High-Resolution Estimates of N2O Emissions from Inland Waters and Wetlands in China.

Inland waters (rivers, lakes, and reservoirs) and wetlands (marshes and coastal wetlands) represent large and continuous sources of nitrous oxide (N2O) emissions, in view of adequate biomass and anaerobic conditions. Considerable uncertainties remain in quantifying spatially explicit N2O emissions from aquatic systems, attributable to the limitations of models and a lack of comprehensive data sets. Herein, we conducted a synthesis of 1659 observations of N2O emission rates to determine the major environmental drivers across five aquatic systems. A framework for spatially explicit estimates of N2O emissions in China was established, employing a data-driven approach that upscaled from site-specific N2O fluxes to robust multiple-regression models. Results revealed the effectiveness of models incorporating soil organic carbon and water content for marshes and coastal wetlands, as well as water nitrate concentration and dissolved organic carbon for lakes, rivers, and reservoirs for predicting emissions. Total national N2O emissions from inland waters and wetlands were 1.02 × 105 t N2O yr-1, with contributions from marshes (36.33%), rivers (27.77%), lakes (25.27%), reservoirs (6.47%), and coastal wetlands (4.16%). Spatially, larger emissions occurred in the Songliao River Basin and Continental River Basin, primarily due to their substantial terrestrial biomass. This study offers a vital national inventory of N2O emissions from inland waters and wetlands in China, providing paradigms for the inventorying work in other countries and insights to formulate effective mitigation strategies for climate change.

Understanding and alleviating informal caregiver burden through the development and validation of a caregiver strain index-based model in Taiwan.

BACKGROUND: Quantifying the informal caregiver burden is important for understanding the risk factors associated with caregiver overload and for evaluating the effectiveness of services provided in Long-term Care (LTC). OBJECTIVE: This study aimed to develop and validate a Caregiver Strain Index (CSI)-based score for quantifying the informal caregiver burden, while the original dataset did not fully cover evaluation items commonly included in international assessments. Subsequently, we utilized the CSI-based score to pinpoint key caregiver burden risk factors, examine the initial timing of LTC services adoption, and assess the impact of LTC services on reducing caregiver burden. METHODS: The study analyzed over 28,000 LTC cases in Southern Taiwan from August 2019 to December 2022. Through multiple regression analysis, we identified significant risk factors associated with caregiver burden and examined changes in this burden after utilizing various services. Survival analysis was employed to explore the relationship between adopting the first LTC services and varying levels of caregiver burden. RESULTS: We identified 126 significant risk factors for caregiver burden. The most critical factors included caregiving for other disabled family members or children under the age of three (ß = 0.74, p < 0.001), the employment status of the caregiver (ß = 0.30-0.53, p < 0.001), the frailty of the care recipient (ß = 0.28-0.31, p < 0.001), and the behavioral symptoms of dementia in care recipients (ß = 0.28-2.60, p < 0.05). Generally, caregivers facing higher burdens sought LTC services earlier, and providing home care services alleviated the caregiver's burden. CONCLUSION: This comprehensive study suggests policy refinements to recognize high-risk caregivers better early and provide timely support to improve the overall well-being of both informal caregivers and care recipients.

COVID-19 information seeking and individuals' protective behaviors: examining the role of information sources and information content.

