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We preprocessed all ncRNA sequences by replacing ‘T’s with ‘U’s since they are both complementary to adenine and similar in structure (‘T’s for representing thymine in DNA, while ‘U’s for uracil in RNA). This resulted in a dataset involving 4 main types of bases (16 counted types of combinations in total: ‘A’, ‘C’, ‘G’... | The large-scale dataset used in the pre-training phase was collected from RNAcentral 47, the largest ncRNA dataset available to date. This dataset is a comprehensive collection of ncRNA sequences, representing all ncRNA types from a broad range of organisms. It combines ncRNA sequences across 47 different databases, re... | Moreover, to minimize redundancy without compromising the size of our dataset (i.e., to preserve as many sequences as possible), we removed duplicate sequences using CD-HIT-EST, which was set to a 100% similarity threshold. After the above preprocessing steps, a final, large-scale dataset consisting of over 23.7 millio... | Although our RNA-FM can alleviate the problem of data scarcity, there is still less structural data available for RNAs than for proteins. As a result, we collected a non-redundant, self-distillation dataset with ground-truth secondary structure from the RNAStralign and bpRNA-1M databases. We filtered this dataset by re... | Below, we detail how we constructed the large-scale non-coding RNA (ncRNA) dataset, followed by model and training details. | B |
=positive momentum virial theorempositive momentum virial theorem\displaystyle\qquad=\qquad\mbox{positive momentum virial theorem}= positive momentum virial theorem | In genetics, the natural time increment to consider is discrete (generation), whereas in physics continuous-time is more natural. Thus, the discrete Price equation pertains to change in a trait after a single generation, whereas the virial theorem is formulated with continuous-time, and is additionally time averaged. H... | The dynamical interpretation of evolutionary theory posits a correspondence between theories of evolution and Newtonian mechanics [46, 23]. In this framework, notions such as selection or mutation in biology are associated to forces in physics [46]. The identical form of equations (4) and (5) can constrain such associa... | The momentum-averaged positions 𝔼(𝐳(t))𝔼𝐳𝑡\mathbb{E}({\bf z}(t))blackboard_E ( bold_z ( italic_t ) ) and velocities ddt𝔼(𝐳(t))𝑑𝑑𝑡𝔼𝐳𝑡\frac{d}{dt}\mathbb{E}({\bf z}(t))divide start_ARG italic_d end_ARG start_ARG italic_d italic_t end_ARG blackboard_E ( bold_z ( italic_t ) ) are discrete analogs of mome... | Ultimately, analogies between the Price equation and the virial theorem point towards potentially productive directions for exploration in both biology and physics. The statistical framing of the virial theorem in (5) highlights phenomena that may have been overlooked in the physics realm. For example, the first term o... | A |
K-fold cross-validation (CV) stands as the predominant methodology for machine learning assessment, with its advantages and limitations extensively explored, particularly in SO scenarios [9, 10, 11]. | 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. Tsamardinos et al. [12] compared double CV, the Tibshirani and Tibshirani method [13], and nested CV in their ability to improve the estima... | In biomarker discovery, the focus is often on optimizing the accuracy of machine learning models using the selected molecular features, while also minimizing the number of features to ensure clinical feasibility and resource efficiency. Characterising all the best compromises (or trade-offs) between predictive value an... | K-fold CV returns a model trained on all the available samples and an estimation of its performance computed by averaging k𝑘kitalic_k CV results. The obtained model performance is usually underestimated when only one hyperparameter configuration is used. However, it tends to be overestimated when multiple configuratio... | Consequently, this does not lead to any improvement in the actual model selection process. On the other hand, strategies that involve subtracting a constant value from model evaluation metrics, used for ranking multiple solutions, fall also short. This approach alone is insufficient to significantly impact the ranking ... | C |
The data set consists of 39 cases with different levels of misalignment between the different b-value image volumes. For each subject, we chose only one slice where the ROI in the right lung was labeled. The images were then cropped to a shape of 96×96969696\times 9696 × 96 and normalized by the 0.99 quantiles of the D... | To ensure the reproducibility of our findings, we established two distinct, non-overlapping groups of 16 cases each for hyperparameter tuning. The composition of these groups was planned to encapsulate a wide array of gestational ages, thereby encompassing nearly the full breadth of ages present in our dataset. We cond... | Figure 7: The correlations between f and the GA in the canalicular stage are calculated in two datasets: group 1 test cases and group 2 test cases. | The tuning process was conducted separately for two distinct groups, each comprising 16 cases, and was performed independently. The criterion used for selecting the optimal hyperparameters was based on the correlation between the IVIM parameter f𝑓fitalic_f and gestational age during the canalicular stage of fetal deve... | Figure 7 displays correlations derived from two test groups, where each group’s test cases include those from the opposing original group (the 16 cases for the hyperparameter tune). Average IVIM parameters were computed in the ROI for each case across all evaluated methods, utilizing the best hyperparameters for each ... | A |
These issues become more significant when using RL to model biological systems, as biological agents rarely behave deterministically even after learning is complete. | This form of cost was employed as biological control costs in bacterial chemotaxis [21] and immunological learning [48]. | The parameter σ∈ℝ≥0𝜎subscriptℝabsent0\sigma\in\mathbb{R}_{\geq 0}italic_σ ∈ blackboard_R start_POSTSUBSCRIPT ≥ 0 end_POSTSUBSCRIPT is the randomness of the agents’ motility, determined by the scaling of time and space. | This persistent stochasticity reflects the inherent randomness of biological processes, making deterministic control strategies costly and hence unnatural [48]. | This principle has been applied to various biological processes from synaptic signaling to embryo fertilization, where the arrival time of the fastest individuals is more critical than the population average. | C |
Although previous studies used a random split[8, 33, 50], we observe that, due to the strong correlation between successive structures sampled by molecular dynamics simulations, a random split allows networks to achieve high validation scores even when they have memorized the training data rather than learned useful ab... | Although previous studies used a random split[8, 33, 50], we observe that, due to the strong correlation between successive structures sampled by molecular dynamics simulations, a random split allows networks to achieve high validation scores even when they have memorized the training data rather than learned useful ab... | The computational costs for training VAMPnets with different token mixers are shown in Figure 8. The simplest GNN using pooling is not much more computationally costly than an MLP that takes distances between Cα atoms as inputs. The GNNs with token mixers are about an order of magnitude more computationally costly but ... | For chignolin, the GNNs clearly outperform a multilayer perceptron (MLP) that takes distances between pairs of Cα atoms as inputs; there is not a significant difference between pooling and the mixers considered. For trp-cage and villin, the GNN with pooling consistently achieves the lowest scores. The distance-based ML... | For each system and token mixer architecture pair that we consider, we independently train three VAMPnets using different random number generator seeds (and the training-validation split described in Section IV.4). We report the training and validation VAMP-2 scores for the different token mixer architectures for each ... | D |
