pwc-archive/papers-with-abstracts huggingface dataset:
The number of cultural traits, evolving genealogies, and the descendant process
Authors: Joe Yuichiro Wakano, Hisashi Ohtsuki, Yutaka Kobayashi, Ellen Baake
Date: 2025-05-13 00:00:00

We consider a Moran-type model of cultural evolution, which describes how traits emerge, are transmitted, and get lost in populations. Our analysis focuses on the underlying cultural genealogies; they were first described by Aguilar (2015) and are closely related to the ancestral selection graph of population genetics, wherefore we call them ancestral learning graphs. We investigate their dynamical behaviour, that is, we are concerned with evolving genealogies. In particular, we consider the total length of the genealogy of a sample of individuals from a stationary population as a function of the (forward) time at which the sample is taken. This quantity shows a sawtooth-like dynamics with linear increase interrupted by collapses to near-zero at random times. We relate this to the metastable behaviour of the stochastic logistic model, which describes the evolution of the number of ancestors, or equivalently, the number of descendants of a given sample. We assume that new inventions appear independently in every individual, and all traits of the cultural parent are transmitted to the learner in any given learning event. The set of traits of an individual then agrees with the set of innovations along its genealogy. The properties of the genealogy thus translate into the properties of the trait set of a sample. In particular, the moments of the number of traits are obtained from the moments of the total length of the genealogy.
Rough Bergomi turns grey
Authors: Antoine Jacquier, Adriano Oliveri Orioles, Zan Zuric
Date: 2025-05-13 00:00:00

We propose a tractable extension of the rough Bergomi model, replacing the fractional Brownian motion with a generalised grey Brownian motion, which we show to be reminiscent of models with stochastic volatility of volatility. This extension breaks away from the log-Normal assumption of rough Bergomi, thereby making it a viable suggestion for the Equity Holy Grail -- the joint SPX/VIX options calibration. For this new (class of) model(s), we provide semi-closed and asymptotic formulae for SPX and VIX options and show numerically its potential advantages as well as calibration results.
Efficient, simulation-free estimators of firing rates with Markovian surrogates
Authors: Zhongyi Wang, Louis Tao, Zhuo-Cheng Xiao
Date: 2025-05-13 00:00:00

Spiking neural networks (SNNs) are powerful mathematical models that integrate the biological details of neural systems, but their complexity often makes them computationally expensive and analytically untractable. The firing rate of an SNN is a crucial first-order statistic to characterize network activity. However, estimating firing rates analytically from even simplified SNN models is challenging due to 1) the intricate dependence between the nonlinear network dynamics and parameters, and 2) the singularity and irreversibility of spikes. In this Letter, we propose a class of computationally efficient, simulation-free estimators of firing rates. This is based on a hierarchy of Markovian approximations that reduces the complexity of SNN dynamics. We show that while considering firing rates alone is insufficient for accurate estimations of themselves, the information of spiking synchrony dramatically improves the estimator's accuracy. This approach provides a practical tool for brain modelers, directly mapping biological parameters to firing rate.
Computational Analysis using Multi-ligand Simultaneous Docking of Withaferin A and Garcinol Reveals Enhanced BCL-2 and AKT-1 Inhibition
Authors: Pronama Biswas, Dishali Mathur, Jinal Dinesh, Hitesh Kumar Dinesh, Belaguppa Manjunath Ashwin Desai
Date: 2025-05-13 00:00:00
Tasks: Molecular Docking

