pwc-archive/papers-with-abstracts huggingface dataset:
Data Sharing in the PRIMED Consortium: Design, implementation, and recommendations for future policymaking
Authors: Johanna L. Smith, Quenna Wong, Whitney Hornsby, Matthew P. Conomos, Benjamin D. Heavner, Iftikhar J. Kullo, Bruce M. Psaty, Stephen S. Rich, Bamidele Tayo, Pradeep Natarajan, Sara C. Nelson, Consortium Data Sharing Working Group, Polygenic Risk Methods in Diverse Populations, Consortium
Date: 2025-02-12 00:00:00

Sharing diverse genomic and other biomedical datasets is critical to advance scientific discoveries and their equitable translation to improve human health. However, data sharing remains challenging in the context of legacy datasets, evolving policies, multi-institutional consortium science, and international stakeholders. The NIH-funded Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium was established to improve the performance of polygenic risk estimates for a broad range of health and disease outcomes with global impacts. Improving polygenic risk score performance across genetically diverse populations requires access to large, diverse cohorts. We report on the design and implementation of data sharing policies and procedures developed in PRIMED to aggregate and analyze data from multiple, heterogeneous sources while adhering to existing data sharing policies for each integrated dataset. We describe two primary data sharing mechanisms: coordinated dbGaP applications and a Consortium Data Sharing Agreement, as well as provide alternatives when individual-level data cannot be shared within the Consortium (e.g., federated analyses). We also describe technical implementation of Consortium data sharing in the NHGRI Analysis Visualization and Informatics Lab-space (AnVIL) cloud platform, to share derived individual-level data, genomic summary results, and methods workflows with appropriate permissions. As a Consortium making secondary use of pre-existing data sources, we also discuss challenges and propose solutions for release of individual- and summary-level data products to the broader scientific community. We make recommendations for ongoing and future policymaking with the goal of informing future consortia and other research activities.
Research on Cumin Peptides Using PBS Extraction and Their Multifunctional Bioactivities
Authors: Yasen Mijiti, Yangjian, Sumairemu subuer, Najeeb Ullah, Ermatov Ismoil, Hailiqian Taoerdahong, Paerhati Rouzi
Date: 2025-02-12 00:00:00

This study employed neutral PBS buffer combined with ammonium sulfate fractionation to isolate peptide-active fractions (PD-30, PD-50, PD-80) from Cuminum cyminum L.seeds (cumin) and systematically evaluated their antimicrobial, antioxidant, hypoglycemic, and anticancer activities. The results demonstrated that the PD-80 fraction exhibited potent antifungal activity against Candida albicans (inhibition zone diameter: 12.5 mm) and significant antioxidant capacity, with DPPH and ABTS radical scavenging rates of 72.4% and 78.9%. The PD-50 fraction showed the strongest antibacterial effect against Escherichia coli (inhibition zone diameter: 11.7mm), while PD-30 displayed the highest inhibitory activity against PTP1B (IC50=18.39 ug/mL), indicating its potential for hypoglycemic applications. Through mass spectrometry and database alignment, 414 peptides were identified for the first time in cumin-derived PBS extracts, including 18 structurally novel monomers comprising 11 antimicrobial peptides, 7 anticancer peptides, and 6 hypoglycemic peptides. Notably, peptide CK12 shares sequence homology (59% similarity) with the HIV fusion inhibitor T20, suggesting potential antiviral activity. This study elucidates the multifunctionality and structural basis of cumin peptides, providing a theoretical foundation for their application in natural pharmaceuticals and functional foods. Future research will focus on chemical modification and in vivo validation to advance their utilization in precision medicine and the modernization of traditional Chinese medicine.
From random walks to epidemic spreading: Compartment model with mortality for vector transmitted diseases
Authors: Téo Granger, Thomas M. Michelitsch, Bernard A. Collet, Michael Bestehorn, Alejandro P. Riascos
Date: 2025-02-12 00:00:00

