Understanding how cells migrate in response to external cues has important implications for biology, medicine, and bioengineering. Chemical, mechanical, and electrical signals are the primary drivers of directed cell migration, and each has been extensively studied over the past decades. Among them, chemical cues were the first to be investigated and remain the most widely studied due to their undeniable role in in vivo guidance. Mechanical signals, particularly substrate stiffness gradients, have gained prominence for their ubiquity across cell types and their potential to direct migration. More recently, growing evidence suggests that electrotaxis offers a highly precise and programmable means to guide cell movement. Despite this, these cues are often studied in isolation, whereas in vivo they typically coexist and interact. Using well-established biophysical models, we investigate how mechanical and electrical signals cooperate and how they can be engineered to compete for control over cell migration. Our model shows that an electric field can override and even reverse mechanotaxis. Still, the specific outcomes strongly depend on the cell type or, in other words, on the model parameters that describe how strongly the sensing molecules activate the signaling network. To address this large variability in controlling cell migration, we propose particular steps toward further exploration. To support such future research, we provide a freely available platform for predicting electro-mechanical interactions in cell migration, based on a given cell's sensing and signaling characteristics, which could tailor the mechanical and electrical signals that arise naturally during organ development, cancer invasion, or tissue regeneration.
Metabolic dysfunction-associated steatotic liver disease (MASLD) affects 25-38% of the global population, yet the contribution of environmental polyethylene terephthalate (PET) microplastics to its pathogenesis remains unclear. PET microplastics accumulate in the liver at approximately 4.6 particles per gram of tissue and have been implicated in metabolic disturbance, oxidative stress, and inflammation, but their molecular targets and mechanisms in MASLD are not well defined. We integrated three GEO microarray cohorts (GSE37031, GSE63067, GSE89632) and performed differential expression analysis, weighted gene co-expression network analysis (WGCNA), and PET target prediction using ChEMBL, PharmMapper, and SwissTargetPrediction. Functional enrichment, protein-protein interaction network analysis, CIBERSORT-based immune deconvolution, molecular docking, and 100 ns molecular dynamics simulations were employed to identify and validate hub genes. Integration of MASLD transcriptomes and PET target predictions yielded 19 overlapping genes enriched in pathways related to lipid metabolism, fatty acid degradation, glycolysis/gluconeogenesis, and chemical carcinogenesis. Network topology consistently highlighted FABP4, PTGS2, and HPGD as central hub genes. Immune deconvolution revealed MASLD-associated alterations characterized by increased M2 macrophages and γδ T cells, with decreased monocytes, dendritic cells, and naive B cells. PTGS2 and FABP4 expression showed strong correlations with innate immune cells. Molecular docking demonstrated favorable PET binding to all three proteins (-6.3 to -6.9 kcal/mol), and molecular dynamics simulations confirmed stable complexes over 100 ns, with predominantly hydrophobic interactions. Through integrated bioinformatics analysis and molecular simulation, this study identifies FABP4, PTGS2, and HPGD as potential molecular targets through which PET microplastics may influence lipid metabolism, prostaglandin signaling, and innate immune responses in MASLD. Molecular docking and dynamics simulations suggest favorable binding interactions between PET and these proteins.
The global health crisis of antimicrobial resistance necessitates the discovery of new antibacterial agents. Underexplored marine microbiomes, particularly from the biodiverse Indian coast, represent a rich potential source of antimicrobial peptides (AMPs). Targeting the urgent threat of multidrug-resistant ESKAPE pathogens, the present study aimed to computationally identify novel, membrane-active AMPs from these unique metagenomic datasets, with a focus on inhibiting Gram-negative bacteria. In this study, we computationally mined Indian marine high-resolution shotgun metagenomic datasets through quality filtering, de novo assembly, and small open reading frame prediction. An ensemble of six machine learning-based AMP prediction tools identified over 51,000 high-confidence candidate AMPs. Subsequent filtering based on physicochemical properties and AlphaFold3-predicted structures prioritized ten peptides with favourable membrane-active characteristics. Two lead candidates, c_AMP_1 and c_AMP_2, were subjected to all-atom molecular dynamics simulations within Gram-negative membrane mimetic models of Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae. Our simulations indicated distinct membrane interaction modes: c_AMP_1 adopted a stable, surface-associated α-helical orientation, while c_AMP_2 displayed a more flexible, membrane-inserting orientation in the simulations. Analysis of the MD simulations revealed distinct predicted peptide-membrane interaction profiles, characterized by specific hydrogen bonding patterns, peptide tilt angles, and membrane thinning, which collectively suggest differing biophysical interaction modes. Taken together, our work suggests the Indian marine microbiome as a promising reservoir for novel AMP candidates and suggests that an integrated computational pipeline - combining machine learning, structural biology, and biophysical simulation - may help prioritize candidate peptides for future experimental validation against critical pathogens.