Developing an effective medicine to combat cancer and elusive stem cells is crucial in the current scenario. Withaferin A and Garcinol, important phytoconstituents of Withania somnifera (Ashwagandha) and Garcinia indica (Kokum) respectively, known for their therapeutic efficiency, have been used for several decades for treating various disorders, because of their anti-cancerous, anti-inflammatory and anti-invasive properties. This study investigates the potentials of withaferin A and garcinol in inhibiting BCL-2 and AKT-1, crucial proteins contributing in cancer cell persistence by evading apoptosis, increased cell proliferation, and inflammation. Molecular docking techniques, including single docking and MLSD, were used to understand the binding interaction of the ligands with BCL-2 and AKT-1. MLSD highlighted inter-ligand interactions among withaferin A and garcinol, against BCL-2, with a binding affinity of -11.88 +- 0.12 kcal/mol, surpassing the binding affinity of venetoclax (-9.73 +- 0.1 kcal/mol) a commercial inhibitor of BCL-2. For AKT-1, the binding affinity of withaferin A and garcinol (-13.74 +- 0.08 kcal/mol) surpassed the binding affinity of melatonin (-7.24 +- 0.06 kcal/mol), a commercial inhibitor of AKT-1. The MLSD results highlight the combined effects of garcinol and withaferin A, highlighting the importance of considering both the interactions of the bioactive compounds in the development of new medicines and strategies targeting cancer and elusive stem cells.
Protein FID: Improved Evaluation of Protein Structure Generative Models
Authors: Felix Faltings, Hannes Stark, Tommi Jaakkola, Regina Barzilay
Date: 2025-05-12 00:00:00

Protein structure generative models have seen a recent surge of interest, but meaningfully evaluating them computationally is an active area of research. While current metrics have driven useful progress, they do not capture how well models sample the design space represented by the training data. We argue for a protein Frechet Inception Distance (FID) metric to supplement current evaluations with a measure of distributional similarity in a semantically meaningful latent space. Our FID behaves desirably under protein structure perturbations and correctly recapitulates similarities between protein samples: it correlates with optimal transport distances and recovers FoldSeek clusters and the CATH hierarchy. Evaluating current protein structure generative models with FID shows that they fall short of modeling the distribution of PDB proteins.
In Silico Prediction and Validation of LmGt Inhibitors Using QSAR and Molecular Docking Approaches
Authors: Pronama Biswas, Madhavi Bhatt, Belaguppa Manjunath Ashwin Desai
Date: 2025-05-12 00:00:00
Tasks: Drug Discovery, Molecular Docking, Specificity

Leishmaniasis caused by Leishmania mexicana relies on Leishmania mexicana gluscose transporter (LmGT) receptors, which play an important role in glucose and ribose uptake at different stages of parasite's life cycle. Previous efforts to identify LmGT inhibitors have been primarily based on in vitro screening. However, this conventional method is limited by inefficiency, high cost, and lack of specificity which leaves a significant gap in the development of targeted therapeutic candidates for LmGT. This study employs computational techniques to address this gap by developing a quantitative structure analysis relationship model, utilizing a support vector machine classifier to identify novel LmGt inhibitor. The QSAR model achieved an accuracy of 0.81 in differentiating active compounds. Molecular docking further validated the identified inhibitors, revealing strong binding affinities with a top score of -9.46. The docking analysis showed that the inhibitors formed multiple hydrogen bonds and occupied the same binding pockets as Phase 3 drug candidate. The tested inhibitors were derived from natural sources, which suggest reduced side effects and improved biocompability. This combined approach demonstrates the power of computational models in accelerating drug discovery, with implication for more efficient and biocompatible therapies against Leishmania mexicana.
Oncolytic mechanisms and immunotherapeutic potential of Newcastle disease virus in cancer therapy
Authors: Umar Ahmad, Surializa Harun, Moussa Moise Diagne, Syahril Abdullah, Khatijah Yusoff, Abhi Veerakumarasivam
Date: 2025-05-09 00:00:00
Tasks: Morphology classification

