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

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/

Effectiveness and safety of electronically delivered prescribing feedback and decision support on antibiotic use for respiratory illness in primary care:REDUCE cluster-randomised trial

Objectives: To evaluate the effectiveness and safety at population-scale of electronically delivered prescribing feedback and decision support interventions at reducing antibiotic (AB) prescribing for self-limiting respiratory infections (RTI).Design: Open-label, two-arm, cluster randomised controlled trialSetting: UK general practices in the Clinical Practice Research DatalinkParticipants: 79 general practices (582,675 patient-years) randomised (1:1) to antimicrobial stewardship (AMS) intervention or usual care.Interventions: The AMS intervention comprised a brief training webinar, automated monthly feedback reports of AB prescribing, and electronic decision support tools to inform appropriate AB prescribing over 12 months. Intervention components were delivered electronically, supported by a local practice ‘champion’.Main outcome measures: The primary outcome was the rate of AB prescriptions for RTI from electronic health records. Serious bacterial complications were evaluated for safety. Analysis was by Poisson regression with general practice as a random effect, adjusting for covariates. Pre-specified sub-group analyses by age-group are reported.Results: There were 41 AMS trial arm practices (323,155 patient-years) and 38 usual care trial arm practices (259,520 patient-years). AB prescribing rate ratios (RR) were: unadjusted, 0.89 (0.86 to 1.16); and adjusted, 0.88 (95% CI, 0.78 to 0.99, P=0.04); with AB prescribing rates of 98.7 per 1,000 patient-years for AMS (31,907 AB prescriptions) and 107.6 per 1,000 for usual care (27,923 AB prescriptions). AB prescribing was reduced most in adults aged 15-84 years (adjusted RR 0.84, 95%CI 0.75 to 0.95), with one antibiotic prescription per year avoided for every 62 (40 to 200) patients. There was no evidence of effect for children less than 15 years (adjusted RR 0.96, 0.82 to 1.12) or adults aged 85 years and older (adjusted RR 0.97, 0.79 to 1.18). There was no evidence that serious bacterial complications increased (adjusted RR 0.92, 0.74 to 1.13).Conclusions: Electronically-delivered interventions, integrated into practice workflow result in moderate reductions AB prescribing for RTI in adults, which are likely to be of importance for public health. Antibiotic prescribing to children or older people requires further evaluation.Trial registration: ISRCTN95232781<br/

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

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

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

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

Trust in government regarding COVID-19 and its associations with preventive health behaviour and prosocial behaviour during the pandemic: a cross-sectional and longitudinal study

Background. The effective implementation of government policies and measures for controlling the coronavirus disease 2019 (COVID-19) pandemic requires compliance from the public. This study aimed to examine cross-sectional and longitudinal associations of trust ingovernment regarding COVID-19 control with the adoption of recommended health behaviours and prosocial behaviours, and potential determinants of trust in government duringthe pandemic.Methods. This study analysed data from the PsyCorona Survey, an international project onCOVID-19 that included 23 733 participants from 23 countries (representative in age andgender distributions by country) at baseline survey and 7785 participants who also completedfollow-up surveys. Specification curve analysis was used to examine concurrent associationsbetween trust in government and self-reported behaviours. We further used structural equation model to explore potential determinants of trust in government. Multilevel linear regressions were used to examine associations between baseline trust and longitudinal behavioural changes.Results. Higher trust in government regarding COVID-19 control was significantly associatedwith higher adoption of health behaviours (handwashing, avoiding crowded space, self-quarantine) and prosocial behaviours in specification curve analyses (median standardised β =0.173 and 0.229, p < 0.001). Government perceived as well organised, disseminating clear messages and knowledge on COVID-19, and perceived fairness were positively associated withtrust in government (standardised β = 0.358, 0.230, 0.056, and 0.249, p < 0.01). Higher trustat baseline survey was significantly associated with lower rate of decline in health behavioursover time ( p for interaction = 0.001).Conclusions. These results highlighted the importance of trust in government in the control of Covid-19

.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