BACKGROUND: Seeking COVID-19 information promotes individuals to adopt preventive behaviors, including wearing a mask, social distancing, staying away from risky places, and washing hands. This study aims to investigate which information and sources individuals relied on in seeking COVID-19 information and further examine their roles in individuals' adoption of preventive behaviors. METHODS: Through a statistical analysis of 1027 valid responses from citizens in different Chinese cities in 2022 to the self-designed items in an online survey, this study identified individuals' preferred information sources and content on COVID-19. Regarding the information sources and content, the study used multiple regression analysis to examine their associations with individuals' preventive behaviors, and further applied fuzzy-set qualitative comparative analysis (fsQCA) to explore their configurations that increase the likelihood of individuals adopting preventive behaviors. RESULTS: Individuals preferred information about the newest prevention and control policies, precautions and treatment, and symptoms from the sources of workplace and community, social media, and social live streaming services. Additionally, individuals' preventive behaviors were positively related to the workplace and community (ß = 0.202, p <.001), social live streaming services (ß = 0.089, p <.01), government department websites (ß = 0.079, p <.05), television (ß = 0.073, p <.05), and online news media (ß = 0.069, p <.05), but were negatively associated with newspapers (ß=-0.087, p <.05). Regarding information content, precautions and treatments (ß = 0.211, p <.001), the newest prevention and control policies (ß = 0.173, p <.001), symptoms (ß = 0.152, p <.001), and official rumor-dispelling information (ß = 0.082, p <.05) had a positive relationship with individuals' preventive behaviors. In addition, fsQCA results presented eight configurations that promote individuals to adopt preventive behaviors. The total coverage and solution consistency values were 0.869 and 0.987, respectively. Furthermore, COVID-19 information content, the sources of social media and interpersonal sources, and official news media played an essential role in increasing the likelihood of individuals adopting preventive behaviors. CONCLUSIONS: Our findings demonstrated that individuals seek various COVID-19 information from multiple sources. The direct and degree of association of information sources and content with individuals' preventive behaviors vary from source to source and from content to content. Information sources and content could combinatorially promote individuals to adopt preventive behaviors through several configurations.

Motor Unit Firing Properties During Force Control Task and Associations With Neurological Tests in Children.

The present study aimed to clarify the development of motor unit (MU) firing properties and the association between those neural properties and force steadiness (FS)/neurological tests in 6- to 12-year-old children. Fifty-eight school-aged children performed maximal voluntary knee extension contraction, a submaximal FS test at 10% of maximal voluntary knee extension contraction, knee extension reaction time to light stimulus test, and single-leg standing test, and data from 38 children who passed the criteria were subject to analysis. During the FS test, high-density surface electromyography was recorded from the vastus lateralis muscle to identify individual MU firing activity. FS was improved with an increase in age (r = -.540, P < .001). The MU firing rate (MUFR) was significantly decreased with an increase in age (r = -.343, P = .035). MUFR variability was not associated with age. Although there was no significant correlation between FS and MUFR, FS was significantly correlated with MUFR variability even after adjustment for the effect of age (r = .551, P = .002). Neither the reaction time nor the single-leg standing test was correlated with any MU firing properties. These findings suggest that MUFR variability makes an important contribution to precise force control in children but does not naturally develop with age.

A clinical risk score enables early prediction of dissatisfaction 1 year after total knee arthroplasty.

PURPOSE: Dissatisfaction after total knee arthroplasty (TKA) is a prevalent and clinically relevant problem that affects approximately 10%-20% of patients. The aim of this study is to identify factors associated with dissatisfaction 1 year after TKA. METHODS: A total of 236 patients undergoing TKA were included in this prospective cohort study. Demographic data, preoperative clinical parameters (e.g., axial alignment, osteoarthritis severity) and patient-reported outcome measures (PROMs) were collected preoperatively, at 1 month and 1 year after TKA, encompassing the Knee Society Score (KSS) and Knee injury and Osteoarthritis Outcome Score (KOOS). The primary outcome was dissatisfaction 1 year after TKA, defined as ≤20 points on the KSS satisfaction scale. A risk score based on multiple regression and area under the curve (AUC) analyses was calculated to predict dissatisfaction. RESULTS: One year after TKA, 16% of the patients were dissatisfied. Dissatisfied patients were significantly younger (p = 0.023) and had a higher body mass index (BMI) (p = 0.007). No differences were observed in preoperative objective (p = 0.903) and functional KSS (p = 0.346), pain (p = 0.306), osteoarthritis severity (p = 0.358), axial knee alignment (p = 0.984) or psychological distress (p = 0.138). The likelihood of dissatisfaction at 1 year was 3.0, 4.0, 7.4, 4.3 and 2.8 times higher amongst patients aged <63.5 years, with a BMI > 30.1 kg/m2, a KOOSPain < 50%, a KSSFunction < 42 points and a KSSExpectation < 9 points (all at 1 month), respectively. Using these variables, a risk score with a maximum of 7 points was developed, demonstrating a high predictive value for dissatisfaction (AUC: 0.792 [95% confidence interval: 0.700-0.884], p < 0.001). CONCLUSION: Dissatisfaction 1 year after TKA can be predicted by a weighted risk score that includes patient age, BMI, pain, subjective functionality and unmet expectation 1 month postoperatively. Using the risk score, early detection of dissatisfaction has the potential to enable targeted interventions and improve patients' quality of life. LEVEL OF EVIDENCE: Level II, Prognostic study.

