pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning Model Predictive Control for Deep Brain Stimulation in Parkinson's Disease
Authors: Sebastian Steffen, Mark Cannon
Date: 2025-04-01 00:00:00
Tasks: Model Predictive Control

We present a nonlinear data-driven Model Predictive Control (MPC) algorithm for deep brain stimulation (DBS) for the treatment of Parkinson's disease (PD). Although DBS is typically implemented in open-loop, closed-loop DBS (CLDBS) uses the amplitude of neural oscillations in specific frequency bands (e.g. beta 13-30 Hz) as a feedback signal, resulting in improved treatment outcomes with reduced side effects and slower rates of patient habituation to stimulation. To date, CLDBS has only been implemented in vivo with simple control algorithms, such as proportional or proportional-integral control. Our approach employs a multi-step predictor based on differences of input-convex neural networks to model the future evolution of beta oscillations. The use of a multi-step predictor enhances prediction accuracy over the optimization horizon and simplifies online computation. In tests using a simulated model of beta-band activity response and data from PD patients, we achieve reductions of more than 20% in both tracking error and control activity in comparison with existing CLDBS algorithms. The proposed control strategy provides a generalizable data-driven technique that can be applied to the treatment of PD and other diseases targeted by CLDBS, as well as to other neuromodulation techniques.
Dynamical model-based experiment design for drug repositioning
Authors: Atte Aalto, La Mi, Diego A. Blanco-Mora, Jorge Goncalves
Date: 2025-04-01 00:00:00
Tasks: model

Computational methods in drug repositioning can help to conserve resources. In particular, methods based on biological networks are showing promise. Considering only the network topology and knowledge on drug target genes is not sufficient for quantitative predictions or predictions involving drug combinations. We propose an iterative procedure alternating between system identification and drug response experiments. Data from experiments are used to improve the model and drug effect knowledge, which is then used to select drugs for the next experiments. Using simulated data, we show that the procedure can identify nearly optimal drug combinations.
Explainable post-training bias mitigation with distribution-based fairness metrics
Authors: Ryan Franks, Alexey Miroshnikov
Date: 2025-04-01 00:00:00
Tasks: Explainable Models, Fairness

We develop a novel optimization framework with distribution-based fairness constraints for efficiently producing demographically blind, explainable models across a wide range of fairness levels. This is accomplished through post-processing, avoiding the need for retraining. Our framework, which is based on stochastic gradient descent, can be applied to a wide range of model types, with a particular emphasis on the post-processing of gradient-boosted decision trees. Additionally, we design a broad class of interpretable global bias metrics compatible with our method by building on previous work. We empirically test our methodology on a variety of datasets and compare it to other methods.
The Effect of Assortativity on Mpox Spreading with Two Core Groups
Authors: Fanni Nedényi, János Benke, Gergely Röst
Date: 2025-04-01 00:00:00

The spread of infectious diseases often concentrates within specific subgroups of a broader population. For instance, during recent mpox outbreaks in non-endemic countries, transmission primarily affected men who have sex with men (MSM). However, the internal structure of these subpopulations plays a crucial role in disease dynamics and should be accurately represented in mathematical models. In this study, we highlight the importance of modeling interactions between distinct subgroups and their impact on transmission patterns. We consider a stochastic SEIR-based model with two core groups embedded into the general population, and investigate the outcome of the outbreak with different levels of symmetry between these groups and assortativity in their contacts. Our results indicate that the efficiency of commonly used non-pharmaceutical interventions is greatly influenced by these factors, hence they should be considered in the design of intervention strategies.
Proper scoring rules for estimation and forecast evaluation
Authors: Kartik Waghmare, Johanna Ziegel
Date: 2025-04-02 00:00:00

Proper scoring rules have been a subject of growing interest in recent years, not only as tools for evaluation of probabilistic forecasts but also as methods for estimating probability distributions. In this article, we review the mathematical foundations of proper scoring rules including general characterization results and important families of scoring rules. We discuss their role in statistics and machine learning for estimation and forecast evaluation. Furthermore, we comment on interesting developments of their usage in applications.
Nonparametric spectral density estimation using interactive mechanisms under local differential privacy
Authors: Cristina Butucea, Karolina Klockmann, Tatyana Krivobokova
Date: 2025-04-01 00:00:00
Tasks: Density Estimation, Time Series