Animals develop specialized cognitive maps during navigation, constructing environmental representations that facilitate efficient exploration and goal-directed planning. The hippocampal CA1 region is implicated as the primary neural substrate for cognitive mapping, housing spatially tuned cells that adapt based on behavioral patterns and internal states. Computational approaches to modeling these biological systems have employed various methodologies. Although labeled graphs with local spatial information and deep neural networks have provided computational frameworks for spatial navigation, significant limitations persist in modeling one-shot adaptive mapping. We introduce a biologically inspired place cell architecture that develops cognitive maps during exploration of novel environments. Our model implements a simulated agent for reward-driven navigation that forms spatial representations online. The architecture incorporates behaviorally relevant information through neuromodulatory signals that respond to environmental boundaries and reward locations. Learning combines rapid Hebbian plasticity, lateral competition, and targeted modulation of place cells. Analysis of the model across a variety of environments demonstrates that online map formation and reward-directed navigation can emerge within a single simulated trial, without the multi-epoch training typically required by reinforcement-learning approaches. The simulation results show that the agent successfully explores and navigates to target locations in various environments, adapting when reward positions change. Analysis of neuromodulated place cells reveals dynamic changes in neuronal density and place field size after behaviorally significant events. These findings align with experimental observations of reward effects on hippocampal spatial cells while providing computational support for the efficacy of biologically inspired approaches to cognitive mapping.
The last decades have seen a great improvement in our understanding of visuospatial working memory (VSWM). Despite this progress, less is known about how information is stored, retained, and removed from VSWM when novel information is presented sequentially. Here, we present a novel computational model of the dynamics of VSWM that extends and improves classical ideas. Our analysis relies on data from three clinical trials involving neurotypicals and people with autism performing a smartphone based sequential VSWM task. In addition, we applied the model to data from a large clinical trial in prodromal Alzheimer's disease. We demonstrate that visual information in our sequential task is stored in independent pools of contrasting resources, with perfect and imperfect retrieval rates. Our findings illustrate how computational models combined with remote cognitive testing are mature enough for applications in large-scale clinical research.
Type I interferons (IFNs) are indispensable antiviral cytokines in nonspecific immunity, yet they play dual roles in bacterial infections in mammals. Recent studies have revealed a subset of strongly cationic type I IFNs possessing potent antimicrobial properties across nonmammalian vertebrates. In this study, we identified a type I IFN gene, CaIFNi, from Cromileptes altivelis that is characterized by a unique triple-disulfide bond architecture. In Vibrio harveyi-challenged models, overexpression of CaIFNi potentiated bacterial clearance capacity in tissues, whereas its knockdown exacerbated bacterial colonization, highlighting its ability to protect the host against bacterial infection in vivo. In vitro assays further confirmed that CaIFNi directly binds to and kills both gram-negative (G-) and gram-positive (G+) bacteria, which first revealed the antibacterial function of new subgroup IFNi within teleost type I IFNs. Furthermore, the α-helical peptide CaIFNi-18 derived from CaIFNi was identified as a novel antimicrobial peptide (AMP) that has broad-spectrum antibacterial efficacy against G- and G+ bacteria and membrane-targeting ability. Further mechanistic studies revealed that CaIFNi has bactericidal effects on both G- and G+ bacteria through membrane depolarization and disruption, alteration of the bacterial ultrastructure, and in vitro binding to genomic DNA. In addition, CaIFNi-18 also has significant in vivo therapeutic efficacy against bacterial infection, highlighting its great potential as an antibacterial agent. Encouragingly, the loss of antibacterial activity in the truncation mutant (rCaIFNiΔ148-165) lacking the CaIFNi-18 segment suggests that this region is essential for the bactericidal function of the full-length protein and likely acts as its core domain. Further computational simulations revealed that the deletion of the CaIFNi-18 region attenuated the interaction between the protein and the bacterial membrane. These findings not only expand the functional scope of type I IFNs beyond their canonical antiviral role but also identify their derivative CaIFNi-18 as both a promising antimicrobial candidate and the essential bactericidal domain of CaIFNi, thereby offering novel therapeutic strategies against bacterial infections in the aquaculture industry and beyond.
