NCHA variable combination as a method to undertake LGBTQ + student subpopulation analyses

2021 ◽  
pp. 1-12
Author(s):  
Joanna Schwartz ◽  
Whitney Ginder
Keyword(s):  
Variable Combination

Re-examining Roman Death Pollution

2020 ◽  
Vol 110 ◽  
pp. 5-27
Author(s):  
Allison L. C. Emmerson
Keyword(s):  
Late Antiquity ◽  
Fear Of Death ◽  
The Dead ◽  
Variable Combination ◽  
The City

AbstractThe idea that the dead were polluting — that is, that corpses posed a danger of making the living unclean, offensive both to their own communities and to the gods — has long occupied a fundamental position in Roman funerary studies. Nevertheless, what that pollution comprised, as well as how it affected living society, remain subject to debate. This article aims to clarify the issue by re-examining the evidence for Roman attitudes towards the dead. Focusing on the city of Rome itself, I conclude that we have little reason to reconstruct a fear of death pollution prior to Late Antiquity; in fact, the term itself has been detrimental to current understandings. No surviving text from the late republican or early imperial periods indicates that corpses were objects of metaphysical fear, and rather than polluted, mourners are better conceived as obligated, bound by a variable combination of emotions and conventions to behave in certain, if certainly changeable, ways following a death.

scholarly journals Algorithm for assessing forest stand productivity index using leaf area index

2019 ◽  
Vol 16 (3) ◽  
pp. 1311
Author(s):  
Faid Abdul Manan ◽  
Muhammad Buce Saleh ◽  
I Nengah Surati Jaya ◽  
Uus Saepul Mukarom
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Basal Area ◽  
Forest Stand ◽  
Productivity Index ◽  
Estimation Model ◽  
Area Index ◽  
Study Objective ◽  
Stand Productivity ◽  
Variable Combination

This paper describes a development of an algorithm for assessing stand productivity by considering the stand variables. Forest stand productivity is one of the crucial information that required to establish the business plan for unit management at the beginning of forest planning activity. The main study objective is to find out the most significant and accurate variable combination to be used for assessing the forest stand productivity, as well as to develop productivity estimation model based on leaf area index. The study found the best stand variable combination in assessing stand productivity were density of poles (X2), volume of commercial tree having diameter at breast height (dbh) 20-40 cm (X16), basal area of commercial tree of dbh >40 cm (X20) with Kappa Accuracy of 90.56% for classifying into 5 stand productivity classes. It was recognized that the examined algorithm provides excellent accuracy of 100% when the stand productivity was classified into only 3 classes. The best model for assessing the stand productivity index with leaf area index is y = 0.6214x - 0.9928 with R2= 0.71, where y is productivity index and x is leaf area index.

scholarly journals Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8051
Author(s):  
Chunwang Dong ◽  
Chongshan Yang ◽  
Zhongyuan Liu ◽  
Rentian Zhang ◽  
Peng Yan ◽  
...  
Keyword(s):  
Spectral Data ◽  
Prediction Accuracy ◽  
Visual Analysis ◽  
Population Analysis ◽  
Scatter Correction ◽  
Principal Component ◽  
Black Tea ◽  
Support Vector ◽  
Epicatechin Gallate ◽  
Variable Combination

Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.

Chronic stable angina

2019 ◽  
pp. 91-106
Author(s):  
Gaetano Antonio Lanza ◽  
Antonio De Vita
Keyword(s):  
Stable Angina ◽  
Myocardial Oxygen Consumption ◽  
Beta Blockers ◽  
Chronic Stable Angina ◽  
Pharmacological Therapy ◽  
Lifestyle Changes ◽  
Channel Blockers ◽  
Variable Combination ◽  
Improve Clinical Outcome ◽  
Risk Factor Burden

Treatment of patients with chronic stable angina has two main objectives: to improve clinical outcome and to reduce angina symptoms. Prognosis is mainly improved by a reduction in cardiovascular risk factor burden, which may be achieved by appropriate lifestyle changes and, for some risk factors (e.g. hypercholesterolaemia, hypertension, diabetes), appropriate pharmacological therapy (including, in particular, statins and renin–angiotensin–aldosterone system inhibitors) and use of antithrombotic agents. Symptoms can be improved by a variable combination of traditional (beta-blockers, calcium channel blockers, nitrates) and novel (e.g. ivabradine, ranolazine) anti-ischaemic drugs, which may act through reduction in myocardial oxygen consumption and/or improvement of myocardial perfusion.

