pwc-archive/papers-with-abstracts huggingface dataset:
Representation of forward performance criteria with random endowment via FBSDE and application to forward optimized certainty equivalent
Authors: Gechun Liang, Yifan Sun, Thaleia Zariphopoulou
Date: 2023-12-29 00:00:00
Tasks: Stochastic Optimization

We extend the notion of forward performance criteria to settings with random endowment in incomplete markets. Building on these results, we introduce and develop the novel concept of forward optimized certainty equivalent (forward OCE), which offers a genuinely dynamic valuation mechanism that accommodates progressively adaptive market model updates, stochastic risk preferences, and incoming claims with arbitrary maturities. In parallel, we develop a new methodology to analyze the emerging stochastic optimization problems by directly studying the candidate optimal control processes for both the primal and dual problems. Specifically, we derive two new systems of forward-backward stochastic differential equations (FBSDEs) and establish necessary and sufficient conditions for optimality, and various equivalences between the two problems. This new approach is general and complements the existing one based on backward stochastic partial differential equations (backward SPDEs) for the related value functions. We, also, consider representative examples for both forward performance criteria with random endowment and forward OCE, and for the case of exponential criteria, we investigate the connection between forward OCE and forward entropic risk measures.
Reassessing the Exon-Foldon correspondence using Frustration Analysis
Authors: Ezequiel A. Galpern, Hana Jaafari, Carlos Bueno, Peter G. Wolynes, Diego U. Ferreiro
Date: 2024-01-04 00:00:00
Tasks: Protein Folding

Protein folding and evolution are intimately linked phenomena. Here, we revisit the concept of exons as potential protein folding modules across 38 abundant and conserved protein families. Taking advantage of genomic exon-intron organization and extensive protein sequence data, we explore exon boundary conservation and assess their foldon-like behavior using energy landscape theoretic measurements. We found deviations in exon size distribution from exponential decay indicating selection in evolution. We describe that there is a pronounced independent foldability of segments corresponding to conserved exons, supporting the exon-foldon correspondence. We further develop a systematic partitioning of protein domains using exon boundary hot spots, unveiling minimal common exons consisting of uninterrupted alpha and/or beta elements for the majority but not all of the studied families.
Isolation and Characterisation of Polypropylene Microplastic-Utilising Bacterium from the Antarctic Soil
Authors: Nur Ain Shuhada Ab Razak, Syahir Habib, Mohd Yunus Abd Shukor, Siti Aisyah Alias, Jerzy Smykla, Nur Adeela Yasid
Date: 2024-01-04 00:00:00

Despite its remoteness from other continents, the Antarctic region cannot escape the aftermath of human activities as it is highly influenced by anthropogenic impacts that occur both in the regional and global context. Contamination by microplastics, mostly caused by the improper disposal of plastic waste, is widely recognised as a serious environmental threat due to its ubiquity. In recent years, most researchers have focused on microplastic pollution in the marine ecosystem of Antarctica, while pollution in the terrestrial environment continues to be neglected. This study was conducted to investigate the ability of Antarctic soil bacteria to use polypropylene (PP) microplastics as the sole carbon source. Bushnell Haas (BH) medium inoculated with bacteria and supplemented PP-microplastics as the sole carbon source was used in the utilisation test. In this study, the growth response of Dermacoccus sp. strain AYDL3 was assessed after exposure to PP-microplastics in a basal medium for 40 days. The weight reduction of the polymer was determined to further support the growth response. The highest and lowest weight loss percentages were observed on day 20 (23.0%) and day 10 (7.75%), respectively. Fourier transforms infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) analyses were used to confirm the utilisation of PP-microplastics by strain AYDL3. Results indicate that the soil bacteria possess a mechanism for breaking down microplastics allowing them to utilise plastics as energy sources without any pre-treatment. This emphasises the significance of these soil bacteria to adapt and subsequently manage the plastic fragments in the soil in the future.
Machine learning-assisted search for novel coagulants: when machine learning can be efficient even if data availability is low
Authors: Andrij Rovenchak, Maksym Druchok
Date: 2024-01-03 00:00:00

Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect -- perfect candidates selectively attach to and influence only targets, leaving off-targets intact. The amount of experimental data about various properties of molecules constantly grows, promoting data-driven approaches. However, the applicability of typical predictive machine learning techniques can be substantially limited by a lack of experimental data about a particular target. For example, there are many known Thrombin inhibitors (acting as anticoagulants), but a very limited number of known Protein C inhibitors (coagulants). In this study, we present our approach to suggest new inhibitor candidates by building an effective representation of chemical space. For this aim, we developed a deep learning model -- autoencoder, trained on a large set of molecules in the SMILES format to map the chemical space. Further, we applied different sampling strategies to generate novel coagulant candidates. Symmetrically, we tested our approach on anticoagulant candidates, where we were able to predict their inhibition towards Thrombin. We also compare our approach with MegaMolBART -- another deep learning generative model, but exploiting similar principles of navigation in a chemical space.
Cell information processing via frequency encoding and excitability
Authors: Alan Givré, Silvina Ponce Dawson
Date: 2023-12-29 00:00:00

Cells continuously interact with their environment mediating their responses through signaling cascades. Very often, external stimuli induce pulsatile behaviors in intermediaries of the cascade of increasing frequency with the stimulus strength. This is characteristic of intracellular Ca$^{2+}$ signals involving Ca$^{2+}$ release through Inositol Trisphosphate Receptors (IP$_3$Rs). The mean frequency of IP$_3$R-mediated Ca$^{2+}$ pulses has been observed to scale exponentially with the stimulus strength in many cell types. In this paper we use a simple ODE model of the intracellular Ca$^{2+}$ dynamics for parameters for which there is one excitable fixed point. Including fluctuations through an additive noise term, we derive the mean escape rate from the stationary state and, thus, the mean interpulse time, as a function of the fraction, $\beta$, of readily openable IP$_3$Rs. Using an IP$_3$R kinetic model, experimental observations of spatially resolved Ca$^{2+}$ signals and previous estimates of the IP$_3$ produced upon stimulation we quantify the fluctuations and relate $\beta$ to [IP$_3$] and the stimulus strength. In this way we determine that the mean interpulse time can be approximated by an exponential function of the latter for ranges such that the covered mean time intervals are similar or larger than those observed experimentally. The study thus provides an easily interpretable explanation, applicable to other pulsatile signaling intermediaries, of the observed exponential dependence between frequency and stimulus, a key feature that makes frequency encoding qualitatively different from other ways commonly used by cells to "read" their environment.
The numerical solution of the free-boundary cell motility problem
Authors: Vitaly Chernik, Pavel Buklemishev
Date: 2023-10-06 00:00:00

The cell motility problem has been investigated for a long time. Today, many biologists, physicists, and mathematicians are looking for new research instruments for this process. A simple 2D model of a free-boundary cell moving on a homogeneous isotropic surface is presented in the paper. It describes the dynamics of the complex actomyosin liquid, whose special properties influence the boundary dynamics and cell motility. The model consists of a system of equations with the free boundary domain and contains a non-local term. In this work, we present a numerical solution of this problem.
DSBplot: Indels in DNA Double-strand Break Repair Experiments
Authors: Tejasvi Channagiri, Margherita Maria Ferrari, Youngkyu Jeon, Penghao Xu, Francesca Storici, Nataša Jonoska
Date: 2023-12-29 00:00:00
Tasks: Position

Double-strand breaks (DSBs) in DNA are naturally occurring destructive events in all organisms that may lead to genome instability. Cells employ various repair methods known as non-homologous end joining (NHEJ), microhomology mediated end joining (MMEJ), and homology-directed recombination (HDR). These repair processes may lead to DNA sequence variations (e.g., nucleotide insertions, deletions, and substitutions) at the location of the break. Studying DNA DSB repair processes often involves the use of high throughput sequencing assays to precisely quantify the sequence variations near the break with software tools. Often methods of assessing and visualizing these data have not taken into account the full complexity of the sequencing data, such as the frequency, type, and position of the sequence variations in a single comprehensive representation. Here we present a method that allows visualization of the overall variation pattern as well as comparison of these patterns among experimental setups.
Co-evolution of replicators and their parasites
Authors: Alexander Spirov
Date: 2023-12-29 00:00:00

