pwc-archive/papers-with-abstracts huggingface dataset:
Timestamp calibration for time-series single cell RNA-seq expression data
Authors: Xiran Chen, Sha Lin, Xiaofeng Chen, Weikai Li, Yifei Li
Date: 2024-12-04 00:00:00
Tasks: Time Series

Timestamp automatic annotation (TAA) is a crucial procedure for analyzing time-series ScRNA-seq data, as they unveil dynamic biological developments and cell regeneration process. However, current TAA methods heavily rely on manual timestamps, often overlooking their reliability. This oversight can significantly degrade the performance of timestamp automatic annotation due to noisy timestamps. Nevertheless, the current approach for addressing this issue tends to select less critical cleaned samples for timestamp calibration. To tackle this challenge, we have developed a novel timestamp calibration model called ScPace for handling noisy labeled time-series ScRNA-seq data. This approach incorporates a latent variable indicator within a base classifier instead of probability sampling to detect noisy samples effectively. To validate our proposed method, we conducted experiments on both simulated and real time-series ScRNA-seq datasets. Cross-validation experiments with different artificial mislabeling rates demonstrate that ScPace outperforms previous approaches. Furthermore, after calibrating the timestamps of the original time-series ScRNA-seq data using our method, we performed supervised pseudotime analysis, revealing that ScPace enhances its performance significantly. These findings suggest that ScPace is an effective tool for timestamp calibration by enabling reclassification and deletion of detected noisy labeled samples while maintaining robustness across diverse ranges of time-series ScRNA-seq datasets. The source code is available at https://github.com/OPUS-Lightphenexx/ScPace.
gghic: A Versatile R Package for Exploring and Visualizing 3D Genome Organization
Authors: Minghao Jiang, Duohui Jing, Jason W. H. Wong
Date: 2024-12-04 00:00:00

Motivation: The three-dimensional (3D) organization of the genome plays a critical role in regulating gene expression and maintaining cellular homeostasis. Disruptions in this spatial organization can result in abnormal chromatin interactions, contributing to the development of various diseases including cancer. Advances in chromosome conformation capture technologies, such as Hi-C, have enabled researchers to study genome architecture at high resolution. However, the efficient visualization and interpretation of these complex datasets remain a major challenge, particularly when integrating genomic annotations and inter-chromosomal interactions. Results: We present gghic, an R package that extends the ggplot2 framework to enable intuitive and customizable visualization of genomic interaction data. gghic introduces novel layers for generating triangular heatmaps of chromatin interactions and annotating them with features such as chromatin loops, topologically associated domains (TADs), gene/transcript models, and data tracks (e.g., ChIP-seq signals). The package supports data from multiple chromosomes, facilitating the exploration of inter-chromosomal interactions. Built to integrate seamlessly with the R/Bioconductor ecosystem, gghic is compatible with widely used genomic data formats, including HiCExperiment and GInteractions objects. We demonstrate the utility of gghic by replicating a published figure showing a translocation event in T-cell acute lymphoblastic leukemia (T-ALL), highlighting its ability to integrate genomic annotations and generate publication-quality figures. Availability and implementation: The R package can be accessed at https://github.com/jasonwong-lab/gghic and is distributed under the GNU General Public License version 3.0.
Multi-Scale Kinetics Modeling for Cell Culture Process with Metabolic State Transition
Authors: Keqi Wang, Sarah W. Harcum, Wei Xie
Date: 2024-12-05 00:00:00
Tasks: Prediction, Prediction Intervals

To advance the understanding of cellular metabolisms and control batch-to-batch variations in cell culture processes, a multi-scale mechanistic model with a bottom-up and top-down structure was developed to simulate the dynamics of cell culture process undergoing metabolic state transitions. This model integrates interactions at the molecular, cellular, and macro-kinetic levels, accounting for inherent variations in metabolic state transitions of individual cells. By incorporating both online (e.g., oxygen uptake, pH) and offline measurements (e.g., viable cell density, metabolite concentrations), the proposed mechanistic model enables accurate long-term prediction of cell culture trajectories and provides reliable prediction intervals quantifying batch-to-batch variations. This work can guide optimal design of experiments and robust process control to improve yield and production stability. Additionally, the proposed multi-scale model has a modular design enables flexible in silico simulations and extrapolation across diverse conditions, providing a robust prediction framework for scalable and flexible biomanufacturing applications.
Artificial intelligence and the internal processes of creativity
Authors: Jaan Aru
Date: 2024-12-05 00:00:00
Tasks: Diversity