A Confidence Interval for the Difference Between Standardized Regression Coefficients.

Researchers are often interested in comparing predictors, a practice commonly done via informal comparisons of standardized regression slopes. However, formal interval-based approaches offer advantages over informal comparison. Specifically, this article examines a delta-method-based confidence interval for the difference between two standardized regression coefficients, building upon previous work on confidence intervals for single coefficients. Using Monte Carlo simulation studies, the proposed approach is evaluated at finite sample sizes with respect to coverage rate, interval width, Type I error rate, and statistical power under a variety of conditions, and is shown to outperform an alternative approach that uses the standard covariance matrix found in regression textbooks. Additional simulations evaluate current software implementations, small sample performance, and multiple comparison procedures for simultaneously testing multiple differences of interest. Guidance on sample size planning for narrow confidence intervals, an R function to conduct the proposed method, and two empirical demonstrations are provided. The goal is to offer researchers a different tool in their toolbox for when comparisons among standardized coefficients are desired, as a supplement to, rather than a replacement for, other potentially useful analyses.

Peak Scores Significantly Depend on the Relationships between Contextual Signals in ChIP-Seq Peaks.

Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) is a central genome-wide method for in vivo analyses of DNA-protein interactions in various cellular conditions. Numerous studies have demonstrated the complex contextual organization of ChIP-seq peak sequences and the presence of binding sites for transcription factors in them. We assessed the dependence of the ChIP-seq peak score on the presence of different contextual signals in the peak sequences by analyzing these sequences from several ChIP-seq experiments using our fully enumerative GPU-based de novo motif discovery method, Argo_CUDA. Analysis revealed sets of significant IUPAC motifs corresponding to the binding sites of the target and partner transcription factors. For these ChIP-seq experiments, multiple regression models were constructed, demonstrating a significant dependence of the peak scores on the presence in the peak sequences of not only highly significant target motifs but also less significant motifs corresponding to the binding sites of the partner transcription factors. A significant correlation was shown between the presence of the target motifs FOXA2 and the partner motifs HNF4G, which found experimental confirmation in the scientific literature, demonstrating the important contribution of the partner transcription factors to the binding of the target transcription factor to DNA and, consequently, their important contribution to the peak score.

Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models.

A Nationwide Nepalese Study to Establish Reference Intervals for Major Biochemical Analytes with Elucidation of Nepalese Features of Reference Values.

Despite immense importance of reference intervals (RIs) for clinical diagnosis, there have been no reliable RIs available for Nepalese. Hence, this nationwide study was organized to establish RIs for 30 common biochemical parameters. This study was conducted following the harmonized protocol provided by IFCC Committee on Reference Interval and Decision Limits (C-RIDL) with recruitment of 617 apparently healthy volunteers (18 - 65 years) by near-equal gender balance from 5 major cities. Fasting blood were collected, serum was separated and measured collectively using Beckman-Coulter/Olympus AU480 chemistry analyzer. The sources of variations of reference values (RVs) were evaluated by multiple regression analysis and nested ANOVA. Latent abnormal values exclusion (LAVE) method was applied to reduce influence of latent diseases. RIs were standardized based on a value-assigned serum panel provided by C-RIDL. By ANOVA, no between-city differences were observed, while sex-related changes were typically noted for urate, creatinine, iron, γ-glutamyl transferase (GGT), immunoglobulin M, and transferrin, but not for high-density lipoprotein cholesterol. Age-related changes were observed for total cholesterol (TC), triglyceride, low-density lipoprotein cholesterol, and C-reactive protein (CRP). RIs were successfully derived all parametrically. The LAVE procedure was effective in lowering upper limits for aspartate aminotransferase, alanine aminotransferase (ALT), and CRP. Compared to other collaborating countries, Nepalese RIs were low for urea, cholesterols, ALT, and high for triglyceride, GGT, CRP, immunoglobulin G, and complements. The RIs for major chemistry analytes were derived and standardized for nationwide use in Nepal. This study distinctly elucidated sources of variation and international features of Nepalese RIs. Supplementary Information: The online version supplementary material available at 10.1007/s12291-023-01123-6.