We propose a compartmental model for vector-transmitted diseases, such as Malaria and Dengue, spreading over complex networks. Individuals are represented by independent random walkers and vectors by infected nodes. Both walkers and nodes can be susceptible (S) or infected (I). Infected walkers may die (entering the dead compartment D), while infected nodes remain alive. Susceptible walkers can be infected by visiting infected nodes, and susceptible nodes by visits from infected walkers. We derive explicit expressions for the basic reproduction numbers $R_0$ (without mortality) and $R_M$ (with mortality), proving that $R_M < R_0$. When $R_M , R_0 > 1$, the healthy state is unstable, and for zero mortality, an endemic equilibrium emerges. We also study the effects of confinement measures. Simulations align well with mean-field predictions on strongly connected graphs but deviate for weakly connected networks. Our model has various interdisciplinary applications which include the modeling of chemical reaction kinetics, contaminant spread, and wildfire propagation.
DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra
Authors: Montgomery Bohde, Mrunali Manjrekar, Runzhong Wang, Shuiwang Ji, Connor W. Coley
Date: 2025-02-13 00:00:00
Tasks: Decoder, De novo molecule generation from MS/MS spectrum (bonus chemical formulae), scientific discovery

Mass spectrometry plays a fundamental role in elucidating the structures of unknown molecules and subsequent scientific discoveries. One formulation of the structure elucidation task is the conditional de novo generation of molecular structure given a mass spectrum. Toward a more accurate and efficient scientific discovery pipeline for small molecules, we present DiffMS, a formula-restricted encoder-decoder generative network that achieves state-of-the-art performance on this task. The encoder utilizes a transformer architecture and models mass spectra domain knowledge such as peak formulae and neutral losses, and the decoder is a discrete graph diffusion model restricted by the heavy-atom composition of a known chemical formula. To develop a robust decoder that bridges latent embeddings and molecular structures, we pretrain the diffusion decoder with fingerprint-structure pairs, which are available in virtually infinite quantities, compared to structure-spectrum pairs that number in the tens of thousands. Extensive experiments on established benchmarks show that DiffMS outperforms existing models on de novo molecule generation. We provide several ablations to demonstrate the effectiveness of our diffusion and pretraining approaches and show consistent performance scaling with increasing pretraining dataset size. DiffMS code is publicly available at https://github.com/coleygroup/DiffMS.
Foliar Uptake of Biocides: Statistical Assessment of Compartmental and Diffusion-Based Models
Authors: Enrico Sangoi, Federica Cattani, Faheem Padia, Federico Galvanin
Date: 2025-02-12 00:00:00
Tasks: Data Augmentation

The global population increase leads to a high food demand, and to reach this target products such as pesticides are needed to protect the crops. Research is focusing on the development of new products that can be less harmful to the environment, and mathematical models are tools that can help to understand the mechanism of uptake of pesticides and then guide in the product development phase. This paper applies a systematic methodology to model the foliar uptake of pesticides, to take into account the uncertainties in the experimental data and in the model structure. A comparison between different models is conducted, focusing on the identifiability of model parameters through dynamic sensitivity profiles and correlation analysis. Lastly, data augmentation studies are conducted to exploit the model for the design of experiments and to provide a practical support to future experimental campaigns, paving the way for further application of model-based design of experiments techniques in the context of foliar uptake.
An affordable, wearable, fiber-free pulsed-mode diffuse speckle contrast flowmetry (PM-DSCF) sensor for noninvasive measurements of deep cerebral blood flow
Authors: Chaebeom Yeo, Xuhui Liu, Mehrana Mohtasebi, Faezeh Akbari, Faraneh Fathi, Guoqiang Yu
Date: 2025-02-11 00:00:00

