Stepwise Multiple Linear Regression Research Articles

Factors Influencing Social and Community Participation of People with Spinal Cord Injury in Karnataka, India.

Social and community participation are major indicators to assess the adequacy of treatment and rehabilitation in patients with spinal cord injury (SCI). This study examined the relationship between functional independence, level of disability, and social and community participation among people with SCI in India. In this cross sectional study, 110 persons with SCI, aged 18 years and above participated in a community setting, in Karnataka, India. Spinal Cord Independence Measure Version III-self-reporting (SCIM III-SR), Craig Handicap Assessment and Reporting Technique (CHART), and WHO Disability Assessment Schedule 2.0 (WHODAS) were the clinical outcome measures. Spearman's correlation and stepwise multiple linear regression were done to determine association and identify the factors determining the community participation of people with SCI. CHART physical independence had a positive correlation with SCIM self-care (R = 0.446) and SCIM mobility (r = 0.434). CHART cognitive independence (R = -0.38) and CHART mobility (R = -0.396) had a weak correlation with WHODAS. SCIM self-care and SCIM mobility (R2 = 0.34) were determinants of CHART cognitive independence. SCIM respiratory and sphincter management and SCIM self-care (R2 = 0.327) were determinants of CHART mobility. Self-care and mobility of people with SCI determine their ability to successfully reintegrate into the community, warranting a comprehensive community rehabilitation program.

Study on the Status of Illegal Trade in Wild Fauna and Flora Based on Multiple Linear Stepwise Regression and DEA

Study on the Status of Illegal Trade in Wild Fauna and Flora Based on Multiple Linear Stepwise Regression and DEA

Seasonal Variations in Household Water Use, Microbiological Water Quality, and Challenges to the Provision of Adequate Drinking Water: A Case of Peri-urban and Informal Settlements of Hosanna Town, Southern Ethiopia.

Several studies have been conducted on household water use and microbial water quality globally. However, studies that considered seasonal variability of household water use and microbial water quality were limited. Therefore, this study investigated the seasonal variability of household water use, microbiological water quality, and challenges to the provision of adequate water in the peri-urban and informal settlements of Hosanna town, Southern Ethiopia. A longitudinal study was conducted on 288 households. The data was gathered using a pretested structured questionnaire, laboratory-analysis, interviews, storage-container inventories, focus group discussions, key-informant interviews, and an observational checklist. The data was analyzed using stepwise-multiple linear regression, bivariate and multivariable logistic regression, thematic-analysis, t-tests, and non-parametric-tests. Households were visited for 7 consecutive days during the dry and rainy seasons to account for changes in daily and seasonal variation of water use. 440 stored water and 12 source samples were analyzed for E. coli presence during dry and rainy seasons. The prevalence of stored water contamination with E. coli was 43.2% and 34.5% during the dry and rainy seasons, respectively. The per capita water consumption was 19.4 and 20.3 l during the dry and rainy seasons, respectively. Piped water on-premises, small family size, volume, and number of water storage containers were significant predictors of per capita water consumption in both seasons. Piped water off-premises, storing water for more than 3 days, uncovered, and wide-mouthed water storage containers were significantly associated with the presence of E. coli in water in both seasons. Seasonal variability of household water use and microbiological water quality was statistically significant, which is a significant public health concern and needs intervention to enhance water quantity and quality to mitigate the risk of waterborne diseases. Findings also suggest seasonal monitoring of the safety of drinking water to ensure that the water is safe and healthy.

Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering.

