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The causes of symptoms in patients with paroxysmal atrial fibrillation (AF) remains unclear. The purpose of this study was to correlate the magnitudes of skin sympathetic nerve activity (SKNA) with symptoms in patients with AF. We prospectively enrolled patients with symptomatic paroxysmal AF for ambulatory electrocardiography and SKNA recording. Heart rhythms at the time of symptoms were categorized as AF or normal sinus rhythm (NSR). Maximal and average skin sympathetic nerve activity (aSKNA) and heart rate (HR) were compared between symptomatic and asymptomatic AF and NSR episodes using mixed effects models to account for within-patient correlations. Among the 31 enrolled patients, 16 (52%) had at least 1 episode of AF, and 24 (77%) endorsed symptoms during the monitoring period. Compared with asymptomatic AF episodes, symptomatic AF episodes had higher maximal aSKNA (1.260 [interquartile range (IQR) 1.114–1.723] μV vs 1.108 [IQR 0.974–1.312] μV; P <0.001) and higher maximal HR (152 ± 24 bpm vs 132 ± 19 bpm; P <.001). Symptomatic NSR episodes were associated with higher maximal aSKNA (1.612 [IQR 1.287–2.027] μV vs 1.332 [IQR 1.033–1.668] μV; P =.001) and higher maximal HR (152 ± 24 bpm vs 105 ± 16 bpm; P <.001) than asymptomatic NSR episodes. Of the symptomatic episodes, 66 (73%) occurred during NSR and 24 (27%) during AF. All P values were obtained from mixed effects models. Symptomatic episodes in patients with paroxysmal AF were more frequently associated with NSR than AF. Symptomatic AF and NSR episodes were associated with higher aSKNA than asymptomatic episodes. In patients with paroxysmal AF, symptoms correlate better with SKNA than heart rhythm. [ABSTRACT FROM AUTHOR]

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Copyright of Forest Research is the property of Forest Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Despite a decrease in the prevalence of low birth weight (LBW) over time, its ongoing significance as a public health concern in Bangladesh remains evident. Low birth weight is believed to be a contributing factor to infant mortality, prolonged health complications, and vulnerability to non-communicable diseases. This study utilizes nationally representative data from the Multiple Indicator Cluster Surveys (MICS) conducted in 2012-2013 and 2019 to explore factors associated with birth weight. Modeling birth weight data considers interactions among factors, clustering in data, and spatial correlation. District-level maps are generated to identify high-risk areas for LBW. The average birth weight has shown a modest increase, rising from 2.93 kg in 2012-2013 to 2.96 kg in 2019. The study employs a regression tree, a popular machine learning algorithm, to discern essential interactions among potential determinants of birth weight. Findings from various models, including fixed effect, mixed effect, and spatial dependence models, highlight the significance of factors such as maternal age, household head's education, antenatal care, and few data-driven interactions influencing birth weight. District-specific maps reveal lower average birth weights in the southwestern region and selected northern districts, persisting across the two survey periods. Accounting for hierarchical structure and spatial autocorrelation improves model performance, particularly when fitting the most recent round of survey data. The study aims to inform policy formulation and targeted interventions at the district level by utilizing a machine learning technique and regression models to identify vulnerable groups of children requiring heightened attention.

Background: We aimed to evaluate the impacts of short-term daily temperature variability (DTV) on blood pressure (BP) among participants with normotension, prehypertension, and hypertension, respectively, and explore the effects in different climate zones and seasons. Methods: A representative population sample (n = 397,173) covering the subtropical, temperate continental, and temperate monsoon zones was obtained from the China Hypertension Survey. DTV was calculated as the standard deviation of daily minimum and maximum temperatures during the exposure days. The linear mixed effect regression model was used to estimate the associations between DTV exposure and BP among normotension, prehypertension, and hypertension, respectively, and further stratified analysis was performed by climate zones and seasons. Results: After adjustment for confounders, per interquartile range (IQR) increase in DTV (2.28 °C) at 0–6 days of exposure was associated with an increase of 0.41 mmHg (95 % confidence interval [CI]: 0.07, 0.75) in systolic BP (SBP) and 0.41 mmHg (95 % CI: 0.09, 0.72) in pulse pressure (PP) among hypertensive participants in the subtropical zone. Similarly, DTV exposure was associated with an increase of 0.31 mmHg (95 % CI: 0.06, 0.55) in SBP and 0.59 mmHg (95 % CI: 0.24, 0.94) in PP among prehypertensive participants in the temperate continental zone. Additionally, during the warm season, DTV was positively associated with SBP among populations with prehypertension and hypertension, and with PP among all three populations. Conclusion: Short-term DTV exposure was associated with an increase in SBP and PP among hypertensive and prehypertensive participants in the subtropical zone and the temperate continental zone. In addition, positive associations of DTV with SBP and PP were observed among participants with prehypertension and hypertension in the warm season. Comprehensive health education and effective intervention strategies should be implemented to mitigate the effects of temperature variations on BP, particularly among prehypertensive and hypertensive populations.

We present general results on D-optimal designs for estimating the mean response in repeated measures growth curve models with metric outcomes. For this situation, we derive a novel equivalence theorem for checking design optimality. The motivation of this work originates from designing a study in psychological item response testing with multiple retests to measure the improvement in ability. Besides introductory linear growth curves for which analytical results can be obtained, we consider two nonlinear growth curve models incorporating an increasing mean ability and a saturation effect. For these models, D-optimal designs are determined by computational methods and are validated by means of the equivalence theorem. • Novel equivalence theorem for repeated measures growth curve models. • Locally D-optimal designs closer to uniform for high variance ratios. • Locally D-optimal designs closer to uniform for small correlations. [ABSTRACT FROM AUTHOR]

Maintenance & Repair costs in heavy-duty trucks are an important component of the total cost of ownership. Due to the very limited availability of real-time data collected from medium- and heavy-duty vehicles using alternative fuels, this topic has not been well studied resulting in a very slow diffusion of alternative fuel vehicles in the market. This study focuses on collecting maintenance data related to diesel and alternative fuels such as natural gas and propane for the school bus, delivery truck, vocational truck, refuse truck, goods movement truck, and transit bus. The novelty of this work lies in identifying the mixed effects in the maintenance data and using a mixed-effect model for developing a single prediction model on clustered longitudinal data. A mixed-effect random forest machine learning model is trained on the maintenance data for estimating the average cost per mile. The model achieved an R2 of 98.96% with a mean square error of 0.0089 $\$ $ /mile for training and an R2 of 94.31% with a mean square error of 0.0312 $\$ $ /mile for the validation dataset. The prediction model is evaluated on each cluster of data and observed to perform well capturing the variations in each cluster very well. Furthermore, the performance of the mixed-effect random forest model is compared with the XGBoost ensemble model.

