Hasil untuk "Physiology"

M. Gibala, J. Little, M. MacDonald et al.

D. Thijssen, M. Black, K. Pyke et al.

G. Semenza

Pragya Singh, Ankush Gupta, Somay Jalan et al.

Emotion recognition from physiological signals has substantial potential for applications in mental health and emotion-aware systems. However, the lack of standardized, large-scale evaluations across heterogeneous datasets limits progress and model generalization. We introduce FEEL, the first large-scale benchmarking study of emotion recognition using electrodermal activity (EDA) and photoplethysmography (PPG) signals across 19 publicly available datasets. We evaluate 16 architectures spanning traditional machine learning, deep learning, and self-supervised pretraining approaches, structured into four representative modeling paradigms. Our study includes both within-dataset and cross-dataset evaluations, analyzing generalization across variations in experimental settings, device types, and labeling strategies. Our results showed that fine-tuned contrastive signal-language pretraining (CLSP) models (71/114) achieve the highest F1 across arousal and valence classification tasks, while simpler models like Random Forests, LDA, and MLP remain competitive (36/114). Models leveraging handcrafted features (107/114) consistently outperform those trained on raw signal segments, underscoring the value of domain knowledge in low-resource, noisy settings. Further cross-dataset analyses reveal that models trained on real-life setting data generalize well to lab (F1 = 0.79) and constraint-based settings (F1 = 0.78). Similarly, models trained on expert-annotated data transfer effectively to stimulus-labeled (F1 = 0.72) and self-reported datasets (F1 = 0.76). Moreover, models trained on lab-based devices also demonstrated high transferability to both custom wearable devices (F1 = 0.81) and the Empatica E4 (F1 = 0.73), underscoring the influence of heterogeneity. More information about FEEL can be found on our website https://alchemy18.github.io/FEEL_Benchmark/.

Md Rakibul Hasan, Md Zakir Hossain, Aneesh Krishna et al.

When someone claims to be empathic, it does not necessarily mean they are perceived as empathic by the person receiving it. Empathy promotes supportive communication, yet the relationship between listeners' trait and state empathy and speakers' perceptions remains unclear. We conducted an experiment in which speakers described a personal incident and one or more listeners responded naturally, as in everyday conversation. Afterwards, speakers reported perceived empathy, and listeners reported their trait and state empathy. Reliability of the scales was high (Cronbach's $α= 0.805$--$0.888$). Nonparametric Kruskal-Wallis tests showed that speakers paired with higher trait-empathy listeners reported greater perceived empathy, with large effect sizes. In contrast, state empathy did not reliably differentiate speaker outcomes. To complement self-reports, we collected electrodermal activity and heart rate from listeners during the conversations, which shows that high trait empathy listeners exhibited higher physiological variability.

Chaoyue Niu, Veronica Rowe, Guy J. Brown et al.

Paediatric obstructive sleep apnoea (OSA) is clinically significant yet difficult to diagnose, as children poorly tolerate sensor-based polysomnography. Acoustic monitoring provides a non-invasive alternative for home-based OSA screening, but limited paediatric data hinders the development of robust deep learning approaches. This paper proposes a transfer learning framework that adapts acoustic models pretrained on adult sleep data to paediatric OSA detection, incorporating SpO2-based desaturation patterns to enhance model training. Using a large adult sleep dataset (157 nights) and a smaller paediatric dataset (15 nights), we systematically evaluate (i) single- versus multi-task learning, (ii) encoder freezing versus full fine-tuning, and (iii) the impact of delaying SpO2 labels to better align them with the acoustics and capture physiologically meaningful features. Results show that fine-tuning with SpO2 integration consistently improves paediatric OSA detection compared with baseline models without adaptation. These findings demonstrate the feasibility of transfer learning for home-based OSA screening in children and offer its potential clinical value for early diagnosis.

Luca Faes, Gorana Mijatovic, Laura Sparacino et al.

