Electronically delivered, multicomponent intervention to reduce unnecessary antibiotic prescribing for respiratory infections in primary care: a cluster randomised trial using electronic health records—REDUCE Trial study original protocol

Introduction Respiratory tract infections (RTIs) account for about 60% of antibiotics prescribed in primary care. This study aims to test the effectiveness, in a cluster randomised controlled trial, of electronically delivered, multicomponent interventions to reduce unnecessary antibiotic prescribing when patients consult for RTIs in primary care. The research will specifically evaluate the effectiveness of feeding back electronic health records (EHRs) data to general practices. Methods and analysis 2-arm cluster randomised trial using the EHRs of the Clinical Practice Research Datalink (CPRD). General practices in England, Scotland, Wales and Northern Ireland are being recruited and the general population of all ages represents the target population. Control trial arm practices will continue with usual care. Practices in the intervention arm will receive complex multicomponent interventions, delivered remotely to information systems, including (1) feedback of each practice's antibiotic prescribing through monthly antibiotic prescribing reports estimated from CPRD data; (2) delivery of educational and decision support tools; (3) a webinar to explain and promote effective usage of the intervention. The intervention will continue for 12?months. Outcomes will be evaluated from CPRD EHRs. The primary outcome will be the number of antibiotic prescriptions for RTIs per 1000 patient years. Secondary outcomes will be: the RTI consultation rate; the proportion of consultations for RTI with an antibiotic prescribed; subgroups of age; different categories of RTI and quartiles of intervention usage. There will be more than 80% power to detect an absolute reduction in antibiotic prescription for RTI of 12 per 1000 registered patient years. Total healthcare usage will be estimated from CPRD data and compared between trial arms. Ethics and dissemination Trial protocol was approved by the National Research Ethics Service Committee (14/LO/1730). The pragmatic design of the trial will enable subsequent translation of effective interventions at scale in order to achieve population impact. <br/

Response of morphological and biochemical traits of maize genotypes under waterlogging stress

Maize (Zea mays L.) is one of the most important cereal crops cultivated around the world. Waterlogging stress is a major production constraint of maize production in rain-fed agricultural systems. The main objective of this experiment was to investigate the effect of continuous waterlogging on morphological and biochemical traits of maize genotypes at the vegetative stage. Ten maize genotypes were treated under no waterlogging (control) and continuous waterlogging of five centimeters depth for 10 days. The treatments were applied to the plants at their 45 days of age. Visual leaf injury scores from Leaf 4 (youngest leaf is the reference point) to Leaf 7 separated tolerant and susceptible genotypes. Waterlogging stress significantly reduced the total number of live leaves and chlorophyll content in leaf tissues in susceptible genotypes. The anatomical study revealed that tolerant maize genotypes produce a large number of aerenchyma cells under waterlogging stress compared to susceptible genotypes. The enzymatic activities of ascorbate peroxidase (APX) and peroxidase (POD) exhibited a greater increase in tolerant genotypes than susceptible genotypes whereas the contents of reactive oxygen species (H2O2) greatly increased in susceptible genotypes than tolerant genotypes under waterlogging stress compared to control. Principal component 2 (PC2) indicated that increasing plant height in the genotypes BHM-14, BHM-13 and BHM-9 was associated with waterlogging tolerance. The findings of this experiment will add value to maize breeding to screen out maize genotypes for waterlogging stress tolerance

Epidemiology, Genetics and Resistance of <em>Alternaria</em> Blight in Oilseed <em>Brassica</em>

Alternaria blight is one of the most deadly diseases of oilseed Brassica. This recalcitrant disease causes up to 50% yield loss across the globe. The disease is mainly caused by Alternaria brassicae and Alternaria brassicicola. These pathogens lack sexual stages and survive as conidia or condiospores on the debris of previous crops and susceptible weeds. Developing resistant oilseed Brassica cultivars to this disease has become a prime concern for researchers over the years. In absence of resistant oilseed Brassica cultivar, identification and introgression of resistance related genes can be a potential source for Alternaria blight resistance. As resistance toward Alternaria blight is governed by polygenes, intercrossing between the tolerant genotypes and subsequent selection will be the most appropriate way to transfer the quantitative resistance. For that reason, future breeding goal should focus on screening of germplasms for selecting genotypes containing resistance genes and structural features that favors resistance, like thick epicuticular wax, biochemical components such as phenols, phytoalexins and lower soluble sugars, reducing sugars and soluble nitrogen. Selected genotypes should be brought under appropriate breeding programs for attaining Alternaria blight resistance

