pwc-archive/papers-with-abstracts huggingface dataset:
Unraveling cell-cell communication with NicheNet by inferring active ligands from transcriptomics data
Authors: Chananchida Sang-aram, Robin Browaeys, Ruth Seurinck, Yvan Saeys
Date: 2024-04-25 00:00:00

Ligand-receptor interactions constitute a fundamental mechanism of cell-cell communication and signaling. NicheNet is a well-established computational tool that infers ligand-receptor interactions that potentially regulate gene expression changes in receiver cell populations. Whereas the original publication delves into the algorithm and validation, this paper describes a best practices workflow cultivated over four years of experience and user feedback. Starting from the input single-cell expression matrix, we describe a "sender-agnostic" approach which considers ligands from the entire microenvironment, and a "sender-focused" approach which only considers ligands from cell populations of interest. As output, users will obtain a list of prioritized ligands and their potential target genes, along with multiple visualizations. In NicheNet v2, we have updated the data sources and implemented a downstream procedure for prioritizing cell-type-specific ligand-receptor pairs. Although a standard NicheNet analysis takes less than 10 minutes to run, users often invest additional time in making decisions about the approach and parameters that best suit their biological question. This paper serves to aid in this decision-making process by describing the most appropriate workflow for common experimental designs like case-control and cell differentiation studies. Finally, in addition to the step-by-step description of the code, we also provide wrapper functions that enable the analysis to be run in one line of code, thus tailoring the workflow to users at all levels of computational proficiency.
Directional intermodular coupling enriches functional complexity in biological neuronal networks
Authors: Nobuaki Monma, Hideaki Yamamoto, Naoya Fujiwara, Hakuba Murota, Satoshi Moriya, Ayumi Hirano-Iwata, Shigeo Sato
Date: 2024-04-25 00:00:00

Hierarchically modular organization is a canonical network topology that is evolutionarily conserved in the nervous systems of animals. Within the network, neurons form directional connections defined by the growth of their axonal terminals. However, this topology is dissimilar to the network formed by dissociated neurons in culture because they form randomly connected networks on homogeneous substrates. In this study, we fabricated microfluidic devices to reconstitute hierarchically modular neuronal networks in culture (in vitro) and investigated how non-random structures, such as directional connectivity between modules, affect global network dynamics. Embedding directional connections in a pseudo-feedforward manner suppressed excessive synchrony in cultured neuronal networks and enhanced the integration-segregation balance. Modeling the behavior of biological neuronal networks using spiking neural networks (SNNs) further revealed that modularity and directionality cooperate to shape such network dynamics. Finally, we demonstrate that for a given network topology, the statistics of network dynamics, such as global network activation, correlation coefficient, and functional complexity, can be analytically predicted based on eigendecomposition of the transition matrix in the state-transition model. Hence, the integration of bioengineering and cell culture technologies enables us not only to reconstitute complex network circuitry in the nervous system but also to understand the structure-function relationships in biological neuronal networks by bridging theoretical modeling with in vitro experiments.
Resting state fMRI-based brain information flow mapping
Authors: Ze Wang
Date: 2024-04-23 00:00:00

Human brain is a massive information generation and processing machine. Studying the information flow may provide unique insight into brain function and brain diseases. We present here a tool for mapping the regional information flow in the entire brain using fMRI. Using the tool, we can estimate the information flow from a single region to the rest of the brain, between different regions, between different days, or between different individuals' brain.
Semantic distance organizes social knowledge: Insights from semantic dementia and cross-modal conceptual space
Authors: Y. Ivette Colón, Matthew Rouse, Matthew A. Lambon Ralph, Timothy T. Rogers
Date: 2024-04-23 00:00:00
Tasks: Anatomy, Triplet

