Temperature-bounded development of Dirofilaria immitis larvae restricts the geographical distribution and seasonality of its transmission: case study and decision support system for canine heartworm management in Australia.

Dirofilaria immitis is the causative agent of canine heartworm disease. We used the established heartworm development unit (HDU) principle to map the extrinsic incubation period (EIP) of D. immitis in Australia using historical weather data from 2013-2022. We found weather conditions suitable for EIP completion showed substantial seasonality and geographical variability. Whilst a considerable percentage of the Australian territory showed suitable weather conditions to always support EIP completion (17%), only 2.7% of the 2021 Australian human population lived in this region. Therefore, 97% of the population lived in an area that changed its EIP suitability within the study period. EIP completion is required prior to D. immitis transmission, meaning that infection risk of D. immitis is seasonal and location-dependent, being disrupted each year for most of the human population's dogs. We developed an online, open access tool allowing us to visualise EIP completion across Australia historically and in near real-time. We aim to support veterinarians to make risk-based recommendations for dirofilariosis prevention by using the tool, available at https://heartworm-mapping.adelaide.edu.au/shiny/.

Whitefly-Plant Interactions: An Integrated Molecular Perspective.

The rapid advances in available transcriptomic and genomic data and our understanding of the physiology and biochemistry of whitefly-plant interactions have allowed us to gain new and significant insights into the biology of whiteflies and their successful adaptation to host plants. In this review, we provide a comprehensive overview of the mechanisms that whiteflies have evolved to overcome the challenges of feeding on phloem sap. We also highlight the evolution and functions of gene families involved in host perception, evaluation, and manipulation; primary metabolism; and metabolite detoxification. We discuss the emerging themes in plant immunity to whiteflies, focusing on whitefly effectors and their sites of action in plant defense-signaling pathways. We conclude with a discussion of advances in the genetic manipulation of whiteflies and the potential that they hold for exploring the interactions between whiteflies and their host plants, as well as the development of novel strategies for the genetic control of whiteflies.

Geographical survey of the mycobiome and microbiome of Southern California glassy-winged sharpshooters.

The glassy-winged sharpshooter, Homalodisca vitripennis Germar, is an invasive xylem-feeding leafhopper with a devastating economic impact on California agriculture through transmission of the plant pathogen, Xylella fastidiosa. While studies have focused on X. fastidiosa or known symbionts of H. vitripennis, little work has been done at the scale of the microbiome (the bacterial community) or mycobiome (the fungal community). Here, we characterize the mycobiome and the microbiome of H. vitripennis across Southern California and explore correlations with captivity and host insecticide resistance status. Using high-throughput sequencing of the ribosomal internal transcribed spacer 1 region and the 16S rRNA gene to profile the mycobiome and microbiome, respectively, we found that while the H. vitripennis mycobiome significantly varied across Southern California, the microbiome did not. We also observed a significant difference in both the mycobiome and microbiome between captive and wild H. vitripennis. Finally, we found that the mycobiome, but not the microbiome, was correlated with insecticide resistance status in wild H. vitripennis. This study serves as a foundational look at the H. vitripennis mycobiome and microbiome across Southern California. Future work should explore the putative link between microbes and insecticide resistance status and investigate whether microbial communities should be considered in H. vitripennis management practices. IMPORTANCE The glassy-winged sharpshooter is an invasive leafhopper that feeds on the xylem of plants and transmits the devastating pathogen, Xylella fastidiosa, resulting in significant economic damage to California's agricultural system. While studies have focused on this pathogen or obligate symbionts of the glassy-winged sharpshooter, there is limited knowledge of the bacterial and fungal communities that make up its microbiome and mycobiome. To address this knowledge gap, we explored the composition of the mycobiome and the microbiome of the glassy-winged sharpshooter across Southern California and identified differences associated with geography, captivity, and host insecticide resistance status. Understanding sources of variation in the microbial communities associated with the glassy-winged sharpshooter is an important consideration for developing management strategies to control this invasive insect. This study is a first step toward understanding the role microbes may play in the glassy-winged sharpshooter's resistance to insecticides.

Advancing novel therapies for neurodegeneration through an innovative model for industry-academia collaborations: A decade of the Eisai-UCL experience.

External innovation initiatives in the pharmaceutical industry have become an integral part of research and development. Collaborations have been built to enhance innovation, mitigate risk, and share cost, especially for neurodegenerative diseases, a therapeutic area that has suffered from high attrition rates. This article outlines the Eisai-University College London (UCL) Drug Discovery and Development Collaboration as a case study of how to implement a productive industry-academic partnership. In the first 10 years, seven projects have been established and the first project, a novel anti-tau antibody for Alzheimer's disease, has entered clinical trials, providing early validation of this collaboration model.

Relative diagnostic accuracy of point-of-care tests to rule-in Dirofilaria immitis infection in clinically suspect dogs: A systematic review and meta-analysis.

Canine heartworm, Dirofilaria immitis, can cause severe disease and sometimes death of the host. Associated clinical signs, lack of preventative usage and regional endemicity are unlikely sufficient by themselves to reach a definitive diagnosis. Several point-of-care (POC) diagnostic tests are commercially available to aid in-clinic diagnosis, however, there is variable diagnostic accuracy reported and no synthesis of published evidence. This systematic review aims at meta-analysing the likelihood ratio of a positive result (LR+) to inform the selection and interpretation of POC tests in practice to rule-in heartworm infection when there is clinical suspicion. Three literature index interfaces (Web of Science, PubMed, Scopus) were searched on November 11th, 2022, for diagnostic test evaluation (DTE) articles assessing at least one currently commercialised POC test. Risk of bias was assessed adapting the QUADAS-2 protocol and articles with no evidence of high risk of bias were meta-analysed if deemed applicable to our review objective. Substantial between DTE heterogeneity was investigated including potential threshold or covariate effects. A total of 324 primary articles were sourced and 18 were retained for full text review of which only three had low risk of bias in all four QUADAS-2 domains. Of the nine heartworm POC tests evaluated, only three, IDEXX SNAP (n DTEs = 6), Zoetis WITNESS (n DTEs = 3) and Zoetis VETSCAN (n DTEs = 5) could be analysed. Both WITNESS and VETSCAN DTEs showed substantial heterogeneity due to a putative threshold effect and no summary point estimates could be reported. SNAP DTEs showed acceptable heterogeneity, and a summary LR+ was estimated at 559.0 (95%CI: 24.3-12,847.4). The quality and heterogeneity of heartworm POC test DTEs was highly variable which restricted our summary of the diagnostic accuracy to only the SNAP test. A positive result from the SNAP test provides strong evidence of the presence of an infection with adult heartworm(s) in a dog patient and this test is warranted to rule-in clinical suspicion(s) in clinics. However, our review did not appraise the literature to assess the fitness of SNAP test, or any other POC tests, to rule-out heartworm infection in dogs without clinical suspicion or following heartworm therapy.

Agricultural expansion raises groundwater and increases flooding in the South American plains.

Regional effects of farming on hydrology are associated mostly with irrigation. In this work, we show how rainfed agriculture can also leave large-scale imprints. The extent and speed of farming expansion across the South American plains over the past four decades provide an unprecedented case of the effects of rainfed farming on hydrology. Remote sensing analysis shows that as annual crops replaced native vegetation and pastures, floods gradually doubled their coverage, increasing their sensitivity to precipitation. Groundwater shifted from deep (12 to 6 meters) to shallow (4 to 0 meters) states, reducing drawdown levels. Field studies and simulations suggest that declining rooting depths and evapotranspiration in croplands are the causes of this hydrological transformation. These findings show the escalating flooding risks associated with rainfed agriculture expansion at subcontinental and decadal scales.

Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape.

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.

Unravelling the impact of insecticide-treated bed nets on childhood malaria in Malawi.

BACKGROUND: To achieve malaria elimination it is essential to understand the impact of insecticide-treated net (ITNs) programmes. Here, the impact of ITN access and use on malaria prevalence in children in Malawi was investigated using Malaria Indicator Survey (MIS) data. METHODS: MIS data from 2012, 2014 and 2017 were used to investigate the relationship between malaria prevalence in children (6-59 months) and ITN use. Generalized linear modelling (GLM), geostatistical mixed regression modelling and non-stationary GLM were undertaken to evaluate trends, spatial patterns and local dynamics, respectively. RESULTS: Malaria prevalence in Malawi was 27.1% (95% CI 23.1-31.2%) in 2012 and similar in both 2014 (32.1%, 95% CI 25.5-38.7) and 2017 (23.9%, 95% CI 20.3-27.4%). ITN coverage and use increased during the same time period, with household ITN access growing from 19.0% (95% CI 15.6-22.3%) of households with at least 1 ITN for every 2 people sleeping in the house the night before to 41.7% (95% CI 39.1-44.4%) and ITN use from 41.1% (95% CI 37.3-44.9%) of the population sleeping under an ITN the previous night to 57.4% (95% CI 55.0-59.9%). Both the geostatistical and non-stationary GLM regression models showed child malaria prevalence had a negative association with ITN population access and a positive association with ITN use although affected by large uncertainties. The non-stationary GLM highlighted the spatital heterogeneity in the relationship between childhood malaria and ITN dynamics across the country. CONCLUSION: Malaria prevalence in children under five had a negative association with ITN population access and a positive association with ITN use, with spatial heterogeneity in these relationships across Malawi. This study presents an important modelling approach that allows malaria control programmes to spatially disentangle the impact of interventions on malaria cases.

