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<|MaskedSetence|> As a result, we collected a non-redundant, self-distillation dataset with ground-truth secondary structure from the RNAStralign and bpRNA-1M databases. <|MaskedSetence|> RhoFold+ was initially trained using only PDB data, which was then used to generate a self-distillation dataset by inferring pseudo-structural labels. We re-trained the model by sampling 25% of the PDB data and 75% of the distillation data for further improvement. <|MaskedSetence|>
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**A**: We filtered this dataset by removing sequences with more than 256 or fewer than 16 nucleotides, resulting in a dataset of 27,732 sequences.
**B**: During training, we masked out pseudo-label residues with pLDDT scores <0.7 and uniformly sub-sampled the MSAs to augment the distillation dataset.
A structure prediction module.
**C**: Although our RNA-FM can alleviate the problem of data scarcity, there is still less structural data available for RNAs than for proteins.
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Author contributions
SL studied the virial theorem while participating in the “Introduction to Astrophysics” cluster in the COSMOS summer program held at UC Irvine from July 9, 2023 to August 4, 2023. <|MaskedSetence|> LP learned of the virial theorem from SL and, in discussing its proof with SL, realized that it must be related to the Price equation. <|MaskedSetence|> CF identified and developed the connection to ecological orbits and simple harmonic motion via the maternal effect. <|MaskedSetence|> CF, SL, and LP edited the final version and code [12]. The authors are ordered alphabetically.
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**A**: Specifically, she used the virial theorem to repeat Zwicky’s Coma cluster mass estimates using modern measurements of velocity dispersion and galaxy positions.
**B**: SL and LP explored the applications and implications of the connection.
**C**: LP drafted the initial manuscript; both SL and LP edited the first version posted on arXiv [24].
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Although various methods have been developed to enhance performance estimation in model selection using k-fold CV, their design and implementation have been limited to SO problems. <|MaskedSetence|> <|MaskedSetence|> These algorithms modify fitness estimations but do not change the model selection process; the chosen model remains the same as it would be using simple hyperparameter optimization with k-fold CV for model evaluation. Automated ML (AutoML) tools offer an approach designed to explore various model and hyperparameter combinations. These tools aim to identify and deliver the most effective model along with an assessment of its performance. In Tsamardinos et al. six AutoML tools were compared [14]. <|MaskedSetence|>
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**A**: [12] compared double CV, the Tibshirani and Tibshirani method [13], and nested CV in their ability to improve the estimation of the fitness for SO problems.
**B**: Of these, only one had a predictive performance estimation strategy that could adjusts for multiple model validations (limitedly to SO problems and not affecting model selection), while most of the tools have the necessity to withold a test set for an unbiased estimation of the performance of the winning model, thus loosing samples from the final model training.
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**C**: Tsamardinos et al.
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5.2 Lung Segmentation Evaluation
Lung segmentation evaluation results are presented in Figure 5. <|MaskedSetence|> <|MaskedSetence|> We also plotted the mean dice coefficient before applying registration. <|MaskedSetence|> For cases involving major motion, IVIM-Morph succeeded in enhancing the dice coefficient achieving superior results for group 2 (dice = 0.854±0.038plus-or-minus0.8540.0380.854\pm 0.0380.854 ± 0.038) than group 1 (dice = 0.812±0.046plus-or-minus0.8120.0460.812\pm 0.0460.812 ± 0.046). Conversely, in scenarios with minor motion, IVIM-Morph, employing both sets of hyperparameters, consistently maintained a high dice coefficient.
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**A**: We calculated the dice twice, one time using the optimal hyperparameters of group 1 and one time using the optimal hyperparameters of group 2.
**B**: The mean dice coefficient for each compared method is plotted as a boxplot, separately for the major and minor motion cases.
**C**: The mean dice before registration in the minor motion cases is 0.878±0.036plus-or-minus0.8780.0360.878\pm 0.0360.878 ± 0.036 and in the major motion cases is 0.771±0.040plus-or-minus0.7710.0400.771\pm 0.0400.771 ± 0.040, which is expected based on the cases’ motion level.
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<|MaskedSetence|> The simplest GNN using pooling is not much more computationally costly than an MLP that takes distances between Cα atoms as inputs. <|MaskedSetence|> We expect the memory and computational requirements to scale with token number quadratically for SubFormer and subquadratically for SubMixer (depending on the expansion dimension in the token-mixing blocks); these requirements should scale linearly with respect to embedding dimension and network depth.
Figure 8: Computational time for training a single VAMPNet. We employ early stopping and stop training when the training VAMP score does not increase for 1000 batches or the validation VAMP score does not increase for 10 batches. <|MaskedSetence|> All times are for training on a single NVIDIA A40 GPU..
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The computational costs for training VAMPnets with different token mixers are shown in Figure 8.
**B**: The GNNs with token mixers are about an order of magnitude more computationally costly but still manageable (hundreds of seconds) even without advanced acceleration techniques such as flash-attention or compilation.
**C**: Times reported are averages over three training runs, with validation performed at each step using the second half of each trajectory, as depicted in Figure S2.
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Even though a relatively small percentage of patients required critical care services, the surge in cases quickly overwhelmed the healthcare system. At the beginning of the COVID epidemic in India, the Ministry of Health and Family Welfare (MoHFW) suggested that around 2.5% of patients needed intensive care. <|MaskedSetence|> As of June 28, 2020, the MoHFW reported 1,055 dedicated COVID hospitals in India, with 177,529 isolation beds and 78,060 oxygen-supported beds. Additionally, there were 2,400 dedicated COVID Health Centres with 140,099 isolation beds and 51,371 oxygen-supported beds. However, this still left a considerable gap, with approximately 120,000 oxygen-supported beds available. <|MaskedSetence|> Securing beds posed a significant challenge, as revealed by a Local Circles survey in April 2021 (www.statista.com). Only 13% of respondents successfully obtained an ICU bed through the standard procedure, while the majority had to rely on personal connections. <|MaskedSetence|> Impact of coronavirus (COVID-19) on securing ICU beds in hospitals across India as of April 2021 (Data source: Link to the source)
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**A**: The survey indicated difficulties securing COVID-19 ICU beds for family and friends (see Fig 1).
Figure 1.
**B**: However, this might be underestimated due to incomplete reporting in some states [26].
**C**: It was estimated that 15% of patients, translating to about 1.5 million individuals in India, would require mild to moderate infection treatment with oxygen beds.
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The proposed model is broadly applicable to various domains, including social interactions, biological systems (e.g., neural or protein interactions), and technological networks (e.g., the spread of computer viruses or resilience of infrastructure systems). <|MaskedSetence|> <|MaskedSetence|> The model not only maintains a strong fit to empirical data but also reveals hidden structural features of the contact network underlying the disease’s spread. Identifying such networks is crucial for effectively targeting at-risk populations—such as through vaccination campaigns—to prevent further transmission. <|MaskedSetence|>
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**A**: This stochastic SIR framework thus provides a versatile tool for modeling infectious diseases and other dynamic processes beyond the scope of traditional SIR models..