S(0)=S0𝑆0subscript𝑆0S(0)=S_{0}italic_S ( 0 ) = italic_S start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT, E(0)=E0𝐸0subscript𝐸0E(0)=E_{0}italic_E ( 0 ) = italic_E start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT, I(0)=I0𝐼0subscript𝐼0I(0)=I_{0}italic_I ( 0 ) = italic_I start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT, Q(0)=Q0𝑄0subscript... | We classify this decision-making framework as a population game, where an individual’s payoff is determined by their own behaviour as well as the collective behaviour of the community. The players are individuals exposed (E(t)𝐸𝑡E(t)italic_E ( italic_t )) to infection. i.e., the strategy update takes place after expo... | We model the human population consisting of six distinct compartments: susceptible (S), exposed (E), infected (I), quarantined (Q), hospitalized (H), and recovered (R). Denoting S,E,I,Q,H and R𝑆𝐸𝐼𝑄𝐻 and RS,E,I,Q,H\text{ and $R$}italic_S , italic_E , italic_I , italic_Q , italic_H and italic_R as the number of ind... | We do not explicitly mention the recovery compartment R𝑅Ritalic_R here in the model equation (2.1), as individuals become completely immune after recovery and the population is closed. x𝑥xitalic_x represents the percentage of exposed individuals who choose to disclose the exposure to infection and eventually be quara... | Figure 4. The dynamics of different trajectories of the model for various values of disease transmission rates, βssubscript𝛽𝑠\beta_{s}italic_β start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT: (a) infected population, (b) quarantine population, (c) hospitalized, and (d) proportion x𝑥xitalic_x of individuals who choose... | C |
At the onset of the epidemic, m𝑚mitalic_m individuals are randomly selected as initially infectious. Each infectious individual remains in this state for a duration drawn from an exponential distribution with rate γ𝛾\gammaitalic_γ. During this period, the individual contacts their immediate neighbors according to a P... | At the onset of the epidemic, m𝑚mitalic_m individuals are randomly selected as initially infectious. Each infectious individual remains in this state for a duration drawn from an exponential distribution with rate γ𝛾\gammaitalic_γ. During this period, the individual contacts their immediate neighbors according to a P... | Figure 1: SIR Dynamics on Network. Blue nodes represent susceptible individuals, while red and pink ones represent the initially infected and secondarily infected individuals, respectively. The black node indicates a removed individual. Dashed half-edges connect uniformly at random to form solid edges. | A stochastic model represents contact patterns during an epidemic as a graph, where nodes correspond to individuals, and edges denote potential transmission routes. The stochastic SIR epidemic process on a network of size n𝑛nitalic_n can be described as follows. | In many applications, it is useful to track the spread of an epidemic while simultaneously constructing the underlying transmission network. This can be achieved by generating a random graph in tandem with modeling the epidemic’s spread. The process, illustrated in Figure 1, begins by assigning to each node a number of... | D |
We observe how different switching statistics can result in qualitative differences in simulated locomotory paths. Our model also shows how elevated input rates from even single classes of neurons can significantly alter switching statistics and therefore C. elegans behavior. | Figure 1: (a) Partial connectome from [47] containing 198 sensory neurons, interneurons, and motor neurons (most motor neurons that form neuromuscular junctions with muscle cells are excluded). The 15 neurons that will be selected as core neurons are shown in purple. Additionally, the partial connectome contains 137 ne... | One of the challenges in building such a model is the extraction of a suitable subnetwork. C. elegans neurons are highly recurrently connected; they do not process information in a feed-forward manner. Many premotor neurons are known to be “hubs" in the network with extensive connections [44]. These network features ob... | A subset of interneurons — premotor neurons — are chiefly responsible for determining the most common locomotory behaviors [38, 17, 50]. | We determine β𝛽\betaitalic_β, 𝐀𝐀\mathbf{A}bold_A, and 𝐝𝐝\mathbf{d}bold_d simultaneously by performing multiple linear regressions to approximate 𝐀𝐀\mathbf{A}bold_A and 𝐝𝐝\mathbf{d}bold_d for different values of β𝛽\betaitalic_β across 22 datasets selected from Ref. [1]. Our criteria for the selection of these ... | B |
Aside from instantaneous energetics, gait transition from walking to running has been attributed to muscle force-velocity behavior [31], interlimb coordination variability [32], mechanical load or stress [33, 18], and cognitive or perceptual factors [34, 35]; see [36] for a review. However, none of these factors can sh... | Subjects used a mixture of walking and running in 90% of the trials at the two intermediate speeds (2.22 and 2.6 ms-1). On average, walking dominates the walk-run mixture at the lower speeds and running dominates the walk-run mixture at the higher speeds (Figure 2a), so that the walk-run mixture gradually changes as sp... | Pure energy optimality in the absence of fatigue or any uncertainty predicts that in the regime where a walk-run mixture is optimal, there should be exactly one switch between walking and running. Multiple switches between walking and running is not optimal. Here, we find that the median number of switches is one for t... | Humans and many other terrestrial animals exhibit a number of different gaits [1, 2, 3, 4, 5]. Humans walk, run, and much more occasionally, skip [6, 7]. Horses walk, trot, canter, and gallop, and more occasionally, use other gaits [3, 8, 9]. Such gait transitions have most commonly been studied using treadmills (Figur... | We have considered one kind of overground gait transition, in which the task is traveling a given distance at a desired average sub-maximal speed. Another kind of ecological overground gait transition that might have been common in our evolutionary past is to be walking normally and then having to accelerate to a highe... | D |
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). 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 r... | In the main manuscript, we expressed the metabolic cost as a function of external force and force rate, using a single-link model. Now, we consider a limb with multiple joints and multiple muscles. As in our experiment, this limb at rest needs to produce a one-parameter family of external forces and force rates, all al... | 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). 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 r... | Here, we focus on developing a metabolic cost model applicable to isometric tasks involving arbitrary time-varying force production based on joint torque and torque rate, which includes constant force as a special case. In previous work, we showed that the metabolic cost of near-constant isometric force scales non-line... | An alternative explanation for different costs for increasing and decreasing is the use of co-contraction to reduce the output force quickly by activating the antagonist muscles to achieve the required negative force rates. Co-contraction or pre-activation of muscles is seen in a variety of ecological tasks, so increas... | D |
Pα=rγ(α)γ(α)(r+λ)+rλ.subscript𝑃𝛼𝑟𝛾𝛼𝛾𝛼𝑟𝜆𝑟𝜆P_{\alpha}=\frac{r\gamma(\alpha)}{\gamma(\alpha)(r+\lambda)+r\lambda}\;.italic_P start_POSTSUBSCRIPT italic_α end_POSTSUBSCRIPT = divide start_ARG italic_r italic_γ ( italic_α ) end_ARG start_ARG italic_γ ( italic_α ) ( italic_r + italic_λ ) + italic_r italic_λ e... | We also studied the evolution of the mutated pathogen during the time it spreads, as presented in Fig. 4. In this figure, it is shown how pathogens with greater values of γ𝛾\gammaitalic_γ appear during the epidemic spread. The figure presents the average and the greatest value of γ𝛾\gammaitalic_γ of the infections in... | Another advantage of the square grid graph is the ability to easily visualize the graph’s structure and better understand the flow of the epidemic in it. Figure 8 displays the mutation of the pathogen during the epidemic spreading in the network. The α𝛼\alphaitalic_α value of each node is presented as a color in the f... | The solution of the integral yields the probability of infection for each pathogen. Eq. (8) agrees with the intuition, in that pathogens with a longer mean lifetime are more likely to infect their susceptible neighbors. Respectively, as time goes on we expect both the number of mutated pathogens, and the values of γ(α... | Figure 2 demonstrates how higher values of α𝛼\alphaitalic_α (and γ𝛾\gammaitalic_γ as a result, since γ𝛾\gammaitalic_γ is dependent on α𝛼\alphaitalic_α, see equation 4) impact the spread of pathogens in the network, where pathogens with higher values of α𝛼\alphaitalic_α (or γ𝛾\gammaitalic_γ) gain more and more dom... | C |
Line (g) is a plot of the logistic equation whose asymptote, N∗superscript𝑁N^{*}italic_N start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT, | N∗superscript𝑁N^{*}italic_N start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT, is the same as a population brought to steady-state by disease | the same broad shape as the 5-locus haplotype distribution of Fig.3, but the linear portion of the distribution is limited to the first | but the values of N∗superscript𝑁N^{*}italic_N start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT emerge as relative abundances or | state, r=αN∗𝑟𝛼superscript𝑁r=\alpha N^{*}italic_r = italic_α italic_N start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT, where N∗superscript𝑁N^{*}italic_N start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT is the steady-state density | A |