The problem of evolutionary complexification of life is considered one of the fundamental aspects in contemporary evolutionary theory. Parasitism is ubiquitous, inevitable, and arises as soon as the first replicators appear, even during the prebiotic stages of evolution. Both in theoretical approaches (computer modeling and analysis) and in real experiments (replication of biological macromolecules), parasitic processes emerge almost immediately. An effective way to avoid the elimination of the host-parasite system is through compartmentalization. In both theory and experiments, the pressure of parasitism leads to the complexification of the host-parasite system into a network of cooperative replicators and their parasites. Parasites have the ability to create niches for new replicators. The co-evolutionary arms race between defense systems and counter-defense mechanisms among parasites and hosts can progress for a considerable duration, involving multiple stages, if not indefinitely.
Modeling arousal potential of epistemic emotions using Bayesian information gain: Inquiry cycle driven by free energy fluctuations
Authors: Hideyoshi Yanagisawa, Shimon Honda
Date: 2023-12-14 00:00:00

Epistemic emotions, such as curiosity and interest, drive the inquiry process. This study proposes a novel formulation of epistemic emotions such as curiosity and interest using two types of information gain generated by the principle of free energy minimization: Kullback-Leibler divergence(KLD) from Bayesian posterior to prior, which represents free energy reduction in recognition, and Bayesian surprise (BS), which represents the expected information gain by Bayesian prior update. By applying a Gaussian generative model with an additional uniform likelihood, we found that KLD and BS form an upward-convex function of surprise (minimized free energy and prediction error), similar to Berlyne's arousal potential functions, or the Wundt curve. We consider that the alternate maximization of BS and KLD generates an ideal inquiry cycle to approach the optimal arousal level with fluctuations in surprise, and that curiosity and interest drive to facilitate the cyclic process. We exhaustively analyzed the effects of prediction uncertainty (prior variance) and observation uncertainty (likelihood variance) on the peaks of the information gain function as optimal surprises. The results show that greater prediction uncertainty, meaning an open-minded attitude, and less observational uncertainty, meaning precise observation with attention, are expected to provide greater information gains through a greater range of exploration. The proposed mathematical framework unifies the free energy principle of the brain and the arousal potential theory to explain the Wundt curve as an information gain function and suggests an ideal inquiry process driven by epistemic emotions.
Informational non-reductionist theory of consciousness that providing maximum accuracy of reality prediction
Authors: E. E. Vityaev
Date: 2023-12-10 00:00:00

The paper considers a non-reductionist theory of consciousness, which is not reducible to theories of reality and to physiological or psychological theories. Following D.I.Dubrovsky's "informational approach" to the "Mind-Brain Problem", we consider the reality through the prism of information about observed phenomena, which, in turn, is perceived by subjective reality through sensations, perceptions, feelings, etc., which, in turn, are information about the corresponding brain processes. Within this framework the following principle of the Information Theory of Consciousness (ITS) development is put forward: the brain discovers all possible causal relations in the external world and makes all possible inferences by them. The paper shows that ITS built on this principle: (1) also base on the information laws of the structure of external world; (2) explains the structure and functioning of the brain functional systems and cellular ensembles; (3) ensures maximum accuracy of predictions and the anticipation of reality; (4) resolves emerging contradictions and (5) is an information theory of the brain's reflection of reality.
Out-of-equlibrium inference of feeding rates through population data from generic consumer-resource stochastic dynamics
Authors: Jose A. Capitan, David Alonso
Date: 2024-01-03 00:00:00
Tasks: parameter estimation