Significance: Measuring cerebral blood flow (CBF) is crucial for diagnosing various cerebral diseases. An affordable, wearable, and fiber-free continuous-wave speckle contrast flowmetry (CW-DSCF) technique has been developed for continuous monitoring of CBF variations. However, its application in adult humans is limited by shallow tissue penetration. Aim: To develop an innovative pulse-mode DSCF (PM-DSCF) system for continuous monitoring of CBF variations in adult humans. Approach: The PM-DSCF utilizes an 808 nm laser diode and a small NanEye camera to capture diffuse laser speckle fluctuations caused by red blood cell movement in the brain (i.e., CBF). Operating in short-pulse mode (duty cycle < 5%), the system maximizes peak pulse light power for deeper tissue penetration, while ensuring that the average power density remains within ANSI safety standards for skin exposure. The PM-DSCF was evaluated on tissue-simulating phantoms and in adult humans. Results: The maximum effective source-detector distance increased from 15 mm (CW-DSCF) to 35 mm (PM-DSCF). The PM-DSCF successfully detected CBF variations in adult brains during head-up-tilting experiments, consistent with physiological expectations. Conclusions: Switching from CW mode to PM mode significantly increases the maximum tissue penetration depth from ~7.5 mm (CW-DSCF) to ~17.5 mm (PM-DSCF), enabling successful CBF measurements in adult humans.
Design of an Automated Ethanol Vapor Generating System for Alcohol Use Disorder(AUD) Animal Studies
Authors: Alexander Pozhitkov, Douglas Ramsay, Peter A Noble
Date: 2025-02-11 00:00:00
Tasks: Diagnostic

Alcohol Use Disorder (AUD) is a prevalent addictive disorder affecting an estimated 29.5 million Americans. It is characterized by impaired control over alcohol consumption despite negative consequences. The number of diagnostic criteria met by an individual typically determines the severity of AUD. Research into AUD focuses on understanding individual susceptibility differences and developing preventive strategies. Alcohol vapor inhalation has emerged as a promising method for pathophysiological investigations in animals, allowing researchers to control the dose and duration of alcohol exposure. This approach is crucial for studying the escalation of voluntary alcohol-drinking behavior. Current commercial systems for alcohol vapor generation have limitations, including combustion risks and the need to adjust multiple parameters. Other methods, like bubbling or blow-over evaporation, face challenges in maintaining equilibrium and avoiding aerosolization. To address these issues, a new type of ethanol vapor generating system is proposed that relies solely on temperature control, creating a vacuum into which ethanol evaporates under thermodynamic control. This approach eliminates the need to adjust multiple parameters and offers improved accuracy and precision in vapor dose delivery. We validated the system as anticipated, achieving stable ethanol vapor after a few priming cycles. Using a 1.2 L cylinder, we obtained approximately 3.6 L of saturated vapor/air mix in 1 minute. Gravimetric results showed that each cycle produced about 100 mg/L or ~10,000 ppm vapor-to-air mixture. The intended use of the ethanol vapor generator is to provide a concentrated ethanol vapor / air mixture to be further diluted before delivering to the animals.
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data
Authors: Luoting Zhuang, Stephen H. Park, Steven J. Skates, Ashley E. Prosper, Denise R. Aberle, William Hsu
Date: 2025-02-11 00:00:00
Tasks: Anomaly Detection, Data Integration

Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today's data-driven research in oncology has primarily focused on cross-sectional analysis using data from a single modality, limiting the ability to fully characterize and interpret the disease's dynamic heterogeneity. Advances in multiscale data collection and computational methods now enable the discovery of longitudinal multimodal biomarkers for precision oncology. Longitudinal data reveal patterns of disease progression and treatment response that are not evident from single-timepoint data, enabling timely abnormality detection and dynamic treatment adaptation. Multimodal data integration offers complementary information from diverse sources for more precise risk assessment and targeting of cancer therapy. In this review, we survey methods of longitudinal and multimodal modeling, highlighting their synergy in providing multifaceted insights for personalized care tailored to the unique characteristics of a patient's cancer. We summarize the current challenges and future directions of longitudinal multimodal analysis in advancing precision oncology.
Quantifying Cryptocurrency Unpredictability: A Comprehensive Study of Complexity and Forecasting
Authors: Francesco Puoti, Fabrizio Pittorino, Manuel Roveri
Date: 2025-02-13 00:00:00
Tasks: Time Series, Time Series Forecasting