The Scabiosa genus of the family Caprifoliaceae comprises around 100 species distributed mainly in the Mediterranean. Studies on Scabiosa species, incentivized by their wide use in folk medicine, highlighted their interesting phytochemical contents and biological activities. In this study, we investigated the antioxidant activity and phenolics contents of the aqueous extracts obtained from the different parts of Scabiosa maritima at four stages of development. Extraction was conducted by maceration, infusion, and decoction methods. Total phenolic content (TPC), total flavonoids content (TFC), condensed tannin content (CTC), flavonols content, ferric reducing power (RP), total antioxidant activity (TAC), and ABTS and DPPH scavenging activity were evaluated. Pigments (β-carotene, chlorophyll a, chlorophyll b , leukopene) contents of the different plant parts were also investigated as well as the chemical composition of essential oils obtained from leaves collected during April (pre-flowering stage) and inflorescence collecting during May (flowering stage). Mixed effect models were fitted to the different datasets, to elucidate the effect of the different intervening factors (organs, sampling date, and extraction method). The studied parameters have shown an organ-dependent variation, with leaves exhibiting in general the highest values. The analysis of random effect plots revealed an opposite behavior of leaves vs. stem and roots across time. Pigments, phenolics contents, and antioxidant activities were the highest in leaves during March, while they attained their peak during either April or June for stem and roots. The extraction method did not drastically affect the variation pattern of different variables across plant parts and sampling date, however, a significant effect on deviation magnitude was recorded. Infusion extraction generally assures the best recovery of phenolics from the different plant parts. Leaves and inflorescence essential oils exhibited a distinct chemical composition with only hexahydrofarnesyl acetone as the common compound. Leaves essential oil composition was dominated by apocarotenoid derivatives while pyridine derivatives were the group of the major compounds identified in inflorescence essential oil. Inflorescence essential oil was also characterized by a considerable amount of mescalin (5.66%). Repeated measure correlation (rmcorr) and multivariate analysis (MFA) highlighted respectively a moderate to strong correlation between extracts' antioxidant activities and phenolics contents and confirm the opposite time evolution pattern displayed by roots and leaves regarding the different analyzed variables. The extreme position on the second axes of the factorial map, occupied by stems collected during June and roots collected during May highlighted the distinctively high antioxidant activities of their decoctions. [Display omitted] • Phenolics contents and antioxidant activity recorded in the different aqueous extracts were shown to be organ dependent, with a significant effect of plant growing stage. • Extraction process (maceration, infusion, decoction) have shown a significant impact on phenolics contents and antioxidant capacity deviation magnitudes rather than their profiles across plant parts and sampling date. • Leaves extracts have shown an opposite deviation pattern over time to those of stem and roots with regard of the different variables. • Scabiosa.maritima essential oils chemical compositions were investigated for the first time. • Hexahydrofarnesyl acetone was the only common compounds to leaves and inflorescence essential oils. • A considerable amount of mescaline was recorded in Scabiosa.maritima inflorescence essential oil. [ABSTRACT FROM AUTHOR]

Tree mortality plays a fundamental role in the dynamics of forest ecosystems, yet it is one of the most difficult phenomena to accurately predict. Various modeling strategies have been developed to improve individual tree mortality predictions. One less explored strategy is the use of a multistage modeling approach. Potential improvements from this approach have remained largely unknown. In this study, we developed a novel multistage approach and compared its performance in individual tree mortality predictions with a more conventional approach using an identical individual tree mortality model formulation. Extensive permanent plot data (n = 9442) covering the Acadian Region of North America and over multiple decades (1965-2014) were used in this study. Our results indicated that the model behavior with the multistage approach better depicted the observed mortality and showed a notable improvement over the conventional approach. The difference between the observed and predicted numbers of dead trees using the multistage approach was much smaller when compared with the conventional approach. In addition, tree survival probabilities predicted by the multistage approach generally were not significantly different from the observations, whereas the conventional approach consistently underestimated mortality across species and overestimated tree survival probabilities over the large range of DBH in the data. The new multistage approach also predictions of zero mortality in individual plots, a result not possible in conventional models. Finally, the new approach was more tolerant of modeling errors because it based estimates on ranked tree mortality rather than error-prone predicted values. Overall, this new multistage approach deserves to be considered and tested in future studies. [ABSTRACT FROM AUTHOR]

The problem that arises in the Cox model is that there are more than two types of covariates and the presence of random effects is a non-proportional hazard (NPH). One example of a case that involves many factors is student retention. Low student retention can lead to dropping out of college or failure in completing studies. The purpose of this study is to overcome the problem of NPH caused by the presence of time-independent covariates, time-dependent covariates, and random effects. The research method uses simulation. Some of the modified models are the stratified Cox model, the extended Cox model, and the frailty model. The developed model is applied to distance education student retention data. The results of the study show that frailty and study programs provide considerable diversity in explaining the total diversity of the model. It can be concluded that frailty needs to be considered by UT to improve the quality of services to students. In addition, other covariates that have a significant effect on UT student learning retention modeling are age, domicile, gender, GPA, marital status, employment status, number of credits taken, and number of registered courses. [ABSTRACT FROM AUTHOR]

Abstract Background To evaluate the association of physical activity (PA) intensity with cognitive performance at baseline and during follow-up. Methods A total of 4039 participants aged 45 years or above from the China Health and Retirement Longitudinal Study were enrolled in visit 1 (2011–2012) and followed for cognitive function in visit 2 (2013–2014), visit 3 (2015–2016), and visit 4 (2017–2018). We analyzed the association of PA intensity with global cognition, episodic memory, and mental intactness at baseline using adjusted regression methods and evaluated the long-term effect of PA intensity using multiple measures of cognition scores by mixed effect model. Results In cross-sectional analysis, mild and moderate PA, rather than vigorous PA, was associated with better cognitive performance. The results remained consistent in multiple sensitivity analyses. During the follow-up, participant with mild PA had a 0.56 (95% CI 0.12–0.99) higher global cognition, 0.23 (95% CI 0.01–0.46) higher episodic memory, and 0.33 (95% CI 0.01–0.64) higher mental intactness, while those with moderate PA had a 0.74 (95% CI 0.32–1.17) higher global score, 0.32 (95% CI 0.09–0.54) higher episodic memory, and 0.43 (95% CI 0.12–0.74) higher mental intactness, compared with individuals without PA. Vigorous PA was not beneficial to the long-term cognitive performance. Conclusions Our study indicates that mild and moderate PA could improve cognitive performance, rather than the vigorous activity. The targeted intensity of PA might be more effective to achieve the greatest cognition improvement considering age and depressive status.