**B**: By transforming the SIR model using dynamical survival analysis within the edge-based configuration network framework, the resulting system of equations captures the intricate dynamics of network-based interactions.
**C**: Despite the complexity of these interactions, the equations remain mathematically tractable, often enabling precise predictions of disease trends (see, for instance, the discussions of related DSA-based approaches given in [8, 24]).
The utility of the Poisson SIR network model is demonstrated through secondary analysis of the data from 2018–2020 Ebola outbreak in the Democratic Republic of the Congo.
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Other authors have studied biophysical network models of premotor neurons. Rakowski et al. (2013) and (2017) simulated the dynamics of a pre-motor and motor circuit. The stationary distributions of the motor neurons were then used to infer synaptic polarities, i.e., whether a synaptic
connection is excitatory or inhibitory [36, 35];
there is little discussion of network dynamics. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|>
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**A**: elegans neuronal network and evaluated the activity of the command neurons [22, 26].
**B**: Lanza et al. (2021) simulated the dynamics of the C.
**C**: Their model predicted that neural activity converges to limit cycles; i.e., all neurons eventually acquire the same periodicity..
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Most humans do not spend their lives on treadmills, so their behavior may not already be energy optimal for such gait transition tasks without considerable learning [19, 20, 21]. Here, in contrast to these treadmill gait transition experiments, we show that overground gait transitions in realistic overground locomotion is more gradual and provide some clues for why there might exist distinct walk-to-run and run-to-walk speeds on a treadmill.
Imagine you need to travel on foot from your home to an important appointment a kilometer away at a particular time (Figure 1). Unlike on a treadmill, where the speed is constrained, in this overground experiment, you can change speed or change gait. <|MaskedSetence|> If you have very little time, you might need to run all the way. <|MaskedSetence|> That is, we show that for such overground tasks, there is not a sharp gait transition speed below which walking is preferred and above which running is preferred. <|MaskedSetence|>
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**A**: What might you do? If you start very early and have plenty of time, you might prefer to walk all the way.
**B**: Having this mixture of walking and running instead of a sharp gait transition speed is energy optimal [1, 22, 23], and was earlier observed over short distance tasks in humans [1], so the primary experimental contribution of the current study is its demonstration over much longer distances.
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**C**: But if you had an intermediate amount of time, what might you do? Here, we perform this experiment for two long distances over 800 meters, and show that humans systematically use a mixture of walking and running when there is an intermediate amount of time.
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Van der Zee and Kuo [28] proposed a model of metabolic rate proportional to the second derivative of force, which is equivalent to the metabolic cost per movement being proportional to the first derivative of force. This is a different cost from our model, which they supported by showing an approximate quadratic scaling of metabolic cost with force frequency. Our model is roughly consistent with their data, also indicating a roughly quadratic with oscillation frequency, though more specifically our model predicts a faster than quadratic scaling of metabolic cost with frequency when the force mean and amplitude are fixed (γ2>2subscript𝛾22{\gamma_{2}>2}italic_γ start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT > 2). Reviewing Van der Zee and Kuo’s data (figure [28]) suggests their data may also be consistent with a slightly faster-than-quadratic scaling with oscillation frequency. <|MaskedSetence|> <|MaskedSetence|> This calcium pumping cost is in addition to the ATP activity that sustains repetitive actomyosin activity required for force maintenance. At the individual muscle level, metabolic measurements have been performed for continuous or intermittent electrical stimulation in-vivo or in-vitro. These studies suggest that the cost for intermittent activation is more than continuous [44, 45, 46, 47] which is an analogous to say that the cost of producing sinusoidal force is more than constant force. <|MaskedSetence|>
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**A**: One reason positive and negative force rate may have different costs may be due to decrease force, the calcium needs to be pumped back to the sarcoplasmic reticulum which incurs a metabolic cost [42, 43].
**B**: But these studies did not perform experiments comprising different activation and relaxation times, which is analogous to having different upward and downward sinusoid slopes in our experiments.
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**C**: In future work, we will consider how well alternative models with higher derivatives fit our or even more diverse data.
We found that decreasing the force is more costly than increasing the force by having different coefficients in the model for positive and negative force rate (3).
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Figure 6: The evolution of a mutated pathogen (similar to Figs. <|MaskedSetence|> <|MaskedSetence|> The pathogen begins its evolution when a few nodes are infected with pathogens having higher value of γ𝛾\gammaitalic_γ, that after some time get dominance over the network, and the average pathogen’s value of γ𝛾\gammaitalic_γ is growing. <|MaskedSetence|> Therefore, the total infection time is pretty similar between them, while random networks of these sizes will present a larger difference in the spreading time..
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**A**: Since scale free networks have a very small diameter, which is almost independent of the size, the average distance in the Deezer netwrok is very close to the average on our generated scale free network even though it is much smaller.
**B**: The initial pathogen’s parameters are set around the epidemic threshold, based on the network structure.
As we expected, the results of the simulation using the Deezer Europe social network are similar to results for the generated scale free networks, as seen in Figure 6.
**C**: 4,5) during its spreading in the Deezer European social network, with ξ=1.2𝜉1.2\xi=1.2italic_ξ = 1.2.
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times, etc. but these frequency multiples are each associated with
a single haplotype that is different in each case (h=1)ℎ1(h=1)( italic_h = 1 ). <|MaskedSetence|> <|MaskedSetence|> The ordinate is
linear and the abscissa is logarithmic. <|MaskedSetence|>
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**A**: The outcome of a plot in which hnℎ𝑛hnitalic_h italic_n is plotted
against n𝑛nitalic_n is highly informative (Figs.4a,b).
**B**: Most high-frequency
multiples are absent.
**C**: The sum of the hnℎ𝑛hnitalic_h italic_n values.
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<|MaskedSetence|> In the presence of an antigen, immune B cells produce neutralizing antibodies that bind to specific target sites on its surface (called antigenic epitope sites) [63]. In a primary infection, a part of the responding B cells is stored as immune memory to protect against future infections by the same pathogen (another part evolves high affinity to their cognate epitope by affinity maturation, a rapid evolutionary process under selection for recognition [16, 64, 65, 66]). Fast-evolving pathogens, however, are moving targets: they change epitope sequences by accumulation of mutations, which can lead to eventual escape from immune recognition and protection. <|MaskedSetence|> <|MaskedSetence|>
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**A**: This process, called antigenic drift, is frequently observed in RNA viruses, including human influenza, norovirus, and SARS-CoV-2 [67, 68, 69, 70].
**B**: VI Complexity of immune recognition
The following example shows how selection on complexity can act in the adaptive immune system, a rapidly evolving recognition system of high global complexity [62].
**C**: Here we develop a minimal biophysical model for the immune recognition of an evolving antigen and its consequences for host fitness.
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A. Tanaka: Conceptualization, NGS sample preparation, NGS data analysis, investigation, performing experiments, project administration, generating figures and tables, funding acquisition, and writing original draft. <|MaskedSetence|> H. Ohta and Y. Ishitsuka: Data investigation, methodology, generating figures, and writing original draft. C. Onishi and H. Tanaka: Assisting plasmid preparation, experiments and analyses. N. Takenouchi, M. Nakagawa and K. Koh: Collecting clinical samples. <|MaskedSetence|> M. Matsuoka: Collecting clinical samples, supervision, funding acquisition, project administration, and writing original draft. <|MaskedSetence|>
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**A**: All authors participated in discussions and interpretation of the data and results.
Acknowledgements..
**B**: J.I. Yasunaga: Collecting clinical samples, data investigation, funding acquisition and experimental advices.
**C**: A. Fujimoto: Assisting NGS data analysis.
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<|MaskedSetence|> Army DEVCOM Army Research Laboratory and was completed under Cooperative Agreement Number W911NF2420013 and by the National Science Foundation grant 2126976. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. <|MaskedSetence|> <|MaskedSetence|> The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
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**A**: Government.
**B**: Army DEVCOM Army Research Laboratory or the U.S.
**C**: Acknowledgements.
This research was sponsored by the U.S.
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<|MaskedSetence|> One of the first papers is ESM-1b (Rives et al. 2021) trained on 250 million protein sequences with a BERT-style strategy. Several other PLMs are proposed and perform well on various downstream tasks(Rao et al. 2021; Elnaggar et al. 2021; Brandes et al. 2022). Especially, ESMFold (Lin et al. 2022) and OmegaFold (Wu et al. <|MaskedSetence|> 2021). The PLM from ESMFold is named ESM-2, which contains various parameter sizes, from 8M up to 15B. Meanwhile, most RLMs employ a similar paradigm of that in PLMs. <|MaskedSetence|> RNA-FM (Chen et al. 2022), Uni-RNA (Wang et al. 2023) and RiNaLMo (Penić et al. 2024) are three representative RLMs. They show great ability in RNA function and secondary structure prediction. While PLMs and RLMs have succeeded in many biological tasks, applying them together remains an unexplored area of research.
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Many efforts have emerged to develop foundation language models to leverage the massive biological sequence data.
**B**: The RLMs are trained on massive non-coding RNA sequences.
**C**: 2022) show the power of PLMs on protein structure prediction, without multiple sequence alignment information as in AlphaFold2 (Jumper et al.
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A robust evaluation metric for generative models must effectively distinguish between varying levels of different noise types. <|MaskedSetence|> This demonstrates its effectiveness in evaluating image quality. The capability to identify corruptions in real images makes our metric a valuable tool for detecting subtle differences caused by various noises. <|MaskedSetence|> <|MaskedSetence|> This monotonic reduction highlights quality improvements in the diffusion process, essential for accurate evaluation of histopathological images from generative models..
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**A**: In our experiments with salt-and-pepper noise and rectangular patch noises, common in histopathology images, our metric, RL2, shows a monotonic increase with rising noise levels as shown in Figure 3 and Figure 4.
**B**: Even when synthetic image data significantly differs from real data, our metric reliably identifies varying levels of various noise.
The results of our evaluation metric on the diffusion process, are illustrated in Figure 5.
**C**: As shown, an increase in the number of diffusion steps correlates with a reduction in noise levels, a pattern our metric effectively captures through lower values.
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Somewhat counterintuitively, negative scaling regimes have been observed in density scaling laws, particularly in the context of housing prices [48, 49], where the price of detached housing decreases with increasing population density at high densities in England. In our study, cities exhibiting negative elasticities are predominantly observed for pertussis cases. These cities are mainly located in the inner regions of Brazil (see Supplementary Figure S4), which are often characterized by smaller populations, lower economic development, and limited healthcare resources compared to larger urban centers. Pertussis, or whooping cough, is a highly contagious respiratory disease that primarily affects infants and young children. It is a vaccine-preventable disease, and Brazil has included the pertussis vaccine in its National Immunization Program since the 1970s, offering it free of charge through the public healthcare system. We hypothesize that as these small and isolated cities grow and become better connected, they may benefit from improved health services and socioeconomic conditions. <|MaskedSetence|> However, these potential benefits appear to saturate as the population and total number of commuters continue to rise. We further hypothesize that a similar mechanism may be at play in the few cities displaying negative elasticities for other diseases. Nevertheless, the specific characteristics of these diseases, such as differing transmission dynamics and latency periods, may attenuate this initial benefit, resulting in a significantly smaller number of cities in this regime.
As previously mentioned, decreasing returns to scale is the predominant response of cities to a proportional increase in both population and commuters. <|MaskedSetence|> As these cities grow and enhance their connectivity, they may experience modest improvements in healthcare and socioeconomic conditions compared to cities with negative scaling while also beginning to encounter challenges typical of larger urban areas, which contribute to increased disease transmission. The balance of these factors may yield sublinear regimes with variations across disease types. In contrast, increasing returns to scale tend to emerge in large, highly connected cities. The transition from decreasing to increasing returns to scale in disease cases is likely multifactorial, involving socioeconomic, infrastructural, and behavioral influences. <|MaskedSetence|> For instance, substandard housing conditions, such as overcrowded spaces and poor ventilation, are more common in large urban centers and may facilitate the airborne transmission of diseases like tuberculosis and influenza. Additionally, large cities often have higher rates of substance abuse, unsafe sexual practices, and transient relationships, which could explain the more than proportional rise in sexually transmitted infections such as HIV/AIDS and syphilis.
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**A**: These improvements may include more aggressive vaccination campaigns and increased awareness of disease prevention, which can more effectively reduce risky behaviors that facilitate the spread of pertussis than in smaller and more isolated populations.
**B**: This regime is more common among cities of intermediate size and connectivity within the commuting network.
**C**: Large, highly connected cities tend to feature high-density areas, more frequent social interactions, increased mobility patterns, and greater socioeconomic inequalities, all of which may contribute to environments where infectious diseases spread more efficiently.
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<|MaskedSetence|> However, the high dimensionality and vast scale of PINs pose significant challenges for direct computational analysis. Consequently, researchers often resort to indirect approaches, employing summarizing features like topological metrics (e.g., indegree, betweenness, clustering coefficient) to represent these networks [7, 38]. <|MaskedSetence|> Network embedding aims to maximally preserve a network’s information while reducing its dimensionality, facilitating higher resolution and better quality of network data [9]. <|MaskedSetence|> Building on this, more advanced AI-powered embedding algorithms, such as those based on Random Walk, Graph Neural Networks (GNNs), and edge sampling, offer promising improvements for leveraging PIN data to identify druggable genes [19, 26, 41].
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**A**: While practical, this utilization leads to a substantial loss of resolution, potentially overlooking subtle but crucial network characteristics essential for identifying druggable genes.
To address this issue, network embedding techniques can be employed [39].
**B**: Shingo Tsuji successfully used a deep neural network (DNN) to embed a PIN into latent space, creating a framework for inferring Alzheimer’s disease targets [37].
**C**: Protein Interaction Network (PIN) offers a detailed and comprehensive view of protein interactions within biological systems, making them valuable for identifying potential targets [17, 31].
