A critical review on the organo-metal(loid)s pollution in the environment: Distribution, remediation and risk assessment.

Toxic metal(loid)s, e.g., mercury, arsenic, lead, and cadmium are known for several environmental disturbances creating toxicity to humans if accumulated in high quantities. Although not discussed critically, the organo-forms of these inorganic metal(loid)s are considered a greater risk to humans than their elemental forms possibly due to physico-chemical modulation triggering redox alterations or by the involvement of biological metabolism. This extensive review describes the chemical and physical causes of organometals and organometal(loid)s distribution in the environment with ecotoxicity assessment and potential remediation strategies. Organo forms of various metal(loid)s, such as mercury (Hg), arsenic (As), lead (Pb), tin (Sn), antimony (Sb), selenium (Se), and cadmium (Cd) have been discussed in the context of their ecotoxicity. In addition, we elaborated on the transformation, speciation and transformation pathways of these toxic metal(loid)s in soil-water-plant-microbial systems. The present review has pointed out the status of toxic organometal(loid)s, which is required to make the scientific community aware of this pressing condition of organometal(loid)s distribution in the environment. The gradual disposal and piling of organometal(loid)s in the environment demand a thorough revision of the past-present status with possible remediation strategies prescribed as reflected in this review.

A Framework for Selecting and Assessing Wearable Sensors Deployed in Safety Critical Scenarios.

Wearable sensors for psychophysiological monitoring are becoming increasingly mainstream in safety critical contexts. They offer a novel solution to capturing sub-optimal states and can help identify when workers in safety critical environments are suffering from states such as fatigue and stress. However, sensors can differ widely in their application, design, usability, and measurement and there is a lack of guidance on what should be prioritized or considered when selecting a sensor. The paper aims to highlight which concepts are important when creating or selecting a device regarding the optimization of both measurement and usability. Additionally, the paper discusses how design choices can enhance both the usability and measurement capabilities of wearable sensors. The hopes are that this paper will provide researchers and practitioners in human factors and related fields with a framework to help guide them in building and selecting wearable sensors that are well suited for deployment in safety critical contexts.

Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review.

Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches.

Comparison of lattice thermal conductivity usingab-initioDFT, machine learning interatomic potentials, and temperature dependent effective potential: a case study of hexagonal BN and BP bilayer.

Discovering high thermal conductivity materials is essential for various practical applications, particularly in electronic cooling. The significance of two-dimensional (2D) materials lies in their unique properties that emerge due to their reduced dimensionality, making them highly promising for a wide range of applications. Hexagonal boron nitride (BN), both monolayer and bilayer forms, has garnered attention for its fascinating properties. In this work, we focus on bilayer boron phosphide (BP), which is isostructural to its BN analogue. The lattice thermal conductivity of both bilayer BN and BP have been calculated usingab-initiodensity functional theory, machine learning with the moment tensor potential method, and the temperature-dependent effective-potential method (TDEP). The TDEP approach gives more accurate results for both BN and BP materials. The lattice thermal conductivity of bilayer BP is lower than that of bilayer BN at room temperature, attributed to increased phonon anharmonicity. This study highlights the importance of understanding phonon scattering mechanisms in determining the thermal conductivity of 2D materials, contributing to the broader understanding and potential applications of these materials in future technologies.

Emerging concern of nano-pollution in agro-ecosystem: Flip side of nanotechnology.

Nanomaterials (NMs) have proven to be a game-changer in agriculture, showcasing their potential to boost plant growth and safeguarding crops. The agricultural sector has widely adopted NMs, benefiting from their small size, high surface area, and optical properties to augment crop productivity and provide protection against various stressors. This is attributed to their unique characteristics, contributing to their widespread use in agriculture. Human exposure from various components of agro-environmental sectors (soil, crops) NMs residues are likely to upsurge with exposure paths may stimulates bioaccumulation in food chain. With the aim to achieve sustainability, nanotechnology (NTs) do exhibit its potentials in various domains of agriculture also have its flip side too. In this review article we have opted a fusion approach using bibliometric based analysis of global research trend followed by a holistic assessment of pros and cons i.e. toxicological aspect too. Moreover, we have also tried to analyse the current scenario of policy associated with the application of NMs in agro-environment.