Newcastle Disease Virus (NDV), classified as Avian orthoavulavirus 1 (avian paramyxovirus type 1), is a promising oncolytic agent that selectively targets and destroys cancer cells while sparing normal tissues. Its oncoselectivity exploits cancer-specific defects in antiviral defenses, particularly impaired Type I interferon signaling, and dysregulated apoptotic pathways, enabling robust viral replication and cytotoxicity in malignancies such as breast, colorectal, and melanoma. NDV induces intrinsic and extrinsic apoptosis through caspase activation and triggers immunogenic cell death via damage-associated molecular patterns, stimulating potent antitumours immune responses. Additionally, NDVs potential as a vaccine vector, expressing tumours-associated antigens, offers prospects for prophylactic and therapeutic cancer applications. This review provides a comprehensive analysis of NDVs morphology, classification, and molecular biology, focusing on its viral entry and replication mechanisms in host cells. It explores NDVs interactions with cancer cells, emphasizing its ability to induce cytotoxicity and immune activation. Understanding these mechanisms is critical for optimizing NDVs oncolytic potential and advancing its clinical translation. Future directions include enhancing NDV through genetic engineering, combining it with therapies like immune checkpoint inhibitors, and developing personalized medicine approaches tailored to tumours genomic profiles. These advancements position NDV as a versatile therapeutic agent in oncolytic virotherapy.
Linear to Neural Networks Regression: QSPR of Drugs via Degree-Distance Indices
Authors: M. J. Nadjafi Arani, S. Sorgun, M. Mirzargar
Date: 2025-03-18 00:00:00
Tasks: Drug Design, Drug Discovery, regression

This study conducts a Quantitative Structure Property Relationship (QSPR) analysis to explore the correlation between the physical properties of drug molecules and their topological indices using machine learning techniques. While prior studies in drug design have focused on degree-based topological indices, this work analyzes a dataset of 166 drug molecules by computing degree-distance-based topological indices, incorporating vertex-edge weightings with respect to different six atomic properties (atomic number, atomic radius, atomic mass, density, electronegativity, ionization). Both linear models (Linear Regression, Lasso, and Ridge Regression) and nonlinear approaches (Random Forest, XGBoost, and Neural Networks) were employed to predict molecular properties. The results demonstrate the effectiveness of these indices in predicting specific physicochemical properties and underscore the practical relevance of computational methods in molecular property estimation. The study provides an innovative perspective on integrating topological indices with machine learning to enhance predictive accuracy, highlighting their potential application in drug discovery and development processes. This predictive may also explain that establishing a reliable relationship between topological indices and physical properties enables chemists to gain preliminary insights into molecular behavior before conducting experimental analyses, thereby optimizing resource utilization in cheminformatics research.
The Environment-Dependent Regulatory Landscape of the E. coli Genome
Authors: Tom Röschinger, Heun Jin Lee, Rosalind Wenshan Pan, Grace Solini, Kian Faizi, Baiyi Quan, Tsui Fen Chou, Madhav Mani, Stephen Quake, Rob Phillips
Date: 2025-05-13 00:00:00

All cells respond to changes in both their internal milieu and the environment around them through the regulation of their genes. Despite decades of effort, there remain huge gaps in our knowledge of both the function of many genes (the so-called y-ome) and how they adapt to changing environments via regulation. Here we describe a joint experimental and theoretical dissection of the regulation of a broad array of over 100 biologically interesting genes in E. coli across 39 diverse environments, permitting us to discover the binding sites and transcription factors that mediate regulatory control. Using a combination of mutagenesis, massively parallel reporter assays, mass spectrometry and tools from information theory and statistical physics, we go from complete ignorance of a promoter's environment-dependent regulatory architecture to predictive models of its behavior. As a proof of principle of the biological insights to be gained from such a study, we chose a combination of genes from the y-ome, toxin-antitoxin pairs, and genes hypothesized to be part of regulatory modules; in all cases, we discovered a host of new insights into their underlying regulatory landscape and resulting biological function.
Pan-genome Analysis of Plastomes from Lamiales using PGR-TK
Authors: Aadhavan Veerendra, Manoj Samanta
Date: 2025-05-12 00:00:00

Chloroplast sequences from the Lamiales order were analyzed using the Pangenome Research Toolkit (PGR-TK). Overall, most genera and families exhibited a high degree of sequence uniformity. However, at the genus level, Utricularia, Incarvillea, and Orobanche stood out as particularly divergent. At the family level, Orobanchaceae, Bignoniaceae and Lentibulariaceae displayed notably complex patterns in the generated plots. The PGR-TK algorithm successfully distinguished most genera within their respective families and often recognized misclassified plants.
Revolutionising Bacterial Genomics: Graph-Based Strategies for Improved Variant Identification
Authors: Fathima Nuzla Ismail, Abira Sengupta
Date: 2025-05-12 00:00:00
Tasks: Diversity

