Datasets:

ahmedabdelwahed

/

Medical_papers_title_and_abstract_NLP_dataset

like 0

Cost-Effective Features for Reidentification in Camera Networks

Networks of smart cameras share large amounts of data to accomplish tasks such as reidentification. We propose a feature-selection method that minimizes the data needed to represent the appearance of objects by learning the most appropriate feature set for the task at hand (person reidentification). The computational cost for feature extraction and the cost for storing the feature descriptor are considered jointly with feature performance to select cost-effective good features. This selection allows us to improve intercamera reidentification while reducing the bandwidth that is necessary to share data across the camera network. We also rank the selected features in the order of effectiveness for the task to enable a further reduction of the feature set by dropping the least effective features when application constraints require this adaptation. We compare the proposed approach with state-of-the-art methods on the iLIDS and VIPeR datasets and show that the proposed approach considerably reduces network traffic due to intercamera feature sharing while keeping the reidentification performance at an equivalent or better level compared with the state of the art.

A distribution independence based method for 3D face shape decomposition

Decomposing a 3D face shape into different attribute components is usually beneficial to many applications, such as 3D face generation and attribute transfer. In this paper, we propose a novel method to learn independent latent representations of 3D face shapes to decompose a given 3D face shape into identity and expression components. We assume that the identity describes the intrinsic geometry of a face while the expression captures the extrinsic one, and thus they are independent of each other. Based on this assumption, we encode a 3D face shape into its identity and expression representations by a variational inference framework, that is equipped with Graph Convolutional Networks (GCN). Furthermore, we introduce a binary discriminator to enforce the latent representations of identity and expression to be distribution independent by adversarial learning. Both qualitative and quantitative experimental results show that the proposed approach can achieve state-of-the-art results in 3D face shape decomposition. Extensive applications on 3D facial expression transfer, 3D face recognition, and 3D face generation further demonstrate that the proposed method can achieve visually better transferred expressions, purer identity representations, and more diverse 3D face shapes, compared with existing state-of-the-art methods.

A Similarity Metric for the Inputs of OO Programs and Its Application in Adaptive Random Testing

Random testing (RT) has been identified as one of the most popular testing techniques, due to its simplicity and ease of automation. Adaptive random testing (ART) has been proposed as an enhancement to RT, improving its fault-detection effectiveness by evenly spreading random test inputs across the input domain. To achieve the even spreading, ART makes use of distance measurements between consecutive inputs. However, due to the nature of object-oriented software (OOS), its distance measurement can be particularly challenging: Each input may involve multiple classes, and interaction of objects through method invocations. Two previous studies have reported on how to test OOS at a single-class level using ART. In this study, we propose a new similarity metric to enable multiclass level testing using ART. When generating test inputs (for multiple classes, a series of objects, and a sequence of method invocations), we use the similarity metric to calculate the distance between two series of objects, and between two sequences of method invocations. We integrate this metric with ART and apply it to a set of open-source OO programs, with the empirical results showing that our approach outperforms other RT and ART approaches in OOS testing.

Arylboration of Enecarbamates for the Synthesis of Borylated Saturated N-Heterocycles

Two catalytic systems have been developed for the arylboration of endocyclic enecarbamates to deliver synthetically versatile borylated saturated N-heterocycles in good regio- and diastereoselectivities. A Cu/Pd dual catalytic reaction enables the synthesis of borylated, α-arylated azetidines, while a Ni-catalysed arylboration reaction efficiently functionalizes 5-, 6-, and 7-membered enecarbamates. In the case of the Cu/Pd-system, a remarkable additive effect was identified that allowed for broader scope. The products are synthetically useful, as demonstrated by manipulations of the boronic ester to access biologically active compounds.

Molecular Dynamic Simulations and Molecular Docking as a Potential Way for Designed New Inhibitor Drug without Resistance

Mycobacterium tuberculosis is the cause of tuberculosis in humans and is responsible for more than 2 million deaths per year. Despite the development of anti-tuberculosis drugs (Isoniazid, Rifampicin, Ethambutol, pyrazinamide, streptomycin, etc.) and the TB vaccine, this disease has claimed the lives of many people around the world. Drug resistance in this disease is increasing day by day. Conventional methods for discovering and developing drugs are usually time-consuming and expensive. Therefore, a better method is needed to identify, design, and manufacture TB drugs without drug resistance. Bioinformatics applications in obtaining new drugs at the structural level include studies of the mechanism of drug resistance, detection of drug interactions, and prediction of mutant protein structure. In the present study, computer-based approaches including molecular dynamics simulation and molecular docking as a novel and efficient method for the identification and investigation of new cases as well as the investigation of mutated proteins and compounds will be examined .

Novelty in Impact of Neurorehabilitation in a Classic Case of Syringomyelia

A fluid-filled hole inside the parenchyma or central canal of the spinal cord causes syringomyelia, a neurological condition. It is most frequently linked to type 1 Chiari malformations. Syringomyelia can be caused by tumors in the spinal cord, trauma, and post-traumatic or infectious adhesive arachnoiditis. Syringomyelia is shown to have a prevalence of 8.4/100,000 to 0.9/10,000 in certain studies, making it one of the few unusual cases. A large proportion of patients are between 20 and 50 years of age. In our case, the patient is a 17-year-old boy who complained of tingling and weakness in both lower extremities, as well as loss of sensation in both hands. MRI of his spine revealed a Chiari I malformation involving evidence of medulla, fourth ventricle, and cerebellar vermis displacement into the foramen magnum. Arnold Chiari's malformation with cord syringomyelia and tonsillar herniation was diagnosed based on the symptoms and investigation findings. The goal of this case is to highlight the benefits of exercise treatment in improving the patient's quality of life, as physiotherapy protocol instillation is not practiced on a daily basis for such conditions.

Positive Organisational Arts-Based Youth Scholarship: Redressing Discourse on Danger, Disquiet, and Distress during COVID-19

This methodological article argues for the potential of positive organisational arts-based youth scholarship as a methodology to understand and promote positive experiences among young people. With reference to COVID-19, exemplars sourced from social media platforms and relevant organisations demonstrate the remarkable creative brilliance of young people. During these difficult times, young people used song, dance, storytelling, and art to express themselves, (re)connect with others, champion social change, and promote health and wellbeing. This article demonstrates the power of positive organisational arts-based youth scholarship to understand how young people use art to redress negativity via a positive lens of agency, peace, collectedness, and calm.

Milli-Hertz Frequency Tuning Architecture Toward High Repeatable Micromachined Axi-Symmetry Gyroscopes

Axi- symmetry micro gyroscopes are increasingly popular for their ultrahigh measurement sensitivity. However, a side effect is the bias repeatability problem. In this article, we propose and demonstrate an ultraprecise frequency tuning solution to achieve state-of-the-art repeatability performance. The gyroscope dynamics are first analyzed and the major error source is confirmed as the frequency split. Then, an advanced frequency tracker and a precision tuning architecture are developed to improve the bias repeatability. The experimental results prove that the frequency tracker can identify the frequency splits at the mHz level. Consequently, a state-of-the-art turn -ON to turn -ON bias repeatability of 3.6(?)/h is conducted that shows orders of magnitude better than conventional solutions.

Characterization of baseline clinical factors associated with incident worsening kidney function in patients with non-valvular atrial fibrillation: the Hokuriku-Plus AF Registry

Evidence suggests that atrial fibrillation (AF) could increase the risk of worsening kidney function (WKF) which is linked to an increased risk of stroke, bleeding, and death in AF patients. However, limited data exist regarding the factors that could lead to WKF in these patients. Therefore, we sought to identify the potential factors associated with the development of WKF in patients with non-valvular AF (NVAF). We analyzed prospectively recruited 1122 NVAF patients [men 71.9%, median age 73.0 years (interquartile range: 66.0-79.0)] with a baseline estimated glomerular filtration rate (eGFR) ≥ 15 mL/min/1.73 m2 from the Hokuriku-Plus AF Registry. The primary outcome was incident WKF, defined as the %eGFR change from the baseline ≥ 30% during the follow-up period. We evaluated the association between baseline variables and incident WKF using univariate and multivariate Cox proportional hazard models. We also evaluated the non-linear association between the identified factors and incident WKF. During a median follow-up period of 3.0 years (interquartile range: 2.7-3.3), incident WKF was observed in 108 patients (32.6 per 1000 person-years). Compared to the patients without incident WKF, the patients with incident WKF were older and had a higher prevalence of heart failure (HF), diabetes mellitus (DM), and vascular disease at baseline. Those who experienced incident WKF also had higher diastolic blood pressure, lower hemoglobin, lower eGFR, higher B-type natriuretic peptide (BNP) and used warfarin more frequently. Upon multivariate analysis, age ≥ 75 years, HF, DM, and anemia were independently associated with incident WKF. Additionally, age and hemoglobin were linearly associated with the risk of incident WKF, whereas a J- or U-shaped association was observed for HbA1c and BNP. Age ≥ 75 years, HF, DM, and anemia were associated with the development of WKF in Japanese patients with NVAF. In patients with these risk factors, a careful monitoring of the kidney function and appropriate interventions may be important when possible.

Forest access restores foraging and ranging behavior in captive sifakas

Captive wildlife benefit from ecologically informed management strategies that promote natural behaviors. The Duke Lemur Center has pioneered husbandry programs rooted in species' ecology for a diversity of lemurs, including housing social groups in multiacre forest enclosures. We systematically document the foraging and ranging patterns of Coquerel's sifakas (Propithecus coquereli) living in these forest enclosures. Coquerel's sifakas are seasonal frugo-folivores that exhibit striking feeding flexibility in the wild. They are also one of the few members of the Indriidae family to persist in captivity. During all-day follows in the spring and summer of 2 consecutive years, we tracked the behavior of 14 sifakas in six forest enclosures. The sifakas' ranging and foraging patterns reflected those of wild sifakas in western Madagascar: On average, DLC sifakas occupied 3-day home ranges of 1.2 ha, traveled 473 m/day, and spent 26% of their time foraging for wild foodstuffs. The sifakas foraged most for young and mature leaves, fruits, nuts, and flowers from 39 plant species, especially red maple (Acer rubrum), tulip poplar (Liriodendron tulipifera), black locust (Robinia pseudoacacia), grapevine (Vitis rotundifolia), hickory (Carya spp.), and white oak (Quercus alba). Foraging patterns varied across seasons, enclosure areas, and groups, potentially reflecting differences in phenology, microhabitats, and individual preferences. While demonstrating that captive-bred primates express wild-like behaviors under ecologically relevant conditions, our results underscore the feeding flexibility of the Coquerel's sifaka. Captive wildlife exhibiting the range of species-specific behaviors are key resources for ecological research and might be best suited for future reintroductions.

Crystal structure determination, molecular docking and dynamics of arylidene cyanoacetates as potential JNK-3 inhibitors for Ischemia reperfusion injury

Ischemia reperfusion injury is a cardiovascular condition which causes hypoxia by means of obstruction of arterial blood flow eventually leads to reduced synthesis of adenosine tri-phosphate in the mitochondria. c-Jun N-terminal kinase-3 are related to several cascade of events like apoptosis, oxidative stress and mitochondrial dysfunction which can be further related to Ischemia-reperfusion injury. The present study was aimed at determining crystal structure of the ligand by x-ray methods and to perform molecular docking and molecular dynamics studies of the arylidene cyano-acetates with c-Jun N-terminal kinase-3. The binding energy of Ethyl (2E)-2-cyano-3-(4-methoxyphenyl)prop-2-enoate is -4.462 kcal/mol and ethyl (2E)-2-cyano-3-phenylprop-2-enoate is -6.135 kcal/mol. This has created a new rational approach to drug design, where the structure of drug is designed, based on its fit to structures of receptor site, rather than basing it on analogies to other active structures. The above compounds are binding strongly with c-Jun N-terminal kinase-3 protein. Communicated by Ramaswamy H. Sarma.

A Light Implementation of a 3D Convolutional Network for Online Gesture Recognition

With the advancement of machine learning techniques and the increased accessibility to computing power, Artificial Neural Networks (ANNs) have achieved state-of-the-art results in image classification and, most recently, in video classification. The possibility of gesture recognition from a video source enables a more natural non-contact human-machine interaction, immersion when interacting in virtual reality environments and can even lead to sign language translation in the near future. However, the techniques utilized in video classification are usually computationally expensive, being prohibitive to conventional hardware. This work aims to study and analyze the applicability of continuous online gesture recognition techniques for embedded systems. This goal is achieved by proposing a new model based on 2D and 3D CNNs able to perform online gesture recognition, i.e. yielding a label while the video frames are still being processed, in a predictive manner, before having access to future frames of the video. This technique is of paramount interest to applications in which the video is being acquired concomitantly to the classification process and the issuing of the labels has a strict deadline. The proposed model was tested against three representative gesture datasets found in the literature. The obtained results suggest the proposed technique improves the state-of-the-art by yielding a quick gesture recognition process while presenting a high accuracy, which is fundamental for the applicability of embedded systems.

Expanding the Phenotypic Spectrum of APMR4 Syndrome Caused by a Novel Variant in LSS Gene and Review of Literature

Alopecia intellectual disability syndromes 4 (APMR4) is a very rare autosomal recessive condition caused by a mutation in the LSS gene present on chromosome 21. This syndrome has a clinical heterogeneity mainly exhibited with variable degrees of intellectual disability (ID) and congenital alopecia, as well. Eight families with 13 cases have been previously reported. Herein, we provide a report on an Egyptian family with two affected siblings and one affected fetus who was diagnosed prenatally. Whole-exome sequencing (WES) revealed a novel pathogenic missense variant (c.1609G > T; p.Val537Leu) in the lanosterol synthase gene (LSS) related to the examined patients. The detected variant was confirmed by Sanger sequencing. Segregation analyses confirmed that the parents were heterozygous. Our patient was presented with typical clinical manifestations of the disease in addition to new phenotypic features which included some dysmorphic facies as frontal bossing and bilateral large ears, as well as bilateral hyperextensibility of the fingers and wrist joints, short stature, umbilical hernia, and teeth mineralization defect. This study is the first study in Egypt and the 9th molecularly proven family to date. The aim is to expand the clinical and mutational spectrum of the syndrome. Moreover, the report gives a hint on the importance of prenatal testing and the proper genetic counseling to help the parents to take their own decision based on their beliefs.

Representations of birds in Etruscan art (6th-late 4th century BC)

From at least the Iron Age up to the Hellenistic period, the Etruscan culture flourished in a large portion of the Italian peninsula, extending from the Po delta and the eastern Alps in the north to Campania in the south. It was characterised by a magnificent and original artistic production that took its inspiration from aspects of the natural environment inserted into mythological contexts of various origins. In Etruscan art, birds occupied a significant place, and were often represented in wall paintings and craft objects. Species still occurring on the Italian mainland, such as swans, ducks, grouse, and partridges, as well as possibly exotic taxa, domestic forms (chickens and pigeons) and other unidentifiable birds were the subject of artistic inspiration. They were depicted not only in purely cult contexts, but also in the backgrounds of naturalistic landscapes.

DSI-Net: Deep Synergistic Interaction Network for Joint Classification and Segmentation With Endoscope Images

Automatic classification and segmentation of wireless capsule endoscope (WCE) images are two clinically significant and relevant tasks in a computer-aided diagnosis system for gastrointestinal diseases. Most of existing approaches, however, considered these two tasks individually and ignored their complementary information, leading to limited performance. To overcome this bottleneck, we propose a deep synergistic interaction network (DSI-Net) for joint classification and segmentation with WCE images, which mainly consists of the classification branch (C-Branch), the coarse segmentation (CS-Branch) and the fine segmentation branches (FS-Branch). In order to facilitate the classification task with the segmentation knowledge, a lesion location mining (LLM) module is devised in C-Branch to accurately highlight lesion regions through mining neglected lesion areas and erasing misclassified background areas. To assist the segmentation task with the classification prior, we propose a category-guided feature generation (CFG) module in FS-Branch to improve pixel representation by leveraging the category prototypes of C-Branch to obtain the category-aware features. In such way, these modules enable the deep synergistic interaction between these two tasks. In addition, we introduce a task interaction loss to enhance the mutual supervision between the classification and segmentation tasks and guarantee the consistency of their predictions. Relying on the proposed deep synergistic interaction mechanism, DSI-Net achieves superior classification and segmentation performance on public dataset in comparison with state-of-the-art methods. The source code is available at https://github.com/CityU-AIM-Group/DSI-Net.

Electromagnetic scattering from a thick circular aperture

Electromagnetic scattering from a circular aperture ill a thick conducting plane is analyzed. Numerical computations art, performed to evaluate the near and far zone radiation fields in terms of the aperture geometry and incident polarization state. A salient difference in the near-zone field behavior between TM and TE wave incidences is discussed. (C) 2003 Wiley Periodicals. Inc.

Discerning the Contributions of Gold Species in Butadiene Hydrogenation: From Single Atoms to Nanoparticles

Identification of the roles of different active sites is vital for the rational design of catalysts. We present a cutting-edge strategy to discern the contributions of different single-atom gold species and nanoparticles in 1,3-butadiene hydrogenation, through coupling of advanced spectroscopic techniques, electron microscopy-based automated image analyses, and steady-state and kinetic studies. While all the carbon-hosted single gold atoms display negligible initial activity, the in situ-evolved gold nanoparticles are highly active. Full metal-species quantification is realized by combining electron-microscopy-based atom recognition statistics and deep-learning-driven nanoparticle segmentation algorithm, allowing the structure-activity correlations for the hybrid catalysts containing different Au architectures to be established. Surface exposure density of Au nanoparticles, as revealed by electron-microscopy-based statistics, is revealed as a new and reliable activity descriptor.

Octreotide for Acquired Chylothorax in Pediatric Patients Post-Cardiothoracic Surgery for Congenital Heart Disease: A Systematic Review

Chylothorax is a life-threatening complication post-corrective congenital heart surgery. Octreotide is used for treatment of refractory chylothoraces, with no standardized treatment protocol and a paucity of literature describing its efficacy. Our aim was to provide an update on the safety and efficacy of octreotide for the treatment of refractory chylothoraces in neonatal and pediatric patients' post-corrective congenital heart surgery. We performed a systematic review of PubMed, Medline, CINAHL, and Cochrane Library databases. Only intravenous octreotide treatment was included. A total of 621 patients across 27 studies were included. Studies included were 11 case series, 5 case studies, and 11 retrospective cohort studies. Variation in treatment regimens were reported. Treatment efficacy was reported in 95% (23/27) of studies. Definitions of treatment efficacy were reported in 33% (9/27) of studies. No prospective or randomized control trials were available for inclusion. Octreotide efficacy is widely reported despite a lack of standardization on criteria for treatment initiation or what defines an appropriate response to therapy.Please check and confirm whether the edit made to the article title is in order.Yes.

Varifocal-Net: A Chromosome Classification Approach Using Deep Convolutional Networks

Chromosome classification is critical for karyotyping in abnormality diagnosis. To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosome's type and polarity using deep convolutional networks. The approach consists of one global-scale network (G-Net) and one local-scale network (L-Net). It follows three stages. The first stage is to learn both global and local features. We extract global features and detect finer local regions via the G-Net. By proposing a varifocal mechanism, we zoom into local parts and extract local features via the L-Net. Residual learning and multi-task learning strategies are utilized to promote high-level feature extraction. The detection of discriminative local parts is fulfilled by a localization subnet of the G-Net, whose training process involves both supervised and weakly supervised learning. The second stage is to build two multi-layer perceptron classifiers that exploit features of both two scales to boost classification performance. The third stage is to introduce a dispatch strategy of assigning each chromosome to a type within each patient case, by utilizing the domain knowledge of karyotyping. The evaluation results from 1909 karyotyping cases showed that the proposed Varifocal-Net achieved the highest accuracy per patient case () of 99.2 for both type and polarity tasks. It outperformed state-of-the-art methods, demonstrating the effectiveness of our varifocal mechanism, multi-scale feature ensemble, and dispatch strategy. The proposed method has been applied to assist practical karyotype diagnosis.

Urban environment art design simulation based on FPGA and neural network

Environmental process and compositions of the interior and exterior spaces period, outside the species is a continuation of mixing. Ability to Cities Climate Progress Street Development (CCPSD) under development and urban ecosystem is of great interest. Like the collaborative process, the chemical composition and natural finish edge has appeared on the stage. Urban development is inseparable from the appearance of human experience, a more complete overall climate. Gradually different base and current methods of life living material, natural integration plan needs a metropolitan area, then specific social nature and philosophy of environmental protection of the city, and more thoughts. In this article, method will focus on metropolitan areas the natural clear plan to complete the examination. Set best-effort edge within the metropolitan area to explicitly check liner Field Programmable Gate Array (FPGA). The purpose and work planning and layout of the system need to be considered.

Totally Percutaneous Repair of an Aortic Arch Dissection: A Case Report

Although the endovascular repair of descending thoracic aorta diseases is an already consolidated procedure, this approach is not well-established for ascending aorta and arch pathologies. A 71-year-old male patient who had undergone an open ascending aorta replacement ten years ago presented with a huge dissected aortic arch aneurysm. Vascular accesses were obtained with ultrasound-guided punctures, followed by aortic arch exclusion using aortic endoprostheses and the chimney-graft technique for preserving supra-aortic branches flow. This case demonstrates the feasibility of a totally percutaneous aortic arch repair provided that careful preprocedural planning and a dedicated team are available for such a challenging intervention.

Frontal View Gait Recognition With Fusion of Depth Features From a Time of Flight Camera

Frontal view gait recognition for people identification has been carried out using single RGB, stereo RGB, Kinect 1.0, and Doppler radar. However, existing methods based on these camera technologies suffer from several problems. Therefore, we propose a four-part method for frontal view gait recognition based on the fusion of multiple features acquired from a Time-of-Flight (ToF) camera. We have developed a gait data set captured by a ToF camera. The data set includes two sessions recorded seven months apart, with 46 and 33 subjects, respectively, each with six walks with five covariates. The four-part method includes: a new human silhouette extraction algorithm that reduces the multiple reflection problem experienced by ToF cameras; a frame selection method based on a new gait cycle detection algorithm; four new gait image representations; and a novel fusion classifier. Rigorous experiments are carried out to compare the proposed method with state-of-the-art methods. The results show distinct improvements over recognition rates for all covariates. The proposed method outperforms all major existing approaches for all covariates and results in 66.1% and 81.0% Rank 1 and Rank 5 recognition rates, respectively, in overall covariates, compared with a best state-of-the-art method performance of 35.7% and 57.7%.

HDRS: Hindi Dialogue Restaurant Search Corpus for Dialogue State Tracking in Task-Oriented Environment

Due to the rapid increase in the development of Task-oriented dialogue systems, the need for labelled dialogue corpus has become inevitable. For the Hindi language, there is no such dialogue corpus yet available. As a first attempt, we release a Hindi Dialogue Restaurant Search (HDRS) corpus and compare various state-of-the-art dialogue state tracking (DST) models on it. The corpus consists of 1.4 k human-to-human typed dialogues collected using Wizard-of-Oz paradigm. The paper starts with a brief description of the corpus by providing the details of features, corpus collection process and statistical analysis, then the performance of baseline NLU and DST models are investigated. Further, we experimented two categories of state-of-the-art belief state trackers: (1) Non-contextual pre-trained word embedding based DST models; (2) Contextual pre-trained BERT based DST models. All belief trackers follow a three-layered generic architecture. The category-1 models use the static domain ontology, while category-2 models have the capability to handle the dynamic ontology. The DST models are compared on joint-goal and turn-request accuracy. Global encoder and Slot-ATtentive decoders (GSAT) outperforms all the models with 83.25% joint-goal accuracy, followed by SUMBT.

Energy-Efficient Intelligent ECG Monitoring for Wearable Devices

Wearable intelligent ECG monitoring devices can perform automatic ECG diagnosis in real time and send out alert signal together with abnormal ECG signal for doctors further analysis. This provides a means for the patient to identify their heart problem as early as possible and go to doctors for medical treatment. For such system the key requirements include high accuracy and low power consumption. However, the existing wearable intelligent ECG monitoring schemes suffer from high power consumption in both ECG diagnosis and transmission in order to achieve high accuracy. In this work, we have proposed an energy-efficient wearable intelligent ECG monitor scheme with two-stage end-to-end neural network and diagnosis-based adaptive compression. Compared to the state-of-the-art schemes, it significantly reduces the power consumption in ECG diagnosis and transmission while maintaining high accuracy.

Exploiting quantization and spatial correlation in virtual-noise modeling for distributed video coding

Aiming for low complexity encoding video coders based on Wyner-Ziv theory are still unsuccessfully trying to match the performance of predictive video coders One of the most important factors concerning the coding performance of distributed coders is modeling and estimating the correlation between the original video signal and its temporal prediction generated at the decoder One of the problems of the state of the art correlation estimators is that their performance is not consistent across a wide range of video content and different coding settings To address this problem we have developed a correlation model able to adapt to changes in the content and the coding parameters by exploiting the spatial correlation of the video signal and the quantization distortion In this paper we describe our model and present experiments showing that our model provides average bit rate gains of up to 12% and average PSNR gains of up to 0 5 dB when compared to the state of the-art models The experiments suggest that the performance of distributed coders can be significantly improved by taking video content and coding parameters into account (c) 2010 Elsevier B V All rights reserved

The Implementation and Role of Antigen Rapid Test for COVID-19 in Hemodialysis Units

As we move into the third year with COVID-19, many countries have attempted to manage the disease as an endemic. However, this is limited by the disease's morbidity and mortality, the emergence of new strains, and the effectiveness of the vaccine. This brief report describes, evaluates, and discusses the implementation of regular antigen rapid tests (ARTs) for COVID-19 in hemodialysis units. We introduced ARTs during the surge in our hemodialysis units. As compliance with the test was mandatory by regulatory requirements, we surveyed patients and caregivers to measure their acceptability, appropriateness, and feasibility of the ART's implementation. Acceptability measured confidence and level of comfort when performing ART tests, while appropriateness measured the perception of the necessity of ARTs, safety in the dialysis unit with the implementation of ARTs, and understanding using a Likert scale. Feasibility measured the perception of the timely start of dialysis treatment and the convenience of the test. Our survey found that ARTs were acceptable to 98% of patients and caregivers, with the majority reporting no discomfort. The majority of the patients agreed that ARTs were appropriate and feasible. We reported successful ART implementation in a healthcare setting with no false-positive or transmission within the unit during this period. Nevertheless, the long-term implementation outcome will require further evaluation.

Injection-locked dual opto-electronic oscillator with ultra-low phase noise and ultra-low spurious level

We report a new injection-locked dual opto-electronic oscillator (OEO) that uses a long optical fiber loop master oscillator to injection lock into a short-loop signal-mode slave oscillator, which showed substantial improvements in reducing the phase noise and spurs compared to current state-of-the-art multiloop OEOs operating at 10 GHz. Preliminary phase-noise measurement indicated approximately 140-dB reduction of the spurious level.