Purely ionically bonded cation paving the way to ultralow thermal conductivity and large thermoelectric figure of merit in Ruddlesden-Popper perovskite Cs2SnI2Br2.

Lower dimensional materials have gained quite a bit of popularity in the last few decades. Perovskite materials have been studied extensively for their photovoltaic properties. But for large scale application of photovoltaic materials, the thermal properties need to be studied. In this work, using first principles calculations, we have studied the thermal conductivity and thermoelectric performance of quasi two-dimensional (2D) Ruddlesden-Popper phase of perovskite, Cs2SnI2Br2. The Cs atoms are found to be ionically bonded to the halogens leading to low elastic constants and hence give rise to weak bonding. The large anharmonicity in this material causes the lattice thermal conductivity to be ultralow having a value of 0.30 W·m-1·K-1at 300 K and therefore the thermoelectric figure of merit has been found to be high with a maximum value of 2.08 at 600 K. This lead-free 2D perovskite can be the precursor to a wide variety of similar materials with ultralow thermal conductivity.

An assessment of the impact of traditional rice cooking practice and eating habits on arsenic and iron transfer into the food chain of smallholders of Indo-Gangetic plain of South-Asia: Using AMMI and Monte-Carlo simulation model.

The current study was designed to investigate the consequences of rice cooking and soaking of cooked rice (CR) with or without arsenic (As) contaminated water on As and Fe (iron) transfer to the human body along with associated health risk assessment using additive main-effects and multiplicative interaction (AMMI) and Monte Carlo Simulation model. In comparison to raw rice, As content in cooked rice (CR) and soaked cooked rice (SCR) enhanced significantly (at p < 0.05 level), regardless of rice cultivars and locations (at p < 0.05 level) due to the use of As-rich water for cooking and soaking purposes. Whereas As content in CR and SCR was reduced significantly due to the use of As-free water for cooking and soaking purposes. The use of As-free water (AFW) also enhanced the Fe content in CR. The overnight soaking of rice invariably enhanced the Fe content despite the use of As-contaminated water in SCR however, comparatively in lesser amount than As-free rice. In the studied area, due to consumption of As-rich CR and SCR children are more vulnerable to health hazards than adults. Consumption of SCR (prepared with AFW) could be an effective method to minimize As transmission and Fe enrichment among consumers.

A quantitative comparison of virtual and physical experimental paradigms for the investigation of pedestrian responses in hostile emergencies.

Modern experiments investigating human behaviour in emergencies are often implemented in virtual reality (VR), due to the increased experimental control and improved ethical viability over physical reality (PR). However, there remain questions regarding the validity of the results obtained from these environments, and no full validation of VR experiments has yet appeared. This study compares the results of two sets of experiments (in VR and PR paradigms) investigating behavioural responses to knife-based hostile aggressors. This study quantitatively analyses these results to ascertain whether the different paradigms generate different responses, thereby assessing the use of virtual reality as a data generating paradigm for emergencies. The results show that participants reported almost identical psychological responses. This study goes on to identify minimal differences in movement responses across a range of predictors, noting a difference in responses between genders. As a result, this study concludes that VR can produce similarly valid data as physical experiments when investigating human behaviour in hostile emergencies, and that it is therefore possible to conduct realistic experimentation through VR environments while retaining confidence in the resulting data. This has major implications for the future of this type of research, and furthermore suggests that VR experimentation should be performed for both existing and new critical infrastructure to understand human responses in hostile scenarios.

Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

STUDY OBJECTIVES: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of 2 levels of OSA severity (ie, moderate-to-severe and severe OSA) in accordance with clinical practice standards. METHODS: We conducted a prospective, simultaneous study using a wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index, results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. RESULTS: A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between apnea-hypopnea index and RDIPSG_TST were identified. In terms of the agreement between the 2 devices, the average difference between PSG-based apnea-hypopnea index and radar-based RDIPSG_TST was 0.59 events/h, and 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. CONCLUSIONS: The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work. CITATION: Lin S-Y, Tsai C-Y, Majumdar A, et al. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med. 2024;20(8):1267-1277.