A significant advancement in bioinformatics is using genome graph techniques to improve variation discovery across organisms. Traditional approaches, such as bwa mem, rely on linear reference genomes for genomic analyses but may introduce biases when applied to highly diverse bacterial genomes of the same species. Pangenome graphs provide an alternative paradigm for evaluating structural and minor variations within a graphical framework, including insertions, deletions, and single nucleotide polymorphisms. Pangenome graphs enhance the detection and interpretation of complex genetic variants by representing the full genetic diversity of a species. In this study, we present a robust and reliable bioinformatics pipeline utilising the PanGenome Graph Builder (PGGB) and the Variation Graph toolbox (vg giraffe) to align whole-genome sequencing data, call variants against a graph reference, and construct pangenomes from assembled genomes. Our results demonstrate that leveraging pangenome graphs over a single linear reference genome significantly improves mapping rates and variant calling accuracy for simulated and actual bacterial pathogens datasets.
FastDup: a scalable duplicate marking tool using speculation-and-test mechanism
Authors: Zhonghai Zhang, Yewen Li, Ke Meng, Chunming Zhang, Guangming Tan
Date: 2025-05-09 00:00:00

Duplicate marking is a critical preprocessing step in gene sequence analysis to flag redundant reads arising from polymerase chain reaction(PCR) amplification and sequencing artifacts. Although Picard MarkDuplicates is widely recognized as the gold-standard tool, its single-threaded implementation and reliance on global sorting result in significant computational and resource overhead, limiting its efficiency on large-scale datasets. Here, we introduce FastDup: a high-performance, scalable solution that follows the speculation-and-test mechanism. FastDup achieves up to 20x throughput speedup and guarantees 100\% identical output compared to Picard MarkDuplicates. FastDup is a C++ program available from GitHub (https://github.com/zzhofict/FastDup.git) under the MIT license.
Detection of Moving Objects Using Self-motion Constraints on Optic Flow
Authors: Hope Lutwak, Bas Rokers, Eero P. Simoncelli
Date: 2025-05-10 00:00:00

As we move through the world, the pattern of light projected on our eyes is complex and dynamic, yet we are still able to distinguish between moving and stationary objects. We propose that humans accomplish this by exploiting constraints that self-motion imposes on retinal velocities. When an eye translates and rotates in a stationary 3D scene, the velocity at each retinal location is constrained to a line segment in the 2D space of retinal velocities. The slope and intercept of this segment is determined by the eye's translation and rotation, and the position along the segment is determined by the local scene depth. Since all possible velocities arising from a stationary scene must lie on this segment, velocities that are not must correspond to objects moving within the scene. We hypothesize that humans make use of these constraints by using deviations of local velocity from these constraint lines to detect moving objects. To test this, we used a virtual reality headset to present rich wide-field stimuli, simulating the visual experience of translating forward in several virtual environments with varied precision of depth information. Participants had to determine if a cued object moved relative to the scene. Consistent with the hypothesis, we found that performance depended on the deviation of the object velocity from the constraint segment, rather than a difference between retinal velocities of the object and its local surround. We also found that the endpoints of the constraint segment reflected the precision of depth information available in the different virtual environments.
Possible mechanisms underlying time perception: decoupling internal and external time
Authors: Irina Kareva, Georgy Karev
Date: 2025-05-12 00:00:00

Alignment between subjective sense of time and chronological time can become skewed as a result of pharmacological interventions, neurodegenerative diseases (such as Parkinson's disease), or even in the moments preceding brain death. Despite increased understanding of mechanisms governing time perception and the activity of the "internal clock" (such as the functionality of the dopamine system in the basal ganglia of the midbrain), there currently exist no mathematical models that allow investigation of changes in time perception through formalizing the decoupling of "internal" and "external" time. Here we propose such a model using a parametrically heterogeneous power equation, and use it to investigate the critical case of indefinite increase in internal time following cardiac arrest and preceding brain death. We identify three critical parameters that determine time to brain death and provide an analysis of the relevant quantities. We hope that our model can lay foundation for further mathematical and theoretical exploration of this complex topic.
Evaluating Cognitive Assessment Tools:A Comparative Analysis of MMSE, RUDAS, SAGE, ADAS and MoCA for Early Dementia Detection
Authors: Saransh Naole, Dhriti Parikh, Sakshi Nayak, Swarna Priya Ramu
Date: 2025-05-12 00:00:00

