Related Covariates Research Articles

Association between Green Space Structure and the Prevalence of Asthma: A Case Study of Toronto.

Asthma is a chronic inflammatory disease that can be caused by various factors, such as asthma-related genes, lifestyle, and air pollution, and it can result in adverse impacts on asthmatics’ mental health and quality of life. Hence, asthma issues have been widely studied, mainly from demographic, socioeconomic, and genetic perspectives. Although it is becoming increasingly clear that asthma is likely influenced by green spaces, the underlying mechanisms are still unclear and inconsistent. Moreover, green space influences the prevalence of asthma concurrently in multiple ways, but most existing studies have explored only one pathway or a partial pathway, rather than the multi-pathways. Compared to greenness (measured by Normalized Difference Vegetation Index, tree density, etc.), green space structure—which has the potential to impact the concentration of air pollution and microbial diversity—is still less investigated in studies on the influence of green space on asthma. Given this research gap, this research took Toronto, Canada, as a case study to explore the two pathways between green space structure and the prevalence of asthma based on controlling the related covariates. Using regression analysis, it was found that green space structure can protect those aged 0–19 years from a high risk of developing asthma, and this direct protective effect can be enhanced by high tree diversity. For adults, green space structure does not influence the prevalence of asthma unless moderated by tree diversity (a measurement of the richness and diversity of trees). However, this impact was not found in adult females. Moreover, the hypothesis that green space structure influences the prevalence of asthma by reducing air pollution was not confirmed in this study, which can be attributed to a variety of causes.

Surgical outcomes for early-stage non-small cell lung cancer at facilities with stereotactic body radiation therapy programs.

8538 Background: Patients undergoing surgery for early-stage non-small cell lung cancer (NSCLC) may be at high-risk for post-operative mortality. Access to stereotactic body radiation therapy (SBRT) offers a less invasive alternative for this population that may facilitate more appropriate patient selection for surgery. Methods: An analysis of all patients with early-stage NSCLC reported to the National Cancer Database between 2004-2015 was performed. Post-operative mortality rates were derived using vital status data. Utilization of SBRT was defined by each facility’s SBRT Experience in years and SBRT-to-Surgery volume ratios, defined by quartiles. Multivariable logistic regression with backward elimination was used to test for independence of associations between exposures of interest and post-operative mortality. Interaction testing was performed to assess the statistical relationship of covariates found to have independent associations. Results: The study cohort consisted of 202,542 patients who underwent surgical resection of clinical stage T1-T2 NSCLC (AJCC 7th edition). The 90-day post-operative mortality rate declined significantly during the study period from 4.6% to 2.6% (p < 0.001). During this period, the proportion of facilities that utilized SBRT increased from 3.3% to 77.5% (p < 0.001) and the proportion of patients treated with SBRT increased significantly from 0.7% to 15.4% (p < 0.001). Lower 90-day post-operative mortality rates were observed at facilities with greater than six years of SBRT experience (OR 0.84, CI 0.76-0.94, p = 0.003) and SBRT-to-Surgery volume ratios above 17% (OR 0.85, CI 0.79-0.92, p < 0.001). Additional covariates associated with 90-day mortality included higher surgical volume, geographic region, year of diagnosis, age, sex, race, insurance status, facility type, Charlson-Deyo score, clinical T stage, histology, anatomic location, surgery type, and prior malignancy. Interaction testing between these covariates was negative, demonstrating that higher SBRT Experience and SBRT-to-Surgery volume ratios were independently associated with lower 90-day surgical mortality. Conclusions: Patients who underwent surgery for early-stage NSCLC at facilities with higher SBRT Experience and SBRT-to-Surgery volume ratios had lower rates of post-operative mortality. These findings suggest that the availability of SBRT may be a surrogate for a more comprehensive and safer approach to matching patients to surgery or SBRT. The observation of higher post-operative mortality rates at facilities without an SBRT program deserves further study.