We study the evolution of recognition sites in the low-mutation regime, where site sequences are updated sequentially by substitutions in an evolving population. The rates of these substitutions depend on the corresponding mutation rates and on selection coefficients scaled by an effective population size N𝑁Nitalic_N,... | Third, the recognition target sequence changes at a rate ρ=κμ𝜌𝜅𝜇\rho=\kappa\muitalic_ρ = italic_κ italic_μ per unit of length (Fig. 2D). This rate is assumed to be comparable to the point mutation rate μ𝜇\muitalic_μ and to be generated by external factors independent of the recognition function similar to compress... | Second, the recognition target sequence can change by extension and compression steps, which include one unit of sequence into the recognition site or exclude one unit from recognition(Fig. 2BC). These changes, which affect the architecture of recognition, are assumed to occur at a much lower rate than point mutations,... | Clearly, the minimal evolutionary model is a broad approximation to the evolutionary dynamics of any specific receptor-target interface. The model neglects many details of actual molecular evolution processes that are not important for conclusions of this paper. Three model features turn out to be crucial for what foll... | Organisms live in dynamic environments. Changing external signals continuously degrade the fidelity of an organism’s recognition units and generate selective pressure for change. Here we argue that stochastic, adaptive evolution of molecular interactions drives the complexity of the underlying sequence codes. By analyt... | B |
For this purpose, we used ATL samples and 77777777 ATAC-seq datasets from 13131313 human primary blood cell types. | We next examined ATL cases by inferring the past cell status before infection with HTLV-1 and the current cell status in terms of immunophenotypes compared with normal hematopoietic cells. | nearly equally to intronic and intergenic regions (Thurman2012, ). Consistent with these data, as shown in Fig. 1, about 10101010% of the ATAC-seq peaks are overlapped with the TSSs and their surrounding regions, whereas the majority of ATAC-seq peaks (about 85858585%) of healthy CD4+{{}^{+}}start_FLOATSUPERSCRIPT + en... | To ascertain whether the mRNA expression in the ATL cells reflects the characteristics of myeloid cells, we analyzed the RNA-seq data from healthy CD4+{{}^{+}}start_FLOATSUPERSCRIPT + end_FLOATSUPERSCRIPT T cells and HTLV-1-infected CD4+{{}^{+}}start_FLOATSUPERSCRIPT + end_FLOATSUPERSCRIPT cells from 4 ATL cases (sampl... | As summarized in Table 1, the majority of ATL samples are close to CD4+{}^{+}start_FLOATSUPERSCRIPT + end_FLOATSUPERSCRIPT T cells, as expected by the above analysis about the past cell status. | D |
For syncopation tasks, Relative Average Betweenness Centrality was significantly lower in the Mutual condition compared to both Uncoupled (ΔμU,M=0.187Δsubscript𝜇𝑈𝑀0.187\Delta\mu_{U,M}=0.187roman_Δ italic_μ start_POSTSUBSCRIPT italic_U , italic_M end_POSTSUBSCRIPT = 0.187, p=0.0085) and Leader-Follower (ΔμLF,M=0.1... | The distance is calculated for each pair of correlation matrices in a given session resulting in a distance matrix for each subject across an entire experimental session, as shown in Fig. 1. Multidimensional scaling (Scikit-learn sklearn.manifold.MDS class Borg and Groenen (2007)) was used to create a 3 dimensional emb... | Brain activity measured at a macroscopic (cm) scale in humans using electroencephalography (EEG) reflects transient quasi-stable patterns that evolve over timeNunez and Srinivasan (2006). An extensive literature characterizes these patterns as functional networks, using correlation, coherence, or mutual information to ... | To validate the method presented in Fig. 1, we tested it using a model with three metastable states to demonstrate its ability to accurately capture transitions between different basins of attraction. Specifically, we aimed to show how the method can detect and characterize the topological features of system dynamics. ... | Our approach to defining symbols departs from other approaches Beim Graben et al. (2016) by employing Gaussian Mixture Models (GMM) to identify the symbols by focusing on regions with a high density of points. A particular characteristic of GMM is its capability of modeling these dense regions as a mixture of Gaussian ... | D |
However, protein-RNA binding is highly flexible. Some proteins bind with RNA through canonical regions while others bind with RNA through intrinsically disordered regions - protein domains characterized by low sequence complexity and highly variable structures (Seufert et al. 2022), making it challenging to model the m... | We first evaluate our model’s performance on PRA310 and PRA201. We divide the baseline methods into sequence- and structure-based. As illustrated in Table 1, the scratch version of CoPRA reaches the best performance on the PRA310 dataset. IPA is the best-performed model without LMs, and we replace the sequential input ... | Several sequence- or structure-based machine learning-based methods have been applied to predict protein-RNA binding affinity. For example, PNAB (Yang and Deng 2019b) is a stacking heterogeneous ensemble framework based on multiple machine learning methods, e.g. SVR and Random Forest. They manually extract different bi... | Several computational methods have been proposed for protein-RNA binding affinity prediction, including sequence-based and structure-based methods. The sequence-based approaches process the protein and RNA sequence separately with different sequence encoders (Yang and Deng 2019a; Pandey et al. 2024), and subsequently m... | Learning from multiple modals can provide the model with multi-source information of the given context (Huang et al. 2021). Multi-modal learning achieves impressive performance improvement compared to its single-modal counterparts and brings new applications(Luo et al. 2024; Li et al. 2023). Contrastive learning is one... | C |
The FocusPath dataset [13] consists of 864 image patches, each with a resolution of 1024×1024102410241024\times 10241024 × 1024 pixels in sRGB format, capturing varying degrees of focus. These patches are cropped from nine distinct whole slide images (WSIs), with 16 different z-levels employed to simulate various out-o... | The FocusPath dataset [13] consists of 864 image patches, each with a resolution of 1024×1024102410241024\times 10241024 × 1024 pixels in sRGB format, capturing varying degrees of focus. These patches are cropped from nine distinct whole slide images (WSIs), with 16 different z-levels employed to simulate various out-o... | For the experiment to filter out low-quality noisy image patches from good quality ones, we used HistoROI dataset [11]. HistoROI dataset is developed to segment WSIs into six key classes: epithelium, stroma, lymphocytes, adipose, artifacts, and miscellaneous. Artifacts in this dataset include out-of-focus areas, tissue... | Additionally, a quality metric trained on real patches of high quality can also be used to filter out low-quality patches from a whole slide image while training and testing deep learning pipelines for weakly supervised learning in histopathology [11]. | In the experiment for filtering out low-quality patches with artifacts from high quality patches, our network trained only on clean patches gives an AUC score of 0.76. Even without being trained on the artifacts, the model is able to identify clean patches from noisy ones based on the their likelihood. | B |
Our study highlights the potential benefits of improved connectivity between cities, particularly regarding public health outcomes. Increased connectivity is likely associated with a combination of socioeconomic advantages, better access to healthcare, and more effective public health interventions. These benefits are ... | Here we bridge this gap by investigating the effect of inter-city interactions on the association between population size and the number of cases for seven infectious diseases across Brazilian cities. To do so, we use the commuting network among cities as a proxy for inter-city interactions, combined with a general sca... | Our study is not without limitations. While our findings provide evidence supporting the suitability of the Cobb-Douglas and translog models in better describing the data, these models lack mechanistic explanations that directly link the holistic concept of interacting cities to their specific functional forms or to th... | We fit the urban scaling (Eq. 1), Cobb-Douglas (Eq. 2), and translog (Eq. 3) models to each of the seven disease types. For the urban scaling model, we estimate the value of βNsubscript𝛽𝑁\beta_{N}italic_β start_POSTSUBSCRIPT italic_N end_POSTSUBSCRIPT using the standard least-squares method applied to the relationshi... | Figure 2 compares the performance of the three models in predicting the number of cases of HIV/AIDS, meningitis, and influenza. A simple visual inspection reveals that urban scaling models provide the poorest predictions (Fig. 2A), significantly underestimating the number of disease cases in large cities. The Cobb-Doug... | B |