Distinguishing correlation from causation is a fundamental challenge in many scientific fields, including biology, especially when interventions like randomized controlled trials are infeasible and only observational data are available. Methods based on statistical tests of conditional independence within the Mendelian Randomization framework can detect causality between two observed variables that are each associated with a third instrumental variable. However, these methods for detecting causal relationships between traits (e.g., two gene expression or clinical traits associated with a genetic variant, all observed in the same population) often assume a linear relationship, thereby hindering the discovery of causal gene networks from genomics data.We have developed NLCD, a method for NonLinear Causal Discovery from genomics data based on nonlinear regression modeling and conditional feature importance scoring. NLCD uses these techniques to extend the statistical tests in an existing linear causal discovery method called the Causal Inference Test (CIT). We benchmarked NLCD against current state-of-the-art methods: CIT, Findr, and MRPC. On simulated datasets, NLCD performs comparably to most methods in detecting linear relations (Average AUPRC (Area Under the Precision-Recall Curve) of NLCD = 0.94, CIT = 0.94, Findr = 0.94, and MRPC = 0.99), and outperforms them in detecting nonlinear (sine and sawtooth type) relations between two genes (Average AUPRC of NLCD = 0.76, CIT = 0.60, Findr = 0.56, and MRPC = 0.73). When tested on a nonlinear subset of a yeast genomic dataset to recover known causal relations involving transcription factors, NLCD and CIT performed comparable to each other and slightly better than Findr and MRPC (Average AUPRC of NLCD = 0.82, CIT = 0.81, Findr = 0.71, and MRPC = 0.54). On application to a human genomic dataset, NLCD revealed active causal gene pairs (IRF1 → PSME1 and HLA-C → HLA-T) in the muscle tissue, and clarified the promises and challenges in discovering causal gene networks in tissues under in vivo human settings.
Effective reinforcement learning requires balancing exploration of uncertain options with exploitation of known outcomes. In real-world contexts, the same action may yield rewards in some situations and punishments in others, yet how these learning processes influence each other remains unclear. Here, we examine the neural mechanisms underlying how reward and punishment learning interact to guide adaptive behavior. We conducted four experiments (N = 159) using an instrumental learning task with binary choices, some of which were exclusive to reward or punishment learning trials, while others appeared in both, allowing assessment of their interaction. When choices were tied to a single learning type, reward learning engages less exploration (i.e., fewer choices of lower-value options) than punishment learning. Critically, when both learning processes were concurrently engaged, reward learning was selectively impaired, accompanied by enhanced exploration and greater activation in exploration-related prefrontal regions revealed by fMRI. Computational modeling showed that impaired reward learning was best explained by sensitivity to prior punishment history associated with reward-learning options, while individual differences in loss aversion predicted the degree of increased exploration. Finally, pharmacological attenuation of dopaminergic signaling via the D2/3 receptor antagonist amisulpride abolished both the increased exploration and the interference with reward learning. These findings suggest that punishment-history interference during reward learning is dopamine-modulated and associated with increased exploration, with individual differences in this exploration linked to loss aversion, providing a mechanistic account of how the brain resolves competing value signals and informing dopamine-related learning disturbances in neuropsychiatric conditions.