Healthcare Management of Human African Trypanosomiasis Cases in the Eastern, Muchinga and Lusaka Provinces of Zambia.

Human African trypanosomiasis (HAT) is a neglected tropical disease that has not received much attention in Zambia and most of the countries in which it occurs. In this study, we assessed the adequacy of the healthcare delivery system in diagnosis and management of rHAT cases, the environmental factors associated with transmission, the population at risk and the geographical location of rHAT cases. Structured questionnaires, focus group discussions and key informant interviews were conducted among the affected communities and health workers. The study identified 64 cases of rHAT, of which 26 were identified through active surveillance and 38 through passive surveillance. We identified a significant association between knowledge of the vector for rHAT and knowledge of rHAT transmission (p < 0.028). In all four districts, late or poor diagnosis occurred due to a lack of qualified laboratory technicians and diagnostic equipment. This study reveals that the current Zambian healthcare system is not able to adequately handle rHAT cases. Targeted policies to improve staff training in rHAT disease detection and management are needed to ensure that sustainable elimination of this public health problem is achieved in line with global targets.

Transcriptome and population structure of glassy-winged sharpshooters (Homalodisca vitripennis) with varying insecticide resistance in southern California.

BACKGROUND: Homalodisca vitripennis Germar, the glassy-winged sharpshooter, is an invasive insect in California and a critical threat to agriculture through its transmission of the plant pathogen, Xylella fastidiosa. Quarantine, broad-spectrum insecticides, and biological control have been used for population management of H. vitripennis since its invasion and subsequent proliferation throughout California. Recently wide-spread neonicotinoid resistance has been detected in populations of H. vitripennis in the southern portions of California's Central Valley. In order to better understand potential mechanisms of H. vitripennis neonicotinoid resistance, we performed RNA sequencing on wild-caught insecticide-resistant and relatively susceptible sharpshooters to profile their transcriptome and population structure. RESULTS: We identified 81 differentially expressed genes with higher expression in resistant individuals. The significant largest differentially expressed candidate gene linked to resistance status was a cytochrome P450 gene with similarity to CYP6A9. Furthermore, we observed an over-enrichment of GO terms representing functions supportive of roles in resistance mechanisms (cytochrome P450s, M13 peptidases, and cuticle structural proteins). Finally, we saw no evidence of broad-scale population structure, perhaps due to H. vitripennis' relatively recent introduction to California or due to the relatively small geographic scale investigated here. CONCLUSIONS: In this work, we characterized the transcriptome of insecticide-resistant and susceptible H. vitripennis and identified candidate genes that may be involved in resistance mechanisms for this species. Future work should seek to build on the transcriptome profiling performed here to confirm the role of the identified genes, particularly the cytochrome P450, in resistance in H. vitripennis. We hope this work helps aid future population management strategies for this and other species with growing insecticide resistance.

How the Air Clean Plan and carbon mitigation measures co-benefited China in PM2.5 reduction and health from 2014 to 2020.