To further assess GAN-TAT’s applicability, we grouped genes by their probability percentiles as assigned by the model and mapped them to Tclin genes to calculate overlaps (Figure 2A). A Fisher’s exact test was performed on these overlaps. Additionally, we compared pathway enrichment scores of the top 5%percent55\%5 % ... | We trained nine models using three different embedding algorithms: Node2Vec, LINE (Large-scale Information Network Embedding) [14, 36], and ImGAGN-GraphSAGE. Each embedding algorithm was paired with three different classifiers: Decision Tree (DT), Random Forest (RF), and XgBoost[33, 3, 6]. All models were trained on an... | The efficacy of various GAN-TAT configurations and frameworks, based on different embedding algorithms, was evaluated using three distinct label sets sourced from Pharos: Tclin genes, Tclin targets for pancreatic intraductal papillary-mucinous neoplasm, and Tclin targets for acute myeloid leukemia[24]. The PIN and the ... | This study aims to address existing constraints in the utilization of the Protein Interaction Network (PIN) for identifying druggable genes. We propose a novel framework, GAN-TAT (Generative Adversarial Network-based Target Assessment Tool), which incorporates a latent representation of the PIN for each gene, serving a... | Our observations indicate that models based on the ImGAGN-GraphSAGE framework consistently outperform those utilizing other embedding algorithms, particularly in label sets characterized by higher imbalance. Among the classifiers evaluated, XgBoost emerged as the most effective across all embedding methods. The data sh... | D |
Rapid Experimental Validation - Data collection and validation are essential parts of the machine learning pipeline. Due to advancements in sequencing technology, databases such as PDB have grown rapidly in size, whilst labels for some applications still remain limited. The ability to rapidly determine protein characte... | Protein folding is a dynamic process that releases energy and are driven by hydrophilic/hydrophobic forces, Van der Walls forces, and conformational entropy [7, 63]. In most cases, the structures stabilize at minimum free entropy, although it is possible that they stabilize at a higher energy level because they are una... | Protein design is an important component of tasks such as bioengineering and drug design, but remains highly challenging. Different sequences may fold into the same target structure, making it a ill-poised problem, and experimental validation in vitro is required to ensure generated sequences can maintain stable folds ... | Dynamic Modelling of Proteins - Standardized challenges and benchmarks such as CASP have played an important role in accelerating our knowledge of the folding process. However, it is important to remember that the challenge is only restricted to a specific formulation of the problem. Current collection of structural in... | Despite this, there are still important problems in structure prediction that remain to be addressed. Essential life functions tend to be carried out through multi-protein complexes, where interactions between multiple structures drive vital processes. Individual protein structures within these complexes remain challen... | C |
VNN architecture and training. The VNN model was pre-trained on a healthy population to glean information about healthy aging. To facilitate ΔΔ\Deltaroman_Δ-Age that is transparent and methodologically interpretable, we used a multi-layer VNN model that yielded representations from the input cortical thickness features... | Explainability of ΔΔ\Deltaroman_Δ-Age. The representations are learned by the VNN, in part, by transforming the input data according to the eigenspectrum of the anatomical covariance matrix [11]. By leveraging this fact, we characterize the explainability of ΔΔ\Deltaroman_Δ-Age by evaluating the inner products between ... | The VNN model consisted of two layers and yielded a representation from the input cortical thickness data via transformation that was dictated by the anatomical covariance matrix. For training this VNN model, we leveraged the cortical thickness features from the healthy control population in the publicly available OASI... | Explainability of ΔΔ\Deltaroman_Δ-Age. Next, we analyzed the inner products between the regional residuals derived from the representations learned by VNNs and the eigenvectors of the anatomical covariance matrix. The eigenvectors of the anatomical covariance matrix were organized from 00 to 67676767, with the eigenvec... | 4RTNI. This dataset was collected as part of the 4-Repeat Tauopathy Neuroimaging Initiative (4RTNI) and used similar MRI acquisition and clinical assessments as the NIFD dataset. This dataset constituted of 59 individuals diagnosed with progressive supranuclear palsy (PSP; age = 70.79±7.65plus-or-minus70.797.6570.79\pm... | B |
For the temporal model, E∗=(u∗,v∗)superscript𝐸superscript𝑢superscript𝑣E^{*}=(u^{*},v^{*})italic_E start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT = ( italic_u start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT , italic_v start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT ) is locally asymptotically stable when C1=−(a11+a22)>0subscrip... | Mk[u1v1]≡[a11−d1k2−λa12−ξu∗k2a21+ηv∗k2a22−d2k2−λ][u1v1]=[00],subscriptM𝑘matrixsubscript𝑢1subscript𝑣1matrixsubscript𝑎11subscript𝑑1superscript𝑘2𝜆subscript𝑎12𝜉superscript𝑢superscript𝑘2subscript𝑎21𝜂superscript𝑣superscript𝑘2subscript𝑎22subscript𝑑2superscript𝑘2𝜆matrixsubscript𝑢1subscript𝑣1matrix0... | J=(a11a12a21a22),Jmatrixsubscript𝑎11subscript𝑎12subscript𝑎21subscript𝑎22\ \textbf{J}=\begin{pmatrix}a_{11}&a_{12}\\ | Jk[u1v1]≡[a11−d1k2−λa12a21a22−d2k2−λ][u1v1]=[00],subscriptJ𝑘matrixsubscript𝑢1subscript𝑣1matrixsubscript𝑎11subscript𝑑1superscript𝑘2𝜆subscript𝑎12subscript𝑎21subscript𝑎22subscript𝑑2superscript𝑘2𝜆matrixsubscript𝑢1subscript𝑣1matrix00\textbf{J}_{k}\begin{bmatrix}u_{1}\\ | (uv)=(u∗v∗)+ϵ(u1v1)exp(λt+i(kxx+kyy)),matrix𝑢𝑣matrixsuperscript𝑢superscript𝑣italic-ϵmatrixsubscript𝑢1subscript𝑣1𝜆𝑡𝑖subscript𝑘𝑥𝑥subscript𝑘𝑦𝑦\displaystyle\begin{pmatrix}u\\ | D |
(a) Antigen Presentation via APCs to activate T cells. Antigens are up-taken by the APCs and then bind to the MHC. Subsequently, the pMHC complex displayed on APCs can bind to some TCRs on T cells. | From a biological perspective, cellular immunity is vital to health by recognizing and eliminating pathogen-infected and abnormal cells. | (b) Recognition of Antigens by T cells. All cells present some peptides via the pMHC. Certain peptides can be recognized by T cells through the pMHC-TCR interaction, leading to their elimination by T cells. | (a) Antigen Presentation via APCs to activate T cells. Antigens are up-taken by the APCs and then bind to the MHC. Subsequently, the pMHC complex displayed on APCs can bind to some TCRs on T cells. | antigen presentation and antigen recognition, essential to adaptive immunity. Antigen-presenting cells (APCs) activate T cells through antigen presentation, while T cells recognize and eliminate abnormal cells via antigen recognition. | B |
We show that, remarkably, slow but non-zero migration can enhance and accelerate the fluctuation-driven eradication of resistant cells (m=10−4−10−3𝑚superscript104superscript103m=10^{-4}-10^{-3}italic_m = 10 start_POSTSUPERSCRIPT - 4 end_POSTSUPERSCRIPT - 10 start_POSTSUPERSCRIPT - 3 end_POSTSUPERSCRIPT in Fig. 4, Exte... | Environmental variations, spatial structure, cellular migration, and fluctuations are all ubiquitous key factors influencing the evolution of cooperative antimicrobial resistance. | Understanding the joint influence of spatial structure and environmental variability on the evolution of microbial populations is therefore an important research avenue with many open questions. | Our results therefore demonstrate the critical and counterintuitive role of spatial migration that, jointly with environmental variability and demographic fluctuations, determines the maintenance or extinction of cooperative antimicrobial resistance. | The dynamic degradation of the drug can however play a critical role in the evolution of cooperative AMR, as shown in Ref. [21], where the fragmentation of the metapopulation into isolated demes enhances the maintenance of resistance. | C |
Furthermore, side chain changes are identified on the P-I-F motif, the NPxxY motif, and the DRY motif, three receptor motifs that are known to undergo distinct rotamer changes in the transition from inactive to active receptor states.Katritch et al. (2013) | Namely, rotameric state changes in the backbone torsions of transmembrane helix TM6, proximal to Asp114, are coupled to the protonation state changes of Asp114. | The recognition of receptor regions where conformational changes are associated with activation and signaling suggests that the Asp114 protonation state and GPCR activation are intertwined. | The SSI values calculated for each residue reveal those parts of the receptor that signal information about the protonation state of Asp114 by coupled conformational state changes between the rotamer states and the Asp114 protonation state (Fig. 7). | This analysis highlights a concerted behaviour of water binding sites and TM6, whereby state changes to both are indicative of the protonation state of Asp114. It shows how the combined analysis of multiple different features and a comprehensive visualization help to find interrelations within a receptor and discover s... | B |
The motivation for this experimental design was to investigate the cognitive processing of speech events. Going from paradigm 1 to 3, the experimental complexity was increased in terms of task difficulty and practicality. In this way, we wished to see how the ERP component is regulated by the distinctiveness of the eve... | The stimuli for paradigms 3 and 4 were generated in a similar way. Text scripts of the stories were first made and then used for audio synthesis using the text-to-speech tool. The audio files were then sliced into different snippets and normalized to have the same RMS amplitude. The text scripts for paradigm 3 were cre... | This paradigm was designed to be similar to the conventional oddball paradigm in which subjects were presented with a sequence of two different classes of spoken words: animal names and cardinal numbers, or color names and cardinal numbers from a loudspeaker situated one meter in front of the subject. The animal names ... | In paradigm 3, the setup was similar to the setup of paradigm 2. The subject was presented with two competing streams from the same two speakers as in paradigm 2. However, in this case, the stimuli in each speaker were not sequences of spoken words but snippets of different stories and each snippet had a duration of ap... | The stimuli of all paradigms were in Danish and were synthesized using the Google Text-to-Speech tool v2.11.1 [36]. The voice configuration was randomly selected between da-DK-Wavenet-A (female) and da-DK-Wavenet-C (male) to generate each snippet in paradigms 3 and 4. In the end, there were 14 out of 20 male voice snip... | D |
The relative fold-change in replication speed is defined as f(Tr)/feq−1𝑓subscript𝑇𝑟subscript𝑓𝑒𝑞1f(T_{r})/f_{eq}-1italic_f ( italic_T start_POSTSUBSCRIPT italic_r end_POSTSUBSCRIPT ) / italic_f start_POSTSUBSCRIPT italic_e italic_q end_POSTSUBSCRIPT - 1 and computed as described above. Eq. 2 reduces to τsτf>1+11... | All-in-all, p1 replication activity and substitution mutation rate have been measured for 213 unique mutant DNA polymerases, as reported in Table S2 from [47]. We plotted and repurposed this data to investigate the relationship between speed and accuracy in Fig. 3. See Extended Fig. E2 and Supplementary Information for... | Results for other distributions are shown and discussed in the Supplementary Information and in Extended Fig. E6. | Real systems can show a distribution of replication times Peq(Trep)superscript𝑃𝑒𝑞subscript𝑇repP^{eq}(T_{\rm{rep}})italic_P start_POSTSUPERSCRIPT italic_e italic_q end_POSTSUPERSCRIPT ( italic_T start_POSTSUBSCRIPT roman_rep end_POSTSUBSCRIPT ) for numerous mechanistic reasons, ranging from kinetic traps in self-a... | Figure E6: Resets are beneficial only for wide distributions of replication times.(i) The residual y=(LHS−RHS)𝑦LHSRHSy=(\rm{LHS-RHS})italic_y = ( roman_LHS - roman_RHS ) of the inequality in Eq. 2 is plotted as function of reset time Trsubscript𝑇𝑟T_{r}italic_T start_POSTSUBSCRIPT italic_r end_POSTSUBSCRIPT and for d... | B |
The current study aimed to apply Neural CDEs to model the disease progression of pulmonary fibrosis and Alzheimer’s Disease using multimodal (structural and image data) irregularly sampled data. | Neural CDEs[10] are a variant of NDEs designed to work specifically with irregularly sampled time series data. As shown in Figure 2, Neural CDEs use a ”time-varying vector” 𝐗𝐗\mathbf{X}bold_X to create a representation of the local dynamics from a calculated interpolation of the irregularly sampled data upon which a ... | Chen et al. 2018 [9] reported a novel approach to deep learning by using ordinary differential equations (ODEs) as continuous-depth models. In this article, the authors demonstrated ODE-based models can outperform traditional ResNet 16 architectures, offering improved efficiency and adaptability. Rubanova et al [17] ha... | The current study aimed to apply Neural CDEs to model the disease progression of pulmonary fibrosis and Alzheimer’s Disease using multimodal (structural and image data) irregularly sampled data. | Neural-controlled differential equations (Neural CDEs) are an extension of NDEs specifically designed to handle irregularly sampled multivariate time-series data[10]. | B |
We used waves 8-14 (2006-2018) from HRS via the RAND preprocessed files [18] (only these waves had all needed variables). Waves are measured every 2 years, but gait and grip are only measured every 4 years. We thus analyzed two sets of preprocessed data: one for predicting FPFP5 deficits (feature selection and predicti... | We used waves 8-14 (2006-2018) from HRS via the RAND preprocessed files [18] (only these waves had all needed variables). Waves are measured every 2 years, but gait and grip are only measured every 4 years. We thus analyzed two sets of preprocessed data: one for predicting FPFP5 deficits (feature selection and predicti... | We did not include our ELSA survival results in the main text because of the very low hazard observed, especially between waves 4 and 6. This is illustrated by Figure S6. HRS and NHANES survival overlap perfectly but ELSA has much higher survival, especially between waves 4 to 6, indicating an anomalously low hazard. W... | We used waves 4 and 6 from ELSA [19] (only these waves had all needed variables). We excluded 1055105510551055 individuals missing both gait and grip measurements, and an additional 13 with top-coded age. ELSA survival estimates were based on end-of-life interviews, which capture only a fraction of the deaths due to a ... | We used data from three national studies: HRS (longitudinal), ELSA (longitudinal), and NHANES (cross-sectional). Our goal is to understand relationships between variables. Definitions necessarily varied across the studies for both the FI and FP. We considered separate exclusions for predicting health deficits versus su... | C |
We pretrain a 7-billion-parameter autoregressive transformer language model, referred to as METAGENE-1, on a novel corpus of diverse metagenomic DNA and RNA sequences comprising over 1.5 trillion base pairs. | Specifically, we pretrain a 7-billion-parameter autoregressive transformer model, which we refer to as METAGENE-1, on a diverse corpus of DNA and RNA sequences comprising over 1.5 trillion base pairs sourced from wastewater samples, which were processed and sequenced using deep metagenomic (next-generation) sequencing ... | This dataset is sourced from a diverse set of human wastewater samples, which were processed and sequenced using deep metagenomic (next-generation) sequencing methods. | The dataset was generated using deep metagenomic sequencing, specifically leveraging Illumina sequencing technology, commonly referred to as next-generation sequencing (NGS) or high-throughput sequencing, in which billions of nucleic acid fragments are simultaneously sequenced in a massively parallel manner. | Our metagenomic foundation model differs from these prior works in a few important ways. First, our pretraining dataset comprises shorter metagenomic sequences (arising from metagenomic next-generation/massively-parallel sequencing methods) performed on samples of human wastewater collected across many locations; these... | B |