Hypertrophic cardiomyopathy (HCM) is associated with marked inter-patient heterogeneity in ventricular electrophysiology, contributing to arrhythmic risk that is insufficiently captured by current clinical methods. Electrocardiographic imaging (ECGI) provides high-density body surface potential (BSP) measurements but remains largely descriptive. Computational modelling offers a mechanistic framework to interpret BSP signals in terms of underlying tissue-level properties. We developed a BSP-driven workflow to construct patient-specific electrophysiology (EP) models of HCM by integrating multimodal clinical imaging with Bayesian model calibration. Anatomically detailed torso-heart finite-element models were generated for 17 HCM patients using thoracic computed tomography (CT), cardiac magnetic resonance imaging (CMR), and 252-electrode BSP recordings. Ventricular depolarisation and repolarisation were simulated using a reaction-eikonal (RE) formulation coupled to a biophysically detailed ToR-ORd-dynCl ionic model. Emulator-based Bayesian history matching (HM) was used to personalise EP parameters, with staged calibration of QRS and T-wave morphology informed by targeted sensitivity analysis. The calibrated cohort reproduced clinical BSP morphology with Pearson correlation coefficient (PCC) [Formula: see text] for a median of 94.0% (IQR: 91.6 to 96.8%) of electrodes, achieving a median PCC of 0.89 (IQR: 0.80 to 0.94) across the full 252-electrode vest. Calibration substantially reduced uncertainty in the high-dimensional EP parameter space while yielding physiologically plausible conduction and repolarisation properties. Models calibrated exclusively to sinus rhythm robustly generalised to right-ventricular (RV) apical pacing without parameter retuning, reproducing clinically observed pacing-induced trends in depolarisation and repolarisation. Exploratory analysis revealed biologically consistent associations between inferred EP parameters and patient demographics. This study demonstrates that high-density BSP data can be used to functionally personalise mechanistic EP in HCM. The framework captures intrinsic patient-specific EP properties and generalises beyond the calibration condition, supporting its use for mechanistic investigation of arrhythmogenic substrate.
Automated drug-drug interaction (DDI) extraction is a cornerstone of global pharmacovigilance, yet its progress is stymied by a fundamental linguistic paradox: relations are signaled by localized morphological cues while being governed by long-range semantic logic. Current monolithic architectures, including Transformer-based models, often face challenges in resolving this feature entanglement, where local clinical descriptors often distort the distal logical chain, leading to noise propagation and reasoning failures. To address this, we present DuSSM, a parallel state-space framework that structurally disentangles surface patterns from semantic evolution. DuSSM implements a bifurcated pipeline: an explicit convolutional stream acting as a local pattern recognizer to isolate syntactic triggers, and an implicit stream leveraging selective state-space modeling (Mamba) to maintain stable semantic states. Although the initial contextual encoding retains a quadratic complexity (𝒪(N2)), this decoupled downstream reasoning module operates with a strictly linear 𝒪(N) complexity. Our extensive experiments across four diverse biomedical benchmarks (DDI-2013, ChemProt, GAD, EU-ADR) demonstrate that this dual-stream feature separation generalizes exceptionally well, achieving a robust Fl-score of 82.27% on the DDI benchmark. Notably, DuSSM yields 94.32% precision on non-interaction cases, effectively mitigating the alert fatigue that can arise from the probabilistic nature of generative large language models (LLMs) in zero-shot settings. By reconciling computational efficiency with mechanistic interpretability, DuSSM provides a scalable and trustworthy paradigm for deciphering complex biological interactions within massive electronic health records. All code and data have been publicly released at: https://github.com/Hero-Legend/DuSSM.