A phylogenetic classification of the world’s tropical forests

Knowledge about the biogeographic affinities of the world’s tropical forests helps to better understand regional differences in forest structure, diversity, composition and dynamics. Such understanding will enable anticipation of region specific responses to global environmental change. Modern phylogenies, in combination with broad coverage of species inventory data, now allow for global biogeographic analyses that take species evolutionary distance into account. Here we present the first classification of the world’s tropical forests based on their phylogenetic similarity. We identify five principal floristic regions and their floristic relationships: (1) Indo-Pacific, (2) Subtropical, (3) African, (4) American, and (5) Dry forests. Our results do not support the traditional Neo- versus Palaeo-tropical forest division, but instead separate the combined American and African forests from their Indo-Pacific counterparts. We also find indications for the existence of a global dry forest region, with representatives in America, Africa, Madagascar and India. Additionally, a northern hemisphere Subtropical forest region was identified with representatives in Asia and America, providing support for a link between Asian and American northern hemisphere forests

Concern with COVID-19 pandemic threat and attitudes towards immigrants: The mediating effect of the desire for tightness

Tightening social norms is thought to be adaptive for dealing with collective threat yet it may have negative consequences for increasing prejudice. The present research investigated the role of desire for cultural tightness, triggered by the COVID-19 pandemic, in increasing negative attitudes towards immigrants. We used participant-level data from 41 countries (N = 55,015) collected as part of the PsyCorona project, a crossnational longitudinal study on responses to COVID-19. Our predictions were tested through multilevel and SEM models, treating participants as nested within countries. Results showed that people’s concern with COVID19 threat was related to greater desire for tightness which, in turn, was linked to more negative attitudes towards immigrants. These findings were followed up with a longitudinal model (N = 2,349) which also showed that people’s heightened concern with COVID-19 in an earlier stage of the pandemic was associated with an increase in their desire for tightness and negative attitudes towards immigrants later in time. Our findings offer insight into the trade-offs that tightening social norms under collective threat has for human groups

OnAIR: Applications of the NASA On-Board Artificial Intelligence Research Platform

Infusing artificial intelligence algorithms into production aerospace systems canbe challenging due to costs, timelines, and a risk-averse industry. We introducethe Onboard Artificial Intelligence Research (OnAIR) platform, an open-sourcesoftware pipeline and cognitive architecture tool that enables full life cycle AIresearch for on-board intelligent systems. We begin with a description and userwalk-through of the OnAIR tool. Next, we describe four use cases of OnAIR forboth research and deployed onboard applications, detailing their use of OnAIRand the benefits it provided to the development and function of each respective scenario. Lastly, we describe two upcoming planned deployments which will leverage OnAIR for crucial mission outcomes. We conclude with remarks onfuture work and goals for the forward progression of OnAIR as a tool to enable alarger AI and aerospace research community

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic

Before vaccines for COVID-19 became available, a set of infection prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection prevention behavior in 56,072 participants across 28 countries, administered in March-May 2020. The machine- learning model predicted 52% of the variance in infection prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual- level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically-derived predictors were relatively unimportant

‘We are all in the same boat’ : how societal discontent affects intention to help during the COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) pandemic has caused a global health crisis. Consequently, many countries have adopted restrictive measures that caused a substantial change in society. Within this framework, it is reasonable to suppose that a sentiment of societal discontent, defined as generalized concern about the precarious state of society, has arisen. Literature shows that collectively experienced situations can motivate people to help each other. Since societal discontent is conceptualized as a collective phenomenon, we argue that it could influence intention to help others, particularly those who suffer from coronavirus. Thus, in the present study, we aimed (a) to explore the relationship between societal discontent and intention to help at the individual level and (b) to investigate a possible moderating effect of societal discontent at the country level on this relationship. To fulfil our purposes, we used data collected in 42 countries (N = 61,734) from the PsyCorona Survey, a cross-national longitudinal study. Results of multilevel analysis showed that, when societal discontent is experienced by the entire community, individuals dissatisfied with society are more prone to help others. Testing the model with longitudinal data (N = 3,817) confirmed our results. Implications for those findings are discussed in relation to crisis management. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement

.Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individuallevel injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant