Ultrastructure of \u3ci\u3eMeelsvirus\u3c/i\u3e: A nuclear virus of arrow worms (phylum Chaetognatha) producing giant tailed virions

Most known giant viruses, i.e., viruses producing giant virions, parasitize amoebae and other unicellular eukaryotes. Although they vary in the level of dependence on host nuclear functions, their virions self-assemble in the host cell\u27s cytoplasm. Here we report the discovery of a new prototype of giant virus infecting epidermal cells of the marine arrow worm Adhesisagitta hispida. Its 1.25 μm-long virions self-assemble and accumulate in the host cell\u27s nucleus. Conventional transmission electron microscopy reveals that the virions have a unique bipartite structure. An ovoid nucleocapsid, situated in a broad head end of the virion is surrounded by a thin envelope. The latter extends away from the head to form a voluminous conical tail filled with electron-dense extracapsidular material. The 31nm-thick capsid wall has a distinctive substructure resulting from a patterned arrangement of subunits; it bears no ultrastructural resemblance to the virion walls of other known giant viruses. The envelope self-assembles coincident with the capsid and remotely from all host membranes. We postulate that transmission to new hosts occurs by rupture of protruding virion-filled nuclei when infected arrow worms mate. Future genomic work is needed to determine the phylogenetic position of this new virus, which we have provisionally named Meelsvirus

Monitoring Storm Impacts on Sandy Coastlines with UAVs

UAV applications have shown the potential to increase the efficiency of collecting high resolution and spatially extensive topographic datasets of sandy coastal systems. These systems are dynamic and sensitive to variability in wave energy, evident in topographic adjustments associated with storm events. Topographic and volumetric changes of a beach-dune system were measured following a post tropical storm event. Using a pre-storm LiDAR and post-storm UAV survey, we identified high magnitude and continuous alongshore erosion of the foredune. Lower magnitude and discontinuous areas of deposition were also recorded, as sediment eroded from the foredune translated seaward and was deposited onto the beach. Overall, a total volumetric loss of ∼11,000 m3 from the beach-dune zone was recorded along the 2.5 km survey extent. Our results highlight the capability of UAVs for rapid monitoring and quantification of storm impacts. Furthermore, confidence in reported topographic changes was improved by implementing quality control measures and handling of data uncertainties (e.g., vegetation). The aim of this chapter is to quantify the impact of a storm event on a beach-dune system and discuss methodological challenges of monitoring sandy coastlines with UAVs

A Tale of Two Sylamores: Understanding Relationships Among Land Use, Nutrients, and Aquatic Communities Across a Subsidy-Stress Gradient

Agricultural land use is known to degrade aquatic systems with high inputs of nutrients, sediments, and pesticides. Increased nutrients can lead to increased algal growth and thus possible hypoxic conditions in slow moving water, while increased sediment loads have been shown to obstruct light and reduce substrate stability. These conditions negatively impact primary producers, macroinvertebrates, and fish. However, small-scale changes in land use can subsidize an aquatic ecosystem instead, where an increase in nutrients allows nutrient-limited biota to flourish, and minor increases in sedimentation may help support populations of collector-filterers. The stimulation in performance caused by small disturbances is part of the subsidy-stress gradient, where increasing perturbation subsidizes an ecosystem until a certain threshold is reached, at which a decline in performance and increased variability starts to occur. The North and South Sylamore watersheds in north Arkansas provide a useful template to investigate the subsidy-stress gradient in relation to land use. North Sylamore flows through the Ozark National Forest and has a heavily forested catchment, while South Sylamore flows through mostly private land, some of which is pasture (23%). Physicochemical, macroinvertebrate, and fish data were collected from multiple sites within each watershed to determine if South Sylamore is exhibiting a response to pasture/agriculture characteristic of a subsidy-stress gradient. Sites within South Sylamore had significantly higher nitrate levels, larger macroinvertebrate populations dominated by collector-filterers, and greater abundance of algivorous fish, suggesting South Sylamore may be subsidized by the surrounding pastoral lands. However, South Sylamore also had a significantly lower proportional abundance of sensitive macroinvertebrate taxa and more unique tolerant fish taxa, suggesting South Sylamore is experiencing stress as well. Habitat quality of South Sylamore could be improved by restoration of trees within the riparian zone. Monitoring aquatic systems for subsidy-stress responses can inform restoration/management decisions and guide intervention prior to watersheds and aquatic communities becoming overly stressed

Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data

Background Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance, empirical research, and clinical intervention. We sought to improve existing OOD electronic phenotypes by incorporating new data types beyond diagnostic codes and by applying several statistical and machine learning methods. Materials and Methods We developed an EHR dataset of emergency department visits involving OOD cases or patients considered at risk for an OOD and ascertained true OOD status through manual chart reviews. We developed and validated prediction models using Random Forest, Extreme Gradient Boost, and Elastic Net models that incorporated 717 features involving primary and second diagnoses, chief complaints, medications prescribed, vital signs, laboratory results, and procedural codes. We also developed models limited to single data types. Results A total of 1718 records involving 1485 patients were manually reviewed; 541 (36.4%) patients had one or more OOD. Prediction performance was similar for all models; sensitivity varied from 94% to 97%; and area under the receiver operating characteristic curve (AUC) was 98% for all methods. The primary diagnosis and chief complaint were the most important contributors to AUC performance; primary diagnoses and medication class contributed most to sensitivity; chief complaint, primary diagnosis, and vital signs were most important for specificity. Models limited to decision support data types available in real time demonstrated robust prediction performance. Conclusions Substantial prediction performance improvements were demonstrated for identifying OODs in EHR data. Our e-phenotypes could be applied in surveillance, retrospective empirical applications, or clinical decision support systems

Chancellor\u27s Citations for Extraordinary Campus Leadership and Service (2014)

The Chancellor’s Citations for Extraordinary Campus Leadership and Service recognize graduating students who are extraordinary campus leaders for their significant service to others

Sheep Updates 2007 - part 3

This session covers seven papers from different authors: PROFITABILITY 1. Benchmarking demonstrates both the potential and realised productivity gains in the sheep and wool industry, Andrew Ritchie, Edward Riggall and James Hall, ICON Agriculture, Darkan 2. Improving sheep genetics will increase farm profitability, Gus Rose, Johan Greeff Department of Agriculture and Food Western Australia, John Young Farming Systems Analysis Service, WA 3. Meat, Merinos and making money in WA Pastoral Zone, M. Alchin, M. Young and T. Johnson, Department of Agriculture and Food Western Australia, GRAZING 4. Nitrogen - farmers\u27 friend or foe? John Lucy and Martin Staines, Department of Agriculture and Food Western Australia 5. Drought proofing grazing systems - a case study from Binnu 2006/7, Tim Wiley & Rob Grima, Department of Agriculture & Food Western Australia 6. Minimising \u27Esperance Storm\u27 livestock losses, Sandra Prosser and Matt Ryan, Department of Agriculture and Food Western Australia 7. Sub-tropical grasses in WA - what is their potential? Geoff Moore, Tony Albertsen, Department of Agriculture & Food Western Australia, Phil Barrett-Lennard, Evergreen Farming, George Woolston, John Titterington, Department of Agriculture and Food Western Australia, Sarah Knight, Irwin-Mingenew Group, Brianna Peake, Liebe Group, Buntine, W

A developmental approach to diversifying neuroscience through effective mentorship practices: perspectives on cross-identity mentorship and a critical call to action.

Many early-career neuroscientists with diverse identities may not have mentors who are more advanced in the neuroscience pipeline and have a congruent identity due to historic biases, laws, and policies impacting access to education. Cross-identity mentoring relationships pose challenges and power imbalances that impact the retention of diverse early career neuroscientists, but also hold the potential for a mutually enriching and collaborative relationship that fosters the mentee\u27s success. Additionally, the barriers faced by diverse mentees and their mentorship needs may evolve with career progression and require developmental considerations. This article provides perspectives on factors that impact cross-identity mentorship from individuals participating in Diversifying the Community of Neuroscience (CNS)-a longitudinal, National Institute of Neurological Disorders and Stroke (NINDS) R25 neuroscience mentorship program developed to increase diversity in the neurosciences. Participants in Diversifying CNS were comprised of 14 graduate students, postdoctoral fellows, and early career faculty who completed an online qualitative survey on cross-identity mentorship practices that impact their experience in neuroscience fields. Qualitative survey data were analyzed using inductive thematic analysis and resulted in four themes across career levels: (1) approach to mentorship and interpersonal dynamics, (2) allyship and management of power imbalance, (3) academic sponsorship, and (4) institutional barriers impacting navigation of academia. These themes, along with identified mentorship needs by developmental stage, provide insights mentors can use to better support the success of their mentees with diverse intersectional identities. As highlighted in our discussion, a mentor\u27s awareness of systemic barriers along with active allyship are foundational for their role

Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP).

OBJECTIVES We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device. DESIGN Interim analysis of a prospective cohort study. SETTING, PARTICIPANTS AND INTERVENTIONS Participants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays. RESULTS A total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO. CONCLUSION Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial. Trial registration number ISRCTN51255782; Pre-results