Climate Field Completion via Markov Random Fields: Application to the HadCRUT4.6 Temperature Dataset

AbstractSurface temperature is a vital metric of Earth’s climate state but is incompletely observed in both space and time: over half of monthly values are missing from the widely used HadCRUT4.6 global surface temperature dataset. Here we apply the graphical expectation–maximization algorithm (GraphEM), a recently developed imputation method, to construct a spatially complete estimate of HadCRUT4.6 temperatures. GraphEM leverages Gaussian Markov random fields (also known as Gaussian graphical models) to better estimate covariance relationships within a climate field, detecting anisotropic features such as land–ocean contrasts, orography, ocean currents, and wave-propagation pathways. This detection leads to improved estimates of missing values compared to methods (such as kriging) that assume isotropic covariance relationships, as we show with real and synthetic data. This interpolated analysis of HadCRUT4.6 data is available as a 100-member ensemble, propagating information about sampling variability available from the original HadCRUT4.6 dataset. A comparison of Niño-3.4 and global mean monthly temperature series with published datasets reveals similarities and differences due in part to the spatial interpolation method. Notably, the GraphEM-completed HadCRUT4.6 global temperature displays a stronger early twenty-first-century warming trend than its uninterpolated counterpart, consistent with recent analyses using other datasets. Known events like the 1877/78 El Niño are recovered with greater fidelity than with kriging, and result in different assessments of changes in ENSO variability through time. Gaussian Markov random fields provide a more geophysically motivated way to impute missing values in climate fields, and the associated graph provides a powerful tool to analyze the structure of teleconnection patterns. We close with a discussion of wider applications of Markov random fields in climate science.

The association between sleep duration and risk of mortality in Chinese older adults: a national cohort study.

Cohort studies about the sleep duration on the risk of death among Chinese older adults are still lacking. The aim of this study was to examine whether extremely long or short sleep duration was associated with mortality in Chinese adults aged 65 years or older. We included participants aged 65 years or older in 2011 at baseline in 23 provinces from the Chinese Longitudinal Healthy Longevity Survey who were followed up in 2014/2018 in China. Sleep duration was categorized as short sleep duration (< 7 hours) and long sleep duration (> 8 hours). We used the Cox proportional hazards model and restricted cubic spline analysis to explore the association between sleep duration and mortality. Among 9578 participants, short sleep duration was associated with an 11% higher risk of death (adjusted hazard ratio [aHR]: 1.11; 95% confidence interval [CI]: 1.02-1.20) and long sleep duration was associated with a 24% higher risk of death (aHR: 1.24; 95% CI: 1.15-1.34), after adjustment for all covariates. There was a U-shaped association between sleep duration and all-cause mortality (nonlinear, P < .0001). Stratified analyses showed that the risk was higher among older people who smoked and with a higher level of education both for short and long sleepers than for those who never smoked and were illiterate (P value for interaction < .05). There was a U-shaped association between sleep duration and all-cause mortality in Chinese older adults, especially in more educated individuals and smokers. Du M, Liu M, Liu J. The association between sleep duration and the risk of mortality in the Chinese older adults: a national cohort study. J Clin Sleep Med. 2021;17(9):1821-1829.

Comparing biological methods for soil health assessments: EL‐FAME, enzyme activities, and qPCR

AbstractSoil health initiatives have categorized assays for enzyme activities (EAs) that measure p‐nitrophenol and ester‐linked fatty acid methyl ester (EL‐FAME) as Tier 2 indicators for biological activity and community structure analysis, respectively. Quantitative polymerase chain reaction (qPCR) assays of functional and taxonomic communities are emerging Tier 3 indicators. To facilitate comparisons of soil biological health between research groups that may employ different methods, we applied these current and emerging indicators to semiarid soils from the Texas High Plains sampled in the growing season and postharvest from 2014 through 2018. Microbial groups via EL‐FAME markers and EAs were strongly correlated (r &gt; .79) with qPCR assays of equivalent taxonomic and functional genes. To further quantify the predictive power of these relationships, we modeled several genes as a function of EA or EL‐FAME markers, combined with other related covariates (e.g., soil texture, pH, irrigation, and soil organic C [SOC]) using a generalized linear model. The latter was trained using data from 2014, which was an average year in terms of temperature and precipitation for the region. Subsequently, the model was tested making predictions for 2015–2018, which represented high variability in climatic conditions, ensuring a thorough assessment of its predictive power. In most cases, soil texture, SOC, and Tier 2 indicators were identified as moderate to strong predictors of the biological responses. Our results suggest that the different approaches for assessing either function or community in these semiarid soils were highly comparable and provided similar information on how microbial communities were responding to both management and climate.

