pwc-archive/papers-with-abstracts huggingface dataset:
Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning
Authors: JunJie Wee, Guo-Wei Wei
Date: 2024-11-19 00:00:00
Tasks: Topological Data Analysis

The fast evolution of SARS-CoV-2 and other infectious viruses poses a grand challenge to the rapid response in terms of viral tracking, diagnostics, and design and manufacture of monoclonal antibodies (mAbs) and vaccines, which are both time-consuming and costly. This underscores the need for efficient computational approaches. Recent advancements, like topological deep learning (TDL), have introduced powerful tools for forecasting emerging dominant variants, yet they require deep mutational scanning (DMS) of viral surface proteins and associated three-dimensional (3D) protein-protein interaction (PPI) complex structures. We propose an AlphaFold 3 (AF3)-assisted multi-task topological Laplacian (MT-TopLap) strategy to address this need. MT-TopLap combines deep learning with topological data analysis (TDA) models, such as persistent Laplacians (PL) to extract detailed topological and geometric characteristics of PPIs, thereby enhancing the prediction of DMS and binding free energy (BFE) changes upon virus mutations. Validation with four experimental DMS datasets of SARS-CoV-2 spike receptor-binding domain (RBD) and the human angiotensin-converting enzyme-2 (ACE2) complexes indicates that our AF3 assisted MT-TopLap strategy maintains robust performance, with only an average 1.1% decrease in Pearson correlation coefficients (PCC) and an average 9.3% increase in root mean square errors (RMSE), compared with the use of experimental structures. Additionally, AF3-assisted MT-TopLap achieved a PCC of 0.81 when tested with a SARS-CoV-2 HK.3 variant DMS dataset, confirming its capability to accurately predict BFE changes and adapt to new experimental data, thereby showcasing its potential for rapid and effective response to fast viral evolution.
Silanization Strategies for Tailoring Peptide Functionalization on Silicon Surfaces: Implications for Enhancing Stem Cell Adhesion
Authors: Melissa Kosovari, Thierry Buffeteau, Laurent Thomas, Andrée-Anne Guay Bégin, Luc Vellutini, James Mcgettrick, Gaétan Laroche, Marie-Christine Durrieu
Date: 2024-11-19 00:00:00

Biomaterial surface engineering and integrating cell-adhesive ligands are crucial in biological research and biotechnological applications. The interplay between cells and their microenvironment, influenced by chemical and physical cues, impacts cellular behavior. Surface modification of biomaterials profoundly affects cellular responses, especially at the cell-surface interface. This work focuses on enhancing cellular activities through material manipulation, emphasizing silanization for further functionalization with bioactive molecules like RGD peptides to improve cell adhesion. The grafting of three distinct silanes onto silicon wafers using both spin coating and immersion methods was investigated. This study sheds light on the effects of different alkyl chain lengths and protecting groups on cellular behavior, providing valuable insights into optimizing silane-based self-assembled monolayers (SAMs) before peptide or protein grafting for the first time. Specifically, it challenges the common use of APTES molecules in this context. These findings advance our understanding of surface modification strategies, paving the way for tailoring biomaterial surfaces to modulate cellular behavior for diverse biotechnological applications.
ukbFGSEA: an R Package for Applying Fast Preranked Gene Set Enrichment Analysis to UK Biobank Exome Data
Authors: Pengjun Guo, He Zhu
Date: 2024-11-18 00:00:00

The Genebass dataset, released by Karczewski et al. (2022), provides a comprehensive resource elucidating associations between genes and 4,529 phenotypes based on nearly 400,000 exomes from the UK Biobank. This extensive dataset enables the evaluation of gene set enrichment across a wide range of phenotypes, facilitating the inference of associations between specified gene sets and phenotypic traits. Despite its potential, no established method for applying gene set enrichment analysis (GSEA) to Genebass data exists. To address this gap, we propose utilizing fast pre-ranked gene set enrichment analysis (FGSEA) as a novel approach to determine whether a specified set of genes is significantly enriched in phenotypes within the UK Biobank. We developed an R package, ukbFGSEA, to implement this analysis, completed with a hands-on tutorial. Our approach has been validated by analyzing gene sets associated with autism spectrum disorder, developmental disorder, and neurodevelopmental disorders, demonstrating its capability to reveal established and novel associations.
RatGene: Gene deletion-addition algorithms using growth to production ratio for growth-coupled production in constraint-based metabolic networks
Authors: Yier Ma, Takeyuki Tamura
Date: 2024-11-18 00:00:00

