Comparison of diagnostic accuracy for eight SARS-CoV-2 serological assays

IntroductionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serological tests have been suggested as an additional diagnostic tool in highly suspected cases with a negative molecular test and determination of seroprevalence in population. We compared the diagnostic performance of eight commercial serological assays for IgA, IgM, and IgG antibodies to the SARS-CoV-2 virus.Materials and methodsThe comparison study was performed on a total of 76 serum samples: 30 SARS-CoV-2 polymerase chain reaction (PCR)-negative and 46 SARS-CoV-2 PCR-positive patients with asymptomatic to severe disease and symptoms duration from 3-30 days. The study included: three rapid lateral flow immunochromatographic assays (LFIC), two enzyme-linked immunosorbent assays (ELISA), and three chemiluminescence immunoassays (CLIA).ResultsAgreement between IgM assays were minimal to moderate (kappa 0.26 to 0.63) and for IgG moderate to excellent (kappa 0.72 to 0.92). Sensitivities improved with > 10 days of symptoms and were: 30% to 89% for IgM; 89% to 100% for IgG; 96% for IgA; 100% for IgA/IgM combination; 96% for total antibodies. Overall specificities were: 90% to 100% for IgM; 85% to 100% for IgG; 90% for IgA; 70% for IgA/IgM combination; 100% for total antibodies. Diagnostic accuracy for IgG ELISA and CIA assays were excellent (AUC ≥ 0.90), without significant difference. IgA showed significantly better diagnostic accuracy than IgM (P < 0.001).ConclusionThere is high variability between IgM assays independently of the assay format, while IgG assays showed moderate to perfect agreement. The appropriate time for testing is crucial for the proper immunity investigation.     

Predicting elections from social media: a three-country,three-method comparative study

ABSTRACT

This study introduces and evaluates the robustness of different volumetric, sentiment, and social network approaches to predict the elections in three Asian countries – Malaysia, India, and Pakistan from Twitter posts. We find that predictive power of social media performs well for India and Pakistan but is not effective for Malaysia. Overall, we find that it is useful to consider the recency of Twitter posts while using it to predict a real outcome, such as an election result. Sentiment information mined using machine learning models was the most accurate predictor of election outcomes. Social network information is stable despite sudden surges in political discussions, for e.g. around elections-related news events. Methods combining sentiment and volume information, or sentiment and social network information, are effective at predicting smaller vote shares, for e.g. vote shares in the case of independent candidates and regional parties. We conclude with a detailed discussion on the caveats of social media analysis for predicting real-world outcomes and recommendations for future work.     

The round-wheel compound bow model revisited: a new extension

An extended offset-eccentric model of an archery twin-round-wheel compound bow is derived. Varying some parameters of the model, the respective effects on the calculated force–draw curve are considered. Two static quality coefficients for the compound bow are introduced. It was found that the twin-round-wheel compound bow can be designed to be more energetic with the help of the model. For a bow with some modifications 18.5% increment of energy was calculated. Also a theoretical limit for the force–draw curve of the compound bow is concluded.     

Factors of Professional Learning Community Implementation and Effect on Student Achievement

ABSTRACT

Professional learning communities (PLCs) promote collaboration among school personnel in an effort to stimulate student learning. Using data obtained from a larger statewide initiative in Missouri, the current study examined data from 181 schools (102 elementary schools, 32 middle schools, 41 high schools, and 6 other schools, average of 428.76 students) to determine (a) the factors that can be used to assess the effects of PLCs, (b) how well PLCs relate to student achievement, and (c) the extent to which teams differentially implement the factors. An exploratory factor analysis and confirmatory factor analyses resulted in two broader constructs that represented PLC attributes, collaborative leadership process and data-driven systems for learning, both of which correlated with student achievement and together provided unique variance in mathematics beyond school variables and achievement scores from before the PLC began. Directions for future research and implications for practice are discussed.     

Measuring Value in Open Access Repositories

Open access institutional repositories were created to promote access to information, encourage scholarly communication, and demonstrate institutional prestige. While these repositories have been widely adopted, the quality of their contents often fails to represent their institution's scholarly output. Moreover, current research uses measurements of quantity, not quality, to assess their value. In response, this article opens new areas of scholarly inquiry by assessing the quality of contents. This is accomplished through a cross-sectional study of repositories at American colleges and universities across the academic spectrum, using citation indexing to identify an institution's articles and authors of highest impact.     

Tools for Reflection: Video-Based Reflection Within a Preservice Community of Practice

Current reform in science education calls for teachers to understand student thinking within a lesson to effectively address students’ needs (NRC in A framework for K-12 science education: practices, crosscutting concepts, and core ideas. National Academy Press, Washington, DC, 2012; NRC in Guide to implementing the Next Generation Science Standards. The National Academies Press, Washington, DC, 2015). This study investigates how to scaffold preservice teachers with learning to attend to students’ thinking for the purpose of guiding curricular decisions. The study focuses on one team teaching a science unit during their early field experience. We sought to understand how participants’ thoughts and abilities changed through participation in a moderated community of practice using video of their own teaching as a reflective tool. We examined how these changes affected both their classroom practice and their decision-making for future lessons. Evidence shows growth in participants’ ability to identify opportunities to elicit, assess, and use students’ thinking to guide instructional decisions. Implications for use of the approach used in this study to begin developing novice teachers’ pedagogical content knowledge for teaching science are discussed.     

Balancing supportive relationships and developing independence: an activity theory approach to understanding feedback in context for master’s students

Abstract

Master’s students are expected to be self-regulating and independent learners. Dialogic feedback has been identified as one way of promoting such independence. There continues to be concern about the extent to which master’s students are achieving this level of functioning. This study aimed to identify feedback practices and contexts which facilitated student engagement and independence. Working with students as co-researchers, interviews were conducted with 27 master’s students from three programmes. Activity theory was used as an analytical tool to generate understanding of feedback in the social context of each programme. Findings indicate there can be tension between factors which promote dialogical feedback and those which promote independence, and that active dialogic feedback with staff may limit student engagement with peer feedback.     

On the relationship between the structural and socioacademic communities of a coauthorship network

This article presents a study that compares detected structural communities in a coauthorship network to the socioacademic characteristics of the scholars that compose the network. The coauthorship network was created from the bibliographic record of a multi-institution, interdisciplinary research group focused on the study of sensor networks and wireless communication. Four different community detection algorithms were employed to assign a structural community to each scholar in the network: leading eigenvector, walktrap, edge betweenness and spinglass. Socioacademic characteristics were gathered from the scholars and include such information as their academic department, academic affiliation, country of origin, and academic position. A Pearson’s χ²test, with a simulated Monte Carlo, revealed that structural communities best represent groupings of individuals working in the same academic department and at the same institution. A generalization of this result suggests that, even in interdisciplinary, multi-institutional research groups, coauthorship is primarily driven by departmental and institutional affiliation.