We address the problem of nonparametric estimation of the spectral density for a centered stationary Gaussian time series under local differential privacy constraints. Specifically, we propose new interactive privacy mechanisms for three tasks: estimating a single covariance coefficient, estimating the spectral density at a fixed frequency, and estimating the entire spectral density function. Our approach achieves faster rates through a two-stage process: we apply first the Laplace mechanism to the truncated value and then use the former privatized sample to gain knowledge on the dependence mechanism in the time series. For spectral densities belonging to H\"older and Sobolev smoothness classes, we demonstrate that our estimators improve upon the non-interactive mechanism of Kroll (2024) for small privacy parameter $\alpha$, since the pointwise rates depend on $n\alpha^2$ instead of $n\alpha^4$. Moreover, we show that the rate $(n\alpha^4)^{-1}$ is optimal for estimating a covariance coefficient with non-interactive mechanisms. However, the $L_2$ rate of our interactive estimator is slower than the pointwise rate. We show how to use these estimators to provide a bona-fide locally differentially private covariance matrix estimator.
Defining the relationship between cathepsin B and esophageal adenocarcinoma: conjoint analysis of Mendelian randomization, transcriptome-wide association studies, and single-cell RNA sequencing data
Authors: Jialin Li, Shaokang Yang, Xinliang Gao, Mingbo Tang, Xiaobo Ma, Suyan Tian, Wei Liu
Date: 2025-04-02 00:00:00

Background: Esophageal cancer poses a significant global health challenge, with the incidence of esophageal adenocarcinoma (EAC), a predominant subtype, increasing notably in Western countries. Cathepsins, a family of lysosomal proteolytic enzymes, have been implicated in the progression of various tumors. However, the causal relationship between the cathepsin family and EAC remains unresolved. Methods: To evaluate these potential causal associations, integrative analyses were conducted, integrating Mendelian randomization (MR), transcriptome-wide association study (TWAS), single-cell RNA sequencing (scRNA-seq), and single-cell expression quantitative trait locus (sc-eQTL) analyses. Results: Univariable and multivariable MR analyses demonstrated that elevated levels of cathepsin B (CTSB) were associated with a reduced risk of EAC. The TWAS analysis identified a negative association between CTSB expression in esophageal tissue and EAC, consistent with experimental validation using immunohistochemistry. The scRNA-seq data analysis indicated that CTSB expression was predominantly localized in macrophages infiltrating EAC. Colocalization analysis incorporating sc-eQTL data specific to macrophages confirmed a shared causal variant between CTSB and macrophages. Additionally, MR analysis of CTSB and macrophage scavenger receptor (MSR) types I and II established their interrelationship, suggesting that CTSB may influence the proinflammatory phenotype of macrophages, ultimately affecting EAC risk. Conclusions: This integrative analysis, utilizing MR, TWAS, scRNA-seq, and sc-eQTL data, identified a significant causal association between CTSB and EAC, potentially mediated through macrophage MSR regulation. These findings suggest that targeting cathepsin B could represent a novel strategy for the diagnosis and treatment of EAC.
LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions
Authors: Muhammad Tahir, Shehroz S. Khan, James Davie, Soichiro Yamanaka, Ahmed Ashraf
Date: 2025-04-01 00:00:00
Tasks: Benchmarking