Our interaction with others largely hinges on how we semantically organize the social world. The organization of such conceptual information is not static -- as we age, our experiences and ever-changing anatomy alter how we represent and arrange semantic information. How does semantic distance between concepts affect this organization, particularly for those with pathological deficits in semantic knowledge? Using triplet judgment responses collected from healthy participants, we compute an ordinal similarity embedding for a set of social words and images that vary in the dimensions of age and gender. We compare semantic distances between items in the space to patterns of error in a word-picture matching task performed by patients with semantic dementia (SD). Error patterns reveal that SD patients retain gender information more robustly than age information, and that age-related errors are a function of linear distance in age from a concept word. The distances between probed and exemplar items in the resulting conceptual map reflect error patterns in SD patient responses such that items semantically closer to a probed concept -- in gender category or in linear age -- are more likely to be erroneously chosen by patients in a word-picture matching task. To our knowledge, this is the first triplet embedding work to embed representations of words and images in a unified space, and to use this space to explain patterns of behavior in patients with impaired social semantic cognition.
How we Learn Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teaching
Authors: Zhong Wang
Date: 2024-04-23 00:00:00
Tasks: Transfer Learning

This article reviews the psychological and neuroscience achievements in concept learning since 2010 from the perspectives of individual learning and social learning, and discusses several issues related to concept learning, including the assistance of machine learning about concept learning. 1 In terms of individual learning, current evidences shown that the brain tends to process concrete concepts through typical features (shared features); And abstract concepts, semantic processing is the most important cognitive way. 2 In terms of social learning, Interpersonal Neuro Synchronization (INS) is considered the main indicator of efficient knowledge transfer (such as teaching activities between teachers and students), but this phenomenon only broadens the channels for concept sources and does not change the basic mode of individual concept learning. Ultimately, this article argues that the way the human brain processes concepts depends on concept's own characteristics, so there are no 'better' strategies in teaching, only more 'suitable' strategies.
Sparse Bayesian Correntropy Learning for Robust Muscle Activity Reconstruction from Noisy Brain Recordings
Authors: Yuanhao Li, Badong Chen, Natsue Yoshimura, Yasuharu Koike, Okito Yamashita
Date: 2024-04-01 00:00:00

Sparse Bayesian learning has promoted many effective frameworks for brain activity decoding, especially for the reconstruction of muscle activity. However, existing sparse Bayesian learning mainly employs Gaussian distribution as error assumption in the reconstruction task, which is not necessarily the truth in the real-world application. On the other hand, brain recording is known to be highly noisy and contains many non-Gaussian noises, which could lead to significant performance degradation for sparse Bayesian learning method. The goal of this paper is to propose a new robust implementation for sparse Bayesian learning, so that robustness and sparseness can be realized simultaneously. Motivated by the great robustness of maximum correntropy criterion (MCC), we proposed an integration of MCC into the sparse Bayesian learning regime. To be specific, we derived the explicit error assumption inherent in the MCC and then leveraged it for the likelihood function. Meanwhile, we used the automatic relevance determination (ARD) technique for the sparse prior distribution. To fully evaluate the proposed method, a synthetic dataset and a real-world muscle activity reconstruction task with two different brain modalities were employed. Experimental results showed that our proposed sparse Bayesian correntropy learning framework improves significantly the robustness in a noisy regression task. The proposed method can realize higher correlation coefficient and lower root mean squared error in the real-world muscle activity reconstruction tasks. Sparse Bayesian correntropy learning provides a powerful tool for neural decoding which can promote the development of brain-computer interfaces.
Pilot Study to Discover Candidate Biomarkers for Autism based on Perception and Production of Facial Expressions
Authors: Megan A. Witherow, Norou Diawara, Janice Keener, John W. Harrington, Khan M. Iftekharuddin
Date: 2024-03-27 00:00:00

