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.

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-1 at 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.

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.

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.

Towards building a trustworthy pipeline integrating Neuroscience Gateway and Open Science Chain

When the scientific dataset evolves or is reused in workflows creating derived datasets, the integrity of the dataset with its metadata information, including provenance, needs to be securely preserved while providing assurances that they are not accidentally or maliciously altered during the process. Providing a secure method to efficiently share and verify the data as well as metadata is essential for the reuse of the scientific data. The National Science Foundation (NSF) funded Open Science Chain (OSC) utilizes consortium blockchain to provide a cyberinfrastructure solution to maintain integrity of the provenance metadata for published datasets and provides a way to perform independent verification of the dataset while promoting reuse and reproducibility. The NSF- and National Institutes of Health (NIH)-funded Neuroscience Gateway (NSG) provides a freely available web portal that allows neuroscience researchers to execute computational data analysis pipeline on high performance computing resources. Combined, the OSC and NSG platforms form an efficient, integrated framework to automatically and securely preserve and verify the integrity of the artifacts used in research workflows while using the NSG platform. This paper presents the results of the first study that integrates OSC-NSG frameworks to track the provenance of neurophysiological signal data analysis to study brain network dynamics using the Neuro-Integrative Connectivity tool, which is deployed in the NSG platform. Database URL: https://www.opensciencechain.org.

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.

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.

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).

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.

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.

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.

Machine learning model for aberrant driving behaviour prediction using heart rate variability: a pilot study involving highway bus drivers

Objectives. Current approaches via physiological features detecting aberrant driving behaviour (ADB), including speeding, abrupt steering, hard braking and aggressive acceleration, are developing. This study proposes using machine learning approaches incorporating heart rate variability (HRV) parameters to predict ADB occurrence. Methods. Naturalistic driving data of 10 highway bus drivers in Taiwan from their daily routes were collected for 4 consecutive days. Their driving behaviours and physiological data during a driving task were determined using a navigation mobile application and heart rate watch. Participants' self-reported data on sleep, driving-related experience, open-source data on weather and the traffic congestion level were obtained. Five machine learning models - logistic regression, random forest, naive Bayes, support vector machine and gated recurrent unit (GRU) - were employed to predict ADBs. Results. Most drivers with ADB had low sleep efficiency (≤80%), with significantly higher scores in driver behaviour questionnaire subcategories of lapses and errors and in the Karolinska sleepiness scale than those without ADBs. Moreover, HRV parameters were significantly different between baseline and pre-ADB event measurements. GRU had the highest accuracy (81.16-84.22%). Conclusions. Sleep deficit may be related to the increased fatigue level and ADB occurrence predicted from HRV-based models among bus drivers.

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.

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 PM_2.5 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 PM_2.5 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 PM_2.5 and PM₁₀ may be a risk factor for OSA.

Conclusion

Because exposure to PM_2.5 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.

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.

Investigating spatial distribution of fluoride in groundwater with respect to hydro-geochemical characteristics and associated probabilistic health risk in Baruipur block of West Bengal, India

Fluoride (F^-) enrichment in groundwater of the lower Gangetic plain in West Bengal, India is a major concern. Fluoride contamination and its toxicity were reported earlier in this region; however, limited evidence was available on the precise site of contamination, hydro-geochemical attributions of F^- mobilization and probabilistic health risk caused by fluoridated groundwater. The present study addresses the research gap by exploring the spatial distribution and physico-chemical parameters of fluoridated groundwater along with the depth-wise sedimental distribution of F^-. Approximately, 10 % of the groundwater samples (n = 824) exhibited high F^- ≥ 1.5 mg/l from 5, out of 19 gram-panchayats and Baruipur municipality area and the maximum F^- was observed in Dhapdhapi-II gram-panchayat with 43.7 % of samples showed ≥1.5 mg/l (n = 167). The distribution patterns of cations and anions in fluoridated groundwater were Na⁺ > Ca²⁺ > Mg²⁺ > Fe > K⁺ and Cl^- > HCO₃^- > SO₄^2- > CO₃^2- > NO₃^- > F^-. Different statistical models like Piper and Gibbs diagram, Chloro Alkaline plot, Saturation index were applied to better understand the hydro-geochemical characteristics for F^- leaching in groundwater. Fluoridated groundwater is of Na-Cl type which implies strong saline character. The intermediate zone between evaporation and rock dominance area controls F^- mobilization along with ion-exchange process occurring between groundwater and host silicate mineral. Furthermore, saturation index proves geogenic activities related to groundwater F^- mobilization. All cations present in sediment samples are closely interlinked with F^- in the depth range of 0-18.3 m. Mineralogical analyses revealed that muscovite is the most responsible mineral for F^- mobilization. The probabilistic health risk assessment disclosed severe health hazard in the order of infants > adults > children > teenagers through F^- tainted groundwater. At P95 percentile dose, all the studied age groups showed THQ >1 from Dhapdhapi-II gram-panchayat. Supply of F^- safe drinking water is required through reliable water supply strategies in the studied area.

