Population development and burial practices in the Hungarian Middle Ages: paleodemographic insights into the adoption of Christianity.

Hepatic Gene Expression Discriminates Responders and Nonresponders in Treatment of Chronic Hepatitis C Viral Infection

BackgroundThe application of novel technologies is now widely used to assist in making optimal decisions. This study aimed to evaluate the performance of linear discriminant analysis (LDA) and flexible discriminant analysis (FDA) in classifying and predicting Friesian cattle’s milk production into low (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:<$$\\end{document}4500 kg), medium (4500–7500 kg), and high (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:>$$\\end{document}7500 kg) categories. A total of 3793 lactation records from cows calved between 2009 and 2020 were collected to examine some predictors such as age at first calving (AFC), lactation order (LO), days open (DO), days in milk (DIM), dry period (DP), calving season (CFS), 305-day milk yield (305-MY), calving interval (CI), and total breeding per conception (TBRD).ResultsThe comparison between LDA and FDA models was based on the significance of coefficients, total accuracy, sensitivity, precision, and F1-score. The LDA results revealed that DIM and 305-MY were the significant (P < 0.001) contributors for data classification, while the FDA was a lactation order. Classification accuracy results showed that the FDA model performed better than the LDA model in expressing accuracies of correctly classified cases as well as overall classification accuracy of milk yield. The FDA model outperformed LDA in both accuracy and F1-score. It achieved an accuracy of 82% compared to LDA’s 71%. Similarly, the F1-score improved from a range of 0.667 to 0.79 for LDA to a higher range of 0.81 to 0.83 for FDA.ConclusionThe findings of this study demonstrated that FDA was more resistant than LDA in case of assumption violations. Furthermore, the current study showed the feasibility and efficacy of LDA and FDA in interpreting and predicting livestock datasets.

Jordan, rich in archaeological sites and the related funerary attributes, has the potential to reconstruct the life of past societies and deduce burial practices that, in turn, may assist in understanding these societies and tracking the changes of social adjustments chronologically. This study utilizes archaeological reports and manuscripts to synthesize the social archaeology in Jordan from the Natufian period to the Persian. The study shows a prominent variability in burial practices over the various archaeological periods that were triggered by culture change, where the latter was imposed by the intertwined socio-political and environmental factors. The simplicity or complexity in burial practices followed those of the society itself, where burial types and practices started as simple during the Natufian period and gradually intensified and complicated in the latter periods.

Genetic diversity in roses is unparalleled to any other flower crops. A large number of rose species is grown all around the world. There is a rich wealth of indigenous rose species such as Rosa brunonii, R. macrophylla, R. moschata, R. spinossisima, R. rubiginosa, R. wichuraiana etc. are grown wild in India. They are the important source of genes responsible for various traits, viz. both biotic and abiotic stress tolerance, fragrance, perpetual flowering, hardiness which need to be protected for further use in breeding programs. Morphological markers or traits basedspecies identification are best for visualization, reproducibility and easy to use, besides serving as a reality check for molecular studies. In this study, 31 rose species were characterized for 18 vegetative characters as per PPV FRA guidelines for DUS testing of rose. Genetic diversity of rose species was done based on dissimilarity calculation, hierarchical cluster analysis, linear discriminant analysis and principal component analysis using R software. R. banksiae - R. chinensis‘Viridiflora’ and R. banksiae - R. rubiginosa showed the highest diversity while R. brunoniiand R. dumalis shows least diversity. Species were divided into five major clusters using hierarchical cluster analysis which was validated by linear discriminant analysis. Here, both Indian originating species R. brunonii and R. moschata fell into the same cluster while significant diversity noticed in R. damascena and R. wichuraiana. Results of principal component analysis follow a similar pattern as of dendrogram. R. banksiae was most distinctly placed which is well supported as only thornless species used in the study. Although no single morphological traits can be used to differentiate species, it is necessary to create a passport data for further studies.