Artificial intelligence (AI) systems capable of generating creative outputs are reshaping our understanding of creativity. This shift presents an opportunity for creativity researchers to reevaluate the key components of the creative process. In particular, the advanced capabilities of AI underscore the importance of studying the internal processes of creativity. This paper explores the neurobiological machinery that underlies these internal processes and describes the experiential component of creativity. It is concluded that although the products of artificial and human creativity can be similar, the internal processes are different. The paper also discusses how AI may negatively affect the internal processes of human creativity, such as the development of skills, the integration of knowledge, and the diversity of ideas.
Predictive Strategies for the Control of Complex Motor Skills: Recent Insights into Individual and Joint Actions
Authors: Marta Russo, Antonella Maselli, Dagmar Sternad, Giovanni Pezzulo
Date: 2024-12-05 00:00:00

Humans perform exquisite sensorimotor skills, both individually and in teams, from athletes performing rhythmic gymnastics to everyday tasks like carrying a cup of coffee. The "predictive brain" framework suggests that mastering these skills relies on predictive mechanisms, raising the question of how we deploy predictions for real-time control and coordination. This review highlights two research lines, showing that during the control of complex objects people make the interaction with 'tools' predictable; and that during dyadic coordination people make their behavior predictable and legible for their partners. These studies demonstrate that to achieve sophisticated motor skills, we play "prediction tricks": we select subspaces of predictable solutions and make sensorimotor interactions more predictable and legible by and for others. This synthesis underscores the critical role of predictability in optimizing control strategies across contexts. Furthermore, it emphasizes the need for novel studies on the scope and limits of predictive mechanisms in motor control.
Effect of Simulated Space Conditions on functional Connectivity
Authors: Parshuram N Aarotale, Jaydip Desai
Date: 2024-12-04 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity, Motor Imagery, Unity

Long duration spaceflight missions can affect the cognitive and behavioral activities of astronauts due to changes in gravity. The microgravity significantly impacts the central nervous system physiology which causes the degradation in the performance and lead to potential risk in the space exploration. The aim of this study was to evaluate functional connectivity at simulated space conditions using an unloading harness system to mimic the body-weight distribution related to Earth, Mars, and International Space Station. A unity model with six directional arrows to imagine six different motor imagery tasks associated with arms and legs were designed for the Oculus Rift S virtual reality headset for testing. An Electroencephalogram (EEG) and functional near infrared spectroscopy (fNIRS) signals were recorded from 10 participants in the distributed weight conditions related to Earth, Mars, and International Space station using the g.Nautilus fNIRS system at sampling rate of 500 Hz. The magnitude squared coherence were estimated from left vs right hemisphere of the brain that represents functional connectivity. The EEG coherence was the higher which shows the strong functional connectivity and fNIRS coherence was lower shows weak functional connectivity between left vs right hemisphere of the brain, during all the tasks and trials irrespective of the simulated space conditions. Further analysis of functional connectivity needed between the intra-regions of the brain.
Multi-scale phylodynamic modelling of rapid punctuated pathogen evolution
Authors: Quang Dang Nguyen, Sheryl L. Chang, Carl J. E. Suster, Rebecca J. Rockett, Vitali Sintchenko, Tania C. Sorrell, Mikhail Prokopenko
Date: 2024-12-05 00:00:00

Computational multi-scale pandemic modelling remains a major and timely challenge. Here we identify specific requirements for a new class of pandemic models operating across three scales: (1) rapid pathogen evolution, punctuated by emergence of new variants, (2) human interactions within a heterogeneous population, and (3) public health responses which constrain individual actions to control the disease transmission. We then present a pandemic modelling framework satisfying these requirements and capable of simulating multi-scale dynamic feedback loops. The developed framework comprises a stochastic agent-based model of pandemic spread, coupled with a phylodynamic model of the within-host pathogen evolution. It is validated with a case study, modelling a rapid punctuated evolution of SARS-CoV-2, based on global and contemporary genomic surveillance data, during the COVID-19 transmission within a large heterogeneous population. We demonstrate that the model captures the essential features of the COVID-19 pandemic and the novel coronavirus evolution, while retaining computational tractability and scalability.
Information Geometry for Maximum Diversity Distributions
Authors: Shinto Eguchi
Date: 2024-12-05 00:00:00
Tasks: Diversity

In recent years, biodiversity measures have gained prominence as essential tools for ecological and environmental assessments, particularly in the context of increasingly complex and large-scale datasets. We provide a comprehensive review of diversity measures, including the Gini-Simpson index, Hill numbers, and Rao's quadratic entropy, examining their roles in capturing various aspects of biodiversity. Among these, Rao's quadratic entropy stands out for its ability to incorporate not only species abundance but also functional and genetic dissimilarities. The paper emphasizes the statistical and ecological significance of Rao's quadratic entropy under the information geometry framework. We explore the distribution maximizing such a diversity measure under linear constraints that reflect ecological realities, such as resource competition or habitat suitability. Furthermore, we discuss a unified approach of the Leinster-Cobbold index combining Hill numbers and Rao's entropy, allowing for an adaptable and similarity-sensitive measure of biodiversity. Finally, we discuss the information geometry associated with the maximum diversity distribution focusing on the cross diversity measures such as the cross-entropy.
One predator and two prey: Coexistence of pumas, guanacos and sheep in Patagonia
Authors: Jhordan Silveira de Borba, Sebastian Gonçalves
Date: 2024-12-04 00:00:00