Objective: This work introduces a framework for multivariate time series analysis aimed at detecting and quantifying collective emerging behaviors in the dynamics of physiological networks. Methods: Given a network system mapped by a vector random process, we compute the predictive information (PI) between the present and past network states and dissect it into amounts quantifying the unique, redundant and synergistic information shared by the present of the network and the past of each unit. Emergence is then quantified as the prevalence of the synergistic over the redundant contribution. The framework is implemented in practice using vector autoregressive (VAR) models. Results: Validation in simulated VAR processes documents that emerging behaviors arise in networks where multiple causal interactions coexist with internal dynamics. The application to cardiovascular and respiratory networks mapping the beat-to-beat variability of heart rate, arterial pressure and respiration measured at rest and during postural stress reveals the presence of statistically significant net synergy, as well as its modulation with sympathetic nervous system activation. Conclusion: Causal emergence can be efficiently assessed decomposing the PI of network systems via VAR models applied to multivariate time series. This approach evidences the synergy/redundancy balance as a hallmark of integrated short-term autonomic control in cardiovascular and respiratory networks. Significance: Measures of causal emergence provide a practical tool to quantify the mechanisms of causal influence that determine the dynamic state of cardiovascular and neural network systems across distinct physiopathological conditions.

Haley N. Green, Tariq Iqbal

With robots becoming increasingly prevalent in various domains, it has become crucial to equip them with tools to achieve greater fluency in interactions with humans. One of the promising areas for further exploration lies in human trust. A real-time, objective model of human trust could be used to maximize productivity, preserve safety, and mitigate failure. In this work, we attempt to use physiological measures, gaze, and facial expressions to model human trust in a robot partner. We are the first to design an in-person, human-robot supervisory interaction study to create a dedicated trust dataset. Using this dataset, we train machine learning algorithms to identify the objective measures that are most indicative of trust in a robot partner, advancing trust prediction in human-robot interactions. Our findings indicate that a combination of sensor modalities (blood volume pulse, electrodermal activity, skin temperature, and gaze) can enhance the accuracy of detecting human trust in a robot partner. Furthermore, the Extra Trees, Random Forest, and Decision Trees classifiers exhibit consistently better performance in measuring the person's trust in the robot partner. These results lay the groundwork for constructing a real-time trust model for human-robot interaction, which could foster more efficient interactions between humans and robots.

Kartikay Tehlan, Thomas Wendler

Dynamic positron emission tomography (PET) with [$^{18}$F]FDG enables non-invasive quantification of glucose metabolism through kinetic analysis, often modelled by the two-tissue compartment model (TCKM). However, voxel-wise kinetic parameter estimation using conventional methods is computationally intensive and limited by spatial resolution. Deep neural networks (DNNs) offer an alternative but require large training datasets and significant computational resources. To address these limitations, we propose a physiological neural representation based on implicit neural representations (INRs) for personalized kinetic parameter estimation. INRs, which learn continuous functions, allow for efficient, high-resolution parametric imaging with reduced data requirements. Our method also integrates anatomical priors from a 3D CT foundation model to enhance robustness and precision in kinetic modelling. We evaluate our approach on an [$^{18}$F]FDG dynamic PET/CT dataset and compare it to state-of-the-art DNNs. Results demonstrate superior spatial resolution, lower mean-squared error, and improved anatomical consistency, particularly in tumour and highly vascularized regions. Our findings highlight the potential of INRs for personalized, data-efficient tracer kinetic modelling, enabling applications in tumour characterization, segmentation, and prognostic assessment.