In computational metabolic design, it is often necessary to modify the original constraint-based metabolic networks to lead to growth-coupled production, where cell growth forces target metabolite production. However, in genome-scale models, finding strategies to simultaneously delete and add genes to induce growth-coupled production is challenging. This is particularly true when heavy computation is necessary due to numerous gene deletions and additions. In this study, we mathematically defined related problems, proved NP-hardness and/or NP-completeness, and developed an algorithm named RatGene that (1) automatically integrates multiple constraint-based metabolic networks, (2) identifies gene deletion-addition strategies by a growth-to-production ratio-based approach, and (3) eliminates redundant gene additions and deletions. The results of computational experiments demonstrated that the RatGene-based approach can significantly improve the success ratio for identifying the strategies for growth-coupled production. RatGene can facilitate a more rational approach to computational metabolic design for the production of useful substances using microorganisms by concurrently considering both gene deletions and additions.
Stability in Reaction Network Models via an Extension of the Next Generation Matrix Method
Authors: Florin Avram, Rim Adenane, Andrei D. Halanay, Matthew D. Johnston
Date: 2024-11-03 00:00:00
Tasks: Epidemiology

In this essay, we investigate some relations between Chemical Reaction Networks (CRN) and Mathematical Epidemiology (ME) and report on several pleasant surprises which we had simply by putting these two topics together. Firstly, we propose a definition of ME models as a subset of CRN models. Secondly, we review a fundamental stability result for boundary points, known in ME as the NGM method since it replaces the investigation of the Jacobian by that of a matrix whose origins lie in probability (the theory of branching processes). This important result seems to be little known outside of ME; even in ME, it has not been made clear before that the method gets sometimes the right answer, even though the conditions of the NGM theorem are not all satisfied. Thus, beyond the theorem, there is a heuristic approach, the validity conditions for which are not sufficiently understood. Thirdly, we show that some simple CRN models with absolute concentration robustness (ACR), are close qualitatively to simple ME models, in the sense that they have an unique disease free equilibrium, and a unique interior fixed point, and the latter enters the positive domain and becomes stable precisely when $R_0:=s_{dfe} \mathcal{R}=\frac{s_{dfe}}{s_e}>1.$ (where $s$ denotes the "ACR species"). Thus, for these "ME type models", a "relay phenomena" takes place: precisely when the DFE loses stability, a new fixed point enters the domain, and takes over. Last but not least, we offer in the associated GitHub repository https://github.com/adhalanay/epidemiology_crns a Mathematica package, Epid-CRN, which is addressed to researchers of both disciplines, and provide illustrative notebooks, which in particular solve a few minor open ME and CRN problems. Our package may also be used to study easy cases of analogue continuous time Markov chain (CTMC) ME and CRN models.
Homogenized $\textit{C. elegans}$ Neural Activity and Connectivity Data
Authors: Quilee Simeon, Anshul Kashyap, Konrad P Kording, Edward S Boyden
Date: 2024-11-18 00:00:00

There is renewed interest in modeling and understanding the nervous system of the nematode $\textit{Caenorhabditis elegans}$ ($\textit{C. elegans}$), as this small model system provides a path to bridge the gap between nervous system structure (connectivity) and function (physiology). However, existing physiology datasets, whether involving passive recording or stimulation, are in distinct formats, and connectome datasets require preprocessing before analysis can commence. Here we compile and homogenize datasets of neural activity and connectivity. Our neural activity dataset is derived from 12 $\textit{C. elegans}$ neuroimaging experiments, while our connectivity dataset is compiled from 9 connectome annotations based on 3 primary electron microscopy studies and 1 signal propagation study. Physiology datasets, collected under varying protocols, measure calcium fluorescence in labeled subsets of the worm's 300 neurons. Our preprocessing pipeline standardizes these datasets by consistently ordering labeled neurons and resampling traces to a common sampling rate, yielding recordings from approximately 900 worms and 250 uniquely labeled neurons. The connectome datasets, collected from electron microscopy reconstructions, represent the entire nervous system as a graph of connections. Our collection is accessible on HuggingFace, facilitating analysis of the structure-function relationship in biology using modern neural network architectures and enabling cross-lab and cross-animal comparisons.
Hierarchical Trait-State Model for Decoding Dyadic Social Interactions
Authors: Qianying Wu, Shigeki Nakauchi, Mohammad Shehata, Shinsuke Shimojo
Date: 2024-11-19 00:00:00
Tasks: Dimensionality Reduction, EEG, Electroencephalogram (EEG)

