pwc-archive/papers-with-abstracts huggingface dataset:
Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
Authors: Devlina Chakravarty, Myeongsang Lee, Lauren L. Porter
Date: 2024-10-18 00:00:00
Tasks: Protein Structure Prediction

In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predicted at all. Here, we review three blind spots that alternative conformations reveal about AF-based protein structure prediction. First, proteins that assume conformations distinct from their training-set homologs can be mispredicted. Second, AF overrelies on its training set to predict alternative conformations. Third, degeneracies in pairwise representations can lead to high-confidence predictions inconsistent with experiment. These weaknesses suggest approaches to predict alternative folds more reliably.
Functionality Determinants and Pore-level Quantitative Structure-Activity Relationship (QSAR) Approach for Water Permeation Rate in Aquaporins
Authors: Juan José Galano-Frutos, Luca Bergamasco, Paolo Vigo, Matteo Morciano, Matteo Fasano, Davide Pirolli, Eliodoro Chiavazzo, Maria Cristina de Rosa
Date: 2024-10-18 00:00:00

Aquaporins play a key role for the regulation of water transport and solute selectivity in biological cells and tissues. Due to their unique properties, during the last years aquaporins (AQPs) have attracted increasing interest for their use in the development of biomimetic membranes for water filtration. Motivated by this fascinating application, and the need for reliable methodologies to rationalize the design of AQPs or other biomimetic molecules with targeted filtration properties, we conduct in this work an all-round analysis of their structural features to provide crucial insights into the key determinants of functionality of these relevant biomolecules. Particularly, we analyze -- on a selected set of experimentally-resolved AQP structures -- the composition and peculiarities of their NPA and Selectivity Filter (SF) motifs, the overall structural and sequence similarities between them, their pore shape and the residue-level pore-lining composition. Then, AQPs' pore structural and physico-chemical descriptors are put into correlation with available data on experimental permeation rate, within the context of a pore-level QSAR modeling. The approach, presented here as a methodological proof of concept, enables the identification of key -- potentially novel -- features related to water permeability, which could contribute to a deeper residue-level understanding of AQP filtering properties. Potential beneficiary areas include the rational design of optimized AQP-based biomimetic membranes and research into the molecular mechanisms underlying AQP-related diseases.
The Interplay Between Physical Activity, Protein Consumption, and Sleep Quality in Muscle Protein Synthesis
Authors: Ayush Devkota, Manakamana Gautam, Uttam Dhakal, Suman Devkota, Gaurav Kumar Gupta, Ujjwal Nepal, Amey Dinesh Dhuru, Aniket Kumar Singh
Date: 2024-10-21 00:00:00
Tasks: Sleep Quality

This systematic review examines the synergistic and individual influences of resistance exercise, dietary protein supplementation, and sleep/recovery on muscle protein synthesis (MPS). Electronic databases such as Scopus, Google Scholar, and Web of Science were extensively used. Studies were selected based on relevance to the criteria and were ensured to be directly applicable to the objectives. Research indicates that a protein dose of 20 to 25 grams maximally stimulates MPS post-resistance training. It is observed that physically frail individuals aged 76 to 92 and middle-aged adults aged 62 to 74 have lower mixed muscle protein synthetic rates than individuals aged 20 to 32. High-whey protein and leucine-enriched supplements enhance MPS more efficiently than standard dairy products in older adults engaged in resistance programs. Similarly, protein intake before sleep boosts overnight MPS rates, which helps prevent muscle loss associated with sleep debt, exercise-induced damage, and muscle-wasting conditions like sarcopenia and cachexia. Resistance exercise is a functional intervention to achieve muscular adaptation and improve function. Future research should focus on variables such as fluctuating fitness levels, age groups, genetics, and lifestyle factors to generate more accurate and beneficial results.
DNA Language Model and Interpretable Graph Neural Network Identify Genes and Pathways Involved in Rare Diseases
Authors: Ali Saadat, Jacques Fellay
Date: 2024-10-20 00:00:00
Tasks: Graph Neural Network, Language Modeling, Language Modelling

Identification of causal genes and pathways is a critical step for understanding the genetic underpinnings of rare diseases. We propose novel approaches to gene prioritization and pathway identification using DNA language model, graph neural networks, and genetic algorithm. Using HyenaDNA, a long-range genomic foundation model, we generated dynamic gene embeddings that reflect changes caused by deleterious variants. These gene embeddings were then utilized to identify candidate genes and pathways. We validated our method on a cohort of rare disease patients with partially known genetic diagnosis, demonstrating the re-identification of known causal genes and pathways and the detection of novel candidates. These findings have implications for the prevention and treatment of rare diseases by enabling targeted identification of new drug targets and therapeutic pathways.
Spike-timing-dependent plasticity and random inputs shape interspike interval regularity of model STN neurons
Authors: Thoa Thieu, Roderick Melnik
Date: 2024-10-21 00:00:00

