pwc-archive/papers-with-abstracts huggingface dataset:
Compensatory Mechanisms in Non-principal Multimedia Learning: The Interplay of Local and Global Information Processing
Authors: Mohammadhosein Ostadi Varnosfaderani, Masoumeh Golmohamadian, Alireza Bosaghzadeh, S. Hamid Amiri, Reza Ebrahimpour
Date: 2024-09-19 00:00:00
Tasks: EEG

Educational multimedia has become increasingly important in modern learning environments because of its cost-effectiveness and ability to overcome the temporal and spatial limitations of traditional methods. However, the complex cognitive processes involved in multimedia learning pose challenges in understanding its neural mechanisms. This study employs network neuroscience to investigate how multimedia design principles influence the underlying neural mechanisms by examining interactions among various brain regions. Two distinct multimedia programs were constructed using identical auditory content but differing visual designs: one adhered to five guidelines for optimizing multimedia instruction, referred to as principal multimedia, while the other intentionally violated these guidelines, referred to as non-principal multimedia. Cortical functional brain networks were then extracted from EEG data to evaluate local and global information processing across the two conditions. Network measurements revealed that principal networks exhibited more efficient local information processing, whereas non-principal networks demonstrated enhanced global information processing and hub formation. Network modularity analysis also indicated two distinct modular organizations, with modules in non-principal networks displaying higher integration and lower segregation than those in principal networks, aligning with initial findings. These observations suggest that the brain may employ compensatory mechanisms to enhance learning and manage cognitive load despite less effective instructional designs.
Separating minimal from radical embodied cognitive neuroscience
Authors: Matthieu M. de Wit
Date: 2024-09-17 00:00:00

Mougenot and Matheson (2024) make a compelling case for the development of a mechanistic cognitive neuroscience that is embodied. However, their analysis of extant work under this header plays down important distinctions between "minimal" and "radical" embodiment. The former remains firmly neurocentric and therefore has limited potential to move the needle in understanding the functional contributions of neural dynamics to cognition in the context of wider organism-environment dynamics.
Definition of Cybernetical Neuroscience
Authors: Alexander Fradkov
Date: 2024-09-14 00:00:00

A new scientific field is introduced and discussed, named cybernetical neuroscience, which studies mathematical models adopted in computational neuroscience by methods of cybernetics -- the science of control and communication in a living organism, machine and society. It also considers the practical application of the results obtained when studying mathematical models. The main tasks and methods, as well as some results of cybernetic neuroscience are considered.
EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG
Authors: Zhengxiao He, Minghong Cai, Letian Li, Siyuan Tian, Ren-Jie Dai
Date: 2024-09-12 00:00:00
Tasks: EEG, Electromyography (EMG)

Motor pattern recognition paradigms are the main forms of Brain-Computer Interfaces(BCI) aimed at motor function rehabilitation and are the most easily promoted applications. In recent years, many researchers have suggested encouraging patients to perform real motor control execution simultaneously in MI-based BCI rehabilitation training systems. Electromyography (EMG) signals are the most direct physiological signals that can assess the execution of movements. Multimodal signal fusion is practically significant for decoding motor patterns. Therefore, we introduce a multimodal motion pattern recognition algorithm for EEG and EMG signals: EEG-EMG FAConformer, a method with several attention modules correlated with temporal and frequency information for motor pattern recognition. We especially devise a frequency band attention module to encode EEG information accurately and efficiently. What's more, modules like Multi-Scale Fusion Module, Independent Channel-Specific Convolution Module(ICSCM), and Fuse Module which can effectively eliminate irrelevant information in EEG and EMG signals and fully exploit hidden dynamics are developed and show great effects. Extensive experiments show that EEG-EMG FAConformer surpasses existing methods on Jeong2020 dataset, showcasing outstanding performance, high robustness and impressive stability.
Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis
Authors: Haokai Zhao, Haowei Lou, Lina Yao, Yu Zhang
Date: 2024-09-11 00:00:00
Tasks: Functional Connectivity

