pwc-archive/papers-with-abstracts huggingface dataset:
Hill Function-based Model of Transcriptional Response: Impact of Nonspecific Binding and RNAP Interactions
Authors: Wenjia Shi, Yao Ma, Peilin Hu, Mi Pang, Xiaona Huang, Yiting Dang, Yuxin Xie, Danni Wu
Date: 2024-03-04 00:00:00
Tasks: Attribute

Hill function is one of the widely used gene transcription regulation models. Its attribute of fitting may result in a lack of an underlying physical picture, yet the fitting parameters can provide information about biochemical reactions, such as the number of transcription factors (TFs) and the binding energy between regulatory elements. However, it remains unclear when and how much biochemical information can Hill function provide in addition to fitting. Here, started from the interactions between TFs and RNA polymerase during transcription regulation and both of their association-dissociation reactions at specific/nonspecific sites on DNA, the regulatory effect of TFs was deduced as fold change. We found that, for weak promoter, fold change can degrade into the regulatory factor (Freg) which is closely correlated with Hill function. By directly comparing and fitting with Hill function, the fitting parameters and corresponding biochemical reaction parameters in Freg were analyzed and discussed, where the single TF and multiple TFs that with cooperativity and basic logic effects were considered. We concluded the strength of promoter and interactions between TFs determine whether Hill function can reflect the corresponding biochemical information. Our findings highlight the role of Hill function in modeling/fitting for transcriptional regulation, which also benefits the preparation of synthetic regulatory elements.
Internal sensory models allow for balance control using muscle spindle acceleration feedback
Authors: Eric Maris
Date: 2024-03-01 00:00:00

Motor control requires sensory feedback, and the nature of this feedback has implications for the tasks of the central nervous system (CNS): for an approximately linear mechanical system (e.g., a freely standing person, a rider on a bicycle), if the sensory feedback does not contain the state variables (i.e., joint position and velocity), then optimal control actions are based on an internal dynamical system that estimates these states from the available incomplete sensory feedback. Such a computational system can be implemented as a recurrent neural network (RNN), and it uses a sensory model to update the state estimates. This is highly relevant for muscle spindle primary afferents whose firing rates scale with acceleration: if fusimotor and skeletomotor control are perfectly coordinated, these firing rates scale with the exafferent joint acceleration component, and in the absence of fusimotor control, they scale with the total joint acceleration (exafferent plus reafferent). For both scenarios, a sensory model exists that expresses the exafferent joint acceleration as a function of the state variables, and for the second scenario, a sensory model exists that corrects for the reafferent joint acceleration. Simulations of standing and bicycle balance control under realistic conditions show that joint acceleration feedback is sufficient for balance control, but only if the reafferent acceleration component is either absent from the feedback or is corrected for in the computational system.
Large language models surpass human experts in predicting neuroscience results
Authors: Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun, Kevin K. Nejad, Felipe Yáñez, Bati Yilmaz, Kangjoo Lee, Alexandra O. Cohen, Valentina Borghesani, Anton Pashkov, Daniele Marinazzo, Jonathan Nicholas, Alessandro Salatiello, Ilia Sucholutsky, Pasquale Minervini, Sepehr Razavi, Roberta Rocca, Elkhan Yusifov, Tereza Okalova, Nianlong Gu, Martin Ferianc, Mikail Khona, Kaustubh R. Patil, Pui-Shee Lee, Rui Mata, Nicholas E. Myers, Jennifer K Bizley, Sebastian Musslick, Isil Poyraz Bilgin, Guiomar Niso, Justin M. Ales, Michael Gaebler, N Apurva Ratan Murty, Leyla Loued-Khenissi, Anna Behler, Chloe M. Hall, Jessica Dafflon, Sherry Dongqi Bao, Bradley C. Love
Date: 2024-03-04 00:00:00

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. To evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors.
History-dependence shapes causal inference of brain-behaviour relationships
Authors: Brandon Caie, Gunnar Blohm
Date: 2024-03-01 00:00:00
Tasks: Causal Inference

