pwc-archive/papers-with-abstracts huggingface dataset:
Sideward contact tracing in an epidemic model with mixing groups
Authors: Dongni Zhang, Martina Favero
Date: 2024-07-16 00:00:00

We consider a stochastic epidemic model with sideward contact tracing. We assume that infection is driven by interactions within mixing events (gatherings of two or more individuals). Once an infective is diagnosed, each individual who was infected at the same event as the diagnosed individual is contact traced with some given probability. Assuming few initial infectives in a large population, the early phase of the epidemic is approximated by a branching process with sibling dependencies. To address the challenges given by the dependencies, we consider sibling groups (individuals who become infected at the same event) as macro-individuals and define a macro-branching process. This allows us to derive an expression for the effective macro-reproduction number which corresponds to the effective individual reproduction number and represents a threshold for the behaviour of the epidemic. Through numerical examples, we show how the reproduction number varies with the distribution of the mixing event size, the mean size, the rate of diagnosis and the tracing probability.
Stochastic branching models for the telomeres dynamics in a model including telomerase activity
Authors: Athanase Benetos, Coralie Fritsch, Emma Horton, Lionel Lenotre, Simon Toupance, Denis Villemonais
Date: 2024-07-16 00:00:00

Telomeres are repetitive sequences of nucleotides at the end of chromosomes, whose evolution over time is intrinsically related to biological ageing. In most cells, with each cell division, telomeres shorten due to the so-called end replication problem, which can lead to replicative senescence and a variety of age-related diseases. On the other hand, in certain cells, the presence of the enzyme telomerase can lead to the lengthening of telomeres, which may delay or prevent the onset of such diseases but can also increase the risk of cancer.In this article, we propose a stochastic representation of this biological model, which takes into account multiple chromosomes per cell, the effect of telomerase, different cell types and the dependence of the distribution of telomere length on the dynamics of the process. We study theoretical properties of this model, including its long-term behaviour. In addition, we investigate numerically the impact of the model parameters on biologically relevant quantities, such as the Hayflick limit and the Malthusian parameter of the population of cells.
High nucleotide skew palindromic DNA sequences function as replication origins due to their unzipping propensity
Authors: Parthasarathi Sahu, Sashikanta Barik, Koushik Ghosh, Hemachander Subramanian
Date: 2024-07-18 00:00:00

Locations of DNA replication initiation in prokaryotes, called "origins of replication", are well-characterized. However, a mechanistic understanding of the sequence-dependence of the local unzipping of double-stranded DNA, the first step towards replication initiation, is lacking. Here, utilizing a Markov chain model that was created to address the directional nature of DNA unzipping and replication, we model the sequence dependence of local melting of double-stranded linear DNA segments. We show that generalized palindromic sequences with high nucleotide skews have a low kinetic barrier for local melting near melting temperatures. This allows for such sequences to function as replication origins. We support our claim with evidence for high-skew palindromic sequences within the replication origins of mitochondrial DNA, bacteria, archaea and plasmids.
Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model
Authors: Jike Wang, Jianwen Feng, Yu Kang, Peichen Pan, Jingxuan Ge, Yan Wang, Mingyang Wang, Zhenxing Wu, Xingcai Zhang, Jiameng Yu, Xujun Zhang, Tianyue Wang, Lirong Wen, Guangning Yan, Yafeng Deng, Hui Shi, Chang-Yu Hsieh, Zhihui Jiang, Tingjun Hou
Date: 2024-07-17 00:00:00
Tasks: Knowledge Distillation, scientific discovery

Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for various tasks. We introduce AMP-Designer, an LLM-based approach for swiftly designing novel antimicrobial peptides (AMPs) with desired properties. Within 11 days, AMP-Designer achieved the de novo design of 18 AMPs with broad-spectrum activity against Gram-negative bacteria. In vitro validation revealed a 94.4% success rate, with two candidates demonstrating exceptional antibacterial efficacy, minimal hemotoxicity, stability in human plasma, and low potential to induce resistance, as evidenced by significant bacterial load reduction in murine lung infection experiments. The entire process, from design to validation, concluded in 48 days. AMP-Designer excels in creating AMPs targeting specific strains despite limited data availability, with a top candidate displaying a minimum inhibitory concentration of 2.0 {\mu}g/ml against Propionibacterium acnes. Integrating advanced machine learning techniques, AMP-Designer demonstrates remarkable efficiency, paving the way for innovative solutions to antibiotic resistance.
Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models
Authors: Jeff Guo, Philippe Schwaller
Date: 2024-07-16 00:00:00
Tasks: Drug Discovery, Retrosynthesis

Synthesizability in generative molecular design remains a pressing challenge. Existing methods to assess synthesizability span heuristics-based methods, retrosynthesis models, and synthesizability-constrained molecular generation. The latter has become increasingly prevalent and proceeds by defining a set of permitted actions a model can take when generating molecules, such that all generations are anchored in "synthetically-feasible" chemical transformations. To date, retrosynthesis models have been mostly used as a post-hoc filtering tool as their inference cost remains prohibitive to use directly in an optimization loop. In this work, we show that with a sufficiently sample-efficient generative model, it is straightforward to directly optimize for synthesizability using retrosynthesis models in goal-directed generation. Under a heavily-constrained computational budget, our model can generate molecules satisfying a multi-parameter drug discovery optimization task while being synthesizable, as deemed by the retrosynthesis model.
SPIN: SE(3)-Invariant Physics Informed Network for Binding Affinity Prediction
Authors: Seungyeon Choi, Sangmin Seo, Sanghyun Park
Date: 2024-07-10 00:00:00

Accurate prediction of protein-ligand binding affinity is crucial for rapid and efficient drug development. Recently, the importance of predicting binding affinity has led to increased attention on research that models the three-dimensional structure of protein-ligand complexes using graph neural networks to predict binding affinity. However, traditional methods often fail to accurately model the complex's spatial information or rely solely on geometric features, neglecting the principles of protein-ligand binding. This can lead to overfitting, resulting in models that perform poorly on independent datasets and ultimately reducing their usefulness in real drug development. To address this issue, we propose SPIN, a model designed to achieve superior generalization by incorporating various inductive biases applicable to this task, beyond merely training on empirical data from datasets. For prediction, we defined two types of inductive biases: a geometric perspective that maintains consistent binding affinity predictions regardless of the complexs rotations and translations, and a physicochemical perspective that necessitates minimal binding free energy along their reaction coordinate for effective protein-ligand binding. These prior knowledge inputs enable the SPIN to outperform comparative models in benchmark sets such as CASF-2016 and CSAR HiQ. Furthermore, we demonstrated the practicality of our model through virtual screening experiments and validated the reliability and potential of our proposed model based on experiments assessing its interpretability.
Decoding Dmrt1: Insights into vertebrate sex determination and gonadal sex differentiation
Authors: Barbora Augstenová, Wen-Juan Ma
Date: 2024-07-15 00:00:00

Dmrt1 is pivotal in testis formation and function by interacting with genes crucial for Sertoli cell differentiation. Dmrt1, or Sox9, forms a conserved antagonistic interaction with Foxl2 across mammals. Across 128 vertebrate species, Dmrt1 exhibits sexually dimorphic expression, implicating its role in master regulation of sex determination and gonadal sex differentiation. Dmrt1 emerges as a master/upstream sex-determining gene in one fish, frog, chicken and turtle. Recent studies suggest epigenetic regulation of Dmrt1 in its promoter methylation, and transposable element insertion introducing epigenetic modification to cis-regulatory elements of Dmrt1, alongside non-coding RNA involvement, in a wide spectrum of sex-determining mechanisms ranging from genetic factors, to interactions between genetic factors with the environment, to solely environmental factors. Additionally, alternative splicing of Dmrt1 was found in all major vertebrate groups except amphibians. Dmrt1 has evolved many lineage-specific isoforms (ranging from 2 to 10), and various isoforms showed sex, tissue or development-specific expression, which is in contrast to the highly conserved sex-specific splicing of its homolog Dsx across insects. Future research should focus on understanding the molecular basis of environmental sex determination from a broader taxon, and the molecular basis of epigenetic regulation. It is also essential to understand why and how multiple alternative splicing variants of Dmrt1 evolve in vertebrates, the specific roles each isoform plays in sex determination and gonadal sex differentiation, as well as the significant differences in the molecular mechanisms and functions of alternative splicing between Dmrt1 in vertebrates and Dsx in insects. Understanding the differences could provide deeper insights into the evolution of sex-determining mechanisms between vertebrates and insects.
SMDP: SARS-CoV-2 Mutation Distribution Profiler for rapid estimation of mutational histories of unusual lineages
Authors: Erin E. Gill, Sheri Harari, Aijing Feng, Fiona S. L. Brinkman, Sarah Otto
Date: 2024-07-15 00:00:00