Impact of lifetime air pollution exposure patterns on the risk of chronic disease

Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages. The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (<20, 20-40, and >40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models. Models with 89% weighting on late-stage exposure showed significant associations between PM_2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes. We conclude that exposure to high-level PM_2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal error rates than models incorporating only late-stage exposures. Future studies regarding long-term air pollution modelling are required considering lifelong exposure pattern. .¹.

Continuous Positive Airway Pressure Reduces Plasma Neurochemical Levels in Patients with OSA: A Pilot Study

Obstructive sleep apnea (OSA) is a risk factor for neurodegenerative diseases. This study determined whether continuous positive airway pressure (CPAP), which can alleviate OSA symptoms, can reduce neurochemical biomarker levels. Thirty patients with OSA and normal cognitive function were recruited and divided into the control (n = 10) and CPAP (n = 20) groups. Next, we examined their in-lab sleep data (polysomnography and CPAP titration), sleep-related questionnaire outcomes, and neurochemical biomarker levels at baseline and the 3-month follow-up. The paired t-test and Wilcoxon signed-rank test were used to examine changes. Analysis of covariance (ANCOVA) was performed to increase the robustness of outcomes. The Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index scores were significantly decreased in the CPAP group. The mean levels of total tau (T−Tau), amyloid-beta-42 (Aβ42), and the product of the two (Aβ42 × T−Tau) increased considerably in the control group (ΔT−Tau: 2.31 pg/mL; ΔAβ42: 0.58 pg/mL; ΔAβ42 × T−Tau: 48.73 pg2/mL2), whereas the mean levels of T−Tau and the product of T−Tau and Aβ42 decreased considerably in the CPAP group (ΔT−Tau: −2.22 pg/mL; ΔAβ42 × T−Tau: −44.35 pg2/mL2). The results of ANCOVA with adjustment for age, sex, body mass index, baseline measurements, and apnea–hypopnea index demonstrated significant differences in neurochemical biomarker levels between the CPAP and control groups. The findings indicate that CPAP may reduce neurochemical biomarker levels by alleviating OSA symptoms.

Associations of the distance-saturation product and low-attenuation area percentage in pulmonary computed tomography with acute exacerbation in patients with chronic obstructive pulmonary disease

Background

Chronic obstructive pulmonary disease (COPD) has high global health concerns, and previous research proposed various indicators to predict mortality, such as the distance-saturation product (DSP), derived from the 6-min walk test (6MWT), and the low-attenuation area percentage (LAA%) in pulmonary computed tomographic images. However, the feasibility of using these indicators to evaluate the stability of COPD still remains to be investigated. Associations of the DSP and LAA% with other COPD-related clinical parameters are also unknown. This study, thus, aimed to explore these associations.

Methods

This retrospective study enrolled 111 patients with COPD from northern Taiwan. Individuals' data we collected included results of a pulmonary function test (PFT), 6MWT, life quality survey [i.e., the modified Medical Research Council (mMRC) scale and COPD assessment test (CAT)], history of acute exacerbation of COPD (AECOPD), and LAA%. Next, the DSP was derived by the distance walked and the lowest oxygen saturation recorded during the 6MWT. In addition, the DSP and clinical phenotype grouping based on clinically significant outcomes by previous study approaches were employed for further investigation (i.e., DSP of 290 m%, LAA% of 20%, and AECOPD frequency of ≥1). Mean comparisons and linear and logistic regression models were utilized to explore associations among the assessed variables.

Results

The low-DSP group (<290 m%) had significantly higher values for the mMRC, CAT, AECOPD frequency, and LAA% at different lung volume scales (total, right, and left), whereas it had lower values of the PFT and 6MWT parameters compared to the high-DSP group. Significant associations (with high odds ratios) were observed of the mMRC, CAT, AECOPD frequency, and PFT with low- and high-DSP groupings. Next, the risk of having AECOPD was associated with the mMRC, CAT, DSP, and LAA% (for the total, right, and left lungs).

Conclusion

A lower value of the DSP was related to a greater worsening of symptoms, more-frequent exacerbations, poorer pulmonary function, and more-severe emphysema (higher LAA%). These readily determined parameters, including the DSP and LAA%, can serve as indicators for assessing the COPD clinical course and may can serve as a guide to corresponding treatments.

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

Objectives

Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features.

Methods

We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening.

Results

The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk.

Conclusions

The established model can be considered for screening for the risk of having moderate-to-severe or severe 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.

