Mosquito control by abatement programmes in the United States: perspectives and lessons for countries in sub-Saharan Africa.

Africa and the United States are both large, heterogeneous geographies with a diverse range of ecologies, climates and mosquito species diversity which contribute to disease transmission and nuisance biting. In the United States, mosquito control is nationally, and regionally coordinated and in so much as the Centers for Disease Control (CDC) provides guidance, the Environmental Protection Agency (EPA) provides pesticide registration, and the states provide legal authority and oversight, the implementation is usually decentralized to the state, county, or city level. Mosquito control operations are organized, in most instances, into fully independent mosquito abatement districts, public works departments, local health departments. In some cases, municipalities engage independent private contractors to undertake mosquito control within their jurisdictions. In sub-Saharan Africa (SSA), where most vector-borne disease endemic countries lie, mosquito control is organized centrally at the national level. In this model, the disease control programmes (national malaria control programmes or national malaria elimination programmes (NMCP/NMEP)) are embedded within the central governments' ministries of health (MoHs) and drive vector control policy development and implementation. Because of the high disease burden and limited resources, the primary endpoint of mosquito control in these settings is reduction of mosquito borne diseases, primarily, malaria. In the United States, however, the endpoint is mosquito control, therefore, significant (or even greater) emphasis is laid on nuisance mosquitoes as much as disease vectors. The authors detail experiences and learnings gathered by the delegation of African vector control professionals that participated in a formal exchange programme initiated by the Pan-African Mosquito Control Association (PAMCA), the University of Notre Dame, and members of the American Mosquito Control Association (AMCA), in the United States between the year 2021 and 2022. The authors highlight the key components of mosquito control operations in the United States and compare them to mosquito control programmes in SSA countries endemic for vector-borne diseases, deriving important lessons that could be useful for vector control in SSA

VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center in 2023.

The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) is a Bioinformatics Resource Center funded by the National Institutes of Health with additional funding from the Wellcome Trust. VEuPathDB supports >600 organisms that comprise invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Since 2004, VEuPathDB has analyzed omics data from the public domain using contemporary bioinformatic workflows, including orthology predictions via OrthoMCL, and integrated the analysis results with analysis tools, visualizations, and advanced search capabilities. The unique data mining platform coupled with >3000 pre-analyzed data sets facilitates the exploration of pertinent omics data in support of hypothesis driven research. Comparisons are easily made across data sets, data types and organisms. A Galaxy workspace offers the opportunity for the analysis of private large-scale datasets and for porting to VEuPathDB for comparisons with integrated data. The MapVEu tool provides a platform for exploration of spatially resolved data such as vector surveillance and insecticide resistance monitoring. To address the growing body of omics data and advances in laboratory techniques, VEuPathDB has added several new data types, searches and features, improved the Galaxy workspace environment, redesigned the MapVEu interface and updated the infrastructure to accommodate these changes

VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center

The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) represents the 2019 merger of VectorBase with the EuPathDB projects. As a Bioinformatics Resource Center funded by the National Institutes of Health, with additional support from the Welllcome Trust, VEuPathDB supports >500 organisms comprising invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Designed to empower researchers with access to Omics data and bioinformatic analyses, VEuPathDB projects integrate >1700 pre-analysed datasets (and associated metadata) with advanced search capabilities, visualizations, and analysis tools in a graphic interface. Diverse data types are analysed with standardized workflows including an in-house OrthoMCL algorithm for predicting orthology. Comparisons are easily made across datasets, data types and organisms in this unique data mining platform. A new site-wide search facilitates access for both experienced and novice users. Upgraded infrastructure and workflows support numerous updates to the web interface, tools, searches and strategies, and Galaxy workspace where users can privately analyse their own data. Forthcoming upgrades include cloud-ready application architecture, expanded support for the Galaxy workspace, tools for interrogating host-pathogen interactions, and improved interactions with affiliated databases (ClinEpiDB, MicrobiomeDB) and other scientific resources, and increased interoperability with the Bacterial & Viral BRC

Guidelines for Genome-Scale Analysis of Biological Rhythms.

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding "big data" that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them