SARS-CoV-2 usually evolves at a relatively constant rate over time. Occasionally, however, lineages arise with higher-than-expected numbers of mutations given the date of sampling. Such lineages can arise for a variety of reasons, including selection pressures imposed by evolution during a chronic infection or exposure to mutation-inducing drugs like molnupiravir. We have developed an open-source web-based application (SMDP: SARS-CoV-2 Mutation Distribution Profiler; https://eringill.shinyapps.io/covid_mutation_distributions) that compares a list of user-submitted lineage-defining mutations or a FASTA file containing a single genome (from which lineage-defining mutations are calculated) with established mutation distributions including those observed during (1) the first nine months of the pandemic, (2) during the global transmission of Omicron, (3) during the chronic infection of immunocompromised patients, and (4) during zoonotic spillover from humans to deer. The application calculates the most likely distribution for the user's mutation list and displays log likelihoods for all distributions. In addition, the transition:transversion ratio of the user's list is calculated to determine whether there is evidence of exposure to a mutation-inducing drug such as molnupiravir and indicates whether the list contains mutations in the proofreading domain of nsp14 which could lead to a higher-than-expected mutation rate in the lineage. This tool will be useful for public health and researchers seeking to rapidly infer evolutionary histories of SARS-CoV-2 variants, which can aid risk assessment and public health responses.
Dy-mer: An Explainable DNA Sequence Representation Scheme using Sparse Recovery
Authors: Zhiyuan Peng, Yuanbo Tang, Yang Li
Date: 2024-07-06 00:00:00

DNA sequences encode vital genetic and biological information, yet these unfixed-length sequences cannot serve as the input of common data mining algorithms. Hence, various representation schemes have been developed to transform DNA sequences into fixed-length numerical representations. However, these schemes face difficulties in learning high-quality representations due to the complexity and sparsity of DNA data. Additionally, DNA sequences are inherently noisy because of mutations. While several schemes have been proposed for their effectiveness, they often lack semantic structure, making it difficult for biologists to validate and leverage the results. To address these challenges, we propose \textbf{Dy-mer}, an explainable and robust DNA representation scheme based on sparse recovery. Leveraging the underlying semantic structure of DNA, we modify the traditional sparse recovery to capture recurring patterns indicative of biological functions by representing frequent K-mers as basis vectors and reconstructing each DNA sequence through simple concatenation. Experimental results demonstrate that \textbf{Dy-mer} achieves state-of-the-art performance in DNA promoter classification, yielding a remarkable \textbf{13\%} increase in accuracy. Moreover, its inherent explainability facilitates DNA clustering and motif detection, enhancing its utility in biological research.
Topological Analysis of Seizure-Induced Changes in Brain Hierarchy Through Effective Connectivity
Authors: Anass B. El-Yaagoubi, Moo K. Chung, Hernando Ombao
Date: 2024-07-18 00:00:00
Tasks: Causal Inference, EEG, Electroencephalogram (EEG), Time Series, Topological Data Analysis