The experiment is designed to evaluate the yield performance and profitability of mung bean varieties and to survey the innovation inclination of the farmers in the study area. The experiment was carried out in the potential environments of the North Shewa zone for two years during the main growing period. As experimental treatments, four mung bean cultivars of Rasa, NLV-1, Arkebe, and local varieties were used and evaluated across the four environments (two farmers’ fields per each environment). The experimental plots were arranged in a simple random block design. The result of the combined analysis of variance revealed that there was highly significant variation (p < 0.01) of grain yield among the genotypes while the environments and genotype by environment interaction are found insignificant. The highest mean grain yield of 1430.6 kg ha−1 was obtained from the improved variety Rasa, which was selected first by the farmers followed by the variety NVL-1. The results also confirmed the existence of a strong and statistically significant association between the actual values rank and the farmers' preference rank for both grain and biomass yields (R = .80, p < .001). Also, the variety Rasa provides the highest (686.6%) marginal rate of return on investment. Therefore, by considering the results of the grain yield performance, farmers’ selection, and the result of the partial budget analysis the variety Rasa was recommended for the study areas.

A mathematical mixed effect model was established to analyse the factors of neonatal hypoglycaemia of gestational diabetes mellitus (GDM). 229 cases of GDM patients were enrolled in this study. The data were analysed by logarithmic transformation of non-normal distribution. Furthermore, the mathematical model was used to analyse influencing factors of hypoglycaemia of neonatal from women with GDM. The results showed that the blood glucose distribution level had a trend of increasing with time, which indicates that it is necessary to strengthen blood glucose intervention of newborns from GDM maternal and provides a data for the timely detection of hypoglycaemia in GDM newborns. Furthermore, we successfully established the GDM newborn blood glucose level mixed mathematical model. From this model, we calculated the GDM newborn blood glucose normal confidence interval based on mixed factors. The results indicate that the minimum value of blood glucose level in GDM newborns did not exceed the risk level 2.2 mmol/L. We concluded that the mathematical mixed effect model is successfully established, from which the change discipline of blood glucose level of newborn from GDM parturient are found. What is already known on this subject? The morbidity of gestational diabetes mellitus (GDM) in China has been increased. However, the clinical data is difficult to be collected and the data that is used for statistics is not enough, which makes it difficult to understand the neonatal hypoglycaemia of GDM more clearly. What do the results of this study add? In this study, we successfully established a mathematical mixed effect model of neonatal hypoglycaemia of women with GDM, which can investigate the influence factor of hypoglycaemia of newborn from women with GDM to find the discipline of blood glucose level of newborn from GDM parturient via mathematical model. What are the implications of these findings for clinical practice and/or further research? Our research helps to better understand and improve the health problem of pregnant women with GDM and their newborn babies. [ABSTRACT FROM AUTHOR]

Despite a substantial number of COVID‐19 related research papers published, it remains unclear as to which factors are associated with the observed variation in global transmission and what are their relative levels of importance. This study applies a rigorous statistical framework to provide robust estimations of the factor effects for a global and integrated perspective on this issue. We developed a mixed effect model exploring the relative importance of potential factors driving COVID‐19 transmission while incorporating spatial and temporal heterogeneity of spread. We use an integrated data set for 87 countries across six continents for model specification and fitting. The best model accounts for 70.4% of the variance in the data analyzed: 10 fixed effect factors explain 20.5% of the variance, random temporal and spatial effects account for 50% of the variance. The fixed effect factors are classified into climatic, demographic and disease control groups. The explained variance in global transmission by the three groups are 0.6%, 1.1%, and 4.4% respectively. The high proportion of variance accounted for by random effects indicated striking differences in temporal transmission trajectories and effects of population mobility among the countries. In particular, the country‐specific mobility‐transmission relationship turns out to be the most important factor in explaining the observed global variation of transmission in the early phase of COVID‐19 pandemic. Plain Language Summary: We have observed substantial variation in global transmission trajectories of COVID‐19. Using statistical analysis, this study aims to investigate the factors that are associated with the observed variation in global transmission and what are their relative levels of importance. We conclude that the variation in transmission trajectories in various countries is mostly accounted for by spatiotemporal heterogeneity in transmission. In particular, disease control policies and population response to COVID‐19 transmission make the largest contribution and demographic features have the least importance. Climatic factors also play a role but turn to be much less important than disease control policies. The mobility‐transmission relationship is country‐specific and turns out to be the most important factor in explaining the observed global variation of transmission. The complexity of COVID‐19 transmission is also demonstrated through the wide range of estimated effects of population mobility on transmission between countries. Key Points: Disease control measures turn out to be the most important factors, followed by climatic factors and demographic characteristicsMost of the variation can be accounted for by the spatiotemporal heterogeneity in the transmission trajectories among the countriesEvaluation of mobility‐transmission relationship and people's response to mobility‐restrictions are vital in combating transmission [ABSTRACT FROM AUTHOR]

Abstract Despite a substantial number of COVID‐19 related research papers published, it remains unclear as to which factors are associated with the observed variation in global transmission and what are their relative levels of importance. This study applies a rigorous statistical framework to provide robust estimations of the factor effects for a global and integrated perspective on this issue. We developed a mixed effect model exploring the relative importance of potential factors driving COVID‐19 transmission while incorporating spatial and temporal heterogeneity of spread. We use an integrated data set for 87 countries across six continents for model specification and fitting. The best model accounts for 70.4% of the variance in the data analyzed: 10 fixed effect factors explain 20.5% of the variance, random temporal and spatial effects account for 50% of the variance. The fixed effect factors are classified into climatic, demographic and disease control groups. The explained variance in global transmission by the three groups are 0.6%, 1.1%, and 4.4% respectively. The high proportion of variance accounted for by random effects indicated striking differences in temporal transmission trajectories and effects of population mobility among the countries. In particular, the country‐specific mobility‐transmission relationship turns out to be the most important factor in explaining the observed global variation of transmission in the early phase of COVID‐19 pandemic.

[Display omitted] • We analyzed the association between urban road network topology and street flooding. • We used NYC 311 complaint data and generalized linear mixed models. • Road network topology significantly impacts street flooding risk at the city scale. • The effect of road network topology is higher in combined sewers and heavy rainfall. • Urban planning strategies are proposed to mitigate street flooding risks. Urban road network is crucial stormwater infrastructure as city roads serve as paths for stormwater runoff and urban flooding. However, it remains unclear how different road networks influence urban flooding. While past studies have examined high-magnitude and high-intensity flooding events at the large river basin scale, they neglected smaller and more frequent street flooding events (SFEs) which could have a higher cumulative cost. Hence, this study used urban road network topology (RNT) factors and social media big data related to street flooding to investigate their associations with data-driven methods for 557 sewer catchments across New York City (NYC). 11,042 SFEs were taken from raw street flooding complaints (SFCs) recorded on the NYC 311 sewer complaints platform between January 1, 2010, and April 28, 2022. We then used generalized linear mixed models to identify the relationship between RNT factors and street flooding risk (SFR) and found that the characteristics of SFR periodically changed with spatial–temporal heterogeneity. While impervious factor was previously found important, RNT emerged as a new significant factor influencing SFR, with the effects being larger in combined sewer systems and heavy precipitation events. We discerned that planning a dendric road network pattern, increasing RNT connectivity in downstream areas, and having wider roads with more intersections could reduce SFR. Decreasing RNT connectivity in upstream areas, road length, the number of roads, and sewer catchment area also mitigated SFR. Our analysis provided informed street flooding mitigation and adaptation strategies to promote a more resilient, healthy, and sustainable urban environment. [ABSTRACT FROM AUTHOR]

