27 research outputs found

    Habitat continuity and geographic distance predict population genetic differentiation in giant kelp

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    Isolation by distance (IBD) models are widely used to predict levels of genetic connectivity as a function of Euclidean distance, and although recent studies have used GIS-landscape ecological approaches to improve the predictability of spatial genetic structure, few if any have addressed the effect of habitat continuity on gene flow. Landscape effects on genetic connectivity are even less understood in marine populations, where habitat mapping is particularly challenging. In this study, we model spatial genetic structure of a habitat-structuring species, the giant kelp Macrocystis pyrifera, using highly variable microsatellite markers. GIS mapping was used to characterize habitat continuity and distance between sampling sites along the mainland coast of the Santa Barbara Channel, and their roles as predictors of genetic differentiation were evaluated. Mean dispersal distance (σ) and effective population size (Ne) were estimated by comparing our IBD slope with those from simulations incorporating habitat continuity and spore dispersal characteristics of the study area. We found an allelic richness of 7–50 alleles/locus, which to our knowledge is the highest reported for macroalgae. The best regression model relating genetic distance to habitat variables included both geographic distance and habitat continuity, which were respectively, positively and negatively related to genetic distance. Our results provide strong support for a dependence of gene flow on both distance and habitat continuity and elucidate the combination of Ne and σ that explained genetic differentiation

    First laboratory confirmation and sequencing of Zaire ebolavirus in Uganda following two independent introductions of cases from the 10th Ebola Outbreak in the Democratic Republic of the Congo, June 2019.

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    Uganda established a domestic Viral Hemorrhagic Fever (VHF) testing capacity in 2010 in response to the increasing occurrence of filovirus outbreaks. In July 2018, the neighboring Democratic Republic of Congo (DRC) experienced its 10th Ebola Virus Disease (EVD) outbreak and for the duration of the outbreak, the Ugandan Ministry of Health (MOH) initiated a national EVD preparedness stance. Almost one year later, on 10th June 2019, three family members who had contracted EVD in the DRC crossed into Uganda to seek medical treatment. Samples were collected from all the suspected cases using internationally established biosafety protocols and submitted for VHF diagnostic testing at Uganda Virus Research Institute. All samples were initially tested by RT-PCR for ebolaviruses, marburgviruses, Rift Valley fever (RVF) virus and Crimean-Congo hemorrhagic fever (CCHF) virus. Four people were identified as being positive for Zaire ebolavirus, marking the first report of Zaire ebolavirus in Uganda. In-country Next Generation Sequencing (NGS) and phylogenetic analysis was performed for the first time in Uganda, confirming the outbreak as imported from DRC at two different time point from different clades. This rapid response by the MoH, UVRI and partners led to the control of the outbreak and prevention of secondary virus transmission

    Future Directions in Brain/Neuronal Computer Interaction (Future BNCI)

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    Brain-Computer Interface (BCI) research has made great progress recently [1-3]. However, this progress has some negative side effects: growing fragmentation among different researchers, confusion about the best research directions, and ongoing disagreement over terms and definitions. Future BNCI is a Coordination and Support Action funded by the European Commission that aims to counteract these trends by helping new and existing researchers identify each other, encouraging effective collaborations, developing roadmaps and frameworks, and establishing standardized terminology. The knowledge developed in Future BNCI will be disseminated through conferences, workshops, journal publications, a book, and a website

    An objective approach to dry eye disease severity. Invest Ophthalmol Vis Sci

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    PURPOSE. A prospective, multisite clinical study (10 sites in the European Union and the United States) evaluated the clinical utility of commonly used tests and tear osmolarity for assessing dry eye disease severity. METHODS. Three hundred fourteen consecutive subjects between the ages of 18 and 82 years were recruited from the general patient population, 299 of which qualified with complete datasets. Osmolarity testing, Schirmer test without anesthesia, tear film breakup time (TBUT), corneal staining, meibomian dysfunction assessment, and conjunctival staining were performed bilaterally. A symptom questionnaire, the Ocular Surface Disease Index (OSDI), was also administered to each patient. Distributions of clinical signs and symptoms against a continuous composite severity index were evaluated. RESULTS. Osmolarity was found to have the highest correlation coefficient to disease severity (r 2 Ï­ 0.55), followed by conjunctival staining (r 2 Ï­ 0.47), corneal staining (r 2 Ï­ 0.43), OSDI (r 2 Ï­ 0.41), meibomian score (r 2 Ï­ 0.37), TBUT (r 2 Ï­ 0.30), and Schirmer result (r 2 Ï­ 0.17). A comparison of standard threshold-based classification with the composite severity index revealed significant overlap between the disease severities of prospectively defined normal and dry eye groups. Fully 63% of the subjects were found to be poorly classified by combinations of clinical thresholds. CONCLUSIONS. Tear film osmolarity was found to be the single best marker of disease severity across normal, mild/moderate, and severe categories. Other tests were found to be informative in the more severe forms of disease; thus, clinical judgment remains an important element in the clinical assessment of dry eye severity. The results also indicate that the initiation and progression of dry eye is multifactorial and supports the rationale for redefining severity on the basis of a continuum of clinical signs. (ClinicalTrials.gov number, NCT00848198.) (Invest Ophthalmol Vis Sci. 2010;51:6125-6130
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