Breast cancer screening patterns in Jamaican women: review of the largest national mammography clinic

BackgroundBreast cancer is the leading cause of cancer and cancer related deaths in Jamaican women. In Jamaica, women often present with advanced stages of breast cancer, despite the availability of screening mammography for early detection. The utilization of screening mammography for early breast cancer diagnosis seems to be limited, and this study investigated the national patterns of mammographic screening and the impact of mammography on the diagnosis of breast cancer in Jamaica.MethodsA retrospective analysis of the records of the largest mammography clinic in Jamaica was done for the period January 2011 to December 2016. Descriptive statistics was performed on relevant patient characteristics with calculation of rates and proportions; cross-tabulations were utilized to assess relationship of covariates being studied on the outcomes of interest. Results are reported in aggregate form with no identifiable patient data.Results48,203 mammograms were performed during the study period. 574 women (1.2%) had mammograms suspicious for breast cancer with median age of 57 years (range 30–95 years); 35% were under the age of 50. 4 women with suspicious findings had undergone ‘screening mammography’, with the remaining having ‘diagnostic mammography’. 38% reported previous mammograms, with a mean interval of 8 years between previous normal mammogram and mammogram suspicious for breast cancer. Median age at first screening mammogram was 51 years (range 41–77).ConclusionBreast cancer screening mammography is underutilized in Jamaica. An organized national breast cancer screening programme is recommended to improve adherence to international breast cancer screening guidelines.

Cage of covariance in calibration modeling: Regressing multiple and strongly correlated response variables onto a low rank subspace of explanatory variables

In analytical chemistry, multivariate calibration is applied when substituting a time-consuming reference measurement (based on e.g. chromatography) with a high-throughput measurement (based on e.g. vibrational spectroscopy). An average error term, of the response variable, is often used to evaluate the performance of a calibration model. However, indirect relationships, between the response and explanatory variables, may be used for calibration. In such cases, model validity cannot necessarily be determined solely by the average error term. One should also consider the use of the models, as well as the validity of the indirect relationships in future samples. If the analyte of interest is partly quantified from signals of interfering compounds, then these interfering compounds will play a hidden role in the calibration. This hidden role may affect future use of the calibration model as strong covariance relationships between analyte estimates and interfering compounds may be imposed. Hence, such model cannot detect changes in the relationship between the analyte and interfering compounds. The problem is called the cage of covariance. This paper discusses the concept cage of covariance and possible consequences of applying models exposed to this issue.

Enhancing the accuracy of machine learning models using the super learner technique in digital soil mapping

Digital soil mapping approaches predict soil properties based on the relationships between soil observations and related environmental covariates using techniques such as machine learning (ML) models. In this research, a wide range of ML models (12 base learners) were tested in predicting and mapping soil properties. Furthermore, a super learner approach was used to improve model accuracy by combining the predictions of the base learners. A major challenge of using super learner and complex models is that the exact contribution of individual covariates in the overall prediction is not always known. To address this issue, permutation feature importance (PFI) analysis was applied as a model-agnostic interpretation tool. The weights assigned to each ML base learner obtained from super learner, and feature importance values obtained from each ML base learner were used to quantify the contribution of individual covariates on the final prediction. The super learner and PFI techniques were tested by predicting a variety of soil physical and chemical properties of the Urmia Lake playa in Iran. As expected, the results indicated that the super learner had substantially higher accuracies for predicting soil properties in comparison to the individual base learners. For instance, the super learner showed an improved performance in comparison to linear regression by decreasing the root mean square error by an average of 46%. The PFI analysis revealed the important contribution of geomorphic and groundwater data in predicting soil properties. Overall, the proposed approach may be used for improving accuracy of ML models in digital soil mapping.