Iron oxide doped rice biochar reduces soil-plant arsenic stress, improves nutrient values: An amendment towards sustainable development goals

Arsenic (As) contamination in paddy soils and its further translocation to the rice is a serious global issue. Arsenic loading to the rice depends on soil physico-chemical parameters and agronomic practices. To minimize this natural threat, as a natural substance, rice straw was used to produce rice biochar (RBC) and doped with iron oxide (IO) nanoparticles, another eco-friendly composite. In this study, RBC was used at three different concentrations- 0.5%, 1%, and 1.5% alone as well as conjugated with fixed 20 ppm IO nanoparticles. These treatments were compared with the control soil and control plants that had only As in the setup, without any amendments. The application of these treatments was efficient in reducing soil As bioavailability by 43.9%, 60.5%, and 57.3% respectively. Experimental data proved a significant percentage of As was adsorbed onto the RBC + IO conjugate. Further, the 1% RBC + IO conjugate was found to be the best treatment in terms of making soil macro-nutrients bioavailable. Rice seedlings grown under this treatment was more stress tolerant and produced less antioxidant enzymes and stress markers compared to the control plants grown under As-stress only. Rice plants from these different growth setups were observed for internal anatomical integrity and found that the RBC alone and RBC + IO conjugate, both improved the internal vascular structure compared to the control plants. To minimize soil As stress in crops, IO-doped RBC was proven to be the best sustainable amendment for improving soil-crop quality and achieving the proposed motto of Sustainable Development Goals by the United Nations.

The impact of signposting and group support pathways on a community-based physical activity intervention grounded in motivational interviewing

BACKGROUND

Brief advice is recommended to increase physical activity (PA) within primary care. This study assessed change in PA levels and mental well-being after a motivational interviewing (MI) community-based PA intervention and the impact of signposting (SP) and social action (SA) (i.e. weekly group support) pathways.

METHODS

Participants (n = 2084) took part in a community-based, primary care PA programme using MI techniques. Self-reported PA and mental well-being data were collected at baseline (following an initial 30-min MI appointment), 12 weeks, 6 months and 12 months. Participants were assigned based upon the surgery they attended to the SP or SA pathway. Multilevel models derived point estimates and 95% confidence intervals for outcomes at each time point and change scores.

RESULTS

Participants increased PA and mental well-being at each follow-up time point through both participant pathways and with little difference between pathways. Retention was similar between pathways at 12 weeks, but the SP pathway retained more participants at 6 and 12 months.

CONCLUSIONS

Both pathways produced similar improvements in PA and mental well-being; however, the addition of a control would have provided further insight as to the effectiveness. Due to lower resources yet similar effects, the SP pathway could be incorporated to support PA in primary care settings.

The impact of signposting and group support pathways on a community-based physical activity intervention grounded in motivational interviewing

BACKGROUND

Brief advice is recommended to increase physical activity (PA) within primary care. This study assessed change in PA levels and mental well-being after a motivational interviewing (MI) community-based PA intervention and the impact of signposting (SP) and social action (SA) (i.e. weekly group support) pathways.

METHODS

Participants (n = 2084) took part in a community-based, primary care PA programme using MI techniques. Self-reported PA and mental well-being data were collected at baseline (following an initial 30-min MI appointment), 12 weeks, 6 months and 12 months. Participants were assigned based upon the surgery they attended to the SP or SA pathway. Multilevel models derived point estimates and 95% confidence intervals for outcomes at each time point and change scores.

RESULTS

Participants increased PA and mental well-being at each follow-up time point through both participant pathways and with little difference between pathways. Retention was similar between pathways at 12 weeks, but the SP pathway retained more participants at 6 and 12 months.

CONCLUSIONS

Both pathways produced similar improvements in PA and mental well-being; however, the addition of a control would have provided further insight as to the effectiveness. Due to lower resources yet similar effects, the SP pathway could be incorporated to support PA in primary care settings.

Associations between risk of Alzheimer's disease and obstructive sleep apnea, intermittent hypoxia, and arousal responses: A pilot study

OBJECTIVES

Obstructive sleep apnea (OSA) may increase the risk of Alzheimer's disease (AD). However, potential associations among sleep-disordered breathing, hypoxia, and OSA-induced arousal responses should be investigated. This study determined differences in sleep parameters and investigated the relationship between such parameters and the risk of AD.

METHODS

Patients with suspected OSA were recruited and underwent in-lab polysomnography (PSG). Subsequently, blood samples were collected from participants. Patients' plasma levels of total tau (T-Tau) and amyloid beta-peptide 42 (Aβ42) were measured using an ultrasensitive immunomagnetic reduction assay. Next, the participants were categorized into low- and high-risk groups on the basis of the computed product (Aβ42 × T-Tau, the cutoff for AD risk). PSG parameters were analyzed and compared.

RESULTS

We included 36 patients in this study, of whom 18 and 18 were assigned to the low- and high-risk groups, respectively. The average apnea-hypopnea index (AHI), apnea, hypopnea index [during rapid eye movement (REM) and non-REM (NREM) sleep], and oxygen desaturation index (≥3%, ODI-3%) values of the high-risk group were significantly higher than those of the low-risk group. Similarly, the mean arousal index and respiratory arousal index (R-ArI) of the high-risk group were significantly higher than those of the low-risk group. Sleep-disordered breathing indices, oxygen desaturation, and arousal responses were significantly associated with an increased risk of AD. Positive associations were observed among the AHI, ODI-3%, R-ArI, and computed product.