Behavioural and neural time series are often correlated with the past. This history-dependence may represent a fundamental property of the measured variables, or may arise from how confounding variables change over time. Here we argue that undecidability about the ground-truth of history-dependence is a general computational property of systems that exchange information with its environment, and show that the resulting uncertainty has a direct impact on causal inference. We first argue that uncertainty in the ground truth of history-dependence is an inherent property of open systems that cannot be explicitly falsified. Simple model systems are then simulated to show how different assumptions about history-dependence can lead to spurious correlations and statistical properties of data distributions that are typically unaccounted for. We then consider this problem from an interventionist perspective, showing that interventions can only be guaranteed to remedy the spurious correlation problem when the latent dynamics between the intervention and measured processes are known a priori, and the effect of the intervention is invariant at the chosen level of analysis. We conclude that uncertainty about history-dependence is a fundamental property of the study of neural systems, and in light of this discuss how causality should be assessed in neuroscience.
Operators' cognitive performance under extreme hot-humid exposure and its physiological-psychological mechanism based on ECG, fNIRS, and Eye Tracking
Authors: Yan Zhang, Ming Jia, Meng Li, Jianyu Wang, XiangMin Hu, Zhihui Xu, Tao Chen
Date: 2024-02-28 00:00:00
Tasks: Functional Connectivity

Operators' cognitive functions are impaired significantly under extreme heat stress, potentially resulting in more severe secondary disasters. This research investigated the impact of elevated temperature and humidity (25 60%RH, 30 70%RH, 35 80%RH, 40 90%RH) on the cognitive functions and performance of operators. Meanwhile, we explored the psychological-physiological mechanism underlying the change in performance by electrocardiogram (ECG), functional near-infrared spectroscopy (fNIRS), and eye tracking physiologically. Psychological aspects such as situation awareness, workload, and working memory were assessed. Eventually, we verified and extended the maximal adaptability model to the extreme condition. Unexpectedly, a temporary improvement in simple reaction tasks but rapid impairment in advanced cognitive functions (i.e. situation awareness, communication, working memory) was obtained above 35 WBGT. The best performance in a suitable environment was due to more effective activation in the prefrontal cortex (PFC). With temperature increasing, more mistakes occurred and comprehension was impaired due to drowsiness and lower arousal levels, according to evidence of compensatory effect in fNIRS. In the extreme environment, the enhanced PFC cooperation with higher functional connectivity resulted in a temporary improvement, while depressed activation in PFC, heavy physical load, and poor regulation of the cardiovascular system restricted it. Our results provide a detailed study of the process of operators' performance and cognitive functions when encountering increasing heat stress, as well as its underlying mechanisms from a neuroergonomics perspective. This can contribute to a better understanding of the interaction between operators' performance and workplace conditions, and help to achieve a more reliable human-centered production system in the promising era of Industry 5.0.
From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling
Authors: Hanwen Wang, Theinmozhi Arulraj, Alberto Ippolito, Aleksander S. Popel
Date: 2024-03-05 00:00:00

Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific manner with models customized to particular clinical settings (e.g., treatment, cancer, and data types). Here, we discuss the challenges for virtual patient generation in immuno-oncology with our most recent experiences, initiatives to develop digital twins, and how research on these two concepts can inform each other.
Postharvest litchi (Litchi chinensis Sonn.) quality preservation by alginate oligosaccharides
Authors: Jianlie Shen, Shulin Wan, Haidong Tan
Date: 2024-03-03 00:00:00

This study investigates the efficacy of alginate oligosaccharides, derived from a novel alginate lyase expressed in E. coli (Pet21a-alginate lyase), in preserving the postharvest quality of litchi (Litchi chinensis Sonn.) fruits. The alginate lyase, characterized by Huang et al. (2013), was employed to produce AOS through enzymatic degradation of alginate. The resulting oligosaccharides were applied to litchi fruits harvested from Guangzhou Zengcheng to evaluate their impact on various quality parameters under controlled storage conditions. The study focused on measuring the effects of alginate oligosaccharide treatment on the fruits' color retention, water loss rate, hardness, and susceptibility to mold infection, under a set relative humidity and temperature. Results demonstrated significant improvements in the treated fruits, with enhanced color retention, reduced water loss, maintained hardness, and lower rates of mold infection compared to untreated controls. These findings suggest that AOS offer a promising natural alternative for extending the shelf life and maintaining the quality of litchi fruits postharvest.
Effects of Marine Reserve Creation in Single Species and Prey-Predator Models
Authors: Aleksander Ćwiszewski, Sławomir Plaskacz
Date: 2024-03-05 00:00:00

Single species fisheries and prey-predator models with marine protected areas (MPA) are studied. The single species case is considered when the fishing effort is around the species extinction threshold and the influence of implementing MPA on catch quantity are studied. In the prey-predator fishery model, the situation with the fishing effort close to the predator extinction value is considered and the effects of implementing MPA are discussed with the focus on MPA sizes assuring an acceptable catch level (i.e. food security) and the sustainability of both the predator and prey populations.
Possible and Impossible Inferences From Reconstructed Evolutionary Processes using Phylogenies as an Example
Authors: Niklas Hohmann
Date: 2024-03-04 00:00:00

