pwc-archive/papers-with-abstracts huggingface dataset:
Finest decomposition coarsening of reaction networks of biochemical systems
Authors: Bryan S. Hernandez, Juan Paolo C. Santos, Patrick Vincent N. Lubenia, Eduardo R. Mendoza
Date: 2024-12-05 00:00:00

Biochemical reaction networks are typically modeled by $\dfrac{dx}{dt}=N\cdot K(x)=Y\cdot I_a\cdot K(x)$, with $x$ and $K(x)$ as the concentration and rate vectors, respectively, and $N$, $Y$, and $I_a$ as the stoichiometric, molecularity, and incidence matrices, respectively. Steady states, which describe their long-term behaviors, are determined by solving $N\cdot K(x)=0$, while complex balanced steady states are found by solving $I_a \cdot K(x)=0$. To investigate these complex networks, decomposition techniques are important, in particular, for computing steady states. Previously, we identified a widespread property across many networks: the existence of independent and incidence-independent decompositions, characterized by the ability to directly sum the stoichiometric and incidence matrices of the subnetworks, respectively, to match those of the entire network. Here, we discover the ubiquitous property that we call the Finest Decomposition Coarsening (FDC), where the finest independent decomposition (FID) is a coarsening of the finest incidence-independent decomposition (FIID). To support the analysis of this property, we introduce a MATLAB package designed to compute both these decompositions. We then characterize the FDC property and its relationship to structural factors such as the invertibility of the molecularity matrix. We also introduce and characterize the Finest Decompositions Equality (FDE) property, where FIID equals FID. Notably, we show that all deficiency zero networks exhibit the FDE property. Furthermore, we establish important relationships of the FID and FIID with decomposition of the network into its connected components. Our results highlight the prevalence of the coarsening property in reaction networks and deepens the understanding of the algebraic structure and dynamics of biochemical networks.
Broker-Trader Partial Information Nash-Equilibria
Authors: Xuchen Wu, Sebastian Jaimungal
Date: 2024-12-23 00:00:00

We study partial information Nash equilibrium between a broker and an informed trader. In this setting, the informed trader, who possesses knowledge of a trading signal, trades multiple assets with the broker in a dealer market. Simultaneously, the broker offloads these assets in a lit exchange where their actions impact the asset prices. The broker, however, only observes aggregate prices and cannot distinguish between underlying trends and volatility. Both the broker and the informed trader aim to maximize their penalized expected wealth. Using convex analysis, we characterize the Nash equilibrium and demonstrate its existence and uniqueness. Furthermore, we establish that this equilibrium corresponds to the solution of a nonstandard system of forward-backward stochastic differential equations (FBSDEs) that involves the two differing filtrations. For short enough time horizons, we prove that a unique solution of this system exists. Finally, under quite general assumptions, we show that the solution to the FBSDE system admits a polynomial approximation in the strength of the transient impact to arbitrary order, and prove that the error is controlled.
A Limit Order Book Model for High Frequency Trading with Rough Volatility
Authors: Yun Chen-Shue, Yukun Li, Jiongmin Yong
Date: 2024-12-22 00:00:00

We introduce a model for limit order book of a certain security with two main features: First, both the limit orders and market orders for the given asset are allowed to appear and interact with each other. Second, the high frequency trading activities are allowed and described by the scaling limit of nearly-unstable multi-dimensional Hawkes processes with power law decay. The model has been derived as a stochastic partial differential equation (SPDE, for short), under certain intuitive identifications. Its diffusion coefficient is determined by a Volterra integral equation driven by a Hawkes process, whose Hurst exponent is less than 1/2 (so that the relevant process is negatively correlated). As a result, the volatility path of the SPDE is rougher than that driven by a (standard) Brownian motion. The well-posedness follows from a result in literature. Hence, a foundation is laid down for further studies in this direction.
Physical principles underpinning molecular-level protein evolution
Authors: J. A. Vila
Date: 2024-12-19 00:00:00

