Academic literature on the topic 'Spatial disease'

Agriculture is an important resource for the global economy, while plant disease causes devastating yield loss. To control plant disease, every country around the world spends trillions of dollars on disease management. Some of the recent solutions are based on the utilization of computer vision techniques in plant science which helps to monitor crop industries such as tomato, maize, grape, citrus, potato and cassava, and other crops. The attention-based CNN network has become effective in plant disease prediction. However, existing approaches are less precise in detecting minute-scale disease in the leaves. Our proposed Channel–Spatial segmentation network will help to determine the disease in the leaf, and it consists of two main stages: (a) channel attention discriminates diseased and healthy parts as well as channel-focused features, and (b) spatial attention consumes channel-focused features and highlights the diseased part for the final prediction process. This investigation forms a channel and spatial attention in a sequential way to identify diseased and healthy leaves. Finally, identified leaf diseases are divided into Mild, Medium, Severe, and Healthy. Our model successfully predicts the diseased leaves with the highest accuracy of 99.76%. Our research study shows evaluation metrics, comparison studies, and expert analysis to comprehend the network performance. This concludes that the Channel–Spatial segmentation network can be used effectively to diagnose different disease degrees based on a combination of image processing and statistical calculation.

Ecological dynamics can produce a variety of striking patterns. On ecological time scales, pattern formation has been hypothesized to be due to the interaction between a species and its local environment. On longer time scales, evolutionary factors must be taken into account. To examine the evolutionary robustness of spatial pattern formation, we construct a spatially explicit model of vegetation in the presence of a pathogen. Initially, we compare the dynamics for vegetation parameters that lead to competition induced spatial patterns and those that do not. Over ecological time scales, banded spatial patterns dramatically reduced the ability of the pathogen to spread, lowered its endemic density and hence increased the persistence of the vegetation. To gain an evolutionary understanding, each plant was given a heritable trait defining its resilience to competition; greater competition leads to lower vegetation density but stronger spatial patterns. When a disease is introduced, the selective pressure on the plant's resilience to the competition parameter is determined by the transmission of the disease. For high transmission, vegetation that has low resilience to competition and hence strong spatial patterning is an evolutionarily stable strategy. This demonstrates a novel mechanism by which striking spatial patterns can be maintained by disease-driven selection.

Spatial transcriptomics (ST) is transforming the way we can study gene expression and its regulation through position-specific resolution within tissues. However, as in bulk RNA-Seq, transposable elements (TEs) are not being studied due to their highly repetitive nature. In recent years, TEs have been recognized as important regulators of gene expression, and thus, TE expression analysis in a spatially resolved manner could further help to understand their role in gene regulation within tissues. We present SpatialTE, a tool to analyze TE expression from ST datasets and show its application in somatic and diseased tissues. The results indicate that TEs have spatially regulated expression patterns and that their expression profiles are spatially altered in ALS disease, indicating that TEs might perform differential regulatory functions within tissue organs. We have made SpatialTE publicly available as open-source software under an MIT license.

The understanding of human mobility and the development of qualitative models as well as quantitative theories for it is of key importance to the research of human infectious disease dynamics on large geographical scales. In our globalized world, mobility and traffic have reached a complexity and volume of unprecedented degree. Long range human mobility is now responsible for the rapid geographical spread of emergent infectious diseases. Multiscale human mobility networks exhibit two prominent features: (1) Networks exhibit a strong heterogeneity, the distribution of weights, traffic fluxes and populations sizes of communities range over many orders of magnitude. (2) Although the interaction magnitude in terms of traffic intensities decreases with distance, the observed power-laws indicate that long range interactions play a significant role in spatial disease dynamics. We will review how the topological features of traffic networks can be incorporated in models for disease dynamics and show, that the way topology is translated into dynamics can have a profound impact on the overall disease dynamics. We will also introduce a class of spatially extended models in which the impact and interplay of both spatial heterogeneity as well as long range spatial interactions can be investigated in a systematic fashion. Our analysis of multiscale human mobility networks is based on a proxy network of dispersing US dollar bills, which we incorporated in a model to produce real-time epidemic forecasts that projected the spatial spread of the recent outbreak of Influenza A(H1N1).