The presence of multiple different pathogen variants within the same infection, referred to as multiplicity of infection (MOI), confounds molecular disease surveillance in diseases such as malaria. Specifically, if molecular/genetic assays yield unphased data, MOI causes ambiguity concerning pathogen haplotypes. Hence, statistical models are required to infer haplotype frequencies and MOI from ambiguous data. Such methods must apply to a general genetic architecture (i.e., multiple, multiallelic markers), when aiming to condition secondary analyses, e.g., population genetic measures such as heterozygosity or linkage disequilibrium, on the background of variants of interest, e.g., drug-resistance associated haplotypes. A statistical method to estimate MOI and pathogen haplotype frequencies, assuming a general genetic architecture, is introduced. The statistical model is formulated and the relation between haplotype frequency, prevalence and MOI is explained. Because no closed solution exists for the maximum-likelihood estimate, the expectation-maximization (EM) algorithm is used to derive the maximum-likelihood estimate. The asymptotic variance of the estimator (inverse Fisher information) is derived. This yields a lower bound for the variance of the estimated model parameters (Cramér-Rao lower bound; CRLB). By numerical simulations, it is shown that the bias of the estimator decreases with sample size, and that its covariance is well approximated by the inverse Fisher information, suggesting that the estimator is asymptotically unbiased and efficient. Computational performance is evaluated using empirical datasets, suggesting that the method is appropriate for up to thirteen polymorphic markers. As an application, a dataset from Cameroon concerning anti-malarial drug resistance is analyzed, showing how the method can be utilized to derive population genetic measures associated with haplotypes of interest. The proposed method has desirable statistical properties and is adequate for handling molecular data consisting of moderate number of multiallelic molecular markers. The EM-algorithm provides a stable iteration to numerically calculate the maximum-likelihood estimates. An implementation of the algorithm alongside a detailed documentation is provided in Supporting information S1 Data.
The cerebral cortex operates in a state of restless activity, even in the absence of external stimuli. Collective neuronal activities, such as neural avalanches and synchronized oscillations, are also found under rest conditions, and these features have been suggested to support sensory processing, brain readiness for rapid responses, and computational efficiency. The rat barrel cortex and thalamus circuit, with its somatotopic organization for processing sensory inputs from the whiskers, provides a powerful system to explore such interplay. To characterize these resting state circuits, we perform simultaneous multi-electrode recordings in rats' barrel cortex and thalamus. During spontaneous activity, oscillations with frequencies centered around 11 Hz are detected concomitantly with slow oscillations below 4 Hz, as well as power-law distributed avalanches. The phase of the lower-frequency oscillation appears to modulate the higher-frequency amplitude, and it has a role in gating avalanche occurrences. We then record neural activity during controlled whisker movements and observe that the 11 Hz barrel circuit active at rest is indeed the one involved in response to whisker stimulation. We finally show how a thalamic-driven firing-rate model can describe the entire phenomenology observed at resting state and predict the response of the barrel cortex to controlled whisker movement, suggesting that the same intrinsic dynamics underlying resting-state activity also shape sensory responses.
Sleep slow oscillations (SOs), characteristic of NREM sleep, are causally tied to cognitive outcomes and the health-promoting homeostatic functions of sleep. Characterization of SO organization during a night of sleep is an active area of research, with most existing work focused on individual SO events rather than the temporal dynamics across sleep cycles or channels. Hence, the probabilistic structure governing the timing and distribution of SOs in one individual across the sleep night remains underexplored. To address this gap, we introduce a computational model characterizing SO emergence over time as a function of sleep cycle and electrode location. SOs were detected in a dataset of nighttime sleep from 22 subjects (9 females), acquired with polysomnography including 64 EEG channels. Modeling of SO occurrence was performed separately for SOs detected during stage N3, and during a combination of stages N2 and N3 (N2&N3). We analyzed SO emergence at two temporal scales. First, we modeled cumulative SO occurrences across successive sleep cycles using a power law fit (across-cycles model). Second, we characterized SO timing within each cycle using a renewal point process (within-cycle model), fitting an inverse Gaussian distribution to the inter-event intervals of SOs and estimating its parameters μ (mean) and λ (shape) for each sleep cycle and channel. Both models were fit to individuals and to a generic idealized 'average' SO emergence behavior, describing both general and individualized patterns. The decay rate of SO count per cycle was 1.70 for N3 and 1.14 for N2&N3, with participant-level variance of 1.00 and 0.53, respectively. Within-cycle modeling showed consistent increases in μ (0.83 ± 0.14) and λ (4.59 ± 0.66) across cycles. This probabilistic framework captures structured SO timing and supports descriptive modeling of large-scale SO dynamics across the night, offering a basis for future investigations of variability in sleep organization.