Since protein mutations are the main driving force of evolution at the molecular level, a proper analysis of them (and the factors controlling them) will enable us to find a response to several crucial queries in evolutionary biology. Among them, we highlight the following: At the molecular level, what factors determine whether protein evolution is repeatable? Aiming at finding an answer to this and several other significant questions behind protein evolvability and the factors that control it (including, but not limited to, the proteins' robustness, the evolutionary pathways, the number of ancestors, the epistasis, the post-translational modifications, and the location and order of mutations) we distinguish two evolutionary models in our analysis: convergent and divergent, based on whether or not a target sequence needs to be reached after n-mutational steps beginning with a wild-type protein sequence (from an unknown ancestor). Preliminary results suggest (regardless of whether the evolution is convergent or divergent) a tight relationship between the thermodynamic hypothesis (or Anfinsen's dogma) and protein evolution at the molecular level. This conjecture will allow us to uncover how fundamental physical principles guide protein evolution, and to gain a deeper grasp of mutationally driven evolutionary processes and the factors that influence them. Breaking down complex evolutionary problems into manageable pieces (without compromising the vision of the problem as a whole) could lead to effective solutions to critical evolutionary biology challenges, paving the way for further progress in this field.
Phenotype-structuring of non-local kinetic models of cell migration driven by environmental sensing
Authors: Tommaso Lorenzi, Nadia Loy, Chiara Villa
Date: 2024-12-20 00:00:00
Tasks: Form

The capability of cells to form surface extensions to non-locally probe the surrounding environment plays a key role in cell migration. The existing mathematical models for migration of cell populations driven by this non-local form of environmental sensing rely on the simplifying assumption that cells in the population share the same cytoskeletal properties, and thus form surface extensions of the same size. To overcome this simplification, we develop a kinetic modelling framework wherein a population of migrating cells is structured by a continuous phenotypic variable that captures variability in structural properties of the cytoskeleton. This framework provides a multiscale representation of cell migration, from single-cell dynamics to population-level behaviours, as we start with a microscopic model that describes the dynamics of single cells in terms of stochastic processes. Next, we formally derive the mesoscopic counterpart of this model, which consists of a phenotype-structured kinetic equation that features a phenotype-dependent non-locality. Then, considering an appropriately rescaled version of this kinetic equation, we formally derive the corresponding macroscopic model, which takes the form of a partial differential equation for the cell number density. To validate the formal procedures employed to derive the macroscopic model from the microscopic model, through the mesoscopic one, we first compare the results of numerical simulations of the two models. We then compare numerical solutions of the macroscopic model with the results of cell locomotion assays, to test the ability of the model to recapitulate qualitative features of experimental observations.
A CNN Approach to Polygenic Risk Prediction of Kidney Stone Formation
Authors: Amr Salem, Anirban Mondal
Date: 2024-12-23 00:00:00

Kidney stones are a common and debilitating health issue, and genetic factors play a crucial role in determining susceptibility. While Genome-Wide Association Studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) linked to kidney stone risk, translating these findings into effective clinical tools remains a challenge. In this study, we explore the potential of deep learning techniques, particularly Convolutional Neural Networks (CNNs), to enhance Polygenic Risk Score (PRS) models for predicting kidney stone susceptibility. Using a curated dataset of kidney stone-associated SNPs from a recent GWAS, we apply CNNs to model non-linear genetic interactions and improve prediction accuracy. Our approach includes SNP selection, genotype filtering, and model training using a dataset of 560 individuals, divided into training and testing subsets. We compare our CNN-based model with traditional machine learning models, including logistic regression, random forest, and support vector machines, demonstrating that the CNN outperforms these models in terms of classification accuracy and ROC-AUC. The proposed model achieved a validation accuracy of 62%, with an ROC-AUC of 0.68, suggesting its potential for improving genetic-based risk prediction for kidney stones. This study contributes to the growing field of genomics-driven precision medicine and highlights the promise of deep learning in enhancing PRS models for complex diseases.
A unified model for the origins of spongiform degeneration and other neuropathological features in prion diseases
Authors: Gerold Schmitt-Ulms, Xinzhu Wang, Joel Watts, Stephanie Booth, Holger Wille, Wenda Zhao
Date: 2024-12-21 00:00:00

