pwc-archive/papers-with-abstracts huggingface dataset:
Mayer-homology learning prediction of protein-ligand binding affinities
Authors: Hongsong Feng, Li Shen, Jian Liu, Guo-Wei Wei
Date: 2024-08-23 00:00:00
Tasks: Descriptive, Drug Design, molecular representation

Artificial intelligence-assisted drug design is revolutionizing the pharmaceutical industry. Effective molecular features are crucial for accurate machine learning predictions, and advanced mathematics plays a key role in designing these features. Persistent homology theory, which equips topological invariants with persistence, provides valuable insights into molecular structures. The calculation of Betti numbers is based on differential that typically satisfy \(d^2 = 0\). Our recent work has extended this concept by employing Mayer homology with a generalized differential that satisfies \(d^N = 0\) for \(N \geq 2\), leading to the development of persistent Mayer homology (PMH) theory and richer topological information across various scales. In this study, we utilize PMH to create a novel multiscale topological vectorization for molecular representation, offering valuable tools for descriptive and predictive analysis in molecular data and machine learning prediction. Specifically, benchmark tests on established protein-ligand datasets, including PDBbind-2007, PDBbind-2013, and PDBbind-2016, demonstrate the superior performance of our Mayer homology models in predicting protein-ligand binding affinities.
Bounding the number of reticulation events for displaying multiple trees in a phylogenetic network
Authors: Yufeng Wu, Louxin Zhang
Date: 2024-08-26 00:00:00

Reconstructing a parsimonious phylogenetic network that displays multiple phylogenetic trees is an important problem in theory of phylogenetics, where the complexity of the inferred networks is measured by reticulation numbers. The reticulation number for a set of trees is defined as the minimum number of reticulations in a phylogenetic network that displays those trees. A mathematical problem is bounding the reticulation number for multiple trees over a fixed number of taxa. While this problem has been extensively studied for two trees, much less is known about the upper bounds on the reticulation numbers for three or more arbitrary trees. In this paper, we present a few non-trivial upper bounds on reticulation numbers for three or more trees.
A review of sequential Monte Carlo methods for real-time disease modeling
Authors: Dhorasso Temfack, Jason Wyse
Date: 2024-08-28 00:00:00

Sequential Monte Carlo methods are a powerful framework for approximating the posterior distribution of a state variable in a sequential manner. They provide an attractive way of analyzing dynamic systems in real-time, taking into account the limitations of traditional approaches such as Markov Chain Monte Carlo methods, which are not well suited to data that arrives incrementally. This paper reviews and explores the application of Sequential Monte Carlo in dynamic disease modeling, highlighting its capacity for online inference and real-time adaptation to evolving disease dynamics. The integration of kernel density approximation techniques within the stochastic Susceptible-Exposed-Infectious-Recovered (SEIR) compartment model is examined, demonstrating the algorithm's effectiveness in monitoring time-varying parameters such as the effective reproduction number. Case studies, including simulations with synthetic data and analysis of real-world COVID-19 data from Ireland, demonstrate the practical applicability of this approach for informing timely public health interventions.
Quantifying the degree of risk aversion of spectral risk measures
Authors: E. Ruben van Beesten
Date: 2024-08-28 00:00:00

I propose a functional on the space of spectral risk measures that quantifies their ``degree of risk aversion''. This quantification formalizes the idea that some risk measures are ``more risk-averse'' than others. I construct the functional using two axioms: a normalization on the space of CVaRs and a linearity axiom. I present two formulas for the functional and discuss several properties and interpretations.
A Tutorial on Brownian Motion for Biostatisticians
Authors: Elvis Han Cui
Date: 2024-08-16 00:00:00