Functional magnetic resonance imaging (fMRI) is an emerging neuroimaging modality that is commonly modeled as networks of Regions of Interest (ROIs) and their connections, named functional connectivity, for understanding the brain functions and mental disorders. However, due to the high cost of fMRI data acquisition and labeling, the amount of fMRI data is usually small, which largely limits the performance of recognition models. With the rise of generative models, especially diffusion models, the ability to generate realistic samples close to the real data distribution has been widely used for data augmentations. In this work, we present a transformer-based latent diffusion model for functional connectivity generation and demonstrate the effectiveness of the diffusion model as an augmentation tool for fMRI functional connectivity. Furthermore, extended experiments are conducted to provide detailed analysis of the generation quality and interpretations for the learned feature pattern. Our code will be made public upon acceptance.
Brain-Inspired AI with Hyperbolic Geometry
Authors: Alexander Joseph, Nathan Francis, Meijke Balay
Date: 2024-09-04 00:00:00

Artificial neural networks (ANNs) were inspired by the architecture and functions of the human brain and have revolutionised the field of artificial intelligence (AI). Inspired by studies on the latent geometry of the brain, in this perspective paper we posit that an increase in the research and application of hyperbolic geometry in ANNs and machine learning will lead to increased accuracy, improved feature space representations and more efficient models across a range of tasks. We examine the structure and functions of the human brain, emphasising the correspondence between its scale-free hierarchical organization and hyperbolic geometry, and reflecting on the central role hyperbolic geometry plays in facilitating human intelligence. Empirical evidence indicates that hyperbolic neural networks outperform Euclidean models for tasks including natural language processing, computer vision and complex network analysis, requiring fewer parameters and exhibiting better generalisation. Despite its nascent adoption, hyperbolic geometry holds promise for improving machine learning models through brain-inspired geometric representations.
The Computational Mechanisms of Detached Mindfulness
Authors: Brendan Conway-Smith, Robert L. West
Date: 2024-09-03 00:00:00

This paper investigates the computational mechanisms underlying a type of metacognitive monitoring known as detached mindfulness, a particularly effective therapeutic technique within cognitive psychology. While research strongly supports the capacity of detached mindfulness to reduce depression and anxiety, its cognitive and computational underpinnings remain largely unexplained. We employ a computational model of metacognitive skill to articulate the mechanisms through which a detached perception of affect reduces emotional reactivity.
Advancing Multiscale Structural Mapping for Alzheimer's Disease using Local Gyrification Index
Authors: Jinhee Jang, Geonwoo Baek, Ikbeom Jang
Date: 2024-08-21 00:00:00

Research question: This study aims to find whether other neurostructural measurements could be added and combined with the state-of-the-art Alzheimer's imaging marker called MSSM to improve sensitivity to neurodegeneration in Alzheimer's disease patients. Findings: By applying various neurostructural measurements such as the local gyrification index and Jacobian white to the existing Multiscale Structural Mapping of Alzheimer's Disease Neurodegeneration, better results were obtained compared to previous methods, with the addition of LGI proving to be the most effective. Meaning: The extended MSSM imaging marker may provide better ability for the detection of degeneration in Alzheimer's disease. This research shows that this method, using a single standard T1- weighted MRI, can support clinical diagnostics and help identify individuals who may need further biomarker evaluation.
The concentration and energetic content of floral nectar sugars: calculation, conversions, and common confusions
Authors: Jonathan G Pattrick, Jennifer Scott, Geraldine A Wright
Date: 2024-10-08 00:00:00

The sugar concentration of floral nectar is a key metric for describing nectar composition and a major factor influencing pollinator visitation to flowers. Across pollination biology research there are multiple approaches in use for describing nectar sugar concentration. With these different approaches there are several potential sources of confusion which, if not accounted for, can lead to errors. Further potential for error arises if researchers wish to make comparisons between the energetic content of nectars containing different ratios of sucrose, fructose and glucose. Regardless of whether concentration is measured per mole or per unit mass, the energetic content differs between the hexose sugars (glucose and fructose) and sucrose. Appropriate conversion is needed for direct comparison. Here we address these two issues with the following aims. We consolidate the literature on this topic with examples of the different methods for reporting nectar sugar concentrations, provide insight into potential sources of error, and derive equations for converting between the different ways of expressing sugar concentration for the three primary nectar sugars: sucrose, glucose and fructose. Second, we discuss the relative energetic content of sucrose, glucose, and fructose, and rationalise adjustment of "energetic value" rather than reporting concentration directly. In this way, we hope to harmonise ongoing work in pollination ecology.
Study on the presence and density of three types of Brachyuran crabs in Shatt Al-Basra Canal, Basrah, Iraq
Authors: Anfas N. Okash
Date: 2024-10-05 00:00:00

The environment of the Shatt Al-Basra Canal suffers from many pollutants like is a sewage channel for all areas of Basra Governorate, in addition, the riverbanks are a dumping ground for butchery waste, dirt, construction waste, and spoiled medicines thrown into the river and its banks, leading to a lack of biodiversity in the region and the low density of existing organisms. The river banks are characterized by their dryness at low tide due to the high temperature, so they are not suitable for the life of many organisms except the eurythermal and euryhaline organisms. Samples of crabs were collected monthly from September 2023 to June 2024 by using a quadrate with a 1 m length end drilled to a depth of 15 cm, the air temperature at the time of sample collection ranged from 8 to 38{\deg}C, While the BOD ranged between 3.5 to 7.3 mg/L, As for TOC, it ranged from 0.25 to 0.54. The highest density recorded for the species Nasima dotilliformis was in December and reached 6ind/m2 , as the highest density of the species Leptochryseus kuwaitensis was 4ind/m2 in February, and the highest density of the species Ilyoplax stevensi was in December and January, reaching 4ind/m2. The species N. dotilloformis was characterized by its appearance throughout the study period, while the other two species were absent for many months, while the the species L. kuwaitensis was characterized by a lower appearance and lower density. Temperature had the greatest impact on the presence and density of the crab community and was the greatest controller of the rest of the other factors and a basic factor in the dryness of the riverbank soil. The statistical analysis results also showed a significant inverse correlation between temperature and biological oxygen demand (BOD) and a non-significant direct correlation between temperature and total organic carbon (TOC).
Omnibenchmark (alpha) for continuous and open benchmarking in bioinformatics
Authors: Izaskun Mallona, Almut Luetge, Ben Carrillo, Daniel Incicau, Reto Gerber, Anthony Sonrel, Charlotte Soneson, Mark D. Robinson
Date: 2024-09-25 00:00:00
Tasks: Benchmarking

Benchmarking in bioinformatics is a process of designing, running and disseminating rigorous performance evaluations of methods (software). Benchmarking systems facilitate the benchmarking process by providing an entrypoint to store, coordinate and execute concrete benchmarks. We describe an alpha version of a new benchmarking system, Omnibenchmark, to facilitate benchmark formalization and execution in solo and community efforts. Omnibenchmark provides a benchmark definition syntax (in a configuration YAML file), a dynamic workflow generation based on Snakemake, S3-compatible storage handling, and reproducible software environments using EasyBuild, lmod, Apptainer or conda. Tutorials and installation instructions are available from https://omnibenchmark.org.
Measures and Models of Brain-Heart Interactions
Authors: Diego Candia-Rivera, Luca Faes, Fabrizio De Vico Fallani, Mario Chavez
Date: 2024-09-24 00:00:00