Decades after their initial observation in prion-infected brain tissues, the identities of virus-like dense particles, varicose tubules, and oval bodies containing parallel bands and fibrils have remained elusive. Our recent work revealed that a phenotype of dilation of the endoplasmic reticulum (ER), most notable for the perinuclear space (PNS), contributes to spongiform degeneration. To assess the significance of this phenotype for the etiology of prion diseases, we explored whether it can be functionally linked to other neuropathological hallmarks observed in these diseases, as this would indicate it to be a central event. Having surveyed the neuropathological record and other distant literature niches, we propose a model in which pathogenic forms of the prion protein poison raft domains, including essential Na+, K+-ATPases (NKAs) embedded within them, thereby triggering an ER-centered cellular rescue program coordinated by the unfolded protein response (UPR). The execution of this program stalls general protein synthesis, causing the deterioration of synaptic spines. As the disease progresses, cells selectively increase sterol biosynthesis, along with ribosome and ER biogenesis. These adaptive rescue attempts cause morphological changes to the ER which manifest as ER dilation or ER hypertrophy in a manner that is influenced by Ca2+ influx into the cell. The nuclear-to-cytoplasmic transport of mRNAs and tRNAs interrupts in late stage disease, thereby depriving ribosomes of supplies and inducing them to aggregate into a paracrystalline form. In support of this model, we share previously reported data, whose features are consistent with the interpretation that 1) the phenotype of ER dilation is observed in major prion diseases, 2) varicose tubules and oval bodies represent ER hypertrophy, and 3) virus-like dense particles are paracrystalline aggregates of inactive ribosomes.
Learning Biomolecular Models using Signal Temporal Logic
Authors: Hanna Krasowski, Eric Palanques-Tost, Calin Belta, Murat Arcak
Date: 2024-12-05 00:00:00

Modeling dynamical biological systems is key for understanding, predicting, and controlling complex biological behaviors. Traditional methods for identifying governing equations, such as ordinary differential equations (ODEs), typically require extensive quantitative data, which is often scarce in biological systems due to experimental limitations. To address this challenge, we introduce an approach that determines biomolecular models from qualitative system behaviors expressed as Signal Temporal Logic (STL) statements, which are naturally suited to translate expert knowledge into computationally tractable specifications. Our method represents the biological network as a graph, where edges represent interactions between species, and uses a genetic algorithm to identify the graph. To infer the parameters of the ODEs modeling the interactions, we propose a gradient-based algorithm. On a numerical example, we evaluate two loss functions using STL robustness and analyze different initialization techniques to improve the convergence of the approach.
Comparison of Spatiotemporal Characteristics of Eye Movements in Non-experts and the Skill Transfer Effects of Gaze Guidance and Annotation Guidance
Authors: Shota Nishijima, Asuka Takai
Date: 2024-12-23 00:00:00