Predicting the efficacy of micropollutant separation through functionalized membranes is an arduous endeavor. The challenge stems from the complex interactions between the physicochemical properties of the micropollutants and the basic principles underlying membrane filtration. This study aimed to compare the effectiveness of a modest dataset on various machine learning tools (ML) tools in predicting micropollutant removal efficiency for functionalized reverse osmosis (RO) and nanofiltration (NF) membranes. The inherent attributes of both the micropollutants and the membranes are utilized as input factors. The chosen ML tools are supervised algorithm (adaptive network-based fuzzy inference system (NF), linear regression framework (linear regression (LR)), stepwise linear regression (SLR) and multivariate linear regression (MVR)), and unsupervised algorithm (support vector machine (SVM) and ensemble boosted tree (BT)). The feature engineering and parametric dependency analysis revealed that characteristics of micropollutants, such as maximum projection diameter (MaxP), minimal projection diameter (MinP), molecular weight (MW), and compound size (CS), exhibited a notably positive impact on the correlation with removal efficiency. Model combination with key variables demonstrated high prediction accuracy in both supervised and unsupervised ML for micropollutant removal efficiency. An NF-grid partitioning (NF-GP) model achieved the highest accuracy with an R 2 value of 0.965, accompanied by low error metrics, specifically an RMSE and MAE of 3.65. It is owed to the handling of the complex spatial and temporal aspects of micropollutant data through division into consistent subsets facilitating improved identification of rejection efficiency and relationships. The inclusion of inputs with both negative and positive correlations introduces variability, amplifies the system responsiveness, and impedes the precision of predictive models. This study identified key micropollutant properties, including MaxP, MinP, MW, and CS, as crucial factors for efficient micropollutant rejection during real-time filtration applications. It also allowed the design of pore size of self-prepared membranes for the enhanced separation of micropollutants from wastewater.

Relationship Between Executive Function Subdomains and Postural Balance in Community-Dwelling Older Adults.

Executive function (EF) deficits are a significant risk factor for falls among older adults (OAs). However, relationship between EF subdomains (shifting, updating, and inhibition), postural balance (PB), and fall risk in healthy OAs, remains poorly understood. This study aimed to investigate the relationship between EF subdomains (shifting, updating, and inhibition) and PB, and to assess their impact on risk of falls in community-dwelling OAs. A cross-sectional study involving 50 OAs aged over 60years (average age of 72years)was conducted. Participants underwent assessments of EF subdomains and PB using validated tests. A correlation analysis was employed to examine the relationships between EF and PB. The study revealed significant correlations between subdomains and PB. Mental set shifting (r = -.539; p < .001) and inhibition (r = -.395; p = .050) exhibited inverse relationships with PB. Stepwise multiple linear regression showed that Trail Making Test Part B was associated with the PB (R2 = .42, p < .001). These findings highlight the importance of assessing EF subdomains, particularly shifting and inhibition, to identify risk of falls. Trail Making Test Part B largely explains the variability of the PB. Integrating PB assessments and EF training, such as the Mini-BESTest, into routine care can be vital for fall prevention strategies. Significance/Implications: This knowledge underscores the need for cognitive training interventions focusing on shifting and inhibition to enhance PB and potentially reduce falls. Additionally, incorporation of EF assessment tools as Trail Making Test Part B and the Mini-BESTest into routine clinical practice for community-dwelling OAs is recommended to address fall prevention strategies.

ATIVIDADE FÍSICA E AUTOESTIMA EM PESSOAS COM DEFICIÊNCIA NA ARÁBIA SAUDITA

ABSTRACT Introduction: Physical activity can play a critical and effective role in the health and self-esteem of the general population. However, the research on the association between participation in physical activity and self-esteem in those with physical disabilities in non-Western countries, especially Saudi Arabia is limited. Objective: To analyze self-esteem levels among those with physical disabilities in Saudi Arabia, and examine the relationship between participation in physical activity and self-esteem among those with physical disabilities in Saudi Arabia. Methods: A participant sample (N = 292) consisting of Saudi Arabian individuals with physical disabilities (male n = 201; female n = 91) aged 18-59 years (M = 36.08 years; SD = 10.64) was recruited to participate in this study. Levels of self-esteem were measured using the Arabic version of the Rosenberg Self-Esteem Scale (RSES). Results: Statistical analysis illustrated that the participants’ overall self-esteem was moderate (M = 3.14; SD = 0.56). A stepwise multiple linear regression demonstrated that participating in physical activity (measured by days per week) was the only predictor of the total RSES scale (ß = −0.304; p = &lt;0.001). Conclusions: The findings suggest that participation in physical activity is a statistically significant predictor of self-esteem levels in those with physical disabilities in Saudi Arabia. Therefore, it is essential to increase awareness of the importance of participation in physical activity among people with physical disabilities. Level of evidence II; Therapeutic Studies - Investigation of Treatment Results.