This manuscript provides an in-depth exploration of Brownian Motion, a fundamental stochastic process in probability theory for Biostatisticians. It begins with foundational definitions and properties, including the construction of Brownian motion and its Markovian characteristics. The document delves into advanced topics such as the Karhunen-Loeve expansion, reflection principles, and Levy's modulus of continuity. Through rigorous proofs and theorems, the manuscript examines the non-differentiability of Brownian paths, the behavior of zero sets, and the significance of local time. The notes also cover important results like Donsker's theorem and Blumenthal's 0-1 law, emphasizing their implications in the study of stochastic processes.
A versatile informative diffusion model for single-cell ATAC-seq data generation and analysis
Authors: Lei Huang, Lei Xiong, Na Sun, Zunpeng Liu, Ka-Chun Wong, Manolis Kellis
Date: 2024-08-27 00:00:00

The rapid advancement of single-cell ATAC sequencing (scATAC-seq) technologies holds great promise for investigating the heterogeneity of epigenetic landscapes at the cellular level. The amplification process in scATAC-seq experiments often introduces noise due to dropout events, which results in extreme sparsity that hinders accurate analysis. Consequently, there is a significant demand for the generation of high-quality scATAC-seq data in silico. Furthermore, current methodologies are typically task-specific, lacking a versatile framework capable of handling multiple tasks within a single model. In this work, we propose ATAC-Diff, a versatile framework, which is based on a latent diffusion model conditioned on the latent auxiliary variables to adapt for various tasks. ATAC-Diff is the first diffusion model for the scATAC-seq data generation and analysis, composed of auxiliary modules encoding the latent high-level variables to enable the model to learn the semantic information to sample high-quality data. Gaussian Mixture Model (GMM) as the latent prior and auxiliary decoder, the yield variables reserve the refined genomic information beneficial for downstream analyses. Another innovation is the incorporation of mutual information between observed and hidden variables as a regularization term to prevent the model from decoupling from latent variables. Through extensive experiments, we demonstrate that ATAC-Diff achieves high performance in both generation and analysis tasks, outperforming state-of-the-art models.
Intracellular order formation through stepwise phase transitions
Authors: Yuika Ueda, Shinji Deguchi
Date: 2024-08-26 00:00:00

Living cells inherently exhibit the ability to spontaneously reorganize their structures in response to changes in both their internal and external environments. Among these responses, the organization of stress fibers composed of actin molecules changes in direct accordance with the mechanical stiffness of their environments. On soft substrates, SFs are rarely formed, but as stiffness increases, they emerge with random orientation, progressively align, and eventually form thicker bundles as stiffness surpasses successive thresholds. These transformations share similarities with phase transitions studied in condensed matter physics, yet despite extensive research on cellular dynamics, the introduction of the statistical mechanics perspective to the environmental dependence of intracellular structures remains underexplored. With this physical framework, we identify key relationships governing these intracellular transitions, highlighting the delicate balance between energy and entropy. Our analysis provides a unified understanding of the stepwise phase transitions of actin structures, offering new insights into related biological mechanisms. Notably, our study suggests the existence of mechanical checkpoints in the G1 phase of the cell cycle, which sequentially regulate the formation of intracellular structures to ensure proper cell cycle progression.
HEK-Omics: The promise of omics to optimize HEK293 for recombinant adeno-associated virus (rAAV) gene therapy manufacturing
Authors: Sai Guna Ranjan Gurazada, Hannah M. Kennedy, Richard D. Braatz, Steven J. Mehrman, Shawn W. Polson, Irene T. Rombel
Date: 2024-08-23 00:00:00

Gene therapy is poised to transition from niche to mainstream medicine, with recombinant adeno-associated virus (rAAV) as the vector of choice. However, this requires robust, scalable, industrialized production to meet demand and provide affordable patient access, which has thus far failed to materialize. Closing the chasm between demand and supply requires innovation in biomanufacturing to achieve the essential step change in rAAV product yield and quality. Omics provides a rich source of mechanistic knowledge that can be applied to HEK293, the prevailing cell line for rAAV production. In this review, the findings from a growing number of disparate studies that apply genomics, epigenomics, transcriptomics, proteomics, and metabolomics to HEK293 bioproduction are explored. Learnings from CHO-Omics, application of omics approaches to improve CHO bioproduction, provide context for the potential of "HEK-Omics" as a multiomics-informed approach providing actionable mechanistic insights for improved transient and stable production of rAAV and other recombinant products in HEK293.
Discovering Motifs to Fingerprint Multi-Layer Networks: a Case Study on the Connectome of C. Elegans
Authors: Deepak Sharma, Matthias Renz, Philipp Hövel
Date: 2024-08-09 00:00:00
Tasks: Subgraph Counting

