pwc-archive/papers-with-abstracts huggingface dataset:
Recovering Time-Varying Networks From Single-Cell Data
Authors: Euxhen Hasanaj, Barnabás Póczos, Ziv Bar-Joseph
Date: 2024-10-01 00:00:00
Tasks: Meta-Learning, Time Series

Gene regulation is a dynamic process that underlies all aspects of human development, disease response, and other key biological processes. The reconstruction of temporal gene regulatory networks has conventionally relied on regression analysis, graphical models, or other types of relevance networks. With the large increase in time series single-cell data, new approaches are needed to address the unique scale and nature of this data for reconstructing such networks. Here, we develop a deep neural network, Marlene, to infer dynamic graphs from time series single-cell gene expression data. Marlene constructs directed gene networks using a self-attention mechanism where the weights evolve over time using recurrent units. By employing meta learning, the model is able to recover accurate temporal networks even for rare cell types. In addition, Marlene can identify gene interactions relevant to specific biological responses, including COVID-19 immune response, fibrosis, and aging.
smICA: an open source repository for mapping the concentration of fluorescently labeled molecules in living cells on the basis of confocal imaging combined with time-correlated single-photon counting
Authors: Tomasz Kalwarczyk, Grzegorz Bubak, Jarosław Michalski, Karina Kwapiszewska, Marta Pilz, Adam Mamot, Jacek Jemielity, Robert Hołyst
Date: 2024-10-01 00:00:00

Advanced microscopy techniques are essential for visualizing and tracking cellular components and molecules in biomedical research. However, conventional fluorescence microscopy methods often struggle with accurately measuring molecule concentrations in cells. To overcome these limitations, we introduce a novel approach that integrates laser scanning confocal microscopy with time-correlated single photon counting (TCSPC), supported by an open-source analysis tool called smICA (single-molecule Image to Concentration Analyzer). Our method, validated against traditional fluorescence correlation spectroscopy (FCS), offers enhanced accuracy in determining fluorescent molecule concentrations, particularly in cases where molecules are immobile or unevenly distributed. This is demonstrated using fluorescently labeled mRNA in living cells, highlighting the approach's effectiveness.
RNA-Protein Interaction Prediction Based on Deep Learning: A Comprehensive Survey
Authors: Danyu Li, Rubing Huang, Chenhui Cui, Dave Towey, Ling Zhou, Jinyu Tian, Bin Zou
Date: 2024-09-30 00:00:00
Tasks: Deep Learning, Survey

The interaction between Ribonucleic Acids (RNAs) and proteins, also called RNA Protein Interaction (RPI), plays an important role in the life activities of organisms, including in various regulatory processes, such as gene splicing, gene localization, and disease pathogenesis. RPI Prediction (RPIP) predicts the interactions between RNAs and proteins, which includes looking for the existence of interactions and the binding sites of interactions, and adding RNA-protein functional annotations (such as immunity regulation, neuroprotection, etc). Due to the huge amounts of complex biological data, Deep Learning-based RPIP (DL-based RPIP) has been widely investigated, as it can extract high-dimensional features from data and make accurate predictions. Over the last decade, there have been many achievements and contributions in DL-based RPIP. Although some previous studies review DL-based RPIP, to the best of our knowledge, there is still a lack of a comprehensive survey. In this paper, we extensively survey DL-based RPIP in terms of its entire process, including: feature encoding, deep learning modeling, results evaluation, RPIP application domains, and available websites and software. We also identify some open research challenges, and discuss the potential future work for DL-based RPIP.
Deciphering Cell Systems: Machine Learning Perspectives And Approaches For The Analysis Of Single-Cell Data
Authors: Yongjian Yang
Date: 2024-09-28 00:00:00

