This article explores the relationship between statistical classifications and comparisons in German colonial statistics between 1885 and 1914. It questions the importance and the characteristics of comparison in terms of space and population in colonial statistics. ZEN-3694 order The aim is to sharpen the view of statistical methods and categories in an imperial context. The results show that the statistical observation of colonies was based on a territorial distinction between metropole and colonies, which led to the use of different methods. I argue that this territorial and methodological distinction was interwoven with a fundamental incomparability between colonized populations and colonizers.Hepatitis infections represent a major health concern worldwide. Numerous computer-aided approaches have been devised for the early detection of hepatitis. In this study, we propose a method for the analysis and classification of cases of hepatitis-B virus ( HBV), hepatitis-C virus (HCV), and healthy subjects using Raman spectroscopy and a multiscale convolutional neural network (MSCNN). In particular, serum samples of HBV-infected patients (435 cases), HCV-infected patients (374 cases), and healthy persons (499 cases) are analyzed via Raman spectroscopy. The differences between Raman peaks in the measured serum spectra indicate specific biomolecular differences among the three classes. The dimensionality of the spectral data is reduced through principal component analysis. Subsequently, features are extracted, and then feature normalization is applied. Next, the extracted features are used to train different classifiers, namely MSCNN, a single-scale convolutional neural network, and other traditional classifiers. Among these classifiers, the MSCNN model achieved the best outcomes with a precision of 98.89%, sensitivity of 97.44%, specificity of 94.54%, and accuracy of 94.92%. Overall, the results demonstrate that Raman spectral analysis and MSCNN can be effectively utilized for rapid screening of hepatitis B and C cases.Artificial intelligence (AI) has attained a new level of maturity in recent years and is becoming the driver of digitalization in all areas of life. AI is a cross-sectional technology with great importance for all areas of medicine employing image data, text data and bio-data. There is no medical field that will remain unaffected by AI, with AI-assisted clinical decision-making assuming a particularly important role. AI methods are becoming established in medical workflow management and for prediction of treatment success or treatment outcome. AI systems are already able to lend support to imaging-based diagnosis and patient management, but cannot suggest critical decisions. The corresponding preventive or therapeutic measures can be more rationally assessed with the help of AI, although the number of diseases covered is currently too low to create robust systems for routine clinical use. Prerequisite for the widespread use of AI systems is appropriate training to enable physicians to decide when computer-assisted decision-making can be relied upon.
Coronavirus disease is a major global public health problem. The contagious disease caused by a newly discovered coronavirus, coronavirus disease 2019 (COVID-19), was declared a pandemic following the outbreak of cases of respiratory illness during 2019. Although studies assessed COVID-19 knowledge, attitude, and practice in Ethiopia the findings were highly variable and inconsistent.

This study assessed the pooled status of knowledge, attitude, and prevention practices regarding COVID-19 in Ethiopia.

International and national electronic databases, including PubMed/MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature, Google Scholar, Science Direct, and Google, were systematically searched. All observational studies on COVID-19 knowledge, attitude, and prevention practices in Ethiopia were included. We assessed heterogeneity among the included studies using the Cochrane
test statistics and

test. Lastly, a random-effects meta-analysis model was fitted to estimate the pooledoward COVID-19 in Ethiopia. The lowest pooled proportion was observed in the Amhara region. These findings indicate the need to revise plans and policies to improve the knowledge, attitudes, and prevention practices of people toward COVID-19 in Ethiopia, especially in the Amhara region. (Curr Ther Res Clin Exp. 2021; 82XXX-XXX) © 2021 Elsevier HS Journals, Inc.Single-cell and single-nucleus sequencing techniques are a burgeoning field with various biological, biomedical and clinical applications. Numerous high- and low-throughput methods have been developed for sequencing the RNA and DNA content of single cells. However, for all these methods, the key requirement is high-quality input of a single-cell or single-nucleus suspension. Preparing such a suspension is the limiting step when working with fragile, archived tissues of variable quality. This hurdle can prevent such tissues from being extensively investigated with single-cell technologies. We describe a protocol for preparing single-nucleus suspensions within the span of a few hours that reliably works for multiple postmortem and archived tissue types using standard laboratory equipment. The stages of the protocol include tissue preparation and dissociation, nuclei extraction, and nuclei concentration assessment and capture. The protocol is comparable to other published protocols but does not require fluorescence-assisted nuclei sorting (FANS) or ultracentrifugation. The protocol can be carried out by a competent graduate student familiar with basic laboratory techniques and equipment. Moreover, these preparations are compatible with single-nucleus (sn)RNA-seq and assay for transposase-accessible chromatin (ATAC)-seq using the 10X Genomics Chromium system. The protocol reliably results in efficient capture of single nuclei for high-quality snRNA-seq libraries.Physiological need states direct decision-making toward re-establishing homeostasis. Using a two-alternative forced choice task for mice that models elements of human decisions, we found that varying hunger and thirst states caused need-inappropriate choices, such as food seeking when thirsty. These results show limits on interoceptive knowledge of hunger and thirst states to guide decision-making. Instead, need states were identified after food and water consumption by outcome evaluation, which depended on the medial prefrontal cortex.ZEN-3694 order