Deep ensemble transfer learning-based approach for classifying hot-rolled steel strips surface defects

Over the last few years, advanced deep learning-based computer vision algorithms are revolutionizing the manufacturing field. Thus, several industry-related hard problems can be solved by training these algorithms, including flaw detection in various materials. Therefore, identifying steel surface defects is considered one of the most important tasks in the steel industry. In this paper, we propose a deep learning-based model to classify six of the most common steel strip surface defects using the NEU-CLS dataset. We investigate the effectiveness of two state-of-the-art CNN architectures (MobileNet-V2 and Xception) combined with the transfer learning approach. The proposed approach uses an ensemble of two pre-trained state-of-the-art Convolutional Neural Networks, which are MobileNet-V2 and Xception. To perform a comparative analysis of the proposed architectures, several evaluation metrics are adopted, including loss, accuracy, precision, recall, F1-score, and execution time. The experimental results show that the proposed deep ensemble learning approach provides higher performance achieving an accuracy of 99.72% compared to MobileNet-V2 (98.61%) and Xception (99.17%) while preserving fast execution time and small models' size.

Fast Run-Length Compression of Point Cloud Geometry

The increase in popularity of point-cloud-oriented applications has triggered the development of specialized compression algorithms. In this paper, a novel algorithm is developed for the lossless geometry compression of voxelized point clouds following an intra-frame design. The encoded voxels are arranged into runs and are encoded through a single-pass application directly on the voxel domain. This is done without representing the point cloud via an octree nor rendering the voxel space through an occupancy matrix, therefore decreasing the memory requirements of the method. Each run is compressed using a context-adaptive arithmetic encoder yielding state-of-the-art compression results, with gains of up to 15% over TMC13, MPEG's standard for point cloud geometry compression. Several proposed contributions accelerate the calculations of each run's probability limits prior to arithmetic encoding. As a result, the encoder attains a low computational complexity described by a linear relation to the number of occupied voxels leading to an average speedup of 1.8 over TMC13 in encoding speeds. Various experiments are conducted assessing the proposed algorithm's state-of-the-art performance in terms of compression ratio and encoding speeds.

Efficacy of the environmentally sustainable microwave heating compared to biocide applications in the devitalization of phototrophic communities colonizing rock engravings of Valle Camonica, UNESCO world heritage site, Italy

The devitalization of lithobionts prior to their removal from engraved rocks is a common conservation practice periodically undertaken in rock art sites. In this study, we assessed in situ the efficacy of three traditional biocides and of an innovative microwave heating system, and compared different application protocols to devitalize foliose and crustose lichens and a cyanobacteria-dominated biofilm on the rock engravings of Valle Camonica (UNESCO site n.94, Italy). The analysis of their vitality and stress responses by monitoring chlorophyll a fluorescence parameters (Fv/Fm, F0, OJIP transient) showed that the common application of biocides by brush is rather ineffective, particularly in the case of the resistant crustose lichens. The heating of rock surfaces to 70 degrees C for a few minutes by the microwave system caused devitalization of lithobionts to a similar extent as the biocide application with cellulose poultice, which, however, introduced high amounts of chemicals in the environment. The microwave irradiation overcame any lithobiontic stress resistance and avoided useless or excessive spread of biocides, appearing a promising sustainable approach for the parallel conservation of rock art and its surrounding natural environment.

Day Ahead Real Time Pricing and Critical Peak Pricing Based Power Scheduling for Smart Homes with Different Duty Cycles

In this paper, we propose a demand side management (DSM) scheme in the residential area for electricity cost and peak to average ratio (PAR) alleviation with maximum users' satisfaction. For this purpose, we implement state-of-the-art algorithms: enhanced differential evolution (EDE) and teacher learning-based optimization (TLBO). Furthermore, we propose a hybrid technique (HT) having the best features of both aforementioned algorithms. We consider a system model for single smart home as well as for a community (multiple homes) and each home consists of multiple appliances with different priorities. The priority is assigned (to each appliance) by electricity consumers and then the proposed scheme finds an optimal solution according to the assigned priorities. Day-ahead real time pricing (DA-RTP) and critical peak pricing (CPP) are used for electricity cost calculation. To validate our proposed scheme, simulations are carried out and results show that our proposed scheme efficiently achieves the aforementioned objectives. However, when we perform a comparison with existing schemes, HT outperforms other state-of-the-art schemes (TLBO and EDE) in terms of electricity cost and PAR reduction while minimizing the average waiting time.

Phelan-McDermid and general anesthesia with different hypnotics

Phelan-McDermid syndrome (PMS) is a rare neurodevelopmental disease, caused by an autosomal dominant mutation due to the terminal deletion of 22q13, leading to a defect in the SHANK3 protein. We present the clinical case of a 12-year-old patient with this syndrome, who underwent three interventions that required general anesthesia. In none of them did she present intraoperative or postoperative complications.

Explicit Lower Bounds on Strong Quantum Simulation

We consider the problem of classical strong (amplitude-wise) simulation of n-qubit quantum circuits, and identify a subclass of simulators we call monotone. This subclass encompasses almost all prominent simulation techniques. We prove an unconditional (i.e. without relying on any complexity-theoretic assumptions) and explicit (n - 2)(2(n-3) - 1) lower bound on the running time of simulators within this subclass. Assuming the Strong Exponential Time Hypothesis (SETH), we further remark that a universal simulator computing any amplitude to precision 2(-n) /2 must take at least 2(n-o(n)) time. We then compare strong simulators to existing SAT solvers, and identify the time-complexity below which a strong simulator would improve on state-of-the-art general SAT solving. Finally, we investigate Clifford+T quantum circuits with t T-gates. Using the sparsification lemma, we identify a time complexity lower bound of 2(2.2451x10-8)t below which a strong simulator would improve on state-of-the-art 3-SAT solving. This also yields a conditional exponential lower bound on the growth of the stabilizer rank of magic states.

A Low-Cost Surveillance and Information System for Museum Using Visible Light Communication

This paper designed and implemented a low-cost surveillance and information system for museums with two new coding schemes using visible light communication. For security, we use a key in place of the clock in the Manchester coding. For the enhanced data transfer, we use the unbalanced time duration for data one and zero. The prototype system was built with two low-cost microcontrollers, an LED, and linear light sensors. With 1-m distance in a dark environment, the maximum data transfer rate was roughly 1.93 kbits/s with a 10 Phi white LED. Under the dark-to-modest ambient brightness of up to 120 lux, the white LEDs were performed well in terms of the transmission distance, compared with LEDs with different colors. The experiments with flashlights reveal that our system can easily detect the prohibited use of flashlight. Experiments with the mobile phones' flashlight also revealed that photographing with the flashlight would have a worse impact on arts than the continuous illumination. We also measured the distance between arts to avoid inevitable interference between illumination sources in neighbor.

Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation

Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably lose the edge information, which contains spatial features of vessels while performing down-sampling, leading to the limited segmentation performance of fine blood vessels. Furthermore, the existing methods ignore the dynamic topological correlations among feature maps in the deep learning framework, resulting in the inefficient capture of the channel characterization. To address these limitations, we propose a novel dual encoder-based dynamic-channel graph convolutional network with edge enhancement (DE-DCGCN-EE) for retinal vessel segmentation. Specifically, we first design an edge detection-based dual encoder to preserve the edge of vessels in down-sampling. Secondly, we investigate a dynamic-channel graph convolutional network to map the image channels to the topological space and synthesize the features of each channel on the topological map, which solves the limitation of insufficient channel information utilization. Finally, we study an edge enhancement block, aiming to fuse the edge and spatial features in the dual encoder, which is beneficial to improve the accuracy of fine blood vessel segmentation. Competitive experimental results on five retinal image datasets validate the efficacy of the proposed DE-DCGCN-EE, which achieves more remarkable segmentation results against the other state-of-the-art methods, indicating its potential clinical application.

Tau interactome and RNA binding proteins in neurodegenerative diseases

Pathological tau aggregation is a primary neuropathological feature of many neurodegenerative diseases. Intriguingly, despite the common presence of tau aggregates in these diseases the affected brain regions, clinical symptoms, and morphology, conformation, and isoform ratio present in tau aggregates varies widely. The tau-mediated disease mechanisms that drive neurodegenerative disease are still unknown. Tau interactome studies are critically important for understanding tauopathy. They reveal the interacting partners that define disease pathways, and the tau interactions present in neuropathological aggregates provide potential insight into the cellular environment and protein interactions present during pathological tau aggregation. Here we provide a combined analysis of 12 tau interactome studies of human brain tissue, human cell culture models and rodent models of disease. Together, these studies identified 2084 proteins that interact with tau in human tissue and 1152 proteins that interact with tau in rodent models of disease. Our combined analysis of the tau interactome revealed consistent enrichment of interactions between tau and proteins involved in RNA binding, ribosome, and proteasome function. Comparison of human and rodent tau interactome studies revealed substantial differences between the two species. We also performed a second analysis to identify the tau interacting proteins that are enriched in neurons containing granulovacuolar degeneration or neurofibrillary tangle pathology. These results revealed a timed dysregulation of tau interactions as pathology develops. RNA binding proteins, particularly HNRNPs, emerged as early disease-associated tau interactors and therefore may have an important role in driving tau pathology.

Ligand-Functional Groups Induced Tuning MOFs' 2D into 1D Pore Channels for Pipeline Natural Gas Purification

The solvothermal reactions of CoCl2 ⋅ 6H2 O, 3,5-pyridinedicarboxylic acid (H2 L) and isonicotinic acid (HL1 )/3-amino isonicotinic acid (HL2 )/3-chloro isonicotinic acid (HL3 ) successfully led to three tfz-d topological pillar-layer [Co4 (μ-F)2 (COO)6 (NC5 H4 )4 ] cluster-based MOFs, namely, [Co4 (μ-F)2 (L)2 (L1 )2 ⋅ 2DMA] ⋅ DMA ⋅ 2H2 O (SNNU-Bai76, SNNU-Bai=Shaanxi Normal University Bai's group), [Co4 (μ-F)2 (L)2 (L2 )2 ⋅ 2H2 O] ⋅ 2DMA ⋅ 2H2 O (SNNU-Bai77) and [Co4 (μ-F)2 (L)2 (L3 )2 ⋅ 2H2 O] ⋅ 2DMF ⋅ 2H2 O (SNNU-Bai78). With the 2D pore channels in SNNU-Bai76 and SNNU-Bai77 being tuned to the 1D pore channel in SNNU-Bai78, C3 H8 and C2 H6 adsorption uptakes are apparently improved and the IAST selectivities of C3 H8 /CH4 and C2 H6 /CH4 almost remain, which indicate that SNNU-Bai78 may be one potential separation material for the pipeline natural gas purification. These were further confirmed by the breakthrough experiments for the simulated pipeline natural gas (C3 H8 /C2 H6 /CH4 : 5/10/85 gas mixture) of three isostructural MOFs. Furthermore, GCMC simulations revealed that due to one of the pore channels blocked by Cl atoms in a couple of 3-chloro isonicotinic acid with the changed conformation as the pillar, the pore wall of the formed 1D pore channel in SNNU-Bai78 may interact with the adsorbed C3 H8 or C2 H6 molecule more strongly, for which more atoms of framework at the new adsorption site will interact with the adsorbed gas molecule by more intermolecular interactions. This was also evidenced by the increased binding energies, being consistent with the tuning of adsorption enthalpies for C3 H8 and C2 H6 gas molecules, and the reduced C3 H8 and C2 H6 gas diffusion coefficients in SNNU-Bai78. Very interestingly, this work is the first example of finely tuning the pore connectivity of MOFs toward strengthened host-guest interactions for the gas adsorption and separation.

MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction

Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation problem, will degrade the imaging quality. In this paper, we propose a novel LDCT reconstruction network that unrolls the iterative scheme and performs in both image and manifold spaces. Because patch manifolds of medical images have low-dimensional structures, we can build graphs from the manifolds. Then, we simultaneously leverage the spatial convolution to extract the local pixel-level features from the images and incorporate the graph convolution to analyze the nonlocal topological features in manifold space. The experiments show that our proposed method outperforms both the quantitative and qualitative aspects of state-of-the-art methods. In addition, aided by a projection loss component, our proposed method also demonstrates superior performance for semi-supervised learning. The network can remove most noise while maintaining the details of only 10% (40 slices) of the training data labeled.

An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts

In general, development of adequately complex mathematical models, such as deep neural networks, can be an effective way to improve the accuracy of learning models. However, this is achieved at the cost of reduced post-hoc model interpretability, because what is learned by the model can become less intelligible and tractable to humans as the model complexity increases. In this paper, we target a similarity learning task in the context of image retrieval, with a focus on the model interpretability issue. An effective similarity neural network (SNN) is proposed not only to seek robust retrieval performance but also to achieve satisfactory post-hoc interpretability. The network is designed by linking the neuron architecture with the organization of a concept tree and by formulating neuron operations to pass similarity information between concepts. Various ways of understanding and visualizing what is learned by the SNN neurons are proposed. We also exhaustively evaluate the proposed approach using a number of relevant datasets against a number of state-of-the-art approaches to demonstrate the effectiveness of the proposed network. Our results show that the proposed approach can offer superior performance when compared against state-of-the-art approaches. Neuron visualization results are demonstrated to support the understanding of the trained neurons.