Background High concentrations of respirable quartz have been reported from workers in construction, foundries, and quarries. Current exposure concentrations in prevalent but presumably lower exposed occupations have been less examined. We aimed to quantify current exposure concentrations of respirable dust and quartz across prevalent occupations and to identify determinants of respirable quartz exposure across these occupations. Methods One hundred and eighty-nine full-shift personal samples of respirable dust of workers within 11 occupations in Denmark were sampled during 2018. Respirable dust was determined gravimetrically and analysed for quartz content with infrared spectrometry. Determinants for respirable quartz exposure, i.e. use of power tools, outdoor or indoor location, and percentage of quartz in respirable dust, were analysed in linear mixed effect models. Results The overall geometric means (geometric standard deviations) for respirable dust and quartz were 216 µg m−3 (4.42) and 16 µg m−3 (4.07), respectively. The highest quartz concentrations were observed among stone cutters and carvers [93 µg m−3 (3.47)], and metal melters and casters [61 µg m−3 (1.71)]. Use of power tools increased exposure concentrations of quartz by a factor of 3.5. Occupations explained 27%, companies within occupations 28%, and differences between workers within companies within occupations 14% of the variability in quartz concentrations. Thirty percent was due to day-to-day variability in exposure concentrations. In total, 19% of the variation in quartz concentration could be explained by type of tool, indoor/outdoor location, and percentage of quartz in respirable dust. Conclusion Current exposure concentrations are generally low, but some occupations in this study had average exposure concentrations to respirable quartz above the ACGIH threshold limit value of 25 µg m−3. Preventive measures to lower excess risk of quartz-related diseases among these workers are still needed. In terms of preventive strategies, use of power tools and quartz content of used materials were identified as main determinants of exposure. Lowering of exposures will be most efficient when focussed on these major determinants, e.g. tool dust control with water, dust extraction, and use of low quartz content materials. [ABSTRACT FROM AUTHOR]

Air pollution caused by fine particulate matter (PM2.5) may affect people's health. At present, aerosol optical depth (AOD) products have been used to estimate PM2.5 concentrations. However, AOD always has a low coverage rate. High-resolution products cannot provide a wide range of data. The purpose of this study was to deal with the missing data problem using the interpolation technique and to estimate high-resolution PM2.5 concentrations by fitting the spatial and temporal variations of AOD and PM2.5. Firstly, we developed a gradient boosting model (XGBoost) to fill in the missing values. Secondly, we corrected the altitude and humidity of meteorological variables. We then used the mixed-effects model with the gap-filling AOD data to estimate the PM2.5 concentrations in this study area. Finally, we obtained the complete PM2.5 concentrations picture. The AOD coverage rate reached 100% after model estimation. Meanwhile, the estimation results of PM2.5 concentrations were also validated and the model worked well. The results indicate that it is necessary to fill in the missing values of AOD data for PM2.5 estimations. This study can provide complete AOD data, which makes the results of PM2.5 estimations more accurate and has some research value. [ABSTRACT FROM AUTHOR]

The expected mean squares (EMS) for the effects in a two-way ANOVA model are derived when sampling from a finite population, for factors A and B. The A-effects and B-effects are represented by i  and  j , respectively, in the model. Model effects are studied for the random and mixed effects models. Thus, in terms of hypothesis testing, we are interested in the expected mean square formulas when the random effects are sampled from finite populations. For balanced data, the EMS for the A, B and AB interaction effects when the effects are sampled from a finite population are the same as the EMS for an infinite population. For unbalanced data, the EMS when the effects are sampled from a finite population in factors A, B and the AB interaction, the EMS are not the same as for an infinite population because the values that multiply the variance components differ.

Abinet Dagnaw Mekuria,1 Assefa Legesse Sisay,2 Kassa Ketsela Hailegiorgies,3 Ayele Mamo Abebe4 1Department of Public Health, College of Health Science, Debre Berhan University, Debre Berhan, Ethiopia; 2Department of Epidemiology and Bio-Statistics, Jimma University, Jimma, Ethiopia; 3Department of Nursing, College of Health Science, Debre Berhan University, Debre Berhan, Ethiopia; 4Pediatrics nursing Department, College of Health Science, Debre Berhan University, Debre Berhan, EthiopiaCorrespondence: Ayele Mamo AbebePediatrics nursing Department, Debre Berhan University, College of Health Science, PO Box 445, Debre Berhan, EthiopiaEmail ayelemamo12@gmail.comBackground: Prevention of mother-to-child transmission of HIV (PMTCT) is a frequently used word for programs and intervention methods to decrease the risk of mother-to-child transmission of HIV. The aim of this study was to identify determinants of the reduction of CD4 count through time and the maternal transmission of HIV to their child on the PMTCT program at health centers in North Shewa Zone, Ethiopia.Methods: The cohort study design was conducted by using secondary data collected from the cohort register of PMTCT starting from September 1, 2014 to November 30, 2017. In this study, a longitudinal study was conducted for two types of result; these were longitudinal response measurements of HIV infected women CD4 count and the time to maternal transmission of HIV taken from 203 patients.Results: The prevalence rate of HIV infection among exposed infants was 5.58%. Baseline CD4 count, visiting times, weight, and interaction between visiting time and baseline CD4 count had a statistically significant effect on the longitudinal biomarker. From the Weibull AFT model, ART start, partner test, clinical stage, educational status, place of delivery, and MUAC were statistically significant. Hence, as a measurement unit decreased in square root CD4 cell count by 1.18 elevates the risk of maternal transmission of HIV.Conclusion: In this study, the determinant of mother-to-child transmission of HIV including loss of weight, ART start (ANC), place of delivery at home, illiterate and mother with severe malnutrition, had a significant effect. The longitudinal biomarker also had a strong association with baseline CD4 and the risk of maternal transmission of HIV. Health education should be given about balanced diet, weight control, and take medication for HIV positive patients by the responsible bodies.Keywords: longitudinal data, mixed effect model, option B+ program, parametric AFT model

Understanding plant species distribution patterns along environmental gradients is fundamental to managing ecosystems, particularly when habitats are fragmented due to intensive human land use pressure. The variation pattern of functional diversity of plant communities along the elevation gradient in the Dindin dry evergreen Afromontane forest was tested. Fifty four plots of 20 x 20 m (400 m2) were established at 200 m intervals starting 2,300–2,900 m a. s. l. and woody species composition, and environmental variables were recorded. Nine functional diversity indices based on functional distances were employed to esimate functional diversity. The mixed effect model was used to determine the effect of elevation, aspect and slope on functional diversity indices. The results showed that functional diversity in communities varied greatly; functional diversity revealed a decrease with increasing elevation and a‘‘humped’’ pattern, with peak diversity appearing at middle elevation. Functional diversity was significantly correlated with elevation, slope, and aspect. Functional diversity was significantly correlated with species richness and evenness. Environmental filtering was important to the functional diversity pattern; the nine indices were all successful in the analysis of functional diversity in the plant community with different effectiveness, and modified functional attribute diversity, plot-based functional diversity, community based functional diversity, functional richness, and community weight mean of woody density performed better than the other four indices in this study.