Weight and Appetite Loss in Cancer

2021 ◽  
pp. 298-302
Author(s):  
Yesne Alici ◽  
Victoria Saltz
Keyword(s):  
Cancer Patients ◽  
Cancer Cachexia ◽  
Sense Of Self ◽  
Resting Energy Expenditure ◽  
Appetite Loss ◽  
Chronic Inflammatory Response ◽  
Variable Combination ◽  
Cancer Anorexia ◽  
Cachexia Syndrome

Weight and appetite loss in cancer patients, referred to as the cancer anorexia-cachexia syndrome, is a complex, multifactorial syndrome, defined by an ongoing loss of skeletal muscle mass, with or without loss of fat mass, which cannot be fully reversed by conventional nutritional support, and may lead to progressive functional impairment. It is a hypercatabolic state in the context of chronic inflammatory response best described in the setting of cancer but can also be seen in other advanced chronic illness. Cancer cachexia occurs in approximately 50% of cancer patients, and in 80% of those with advanced cancer. It impacts adversely on function, treatment tolerability and treatment response, and health service utilization, but most importantly, dignity, sense of self, quality of life, and survival. The pathophysiology of cancer cachexia is complex and multifactorial. It is characterized by a negative protein and energy balance, driven by a variable combination of reduced food intake, increased resting energy expenditure, and net loss of lean tissue. The best approach to weight and appetite loss among cancer patients is a multimodal therapy, in which a personalized combination of pharmacologic and nonpharmacologic treatments is implemented. This chapter will provide an overview of the cancer anorexia cachexia syndrome as relevant to the practice of clinicians of all disciplines managing cancer patients.

scholarly journals Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7248
Author(s):  
Fugen Jiang ◽  
Mykola Kutia ◽  
Arbi J. Sarkissian ◽  
Hui Lin ◽  
Jiangping Long ◽  
...  
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Texture Features ◽  
Estimation Accuracy ◽  
Stem Volume ◽  
Estimation Model ◽  
Red Edge ◽  
Variable Combination ◽  
Coniferous Plantations ◽  
Sentinel 2

Forest growing stem volume (GSV) reflects the richness of forest resources as well as the quality of forest ecosystems. Remote sensing technology enables robust and efficient GSV estimation as it greatly reduces the survey time and cost while facilitating periodic monitoring. Given its red edge bands and a short revisit time period, Sentinel-2 images were selected for the GSV estimation in Wangyedian forest farm, Inner Mongolia, China. The variable combination was shown to significantly affect the accuracy of the estimation model. After extracting spectral variables, texture features, and topographic factors, a stepwise random forest (SRF) method was proposed to select variable combinations and establish random forest regressions (RFR) for GSV estimation. The linear stepwise regression (LSR), Boruta, Variable Selection Using Random Forests (VSURF), and random forest (RF) methods were then used as references for comparison with the proposed SRF for selection of predictors and GSV estimation. Combined with the observed GSV data and the Sentinel-2 images, the distributions of GSV were generated by the RFR models with the variable combinations determined by the LSR, RF, Boruta, VSURF, and SRF. The results show that the texture features of Sentinel-2’s red edge bands can significantly improve the accuracy of GSV estimation. The SRF method can effectively select the optimal variable combination, and the SRF-based model results in the highest estimation accuracy with the decreases of relative root mean square error by 16.4%, 14.4%, 16.3%, and 10.6% compared with those from the LSR-, RF-, Boruta-, and VSURF-based models, respectively. The GSV distribution generated by the SRF-based model matched that of the field observations well. The results of this study are expected to provide a reference for GSV estimation of coniferous plantations.