Neuronal oscillations are closely related to the symptoms of Parkinson's disease (PD). In this study, we explore how random fluctuations (or "stochastic inputs") affect these oscillations in brain states, which reflect the collective activity of interconnected neurons. These random inputs are modeled in the context of the subthalamic nucleus (STN), a brain region implicated in PD, and their interaction with synaptic dynamics and spike-timing-dependent plasticity (STDP) in both healthy and PD-affected neurons. Specifically, we investigate the effects of random synaptic inputs and their correlations on the membrane potential of STN neurons. Our results show that these random inputs significantly influence the firing patterns of STN neurons, both in healthy cells and in those affected by PD under deep brain stimulation (DBS) treatment. We also find that STDP increases the regularity of the interspike intervals (ISI) in spike trains of output neurons. However, the introduction of random refractory periods and fluctuating input currents can induce greater irregularity in the spike trains. Furthermore, when random inputs and STDP are combined, the correlation between the activity of different neurons increases. These findings suggest that the stochastic dynamics of STN neurons, in conjunction with STDP, could offer insights into the mechanisms underlying PD symptoms and their potential management.
How EEG preprocessing shapes decoding performance
Authors: Roman Kessler, Alexander Enge, Michael A. Skeide
Date: 2024-10-18 00:00:00
Tasks: Binary Classification, EEG, ERP

EEG preprocessing varies widely between studies, but its impact on classification performance remains poorly understood. To address this gap, we analyzed seven experiments with 40 participants drawn from the public ERP CORE dataset. We systematically varied key preprocessing steps, such as filtering, referencing, baseline interval, detrending, and multiple artifact correction steps. Then we performed trial-wise binary classification (i.e., decoding) using neural networks (EEGNet), or time-resolved logistic regressions. Our findings demonstrate that preprocessing choices influenced decoding performance considerably. All artifact correction steps reduced decoding performance across experiments and models, while higher high-pass filter cutoffs consistently increased decoding performance. For EEGNet, baseline correction further increased decoding performance, and for time-resolved classifiers, linear detrending, and lower low-pass filter cutoffs increased decoding performance. The influence of other preprocessing choices was specific for each experiment or event-related potential component. The current results underline the importance of carefully selecting preprocessing steps for EEG-based decoding. While uncorrected artifacts may increase decoding performance, this comes at the expense of interpretability and model validity, as the model may exploit structured noise rather than the neural signal.
Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios
Authors: Shihan Ma, Bo Hu, Tianyu Jia, Alexander Kenneth Clarke, Blanka Zicher, Arnault H. Caillet, Dario Farina, Jose C. Principe
Date: 2024-10-04 00:00:00
Tasks: EEG, Electromyography (EMG)

The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroencephalography (EEG) and electromyography (EMG) recordings. However, current approaches for capturing high-level and contextual connectivity between these recordings have important limitations. Here, we present a novel application of statistical dependence estimators based on orthonormal decomposition of density ratios to model the relationship between cortical and muscle oscillations. Our method extends from traditional scalar-valued measures by learning eigenvalues, eigenfunctions, and projection spaces of density ratios from realizations of the signal, addressing the interpretability, scalability, and local temporal dependence of cortico-muscular connectivity. We experimentally demonstrate that eigenfunctions learned from cortico-muscular connectivity can accurately classify movements and subjects. Moreover, they reveal channel and temporal dependencies that confirm the activation of specific EEG channels during movement. Our code is available at https://github.com/bohu615/corticomuscular-eigen-encoder.
Mathematical modelling to inform outbreak response vaccination
Authors: Manjari Shankar, Anna-Maria Hartner, Callum R. K. Arnold, Ezra Gayawan, Hyolim Kang, Jong-Hoon Kim, Gemma Nedjati Gilani, Anne Cori, Han Fu, Mark Jit, Rudzani Muloiwa, Allison Portnoy, Caroline Trotter, Katy A. M. Gaythorpe
Date: 2024-10-17 00:00:00
Tasks: Decision Making