Purpose: Facial expression production and perception in autism spectrum disorder (ASD) suggest potential presence of behavioral biomarkers that may stratify individuals on the spectrum into prognostic or treatment subgroups. Construct validity and group discriminability have been recommended as criteria for identification of candidate stratification biomarkers. Methods: In an online pilot study of 11 children and young adults diagnosed with ASD and 11 age- and gender-matched neurotypical (NT) individuals, participants recognize and mimic static and dynamic facial expressions of 3D avatars. Webcam-based eye-tracking (ET) and facial video tracking (VT), including activation and asymmetry of action units (AUs) from the Facial Action Coding System (FACS) are collected. We assess validity of constructs for each dependent variable (DV) based on the expected response in the NT group. Then, the Boruta statistical method identifies DVs that are significant to group discriminability (ASD or NT). Results: We identify one candidate ET biomarker (percentage gaze duration to the face while mimicking static 'disgust' expression) and 14 additional DVs of interest for future study, including 4 ET DVs, 5 DVs related to VT AU activation, and 4 DVs related to AU asymmetry in VT. Based on a power analysis, we provide sample size recommendations for future studies. Conclusion: This pilot study provides a framework for ASD stratification biomarker discovery based on perception and production of facial expressions.
Mechanisms promoting biodiversity in ecosystems
Authors: Ju Kang, Yiyuan Niu, Xin Wang
Date: 2024-04-23 00:00:00

Explaining biodiversity is a central focus in theoretical ecology. A significant obstacle arises from the Competitive Exclusion Principle (CEP), which states that two species competing for the same type of resources cannot coexist at constant population densities, or more generally, the number of consumer species cannot exceed that of resource species at steady states. The conflict between CEP and biodiversity is exemplified by the paradox of the plankton, where a few types of limiting resources support a plethora of plankton species. In this review, we introduce mechanisms proposed over the years for promoting biodiversity in ecosystems, with a special focus on those that alleviate the constraints imposed by the CEP, including mechanisms that challenge the CEP in well-mixed systems at a steady state or those that circumvent its limitations through contextual differences.
Assessing Human Judgment Forecasts in the Rapid Spread of the Mpox Outbreak: Insights and Challenges for Pandemic Preparedness
Authors: Thomas McAndrew, Maimuna S. Majumder, Andrew A. Lover, Srini Venkatramanan, Paolo Bocchini, Tamay Besiroglu, Allison Codi, Gaia Dempsey, Sam Abbott, Sylvain Chevalier, Nikos I. Bosse, Juan Cambeiro, David Braun
Date: 2024-04-23 00:00:00

In May 2022, mpox (formerly monkeypox) spread to non-endemic countries rapidly. Human judgment is a forecasting approach that has been sparsely evaluated during the beginning of an outbreak. We collected -- between May 19, 2022 and July 31, 2022 -- 1275 forecasts from 442 individuals of six questions about the mpox outbreak where ground truth data are now available. Individual human judgment forecasts and an equally weighted ensemble were evaluated, as well as compared to a random walk, autoregressive, and doubling time model. We found (1) individual human judgment forecasts underestimated outbreak size, (2) the ensemble forecast median moved closer to the ground truth over time but uncertainty around the median did not appreciably decrease, and (3) compared to computational models, for 2-8 week ahead forecasts, the human judgment ensemble outperformed all three models when using median absolute error and weighted interval score; for one week ahead forecasts a random walk outperformed human judgment. We propose two possible explanations: at the time a forecast was submitted, the mode was correlated with the most recent (and smaller) observation that would eventually determine ground truth. Several forecasts were solicited on a logarithmic scale which may have caused humans to generate forecasts with unintended, large uncertainty intervals. To aide in outbreak preparedness, platforms that solicit human judgment forecasts may wish to assess whether specifying a forecast on logarithmic scale matches an individual's intended forecast, support human judgment by finding cues that are typically used to build forecasts, and, to improve performance, tailor their platform to allow forecasters to assign zero probability to events.
Harnessing Big Data and Artificial Intelligence to Study Plant Stress
Authors: Eugene Koh, Rohan Shawn Sunil, Hilbert Yuen In Lam, Marek Mutwil
Date: 2024-04-24 00:00:00