This chapter addresses the nobility of Central Europe—first reviewing their self-perception and the modern understanding of what it meant to be noble in medieval Central Europe, then elaborating the stratification of the region’s aristocracies by their roles in administration and land-holding. The emergence of the nobility in medieval Central Europe, part of monarchical or princely administrative social structures, was a mark of increasing sociocultural complexity. It consolidated a hereditary category of landowners (called nobles or boyars) who dominated the exercise of power either on their own demesnes or as subordinates of the monarchs. The nobility, with access to varying economic resources, mainly landed property, were organized in hierarchical ranks, from a wealthy top layer to a low stratum of impoverished nobles barely able to maintain their status. The development of this category of the population in the societies of Central Europe was a phenomenon that began before the turn of the first millennium and lasted well into the modern times. The chapter pays special attention to the lesser nobility—a circle that has often been disregarded in twentieth-century historiography.

Background: Linear Discriminant Analysis (LDA) is a powerful and widely used technique for classification with correlated variables. Principal Components (PCs) group these variables into linear combinations and produce independent variables. The LDA on these PC’s may provide better classification accuracy in clinical diagnostics than on usual measurements. Methodology: Two datasets were utilized for demonstration: one from a Sudden Sensorineural Hearing Loss (SSNHL) case-control study and the other from a Gall Bladder (GB) case-control study. Linear Discriminant Analysis (LDA) was conducted on the actual correlated measured variables for group classification, as well as on the derived principal component variables, to compare their classification accuracies. Performance metrics including Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Classification Accuracy, and F1 Score were assessed. For validation, a third simulated dataset was employed. Additionally, LDA was performed on each dataset using eigenvectors of the control group applied to the cases and vice versa, revealing a strong agreement in classification as measured by the kappa statistic. Results: When LDA was applied to the actual lipid measurements in the SSNHL dataset, the classification accuracy was 57.2%, and the F1 score was 39.7%. However, when LDA was performed using principal components (PCs), the classification accuracy markedly improved to 99.2%, with an F1 score of 98.5%. Similarly, for the GB cancer dataset, the classification accuracy and F1 score were initially 77.2% and 77.3%, respectively. Upon applying LDA with the PCs, these metrics were significantly enhanced to 98.4% and 98.3%, respectively. For the simulated dataset, both the classification accuracy and F1 score were 99.1%. The study also demonstrated that the classification accuracy and F1 score remained consistent regardless of whether the eigenvectors from the cases or controls were used to classify new subjects (Kappa Statistic = 0.962, P &lt; 0.001). Conclusion: In group separation, utilizing principal components significantly improves classification accuracy and overall performance metrics, outperforming the use of the original correlated predictors.

Coal analysis by diffuse reflectance near-infrared spectroscopy: Hierarchical cluster and linear discriminant analysis

Logistic, linear, and quadratic discriminant analyses were compared in their ability to differentiate hypercalcemic patients with primary hyperparathyroidism from those with malignancy. Linear and quadratic discriminant analyses were performed by use of both untransformed and logarithmically transformed data. Application of principal components analysis with varimax rotation was helpful in revealing the underlying relationships between variables. All discriminant methods identified serum albumin as the best single discriminating test, with the log-quadratic discriminant analysis classifying 81% of patients correctly. The combination of albumin, carboxy-terminal parathyroid hormone, and chloride improved classification accuracy (92% by use of log-quadratic discriminant analysis). Logistic discriminant analysis, using all 20 variables, gave a classification accuracy of 100%. Quadratic discriminant analysis gave better classification than linear discriminant analysis, and both methods performed better when log-transformed data were used. Logistic discriminant analysis followed by discrimination procedures using log-transformed data yielded the highest classification accuracy and reliability of the methods used.

