pwc-archive/papers-with-abstracts huggingface dataset:
Time-varying EEG spectral power predicts evoked and spontaneous fMRI motor brain activity
Authors: Neil Mehta, Ines Goncalves, Alberto Montagna, Mathis Fleury, Gustavo Caetano, Ines Esteves, Athanasios Vourvopoulos, Pulkit Grover, Patricia Figueiredo
Date: 2025-04-14 00:00:00
Tasks: EEG, Rhythm

Simultaneous EEG-fMRI recordings are increasingly used to investigate brain activity by leveraging the complementary high spatial and high temporal resolution of fMRI and EEG signals respectively. It remains unclear, however, to what degree these two imaging modalities capture shared information about neural activity. Here, we investigate whether it is possible to predict both task-evoked and spontaneous fMRI signals of motor brain networks from EEG time-varying spectral power using interpretable models trained for individual subjects with Sparse Group Lasso regularization. Critically, we test the trained models on data acquired from each subject on a different day and obtain statistical validation by comparison with appropriate null models as well as the conventional EEG sensorimotor rhythm. We find significant prediction results in most subjects, although less frequently for resting-state compared to task-based conditions. Furthermore, we interpret the model learned parameters to understand representations of EEG-fMRI coupling in terms of predictive EEG channels, frequencies, and haemodynamic delays. In conclusion, our work provides evidence of the ability to predict fMRI motor brain activity from EEG recordings alone across different days, in both task-evoked and spontaneous conditions, with statistical significance in individual subjects. These results present great potential for translation to EEG neurofeedback applications.
Selectivity gain in olfactory receptor neuron at optimal odor concentration
Authors: A. K. Vidybida
Date: 2025-04-14 00:00:00

It has been discovered before (arXiv:2306.07676) that for the selectivity gain due to fluctuations in the process of primary odor reception by olfactory receptor neuron (ORN) there exists an optimal concentration of odors at which increased selectivity is mostly manifested. We estimate by means of numerical simulation what could be the gain value at that concentration by modeling ORN as a leaky integrate-and-fire neuron with membrane populated by receptor proteins R which bind and release odor molecules randomly. Each R is modeled as a ligand-gated ion channel, and binding-releasing is modeled as a Markov stochastic process. Possible values for the selectivity gain are calculated for ORN parameters suggested by experimental data. Keywords: ORN, selectivity, receptor proteins, fluctuations, stochastic process, Markov process
Measuring amount of computation done by C.elegans using whole brain neural activity
Authors: Junang Li, Andrew M. Leifer, David H. Wolpert
Date: 2025-04-14 00:00:00
Tasks: Time Series

Many dynamical systems found in biology, ranging from genetic circuits to the human brain to human social systems, are inherently computational. Although extensive research has explored their resulting functions and behaviors, the underlying computations often remain elusive. Even the fundamental task of quantifying the \textit{amount} of computation performed by a dynamical system remains under-investigated. In this study we address this challenge by introducing a novel framework to estimate the amount of computation implemented by an arbitrary physical system based on empirical time-series of its dynamics. This framework works by forming a statistical reconstruction of that dynamics, and then defining the amount of computation in terms of both the complexity and fidelity of this reconstruction. We validate our framework by showing that it appropriately distinguishes the relative amount of computation across different regimes of Lorenz dynamics and various computation classes of cellular automata. We then apply this framework to neural activity in \textit{Caenorhabditis elegans}, as captured by calcium imaging. By analyzing time-series neural data obtained from the fluorescent intensity of the calcium indicator GCaMP, we find that high and low amounts of computation are required, respectively, in the neural dynamics of freely moving and immobile worms. Our analysis further sheds light on the amount of computation performed when the system is in various locomotion states. In sum, our study refines the definition of computational amount from time-series data and highlights neural computation in a simple organism across distinct behavioral states.
Sequence models for by-trial decoding of cognitive strategies from neural data
Authors: Rick den Otter, Gabriel Weindel, Sjoerd Stuit, Leendert van Maanen
Date: 2025-04-14 00:00:00
Tasks: Decision Making