This paper offers a thorough examination of the univariate predictability in cryptocurrency time-series. By exploiting a combination of complexity measure and model predictions we explore the cryptocurrencies time-series forecasting task focusing on the exchange rate in USD of Litecoin, Binance Coin, Bitcoin, Ethereum, and XRP. On one hand, to assess the complexity and the randomness of these time-series, a comparative analysis has been performed using Brownian and colored noises as a benchmark. The results obtained from the Complexity-Entropy causality plane and power density spectrum analysis reveal that cryptocurrency time-series exhibit characteristics closely resembling those of Brownian noise when analyzed in a univariate context. On the other hand, the application of a wide range of statistical, machine and deep learning models for time-series forecasting demonstrates the low predictability of cryptocurrencies. Notably, our analysis reveals that simpler models such as Naive models consistently outperform the more complex machine and deep learning ones in terms of forecasting accuracy across different forecast horizons and time windows. The combined study of complexity and forecasting accuracies highlights the difficulty of predicting the cryptocurrency market. These findings provide valuable insights into the inherent characteristics of the cryptocurrency data and highlight the need to reassess the challenges associated with predicting cryptocurrency's price movements.
Quantum Powered Credit Risk Assessment: A Novel Approach using hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis
Authors: Rath Minati, Date Hema
Date: 2025-02-06 00:00:00
Tasks: Deep Learning, Financial Analysis

The integration of Quantum Deep Learning (QDL) techniques into the landscape of financial risk analysis presents a promising avenue for innovation. This study introduces a framework for credit risk assessment in the banking sector, combining quantum deep learning techniques with adaptive modeling for Row-Type Dependent Predictive Analysis (RTDPA). By leveraging RTDPA, the proposed approach tailors predictive models to different loan categories, aiming to enhance the accuracy and efficiency of credit risk evaluation. While this work explores the potential of integrating quantum methods with classical deep learning for risk assessment, it focuses on the feasibility and performance of this hybrid framework rather than claiming transformative industry-wide impacts. The findings offer insights into how quantum techniques can complement traditional financial analysis, paving the way for further advancements in predictive modeling for credit risk.
Utilizing Pre-trained and Large Language Models for 10-K Items Segmentation
Authors: Hsin-Min Lu, Yu-Tai Chien, Huan-Hsun Yen, Yen-Hsiu Chen
Date: 2025-02-13 00:00:00
Tasks: Segmentation

Extracting specific items from 10-K reports remains challenging due to variations in document formats and item presentation. Traditional rule-based item segmentation approaches often yield suboptimal results. This study introduces two advanced item segmentation methods leveraging language models: (1) GPT4ItemSeg, using a novel line-ID-based prompting mechanism to utilize GPT4 for item segmentation, and (2) BERT4ItemSeg, combining BERT embeddings with a Bi-LSTM model in a hierarchical structure to overcome context window constraints. Trained and evaluated on 3,737 annotated 10-K reports, BERT4ItemSeg achieved a macro-F1 of 0.9825, surpassing GPT4ItemSeg (0.9567), conditional random field (0.9818), and rule-based methods (0.9048) for core items (1, 1A, 3, and 7). These approaches enhance item segmentation performance, improving text analytics in accounting and finance. BERT4ItemSeg offers satisfactory item segmentation performance, while GPT4ItemSeg can easily adapt to regulatory changes. Together, they offer practical benefits for researchers and practitioners, enabling reliable empirical studies and automated 10-K item segmentation functionality.
The Relative Entropy of Expectation and Price
Authors: Paul McCloud
Date: 2025-02-12 00:00:00

