Identification of the Sex Pheromone of the Webbing Coneworm Moth, Dioryctria disclusa (Lepidoptera: Pyralidae)

A sex pheromone component for the webbing coneworm, Dioryctria disclusa, has been identified as (Z)-9-tetradecenyl acetate. Traps baited with rubber septa impregnated with this compound at loadings of 30 to 300 μg caught the same as or significantly better than traps baited with two virgin female moths. Addition of 3 to 30% of the E isomer did not increase trap catch, and only (Z)-9-tetradecenyl acetate was found in the female extrac

Uptake of Per- and Polyfluoroalkyl Substances by Fish, Mussel, and Passive Samplers in Mobile-Laboratory Exposures Using Groundwater from a Contamination Plume at a Historical Fire Training Area, Cape Cod, Massachusetts

Aqueous film-forming foams historically were used during fire training activities on Joint Base Cape Cod, Massachusetts, and created an extensive per- and polyfluoroalkyl substances (PFAS) groundwater contamination plume. The potential for PFAS bioconcentration from exposure to the contaminated groundwater, which discharges to surface water bodies, was assessed with mobile-laboratory experiments using groundwater from the contamination plume and a nearby reference location. The on-site continuous-flow 21-day exposures used male and female fathead minnows, freshwater mussels, polar organic chemical integrative samplers (POCIS), and polyethylene tube samplers (PETS) to evaluate biotic and abiotic uptake. The composition of the PFAS-contaminated groundwater was complex and 9 PFAS were detected in the reference groundwater and 17 PFAS were detected in the contaminated groundwater. The summed PFAS concentrations ranged from 120 to 140 ng L–1 in reference groundwater and 6100 to 15,000 ng L–1 in contaminated groundwater. Biotic concentration factors (CFb) for individual PFAS were species, sex, source, and compound-specific and ranged from 2.9 to 1000 L kg–1 in whole-body male fish exposed to contaminated groundwater for 21 days. The fish and mussel CFb generally increased with increasing fluorocarbon chain length and were greater for sulfonates than for carboxylates. The exception was perfluorohexane sulfonate, which deviated from the linear trend and had a 10-fold difference in CFb between sites, possibly because of biotransformation of precursors such as perfluorohexane sulfonamide. Uptake for most PFAS in male fish was linear over time, whereas female fish had bilinear uptake indicated by an initial increase in tissue concentrations followed by a decrease. Uptake of PFAS was less for mussels (maximum CFb = 200) than for fish, and mussel uptake of most PFAS also was bilinear. Although abiotic concentration factors were greater than CFb, and values for POCIS were greater than for PETS, passive samplers were useful for assessing PFAS that potentially bioconcentrate in fish but are present at concentrations below method quantitation limits in water. Passive samplers also accumulate short-chain PFAS that are not bioconcentrated

Distinct White Matter Changes Associated with Cerebrospinal Fluid Amyloid-β\u3csub\u3e1-42\u3c/sub\u3e and Hypertension

BACKGROUND: Alzheimer\u27s disease (AD) pathology and hypertension (HTN) are risk factors for development of white matter (WM) alterations and might be independently associated with these alterations in older adults. OBJECTIVE: To evaluate the independent and synergistic effects of HTN and AD pathology on WM alterations. METHODS: Clinical measures of cerebrovascular disease risk were collected from 62 participants in University of Kentucky Alzheimer\u27s Disease Center studies who also had cerebrospinal fluid (CSF) sampling and MRI brain scans. CSF Aβ1-42 levels were measured as a marker of AD, and fluid-attenuated inversion recovery imaging and diffusion tensor imaging were obtained to assess WM macro- and microstructural properties. Linear regression analyses were used to assess the relationships among WM alterations, cerebrovascular disease risk, and AD pathology. Voxelwise analyses were performed to examine spatial patterns of WM alteration associated with each pathology. RESULTS: HTN and CSF Aβ1-42 levels were each associated with white matter hyperintensities (WMH). Also, CSF Aβ1-42 levels were associated with alterations in normal appearing white matter fractional anisotropy (NAWM-FA), whereas HTN was marginally associated with alterations in NAWM-FA. Linear regression analyses demonstrated significant main effects of HTN and CSF Aβ1-42 on WMH volume, but no significant HTN×CSF Aβ1-42 interaction. Furthermore, voxelwise analyses showed unique patterns of WM alteration associated with hypertension and CSF Aβ1-42. CONCLUSION: Associations of HTN and lower CSF Aβ1-42 with WM alteration were statistically and spatially distinct, suggesting independent rather than synergistic effects. Considering such spatial distributions may improve diagnostic accuracy to address each underlying pathology

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

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