Statistical models are often structurally unidentifiable, because different sets of parameters can lead to equal model outcomes. To be useful for prediction and parameter inference from data, stochastic population models need to be identifiable, this meaning that model parameters can be uniquely inferred from a large number of model observations. In particular, precise estimation of feeding rates in consumer-resource dynamics is crucial, because consumer-resource processes are central in determining biomass transport across ecosystems. Model parameters are usually estimated at stationarity, because in that case model analyses are often easier. In this contribution we analyze the problem of parameter redundancy in a multi-resource consumer-resource model, showing that model indentifiability depends on whether the dynamics have reached stationarity or not. To be precise, we: (i) Calculate the steady-state and out-of-equilibrium probability distributions of predator's abundances analytically using generating functions, which allow us to unveil parameter redundancy and carry out proper maximum likelihood estimation. (ii) Conduct \emph{in silico} experiments by tracking the abundance of consumers that are either searching for or handling prey, data then used for maximum likelihood parameter estimation. (iii) Show that, when model observations are recorded out of equilibrium, feeding parameters are truly identifiable, whereas if sampling is done solely at stationarity, only ratios of rates can be inferred from data (i.e., parameters are redundant). We discuss the implications of our results when inferring parameters of general dynamical models.
Walsh coefficients and circuits for several alleles
Authors: Kristina Crona, Devin Greene
Date: 2024-01-01 00:00:00

Walsh coefficients have been applied extensively to biallelic systems for quantifying pairwise and higher order epistasis, in particular for demonstrating the empirical importance of higher order interactions. Circuits, or minimal dependence relations, and related approaches that use triangulations of polytopes have also been applied to biallelic systems. Here we provide biological interpretations of Walsh coefficients for several alleles, and discuss circuits in the same general setting.
Detection of evolutionary shifts in variance under an Ornsten-Uhlenbeck model
Authors: Wensha Zhang, Lam Si Tung Ho, Toby Kenney
Date: 2023-12-29 00:00:00
Tasks: Variable Selection

Abrupt environmental changes can lead to evolutionary shifts in not only the optimal trait value, but also the rate of adaptation and the diffusion variance in trait evolution. While several methods exist for detecting shifts in optimal values, few explicitly model shifts in both evolutionary variance and adaptation rates. We use a multi-optima and multi-variance Ornstein-Uhlenbeck (OU) process model to describe trait evolution with shifts in both optimal value and diffusion variance and analyze how covariance between species is affected when shifts in variance occur along the phylogeny. We propose a new method that simultaneously detects shifts in both variance and optimal values by formulating the problem as a variable selection task using an L1-penalized loss function. Our method is implemented in the R package ShiVa (Detection of evolutionary Shifts in Variance). Through simulations, we compare ShiVa with methods that only consider shifts in optimal values (l1ou; PhylogeneticEM), and PCMFit. Our method demonstrates improved predictive ability and significantly reduces false positives in detecting optimal value shifts when variance shifts are present. When only shifts in optimal value occur, our method performs comparably to existing approaches. Applying ShiVa to empirical data from cordylid lizards , we find that it outperforms l1ou and PhylogeneticEM, achieving the highest log-likelihood and lowest BIC.
A model-based assessment of social isolation practices for COVID-19 outbreak response in residential care facilities
Authors: Cameron Zachreson, Ruarai Tobin, Camelia Walker, Eamon Conway, Freya M Shearer, Jodie McVernon, Nicholas Geard
Date: 2023-12-29 00:00:00