The ecosystem considered in this study is the outcome of a lengthy sequence of historical and ecological events. Patagonia's indigenous fauna comprises survivors of five significant extinction events, with the notable presence of the puma and the guanaco, two of the largest native mammals. In addition to these, European immigrants introduced sheep into the ecosystem. Together, these three species form a straightforward trophic network, featuring one predator and two prey species, all competing within the Patagonian steppe. For ranchers, guanacos and pumas are frequently perceived as threats to their economic interests. In recent decades, the field of biology, particularly ecology, has witnessed a substantial increase in the development of equation-based models. Scientists are interested in the ability to systematize hypotheses and gain insights into the behavior of complex biological systems, such as the one presented in this study. However, the nonlinear nature and the large number of parameters of models, represent a challenge when one wants to explore the parameter space. To overcome this and, at the same time, improve the understanding of the Patagonia ecosystem, we start by building an equation-based model based on previous contributions, and we reduce it to the essential minimum set of parameters. Then, we introduce two tools, a generalization of ternary graphs and a perceptron based ML, to help understand the response of the system equation to the key parameters. The perceptron tool allows us to visualize/interpret the influence of each parameter on the survival or extinction of each species. Through the generalization of the ternary graph, it was possible to conveniently visualize how the system responds to different combinations/variations of the five parameters of the reduced system equation in a single graphical representation.
Asymptotic enumeration of normal and hybridization networks via tree decoration
Authors: Michael Fuchs, Mike Steel, Qiang Zhang
Date: 2024-12-04 00:00:00
Tasks: ARC

Phylogenetic networks provide a more general description of evolutionary relationships than rooted phylogenetic trees. One way to produce a phylogenetic network is to randomly place $k$ arcs between the edges of a rooted binary phylogenetic tree with $n$ leaves. The resulting directed graph may fail to be a phylogenetic network, and even when it is (and thereby a `tree-based' network), it may fail to be a tree-child or normal network. In this paper, we first show that if $k$ is fixed, the proportion of arc placements that result in a normal network tends to 1 as $n$ grows. From this result, the asymptotic enumeration of normal networks becomes straightforward and provides a transparent meaning to the combinatorial terms that arise. Moreover, the approach extends to allow $k$ to grow with $n$ (at the rate $o(n^\frac{1}{3})$), which was not handled in earlier work. We also investigate a subclass of normal networks of particular relevance in biology (hybridization networks) and establish that the same asymptotic results apply.
A mathematical language for linking fine-scale structure in spikes from hundreds to thousands of neurons with behaviour
Authors: Alexandra N. Busch, Roberto C. Budzinski, Federico W. Pasini, Ján Mináč, Jonathan A. Michaels, Megan Roussy, Roberto A. Gulli, Benjamin W. Corrigan, J. Andrew Pruszynski, Julio Martinez-Trujillo, Lyle E. Muller
Date: 2024-12-05 00:00:00

Recent advances in neural recording technology allow simultaneously recording action potentials from hundreds to thousands of neurons in awake, behaving animals. However, characterizing spike patterns in the resulting data, and linking these patterns to behaviour, remains a challenging task. The lack of a rigorous mathematical language for variable numbers of events (spikes) emitted by multiple agents (neurons) is an important limiting factor. We introduce a new mathematical operation to decompose complex spike patterns into a set of simple, structured elements. This creates a mathematical language that allows comparing spike patterns across trials, detecting sub-patterns, and making links to behaviour via a clear distance measure. We apply the method to dual Utah array recordings from macaque prefrontal cortex, where this technique reveals previously unseen structure that can predict both memory-guided decisions and errors in a virtual-reality working memory task. These results demonstrate that this technique provides a powerful new approach to understand structure in the spike times of neural populations, at a scale that will continue to grow more and more rapidly in upcoming years.
A novel approach to differential expression analysis of co-occurrence networks for small-sampled microbiome data
Authors: Nandini Gadhia, Michalis Smyrnakis, Po-Yu Liu, Damer Blake, Melanie Hay, Anh Nguyen, Dominic Richards, Dong Xia, Ritesh Krishna
Date: 2024-12-04 00:00:00

