Hasil untuk "Physiology"

Nischal Mandal

Understanding and predicting human emotional and physiological states using wearable sensors has important applications in stress monitoring, mental health assessment, and affective computing. This study presents a novel Multi-Task Physics-Informed Neural Network (PINN) that performs Electrodermal Activity (EDA) prediction and emotion classification simultaneously, using the publicly available WESAD dataset. The model integrates psychological self-report features (PANAS and SAM) with a physics-inspired differential equation representing EDA dynamics, enforcing biophysically grounded constraints through a custom loss function. This loss combines EDA regression, emotion classification, and a physics residual term for improved interpretability. The architecture supports dual outputs for both tasks and is trained under a unified multi-task framework. Evaluated using 5-fold cross-validation, the model achieves an average EDA RMSE of 0.0362, Pearson correlation of 0.9919, and F1-score of 94.08 percent. These results outperform classical models such as SVR and XGBoost, as well as ablated variants like emotion-only and EDA-only models. In addition, the learned physical parameters including decay rate (alpha_0), emotional sensitivity (beta), and time scaling (gamma) are interpretable and stable across folds, aligning with known principles of human physiology. This work is the first to introduce a multi-task PINN framework for wearable emotion recognition, offering improved performance, generalizability, and model transparency. The proposed system provides a foundation for future interpretable and multimodal applications in healthcare and human-computer interaction.

Axel Loewe, Peter J. Hunter, Peter Kohl

Since the turn of the millennium, computational modelling of biological systems has evolved remarkably and sees matured use spanning basic and clinical research. While the topic of the peri-millennial debate about the virtues and limitations of 'reductionism and integrationism' seems less controversial today, a new apparent dichotomy dominates discussions: mechanistic vs. data-driven modelling. In light of this distinction, we provide an overview of recent achievements and new challenges with a focus on the cardiovascular system. Attention has shifted from generating a universal model of the human to either models of individual humans (digital twins) or entire cohorts of models representative of clinical populations to enable in silico clinical trials. Disease-specific parameterisation, inter-individual and intra-individual variability, uncertainty quantification as well as interoperable, standardised, and quality-controlled data are important issues today, which call for open tools, data and metadata standards, as well as strong community interactions. The quantitative, biophysical, and highly controlled approach provided by in silico methods has become an integral part of physiological and medical research. In silico methods have the potential to accelerate future progress also in the fields of integrated multi-physics modelling, multi-scale models, virtual cohort studies, and machine learning beyond what is feasible today. In fact, mechanistic and data-driven modelling can complement each other synergistically and fuel tomorrow's artificial intelligence applications to further our understanding of physiology and disease mechanisms, to generate new hypotheses and assess their plausibility, and thus to contribute to the evolution of preventive, diagnostic, and therapeutic approaches.

Ziqian Zhang, Min Huang, Zhongzhe Xiao

Speech emotion recognition (SER) has advanced significantly for the sake of deep-learning methods, while textual information further enhances its performance. However, few studies have focused on the physiological information during speech production, which also encompasses speaker traits, including emotional states. To bridge this gap, we conducted a series of experiments to investigate the potential of the phonation excitation information and articulatory kinematics for SER. Due to the scarcity of training data for this purpose, we introduce a portrayed emotional dataset, STEM-E2VA, which includes audio and physiological data such as electroglottography (EGG) and electromagnetic articulography (EMA). EGG and EMA provide information of phonation excitation and articulatory kinematics, respectively. Additionally, we performed emotion recognition using estimated physiological data derived through inversion methods from speech, instead of collected EGG and EMA, to explore the feasibility of applying such physiological information in real-world SER. Experimental results confirm the effectiveness of incorporating physiological information about speech production for SER and demonstrate its potential for practical use in real-world scenarios.

Ye Sun, Bowei Zhao, Dezhong Yao et al.