Understanding the sequence of cognitive operations that underlie decision-making is a fundamental challenge in cognitive neuroscience. Traditional approaches often rely on group-level statistics, which obscure trial-by-trial variations in cognitive strategies. In this study, we introduce a novel machine learning method that combines Hidden Multivariate Pattern analysis with a Structured State Space Sequence model to decode cognitive strategies from electroencephalography data at the trial level. We apply this method to a decision-making task, where participants were instructed to prioritize either speed or accuracy in their responses. Our results reveal an additional cognitive operation, labeled Confirmation, which seems to occur predominantly in the accuracy condition but also frequently in the speed condition. The modeled probability that this operation occurs is associated with higher probability of responding correctly as well as changes of mind, as indexed by electromyography data. By successfully modeling cognitive operations at the trial level, we provide empirical evidence for dynamic variability in decision strategies, challenging the assumption of homogeneous cognitive processes within experimental conditions. Our approach shows the potential of sequence modeling in cognitive neuroscience to capture trial-level variability that is obscured by aggregate analyses. The introduced method offers a new way to detect and understand cognitive strategies in a data-driven manner, with implications for both theoretical research and practical applications in many fields.
The Effects of Socioeconomic Status and Depression on The Neural Correlates of Error Monitoring. An Event-Related Potential Study
Authors: Hiran Shanake Perera, Rozainee Khairudin, Khazriyati Salehuddin
Date: 2025-04-13 00:00:00
Tasks: Diagnostic

Existing evidence suggests that neural responses to errors were exaggerated in individuals at risk of depression and anxiety. This phenomenon has led to the possibility that the error-related negativity (ERN), a well-known neural correlate of error monitoring could be used as a diagnostic tool for several psychological disorders. However, conflicting evidence between psychopathology and the ERN suggests that this phenomenon is modulated by variables are yet to be identified. Socioeconomic status (SES) could potentially play a role in the relationship between the ERN and psychopathological disorders, given that SES is known to be associated with depression and anxiety. In the current study, we first tested whether SES was related to ERN amplitude. Second, we examined whether the relationship between the ERN and depression was explained by differences in SES. We measured error-related negativity (ERN) from a sample of adult participants from low to high socioeconomic backgrounds while controlling their depression scores. Results show that SES correlated with variations in ERN amplitude. Specifically, we found that low-SES individuals had a larger ERN than wealthier individuals. In addition, the relationship between depression and the ERN was fully accounted for by variations in SES. Overall, our results indicate that SES predicts neural responses to errors. Findings also indicate that the link between depression and ERN may be the result of SES variations. Future research examining the links between psychopathology and error monitoring should control SES differences, and caution is needed if they are to be used as a diagnostic tool in low-income communities.
Diagnostic Uncertainty Limits the Potential of Early Warning Signals to Identify Epidemic Emergence
Authors: Callum R. K. Arnold, Matthew J. Ferrari
Date: 2025-04-15 00:00:00
Tasks: Diagnostic, Time Series

Methods to detect the emergence of infectious diseases, and approach to the "critical transition" RE = 1, have to potential to avert substantial disease burden by facilitating preemptive actions like vaccination campaigns. Early warning signals (EWS), summary statistics of infection case time series, show promise in providing such advanced warnings. As EWS are computed on test positive case data, the accuracy of this underlying data is integral to their predictive ability, but will vary with changes in the diagnostic test accuracy and the incidence of the target disease relative to clinically-compatible background noise. We simulated emergent and null time series as the sum of an SEIR-generated measles time series, and background noise generated by either independent draws from a Poisson distribution, or an SEIR simulation with rubella-like parameters. We demonstrate that proactive outbreak detection with EWS metrics is resilient to decreasing diagnostic accuracy, so long as background infections remain proportionally low. Under situations with large, episodic, noise, imperfect diagnostic tests cannot appropriately discriminate between emergent and null periods. Not all EWS metrics performed equally: we find that the mean was the least affected by changes to the noise structure and magnitude, given a moderately accurate diagnostic test (>= to 95% sensitive and specific), and the autocovariance and variance were the most predictive when the noise incidence did not exhibit large temporal variations. In these situations, diagnostic test accuracy should not be a precursor to the implementation of an EWS metric-based alert system.
Can genomic analysis actually estimate past population size?
Authors: Janeesh K. Bansal, Richard A. Nichols
Date: 2025-04-14 00:00:00
Tasks: Diversity