The exploration of brain-heart interactions within various paradigms, including affective computing, human-computer interfaces, and sensorimotor evaluation, stands as a significant milestone in biomarker development and neuroscientific research. A range of techniques, spanning from molecular to behavioral approaches, has been proposed to measure these interactions. Different frameworks use signal processing techniques, from the estimation of brain responses to individual heartbeats to higher-order dynamics linking cardiac inputs to changes in brain organization. This review provides an overview to the most notable signal processing strategies currently used for measuring and modeling brain-heart interactions. It discusses their usability and highlights the main challenges that need to be addressed for future methodological developments. Current methodologies have deepened our understanding of the impact of neural disruptions on brain-heart interactions, solidifying it as a biomarker for evaluation of the physiological state of the nervous system and holding immense potential for disease stratification. The vast outlook of these methods becomes apparent specially in neurological and psychiatric disorders. As we tackle new methodological challenges, gaining a more profound understanding of how these interactions operate, we anticipate further insights into the role of peripheral neurons and the environmental input from the rest of the body in shaping brain functioning.
Building a continuous benchmarking ecosystem in bioinformatics
Authors: Izaskun Mallona, Charlotte Soneson, Ben Carrillo, Almut Luetge, Daniel Incicau, Reto Gerber, Anthony Sonrel, Mark D. Robinson
Date: 2024-09-23 00:00:00
Tasks: Benchmarking

Benchmarking, which involves collecting reference datasets and demonstrating method performances, is a requirement for the development of new computational tools, but also becomes a domain of its own to achieve neutral comparisons of methods. Although a lot has been written about how to design and conduct benchmark studies, this Perspective sheds light on a wish list for a computational platform to orchestrate benchmark studies. We discuss various ideas for organizing reproducible software environments, formally defining benchmarks, orchestrating standardized workflows, and how they interface with computing infrastructure.
Evaluation of drought tolerance of some almond genotypes by morphological, phytochemical and molecular markers in Sulaymaniyah governorate
Authors: Anwar Mohammed Raouf Mahmood
Date: 2024-09-22 00:00:00

The study was carried out during 2017 to 2019 growing seasons at four locations in Sulaimani governorate and one location in Halabja governorate, in the Iraqi Kurdistan region including SH, M, Q, B and H. A huge number almond trees were observed for all locations, among them 38 trees were selected with the best morphological characteristics which were chosen 9,3,5,7 and 14 trees depending on the locations, respectively. A simple experiment was conducted using RCBD for this experiment and means was separated by Duncans test. In order to evaluate their tolerances to drought in glass house, an experiment was conducted at Department of Horticulture, College of Agricultural Engineering Sciences, University of Sulaimani, that seeds were taken from those genotype trees and stratified then sown in pots. A factorial RCBD experiment was used with two factors genotypes and irrigation intervals. Therefore, thirty eight seedling genotypes grown in pots under glasshouse condition were exposed to three irrigation intervals 10,20 and 40 days after 10 days from seedling emergence. Therefore, the number of treatment combinations was 114 seedlings for each replicate and with a total 342 seedlings for the whole experiment. Analysis of variance was carried out and the means were compared according to LSD 0.05. As a result of the study, the seedlings showed different levels of adaptation to drought that can be used to future breeding programs as rootstocks. The objectives of this study were to identify morphological, phytochemical and genetic diversity with relatedness among the most important almond genotypes in Sulaimani Region which related to drought tolerance to and relationship between morphological, biochemical and molecular data.
Transdisciplinary collaborations for advancing sustainable and resilient agricultural systems
Authors: Vesna Bacheva, Imani Madison, Mathew Baldwin, Mark Beilstein, Douglas F. Call, Jessica A. Deaver, Kirill Efimenko, Jan Genzer, Khara Grieger, April Z. Gu, Mehmet Mert Ilman, Jen Liu, Sijin Li, Brooke K. Mayer, Anand Kumar Mishra, Juan Claudio Nino, Gloire Rubambiza, Phoebe Sengers, Robert Shepherd, Jesse Woodson, Hakim Weatherspoon, Margaret Frank, Jacob Jones, Rosangela Sozzani, Abraham Stroock
Date: 2024-09-18 00:00:00