This dissertation explores the application of machine learning in molecular biology, focusing on gene expression regulation and cellular behavior at the single-cell level. Using modern neural networks, the research addresses key challenges in cell-cell communication, gene function inference, and protein expression analysis, with a special focus on single-cell RNA sequencing (scRNA-seq) data. Advanced computational methodologies integrating systems biology and neural network techniques were developed to handle the complexity and high-dimensionality of single-cell data, leading to a deeper understanding of genotype-phenotype relationships. This work proposes novel solutions to optimization problems in manifold learning, explores generative models for gene regulatory networks, and simulates gene knockout at single-cell resolution. Furthermore, the research enhances the interpretability of black-box neural models for multimodal data. Key contributions to cellular biology include the analysis of cell-cell interactions and their role in shaping cellular behavior, gene function prediction through knockout simulations, and the investigation of how gene expression patterns translate into protein expression. These findings have implications for understanding disease mechanisms and therapeutic development. Overall, this dissertation advances computational biology by providing new tools and insights into single-cell analysis, offering a valuable resource for future studies on cellular behavior and potential treatments for various diseases.
Towards Precision Feeding Using Behavioral Monitoring in Marine Cages
Authors: Dimitra Georgopoulou, Charalabos Vouidaskis, Nikos Papandroulakis
Date: 2024-09-27 00:00:00

Aquaculture is expected to account for two-thirds of global fish consumption by 2030, highlighting the need for sustainable and efficient practices. Feeding is crucial to aquaculture success, influenced by factors like fish size, environment, and health. This study addresses a gap in feeding control for sea cages by developing a real-time monitoring system, using AI models and computer vision to analyze feeding behavior with European sea bass as pilot species. Key metrics like fish speed and a new feeding behavior index (FBI) were used to assess responses to different feeding scenarios. The results revealed distinct behavior patterns based on feeding quantity, with imbalances in activity when fish are overfed or underfed. The results can be used for predicting the level of satiation of the fish and controlling feeding duration.
A Grasping Movement Intention Estimator for Intuitive Control of Assistive Devices
Authors: Etienne Moullet, François Bailly, Justin Carpentier, Christine Azevedo Coste
Date: 2024-09-25 00:00:00
Tasks: Object

This study introduces i-GRIP, an innovative movement goal estimator designed to facilitate the control of assistive devices for grasping tasks in individuals with upperlimb impairments. The algorithm operates within a collaborative control paradigm, eliminating the need for specific user actions apart from naturally moving their hand toward a desired object. i-GRIP analyzes the hand's movement in an object-populated scene to determine its target and select an appropriate grip. In an experimental study involving 11 healthy participants, i-GRIP showed promising goal estimation performances and responsiveness and the potential to facilitate the daily use of assistive devices for individuals with upper-limb impairments in the future.
GATher: Graph Attention Based Predictions of Gene-Disease Links
Authors: David Narganes-Carlon, Anniek Myatt, Mani Mudaliar, Daniel J. Crowther
Date: 2024-09-23 00:00:00
Tasks: Drug Discovery, Graph Attention, Specificity

Target selection is crucial in pharmaceutical drug discovery, directly influencing clinical trial success. Despite its importance, drug development remains resource-intensive, often taking over a decade with significant financial costs. High failure rates highlight the need for better early-stage target selection. We present GATher, a graph attention network designed to predict therapeutic gene-disease links by integrating data from diverse biomedical sources into a graph with over 4.4 million edges. GATher incorporates GATv3, a novel graph attention convolution layer, and GATv3HeteroConv, which aggregates transformations for each edge type, enhancing its ability to manage complex interactions within this extensive dataset. Utilizing hard negative sampling and multi-task pre-training, GATher addresses topological imbalances and improves specificity. Trained on data up to 2018 and evaluated through 2024, our results show GATher predicts clinical trial outcomes with a ROC AUC of 0.69 for unmet efficacy failures and 0.79 for positive efficacy. Feature attribution methods, using Captum, highlight key nodes and relationships, enhancing model interpretability. By 2024, GATher improved precision in prioritizing the top 200 clinical trial targets to 14.1%, an absolute increase of over 3.5% compared to other methods. GATher outperforms existing models like GAT, GATv2, and HGT in predicting clinical trial outcomes, demonstrating its potential in enhancing target validation and predicting clinical efficacy and safety.
Hybrid methods in reaction-diffusion equations
Authors: Tomás Alarcón, Natalia Briñas-Pascual, Juan Calvo, Pilar Guerrero, Daria Stepanova
Date: 2024-09-20 00:00:00

