Diurnal evolution of urban tree temperature at a city scale

Despite the importance of urban trees’ surface temperature in assessing micro-climate interactions between trees and the surrounding environment, their diurnal evolution has been largely understudied at a city-wide scale due to a lack of effective thermal observations. By downscaling ECOSTRESS land surface temperature imaginary over New York City, we provide the first diurnal analysis of city-scale canopy temperature. Research reveals a remarkable spatial variation of the canopy temperature during daytime up to 5.6 K (standard deviation, STD), while the nighttime STD remains low at 1.7 K. Further, our analysis shows that the greenspace coverage and distance to bluespaces play an important role in cooling the local canopy during daytime, explaining 25.0–41.1% of daytime spatial variation of canopy temperatures while surrounding buildings modulate canopy temperature asymmetrically diurnally: reduced daytime warming and reduced nocturnal cooling. Built on space-borne observations and a flexible yet robust statistical method, our research design can be easily transferable to explore urban trees’ response to local climate across cities, highlighting the potentials of advancing the science and technologies for urban forest management.

Magnitude Estimation of Overpressure Generation Mechanisms Using Quantitative Stochastic 2D Basin Models: A Case Study from the Danube-Tisza Interfluve Area in Hungary

The overpressure formation in the Pannonian basin, Hungary, was investigated but has not been properly understood for the last 40 years because at least two different explanations were delineated. The first explanation considers the hydrocarbon generation as the main overpressure generation mechanism with some undercompaction contribution. On the contrary, another explanation assumes tectonic stress as the main trigger of abnormal pressure. The following research delivers a suitable workflow to understand which generation mechanisms were active in the study area and estimate the quantitative contribution of the mechanisms. The developed workflow relies on the basin modeling principles that were designed to simulate subsurface processes on a geological timeframe. Moreover, the uncertainty of input parameters was considered, and the joint application of a heuristic Monte Carlo simulation scheme and improved basin modeling resulted in stochastic pore pressure models. The most frequent value (MFV) method was applied on the simulated values to test a robust statistical method in pore pressure prediction. The study has identified not only the four main overpressure generation mechanisms, but it could calculate the individual contribution to the subsurface pressure. Finally, two independent and stochastic pore pressure prediction methods have been developed that could be used in the pre-drill well planning phase and the real-time prediction during drilling.

Georeferencing of Multi-Sheet Maps Based on Least Squares with Constraints—First Military Mapping Survey Maps in the Area of Czechia

The article deals with the possibility of georeferencing old multi-sheet map works. Various approaches to problem solving and a workable method for using the least squares method with the conditions of the adjacency of map sheets are discussed. To increase reliability, the IRLS robust statistical method is used, which uses iterative weighting of individual measurements based on Huber’s M-estimate. The method is applied to the First Military Mapping of the Habsburg monarchy as a typical representative of old topographic maps, which are not easy to georeference due to unknown parameters of the used cartographic projection. A georeferenced layer of the above mentioned mapping is available on the Mapire.eu portal as well. A basic analysis of the comparison of georeferencing results using our method and the mentioned portal is performed.

An uncertainty estimate of the prevalence of stunting in national surveys: the need for better precision

Background Stunting is determined by using the World Health Organization (WHO) child growth standard which was developed using precise measurements. However, it is unlikely that large scale surveys maintain the same level of rigour and precision when measuring the height of children. The population measure of stunting in children is sensitive to over-dispersion, and the high prevalence of stunting observed in surveys in low and middle-income countries (LMIC) could partly be due to lower measurement precison. Objectives To quantify the incongruence in the dispersion of height-for-age in national surveys of < 5 y children, in relation to the standard WHO Multicenter Growth Reference Study (MGRS), and propose a measure of uncertainty in population measures of stunting. Methods An uncertainty factor was proposed and measured from the observed incongruence in dispersion of the height-for-age of < 5 y children in the MGRS against carefully matched populations from the Demographic Health Survey of 17 countries (‘test datasets’, based on the availability of data). This also allowed for the determination of uncertainty-corrected prevalence of stunting (height-for-age Z score < − 2) in < 5 y children. Results The uncertainty factor was estimated for 17 LMICs. This ranged from 0.9 to 2.1 for Peru and Egypt respectively (reference value 1). As an explicit country example, the dispersion of height-for-age in the Indian National Family Health Survey-4 test dataset was 39% higher than the MGRS study, with an uncertainty factor of 1.39. From this, the uncertainty-adjusted Indian national stunting prevalence estimate reduced to 18.7% from the unadjusted estimate of 36.2%. Conclusions This study proposes a robust statistical method to estimate uncertainty in stunting prevalence estimates due to incongruent dispersions of height measured in national surveys for children < 5 years in relation to the WHO height-for-age standard. The uncertainty is partly due to population heterogeneity, but also due to measurement precision, and calls for better quality in these measurements.

A Robust Statistical method to Estimate the Intervention Effect with Longitudinal Data

Segmented regression is a standard statistical procedure used to estimate the effect of a policy intervention on time series outcomes. This statistical method assumes the normality of the outcome variable, a large sample size, no autocorrelation in the observations, and a linear trend over time. Also, segmented regression is very sensitive to outliers. In a small sample study, if the outcome variable does not follow a Gaussian distribution, then using segmented regression to estimate the intervention effect leads to incorrect inferences. To address the small sample problem and non-normality in the outcome variable, including outliers, we describe and develop a robust statistical method to estimate the policy intervention effect in a series of longitudinal data. A simulation study is conducted to demonstrate the effect of outliers and non-normality in the outcomes by calculating the power of the test statistics with the segmented regression and the proposed robust statistical methods. Moreover, since finding the sampling distribution of the proposed robust statistic is analytically difficult, we use a nonparametric bootstrap technique to study the properties of the sampling distribution and make statistical inferences. Simulation studies show that the proposed method has more power than the standard t-test used in segmented regression analysis under the non-normality error distribution. Finally, we use the developed technique to estimate the intervention effect of the Istanbul Declaration on illegal organ activities. The robust method detected more significant effects compared to the standard method and provided shorter confidence intervals.

PERIPHERAL BLOOD AS TOOL TO DETERMINE GENE EXPRESSION PATTERNS IN PATIENTS WITH PSYCHIATRIC, NEUROLOGICAL AND OTHER COMMON DISORDERS: A SYSTEMATIC REVIEW AND META-ANALYSIS PROTOCOL

ABSTRACTIntroductionCommon diseases are influenced by a variety of factors that can enhance one person’s susceptibility to developing a specific condition. Complex traits have been investigated in several biological levels. One that reflects the high interconnectivity and interaction of genes, proteins and transcription factors is the transcriptome. In this study, we disclose the protocol for a systematic review and meta-analysis aiming at summarizing the available evidence regarding transcriptomic gene expression levels of peripheral blood samples comparing subjects with psychiatric, neurological and other common disorders to healthy controls.Methods and analysisThe investigation of the transcriptomic levels in the peripheral blood enables the unique opportunity to unravel the etiology of common diseases in patients ex-vivo. However, the experimental results should be minimally consistent across studies for them to be considered as the best approximation of the true effect. In order to test this, we will systematically identify all transcriptome studies that compared subjects with common disorders to their respective control samples. We will apply meta-analyses to assess the overall differentially expressed genes throughout the studies of each condition.Ethics and disseminationThe data that will be used to conduct this study are available online and have already been published following their own ethical laws. Therefore this study requires no further ethical approval. The results of this study will be published in leading peer-reviewed journals of the area and also presented at relevant national and international conferences.Strengths and limitations of this study➣We present a new and systematically centered method to assess the overall effect of transcriptomic levels in the blood of subjects with common conditions.➣Meta-analyses are a robust statistical method to assess effect sizes across studies.➣The analysis is limited by the availability of studies, as well as their quality and comprehensiveness.➣Subgroup and meta-regression analyses will be also limited by the amount and quality of sample characterization variables made available by original studies.