In mammalian and vertebrate genomes, the promoter regions of the gene and their distal enhancers may be located millions of base-pairs from each other, while a promoter may not interact with the closest enhancer. Since base-pair proximity is not a good indicator of these interactions, there is considerable work toward developing methods for predicting Enhancer-Promoter Interactions (EPI). Several machine learning methods have reported increasingly higher accuracies for predicting EPI. Typically, these approaches randomly split the dataset of Enhancer-Promoter (EP) pairs into training and testing subsets followed by model training. However, the aforementioned random splitting causes information leakage by assigning EP pairs from the same genomic region to both testing and training sets, leading to performance overestimation. In this paper we propose to use a more thorough training and testing paradigm i.e., Leave-one-chromosome-out (LOCO) cross-validation for EPI-prediction. We demonstrate that a deep learning algorithm, which gives higher accuracies when trained and tested on random-splitting setting, drops drastically in performance under LOCO setting, confirming overestimation of performance. We further propose a novel hybrid deep neural network for EPI-prediction that fuses k-mer features of the nucleotide sequence. We show that the hybrid architecture performs significantly better in the LOCO setting, demonstrating it can learn more generalizable aspects of EP interactions. With this paper we are also releasing the LOCO splitting-based EPI dataset. Research data is available in this public repository: https://github.com/malikmtahir/EPI
Celler:A Genomic Language Model for Long-Tailed Single-Cell Annotation
Authors: Huan Zhao, Yiming Liu, Jina Yao, Ling Xiong, Zexin Zhou, Zixing Zhang
Date: 2025-03-28 00:00:00
Tasks: Language Modeling, Language Modelling

Recent breakthroughs in single-cell technology have ushered in unparalleled opportunities to decode the molecular intricacy of intricate biological systems, especially those linked to diseases unique to humans. However, these progressions have also ushered in novel obstacles-specifically, the efficient annotation of extensive, long-tailed single-cell data pertaining to disease conditions. To effectively surmount this challenge, we introduce Celler, a state-of-the-art generative pre-training model crafted specifically for the annotation of single-cell data. Celler incorporates two groundbreaking elements: First, we introduced the Gaussian Inflation (GInf) Loss function. By dynamically adjusting sample weights, GInf Loss significantly enhances the model's ability to learn from rare categories while reducing the risk of overfitting for common categories. Secondly, we introduce an innovative Hard Data Mining (HDM) strategy into the training process, specifically targeting the challenging-to-learn minority data samples, which significantly improved the model's predictive accuracy. Additionally, to further advance research in this field, we have constructed a large-scale single-cell dataset: Celler-75, which encompasses 40 million cells distributed across 80 human tissues and 75 specific diseases. This dataset provides critical support for comprehensively exploring the potential of single-cell technology in disease research. Our code is available at https://github.com/AI4science-ym/HiCeller.
Dual mechanism of Anti-Seizure Medications in controlling seizure activity
Authors: Guillermo M. Besne, Emmanuel Molefi, Sarah J. Gascoigne, Nathan Evans, Billy Smith, Chris Thornton, Fahmida A. Chowdhury, Beate Diehl, John S. Duncan, Andrew W. McEvoy, Anna Miserocchi, Jane de Tisi, Matthew Walker, Peter N. Taylor, Yujiang Wang
Date: 2025-04-02 00:00:00
Tasks: EEG

Background: Anti-seizure medications (ASMs) can reduce seizure duration, but their precise modes of action are unclear. Specifically, it is unknown whether ASMs shorten seizures by simply compressing existing seizure activity into a shorter time frame or by selectively suppressing certain seizure activity patterns. Methods: We analysed intracranial EEG (iEEG) recordings of 457 seizures from 28 people with epilepsy undergoing ASM tapering. Beyond measuring seizure occurrence and duration, we categorized distinct seizure activity patterns (states) based on spatial and frequency power characteristics and related these to different ASM levels. Results: We found that reducing ASM levels led to increased seizure frequency (r = 0.87, p < 0.001) and longer seizure duration ($\beta$ = -0.033, p < 0.001), consistent with prior research. Further analysis revealed two distinct mechanisms in which seizures became prolonged: Emergence of new seizure patterns - In approx. 40% of patients, ASM tapering unmasked additional seizure activity states, and seizures containing these 'taper-emergent states' were substantially longer (r = 0.49, p < 0.001). Prolongation of existing seizure patterns - Even in seizures without taper-emergent states, lower ASM levels still resulted in approx. 12-224% longer durations depending on the ASM dosage and tapering ($\beta$ = -0.049, p < 0.001). Conclusion: ASMs influence seizures through two mechanisms: they (i) suppress specific seizure activity patterns (states) in an all-or-nothing fashion and (ii) curtail the duration of other seizure patterns. These findings highlight the complex role of ASMs in seizure modulation and could inform personalized dosing strategies for epilepsy management. These findings may also have implications in understanding the effects of ASMs on cognition and mood.
Tinnitus, lucid dreaming and awakening. An online survey and theoretical implications
Authors: Robin Guillard, Nicolas Dauman, Aurélien Cadix, Charlotte Glabasnia Linck, Marco Congedo, Dirk de Ridder, Alain Londero
Date: 2025-04-02 00:00:00