Simulation of stochastic spatially-extended systems is a challenging problem. The fundamental quantities in these models are individual entities such as molecules, cells, or animals, which move and react in a random manner. In big systems, accounting for each individual is inefficient. If the number of entities is large enough, random effects are negligible, and often partial differential equations (PDEs) are used in which the fluctuations are neglected. When the system is heterogeneous, so that the number of individuals is large in certain regions and small in others, the PDE description becomes inaccurate in certain regions. To overcome this problem, the so-called hybrid schemes have been proposed that couple a stochastic description in parts of the domain with its mean field limit in the others. In this chapter, we review the different formulations of this approach and our recent contributions to overcome several of the limitations of previous schemes, including the extension of the concept to multiscale models of cell populations.
pAE: An Efficient Autoencoder Architecture for Modeling the Lateral Geniculate Nucleus by Integrating Feedforward and Feedback Streams in Human Visual System
Authors: Moslem Gorji, Amin Ranjbar, Mohammad Bagher Menhaj
Date: 2024-09-20 00:00:00

The visual cortex is a vital part of the brain, responsible for hierarchically identifying objects. Understanding the role of the lateral geniculate nucleus (LGN) as a prior region of the visual cortex is crucial when processing visual information in both bottom-up and top-down pathways. When visual stimuli reach the retina, they are transmitted to the LGN area for initial processing before being sent to the visual cortex for further processing. In this study, we introduce a deep convolutional model that closely approximates human visual information processing. We aim to approximate the function for the LGN area using a trained shallow convolutional model which is designed based on a pruned autoencoder (pAE) architecture. The pAE model attempts to integrate feed forward and feedback streams from/to the V1 area into the problem. This modeling framework encompasses both temporal and non-temporal data feeding modes of the visual stimuli dataset containing natural images captured by a fixed camera in consecutive frames, featuring two categories: images with animals (in motion), and images without animals. Subsequently, we compare the results of our proposed deep-tuned model with wavelet filter bank methods employing Gabor and biorthogonal wavelet functions. Our experiments reveal that the proposed method based on the deep-tuned model not only achieves results with high similarity in comparison with human benchmarks but also performs significantly better than other models. The pAE model achieves the final 99.26% prediction performance and demonstrates a notable improvement of around 28% over human results in the temporal mode.
CMINNs: Compartment Model Informed Neural Networks -- Unlocking Drug Dynamics
Authors: Nazanin Ahmadi Daryakenari, Shupeng Wang, George Karniadakis
Date: 2024-09-19 00:00:00
Tasks: parameter estimation

In the field of pharmacokinetics and pharmacodynamics (PKPD) modeling, which plays a pivotal role in the drug development process, traditional models frequently encounter difficulties in fully encapsulating the complexities of drug absorption, distribution, and their impact on targets. Although multi-compartment models are frequently utilized to elucidate intricate drug dynamics, they can also be overly complex. To generalize modeling while maintaining simplicity, we propose an innovative approach that enhances PK and integrated PK-PD modeling by incorporating fractional calculus or time-varying parameter(s), combined with constant or piecewise constant parameters. These approaches effectively model anomalous diffusion, thereby capturing drug trapping and escape rates in heterogeneous tissues, which is a prevalent phenomenon in drug dynamics. Furthermore, this method provides insight into the dynamics of drug in cancer in multi-dose administrations. Our methodology employs a Physics-Informed Neural Network (PINN) and fractional Physics-Informed Neural Networks (fPINNs), integrating ordinary differential equations (ODEs) with integer/fractional derivative order from compartmental modeling with neural networks. This integration optimizes parameter estimation for variables that are time-variant, constant, piecewise constant, or related to the fractional derivative order. The results demonstrate that this methodology offers a robust framework that not only markedly enhances the model's depiction of drug absorption rates and distributed delayed responses but also unlocks different drug-effect dynamics, providing new insights into absorption rates, anomalous diffusion, drug resistance, peristance and pharmacokinetic tolerance, all within a system of just two (fractional) ODEs with explainable results.
What is in a Scent? Understanding the role of scent marking in social dynamics and territoriality of free-ranging dogs
Authors: Sourabh Biswas, Kalyan Ghosh, Swarnali Ghosh, Akash Biswas, Anindita Bhadra
Date: 2024-09-18 00:00:00