Modern medical services, a double-edged sword manages symptoms, but accumulates genetic background of cardiovascular diseases: A cross populational analysis of 217 countries.

Through reduced natural selection, measured with Biological State Index (I bs), modern medicine enables most people to survive well beyond the reproductive lifespan leading to deleterious gene accumulation in population. This study explored the role of reduced natural selection in increasing cardiovascular disease (CVD) incidence worldwide. Country-specific estimates of CVD incidence and the index of reduced natural selection were captured for analysis of their correlation. Aging, affluence, obesity prevalence, and urbanization were considered as the potential confounders in the analyses. Worldwide, I bs was significantly correlated with CVD incidence in the bivariate correlation analyses. This relationship remains when the contributing effects from aging, affluence, obesity prevalence, and urbanization are removed in partial correlation model. Multiple linear regression (enter) shows that I bs is a significant predictor of CVD incidence. Stepwise multiple linear regression selects I bs as the variable having the second greatest influence on CVD incidence after ageing. I bs showed a significantly greater correlation with CVD incidence in low- and middle-income countries (LMICs) than in high-income countries. Worldwide, through reducing natural selection, the side effects of healthcare services may have been partially contributing to the increase of CVD incidence worldwide with special regard to LMICs.

Multifactorial assessment of braking time predictors in a driving simulator among older adults according to gender

Vehicle driving depends on the integration of motor, visual, and cognitive skills to respond appropriately to different situations that occur in traffic. To analyze a model of performance predictor for braking time in the driving simulator, using a battery of tests divided by gender. Selected were 100 male drivers with a mean age of 72.6 ± 5.7 years. Sociodemographic variables, braking time in the driving simulator, and motor, visual, and cognitive skills were evaluated. Comparing genders, men were older than women (p = 0.002) and had longer driving times (p = 0.001). Men had more strength in hand grip (p ≤ 0.001). In the linear regression analysis, the model explained 68 % of the braking time in men and 50.8 % in women. In the stepwise multiple linear regression analysis, the variable that remained in the model was the strength of the right plantar flexors, which explained 13 % of the braking time in women and men, and the cognitive variables explained 38.9 %. Sociodemographic, motor, visual, and cognitive variables, explained a substantial portion of the variability in braking time for both older women and men, the specific variables driving this performance differed between the sexes. For older women, factors such as muscle strength emerged as critical determinants of braking ability, highlighting the importance of physical health in maintaining driving skills. On the other hand, cognitive conditions emerged as the primary predictor of braking performance in older men, underscoring the role of mental acuity and decision-making processes in safe driving.

Comparison of ground reaction forces as running speed increases between male and female runners.

Introduction: The biomechanics associated with human running are affected by gender and speed. Knowledge regarding ground reaction force (GRF) at various running speeds is pivotal for the prevention of injuries related to running. This study aimed to investigate the gait pattern differences between males and females while running at different speeds, and to verify the relationship between GRFs and running speed among both males and females. Methods: GRF data were collected from forty-eight participants (thirty male runners and eighteen female runners) while running on an overground runway at seven discrete speeds: 10, 11, 12, 13, 14, 15 and 16km/h. Results: The ANOVA results showed that running speed had a significant effect (p < 0.05) on GRFs, propulsive and vertical forces increased with increasing speed. An independent t-test also showed significant differences (p < 0.05) in vertical and anterior-posterior GRFs at all running speeds, specifically, female runners demonstrated higher propulsive and vertical forces than males during the late stance phase of running. Pearson correlation and stepwise multiple linear regression showed significant correlations between running speed and the GRF variables. Discussion: These findings suggest that female runners require more effort to keep the same speed as male runners. This study may provide valuable insights into the underlying biomechanical factors of the movement patterns at GRFs during running.

Cognitive assessment in preterms by Bayley-III: development in the first year and associated factors.