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<|MaskedSetence|> <|MaskedSetence|> Graphs are a highly general representation for different data types, and can also be used to represent 1D, 2D, and 3D Euclidean data by treating the inputs as a grid. Equivalents of convolution, pooling, and attention operators in Euclidean space data are also used for feature extraction for GNNs. <|MaskedSetence|> GNNs all involve a convolutional operator, ψ𝜓\psiitalic_ψ, a pooling operator, ⨁direct-sum\bigoplus⨁, and a non-linear activation function, ϕitalic-ϕ\phiitalic_ϕ. Variations between them can be classified depending on the weighting methodology for features from neighbouring nodes. Figure taken from [16] .
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Graph Convolutional Neural Networks (GCNNs) - GCNNs operate on graph data, where sample inputs consists of vertices and edges between them.
**B**: Figure 2 gives a generalized overview of different GNN structures.
Figure 2: A simplified overview of the main classes of GNNs.
**C**: Features are represented as vertices, and edges are used to determine feature aggregation between vertices during learning.
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For training, we randomly split the available cortical thickness data into a training set of 568568568568 individuals and a test set of 63636363 individuals. The anatomical covariance matrix was estimated from the cortical thickness data of the training set. <|MaskedSetence|> The VNN was trained to predict chronological age on the subset of 498498498498 individuals with mean squared error loss optimized using stochastic gradient descent with Adam optimizer for up to 100100100100 epochs. <|MaskedSetence|> Thus, in total, VNN model consisted of 22,5702257022,57022 , 570 learnable parameters. The batch size used for training was 10101010 and the learning rate was 0.150.150.150.15. The hyperparameters for the VNN architecture and training were decided during a hyperoptimization procedure based on Optuna [24]. <|MaskedSetence|> These models achieved a prediction performance of 7.25±0.51plus-or-minus7.250.517.25\pm 0.517.25 ± 0.51 years on the test set and 6.336.336.336.33 years on the complete dataset, with a Pearson’s correlation of 0.44±0.014plus-or-minus0.440.0140.44\pm 0.0140.44 ± 0.014. Thus, the statistical evidence suggested that VNNs learned information about healthy aging, even though they were weak predictors of chronological age. The results reported in this paper are derived from one pre-trained VNN model among the 10101010 that were pre-trained using the above procedure..
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**A**: The training set was further split into a subset of 498498498498 individuals and a validation set of 70707070 individuals.
**B**: Using this strategy, we trained 10101010 distinct VNN models with different permutations of the training set.
**C**: The configuration with the best performance on the validation set of 70707070 individuals was selected.
The first layer of VNN consisted of 2222 filter taps and the second layer consisted of 6666 filter taps, with width 61616161.
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7 Conclusions
Inducible defences, a form of phenotypic plasticity, have the ability to significantly influence direct interactions within ecological communities, generating trait-mediated indirect effects [66, 67]. These defences arise when prey exhibit adaptive behavioural, morphological, or physiological traits in response to their predators, effectually minimizing direct encounters with predators. <|MaskedSetence|> Therefore, by changing the dynamics of interactions, inducible defences can have cascade trait-mediated impacts on prey, predators, and the prey’s resources [68]. <|MaskedSetence|> There are various predator-prey systems have been explored to elucidate the implications of inducible defences at both populations as well as community levels [71, 72, 73, 74, 75]. <|MaskedSetence|>
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**A**: An evolutionary ecological theory posits that inducible defences are vouched for over constitutive ones when these defensive traits impose exceptional costs on prey [69, 70].
**B**: However, such defences often come with associated costs- either through a reduction in prey growth rates (metabolic costs) or by impairing prey-resource interactions (feeding costs).
**C**: However, the focus of our study is to look over the impact of inducible defences on the dynamic behaviour of predator-prey interactions, particularly in the context of predator interference and the coupled effects of repulsive and attractive taxis.
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Figure 1:
The Role of pMHC-TCR in Adaptive Immunity and the Correspondence between Our Model Architecture and the Biological Process. <|MaskedSetence|> Antigens are up-taken by the APCs and then bind to the MHC. <|MaskedSetence|>
(b) Recognition of Antigens by T cells. All cells present some peptides via the pMHC. <|MaskedSetence|> .
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**A**: Certain peptides can be recognized by T cells through the pMHC-TCR interaction, leading to their elimination by T cells.
**B**: Subsequently, the pMHC complex displayed on APCs can bind to some TCRs on T cells.
**C**: (More details will be introduced in Section 3.1.)
(a) Antigen Presentation via APCs to activate T cells.
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Acknowledgements
The authors would like to thank M. Asker, J. Jiménez, S. Muñoz Montero, M. Pleimling, A. M. <|MaskedSetence|> Swailem for fruitful discussions.
L. <|MaskedSetence|> N. and M. <|MaskedSetence|> gratefully acknowledge funding from the U.K. Engineering and Physical Sciences Research Council (EPSRC) under the Grant No. EP/V014439/1 for the project ‘DMS-EPSRC Eco-Evolutionary Dynamics of Fluctuating Populations’ (https://eedfp.com/)..
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**C**: Rucklidge, and M.
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<|MaskedSetence|> <|MaskedSetence|> (2015) generative deep learning methods;Noé et al. (2019) Markov model analyses; Husic and Pande (2018); Nüske et al. (2017) and neural-network based analyses.Fraccalvieri et al. (2011); Ward et al. (2021) For example, DiffNetsWard et al. (2021) successfully identified mutation sites that affect the signalling profile of the oxytocin receptor.Malik et al. <|MaskedSetence|> They also frequently require fine-tuning for a particular molecular system..
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**A**: (2021)
However, the available methods are generally computationally costly, difficult to apply, and/or not easily interpretable.
**B**: The necessary analysis is the bottleneck of many biomolecular simulation projects, as it can take weeks of dedicated work if performed by eye and by one-off scripts, and a focus on preconceived candidate mechanisms can lead to missing unexpected effects.
In light of these hurdles, the strong interest in ensemble analyses over the past two decades has led to development of ensemble databases with inbuilt analysis toolsZivanovic et al.
**C**: (2020) and the availability of more powerful, systematic and quantitative approaches, including: single-score similarity measures between two ensembles,Brüschweiler (2003); Lindorff-Larsen and Ferkinghoff-Borg (2009) implemented in libraries such as Encore;Tiberti et al.
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Paradigm 2 was an extension of paradigm 1, using similar sequences of twenty discrete spoken words. <|MaskedSetence|> <|MaskedSetence|> The subject was instructed to passively count the number of target events in the attended stream and report the count after the trial. <|MaskedSetence|> Additionally, the attended stream was randomly changed but balanced between the left and right speakers to avoid bias towards a particular listening direction.
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**A**: In each trial, the subject was asked to pay attention to only the target events in one of the streams and completely ignore the other stream.
**B**: The target events in the twenty trials were balanced between the two classes of events.
**C**: However, instead of a single stream of words, two competing streams were presented simultaneously by two speakers located at equal distances on either side of the subject, placed 60 degrees to the left and right (see figure 2).