Tyler N. Akonom, Mary A. Allen, Pei-San Tsai

Sexual interactions have previously been shown to improve reproductive health through unknown mechanisms. In this study, we used RNA-Seq to examine sex-induced gene expression changes in the preoptic area (POA), a critical reproductive brain region. Using a mouse model defective in fibroblast growth factor signaling (dnFGFR mouse), previously shown to disrupt the gonadotropin-releasing hormone (GnRH) system, we examined the impact of opposite sex (OS) housing on gene expression in the POA of a reproductively compromised animal. Bulk RNA-Seq followed by gene set enrichment analysis (GSEA) were used to analyze changes in gene expression and biological processes in control and dnFGFR mice after 300 days of cohabitation with a same sex or OS partner. OS housing of dnFGFR mice, but not control mice, significantly improved reproductive anatomy and gonadotropins in dnFGFR mice. These changes occurred concomitantly with novel biological processes related to estradiol metabolism and neuron excitation. Our results suggest a new role of neuron- or astrocyte-derived estradiol in the plasticity of the GnRH neuron population and offer a promising new direction for the treatment of reproductive disorders stemming from GnRH deficiency.

F. Engelmann

D. Prescott

Ulfat Jahan Farha, Zarin Subah, Md Helal Uddin et al.

Changing temperature, precipitation regimes, and sea level rise, often associated with climate change, cause salinity intrusion into groundwater and surface water, affecting aquatic ecosystems. This study investigates the impacts of salinity on the physiological traits of freshwater hydrophytes, including Water Hyacinth (Eichhornia crassipes), Buffalo Spinach (Enhydra fluctuans), and Taro (Colocasia esculenta). The plants were exposed to salinity concentrations of 0, 10, 20, and 30 ppt for 48 hours. Parameters such as biomass, stomata density, transpiration rate, chlorophyll content, relative water content, and histo-architectural changes were analyzed. The results showed a decline in biomass, stomatal density, and relative water content with increasing salinity. Taro demonstrated higher salt tolerance compared to other species. Histological observations revealed deformities in root and tuber tissues under saline stress. These findings highlight the critical impacts of climate change-induced salinity on aquatic plant ecosystems.

Yan Zhang, Ming Jia, Meng Li et al.

Operators' cognitive functions are impaired significantly under extreme heat stress, potentially resulting in more severe secondary disasters. This research investigated the impact of elevated temperature and humidity (25 60%RH, 30 70%RH, 35 80%RH, 40 90%RH) on the cognitive functions and performance of operators. Meanwhile, we explored the psychological-physiological mechanism underlying the change in performance by electrocardiogram (ECG), functional near-infrared spectroscopy (fNIRS), and eye tracking physiologically. Psychological aspects such as situation awareness, workload, and working memory were assessed. Eventually, we verified and extended the maximal adaptability model to the extreme condition. Unexpectedly, a temporary improvement in simple reaction tasks but rapid impairment in advanced cognitive functions (i.e. situation awareness, communication, working memory) was obtained above 35 WBGT. The best performance in a suitable environment was due to more effective activation in the prefrontal cortex (PFC). With temperature increasing, more mistakes occurred and comprehension was impaired due to drowsiness and lower arousal levels, according to evidence of compensatory effect in fNIRS. In the extreme environment, the enhanced PFC cooperation with higher functional connectivity resulted in a temporary improvement, while depressed activation in PFC, heavy physical load, and poor regulation of the cardiovascular system restricted it. Our results provide a detailed study of the process of operators' performance and cognitive functions when encountering increasing heat stress, as well as its underlying mechanisms from a neuroergonomics perspective. This can contribute to a better understanding of the interaction between operators' performance and workplace conditions, and help to achieve a more reliable human-centered production system in the promising era of Industry 5.0.

Archana Thiruppathi, Shubham Rajaram Salunkhe, Shobica Priya Ramasamy et al.