Mathematical models are established tools to assist in outbreak response. They help characterise complex patterns in disease spread, simulate control options to assist public health authorities in decision-making, and longer-term operational and financial planning. In the context of vaccine-preventable diseases (VPDs), vaccines are one of the most-cost effective outbreak response interventions, with the potential to avert significant morbidity and mortality through timely delivery. Models can contribute to the design of vaccine response by investigating the importance of timeliness, identifying high-risk areas, prioritising the use of limited vaccine supply, highlighting surveillance gaps and reporting, and determining the short- and long-term benefits. In this review, we examine how models have been used to inform vaccine response for 10 VPDs, and provide additional insights into the challenges of outbreak response modelling, such as data gaps, key vaccine-specific considerations, and communication between modellers and stakeholders. We illustrate that while models are key to policy-oriented outbreak vaccine response, they can only be as good as the surveillance data that inform them.
Epidemic waves for a two-group SIRS model with double nonlocal effects in a patchy environment
Authors: Chufen Wu, Yonghui Xia, Jianshe Yu
Date: 2024-10-17 00:00:00

We propose a lattice dynamical system that arises in a discrete diffusive two-group epidemic model with latency in a patchy environment. The model considers the SIS form and latency of the disease in group 1, while the SIR form without latency of the disease in group 2. The system includes double nonlocal effects, one effect is the nonlocal diffusion of individuals in isolated patches or niches, while the other effect is the distributed transmission delay representing the incubation of the disease. We demonstrate that there is a threshold value $c^*$ that can determine the persistence or disappearance of the disease. If $c\geq c^*$, then there is an epidemic wave connecting the disease-free equilibrium and endemic equilibrium. In this case, the disease will evolve to endemic. If $0<c<c^*$, then the disease will die out.
Turing chemotactic instability in an HIV model
Authors: Florinda Capone, Roberta De Luca, Vincenzo Luongo
Date: 2024-10-12 00:00:00
Tasks: model

A ternary reaction-diffusion model for early HIV infection dynamics, incorporating logistic growth of target cells, is introduced. According to in vitro and in vivo studies, random movement of target cells, infected cells, and virions and a chemotactic attraction of target cells by cytokines, are included. The research explores the existence of disease-free and coexistence equilibria, conducting linear stability analyses for homogeneous and heterogeneous scenarios. Specifically, conditions for chemotactic-self diffusion instability of the endemic equilibrium are found, indicating that Turing patterns may emerge when the chemotactic effect surpasses a critical threshold. This threshold is lower than in models without logistic growth of target cells, suggesting that the logistic model provides better insights into infection hot spots in the early stages. The location and shape of these patterns, crucial for developing infection control strategies, are investigated using weakly nonlinear analysis and demonstrated through numerical simulations.
Interpreting Microbiome Relative Abundance Data Using Symbolic Regression
Authors: Swagatam Haldar, Christoph Stein-Thoeringer, Vadim Borisov
Date: 2024-10-18 00:00:00
Tasks: Diagnostic, Knowledge Distillation, regression, Symbolic Regression

Understanding the complex interactions within the microbiome is crucial for developing effective diagnostic and therapeutic strategies. Traditional machine learning models often lack interpretability, which is essential for clinical and biological insights. This paper explores the application of symbolic regression (SR) to microbiome relative abundance data, with a focus on colorectal cancer (CRC). SR, known for its high interpretability, is compared against traditional machine learning models, e.g., random forest, gradient boosting decision trees. These models are evaluated based on performance metrics such as F1 score and accuracy. We utilize 71 studies encompassing, from various cohorts, over 10,000 samples across 749 species features. Our results indicate that SR not only competes reasonably well in terms of predictive performance, but also excels in model interpretability. SR provides explicit mathematical expressions that offer insights into the biological relationships within the microbiome, a crucial advantage for clinical and biological interpretation. Our experiments also show that SR can help understand complex models like XGBoost via knowledge distillation. To aid in reproducibility and further research, we have made the code openly available at https://github.com/swag2198/microbiome-symbolic-regression .
Muscle coactivation primes the nervous system for fast and task-dependent feedback control
Authors: Philipp Maurus, Daniel P. Armstrong, Stephen H. Scott, Tyler Cluff
Date: 2024-10-21 00:00:00

Humans and other animals coactivate agonist and antagonist muscles in many motor actions. Increases in muscle coactivation are thought to leverage viscoelastic properties of skeletal muscles to provide resistance against limb motion. However, coactivation also emerges in scenarios where it seems paradoxical because the goal is not to resist limb motion but instead to rapidly mobilize the limb(s) or body to launch or correct movements. Here, we present a new perspective on muscle coactivation: to prime the nervous system for fast, task-dependent responses to sensory stimuli. We review distributed neural control mechanisms that may allow the healthy nervous system to leverage muscle coactivation to produce fast and flexible responses to sensory feedback.
Dynamic Factor Allocation Leveraging Regime-Switching Signals
Authors: Yizhan Shu, John M. Mulvey
Date: 2024-10-18 00:00:00