The understanding of human mobility and the development of qualitative models as well as quantitative theories for it is of key importance to the research of human infectious disease dynamics on large geographical scales. In our globalized world, mobility and traffic have reached a complexity and volume of unprecedented degree. Long range human mobility is now responsible for the rapid geographical spread of emergent infectious diseases. Multiscale human mobility networks exhibit two prominent features: (1) Networks exhibit a strong heterogeneity, the distribution of weights, traffic fluxes and populations sizes of communities range over many orders of magnitude. (2) Although the interaction magnitude in terms of traffic intensities decreases with distance, the observed power-laws indicate that long range interactions play a significant role in spatial disease dynamics. We will review how the topological features of traffic networks can be incorporated in models for disease dynamics and show, that the way topology is translated into dynamics can have a profound impact on the overall disease dynamics. We will also introduce a class of spatially extended models in which the impact and interplay of both spatial heterogeneity as well as long range spatial interactions can be investigated in a systematic fashion. Our analysis of multiscale human mobility networks is based on a proxy network of dispersing US dollar bills, which we incorporated in a model to produce real-time epidemic forecasts that projected the spatial spread of the recent outbreak of Influenza A(H1N1).

This thesis is concerned with providing further statistical development in the area of space-time modelling with particular application to disease data. We briefly consider the non-Bayesian approaches of empirical mode decomposition and generalised linear modelling for analysing space-time data, but our main focus is on the increasingly popular Bayesian hierarchical approach and topics surrounding that. We begin by introducing the hierarchical Poisson regression model of Mugglin et al. [36] and a data set provided by NHS Direct which will be used to illustrate our results through-out the remainder of the thesis. We provide details of how a Bayesian analysis can be performed using Markov chain Monte Carlo (MCMC) via the software LinBUGS then go on to consider two particular issues associated with such analyses. Firstly, a problem with the efficiency of MCMC for the Poisson regression model is likely to be due to the presence of non-standard conditional distributions. We develop and test the 'improved auxiliary mixture sampling' method which introduces auxiliary variables to the conditional distribution in such a way that it becomes multivariate Normal and an efficient block Gibbs sampling scheme can be used to simulate from it. Secondly, since MCMC allows modelling of such complexity, inputs such as priors can only be elicited in a casual way thereby increasing the need to check how sensitive our output is to changes to the prior. We therefore develop and test the 'marginal sensitivity' method which, using only one MCMC output sample, quantifies how sensitive the marginal posterior distributions are to changes to prior parameters

The objective of this thesis is to analyze how existing data can address Chagas disease transmission risk in South America given data availability. A literature review was conducted to determine prominent variables that models use to assist with Chagas disease mitigation efforts, followed by a Web search to collect publicly available spatial data pertaining to these variables. The data were then used to create maps of data availability and in an agent-based model to identify which variables are most associated with disease transmission risk. Data availability varied widely across South America, and model results indicate that datasets related to household size and spatial housing arrangement are most important to Chagas disease infection in urban areas. Governments can use this information to better direct their resources to collect data and control the spread of triatomine vectors and Chagas disease more effectively, and potentially identify more cost-effective strategies for vector elimination.

