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Studies of the virome of olive trees with symptoms of leaf mottling by high-throughput sequencing (HTS) revealed the presence of a new virus. Full coding genome sequences of two isolates were determined and consisted of a single RNA segment of 16,516 nt and 16,489, respectively. The genomic organization contained 10 open reading frames (ORFs) from 5′ to 3′: ORF1a, ORF1b (RdRp), ORF2 (p22), ORF3 (p7), ORF4 (HSP70h), ORF5 (HSP90h), ORF6 (CP), ORF7 (p19), ORF8 (p12), ORF9 (p23) and ORF10 (p9). Phylogenetic analyses clustered this virus in the genus Olivavirus, family Closteroviridae, with the closest species being Olivavirus flaviolae, commonly named olive leaf yellowing-associated virus (OLYaV). However, amino acid sequences of all taxonomically relevant proteins showed, in all cases, a divergence higher than 25% between OLYaV and the new virus, indicating that it represents a new species in the genus Olivavirus for which the common name of olive leaf mottling virus (OLMV) is proposed. This study represents an advance in the genus Olivavirus and provides new insights into the olive virome. [ABSTRACT FROM AUTHOR]

Propensity score weighting is one of the techniques used in controlling for selection biases in nonexperimental studies. Propensity scores can be used as weights to account for selection assignment differences between treatment and comparison groups. One of the advantages of this approach is that all the individuals in the study can be used for the outcomes evaluation. In this paper, we demonstrate how to conduct propensity score weighting using R. The purpose is to provide a step-by-step guide to propensity score weighting implementation for practitioners. In addition to strengths, some limitations of propensity score weighting are discussed.

This paper addresses the issue of LED short-circuit fault detection in signaling and lighting systems in the automotive industry. The conventional diagnostic method commonly implemented in newer vehicles relies on measuring the voltage drop across different LED branches and comparing it with threshold values indicating faults caused by open circuits or LED short circuits. With this algorithm, detecting cases of a few LEDs short-circuited within a branch, particularly a single malfunctioning LED, is particularly challenging. In this work, two easily implementable algorithms are proposed to address this issue within the vehicle's control unit. One is based on a mathematical prediction model, while the other utilizes a neural network. The results obtained offer a 100% LED short-circuit fault detection rate in the majority of analyzed cases, representing a significant improvement over the conventional method, even in scenarios involving a single malfunctioning LED within a branch. Additionally, the neural network-based model can accurately predict the number of failed LEDs. [ABSTRACT FROM AUTHOR]

The increasing prevalence of exploratory factor analysis (EFA) applications in scholarly literature reflects its popularity and the convenience of computer‐assisted analysis. With advancements in computer hardware and software, the complexity and variations of EFA analysis have also grown. Despite the availability of sophisticated computer programming, the appropriate utilization of EFA necessitates users to make informed judgments. Additionally, users are responsible for searching and identifying suitable statistical software to accommodate their data and analysis requirements. This review aims to enhance understanding of the EFA technique and summarize the analysis options available for EFA in R packages. A total of 50 packages were examined in this study. Specifically, the review focuses on (1) diagnostic functions, (2) factor extraction, (3) factor retention, (4) factor rotation, and (5) complex data and technique features provided by these packages. The review summarizes the available function options in R packages by outlining these five crucial steps in conducting an EFA analysis. This synthesis offers an overview of the similarities and distinctive features of each package, serving as a valuable resource for users in selecting a suitable EFA technique. It is important to note that there is no definitive approach to conducting an exploratory factor analysis. Users need to deliberately select and combine appropriate techniques to achieve optimal results. This article is categorized under:Statistical and Graphical Methods of Data Analysis > Dimension ReductionSoftware for Computational Statistics > Software/Statistical Software [ABSTRACT FROM AUTHOR]

The MAST-EL University and School Partnership supports a strategic infrastructure for preparing preservice and in-service teachers ensuring high quality mathematics and science instruction for English learners (ELs). The partnership in its second year consists of thirty-two in-service elementary teachers, three instructional coaches, thirty preservice elementary teachers, five faculty members, and four principals. Preservice and in-service teachers are mentored, supported, and observed by school-based instructional coaches, principals, and faculty; attend a week long summer institute focused on content knowledge and pedagogical practices for ELs learning mathematics, science, and language/ literacy; and attend monthly professional learning community (PLCs) meetings during the school year. During the second year of the project, we have learned that a professional community is being built, that the summer institute was positively perceived, and that PLCs are supporting teachers' and preservice teachers' quests to become effective mathematics and science educators.

Peach latent mosaic viroid (PLMVd) is an important pathogen that causes disease in peaches. Control of this viroid remains problematic because most PLMVd variants are symptomless, and although there are many detection tests in use, the reliability of PCR-based methods is compromised by the complex, branched secondary RNA structure of the viroid and its genetic diversity. In this study, a duplex RT-qPCR method was developed and validated against two previously published single RT-qPCRs, which were potentially able to detect all known PLMVd variants when used in tandem. In addition, in order to simplify the sample preparation, rapid-extraction protocols based on the use of crude sap or tissue printing were compared with commercially available RNA purification kits. The performance of the new procedure was evaluated in a test performance study involving five participant laboratories. The new method, in combination with rapid-sample-preparation approaches, was demonstrated to be feasible and reliable, with the advantage of detecting all different PLMVd isolates/variants assayed in a single reaction, reducing costs for routine diagnosis. [ABSTRACT FROM AUTHOR]

The behavior of eight measures of fit used to evaluate confirmatory factor analysis models was studied through Monte Carlo simulation to determine the extent to which sample size, model size, estimation procedure, and level of nonnormality affect fit when analyzing polytomous data. Implications of results for evaluating fit are discussed. (SLD)

The analysis by high throughput sequencing (HTS) and RT-PCR of Spanish pomegranate fruits showing yellow rings revealed the presence of viroid isolates closely related to fig isolates of apple dimple fruit viroid (ADFVd). The analysis of pomegranate public RNASeq data (Sequence Reads Archives, SRAs) from Israel provided evidence for the presence of similar ADFVd isolates in pomegranate trees in this country. In addition, reads or contigs of plum viroid I (PVd-I) isolates were also identified in two of the analyzed SRA datasets from Israel, suggesting the presence of this second viroid in pomegranate. Full length ADFVd genomic sequences have been recovered, increasing knowledge on the diversity of this viroid and on the pomegranate virome in which only four viruses and one viroid had previously been reported. [ABSTRACT FROM AUTHOR]