To analyze the cognitive development of preterm infants at six and 12 months of corrected age and the associations with perinatal and socioeconomic factors. Cognitive development of 40 infants (20 preterm and 20 full-term) at six and 12 months of age was evaluated using the Bayley-III scale. Correlations between cognitive outcome and associated factors were assessed using Spearman correlation. Stepwise multiple linear regression analysis with covariance was applied to identify changes on cognitive score between six and 12 months. Bayley-III cognitive score in preterm group was significantly lower than in full-term group at both six and 12 months of age. Birth weight correlated with cognitive performance at six months and head circumference at birth at 12 months, in full-terms infants. The occurrence of necrotizing enterocolitis was inversely associated with cognitive score in preterms at 12 months. An increase in cognitive score was observed between six and 12 months in both groups, but the gain was more pronounced in preterms. These findings suggest some cognitive recovery capacity in the first year despite the restrictions imposed by premature birth and emphasize the importance of early interventions in this population.

Profiling of phenolic composition in camellia oil and its correlative antioxidant properties analysis.

Less research has been conducted on the association between camellia oil's (CO) phenolic composition and antioxidant capability. In this study, the phenolic profile of CO and its connection to antioxidant capacity were examined utilizing a combination of widely-targeted phenolic metabolomics and multivariate statistical analysis. A total of 751 phenolics were discovered. The WGCNA was used to link phenols to antioxidants, yielding 161 antioxidant-related phenols from the blue module. In response to several antioxidant assays, 59 (FRAP), 59 (DPPH), and 53 (ABTS) phenolics were identified as differential phenolic markers (DPMs). Further stepwise multiple linear regression revealed six DPMs that substantially influenced the antioxidant capacities. Nine metabolic pathways and their associated network mechanisms for the most significant phenolics were developed. This study sheds light on the phenolic content of CO, elucidates their role in antioxidant activity, and lays the groundwork for improving extraction techniques and generating improved product.

Relationships between regional retinal sensitivities assessed by microperimetry and their corresponding retinal layer thicknesses in healthy middle‐aged subjects

Aims/Purpose: To investigate relationships between regional differential light sensitivity (DLS) and inner and outer retinal layer (IRL and ORL) thicknesses in healthy middle‐aged adults.Methods: Measurements were made in 42 eyes of 42 healthy individuals of mean age 51.3 ± 5.4 years (range 40–59 years), spherical equivalent (SE) between ±3.50 D and good visual acuity. Mesopic fundus‐controlled perimetry was used to measure regional DLS. Spectral‐domain optical coherence tomography (SD‐OCT) was used to measure IRL (ganglion cell‐related layer) and ORL thicknesses. DLS and retinal thickness measurements were recorded for five subfields: central fovea (1 mm diameter) and the quadrants temporal, nasal, superior, and inferior of a parafoveal ring of outer diameter 3 mm and inner diameter 1 mm. Relationships between regional DLS and corresponding IRL and ORL thicknesses were assessed through univariate and multivariate analysis.Results: Mean DLS measured in the inferior parafoveal ring quadrant was significantly correlated with mean IRL thickness (rho = −0.50; p = 0.0045). This correlation was negative, meaning that a thicker IRL was related to a worse DLS. No significant correlations emerged between mean DLS and corresponding ORL or IRL thicknesses for the other subfields. Subsequent forward stepwise multiple linear regression analysis to correct for potential confounding factors revealed that IRL thickness and SE emerged as significant predictors of DLS in the inferior quadrant (R2 model = 33%; p = 0.0004).Conclusions: A thicker macular IRL, possibly indicating transient gliosis, was associated with a worse DLS in healthy middle‐aged eyes.