Feeding the growing human population sustainably amidst climate change is one of the most important challenges in the 21st century. Current practices often lead to the overuse of agronomic inputs, such as synthetic fertilizers and water, resulting in environmental contamination and diminishing returns on crop productivity. The complexity of agricultural systems, involving plant-environment interactions and human management, presents significant scientific and technical challenges for developing sustainable practices. Addressing these challenges necessitates transdisciplinary research, involving intense collaboration among fields such as plant science, engineering, computer science, and social sciences. Here, we present five case studies from two research centers demonstrating successful transdisciplinary approaches toward more sustainable water and fertilizer use. These case studies span multiple scales. Starting from whole-plant signaling, we explore how reporter plants can transform our understanding of plant communication and enable efficient application of water and fertilizers. We then show how new fertilizer technologies could increase the availability of phosphorus in the soil. To accelerate advancements in breeding new cultivars, we discuss robotic technologies for high-throughput plant screening in different environments at a population scale. At the ecosystem scale, we investigate phosphorus recovery from aquatic systems and methods to minimize phosphorus leaching. Finally, as agricultural outputs affect all people, we show how to integrate stakeholder perspectives and needs into the research. With these case studies, we hope to encourage the scientific community to adopt transdisciplinary research and promote cross-training among biologists, engineers, and social scientists to drive discovery and innovation in advancing sustainable agricultural systems.
Information Clustering and Pathogen Evolution
Authors: Baptiste Filoche, Stefan Hohenegger
Date: 2024-10-08 00:00:00
Tasks: Clustering

Recent outbreaks of infectious diseases have been monitored closely from an epidemiological and microbiological perspective. Extracting from this wealth of data the information that is relevant for the evolution of the pathogen and predict the further dynamic of the epidemic is a difficult task. We therefore consider clusterings of these data to condense this information. We interpret the relative abundance of (genetic) variants of the pathogen as a time-dependent probability distribution and consider clusterings that keep the Fisher information (approximately) invariant, in order to ensure that they capture the dynamics of the pandemic. By first studying analytic models, we show that this condition groups variants together that interact in a similar fashion with the population and show comparable adaptation to the epidemiological situation. Moreover, we demonstrate that the same clustering can be achieved by grouping variants together according to the time-derivative of their information, which is defined as a function of the probabilities alone. A computationally simple clustering based on the probability distribution therefore allows us to probe interactions of different variants of the pathogen with its environment. To validate these findings, we consider data of 551.459 amino acid sequences of the spike protein of SARS-CoV-2 in France over the course of almost 4 years. We demonstrate that our proposed clustering enables us to identify and track point mutations that allow variants to become dominant and identify temporal correlations among such mutations. We identify indicators which point out dangerous variants, with a potential to grow to large probabilities. We show that we can accurately predict the temporal dynamics of such variants by using a universal model discussed in previous work.
Adaptive efficiency of information processing in immune-pathogen co-evolution
Authors: Quinn N Bellamy, Zachary Montague, Luca Peliti, Armita Nourmohammad
Date: 2024-10-07 00:00:00

Organisms have evolved immune systems that can counter pathogenic threats. The adaptive immune system in vertebrates consists of a diverse repertoire of immune receptors that can dynamically reorganize to specifically target the ever-changing pathogenic landscape. Pathogens in return evolve to escape the immune challenge, forming an co-evolutionary arms race. We introduce a formalism to characterize out-of-equilibrium interactions in co-evolutionary processes. We show that the rates of information exchange and entropy production can distinguish the leader from the follower in an evolutionary arms races. Lastly, we introduce co-evolutionary efficiency as a metric to quantify each population's ability to exploit information in response to the other. Our formalism provides insights into the conditions necessary for stable co-evolution and establishes bounds on the limits of information exchange and adaptation in co-evolving systems.