Life finds a way. For sessile organisms like plants, the need to adapt to changes in the environment is even more poignant. For humanity, the need to develop crops that can grow in diverse environments and feed our growing population is an existential one. The advent of the genomics era enabled the generation of high-throughput data and computational methods that serve as powerful hypothesis-generating tools to understand the genomic and gene functional basis of stress resilience. Today, the proliferation of artificial intelligence (AI) allows scientists to rapidly screen through high-throughput datasets to uncover elusive patterns and correlations, enabling us to create more performant models for prediction and hypothesis generation in plant biology. This review aims to provide an overview of the availability of large-scale data in plant stress research and discuss the application of AI tools on these large-scale datasets in a bid to develop more stress-resilient plants.
Detection of circular permutations by Protein Language Models
Authors: Yue Hu, Bin Huang, Chunzi Zang
Date: 2024-04-23 00:00:00
Tasks: Computational Efficiency, Language Modeling, Language Modelling, Protein Language Model

Protein circular permutations are crucial for understanding protein evolution and functionality. Traditional detection methods, sequence-based or structure-based, struggle with accuracy and computational efficiency, the latter also limited by treating proteins as rigid bodies. The plmCP method, utilizing a protein language model, not only speeds up the detection process but also enhances the accuracy of identifying circular permutations, contributing significantly to protein research and engineering by acknowledging structural flexibility.
Frailty Assessment in Aortic Stenosis based on Dynamic Interconnection between Cardiac and Motor Systems
Authors: Patricio Arrué, Kaveh Laksari, Nancy Sweitzer, Mindy Fain, Nima Toosizadeh
Date: 2024-04-16 00:00:00

Background: Aortic stenosis (AS) is the most common acquired valvar disease and is associated with increased risk for frailty. Frailty as a geriatric syndrome is associated with muscle weakness and a compromised autonomic nervous system (ANS) performance in older adults. The purpose of the current work was to assess differences in both motor and ANS performance, and interaction between them, as symptoms of frailty in community dwelling older adults with and without AS. Results: Eighty-six participants were recruited, including 30 with (age=72$\pm$11, 10 non-frail and 20 pre-frail/frail) and 56 without AS (age=80$\pm$8, 12 non-frail and 44 pre-frail/frail). There was a significant difference in UEF motor score between older adults with and without AS (p<0.01, mean values of 0.57$\pm$0.25 and 0.48$\pm$0.23, respectively). Differences in UEF motor score was also observed between the frailty groups (p=0.02, mean values of 0.55$\pm$0.24 and 0.40$\pm$0.20 for pre-frail/frail and non-frail, respectively). CCM parameters showed significant differences between the frailty groups (p=0.02, mean CCM of 0.69$\pm$0.05 for non-frail and 0.54$\pm$0.03 for pre-frail/frail), but not between the AS groups (p>0.70). No significant interaction was observed between frailty and AS condition (p>0.08). Conclusion: Current findings suggest that ANS measures may be highly associated with frailty regardless of AS condition. Combining motor and HR dynamics parameters in a multimodal model may provide a promising tool for frailty assessment
VASARI-auto: equitable, efficient, and economical featurisation of glioma MRI
Authors: James K Ruffle, Samia Mohinta, Kelly Pegoretti Baruteau, Rebekah Rajiah, Faith Lee, Sebastian Brandner, Parashkev Nachev, Harpreet Hyare
Date: 2024-04-03 00:00:00
Tasks: Survival Prediction