Emplacement ages and petrogenesis of intermediate‐acid intrusive rocks in the Lajishan region of the Central Qilian Belt, China: Constraints from zircon U–Pb ages, Hf isotopes, and whole‐rock geochemistry

As a major tectonic unit along the southwestern rim of the Central Qilian Belt, the Lajishan region plays an important role in the amalgamation of continental plates in the Qilian Orogenic Belt. In this study, zircon laser ablation inductively coupled plasma mass spectrometry for U–Pb dating and Hf isotope analysis, as well as geochemical analysis of intermediate‐acid intrusive rocks from the Lajishan area were conducted. Results show that formation ages of the intermediate‐acid intrusive rocks are 438–452 Ma. Whole‐rock geochemistry results show that the diorite and monzodiorite are metaluminous, low‐ to medium‐K series, and characterized by obvious Nb, Ta, and Ti depletions as well as large‐ion lithophile element and light rare earth element enrichments. The εHf(t) values of zircons from monodiorite range from +2.71 to +10.59 and the two‐stage model ages range from 751 to 1,253 Ma. A large transversal span of εHf(t) values suggests that the source of depleted mantle was mixed with a small amount of older crustal material. The diorite and monzodiorite show a covariant relationship between εHf(t) values and Th/U ratios, which imply the existence of source heterogeneity. The diorites and monzodiorites were most likely produced in an arc setting. The granites are low K in composition and metaluminous to weakly peraluminous. In view of obvious Nb, Ta, and Ti depletions as well as large‐ion lithophile element and light rare earth element enrichments, parent magmas of the granites are suggested to have formed with the participation of crustal source materials. The εHf(t) values of zircons from granite range from +5.82 to +8.90, and the two‐stage model ages range from 861 to 1,057 Ma, indicating that magmas of the granites were mainly derived from partial melting of depleted mantle or juvenile thickened lower crust. The granites were most likely produced in an arc setting and share a similar source with the diorite and monzodiorite. Correlation between major oxides and SiO2 show that during the formation of the three kinds of rocks, crystallization and differentiation both played important roles. Combined with published data from the Central Qilian Belt and its vicinity, our results conclude that the northward subduction of oceanic crust contributed to the formation of an Early Palaeozoic subduction–accretion system in the Lajishan region. This model reveals a tectonic–magmatic thermal event and provides important insights into the tectonic evolution process in the Qilian region, as well as the Central Orogenic Belt during the Early Palaeozoic.

Conditional distribution modeling as an alternative method for covariates simulation: Comparison with joint multivariate normal and bootstrap techniques.

Clinical trial simulation (CTS) is a valuable tool in drug development. To obtain realistic scenarios, the subjects included in the CTS must be representative of the target population. Common ways of generating virtual subjects are based upon bootstrap (BS) procedures or multivariate normal distributions (MVNDs). Here, we investigated the performance of an alternative method based on conditional distributions (CDs). Covariate data from a hypertension drug development program were used. The methods were evaluated based on the original data set (internal evaluation) and on their ability to reproduce an older, unobserved population (extrapolation). Similar results were obtained in the internal evaluation for summary statistics, yet BS was able to preserve the correlation structure of the empirical distribution, which was not adequately reproduced by MVND; CD was in between BS and MVND. BS does not allow to extrapolate to an unobserved population. When the data set used to inform the extrapolation was well approximated by an MVND, the results from CD and MVND were comparable. However, improved extrapolation performance was observed for CD when deviations from normality assumptions occurred. If CTS is used to simulate within the observed distribution, BS is the preferred method. When extrapolating to new populations, a parametric method like CD/MVND is needed. In case the empirical multivariate distribution is characterized by linearly related covariates and unimodal marginal distributions, MVND can be used because of the simpler statistical framework and well‐established use; however, if uncertainty about the MVND assumptions exists, CD will increase the confidence in the simulations compared to MVND.