Traits are patterns of brain signals and behaviors that are stable over time but differ across individuals, whereas states are phasic patterns that vary over time, are influenced by the environment, yet oscillate around the traits. The quality of a social interaction depends on the traits and states of the interacting agents. However, it remains unclear how to decipher both traits and states from the same set of brain signals. To explore the hidden neural traits and states in relation to the behavioral ones during social interactions, we developed a pipeline to extract latent dimensions of the brain from electroencephalogram (EEG) data collected during a team flow task. Our pipeline involved two stages of dimensionality reduction: first, non-negative matrix factorization (NMF), followed by linear discriminant analysis (LDA). This pipeline resulted in an interpretable, seven-dimensional EEG latent space that revealed a trait-state hierarchical structure, with macro-segregation capturing neural traits and micro-segregation capturing neural states. Out of the seven latent dimensions, we found that three that significantly contributed to variations across individuals and task states. Using representational similarity analysis, we mapped the EEG latent space to a skill-cognition space, establishing a connection between hidden neural signatures and social interaction behaviors. Our method demonstrates the feasibility of representing both traits and states within a single model that correlates with changes in social behavior.
Chikungunya: The Silent Threat in the Shadows
Authors: Ambreen Talib, Rabbya Rayan Shah, Rameen Atique, Hafiza Arshi Saeed, Ayesha Haidar, Ayesha Nadeem, Areesha Naveed, Javeria Sharif, Abdul Samad
Date: 2024-11-19 00:00:00

Chikungunya virus (CHIKV) is one of the most relevant arboviruses affecting public health today. It belongs to the Togaviridae family and alphavirus genus, causing an arthritogenic disease known as Chikungunya fever (CHIKF). This multifaceted disease is distinguished from other arbovirus infections by intense arthralgia, which can persist for months or even years in some individuals. The virus has re-emerged as a global health threat in recent decades, originating in Africa and spreading across Asia and America, leading to widespread outbreaks affecting millions. Despite more than 50 years of research on CHIKV pathogenesis, no drugs or vaccines are available. Current management focuses on supportive care to alleviate symptoms and improve patient's quality of life. The ongoing threat posed by CHIKV highlights the need to understand its pathogenesis better. This review provides a comprehensive overview of CHIKV, focusing on host factors, vector-related factors, and complex viral genetic interactions. By exploring these intricate connections, we aim to offer insights that may lead to more effective strategies for preventing and managing this re-emerging global health threat.
A general relationship between extinction risk and carrying capacity
Authors: Thomas S Ball, Ben Balmford, Andrew Balmford, Daniele Rinaldo, Piero Visconti, Rhys Green
Date: 2024-11-20 00:00:00

Understanding the relationship between a populations probability of extinction and its carrying capacity frames conservation status assessments and guides efforts to understand and mitigate the ongoing biodiversity crisis. Despite this, our understanding of the mathematical form of this relationship remains limited. We conducted ~5 billion population viability assessments that jointly converge on a modified Gompertz curve. This pattern is consistent across >1700 distinct model populations, representing different breeding systems and widely varying rates of population growth, levels of environmental stochasticity, adult survival rate, age at first breeding, and initial population size. Analytical treatment of the underlying dynamics shows that few assumptions suffice to show that the relationship holds for any extant population subject to density-dependent growth. Finally, we discuss the implications of these results and consider the practical use of our findings by conservationists.
Computational and Experimental Exploration of Protein Fitness Landscapes: Navigating Smooth and Rugged Terrains
Authors: Mahakaran Sandhu, John Chen, Dana Matthews, Matthew A Spence, Sacha B Pulsford, Barnabas Gall, James Nichols, Nobuhiko Tokuriki, Colin J Jackson
Date: 2024-11-20 00:00:00