An extensive review of arsenic dynamics and its distribution in soil-aqueous-rice plant systems in south and Southeast Asia with bibliographic and meta-data analysis.

Millions of people worldwide are affected by arsenic (As) contamination, particularly in South and Southeast Asian countries, where large-scale dependence on the usage of As-contaminated groundwater in drinking and irrigation is a familiar practice. Rice (Oryza sativa) cultivation is commonly done in South and Southeast Asian countries as a preferable crop which takes up more As than any other cereals. The present article has performed a scientific meta-data analysis and extensive bibliometric analysis to demonstrate the research trend in global rice As contamination scenario in the timeframe of 1980-2023. This study identified that China contributes most with the maximum number of publications followed by India, USA, UK and Bangladesh. The two words 'arsenic' and 'rice' have been identified as the most dominant keywords used by the authors, found through co-occurrence cluster analysis with author keyword association study. The comprehensive perceptive attained about the factors affecting As load in plant tissue and the nature of the micro-environment augment the contamination of rice cultivars in the region. This extensive review analyses soil parameters through meta-data regression assessment that influence and control As dynamics in soil with its further loading into rice grains and presents that As content and OM are inversely related and slightly correlated to the pH increment of the soil. Additionally, irrigation and water management practices have been found as a potential modulator of soil As concentration and bioavailability, presented through a linear fit with 95% confidence interval method.

Sustainable water management in rice cultivation reduces arsenic contamination, increases productivity, microbial molecular response, and profitability.

Arsenic (As) and silicon (Si) are two structurally competitive natural elements where Si minimises As accumulation in rice plants, and based on this two-year field trial, the study proposes adopting alternating wetting and drying (AWD) irrigation as a sustainable water management strategy allowing greater Si availability. This field-based project is the first report on AWD's impact on As-Si distribution in fluvio-alluvial soils of the entire Ganga valley (24 study sites, six divisions), seasonal variance (pre-monsoon and monsoon), rice plant anatomy and productivity, soil microbial diversity, microbial gene ontology profiling and associated metabolic pathways. Under AWD to flooded and pre-monsoon to monsoon cultivations, respectively, greater Si availability was achieved and As-bioavailability was reduced by 8.7 ± 0.01-9.2 ± 0.02% and 25.7 ± 0.09-26.1 ± 0.01%. In the pre-monsoon and monsoon seasons, the physiological betterment of rice plants led to the high rice grain yield under AWD improved by 8.4 ± 0.07% and 10.0 ± 0.07%, proving the economic profitability. Compared to waterlogging, AWD evidences as an optimal soil condition for supporting soil microbial communities in rice fields, allowing diverse metabolic activities, including As-resistance, and active expression of As-responsive genes and gene products. Greater expressions of gene ontological terms and complex biochemical networking related to As metabolism under AWD proved better cellular, genetic and environmental responsiveness in microbial communities. Finally, by implementing AWD, groundwater usage can be reduced, lowering the cost of pumping and field management and generating an economic profit for farmers. These combined assessments prove the acceptability of AWD for the establishment of multiple sustainable development goals (SDGs).

Computation of X-ray and Neutron Scattering Patterns to Benchmark Atomistic Simulations against Experiments.

Molecular Dynamics simulations study material structure and dynamics at the atomic level. X-ray and neutron scattering experiments probe exactly the same time- and length scales as the simulations. In order to benchmark simulations against measured scattering data, a program is required that computes scattering patterns from simulations with good single-core performance and support for parallelization. In this work, the existing program Sassena is used as a potent solution to this requirement for a range of scattering methods, covering pico- to nanosecond dynamics, as well as the structure from some Ångströms to hundreds of nanometers. In the case of nanometer-level structures, the finite size of the simulation box, which is referred to as the finite size effect, has to be factored into the computations for which a method is described and implemented into Sassena. Additionally, the single-core and parallelization performance of Sassena is investigated, and several improvements are introduced.

Evaluation of iron-modified biochar on arsenic accumulation by rice: a pathway to assess human health risk from cooked rice.