Genomic data can be used to reconstruct population size over thousands of generations, using a new class of algorithms (SMC methods). These analyses often show a recent decline in $N_e$ (effective size), which at face value implies a conservation or demographic crisis: a population crash and loss of genetic diversity. This interpretation is frequently mistaken. Here we outline how SMC methods work, why they generate this misleading signal, and suggest simple approaches for exploiting the rich information produced by these algorithms. In most species, genomic patterns reflect major changes in the species' range and subdivision over tens or hundreds of thousands of years. Consequently, collaboration between geneticists, palaeoecologists, palaeoclimatologists, and geologists is crucial for evaluating the outputs of SMC algorithms.
Failure and Success in Single-Drug Control of Antimicrobial Resistance
Authors: Alejandro Anderson, Rami Katz, Francesca Calà Campana, Giulia Giordano
Date: 2025-04-13 00:00:00

We propose a mathematical model of Antimicrobial Resistance in the host to predict the failure of two antagonists of bacterial growth: the immune response and a single-antibiotic therapy. After characterising the initial bacterial load that cannot be cleared by the immune system alone, we define the success set of initial conditions for which an infection-free equilibrium can be reached by a viable single-antibiotic therapy, and we provide a rigorously defined inner approximation of the set. For initial conditions within the success set, we propose an optimal control framework to design single-drug therapies.
Enhancing U.S. swine farm preparedness for infectious foreign animal diseases with rapid access to biosecurity information
Authors: Christian Fleming, Kelsey Mills, Nicolas Cardenas, Jason A. Galvis, Gustavo Machado
Date: 2025-04-12 00:00:00
Tasks: FAD

The U.S. launched the Secure Pork Supply (SPS) Plan for Continuity of Business, a voluntary program providing foreign animal disease (FAD) guidance and setting biosecurity standards to maintain business continuity amid FAD outbreaks. The role of biosecurity in disease prevention is well recognized, yet the U.S. swine industry lacks knowledge of individual farm biosecurity plans and the efficacy of existing measures. We describe a multi-sector initiative that formed the Rapid Access Biosecurity (RAB) app consortium with the swine industry, government, and academia. We (i) summarized 7,625 farms using RABapp, (ii) mapped U.S. commercial swine coverage and areas of limited biosecurity, and (iii) examined associations between biosecurity and occurrences of porcine reproductive and respiratory syndrome virus (PRRSV) and porcine epidemic diarrhea virus (PEDV). RABapp, used in 31 states, covers ~47% of U.S. commercial swine. Of 307 Agricultural Statistics Districts with swine, 78% (238) had <50% of those animals in RABapp. We used a mixed-effects logistic regression model, accounting for production company and farm type (breeding vs. non-breeding). Requiring footwear/clothing changes, having multiple carcass disposal locations, hosting other businesses, and greater distance to swine farms reduced infection odds. Rendering carcasses, manure pit storage or land application, multiple perimeter buffer areas, and a larger animal housing area increased risk. This study leveraged RABapp to assess U.S. swine farm biosecurity, revealing gaps in SPS plan adoption that create vulnerable regions. Some biosecurity practices (e.g., footwear changes) lowered PRRSV/PEDV risk, while certain disposal and manure practices increased it. Targeted biosecurity measures and broader RABapp adoption can bolster industry resilience against foreign animal diseases.
An evolutionary medicine and life history perspective on aging and disease: Trade-offs, hyperfunction, and mismatch
Authors: Jacob E. Aronoff, Benjamin C. Trumble
Date: 2025-04-11 00:00:00