China implemented a stringent Air Clean Plan (ACP) since 2013 to address environmental and health risks caused by ambient fine particulate matter (PM2.5). However, the policy effectiveness of ACP and co-benefits of carbon mitigation measures to environment and health are still largely unknown. Using satellite-based PM2.5 products produced in our previous study, concentration-response functions, and the logarithmic mean Divisia index (LMDI) method, we analyzed the spatiotemporal dynamics of premature deaths attributable to PM2.5 exposure, and quantitatively estimated the policy benefits of ACP and carbon mitigation measures. We found the annual PM2.5 concentrations in China decreased by 33.65 % (13.41 µg m-3) from 2014 to 2020, accompanied by a decrease in PM2.5-attributable premature deaths of 0.23 million (95 % confidence interval (CI): 0.22-0.27), indicating the huge benefits of China ACP for human health and environment. However, there were still 1.12 million (95 % CI: 0.79-1.56) premature deaths caused by the exposure of PM2.5 in mainland China in 2020. Among all ACP measures, clean production (contributed 55.98 % and 51.14 % to decrease in PM2.5 and premature deaths attributable to PM2.5) and energy consumption control (contributed 32.58 % and 29.54 % to decrease in PM2.5 and premature deaths attributable to PM2.5) made the largest contribution during the past seven years. Nevertheless, the environmental and health benefits of ACP are not fully synergistic in different regions, and the effectiveness of ACP measures reduced from 2018 to 2020. The co-effects of CO2 and PM2.5 has become one of the major drivers for PM2.5 and premature deaths reduction since 2018, confirming the clear environment and health co-benefits of carbon mitigation measures. Our study suggests, with the saturation of clean production and source control, more targeted region-specific strategies and synergistic air pollution-carbon mitigation measures are critical to achieving the WHO's Air Quality Guideline target and the UN's Sustainable Development Goal Target in China.

Gene Editing and Genetic Control of Hemipteran Pests: Progress, Challenges and Perspectives.

The origin of the order Hemiptera can be traced to the late Permian Period more than 230 MYA, well before the origin of flowering plants 100 MY later in during the Cretaceous period. Hemipteran species consume their liquid diets using a sucking proboscis; for phytophagous hemipterans their mouthparts (stylets) are elegant structures that enable voracious feeding from plant xylem or phloem. This adaptation has resulted in some hemipteran species becoming globally significant pests of agriculture resulting in significant annual crop losses. Due to the reliance on chemical insecticides for the control of insect pests in agricultural settings, many hemipteran pests have evolved resistance to insecticides resulting in an urgent need to develop new, species-specific and environmentally friendly methods of pest control. The rapid advances in CRISPR/Cas9 technologies in model insects such as Drosophila melanogaster, Tribolium castaneum, Bombyx mori, and Aedes aegypti has spurred a new round of innovative genetic control strategies in the Diptera and Lepidoptera and an increased interest in assessing genetic control technologies for the Hemiptera. Genetic control approaches in the Hemiptera have, to date, been largely overlooked due to the problems of introducing genetic material into the germline of these insects. The high frequency of CRISPR-mediated mutagenesis in model insect species suggest that, if the delivery problem for Hemiptera could be solved, then gene editing in the Hemiptera might be quickly achieved. Significant advances in CRISPR/Cas9 editing have been realized in nine species of Hemiptera over the past 4 years. Here we review progress in the Hemiptera and discuss the challenges and opportunities for extending contemporary genetic control strategies into species in this agriculturally important insect orderr.

Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning.

Accurate simulation of tumor growth during chemotherapy has significant potential to alleviate the risk of unknown side effects and optimize clinical trials. In this study, a 3D simulation model encompassing angiogenesis and tumor growth was developed to identify the vascular endothelial growth factor (VEGF) concentration and visualize the formation of a microvascular network. Accordingly, three anti-angiogenic drugs (Bevacizumab, Ranibizumab, and Brolucizumab) at different concentrations were evaluated in terms of their efficacy. Moreover, comprehensive mechanisms of tumor cell proliferation and endothelial cell angiogenesis are proposed to provide accurate predictions for optimizing drug treatments. The evaluation of simulation output data can extract additional features such as tumor volume, tumor cell number, and the length of new vessels using machine learning (ML) techniques. These were investigated to examine the different stages of tumor growth and the efficacy of different drugs. The results indicate that brolucizuman has the best efficacy by decreasing the length of sprouting new vessels by up to 16%. The optimal concentration was obtained at 10 mol m-3 with an effectiveness percentage of 42% at 20 days post-treatment. Furthermore, by performing comparative analysis, the best ML method (matching the performance of the reference simulations) was identified as reinforcement learning with a 3.3% mean absolute error (MAE) and an average accuracy of 94.3%.

Efficient CRISPR/Cas9-mediated genome modification of the glassy-winged sharpshooter Homalodisca vitripennis (Germar).