The association between hordein polypeptide banding and agronomic traits in partitioning genetic diversity in six-rowed Ethiopian barley lines (Hordeum vulgare L.).

BACKGROUND: Evaluation of the extent of genetic variation within and between the populations of crop genetic resources are of paramount importance in any breeding program. An experiment aimed at assessing the extent of variation among barley lines and the degree of association between hordein polypeptide and agronomic traits was hence executed. METHODS: Field experiment was conducted in six environments between 2017-2019 involving 19 barley lines. Hordein bands were separated using vertical Sodium Dodecyl Sulphate Poly- acrylamide Gel Electrophoresis (SDS-PAGE). RESULTS: The analysis of variance revealed significant variation among lines and wider range units were observed for the agronomic traits. The line (Acc# 16,811-6) was superior, producing the highest grain yield (2.97 ton ha-1) across environments, 3.6 ton ha-1 at Holleta, and 1.93 ton ha-1 at Chefedonsa. At Arsi Negelle a different line Acc# 17146-9 was the highest yielding (3.15ton ha-1). SDS-PAGE-based analysis of barley lines separated 12 hordein bands between C (four bands) and B (eight bands) subunits. Interestingly bands 52, 46a, and 46b were uniquely conserved in the four naked barley lines (Acc#16809-14,16956-11, 17240-3, 17244-19). A considerably high proportion of genetic diversity within the populations than among the populations could be a repercussion of high gene flow which substantiates the longstanding and dominant informal seed exchange system among the farmers. The significant positive association between grain yield and band 50 evocates the expression of this allele may code for higher grain yield. The negative association between days to maturity and band 52 perhaps stipulates earliness in barely lines upon the manifestation of the band. Band 52 and 60 appeared to be associated with more than one agronomic trait (days to maturity and thousand kernel weight; grain filling period and grain yield respectively) and could be the result of pleiotropic characteristics of the genes residing in these banding regions. CONCLUSION: The barley lines exhibited substantial variation for hordein protein and agronomic traits. However, imparted the need for the implementation of decentralized breeding as a consequence of genotype-by-environment interaction. Significant hordein polypeptide and agronomic traits association advocated the utilization of hordein as a protein marker and perhaps consider them in the parental line selection.

The first rib as a method of adult age-at-death estimation in a modern South African sample.

An age-at-death estimation method using the first rib may be particularly advantageous as this rib is relatively easy to identify, not easily damaged postmortem, and associated with less mechanical stresses compared to other age indicators. Previously, mixed results have been achieved using the first rib to estimate age-at-death. This study aimed to develop and test an age-at-death estimation method using the first rib. An identified modern black South African sample of 260 skeletons were used to collect age-related data from the first rib. Multiple linear regression analysis equations were created from this data for male, female, and combined samples. When tested on a hold-out sample, equations generated mean inaccuracies of 7-13 years for point estimates. The 95% confidence intervals contained the true age in 11-33% of individuals depending on the equation used, but wider intervals generated using 95% prediction intervals contained true ages for 100% of individuals. Point estimate inaccuracies are comparable to other age-at-death estimation methods and may be useful if single indicator estimation is unavoidable in the case of missing or damaged bones. However, combined methods that use indicators from many areas of the skeleton are preferable and may reduce interval widths.

Longitudinal development of muscle strength and relationship with motor unit activity and muscle morphological characteristics in youth athletes.