CONCLUSIONS

Recurrent sleep-disordered breathing, intermittent hypoxia, and arousal responses, including those occurring during the NREM stage, were associated with AD risk. However, a longitudinal study should be conducted to investigate the causal relationships among these factors.

Automatic Detection of Tuberculosis Using VGG19 with Seagull-Algorithm

Due to various reasons, the incidence rate of communicable diseases in humans is steadily rising, and timely detection and handling will reduce the disease distribution speed. Tuberculosis (TB) is a severe communicable illness caused by the bacterium Mycobacterium-Tuberculosis (M. tuberculosis), which predominantly affects the lungs and causes severe respiratory problems. Due to its significance, several clinical level detections of TB are suggested, including lung diagnosis with chest X-ray images. The proposed work aims to develop an automatic TB detection system to assist the pulmonologist in confirming the severity of the disease, decision-making, and treatment execution. The proposed system employs a pre-trained VGG19 with the following phases: (i) image pre-processing, (ii) mining of deep features, (iii) enhancing the X-ray images with chosen procedures and mining of the handcrafted features, (iv) feature optimization using Seagull-Algorithm and serial concatenation, and (v) binary classification and validation. The classification is executed with 10-fold cross-validation in this work, and the proposed work is investigated using MATLAB^® software. The proposed research work was executed using the concatenated deep and handcrafted features, which provided a classification accuracy of 98.6190% with the SVM-Medium Gaussian (SVM-MG) classifier.

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

Assessing Cognitive Workload Using Cardiovascular Measures and Voice

Monitoring cognitive workload has the potential to improve both the performance and fidelity of human decision making. However, previous efforts towards discriminating further than binary levels (e.g., low/high or neutral/high) in cognitive workload classification have not been successful. This lack of sensitivity in cognitive workload measurements might be due to individual differences as well as inadequate methodology used to analyse the measured signal. In this paper, a method that combines the speech signal with cardiovascular measurements for screen and heartbeat classification is introduced. For validation, speech and cardiovascular signals from 97 university participants and 20 airline pilot participants were collected while cognitive stimuli of varying difficulty level were induced with the Stroop colour/word test. For the trinary classification scheme (low, medium, high cognitive workload) the prominent result using classifiers trained on each participant achieved 15.17 ± 0.79% and 17.38 ± 1.85% average misclassification rates indicating good discrimination at three levels of cognitive workload. Combining cardiovascular and speech measures synchronized to each heartbeat and consolidated with short-term dynamic measures might therefore provide enhanced sensitivity in cognitive workload monitoring. The results show that the influence of individual differences is a limiting factor for a generic classification and highlights the need for research to focus on methods that incorporate individual differences to achieve even better results. This method can potentially be used to measure and monitor workload in real time in operational environments.

An assessment of various potentially toxic elements and associated health risks in agricultural soil along the middle Gangetic basin, India

The present study analysed the levels of potentially toxic elements along with physico-chemical properties of agricultural soil samples (n = 59) collected from fields situated along the path of river Ganga in the middle Gangetic floodplain in two districts, Ballia and Ghazipur. Arsenic (As), chromium (Cr), copper (Cu), nickel (Ni), zinc (Zn), lead (Pb), iron (Fe) and manganese (Mn) levels were analysed by Wavelength Dispersive-X-Ray Fluorescence Spectroscopy (WD-XRF) and the associated health risks along with diverse indices were calculated. The mean concentrations of As, Cu, Cr, Pb, Zn and Ni were found to be 15, 42, 85, 18, 87 and 47 mg kg^-1, respectively in Ballia and 13, 31, 73, 22, 77 and 34 mg kg^-1, respectively in Ghazipur. Physico-chemical properties like pH, ORP and organic matter were found to be 7.91, 209 and 1.20, respectively in Ballia and 8.51, 155 and 1.25, respectively in Ghazipur. The calculated health quotient (HQ) for all the elements was observed to be within the threshold value of one, however with few exemptions. Therefore, the present study showcases the contamination of potentially toxic elements in agricultural fields and possible health hazards for people.

Potent Blockchain-Enabled Socket RPC Internet of Healthcare Things (IoHT) Framework for Medical Enterprises

Present-day intelligent healthcare applications offer digital healthcare services to users in a distributed manner. The Internet of Healthcare Things (IoHT) is the mechanism of the Internet of Things (IoT) found in different healthcare applications, with devices that are attached to external fog cloud networks. Using different mobile applications connecting to cloud computing, the applications of the IoHT are remote healthcare monitoring systems, high blood pressure monitoring, online medical counseling, and others. These applications are designed based on a client-server architecture based on various standards such as the common object request broker (CORBA), a service-oriented architecture (SOA), remote method invocation (RMI), and others. However, these applications do not directly support the many healthcare nodes and blockchain technology in the current standard. Thus, this study devises a potent blockchain-enabled socket RPC IoHT framework for medical enterprises (e.g., healthcare applications). The goal is to minimize service costs, blockchain security costs, and data storage costs in distributed mobile cloud networks. Simulation results show that the proposed blockchain-enabled socket RPC minimized the service cost by 40%, the blockchain cost by 49%, and the storage cost by 23% for healthcare applications.

Antioxidant enzymes and transporter genes mediate arsenic stress reduction in rice (Oryza sativa L.) upon thiourea supplementation

Thiourea (TU) is a chemo-priming agent and non-physiological reactive oxygen species (ROS) scavenger whose application has been found to reduce As accumulation in rice grains along with improved growth and yield. The present field study explored TU-mediated mechanistic changes in silicon (Si) assimilation in root/shoot, biochemical and molecular mechanisms of arsenic (As) stress amelioration in rice cultivars. Gosai and Satabdi (IET-4786) rice cultivars were selected for field experiment at three different places; control field and two other As contaminated experimental fields (EF1 and EF2) in West Bengal, India. The average As reduction was observed to be 9.5% and 19.8% whereas the yield increment was 8.8% and 17.7% for gosai and satabdi, respectively among all the three experimental fields. The positive interrelation was also observed between improved internal ultrastructure anatomy and enhanced Si assimilation (36%-423%) upon TU application. The level of photosynthetic pigments was increased by 29.8%-99.2%. Further, activities of antioxidant enzymes were harmonically altered in TU supplemented plants. The expression of various As related transporter genes in flag leaf and developing grains (inflorescence) was changed in both the rice cultivars (gosai and satabdi). It was also presumably responsible for observed As reduction in grains. Thus, TU application was found to be an efficient and sustainable agronomic practice for amelioration of As toxicity in rice plants in As contaminated field conditions.

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications’ execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.

A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer's Disease

The early prediction of Alzheimer's disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.

Enhanced phytoremediation of Metal(loid)s via spiked ZVI nanoparticles: An urban clean-up strategy with ornamental plants

The increasing industrialization and urbanization are also triggering environmental pollution, mostly unnoticed, in the case of soil pollution due to uncontrolled contamination by toxic elemental dispersion. The present study focused on this aspect and studied the clean-up of urban soil in a low-cost and eco-friendly way to restrict arsenic (As), lead (Pb) and mercury (Hg) contamination. Four potential ornamental plants, Catharanthus roseus (vinca), Cosmos bipinnatus (cosmos), Gomphrena globose (globosa) and Impatiens balsamina (balsamina) were used along with zero valent iron (ZVI) nanoparticles (Fe NPs) for remediation of the soil spiked with As (70 mg kg-1), Pb (600 mg kg-1) and Hg (15 mg kg-1) in a 60 d pot experiment. All plants were divided into four groups viz. control, spiked, spiked+20 mg kg-1 ZVI NP and spiked+50 mg kg-1 ZVI NP. FTIR and SEM were used for ZVI NP characterization. Soil and plant analyses and elemental assessments were done using ICP-MS, XRF and SEM. Among the four plants, cosmos showed the maximum accumulation of toxic elements (41.24 ± 0.022 mg kg-1 As, 139.15 ± 11.2 mg kg-1 Pb and 15.57 ± 0.27 mg kg-1 Hg) at 60 d. The application of ZVI NP at 20 mg kg-1 dosage was found to further augment plants' potential for metal(loid)s accumulation without negatively hampering their growth. Cosmos were observed to reduce soil As from 81.35 ± 1.34 mg kg-1 to 28.16 ± 1.38 mg kg-1 (65.38%), Pb from 1132.47 ± 4.66 to 516.09 ± 3.15 mg kg-1 (54.42%) and Hg from 17.35 ± 0.88 to 6.65 ± 0.4 mg kg-1 (61.67%) at 60 d in spiked + 20 mg kg-1 ZVI NP treatment. Balsamina was the most sensitive plant and showed the least metal(loid)s accumulation. In conclusion, three of these plants are potent enough to use together for a better and enhanced removal of toxic elements from the contaminated soil with cosmos to be the best amongst these in urban areas.

Comparison of molecular dynamics simulations of water with neutron and X-ray scattering experiments

The atomistic structure and dynamics obtained from molecular dynamics (MD) simulations with the example of TIP3P (rigid and flexible) and TIP4P/2005 (rigid) water is compared to neutron and X-ray scattering data at ambient conditions. Neutron and X-ray diffractograms are calculated from the simulations for four isotopic substitutions as well as the incoherent intermediate scattering function for neutrons. The resulting curves are compared to each other and to published experimental data. Differences between simulated and measured intermediate scattering functions are quantified by fitting an analytic model to the computed values. The sensitivity of the scattering curves to the parameters of the MD simulations is demonstrated on the example of two parameters, bond length and angle.

Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile

(a) Objective: Obstructive sleep apnea syndrome (OSAS) is typically diagnosed through polysomnography (PSG). However, PSG incurs high medical costs. This study developed new models for screening the risk of moderate-to-severe OSAS (apnea-hypopnea index, AHI ≥15) and severe OSAS (AHI ≥30) in various age groups and sexes by using anthropometric features in the Taiwan population.(b) Participants: Data were derived from 10,391 northern Taiwan patients who underwent PSG.(c) Methods: Patients' characteristics - namely age, sex, body mass index (BMI), neck circumference, and waist circumference - was obtained. To develop an age- and sex-independent model, various approaches - namely logistic regression, k-nearest neighbor, naive Bayes, random forest (RF), and support vector machine - were trained for four groups based on sex and age (men or women; aged <50 or ≥50 years). Dataset was separated independently (training:70%; validation: 10%; testing: 20%) and Cross-validated grid search was applied for model optimization. Models demonstrating the highest overall accuracy in validation outcomes for the four groups were used to predict the testing dataset.(d) Results: The RF models showed the highest overall accuracy. BMI was the most influential parameter in both types of OSAS severity screening models.(e) Conclusion: The established models can be applied to screen OSAS risk in the Taiwan population and those with similar craniofacial features.

Associations among sleep-disordered breathing, arousal response, and risk of mild cognitive impairment in a northern Taiwan population

STUDY OBJECTIVES

Dementia is associated with sleep disorders. However, the relationship between dementia and sleep arousal remains unclear. This study explored the associations among sleep parameters, arousal responses, and risk of mild cognitive impairment (MCI).

METHODS

Participants with the chief complaints of memory problems and sleep disorders were screened from the sleep center database of Taipei Medical University Shuang-Ho Hospital, and the parameters related to the Cognitive Abilities Screening Instrument (CASI), Clinical Dementia Rating (CDR), and polysomnography (PSG) were determined. All the examinations were conducted within 6 months and without a particular order. The participants were divided into those without cognitive impairment (CDR = 0) and those with MCI (CDR = 0.5). Mean comparison, linear regression models, and logistic regression models were employed to investigate the associations among obtained variables.

RESULTS

This study included 31 participants without MCI and 37 with MCI (17 with amnestic MCI; 20 with multidomain MCI). Patients with MCI had significantly higher mean values of the spontaneous arousal index (SpArI) and SpArI in the nonrapid eye movement (NREM) stage (SpArI_NREM) than those without MCI. An increased risk of MCI was significantly associated with an increase SpArI and SpArI_NREM with various adjustments. Significant associations between the CASI scores and the oximetry parameters and sleep disorder indexes were observed.

CONCLUSIONS

Repetitive respiratory events with hypoxia were associated with cognitive dysfunction. Spontaneous arousal, especially in NREM sleep, was related to the risk of MCI. However, additional longitudinal studies are required to confirm their causality.

Pressure-induced order-disorder transitions in β-In2S3: an experimental and theoretical study of structural and vibrational properties

This joint experimental and theoretical study of the structural and vibrational properties of β-In₂S₃ upon compression shows that this tetragonal defect spinel undergoes two reversible pressure-induced order-disorder transitions up to 20 GPa. We propose that the first high-pressure phase above 5.0 GPa has the cubic defect spinel structure of α-In₂S₃ and the second high-pressure phase (ϕ-In₂S₃) above 10.5 GPa has a defect α-NaFeO₂-type (R3̄m) structure. This phase, related to the NaCl structure, has not been previously observed in spinels under compression and is related to both the tetradymite structure of topological insulators and to the defect LiTiO₂ phase observed at high pressure in other thiospinels. Structural characterization of the three phases shows that α-In₂S₃ is softer than β-In₂S₃ while ϕ-In₂S₃ is harder than β-In₂S₃. Vibrational characterization of the three phases is also provided, and their Raman-active modes are tentatively assigned. Our work shows that the metastable α phase of In₂S₃ can be accessed not only by high temperature or varying composition, but also by high pressure. On top of that, the pressure-induced β-α-ϕ sequence of phase transitions evidences that β-In₂S₃, a BIII2XV3 compound with an intriguing structure typical of A^IIBIII2XVI4 compounds (intermediate between thiospinels and ordered-vacancy compounds) undergoes: (i) a first phase transition at ambient pressure to a disordered spinel-type structure (α-In₂S₃), isostructural with those found at high pressure and high temperature in other BIII2XV3 compounds; and (ii) a second phase transition to the defect α-NaFeO₂-type structure (ϕ-In₂S₃), a distorted NaCl-type structure that is related to the defect NaCl phase found at high pressure in A^IIBIII2XVI4 ordered-vacancy compounds and to the defect LiTiO₂-type phase found at high pressure in A^IIBIII2XVI4 thiospinels. This result shows that In₂S₃ (with its intrinsic vacancies) has a similar pressure behaviour to thiospinels and ordered-vacancy compounds of the A^IIBIII2XVI4 family, making β-In₂S₃ the union link between such families of compounds and showing that group-13 thiospinels have more in common with ordered-vacancy compounds than with oxospinels and thiospinels with transition metals.