The rise in chronic diseases over the last century presents a significant health and economic burden globally. Here we apply evolutionary medicine and life history theory to better understand their development. We highlight an imbalanced metabolic axis of growth and proliferation (anabolic) versus maintenance and dormancy (catabolic), focusing on major mechanisms including IGF-1, mTOR, AMPK, and Klotho. We also relate this axis to the hyperfunction theory of aging, which similarly implicates anabolic mechanisms like mTOR in aging and disease. Next, we highlight the Brain-Body Energy Conservation model, which connects the hyperfunction theory with energetic trade-offs that induce hypofunction and catabolic health risks like impaired immunity. Finally, we discuss how modern environmental mismatches exacerbate this process. Following our review, we discuss future research directions to better understand health risk. This includes studying IGF-1, mTOR, AMPK, and Klotho and how they relate to health and aging in human subsistence populations, including with lifestyle shifts. It also includes understanding their role in the developmental origins of health and disease as well as the social determinants of health disparities. Further, we discuss the need for future studies on exceptionally long-lived species to understand potentially underappreciated trade-offs and costs that come with their longevity. We close with considering possible implications for therapeutics, including (1) compensatory pathways counteracting treatments, (2) a Goldilocks zone, in which suppressing anabolic metabolism too far introduces catabolic health risks, and (3) species constraints, in which therapeutics tested in shorter lived species with greater anabolic imbalance will be less effective in humans.
Elucidating the Design Space of Multimodal Protein Language Models
Authors: Cheng-Yen Hsieh, Xinyou Wang, Daiheng Zhang, Dongyu Xue, Fei Ye, ShuJian Huang, Zaixiang Zheng, Quanquan Gu
Date: 2025-04-15 00:00:00
Tasks: Diversity, Representation Learning

Multimodal protein language models (PLMs) integrate sequence and token-based structural information, serving as a powerful foundation for protein modeling, generation, and design. However, the reliance on tokenizing 3D structures into discrete tokens causes substantial loss of fidelity about fine-grained structural details and correlations. In this paper, we systematically elucidate the design space of multimodal PLMs to overcome their limitations. We identify tokenization loss and inaccurate structure token predictions by the PLMs as major bottlenecks. To address these, our proposed design space covers improved generative modeling, structure-aware architectures and representation learning, and data exploration. Our advancements approach finer-grained supervision, demonstrating that token-based multimodal PLMs can achieve robust structural modeling. The effective design methods dramatically improve the structure generation diversity, and notably, folding abilities of our 650M model by reducing the RMSD from 5.52 to 2.36 on PDB testset, even outperforming 3B baselines and on par with the specialized folding models. Project page and code: https://bytedance.github.io/dplm/dplm-2.1/.
Automatic Raman Measurements in a High-Throughput Bioprocess Development Lab
Authors: Christoph Lange, Simon Seidel, Madeline Altmann, Daniel Stors, Annina Kemmer, Linda Cai, Stefan Born, Peter Neubauer, M. Nicolas Cruz Bournazou
Date: 2025-04-14 00:00:00

This study presents a collection of physical devices and software services that fully automate Raman spectra measurements for liquid samples within a robotic facility. This method is applicable to various fields, with demonstrated efficacy in biotechnology, where Raman spectroscopy monitors substrates, metabolites, and product-related concentrations. Our system specifically measures 50 $\micro L$ samples using a liquid handling robot capable of taking 8 samples simultaneously. We record multiple Raman spectra for 10s each. Furthermore, our system takes around 20s for sample handling, cleaning, and preparation of the next measurement. All spectra and metadata are stored in a database, and we use a machine learning model to estimate concentrations from the spectra. This automated approach enables gathering spectra for various applications under uniform conditions in high-throughput fermentation processes, calibration procedures, and offline evaluations. This allows data to be combined to train sophisticated machine learning models with improved generalization. Consequently, we can develop accurate models more quickly for new applications by reusing data from prior applications, thereby reducing the need for extensive calibration data.
Domain-Adversarial Neural Network and Explainable AI for Reducing Tissue-of-Origin Signal in Pan-cancer Mortality Classification
Authors: Cristian Padron-Manrique, Juan José Oropeza Valdez, Osbaldo Resendis-Antonio
Date: 2025-04-14 00:00:00