Proteins evolve through complex sequence spaces, with fitness landscapes serving as a conceptual framework that links sequence to function. Fitness landscapes can be smooth, where multiple similarly accessible evolutionary paths are available, or rugged, where the presence of multiple local fitness optima complicate evolution and prediction. Indeed, many proteins, especially those with complex functions or under multiple selection pressures, exist on rugged fitness landscapes. Here we discuss the theoretical framework that underpins our understanding of fitness landscapes, alongside recent work that has advanced our understanding - particularly the biophysical basis for smoothness versus ruggedness. Finally, we address the rapid advances that have been made in computational and experimental exploration and exploitation of fitness landscapes, and how these can identify efficient routes to protein optimization.
Estimating the tails of the spectrum of the Hessian of the log-likelihood for \textit{ab-initio} single-particle reconstruction in electron cryomicroscopy
Authors: Aaditya V. Rangan, Wai-Shing Tang, Pilar Cossio, Kexin Zhang, Nikolaus Grigorieff
Date: 2024-11-20 00:00:00
Tasks: Sensitivity

Electron cryomicroscopy (cryo-EM) is a technique in structural biology used to reconstruct accurate volumetric maps of molecules. One step of the cryo-EM pipeline involves solving an inverse-problem. This inverse-problem, referred to as \textit{ab-initio} single-particle reconstruction, takes as input a collection of 2d-images -- each a projection of a molecule from an unknown viewing-angle -- and attempts to reconstruct the 3d-volume representing the underlying molecular density. Most methods for solving this inverse-problem search for a solution which optimizes a posterior likelihood of generating the observed image-data, given the reconstructed volume. Within this framework, it is natural to study the Hessian of the log-likelihood: the eigenvectors and eigenvalues of the Hessian determine how the likelihood changes with respect to perturbations in the solution, and can give insight into the sensitivity of the solution to aspects of the input. In this paper we describe a simple strategy for estimating the smallest eigenvalues and eigenvectors (i.e., the `softest modes') of the Hessian of the log-likelihood for the \textit{ab-initio} single-particle reconstruction problem. This strategy involves rewriting the log-likelihood as a 3d-integral. This interpretation holds in the low-noise limit, as well as in many practical scenarios which allow for noise-marginalization. Once we have estimated the softest modes, we can use them to perform many kinds of sensitivity analysis. For example, we can determine which parts of the reconstructed volume are trustworthy, and which are unreliable, and how this unreliability might depend on the data-set and the imaging parameters. We believe that this kind of analysis can be used alongside more traditional strategies for sensitivity analysis, as well as in other applications, such as free-energy estimation.
bia-binder: A web-native cloud compute service for the bioimage analysis community
Authors: Craig T. Russell, Jean-Marie Burel, Awais Athar, Simon Li, Ugis Sarkans, Jason Swedlow, Alvis Brazma, Matthew Hartley, Virginie Uhlmann
Date: 2024-11-19 00:00:00

We introduce bia-binder (BioImage Archive Binder), an open-source, cloud-architectured, and web-based coding environment tailored to bioimage analysis that is freely accessible to all researchers. The service generates easy-to-use Jupyter Notebook coding environments hosted on EMBL-EBI's Embassy Cloud, which provides significant computational resources. The bia-binder architecture is free, open-source and publicly available for deployment. It features fast and direct access to images in the BioImage Archive, the Image Data Resource, and the BioStudies databases. We believe that this service can play a role in mitigating the current inequalities in access to scientific resources across academia. As bia-binder produces permanent links to compiled coding environments, we foresee the service to become widely-used within the community and enable exploratory research. bia-binder is built and deployed using helmsman and helm and released under the MIT licence. It can be accessed at binder.bioimagearchive.org and runs on any standard web browser.
Cancer incidence estimation from mortality data: a validation study within a population-based cancer registry
Authors: Daniel Redondo-Sánchez, Miguel Rodríguez-Barranco, Alberto Ameijide, Francisco J. Alonso, Pablo Fernández-Navarro, Jose Juan Jiménez-Moleón, María-José Sánchez
Date: 2024-11-19 00:00:00