An analysis of COVID-19 clusters in India

BackgroundIn this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread.MethodsA cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments.ResultsThe clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control.ConclusionsThe cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

Identifying Risk Factors and Spatial Clustering of HIV Infection Among Female Sex Workers in India

Background: The human immunodeficiency virus (HIV) epidemic in India is generally considered to be more concentrated, with the focus on high-risk groups including female sex workers (FSWs). The Integrated Biological and Behavioral Surveillance (IBBS), the first nationwide surveillance conducted during 2014-2015, collected many key indicators, including indicators related to HIV/STI transmission. The purpose of this study was to develop an index score for each domain surveyed and to identify focus areas for interventions among FSWs. Methods: The study population consisted of 27,007 FSWs. Forty high-risk related covariates of HIV/STI transmission, demographic characteristics, sexual history, condom practices, knowledge of HIV/STI and biological variables were considered. The original data set was examined using the correlation matrix and was reduced to 15 highly-correlated factors using principal component analysis. The factors were further improved using varimax rotation and the percentage of variation was used as weights to obtain the initial score for each domain, which were then standardized for comparison. Bartlett’s test of sphericity was examined before the factor extraction. Results: Six factors were extracted, which together explained about 73% of the total variation. The factors were: (1) more number of clients; (2) younger FSW and started selling sex at younger age; (3) experiencing condom breakage; (4) having occasional clients and poor HIV/AIDS knowledge; (5) illiteracy; and (6) a longer period of sex work. Six domains with an index score of above 80, from the states of Maharashtra, Rajasthan, Arunachal Pradesh, Uttar Pradesh, and Jharkhand need greater intervention. Conclusion and Implications for Translation: FSWs’ current age, age at commencement of sex work, and the number of clients were the indicators most-associated with HIV infection. Therefore, program and policy interventions should focus on FSWs who are younger than &lt;25 years, who started selling sex at &lt;22 years, and who have &gt;10 clients. Key words: • Female Sex Worker • Kriged Map • Factor Analysis • Principle Component Analysis • HIV • Sexually Transmitted Infections Copyright © 2021 Elangovan et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in this journal, is properly cited.

Association between obstructive sleep apnea and health-related quality of life in untreated adults: a systematic review.

The objective of this systematic review was to answer the question: "Is there association between obstructive sleep apnea (OSA) and health-related quality of life (HRQoL) in untreated adults?" We included observational studies that evaluated the health-related quality of life of patients with OSA vs control groups, through generic and disease-specific questionnaires. The searches were conducted in six databases: Embase, Lilacs, PsycINFO, PubMed, Scopus, and Web of Science. Additional search in the grey literature and hand search were performed, and also experts were consulted. Risk of bias was performed by using Joanna Briggs Institute Critical Appraisal Checklist for cross-sectional, cohort, and case-control studies. We analyzed the data using a narrative synthesis. The Grading of Recommendations Assessment, Development, and Evaluation evidence profile was used to verify the overall certainty of the assessed evidence. Nineteen studies were included for qualitative analysis. Generic questionnaires showed worse HRQoL in the OSA group compared to the control group in at least one domain of the HRQoL questionnaires. The affected domains that showed statistical and clinically relevant differences were physical functioning, physical role, pain, general health, vitality, emotional role, and mental health. The certainty of evidence assessment was very low. The available literature suggests that OSA in untreated adults is associated with worse HRQoL. However, this association seems to disappear when we consider only studies adjusted for related covariates. CRD42018114746.

Asymptotic distribution of sample Shannon entropy in the case of an underlying finite, regular Markov chain.