Traditional Topological Data Analysis (TDA) methods, such as Persistent Homology (PH), rely on distance measures (e.g., cross-correlation, partial correlation, coherence, and partial coherence) that are symmetric by definition. While useful for studying topological patterns in functional brain connectivity, the main limitation of these methods is their inability to capture the directional dynamics - which is crucial for understanding effective brain connectivity. We propose the Causality-Based Topological Ranking (CBTR) method, which integrates Causal Inference (CI) to assess effective brain connectivity with Hodge Decomposition (HD) to rank brain regions based on their mutual influence. Our simulations confirm that the CBTR method accurately and consistently identifies hierarchical structures in multivariate time series data. Moreover, this method effectively identifies brain regions showing the most significant interaction changes with other regions during seizures using electroencephalogram (EEG) data. These results provide novel insights into the brain's hierarchical organization and illuminate the impact of seizures on its dynamics.
Evaluating the evolution and inter-individual variability of infant functional module development from 0 to 5 years old
Authors: Lingbin Bian, Nizhuan Wang, Yuanning Li, Adeel Razi, Qian Wang, Han Zhang, Dinggang Shen, the UNC/UMN Baby Connectome Project Consortium
Date: 2024-07-18 00:00:00
Tasks: Functional Connectivity

The segregation and integration of infant brain networks undergo tremendous changes due to the rapid development of brain function and organization. Traditional methods for estimating brain modularity usually rely on group-averaged functional connectivity (FC), often overlooking individual variability. To address this, we introduce a novel approach utilizing Bayesian modeling to analyze the dynamic development of functional modules in infants over time. This method retains inter-individual variability and, in comparison to conventional group averaging techniques, more effectively detects modules, taking into account the stationarity of module evolution. Furthermore, we explore gender differences in module development under awake and sleep conditions by assessing modular similarities. Our results show that female infants demonstrate more distinct modular structures between these two conditions, possibly implying relative quiet and restful sleep compared with male infants.
Feature interpretability in BCIs: exploring the role of network lateralization
Authors: Juliana Gonzalez-Astudillo, Fabrizio De Vico Fallani
Date: 2024-07-16 00:00:00
Tasks: Benchmarking, EEG, Functional Connectivity, Motor Imagery

Brain-computer interfaces (BCIs) enable users to interact with the external world using brain activity. Despite their potential in neuroscience and industry, BCI performance remains inconsistent in noninvasive applications, often prioritizing algorithms that achieve high classification accuracies while masking the neural mechanisms driving that performance. In this study, we investigated the interpretability of features derived from brain network lateralization, benchmarking against widely used techniques like power spectrum density (PSD), common spatial pattern (CSP), and Riemannian geometry. We focused on the spatial distribution of the functional connectivity within and between hemispheres during motor imagery tasks, introducing network-based metrics such as integration and segregation. Evaluating these metrics across multiple EEG-based BCI datasets, our findings reveal that network lateralization offers neurophysiological plausible insights, characterized by stronger lateralization in sensorimotor and frontal areas contralateral to imagined movements. While these lateralization features did not outperform CSP and Riemannian geometry in terms of classification accuracy, they demonstrated competitive performance against PSD alone and provided biologically relevant interpretation. This study underscores the potential of brain network lateralization as a new feature to be integrated in motor imagery-based BCIs for enhancing the interpretability of noninvasive applications.
Generalisation to unseen topologies: Towards control of biological neural network activity
Authors: Laurens Engwegen, Daan Brinks, Wendelin Böhmer
Date: 2024-06-17 00:00:00

Novel imaging and neurostimulation techniques open doors for advancements in closed-loop control of activity in biological neural networks. This would allow for applications in the investigation of activity propagation, and for diagnosis and treatment of pathological behaviour. Due to the partially observable characteristics of activity propagation, through networks in which edges can not be observed, and the dynamic nature of neuronal systems, there is a need for adaptive, generalisable control. In this paper, we introduce an environment that procedurally generates neuronal networks with different topologies to investigate this generalisation problem. Additionally, an existing transformer-based architecture is adjusted to evaluate the generalisation performance of a deep RL agent in the presented partially observable environment. The agent demonstrates the capability to generalise control from a limited number of training networks to unseen test networks.
Building Artificial Intelligence with Creative Agency and Self-hood
Authors: Liane Gabora, Joscha Bach
Date: 2024-06-09 00:00:00

