Climate change is likely to worsen the food security situation through its impact on food production, which may indirectly affect fertility behaviour. This study examines the direct and indirect effects of climate change (e.g., temperature and precipitation) via the production of major crops, as well as their short- and long-term effects on the total fertility rate (TFR) in Bangladesh. We used structural equation modelling (SEM) to perform path analysis and distinguish the direct influence of climate change on fertility and its indirect influence on fertility through food security. We also applied the error correction model (ECM) to analyze the time-series data on temperature and precipitation, crop production and fertility rate of Bangladesh from 1966 to 2015. The results show that maximum temperature has a direct effect and indirect negative effect-via crop production-on TFR, while crop production has a direct positive effect and indirect negative effect-via infant mortality-on TFR. In the short term, TFR responds negatively to the maximum temperature but positively in the long term. The effect of rainfall on TFR is found to be direct, positive, but mainly short-term. Although indicators of economic development play an important part in the fertility decline in Bangladesh, some climate change parameters and crop production are non-negligible factors.

Methods for meta-analysis of studies with individual participant data and continuous exposure variables are well described in the statistical literature but are not widely used in clinical and epidemiological research. The purpose of this case study is to make the methods more accessible.
A two-stage process is demonstrated. Response curves are estimated separately for each study using fractional polynomials. The study-specific curves are then averaged pointwise over all studies at each value of the exposure. The averaging can be implemented using fixed effects or random effects methods.
The methodology is illustrated using samples of real data with continuous outcome and exposure data and several covariates. The sample data set, segments of Stata and R code, and outputs are provided to enable replication of the results.
These methods and tools can be adapted to other situations, including for time-to-event or categorical outcomes, different ways of modelling exposure-outcome curves, and different strategies for covariate adjustment.

BACKGROUND: The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination.
RESULTS: The shape based PCA was used for a statistical analysis of 140 sugar beet roots of different cultivars. The calculation of the mean sugar beet root shape and the description of the main variations was possible. Furthermore, unknown and individual tap roots could be attributed to their cultivar by means of a robust classification tool based on the PCA results.
CONCLUSIONS: The method demonstrates that it is possible to identify principal modes of root shape variations automatically and to quantify associated variances out of laserscanned 3D sugar beet tap root models. The introduced approach is not limited to the 3D shape description by laser scanning. A transfer to 3D MRI or radar data is also conceivable.

Parkinson\'s disease (PD) is a neurodegenerative disease inducing dystrophy of the motor system. Automatic movement analysis systems have potential in improving patient care by enabling personalized and more accurate adjust of treatment. These systems utilize machine learning to classify the movement properties based on the features derived from the signals. Smartphones can provide an inexpensive measurement platform with their built-in sensors for movement assessment. This study compared three feature selection and nine classification methods for identifying PD patients from control subjects based on accelerometer and gyroscope signals measured with a smartphone during a 20-step walking test. Minimum Redundancy Maximum Relevance (mRMR) and sequential feature selection with both forward (SFS) and backward (SBS) propagation directions were used in this study. The number of selected features was narrowed down from 201 to 4-15 features by applying SFS and mRMR methods. From the methods compared in this study, the highest accuracy for individual steps was achieved with SFS (7 features) and Naive Bayes classifier (accuracy 75.3%), and the second highest accuracy with SFS (4 features) and k Nearest neighbours (accuracy 75.1%). Leave-one-subject-out cross-validation was used in the analysis. For the overall classification of each subject, which was based on the majority vote of the classified steps, k Nearest Neighbors provided the most accurate result with an accuracy of 84.5% and an error rate of 15.5%. This study shows the differences in feature selection methods and classifiers and provides generalizations for optimizing methodologies for smartphone-based monitoring of PD patients. The results are promising for further developing the analysis system for longer measurements carried out in free-living conditions.

The 2019 novel-coronavirus (COVID-19) has affected 181 countries with approximately 1197405 confirmed cases (by 5th April). Understanding the transmission dynamics of the infection in each country which got affected on a daily basis and evaluating the effectiveness of control policies are critical for our further actions. To date, the statistics of COVID-19 reported cases show that more than 80% of infected are mild cases of disease, around 14% of infected have severe complications, and about 5% are categorized as critical disease victims. Today\'s report (5th April 2020; daily updates in the prepared website) shows that the confirmed cases of COVID-19 in the United States, Spain, Italy, and Germany are 308850, 126168, 124632, and 96092, respectively. Calculating the total case fatality rate (CFR) of Italy (4th April 2020), about 13.3% of confirmed cases have passed away. Compared with South Korea\'s rate of 1.8% (seven times lower than Italy) and China\'s 4% (69% lower than Italy), the CFR of Italy is too high. Some effective policies that yielded significant changes in the trend of cases were the lockdown policy in China, Italy, and Spain (the effect observed after some days), the shutdown of all nonessential companies in Hubei (the effect observed after 5 days), combined policy in South Korea, and reducing working hours in Iran.

Microbial community assembly (MCA) of both human and nonhuman animal carcasses provides indicators useful for estimating the postmortem interval (PMI) in terrestrial settings. However, there are fewer studies estimating postmortem submersion intervals (PMSIs) in aquatic habitats. No aquatic studies to date assessed MCA in the context of a death investigation, with all previous studies focusing on important basic ecological questions. Within the context of a cold case investigation, we performed an experiment using replicate adult swine carcasses to describe postmortem MCA variability within a nonflowing aquatic habitat. Using high-throughput sequencing of carcass postmortem microbiomes, we described MCA variability and identified key taxa associated with decomposition in an aquatic habitat similar to the cold case body recovery site. We also modeled key taxa for estimating PMSIs, modeling within ±3 days (mean square error) postmortem using random forest regression. Our findings show significant changes in microbial communities as decomposition progressed, and several taxa were identified as important indicator taxa which may be useful for future estimates of PMSI. While descriptive, this study provides initial findings quantifying MCA variability within a nonflowing aquatic habitat. Within the context of the cold case investigation, we discuss how postmortem microbial samples collected at the time of body recovery could have been an important piece of evidence for understanding the PMSI of recovered remains. Additional experimental studies are needed to explicitly test and identify mechanisms associated with postmortem MCA variability in other habitats and under different temperature (e.g., seasons) conditions.

The relative scale adjusts for baseline variability and therefore may lead to findings that can be generalized more widely. It is routinely used for the analysis of binary outcomes but only rarely for continuous outcomes. Our objective was to compare relative vs absolute scale pooled outcomes using data from a recently published Cochrane systematic review that reported only absolute effects of inhaled β2-agonists on exercise-induced decline in forced-expiratory volumes in 1 s (FEV1).
From the Cochrane review, we selected placebo-controlled cross-over studies that reported individual participant data (IPD). Reversal in FEV1 decline after exercise was modeled as a mean uniform percentage point (pp) change (absolute effect) or average percent change (relative effect) using either intercept-only or slope-only, respectively, linear mixed-effect models. We also calculated the pooled relative effect estimates using standard random-effects, inverse-variance-weighting meta-analysis using study-level mean effects.
Fourteen studies with 187 participants were identified for the IPD analysis. On the absolute scale, β2-agonists decreased the exercise-induced FEV1 decline by 28 pp., and on the relative scale, they decreased the FEV1 decline by 90%. The fit of the statistical model was significantly better with the relative 90% estimate compared with the absolute 28 pp. estimate. Furthermore, the median residuals (5.8 vs. 10.8 pp) were substantially smaller in the relative effect model than in the absolute effect model. Using standard study-level meta-analysis of the same 14 studies, β2-agonists reduced exercise-induced FEV1 decline on the relative scale by a similar amount: 83% or 90%, depending on the method of calculating the relative effect.
Compared with the absolute scale, the relative scale captures more effectively the variation in the effects of β2-agonists on exercise-induced FEV1-declines. The absolute scale has been used in the analysis of FEV1 changes and may have led to sub-optimal statistical analysis in some cases. The choice between the absolute and relative scale should be determined based on biological reasoning and empirical testing to identify the scale that leads to lower heterogeneity.

Following up on articles recently published in this journal, the present contribution tells (some of) \"the rest of the story\" about the value of randomization in single-case intervention research investigations. Invoking principles of internal, statistical-conclusion, and external validity, we begin by emphasizing the critical distinction between design randomization and analysis randomization, along with the necessary correspondence between the two. Four different types of single-case design-and-analysis randomization are then discussed. The persistent negative influence of serially dependent single-case outcome observations is highlighted, accompanied by examples of inappropriate applications of parametric and nonparametric tests that have appeared in the literature. We conclude by presenting valid applications of single-case randomization procedures in various single-case intervention contexts, with specific reference to a freely available Excel-based software package that can be accessed to incorporate the present randomization schemes into a wide variety of single-case intervention designs and analyses.

We present an approach to draw multiple and powerful inferences for each data aspect of single-case ABAB phase designs: level, trend, variability, overlap, immediacy, and consistency of data patterns. We show step-by-step how effect size measures can be calculated for each data aspect and subsequently integrated as test statistics in multiple randomization tests. To control for Type I errors, we discuss three methods for adjusting the obtained p-values based on the false discovery rate: the multiple testing correction proposed by Benjamini and Hochberg (1995), the adaptive correction suggested by Benjamini and Hochberg (2000), and the correction taking into account the dependency between the tests (Benjamini & Yekutieli, 2001). We apply this approach to a published data set and compare the results to the conclusions drawn by the authors based on visual analysis. The multiple randomization testing procedure can give more detailed information about which data aspects are affected by the single-case intervention. We provide generic R-code to execute the presented analyses.

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