Abstract Background Patients infected with HIV may experience a succession of clinical stages before the disease diagnosis and their health status may be followed-up by tracking disease biomarkers. In this study, we present a joint multistate model for predicting the clinical progression of HIV infection which takes into account the viral load and CD4 count biomarkers. Methods The data is from an ongoing prospective cohort study conducted among antiretroviral treatment (ART) naïve HIV-infected women in the province of KwaZulu-Natal, South Africa. We presented a joint model that consists of two related submodels: a Markov multistate model for CD4 cell count transitions and a linear mixed effect model for longitudinal viral load dynamics. Results Viral load dynamics significantly affect the transition intensities of HIV/AIDS disease progression. The analysis also showed that patients with relatively high educational levels (β = − 0.004; 95% confidence interval [CI]:-0.207, − 0.064), high RBC indices scores (β = − 0.01; 95%CI:-0.017, − 0.002) and high physical health scores (β = − 0.001; 95%CI:-0.026, − 0.003) were significantly were associated with a lower rate of viral load increase over time. Patients with TB co-infection (β = 0.002; 95%CI:0.001, 0.004), having many sex partners (β = 0.007; 95%CI:0.003, 0.011), being younger age (β = 0.008; 95%CI:0.003, 0.012) and high liver abnormality scores (β = 0.004; 95%CI:0.001, 0.01) were associated with a higher rate of viral load increase over time. Moreover, patients with many sex partners (β = − 0.61; 95%CI:-0.94, − 0.28) and with a high liver abnormality score (β = − 0.17; 95%CI:-0.30, − 0.05) showed significantly reduced intensities of immunological recovery transitions. Furthermore, a high weight, high education levels, high QoL scores, high RBC parameters and being of middle age significantly increased the intensities of immunological recovery transitions. Conclusion Overall, from a clinical perspective, QoL measurement items, being of a younger age, clinical attributes, marital status, and educational status are associated with the current state of the patient, and are an important contributing factor to extend survival of the patients and guide clinical interventions. From a methodological perspective, it can be concluded that a joint multistate model approach provides wide-ranging information about the progression and assists to provide specific dynamic predictions and increasingly precise knowledge of diseases.

Background: The relationship between the severe trauma patient volume and outcomes is still being debated. The aim of this study was to evaluate the relationship between severe trauma patient volume, hospital mortality, and door-to-definitive treatment time. Methods: This was a retrospective cohort study that used recorded data from the Japan Trauma Data Bank. We included severe trauma patients who had an Injury Severity Score greater than 16. Hospitals were categorized according to their annual severe trauma patient volume: low volume, 1–49 (reference), medium volume, 50–99, and high volume, ≥ 100]. The association of volume categories with in-hospital mortality was evaluated by use of a mixed-effects model adjusted for patient demographics and trauma severity. Additionally, the association of volume categories with in-hospital mortality among subgroups and with door-to-definitive treatment time were also evaluated. Results: A total of 74,957 severe trauma patients from 213 hospitals were analyzed. In-hospital mortality was 15.7%, 15.2%, and 12.8% in the low volume, medium volume, and high volume groups, respectively. High volume was associated with reduced in-hospital mortality compared to low volume (odds ratio = 0.757, 95% confidence interval = 0.626–0.916). However, medium volume was not associated with reduced in-hospital mortality. Among subgroups, high volume was associated with reduced in-hospital mortality only in the probability of survival ≥ 0.5. Door-to-definitive treatment time was decreased in high volume, however, high volume was not associated with reduced in-hospital mortality among the patients who underwent definitive treatment and the patients whose ps < 0.5. Conclusions: Severe trauma patient volume was associated with decreased mortality by decreasing preventable trauma death. [ABSTRACT FROM AUTHOR]

Background: To evaluate the association of physical activity (PA) intensity with cognitive performance at baseline and during follow-up. Methods: A total of 4039 participants aged 45 years or above from the China Health and Retirement Longitudinal Study were enrolled in visit 1 (2011–2012) and followed for cognitive function in visit 2 (2013–2014), visit 3 (2015–2016), and visit 4 (2017–2018). We analyzed the association of PA intensity with global cognition, episodic memory, and mental intactness at baseline using adjusted regression methods and evaluated the long-term effect of PA intensity using multiple measures of cognition scores by mixed effect model. Results: In cross-sectional analysis, mild and moderate PA, rather than vigorous PA, was associated with better cognitive performance. The results remained consistent in multiple sensitivity analyses. During the follow-up, participant with mild PA had a 0.56 (95% CI 0.12–0.99) higher global cognition, 0.23 (95% CI 0.01–0.46) higher episodic memory, and 0.33 (95% CI 0.01–0.64) higher mental intactness, while those with moderate PA had a 0.74 (95% CI 0.32–1.17) higher global score, 0.32 (95% CI 0.09–0.54) higher episodic memory, and 0.43 (95% CI 0.12–0.74) higher mental intactness, compared with individuals without PA. Vigorous PA was not beneficial to the long-term cognitive performance. Conclusions: Our study indicates that mild and moderate PA could improve cognitive performance, rather than the vigorous activity. The targeted intensity of PA might be more effective to achieve the greatest cognition improvement considering age and depressive status. [ABSTRACT FROM AUTHOR]

The count response variables are usually included of extra zeros. A useful tool for modeling such data is zero-inflated regression models. In the last decade, the Conway–Maxwell–Poisson model is applied for analyzing count data that can handle under- and over-dispersed data, besides that can encompass the Poisson and negative binomial. Sometimes, due to the sampling design or the data collection procedure, the data simultaneously are clustered or correlated with extra zeros and under- or over-dispersion. We applied a three-level zero-inflated Conway–Maxwell–Poisson regression model to overcome these problems. An expectation-maximization algorithm is used to estimate the model parameters of an appropriate penalized log-likelihood function. Model flexibility and finite-sample properties of this methodology have been investigated by extensive simulation study. The method has been illustrated with an application on real data in the health survey. Furthermore, we compared the results of the model with a three-level zero-inflated negative binomial regression model. [ABSTRACT FROM AUTHOR]

Sleep is essential in maintaining physiological homeostasis in the brain. While the underlying mechanism is not fully understood, a ‘synaptic homeostasis’ theory has been proposed that synapses continue to strengthen during awake and undergo downscaling during sleep. This theory predicts that brain excitability increases with sleepiness. Here, we collected transcranial magnetic stimulation measurements in 38 subjects in a 34 hr program and decoded the relationship between cortical excitability and self-report sleepiness using advanced statistical methods. By utilizing a combination of partial least squares regression and mixed-effect models, we identified a robust pattern of excitability changes, which can quantitatively predict the degree of sleepiness. Moreover, we found that synaptic strengthen occurred in both excitatory and inhibitory connections after sleep deprivation. In sum, our study provides supportive evidence for the synaptic homeostasis theory in human sleep and clarifies the process of synaptic strength modulation during sleepiness.