Neural and morphological adaptations determine gains of muscle strength. For youth athletes, the importance of morphological adaptation is typically highlighted based on the change in maturity status. However, the long-term development of neural components in youth athletes remains unclear. The present study investigated the longitudinal development of muscle strength, muscle thickness (MT), and motor unit firing activity of the knee extensor and their relationships in youth athletes. Seventy male youth soccer players (mean ± SD age = 16.3 ± 0.6 years) performed neuromuscular, maximal voluntary isometric contraction (MVC), and submaximal ramp contraction (at 30 and 50% MVC) tests with knee extensors, two times with a 10-month measurement interval. High-density surface electromyography was recorded from the vastus lateralis and decomposed to identify each individual motor unit activity. MT was evaluated by the sum of the vastus lateralis and vastus intermedius thicknesses. Finally, sixty-four participants were employed to compare MVC and MT, and 26 participants were employed to analyze motor unit activity. MVC and MT were increased from pre to post (p < 0.05, 6.9 and 1.7% for MVC and MT, respectively). Y-intercept of the regression line between median firing rate vs. recruitment threshold was also increased (p < 0.05, 13.3%). Multiple regression analysis demonstrated that the gains of both MT and Y-intercept were explanatory variables for the gain of strength. These findings suggest that the neural adaptation could also make the important contribution to the strength gain for the youth athletes over a 10-month training period.

Multiobjective optimization of frozen and freeze-dried Lactobacillus delbrueckii subsp. bulgaricus CFL1 production via the modification of fermentation conditions.

AIM: This study investigates the individual and combined effects of fermentation parameters for improving cell biomass productivity and the resistance to freezing, freeze-drying, and freeze-dried storage of Lactobacillus delbrueckii subsp. bulgaricus CFL1. METHODS AND RESULTS: Cells were cultivated at different temperatures (42°C and 37°C) and pH values (5.8 and 4.8) and harvested at various growth phases (mid-exponential, deceleration, and stationary growth phases). Specific acidifying activity was determined after fermentation, freezing, freeze-drying, and freeze-dried storage. Multiple regression analyses were performed to identify the effects of fermentation parameters on the specific acidifying activity losses and to generate the corresponding 3D response surfaces. A multiobjective decision approach was applied to optimize biomass productivity and specific acidifying activity. The temperature positively influenced biomass productivity, whereas low pH during growth reduced the loss of specific acidifying activity after freezing and freeze-drying. Furthermore, freeze-drying resistance was favored by increased harvest time. CONCLUSIONS: Productivity, and freezing and freeze-drying resistances of L. delbrueckii subsp. bulgaricus CFL1 were differentially affected by the fermentation parameters studied. There was no single fermentation condition that improved both productivity and resistance to freezing and freeze-drying. Thus, Pareto fronts were helpful to optimize productivity and resistance, when cells were grown at 42°C, pH 4.8, and harvested at the deceleration phase.

An acceptance analysis of a sexual health education digital tool in resource-poor regions of Kenya: an UTAUT based survey study.

BACKGROUND: Unplanned pregnancies and sexually transmitted diseases are a concern in Sub-Saharan Africa, particularly in low-income areas. Access to sexual health information is limited, partly due to the absence of comprehensive sex education in the national school curriculum and social taboos. In response to these challenges, this study introduces a web-based prototype, designed to provide essential sexual health information, targeting 18 to 35-year-old Kenyans, focusing on contraception, menstruation, and female genital mutilation. METHOD: Aiming to investigate young adults' behavioral intention to use a digital tool for sexuality education, by analyzing factors affecting acceptance and usability in low-income and resource-poor regions in Kenya. To explore the acceptability and use of the developed digital tool, this study used a modified version of the Unified Theory of Acceptance and Use of Technology (UTAUT), complemented by the System Usability Scale (SUS) questionnaire. For statistical analysis, a Structural Equation Model (SEM) including Confirmatory Factor Analysis (CFA) and Linear Regression was used. Regarding the reporting of the E-survey results, the Checklist for Reporting Results of Internet E-surveys (CHERRIES), was considered. RESULTS: Survey information from 77 persons (69 female, 7 male, 1 diverse) were collected. A modified UTAUT appears as an appropriate model for measuring the constructs and integrating evidence-based approaches to advanced and safe sexual healthcare information. Results from the SEM showed perceived usefulness, attitude towards healthcare integrated evidence technology, and usability as well as having a significant positive impact on the acceptance, the intention to use as well as wellbeing. Having the resources and knowledge necessary for the usage of a digital tool turns out to have a significant negative impact. A SUS score of 67.3 indicates the usability of the tool for sexual health information, assessed as okay. CONCLUSIONS: The study adopts validated methods to assess the acceptability and usability of a digital sexual health education tool in Kenya. Emphasizing its potential effectiveness and highlighting the influence of cultural and contextual factors on technology adoption.

