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As the process yield has significantly raised because of the advanced development of manufacturing technology today, engineers would logically attempt to inspect fewer sample items for the quality evaluation of processes or products. Therefore, in this paper, an efficient sampling inspection method based on the process yield index Spk is developed for lot sentencing, wherein the inspection is performed only on a fractional submitted lot rather than examining every following submission. Both the average sample number (ASN) and operating characteristic (OC) functions of the proposed method are derived on the basis of the Markov chain technique. Further, an optimization model that minimizes the ASN and constrains two OC functions restricted to given quality requirements and tolerable risks is constructed. Performance comparisons in terms of economy and discriminatory power are analyzed by contrasting ASN and OC curves with existing Spk-based methods under the same quality conditions to emphasize the superiority of the proposed method. For easy implementation, we prove the applicability of the proposed method by demonstrating a case study taken from an integrated circuit packaging company.

Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract

PurposeA new high-resolution next-generation sequencing (NGS)-based method was established to type closely related European type II Toxoplasma gondii strains.MethodsT. gondii field isolates were collected from different parts of Europe and assessed by whole genome sequencing (WGS). In comparison to ME49 (a type II reference strain), highly polymorphic regions (HPRs) were identified, showing a considerable number of single nucleotide polymorphisms (SNPs). After confirmation by Sanger sequencing, 18 HPRs were used to design a primer panel for multiplex PCR to establish a multilocus Ion AmpliSeq typing method. Toxoplasma gondii isolates and T. gondii present in clinical samples were typed with the new method. The sensitivity of the method was tested with serially diluted reference DNA samples.ResultsAmong type II specimens, the method could differentiate the same number of haplotypes as the reference standard, microsatellite (MS) typing. Passages of the same isolates and specimens originating from abortion outbreaks were identified as identical. In addition, seven different genotypes, two atypical and two recombinant specimens were clearly distinguished from each other by the method. Furthermore, almost all SNPs detected by the Ion AmpliSeq method corresponded to those expected based on WGS. By testing serially diluted DNA samples, the method exhibited a similar analytical sensitivity as MS typing.ConclusionThe new method can distinguish different T. gondii genotypes and detect intra-genotype variability among European type II T. gondii strains. Furthermore, with WGS data additional target regions can be added to the method to potentially increase typing resolution.

Background Subjective burden is a central variable describing the situation encountered by family caregivers. The 10-item short version of the Burden Scale for Family Caregivers (BSFC-short/BSFC-s) was developed to provide an economical measure of this variable. The present study examined the reliability and validity of the BSFC-s. Methods Comprehensive data from “the IDA project” were the basis of the calculations, which included 351 dyads and examined medical data on people with dementia, interview data from their family caregivers, and health insurance data. A factor analysis was performed to explore the structure of the BSFC-s; Cronbach’s alpha was used to evaluate the internal consistency of the scale. The items were analyzed to determine the item difficulty and the discriminatory power. Construct validity was tested with five hypotheses. To establish the predictive validity of the BSFC-s, predictors of institutionalization at a follow-up time of 2.5 years were analyzed (binary logistic regression). Results The BSFC-s score adhered to a one-factor structure. Cronbach's alpha for the complete scale was .92. A significant increase in the BSFC-s score was observed when dementia progressed, disturbing behavior occurred more frequently, care requirements increased, and when caregivers were diagnosed with depression. Caregiver burden was the second strongest predictor of institutionalization out of a total of four significant predictors. Conclusions All hypotheses that referred to the construct validity were supported. The BSFC-short with its ten items is a very economical instrument for assessing the caregiver’s total subjective burden in a short time frame. The BSFC-s score has predictive validity for the institutionalization of people with dementia. Therefore it is an appropriate outcome measure to evaluate caregiver interventions. The scale is available for free in 20 languages ( http://www.caregiver-burden.eu ). This availability facilitates the comparison of international research findings.

The aim of this study is to assess the effects of substituting soybean meal with extruded urea in the diet of crossbred Texel x no defined racial pattern lambs under continuous grazing on Brachiaria ssp., focusing on both their productive and nutritional performance. 60 Texel crossbred lambs (12 animals for each treatment) were used, with an average initial weight of 20.7 ± 0.87 kg and an average age of 2.5 ± 0.70 months, fed treatments with increasing levels of UE (Urea extruded Amireia® 200S): 0; 6; 12; 18 and 24 grams of EU 100/kg of body weight, with trial period was 5 months, using the multivariate technique. The data were subjected to principal component and canonical discriminant analysis to check possible differences between the evaluated treatments and identify the variables that best discriminate and use these variables to create a discriminant function that represents the differences between treatments. Of the 12 variables initially used, we observed that 9 were used by the main components, but 6 were those that presented the greatest discriminatory power for the study. Main component 1 was characterized by biometric measurements and showed the greatest power of variation in the study (60%), followed by main component 2, represented by slaughter weight and empty body weight (13%). These correlations indicate that biometric measurements can serve as reliable indirect indicators for estimating carcass traits in sheep, offering a practical alternative to visual assessments.

The purpose of this study is to provide an efficient method for the selection of input–output indicators in the data envelopment analysis (DEA) approach, in order to improve the discriminatory power of the DEA method in the evaluation process and performance analysis of homogeneous decision-making units (DMUs) in the presence of negative values and data. For this purpose, the Shannon entropy technique is used as one of the most important methods for determining the weight of indicators. Moreover, due to the presence of negative data in some indicators, the range directional measure (RDM) model is used as the basic model of the research. Finally, to demonstrate the applicability of the proposed approach, the food and beverage industry has been selected from the Tehran stock exchange (TSE) as a case study, and data related to 15 stocks have been extracted from this industry. The numerical and experimental results indicate the efficacy of the hybrid data envelopment analysis–Shannon entropy (DEASE) approach to evaluate stocks under negative data. Furthermore, the discriminatory power of the proposed DEASE approach is greater than that of a classical DEA model.

Polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs), and per- and poly-fluoroalkyl substances (PFAS) are persistent organic pollutants (POPs) that remain detrimental to critical subpopulations, namely pregnant women. Required tests for biomonitoring are quite expensive. Moreover, statistical models aiming to discover the relationships between pollutants levels and human characteristics have their limitations. Therefore, the objective of this study is to use machine learning predictive models to further examine the pollutants’ predictors, while comparing them. Levels of 33 congeners were measured in the serum of 269 pregnant women, from whom data was collected regarding sociodemographic, dietary, environmental, and anthropometric characteristics. Several machine learning algorithms were compared using “Python” for each pollutant: support vector machine (SVM), random forest, XGBoost, and neural networks. The aforementioned characteristics were included in the model as features. Prediction, accuracy, precision, recall, F1-score, area under the ROC curve (AUC), sensitivity, and specificity were retrieved to compare the models between them and among pollutants. The highest performing model for all pollutants was Random Forest. Results showed a moderate to acceptable performance and discriminative power among all POPs, with OCPs’ model performing slightly better than all other models. Top related features for each model were also presented using SHAP analysis, detailing the predictors’ negative or positive impact on the model. In conclusion, developing such a tool is of major importance in a context of limited financial and research resources. Nevertheless, machine learning models should always be interpreted with caution by exploring all evaluation metrics.

Background Although larger amounts of scientific attention have been directed toward the concept of positive aspects of caregiving (PAC) in recent years, a globally uniform definition and a suitable, scientifically valid questionnaire for all informal caregivers have yet to be developed. On the basis of the questionnaires that already exist for measuring PAC, the authors aimed to (a) concretize the concept and (b) develop a new scale by focusing only on items that show that family caregivers experience a benefit for themselves and that the benefit they experience is the result of their caregiving activities. Methods The Benefits of Being a Caregiver Scale (BBCS) was validated on data from 961 informal caregivers. Cronbach's alpha was calculated to assess the internal consistency of the items, and a factor analysis was conducted to determine the structure of the BBCS. The discriminatory power and item difficulties were examined. Construct validity was established by testing four hypotheses. Results The factor analysis confirmed the single-factor structure of the BBCS. Cronbach's alpha for the total scale was 0.922. One of the 15 items did not show good to very good discriminatory power and was excluded from the final version of the scale. A higher BBCS score was observed if the caregiver experienced more positive aspects of caregiving and tended to have better general coping skills and a positive relationship with the care-receiver. The BBCS score was not associated with the subjective burden of the caregiver. Results confirmed the validity of the BBCS. Conclusion The BBCS is a valid assessment instrument for measuring the benefits that caregivers experience from their caregiving work and can easily be used in research and practice. The BBCS is available free of charge in English and German ( http://www.caregiver-benefits.de ).

To compare the discriminatory power of pulsed-field gel electrophoresis (PFGE) and sequence-based typing (SBT) in outbreaks for determining the infection source. Twenty-five investigations of Legionnaires' disease were analyzed by PFGE, SBT and Dresden monoclonal antibody. The results suggested that monoclonal antibody could reduce the number of isolates to be characterized by molecular methods. The epidemiological concordance PFGE-SBT was 100%, while the molecular concordance was 64%. Adjusted Wallace index (AW) showed that PFGE has better discriminatory power than SBT (AW = 0.767; AW = 1). The discrepancies appeared mostly in sequence type (ST) 1, a worldwide distributed ST for which PFGE discriminated different profiles. SBT discriminatory power was not sufficient verifying the infection source, especially in worldwide distributed STs, which were classified into different PFGE patterns.

Background The low cost and rapidity of microsatellite analysis have led to the development of several markers for many species. Because in non-invasive genetics it is recommended to genotype individuals using few loci, generally a subset of markers is selected. The choice of different marker panels by different research groups studying the same population can cause problems and bias in data analysis. A priority issue in conservation genetics is the comparability of data produced by different labs with different methods. Here, we compared data from previous and ongoing studies to identify a panel of microsatellite loci efficient for the long-term monitoring of Apennine brown bears ( Ursus arctos marsicanus ), aiming at reducing genotyping uncertainty and allowing reliable individual identifications overtimes. Results We examined all microsatellite markers used up to now and identified 19 candidate loci. We evaluated the efficacy of 13 of the most commonly used loci analyzing 194 DNA samples belonging to 113 distinct bears selected from the Italian national biobank. We compared data from 4 different marker subsets on the basis of genotyping errors, allelic patterns, observed and expected heterozygosity, discriminatory powers, number of mismatching pairs, and probability of identity. The optimal marker set was selected evaluating the low molecular weight, the high discriminatory power, and the low occurrence of genotyping errors of each primer. We calibrated allele calls and verified matches among genotypes obtained in previous studies using the complete set of 13 STRs (Short Tandem Repeats), analyzing six invasive DNA samples from distinct individuals. Differences in allele-sizing between labs were consistent, showing a substantial overlap of the individual genotyping. Conclusions The proposed marker set comprises 11 Ursus specific markers with the addition of cxx20, the canid-locus less prone to genotyping errors, in order to prevent underestimation (maximizing the discriminatory power) and overestimation (minimizing the genotyping errors) of the number of Apennine brown bears. The selected markers allow saving time and costs with the amplification in multiplex of all loci thanks to the same annealing temperature. Our work optimizes the available resources by identifying a shared panel and a uniform methodology capable of improving comparisons between past and future studies.