Ghost quantitative trait loci (QTL) are the false discoveries in QTL mapping, that arise due to the "accumulation" of the polygenic effects, uniformly distributed over the genome. The locations on the chromosome that are strongly correlated with the total of the polygenic effects depend on a specific sample correlation structure determined by the genotypes at all loci. The problem is particularly severe when the same genotypes are used to study multiple QTL, e.g. using recombinant inbred lines or studying the expression QTL. In this case, the ghost QTL phenomenon can lead to false hotspots, where multiple QTL show apparent linkage to the same locus. We illustrate the problem using the classic backcross design and suggest that it can be solved by the application of the extended mixed effect model, where the random effects are allowed to have a nonzero mean. We provide formulas for estimating the thresholds for the corresponding t-test statistics and use them in the stepwise selection strategy, which allows for a simultaneous detection of several QTL. Extensive simulation studies illustrate that our approach eliminates ghost QTL/false hotspots, while preserving a high power of true QTL detection. [ABSTRACT FROM AUTHOR]

The expected mean squares (EMS) for the effects in a two-way ANOVA model are derived when sampling from a finite population, for factors A and B. The A-effects and B-effects are represented by τi and βj, respectively, in the model. Model effects are studied for the random and mixed effects models. Thus, in terms of hypothesis testing, we are interested in the expected mean square formulas when the random effects are sampled from finite populations. For balanced data, the EMS for the A, B and AB interaction effects when the effects are sampled from a finite population are the same as the EMS for an infinite population. For unbalanced data, the EMS when the effects are sampled from a finite population in factors A, B and the AB interaction, the EMS are not the same as for an infinite population because the values that multiply the variance components differ. [ABSTRACT FROM AUTHOR]

Social bonding is fundamental to human society, and romantic interest involves an important type of bonding. Speed dating research paradigms offer both high external validity and experimental control for studying romantic interest in real-world settings. While previous studies focused on the effect of social and personality factors on romantic interest, the role of non-verbal interaction has been little studied in initial romantic interest, despite being commonly viewed as a crucial factor. The present study investigated whether romantic interest can be predicted by non-verbal dyadic interactive body sway, and enhanced by movement-promoting ('groovy') background music. Participants' body sway trajectories were recorded during speed dating. Directional (predictive) body sway coupling, but not body sway similarity, predicted interest in a long-term relationship above and beyond rated physical attractiveness. In addition, presence of groovy background music promoted interest in meeting a dating partner again. Overall, we demonstrate that romantic interest is reflected by non-verbal body sway in dyads in a real-world dating setting. This novel approach could potentially be applied to investigate non-verbal aspects of social bonding in other dynamic interpersonal interactions such as between infants and parents and in non-verbal populations including those with communication disorders. [ABSTRACT FROM AUTHOR]

Abstract Background Management of chronic hepatitis C (CHC) has significantly accelerated in the last few years. Currently, second generation direct acting antivirals (DAAs) promise clearance of infection in most of patients. Here we present the results of the first analysis carried out on data of Lazio clinical network for DAAs. Methods The study was designed as a multicenter cohort: a) to assess the evolution of treatment during the first 24 months of the activity of the Clinical Network; b) to report overall efficacy of treatments; c) to analyze potential factors associated with lack of virological response at 12 weeks after therapy (SVR12); d) to evaluate the variation of ALT at baseline and 12 weeks after therapy in those who achieved SVR12 in comparison to those who did not. Analyses of efficacy were carried out with multilevel mixed effect logistic regression model. ALT temporal variation was assessed by mixed effect model mixed models with random intercept at patient’s level and random slope at the level of the time; i.e. either before or after therapy. Results Between 30 December 2014 and 31 December 2016 5279 patients started a DAA treatment; of those, 5127 (in 14 clinical centers) had completed the 12-week follow-up. Overall proportion of SVR12 was 93.41% (N = 4780) with no heterogeneity between the 14 clinical centers. Interruption as the consequence of severe side effect was very low (only 23 patients). Unadjusted analysis indicates that proportion of SVR12 significantly changes according to patient’s baseline characteristics, however after adjusting for potential confounders only adherence to current guidelines, stage of liver diseases, gender, transplant and HIV status were independently associated with the response to therapy. Analysis of ALT temporal variation showed that ALT level normalized in most, but not, all patients who achieved SVR12. Conclusion Our study confirmed the extraordinary efficacy of DAAs outside clinical trials. The advantage of DAAs was particularly significant for those patients who were previously considered as difficult-to-treat and did not have treatment options before DAAs era. Intervention based on network of select centers and prioritization of patients according to diseases severity was successful. Further studies are needed to establish whether clearance of HCV after DAAs therapy can arrest or even revert liver fibrosis in non-cirrhotic patients and/or improve life quality and expectancy in those who achieve SVR12 with cirrhosis.

Ecological Momentary Assessment (EMA) studies aim to explore the interaction between subjects' psychological states and real environmental factors. During the EMA studies, participants can receive prompted assessments intensively across days and within each day, which results in three-level longitudinal data, e.g., subject-level (level-3), day-level nested in subject (level-2) and assessment-level nested in each day (level-1). Those three-level data may exhibit complex longitudinal correlation structure but ignoring or mis-specifying the within-subject correlation structure can lead to bias on the estimation of the key effects and the intraclass correlation. Given the three-level EMA data and the time stamps of the responses, we proposed a linear mixed effects model with random effects at each level. In this model, we accounted for level-2 autocorrelation and level-1 autocorrelation and showed how structural information from the three-level data improved the fit of the model. With real time stamps of the assessments, we also provided a useful extension of this proposed model to deal with the issue of irregular-spacing in EMA assessments. [ABSTRACT FROM AUTHOR]