Aberrant Topological Properties of Brain Functional Network in Children with Obstructive Sleep Apnea Derived from Resting-State fMRI

To examine the difference in the topological properties of brain functional network between the children with obstructive sleep apnea (OSA) and healthy controls, and to explore the relationships between these properties and cognitive scores of OSA children. Twenty-four OSA children (6.5 ± 2.8 years, 15 males) and 26 healthy controls (8.0 ± 2.9 years, 11 males) underwent resting-state fMRI (rs-fMRI), based on which brain functional networks were constructed. We compared the global and regional topological properties of the network between OSA children and healthy controls. Partial correlation analysis was performed between topological properties and cognitive scores across OSA children. When comparing the OSA children with the healthy controls, lower full-scale intelligent quotient (FIQ) and verbal intelligent quotient (VIQ) were observed. Additionally, nodal degree centrality decreased in the bilateral anterior cingulate and paracingulate gyrus, but increased in the right middle frontal gyrus, the left fusiform gyrus, and the left supramarginal gyrus. Nodal efficiency decreased in the right precentral gyrus, and the bilateral anterior cingulate and paracingulate gyrus, but increased in the left fusiform gyrus. Nodal betweenness centrality increased in the dorsolateral part of the right superior frontal gyrus, the left fusiform gyrus, and the left supramarginal gyrus. Further, the nodal degree centrality in the left supramarginal gyrus was positively correlated with FIQ. In contrast, none of global topological properties showed difference between those two groups. The outcomes of OSA may impaired the regional topological properties of the brain functional network of OSA children, which may be potential neural mechanism underlying the cognitive declines of these patients.

Design, synthesis and anticancer evaluation of novel Se-NSAID hybrid molecules: Identification of a Se-indomethacin analog as a potential therapeutic for breast cancer

A total of twenty-five novel carboxylic acid, methylester, methylamide or cyano nonsteroidal anti-inflammatory drug (NSAID) derivatives incorporating Se in the chemical form of selenoester were reported. Twenty Se-NSAID analogs exhibited an increase in cytotoxic potency compared with parent NSAID scaffolds (aspirin, salicylic acid, naproxen, indomethacin and ketoprofen). Top five analogs were selected to further study their cytotoxicity in a larger panel of cancer cells and were also submitted to the DTP program of the NCI's panel of 60 cancer cell lines. Compounds 4a and 4d stood out with IC50 values below 10 μM in several cancer cells along with a selectivity index higher than 5 in breast cancer cells. Remarkably, analog 4d was found to inhibit cell growth notably in two breast cancer cell lines by inducing apoptosis, and to be metabolized to release the parent NSAID along with the Se fragment. Taken together, our results show that Se-NSAID analog 4d could be a potential chemotherapeutic drug for breast cancer.

Neural Segmentation of Seeding ROIs (sROIs) for Pre-Surgical Brain Tractography

White matter tractography mapping is an important tool for neuro-surgical planning and navigation. It relies on the accurate manual delineation of anatomical seeding ROIs (sROIs) by neuroanatomy experts. Stringent pre-operative time-constraints and limited availability of experts suggest that automation tools are strongly needed for the task. In this article, we propose and compare several multi-modal fully convolutional network architectures for segmentation of sROIs. Inspired by their manual segmentation practice, anatomical information from T1w maps is fused by the network with directionally encoded color (DEC) maps to compute the segmentation. Qualitative and quantitative validation was performed on image data from 75 real tumor resection candidates for the sROIs of the motor tract, the arcuate fasciculus, and optic radiation. Favorable comparison was also obtained with state-of-the-art methods for the tumor dataset as well as the ISMRM 2017 traCED challenge dataset. The proposed networks showed promising results, indicating they may significantly improve the efficiency of pre-surgical tractography mapping, without compromising its quality.

Can lateral mobility be restored along a highly domesticated low-energy gravel-bed river?

Fluvial engineering works such as weirs, rip-rap, groynes, and dykes have constrained for decades and more the lateral mobility of rivers, one of the key drivers of aquatic and riparian diversity. Preserving or restoring a sufficient space for river mobility has therefore become a major river management focus. Because the success and relevance of management actions are conditioned by the level of energy and sediment supply of rivers, such actions are generally considered unsuitable for low-energy rivers. However, some low-energy rivers have numerous ancient engineering works along their length, especially bank protections, suggesting a potential capacity for bed migration. In this context, it is essential to determine to what extent planform dynamics is disturbed, and if lateral mobility can be restored. Herein, a case study was done on a 146 km stretch of the low-energy meandering gravel-bed Cher River (France). The goal of the study was to estimate the remnant shifting capacity, identify the factors controlling the location and intensity of lateral erosion, determine the potential for preserving and restoring lateral mobility, and examine management measures that could be implemented to this end. For that, field surveys, analysis of existing databases, aerial photographs, and laser imaging detection and ranging digital elevation model (LiDAR DEM) data were combined. The study revealed a strong longitudinal fragmentation of the river, with most of it laterally constrained due to the presence of anthropogenic structures such as bank protections, former gravel pits in the alluvial plain, bridges, and weirs. The river is now composed of a string of constrained and unconstrained reaches, and the space available for river shifting has been dramatically reduced. Due to these fluvial engineering works and anthropogenic legacies, the potential for lateral movement of the riverbed, and, therefore, diversification of riparian and aquatic habitats, is limited. Furthermore, lateral mobility could be preserved or restored only for very short sections of the river. It is therefore highly unlikely that good ecological status could be achieved on the entire river corridor through removal of bank protections. Nevertheless, a possible solution could be combining bank protection removals with a series of gravel augmentations close to each other.

Elevated frequencies of micronucleated erythrocytes in infants exposed to zidovudine in utero and postpartum to prevent mother-to-child transmission of HIV

Zidovudine-based antiretroviral therapies (ARTs) for treatment of HIV-infected pregnant women have markedly reduced mother-to-child transmission of the human immunodeficiency virus (HIV-1) from similar to 25% to < 1%. However, zidovudine (ZDV; AZT), a nucleoside analogue, induces chromosomal damage, gene mutations, and cancer in animals following direct or transplacental exposure. To determine if chromosomal damage is induced by ZDV in infants exposed transplacentally, we evaluated micronucleated reticulocyte frequencies (%MN-RET) in 16 HIV-infected ART-treated mother-infant pairs. Thirteen women received prenatal ART containing ZDV; three received ART without ZDV. All infants received ZDV for 6 weeks postpartum. Venous blood was obtained from women at delivery and from infants at 1-3 days, 4-6 weeks, and 4-6 months of life; cord blood was collected immediately after delivery. Ten cord blood samples (controls) were obtained from infants of HIV-uninfected women who did not receive ART. %MN-RET was measured using a single laser 3-color flow cytometric system. Tenfold increases in %MNRET were seen in women and infants who received ZDV-containing ART prenatally; no increases were detected in three women and infants who received prenatal ART without ZDV. Specifically, mean %MNRET in cord blood of ZDV-exposed infants was 1.67 +/- 0.34 compared with 0.16 +/- 0.06 in non-ZDV ART-exposed infants (P=0.006) and 0.12 +/- 0.02 in control cord bloods (P < 0.0001). %MN-RET in ZDV-exposed newborns decreased over the first 6 months of life to levels comparable to cord blood controls. These results demonstrate that transplacental ZDV exposure is genotoxic in humans. Long-term monitoring of HIV-uninfected ZDV-exposed infants is recommended to ensure their continued health. Environ. Mol. Mutagen. 48:322-329, 2007. Published 2007 Wiley-Liss, Inc.

Side Lobe Suppression of Concentric Circular Antenna Array Using Social Spider Algorithm

This paper presents an efficient method to improve the far-field radiation pattern of concentric circular antenna array (CCAA) design using two stochastic optimization algorithms known as social spider algorithm (SSA) and modified social spider algorithm (MSSA). Low side lobe level (SLL) plays a crucial role in reducing the interference with the other frequency components along the entire side lobes of the far-field radiation pattern. SSA and MSAA are the state-of-the-art evolutionary optimization techniques which are applied here to determine the optimal current amplitude and the inter-element distance between two consecutive antennae of the 3-ring CCAA. In this paper, the optimal results achieved by using SSA, MSSA for (4, 6, 8) elements and (8, 10, 12) elements 3-ring CCAAs, with and without centre elements are reported. The results achieved by employing SSA and MSSA show a considerable improvement in SLL reduction as compared to the uniform and the other array patterns reported in the state-of-the-art literature.

An automated method for mining high-quality assertion sets

Assertion-Based Verification (ABV) is one of the promising ways of functional verification. The efficiency of ABV largely depends on the quality of the assertions in terms of how accurately they capture the consistency between implementation and specification. To this end, several assertion miners have been developed to automatically generate assertions. However, existing automatic assertion miners typically generate a huge amount of assertions which can lead to overhead in the verification process. Assertion evaluation, on the other hand, has recently appeared to evaluate and select high-quality assertions among the huge generated assertion set. These methods typically measure the quality of an assertion based on different metrics. These metrics nonetheless, consider dissimilar and distinct aspects which lead to difficulties in deciding what metric should influence more in assertion evaluation. Thereby, to exceed the state-of-the-art, a flow is proposed in which an assertion miner and an assertion evaluator are introduced. The assertion miner is capable of generating a set of readable and compact assertions. The assertion evaluator instead estimates the quality of the assertion set with a data-mining-based algorithm called dominance. Dominance is able to analyze the outcome of different metrics to unify them. Experimental results present the effectiveness of the proposed flow by comparing them to the state-of-the-art.

Hardware Division by Small Integer Constants

This article studies the design of custom circuits for division by a small positive constant. Such circuits can be useful for specific FPGA and ASIC applications. The first problem studied is the Euclidean division of an unsigned integer by a constant, computing a quotient and remainder. Several new solutions are proposed and compared against the state-of-the-art. As the proposed solutions use small look-up tables, they match well with the hardware resources of an FPGA. The article then studies whether the division by the product of two constants is better implemented as two successive dividers or as one atomic divider. It also considers the case when only a quotient or only a remainder is needed. Finally, it addresses the correct rounding of the division of a floating-point number by a small integer constant. All these solutions, and the previous state-of-the-art, are compared in terms of timing, area, and area-timing product. In general, the relevance domains of the various techniques are different on FPGA and on ASIC.

Dietary restriction and medical therapy drives PPARα-regulated improvements in early diabetic kidney disease in male rats

The attenuation of diabetic kidney disease (DKD) by metabolic surgery is enhanced by pharmacotherapy promoting renal fatty acid oxidation (FAO). Using the Zucker Diabetic Fatty and Zucker Diabetic Sprague Dawley rat models of DKD, we conducted studies to determine if these effects could be replicated with a non-invasive bariatric mimetic intervention. Metabolic control and renal injury were compared in rats undergoing a dietary restriction plus medical therapy protocol (DMT; fenofibrate, liraglutide, metformin, ramipril, and rosuvastatin) and ad libitum-fed controls. The global renal cortical transcriptome and urinary 1H-NMR metabolomic profiles were also compared. Kidney cell type-specific and medication-specific transcriptomic responses were explored through in silico deconvolution. Transcriptomic and metabolomic correlates of improvements in kidney structure were defined using a molecular morphometric approach. The DMT protocol led to ∼20% weight loss, normalized metabolic parameters and was associated with reductions in indices of glomerular and proximal tubular injury. The transcriptomic response to DMT was dominated by changes in fenofibrate- and peroxisome proliferator-activated receptor-α (PPARα)-governed peroxisomal and mitochondrial FAO transcripts localizing to the proximal tubule. DMT induced urinary excretion of PPARα-regulated metabolites involved in nicotinamide metabolism and reversed DKD-associated changes in the urinary excretion of tricarboxylic acid (TCA) cycle intermediates. FAO transcripts and urinary nicotinamide and TCA cycle metabolites were moderately to strongly correlated with improvements in glomerular and proximal tubular injury. Weight loss plus pharmacological PPARα agonism is a promising means of attenuating DKD.

Scalable Iterative Classification for Sanitizing Large-Scale Datasets

Cheap ubiquitous computing enables the collection of massive amounts of personal data in a wide variety of domains. Many organizations aim to share such data while obscuring features that could disclose personally identifiable information. Much of this data exhibits weak structure (e.g., text), such that machine learning approaches have been developed to detect and remove identifiers from it. While learning is never perfect, and relying on such approaches to sanitize data can leak sensitive information, a small risk is often acceptable. Our goal is to balance the value of published data and the risk of an adversary discovering leaked identifiers. We model data sanitization as a game between 1) a publisher who chooses a set of classifiers to apply to data and publishes only instances predicted as non-sensitive and 2) an attacker who combines machine learning and manual inspection to uncover leaked identifying information. We introduce a fast iterative greedy algorithm for the publisher that ensures a low utility for a resource-limited adversary. Moreover, using five text data sets we illustrate that our algorithm leaves virtually no automatically identifiable sensitive instances for a state-of-the-art learning algorithm, while sharing over 93 percent of the original data, and completes after at most five iterations.

Performance and methane potential of up-flow anaerobic sludge blanket treating thermal hydrolyzed sludge dewatering liquor

Thermal hydrolysis pretreatment could release organic sufficiently from solid into liquid phase to accelerate the high solid sludge anaerobic digestion. Thus, up-flow anaerobic sludge blanket (UASB) could be a promising energy recovery process to treat thermal hydrolyzed sludge dewatering liquor with significantly augmented the organic loading rate (OLR). In this study, its performance was investigated using a lab-scale UASB to treat sludge dewatering liquor after 165 °C, 30 min thermal hydrolysis pretreatment. The results show that 85.57% of the organic in thermal hydrolyzed sludge dewatering liquor could be converted to methane. The UASB adapts to high OLR stably, and the COD removal efficiency was 71.98 ± 1.95% at OLR of 18.35 ± 0.78 kgCOD·(m3·d)-1, and the gap between the maximum potential and experimental methane production yields could be observed during different OLRs. It could be explained as the methanogenesis rate decreased due to the shift of dominant pathway from acetoclastic methanogenesis to syntrophic acetate oxidation following hydrogenotrophic methanogenesis. Methanospirillum became the dominant methanogen with the increase of OLR. In addition, the methane production yield and rate would be hindered till the ammonia nitrogen concentration exceeds 4 g·L-1. Direct interspecies electron transfer could be promising methods to improve UASB performance treating thermal hydrolyzed dewatering liquor.

HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery

Wide Area Motion Imagery (WAMI) yields high resolution images with a large number of extremely small objects. Target objects have large spatial displacements throughout consecutive frames. This nature of WAMI images makes object tracking and detection challenging. In this paper, we present our deep neural network-based combined object detection and tracking model, namely, Heat Map Network (HM-Net). HM-Net is significantly faster than state-of-the-art frame differencing and background subtraction-based methods, without compromising detection and tracking performances. HM-Net follows object center-based joint detection and tracking paradigm. Simple heat map-based predictions support unlimited number of simultaneous detections. The proposed method uses two consecutive frames and the object detection heat map obtained from the previous frame as input, which helps HM-Net monitor spatio-temporal changes between frames and keep track of previously predicted objects. Although reuse of prior object detection heat map acts as a vital feedback-based memory element, it can lead to unintended surge of false positive detections. To increase robustness of the method against false positives and to eliminate low confidence detections, HM-Net employs novel feedback filters and advanced data augmentations. HM-Net outperforms state-of-the-art WAMI moving object detection and tracking methods on WPAFB dataset with its 96.2% F1 and 94.4% mAP detection scores, while achieving 61.8 % mAP tracking score on the same dataset. This performance corresponds to an improvement of 2.1% for F1, 6.1% for mAP scores on detection, and 9.5% for mAP score on tracking over state-of-the-art.

The critical role of the endolysosomal system in cerebral ischemia

Cerebral ischemia is a serious disease that triggers sequential pathological mechanisms, leading to significant morbidity and mortality. Although most studies to date have typically focused on the lysosome, a single organelle, current evidence supports that the function of lysosomes cannot be separated from that of the endolysosomal system as a whole. The associated membrane fusion functions of this system play a crucial role in the biodegradation of cerebral ischemia-related products. Here, we review the regulation of and the changes that occur in the endolysosomal system after cerebral ischemia, focusing on the latest research progress on membrane fusion function. Numerous proteins, including N-ethylmaleimide-sensitive factor and lysosomal potassium channel transmembrane protein 175, regulate the function of this system. However, these proteins are abnormally expressed after cerebral ischemic injury, which disrupts the normal fusion function of membranes within the endolysosomal system and that between autophagosomes and lysosomes. This results in impaired "maturation" of the endolysosomal system and the collapse of energy metabolism balance and protein homeostasis maintained by the autophagy-lysosomal pathway. Autophagy is the final step in the endolysosomal pathway and contributes to maintaining the dynamic balance of the system. The process of autophagosome-lysosome fusion is a necessary part of autophagy and plays a crucial role in maintaining energy homeostasis and clearing aging proteins. We believe that, in cerebral ischemic injury, the endolysosomal system should be considered as a whole rather than focusing on the lysosome. Understanding how this dynamic system is regulated will provide new ideas for the treatment of cerebral ischemia.

Joint Learning of Local and Global Context for Temporal Action Proposal Generation

Temporal action proposal generation is an important yet challenging problem, since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content. This problem requires methods not only generating proposals with precise temporal boundaries, but also retrieving proposals to cover ground truth action instances with high recall and high overlap using relatively fewer proposals. To address these difficulties, we introduce an effective and efficient proposal generation method, named Local-Global Network (LGN), by which local and global contexts are jointly learned to generate high quality proposals. Locally, LGN first locates temporal boundaries with high starting and ending probabilities separately, then directly combines these boundaries as proposals. Globally, LGN evaluates the actionness probability of multiple-durations temporal regions simultaneously using temporal convolutional layers and anchor mechanism. Finally, we combine the boundary probabilities of each proposal with actionness probability of matched temporal regions as the confidence score, which is used for retrieving proposals. We conduct experiments on two datasets: ActivityNet-1.3 and THUMOS-14, where LGN outperforms other state-of-the-art methods with both high recall and high temporal precision. Finally, further experiments demonstrate that by combining existing action classifiers, our method significantly improves the state-of-the-art temporal action detection performance.

A Novel Multiplier Hardware Organization for Finite Fields Defined by All-One Polynomials

In this brief, a novel, energy-efficient hardware organization for a finite-field multiplier based on irreducible all-one polynomials (AOPs) is proposed. The proposed AOP multiplier organization deploys three distinct submodules, which constitute a left-shifting network (reduction), an AND network (multiplication), and a three-input XOR tree (accumulation). Previously reported state-of-the-art implementation distributes these operations to systolic arrays, which are elegant in layout but do not yield the most efficient solution. The advantages of the proposed organization compared with those reported in the literature include reduced cost and power dissipation for a given clock frequency constraint (or increased clock frequency for a given power constraint) and the absence of bypassing problems due to fewer pipeline stages. Both the previously reported and the proposed organizations have been implemented in Verilog for three different binary-field sizes using the TSMC 90 nm standard cell library and have been synthesized for three distinct frequency targets using Cadence Genus Synthesis tool. The proposed organization achieves 18%, 31%, and 19% reduction in average leakage, dynamic capacitance, and area, respectively, compared with state-of-the-art schemes and thus can be considered for energy-efficient, compact portable systems, including wireless sensors.

Long-Term Prostaglandin E1 Infusion for Newborns with Critical Congenital Heart Disease

Prostaglandin E1 is crucial for keeping the patent ductus arteriosus in critical congenital heart disease for the survival and palliation of particularly prematurely born babies until a cardiosurgical intervention is available. In this study, the side effects of prostaglandin E1 in newborns with critical congenital heart disease and clinical outcomes were evaluated. Thirty-five newborns diagnosed with critical congenital heart disease were treated with prostaglandin E1 between January 2012 and September 2014 at our hospital. Patient charts were examined for prostaglandin E1 side effects (metabolic, gastric outlet obstruction, apnea), clinical status, and prognosis. Acquired data were analyzed in the SPSS 20.0 program. Patients with birth weight under 2500 g needed more days of prostaglandin E1 infusion than ones with birthweight over 2500 g (P = 0.016). The ratio of patients with birth weight under 2500 g who received prostaglandin E1 longer than 7 days was higher than the patients with birth weight over 2500 g (P = 0.02). Eighteen side effects were encountered in 11 of 35 patients (31%). Of these side effects, 1 patient had 4, 4 patients had 2, and 6 patients had only 1 side effect. Discontinuation of the therapy was never needed. Prostaglandin E1 is an accepted therapy modality for survival and outcome in critical congenital heart disease in particularly low-birth-weight babies until a surgical intervention is available. Side effects are not less encountered but are almost always manageable, and discontinuation is not needed.

High-throughput-derived biologically-inspired features for unconstrained face recognition

Many modern computer vision algorithms are built atop of a set of low-level feature operators (such as SIFT [23,24]; HOG [8,3]; or LBP [1,2]) that transform raw pixel values into a representation better suited to subsequent processing and classification. While the choice of feature representation is often not central to the logic of a given algorithm, the quality of the feature representation can have critically important implications for performance. Here, we demonstrate a large-scale feature search approach to generating new, more powerful feature representations in which a multitude of complex, nonlinear, multilayer neuromorphic feature representations are randomly generated and screened to find those best suited for the task at hand. In particular, we show that a brute-force search can generate representations that, in combination with standard machine learning blending techniques, achieve state-of-the-art performance on the Labeled Faces in the Wild (LFW) [19] unconstrained face recognition challenge set. These representations outperform previous state-of-the-art approaches, in spite of requiring less training data and using a conceptually simpler machine learning backend. We argue that such large-scale-search-derived feature sets can play a synergistic role with other computer vision approaches by providing a richer base of features with which to work. (C) 2012 Elsevier B.V. All rights reserved.

Bidirectional LSTM-RNN-based hybrid deep learning frameworks for univariate time series classification

Time series classification (TSC) has been around for recent decades as a significant research problem for industry practitioners as well as academic researchers. Due to the rapid increase in temporal data in a wide range of disciplines, an incredible amount of algorithms have been proposed. This paper proposes robust approaches based on state-of-the-art techniques, bidirectional long short-term memory (BiLSTM), fully convolutional network (FCN), and attention mechanism. A BiLSTM considers both forward and backward dependencies, and FCN is proven to be good at feature extraction as a TSC baseline. Therefore, we augment BiLSTM and FCN in a hybrid deep learning architecture, BiLSTM-FCN. Moreover, we similarly explore the use of the attention mechanism to check its efficiency on BiLSTM-FCN and propose another model ABiLSTM-FCN. We validate the performance on 85 datasets from the University of California Riverside (UCR) univariate time series archive. The proposed models are evaluated in terms of classification testing error and f1-score and also provide performance comparison with various existing state-of-the-art techniques. The experimental results show that our proposed models perform comprehensively better than the existing state-of-the-art methods and baselines.

Simultaneously Stabilizing Both Electrodes and Electrolytes by a Self-Separating Organometallics Interface for High-Performance Zinc-Ion Batteries at Wide Temperatures

Rechargeable aqueous zinc-ion batteries are of great potential as one of the next-generation energy-storage devices due to their low cost and high safety. However, the development of long-term stable electrodes and electrolytes still suffers from great challenges. Herein, a self-separation strategy is developed for an interface layer design to optimize both electrodes and electrolytes simultaneously. Specifically, the coating with an organometallics (sodium tricyanomethanide) evolves into an electrically responsive shield layer composed of nitrogen, carbon-enriched polymer network, and sodium ions, which not only modulates the zinc-ion migration pathways to inhibit interface side reactions but also adsorbs onto Zn perturbations to induce planar zinc deposition. Additionally, the separated ions from the coating can diffuse to the electrolyte to affect the Zn2+ solvation structure and maintain the cathode structural stability by forming a stable cathode-electrolyte interface and sodium ions' equilibrium, confirmed by in situ spectroscopy and electrochemical analysis. Due to these unique advantages, the symmetric zinc batteries exhibit an extralong cycling lifespan of 3000 h and rate performance at 20 mA cm-2 at wide temperatures. The efficiency of the self-separation strategy is further demonstrated in practical full batteries with an ultralong lifespan over 10 000 cycles from -35 to 60 °C.

An Efficient Optimization Framework for Multi-Region Segmentation Based on Lagrangian Duality

We introduce a multi-region model for simultaneous segmentation of medical images. In contrast to many other models, geometric constraints such as inclusion and exclusion between the regions are enforced, which makes it possible to correctly segment different regions even if the intensity distributions are identical. We efficiently optimize the model using a combination of graph cuts and Lagrangian duality which is faster and more memory efficient than current state of the art. As the method is based on global optimization techniques, the resulting segmentations are independent of initialization. We apply our framework to the segmentation of the left and right ventricles, myocardium and the left ventricular papillary muscles in magnetic resonance imaging and to lung segmentation in full-body X-ray computed tomography. We evaluate our approach on a publicly available benchmark with competitive results.

Biogeography-based optimization scheme for solving the coverage and connected node placement problem for wireless sensor networks

In wireless sensor networks, coverage and connectivity are the fundamental problems for monitoring the targets and guaranteed information dissemination to the far away base station from each node which covers the target. This problem has been proved NP-complete problem, where a set of target points are given, the objective is to find optimal number of suitable positions to organize sensor nodes such that it must satisfy both k-coverage and m-connectivity requirements. In this paper, a biogeography-based optimization (BBO) scheme is used to solve this problem. The proposed BBO-based scheme provides an efficient encoding scheme for the habitat representation and formulates an objective function along with the BBO's migration and mutation operators. Simulation results show the performance of the proposed scheme to find approximate optimal number of suitable positions under different combinations of k and m. In addition, a comparative study with state-of-art schemes has also been done and its analysis confirms the superiority of the proposed BBO-based scheme over state-of-art schemes.

VOC exposures in a mixed-use university art building

Despite a sizable educational art enterprise in the United States there is a dearth of rigorously performed studies of exposures to persons engaged in such activities. Exposures to 45 EPA-designated volatile organic compounds were examined in printmakers in a mixed-use university art school served by a 100% exhausted mechanical ventilation system. Personal exposures (n=90) were compared with area concentrations (n=36) in the studio area and at a second location at the same facility. For personal exposure assessments a cohort of 12 students wore passive dosimeters twice weekly over a 6-week period. Numerous compounds were found, the most prevalent being toluene at an average concentration of 64.6 mug/m(3) (17.1 ppb; range <1-319 ppb); 1,1,1, trichloroethane at 40.5 mug/m(3) (7.5 ppb; range <11-211 ppb); xylenes at 8.0 mug/m(3) (1.8 ppb; range <1-43 ppb); 1,3,5-trimethyl benzene at 6.2 mug/m(3) (1.3 ppb; range <.3-32 ppb); propyl benzene at 5.0 mug/m(3) (1.0 ppb; range <.5-27 ppb); methylene chloride at 4.9 mug/m(3) (1.4 ppb; range <1-10 ppb); and ethyl benzene at 4.5 mug/m(3) (1.1 ppb; range <.4-23 ppb). Personal exposures were considerably higher than average area air concentrations, with the exception of methylene chloride concentrations, which were five times higher at the print cleaning operation. Floors where solvents were not used had no detectable exposures (typical lower limit of detection 1 ppb) and were free of solvent odors. Despite frequent solvent contact with skin, personal protective equipment was seldom used. Results indicate that in mixed-use facilities such as this, nonrecirculating general ventilation systems can effectively eliminate indoor air quality issues between floors, despite perceptible odors on solvent use floors. For total exposure assessments in such processes, contact exposures from printmaking solvents during cleaning procedures are a potentially important consideration.