Scent marks play a crucial role in both territorial and sexual communication in many species. We investigated how free-ranging dogs respond to scent marks from individuals of different identities in terms of sex and group, across varying strategic locations within their territory. Both male and female dogs showed heightened interest in scent marks compared to control, exhibiting stronger territorial responses,. with males being more territorial than females. Overmarking behaviour was predominantly observed in males, particularly in response to male scent marks and those from neighbouring groups. Behavioural cluster analysis revealed distinct responses to different scent marks, with neighbouring group male scents eliciting the most distinct reactions. Our findings highlight the multifaceted role of scent marks in free-ranging dog communication, mediating both territorial defence and intrasexual competition. The differential responses based on the identity and gender of the scent-marker emphasize the complexity of olfactory signalling in this species. This study contributes to understanding the social behaviour of dogs in their natural habitat, and opens up possibilities for future explorations in the role of olfactory cues in the social dynamics of the species.
Physics-Informed Neural Networks can accurately model cardiac electrophysiology in 3D geometries and fibrillatory conditions
Authors: Ching-En Chiu, Aditi Roy, Sarah Cechnicka, Ashvin Gupta, Arieh Levy Pinto, Christoforos Galazis, Kim Christensen, Danilo Mandic, Marta Varela
Date: 2024-09-18 00:00:00
Tasks: Rhythm

Physics-Informed Neural Networks (PINNs) are fast becoming an important tool to solve differential equations rapidly and accurately, and to identify the systems parameters that best agree with a given set of measurements. PINNs have been used for cardiac electrophysiology (EP), but only in simple 1D and 2D geometries and for sinus rhythm or single rotor dynamics. Here, we demonstrate how PINNs can be used to accurately reconstruct the propagation of cardiac action potential in more complex geometries and dynamical regimes. These include 3D spherical geometries and spiral break-up conditions that model cardiac fibrillation, with a mean RMSE $< 5.1\times 10^{-2}$ overall. We also demonstrate that PINNs can be used to reliably parameterise cardiac EP models with some biological detail. We estimate the diffusion coefficient and parameters related to ion channel conductances in the Fenton-Karma model in a 2D setup, achieving a mean relative error of $-0.09\pm 0.33$. Our results are an important step towards the deployment of PINNs to realistic cardiac geometries and arrhythmic conditions.
SEE: Semantically Aligned EEG-to-Text Translation
Authors: Yitian Tao, Yan Liang, Luoyu Wang, Yongqing Li, Qing Yang, Han Zhang
Date: 2024-09-14 00:00:00
Tasks: Brain Computer Interface, EEG, Language Modeling, Language Modelling, Translation

Decoding neurophysiological signals into language is of great research interest within brain-computer interface (BCI) applications. Electroencephalography (EEG), known for its non-invasiveness, ease of use, and cost-effectiveness, has been a popular method in this field. However, current EEG-to-Text decoding approaches face challenges due to the huge domain gap between EEG recordings and raw texts, inherent data bias, and small closed vocabularies. In this paper, we propose SEE: Semantically Aligned EEG-to-Text Translation, a novel method aimed at improving EEG-to-Text decoding by seamlessly integrating two modules into a pre-trained BART language model. These two modules include (1) a Cross-Modal Codebook that learns cross-modal representations to enhance feature consolidation and mitigate domain gap, and (2) a Semantic Matching Module that fully utilizes pre-trained text representations to align multi-modal features extracted from EEG-Text pairs while considering noise caused by false negatives, i.e., data from different EEG-Text pairs that have similar semantic meanings. Experimental results on the Zurich Cognitive Language Processing Corpus (ZuCo) demonstrate the effectiveness of SEE, which enhances the feasibility of accurate EEG-to-Text decoding.
An asymmetric surface coating strategy for promotes rapid endothelialization in the rabbit carotid artery
Authors: Lili Tan, Zhiyi Ye, Suhua Yu, Jinxuan Wang, Chenxi Ouyang, Zhengcai Zhang, Robert Guidoin, Guixue Wang
Date: 2024-10-06 00:00:00