Camera trapping, paired with analytical methods for estimating species occurrence, population size or density, can yield information with direct consequences for wildlife management and conservation. Detectability, the ability to detect a species or individual if it is present, affects the reliability and efficiency of camera trap surveys and, in turn, varies across species, space and time. Greater detectability means greater sample size, and a common approach to boost detectability of wildlife by camera traps involves the application of olfactory lures. Using a camera trap study on sympatric mesocarnivores (European badger Meles meles, red fox Vulpes vulpes, pine marten Martes martes and domestic cat Felis catus), we quantified three elements of detectability: (1) the time until first detection ('sooner', conditional on being present), (2) the proximity to a focal point in front of the camera ('closer', conditional on being detected) and (3) the duration of exposure to the camera ('longer', conditional on being detected). A hierarchical analytical approach and a quasi‐experimental setup allowed us to test for and quantify the species‐specific effect of olfactory lures on these aspects of detectability. Depending on species, average median time to first detection ranged from 4 to 28 days, distance to the focal point from 0.3 to 0.8 body lengths, and median time to departure from 2 to 6 seconds. Credible intervals overlapped substantially between most species in all three measures, and variation between observations was extensive. We detected effects of lures on time to first detection for cats (castoreum; American beaver Castor canadensis scent), distance to focal point for badgers (striped skunk Mephitis mephitis scent) and martens (castoreum, fox and skunk scents), and the duration of exposure for foxes (fox and skunk scents). We discuss how a multifaceted perspective on detectability in camera trap studies, linked with species biology, can give investigators a more structured approach to selecting and testing measures intended to boost detection probability. [ABSTRACT FROM AUTHOR]

With the advances of personal computer and application, data analysis has been familiar. Clinicians can easily analyze their own data. As an editor and reviewer for the submitted manuscripts, I often faces fundamental misuse of statistical analysis. In this manuscript, basic concepts of statistics are described to prevent common errors and misuse. For the application of regression analysis, advanced description are presented. [ABSTRACT FROM AUTHOR]

Data observed in multiregional clinical trials are structurally hierarchical in the sense that the patient population consists of several regions and patients are nested within their own regions. To reflect such hierarchical structure, in this article, we propose two-level hierarchical linear models in which the level-1 model is based on patient-level data such as treatment indicator and age, and the level-2 model is based on region-level data such as medical practices. The fixed effect model and the continuous random effect model are shown to be special cases of hierarchical linear models. We conducted simulation studies to investigate the empirical Type I error rates of three methods for testing the overall treatment effect. The performance of the testing method with sample ratios as weights and the empirical Bayes estimator for between-region variability is better than that of the other two testing methods. [ABSTRACT FROM AUTHOR]

Exposure to ambient air pollutants such as ozone (O3) and particulate matter (PM) is associated with increased cardiovascular morbidity and rate of mortality, but the underlying biological mechanisms have yet to be described. Emerging evidence shows that extracellular vehicle (EV) microRNAs (miRNAs) may facilitate cell-to-cell and organ-to-organ communications and play a role in the air pollution-induced cardiovascular effects. This study aims to explore the association between air pollutant exposure and miRNA changes related to cardiovascular diseases. Using a panel study design, 14 participants with coronary artery diseases were enrolled in this study. Each participant had up to 10 clinical visits and their plasma samples were collected and measured for expression of miRNA-21 (miR-21), miR-126, miR-146, miR-150, and miR-155. Mixed effects models adjusted for temperature, humidity, and season were used to examine the association between miRNA levels and exposure to 8-hr O3 or 24-hr PM2.5 up to 4 days prior. Results demonstrated that miR-150 expression was positively associated with O3 exposure at 1–4 days lag and 5day moving average while miR-155 expression tracked with O3 exposure at lag 0. No significant association was found between miRNA expression and ambient PM2.5 at any time point. β-blocker and diabetic medication usage significantly modified the correlation between O3 exposure and miR-150 expression where the link was more prominent among non-users. In conclusion, evidence indicated an association between exposure to ambient O3 and circulating levels of EV miR-150 and miR-155 was observed. These findings pointed to a future research direction involving miRNA-mediated mechanisms of O3-induced cardiovascular effects. [ABSTRACT FROM AUTHOR]

Agriculture crop residue burning is a serious issue in Northern India due to ineffective control policies. An epidemiological study has been done on school going children to evaluate the trends in their physiological parameters due to crop residue burning activity in the affected area. Total 600 children were recruited and continuously inspected for three years at three different sites. Season wise, the level of fine particulate matter (PM10 and PM2.5) was higher in rice crop residue burning seasons than in wheat crop residue burning seasons. As per dose–response relationship, maximum degradation was observed in FVC (−7.62%) and PEF (−6.23%) parameters from their baseline values due to burning activities. With adjustment of socioeconomic, anthropometric parameters of selected subjects and meteorological parameters in multivariate mixed effect model, prediction equations have been purposed to observe the future trends in physiological parameters of children. Based on Tiffenue index, an unrecoverable effect has been observed in lung parameters. Trends were alarming and may cause serious complications in early age of human subjects. [ABSTRACT FROM AUTHOR]

Over the past 40 years, PM2.5 pollution in North China has become increasingly serious and progressively exposes the densely populated areas to pollutants However, due to limited ground data, it is challenging to estimate accurate PM2.5 exposure levels, further making it unfavorable for the prediction and prevention of PM2.5 pollutions. This paper therefore uses the mixed effect model to estimate daily PM2.5 concentrations of North China between 2003 and 2015 with ground observation data and MODIS AOD satellite data. The tempo-spatial characteristics of PM2.5 and the influence of meteorological elements on PM2.5 is discussed with EOF and canonical correlation analysis respectively. Results show that overall R2 is 0.36 and the root mean squared predicted error was 30.1 µg/m3 for the model prediction. Our time series analysis showed that, the Taihang Mountains acted as a boundary between the high and low pollution areas in North China; while the northern part of Henan Province, the southern part of Hebei Province and the western part of Shandong Province were the most polluted areas. Although, in 2004, 2009 and dates after 2013, PM2.5 concentrations were relatively low. Meteorological/topography conditions, that include high surface humidity of area in the range of 34°–40°N and 119°–124°E, relatively low boundary layer heights, and southerly and easterly winds from the east and north area were common factors attributed to haze in the most polluted area. Overall, the spatial distribution of increasingly concentrated PM2.5 pollution in North China are consistent with the local emission level, unfavorable meteorological conditions and topographic changes. [ABSTRACT FROM AUTHOR]