Brightfield time-lapse imaging is widely used in cardiac tissue engineering, yet the absence of standardized, interpretable analytical frameworks limits reproducibility and cross-platform comparison. We present an open, scalable computational pipeline for quantifying spatiotemporal contractile dynamics in microscopy videos of human induced pluripotent stem cell-derived cardiac microbundles. Building on our open-source tools "MicroBundleCompute" and "MicroBundlePillarTrack," we define a suite of 16 interpretable structural, functional, and spatiotemporal metrics that capture tissue deformation, synchrony, and heterogeneity. The framework integrates full-field displacement tracking, strain reconstruction, spatial registration, dimensionality reduction, and topology-based vector-field analysis within a unified workflow. Applied to a dataset of 670 cardiac microbundles spanning 20 experimental conditions, the pipeline reveals continuous variation in contractile phenotypes rather than discrete condition-specific clustering, with intra-condition variability often exceeding inter-condition differences. Redundancy analysis identifies a reduced core set of 10 metrics that retain most informational content while minimizing multicollinearity. Analysis of denoised displacement fields shows that contraction is dominated by a global isotropic mode, with localized saddle-type deformation patterns present in approximately half of the samples. All software and workflows are released openly to enable reproducible, scalable analysis of dynamic tissue mechanics.
Single-cell transcriptomic data provide critical insights into cellular states and disease mechanisms, and foundation models have recently emerged as powerful tools for learning gene-gene relationships from these data. However, current approaches often overlook key challenges, including the mismatch between model design and the rank-ordered structure of gene expression profiles, as well as the unclear benefits of large-scale pretraining for biological applications. Here, we present GFCAB, a modified modeling framework designed to better capture the structural properties of ranked single-cell transcriptomic data. GFCAB incorporates a cumulative assignment mechanism to suppress repeated gene predictions and a similarity-based regularization strategy to promote diversity in model outputs. Across multiple evaluation settings, including pretraining behavior, biologically relevant classification tasks, and cross-dataset analyzes, GFCAB consistently reduces redundancy and enhances the recovery of low-frequency genes with known functional and disease relevance while maintaining or improving predictive accuracy. In downstream applications, including classification and zero-shot batch effect correction, the model achieves competitive or improved performance compared to existing approaches. We further show that indiscriminately increasing the pretraining data scale does not uniformly improve performance. Instead, models trained on substantially smaller datasets can match or exceed the performance of larger models and often demonstrate improved generalization across datasets. Together, these findings highlight the importance of aligning model design with the intrinsic structure of biological data and suggest that architectural innovation can reduce reliance on large-scale training data. GFCAB provides a framework for developing more efficient and biologically informative models for single-cell analysis, with potential applications in disease characterization and precision biology.
Understanding how cells migrate through confined environments is crucial for elucidating fundamental biological processes, including cancer invasion, immune surveillance, and tissue morphogenesis. The nucleus, as the largest and stiffest cellular organelle, often limits cellular deformability, making it a key factor in migration through narrow pores or highly constrained spaces. In this work, we introduce a geometric surface partial differential equation (GS-PDE) model in which the cell plasma membrane and nuclear envelope are described as evolving energetic closed surfaces governed by force-balance equations. We replicate the results of a biophysical experiment, in which a microfluidic device is used to impose compressive stresses on cells by driving them through narrow microchannels under a controlled pressure gradient. The model is validated by reproducing cell entry into the microchannels. A parametric sensitivity analysis highlights the dominant influence of specific parameters, whose accurate estimation is essential to faithfully capture the experimental setup. We found that surface tension and confinement geometry emerge as key determinants of translocation efficiency. Although tailored to this specific setup for validation purposes, the framework is sufficiently general to be applied to a broad range of cell mechanics scenarios, providing a robust and flexible tool for investigating the interplay between cell mechanics and confinement. It also offers a solid foundation for future extensions integrating more complex biochemical processes such as active confined migration.