Comparative analysis of SMLR, ANN, Elastic net and LASSO based models for rice crop yield prediction in Uttarakhand

The study was aimed to develop the yield forecast model for rice crop yield. Four different techniques i.e. Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN), Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ELNET)were used to build the prediction models. Dataset of meteorological data and crop yield data of 15 years have been used to develop the forecast models. The developed models were also validated on the dataset of three years. The assessment of the developed models wasdone by using root mean square error (RMSE),normalized root mean square error (nRMSE),Mean Absolute Error (MAE) and on the basis of coefficient of determination (R2). The experimental analysis suggested that the performance for Artificial Neural Network (R2=0.99, RMSE=0.07, nRMSE=2.20, MAE=0.06) is better as compared to SMLR(R2=0.97, RMSE=0.08, nRMSE=2.34, MAE=0.05), LASSO (R2=0.62, RMSE=0.26, nRMSE=7.81, MAE=0.24) and ELNET (R2=0.54, RMSE=0.38, nRMSE=11.41, MAE=0.37) for the predictionof rice crop yield for Udham Singh Nagar (USN) district of Uttarakhand. Therefore, for the prediction of rice yield, ANN technique can be well utilised for Udham Singh Nagar district of Uttarakhand.&#x0D;

The effects of dental hygiene students’ adult attachment, self-respect, and sociable support on their interpersonal competence

Objectives: This study sought to identify dental hygiene students’ adult attachment, interpersonal competence, self-respect, and sociable support, and to identify factors that affect interpersonal competence. Method: A self-administered survey was conducted on 180 students attending dental hygiene schools nationwide from 14 February to 30 May, 2023. Descriptive statistics, t-test, one-way ANOVAs, Pearson correlation coefficient, and stepwise multiple linear regression analys were used, as determined by the SPSS 26.0 program. Results: Dental hygiene students’ interpersonal competence measured 3.61 points, adult attachment measured 3.44 points, self-respect measured 3.44 points, and sociable support measured 4.00 points. In terms of general characteristics, significant differences were found in satisfaction with college life and club membership. Interpersonal competence was positively correlated with adult attachment, self-respect, and sociable support. The factor that had the greatest impact on interpersonal competence of dental hygiene students was sociable support, with an explanatory power of 37.3%. Conclusions: In order to improve the sociable support for dental hygiene students, a social activity support network within and outside of school is needed.

Spatial and variety distributions, risk assessment, and prediction model for heavy metals in rice grains in China.

In this study, 6229 brown rice grains from three major rice-producing regions were collected to investigate the spatial and variety distributions of heavy metals in rice grains in China. The potential sources of heavy metals in rice grains were identified using the Pearson correlation matrix and principal component analysis, and the health risks of dietary exposure to heavy metals via rice consumption were assessed using the hazard index (HI) and total carcinogenic risk (TCR) method, respectively. Moreover, 48 paired soil and rice samples from 11 cities were collected to construct a predicting model for Cd accumulation in rice grains using the multiple linear stepwise regression analysis. The results indicated that Cd and Ni were the main heavy metal pollutants in rice grains in China, with approximately 10% of samples exceeding their corresponding maximum allowable limits. The Yangtze River basin had heavier pollution of heavy metals than the Southeast Coastal Region and Northeast Plain, and the indica rice varieties had higher heavy metal accumulation abilities compared with the japonica rice. The Cu, Pb, and Cd mainly originated from anthropogenic sources, while As, Hg, Cr, and Ni originated from both natural and anthropogenic sources. The mean HI and TCR values of dietary exposure to heavy metals via rice consumption ranged from 2.92 to 4.31 and 9.74 × 10-3 to 1.44 × 10-2, respectively, much higher than the acceptable range, and As and Ni were the main contributor to the HI and TCR for Chinese adults and children, respectively. The available Si (ASi), total Cd (TCd), available Mo (AMo), and available S (AS) were the main soil factors determining grain Cd accumulation. A multiple linear stepwise regression model was constructed based on ASi, TCd, AMo, and AS in soils with good accuracy and precision, which could be applied to predict Cd accumulation in rice grains and guide safe rice production in contaminated paddy fields.