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In self-assembly, our results suggest that disassembly pathways can provide time-efficient error correction when combined with misincorporation-induced pauses seen in natural systems such ribosomal assembly checkpoints [83] and synthetic systems [55, 45, 84]. Our results suggest, in a twist, that classic annealing protocols
[85] for reducing defects in crystal growth can also increase net growth rate under some conditions.
Finally, resets have been shown to be a broadly relevant strategy for speeding up search in a broad range of contexts [40, 62, 60, 65, 63, 86]. Our work points out that in addition to saving time, reset mechanisms effectively reduce the entropy of paths used to reach a destination state. <|MaskedSetence|> <|MaskedSetence|> assembled structures or copied polymers. <|MaskedSetence|>
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**A**: Such ‘canalization’ into a few paths can be seen as a non-equilibrium version of Waddington’s homeorhesis [87].
**B**: As a consequence, complex systems can achieve stereotyped reproducible behaviors, despite living in high-dimensional disordered state spaces, through simple non-equilibrium mechanisms that also provide speed benefits..
**C**: The reduction in trajectory entropy can show up as higher observable order in, e.g.
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<|MaskedSetence|> During training, it achieves an RMSE of 0.1360, maintaining its effectiveness in the validation phase with an RMSE of 0.3465. The model’s performance remains stable in the testing phase as well, with an RMSE of 0.2912. <|MaskedSetence|> <|MaskedSetence|> Notably, the Rectilinear interpolation model achieves the best result in the testing phase with an RMSE of 0.2570. These findings highlight the advantage of the Rectilinear interpolation model in minimizing prediction errors compared to the Cubic Hermite splines approach..
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IV-B3 Neural CDE interpolation strategies
As shown in Table V, the Cubic Hermite splines model exhibits consistent results throughout the process.
**B**: It starts with a slightly lower RMSE of 0.1278 during training, which carries over into the validation phase with an RMSE of 0.3371.
**C**: In contrast, the Rectilinear interpolation approach outperforms the Cubic Hermite splines model in terms of predictive accuracy.
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<|MaskedSetence|> The FI that includes the FPFP5 performed the best (green upside-down triangles). Similarly, when the default FI (red triangles) was compared to leave-one-out NFPFP5 (cyan squares), the FI was superior. Both the NFPFP5 and FI typically out-performed chronological age, except for predicting weakness (deficit grip strength). Only NFPFP5 including all deficits (purple circles) performed comparably to the default FI. <|MaskedSetence|> Leave-one-out excludes the outcome deficit from the predictor. <|MaskedSetence|>
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**A**: Error bars are standard errors..
**B**: The AUC is the probability that a metric will correctly rank positive individuals as higher than negative individuals [31] (dotted line at 0.5 indicates a random guess).
**C**:
Figure 1: The FI predicts future FPFP5 deficits better than NFPFP5.
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<|MaskedSetence|> The metric used for all evaluations is MCC. <|MaskedSetence|> <|MaskedSetence|> All models were trained with a consistent set of hyperparameters: DNABERT (Zhou et al., 2024) variants undergo full-model fine-tuning, while Nucleotide Transformer (NT) (Dalla-Torre et al., 2023) variants and METAGENE-1 are fine-tuned using low-rank adapters (LoRA) (Hu et al., 2021). For sequence-level classification, we use the built-in pooler for DNABERT and NT models provided in HuggingFace Transformers (Wolf, 2019), and use mean-pooled representations for METAGENE-1. Additional experimental details can be found in Section C.1..
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**A**: The header row reports macro-averaged performance metrics.
**B**:
Table 2: Results on the Pathogen Detection benchmark.
**C**: See Section 5.2 for details.
We evaluate the performance of METAGENE-1 and other genomic foundation models on the pathogen detection datasets, measured using the Matthews correlation coefficient (MCC).
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We employ two age grouping strategies. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> This strategy divides the age range into four segments: [0-34), [34-60), [60-78), and 78+, aligning with significant biological and proteomic changes that correspond to shifts in aging patterns.
Within each age group, we calculate Pearson correlation coefficients for DNA methylation data to identify CpG sites most strongly correlated with age. This allows us to capture localized, linear relationships within each age range, potentially identifying age-specific biomarkers.
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**A**: The second grouping is based on research by [24], which identified key inflection points in aging at approximately 34,60, and 78 years.
**B**: The first divides the age range into decade-sized intervals: [0−10),[10−20),[20−30),…,[90−100),[100+[0-10),[10-20),[20-30),\ldots,[90-100),[100+[ 0 - 10 ) , [ 10 - 20 ) , [ 20 - 30 ) , … , [ 90 - 100 ) , [ 100 +.
**C**: This approach is motivated by its interpretability, as decade intervals are commonly used and easily understood, making the results accessible to a broad audience.
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A key mechanism of our model is the refractoriness of plasticity which prevents a continuous update of the post-synaptic neuron’s incoming weights while it is bursting. <|MaskedSetence|> Interestingly, this refractory period has also been observed in in vitro experiments [9]. Also note that without a refractory period this model will learn to a non-factorised, winner-take-all representation similar to the one learned by the continuous model (see Appendix figures C.5 and C.6). Varying the threshold for bursting does not affect the learning much unless we set it to zero, in which case the network seems to diverge from the discrete version (Fig. <|MaskedSetence|> Varying the hold period (i.e. the number of iterations the stimuli is held for the network to reach a stable state) does affect the learning trajectory (Fig. 2F) which is interesting since the standard version of discrete network (which uses the same learning rule as our model - Hebbian) stops learning as the hold period goes below 150 (see Appendix figures A.1).
We further explore how learning differs on these models by counting the number of synapses that are updated (i.e. have gradient entry different from 0). <|MaskedSetence|> As expected, the discrete network has a stair-case like shape since it only updates once every 500 steps (i.e. the hold period for this simulation). It is interesting to note that the asynchronous network follows a very similar trajectory to the discrete network for a random untrained network (Fig. 2G). However, as we train the networks, the discrete model seems to increase the number of updates while the asynchronous model slightly decreases them (Fig. 2I).
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**A**: Figure 2G and 2H show the number of synapses updated at each Euler step during a small simulation window.
**B**: 2E).
**C**: Figure 2D shows that refrectoriness is quite important for the asynchronous model to approximate the learning trajectory of the discrete model, as non-existent (1 step) or small (10 steps) refractoriness lead to poor average weight similarity.
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<|MaskedSetence|> The key element for achieving this unification is the condition map, which transforms complex geometric conditions to match the diffusion model’s configuration space, thereby enabling self-guidance without the need for external models. <|MaskedSetence|> Moreover, our method is the most versatile, extending beyond guiding molecular structures to leveraging complex geometric conditions such as volumes, surfaces, and densities, thereby enabling the unified tackling of diverse drug discovery tasks. For complex conditions specifically, previous works primarily rely on conditional diffusion models for effective condition encoding [12, 13, 14]. <|MaskedSetence|> With performance either on par with or superior to tailored models, we conclude that UniGuide offers advantages beyond its unification. Firstly, while the novelty of conditional models often stems from the condition incorporation, our method redirects focus to advancing unconditional generation, which directly benefits multiple applications. Furthermore, this separation of model training and conditioning allows us to tackle tasks with minimal data, a common scenario in the biological domain.
