Unraveling birth weight determinants: Integrating machine learning, spatial analysis, and district-level mapping

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.

Association between short-term daily temperature variability and blood pressure in the Chinese population: From the China hypertension survey

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.

A daily and complete PM2.5 dataset derived from space observations for Vietnam from 2012 to 2020

PM_2.5 pollution is a serious problem in Vietnam and around the world, having bad impacts on human health, animals and environment. Regular monitoring at a large scale is important to assess the status of air pollution, develop solutions and evaluate the effectiveness of policy implementation. However, air quality monitoring stations in Vietnam are limited. In this article, we propose an approach to estimate daily PM_2.5 concentration from 2012 to 2020 over the Vietnamese territory, which is strongly affected by cloudy conditions, using a modern statistical model named Mixed Effect Model (MEM) on a dataset consisting of ground PM_2.5 measurements, integrated satellite Aerosol Optical Depth (AOD), meteorological and land use maps. The result of this approach is the first long-term, full coverage and high quality PM_2.5 dataset of Vietnam. The daily mean PM_2.5 maps have high validation results in comparison with ground PM_2.5 measurement (Pearson r of 0.87, R² of 0.75, RMSE of 11.76 μg/m³, and MRE of 36.57 % on a total of 13,886 data samples). The aggregated monthly and annual average maps from 2012 to 2020 in Vietnam have outstanding quality when compared with another global PM_2.5 product. The PM_2.5 concentration maps has shown spatial distribution and seasonal variations of PM_2.5 concentration in Vietnam in a long period from 2012 to 2020 and has been used in other studies and applications in the environment and public health at the national scale, which has not been possible before because of the lack of monitoring stations and an appropriate PM_2.5 modeling approach.

Exploring farmer perceptions and evaluating the performance of mung bean (Vigna radiata L) varieties in Amhara region, Ethiopia

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 new mathematical mixed effect model was used for analysing the influencing factors of hypoglycaemia of newborns from women with gestational diabetes mellitus

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. Impact statementWhat 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.

High-intensity physical activity is not associated with better cognition in the elder: evidence from the China Health and Retirement Longitudinal Study

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00923-3.

Effects of cold and hot temperature on metabolic indicators in adults from a prospective cohort study

BACKGROUND

Previous studies have found that exposed to low and high outdoor temperature was associated with cardiovascular diseases morbidity and mortality. The risk factors for cardiovascular disease include high blood lipid, high uric acid (UA) and high fasting plasma glucose (FPG). However, few studies have explored the effects of low and high temperature on these metabolic indicators.

OBJECTIVE

To explore the effect of low and high temperature on metabolic indicators in adults from northwest of China.

METHODS

Based on a prospective cohort study, a total of 30,759 individuals who participated in both baseline and first follow-up from 2011 to 2015 were selected in this study. The meteorological observation data and environmental monitoring data were collected in the same period. Associations between cold and hot temperature and blood lipid (total cholesterol (TC), triglycerides (TG), low density lipoprotein-cholesterol (LDL-C), and high density lipoprotein-cholesterol (HDL-C)), UA and FPG were conducted with mixed effect models after adjusting for confounding factors.

RESULTS

A nonlinear relationship between outdoor temperature and metabolic indicators was found. For the cold effects, each 5 °C decrease of mean temperature was associated with an increase of 5.07% (95% CI: 3.52%, 6.63%) in TG and 2.85% (95% CI: 2.18%, 3.53%) in UA, While a decrease of 3.38% (95% CI: 2.67%, 4.09%) in HDL-C and 1.26% (95% CI: 0.48%, 2.04%) in LDL-C. For the heat effects, each 5 °C increase in mean temperature was associated with 1.82% (95% CI: 0.89%, 2.76%), 0.56% (95% CI: 0.11%, 1.00%), 5.82% (95% CI: 4.58%, 7.06%), 9.02% (95% CI: 7.17%, 10.87%), 0.20% (95% CI: 0.01%, 0.40%), and 1.22% (95% CI: 0.19%, 2.24%) decrease in TC, TG, HDL-C, LDL-C, UA and FPG. Age, smoking, drinking, high-oil diet and hyperlipidemia might modify the association between mean temperature and metabolic indicators.

CONCLUSION

There was a significant effect of cold and hot temperature on metabolic indicators in a high altitude area of northwestern China. These results provide a basis for understanding the underlying mechanism of the influence of temperature on metabolic diseases.