Predictors of Professional Quality of Life, Stress and Coping Styles Among Frontline Nurses During Covıd-19: A Cross-Sectional Study

COVID-19 may cause traumatic experiences and create stressful work environments for nurses, adversely affecting their psychosocial status. This study analyzes the professional quality of life, perceived stress levels, and coping styles of frontline nurses during the COVID-19 pandemic. Web-based survey using a descriptive cross-sectional design and a convenience sampling were used to select 244 nurses working with COVID-19 patients in inpatient, emergency, or intensive care units in Istanbul, Turkey. All participants completed a research questionnaire via Google Forms between August 7 and December 25, 2020. Multiple stepwise linear regression was used to analyze data. Participants experienced moderate levels of burnout, compassion satisfaction and stress, and high levels of compassion fatigue. They mostly used the self-confident approach coping style. Lack of training on the use of personal protective equipment, inadequate social support and the demands of emergency unit work were associated with compassion fatigue, burnout, and decreased level of compassion satisfaction. Organizational support was a predictor of seeking social support coping style and perceived stress was a predictor of a submissive and helpless coping style. This study demonstrates that interventions and coping programs are needed to improve frontline nurses’ ability to cope with stress.

Coniferous Forests Aboveground Biomass Inversion in Typical Regions of China with Joint Sentinel-1 and Sentinel-2 Remote Sensing Data Supported by Different Feature Optimizing Algorithms

Multispectral remote sensing (RS) data and synthetic aperture radar (SAR) data can provide horizontal and vertical information about forest AGB under different stand conditions. With the abundance of RS features extracted from multispectral and SAR datasets, a key point for accurate forest AGB estimation is to use suitable feature optimization inversion algorithms. In this study, feature optimization inversion algorithms including multiple linear stepwise regression (MLSR), K-nearest neighbor with fast iterative feature selection (KNN-FIFS), and random forest (RF) were explored, with a total of 93 RS features working as inversion model input for forest AGB inversion. The results showed that KNN-FIFS with the combination of Sentinel-1 and Sentinel-2 performed best at both test sites (R2 = 0.568 and RMSE = 15.05 t/hm2 for Puer and R2 = 0.511 and RMSE = 32.29 t/hm2 for Genhe). Among the three feature optimization inversion algorithms, RF performed worst for forest AGB estimation with R2 = 0.348 and RMSE = 18.06 t/hm2 for Puer and R2 = 0.345 and RMSE = 35.98 t/hm2 for Genhe using the feature combination of Sentinel-1 and Sentinel-2. The results indicated that a combination of features extracted from Sentinel-1 and Sentinel-2 can improve the inversion accuracy of forest AGB, and the KNN-FIFS algorithm has robustness and transferability in forest AGB inversions.

Analysis of dynamic functional connectivity states and influencing factors of brain network in male patients with obstructive sleep apnea

Objective: To analyze dynamic functional connectivity (dFNC) states and influencing factors of brain network in male patients with obstructive sleep apnea (OSA). Methods: A total of 111 male patients diagnosed with obstructive sleep apnea or presenting with simple snoring, who visited the Sleep Clinic at the Second Affiliated Hospital of Soochow University between August 2020 and December 2021, were prospectively selected for this study. General information was collected, and polysomnography (PSG) was performed. Based on the oxygen desaturation index (ODI), the participants were divided into three groups: primary snoring group (ODI<5 events/hour, n=34), mild to moderate OSA group (5 events/hour≤ODI<30 events/hour, n=43), and sever OSA group (ODI≥30 events/hour, n=34). Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) scale, and daytime sleepiness was evaluated using the Epworth Sleepiness Scale (ESS). Resting-state functional magnetic resonance imaging (fMRI) data were collected and preprocessed. dFNC matrices were constructed using a sliding time window approach. The number of dFNC states was determined using k-means clustering analysis. Three parameters, namely, fractional time (FT), mean dwell time (MDT), and number of transitions (NT), were used to characterize the temporal properties of dFNC states. Differences in the temporal properties of dFNC states among the groups were compared. The correlations between temporal properties and PSG parameters, as well as MoCA and ESS scores, were further analyzed. Multiple stepwise linear regression analysis was performed to identify the influencing factors of the temporal properties of dFNC states. Results: The age of the patients was (40.2±8.6) years (range: 25-65 years). There were no significant differences in age, smoking history and alcohol history, and MoCA scores among the three groups (all P>0.05). Three dFNC states were extracted through k-means clustering analysis: state 1, characterized by strong connections within the visual and sensorimotor networks with a frequency of 31.7% (4 611/14 541); state 2, characterized by strong connections within the default mode network, attention network, and other cognitive networks, with the lowest frequency of 22.1% (3 213/14 541); state 3, characterized by weaker connections across the whole brain, with the highest frequency of 46.2% (6 717/14 541). The FT [0.28 (0.05, 0.35) vs 0.39 (0.26, 0.53)] and MDT [8.20 (4.35, 12.54) vs 11.68 (8.50, 16.69)] of state 2 in the sever OSA group were lower than those in the primary snoring group (both P<0.05), while there were no significant differences in the temporal properties of states 1 and 3 among the three groups (all P>0.05). The FT and MDT of state 2 were correlated with body mass index (BMI), apnea-hypopnea index (AHI), ODI, and minimum oxygen saturation (MinSaO2) (FT: r values were -0.218, -0.230, -0.249, 0.198, respectively; MDT: r values were 0.269, -0.253, -0.265, 0.209, respectively; all P<0.05). There were no significant correlations between the temporal properties and MoCA or ESS scores (all P>0.05). ODI was found to be an influencing factor for the temporal properties of state 2 (FT: β=-0.225, 95%CI:-0.227 to -0.223; MDT: β=-0.241, 95%CI:-0.289 to -0.195). Conclusions: Male patients with OSA exhibit alterations in specific temporal properties of brain network dynamic functional connectivity, which are associated with nocturnal oxygen parameters. This may be one of the mechanisms underlying brain functional damage in patients with OSA.