We assessed the validity of one of the most frequently used methods to estimate cancer incidence, on the basis of cancer mortality data and the incidence-to-mortality ratio IMR, the IMR method. Using the previous 15 year cancer mortality time series, we derived the expected yearly number of cancer cases in the period 2004 to 2013 for six cancer sites for each sex. Generalized linear mixed models, including a polynomial function for the year of death and smoothing splines for age, were adjusted. Models were fitted under a Bayesian framework based on Markov chain Monte Carlo methods. The IMR method was applied to five scenarios reflecting different assumptions regarding the behavior of the IMR. We compared incident cases estimated with the IMR method to observed cases diagnosed in 2004 to 2013 in Granada. A goodness-of-fit GOF indicator was formulated to determine the best estimation scenario. The relative differences between the observed and predicted numbers of cancer cases were less than 10 percent for most cancer sites. The constant assumption for the IMR trend provided the best GOF for colon, rectal, lung, bladder, and stomach cancers in men and colon, rectum, breast, and corpus uteri in women.
A Review of Reinforcement Learning in Financial Applications
Authors: Yahui Bai, Yuhe Gao, Runzhe Wan, Sheng Zhang, Rui Song
Date: 2024-11-01 00:00:00
Tasks: Benchmarking, Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, there has been a growing trend of applying Reinforcement Learning (RL) in financial applications. This approach has shown great potential to solve decision-making tasks in finance. In this survey, we present a comprehensive study of the applications of RL in finance and conduct a series of meta-analyses to investigate the common themes in the literature, such as the factors that most significantly affect RL's performance compared to traditional methods. Moreover, we identify challenges including explainability, Markov Decision Process (MDP) modeling, and robustness that hinder the broader utilization of RL in the financial industry and discuss recent advancements in overcoming these challenges. Finally, we propose future research directions, such as benchmarking, contextual RL, multi-agent RL, and model-based RL to address these challenges and to further enhance the implementation of RL in finance.
Chat Bankman-Fried: an Exploration of LLM Alignment in Finance
Authors: Claudia Biancotti, Carolina Camassa, Andrea Coletta, Oliver Giudice, Aldo Glielmo
Date: 2024-11-01 00:00:00

Advancements in large language models (LLMs) have renewed concerns about AI alignment - the consistency between human and AI goals and values. As various jurisdictions enact legislation on AI safety, the concept of alignment must be defined and measured across different domains. This paper proposes an experimental framework to assess whether LLMs adhere to ethical and legal standards in the relatively unexplored context of finance. We prompt twelve LLMs to impersonate the CEO of a financial institution and test their willingness to misuse customer assets to repay outstanding corporate debt. Beginning with a baseline configuration, we adjust preferences, incentives and constraints, analyzing the impact of each adjustment with logistic regression. Our findings reveal significant heterogeneity in the baseline propensity for unethical behavior of LLMs. Factors such as risk aversion, profit expectations, and regulatory environment consistently influence misalignment in ways predicted by economic theory, although the magnitude of these effects varies across LLMs. This paper highlights both the benefits and limitations of simulation-based, ex post safety testing. While it can inform financial authorities and institutions aiming to ensure LLM safety, there is a clear trade-off between generality and cost.
Risk-Neutral Pricing Model of Uniswap Liquidity Providing Position: A Stopping Time Approach
Authors: Liang Hou, Hao Yu, Guosong Xu
Date: 2024-11-19 00:00:00
Tasks: Position

In this paper, we introduce a novel pricing model for Uniswap V3, built upon stochastic processes and the Martingale Stopping Theorem. This model innovatively frames the valuation of positions within Uniswap V3. We further conduct a numerical analysis and examine the sensitivities through Greek risk measures to elucidate the model's implications. The results underscore the model's significant academic contribution and its practical applicability for Uniswap liquidity providers, particularly in assessing risk exposure and guiding hedging strategies.