Strategies to enhance rice productivity in response to global demand have been the paramount focus of breeders worldwide. Multiple factors, including agronomical traits such as plant architecture and grain formation and physiological traits such as photosynthetic efficiency and NUE (nitrogen use efficiency), as well as factors such as phytohormone perception and homeostasis and transcriptional regulation, indirectly influence rice grain yield. Advances in genetic analysis methodologies and functional genomics, numerous genes, QTLs (Quantitative Trait Loci), and SNPs (Single-Nucleotide Polymorphisms), linked to yield traits, have been identified and analyzed in rice. Genome editing allows for the targeted modification of identified genes to create novel mutations in rice, avoiding the unintended mutations often caused by random mutagenesis. Genome editing technologies, notably the CRISPR/Cas9 system, present a promising tool to generate precise and rapid modifications in the plant genome. Advancements in CRISPR have further enabled researchers to modify a larger number of genes with higher efficiency. This paper reviews recent research on genome editing of yield-related genes in rice, discusses available gene editing tools, and highlights their potential to expedite rice breeding programs.

Yoshitomo Kurogi, Yosuke Mizuno, Takumi Kamiyama et al.

Abstract Intestinal stem cells (ISCs) of the fruit fly, Drosophila melanogaster, offer an excellent genetic model to explore homeostatic roles of ISCs in animal physiology. Among available genetic tools, the escargot (esg)-GAL4 driver, expressing the yeast transcription factor gene, GAL4, under control of the esg gene promoter, has contributed significantly to ISC studies. This driver facilitates activation of genes of interest in proximity to a GAL4-binding element, Upstream Activating Sequence, in ISCs and progenitor enteroblasts (EBs). While esg-GAL4 has been considered an ISC/EB-specific driver, recent studies have shown that esg-GAL4 is also active in other tissues, such as neurons and ovaries. Therefore, the ISC/EB specificity of esg-GAL4 is questionable. In this study, we reveal esg-GAL4 expression in the corpus allatum (CA), responsible for juvenile hormone (JH) production. When driving the oncogenic gene, Ras V12 , esg-GAL4 induces overgrowth in ISCs/EBs as reported, but also increases CA cell number and size. Consistent with this observation, animals alter expression of JH-response genes. Our data show that esg-GAL4-driven gene manipulation can systemically influence JH-mediated animal physiology, arguing for cautious use of esg-GAL4 as a “specific” ISC/EB driver to examine ISC/EB-mediated animal physiology.

Danai Kotoula, Eleni G. Papazoglou, Garifalia Economou et al.

The aim of this study was to assess the phytoremediation potential of fiber flax (<i>Linum usitatissimatum</i> L., var. Calista) cultivated in a soil contaminated with multiple metals, under real field conditions. A two-year (2022 and 2023) field experiment was conducted in a site contaminated with elevated concentrations of Cd, Ni, Cu, Pb, and Zn due to mining and metallurgical activities. Three different nitrogen fertilization levels were tested (N0: 0 kg N ha⁻¹, N1: 30 kg N ha⁻¹, N2: 60 kg N ha⁻¹), and both spring and winter sowings were conducted. At full maturity, growth parameters and yields were measured. The phytoremediation potential of flax was assessed in terms of the metal concentrations in the above-ground biomass and of the metal uptake (i.e., the potential removal of the soil metals in g ha⁻¹ and per year). Flax demonstrated a shorter growth cycle, with shorter and thicker plants and higher yields when sown in spring compared to winter sowing. Plant growth and productivity were not evidently influenced by additional nitrogen fertilization during plant growth. The cadmium bioaccumulation factor was 1.06, indicating that flax accumulates this metal. For Ni, Cu, Pb, and Zn, the corresponding values were 0.0, 0.04, 0.004, and 0.02, suggesting that this crop excludes these metals. The order of the higher uptake in plant tissues was as follows: Zn > Pb > Cd > Cu > Ni. In conclusion, flax demonstrated tolerance to heavy metals in the soil, effectively supporting soil restoration through cultivation. Additionally, flax showed potential as a cadmium accumulator while excluding nickel, copper, lead, and zinc.

W. Stewart

W. Hoar

F. Holly, M. Lemp