A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images

Obtaining the complete segmentation map of retinal lesions is the first step toward an automated diagnosis tool for retinopathy that is interpretable in its decision-making. However, the limited availability of ground truth lesion detection maps at a pixel level restricts the ability of deep segmentation neural networks to generalize over large databases. In this paper, we propose a novel approach for training a convolutional multi-task architecture with supervised learning and reinforcing it with weakly supervised learning. The architecture is simultaneously trained for three tasks: segmentation of red lesions and of bright lesions, those two tasks done concurrently with lesion detection. In addition, we propose and discuss the advantages of a new preprocessing method that guarantees the color consistency between the raw image and its enhanced version. Our complete system produces segmentations of both red and bright lesions. The method is validated at the pixel level and per-image using four databases and a cross-validation strategy. When evaluated on the task of screening for the presence or absence of lesions on the Messidor image set, the proposed method achieves an area under the ROC curve of 0.839, comparable with the state-of-the-art.

The History of Acta geographica Slovenica

Acta geographica Slovenica is a research journal for geography and related disciplines published by the Anton Melik Geographical Institute of Scientific Research Centre of the Slovenian Academy of Sciences and Arts. It has been published since 1952 and is the second-oldest Slovenian geographical journal. Volume 50 was published in 2010, and this article is dedicated to this special anniversary. The journal was only published occasionally until 1976, when the volume 14 appeared, but afterwards it began to be published annually, with two volumes a year since 2003 (volume 43). With volume 43, the journal was included in Science Citation Index Expanded (SCIE). Since 2010, it has also had an impact factor. For 2009, this factor was 0.714, which ranks the journal in third place among all indexed Slovenian journals. In all the volumes, a total of 273 research articles have been published on more than 12,000 pages; half of these articles were written by the institute members.

A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification

Convolutional autoencoders (CAEs) have shown their remarkable performance in stacking to deep convolutional neural networks (CNNs) for classifying image data during the past several years. However, they are unable to construct the state-of-the-art CNNs due to their intrinsic architectures. In this regard, we propose a flexible CAE (FCAE) by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional CAE. We also design an architecture discovery method by exploiting particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed FCAE with much less computational resource and without any manual intervention. We test the proposed approach on four extensively used image classification data sets. Experimental results show that our proposed approach in this paper significantly outperforms the peer competitors including the state-of-the-art algorithms.

Total Deep Variation: A Stable Regularization Method for Inverse Problems

Various problems in computer vision and medical imaging can be cast as inverse problems. A frequent method for solving inverse problems is the variational approach, which amounts to minimizing an energy composed of a data fidelity term and a regularizer. Classically, handcrafted regularizers are used, which are commonly outperformed by state-of-the-art deep learning approaches. In this work, we combine the variational formulation of inverse problems with deep learning by introducing the data-driven general-purpose total deep variation regularizer. In its core, a convolutional neural network extracts local features on multiple scales and in successive blocks. This combination allows for a rigorous mathematical analysis including an optimal control formulation of the training problem in a mean-field setting and a stability analysis with respect to the initial values and the parameters of the regularizer. In addition, we experimentally verify the robustness against adversarial attacks and numerically derive upper bounds for the generalization error. Finally, we achieve state-of-the-art results for several imaging tasks.

SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map

Modern data augmentation strategies such as Cutout, Mixup, and CutMix, have achieved good performance in image recognition tasks. Particularly, the data augmentation approaches, such as Mixup and CutMix, that mix two images to generate a mixed training image, could generalize convolutional neural networks better than single image-based data augmentation approaches such as Cutout. We focus on the fact that the mixed image can improve generalization ability, and we wondered if it would be effective to apply it to a single image. Consequently, we propose a new data augmentation method to produce a self-mixed image based on a saliency map, called SalfMix. Furthermore, we combined SalfMix with state-of-the-art two images-based approaches, such as Mixup, SaliencyMix, and CutMix, to increase the performance, called HybridMix. The proposed SalfMix achieved better accuracies than Cutout, and HybridMix achieved state-of-the-art performance on three classification datasets: CIFAR-10, CIFAR-100, and TinyImageNet-200. Furthermore, HybridMix achieved the best accuracy in object detection tasks on the VOC dataset, in terms of mean average precision.

Fuzzy ART neural network algorithm for classifying the power system faults

This paper introduces advanced pattern recognition algorithm for classifying the transmission line faults, based on combined use of neural network and fuzzy logic. The approach utilizes self-organized, supervised Adaptive Resonance Theory (ART) neural network with fuzzy decision rule applied on neural network outputs to improve algorithm selectivity for a variety of real events not necessarily anticipated during training. Tuning of input signal preprocessing steps and enhanced supervised learning are implemented, and their influence on the algorithm classification capability is investigated. Simulation results show improved algorithm recognition capabilities when compared to a previous version of ART algorithm for each of the implemented scenarios.

Socioeconomic inequalities in molecular risk for chronic diseases observed in young adulthood

Many common chronic diseases of aging are negatively associated with socioeconomic status (SES). This study examines whether inequalities can already be observed in the molecular underpinnings of such diseases in the 30s, before many of them become prevalent. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a large, nationally representative sample of US subjects who were followed for over two decades beginning in adolescence. We now have transcriptomic data (mRNA-seq) from a random subset of 4,543 of these young adults. SES in the household-of-origin and in young adulthood were examined as covariates of a priori-defined mRNA-based disease signatures and of specific gene transcripts identified de novo. An SES composite from young adulthood predicted many disease signatures, as did income and subjective status. Analyses highlighted SES-based inequalities in immune, inflammatory, ribosomal, and metabolic pathways, several of which play central roles in senescence. Many genes are also involved in transcription, translation, and diverse signaling mechanisms. Average causal-mediated effect models suggest that body mass index plays a key role in accounting for these relationships. Overall, the results reveal inequalities in molecular risk factors for chronic diseases often decades before diagnoses and suggest future directions for social signal transduction models that trace how social circumstances regulate the human genome.

Knowledge Graph Embedding via Graph Attenuated Attention Networks

Knowledge graphs contain a wealth of real-world knowledge that can provide strong support for artificial intelligence applications. Much progress has been made in knowledge graph completion, state-of-the-art models are based on graph convolutional neural networks. These models automatically extract features, in combination with the features of the graph model, to generate feature embeddings with a strong expressive ability. However, these methods assign the same weights on the relation path in the knowledge graph and ignore the rich information presented in neighbor nodes, which result in incomplete mining of triple features. To this end, we propose Graph Attenuated Attention networks(GAATs), a novel representation method, which integrates an attenuated attention mechanism to assign different weight in different relation path and acquire the information from the neighborhoods. As a result, entities and relations can be learned in any neighbors. Our empirical research provides insight into the effectiveness of the attenuated attention-based models, and we show significant improvement compared to the state-of-the-art methods on two benchmark datasets WN18RR and FB15k-237.

Quality of counselling assessed by patients after total knee arthroplasty: A cross-sectional study

Patient counselling is a key function in nursing. High-quality counselling promotes adherence to treatment and reduces complications. The purpose of the study was to describe the quality of counselling experienced by total knee arthroplasty patients following surgery. The study was a descriptive cross-sectional study. The data were collected from patients following total knee arthroplasty (N = 60) in 2016 with a modified Quality of Counselling Instrument, and analysed using statistical methods. Over half of the patients (58%) were women and the mean age was 68 years (range 49-84). Over a quarter of patients (28.9%) lived alone, and about two-thirds were overweight (42.1%), or obese (31.6%). After surgery, many patients (88%) experienced moderate pain. Half of patients (52.6%) received a good quality of counselling for the disease and its treatment, and counselling for recovery from treatment (81.6%) was good. Most patients (92.1%) received satisfactory counselling about physical activity. There was a correlation between the disease and its treatment counselling and quality of life (r = -0.553, p = 0.003) and pain (r = -0657, p = 0.000). Interaction during counselling was good (97.4%) and it was implemented in a patient-centred way (89.5%). High-quality counselling implemented in a patient-centred manner can play a part in reducing pain and increasing patients' quality of life.

How to activate intuitive and reflective thinking in behavior research? A comprehensive examination of experimental techniques

Experiments comparing intuitive and reflective decisions provide insights into the cognitive foundations of human behavior. However, the relative strengths and weaknesses of the frequently used experimental techniques for activating intuition and reflection remain unknown. In a large-scale preregistered online experiment (N = 3667), we compared the effects of eight reflection, six intuition, and two within-subjects manipulations on actual and self-reported measures of cognitive performance. Compared to the overall control, the long debiasing training was the most effective technique for increasing actual reflection scores, and the emotion induction was the most effective technique for increasing actual intuition scores. In contrast, the reason and the intuition recall, the reason induction, and the brief time delay conditions failed to achieve the intended effects. We recommend using the debiasing training, the decision justification, or the monetary incentives technique to activate reflection, and the emotion induction, the cognitive load, or the time pressure technique to activate intuition.

Looking through past records: The use of historical documents in cave art spatial studies and its application to La Pasiega (Puente Viesgo, Cantabria, Spain)

In the course of the last decades, new cave art discoveries such as La Garma, Chauvet-Pont-d'Arc, Le Reseau Clastres in the Niaux Cave, Cosquer and Cussac have allowed researchers to advance in context and spatial studies related to the art. This has been possible because the decorated chambers were intact at the moment of the discovery and, soon after, protocols were put in place to protect these invaluable records. These types of caves are a minority. In the Cantabrian region, most of the discoveries took place at the beginning of the 20th century and, in some cases, a few years after the first studies were published, the caves were greatly modified to prepare them for tourist visits in the 1950s. However, the study of historical documents can provide information regarding the context and the original spatial distribution of the caves. Using the available data from different historical sources such as pictures, descriptions, sketches, plans, etc. available in publications and unpublished materials, we can reconstruct, to a limited extent, the appearance of a cave in the moment of its discovery. The information gathered by the different researchers in the last hundred years to advance in the knowledge of La Pasiega cave in Puente Viesgo (Cantabria) is used to prove the validity of this approach. The results, combining information from the available sources and careful observation in the cave, are positive, allowing us to advance significantly in the understanding of the cave's spatial characteristics. (C) 2016 Elsevier Ltd and INQUA. All rights reserved.

A Qualification Approach to DAC Mismatch-Shaping Methods

Linearizing a digital-to-analog converter by mismatch noise shaping is popular particularly in the art of Delta-Sigma converters. This work proposes a procedure for characterizing and benchmarking the shaping methods. The proposed approach provides five graphical figures of merit related to in-band spurious level and noise floor. In our case-studies, we will concentrate on the data-weighted averaging method with low-and bandpass shaping alongside a combination of two-tone suppression methods.

NSGA-II with objective-specific variation operators for multiobjective vehicle routing problem with time windows

Vehicle routing problem with time windows (VRPTW) is a pivotal problem in logistics domain as it possesses multiobjective characteristics in real-world applications. Literature contains a general multiobjective VRPTW (MOVRPTW) with five objectives along with MOVRPTW benchmark instances that are derived from real-world data. In this paper, we have proposed a nondominated sorting genetic algorithm II (NSGA-II) based approach with objective-specific variation operators to address the MOVRPTW. In the proposed NSGA-II approach, the crossover and mutation operators are designed by exploiting the problem characteristics as well as the attributes of each objective. The performance of the proposed approach is evaluated on the standard benchmark instances of the problem and compared with the state-of-the-art approach available in literature. The computational results demonstrate the superiority of our approach over the state-of-the-art approach for the MOVRPTW.

Toward Multicenter Skin Lesion Classification Using Deep Neural Network With Adaptively Weighted Balance Loss

Recently, deep neural network-based methods have shown promising advantages in accurately recognizing skin lesions from dermoscopic images. However, most existing works focus more on improving the network framework for better feature representation but ignore the data imbalance issue, limiting their flexibility and accuracy across multiple scenarios in multi-center clinics. Generally, different clinical centers have different data distributions, which presents challenging requirements for the network's flexibility and accuracy. In this paper, we divert the attention from framework improvement to the data imbalance issue and propose a new solution for multi-center skin lesion classification by introducing a novel adaptively weighted balance (AWB) loss to the conventional classification network. Benefiting from AWB, the proposed solution has the following advantages: 1) it is easy to satisfy different practical requirements by only changing the backbone; 2) it is user-friendly with no tuning on hyperparameters; and 3) it adaptively enables small intraclass compactness and pays more attention to the minority class. Extensive experiments demonstrate that, compared with solutions equipped with state-of-the-art loss functions, the proposed solution is more flexible and more competent for tackling the multi-center imbalanced skin lesion classification task with considerable performance on two benchmark datasets. In addition, the proposed solution is proved to be effective in handling the imbalanced gastrointestinal disease classification task and the imbalanced DR grading task. Code is available at https://github.com/Weipeishan2021.

A Case Report of a Normal Pregnancy in a Bicornuate Uterus Through In Vitro Fertilization

The case report has been done to examine the possibility of normal pregnancy achieved in the case of a rare congenital anomaly, the bicornuate uterus. A bicornuate uterus is a very rare congenital anomaly of the uterus, which falls in the class 4 category according to the classification of Mullerian duct anomalies given by the American Society of Reproductive Medicine and is associated with several obstetrics complications like malpresentation, recurrent abortions, and growth restrictions. However, to have a normal pregnancy in a bicornuate uterus, close antenatal monitoring is required, and, depending on the individual, surgical unification can be done. A 30-year-old woman with G3A2 with 34.3 weeks of gestational age with in vitro fertilization (IVF) conception came with cervical stitch in situ and oligohydramnios with liquor index 7 for safe confinement. At the time of admission, amenorrhea was present for nine months. Ultrasound at 33 weeks three days showed a single uterine live fetus weighing about 2187 grams. The interpretation of the color Doppler was also normal. Fetal heart sound was heard in the Doppler. She was operated on at 36 weeks as an emergency lower-section cesarean section procedure. The indication was that it was an IVF baby, and the female had presented with oligohydramnios on performing investigations. The patient was counseled accordingly and discharged on 22 February 2022. She was advised to come back after 15 days or SOS at the time of emergency. All the measures were suggested, including adequate rest, plenty of fluids, and a good protein diet. Most cases of the bicornuate uterus do not present with any symptoms, i.e., they are asymptomatic and can be detected during routine evaluation of the patient. However, some patients can also have symptoms like menstrual problems such as dysmenorrhea and menorrhagia. Also, along with this anomaly, associated anomalies may be present, including agenesis of the kidney and ureter. The first and foremost investigation to be done is ultrasonography, which tells about the diagnosis of the bicornuate uterus. Magnetic resonance imaging is the gold standard test for its diagnosis. However, the diagnosis in the case of asymptomatic patients is relatively tricky and requires aggressive prenatal monitoring and needs to be kept in observation to make the pregnancy successful.