Physicochemical characteristics and oxidative potential of size-segregated respirable coal mine dust: Implications for potentially hazardous agents and health risk assessment

The health risks associated with exposure to respirable dust in coal mines have been attracted much attention by an increasing number of researchers. However, the accurate identification of potentially hazardous agents in respirable dusts and the evaluation of the potential health risks arising from it still remains controversial to varying degrees. A comprehensive understanding of the physicochemical properties of respirable dust is a prerequisite and an important basis for resolving this controversy. Therefore, in this study, the particle size distribution and morphology, pore structure, mineralogical and geochemical patterns, and oxidative potential (OP) of respirable coal mine dust were comprehensively investigated. Stepwise multiple linear regression was employed to identify dust components driving OP, such as anatase, tobelite, quartz, and ankerite, in respirable coal mine dust, along with Na, Ni, Se, W, and As. On this basis, we performed a single-factor risk prediction for different coal mines by considering factors that may impact miners' health, with the analyses yielding somewhat contradictory results. Therefore, a multifactor integrated prediction model is proposed using an entropy-based technique for order preference by similarity to the ideal solution to categorize coal mines in the study area into three risk categories, high-, medium-, and low-risk dust mines, which is important for the hierarchical classification and control of coal mines and for formulating appropriate dust prevention and control measures.

GC-IMS determination of volatile organic compounds as potential indicators of wheat germination rate

The objective of this research was to investigate the correlation between volatile organic compounds (VOCs) and the germination rate of stored wheat, as well as to identify the potential markers for predicting the germination rate. The findings of this study are expected to contribute to the advancement of a non-destructive and rapid method for evaluating the quality of wheat storage, thereby establishing a theoretical foundation for further research in this area. Gas chromatography-ion mobility spectrometry (GC-IMS) was utilized to analyze the VOCs of wheat samples subjected to accelerated storage condition at 40 °C, RH 65%, as well as natural storage condition at room temperature. A total of 68 and 79 VOCs were detected under accelerated and natural storage conditions, respectively. The germination rate of wheat was found to decreased significantly with prolonged storage time under both storage conditions. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were utilized to differentiate wheat samples stored for varying durations. Furthermore, correlation analysis revealed that 31 VOCs exhibited a significant correlation (p < 0.05) with the germination rate. Through multiple linear stepwise regression analysis, four VOCs—namely isoamyl butyrate, (Z)-3-nonen-1-ol, 1-propanol, and Propanal—were identified as potential markers. Subsequently, a prediction model was constructed using these markers. The results of this study demonstrate that these VOCs can serve as indicators for predicting the germination rate, thus facilitating the evaluation of the germination ability and quality of stored wheat. This research holds implications for enhancing the efficiency of wheat storage management and ensuring food security.