Non-farm employment, natural resource extraction, and poverty: evidence from household data for rural Vietnam

Natural resources are important in sustaining the livelihoods of rural households and the environment. However, over-exploitation is causing an alarming depletion of natural resources in many developing countries. At the same time, rapid economic growth has created non-farm employment opportunities for local people. In this context, examining the interrelationship between non-farm employment and natural resource extraction provides useful information for reducing resource extraction and improving rural households’ welfare. In this study, we use a dataset of 1780 identical households from three survey waves undertaken in 2010, 2013, and 2016 in Vietnam to (i) identify the determinants of rural households’ participation in non-farm activities, (ii) examine the interrelationship between non-farm employment and natural resource extraction, and (iii) investigate the impact of non-farm employment on rural households’ welfare. The findings from pooled sample estimations reveal that (i) cable internet at home and rural road quality positively affect households’ decisions to participate in non-farm employment; (ii) non-farm income and income from natural resource extraction have a negative association; and (iii) non-farm income significantly contributes to poverty reduction in both relative and absolute terms. Our findings suggest that improved provision of non-farm opportunities and increased investment in infrastructure and telecommunication are needed to improve rural households’ welfare and consequently reduce their natural resource exploitation. © 2022, The Author(s)

Prognostic significance of endogenous erythropoietin in long-term outcome of patients with acute decompensated heart failure

Aims Although previous reports suggest that an elevated endogenous erythropoietin (EPO) level is associated with worse clinical outcomes in chronic heart failure (HF) patients, the prognostic implication of EPO in patients with acute decompensated HF (ADHF) and underlying mechanisms of the high EPO level in severe HF patients who have a poor prognosis remain unclear. Methods and results We examined 539 consecutive ADHF patients with EPO measurement on admission from our registry. During a median follow-up period of 329 days, a higher EPO level on admission was independently associated with worse clinical outcomes [hazard ratio (HR) 1.25, 95% confidence interval (CI) 1.06–1.48, P = 0.008], and haemoglobin level was the strongest determinant of EPO level (P < 0.001), whereas estimated glomerular filtration rate (eGFR) was not significant in multivariate regression analysis. In the anaemic subgroup of 318 patients, a higher EPO level than expected on the basis of their haemoglobin level was related to increased adverse events (HR 1.63, 95% CI 1.05–2.49, P = 0.028). Moreover, estimated plasma volume excess rate was positively associated with EPO level (P = 0.003), and anaemic patients with a higher than expected EPO level tended to have a higher estimated plasma volume excess rate and plasma lactate level, and lower systemic oxygen saturation level with the preservation of the reticulocyte production index than those with a lower than expected EPO level. Conclusion A high EPO level predicts long-term worse clinical outcomes in ADHF patients, independent of anaemia and impaired renal function. Anaemia and hypoxia due to severe congestion may synergistically contribute to a high EPO level in high-risk HF patients

Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data

We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ER), RNA poly- merase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ER binding. We use the method to identify a genome-wide confident set of ER target genes and their regulatory enhancers genome- wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ER binding proximity alone.Peer reviewe

Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays

Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcription time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor alpha activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.Peer reviewe

A two-level ILU preconditioner for electromagnetic applications

[EN] Computational electromagnetics based on the solution of the integral form of Maxwell s equations with boundary element methods require the solution of large and dense linear systems. For large-scale problems the solution is obtained by using iterative Krylov-type methods provided that a fast method for performing matrix vector products is available. In addition, for ill-conditioned problems some kind of preconditioning technique must be applied to the linear system in order to accelerate the convergence of the iterative method and improve its performance. For many applications it has been reported that incomplete factorizations often suffer from numerical instability due to the indefiniteness of the coefficient matrix. In this context, approximate inverse preconditioners based on Frobenius-norm minimization have emerged as a robust and highly parallel alternative. In this work we propose a two-level ILU preconditioner for the preconditioned GMRES method. The computation and application of the preconditioner is based on graph partitioning techniques. Numerical experiments are presented for different problems and show that with this technique it is possible to obtain robust ILU preconditioners that perform competitively compared with Frobenius-norm minimization preconditioners.This work was supported by the Spanish Ministerio de Economía y Competitividad under grant MTM2014-58159-P and MTM2015-68805-REDT.Cerdán Soriano, JM.; Marín Mateos-Aparicio, J.; Mas Marí, J. (2017). A two-level ILU preconditioner for electromagnetic applications. Journal of Computational and Applied Mathematics. 309:371-382. https://doi.org/10.1016/j.cam.2016.03.012S37138230

Assessment of chemical and mineralogical characteristics of airborne dust in the Sistan region, Iran

Windblown transport and deposition of dust is widely recognized as an important physical and chemical concern to climate, human health and ecosystems. Sistan is a region located in southeast Iran with extensive wind erosion, severe desertification and intense dust storms, which cause adverse effects in regional air quality and human health. To mitigate the impact of these phenomena, it is vital to ascertain the physical and chemical characteristics of airborne and soil dust. This paper examines for the first time, the mineralogical and chemical properties of dust over Sistan by collecting aerosol samples at two stations established close to a dry-bed lake dust source region, from August 2009 to August 2010. Furthermore, soil samples were collected from topsoil (0–5 cm depth) at several locations in the dry-bed Hamoun lakes and downwind areas. These data were analyzed to investigate the chemical and mineralogical characteristics of dust, relevance of inferred sources and contributions to air pollution. X-ray Diffraction (XRD) analysis of airborne and soil dust samples shows that the dust mineralogy is dominated mainly by quartz (30–40%), calcite (18–23%), muscovite (10–17%), plagioclase (9–12%), chlorite ( 6%) and enstatite ( 3%), with minor components of dolomite, microcline, halite and gypsum. X-ray Fluorescence (XRF) analyses of all the samples indicate that the most important oxide compositions of the airborne and soil dust are SiO2, CaO, Al2O3, Na2O, MgO and Fe2O3, exhibiting similar percentages for both stations and soil samples. Estimates of Enrichment Factors (EFs) for all studied elements show that all of them have very low EF values, suggesting natural origin from local materials. The results suggest that a common dust source region can be inferred, which is the eroded sedimentary environment in the extensive Hamoun dry lakes lying to the north of Sistan.http://www.elsevier.com/locate/chemospherehb201

Screening rules for growth to detect celiac disease: A case-control simulation study

Background: It is generally assumed that most patients with celiac disease (CD) have a slowed growth in terms of length (or height) and weight. However, the effectiveness of slowed growth as a tool for identifying children with CD is unknown. Our aim is to study the diagnostic efficiency of several growth criteria used to detect CD children. Methods: A case-control simulation study was carried out. Longitudinal length and weight measurements from birth to 2.5 years of age were used from three groups of CD patients (n = 134) (one group diagnosed by screening, two groups with clinical manifestations), and a reference group obtained from the Social Medical Survey of Children Attending Child Health Clinics (SMOCC) cohort (n = 2,151) in The Netherlands. The main outcome measures were sensitivity, specificity and positive predictive value (PPV) for each criterion. Results: Body mass index (BMI) performed best for the groups with clinical manifestations. Thirty percent of the CD children with clinical manifestations and two percent of the reference children had a BMI Standard Deviation Score (SDS) less than -1.5 and a decrease in BMI SDS of at least -2.5 (PPV = 0.85%). The growth criteria did not discriminate between the screened CD group and the reference group. Conclusion: For the CD children with clinical manifestations, the most sensitive growth parameter is a decrease in BMI SDS. BMI is a better predictor than weight, and much better than length or height. Toddlers with CD detected by screening grow normally at this stage of the disease