Motif discovery is a powerful and insightful method to quantify network structures and explore their function. As a case study, we present a comprehensive analysis of regulatory motifs in the connectome of the model organism Caenorhabditis elegans (C. elegans). Leveraging the Efficient Subgraph Counting Algorithmic PackagE (ESCAPE) algorithm, we identify network motifs in the multi-layer nervous system of C. elegans and link them to functional circuits. We further investigate motif enrichment within signal pathways and benchmark our findings with random networks of similar size and link density. Our findings provide valuable insights into the organization of the nerve net of this well documented organism and can be easily transferred to other species and disciplines alike.
From Neuronal Packets to Thoughtseeds: A Hierarchical Model of Embodied Cognition in the Global Workspace
Authors: Prakash Chandra Kavi, Gorka Zamora Lopez, Daniel Ari Friedman
Date: 2024-08-28 00:00:00
Tasks: Decision Making

The emergence of cognition requires a framework that bridges evolutionary principles with neurocomputational mechanisms. This paper introduces the novel "thoughtseed" framework, proposing that cognition arises from the dynamic interaction of self-organizing units of embodied knowledge called "thoughtseeds" within the Global Workspace of consciousness. Leveraging foundational concepts from evolutionary theory, neuronal packets, and free energy principle, we propose a hierarchical model of cognitive states, comprising Neuronal Packet Domains (NPDs), Knowledge Domains (KDs), the thoughtseed network, and meta-cognition. This hierarchical interplay, mediated by nested Markov blankets and reciprocal message passing, facilitates the emergence of thoughtseeds as coherent patterns of activity that guide perception, action, and learning. Thoughtseeds, posited as fundamental units of thought, compete for dominance within the Global Workspace, with the dominant thoughtseed shaping conscious experience and guiding behavior. We present a mathematical framework grounded in active inference and dynamical systems theory to model thoughtseed dynamics and their contribution to the unitary nature of consciousness. The thoughtseed framework offers a promising step towards a novel, biologically-grounded model for understanding the organizing principles and emergence of embodied cognition, offering a unified account of cognitive phenomena, with potential applications in understanding consciousness, attention, and decision-making.
A Model-Free Method to Quantify Memory Utilization in Neural Point Processes
Authors: Gorana Mijatovic, Sebastiano Stramaglia, Luca Faes
Date: 2024-08-28 00:00:00
Tasks: Point Processes