Let X𝑋Xitalic_X represent the DNA methylation data matrix, where X𝑋Xitalic_X is an N×M𝑁𝑀N\times Mitalic_N × italic_M matrix, with N𝑁Nitalic_N representing the number of samples and M𝑀Mitalic_M the total number of CpG sites. The vector y𝑦yitalic_y, which corresponds to the chronological ages of the N𝑁Nitalic_N s... | In the 0-10 age range, the correlation is remarkably high, indicating a rapid rate of aging or growth during this period. A similarly fast rate of change is observed in the 10-20 age range. However, the aging rate appears to slow down in the 20-30 age range, and further deceleration is observed in the 40-50 and 70-80 a... | iTARGET-(34-60-78): This approach segments ages into biologically significant intervals: [0-34), [34-60), [60-78), and 78+. | We employ two age grouping strategies. 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 +. This approach is motivated by its interpretability, as decade interv... | The third set of experiments compares two age grouping strategies for DNA methylation age prediction. The first strategy uses decade-sized intervals (e.g., [0-10), [10-20), …, [90-100)) for ease of interpretability. The second strategy, informed by [24], divides ages into segments at key inflection points: [0-34), [34-... | C |
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. Figure 2D shows that refrectoriness is quite important for the asynchronous model to approximate the learning trajectory of the discrete model, as non-... | 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). Figure 2G and 2H show the number of synapses updated at each Euler step during a small simulation window. As expected, the discrete network has a stair-case like shape... | To assess the similarity between learning dynamics, we compare the learning trajectories of both asynchronous and continuous models with the discrete model. We initialize all models with the same weights and present the same stimulus sequence, and measure the cosine similarity of each neuron’s incoming weights (see App... | Figure 2: The discrete and asynchronous models learn very similar representations. (A) Histogram of cosine similarities of the feed-forward weight between the discrete model and the continuous (orange) and asynchronous (blue) model. (B) As A, for the recurrent weights. (C) Average cosine similarity of feed-forward weig... | 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. Figure 2D shows that refrectoriness is quite important for the asynchronous model to approximate the learning trajectory of the discrete model, as non-... | A |
We demonstrate that UniGuide performs competitively or even surpasses specialised baseline models, underscoring its practical relevance and transferability to diverse drug discovery scenarios. | Table 1: Ligand-Based Drug Design. Results taken from Chen et al. [14] are indicated with (∗)(^{*})( start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT ). We highlight the best conditioning approach for | Table 2: Structure-Based Drug Design. Quantitative comparison of generated ligands for target pockets from the CrossDocked and Binding MOAD test sets. Results taken from the respective works are indicated with (∗)(^{*})( start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT ). | conditioning approach for the DiffSBDD backbone in bold and underline the best approach over all methods. | Table 3: Linker Design. Results taken from Igashov et al. [13] are indicated with (∗)(^{*})( start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT ). We underline the best method overall. | A |
For the design of peptide sequences, ProT-Diff (Wang et al., 2024d) combines a pre-trained protein language model (PLM) ProtT5-XL-UniRef50 (Elnaggar et al., 2020) with an improved diffusion model to generate de novo candidate sequences for antimicrobial peptides (AMPs). AMP-Diffusion (Chen et al., 2024c) uses PLM ESM2 ... | Diff-AMP (Wang et al., 2024a) integrates thermodynamic diffusion and attention mechanisms into reinforcement learning to advance research on AMP generation. Sequence-based diffusion models complement structure-based approached by aiding in sequence-to-function or optimizing sequence design for structural goals. | ForceGen (Ni et al., 2024) develops a pLDM by combining the ESM Metagenomic Atlas (Lin et al., 2023), a model of the ESM family, with an attention-based diffusion model (Ni et al., 2023) to generate a protein sequence and structure with non-mechanical properties. | For the design of peptide sequences, ProT-Diff (Wang et al., 2024d) combines a pre-trained protein language model (PLM) ProtT5-XL-UniRef50 (Elnaggar et al., 2020) with an improved diffusion model to generate de novo candidate sequences for antimicrobial peptides (AMPs). AMP-Diffusion (Chen et al., 2024c) uses PLM ESM2 ... | ProteinGenerator (Lisanza et al., 2023) is a sequence space diffusion model based on RoseTTAFold that simultaneously generates protein sequences and structures. The success rate of ProteinGenerator in generating long sequences that fold to the designed structure is lower than RFDiffusion, this may reflect the intrinsic... | A |
The subunits of GABAA receptors include α𝛼\alphaitalic_α (GABRA1, GABRA2, GABRA3, GABRA4, GABRA5, GABRA6), β𝛽\betaitalic_β (GABRB1, GABRB2, GABRB3), γ𝛾\gammaitalic_γ (GABRG1, GABRG2, GABRG3), δ𝛿\deltaitalic_δ (GABRD), ϵitalic-ϵ\epsilonitalic_ϵ (GABRE), π𝜋\piitalic_π (GABRP), θ𝜃\thetaitalic_θ (GABRQ), and ρ𝜌\rhoi... | We extracted the corresponding 24 PPI networks by sequentially entering these gene names into the STRING database. Within each network, there is a core sub-network of proteins that interact directly with the GABA receptor, while the directly and indirectly interacting proteins together form the global network. We limit... | Compounds that act as agonists or antagonists of the GABA receptor exhibit pharmacological effects in anesthesia, which encourages the search for additional compounds that bind to the GABA receptor. The desired drugs must demonstrate specificity for the target protein without causing adverse side effects on other prote... | Protemic technology has increasingly presented a vast potential in anesthesia [8], and the use of proteomic tools to study anesthetic binding sites has offered a better understanding of the mechanisms of anesthetic action. Protein-protein interaction (PPI) networks at the proteomics level provide a systematic framework... | Figure 1: Flowchart of nearly optimal lead compounds screening for Gamma-aminobutyric acid (GABA) receptor agonists. a: The protein-protein interaction (PPI) networks of 24 GABA receptor subtypes involve 4824 proteins, and each receptor subtype has a core and global PPI network. Here only two PPI networks (GABRA1 and G... | A |
The performance of the models were quantified by correlating features of the reconstructions with the stimuli. Specifically, features from reconstructed images and original stimuli are extracted using a pretrained AlexNet model at conv1, conv2, conv3, conv4, conv5 FC6, FC7, and FC8. Subsequently, each feature layer is ... | The introduction of the IRFA model contributes several advancements to the field. First, it demonstrates that incorporating feature-based selective attention, alongside spatial attention, into brain representations significantly enhances the quality of natural image reconstruction from evoked brain responses in the V1,... | In our investigation, we explored the impact of varying the number of dedicated feature channels within our model, specifically training with 4, 16, 32, and 64 features, to determine if increasing feature separability leads to enhanced model performance. Given the convolutional nature of the model, there is an inherent... | The U-NET’s output is compared with the target using three losses (an adversarial loss, a feature loss (VGG) and an L1 loss). The adversarial loss is a discriminator that is trained in parallel of the reconstruction model, which consists of 5 convolutional layers (see Fig. 1B). The feature loss uses the full set of con... | We systematically trained the model to reconstruct images using sets of 4, 16, 32, and 64 learnable attention maps. This approach allows us to evaluate the optimal number of features required for effective reconstruction. The quality of the reconstructions is quantitatively compared with a baseline model, based on the ... | B |
As we argued, the formation of self-conscious awareness in CTM should be a procedure instead of a single activity of any processor. Since self-conscious and conscious are two analogical concepts, we assume that they are generated by the same procedure. Also, the duality of self-consciousness requires the CTM could be a... | Self-conscious Awareness: the Self-conscious Content broadcast to all LTM processors and gets received. | Self-conscious Content: a chunk that wins the competition tree and reaches STM, in addition, this chunk should be made by MIT(But not all chunks made by MIT could be self-conscious content). | In this section, we discussed some functions of MIT, but there are still some issues that remain. Not all chunks generated by MIT will ultimately become self-conscious content, as we have mentioned in the definition. For example, the understanding of the outer world generated by MoTW does not involve self-awareness, ev... | Based on the definition of conscious content and conscious awareness, we here present the definition of Self-conscious Content and Self-conscious Awareness as follows: | D |