Methods for converting the tacit knowledge of experts into explicit knowledge have drawn increasing attention. Gaze data has emerged as a valuable approach in this effort. However, the effective transfer of tacit knowledge remains a challenge. No studies have directly compared the effects of gaze-based and annotation-based guidance or adequately examined their impacts on skill improvement after instruction. This study examined the effects of gaze and annotation guidance on the spatiotemporal characteristics of eye movements and the skill transfer of expert evaluation techniques in karate kata performances. 28 non-expert participants were assigned to three groups: a gaze guidance group, an annotation guidance group, and a control group. Participants were presented with instructional slideshows based on the expert's gaze data and annotations. Before and after instruction, participants were asked to evaluate karate kata performance videos performed by practitioners with different skill levels. The results showed that the annotation guidance group exhibited an effect of directing gaze toward the presented areas of focus, with a trend of increased total number of fixation areas. On the other hand, while the gaze guidance group was not encouraged to make fixations on multiple areas, the possibility of promoting peripheral vision was inferred based on measurements from the eye-tracking system. Regarding ranking results, 71.4% of participants in the gaze guidance group showed an improvement in ranking skills, with a trend toward better scoring ability compared to the other groups. This demonstrates the necessity of selecting appropriate methods based on instructional goals to transfer tacit knowledge effectively. Gaze-based instructional methods are expected to be versatile and applicable to karate kata and fields such as industry, medicine, and other sports.
How connection probability shapes fluctuations of neural population dynamics
Authors: Nils E. Greven, Jonas Ranft, Tilo Schwalger
Date: 2024-12-20 00:00:00

Mean-field models of neuronal populations in the brain have proven extremely useful to understand network dynamics and response to stimuli, but these models generally lack a faithful description of the fluctuations in the biologically relevant case of finite network size and connection probabilities $p<1$ (non-full connectivity). To gain insight into the different fluctuation mechanisms underlying the neural variability of populations of spiking neurons, we derive and analyze a stochastic mean-field model for finite-size networks of Poisson neurons with random, non-full connectivity, external noise and disordered mean inputs. We treat the quenched disorder of the connectivity by an annealed approximation that enables a reduction to a low-dimensional closed system of coupled Langevin equations for the mean and variance of the neuronal membrane potentials as well as a variable capturing finite-size fluctuations arising specifically in the case $p<1$. Comparing to microscopic simulations, we find that the mesoscopic model describes the fluctuations and nonlinearities well and outperforms previous mesoscopic models that neglected the recurrent noise effect caused by the non-full connectivity. This effect can be analytically understood by a softening of the effective nonlinearity and the multiplicative character of finite-size spiking noise. The mesoscopic theory shows that quenched disorder can stabilize the asynchronous state, and it correctly predicts large quantitiative and non-trivial qualitative effects of connection probability on the variance of the population firing rate and its dependence on stimulus strength. Our theory thus elucidates how disordered connectivity shapes nonlinear dynamics and fluctuations of neural populations at the mesoscopic scale and showcases a useful mean-field method to treat non-full connectivity in finite-size, spiking neural networks.
A Proposal for Extending the Common Model of Cognition to Emotion
Authors: Paul S. Rosenbloom, John E. Laird, Christian Lebiere, Andrea Stocco, Richard H. Granger, Christian Huyck
Date: 2024-12-19 00:00:00

Cognition and emotion must be partnered in any complete model of a humanlike mind. This article proposes an extension to the Common Model of Cognition -- a developing consensus concerning what is required in such a mind -- for emotion that includes a linked pair of modules for emotion and metacognitive assessment, plus pervasive connections between these two new modules and the Common Model's existing modules and links.
Investigation of phytochemicals, spectral properties, anticancer, antidiabetic, and antimicrobial activities of chosen Ayurvedic remedies
Authors: T. H. Mohamed Ahadu Shareef, Irfan Navabshan, M Mohamed Divan Masood, T. Eswara Yuvaraj, A. Sherif
Date: 2024-12-22 00:00:00