(1) Background: Tinnitus is the perception of phantom sound in the absence of a corresponding external source. Previous studies reported that the presence of tinnitus is notably absent during dreams. This study aimed at replicating previous findings regarding tinnitus-free dreams, while also gaining a deeper understanding of tinnitus manifestations during dreams and after awakening. (2) Methods: For this observational study, 195 tinnitus patients answered an online survey on the mutual-help community Siopi. (3) Results: 160 patients could recall their dreams. Among them, 92.5% state they do not hear their tinnitus while dreaming. The rest (7.5%) report higher tinnitus burden, higher stress and more often exhibit objective tinnitus and/or tinnitus related to peripheral auditory pathology and/or drug intake. 13% of the participants frequently experience lucid dreams. Among them, 36% could perceive their tinnitus during lucid dreams, and this was strongly associated with the concomitant perception of external sounds during lucid dreaming. While the majority of patients report perceiving their tinnitus instantly upon awakening, during nocturnal awakenings, 18% declared they could be awakened by their tinnitus and 9.8% mentioned that their tinnitus can temporarily cease. (4) Conclusions: Our findings confirm the previous findings: tinnitus is rarely perceived during dreams. Remarkably, our study is the first to document the case of tinnitus during lucid dreaming. 64% of these patients gain higher-order consciousness attributes while still experiencing a tinnitus-free state. Our observations suggest that the presence or absence of gating of external auditory information during dreams acts as a tinnitus on-off switch, refining the previously proposed integrative model of auditory phantom perception.
Emotional Responses to Auditory Hierarchical Structures is Shaped by Bodily Sensations and Listeners' Sensory Traits
Authors: Maiko Minatoya, Tatsuya Daikoku, Yasuo Kuniyoshi
Date: 2025-04-02 00:00:00

Emotional responses to auditory stimuli are a common part of everyday life. However, for some individuals, these responses can be distressing enough to interfere with daily functioning. Despite their prevalence, the mechanisms underlying auditory-induced emotion remain only partially understood. Prior research has identified contributing factors such as auditory features, listener traits, and bodily sensations. However, most studies have focused on acoustic features, leaving the role of syntactic structure largely unexplored. This study specifically investigates how hierarchical syntactic structures influence emotional experience, in conjunction with listener traits and bodily sensations. An online experiment was conducted with 715 participants, who listened to 26 sound sequences varying systematically in hierarchical syntactic complexity. Sequences were generated by combining three types of local pitch movement with three types of global pitch movement in ascending and descending pitch directions, resulting in nine complexity levels. Participants rated the valence and arousal of each sequence and indicated any bodily sensations on a body map. Measures of sensory processing patterns were also collected. Results showed that emotional valence was associated with the complex interplay of moderate syntactic complexity ("not too simple, not too complex"), sensory sensitivity, and upper torso sensations. These findings expand existing research by identifying syntactic features that shape auditory-induced emotional experience and highlight the link between bodily sensation and emotional response. They also suggest potential applications for incorporating syntactic design into therapeutic approaches to emotion regulation.
BOLDSimNet: Examining Brain Network Similarity between Task and Resting-State fMRI
Authors: Boseong Kim, Debashis Das Chakladar, Haejun Chung, Ikbeom Jang
Date: 2025-04-02 00:00:00