Our understanding of past evolutionary change is often based on reconstructions based on incomplete data, raising fundamental questions about the degree to which we can make reliable inferences about past evolutionary processes. This was demonstrated by Louca and Pennell (2020), who showed that each pure-birth process can be generated by an infinite number of birth-death processes. Here, I explore what it means to reconstruct past evolutionary change with three approaches from measure theory, group theory, and homotopy theory to better understand structural constraint and origins of (non)identifiability. As an example, the developed framework is applied to the case of birth-death processes.
A Novel Method for Clustering Cellular Data to Improve Classification
Authors: Diek W. Wheeler, Giorgio A. Ascoli
Date: 2024-03-05 00:00:00
Tasks: Clustering

Many fields, such as neuroscience, are experiencing the vast proliferation of cellular data, underscoring the need for organizing and interpreting large datasets. A popular approach partitions data into manageable subsets via hierarchical clustering, but objective methods to determine the appropriate classification granularity are missing. We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters. Here we present the corresponding protocol to classify cellular datasets by combining data-driven unsupervised hierarchical clustering with statistical testing. These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values, including molecular, physiological, and anatomical datasets. We demonstrate the protocol using cellular data from the Janelia MouseLight project to characterize morphological aspects of neurons.
Assessing the distribution of cancer stem cells in tumorspheres
Authors: Jerónimo Fotinós, María Paula Marks, Lucas Barberis, Luciano Vellón
Date: 2024-03-05 00:00:00

In previous theoretical research, we inferred that cancer stem cells (CSCs), the cells that presumably drive tumor growth and resistance to conventional cancer treatments, are not uniformly distributed in the bulk of a tumorsphere. To confirm this theoretical prediction, we cultivated tumorspheres enriched in CSCs, and performed immunofluorecent detection of the stemness marker SOX2 using a confocal microscope. In this article, we present a method developed to process the images that reconstruct the amount and location of the CSCs in the tumorspheres. Its advantage is the use of a statistical criterion to classify the cells in stem and differentiated instead of setting an arbitrary threshold. From the analysis of the results of the methods using graph theory and computational modeling, we concluded that the distribution of Cancer Stem Cells in an experimental tumorsphere is non-homogeneous. This method is independent of the tumorsphere assay being useful for analyzing images in which several different kinds of cells are stained with different markers.
Drug Resistance Predictions Based on a Directed Flag Transformer
Authors: Dong Chen, Gengzhuo Liu, Hongyan Du, Benjamin Jones, JunJie Wee, Rui Wang, Jiahui Chen, Jana Shen, Guo-Wei Wei
Date: 2024-03-05 00:00:00
Tasks: Drug Discovery

The continuous evolution of the SARS-CoV-2 virus poses a significant challenge to global public health. Of particular concern is the potential resistance to the widely prescribed drug PAXLOVID, of which the main ingredient nirmatrelvir inhibits the viral main protease (Mpro). Here, we developed CAPTURE (direCted flAg laPlacian Transformer for drUg Resistance prEdictions) to analyze the effects of Mpro mutations on nirmatrelvir-Mpro binding affinities and identify potential drug-resistant mutations. CAPTURE combines a comprehensive mutation analysis with a resistance prediction module based on DFFormer-seq, which is a novel ensemble model that leverages a new Directed Flag Transformer and sequence embeddings from the protein and small-molecule-large-language models. Our analysis of the evolution of Mpro mutations revealed a progressive increase in mutation frequencies for residues near the binding site between May and December 2022, suggesting that the widespread use of PAXLOVID created a selective pressure that accelerated the evolution of drug-resistant variants. Applied to mutations at the nirmatrelvir-Mpro binding site, CAPTURE identified several potential resistance mutations, including H172Y and F140L, which have been experimentally confirmed, as well as five other mutations that await experimental verification. CAPTURE evaluation in a limited experimental data set on Mpro mutants gives a recall of 57\% and a precision of 71\% for predicting potential drug-resistant mutations. Our work establishes a powerful new framework for predicting drug-resistant mutations and real-time viral surveillance. The insights also guide the rational design of more resilient next-generation therapeutics.
Tractography derived quantitative estimates of tissue microstructure depend on streamline length: A characterization and method of adjustment
Authors: Richard G. Carson, Alexander Leemans
Date: 2024-03-04 00:00:00