Unsupervised clustering analysis of comprehensive health status and its influencing factors on women of childbearing age: a cross-sectional study from a province in central China.

BACKGROUND: Most previous studies on women of childbearing age have focused on reproductive health and fertility intentions, and evidence regarding the comprehensive health status of women of childbearing age is limited. This study aimed to comprehensively examine the health status of women of childbearing age through a multi-method and multi-indicator evaluation, analyze the factors that influence their overall health, and provide sound recommendations for the improvement and promotion of healthy behaviors. METHODS: Data on women of childbearing age living in Shanxi Province were collected between September 2021 and January 2022 through online and offline surveys. The k-means algorithm was used to assess health-related patterns in women, and multivariate nonconditional logistic regression was used to assess the influencing factors of women's overall health. RESULTS: In total, 1,258 of 2,925 (43%) participants were classified as having a good health status in all five domains of the three health dimensions: quality of life, mental health, and illness. Multivariate logistic regression showed that education level, gynecological examination status, health status of family members, access to medical treatment, age, cooking preferences, diet, social support, hand washing habits, attitude toward breast cancer prevention, and awareness of reproductive health were significantly associated with different health patterns. CONCLUSIONS: The comprehensive health status of women of childbearing age in Shanxi Province is generally good; however, a large proportion of women with deficiencies in some dimensions remains. Since lifestyle greatly impacts women's health, health education on lifestyle and health-related issues should be strengthened.

Estimation methods to detect changes in cardiorespiratory fitness due to exercise training and subsequent detraining.

PURPOSE: To determine whether estimated maximal oxygen consumption ([Formula: see text]) can detect cardiorespiratory fitness (CRF) changes by behavioral modifications. This study compared changes in measured [Formula: see text]O2max (m[Formula: see text]O2max) through exercise intervention with e[Formula: see text]O2max using a multiple regression model (MRM) and linear extrapolation method (LEM). METHODS: A cross-sectional analysis involving 173 adults was conducted to establish an MRM by including age, sex, body mass index, questionnaire score, heart rate (HR) from step test, and m[Formula: see text]O2max. Subsequently, 15 men participated in an intervention experiment comprising an 8-week, high-intensity interval training, followed by 8-week detraining, and completed anthropometric measurements, questionnaires, step tests, and m[Formula: see text]O2max tests. m[Formula: see text]O2max changes throughout the intervention were compared to e[Formula: see text]O2max changes calculated using the MRM and LEM. The LEM used the HR during the step test with constant values (predetermined [Formula: see text]O2), such as the Chester step test. RESULTS: Inclusion of the step test HR in a questionnaire-based MRM improved the estimation power, although the MRM underestimated higher m[Formula: see text]O2max values. In the intervention, m[Formula: see text]O2max increased by 20.0 ± 14.1% (P < 0.01) and subsequently decreased by 9.5 ± 6.6% (P < 0.01) after exercise training and detraining, respectively. Significant method × time interactions were observed between m[Formula: see text]O2max and e[Formula: see text]O2max in the MRM but not in the LEM, i.e., an apparent systematic error (underestimation of high values) of the MRM was absent in the LEM, although the correlation between m[Formula: see text]O2max and e[Formula: see text]O2max using the LEM was moderate. CONCLUSION: e[Formula: see text]O2max, particularly using the MRM with HR as an explanatory factor, is not an appropriate method for detecting CRF changes along with behavioral modifications. CLINICAL TRIAL REGISTRATION: Registered number, UMIN000041031; Registered date, 2020/07/08; URL, https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046855.

Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose.