Motor brain-computer interfaces (BCIs) enable the control of external devices by decoding neural signals. However, most existing systems rely on a direct "brain-machine" mapping, overlooking the hierarchical physiological pathway of natural movement, namely the "brain-muscle-joint" cascade. Due to the lack of explicit modeling and enhancement of this pathway, current systems are often constrained by the low amplitude and high noise of EEG signals, resulting in motor outputs that are unstable, discontinuous, and insufficiently natural.To address these limitations, this study introduces the concept of a brain-muscle atlas, designed to systematically characterize the mapping between motor cortical activity and corresponding muscle activation, thereby establishing a movement decoding framework that better aligns with neuromuscular physiology. Using synchronously recorded EEG-EMG data, we constructed the first brain-muscle atlas for elbow flexion-extension, achieving a structured mapping from cortical activity to muscle activation.Offline experiments demonstrate that the proposed atlas accurately reconstructs the temporal activation patterns of primary elbow agonists, achieving a maximum correlation coefficient of 0.8314, thereby validating its ability to capture cortical-muscular mapping. Furthermore, by leveraging atlas-derived muscle activation representations, we enabled continuous real-time control of a virtual elbow joint. All ten participants successfully completed the online flexion-extension task, indicating that the system robustly extracts motor intent even under low-SNR EEG conditions.

L. Gannes, Carlos Martínez del Rio, Paul L. Koch

Heming Huang, Charles S. Peskin

In the physiology of oxygen-hemoglobin binding, an important role is played by the influence of $\text{H}^+$ and $\text{CO}_2$ on the affinity of hemoglobin for $\text{O}_2$. Here we extend the allosteric model of hemoglobin to include these effects. We assume purely allosteric modulation, i.e., that the modulatory effects of $\text{H}^+$ and $\text{CO}_2$ on oxygen binding occur only because of their influence on the T $\leftrightarrow$ R transition, in which all four subunits of the hemoglobin molecule participate simultaneously. We assume, moreover, that these modulatory influences occur only through the interaction of $\text{H}^+$ and $\text{CO}_2$ with the amino group at the N-terminal of each of the four polypeptide chains of the hemoglobin molecule. We fit the model to experimental data and obtain reasonable agreement with the observed shifts in oxygen-hemoglobin binding that occur when the concentrations of $\text{H}^+$ and $\text{CO}_2$ are changed.

Philippe Rauffet, Sorin Moga, Alexandre Kostenko

This study examines the relationship between mental workload and the cognitive control implemented in multitasking activity. A MATB-II experiment was conducted to simulate different conditions of multitasking demand, and to collect the behavioral and physiological activities of 17 participants. The results show that implementation of different modes of cognitive control can be detected with physiological indicators, and that cognitive control could be seen as a moderator of the effect of mental stress (task demand) upon mental strain (physiological responses).

Anggraini Kartika Sari Prita, Amelia Kharina, Cahyani Frameiza

Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease that can attack many organs with varying degrees of severity. This can affect quality of life (QOL). SF36 is a commonly used QOL test. This study aims to report the validity and reliability test of the Indonesian Version of RAND SF-36 in SLE patients. This research uses a cross sectional method and tested it on 19 eligible respondents. To test the reliability and validity of the questionnaire, analysis of the Cronbach coefficient and Pearson correlation was carried out. All subjects were women with an average age of 22.37 ± 5.10 years, the majority had secondary education (66.7%), were not married (79.2%), had no comorbidities (31.6%), and the duration of SLE was more than 3 years (62.5%). All of them used steroids as SLE therapy and also Mycophenolate mofetil (68.8%). The total value of Cronbach's alpha is 0.723 > 0.7, only two items were deemed appropriate RE scale (0.778) and GH scale (0.724). The validity sig value is < 0.005. In general, this study provides evidence that the Indonesian version of the RAND-SF 36 can be used to assess the QOL of SLE patients. However, there are limitations to the reliability of the scales. Further research or adjustments to the questions in the Indonesian version are required to enhance the reliability of the assessment.