Traditional causal connectivity methods in task-based and resting-state functional magnetic resonance imaging (fMRI) face challenges in accurately capturing directed information flow due to their sensitivity to noise and inability to model multivariate dependencies. These limitations hinder the effective comparison of brain networks between cognitive states, making it difficult to analyze network reconfiguration during task and resting states. To address these issues, we propose BOLDSimNet, a novel framework utilizing Multivariate Transfer Entropy (MTE) to measure causal connectivity and network similarity across different cognitive states. Our method groups functionally similar regions of interest (ROIs) rather than spatially adjacent nodes, improving accuracy in network alignment. We applied BOLDSimNet to fMRI data from 40 healthy controls and found that children exhibited higher similarity scores between task and resting states compared to adolescents, indicating reduced variability in attention shifts. In contrast, adolescents showed more differences between task and resting states in the Dorsal Attention Network (DAN) and the Default Mode Network (DMN), reflecting enhanced network adaptability. These findings emphasize developmental variations in the reconfiguration of the causal brain network, showcasing BOLDSimNet's ability to quantify network similarity and identify attentional fluctuations between different cognitive states.
Factors Influencing Farmers' Motivation to Adopt Smart Farm Technology in South Korea
Authors: Jihyuk Bang, Ji Woo Han
Date: 2025-04-02 00:00:00

Smart farming technologies have recently become a major focus because they promise improved agricultural productivity together with sustainability benefits. The rate at which farmers adopt new technologies differs because of various socio-economic and technological factors. This research investigates the main factors affecting smart farm adoption through an evaluation of farmer age, education attainment, land size, government banking, technological hurdles and financial limitations. Statistical analysis of survey responses reveals that farmers under thirty years old and possessing advanced education along with operating expansive farms demonstrate greater willingness to adopt smart farming solutions. Government policies that offer financial assistance and training programs drive farmers to adopt new systems yet technical and financial difficulties prevent broad adoption. The research results demonstrate the requirement for targeted policies that should help farmers by providing specific financial help and digital education programs. This study delivers important knowledge which helps government officials together with agricultural leaders to accelerate the adaptation speed of smart farming technology.
Group centrality in optimal and suboptimal vaccination for epidemic models in contact networks
Authors: J. Orestes Cerdeira, Fabio A. C. C. Chalub, Matheus Hansen
Date: 2025-04-01 00:00:00
Tasks: Epidemiology

The pursuit of strategies that minimize the number of individuals needing vaccination to control an outbreak is a well-established area of study in mathematical epidemiology. However, for certain diseases, public policy tends to prioritize immunizing vulnerable individuals over epidemic control. As a result, optimal vaccination strategies may not always be effective in supporting real-world public policies. A similar situation happens when a new vaccine is introduced and is in short supply, as target priority groups for vaccination have to be defined. In this work, we focus on a disease that results in long-term immunity and spreads through a heterogeneous population, represented by a contact network. We study four well-known group centrality measures and show that the GED-Walk offers a reliable means of estimating the impact of vaccinating specific groups of individuals, even in suboptimal cases. Additionally, we depart from the search for target individuals to be vaccinated and provide proxies for identifying optimal groups for vaccination. While the GED-Walk is the most useful centrality measure for suboptimal cases, the betweenness (a related, but different centrality measure) stands out when looking for optimal groups. This indicates that optimal vaccination is not concerned with breaking the largest number of transmission routes, but interrupting geodesic ones.
Why risk matters for protein binder design
Authors: Tudor-Stefan Cotet, Igor Krawczuk
Date: 2025-03-31 00:00:00
Tasks: Bayesian Optimization, Model Selection, Portfolio Optimization

Bayesian optimization (BO) has recently become more prevalent in protein engineering applications and hence has become a fruitful target of benchmarks. However, current BO comparisons often overlook real-world considerations like risk and cost constraints. In this work, we compare 72 model combinations of encodings, surrogate models, and acquisition functions on 11 protein binder fitness landscapes, specifically from this perspective. Drawing from the portfolio optimization literature, we adopt metrics to quantify the cold-start performance relative to a random baseline, to assess the risk of an optimization campaign, and to calculate the overall budget required to reach a fitness threshold. Our results suggest the existence of Pareto-optimal models on the risk-performance axis, the shift of this preference depending on the landscape explored, and the robust correlation between landscape properties such as epistasis with the average and worst-case model performance. They also highlight that rigorous model selection requires substantial computational and statistical efforts.