The inference of Shannon entropy out of sample histograms is known to be affected by systematic and random errors that depend on the finite size of the available data set. This dependence was mostly investigated in the multinomial case, in which states are visited in an independent fashion. In this paper the asymptotic behavior of the distribution of the sample Shannon entropy, also referred to as plug-in estimator, is investigated in the case of an underlying finite Markov process characterized by a regular stochastic matrix. As the size of the data set tends to infinity, the plug-in estimator is shown to become asymptotically normal, though in a way that substantially deviates from the known multinomial case. The asymptotic behavior of bias and variance of the plug-in estimator are expressed in terms of the spectrum of the stochastic matrix and of the related covariance matrix. Effects of initial conditions are also considered. By virtue of the formal similarity with Shannon entropy, the results are directly applicable to the evaluation of permutation entropy.

Immigrant-Native Disparities in Happiness among Middle-Aged and Older Adults in Western European Countries: The Moderating Role of Social Capital.

Objective: This study investigated native-immigrant disparities in happiness among middle-aged and older adults in Denmark, Germany, and the Netherlands and the moderating role of social capital associated with such disparities. Methods: Cross-sectional data from 2583 respondents aged 50 years or older from 10 origin countries from the 2015 wave of the Migrants' Welfare State Attitudes survey were used to estimate linear regression models. Results: Older immigrants experienced different levels of happiness compared with native-born individuals. Immigrants' socioeconomic status and other standard predictors accounted for much of the native-immigrant gap. Depending on origin countries, social capital had differential moderating roles compared to nonimmigrants. Conclusion: Immigrants were not always worse off than native-born residents; they showed a "happiness advantage" after controlling for socioeconomic status and related covariates. Social capital plays an important role in narrowing the immigrant-native gap in happiness.

A feature reconstruction-based multi-task regression model for cyanobacterial distribution forecasting along the water column

Cyanobacterial water pollution has been threatening the cleaner ecosystem and urban sustainability due to the harmfulness to aquatic ecosystems and human health, which triggers the development of an effective forecasting tool for cyanobacterial blooms. Along the water column, the variations in cyanobacteria cell densities show various distribution patterns and are influenced by multiple environmental factors. Most data-driven models treat cyanobacteria forecasting at a specific water depth as a single task, which fails to share knowledge amongst water depths, resulting in unfavourable forecasting accuracy. This is why an increasing number of nonlinear black-box models have been built for cyanobacteria forecasting but at the expense of model interpretability. This study aims to investigate whether forecasting accuracy and model interpretability can be enhanced by (i) using easily accessible predictors and (ii) developing a feature reconstruction-based multi-task regression model with knowledge sharing amongst water depths. Real-world data from a tropical lake are used to evaluate the effectiveness of the model. For the studied lake, the highest average cyanobacteria cell density occurs at 1.0 m, after which it decreases by over 30% at 5.5 m. The correlation coefficients of time-serial cyanobacteria cell densities between adjacent water depths are greater than 0.95 (P < 0.001). The forecasting results indicate that, compared to single-task nonlinear models, 20.59%, 16.25%, and 22.70% error reductions, measured by the mean square error, are achieved for one-day-ahead, two-day-ahead, and three-day-ahead cyanobacterial bloom forecasts. The accurate bloom and non-bloom signals under the proposed model are up to 94.81% and 98.28%. Based on the proposed model, the relative importance of predictors, the sparsity of regression coefficients, and the covariance relationship of regression coefficients can interpret the model adequately and elucidate the mechanism of knowledge sharing and forecasting accuracy improvement.

Fecal transplantation alleviates acute liver injury in mice through regulating Treg/Th17 cytokines balance

Changes in intestinal microecology during acute liver failure (ALF) directly affect the occurrence and development of the disease. The study aimed to investigate the relationship between the intestinal microbiota and the key immune cells. Fecal microbiota transplantation (FMT) was used to determine whether ALF can balance Th17/Treg cytokines. The relationship between gut microbiota and clinical indicators was analyzed. BALB/c mice were treated with d-galactosamine (d-GalN) to induce a murine ALF model. FMT to d-GalN mice was conducted to test for liver function indicators. Results showed that the proportions of Lachnospiraceae, Prevotella, S24-7, Odoribacter and Rikenellaceae in d-GalN mice with intestinal microbiota disorder were restored after FMT. Further, CIA analysis showed that bacteria had a covariant relationship with clinical indicators. Microbiota could account for changes in 49.9% of the overall clinical indicators. Adonis analysis showed that Ruminococcus, and Enterococcus have a greater impact on clinical indicators. FMT down-regulated the expression of IL-17A, TNF-α, and TGF-β, while up-regulated IL-10 and IL-22. Transplantation of feces from Saccharomyces boulardii donor mice improved GalN-induced liver damage. These findings indicate that FMT attenuates d-GalN-induced liver damage in mice, and a clinical trial is required to validate the relevance of our findings in humans, and to test whether this therapeutic approach is effective for patients with ALF.