This article explores dynamic factor allocation by analyzing the cyclical performance of factors through regime analysis. The authors focus on a U.S. equity investment universe comprising seven long-only indices representing the market and six style factors: value, size, momentum, quality, low volatility, and growth. Their approach integrates factor-specific regime inferences of each factor index's active performance relative to the market into the Black-Litterman model to construct a fully-invested, long-only multi-factor portfolio. First, the authors apply the sparse jump model (SJM) to identify bull and bear market regimes for individual factors, using a feature set based on risk and return measures from historical factor active returns, as well as variables reflecting the broader market environment. The regimes identified by the SJM exhibit enhanced stability and interpretability compared to traditional methods. A hypothetical single-factor long-short strategy is then used to assess these regime inferences and fine-tune hyperparameters, resulting in a positive Sharpe ratio of this strategy across all factors with low correlation among them. These regime inferences are then incorporated into the Black-Litterman framework to dynamically adjust allocations among the seven indices, with an equally weighted (EW) portfolio serving as the benchmark. Empirical results show that the constructed multi-factor portfolio significantly improves the information ratio (IR) relative to the market, raising it from just 0.05 for the EW benchmark to approximately 0.4. When measured relative to the EW benchmark itself, the dynamic allocation achieves an IR of around 0.4 to 0.5. The strategy also enhances absolute portfolio performance across key metrics such as the Sharpe ratio and maximum drawdown.
Reinforcement Learning in Non-Markov Market-Making
Authors: Luca Lalor, Anatoliy Swishchuk
Date: 2024-10-18 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We develop a deep reinforcement learning (RL) framework for an optimal market-making (MM) trading problem, specifically focusing on price processes with semi-Markov and Hawkes Jump-Diffusion dynamics. We begin by discussing the basics of RL and the deep RL framework used, where we deployed the state-of-the-art Soft Actor-Critic (SAC) algorithm for the deep learning part. The SAC algorithm is an off-policy entropy maximization algorithm more suitable for tackling complex, high-dimensional problems with continuous state and action spaces like in optimal market-making (MM). We introduce the optimal MM problem considered, where we detail all the deterministic and stochastic processes that go into setting up an environment for simulating this strategy. Here we also give an in-depth overview of the jump-diffusion pricing dynamics used, our method for dealing with adverse selection within the limit order book, and we highlight the working parts of our optimization problem. Next, we discuss training and testing results, where we give visuals of how important deterministic and stochastic processes such as the bid/ask, trade executions, inventory, and the reward function evolved. We include a discussion on the limitations of these results, which are important points to note for most diffusion models in this setting.
Decentralized Finance: Impact on Financial Services and required DeFi Literacy in 2034
Authors: Daniel Liebau
Date: 2024-10-18 00:00:00
Tasks: Management

This study explores the potential future impact of Decentralized Finance (DeFi) on the financial services industry and the competencies financial professionals will require in a decade from now, in 2034. Using a survey of 109 experts from industry, academia, and regulatory bodies, the research highlights growing confidence in DeFi's potential to reshape critical areas such as risk management and operations. Over 40% of respondents anticipate high levels of DeFi adoption by 2034. However, the study identifies critical issues that need to be addressed, particularly in the areas of data management & privacy, and security. These concerns, alongside regulatory challenges, underscore the need for financial institutions to prepare carefully. The findings also suggest that strategic competencies, sector-specific domain expertise, and technological skills will become increasingly vital. The insights offered are valuable for regulators, policymakers, and industry professionals, emphasizing the need for continuous upskilling to remain competitive in an evolving financial services landscape.
Corporate Non-Disclosure Disputes: equilibrium settlement where increasing legal liability encourages voluntary disclosures
Authors: Miles B. Gietzmann, Adam J. Ostaszewski
Date: 2024-10-02 00:00:00
Tasks: Management

How should a court resolve a shareholder-management dispute after an unexpected price drop, when it is suspected that at an earlier time management chose not to update (disclose to) the market about a material event that was privately observed? An earlier fundamental result in this area (Dye, 2017) has shown that if the court chooses to make public that it will increase awards of damages to try and deter non-disclosure, then this may have the perverse effect that management may rationally choose to disclose less. Schantl and Wagenhofer (2024) call this the pure-insurance effect shareholders receive from higher damages payments. They show that the result may be relaxed if management also face a fixed exogenous reputational cost from non-disclosure. In this research we probe the increased-damages versus reduced-disclosure result via a different route. We introduce a dynamic continuous-time model of management's equilibrium disclosure decision and show that as awards of damages increase this has in a dynamic setting a hitherto unrecognized effect: management rationally switch their disclosure strategy. We characterize the range of damage awards, which we term the legal consistency zone, in which increased awards of damages evoke an endogenous increase in voluntary disclosure.