CRISPR/Cas9 technology enables the extension of genetic techniques into insect pests previously refractory to genetic analysis. We report the establishment of genetic analysis in the glassy-winged sharpshooter (GWSS), Homalodisca vitripennis, which is a significant leafhopper pest of agriculture in California. We use a novel and simple approach of embryo microinjection in situ on the host plant and obtain high frequency mutagenesis, in excess of 55%, of the cinnabar and white eye pigmentation loci. Through pair matings, we obtained 100% transmission of w and cn alleles to the G3 generation and also established that both genes are located on autosomes. Our analysis of wing phenotype revealed an unexpected discovery of the participation of pteridine pigments in wing and wing-vein coloration, indicating a role for these pigments beyond eye color. We used amplicon sequencing to examine the extent of off-target mutagenesis in adults arising from injected eggs, which was found to be negligible or non-existent. Our data show that GWSS can be easily developed as a genetic model system for the Hemiptera, enabling the study of traits that contribute to the success of invasive pests and vectors of plant pathogens. This will facilitate novel genetic control strategies.

A taxonomic-based joint species distribution model for presence-only data.

Species distribution models (SDMs) are an important class of model for mapping taxa spatially and are a key tool for tackling biodiversity loss. However, most common SDMs depend on presence-absence data and, despite the accumulation and exponential growth of biological occurrence data across the globe, the available data are predominantly presence-only (i.e. they lack real absences). Although presence-only SDMs do exist, they inevitably require assumptions about absences of the considered taxa and they are specified mostly for single species and, thus, do not exploit fully the information in related taxa. This greatly limits the utility of global biodiversity databases such as GBIF. Here, we present a Bayesian-based SDM for multiple species that operates directly on presence-only data by exploiting the joint distribution between the multiple ecological processes and, crucially, identifies the sampling effort per taxa which allows inference on absences. The model was applied to two case studies. One, focusing on taxonomically diverse taxa over central Mexico and another focusing on the monophyletic family Cactacea over continental Mexico. In both cases, the model was able to identify the ecological and sampling effort processes for each taxon using only the presence observations, environmental and anthropological data.

Unmet need for COVID-19 vaccination coverage in Kenya.

COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 - 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 - 16.74) - 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 - 36.96) - 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.

A Semi-Supervised Deep Rule-Based Approach for Complex Satellite Sensor Image Analysis.

Large-scale (large-area), fine spatial resolution satellite sensor images are valuable data sources for Earth observation while not yet fully exploited by research communities for practical applications. Often, such images exhibit highly complex geometrical structures and spatial patterns, and distinctive characteristics of multiple land-use categories may appear at the same region. Autonomous information extraction from these images is essential in the field of pattern recognition within remote sensing, but this task is extremely challenging due to the spectral and spatial complexity captured in satellite sensor imagery. In this research, a semi-supervised deep rule-based approach for satellite sensor image analysis (SeRBIA) is proposed, where large-scale satellite sensor images are analysed autonomously and classified into detailed land-use categories. Using an ensemble feature descriptor derived from pre-trained AlexNet and VGG-VD-16 models, SeRBIA is capable of learning continuously from both labelled and unlabelled images through self-adaptation without human involvement or intervention. Extensive numerical experiments were conducted on both benchmark datasets and real-world satellite sensor images to comprehensively test the validity and effectiveness of the proposed method. The novel information mining technique developed here can be applied to analyse large-scale satellite sensor images with high accuracy and interpretability, across a wide range of real-world applications.

Deep learning-based landslide susceptibility mapping.

Landslides are considered as one of the most devastating natural hazards in Iran, causing extensive damage and loss of life. Landslide susceptibility maps for landslide prone areas can be used to plan for and mitigate the consequences of catastrophic landsliding events. Here, we developed a deep convolutional neural network (CNN-DNN) for mapping landslide susceptibility, and evaluated it on the Isfahan province, Iran, which has not previously been assessed on such a scale. The proposed model was trained and validated using training (80%) and testing (20%) datasets, each containing relevant data on historical landslides, field records and remote sensing images, and a range of geomorphological, geological, environmental and human activity factors as covariates. The CNN-DNN model prediction accuracy was tested using a wide range of statistics from the confusion matrix and error indices from the receiver operating characteristic (ROC) curve. The CNN-DNN model was evaluated comprehensively by comparing it to several state-of-the-art benchmark machine learning techniques including the support vector machine (SVM), logistic regression (LR), Gaussian naïve Bayes (GNB), multilayer perceptron (MLP), Bernoulli Naïve Bayes (BNB) and decision tree (DT) classifiers. The CNN-DNN model for landslide susceptibility mapping was found to predict more accurately than the benchmark algorithms, with an AUC = 90.9%, IRs = 84.8%, MSE = 0.17, RMSE = 0.40, and MAPE = 0.42. The map provided by the CNN-DNN clearly revealed a high-susceptibility area in the west and southwest, related to the main Zagros trend in the province. These findings can be of great utility for landslide risk management and land use planning in the Isfahan province.