This study examines the phytochemical characteristics of Ayurvedic products. An analysis was performed on Kottakkal Ayurveda Triphala (T), Kottakkal Ayurveda Hinguvachadi Churnam (H), and Kottakkal Ayurveda Jirakadyarishtam (J) using GC-MS and LC-MS techniques to determine their bioactive constituents, while also assessing their antimicrobial, docking, anticancer, and anti-diabetic activities. The GC-MS analysis identified 30, 45, and 8 chemical components in Kottakkal Ayurveda Triphala (T), Kottakkal Ayurveda Hinguvachadi Churnam (H), and Kottakkal Ayurveda Jirakadyarishtam (J), respectively. The LC-MS analysis produced 15, 20, and 16 peaks for Kottakkal Ayurveda Triphala (T), Kottakkal Ayurveda Hinguvachadi Churnam (H), and Kottakkal Ayurveda Jirakadyarishtam (J), with m/z values of 982, 981, 972, and 933; 987, 985, 974, and 945; and 969, 965, 951, and 941, respectively, confirming their precision. Moreover, characterization of the Ayurvedic products was carried out using FT-IR, UV-vis, and 1H-NMR spectroscopy to identify significant functional groups and chemical substances. Kottakkal Ayurveda Triphala (T) was evaluated for antibacterial activity against Gram-positive bacteria (Streptococcus pneumoniae and Staphylococcus aureus) along with Gram-negative bacteria (Escherichia coli and Klebsiella pneumoniae), yielding a P value of 0.0650 (P < 0.0001). Both Kottakkal Ayurveda Hinguvachadi Churnam (H) and Kottakkal Ayurveda Jirakadyarishtam (J) were subjected to analysis for their effectiveness against Aspergillus niger and Aspergillus fumigatus, also revealing a P value within the acceptable range of 0.0650 (P < 0.0001). The anti-diabetic properties of Kottakkal Ayurveda Triphala (T) were assessed using the {\alpha}-glucosidase inhibitory method, which exhibited a significant inhibitory effect on {\alpha}-glucosidase, resulting in an average P value of 0.001 (P < 0.0001).
Should public health policy exempt cases with low viral load from isolation during an epidemic?: a modelling study
Authors: Jiahao Diao, Rebecca H. Chisholm, Nicholas Geard, James M. McCaw
Date: 2024-12-23 00:00:00

During the COVID-19 pandemic, case isolation emerged as a key non-pharmaceutical intervention in pandemic response. Its effectiveness hinges on the timing of isolation, which is often dictated by the onset of contagiousness. While minimizing isolation delays is crucial, the impact of evolving epidemic dynamics, such as changes in viral load distributions among cases, is less understood. These dynamics could inform more efficient isolation strategies. We developed a multi-scale agent-based model to assess isolation policies that consider viral loads. Our model compares the outcomes of universal isolation with strategies that exempt low viral load cases post-peak. We found that most low viral load cases identified after the peak are less contagious, raising the question of their need for isolation. Our analysis reveals that exempting these individuals slightly increases new infections and, unexpectedly, reduces efficiency, defined as infections averted per isolation. These results advise caution with adaptive isolation policies. Our flexible model can evaluate complex strategies, aiding future public health planning.
Weak signals, strong debates: Density dependence and population regulation through the lens of model uncertainty
Authors: Evan C. Johnson
Date: 2024-12-23 00:00:00

Ecologists have long argued about the strength of density dependence and population regulation, respectively defined as the short-term and long-term rates of return to equilibrium. Here, I give three arguments for the intractability of population regulation. First, the ecological literature flip-flops on the strength of evidence for population regulation; by simple induction, population regulation should remain uncertain. Second, there is an analogous debate in economics about whether shocks to Gross Domestic Product have transient or permanent effects. This literature is extensive and sophisticated, yet there is no consensus, implying that more research will not resolve the issue in ecology. Third, using a variety of time series models and the Global Population Dynamics Database, I show that one's conclusions about population regulation are almost entirely dependent on model structure. This insurmountable model uncertainty explains why the strength of regulation is unresolved despite decades of research. However, it is possible to achieve the more modest goal of estimating density dependence. I introduce a novel measure of density dependence -- the effective autoregressive parameter -- which is conceptually intuitive and easy to calculate with simulations. The strength of density dependence varies significantly across populations, with an average that can be characterized as moderate: perturbations have an average half-life of 3 years. Rather than a universal balance of nature, stability varies widely across populations in ways that correlate with life history and taxonomy.