In this review, we aim to offer an in-depth exploration of the diverse dynamical behaviors encapsulated within the FHN model. The widespread adoption of the FHN model across physics and biology can be attributed to the model’s remarkable versatility in capturing a wide array of dynamical phenomena while maintaining a r... | In conclusion, we hope our review will serve as a guide for understanding and using the diverse dynamical behaviors offered by the FHN model. Throughout our analysis, stability analyses and bifurcation studies provided insights into the observed dynamics. By exploring its applications across multiple disciplines, we ai... | Our review is structured around delineating the most prominent dynamical behaviors observed within the FHN model. We categorize our analysis into three primary sections: (i) examining the foundational FHN model, characterized by a system of two nonlinear coupled ordinary differential equations (ODEs) [Eq. 8]; (ii) stud... | Lastly, we explored discretely coupled FHN equations [Eq. 11]. This is the broadest category as here one can consider a multitude of different network topologies and coupling terms. We focussed on synchronization properties in two coupled FHN modules, the existence of traveling waves when transitioning from continuous ... | We structured our analysis into three primary sections. Firstly, we examined the original FHN model [Eq. 8], discussing widely observed dynamical regimes such as monostability, multistability, relaxation oscillations, and excitability. We examined the role of local and global bifurcations in shaping these regimes, emph... | B |
Intermediate Coupling (0.017<c<0.2950.017𝑐0.2950.017<c<0.2950.017 < italic_c < 0.295). At this level of coupling, the external system transitions to an excitable state in response to the internal pulse. Consequently, when an oscillation from a non-driven region intersects with a driven region, it triggers the latter t... | Comparable to the previous case, phase waves that are synchronized with the driving pulse emerge within the system (Fig. 8A). In addition, the system becomes excitable under the influence of the driving pulse and is subject to perturbations from adjacent oscillatory regions. These perturbations trigger an extended resp... | Intermediate Coupling (0.017<c<0.2950.017𝑐0.2950.017<c<0.2950.017 < italic_c < 0.295). At this level of coupling, the external system transitions to an excitable state in response to the internal pulse. Consequently, when an oscillation from a non-driven region intersects with a driven region, it triggers the latter t... | Our simple setup of interconnected FitzHugh-Nagumo (FHN) models, inspired by cellular structures, captures a wide range of phase-related dynamics. In this setup, a traveling wave within the internal system, analogous to a cell’s cytoplasm, drives the dynamics of the external system, similar to a cell’s cortex, without ... | Strong Coupling (c>0.295𝑐0.295c>0.295italic_c > 0.295). With strong coupling, the external system becomes non-excitable during the passage of the internal pulse, yet minor perturbations from equilibrium are still possible. Phase waves closely tied to the internal pulse form and traverse the ring, ultimately self-annih... | D |
Table 1: Average Test set reconstruction results of our discrete auto-encoding method for several down-sampling ratios and (implicit) codebook sizes. For CASP-15 we report the median of the metrics due to the limited dataset size. | Note that a RMSD below 2222Å is considered of the order of experimental resolution and two proteins with a TM-score >0.5absent0.5>0.5> 0.5 are considered to have the same fold. | The root mean square distance (RMSD) between two structures is computed by calculating the square root of the average of the squared distances between corresponding atoms of the structures after the optimal superposition has been found. The TM-score (Y. and J., 2005) is a normalised measure of how similar two structure... | For context, two structures are considered to have similar fold when their TM-score exceeds 0.5 (Xu and Y., 2010) and a RMSD below 2Å is usually seen as approaching experimental resolution. | Table 2: Structure generation metrics for our method alongside baselines (and nature)specifically designed for protein structure generation. Self-consistent TM-score (scTM) and self-consistent RMSD (scRMSD) are two different ways to asses the designability of the generated structure. Note that while high novelty score ... | A |
A future research direction is deriving the brain’s slow dynamics and learning mechanisms. Training many parameters over extended periods allows black-box models, such as neural networks representing protein networks, to act as homeostatic-control agents within cells. Promising results have emerged in simplified neuron... | Limitations of the method relate to the fact that brain models extend beyond the cable equation: spike propagation with delays, stochastic models, reaction kinetics and ion dynamics, and Nernst potentials are not included in the method. These can be solved mathematically but would require modifications to the brain sim... | In this section, we explain how the gradient model is derived from the cable equations and evaluate the approach. We start with a description of the cable equation as used in brain-simulation software, followed by a short description of the sensitivity equation. We then combine these equations to form the gradient mode... | In this brief, we introduced gradient diffusion, a methodology that facilitates the calculation of parameter gradients for any existing, unmodified model-and-neurosimulator combination, thereby enabling support for homeostatic control. This approach allows for the efficient tuning of realistic neuron models and the imp... | The cable equation in eq. 8 is taken from the Arbor brain simulator, but the same equation is solved by NEURON or EDEN. | A |
The parameter κ𝜅\kappaitalic_κ, which sets the relative lifespan of phages versus hosts and thus impacts the density of phages in the environment and accordingly the rate of infections, has surprisingly little impact on the dynamics, see appendix 15. | In both the phage therapy and biodetection examples, the crucial requirements are that bacteria are the limiting agent and that adsorption happens quickly (Goodridge, 2008). Both can be accomplished by high phage densities, creating potential scenarios where many adsorption events occur in a small time window and openi... | Our work focuses on the ecological impact of simultaneous infections but suggests an interesting evolutionary question. In our model, host death is inevitable after infection. However, the burst size λ𝜆\lambdaitalic_λ influences phage density and accordingly the rate at which hosts are infected and then lyse. Conseque... | 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 ... | Our model describes a phage-host system where multiple phages can simultaneously infect a single host. Considering the high densities of phage proposed for use in various applications, as well as the high densities of phage in many natural settings, simultaneous infections are a natural and relevant infection dynamic t... | D |
While it is certainly impressive that test tubes filled with some chemicals can be used for handwritten number recognition, it should of course be noted that this is certainly not the most efficient approach if the recognition of handwritten numbers is our primary goal. If the numbers are primarily a proof of principle... | Moreover, there can be contexts where having a neural network that operates slower can actually be an advantage.333Thanks to Raphael Wittkowski for bringing this to my attention. A good example would be a network that processes temporal input signals, as is required, e.g., in speech recognition. In this case, it is adv... | In this chapter, I have provided a brief introduction to neural networks consisting of DNA, using as an example the winner-take-all network proposed in Ref. Cherry and Qian (2018). The input data is provided as a DNA strand and is processed via biochemical reactions. On this basis, it is possible to recognize handwritt... | In a nutshell, reservoir computing employs a dynamical system (the reservoir) that is driven by an input signal. The response of the system then serves as the input for a neural network with a single layer (the readout layer) that converts this response into the output layer. This readout layer is the only part of the ... | DNA neural networks require the input signal to have the form of a DNA strand. In general, this is a disadvantage since converting general input signals to DNA is quite an effort. This aspect can, however, turn into an advantage in contexts where the input signal takes the form of DNA strands (or at least that of biomo... | D |