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**A**: With our method, we are able to tackle the same tasks, while overcoming major drawbacks: UniGuide eliminates the need for additional training and, more importantly, avoids constraining the model to specific tasks.
We demonstrate the wide applicability of UniGuide by tackling a variety of geometry-constrained drug discovery tasks.
**B**: We address the challenge of adaptability by introducing UniGuide, a method that unifies guidance for geometry-conditioned molecular generation, see Fig. 1.
**C**: Like other guidance-based approaches, UniGuide does not constrain the generality of the underlying model.
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Figure 4: Overview of EDM (Equivariant Diffusion Models) and its extensions for molecular generation tasks. The top box represents the foundational EDM model, which uses 3D point cloud representation with E(3) equivariance to handle molecular structures. <|MaskedSetence|> It demonstrates how subsequent models address these challenges through novel methods. Irregular Training Space: GeoLDM uses latent space encoding but performs poorly in generating realistic molecules. SubDiff solves this issue by introducing a subgraph extraction process to improve generation quality. <|MaskedSetence|> PMDM incorporates a dual equivariant encoder and Gaussian noise to handle complex protein-ligand interactions. Limited Modality: MiDi combines 2D connectivity graphs and 3D point clouds but struggles with poor adaptation to the data distribution. EQGAT-Diff enhances performance by introducing an EQGAT encoder for better data alignment. Unrealistic Molecules: MolDiff generates molecules with inaccurate ligand interactions. <|MaskedSetence|> The topic of generating molecules using diffusion models is equivalent to the following question: How to generate attributed graphs using diffusion models? To answer this question, there are two main challenges:.
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**A**: The figure highlights the key limitations of earlier models (shown in blue boxes).
**B**: Scalability to Complex Molecules: MDM considers covalent bonds and Van der Waals forces but cannot adapt to target-specific molecular pockets.
**C**: MolSnapper improves molecular realism by accurately representing ligand interactions within target pockets.
Molecules live in physical 3D space, there is a high need to better understand the design space of diffusion models for molecular modeling.
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<|MaskedSetence|> <|MaskedSetence|> Since ML approaches have discovered two primary evolutionary mechanisms of SARS-CoV-2 [6, 48], they aid in discovering new mechanisms of action and drug targets, providing robust support for the development of innovative anesthetics.
In this study, we constructed a proteomics-based ML system aimed at exploring novel anesthetic drugs targeting GABA receptors. <|MaskedSetence|> We then collected experimental binding affinity (BA) data for these target proteins from the ChEMBL database and developed ML models based on this information. Compound information was transformed into two different latent vector fingerprints through transformer networks and autoencoder models. These molecular fingerprints, combined with support vector machines, formed our BA prediction model.
By cross-predicting over 180,000 compounds, we assessed their potential for side effects and reuse value. Using these models, we screened for promising lead compounds and conducted an in-depth analysis of side effects for FDA-approved drugs and other existing medications. Additionally, we optimized the molecular structures of existing drugs to reduce side effects and improve their pharmacokinetic properties. During the compound screening process, we also comprehensively considered pharmacokinetic parameters, namely absorption, distribution, metabolism, excretion, and toxicity (ADMET), as well as synthetic feasibility. Our platform is expected to facilitate the development process of anesthetic drugs..
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**A**: ML technologies process and analyze vast amounts of biomedical data, thereby enhancing the efficiency of drug design and screening [40], predicting the biological activity and pharmacological properties of new molecules, optimizing drug structures, and improving binding specificity to GABA receptors.
**B**: Moreover, they can predict the potential toxicity and side effects of drugs, enabling the screening of safer candidate drugs.
**C**: Utilizing the String v11 database, we extracted the protein interaction networks of 24 GABA receptor subtypes, considering these related proteins as potential therapeutic targets and sites that may induce side effects.
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5.1 Broader impact
Neural decoding models, particularly those tasked with reconstructing naturalistic images, significantly deepen our understanding of the relationship between neural activity and the stimuli that evoke it. <|MaskedSetence|> <|MaskedSetence|> Human engagement with the environment extends beyond simple perception and attention, involving complex behaviours and neuroplastic adaptations that generalized models, based solely on image data, may fail to capture. For example, visual feedback from motor outputs presents a layer of complexity not accounted for in static image training. <|MaskedSetence|> Such discrepancies highlight the critical need for nuanced model development and the cautious interpretation of model outputs in practical applications.
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**A**: These models hold substantial promise for applications such as visual neuroprosthetics, aiming to restore lost visual experiences.
**B**: However, their deployment must be approached with caution due to the inherent complexities of brain function and environmental interactions.
**C**: Moreover, using these models to map stimulation locations for neuroprosthetic purposes may not yield accurate replications of natural neural responses, as the act of stimulating brain areas does not mimic the dynamics of natural sensory recording.
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The ability to handle data both globally and locally.
A CTM can surely deal with data both globally and locally. By globally we mean the CTM invokes all processors to handle the data(or to say, the data is available to all processors) and in contrast, by locally we mean it only invokes some of the processors. <|MaskedSetence|> <|MaskedSetence|> After all, processors have produced new chunks, those chunks will be sent to the competition Up-tree to battle for entering the STM. <|MaskedSetence|> That indicates the CTM model can globally handle information. The ability of processing information locally is hidden in the whole processing-competition procedure. For example, if a CTM only wants to handle chunks from specific processors, it would just give those irrelevant chunks from other processors much lower weight so that those processors almost have no chance to ’hand in’ their chunks to the STM, which is equivalent to make those irrelevant processors ’sleep’(stop producing chunks) for a while.
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**A**: There isn’t an explicit boundary between ’globally’ and ’locally’ in CTM.
**B**: All those data come in the form of chunks conveyed to LTM processors at time t𝑡titalic_t.
**C**: It’s clear that in a probabilistic CTM, those chunks’ weights are comparable in the competition without any unit conversion, therefor we believe all processors are using a common system of units to calculate the weight of each chunk.
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We structured our analysis into three primary sections. <|MaskedSetence|> We examined the role of local and global bifurcations in shaping these regimes, emphasizing the importance of time scale separation.
Secondly, we explored the diffusively coupled FHN model [Eq. 10], introducing spatial coupling through diffusion. Through theoretical analysis, we investigated stationary homogeneous solutions, their linear stability, and spatially structured dynamic solutions, including traveling structures and spatially extended patterns. We studied the emergence of a Turing instability and the resulting spatially structured Turing patterns. <|MaskedSetence|> <|MaskedSetence|> We focussed on synchronization properties in two coupled FHN modules, the existence of traveling waves when transitioning from continuous diffusive coupling to discrete coupling, and the emergence of chimera states characterized by spatio-temporal patterns of coherent and incoherent behavior.