This paper is an invited layperson summary for The Academic of the paper referenced on the last page. We summarize how the formal framework of autocatalytic networks offers a means of modeling the origins of self-organizing, self-sustaining structures that are sufficiently complex to reproduce and evolve, be they organisms undergoing biological evolution, novelty-generating minds driving cultural evolution, or artificial intelligence networks such as large language models. The approach can be used to analyze and detect phase transitions in vastly complex networks that have proven intractable with other approaches, and suggests a promising avenue to building an autonomous, agentic AI self. It seems reasonable to expect that such an autocatalytic AI would possess creative agency akin to that of humans, and undergo psychologically healing -- i.e., therapeutic -- internal transformation through engagement in creative tasks. Moreover, creative tasks would be expected to help such an AI solidify its self-identity.
Branch length statistics in phylogenetic trees under constant-rate birth-death dynamics
Authors: Tobias Dieselhorst, Johannes Berg
Date: 2024-07-18 00:00:00

Phylogenetic trees represent the evolutionary relationships between extant lineages, where extinct or non-sampled lineages are omitted. Extending the work of Stadler and collaborators, this paper focuses on the branch lengths in phylogenetic trees arising under a constant-rate birth-death model. We derive branch length distributions of phylogenetic branches with and without random sampling of individuals of the extant population under two distinct statistical scenarios: a fixed age of the birth-death process and a fixed number of individuals at the time of observation. We find that branches connected to the tree leaves (pendant branches) and branches in the interior of the tree behave very differently under sampling; pendant branches grow longer without limit as the sampling probability is decreased, whereas the interior branch lengths quickly reach an asymptotic distribution that does not depend on the sampling probability.
Bridging Wright-Fisher and Moran models
Authors: Arthur Alexandre, Alia Abbara, Cecilia Fruet, Claude Loverdo, Anne-Florence Bitbol
Date: 2024-07-17 00:00:00

The Wright-Fisher model and the Moran model are both widely used in population genetics. They describe the time evolution of the frequency of an allele in a well-mixed population with fixed size. We propose a simple and tractable model which bridges the Wright-Fisher and the Moran descriptions. We assume that a fixed fraction of the population is updated at each discrete time step. In this model, we determine the fixation probability of a mutant and its average fixation and extinction times, under the diffusion approximation. We further study the associated coalescent process, which converges to Kingman's coalescent, and we calculate effective population sizes. We generalize our model, first by taking into account fluctuating updated fractions or individual lifetimes, and then by incorporating selection on the lifetime as well as on the reproductive fitness.
A Coupled Two-Tier Mathematical Transmission Model to Explore Virulence Evolution in Vector-Borne Diseases
Authors: Daniel A. M. Villela
Date: 2024-07-16 00:00:00

The emergence or adaptation of pathogens may lead to epidemics, highlighting the need for a thorough understanding of pathogen evolution. The tradeoff hypothesis suggests that virulence evolves to reach an optimal transmission intensity relative to the mortality caused by the disease. This study introduces a mathematical model that incorporates key factors such as recovery times and mortality rates, focusing on the diminishing effects of parasite growth on transmission, with a focus on vector-borne diseases. The analysis reveals conditions under which heightened virulence occurs in hosts, indicating that these factors can support vector-host transmission of a pathogen, even if the host-only component is insufficient for sustainable transmission. This insight helps explain the significant presence of pathogens with high fatality rates, such as those in vector-borne diseases. The findings underscore an elevated risk for future outbreaks involving such diseases. Enhanced surveillance of mortality rates and techniques to monitor pathogen evolution are vital to effectively control future epidemics. This study provides essential insights for epidemic preparedness and highlights the need for ongoing research into pathogen evolution.