Quantifying the predictive capacity of a neural system, intended as the capability to store information and actively use it for dynamic system evolution, is a key component of neural information processing. Information storage (IS), the main measure quantifying the active utilization of memory in a dynamic system, is only defined for discrete-time processes. While recent theoretical work laid the foundations for the continuous-time analysis of the predictive capacity stored in a process, methods for the effective computation of the related measures are needed to favor widespread utilization on neural data. This work introduces a method for the model-free estimation of the so-called memory utilization rate (MUR), the continuous-time counterpart of the IS, specifically designed to quantify the predictive capacity stored in neural point processes. The method employs nearest-neighbor entropy estimation applied to the inter-spike intervals measured from point-process realizations to quantify the extent of memory used by a spike train. An empirical procedure based on surrogate data is implemented to compensate the estimation bias and detect statistically significant levels of memory. The method is validated in simulated Poisson processes and in realistic models of coupled cortical dynamics and heartbeat dynamics. It is then applied to real spike trains reflecting central and autonomic nervous system activities: in spontaneously growing cortical neuron cultures, the MUR detected increasing memory utilization across maturation stages, associated to emergent bursting synchronized activity; in the study of the neuro-autonomic modulation of human heartbeats, the MUR reflected the sympathetic activation occurring with postural but not with mental stress. The proposed approach offers a computationally reliable tool to analyze spike train data in computational neuroscience and physiology.
Automatic recognition and detection of aphasic natural speech
Authors: Mara Barberis, Pieter De Clercq, Bastiaan Tamm, Hugo Van hamme, Maaike Vandermosten
Date: 2024-08-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Aphasia is a language disorder affecting one third of stroke patients. Current aphasia assessment does not consider natural speech due to the time consuming nature of manual transcriptions and a lack of knowledge on how to analyze such data. Here, we evaluate the potential of automatic speech recognition (ASR) to transcribe Dutch aphasic speech and the ability of natural speech features to detect aphasia. A picture-description task was administered and automatically transcribed in 62 persons with aphasia and 57 controls. Acoustic and linguistic features were semi-automatically extracted and provided as input to a support vector machine (SVM) classifier. Our ASR model obtained a WER of 24.5%, outperforming earlier ASR models for aphasia. The SVM shows high accuracy (86.6%) at the individual level, with fluency features as most dominant to detect aphasia. ASR and semi-automatic feature extraction can thus facilitate natural speech analysis in a time efficient manner in clinical practice.
A CRH-HCN Theory of Obsessive-Compulsive Disorder (OCD)
Authors: Ari Rappoport
Date: 2024-08-13 00:00:00
Tasks: All

I present the first complete theory of OCD. OCD occurs when excessive CRH is released in the prefrontal cortex, activating cAMP. cAMP is a major inducer of HCN channels, which promote repeated neural firing. The combination of CRH, which is strongly associated with stress, and repeated firing that cannot be controlled, explains all of the features of OCD, including obsessions and compulsions of all kinds.
RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier
Authors: Maryam Ostadsharif Memar, Navid Ziaei, Behzad Nazari, Ali Yousefi
Date: 2024-08-12 00:00:00
Tasks: Brain Computer Interface, Decoder

Intracranial electroencephalography (iEEG) is increasingly used for clinical and brain-computer interface applications due to its high spatial and temporal resolution. However, inter-subject variability in electrode implantation poses a challenge for developing generalized neural decoders. To address this, we introduce a novel decoder model that is robust to inter-subject electrode implantation variability. We call this model RISE-iEEG, which stands for Robust to Inter-Subject Electrode Implantation Variability iEEG Classifier. RISE-iEEG employs a deep neural network structure preceded by a participant-specific projection network. The projection network maps the neural data of individual participants onto a common low-dimensional space, compensating for the implantation variability. In other words, we developed an iEEG decoder model that can be applied across multiple participants' data without requiring the coordinates of electrode for each participant. The performance of RISE-iEEG across multiple datasets, including the Music Reconstruction dataset, and AJILE12 dataset, surpasses that of advanced iEEG decoder models such as HTNet and EEGNet. Our analysis shows that the performance of RISE-iEEG is about 7\% higher than that of HTNet and EEGNet in terms of F1 score, with an average F1 score of 0.83, which is the highest result among the evaluation methods defined. Furthermore, Our analysis of the projection network weights reveals that the Superior Temporal and Postcentral lobes are key encoding nodes for the Music Reconstruction and AJILE12 datasets, which aligns with the primary physiological principles governing these regions. This model improves decoding accuracy while maintaining interpretability and generalization.
Individual or collective treatments: how to target antimicrobial use to limit the spread of \textit{Mannheimia haemolytica} among beef cattle?
Authors: Baptiste Sorin-Dupont, Antoine Poyard, Sebastien Assie, Sebastien Picault, Pauline Ezanno
Date: 2024-08-29 00:00:00

