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The constant changes experienced in agricultural activities due to climate change pose a great challenge to melon production. Hence, this research examined the determinants of melon farmers' adaptation strategies to cope with climate change hazards in southern–southern Nigeria. The research ultimately depended on primary data collected by using a set of questionnaires and interviews. The data were obtained from 260 samples retrieved from melon farmers by using multistage sampling techniques. The data were analyzed using the multivariate probit (MVP) model and partial eta squared test. The results of the MVP model showed that age (− 0.009), marital status (0.200), access to information on climate change (0.567) and crop insurance (0.214) were significant at the 0.01 level, while household size (− 0.030) was significant at the 0.05 level and determined the adoption of crop diversification. Educational level (0.012), extension contact (0.138) and access to credit (0.122) were significant at the 0.05 level, while access to information on climate change (0.415) was significant at the 0.01 level and determined the adoption of change in planting dates. Age (− 0.010) and access to information on climate change (0.381) were significant at the 0.01 level, while sex (− 0.139), marital status (0.158) and off-farm income (− 2.3E-7) were significant at the 0.05 level and determined the adoption of mixed farming. Farming experience (0.005) is significant at the 0.05 level, while access to information on climate change (0.529) and crop insurance (0.272) are significant at the 0.01 level and determine the adoption of drought-tolerant crop species. Access to information on climate change (0.536) is significant at the 0.01 level, indicating the adoption of improved crop species. Age (− 0.010), farm size (− 0.085) and crop insurance (0.206) were significant at the 0.05 level, while access to information on climate change (0.353) was significant at the 0.01 level and determined the adoption of off-farm job opportunities. The study recommends the availability and accessibility of credit, climate-smart agricultural practices, and the establishment of public‒private partnerships, among others. [ABSTRACT FROM AUTHOR]

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome. A multifaceted computational strategy identifies 16 genetic variants contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing dataset of a cohort of Italian patients. [ABSTRACT FROM AUTHOR]