Arsenic dynamics and flux assessment under drying-wetting irrigation and enhanced microbial diversity in paddy soils: A four year study in Bengal delta plain

Arsenic (As) assessment in agricultural soils and corresponding crops is necessary from the global health safety perspective. To the best of our knowledge, we are reporting for the first time, As flux determining parametric equations for paddy field with seasonal rice cultivation under conventional flooding and dry-wet irrigation approaches. Rigorous field experiments and measuring quantitative parameters, flushed out or percolated into the deeper soil As flux was assessed. A wintery (boro)-monsoonal (aman) study from 2016 to 2019 has been conducted showing the efficiency of dry-wet irrigation on reduction of soil As bioavailability. The reduction in boro was 52.4% in 2016 to 64.8% in 2019 while in aman, it was 61% in 2016 to 74.9% in 2019. Low bioavailability was correlated to plant's internal vascular structure that was found more rigid and firm in dry-wet field grown plants. Observed soil physico-chemical parameters clearly influenced As bioavailability as well as soil microbial community. Assessment of microbial diversity using metagenomics under altered water regime was done by population analysis, relative abundance, species richness, Krona chart comparison. Dry-wet field was found to be more diverse and enriched in microbial community than that of the flooded soil indicating an affective reduction of As bioavailability under biotic-abiotic factors.

389 The Association between Arousals and Neurochemical Biomarkers Accumulation in Obstructive Sleep Apnea with Low Arousal Threshold

Introduction

Previous studies indicated the accumulation of neurodegenerative protein may be caused by higher Obstructive sleep apnea syndrome (OSAS) severity. However, the association between arousal-related parameters induced by OSAS and the amyloid burden remains unclear. The aim of this study is to investigate the association between arousal threshold (ArTH) and neurochemical protein accumulation in OSAS patients.

Methods

Suspected OSAS participants were performed Mini-mental status examination (MMSE) and full-night polysomnography (PSG) in the sleep center of Taipei Medical University Shuang Ho Hospital, Taiwan. On the same morning, the blood samples were obtained from the participants. The concentrations of total Tau (T-Tau) and amyloid beta peptide 42 (Aβ42) were quantified by ultra-sensitive immunomagnetic reduction assays. An overall of 23 participants were enrolled and classified into Low ArTH group (n=12) and High ArTH group (n=11) based on low ArTH criteria. Regarding the statistical methods, for categorical variables and continuous variables, Fisher’s exact test and Mann-Whitney U test were performed to investigate the differences between groups, respectively. The associations between biomarkers concentrations and PSG parameters were assessed by Spearman’s correlation.

Results

Regarding the demographic characteristics in two subgroups, significantly lower body-mass index and OSAS severity were noted in Low ArTH group (p&lt;0.05). The MMSE was in normal range in both groups and had no significant differences in subgroups. For PSG parameters, there were significantly lower desaturation index, AHI and higher spontaneous arousals index in each sleep stage in Low ArTH group (p&lt;0.01). Nevertheless, in the plasma neurochemical biomarkers, Aβ42 and Aβ42 X T-Tau were significantly higher in Low ArTH group (p&lt;0.05). Moreover, in Low ArTH group, T-Tau was positively correlated with respiratory arousals index (r=0.61, p&lt;0.05) and all arousals index (r=0.76, p&lt;0.01), respectively. The positive correlations between Aβ42 X T-Tau and respiratory arousals index (r=0.62, p&lt;0.05), all arousals index (r=0.75, p&lt;0.01) could also be observed. There were no significant correlations noted in High ArTH group.

Conclusion

OSAS patients with low ArTH have higher neurochemical biomarker levels. Also, the significantly positive correlations between arousals and biomarkers were observed in that group.

Support (if any):

Chemical intervention for enhancing growth and reducing grain arsenic accumulation in rice

Arsenic (As) is a ubiquitous environmental carcinogen that enters the human food chain mainly through rice grains. In the present study, we evaluated the potential of thiourea (TU; non-physiological reactive oxygen species scavenger) in mitigating the negative effects of arsenic (As) stress in indica rice variety IR64, with the overall aim to reduce grain As accumulation. At seedling stage, As + TU treatment induced the formation of more numerous and longer crown roots compared with As alone. The As accumulation in main root, crown root, lower leaf and upper leaf was significantly reduced to 0.1-, 0.14-, 0.16-, 0.14-fold, respectively in As + TU treated seedlings compared with those of As alone. This reduced As accumulation was also coincided with light-dependent suppression in the expression levels of aquaporins and photosynthesis-related genes in As + TU treated roots. In addition, the foliar-supplemented TU under As-stress maintained reducing redox conditions which decreased the rate of As accumulation in flag leaves and, eventually grain As by 0.53-fold compared with those of As treatment. The agronomic feasibility of TU was validated under naturally As contaminated sites of Nadia (West Bengal, India). The tiller numbers and crop productivity (kg seed/ha) of TU-sprayed plants were increased by 1.5- and 1.18-fold, respectively; while, grain As accumulation was reduced by 0.36-fold compared with those of water-sprayed control. Thus, this study established TU application as a sustainable solution for cultivating rice in As-contaminated field conditions.