The overuse of antibiotics has become a major global concern due to its role in diminishing treatment effectiveness and positively selecting antibiotic-resistant bacterial strains. This issue is particularly important in the beef cattle sector, where Bovine Respiratory Diseases (BRD) impose significant economic and welfare burdens. BRD are complex, multifactorial conditions primarily affecting young calves and feedlot cattle, caused by a combination of viral and bacterial pathogens, environmental factors, and stressors. Despite efforts to reduce antimicrobial use (AMU), the cattle production system remains heavily reliant on antibiotics to control BRD, often through the implementation of collective treatments to prevent outbreaks. This study aimed at evaluating the impact of various treatment practices on the spread of BRD, specifically focusing on criteria for implementing collective treatments. Using a mechanistic stochastic model, we simulated the spread of \textit{Mannheimia haemolytica} in a multi-pen fattening operation under sixteen different scenarios, considering pen composition, individual risk levels, and treatment strategies. Our findings suggest that an alternative criterion for collective treatments based on the speed of the disease spread, could reduce BRD incidence and AMU more effectively than conventional methods. This research highlights the importance of responsible treatment practices and the potential benefits of novel criteria for collective treatment strategies in improving animal health. Moreover, it emphasizes the need for transparency on the exposure to risk factors along the production chain.
Transformations to simplify phylogenetic networks
Authors: Johanna Heiss, Daniel H. Huson, Mike Steel
Date: 2024-08-28 00:00:00

The evolutionary relationships between species are typically represented in the biological literature by rooted phylogenetic trees. However, a tree fails to capture ancestral reticulate processes, such as the formation of hybrid species or lateral gene transfer events between lineages, and so the history of life is more accurately described by a rooted phylogenetic network. Nevertheless, phylogenetic networks may be complex and difficult to interpret, so biologists sometimes prefer a tree that summarises the central tree-like trend of evolution. In this paper, we formally investigate methods for transforming an arbitrary phylogenetic network into a tree (on the same set of leaves) and ask which ones (if any) satisfy a simple consistency condition. This consistency condition states that if we add additional species into a phylogenetic network (without otherwise changing this original network) then transforming this enlarged network into a rooted phylogenetic tree induces the same tree on the original set of species as transforming the original network. We show that the LSA (lowest stable ancestor) tree method satisfies this consistency property, whereas several other commonly used methods (and a new one we introduce) do not. We also briefly consider transformations that convert arbitrary phylogenetic networks to another simpler class, namely normal networks.
A framework for the modelling and the analysis of epidemiological spread in commuting populations
Authors: Pierre-Alexandre Bliman, Boureima Sangaré, Assane Savadogo
Date: 2024-08-28 00:00:00
Tasks: Epidemiology

In the present paper, our goal is to establish a framework for the mathematical modelling and the analysis of the spread of an epidemic in a large population commuting regularly, typically along a time-periodic pattern, as is roughly speaking the case in populous urban center. We consider a large number of distinct homogeneous groups of individuals of various sizes, called subpopulations, and focus on the modelling of the changing conditions of their mixing along time and of the induced disease transmission. We propose a general class of models in which the 'force of infection' plays a central role, which attempts to 'reconcile' the classical modelling approaches in mathematical epidemiology, based on compartmental models, with some widely used analysis results (including those by P. van den Driessche and J. Watmough in 2002), established for apparently less structured systems of nonlinear ordinary-differential equations. We take special care in explaining the modelling approach in details, and provide analysis results that allow to compute or estimate the value of the basic reproduction number for such general periodic epidemic systems.
The complex world of honey bee vibrational signaling: A response to Ramsey et al. (2017)
Authors: Heather C Bell, Parry M Keitzman, James C Nieh
Date: 2024-08-26 00:00:00

Ramsey et al. report on the characteristics and temporal distribution of an interesting vibrational signal that they term the whooping signal, primarily based upon a long-term study of vibrations recorded by accelerometers placed inside two honey bee (Apis mellifera) colonies, one in France and one in the United Kingdom. The goal of the study, the long-term automated recording and analysis of honey bee vibrational signaling, is worthwhile. However, we believe that some of the conclusions drawn by the authors are not well supported, given the evidence.