Tracking Pattern of Total Cholesterol Levels from Childhood to Adolescence in Japan

Aims: This study aimed to evaluate the tracking pattern of serum total cholesterol (TC) levels among Japanese children using data collected continuously for 9 years and examine the relationship between childhood and adulthood TC levels.

Methods: TC levels of 2,608 first grade primary school children enrolled during 1981-2014 from two Japanese towns were measured during annual health check-ups. Nine-year trajectories of estimated TC levels stratified by TC quartiles in the first grade were analyzed using a mixed effects model. Adulthood TC levels were measured in participants who underwent health check-ups in the same area.

Results: Overall, 1,322 boys and 1,286 girls in the first grade of a primary school were followed for 9 years. Trajectories of TC levels during the period stratified by TC quartiles in the first grade differed significantly and did not cross each other for both sexes. Childhood data of 242 adult participants were linked with their adulthood data; the mean of age was late 20s for both sexes. The average TC levels in adulthood increased from the first to the fourth quartile in the first grade. Additionally, trajectories of TC levels differed between boys and girls. The later the admission year, the more elevated the TC levels in girls.

Conclusion: Among Japanese children, TC levels were strongly tracked from childhood to adolescence for 9 years, and elevated TC levels in childhood were related to elevated TC levels in adulthood. Maintaining appropriate TC levels during childhood may be important to prevent future coronary artery diseases.

A comparison of methods accounting for batch effects in differential expression analysis of UMI count based single cell RNA sequencing

Accounting for batch effects, especially latent batch effects, in differential expression (DE) analysis is critical for identifying true biological effects. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for quantifying cell-to-cell variation in transcript abundance and characterizing cellular dynamics. Although many scRNA-seq DE analysis methods accommodate known batch variables, their performance has not been systematically evaluated. Moreover, the challenge of accounting for latent batch variables in scRNA-seq DE analysis is largely unmet. In contrast, many methods have been developed to account for batch variables (either known or latent) in other high-dimensional data, especially bulk RNA-seq. We extensively evaluate 11 methods for batch variables in different scRNA-seq DE analysis scenarios, with a primary focus on latent batch variables. We demonstrate that for known batch variables, incorporating them as covariates into a regression model outperformed approaches using a batch-corrected matrix. For latent batches, fixed effects models have inflated FDRs, whereas aggregation-based methods and mixed effects models have significant power loss. Surrogate variable based methods generally control the FDR well while achieving good power with small group effects. However, their performance (except that of SVA) deteriorated substantially in scenarios involving large group effects and/or group label impurity. In these settings, SVA achieves relatively good performance despite an occasionally inflated FDR (up to 0.2). Finally we make the following recommendations for scRNA-seq DE analysis: 1) incorporate known batch variables instead of using batch-corrected data; and 2) employ SVA for latent batch correction. However, better methods are still needed to fully unleash the power of scRNA-seq.

Severe trauma patient volume was associated with decreased mortality

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.

Modelling of viral load dynamics and CD4 cell count progression in an antiretroviral naive cohort: using a joint linear mixed and multistate Markov model

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.

Large-sample estimation and inference in multivariate single-index models

By optimizing index functions against different outcomes, we propose a multivariate single-index model (SIM) for development of medical indices that simultaneously work with multiple outcomes. Fitting of a multivariate SIM is not fundamentally different from fitting a univariate SIM, as the former can be written as a sum of multiple univariate SIMs with appropriate indicator functions. What have not been carefully studied are the theoretical properties of the parameter estimators. Because of the lack of asymptotic results, no formal inference procedure has been made available for multivariate SIMs. In this paper, we examine the asymptotic properties of the multivariate SIM parameter estimators. We show that, under mild regularity conditions, estimators for the multivariate SIM parameters are indeed n-consistent and asymptotically normal. We conduct a simulation study to investigate the finite-sample performance of the corresponding estimation and inference procedures. To illustrate its use in practice, we construct an index measure of urine electrolyte markers for assessing the risk of hypertension in individual subjects.