Sensitivity and Uncertainty Analysis of BEAVRS Benchmark

The BEAVRS benchmark problem describes detailed information such as the geometry and material specifications to construct a neutronic calculation model of a commercial Pressurized Water Reactor (PWR) core. The benchmark was modified several times; the main purpose of the current study is to investigate the effect of the modifications in the BEAVRS benchmark on sensitivities and uncertainties in cross section that in turn affect the multiplication factors and reactivity coefficients. The sensitivity coefficients are calculated for keff due to perturbation in cross-sections using the KSEN card of MCNP6 code for the two cores, where all control rods are in and out. Since MCNP6 cannot calculate uncertainties, the NJOY2021 was used to compute the relative covariance matrices of the cross-sections data library, thereafter, a python script was developed to calculate the uncertainties of keff as well as sensitivities and uncertainties of reactivity due to the control rods insertion. The results indicate that the uncertainties in reactivities caused by control rods insertion for the BV2 model is greater than the BV1 model, due to the effect of using higher coolant density in the nozzle and support plate structures in addition to the effect of the burnable absorber designs in the BV2 model.

Under-Five Child Growth and Nutrition Status: Spatial Clustering of Indian Districts

Variation in human growth and the genetic and environmental factors that are influencing it have been described worldwide. The objective of this study is to assess the geographical variance of under-five nutritional status and its related covariates across Indian districts. We use the most recent fourth round of the Indian National Family Health Survey conducted in 2015–2016, which for the first time offers district level information. We employ principal component analysis (PCA) on the demographic and socio-economic determinants of childhood morbidity and conduct hierarchical clustering analysis to identify geographical patterns in nutritional status at the district level. Our results reveal strong geographical clustering among the districts of India, often crossing state borders. Throughout most of Southern India, children are provided with relatively better conditions for growth and improved nutritional status, as compared to districts in the central, particularly rural parts of India along the so called “tribal belt”. Here is also where girls are on average measured to have less weight and height compared to boys. Looking at average weight, as well as the proportion of children that suffer from underweight and wasting, north-eastern Indian districts offer living conditions more conducive to healthy child development. The geographical clustering of malnutrition, as well as below-average child height and weight coincides with high poverty, low female education, lower BMI among mothers, higher prevalence of both parity 4 + and teenage pregnancies. The present study highlights the importance of combining PCA and cluster analysis in studying variation in under-five child growth and of conducting this analysis at the district level. We identify the geographical areas, where children are under severe risk of undernutrition, stunting and wasting and contribute to formulating policies to improve child nutrition in India.

Data-Driven Feature-Based Newsvendor: A Distributionally Robust Approach

In this paper, we study the feature-based newsvendor problem in the context of available historical demand observations and related demand covariates. The demand distribution conditioned on these features is based on general kernel estimation methods that can incorporate machine learning algorithms, such as the random forest. To account for estimation errors, we propose a distributionally robust optimization (DRO) approach that contains all distributions close to the estimated conditional demand distribution under a discrepancy measure based on the cumulative distribution functions. Interestingly, we show that the DRO problem with such an ambiguity set admits a closed-form solution for the newsvendor loss. In addition, we show that the optimal solution produced by this approach converges to the optimal inventory decision asymptotically and provides a finite-sample performance guarantee. The results of numerical experiments with synthetic and real-world data sets show that compared with other approaches, our model performs well in terms of the out-of-sample cost and computational time.