Preliminary chemometric study of minerals and trace elements in Spanish infant formulae

This chapter focuses on the practices of the world of Jewish communities in medieval Central Europe. It compares their structures, legal frameworks, economy, rabbinic movements, intellectual life, and culture across the region, and sums up their influence on the key modern anti-Semitic and anti-Jewish views projected during the nineteenth and twentieth century. Textual sources suggest that the earliest Jewish inhabitants of medieval Central Europe were merchants. During the thirteenth century, the number of the Jewish communities in East Central Europe increased dramatically. This development was directly connected to the process of urbanization; the new towns offered opportunities for Jews to settle and earn a living. Several Central European monarchs issued letters of privilege that defined the basic legal framework of Jewish life. However, violent persecutions and expulsions reshaped the settlement structure of Jews in Austria, Moravia, and Silesia from the end of the fourteenth century on.

This chapter highlights urban production and commerce in medieval Central Europe. It comparatively addresses the circumstances that prompted the region’s trade, such as minting, credit management, and monetary reforms. Mining and the extraction of mineral wealth had particular importance in the countries of medieval Central Europe, especially precious metal production in the Czech lands and Hungary and salt and lead production in Poland. Mining particularly faced technological challenges and was also a field for introducing technological innovations. Mining production required significant monetary investments, thus there are some examples of large-scale financial investors. Royal finances also derived an essential part of their income from activities connected to mining and salt production. The chapter then focuses on the flow of locally and trans-regionally exchanged goods, and touches upon long-distance routes that connected Central Europe with other parts of Europe. This raises some new questions, such as common difficulties in urban production and commerce, indicating some new types of primary sources that need to be scrutinized in future research.

Accurate and rapid identification of pathogenic microorganisms is of critical importance in disease treatment and public health. Conventional work flows are time-consuming, and procedures are multifaceted. MS can be an alternative but is limited by low efficiency for amino acid sequencing as well as low reproducibility for spectrum fingerprinting. We systematically analyzed the feasibility of applying MS for rapid and accurate bacterial identification. Directly applying bacterial colonies without further protein extraction to MALDI-TOF MS analysis revealed rich peak contents and high reproducibility. The MS spectra derived from 57 isolates comprising six human pathogenic bacterial species were analyzed using both unsupervised hierarchical clustering and supervised model construction via the Genetic Algorithm. Hierarchical clustering analysis categorized the spectra into six groups precisely corresponding to the six bacterial species. Precise classification was also maintained in an independently prepared set of bacteria even when the numbers of m/z values were reduced to six. In parallel, classification models were constructed via Genetic Algorithm analysis. A model containing 18 m/z values accurately classified independently prepared bacteria and identified those species originally not used for model construction. Moreover bacteria fewer than 10(4) cells and different species in bacterial mixtures were identified using the classification model approach. In conclusion, the application of MALDI-TOF MS in combination with a suitable model construction provides a highly accurate method for bacterial classification and identification. The approach can identify bacteria with low abundance even in mixed flora, suggesting that a rapid and accurate bacterial identification using MS techniques even before culture can be attained in the near future.

This chapter describes musical culture in medieval Central Europe. It challenges traditional interpretations that profiled the musical culture of Central Europe in its relation to the European West, showing how in different parts of the region various models arose from the influence of France, the Netherlands, and Italy, and also how they transformed into specific musical forms that are nowadays recognized as a distinctive regional contribution to the broader identity of medieval Europe. In seeking the beginnings of notated professional music in Central Europe, one has to reach back to the times of Charlemagne, when the first notations of plainchant appeared. During the Middle Ages, there was a whole range of musical genres used in church ceremonies which did not belong to liturgical chants such as antiphons, responsories, and mass sections. Minnesang, a German tradition of courtly lyric and secular monophony, also held an important place in the song tradition of Central Europe. The chapter then looks at composers such as Mikołaj Radomski, Hermann Edlerawer, and Johannes Tourout, before considering the development of polyphonic music.

A chemosensor array for the colorimetric identification of some carboxylic acids in human urine samples

Use of unsupervised and supervised artificial neural networks for the identification of lactic acid bacteria on the basis of SDS-PAGE patterns of whole cell proteins