[Short-term effects of air pollution on lung function of school-age children in Hangzhou]

A total of 1 685 school-age children selected from Hangzhou received lung function testing to evaluate the short-term effects of air pollution on their lung function. The results showed that in every 10 μg/m(3) increase of average concentration of PM(2.5) and PM(10) on the day of the test and the day before the test,peak expiratory flow (PEF) decreased 0.039 (95%CI: 0.012-0.067) L/s and 0.031 (95% CI:0.011-0.051) L/s,respectively. When the average concentration of SO(2) increased 10 μg/m(3) on the day of test and the day prior to the test, PEF and 75% of the forced vital capacity that has not been exhaled (MEF(75)) decreased 0.437 (95%CI: 0.217-0.658) L/s and 0.396 (95%CI: 0.180-0.613) L/s. After being adjusted for NO(2),with every 10 μg/m(3) increase of average concentration of PM(2.5) and PM(10) on the day of the test and the day before the test,PEF and MEF(75) decreased 0.056 (95%CI: 0.028-0.085), 0.053(95%CI: 0.027-0.081) and 0.047 (95%CI: 0.026-0.068) L/s,0.044 (95%CI: 0.023-0.065) L/s on the day before the test, respectively. The results indicate that air pollution have short-term and lag effects on lung function of school-age children in Hangzhou.

The association of socioeconomic status with quality of life in cancer patients over a 6-month period using individual growth models

PURPOSE

Studies examining longitudinal associations between socioeconomic factors and quality of life (QoL) in cancer patients are rare. This study investigates changes in QoL over a 6-month period.

METHODS

Four hundred forty-two cancer patients (mean age 64, SD = 11, 70% male) completed standardized questionnaires at the beginning (t1) and end (t2) of their hospital stay and 3 (t3) and 6 months (t4) thereafter. QoL was assessed with the EORTC QLQ-C30 core questionnaire. Mixed effect models were employed to analyze individual changes in QoL in relation to socioeconomic status (education, income, job status) over the four timepoints. Age, sex, cohabitation, disease and treatment factors, and comorbidity were included as covariates in the models.

RESULTS

Income was a predictive factor for QoL. Patients with a low income had 8.8 percentage points (PP) lower physical, 4.9 PP lower emotional, and 11.4 PP lower role functioning. They also had 6.6 PP lower global QoL. Lower social functioning (6.2 PP) was found in patients with higher education or university degrees compared with those who were less educated or had not undergone an apprenticeship. Income also influenced trajectories of role functioning. There was no evidence that primary or secondary education and job type were related to QoL.

CONCLUSIONS

The fact that income is negatively associated with many aspects of quality of life should be considered during and after treatment with a focus on patients with special needs.

Impact of new DAA therapy on real clinical practice: a multicenter region-wide cohort study

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.

Spatial scale and seasonal dependence of land use impacts on riverine water quality in the Huai River basin, China

Land use pattern is an effective reflection of anthropic activities, which are primarily responsible for water quality deterioration. A detailed understanding of relationship between water quality and land use is critical for effective land use management to improve water quality. Linear mixed effects and multiple regression models were applied to water quality data collected from 2003 to 2010 from 36 stations in the Huai River basin together with topography and climate data, to characterize the land use impacts on water quality and their spatial scale and seasonal dependence. The results indicated that the influence of land use categories on specific water quality parameter was multiple and varied with spatial scales and seasons. Land use exhibited strongest association with dissolved oxygen (DO) and ammonia nitrogen (NH₃-N) concentrations at entire watershed scale and with total phosphorus (TP) and fluoride concentrations at finer scales. However, the spatial scale, at which land use exerted strongest influence on instream chemical oxygen demand (COD) and biochemical oxygen demand (BOD) levels, varied with seasons. In addition, land use composition was responsible for the seasonal pattern observed in contaminant concentrations. COD, NH₃-N, and fluoride generally peaked during dry seasons in highly urbanized regions and during rainy seasons in less urbanized regions. High proportion of agricultural and rural areas was associated with high nutrient contamination risk during spring. The results highlight the spatial scale and seasonal dependence of land use impacts on water quality and can provide scientific basis for scale-specific land management and seasonal contamination control.

[Acute effect of ambient air pollution on small airway lung functions among school children in Shanghai]

Objective: To study the acute effects of compound ambient air pollution on small airway lung functions among school children in Shanghai. Method: A longitudinal survey on lung functions was conducted among 233 school-children from three schools (A, B and C, located in innerring, mid-ring and outer-ring areas). Lung function test was performed once a week for 3 times respectively, among children in school A and B in Dec. 2013 and in school C in Dec. 2014. The fourth lung function test was tested in Jun. 2014 and May 2015 in the respective schools. Results: from the lung function would include items as: forced mid-expiratory flow at 25% of forced vital capacity (MEF(25%)), mid-expiratory flow at 50% of forced vital capacity (MEF(50%)), mid-expiratory flow at 75% of forced vital capacity (MEF(75%)) and mid-expiratory flow between 25% and 75% of the forced vital capacity (FEF(25%-75%)). Data regarding the daily air quality real-time of PM(2.5), PM(10), SO(2) and NO(2) in Dec. 2013, Dec. 2014, Jun. 2014 and May. 2015 from the three environmental monitoring spots and meteorological data from the Shanghai Meteorological Service system which were physically close to the three schools, were collected simultaneously. Linear mixed effect model was used to examine the levels of correlation between lung function indicators and ambient air pollutants. Results When confounding factors on meteorology and individuals were controlled, the lag effects and accumulated lag effects were found to have existed between the internal quarter rang (IQR) concentration of PM(2.5) and PM(10) in lag2 day and lag02 days, IQR concentration of SO(2) in lag02 day and IQR concentration of NO(2) lag0 day, when small airway lung functions like MEF(25%), MEF(50%), MEF(75%) and FEF(25%-75%)(P<0.05) were inspected. Results from the two air pollutants model analysis showed that SO(2) and NO(2) presenting interactive effects with PM(2.5), PM(10) and lag effects more significant than the individual SO(2) and NO(2), respectively (P<0.05). Conclusion: Contents on the ambient air pollutants as PM(2.5), PM(10), SO(2) and NO(2) were negatively associated with the lung functions in the small airways of children, in Shanghai.

A comparison of observation-level random effect and Beta-Binomial models for modelling overdispersion in Binomial data in ecology & evolution

Overdispersion is a common feature of models of biological data, but researchers often fail to model the excess variation driving the overdispersion, resulting in biased parameter estimates and standard errors. Quantifying and modeling overdispersion when it is present is therefore critical for robust biological inference. One means to account for overdispersion is to add an observation-level random effect (OLRE) to a model, where each data point receives a unique level of a random effect that can absorb the extra-parametric variation in the data. Although some studies have investigated the utility of OLRE to model overdispersion in Poisson count data, studies doing so for Binomial proportion data are scarce. Here I use a simulation approach to investigate the ability of both OLRE models and Beta-Binomial models to recover unbiased parameter estimates in mixed effects models of Binomial data under various degrees of overdispersion. In addition, as ecologists often fit random intercept terms to models when the random effect sample size is low (<5 levels), I investigate the performance of both model types under a range of random effect sample sizes when overdispersion is present. Simulation results revealed that the efficacy of OLRE depends on the process that generated the overdispersion; OLRE failed to cope with overdispersion generated from a Beta-Binomial mixture model, leading to biased slope and intercept estimates, but performed well for overdispersion generated by adding random noise to the linear predictor. Comparison of parameter estimates from an OLRE model with those from its corresponding Beta-Binomial model readily identified when OLRE were performing poorly due to disagreement between effect sizes, and this strategy should be employed whenever OLRE are used for Binomial data to assess their reliability. Beta-Binomial models performed well across all contexts, but showed a tendency to underestimate effect sizes when modelling non-Beta-Binomial data. Finally, both OLRE and Beta-Binomial models performed poorly when models contained <5 levels of the random intercept term, especially for estimating variance components, and this effect appeared independent of total sample size. These results suggest that OLRE are a useful tool for modelling overdispersion in Binomial data, but that they do not perform well in all circumstances and researchers should take care to verify the robustness of parameter estimates of OLRE models.