Tractography algorithms are used extensively to delineate white matter structures, by operating on the voxel-wise information generated through the application of diffusion tensor imaging (DTI) or other models to diffusion weighted (DW) magnetic resonance imaging (MRI) data. We demonstrate that these methods commonly yield systematic streamline length dependent distortions of tractography derived tissue microstructure parameters, such as fractional anisotropy (FA). This dependency may be described as piecewise linear. For streamlines shorter than an inflection point (determined for a group of tracts delineated for each individual brain), estimates of tissue microstructure exhibit a positive linear relation with streamline length. For streamlines longer than the point of inflection, the association is weaker, with the slope of the relationship between streamline length and tissue microstructure differing only marginally from zero. As the dependency is most pronounced for a range of streamline lengths encountered typically in DW imaging of the human brain (less than ~100 mm), our results suggest that some previous estimates of tissue microstructure should be treated with considerable caution. A method is described, whereby an Akaike information weighted average of linear, Blackman and piecewise linear model predictions, may be used to compensate effectively for the dependence of FA (and other estimates of tissue microstructure) on streamline length, across the entire range of streamline lengths present in each specimen.
Renal function changes in chronic hepatitis B patients
Authors: Jinhua Zhao, Lili Wu, Xiaoan Yang, Zhilaing Gao, Hong Deng
Date: 2024-03-04 00:00:00

The best way to treat chronic hepatitis B is with pegylated interferon alone or with oral antiviral drugs. There is limited research comparing the renal safety of entecavir and tenofovir when used with pegylated interferon. This study will compare changes in renal function in chronic hepatitis B patients treated with pegylated interferon and either entecavir or tenofovir. The study included a cohort of 836 patients with chronic hepatitis B (CHB) who received treatment with pegylated interferon (IFN) either alone or in combination with entecavir (ETV) and tenofovir (TDF) between the years 2018 and 2021. Of these patients, 713 were included in a matched analysis comparing outcomes between those who were cured and those who were uncured, while 123 patients received IFN alone as a control group for comparison with the ETV and TDF treatment groups. The primary outcome measured was the change in renal function, specifically estimated glomerular filtration rate (eGFR), cystatin C (CysC), and inorganic phosphorus (IPHOS). Patients were categorized into stage 1 or stage 2 based on a baseline eGFR of less than 90 ml/min/m^2 Results: 125 CHB patients were matched 1:1 in both the combined treatment and cured groups. Baseline eGFR, CysC, and IPHOS levels were similar between the groups. Renal function in stage 1 and stage 2 groups showed a decreasing trend at 48 weeks after an initial increase.Correlation analysis showed significant relationships between changes in ALT and eGFR values at 12 weeks in both non-cured and cured groups. Conclusions: Over the 48-week duration of combined treatment in patients with chronic hepatitis B (CHB), it was found that both Tenofovir Disoproxil Fumarate (TDF) and Entecavir (ETV) did not lead to an increase in renal injury.
Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences
Authors: Michael J. Plank, Matthew J. Simpson
Date: 2024-03-04 00:00:00
Tasks: Caricature, Uncertainty Quantification

Parameter inference and uncertainty quantification are important steps when relating mathematical models to real-world observations, and when estimating uncertainty in model predictions. However, methods for doing this can be computationally expensive, particularly when the number of unknown model parameters is large. The aim of this study is to develop and test an efficient profile likelihood-based method, which takes advantage of the structure of the mathematical model being used. We do this by identifying specific parameters that affect model output in a known way, such as a linear scaling. We illustrate the method by applying it to three caricature models from different areas of the life sciences: (i) a predator-prey model from ecology; (ii) a compartment-based epidemic model from health sciences; and, (iii) an advection-diffusion-reaction model describing transport of dissolved solutes from environmental science. We show that the new method produces results of comparable accuracy to existing profile likelihood methods, but with substantially fewer evaluations of the forward model. We conclude that our method could provide a much more efficient approach to parameter inference for models where a structured approach is feasible. Code to apply the new method to user-supplied models and data is provided via a publicly accessible repository.
Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification
Authors: Leandro Y. S. Okimoto, Rayol Mendonca-Neto, Fabíola G. Nakamura, Eduardo F. Nakamura, David Fenyö, Claudio T. Silva
Date: 2024-03-01 00:00:00
Tasks: Diagnostic

Background: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. Results: This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature. Conclusions: The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.