El programa d'eradicació de la malaltia d'Aujeszky va començar a Espanya l'any 1995, però no va ser fins el 2003, quan a causa de les garanties suplementàries establertes en els intercanvis intracomunitaris de l'espècie porcina amb relació a la malaltia d'Aujeszky, que aquest programa es va reforçar i es varen establir les bases del programa coordinat de lluita, control i eradicació de la malaltia. L'objectiu d'aquest estudi és realitzar una anàlisi espacial de l'eradicació de la malaltia d'Aujeszky a Catalunya (Espanya) des del 2003 fins el 2007. L'estudi s'ha dividit en quatre períodes, en base a les diferents etapes establertes al programa d'eradicació a Catalunya.
A la primera part de l'estudi, varem analitzar si la distribució espacial de la malaltia d'Aujeszky a Catalunya ha estat homogènia o hi han hagut zones d'alt risc (conglomerats) durant les diferents etapes del programa d'eradicació. Per fer-ho, en cada període varem realitzar diferents anàlisis espacials amb el programa SaTScan v6.1 basats en el model de Bernoulli. En els quatre períodes d'estudi, varem identificar conglomerats de granges positives de truges (cicle obert i cicle tancat) i/o de granges positives d'engreixos, tant a l'oest com al centre com a l'est de Catalunya. Com que el risc d'infecció va disminuir més ràpidament fora dels conglomerats que dintre, els valors del ràtio de prevalença d'aquests conglomerats augmenten al llarg del temps. Per analitzar l'evolució de la malaltia, varem estudiar si hi havia àrees en les que la proporció de granges que s'havien reinfectat o que havien eliminat la infecció era més gran. Aquestes anàlisis van demostrar que hi havia zones en les que la proporció de granges que havien eliminat la infecció era més alta, i per tant que l'eradicació de la malaltia té també un component espacial.
En els quatre períodes d'estudi, també es van detectar àrees en les que la proporció de granges reinfectades havia estat més alta. El risc relatiu d'aquests conglomerats era més gran que el dels conglomerats descrits abans. D'altra banda, existeix una associació geogràfica entre els conglomerats de granges de mares positives, granges d'engreix positives i granges de mares reinfectades. Aquesta associació podria ser deguda a la transmissió a nivell local del virus d'Aujeszky. Ja que la densitat de granges a una zona podria ser un factor relacionat amb aquesta transmissió local, varem analitzar aquesta variable en conglomerats d'eradicació i de reinfeccions. La densitat mitjana de granges de porc als conglomerats d'eradicació és de 0.4 granges per quilòmetre quadrat (mitjana de 0.28 i desviació estàndard de 0.33) i de 1.51 (mitjana de 0.7 i desviació estàndard de 1.61) als conglomerats on més granges de truges s'han reinfectat (valor de p<0.05).
En base a aquests resultats, a la segona part de l'estudi varem analitzar el paper que podien exercir factors geogràfics en la transmissió a nivell local del virus i en la persistència de la malaltia d'Aujeszky a determinades zones. Per fer-ho, varem usar un model jeràrquic bayesià, en el que varem incloure diferents variables geogràfiques que podien estar implicades en la transmissió a nivell local del virus; com són la distància a l'escorxador més proper, distància a la carretera més pròxima, nombre d'animals d'engreix positius pròxims a la granja (radi de 750 metres) i nombre de truges positives pròximes a la granja (radi de 750 metres). Al model també varem incloure una altra variable no geogràfica: tipus de granja (cicle obert o cicle tancat). L'ús d'aquests models jeràrquics bayesians permet d'incorporar un terme que té en compte la dependència espacial (autocorrelació) existent a les dades. La dependència espacial va ser inclosa al model mitjançant una distribució normal condicionalment autoregressiva (CAR) basada en un nombre de veïns. Aquests veïns van ser definits com aquelles granges localitzades en un radi de 500 metres de cada granja de truges.
De les quatre variables geogràfiques incloses al model, només la presència d'animals d'engreix positius presents a la proximitat d'una granja de truges incrementava la probabilitat d'infecció pel virus d'Aujeszky. Al primer període, per cada 1000 porcs d'engreix al voltant de cada granja de mares, l'odds (raó de probabilitats) de cada granja d'ésser positiva s'incrementava per un factor entre 1.005 i 1.36. En el període 2.2, tenir porcs d'engreix al voltant augmentava la raó de probabilitats d'infecció per un valor d'entre 1.84 i 3.22. En el període 2.1 i en el període 3, cap de les variables va influir de forma significativa en la probabilitat de ser una granja positiva. El tipus de granja (cicle obert o cicle tancat) tampoc es va relacionar amb la probabilitat de ser una granja positiva en cap dels períodes de l'estudi. El patró geogràfic dels residus (observats versus predits) del model binomial jeràrquic bayesià va ser molt similar al dels observats, en tots els períodes de l'estudi. Aquest resultat evidencia que la transmissió a nivell local del virus d'Aujeszky probablement no hagi estat el principal factor relacionat amb la persistència del virus en granges de truges. Altres factors, específics de cada granja, probablement han tingut una relació més alta en la probabilitat d'infecció que les variables geogràfiques incloses en aquesta anàlisi.