Ochuko L. Erukainure, Aliyu Muhammad, Rahul Ravichandran et al.

The multiple mutation of the spike (S) protein of the Omicron SARS-CoV-2 variant is a major concern, as it has been implicated in the severity of COVID-19 and its complications. These mutations have been attributed to COVID-19-infected immune-compromised individuals, with HIV patients being suspected to top the list. The present study investigated the mutation of the S protein of the omicron variant in comparison to the Delta and Wuhan variants. It also investigated the molecular interactions of antiretroviral drugs (ARVd) vis-à-vis dolutegravir, lamivudine, tenofovir-disoproxilfumarate and lenacapavir with the initiation and termination codons of the mRNAs of the mutated proteins of the omicron variant using computational tools. The complete genome sequences of the respective S proteins for omicron (OM066778.1), Delta (OK091006.1) and Wuhan (NC 045512.2) SARS-CoV-2 variants were retrieved from the National Center for Biotechnology Information (NCBI) database. Evolutionary analysis revealed high trends of mutations in the S protein of the omicron SARS-CoV-2 variant compared to the delta and Wuhan variants coupled with 68 % homology. The sequences of the translation initiation sites (TISs), translation termination sites (TTSs), high mutation region-1 (HMR1) and high region mutation-2 (HMR2) mRNAs were retrieved from the full genome of the omicron variant S protein. Molecular docking analysis revealed strong molecular interactions of ARVd with TISs, TTSs, HMR1 and HMR2 of the S protein mRNA. These results indicate mutations in the S protein of the Omicron SARS-CoV-2 variant compared to the Delta and Wuhan variants. These mutation points may present new therapeutic targets for COVID-19.

Hirofumi Tanaka, D. Seals

Oran Lang, Doron Yaya-Stupp, Ilana Traynis et al.

AI models have shown promise in many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust in AI-based models, and could enable novel scientific discovery by uncovering signals in the data that are not yet known to experts. In this paper, we present a method for automatic visual explanations leveraging team-based expertise by generating hypotheses of what visual signals in the images are correlated with the task. We propose the following 4 steps: (i) Train a classifier to perform a given task (ii) Train a classifier guided StyleGAN-based image generator (StylEx) (iii) Automatically detect and visualize the top visual attributes that the classifier is sensitive towards (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, we present the discovered attributes to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health. We demonstrate results on eight prediction tasks across three medical imaging modalities: retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples of attributes that capture clinically known features, confounders that arise from factors beyond physiological mechanisms, and reveal a number of physiologically plausible novel attributes. Our approach has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models. Importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors. Finally, we intend to release code to enable researchers to train their own StylEx models and analyze their predictive tasks.

Sitara Afzal, Haseeb Ali Khan, Imran Ullah Khan et al.

As the name suggests, affective computing aims to recognize human emotions, sentiments, and feelings. There is a wide range of fields that study affective computing, including languages, sociology, psychology, computer science, and physiology. However, no research has ever been done to determine how machine learning (ML) and mixed reality (XR) interact together. This paper discusses the significance of affective computing, as well as its ideas, conceptions, methods, and outcomes. By using approaches of ML and XR, we survey and discuss recent methodologies in affective computing. We survey the state-of-the-art approaches along with current affective data resources. Further, we discuss various applications where affective computing has a significant impact, which will aid future scholars in gaining a better understanding of its significance and practical relevance.

Madeleine Héger, Pierre Noiset, Kiatoko Nkoba et al.