Thiourea supplementation mediated reduction of grain arsenic in rice (Oryza sativa L.) cultivars: A two year field study

The present study delineates the interactions of arsenic (As), a carcinogenic metalloid, and thiourea (TU), a non-physiological reactive oxygen species (ROS) scavenger, in rice plants grown in As contaminated fields in West Bengal, India. The study was performed for four consecutive seasons (two boro and two aman) in 2016 and 2017 with two local rice cultivars; Gosai and Satabdi (IET-4786) in a control and two As contaminated experimental fields. Thiourea (0.05% wt/vol) treatment was given in the form of seed priming and foliar spray. Thiourea significantly improved growth and yield of rice plants and reduced As concentration in root, shoot, husk and grains in both cultivars and fields. The reduction in As concentration ranged from 10.3% to 27.5% in four seasons in different fields. The average (four seasons) increase in yield was recorded about ~8.1% and ~11.5% in control, ~20.2% and ~18.6% in experimental field 1, and ~16.2% and ~24.1% in experimental field 2, for gosai and satabdi, respectively. Mean hazard quotient (HQ) and incremental lifetime cancer risk (ILCR) values of As reduced upon TU supplementation for both cultivars as compared to that of non-TU plants. Hence, TU can be effectively used to cultivate rice safely in As contaminated fields.

Practical consensus recommendations on fertility preservation in patients with breast cancer

Young women diagnosed with cancer today have a greater chance of long-term survival than ever before. Successful survivorship for this group of patients includes maintaining a high quality of life after a cancer diagnosis and treatment; however, lifesaving treatments such as chemotherapy, radiation, and surgery can impact survivors by impairing reproductive and endocrine health. Expert oncologists along with reproductive medicine specialists discuss fertility preservation options in this chapter since fertility preservation is becoming a priority for young women with breast cancer. This expert group used data from published literature, practical experience and opinion of a large group of academic oncologists to arrive at these practical consensus recommendations for the benefit of community oncologists.

Tracking respiratory mechanics around natural breathing rates via variable ventilation

Measuring respiratory resistance and elastance as a function of time, tidal volume, respiratory rate, and positive end-expiratory pressure can guide mechanical ventilation. However, current measurement techniques are limited since they are assessed intermittently at non-physiological frequencies or involve specialized equipment. To this end, we introduce ZVV, a practical approach to continuously track resistance and elastance during Variable Ventilation (VV), in which frequency and tidal volume vary from breath-to-breath. ZVV segments airway pressure and flow recordings into individual breaths, calculates resistance and elastance for each breath, bins them according to frequency or tidal volume and plots the results against bin means. ZVV's feasibility was assessed clinically in five human patients with acute lung injury, experimentally in five mice ventilated before and after lavage injury, and computationally using a viscoelastic respiratory model. ZVV provided continuous measurements in both settings, while the computational study revealed <2% estimation errors. Our findings support ZVV as a feasible technique to assess respiratory mechanics under physiological conditions. Additionally, in humans, ZVV detected a decrease in resistance and elastance with time by 12.8% and 6.2%, respectively, suggesting that VV can improve lung recruitment in some patients and can therefore potentially serve both as a dual diagnostic and therapeutic tool.

Mitigating the Climate Forcing of Aircraft Contrails by Small-Scale Diversions and Technology Adoption

The climate forcing of contrails and induced-cirrus cloudiness is thought to be comparable to the cumulative impacts of aviation CO₂ emissions. This paper estimates the impact of aviation contrails on climate forcing for flight track data in Japanese airspace and propagates uncertainties arising from meteorology and aircraft black carbon (BC) particle number emissions. Uncertainties in the contrail age, coverage, optical properties, radiative forcing, and energy forcing (EF) from individual flights can be 2 orders of magnitude larger than the fleet-average values. Only 2.2% [2.0, 2.5%] of flights contribute to 80% of the contrail EF in this region. A small-scale strategy of selectively diverting 1.7% of the fleet could reduce the contrail EF by up to 59.3% [52.4, 65.6%], with only a 0.014% [0.010, 0.017%] increase in total fuel consumption and CO₂ emissions. A low-risk strategy of diverting flights only if there is no fuel penalty, thereby avoiding additional long-lived CO₂ emissions, would reduce contrail EF by 20.0% [17.4, 23.0%]. In the longer term, widespread use of new engine combustor technology, which reduces BC particle emissions, could achieve a 68.8% [45.2, 82.1%] reduction in the contrail EF. A combination of both interventions could reduce the contrail EF by 91.8% [88.6, 95.8%].