El programa de erradicación de la enfermedad de Aujeszky comenzó en España en 1995, pero no fue hasta el 2003, cuando debido a las garantías suplementarias establecidas en los intercambios intracomunitarios de la especie porcina en relación a la enfermedad de Aujeszky, que dicho programa se reforzó y se establecieron las bases del programa coordinado de lucha, control y erradicación de la enfermedad. El objetivo de este estudio es realizar un análisis espacial de la erradicación de la enfermedad de Aujeszky en Cataluña (España) desde el 2003 hasta el 2007. El estudio se ha dividido en cuatro periodos, en base a las diferentes etapas establecidas en el programa de erradicación en Cataluña.
En la primera parte del estudio, analizamos si la distribución espacial de la enfermedad de Aujeszky en Cataluña ha sido homogénea o han existido zonas de alto riesgo (conglomerados) durante las distintas etapas del programa de erradicación. Para ello, en cada periodo realizamos diferentes análisis espaciales con el programa SaTScan® v6.1 basados en el modelo de Bernoulli. En los cuatro periodos de estudio, identificamos conglomerados de granjas positivas de cerdas (ciclo abierto y ciclo cerrado) y/o de granjas positivas de engordes, tanto en la parte oeste como en el centro y este de Cataluña. Debido a que el riesgo de infección disminuyó más rápido fuera de los conglomerados que dentro, los valores del ratio de prevalencia de estos conglomerados aumentaron a lo largo del tiempo. Para analizar la evolución de la enfermedad, estudiamos si había áreas en las que la proporción de granjas que se habían reinfectado
o que habían eliminado la infección era mayor. Estos análisis demostraron que había zonas en las que la proporción de granjas que habían eliminado la infección era más alta, y por lo tanto que la erradicación de la enfermedad tiene también un componente espacial.
En los cuatro periodos de estudio, también se detectaron áreas en las que la proporción de granjas reinfectadas fue más alta. El riesgo relativo de estos conglomerados era mayor que el de los otros análisis de conglomerados. Por otro lado, existía una asociación geográfica entre los conglomerados de granjas de madres positivas, granjas de engorde positivas y granjas de madres reinfectadas. Esta asociación podría ser debida a la transmisión a nivel local del virus de Aujeszky. Ya que la densidad de granjas en una zona podría ser un factor relacionado con esta transmisión local, analizamos esta variable en conglomerados de erradicación y de reinfecciones. La densidad media de granjas de porcino en los conglomerados de erradicación fue de 0.4 granjas por kilómetro cuadrado (mediana de 0.28 y desviación estándar de 0.33) y de 1.51 (mediana de 0.7 y desviación estándar de 1.61) en los conglomerados donde más granjas de cerdas se habían reinfectado (valor de p<0.05).
En base a estos resultados, en la segunda parte del estudio analizamos el papel que podían desempeñar factores geográficos en la transmisión a nivel local del virus y en la persistencia de la enfermedad de Aujeszky en determinadas zonas. Para ello, usamos un modelo jerárquico bayesiano y en él incluimos diferentes variables geográficas que podían estar implicadas en la transmisión a nivel local del virus; como son la distancia al matadero más cercano, distancia a la carretera más próxima, número de animals de engorde positivos próximos a la granja (radio de 750 metros) y número de cerdas positivas próximas a la granja (radio de 750 metros). En el modelo también incluimos otra variable no geográfica: tipo de granja (ciclo abierto o ciclo cerrado). El uso de estos modelos jerárquicos bayesianos permite incorporar un término que tiene en cuenta la dependencia espacial (autocorrelación) existente en los datos. La dependencia espacial fue incluida en el modelo mediante una distribución normal condicionalmente autoregresiva (CAR) basada en un número de vecinos. Dichos vecinos fueron definidos como aquellas granjas localizadas en un radio de 500 metros de cada granja de cerdas.
De las cuatro variables geográficas incluidas en el modelo, sólo la presencia de animales de engorde positivos presentes en la proximidad de una granja de cerdas incrementaba la probabilidad de infección por el virus de Aujeszky. En el primer periodo, por cada 1000 cerdos de engorde en la vecindad de cada granja de madres, el odds (razón de probabilidades) de ser positiva de cada granja se incrementaba por un factor entre 1.005 y 1.36. En el periodo 2.2, tener cerdos de engorde en la vecindad aumentaba la razón de probabilidades de infección por un valor entre 1.84 y 3.22. En el periodo 2.1 y en el periodo 3, ninguna de las variables influyó de forma significativa en la probabilidad de ser una granja positiva. El tipo de granja (ciclo abierto o ciclo cerrado) tampoco se relacionó con la probabilidad de ser una granja positiva en ninguno de los periodos del estudio. El patrón geográfico de los residuos (observados versus predichos) del modelo binomial jerárquico bayesiano fue muy similar al de los observados, en todos los periodos del estudio. Este resultado evidencia que la transmisión a nivel local del virus de Aujeszky probablemente no haya sido el principal factor relacionado con la persistencia del virus en granjas de cerdas. Otros factores, específicos de cada granja, probablemente tengan una relación más alta en la probabilidad de infección que las variables geográficas incluidas en este análisis.