Abstract Background Stingless bee honey (SBH) is a natural remedy and therapeutic agent traditionally used by local communities across the (sub-)tropics. Forest SBH represents a prime non-timber forest product (NTFP) with a potential to revitalize indigenous foodways and to generate income in rural areas, yet it is also used in a variety of non-food contexts that are poorly documented in sub-Saharan Africa and that collectively represent a significant part of the local traditional ecological knowledge (TEK) passed on across generations. Documenting TEK of local communities in African tropical forests facing global change is a pressing issue to recognize the value of their insights, to evaluate their sustainability, to determine how they contribute to enhancing conservation efforts, and how TEK generally contributes to the well-being of both the natural environment and the communities that rely on it. This is particularly important to achieve in Kenya’s only tropical rainforest at Kakamega where SBH production and non-food uses have evolved and diversified to a remarkable extent. Methods We used ethnographic techniques and methods, including semi-structured questionnaires and recorded interviews. We used snowball sampling, a non-probability sampling method where new interviewees were recruited by other respondents, to collectively form a sample consisting of 36 interviewees (including only one woman). Results Our results indicate that local communities in Kakamega were able to discriminate between six different and scientifically recognized stingless bee species, and they provided detailed accounts on the species-specific non-food uses of these SBH. Collectively, we recorded an array of 26 different non-food uses that are all passed on orally across generations in the Kakamega community. Conclusion Our results uncover the vast and hitherto unexpected diversity of TEK associated with SBH and pave the way for a systematic survey of SBH and their non-food uses across a network of communities in different environments and with different cultural backgrounds in the Afrotropics. This, along with parallel and more in-depth investigations into honey chemistry, will help develop a comprehensive understanding of SBH, offering insights into holistic ecosystem management, resilience and adaptation while in the mid- to long-term promoting cross-cultural exchanges and pathways for the revitalization of cultural practices and traditions.

S. Harridge, N. Lazarus

P. Montgomery, D. Pyne, C. Minahan

W. Richardson, Samantha A. Clarke, T. A. Quinn et al.

Once myocardium dies during a heart attack, it is replaced by scar tissue over the course of several weeks. The size, location, composition, structure, and mechanical properties of the healing scar are all critical determinants of the fate of patients who survive the initial infarction. While the central importance of scar structure in determining pump function and remodeling has long been recognized, it has proven remarkably difficult to design therapies that improve heart function or limit remodeling by modifying scar structure. Many exciting new therapies are under development, but predicting their long‐term effects requires a detailed understanding of how infarct scar forms, how its properties impact left ventricular function and remodeling, and how changes in scar structure and properties feed back to affect not only heart mechanics but also electrical conduction, reflex hemodynamic compensations, and the ongoing process of scar formation itself. In this article, we outline the scar formation process following a myocardial infarction, discuss interpretation of standard measures of heart function in the setting of a healing infarct, then present implications of infarct scar geometry and structure for both mechanical and electrical function of the heart and summarize experiences to date with therapeutic interventions that aim to modify scar geometry and structure. One important conclusion that emerges from the studies reviewed here is that computational modeling is an essential tool for integrating the wealth of information required to understand this complex system and predict the impact of novel therapies on scar healing, heart function, and remodeling following myocardial infarction. © 2015 American Physiological Society. Compr Physiol 5:1877‐1909, 2015.

S. Gordon, L. Martínez-Pomares

Macrophages are present in mammals from midgestation, contributing to physiologic homeostasis throughout life. Macrophages arise from yolk sac and foetal liver progenitors during embryonic development in the mouse and persist in different organs as heterogeneous, self-renewing tissue-resident populations. Bone marrow-derived blood monocytes are recruited after birth to replenish tissue-resident populations and to meet further demands during inflammation, infection and metabolic perturbations. Macrophages of mixed origin and different locations vary in replication and turnover, but are all active in mRNA and protein synthesis, fulfilling organ-specific and systemic trophic functions, in addition to host defence. In this review, we emphasise selected properties and non-immune functions of tissue macrophages which contribute to physiologic homeostasis.

Jörn Rittweger, G. Beller, D. Felsenberg

A. Rehman, H. Ahsan, F. Khan

R. Premont, R. Gainetdinov