Arsenic (As) contamination of rice grain poses a serious threat to human health. Therefore, it is crucial to reduce the bioavailability of As in the soil and its accumulation in rice grains to ensure the safety of food and human health. In this study, mango (Mangifera indica) leaf-derived biochars (MBC) were synthesized and modified with iron (Fe) to produce FeMBC. In this study, 0.5 and 1% (w/w) doses of MBC and FeMBC were used. The results showed that 1% FeMBC enhanced the percentage of filled grains/panicle and biomass yield by 17 and 27%, respectively, compared to the control. The application of 0.5 and 1% FeMBC significantly (p < 0.05) reduced bioavailable soil As concentration by 33 and 48%, respectively, in comparison to the control. The even higher As flux in the control group as compared to the biochar-treated groups indicates the lower As availability to biochar-treated rice plant. The concentration of As in rice grains was reduced by 6 and 31% in 1% MBC and 1% FeMBC, respectively, compared to the control. The reduction in As concentration in rice grain under 1% FeMBC was more pronounced due to reduced bioavailability of As and enhanced formation of Fe-plaque. This may restrict the entry of As through the rice plant. The concentrations of micronutrients (such as Fe, Zn, Se, and Mn) in brown rice were also improved after the application of both MBC and FeMBC in comparison to the control. This study indicates that the consumption of parboiled rice reduces the health risk associated with As compared to cooked sunned rice. It emphasizes that 1% MBC and 1% FeMBC have great potential to decrease the uptake of As in rice grains.

Selenium - An environmentally friendly micronutrient in agroecosystem in the modern era: An overview of 50-year findings.

Agricultural productivity is constantly being forced to maintain yield stability to feed the enormously growing world population. However, shrinking arable and nutrient-deprived soil and abiotic and biotic stressor (s) in different magnitudes put additional challenges to achieving global food security. Though well-defined, the concept of macro, micronutrients, and beneficial elements is from a plant nutritional perspective. Among various micronutrients, selenium (Se) is essential in small amounts for the life cycle of organisms, including crops. Selenium has the potential to improve soil health, leading to the improvement of productivity and crop quality. However, Se possesses an immense encouraging phenomenon when supplied within the threshold limit, also having wide variations. The supplementation of Se has exhibited promising outcomes in lessening biotic and abiotic stress in various crops. Besides, bulk form, nano-Se, and biogenic-Se also revealed some merits and limitations. Literature suggests that the possibilities of biogenic-Se in stress alleviation and fortifying foods are encouraging. In this article, apart from adopting a combination of a conventional extensive review of the literature and bibliometric analysis, the authors have assessed the journey of Se in the "soil to spoon" perspective in a diverse agroecosystem to highlight the research gap area. There is no doubt that the time has come to seriously consider the tag of beneficial elements associated with Se, especially in the drastic global climate change era.

Association between air pollutant exposure, body water distribution and sleep disorder indices in individuals with low-arousal-threshold obstructive sleep apnoea.

BACKGROUND: Air pollution may alter body water distribution, it may also be linked to low-arousal-threshold obstructive sleep apnoea (low-ArTH OSA). Here, we explored the mediation effects of air pollution on body water distribution and low-ArTH OSA manifestations. METHODS: In this retrospective study, we obtained sleep centre data from healthy participants and patients with low-ArTH OSA (N=1924) in northern Taiwan. Air pollutant exposure at different time intervals (1, 3, 6 and 12 months) was estimated using the nearest station estimation method, and government air-quality data were also obtained. Regression models were used to assess the associations of estimated exposure, sleep disorder indices and body water distribution with the risk of low-ArTH OSA. Mediation analysis was performed to explore the relationships between air pollution, body water distribution and sleep disorder indices. RESULTS: First, exposure to particulate matter (PM) with a diameter of ≤10 µm (PM10) for 1 and 3 months and exposure to PM with a diameter of ≤2.5 µm (PM2.5) for 3 months were significantly associated with the Apnoea-Hypopnoea Index (AHI), Oxygen Desaturation Index (ODI), Arousal Index (ArI) and intracellular-to-extracellular water ratio (I-E water ratio). Significant associations were observed between the risk of low-ArTH OSA and 1- month exposure to PM10 (OR 1.42, 95% CI 1.09 to 1.84), PM2.5 (OR 1.33, 95% CI 1.02 to 1.74) and ozone (OR 1.27, 95% CI 1.01 to 1.6). I-E water ratio alternation caused by 1-month exposure to PM10 and 3-month exposure to PM2.5 and PM10 had partial mediation effects on AHI and ODI. CONCLUSION: Air pollution can directly increase sleep disorder indices (AHI, ODI and ArI) and alter body water distribution, thus mediating the risk of low-ArTH OSA.

Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles.

Objective: Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. Methods: Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. Results: InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. Conclusions: The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

Evaluation of efficacy of non-coding RNA in abiotic stress management of field crops: Current status and future prospective.

Abiotic stresses are responsible for the major losses in crop yield all over the world. Stresses generate harmful ROS which can impair cellular processes in plants. Therefore, plants have evolved antioxidant systems in defence against the stress-induced damages. The frequency of occurrence of abiotic stressors has increased several-fold due to the climate change experienced in recent times and projected for the future. This had particularly aggravated the risk of yield losses and threatened global food security. Non-coding RNAs are the part of eukaryotic genome that does not code for any proteins. However, they have been recently found to have a crucial role in the responses of plants to both abiotic and biotic stresses. There are different types of ncRNAs, for example, miRNAs and lncRNAs, which have the potential to regulate the expression of stress-related genes at the levels of transcription, post-transcription, and translation of proteins. The lncRNAs are also able to impart their epigenetic effects on the target genes through the alteration of the status of histone modification and organization of the chromatins. The current review attempts to deliver a comprehensive account of the role of ncRNAs in the regulation of plants' abiotic stress responses through ROS homeostasis. The potential applications ncRNAs in amelioration of abiotic stresses in field crops also have been evaluated.

Associations between air pollution, intracellular-to-extracellular water distribution, and obstructive sleep apnea manifestations.

Background: Exposure to air pollution may be a risk factor for obstructive sleep apnea (OSA) because air pollution may alter body water distribution and aggravate OSA manifestations. Objectives: This study aimed to investigate the mediating effects of air pollution on the exacerbation of OSA severity through body water distribution. Methods: This retrospective study analyzed body composition and polysomnographic data collected from a sleep center in Northern Taiwan. Air pollution exposure was estimated using an adjusted nearest method, registered residential addresses, and data from the databases of government air quality motioning stations. Next, regression models were employed to determine the associations between estimated air pollution exposure levels (exposure for 1, 3, 6, and 12 months), OSA manifestations (sleep-disordered breathing indices and respiratory event duration), and body fluid parameters (total body water and body water distribution). The association between air pollution and OSA risk was determined. Results: Significant associations between OSA manifestations and short-term (1 month) exposure to PM2.5 and PM10 were identified. Similarly, significant associations were identified among total body water and body water distribution (intracellular-to-extracellular body water distribution), short-term (1 month) exposure to PM2.5 and PM10, and medium-term (3 months) exposure to PM10. Body water distribution might be a mediator that aggravates OSA manifestations, and short-term exposure to PM2.5 and PM10 may be a risk factor for OSA. Conclusion: Because exposure to PM2.5 and PM10 may be a risk factor for OSA that exacerbates OSA manifestations and exposure to particulate pollutants may affect OSA manifestations or alter body water distribution to affect OSA manifestations, mitigating exposure to particulate pollutants may improve OSA manifestations and reduce the risk of OSA. Furthermore, this study elucidated the potential mechanisms underlying the relationship between air pollution, body fluid parameters, and OSA severity.

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability.

OBJECTIVE: This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks. BACKGROUND: Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages. METHOD: This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance. RESULTS: Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values. CONCLUSION: HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs. APPLICATION: The established models can be used in realistic driving scenarios.

Association of Low Arousal Threshold Obstructive Sleep Apnea Manifestations with Body Fat and Water Distribution.

Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E-I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E-I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA.