Data_Sheet_1_Nutritional deficiencies in low-sociodemographic-index countries: a population-based study.docx

BackgroundWe aimed to estimate the burden of nutritional deficiency according to sex and age in countries with a low sociodemographic index (SDI).MethodsFollowing the methods of the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, estimated annual percentage changes (EAPCs) were calculated to determine trends in the age-standardized rates of incidence and disability-adjusted life-years (DALYs) of nutritional deficiency and its main subcategories from 1990 to 2019 in low-SDI countries.FindingsFrom 1990 to 2019, the age-standardized incidence and DALY rates of nutritional deficiency showed decreasing trends, with EAPCs of −0.90 [95% confidence interval (CI), 1.06 to −0.75] and −3.20 (95% CI, −3.29 to −3.10), respectively, in low-SDI countries. In 2019, of the subcategories analyzed, vitamin A deficiency had the highest age-standardized incidence rate and protein–energy malnutrition had the highest age-standardized DALY rate. From 1990 to 2019, the greatest decrease in the age-standardized incidence rate was observed for vitamin A deficiency and the greatest decrease in the age-standardized DALY rate was observed for protein–energy malnutrition. At the national level, from 1990 to 2019, the greatest increase in the age-standardized incidence rate of overall nutritional deficiency was observed in males in Afghanistan (EAPC: 0.28; 95% CI, 0.07 to 0.49). Of the age groups analyzed, the highest incidence and DALY rates of overall nutritional deficiency and dietary iron deficiency were observed in children aged 1–4 years.InterpretationThe age-standardized incidence and DALY rates of nutritional deficiency decreased significantly from 1990 to 2019, especially for vitamin A deficiency and protein–energy malnutrition. Overall nutritional deficiency and dietary iron deficiency occurred primarily in children aged 1–4 years.</p

Model-Free Statistical Inference on High-Dimensional Data*

This paper aims to develop an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduction framework. With the aid of new reformulation, we propose a new test statistic and show that its asymptotic distribution is χ2 distribution whose degree of freedom does not depend on the unknown population distribution. We further conduct power analysis under local alternative hypotheses. In addition, we study how to control the false discovery rate of the proposed χ2 tests, which are correlated, to identify important predictors under a model-free framework. To this end, we propose a multiple testing procedure and establish its theoretical guarantees. Monte Carlo simulation studies are conducted to assess the performance of the proposed tests and an empirical analysis of a real-world data set is used to illustrate the proposed methodology.</p

Two-Fold Anisotropy Governs Morphological Evolution and Stress Generation in Sodiated Black Phosphorus for Sodium Ion Batteries

Phosphorus represents a promising anode material for sodium ion batteries owing to its extremely high theoretical capacity. Recent in situ transmission electron microscopy studies evidenced anisotropic swelling in sodiated black phosphorus, which may find an origin from the two intrinsic anisotropic properties inherent to the layered structure of black phosphorus: sodium diffusional directionality and insertion strain anisotropy. To understand the morphological evolution and stress generation in sodiated black phosphorus, we develop a chemo-mechanical model by incorporating the intrinsic anisotropic properties into the large elasto-plastic deformation. Our modeling results reveal that the apparent morphological evolution in sodiated black phosphorus is critically controlled by the coupled effect of the two intrinsic anisotropic properties. In particular, sodium diffusional directionality generates sharp interphases along the [010] and [001] directions, which constrain anisotropic development of the insertion strain. The coupled effect renders distinctive stress-generation and fracture mechanisms when sodiation starts from different crystal facets. In addition to providing a powerful modeling framework for sodiation and lithiation of layered structures, our findings shed significant light on the sodiation-induced chemo-mechanical degradation of black phosphorus as a promising anode for the next-generation sodium ion batteries

Type I errors (<i>δ</i> = 0) and power (<i>δ</i> ≠ 0) given by the investigated test statistics based on permutation approach in detecting location shift between two samples generated from the four pairs of <i>F</i>(<i>x</i>) and <i>G</i>(<i>x</i>) with the non-independent variance-covariance matrix and sample sizes <i>n</i> = <i>m</i> = 20.

<p>Type I errors (<i>δ</i> = 0) and power (<i>δ</i> ≠ 0) given by the investigated test statistics based on permutation approach in detecting location shift between two samples generated from the four pairs of <i>F</i>(<i>x</i>) and <i>G</i>(<i>x</i>) with the non-independent variance-covariance matrix and sample sizes <i>n</i> = <i>m</i> = 20.</p

Boxplots of mental health for treatment and control groups at baseline and 6-month visits.

<p>Boxplots of mental health for treatment and control groups at baseline and 6-month visits.</p

Type I errors (<i>δ</i> = 0) and power (<i>δ</i> ≠ 0) given by the investigated test statistics based on permutation approach in detecting location shift between two samples generated from the four pairs of <i>F</i>(<i>x</i>) and <i>G</i>(<i>x</i>) with variance-covariance matrix <i>I</i>_4×4 and sample sizes <i>n</i> = <i>m</i> = 50.

<p>Type I errors (<i>δ</i> = 0) and power (<i>δ</i> ≠ 0) given by the investigated test statistics based on permutation approach in detecting location shift between two samples generated from the four pairs of <i>F</i>(<i>x</i>) and <i>G</i>(<i>x</i>) with variance-covariance matrix <i>I</i>_4×4 and sample sizes <i>n</i> = <i>m</i> = 50.</p

Values of test statistics and corresponding <i>p</i>-values for comparison of intervention and control groups at baseline and 6-month visits and for comparison of baseline (upper panel) and 6-month visits within each group (lower panel).

<p>Values of test statistics and corresponding <i>p</i>-values for comparison of intervention and control groups at baseline and 6-month visits and for comparison of baseline (upper panel) and 6-month visits within each group (lower panel).</p

Type I errors (<i>δ</i> = 0) and power (<i>δ</i> ≠ 0) given by the investigated test statistics based on permutation approach in detecting location shift between two samples generated from the four pairs of <i>F</i>(<i>x</i>) and <i>G</i>(<i>x</i>) with variance-covariance matrix <i>I</i>_4×4 and sample sizes <i>n</i> = <i>m</i> = 20.

<p>Type I errors (<i>δ</i> = 0) and power (<i>δ</i> ≠ 0) given by the investigated test statistics based on permutation approach in detecting location shift between two samples generated from the four pairs of <i>F</i>(<i>x</i>) and <i>G</i>(<i>x</i>) with variance-covariance matrix <i>I</i>_4×4 and sample sizes <i>n</i> = <i>m</i> = 20.</p

Type I errors (<i>δ</i> = 0) and power (<i>δ</i> ≠ 0) given by the investigated test statistics based on permutation approach in detecting location shift between two samples generated from the four pairs of <i>F</i>(<i>x</i>) and <i>G</i>(<i>x</i>) with the non-independent variance-covariance matrix and sample sizes <i>n</i> = <i>m</i> = 50.

<p>Type I errors (<i>δ</i> = 0) and power (<i>δ</i> ≠ 0) given by the investigated test statistics based on permutation approach in detecting location shift between two samples generated from the four pairs of <i>F</i>(<i>x</i>) and <i>G</i>(<i>x</i>) with the non-independent variance-covariance matrix and sample sizes <i>n</i> = <i>m</i> = 50.</p

Boxplots of visual load for treatment and control groups at baseline and 6-month visits.

<p>Boxplots of visual load for treatment and control groups at baseline and 6-month visits.</p