Ultra low phase noise sapphire - SiGe HBT oscillator

A state of the art C band oscillator is presented. It is based on a high Q WGM sapphire resonator and on a low residual phase noise SiGe HBT amplifier. A two oscillator experiment performed on this system has revealed a phase noise level of -133 dBc/Hz at 1 kHz offset from the 4.85 GHz carrier, which is the best published phase noise result for a single loop, free running microwave oscillator.

Beyond the science and art of the healthy buildings daylighting dynamic control's performance prediction and validation

Further advance of the healthy building's energy efficiency and sustainability is inextricably linked to the building's envelopes/facades physics study, particularly fundamentals of the dynamic control of sunlight and optimal control of solar heat gains. Based on the improved understanding of mechanism which physically control specific materials intensity, e.g. absorption, reflection and transmission of solar radiation, are to be improved strategies to dynamically control separation of the daylight admittance from the solar heat gains. Relevant dynamic control mathematical models and algorithms, as well as infrastructure/hardware and software integrated performance prediction and validation are to be further developed. This paper reviews the most recent research and development results, the current state of the science and art, as well as some of the ongoing R&D at the edge of new breakthroughs of the healthy buildings daylighting dynamic control's performance prediction and validation. Finally defined is a challenging future research goal-tuning control of buildings glazing's transmittance dependence on the solar radiation wavelength to optimize daylighting and building's energy efficiency. (c) 2011 Elsevier B.V. All rights reserved.

Robust Prostate Segmentation Using Intrinsic Properties of TRUS Images

Accurate segmentation is usually crucial in transrectal ultrasound (TRUS) image based prostate diagnosis; however, it is always hampered by heavy speckles. Contrary to the traditional view that speckles are adverse to segmentation, we exploit intrinsic properties induced by speckles to facilitate the task, based on the observations that sizes and orientations of speckles provide salient cues to determine the prostate boundary. Since the speckle orientation changes in accordance with a statistical prior rule, rotation-invariant texture feature is extracted along the orientations revealed by the rule. To address the problem of feature changes due to different speckle sizes, TRUS images are split into several arc-like strips. In each strip, every individual feature vector is sparsely represented, and representation residuals are obtained. The residuals, along with the spatial coherence inherited from biological tissues, are combined to segment the prostate preliminarily via graph cuts. After that, the segmentation is fine-tuned by a novel level sets model, which integrates 1) the prostate shape prior, 2) dark-to-light intensity transition near the prostate boundary, and 3) the texture feature just obtained. The proposed method is validated on two 2-D image datasets obtained from two different sonographic imaging systems, with the mean absolute distance on the mid gland images only 1.06 +/- 0.53 mm and 1.25 +/- 0.77 mm, respectively. The method is also extended to segment apex and base images, producing competitive results over the state of the art.

Information overload's double-edged sword effect on sense of safety: Examining the moderating role of hypervigilance

Since the COVID-19 pandemic outbreak, long-term overlooked motives concerning a sense of safety have become a primary concern. People's sense of safety largely depends on the information they receive. Indeed, a tsunami of information about the virus has been disseminated by all forms of media to people's electronic devices, thus permeating their lives. This study proposed that the over-abundance of information, known as information overload, could endanger individuals' sense of safety by increasing their rumination about COVID-19. However, it could also enhance their sense of safety by increasing their positive attitudes toward COVID-19 precautions. Furthermore, we proposed that individuals' hypervigilance could strengthen the relationship between information overload and rumination about COVID-19 and attitudes toward COVID-19 precautions. We tested these hypotheses using a cross-sectional survey study (N = 403) in February 2021 and a diary study (N = 98) in July 2021 in China. The results of both studies support the dual mediating paths of the relationship between information overload and sense of safety. We also found that hypervigilance moderated the relationship between information overload and rumination about COVID-19. Overall, our study offers insights into how social media may influence people's sense of safety and how individual differences in hypervigilance play a role in the process.

Association between vitamin D status and serum parathyroid hormone concentration and calcaneal stiffness in Japanese adolescents: sex differences in susceptibility to vitamin D deficiency

There is currently insufficient information on serum 25-hydroxyvitamin D (25OHD) and parathyroid hormone (PTH) concentrations, and bone mineral status in healthy adolescents to allow reference values to be set. This study aimed to provide comparable data on vitamin D status in Japanese adolescents and to assess sex differences in susceptibility to vitamin D insufficiency. Serum 25OHD and PTH concentrations were measured in 1,380 healthy adolescents (aged 12-18 years). Subjects completed a questionnaire on exercise history, diet, and lifestyle factors. Calcaneal stiffness was evaluated by quantitative ultrasound. Serum 25OHD concentrations in boys and girls were 60.8 ± 18.3 and 52.8 ± 17.0 nmol/L, respectively. Approximately 30 % of boys and 47 % of girls had suboptimal 25OHD concentrations (<50 nmol/L). Serum PTH concentration was negatively correlated with serum 25OHD concentration in boys, but negatively correlated with calcium intake rather than serum 25OHD in girls. In contrast, the increment in calcaneal stiffness as a result of elevation of serum 25OHD was higher in girls than in boys. As vitamin D deficiency is common in Japanese adolescents, it was estimated that intakes of ≥12 and ≥14 μg/day vitamin D would be required to reach 25OHD concentrations of 50 nmol/L in boys and girls, respectively. Moreover, the results of the present study indicate that vitamin D deficiency has a greater association with calcaneal stiffness in girls than in boys.

First Report of Leaf Spot on Ipomoea nil Caused by Alternaria alternata in China

Ipomoea nil (Linnaeus) Roth, belonging to the Convolvulaceae family, is an ornamental and medicinal plant in China, which has the function of diuretic and expectorant, and it is also a common weed in the field. In October 2021, a leaf spot disease was observed on I. nil in a field as weed in Jingzhou (N 30° 21', E 112° 19'), Hubei Province, China. Symptoms began as small brown blotches, then developed into oval or irregularly shaped brown necrotic lesions. In severe cases, the leaves were completely necrotic and detached. In the surveyed area, the incidence was between 30% - 40%. To isolate the pathogen, twenty-one leaf pieces (5×5 mm) were cut from the lesion edges of seven symptomatic leaves, disinfected with 70% ethanol and 2% sodium hypochlorite (NaOCl), rinsed with sterile water five times, then placed on three potato dextrose agar (PDA) modified with 50 μg/mL kanamycin, and incubated at 25 °C in dark for 5 days. The isolates were subcultured by transferring mycelium tips. Sixteen fungal strains were isolated from the tissues, and nine of them showed similar morphological characteristics. After cultured 7 days on PDA at 25 °C, the nine colonies were initially white, then turned greenish brown to black in the center and had abundant fine villous aerial mycelia up to 61.5 mm in average diameter. To examine its conidial morphology, the fungi were cultured for 7 days on potato carrot agar (PCA) at 22°C with a light/dark period of 8/16 h. On PCA, conidia were brown or olive-brown, obclavate to obpyriform, with a short beak, one to five transverse and zero to three longitudinal septa. They formed chains of 1 - 8 conidia, with branches. Conidia were 16 - 46 µm long and 8 - 14 µm wide (n=50). These morphological features were similar to those described in Alternaria spp. (Simmons 2007). A single isolate "Q2" was selected for molecular identification because it was the most aggressive in preliminary leaf pathogenicity assays. The internal transcribed spacer (ITS) region of rDNA and histone 3 (H3) gene were amplified and sequenced using primers ITS1/ITS4 (White et al. 1990) and H3-1a/H3-1b (Zheng et al. 2015). BLAST analysis revealed that the sequences (ITS, ON360984; H3, ON375577) were 100% identical to Alternaria alternata (ITS, MK396607; H3, MN840996), respectively. Maximum likelihood analysis based on combined two gene sequences was conducted with an evolutionary model of GTR+I+G under 1000 bootstrap replicates. Phylogenetic tree showed that Q2 and Alternaria alternata 21-5 and BLH-YB-11 located in one clade supported with 99% bootstrap values. The pathogen was identified as A. alternata. To fulfill Koch's postulate, 10 ml conidia (106 spores/ml) of Q2 was sprayed on five healthy seedlings, with sterile distilled water as a control. All leaves were rinsed three times with sterile water before inoculation. All seedlings were placed in sealed plastic bags with air valves, and grown in a greenhouse (25 ± 2 ˚C, RH 65%). The test was repeated twice. After 10 days, symptoms typical of brown blotches similar to those observed in the field were observed on leaves of inoculated plants, while control remained healthy. A. alternata was re-isolated from the inoculated symptomatic leaves with a frequency of 100% based on morphological and molecular characters, thus Koch's postulate was confirmed. To the best of our knowledge, this is the first report of A. alternata causing leaf spot on I. nil in China. Our findings extended the host range of the pathogen A. alternata on characteristic plants.

[Innovating in the hospital of tomorrow]

The hospital of tomorrow must aim not only to be an innovative establishment, but also to become an establishment that innovates. The objective is to bring up initiatives and the expression of needs of the hospital community, to evaluate and prioritize them, and then to implement the selected projects.

Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images

Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by using deep neural networks. However, training supervised deep models such as convolutional neural networks (CNNs) usually requires sufficient annotated image data, which is prohibitively expensive or unavailable in some applications. Additionally, when applying a CNN to new datasets, it is common to annotate individual cells/nuclei in those target datasets for model re-learning, leading to inefficient and low-throughput image analysis. To tackle these problems, we present a bidirectional, adversarial domain adaptation method for nucleus detection on cross-modality microscopy image data. Specifically, the method learns a deep regression model for individual nucleus detection with both source-to-target and target-to-source image translation. In addition, we explicitly extend this unsupervised domain adaptation method to a semi-supervised learning situation and further boost the nucleus detection performance. We evaluate the proposed method on three cross-modality microscopy image datasets, which cover a wide variety of microscopy imaging protocols or modalities, and obtain a significant improvement in nucleus detection compared to reference baseline approaches. In addition, our semi-supervised method is very competitive with recent fully supervised learning models trained with all real target training labels.

Deep Geodesic Learning for Segmentation and Anatomical Landmarking

In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space. The overall approach employs three inter-related steps. In the first step, we propose a deep neural network architecture with carefully designed regularization, and network hyper-parameters to perform image segmentation without the need for data augmentation and complex post-processing refinement. In the second step, we formulate the landmark localization problem directly on the geodesic space for sparsely-spaced anatomical landmarks. In the third step, we utilize a long short-term memory network to identify the closely-spaced landmarks, which is rather difficult to obtain using other standard networks. The proposed fully automated method showed superior efficacy compared to the state-of-the-art mandible segmentation and landmarking approaches in craniofacial anomalies and diseased states. We used a very challenging CBCT data set of 50 patients with a high-degree of craniomaxillofacial variability that is realistic in clinical practice. The qualitative visual inspection was conducted for distinct CBCT scans from 250 patients with high anatomical variability. We have also shown the state-of-the-art performance in an independent data set from the MICCAI Head-Neck Challenge (2015).

Physically plausible propeller noise prediction via recursive corrections leveraging prior knowledge and experimental data

For propeller-driven vessels, cavitation is the most dominant noise source producing both structure-borne and radiated noise impacting wildlife, passenger comfort, and underwater warfare. Physically plausible and accurate predictions of the underwater radiated noise at design stage, i.e., for previously untested geometries and operating conditions, are fundamental for designing silent and efficient propellers. State-of-the-art predictive models are based on physical, data-driven, and hybrid approaches. Physical models (PMs) meet the need for physically plausible predictions but are either too computationally demanding or not accurate enough at design stage. Data-driven models (DDMs) are computationally inexpensive ad accurate on average but sometimes produce physically implausible results. Hybrid models (HMs) combine PMs and DDMs trying to take advantage of their strengths while limiting their weaknesses but state-of-the-art hybridisation strategies do not actually blend them, failing to achieve the HMs full potential. In this work, for the first time, we propose a novel HM that recursively correct a state-of-the-art PM by means of a DDM which simultaneously exploits the prior physical knowledge in the definition of its feature set and the data coming from a vast experimental campaign at the Emerson Cavitation Tunnel on the Meridian standard propeller series behind different severities of the axial wake. Results in different extrapolating conditions, i.e., extrapolation with respect to propeller rotational speed, wakefield, and geometry, will support our proposal both in terms of accuracy and physical plausibility.

Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images

Human activities in the sea, such as intensive fishing and exploitation of offshore wind farms, may impact negatively on the marine mega fauna. As an attempt to control such impacts, surveying, and tracking of marine animals are often performed on the sites where those activities take place. Nowadays, thank to high resolution cameras and to the development of machine learning techniques, tracking of wild animals can be performed remotely and the analysis of the acquired images can be automatized using state-of-the-art object detection models. However, most state-of-the-art detection methods require lots of annotated data to provide satisfactory results. Since analyzing thousands of images acquired during a flight survey can be a cumbersome and time consuming task, we focus in this article on the weakly supervised detection of marine animals. We propose a modification of the patch distribution modeling method (PaDiM), which is currently one of the state-of-the-art approaches for anomaly detection and localization for visual industrial inspection. In order to show its effectiveness and suitability for marine animal detection, we conduct a comparative evaluation of the proposed method against the original version, as well as other state-of-the-art approaches on two high-resolution marine animal image datasets. On both tested datasets, the proposed method yielded better F1 and recall scores (75% recall/41% precision, and 57% recall/60% precision, respectively) when trained on images known to contain no object of interest. This shows a great potential of the proposed approach to speed up the marine animal discovery in new flight surveys. Additionally, such a method could be adopted for bounding box proposals to perform faster and cheaper annotation within a fully-supervised detection framework.

Short-Term Load Forecasting Using Recurrent Neural Networks With Input Attention Mechanism and Hidden Connection Mechanism

Short-term load forecasting is a critical task in the smart grid, which can be used to optimize power deployment and reduce power losses. Recurrent neural networks (RNNs) are the most popular deep learning models for short-term load forecasting. However, despite of achieving better forecasting accuracy than the traditional models, the performance of the existing RNN-based load forecasting approaches is still unsatisfactory for practical usage. Therefore, in this work, we have proposed input attention mechanism (IAM) and hidden connection mechanism (HCM) to greatly enhance the accuracy and efficiency of RNN-based load forecasting models. Specifically, we use IAM to assign the importance weights on input layers, which have better performances in both efficiency and accuracy than traditional attention mechanisms. To further enhance the models' efficiency, HCM is then applied to utilize residual connections to enhance the model's converging speed. We have applied both IAM and HCM on four state-of-the-art RNN implementations, and then conducted extensive experimental studies on two public datasets. Experimental results show that the proposed RNNs with IAM and HCM models achieve much better performances than the state-of-the-art baselines in both accuracy and efficiency. Ablation studies show that both IAM and HCM are essential to achieve such superior performances.

A Spectral Approach to Inter-Carrier Interference Mitigation in OFDM Systems

In this paper, we propose a new method for intercarrier interference (ICI) mitigation in orthogonal frequency-division multiplexing (OFDM) systems. The proposed approach views the signal reconstruction problem at the receiver end as an integer least squares (ILS) problem, and uses a recently developed spectral approach called sequential probabilistic ILS (SPILS) to solve it. The proposed approach outperforms other state-of-the-art approaches while having the same computational complexity. In addition, we present a novel extension to the SPILS scheme that allows the generation of soft decisions (for communication systems which use soft-decision decoding). The use of soft-decision decoding (naturally) brings significant improvement in the detection reliability, and we show that the proposed method again outperforms other state-of-the-art approaches. To better address the tradeoff between performance and complexity, we first suggest a novel method to reduce the number of matrix inversions required and hence, to reduce the implementation complexity without any degradation in performance. We also introduce a novel low complexity scheme termed Quick SPILS (QSPILS) in which we lose a little in detection reliability, but significantly reduce the implementation complexity.

Video Deraining Using the Visual Properties of Rain Streaks

In computer vision applications, the visibility of the video content is crucial to perform analysis for better accuracy. The visibility can be affected by several atmospheric interferences in challenging weather - one such interference is the appearance of rain streaks. Recently, rain streak removal has achieved plenty of interest among researchers, as it has some exciting applications such as autonomous cars, intelligent traffic monitoring systems, multimedia, etc. In this paper, we propose a novel and simple method of rain streak removal by combining three novel extracted visual features focusing on the temporal appearance, wide shape and relative location of the rain streak. We called it the TAWL (Temporal Appearance, Width, and Location) method. The proposed TAWL method adaptively uses features from different resolutions and frame rates. Moreover, it progressively processes features from the upcoming frames so that it can remove rain in real-time. Experiments have been conducted using video sequences with both real rain and synthetic rain to compare the performance of the proposed method against the relevant state-of-the-art methods. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods by removing more rain streaks while keeping other moving regions.

Direct localisation of molecules in tissue sections of growing antler tips using MALDI imaging

The astonishing growth rate of deer antlers offers a valuable model for the discovery of novel factors and regulatory systems controlling rapid tissue growth. Numerous molecules have been identified in growing antlers using a variety of techniques. However, little is known about the spatial distribution of these molecules in situ. A technique that has the potential to help in this regard is direct proteomic analysis of tissue sections by matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS). The present study applied this technique to spatially map molecules in antler tissue sections. Two protonated molecular ions were selected: m/z 6679 and m/z 6200 corresponding to VEGF and thymosin beta-10, respectively. Superimposition of the respective ion images on to histologically stained samples showed distinct spatial distribution across the antler tissue sections which were consistent with the previous reports using in situ hybridization. Two other molecular ions specifically m/z 8100 and m/z 11,800 were also selected, corresponding to reported masses of urocortin precursor and thioredoxin, respectively. As the spatial distribution of these proteins is not specifically known, MALDI-IMS was used as a potential technique to obtain information on their distribution on antler tips. The presence of all these molecules in deer antlers were further confirmed using LC-MS/MS data. The present study also demonstrated that MALDI-IMS could be further used to image antler sections with an extended ion mass range of up to m/z 45,000, thus potentially increasing the ability to discover the distribution of a larger set of molecules that may play an important role in antler growth. We have thus demonstrated that MALDI-IMS is a promising technique for generating molecular maps with high spatial resolution which can aid in evaluating the function of novel molecules during antler growth.

Cost standard set program: moving forward to standardization of cost assessment based on clinical condition

This communication piece is reporting the launching of the International Cost Standard set program, aiming to introduce standardized frameworks to measure costs for specific clinical conditions worldwide. A scientific committee including 16 international healthcare cost assessment experts from several countries, and International Consortium for Health Outcomes Measurement was formed to introduce the program. The committee got together in Lisbon for a first scientific meeting, followed by an international conference where time-driven activity-based costing applied studies were shared with the community. The cost standard set program start to offer instruments for people to measure with real-world data, the financial impact of having access to health technologies, improving the ability to evaluate inequity. Those advances might represent a paradigm shift in our ability to generate cost information on an individual level.

Consistent 3D human body segmentation based on combinatorial descriptor in spectral domain

Consistent 3D human body segmentation plays a vital role in many human-oriented applications. Recently research used supervised methods to achieve state-of-the-art performance. However, requiring massive labelled data and tedious training is a costly process. Unsupervised methods do not need labelling and training but struggle to achieve consistent segmentation for a non-rigid deformable mesh. Moreover, the segmentation style is also fixed. In the paper, we aim to achieve high-performance, consistent 3D human mesh segmentation avoiding fully-labelled data and time-consuming training. Specifically, this paper designs a Laplacian operator by incorporating mesh saliency, in which a face-level filter is proposed to improve the detection of concave vertices. Accordingly, we construct a combinatorial descriptor by explicitly employing the global and local attributes derived from the spectrum of the proposed saliency Laplacian operator to achieve consistent segmentation in the spectral domain. An automatic determination mechanism is adopted to determine the number of segments. Extensive experimental results demonstrate that the presented method is effective and efficient for many 3D meshes, especially for the human body shape. The segmentation results are comparable to other state-of-the-art performances without requiring time-consuming labelling and training on large-scale datasets.

Acute Mitral Valve Regurgitation Presenting With Right Upper Lobe Opacification

There is literature describing unilateral or focal pulmonary edema due to mitral regurgitation. The proposed mechanism is a regurgitant jet propelling blood towards the orifice of a particular pulmonary vein within the left atrium, which selectively pressurizes that vein. The increased hydrostatic pressure is transmitted to the pulmonary capillaries that drain into that vein, causing focal consolidation. A 62-year-old female presented with acute hypoxic respiratory failure. Her dyspnea started suddenly and she was unresponsive when she arrived at the emergency department via emergency medical services. Her initial oxygen saturation was 23% and she was immediately intubated. Sequential chest radiographs demonstrated dense consolidation in the right upper lung field and then opacification of the right hemithorax. These asymmetric lung findings were suspicious for infectious etiology but she was afebrile with no respiratory secretions and had normal inflammatory markers. Echocardiography showed a ruptured anterior papillary muscle causing a flail mitral valve leaflet with severe mitral regurgitation. The patient developed cardiogenic shock; she had an intra-aortic balloon pump placed for afterload reduction and was taken to the operating room for an emergency mitral valve replacement. Her clinical status rapidly improved and she made a full recovery. As in this case, acute mitral regurgitation can present with sudden life-threatening respiratory failure and cardiogenic shock so prompt diagnosis is critical. This is often misdiagnosed as pneumonia or other respiratory illnesses. Awareness, early diagnosis, and treatment of this entity could provide significant morbidity and mortality benefits for patients.

Fast, robust and efficient 2D pattern recognition for re-assembling fragmented images

We discuss the realization of a fast, robust and accurate pattern matching algorithm for comparison of digital images implemented by discrete Circular Harmonic expansions based on sampling theory. The algorithm and its performance for reassembling fragmented digital images are described in detail and illustrated by examples and data from the experimentation on an art fresco real problem. Because of the huge database of patterns and the large-scale dimension, the results of the experimentation are relevant to describe the power of discrimination and the efficiency of such method. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Same as it ever was? The Aurignacian of the Swabian Jura and the origins of Palaeolithic art

The Aurignacian of the Swabian Jura constitutes a key region for the understanding of the behaviour of the first populations of modern humans in Europe. The region has yielded works of figurative art and musical instruments that are among the oldest in the world. The objects are evidence for the existence of a new type of society distinct from those known in previous phases of human prehistory. This article highlights the innovations intrinsic to the beginning of the Upper Palaeolithic and contests the idea of a gradual evolution, which erodes the clear distinction between the Middle Palaeolithic and the Upper Palaeolithic at some point in the transition. (C) 2017 Published by Elsevier Ltd.

State of the Art in Sensor Technologies for Sewer Inspection

This paper reviews the state of the art in sensors and automated inspection devices for enhanced sewer inspection. Efficiency, safety, environmental, and legislative concerns have made inspection and assessment of communal sewers a central issue to water and sewerage companies. Nowadays, the standard sewer inspection system is based on a wheeled platform on which a closed circuit television (CCTV) camera is mounted. One of the disadvantages of camera inspection systems is that they can only detect a small proportion of all possible damage in a sewer. The inspection outcome of such systems relies not only on the quality of the acquired images, but also on the off-line recognition and classification conducted by human operators. In consequence, CCTV-based platforms are frequently not effective. Infrared, microwave, optical, and ultrasonic-based sensors have been proposed to complement the existing CCTV-based approach and to improve inspection results. New inspection devices employing multiple sensors and being capable of carrying out remote sewer inspection tasks are under research.

Color Pixel Reconstruction for a Monolithic RGB-Z CMOS Imager

In this paper, we introduce the challenges, intrinsic and extrinsic, of color and depth sensors integration in the same matrix for a monolithic RBG-Z CMOS imager system. Due to the fact that the technology to conceive this type of circuit is still under development, the challenge that we address is the extrinsic one. It is a consequence of the heterogeneity of the matrix, where information is missing compared to what can be provided by separate RGB and Z systems. For that a first evaluation is done taking into account how the RGB-Z patterns could impact the demosaicing step. The evaluated pattern are in function of the different sizes between color and depth pixels. For the missing color reconstruction we have evaluated the state of the art algorithms, adapted to the missing information, and we propose an original adaptive algorithm using a new operator called semi-gradient (SG). To fill the lack of a mature technology for which real images are missing for this type of CMOS imager, a test environment was created and then used with three different databases, Kodak, McMaster, HDR+burst. The results show improvements on edges, corners, and narrow lines reconstruction, and a reduction of color and structural artefacts compared to the state-of-the-art reconstruction algorithms.

Study on Distributed Consistent Cooperative Control of Multi-ART in Automated Container Terminals

In order to address the congestion problem of vehicles in quay of automated container terminal with parallel layout and side-loading operations, the study regards the terminal operation system as a multi-agent system (MAS). Then, A dynamic cooperative speed regulation strategy for a group intelligence-oriented multi-ART (Artificial Intelligence Robot of Transportation) is proposed to realize the smart and distributed traffic control of container terminals. Via the real-time data interchange and message passing with ART group, the individual ART implements the collaborative speed regulation with the combination of the dynamic speed regulation strategy. Through the above-mentioned method, the sequence of ARTs arriving at the quay can be adjusted, which can decrease the waiting time of ARTs at the quay. Considering the consistency problem of individual and group agent among the dynamic speed regulation of ARTs, a novel distributed consensus protocol is established to assure that the individual status of ART will tend to converge basically during the collaborative speed regulation process, and the convergence of system can be guaranteed. Based on the case of automated container terminal of Tianjin Port, the paper establishes a multi-agent based simulation model, and simulates the decision-making process, and validates the effectiveness of mentioned model and strategy. The results demonstrate that the strategy can decrease the waiting time of ARTs under uncertainty environment of terminal and improve the operation efficiency of system.

Association between Economic Growth, Mortality, and Healthcare Spending in 31 High-Income Countries

This study aims to investigate the association between gross domestic product (GDP), mortality rate (MR) and current healthcare expenditure (CHE) in 31 high-income countries. We used panel data from 2000 to 2017 collected from WHO and OECD databases. The association between CHE, GDP and MR was investigated through a random-effects model. To control for reverse causality, we adopted a test of Granger causality. The model shows that the MR has a statistically significant and negative effect on CHE and that an increase in GDP is associated with an increase of CHE (p < 0.001). The Granger causality analysis shows that all the variables exhibit a bidirectional causality. We found a two-way relationship between GDP and CHE. Our analysis highlights the economic multiplier effect of CHE. In the debate on the optimal allocation of resources, this evidence should be taken into due consideration.