Exhaled nitric oxide trace gas at the ppb level is a biomarker of human airway inflammation. To detect this, we developed a method for the collection of active pumping electronic nose bionic chamber gas. An optimization algorithm based on multivariate regression (MR) and genetic algorithm-back propagation (GA-BP) was proposed to improve the accuracy of trace-level gas detection. An electronic nose was used to detect NO gas at the ppb level by substituting breathing gas with a sample gas. The impact of the pump suction flow capacity variation on the response of the electronic nose system was determined using an ANOVA. Further, the optimization algorithm based on MR and GA-BP was studied for flow correction. The results of this study demonstrate an increase in the detection accuracy of the system by more than twofold, from 17.40%FS before correction to 6.86%FS after correction. The findings of this research lay the technical groundwork for the practical application of electronic nose systems in the daily monitoring of FeNO.

Field Measurement of the Dynamic Interaction between Urban Surfaces and Microclimates in Humid Subtropical Climates with Multiple Sensors.

Forcing pathways between urban surfaces (impervious and pervious pavers) and near-surface air temperature were measured and investigated with a network of multiple sensors. Utilizing field data measured between April 2021 and May 2022, and assuming that the influential variables follow the basic heat-transfer energy-balance equations, multiple regression-based statistical models were built to predict the surface temperature and near-surface air temperature (0.05 m, 0.5 m, 1 m, 2 m, and 3 m) of one impervious paver site and one pervious paver site in Taipei City, Taiwan. Evaporative cooling was found to be more influential on the pervious paver with a statistically significant influence on the microclimate up to 1.8 m (and up to 0.7 m for the impervious paver), using in situ data with an ambient air temperature higher than 24 °C. The surface temperature is mainly affected by solar shortwave radiation and ambient air temperature. As for near-surface air temperature, ambient air temperature is the most influential factor, followed by surface temperature. The importance of surface temperature indicates the influence of upwelling longwave radiation on the microclimate. The predictive equations show that pervious surfaces can help cities with hot and humid climates fight the changing climate in the future.

Differences in periodontal parameters between SPT patients who regularly and irregularly visited the dental clinic analyzed at tooth level: a 14-year retrospective cohort study.

The purpose of this study was to evaluate the periodontal status of patients who routinely did SPT, when compared to patients that did not SPT. This retrospective cohort study was conducted at a general dental office from 2001 to 2019. Patients aged 18 to 81 years who visited the dental office over a 10-year period were assigned into two groups: an SPT group, which included patients who continually visited the dental office for SPT one or more times every year, and an irregular group, consisting of patients who did not visit the dental office at least once a year. A total of 7307 teeth (SPT group) and 4659 teeth (irregular group) were evaluated, and the periodontal conditions were compared between the first and latest visits. Multiple regression analysis was used to analyze the results. The mean follow-up time was 13.74 years. The risk factors for improvements in probing pocket depth included age, sex, smoking, diabetes mellitus, molar tooth, and irregular SPT group (p < 0.001), and that for a positive bleeding on probing site was the irregular group (odds ratio 2.94; 95% confidence interval 2.63-3.29). This study showed that lack of routine in attending the SPT program significantly decreased the periodontal parameters, thus highlighting the importance of continuing with the program to maintain the periodontal health.

Common, uncommon, and novel applications of random forest in psychological research.

Recent reform efforts have pushed toward a better understanding of the distinction between exploratory and confirmatory research, and appropriate use of each. As some utilize more exploratory tools, it may be tempting to employ multiple linear regression models. In this paper, we advocate for the use of random forest (RF) models. RF is able to obtain better predictive performance than traditional regression, while also inherently protecting against overfitting as well as detecting nonlinear effects and interactions among predictors. Given the advantages of RF compared to other statistical procedures, it is a tool commonly used within a plethora of industries, including stock trading, banking, pharmaceuticals, and patient healthcare planning. However, we find RF is used within the field of psychology comparatively less frequently. In the current paper, we advocate for RF as an important statistical tool within the context of behavioral and psychological research. In hopes of increasing the use of RF in the field of psychology, we provide information pertaining to the limitations one might confront in using RF and how to overcome such limitations. Moreover, we discuss various methods for how to optimally utilize RF with psychological data, such as nonparametric modeling, interaction and nonlinearity detection, variable selection, prediction and classification modeling, and assessing parameters of Monte Carlo simulations. Throughout, we illustrate the use of RF with visualization strategies, aimed to make RF models more comprehensible and intuitive.