Aujeszky's disease (AD) eradication programme started in Spain in 1995, but it was not until 2003, due to the additional guarantees in intra-community trade relating to Aujeszky's, that AD eradication programme was adapted and ensured. The aim of this study is to conduct a spatial analysis of the Aujeszky's disease (AD) eradication programme in Catalonia, Spain, from 2003 to 2007. The study has been divided in four periods, based on the phases designed in the AD eradication programme in Catalonia.
In the first part of the study, we explore for high risk areas (clusters) in order to test whether the spatial distribution of AD in the region during the consecutive eradication periods was homogeneously distributed over the territory or clustered in space. Different purely spatial analyses, based on the Bernoulli model, were run with SaTScan® v6.1 in each period. Clusters of positive sow farms (farrow to weaning and farrow to finish) and/or fattening farms were identified in the four study periods in the western, central and north eastern part of the region. The prevalence ratio values of these clusters increased throughout the study period due to the fact that the risk of disease decreased faster outside the clusters than inside the clusters. In order to study the evolution of the disease, we explored for areas where more negative sow farms became infected and areas where more sow farms eliminated the infection. These analyses demonstrated areas with significantly higher proportions of sow farms that became negative, which indicates that the eradication of the disease has a spatial component. Clusters of negative sow farms that were infected again (reinfections) were also detected in the four study periods. The relative risk values of these clusters were much higher compared to the other cluster analyses. There was a geographical association between the clusters of positive sow farms, positive fattening farms and re-infected sow farms. This association could be attributable to the local spread of Aujeszky´s disease virus. Pig farm density could be a factor influencing the local spread of infection and was therefore evaluated for clusters of re-infected sow farms and clusters of sow farms that eliminated the infection. The mean density of pig farms was 0.40 farms per square Km (median of 0.28 and standard deviation of 0.33) in clusters of sow farms that became negative and 1.51 (median of 0.70 and standard deviation of 1.61) in clusters where more sow farms became positive (p-value<0.05).
Based on these results, in the second part of the study, we tested the role of geographical factors that could be implicated in local spread and persistence of AD in certain areas. Several geographic variables describing the possible risk factors associated to neighbourhood transmission: Distance to the nearest slaughterhouse, distance to conventional roads, mean number of AD serological positive sows and serological positive fattening pigs in the neighbourhood (750 meters radius) of each sow farm were included in a hierarchical Bayesian binomial model. A non geographic variable; type of farm (farrow to weaning versus farrow to finish) was also included. The use of Bayesian models allowed us to take into account the spatial dependence (autocorrelation) among the data; included in the model as a random effect. Spatial dependence was parameterised with a conditional autoregressive distribution (CAR) based on a set of neighbours. The set of neighbours was defined as those farms located in a 500 meters buffer radius around each sow farm.
From the four geographical variables included in the model, only positive fattening animals in the neighbourhood of sow farms increased the probability of being AD positive. In the first period, 1,000 positive fattening pigs in the neighbourhood (750 meters buffer radius) increase the odds of each sow farm being AD positive by a factor between 1.005 and 1.36. In period 2.2, having positive fattening animals in the neighbourhood increased the likelihood of each sow farm to be AD positive between
1.84 and 3.22. In period 2.1 and period 3, none of the variables had a positive relation with the probability of being positive. The type of farm (farrow to weaning or farrow to finish) also did not influence the probability of being AD positive in any period. The geographical pattern of the residuals of the hierarchical bayesian binomial model (observed versus predicted) was very similar to the observed infection in sow farms in all the eradication periods, showing that neighbourhood transmission might not be the main factor related to the eradication of Aujeszky-s disease in sow farms. Other herd¬specific risk factors might be much more related to the probability of AD infection than the geographical variables included in this study.

Alligatorweed leaf spot is a disease of invasive Alternanthera philoxeroides (Alligatorweed) in the southern US, caused by Alternaria alternantherae. However, little is known about when or where this pathogen naturally occurs. To better understand this species’ life history, we examined temporal (every 2–3 weeks) and spatial (latitudinal) patterns of A. alternantherae occurrence at sites in Louisiana for 2 y. Pathogen presence reflected clear within-year temporal and spatial patterns. Overall, the percentage of leaves infected with A. alternantherae was low during spring each year (0–20% infected) but increased throughout summer (maximum of 50% infected), and plants in northern sites had lower frequency of infection relative to southern sites until later in the year (late summer/early fall) but only in 1 of the 2 years of our study. The mean proportion of leaves infected with A. alternantherae declined with latitude both years (P = 0.01) and variability increased with latitude (P = 0.04), a pattern suggestive of range limitation in northern areas. We estimate a northern distributional limit of 34°N for A. alternantherae in Louisiana, but Alligatorweed occurs farther north. Although we did not directly examine disease impacts to Alligatorweed during the study, they may be greatest in southern areas, where the pathogen is more common early and throughout the growing season, and thus may be less likely to provide control in northern infestations of the invasive Alligatorweed.

The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).

Large-scale patterns of plant invasions may reflect regional heterogeneity in biotic and abiotic factors and genetic variation within and between invading populations. Having information on how effects of biotic resistance vary spatially can be especially important when implementing biological control because introduced agents may have different Impacts through interactions with host-plant genotype, local environment, or other novel enemies. We conducted a series of field surveys and laboratory studies to determine whether there was evidence of biotic resistance, as foliar fungal pathogens, in two introduced genotypes (triploid G1, diploid G4) of the Eurasian wetland weed, Butomus umbellatus L. in the USA. We tested whether genotypes differed in disease attack and whether spatial patterns in disease incidence were related to geographic location or climate for either genotype. After accounting for location (latitude, climate), G1 plants had lower disease incidence than G4 plants in the field (38% vs. 70%) but similar pathogen richness. In contrast, bioassays revealed G1 plants consistently received a higher damage score and had larger leaf lesions regardless of pathogen. These results demonstrate that two widespread B. umbellatus genotypes exhibit different susceptibility to pathogens and effectiveness of pathogen biological controls may depend on local conditions.

This paper presents an overview of how health outcomes vary across cities in Latin America and discusses some of the known drivers of this variation. There are large disparities in outcomes across cities and across neighborhoods of the same city. Because health is closely related to the socioeconomic conditions of individuals, part of the spatial variation reflects residential segregation by income. Local characteristics also have a direct effect on health outcomes, shaping individuals' access to health services and the prevalence of unhealthy lifestyles. In addition, urban environments affect health through natural atmospheric conditions, through local infrastructure in particular water, sanitation, and urban transit and through the presence of urban externalities such as traffic congestion, pollution, crime, and the spread of transmissible diseases. The COVID-19 pandemic illustrates many of these patterns, since the impact of the disease has differed sharply across cities, and much of this variation can be explained by observable local characteristics particularly population, connectivity with other cities and countries, income levels, and residential overcrowding.