Residential aged-care facilities (RACFs, also called long-term care facilities, aged care homes, or nursing homes) have elevated risks of respiratory infection outbreaks and associated disease burden. During the COVID-19 pandemic, social isolation policies were commonly used in these facilities to prevent and mitigate outbreaks. We refer specifically to general isolation policies that were intended to reduce contact between residents, without regard to confirmed infection status. Such policies are controversial because of their association with adverse mental and physical health indicators and there is a lack of modelling that assesses their effectiveness. We developed an agent-based model of COVID-19 transmission in a structured population, intended to represent the salient characteristics of a residential care environment. Using our model, we generated stochastic ensembles of simulated outbreaks and compared summary statistics of outbreaks simulated} under different mitigation conditions. Our study focuses on the marginal impact of general isolation (reducing social contact between residents), regardless of confirmed infection. In the absence of any asymptomatic screening, general isolation of residents to their rooms reduced median cumulative cases by approximately 27%. However, when conducted concurrently with asymptomatic screening and isolation of confirmed cases, general isolation reduced the median number of cumulative infections by only 12% in our simulations. Our simulations showed that general isolation of residents did not provide substantial benefits beyond those achieved through screening, isolation of confirmed cases, and deployment of PPE. Our conclusions are sensitive to assumptions about the proportion of total contacts in a facility accounted for by casual interactions between residents.
Exploring the Impacts of Land Use/Cover Change on Ecosystem Services in Multiple Scenarios --The Case of Sichuan-Chongqing Region, China
Authors: Ran Chen, Jing Zhao, Xiaomin Luo, Xinxue Yan, Xi Zheng, Yijun Mao, Xiaoping Fu, Xueqi Yao, Sijia Jiang
Date: 2023-12-19 00:00:00

To improve the environment of the ecosystem, China has implemented the Green-forGrain Program for two decades, which has resulted in an imbalance among ecology.economy and food. This study focuses on the "ecology-food" imbalance problem.taking Sichuan-Chongqing Region as an example, to set up future scenarios topredicate the distribution of ESs. We first forecast land use/cover change in 2050under four different scenarios: Natural Development Scenarios; Arable LandConservation Scenarios; Ecological Priority Scenarios; Ecology-Arable LandHarmonization Scenarios. Then we assess changes in five ESs: habitat quality ,cropproduction, soil conservation, water yield, and carbon storage from 1990 to 2020 and2050. Finally, we reveal the spacial distribution of ESs. The following conclusions areobtained: (1) From 1990-2020, CS, SC, and HQ reveal an increasing trend with growthrates of 1.68%, 0.08%, and 0.46%: CP reveals a reduce rate of 2.75% . (2) S4 has anincrease in arable land, and CP has increased by 7.56% compared to S1, reversingthe trend of reduced CP under S1. (3) The high-high anomalies area of CP under S4 isoasically the same as that under S2, which proves that S4 is a scenario policy that canbe referred to for future development.
Accelerating Discovery of Novel and Bioactive Ligands With Pharmacophore-Informed Generative Models
Authors: Weixin Xie, Jianhang Zhang, Qin Xie, Chaojun Gong, Youjun Xu, Luhua Lai, Jianfeng Pei
Date: 2024-01-02 00:00:00
Tasks: Drug Discovery

Deep generative models have gained significant advancements to accelerate drug discovery by generating bioactive chemicals against desired targets. Nevertheless, most generated compounds that have been validated for potent bioactivity often exhibit structural novelty levels that fall short of satisfaction, thereby providing limited inspiration to human medicinal chemists. The challenge faced by generative models lies in their ability to produce compounds that are both bioactive and novel, rather than merely making minor modifications to known actives present in the training set. Recognizing the utility of pharmacophores in facilitating scaffold hopping, we developed TransPharmer, an innovative generative model that integrates ligand-based interpretable pharmacophore fingerprints with generative pre-training transformer (GPT) for de novo molecule generation. TransPharmer demonstrates superior performance across tasks involving unconditioned distribution learning, de novo generation and scaffold elaboration under pharmacophoric constraints. Its distinct exploration mode within the local chemical space renders it particularly useful for scaffold hopping, producing compounds that are structurally novel while pharmaceutically related. The efficacy of TransPharmer is validated through two case studies involving the dopamine receptor D2 (DRD2) and polo-like kinase 1 (PLK1). Notably in the case of PLK1, three out of four synthesized designed compounds exhibit submicromolar activities, with the most potent one, IIP0943, demonstrating a potency of 5.1 nM. Featuring a new scaffold of 4-(benzo[b]thiophen-7-yloxy)pyrimidine, IIP0943 also exhibits high selectivity for PLK1. It was demonstrated that TransPharmer is a powerful tool for discovery of novel and bioactive ligands.