Spatial analysis of G.f.fuscipes abundance in Uganda using Poisson and Zero-Inflated Poisson regression models.

BACKGROUND: Tsetse flies are the major vectors of human trypanosomiasis of the form Trypanosoma brucei rhodesiense and T.b.gambiense. They are widely spread across the sub-Saharan Africa and rendering a lot of challenges to both human and animal health. This stresses effective agricultural production and productivity in Africa. Delimiting the extent and magnitude of tsetse coverage has been a challenge over decades due to limited resources and unsatisfactory technology. In a bid to overcome these limitations, this study attempted to explore modelling skills that can be applied to spatially estimate tsetse abundance in the country using limited tsetse data and a set of remote-sensed environmental variables. METHODOLOGY: Entomological data for the period 2008-2018 as used in the model were obtained from various sources and systematically assembled using a structured protocol. Data harmonisation for the purposes of responsiveness and matching was carried out. The key tool for tsetse trapping was itemized as pyramidal trap in many instances and biconical trap in others. Based on the spatially explicit assembled data, we ran two regression models; standard Poisson and Zero-Inflated Poisson (ZIP), to explore the associations between tsetse abundance in Uganda and several environmental and climatic covariates. The covariate data were constituted largely by satellite sensor data in form of meteorological and vegetation surrogates in association with elevation and land cover data. We finally used the Zero-Inflated Poisson (ZIP) regression model to predict tsetse abundance due to its superiority over the standard Poisson after model fitting and testing using the Vuong Non-Nested statistic. RESULTS: A total of 1,187 tsetse sampling points were identified and considered as representative for the country. The model results indicated the significance and level of responsiveness of each covariate in influencing tsetse abundance across the study area. Woodland vegetation, elevation, temperature, rainfall, and dry season normalised difference vegetation index (NDVI) were important in determining tsetse abundance and spatial distribution at varied scales. The resultant prediction map shows scaled tsetse abundance with estimated fitted numbers ranging from 0 to 59 flies per trap per day (FTD). Tsetse abundance was found to be largest at low elevations, in areas of high vegetative activity, in game parks, forests and shrubs during the dry season. There was very limited responsiveness of selected predictors to tsetse abundance during the wet season, matching the known fact that tsetse disperse most significantly during wet season. CONCLUSIONS: A methodology was advanced to enable compilation of entomological data for 10 years, which supported the generation of tsetse abundance maps for Uganda through modelling. Our findings indicate the spatial distribution of the G. f. fuscipes as; low 0-5 FTD (48%), medium 5.1-35 FTD (18%) and high 35.1-60 FTD (34%) grounded on seasonality. This approach, amidst entomological data shortages due to limited resources and absence of expertise, can be adopted to enable mapping of the vector to provide better decision support towards designing and implementing targeted tsetse and tsetse-transmitted African trypanosomiasis control strategies.

Improved draft reference genome for the Glassy-winged Sharpshooter (Homalodisca vitripennis), a vector for Pierce's disease.

Homalodisca vitripennis (Hemiptera: Cicadellidae), known as the glassy-winged sharpshooter, is a xylem feeding leafhopper and an important agricultural pest as a vector of Xylella fastidiosa, which causes Pierce's disease in grapes and a variety of other scorch diseases. The current H. vitripennis reference genome from the Baylor College of Medicine's i5k pilot project is a 1.4-Gb assembly with 110,000 scaffolds, which still has significant gaps making identification of genes difficult. To improve on this effort, we used a combination of Oxford Nanopore long-read sequencing technology combined with Illumina sequencing reads to generate a better assembly and first-pass annotation of the whole genome sequence of a wild-caught Californian (Tulare County) individual of H. vitripennis. The improved reference genome assembly for H. vitripennis is 1.93-Gb in length (21,254 scaffolds, N50 = 650 Mb, BUSCO completeness = 94.3%), with 33.06% of the genome masked as repetitive. In total, 108,762 gene models were predicted including 98,296 protein-coding genes and 10,466 tRNA genes. As an additional community resource, we identified 27 orthologous candidate genes of interest for future experimental work including phenotypic marker genes like white. Furthermore, as part of the assembly process, we generated four endosymbiont metagenome-assembled genomes, including a high-quality near complete 1.7-Mb Wolbachia sp. genome (1 scaffold, CheckM completeness = 99.4%). The improved genome assembly and annotation for H. vitripennis, curated set of candidate genes, and endosymbiont MAGs will be invaluable resources for future research of H. vitripennis.