Background: Patients infected with HIV may experience a succession of clinical stages before the disease diagnosis and their health status may be followed-up by tracking disease biomarkers. In this study, we present a joint multistate model for predicting the clinical progression of HIV infection which takes into account the viral load and CD4 count biomarkers.Methods: The data is from an ongoing prospective cohort study conducted among antiretroviral treatment (ART) naïve HIV-infected women in the province of KwaZulu-Natal, South Africa. We presented a joint model that consists of two related submodels: a Markov multistate model for CD4 cell count transitions and a linear mixed effect model for longitudinal viral load dynamics.Results: Viral load dynamics significantly affect the transition intensities of HIV/AIDS disease progression. The analysis also showed that patients with relatively high educational levels (β = - 0.004; 95% confidence interval [CI]:-0.207, - 0.064), high RBC indices scores (β = - 0.01; 95%CI:-0.017, - 0.002) and high physical health scores (β = - 0.001; 95%CI:-0.026, - 0.003) were significantly were associated with a lower rate of viral load increase over time. Patients with TB co-infection (β = 0.002; 95%CI:0.001, 0.004), having many sex partners (β = 0.007; 95%CI:0.003, 0.011), being younger age (β = 0.008; 95%CI:0.003, 0.012) and high liver abnormality scores (β = 0.004; 95%CI:0.001, 0.01) were associated with a higher rate of viral load increase over time. Moreover, patients with many sex partners (β = - 0.61; 95%CI:-0.94, - 0.28) and with a high liver abnormality score (β = - 0.17; 95%CI:-0.30, - 0.05) showed significantly reduced intensities of immunological recovery transitions. Furthermore, a high weight, high education levels, high QoL scores, high RBC parameters and being of middle age significantly increased the intensities of immunological recovery transitions.Conclusion: Overall, from a clinical perspective, QoL measurement items, being of a younger age, clinical attributes, marital status, and educational status are associated with the current state of the patient, and are an important contributing factor to extend survival of the patients and guide clinical interventions. From a methodological perspective, it can be concluded that a joint multistate model approach provides wide-ranging information about the progression and assists to provide specific dynamic predictions and increasingly precise knowledge of diseases. [ABSTRACT FROM AUTHOR]

The runoff infiltration partitioning has direct consequences on preservation of water resources in rural territories, both in agricultural plots and uncultivated areas (e.g. ditches, channels, grass strips), and requires a better understanding of the variability of soil infiltration capacity by disentangling the complex links between soil, vegetation and management. The general objective of the study was to investigate the temporal variation in quasi-steady ponded infiltration rates of a fluvisol soil under bare and different cover crop type (a Malvaceae with a tap-root system and a Poaceae with a fibrous root system) and management conditions (burning, mowing, and chemical weeding) that are commonly found in the Mediterranean vineyards. A modified double-ring infiltration method was used to repeat measurement of the quasi-steady ponded infiltration, f cp , on the same location over time. Placed on a 64 m2 plot area with minimal distances between individual measurements of 30 cm, the setup allowed evaluation of variability among measurements that were conducted within the plot. The results showed a significantly lower f cp for bare soil than covered soil, and a two-fold higher f cp for soil covered by Malvaceae than Poaceae. A seasonal effect in f cp was observed, with the highest f cp in summer and the lowest in winter. The study revealed a strong spatial variability in f cp along a transect of a few tens of centimeter, and showed no significant effect of management strategies compared to the vegetated control. The results revealed the importance of considering both plant traits and season rather than vegetation management strategies to explain quasi-steady ponded infiltration rates. • Infiltration rates are a key parameter in the partitioning of hydrological resources. • Measurement of quasi-steady ponded infiltration rate at local scale is challenging. • We proposed an innovative set up to measure impacts of different plant covers and management strategies. • The study reveals a significant effect of type of plant cover on quasi-steady ponded infiltration rates. • No effect of management practices were noticed on quasi-steady ponded infiltration rates. [ABSTRACT FROM AUTHOR]

• Short-term temperature variability exposure increased SBP and pulse pressure. • Hypertensive and prehypertensive adults were more sensitive to temperature variability. • Temperature variability increased SBP and pulse pressure only during the warm season. We aimed to evaluate the impacts of short-term daily temperature variability (DTV) on blood pressure (BP) among participants with normotension, prehypertension, and hypertension, respectively, and explore the effects in different climate zones and seasons. A representative population sample (n = 397,173) covering the subtropical, temperate continental, and temperate monsoon zones was obtained from the China Hypertension Survey. DTV was calculated as the standard deviation of daily minimum and maximum temperatures during the exposure days. The linear mixed effect regression model was used to estimate the associations between DTV exposure and BP among normotension, prehypertension, and hypertension, respectively, and further stratified analysis was performed by climate zones and seasons. After adjustment for confounders, per interquartile range (IQR) increase in DTV (2.28 °C) at 0–6 days of exposure was associated with an increase of 0.41 mmHg (95 % confidence interval [CI]: 0.07, 0.75) in systolic BP (SBP) and 0.41 mmHg (95 % CI: 0.09, 0.72) in pulse pressure (PP) among hypertensive participants in the subtropical zone. Similarly, DTV exposure was associated with an increase of 0.31 mmHg (95 % CI: 0.06, 0.55) in SBP and 0.59 mmHg (95 % CI: 0.24, 0.94) in PP among prehypertensive participants in the temperate continental zone. Additionally, during the warm season, DTV was positively associated with SBP among populations with prehypertension and hypertension, and with PP among all three populations. Short-term DTV exposure was associated with an increase in SBP and PP among hypertensive and prehypertensive participants in the subtropical zone and the temperate continental zone. In addition, positive associations of DTV with SBP and PP were observed among participants with prehypertension and hypertension in the warm season. Comprehensive health education and effective intervention strategies should be implemented to mitigate the effects of temperature variations on BP, particularly among prehypertensive and hypertensive populations. [ABSTRACT FROM AUTHOR]

A comparative analysis of energy poverty transitions and persistence can provide valuable suggestions for long-term policy actions. This study examines the dynamics of energy poverty in 17 European countries based on the longitudinal household data from the EU Survey on Income and Living Conditions, waves 2015–2018. The study pursues two goals. First, we explore households’ chances of transitioning in and out of energy poverty in each country following the discrete-time Markov process. On average, the probability to stay in energy poverty is 51.5%, and there is a lot of heterogeneity across countries. Households in Bulgaria, Greece, Romania, and Lithuania are quite close to the energy poverty trap. Second, we identify factors that help energy-poor households leave energy poverty. Demographic, technical, and socio-economic factors are the drivers in escaping energy poverty, which suggests common EU policy.

This paper aims to explore which macroeconomic factors affect the volatility of the automakers stock prices by employing a multifactor model. The study uses quarterly panel data of 39 automakers quoted on the stock exchanges in the 11 countries. It studies the effects of 19 macroeconomic variables from January 2000 to December 2017, and proposes the mixed-effect model constructed based on employing genetic algorithm and AIC criterion, and compares its explanatory power with the existing multifactor model (El Khoury, 2015). This paper suggests that the proposed model can shed more light on explaining the variability of stock prices of the quoted automakers. The findings show there are positive linkages between automaker's stock return volatility and explanatory variables such as stock market development, GDP and unemployment. Conversely, an inverse linkage between the dependent variable and money supply and IPI was found. The study demonstrates that selected macroeconomic factors can also be used as predictors. [ABSTRACT FROM AUTHOR]