Quantitative Analysis of Cadmium Content in Tomato Leaves Based on Hyperspectral Image and Feature Selection

2018 ◽  
Vol 34 (5) ◽  
pp. 789-798 ◽  
Author(s):  
Yuechun Zhang ◽  
Jun Sun ◽  
Junyan Li ◽  
Xiaohong Wu ◽  
Chunmei Dai
Keyword(s):  
Feature Selection ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Population Analysis ◽  
Cadmium Stress ◽  
Hyperspectral Images ◽  
Cadmium Content ◽  
Variable Combination ◽  
Non Destructive

Abstract.In order to ensure that safe and healthy tomatoes can be provided to people, a method for quantitative determination of cadmium content in tomato leaves based on hyperspectral imaging technology was put forward in this study. Tomato leaves with seven cadmium stress gradients were studied. Hyperspectral images of all samples were firstly acquired by the hyperspectral imaging system, then the spectral data were extracted from the hyperspectral images. To simplify the model, three algorithms of competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA) and bootstrapping soft shrinkage (BOSS) were used to select the feature wavelengths ranging from 431 to 962 nm. Final results showed that BOSS can improve prediction performance and greatly reduce features when compared with the other two selection methods. The BOSS model got the best accuracy in calibration and prediction with R2c of 0.9907 and RMSEC of 0.4257mg/kg, R2p of 0.9821, and RMSEP of 0.6461 mg/kg. Hence, the method of hyperspectral technology combined with the BOSS feature selection is feasible for detecting the cadmium content of tomato leaves, which can potentially provide a new method and thought for cadmium content detection of other crops. Keywords: Feature selection, Hyperspectral image technology, Non-destructive analysis, Regression model, Tomato leaves.

Clinical Characteristics and Genotype–Phenotype Correlation in Children with KMT2E Gene-Related Neurodevelopmental Disorders: Report of Two New Cases and Review of Published Literature

2020 ◽  
Author(s):  
Indar Kumar Sharawat ◽  
Prateek Kumar Panda ◽  
Lesa Dawman
Keyword(s):  
Developmental Delay ◽  
Neurodevelopmental Disorder ◽  
Copy Number Variants ◽  
Cerebral White Matter ◽  
Global Developmental Delay ◽  
Missense Mutations ◽  
Search Terms ◽  
Pathogenic Factors ◽  
The Common ◽  
Variable Combination

Abstract Background In recent years, many new candidate genes are being identified as putative pathogenic factors in children with developmental delay and autism. Recently, heterozygous mutations in the KMT2E gene have been identified as a cause of a unique neurodevelopmental disorder with variable combination of global developmental delay or isolated speech delay, intellectual disability, autistic features, and seizures. Methods Here, we present two new cases of KMT2E mutation-associated neurodevelopmental disorder in a 4-year-old girl and 5-year-old boy. We also performed a pooled review of the previously published cases of KMT2E-related neurodevelopmental disorder. Articles were identified through search engines using appropriate search terms. Results Along with the presented 2 cases, 40 cases were analyzed. Out of them, 30, 6, and 4 children had protein-truncating mutations, missense mutations, and copy number variants, respectively. The common features were global developmental delay (97%) followed by macrocephaly (35%), seizures (30%), and autism (25%). Children with missense variants had severe phenotype, with microcephaly, profound developmental delay, and increased frequency of seizures. Neuroimaging revealed nonspecific changes, including cerebral white matter signal abnormalities. Conclusion KMT2E-related neurodevelopmental disorder remains one of the clinical differentials in children with global developmental delay and/or autistic features/seizure. With the reporting of more cases in the future, the already heterogeneous clinical spectrum of this disease is likely to be widened.

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