Early detection of dementia is very crucial to ensure treatment begins on time, however it is difficult to choose appropriate cognitive assessment tools because each test is designed differently and may not be tailored to the needs of a patient. This review compares five commonly used tests the Mini-Mental State Examination (MMSE), Rowland Universal Dementia Assessment Scale (RUDAS), Self-Administered Gerocognitive Examination (SAGE), Alzheimer's Disease Assessment Scale (ADAS), and Montreal Cognitive Assessment (MoCA). Each test has different criteria's and vary in their coverage of cognitive domains. MMSE focuses on memory and language but lacks in the evaluation of executive and visuospatial abilities. RUDAS and SAGE focus on memory, language and visual thinking while ADAS mainly targets memory, executive function and language. The MoCA is most complete as it focuses on areas like attention, memory skills, problem solving and visual skills. This review evaluates how accurate and reliable these tools are to help doctors decide the most efficient tool for diagnosis.
NeuroPal: A Clinically-Informed Multimodal LLM Assistant for Mental Health Combining Sleep Chronotherapy, Cognitive Behavioral Reframing, and Adaptive Phytochemical Intervention
Authors: Xiaoran Han
Date: 2025-05-10 00:00:00
Tasks: Large Language Model, Rhythm, Sleep Quality

Due to time constraints, mental health professionals in China are unable to offer patients prolonged talk therapy, leaving a gap in care for patients with psychological disorders, including aberrant sleep and eating patterns, maladaptive explanatory styles, and gastrointestinal dysfunction. To bridge this gap in care and address these problems in a large-scale manner, we built NeuroPal, a large language model (LLM)-assistant that provides scalable, evidence-based interventions with three clinically validated modules: (1) a sleep chronotherapy planner to output personalized circadian rhythm correction protocols, (2) a cognitive-behavioral reframing engine grounded in CBT and humanistic principles to shift negative attributional biases, and (3) a biochemical regulation advisor to output phytotherapy formulations to regulate sleep-metabolism-gut-axis imbalances. In collaboration with Peking Union Medical College Hospital and Xiangya Hospital Central South University, we ran an RCT protocol with 513 participants with mood/anxiety disorders and showed statistically significant improvements towards primary endpoints (> p<.01). Experiment shows 37.2% drop in the Pittsburgh Sleep Quality Index (PSQI), 28.6% rise in positive affective word usages (LIWC analysis), and 23.4% improvement in patient-reported digestive comfort. The assistant also reached 89.1% adherence rates, significantly higher than the human-guided therapy intervention (72.3%) in matched controls. Our results indicate that an LLM-driven multimodal intervention is able to successfully bridge time-constrained clinical practice while preserving therapeutic effectiveness. For next steps, we plan to explore longitudinal outcome tracking and FDA/CFDA certification routes.
The cognitive triple-slit experiment
Authors: Luca Sassoli de Bianchi, Massimiliano Sassoli de Bianchi
Date: 2025-05-06 00:00:00

Quantum cognition has made it possible to model human cognitive processes very effectively, revealing numerous parallels between the properties of conceptual entities tested by the human mind and those of microscopic entities tested by measurement apparatuses. The success of quantum cognition has also made it possible to formulate an interpretation of quantum mechanics, called the conceptuality interpretation, which ascribes to quantum entities a conceptual nature similar to that of human concepts. The present work fits into these lines of research by analyzing a cognitive version of single, double, and triple-slit experiments. The data clearly show the formation of the typical interference fringes between the slits as well as the embryos of secondary fringes. Our analysis also shows that while quantum entities and human concepts may share a same conceptual nature, the way they manifest it in specific contexts can be quite different. This is also evident from the significant deviation from zero observed for the Sorkin parameter, indicating the presence of strong irreducible third-order interference contributions in human decision.