As operators acting on the undetermined final settlement of a derivative security, expectation is linear but price is non-linear. When the market of underlying securities is incomplete, non-linearity emerges from the bid-offer around the mid price that accounts for the residual risks of the optimal funding and hedging strategy. At the extremes, non-linearity also arises from the embedded options on capital that are exercised upon default. In this essay, these convexities are quantified in an entropic risk metric that evaluates the strategic risks, which is realised as a cost with the introduction of bilateral margin. Price is then adjusted for market incompleteness and the risk of default caused by the exhaustion of capital. In the complete market theory, price is derived from a martingale condition. In the incomplete market theory presented here, price is instead derived from a log-martingale condition: \begin{equation} p=-\frac{1}{\alpha}\log\mathbb{E}\exp[-\alpha P] \notag \end{equation} for the price $p$ and payoff $P$ of a funded and hedged derivative security, where the price measure $\mathbb{E}$ has minimum entropy relative to economic expectations, and the parameter $\alpha$ matches the risk aversion of the investor. This price principle is easily applied to standard models for market evolution, with applications considered here in model risk analysis, deep hedging and quantum information.
Marginal Price Optimization
Authors: Stefan Loesch, Mark Bentley Richardson
Date: 2025-02-12 00:00:00

We introduce a new framework for optimal routing and arbitrage in AMM driven markets. This framework improves on the original best-practice convex optimization by restricting the search to the boundary of the optimal space. We can parameterize this boundary using a set of prices, and a potentially very high dimensional optimization problem (2 optimization variables per curve) gets reduced to a much lower dimensional root finding problem (1 optimization variable per token, regardless of the number of the curves). Our reformulation is similar to the dual problem of a reformulation of the original convex problem. We show our reformulation of the problem is equivalent to the original formulation except in the case of infinitely concentrated liquidity, where we provide a suitable approximation. Our formulation performs far better than the original one in terms of speed - we obtain an improvement of up to 200x against Clarabel, the new CVXPY default solver - and robustness, especially on levered curves.
Analyzing Communicability and Connectivity in the Indian Stock Market During Crises
Authors: Pawanesh Pawanesh, Charu Sharma, Niteesh Sahni
Date: 2025-02-12 00:00:00

In financial networks, information does not always follow the shortest path between two nodes but may also take alternate routes. Communicability, a network measure, resolves this complexity and, in diffusion-like processes, provides a reliable measure of the ease with which information flows between nodes. As a result, communicability appears to be an important measure for detecting disturbances in connectivity within financial systems, similar to instability caused by periods of high volatility. This study investigates the evolution of communicability measures in the stock networks during periods of crises, showing how systemic shocks strengthen the pairwise interdependence between stocks in the financial market. In this study, the permutation test reveals that approximately 83.5 per cent of stock pairs were found to be statistically significant at the significance level of 0.001 and have an increase in the shortest communicability path length during the crisis than the normal days, indicating enhanced interdependence and heightened information flow in the market. Furthermore, we show that when employed as features in the classification model, the network shortest path-based measures, along with communicability measures, are able to accurately classify between the times periods of market stability and volatility. Additionally, our results show that the geometric measures perform better in terms of classification accuracy than topological measures. These findings provide important insights into market behaviour during times of increased volatility and advance our understanding of the financial market crisis.
WENDy for Nonlinear-in-Parameters ODEs
Authors: Nic Rummel, Daniel A. Messenger, Stephen Becker, Vanja Dukic, David M. Bortz
Date: 2025-02-13 00:00:00
Tasks: parameter estimation

The Weak-form Estimation of Non-linear Dynamics (WENDy) algorithm is extended to accommodate systems of ordinary differential equations that are nonlinear-in-parameters. The extension rests on derived analytic expressions for a likelihood function, its gradient and its Hessian matrix. WENDy makes use of these to approximate a maximum likelihood estimator based on optimization routines suited for non-convex optimization problems. The resulting parameter estimation algorithm has better accuracy, a substantially larger domain of convergence, and is often orders of magnitude faster than the conventional output error least squares method (based on forward solvers). The algorithm is efficiently implemented in Julia. We demonstrate the algorithm's ability to accommodate the weak form optimization for both additive normal and multiplicative log-normal noise, and present results on a suite of benchmark systems of ordinary differential equations. In order to demonstrate the practical benefits of our approach, we present extensive comparisons between our method and output error methods in terms of accuracy, precision, bias, and coverage.