A cross-sectional study on the prevalence and risk factors for foot and limb lesions in piglets on commercial farms in Ireland

A cross-sectional survey of 68 integrated Irish pig farms was conducted to determine the prevalence and risk factors for foot and limb lesions in 2948 piglets from 272 litters. One litter was selected per age category; 3-7 days, 8-14 days, 15-21 days and 22-28 days per farm. All piglets were examined for sole bruising, sole erosion, coronary band injuries, limb abrasions, alopecia, swollen limbs and swollen feet and scored from 0 to 3 based on relative size. Environmental parameters were recorded for each litter examined. A questionnaire was completed on management, health and performance factors for each farm. The overall prevalence of each lesion was calculated and multilevel mixed effect logistic regression models were used to elucidate risk factors. The prevalence (farm range) of lesions were: sole bruising=61.5% (7-100%), sole erosion=34.1% (0-100%), coronary band injuries=11.3% (0-46%), limb abrasions=55.7% (11-98%), alopecia=24.8% (0-83%), swollen limbs=2.4% (0-11%) and swollen feet=4.4% (0-14%). Age was negatively associated with sole bruising (OR 0.42; CI 0.37, 0.50) and coronary band injury (OR 0.69; CI 0.60, 0.81) and positively associated with limb abrasions (OR 1.54; CI 1.12, 2.14). There was a reduced risk of sole bruising in piglets in pens with plastic slats with oval voids in the piglet area of the pen with a plastic solid area for piglets and metal slats under the crate (OR 0.32; CI 0.15, 0.70) compared with plastic stats throughout and a plastic solid area for piglets. There was an increased risk of sole erosion (OR 1.81; CI 1.07, 3.09) and foot and limb swellings (OR 1.90; CI 1.01, 3.57) in pigs in pens with metal slats only and a solid plastic area for piglets compared with plastic stats throughout and a solid plastic area for piglets. There was an increased risk of coronary band injury in pens with metal slats throughout and a metal solid area for piglets (OR 4.25; CI 1.96, 3.57) compared with plastic stats throughout and a plastic solid area for piglets. We conclude no single floor type was ideal for piglet foot and limb health with all floors influencing different lesions in different ways, however, the positive association between sole erosions, coronary band injury and foot and limb swellings and metal slats suggest that this floor type was most detrimental and most likely to be associated with joint infections that lead to severe health and welfare concerns.

Meta-analysis of D-values of proteolytic Clostridium botulinum and its surrogate strain Clostridium sporogenes PA 3679

Foodborne botulism is a serious disease resulting from ingestion of preformed Clostridium botulinum neurotoxin in foodstuff. Since the 19th century, the heat resistance of this spore forming bacteria has been extensively studied in order to guarantee the public health associated with low acidic, ambient stable products. The most largely used heat resistance parameters in thermal settings of such products are the D121.1°C values (time required to have a 10-fold decrease of the spore count, at 121.1°C) and the z-values (temperature increase to have a 10-fold decrease of D-values). To determine D121.1°C and z-values of proteolytic C. botulinum and its nontoxigenic surrogate strain C. sporogenes PA3679, a dataset of 911 D-values was collected from 38 scientific studies. Within a meta-analysis framework, a mixed-effect linear model was developed with the log D-value (min) as response and the heat treatment temperature as explicative variable. The studies (38), the C. botulinum strains (11), and the heat treatment media (liquid media and various food matrices, split into nine categories in total) were considered as co-variables having a random effect. The species (C. botulinum and C. sporogenes) and the pH (five categories) were considered as co-variables having a fixed effect. Overall, the model gave satisfactory results with a residual standard deviation of 0.22. The heat resistance of proteolytic C. botulinum was found significantly lower than the C. sporogenes PA 3679 one: the mean D-values at the reference temperature of 121.1°C, in liquid media and pH neutral, were estimated to 0.19 and 1.28min for C. botulinum and C. sporogenes, respectively. On the other hand, the mean z-values of the two species were similar: 11.3 and 11.1°C for C. botulinum and C. sporogenes, respectively. These results will be applied to thermal settings of low-acid ambient stable products.

Maximum likelihood estimation for second level fMRI data analysis with expectation trust region algorithm

The trust region method which originated from the Levenberg-Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare the proposed methods with the conventional expectation-maximization (EM) algorithm based on a series of datasets (synthetic and real human fMRI datasets). From simulation studies, we found a higher damping factor for the LM algorithm is better than lower damping factor for the fMRI data analysis. More importantly, in most cases, the expectation trust region algorithm is superior to the EM algorithm in terms of accuracy if the random effect variance is large. We also compare these algorithms on real human datasets which comprise repeated measures of fMRI in phased-encoded and random block experiment designs. We observed that the proposed method is faster in computation and robust to Gaussian noise for the fMRI analysis. The advantages and limitations of the suggested methods are discussed.