We used time-frequency decompositions (TFDs) as a unified representation for both the input (speech signal) and the output (MEG signal). These were computed using Short-Time Fourier Transform (STFT) applied to 3-second windows defined as described above. | The Short-Time Fourier Transform (STFT) was applied to 3-second windows, as previously defined. The STFT parameters, including the number of Fast Fourier Transform points (n-FFT) and the overlap between frames (hop length), were adjusted to ensure temporal alignment between the MEG and speech signals. This setup produc... | In contrast, the temporal resolution offered by Magnetoencephalography (MEG), despite other limitations (e.g. lower sensitivity in deep brain structures), could provide a more detailed and dynamic insight into neural mechanisms underlying language comprehension and generation. In this work, we aimed to develop encoding... | We used data from the MEG-MASC dataset (Gwilliams et al., 2023), specifically selecting 8 subjects as in the study by Oota et al. (2023). The dataset includes recordings from 208 MEG sensors as the subjects listened to a series of naturalistic spoken stories, selected from the Open American National Corpus, namely “Cab... | We used time-frequency decompositions (TFDs) as a unified representation for both the input (speech signal) and the output (MEG signal). These were computed using Short-Time Fourier Transform (STFT) applied to 3-second windows defined as described above. | A |
Motivated by this, in the present paper, we formulate a reinforcement learning (RL) strategy where an agent performs run-and-tumble motion in an environment with inhomogeneous concentration of attractant. For simplicity, we consider one spatial dimension here. The agent can either persist moving in the same direction, ... | In the case when all the concentration peaks are of the same size, then starting from a uniform initial position, the agent is able to localize most strongly near the peak regions when exploration rate is low and learning rate is high. However, when the agent starts from the vicinity of one particular attractant peak, ... | In this work, we have considered an RL agent which is exploring its environment via run-and-tumble motion. We are interested in the question: under what condition the RL strategy is most efficient. We quantify efficiency by the probability to find the agent in the attractant-rich region in the long time limit, and also... | To capture the trapping effect, therefore, we define another performance criterion, which measures if all the favorable regions in the environment has been sampled by the agent. Starting from the vicinity of one peak of [L](x)delimited-[]𝐿𝑥[L](x)[ italic_L ] ( italic_x ), we measure the probability to find the agent... | We are interested in the long time behavior of the agent. In particular, we measure the effectiveness of the RL algorithm by evaluating the performance of the agent at large times. We use different performance criteria like how strongly the agent is able to localize in the high attractant zones, or how quickly it is ab... | D |
The receptive field (RF) size corresponds to the size of the result of the affine transformation applied to the original image. Specifically, this transformed region determines the portion of the original image contributing to the neural response at the layer being analyzed. By using the parameters of the best-performi... | Our analysis shows that the predicted activity from the AFRT model correlate higher with ground truth signals compared to the predicted activity from the baseline model Linear-AlexNet (Fig. 2). We plotted the correlation values for all the best performing models from conv1, conv2 and conv3 layers. Each point represents... | To evaluate performance, we trained three encoding models for each MUA signal, using features from layers 1, 2, and 5. For each MUA, we selected the best-performing model out of the three trained models based on the Pearson correlation value between the predicted response and the target response. This method not only p... | Our model demonstrates substantial enhancements in predicting multi-unit activity (MUA) across the V1, V4, and IT regions of the macaque, outperforming traditional models that lack biologically-inspired constraints. Additionally, AFRT significantly reduces the number of required parameters by transforming feature respo... | Figure 2: Comparison between the performance values of the AFRT model (blue) and the baseline model (red). Single blue dots show correlation values for trained AFRT models trained and red dots show the values of baseline models trained. The dashed line show the average across all models. Both models are trained on the ... | A |
Insights gained from this study can be extended to other diseases whose mechanisms are yet to be clarified. | This study was supported by grants from the Ministerio de Ciencia e Innovación (PID2021-126961OB-I00, PLEC2022-009401); Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación and European Regional Development Fund (ERDF A way of making Europe) (Red de Terapias Avanzadas, RD21/0017/0020); European Union NextG... | This study is based on striatal snRNA-seq obtained from two post-natal stages, 8 and 12 weeks old, from wild-type (WT) and an HD mouse model [9]. First, we generated the single-cell gene count matrices using CellRanger [10]. Next, we used Seurat [11] to normalize the counts and identify highly variable genes, resulting... | To conduct the XAI analysis, we used the KernelExplainer from SHAP, which uses a special weighted linear regression to compute the importance of each feature. An explainer was created using the training data set as the background to generate explanations for the HD cells in each cluster from the test set. We used this ... | Figure 3: Barplot displaying top 20 DEGs from DESEq2 based on absolute LFC for clusters iSPN (left) and dSPN (right). Bars are colour-coded to indicate HD upregulated genes (blue) and down-regulated (red). | A |
Different input file types are supported with their idiosyncratic options, which all are represented by a uniform data type that we call a (potential) Variant. A Variant describes a single position on a chromosome, here, position 123 on chromosome Chr1, and stores the reference and alternative base for file formats tha... | For each sample of the input (e. g., read groups in SAM files, columns in mpileup files, or sample frequencies from tabular formats), the nucleotide base counts (ACGT) of the pooled reads are stored, including counts for "any" (N) and "deletion" (D), which are however ignored in most statistics. | If a reference genome is provided, it is used to fill in the reference bases when using file formats that do not store these. When multiple input files are provided (even of different formats, and with missing data), they are traversed in parallel, using either the intersection or the union of the genomic positions pre... | 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. Our implementation however can also be used with inferred or adjusted allele frequencies as input, for instance using information from the haplotype fr... | In contrast, and in addition to these formats, grenedalf can directly work with other standard file formats such as sam/bam (12), cram (13), vcf (using the "AD" allelic depth field) (14), and a variety of simple table formats, for reading allele counts or allele frequencies from pool sequencing data. All formats can al... | A |
Minimum RMSD: This metric provides insight into the average best-case alignment between the generated conformations and the reference set, indicating the overall accuracy of the model. | Maximum RMSD: The maximum RMSD highlights the worst outliers among the generated conformations, revealing cases where the model may struggle to produce accurate structures. | Minimum RMSD: This metric provides insight into the average best-case alignment between the generated conformations and the reference set, indicating the overall accuracy of the model. | MAT-P (Mean RMSD-Precision): MAT-P scores reflect the mean RMSD between each generated conformation and its nearest reference counterpart. It calculates the average structural deviation between the generated and reference conformations. Low MAT-P scores indicate that the generated conformations closely resemble the ref... | Evaluating the performance of conformation generation models involves assessing their ability to produce a diverse set of accurate molecular structures. The root-mean-square deviation (RMSD) metric is a crucial measure in this context, which quantifies the structural differences between the generated conformations and ... | A |
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