Review question / Objective: According to recent studies, long non-coding RNA (lncRNAs) i.e., OPA-interacting protein 5 antisense RNA 1 (OIP5-AS1) has an important role in various carcinomas. However, its role in the cancer is contradictory. Therefore, we aimed to evaluate the link between OIP5-AS1 and cancer patients' clinicopathological characteristics and prognosis to better understand OIP5-AS1's role in cancer. Condition being studied: Reported studies have revealed that long non-coding RNA (lncRNAs) are considerably involved in crucial physiological events in several carcinomas, it can inhibit or promote the occurrence and development of tumors by changing the sequence and spatial structure, modulating epigenetic, regulating the expression level and interacting with binding proteins. However, the mechanism of cancer regulation via lncRNAs was incompletely understood. Hence, clarifying the application value of lncRNAs in preclinical and clinical disease diagnosis and treatment was therefore the prime objective in the field of cancer research at the time.

Project Title: "Development of a plasmid-based reverse genetics system for the Bluetongue and Epizootic Hemorrhagic Disease viruses to allow comparative characterization of the function of the NS3 viroporin in viral egress". Project details: No - IS-4192-09; Participants – Ehrlich M. (Tel Aviv University), Parker J.S. (Cornell University), DermodyT.S. (Vanderbilt University); Period - 2009-2013. Orbiviruses are insect-borne infectious agents of ruminants that cause diseases with considerable economical impact in Israel and the United States. The recent outbreaks of BTV in Europe and of Epizootic Hemorrhagic Disease Virus (EHDV) in Israel, underscore the need for: (i) a better comprehension of the infection process of orbiviruses, (ii) the identification of unique vs. common traits among different orbiviruses, (iii) the development of novel diagnosis and treatment techniques and approaches; all aimed at the achievement of more effective control and treatment measures. It is the context of these broad goals that the present project was carried out. To fulfill our long-term goal of identifying specific viral determinants of virulence, growth, and transmission of the orbiviruses, we proposed to: (i) develop reverse genetics systems for BTV and EHDV2-Ibaraki; and (ii) identify the molecular determinants of the NS3 nonstructural protein related to viroporin/viral egress activities. The first objective was pursued with a two-pronged approach: (i) development of a plasmid-based reverse genetics system for BTV-17, and (ii) development of an "in-vitro" transcription-based reverse genetics system for EHDV2-Ibaraki. Both approaches encountered technical problems that hampered their achievement. However, dissection of the possible causes of the failure to achieve viral spread of EHDV2-Ibaraki, following the transfection of in-vitro transcribed genomic segments of the virus, revealed a novel characteristic of EHDV2-Ibaraki infection: an uncharacteristically low fold increase in titer upon infection of different cell models. To address the function and regulation of NS3 we employed the following approaches: (i) development (together with Anima Cell Metrology) of a novel technique (based on the transfection of fluorescently-labeledtRNAs) that allows for the detection of the levels of synthesis of individual viral proteins (i.e. NS3) in single cells; (ii) development of a siRNA-mediated knockdown approach for the reduction in levels of expression of NS3 in EHDV2-Ibaraki infected cells; (iii) biochemical and microscopy-based analysis of the localization, levels and post-translational modifications of NS3 in infected cells. In addition, we identified the altered regulation and spatial compartmentalization of protein synthesis in cells infected with EHDV2-Ibaraki or the mammalian reovirus. In EHDV2-Ibaraki-infected cells such altered regulation in protein synthesis occurs in the context of a cell stress reponse that includes the induction of apoptosis, autophagy and activation of the stressrelated kinase c-Jun N-terminal Kinase (JNK). Interestingly, inhibition of such stress-related cellular processes diminishes the production of infectious virions, suggesting that EHDV usurps these responses for the benefit of efficient infection. Taken together, while the present project fell short of the generation of novel reverse genetics systems for orbiviruses, the development of novel experimental approaches and techniques, and their employment in the analysis of EHDV-infected cells, yielded novel insights in the interactions of orbiviruses with mammalian cells.