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**A**: This is the broadest category as here one can consider a multitude of different network topologies and coupling terms.
**B**: Additionally, we examined front solutions, localized states, traveling pulses, and pacemaker-driven waves within the oscillatory domain, highlighting the richness of patterns that arose in different spatial dimensions.
Lastly, we explored discretely coupled FHN equations [Eq. 11].
**C**: Firstly, we examined the original FHN model [Eq. 8], discussing widely observed dynamical regimes such as monostability, multistability, relaxation oscillations, and excitability.
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Next, we explore the significance of phase waves across various disciplines, illustrating the versatility of the phenomena observed in our system. The occurrence of these regimes in different geometries suggests that the presence of a driving and a driven system, even with minimal diffusion, is sufficient for these behaviors to manifest. <|MaskedSetence|> <|MaskedSetence|> In nanophotonics, a nuanced approach involves controlling electromagnetic wave phases [41], while in magnetostatics, phase shifts occur as spin waves traverse domain walls [42]. <|MaskedSetence|>
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**A**: Although the underlying mechanisms driving these phase phenomena may differ, their widespread applicability is evident, and alternative mechanisms might unveil novel applications for these dynamics.
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**B**: In chemistry, phase waves have been pivotal in understanding the Belousov–Zhabotinsky reaction’s shift from triggering mechanisms to phase wave dynamics [39], and as a distinctive regime in oscillatory heterogeneous systems [40].
**C**: This opens the door to replicating these setups in diverse fields.
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<|MaskedSetence|> Foldseek (van Kempen et al., 2024) introduces a quantized autoencoder to encode local protein geometry, demonstrating success in database search tasks. However, as it focuses solely on local features at the residue level, it lacks the capacity to provide global representation of protein structures. This limitation restricts its application in tasks like structure generation or binding prediction, where global information is critical (Krapp et al., 2023). <|MaskedSetence|> (2024); Heinzinger et al. (2023) propose structure-aware protein language models that integrate structure tokens with sequence tokens. Additionally, Li et al. <|MaskedSetence|>
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**A**: (2024) combines a structural autoencoder with K-means clustering applied to the latent representation of a fixed reference dataset.
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**B**: Building on the 3Di-alphabet introduced by Foldseek, Su et al.
**C**:
Discrete representation learning for protein structures has recently garnered increasing attention.
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Tuning of these parameters can take place offline, externally to the simulator, or online, inside the simulator, emulating biological homeostatic control [1]. <|MaskedSetence|> Gradients represent how changes in model parameters affect simulation output. Currently, gradient-free methods are the dominant approach for offline tuning of realistic brain models using existing brain simulations: manual parameter variation, evolutionary search or randomized search [2, 3, 4, 5]. <|MaskedSetence|> Furthermore, they do not allow for tuning parameters during simulation, which at the moment requires carefully hand-crafted homeostatic control programs[1]. Instead, gradient-based methods do not suffer from this curse of dimensionality and scale to the tuning of billions of parameters, as exemplified by current large artificial intelligence models. <|MaskedSetence|> The construction of new, gradient-enabled simulators from scratch is not a task to be taken up lightly. Maintaining feature compatibility and simulation consistency across simulators is heavily dependent on the simulator engine(s) selected and on the support of ecosystem software tools. There are currently two ongoing efforts building new simulators in an automatic-gradient environment [6, 7]. However, these simulators have still not reached NEURON-level compatibility and existing brain models are not supported in this format. Furthermore, they do not support online tuning, i.e., homeostatic control. In contrast, in this brief communication, we advocate for a methodology that enables the calculation of parameter gradients using any unmodified, existing model-and-neurosimulator combination and, subsequently, the support for homeostatic control..
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**A**: What is more, online learning methods can also be developed based on gradients.
Unfortunately, existing brain simulators do not provide gradient calculation.
**B**: General parameter-tuning methods can be divided into gradient-free or gradient-based ones.
**C**: However, these are known to suffer from the curse of dimensionality: higher-dimensional parameter spaces take exponentially longer to tune.
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Phages infect host cells by adsorbing (attaching) to receptors on the host cell wall and then delivering the genomic content into the host cytoplasm. Phages are much smaller than bacteria and each host cell presents multiple receptors that phages can bind to, so multiple phages can adsorb to a single host cell, though not all adsorptions necessarily lead to infection. Multiple adsorptions become increasingly likely at higher phage densities (Turner and Duffy, 2008; Christen et al., 1990) and can become the dominant transmission mode at sufficiently high densities (Turner and Chao, 1999). <|MaskedSetence|> Here, we explore the impact of simultaneous infections on phage-host ecology. We define simultaneous infection as infections that occur within a very small time window and distinguish between simultaneous infection and previously studied forms of co-infection, where after a pause an already infected host cell is infected again. Interestingly, given sufficient time phages can prevent multiple, sequential infections through host cell manipulations (Joseph et al., 2009) but these mechanisms are not applicable to the small time window relevant for simultaneous infections.
To demonstrate the relevance of simultaneous infections consider phage therapy, or the use of phages as an antibiotic. <|MaskedSetence|> In order for a bacterial population to be eliminated, all bacteria must be infected by at least one phage. However, due to the stochastic nature of phage adsorption, in order for all bacteria to be infected at least once high densities of phage must be added, and many bacteria will be adsorbed to multiple times (Abedon, 2016). Further, for both efficacy and in the interest of circumventing evolutionary arms races between phages and hosts (Hampton et al., 2020), it is preferable for adsorption and infection to happen quickly relative to the handling time of the sample and bacterial replication times (Goodridge, 2008). <|MaskedSetence|>
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**A**: If phage densities are very high, it is possible that multiple phages simultaneously adsorb to and then infect the same host cell.
**B**: The basic premise of phage therapy is to use phages to lyse target bacterial populations (Kortright et al., 2019).
**C**: This creates a potential scenario where many adsorption events happen over a short time window, and it seems likely that simultaneous infection events would occur..
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DNA, the carrier of genetic information, naturally suggests itself for such approaches. It is a soft matter system that has evolved specifically for the purpose111Provided one can speak about things like “purposes” of natural objects in a scientific context, see Ref. Hundertmark (forthcoming) for a discussion. <|MaskedSetence|> <|MaskedSetence|> Therefore, a number of authors Kim et al. (2004); Qian et al. (2011); Xiong et al. (2022); Genot et al. (2013); Evans et al. <|MaskedSetence|>
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**A**: Moreover, there is an established tradition of using DNA for performing artificial computational tasks in the framework of DNA computing Adleman (1994).
**B**: (2024) have explored the possibility of using DNA as a basis for artificial neural networks.
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**C**: of storing and processing information.
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<|MaskedSetence|> Negative correlations are observed in the parietal areas, indicating reduced engagement of these regions in low-frequency processing during auditory stimuli. <|MaskedSetence|> Positive correlations are observed in the temporal and parietal regions, indicating theta rhythms’ role in auditory information processing and memory integration. The Text model shows reduced but still present positive correlations in the temporal lobe, highlighting the involvement of theta rhythms in cognitive processing of text-encoded auditory stimuli.