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All life depends on the reliable translation of RNA to protein according to complex interactions between translation machinery and RNA sequence features. While ribosomal occupancy and codon frequencies vary across coding regions, well-established metrics for computing coding potential of RNA do not capture such positional dependence. Here, we investigate positional bias in codon usage, which contextually accounts for the position of protein-coding signals embedded within coding regions. We demonstrate the existence of position-dependent patterns of codon frequency in the human transcriptome and describe these patterns using our POsition-Specific Codon Occurrence (POSCO) score that is more consistently associated with translation-initiating codons than other common sequence features. We further show that the patterns described by POSCO are not accounted for by other common scores, including position-dependent GC content, consensus sequences, and the presence of signal peptides in the translation product. More importantly, POSCO defines a spectrum of translational efficiency and local tRNA adaptation index (tAI). High POSCO scores correspond to the highest initial tAI values, which increase over the downstream length of the transcript, forming a translational highway. Meanwhile, low POSCO scores exhibit the lowest initial tAI values followed by a previously undescribed translational valley. An inverse correlation was found between POSCO score and ribosomal occupancy near the start codon. We also find that POSCO defines a spectrum of local folding energies, with high-POSCO transcripts showing the most stable folding immediately after the start codon. Finally, we examine the relationship between POSCO intensity and functional enrichment. We find that transcripts with start codons showing the highest POSCO are enriched for functions relating to development of musculoskeletal, cardiovascular, neurological, gastrointestinal, sensory, and other body systems. Furthermore, transcripts with high POSCO are depleted for functions related to immune response and detection of chemical stimulus. These findings lay important groundwork to improve our understanding of the regulation of translation, the calculation of coding potential, and the classification of RNA transcripts.
Surface plasmon resonance (SPR) enables label-free detection of binding kinetics and has been widely applied to the biophysical characterization of molecular interactions such as antibody-antigen binding. With the advent of high-throughput SPR (HT-SPR) instruments, hundreds of binding interactions can be detected simultaneously, combining the details of kinetic measurements with the capability of large-panel biomolecule screening. However, binding kinetics analysis for large panels of antibody or antigen often requires a combination of fitting strategies to address different types of sensorgrams. While software packages exist for SPR binding kinetics data analysis, they are associated with a number of limitations: 1) currently most of the software packages are proprietary, prohibiting widespread use; 2) most of the software packages, including open source packages, are designed primarily for low-throughput data analysis, making analyzing a large number of kinetics data sets labor-intensive; 3) the software typically requires multiple iterative user-interface interactions when analyzing large data sets. Here, we present htrSPRanalysis, an open source R package designed primarily for high-throughput binding kinetics data analysis, currently focusing on 1:1 binding analysis. htrSPRanalysis leverages the increasingly commonplace multi-core computing architecture to efficiently analyze a large number of sensorgrams with minimal user-interface interaction. It also offers automated generation of analysis output for all sensorgrams. Furthermore, beyond manual optimization of sensorgram fitting strategies, htrSPR analysis accelerates the analysis process by providing automated procedures to determine the optimal concentration range, choose the optimal dissociation window for fitting, and detect bulk shift. The high-throughput functionalities and automation of fitting optimization makes htrSPRanalysis especially useful for speeding up data analysis to get results for implementing further steps in therapeutic antibody discovery research.
Social situations can be overwhelming for some people, triggering avoidance and social anxiety (SA). However, it remains unknown why identical situations lead to different interpretations. Here, we developed a Social Prism Model to systematically address mechanisms underlying atypical social cognition in SA within a Bayesian cognitive framework. Through eight social valence judgment experiments (N = 541), we demonstrated that social anxiety may be shaped not by how sensory evidence is processed, but by robust dichotomous prior biases depending on social cues. The dichotomous prior biases indicated two parallel mental shortcuts that predispose individuals toward social fear, where negative prior biases were associated with an intolerance to uncertainty and a fear of social evaluations, and over-positive prior biases were associated with negative social learning. Our Bayesian simulations further demonstrated how variations in prior expectations parameters can give rise to biased social judgments, providing mechanistic support for the proposed framework. Collectively, these findings showed that the negative interpretation of the social situations can be understood within the Bayesian framework, highlighting a key role of prior expectation in shaping human social cognition.