Tickborne Encephalitis in Naturally Exposed Monkey (Macaca sylvanus)

We describe tickborne encephalitis (TBE) in a monkey (Macaca sylvanus) after natural exposure in an area at risk for TBE. TBE virus was present in the brain and could be identified as closely related to the European subtype, strain Neudoerfl

Vitamin D levels and perinatal depressive symptoms in women at risk: a secondary analysis of the mothers, omega-3, and mental health study

Abstract Background Vitamin D insufficiency may be associated with depressive symptoms in non-pregnant adults. We performed this study to evaluate whether low maternal vitamin D levels are associated with depressive symptoms in pregnancy. Methods This study was a secondary analysis of a randomized trial designed to assess whether prenatal omega-3 fatty acid supplementation would prevent depressive symptoms. Pregnant women from Michigan who were at risk for depression based on Edinburgh Postnatal Depression Scale Score or history of depression were enrolled. Participants completed the Beck Depression Inventory (BDI) and Mini International Neuropsychiatric Interview at 12–20 weeks, 26–28 weeks, 34–36 weeks, and 6–8 weeks postpartum. Vitamin D levels were measured at 12–20 weeks (N = 117) and 34–36 weeks (N = 112). Complete datasets were available on 105 subjects. Using regression analyses, we evaluated the relationship between vitamin D levels with BDI scores as well as with MINI diagnoses of major depressive disorder and generalized anxiety disorder. Our primary outcome measure was the association of maternal vitamin D levels with BDI scores during early and late pregnancy and postpartum. Results We found that vitamin D levels at 12–20 weeks were inversely associated with BDI scores both at 12—20 and at 34–36 weeks’ gestation (P < 0.05, both). For every one unit increase in vitamin D in early pregnancy, the average decrease in the mean BDI score was .14 units. Vitamin D levels were not associated with diagnoses of major depressive disorder or generalized anxiety disorder. Conclusions In women at risk for depression, early pregnancy low vitamin D levels are associated with higher depressive symptom scores in early and late pregnancy. Future investigations should study whether vitamin D supplementation in early pregnancy may prevent perinatal depressive symptoms. Trial registration https://clinicaltrials.gov/ Registration Number: NCT00711971http://deepblue.lib.umich.edu/bitstream/2027.42/134615/1/12884_2016_Article_988.pd

Sun, Moon, Stars, Rain, Vol. 7 No. 11

Official publication of the Sigma Tau Delta English Honor Society, Alpha Zet Chapter, Stephen F. Austin State University. Published one a year in the Fall Semester, in cooperation with the English Department of Stephen F. Austin State University.https://scholarworks.sfasu.edu/smsr/1000/thumbnail.jp

Sun, Moon, Stars, Rain, Vol. 7 No. 11

Official publication of the Sigma Tau Delta English Honor Society, Alpha Zet Chapter, Stephen F. Austin State University. Published one a year in the Fall Semester, in cooperation with the English Department of Stephen F. Austin State University

Baseline tumor-infiltrating lymphocyte patterns and response to immune checkpoint inhibition in metastatic cutaneous melanoma

Introduction: The presence of tumor-infiltrating lymphocytes (TILs) in melanoma has been linked to survival. Their predictive capability for immune checkpoint inhibition (ICI) response remains uncertain. Therefore, we investigated the association between treatment response and TILs in the largest cohort to date and analyzed if this association was independent of known clinical predictors. Methods: In this multicenter cohort study, patients who received first-line anti-PD1 ± anti-CTLA4 for advanced melanoma were identified. TILs were scored on hematoxylin and eosin (H&E) slides of primary melanoma and pre-treatment metastases using the validated TILs-WG, Clark and MIA score. The primary outcome was objective response rate (ORR), with progression free survival and overall survival being secondary outcomes. Univariable and multivariable logistic regression and Cox proportional hazard were performed, adjusting for known clinical predictors. Results: Metastatic melanoma specimens were available for 650 patients and primary specimens for 565 patients. No association was found in primary melanoma specimens. In metastatic specimens, a 10-point increase in the TILs-WG score was associated with a higher probability of response (aOR 1.17, 95 % CI 1.07–1.28), increased PFS (HR 0.93, 95 % CI 0.87–0.996), and OS (HR 0.94, 95 % CI 0.89–0.99). When categorized, patients in the highest tertile TILs-WG score (15–100 %) compared to the lowest tertile (0 %) had a longer median PFS (13.1 vs. 7.3 months, p = 0.04) and OS (49.4 vs. 19.5 months, p = 0.003). Similar results were noted using the MIA and Clark scores. Conclusion: In advanced melanoma patients, TIL patterns on H&E slides of pre-treatment metastases, regardless of measurement method, are independently associated with ICI response. TILs are easily scored on readily available H&Es, facilitating the use of this biomarker in clinical practice

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation

Constrained Parameter Inference as a Principle for Learning

Learning in biological and artificial neural networks is often framed as a problem in which targeted error signals are used to directly guide parameter updating for more optimal network behaviour. Backpropagation of error (BP) is an example of such an approach and has proven to be a highly successful application of stochastic gradient descent to deep neural networks. However, BP relies on the transmission of gradient information directly to parameters, and frames learning as two completely separated passes. We propose constrained parameter inference (COPI) as a new principle for learning. The COPI approach to learning proposes that parameters might infer their updates based upon local neuron activities. This estimation of network parameters is possible under the constraints of decorrelated neural inputs and top-down perturbations of neural states, where credit is assigned to units instead of parameters directly. The form of the top-down perturbation determines which credit assignment method is being used, and when aligned with BP it constitutes a mixture of the forward and backward passes. We show that COPI is not only more biologically plausible but also provides distinct advantages for fast learning when compared to BP