The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and seldom used in clinical practice. This is a problem that machine learning could plausibly automate. Using glioma data from 1172 patients, we developed VASARI-auto, an automated labelling software applied to both open-source lesion masks and our openly available tumour segmentation model. In parallel, two consultant neuroradiologists independently quantified VASARI features in a subsample of 100 glioblastoma cases. We quantified: 1) agreement across neuroradiologists and VASARI-auto; 2) calibration of performance equity; 3) an economic workforce analysis; and 4) fidelity in predicting patient survival. Tumour segmentation was compatible with the current state of the art and equally performant regardless of age or sex. A modest inter-rater variability between in-house neuroradiologists was comparable to between neuroradiologists and VASARI-auto, with far higher agreement between VASARI-auto methods. The time taken for neuroradiologists to derive VASARI was substantially higher than VASARI-auto (mean time per case 317 vs. 3 seconds). A UK hospital workforce analysis forecast that three years of VASARI featurisation would demand 29,777 consultant neuroradiologist workforce hours ({\pounds}1,574,935), reducible to 332 hours of computing time (and {\pounds}146 of power) with VASARI-auto. The best-performing survival model utilised VASARI-auto features as opposed to those derived by neuroradiologists. VASARI-auto is a highly efficient automated labelling system with equitable performance across patient age or sex, a favourable economic profile if used as a decision support tool, and with non-inferior fidelity in downstream patient survival prediction. Future work should iterate upon and integrate such tools to enhance patient care.
Bioregionalization analyses with the bioregion R-package
Authors: Pierre Denelle, Boris Leroy, Maxime Lenormand
Date: 2024-03-28 00:00:00
Tasks: Clustering, Community Detection

Bioregionalization consists in the identification of spatial units with similar species composition and is a classical approach in the fields of biogeography and macroecology. The recent emergence of global databases, improvements in computational power, and the development of clustering algorithms coming from the network theory have led to several major updates of the bioregionalizations of many taxa. A typical bioregionalization workflow involves five different steps: formatting the input data, computing a (dis)similarity matrix, selecting a bioregionalization algorithm, evaluating the resulting bioregionalization, and mapping and interpreting the bioregions. For most of these steps, there are many options available in the methods and R packages. Here, we present bioregion, a package that includes all the steps of a bioregionalization workflow under a single architecture, with an exhaustive list of the bioregionalization algorithms used in biogeography and macroecology. These algorithms include (non-)hierarchical algorithms as well as community detection algorithms coming from the network theory. Some key methods from the literature, such as the network community detection algorithm Infomap or OSLOM (Order Statistics Local Optimization Method), that were not available in the R language are included in bioregion. By combining different methods coming from different fields to communicate easily, bioregion will allow a reproducible and complete comparison of the different bioregionalization methods, which is still missing in the literature.
Anti-pig Antibodies in Swine Veterinarian Serum: Implications for Clinical Xenotransplantation
Authors: Guerard Byrne, Christopher McGregor
Date: 2024-04-23 00:00:00

Recent clinical xenotransplantation and human decedent studies demonstrate that clinical hyperacute rejection of genetically engineered porcine organs can be reliably avoided but that antibody mediated rejection continues to limit graft survival. We previously identified porcine glycans and proteins which are immunogenic after cardiac xenotransplantation in nonhuman primates, but the clinical immune response to antigens present in glycan depleted triple knockout (TKO) donor pigs is poorly understood. In this study we use fluorescence barcoded HEK cells and HEK cell lines expressing porcine glycans (Gal and SDa) or proteins (CD9, CD46, CD59, PROCR and ANXA2) to screen antibody reactivity in human serum from 160 swine veterinarians, a serum source with potential occupational immune challenge from porcine tissues and pathogens. High levels of anti-Gal IgM were present in all samples and lower levels of anti-SDa IgM were present in 41% of samples. IgM binding to porcine proteins, primarily CD9 and CD46, previously identified as immunogenic in pig to non-human primate cardiac xenograft recipients, was detected in 28 of the 160 swine veterinarian samples. These results suggest that barcoded HEK cell lines expressing porcine protein antigens can be useful for screening human patient serum. A comprehensive analysis of sera from clinical xenotransplant recipients to define a panel of commonly immunogenic porcine antigens will likely be necessary to establish an array of porcine non-Gal antigens for effective monitoring of patient immune responses and allow earlier therapies to reverse antibody mediated rejection.