Remarks on the Zeros of the Associated Legendre Functions with Integral Degree

We present some formulas for the computation of the zeros of the integral-degree associated Legendre functions with respect to the order.Comment: 7 pages, 2 figure

Theories of change and evaluation of Malaysian rural internet centers

This early stage of the research generates three different theories of change for three categories of Rural Internet Centers (RICs) in Malaysia. The cyber café model, the community service center model and the entrepreneurial center models emerged through local community needs. The paper describes findings from the early phase of research that was conducted to evaluate the impact of utilization of connectedness and empowerment through rural internet centres (RIC)

IGF2BP2 alternative variants associated with glutamic acid decarboxylase antibodies negative diabetes in Malaysian subjects

Background: The association of Insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) common variants (rs4402960 and rs1470579) with type 2 diabetes (T2D) has been performed in different populations. The aim of this study was to evaluate the association of alternative variants of IGF2BP2; rs6777038, rs16860234 and rs7651090 with glutamic acid decarboxylase antibodies (GADA) negative diabetes in Malaysian Subjects. Methods/Principal Findings: IGF2BP2; rs6777038, rs16860234 and rs7651090 single nucleotide polymorphisms (SNPs) were genotyped in 1107 GADA negative diabetic patients and 620 control subjects of Asian from Malaysia. The additive genetic model adjusted for age, race, gender and BMI showed that alternative variants; rs6777038, rs16860234 and rs7651090 of IGF2BP2 associated with GADA negative diabetes (OR = 1.21; 1.36; 1.35, P = 0.03; 0.0004; 0.0002, respectively). In addition, the CCG haplotype and diplotype CCG-TCG increased the risk of diabetes (OR = 1.51, P = 0.01; OR = 2.36, P = 0.009, respectively). Conclusions/Significance: IGF2BP2 alternative variants were associated with GADA negative diabetes. The IGF2BP2 haplotypes and diplotypes increased the risk of diabetes in Malaysian subject

Prospects for Genomic Selection in Cassava Breeding

Article purchased; Published online: 28 Sept 2017Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross-validation within populations, cross-population prediction and cross-generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross-validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross-population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within-generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one-third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden.Bill & Melinda Gates FoundationUKaidCGIAR Research Program on Roots, Tubers and BananasPeer Revie

Crystallinity Improvement of Co3O4 by Adding Thiourea

Tricobalt tetraoxide (Co3O4) samples having different thiourea/Co molar ratio of 0, 5 and 10 were prepared by wet chemical synthesis. The effects of thiourea content on the crystal structure-related parameters of Co3O4 were determined. The increase in the amount of thiourea caused a gradual decrease in the lattice parameters and specific surface area and an increase in the crystallinity and crystallite size. The experimental analysis results showed that thiourea content can be used to control the crystal structure-related parameters of Co3O4

Training population optimization for prediction of cassava brown streak disease resistance in west African clones

Published online: 29 Oct 2018; Open Access JournalCassava production in the central, southern and eastern parts of Africa is under threat by cassava brown streak virus (CBSV). Yield losses of up to 100% occur in cases of severe infections of edible roots. Easy illegal movement of planting materials across African countries, and long-range movement of the virus vector (Bemisia tabaci) may facilitate spread of CBSV to West Africa. Thus, effort to pre-emptively breed for CBSD resistance in W. Africa is critical. Genomic selection (GS) has become the main approach for cassava breeding, as costs of genotyping per sample have declined. Using phenotypic and genotypic data (genotyping-by-sequencing), followed by imputation to whole genome sequence (WGS) for 922 clones from National Crops Resources Research Institute, Namulonge, Uganda as a training population (TP), we predicted CBSD symptoms for 35 genotyped W. African clones, evaluated in Uganda. The highest prediction accuracy (r = 0.44) was observed for cassava brown streak disease severity scored at three months (CBSD3s) in the W. African clones using WGS-imputed markers. Optimized TPs gave higher prediction accuracies for CBSD3s and CBSD6s than random TPs of the same size. Inclusion of CBSD QTL chromosome markers as kernels, increased prediction accuracies for CBSD3s and CBSD6s. Similarly, WGS imputation of markers increased prediction accuracies for CBSD3s and for cassava brown streak disease root severity (CBSDRs), but not for CBSD6s. Based on these results we recommend TP optimization, inclusion of CBSD QTL markers in genomic prediction models, and the use of high-density (WGS-imputed) markers for CBSD predictions across population

Synthesis and Structural Characterization of Y-doped Pyramidal ZnO Powders

The present study focuses on the structural changes in ZnO powder induced by doping of a rare earth metal of Y. For this aim, we synthesized four ZnO samples with different Y-content using the combustion reaction method. X-ray powder diffraction (XRPD) technique and scanning electron microscopy (SEM) results confirm that the as-investigated structural parameters and morphology of the ZnO structure were affected directly by the concentration of Y dopant. For each Y-doped sample, randomly-oriented pyramidal morphology and the formation of a minority phase of Y2O3 were observed. A gradual increase in both lattice parameters and unit cell volume was detected with increasing Y content. All samples were found to be thermally stable in the temperature interval of 25-950 °C

A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well

The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings

The forward physics facility at the high-luminosity LHC

High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe standard model (SM) processes and search for physics beyond the standard model (BSM). In this report, we review the status of the civil engineering plans and the experiments to explore the diverse physics signals that can be uniquely probed in the forward region. FPF experiments will be sensitive to a broad range of BSM physics through searches for new particle scattering or decay signatures and deviations from SM expectations in high statistics analyses with TeV neutrinos in this low-background environment. High statistics neutrino detection will also provide valuable data for fundamental topics in perturbative and non-perturbative QCD and in weak interactions. Experiments at the FPF will enable synergies between forward particle production at the LHC and astroparticle physics to be exploited. We report here on these physics topics, on infrastructure, detector, and simulation studies, and on future directions to realize the FPF's physics potential

Repositioning of the global epicentre of non-optimal cholesterol

High blood cholesterol is typically considered a feature of wealthy western countries1,2. However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health4,5. However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol—which is a marker of cardiovascular risk—changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million–4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world.</p