Graph-based machine learning methods are useful tools in the identification and prediction of variation in genetic data. In particular, the comprehension of phenotypic effects at the cellular level is an accelerating research area in pharmacogenomics. In this article, a novel graph theoretic approach is proposed to infer a co-occurrence network from 16S microbiome data. The approach is specialised to handle datasets containing a small number of samples. Small datasets exacerbate the significant challenges faced by biological data, which exhibit properties such as sparsity, compositionality, and complexity of interactions. Methodologies are also proposed to enrich and statistically filter the inferred networks. The utility of the proposed method lies in that it extracts an informative network from small sampled data that is not only feature-rich, but also biologically meaningful and statistically significant. Although specialised for small data sets, which are abundant, it can be generally applied to any small-sampled dataset, and can also be extended to integrate multi-omics data. The proposed methodology is tested on a data set of chickens vaccinated against and challenged by the protozoan parasite Eimeria tenella. The raw genetic reads are processed, and networks inferred to describe the ecosystems of the chicken intestines under three different stages of disease progression. Analysis of the expression of network features derive biologically intuitive conclusions from purely statistical methods. For example, there is a clear evolution in the distribution of node features in line with the progression of the disease. The distributions also reveal clusters of species interacting mutualistically and parasitically, as expected. Moreover, a specific sub-network is found to persist through all experimental conditions, representative of a persistent microbiome.
A personalized model and optimization strategy for estimating blood glucose concentrations from sweat measurements
Authors: Xiaoyu Yin, Elisabetta Peri, Eduard Pelssers, Jaap den Toonder, Lisa Klous, Hein Daanen, Massimo Mischi
Date: 2024-12-03 00:00:00

Background and objective: Diabetes is one of the four leading causes of death worldwide, necessitating daily blood glucose monitoring. While sweat offers a promising non-invasive alternative for glucose monitoring, its application remains limited due to the low to moderate correlation between sweat and blood glucose concentrations, which has been obtained until now by assuming a linear relationship. This study proposes a novel model-based strategy to estimate blood glucose concentrations from sweat samples, setting the stage for non-invasive glucose monitoring through sweat-sensing technology. Methods: We first developed a pharmacokinetic glucose transport model that describes the glucose transport from blood to sweat. Secondly, we designed a novel optimization strategy leveraging the proposed model to solve the inverse problem and infer blood glucose levels from measured glucose concentrations in sweat. To this end, the pharmacokinetic model parameters with the highest sensitivity were also optimized so as to achieve a personalized estimation. Our strategy was tested on a dataset composed of 108 samples from healthy volunteers and diabetic patients. Results: Our glucose transport model improves over the state-of-the-art in estimating sweat glucose concentrations from blood levels (higher accuracy, p<0.001). Additionally, our optimization strategy effectively solved the inverse problem, yielding a Pearson correlation coefficient of 0.98 across all 108 data points, with an average root-mean-square-percent-error of 12%+/-8%. This significantly outperforms the best sweat-blood glucose correlation reported in the existing literature (0.75). Conclusion: Our innovative optimization strategy, also leveraging more accurate modeling, shows promising results, paving the way for non-invasive blood glucose monitoring and, possibly, improved diabetes management.
Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations
Authors: Yunhua Pei, Jin Zheng, John Cartlidge
Date: 2024-12-05 00:00:00
Tasks: Contrastive Learning, Graph Learning, Stock Trend Prediction

Temporal Graph Learning (TGL) is crucial for capturing the evolving nature of stock markets. Traditional methods often ignore the interplay between dynamic temporal changes and static relational structures between stocks. To address this issue, we propose the Dynamic Graph Representation with Contrastive Learning (DGRCL) framework, which integrates dynamic and static graph relations to improve the accuracy of stock trend prediction. Our framework introduces two key components: the Embedding Enhancement (EE) module and the Contrastive Constrained Training (CCT) module. The EE module focuses on dynamically capturing the temporal evolution of stock data, while the CCT module enforces static constraints based on stock relations, refined within contrastive learning. This dual-relation approach allows for a more comprehensive understanding of stock market dynamics. Our experiments on two major U.S. stock market datasets, NASDAQ and NYSE, demonstrate that DGRCL significantly outperforms state-of-the-art TGL baselines. Ablation studies indicate the importance of both modules. Overall, DGRCL not only enhances prediction ability but also provides a robust framework for integrating temporal and relational data in dynamic graphs. Code and data are available for public access.
Correlation without Factors in Retail Cryptocurrency Markets
Authors: Graham L. Giller
Date: 2024-12-05 00:00:00

A simple model-free and distribution-free statistic, the functional relationship between the number of "effective" degrees of freedom and portfolio size, or N*(N), is used to discriminate between two alternative models for the correlation of daily cryptocurrency returns within a retail universe of defined by the list of tradable assets available to account holders at the Robinhood brokerage. The average pairwise correlation between daily cryptocurrency returns is found to be high (of order 60%) and the data collected supports description of the cross-section of returns by a simple isotropic correlation model distinct from a decomposition into a linear factor model with additive noise with high confidence. This description appears to be relatively stable through time.