Studying surface modification has long been a key area for enhancing the effects of vascular stents after surgery. The study aimed to develop an asymmetric drug-eluting stent (ADES) with differential drug loading on its inner and outer surfaces, hypothesizing that this design would enhance drug delivery efficacy for percutaneous coronary interventions (PCIs) compared to uniformly coated drug-eluting stents (UDES). An ultrasonic atomization spraying device was utilized to fabricate the ADES, which was subsequently evaluated for drug release patterns, hemocompatibility, and biocompatibility. In vitro, assessments demonstrated favorable hemocompatibility and showed targeted drug delivery capabilities of ADES within artificial blood vessels. Furthermore, in vivo testing using a rabbit carotid artery model revealed significant endothelialization on stented segments treated with the ADES. These findings suggest that the ADES holds promise as a minimally invasive platform for improving cardiovascular disease treatment outcomes by addressing thrombus formation and neointima proliferation more effectively than traditional stents.
Sonic Entanglements with Electromyography: Between Bodies, Signals, and Representations
Authors: Courtney N Reed, Landon Morrison, Andrew P McPherson, David Fierro, Atau Tanaka
Date: 2024-09-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Electromyography (EMG)

This paper investigates sound and music interactions arising from the use of electromyography (EMG) to instrumentalise signals from muscle exertion of the human body. We situate EMG within a family of embodied interaction modalities, where it occupies a middle ground, considered as a ''signal from the inside'' compared with external observations of the body (e.g., motion capture), but also seen as more volitional than neurological states recorded by brain electroencephalogram (EEG). To understand the messiness of gestural interaction afforded by EMG, we revisit the phenomenological turn in HCI, reading Paul Dourish's work on the transparency of ''ready-to-hand'' technologies against the grain of recent posthumanist theories, which offer a performative interpretation of musical entanglements between bodies, signals, and representations. We take music performance as a use case, reporting on the opportunities and constraints posed by EMG in workshop-based studies of vocal, instrumental, and electronic practices. We observe that across our diverse range of musical subjects, they consistently challenged notions of EMG as a transparent tool that directly registered the state of the body, reporting instead that it took on ''present-at-hand'' qualities, defamiliarising the performer's own sense of themselves and reconfiguring their embodied practice.
Zeolitic Imidazolate Framework-8 offers an anti-inflammatory and antifungal method in the treatment of Aspergillus fungus keratitis in vitro and in vivo
Authors: Xueyun Fu, Xue Tian, Jing Lin, Qian Wang, Lingwen Gu, Ziyi Wang, Menghui Chi, Bing Yu, Zhuhui Feng, Wenyao Liu, Lina Zhang, Cui Li, Guiqiu Zhao
Date: 2024-09-30 00:00:00
Tasks: X-Ray Diffraction (XRD)

Background: Fungal keratitis is a serious blinding eye disease. Traditional drugs used to treat fungal keratitis commonly have the disadvantages of low bioavailability, poor dispersion, and limited permeability. Purpose: To develop a new method for the treatment of fungal keratitis with improved bioavailability, dispersion, and permeability. Purpose: To develop a new method for the treatment of fungal keratitis with improved bioavailability, dispersion, and permeability. Methods: Zeolitic Imidazolate Framework-8 (ZIF-8) was formed by zinc ions and 2-methylimidazole linked by coordination bonds and characterized by Scanning electron microscopy (SEM), X-ray diffraction (XRD), and Zeta potential. The safety of ZIF-8 on HCECs and RAW 264.7 cells was detected by Cell Counting Kit-8 (CCK-8). The anti-inflammatory effects of ZIF-8 on RAW 246.7 cells were evaluated by Quantitative Real-Time PCR Experiments (qPCR) and Enzyme-linked immunosorbent assay (ELISA). Clinical score, Colony-Forming Units (CFU). In vivo, treatment with ZIF-8 reduced corneal fungal load and mitigated neutrophil infiltration in fungal keratitis, which effectively reduced the severity of keratitis in mice and alleviated the infiltration of inflammatory factors in the mouse cornea. In addition, ZIF-8 reduces the inflammatory response by downregulating the expression of pro-inflammatory cytokines TNF-{\alpha}, IL-6, and IL-1\b{eta} after Aspergillus fumigatus infection in vivo and in vitro. Conclusion: ZIF-8 has a significant anti-inflammatory and antifungal effect, which provides a new solution for the treatment of fungal keratitis.