Tissue-of-origin signals dominate pan-cancer gene expression, often obscuring molecular features linked to patient survival. This hampers the discovery of generalizable biomarkers, as models tend to overfit tissue-specific patterns rather than capture survival-relevant signals. To address this, we propose a Domain-Adversarial Neural Network (DANN) trained on TCGA RNA-seq data to learn representations less biased by tissue and more focused on survival. Identifying tissue-independent genetic profiles is key to revealing core cancer programs. We assess the DANN using: (1) Standard SHAP, based on the original input space and DANN's mortality classifier; (2) A layer-aware strategy applied to hidden activations, including an unsupervised manifold from raw activations and a supervised manifold from mortality-specific SHAP values. Standard SHAP remains confounded by tissue signals due to biases inherent in its computation. The raw activation manifold was dominated by high-magnitude activations, which masked subtle tissue and mortality-related signals. In contrast, the layer-aware SHAP manifold offers improved low-dimensional representations of both tissue and mortality signals, independent of activation strength, enabling subpopulation stratification and pan-cancer identification of survival-associated genes.
Non-Destructive Carotenoid Quantification in Leaves via Raman Spectroscopy: Optimizing Treatment for Linear Discriminant Analysis
Authors: Miri Park, Annette Somborn, Dennis Schlehuber, Hyun Jeong Lim, Volkmar Keuter
Date: 2025-04-14 00:00:00

This study introduces a novel method for quantifying challenging carotenoids in leaf tissues, which typically produce less stable signals than fruits, grains, and roots, by applying Linear Discriminant Analysis (LDA) modeling to interpret Raman spectroscopy data. The model's performance was assessed across different spectral preprocessing techniques (smoothing, normalization, baseline correction) and through various subsets of relevant Raman shifts. To generate a broad range of carotenoid contents, genetically modified Arabidopsis thaliana mutants with controlled synthesis and more conventional Spinacia oleracea samples under dark and salt stress were utilized, allowing for the evaluation of model robustness and practical applicability. Transition scores for Arabidopsis thaliana reached 77.27-95.45% in all quantifications, while Spinacia oleracea showed 75-83.33% in 3- and 4-level modeling, demonstrating the LDA model's strong potential for effective application. Among the spectral preprocessing, smoothing had the greatest impact on model performance, enhancing results for Arabidopsis thaliana but showing better outcomes without smoothing for Spinacia oleracea. Overall, this study highlights the potential of LDA modeling combined with Raman spectroscopy as a robust and non-destructive tool for metabolite quantification in herbal plants, with promising applications in agricultural monitoring and quality control.
smFISH_batchRun: A smFISH image processing tool for single-molecule RNA Detection and 3D reconstruction
Authors: Nimmy S. John, ChangHwan Lee
Date: 2025-04-13 00:00:00
Tasks: 3D Reconstruction

Single-molecule RNA imaging has been made possible with the recent advances in microscopy methods. However, systematic analysis of these images has been challenging due to the highly variable background noise, even after applying sophisticated computational clearing methods. Here, we describe our custom MATLAB scripts that allow us to detect both nuclear nascent transcripts at the active transcription sites (ATS) and mature cytoplasmic mRNAs with single-molecule precision and reconstruct the tissue in 3D for further analysis. Our codes were initially optimized for the C. elegans germline but were designed to be broadly applicable to other species and tissue types.