For the Alpha band (8-12 Hz), the Audio model shows positive correlations in the occipital and parietal regions, consistent with alpha rhythms’ association with relaxed states and sensory processing. Negative correlations are prominent in the frontal cortex, suggesting active suppression of irrelevant information during auditory processing. The Text model exhibits a similar pattern with pronounced negative correlations in the frontal regions, indicating alpha rhythms are engaged during both auditory and text-encoded auditory processing, with significant involvement of cognitive control regions.
The Beta band (12-30 Hz) in the Audio model displays mixed areas with positive correlations in the frontal cortex and motor areas, linked to active thinking, focus, and motor planning. Scattered negative correlations are present across the brain, indicating variable engagement of different regions during auditory processing. The Text model shows a similar mixed pattern but with less pronounced correlations compared to the Audio model. Positive correlations in the frontal cortex suggest involvement in cognitive and executive functions during text-encoded auditory processing.
Overall, the topomaps reveal that both types of auditory stimuli engage broad and overlapping brain regions, with distinct patterns of correlation across frequency bands. The frontal cortex, including the prefrontal and motor areas, shows high positive correlations across multiple frequency bands, indicating its critical role in attention, executive functions, and motor planning during auditory processing. <|MaskedSetence|>
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**A**: In the Delta band (1-4 Hz), the Audio model shows a mix of positive and negative correlations, with positive correlations scattered in the frontal cortex and medial temporal lobe, regions associated with attention and memory processes crucial during auditory tasks.
**B**: The superior temporal gyrus and medial temporal lobe exhibit significant correlations, emphasizing their importance in primary auditory processing and memory integration..
**C**: The Text model shows a similar mixed pattern of correlations, with less intensity in positive regions, suggesting that delta band activity is less influenced by text-encoded auditory stimuli.
The Theta band (4-8 Hz) reveals significant negative correlations in the frontal regions for both models, particularly in the prefrontal cortex, involved in working memory and executive functions during auditory processing.
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<|MaskedSetence|> Certain bacteria like E.coli, S.typhimurium, B.subtilis are known to show chemotaxis where they can move along a chemical gradient in their environment [18, 19, 20]. <|MaskedSetence|> <|MaskedSetence|> In a homogeneous attractant environment, after a large number of runs and tumbles the net displacement of the cell is zero. But in presence of an attractant concentration gradient, runs in the favorable direction are extended and those in the opposite direction are shortened, giving rise to a chemotactic drift [27, 28, 29, 30, 31].
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**A**: This migration happens via run-and-tumble motion, which is characterized by persistent movement along a particular direction (run), punctuated by abrupt change of direction (tumble) [25, 26].
**B**:
In this work, we use reinforcement learning to study a model that has been motivated by the phenomenon of bacterial chemotaxis [15, 16, 17].
**C**: When these microorganisms experience concentration gradient of an attractant chemical in their surroundings, they show a tendency to migrate towards regions of higher attractant concentration [21, 22, 23, 24].
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<|MaskedSetence|> The dataset was divided into 22,348 training samples and 100 test samples. The linear weights w𝑤witalic_w were initialized to uniform average pooling. Each model is trained 100 epochs with a batch size of 100 samples. <|MaskedSetence|> The architectures and training loops are implemented with the Mxnet library [23]. <|MaskedSetence|>
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**A**: All models were optimized using Adam with learning rate 0.0002 and batch size 4 and 100 epochs.
**B**:
Training parameters
For our AFRT model, the affine warps 𝒜𝒜\mathcal{A}caligraphic_A were initialized to identity.
**C**: The source code and detailed implementation can be found in our repository 222https://github.com/lelynn/AFRT..
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<|MaskedSetence|> We hypothesize this could be due to the fact that there are no alterations for this cell-type in HD.
Overall, the NN model’s performance evaluated just on HD cells achieves a precision of 0.95 and recall of 0.91, resulting in an F1-score of 0.93. This indicates that the model is highly effective at identifying HD cells with high F1 and low false positive rates. Similarly, when evaluating the model just with WT cells, the precision is 0.91, with a recall of 0.94 and an F1-score of 0.93. <|MaskedSetence|> <|MaskedSetence|>
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**A**: In contrast, the NN model shows low classification performance for Perivascular pericytes.
**B**: These metrics suggest a well-balanced performance in identifying WT cells, with a slightly higher recall compared to precision.
**C**: With an overall accuracy of 0.93, the model shows a robust performance.
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<|MaskedSetence|> Because of implementation differences of estimates available in these packages, we re-derived population genetic estimates and examined their differences (see Supplement).
Most commonly, our input are sequence reads or read-derived allele counts, as those fully capture the effects of both sources of noise, which can then be corrected for. <|MaskedSetence|> These can elevate the effective coverage, and thus improve the calling of low-frequency alleles, which can otherwise be difficult to distinguish from sequencing errors (2). <|MaskedSetence|> It is hence convenient to be able to use the same framework for these data, which existing implementations do not offer.
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**A**: With these reconstructed allele frequencies, the correction for read depth is less relevant, but the correction for pool size remains important.
**B**: Our implementation however can also be used with inferred or adjusted allele frequencies as input, for instance using information from the haplotype frequencies of the founder generation in E&R experiments (7, 8).
**C**: Several of these estimators were previously available in multiple software packages implemented in Perl (3, 4), R (5, 6), or C (2).
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<|MaskedSetence|> Yet, this modular approach, relying on these intermediate descriptors, introduces certain inefficiencies and complexities during both the training phase and sample generation. Approaches like those proposed by [18] and [19] involve predicting interatomic distance matrices and subsequently applying Distance Geometry solutions to derive spatial coordinates [20].
Although CONFVAE represented a step forward with its unified, bilevel optimization-based end-to-end framework [26], these techniques still grapple with the issue of error magnification. Inaccuracies in distance estimations often lead to misguidance of the coordinate determination mechanisms, resulting in the creation of molecular structures that are not only inaccurate but sometimes structurally implausible.
In an attempt to tackle this challenge, the CONFGF model, as introduced in works by [27, 28], aimed to learn the gradient of log-likelihood concerning coordinates. Nevertheless, in practical applications, the model still depended on intermediate geometric elements. <|MaskedSetence|> <|MaskedSetence|> Consequently, the model acquired knowledge from these incorrect distance matrices but was evaluated using valid ones computed from coordinates. This discrepancy between training and testing data resulted in a significant out-of-distribution issue [31]..
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**A**: Unfortunately, by utilizing DSM (Distance Gradient-based learning), the model was trained using perturbed distance matrices, which had the potential to violate the triangular inequality or contain negative values.
**B**: It estimated the gradient with respect to interatomic distances using denoising score matching (DSM) [29, 30] and subsequently applied the chain rule to derive the gradient of coordinates.
**C**: In response to this limitation, newer models have turned to intermediate structural descriptors, such as atomic distances and dihedral angles, known for their roto-translationally invariant nature, which is essential for accurately representing molecular shapes[25].
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