Locked and loading megathrust linked to active subduction beneath the Indo-Burman Ranges

The Indo-Burman mountain rangesmarkthe boundary between the Indian and Eurasian plates, north of the Sumatra–Andaman subduction zone. Whether subduction still occurs along this subaerial section of the plate boundary, with 46mm/yr of highly oblique motion, is contentious. About 21mm/yr of shear motion is taken up along the Sagaing Fault, on the eastern margin of the deformation zone. It has been suggested that the remainder of the relative motion is taken up largely or entirely by horizontal strike-slip faulting and that subduction has stopped. Here we present GPS measurements of plate motions in Bangladesh, combined with measurements from Myanmar and northeast India, taking advantage of a more than 300 km subaerial accretionary prism spanning the Indo-Burman Ranges to the Ganges–Brahmaputra Delta. They reveal 13–17mm/yr of plate convergence on an active, shallowly dipping and locked megathrust fault. Most of the strike-slip motion occurs on a few steep faults, consistent with patterns of strain partitioning in subduction zones. Our results strongly suggest that subduction in this region is active, despite the highly oblique plate motion and thick sediments. We suggest that the presence of a locked megathrust plate boundary represents an underappreciated hazard in one of the most densely populated regions of the world

The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

Understanding the circumstances that lead to pandemics is important for their prevention. Here, we analyze the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted A and B. Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October–8 December), while the separate introduction of lineage A likely occurred within weeks of this event. These findings indicate that it is unlikely that SARS-CoV-2 circulated widely in humans prior to November 2019 and define the narrow window between when SARS-CoV-2 first jumped into humans and when the first cases of COVID-19 were reported. As with other coronaviruses, SARS-CoV-2 emergence likely resulted from multiple zoonotic events

The Neurokinin 1 Receptor Antagonist, Ezlopitant, Reduces Appetitive Responding for Sucrose and Ethanol

Abstract Background: The current obesity epidemic is thought to be partly driven by over-consumption of sugar-sweetened diets and soft drinks. Loss-of-control over eating and addiction to drugs of abuse share overlapping brain mechanisms including changes in motivational drive, such that stimuli that are often no longer ‘liked’ are still intensely ‘wanted’ [7,8]. The neurokinin 1 (NK1) receptor system has been implicated in both learned appetitive behaviors and addiction to alcohol and opioids; however, its role in natural reward seeking remains unknown. Methodology/Principal Findings: We sought to determine whether the NK1-receptor system plays a role in the reinforcing properties of sucrose using a novel selective and clinically safe NK1-receptor antagonist, ezlopitant (CJ-11,974), in three animal models of sucrose consumption and seeking. Furthermore, we compared the effect of ezlopitant on ethanol consumption and seeking in rodents. The NK1-receptor antagonist, ezlopitant decreased appetitive responding for sucrose more potently than for ethanol using an operant self-administration protocol without affecting general locomotor activity. To further evaluate the selectivity of the NK1-receptor antagonist in decreasing consumption of sweetened solutions, we compared the effects of ezlopitant on water, saccharin-, and sodium chloride (NaCl) solution consumption. Ezlopitant decreased intake of saccharin but had no effect on water or salty solution consumption. Conclusions/Significance: The present study indicates that the NK1-receptor may be a part of a common pathway regulating the self-administration, motivational and reinforcing aspects of sweetened solutions, regardless of caloric value, and those of substances of abuse. Additionally, these results indicate that the NK1-receptor system may serve as a therapeutic target for obesity induced by over-consumption of natural reinforcers

Phase 1 Gene Therapy for Duchenne Muscular Dystrophy Using a Translational Optimized AAV Vector

Efficient and widespread gene transfer is required for successful treatment of Duchenne muscular dystrophy (DMD). Here, we performed the first clinical trial using a chimeric adeno-associated virus (AAV) capsid variant (designated AAV2.5) derived from a rational design strategy. AAV2.5 was generated from the AAV2 capsid with five mutations from AAV1. The novel chimeric vector combines the improved muscle transduction capacity of AAV1 with reduced antigenic crossreactivity against both parental serotypes, while keeping the AAV2 receptor binding. In a randomized double-blind placebo-controlled phase I clinical study in DMD boys, AAV2.5 vector was injected into the bicep muscle in one arm, with saline control in the contralateral arm. A subset of patients received AAV empty capsid instead of saline in an effort to distinguish an immune response to vector versus minidystrophin transgene. Recombinant AAV genomes were detected in all patients with up to 2.56 vector copies per diploid genome. There was no cellular immune response to AAV2.5 capsid. This trial established that rationally designed AAV2.5 vector was safe and well tolerated, lays the foundation of customizing AAV vectors that best suit the clinical objective (e.g., limb infusion gene delivery) and should usher in the next generation of viral delivery systems for human gene transfer

Spontaneous honeybee behaviour is altered by persistent organic pollutants

The effect of environmental pollutants on honeybee behaviour has focused mainly on currently used pesticides. However, honeybees are also exposed to persistent organic pollutants (POPs). The aim of this laboratory based study was to determine if exposure to sublethal field-relevant concentrations of POPs altered the spontaneous behaviour of foraging-age worker honeybees. Honeybees (Apis mellifera) were orally exposed to either a sublethal concentration of the polychlorinated biphenyl (PCB) mixture Aroclor 1254 (100 ng/ml), the organochlorine insecticide lindane (2.91 ng/ml) or vehicle (0.01% DMSO, 0.00015% ethanol in 1M sucrose) for 1–4 days. The frequency of single event behaviours and the time engaged in one of four behavioural states (walking, flying, upside down and stationary) were monitored for 15 min after 1, 2, 3 and 4 days exposure. Exposure to Aroclor 1254 but not lindane increased the frequency and time engaged in honeybee motor activity behaviours in comparison to vehicle. The Aroclor 1254—induced hyperactivity was evident after 1 day of exposure and persisted with repeated daily exposure. In contrast, 1 day of exposure to lindane elicited abdominal spasms and increased the frequency of grooming behaviours in comparison to vehicle exposure. After 4 days of exposure, abdominal spasms and increased grooming behaviours were also evident in honeybees exposed to Aroclor 1254. These data demonstrate that POPs can induce distinct behavioural patterns, indicating different toxicokinetic and toxicodynamic properties. The changes in spontaneous behaviour, particularly the PCB-induced chronic hyperactivity and the associated energy demands, may have implications for colony health

A global phylogeny of butterflies reveals their evolutionary history, ancestral hosts and biogeographic origins

Butterflies are a diverse and charismatic insect group that are thought to have evolved with plants and dispersed throughout the world in response to key geological events. However, these hypotheses have not been extensively tested because a comprehensive phylogenetic framework and datasets for butterfly larval hosts and global distributions are lacking. We sequenced 391 genes from nearly 2,300 butterfly species, sampled from 90 countries and 28 specimen collections, to reconstruct a new phylogenomic tree of butterflies representing 92% of all genera. Our phylogeny has strong support for nearly all nodes and demonstrates that at least 36 butterfly tribes require reclassification. Divergence time analyses imply an origin similar to 100 million years ago for butterflies and indicate that all but one family were present before the K/Pg extinction event. We aggregated larval host datasets and global distribution records and found that butterflies are likely to have first fed on Fabaceae and originated in what is now the Americas. Soon after the Cretaceous Thermal Maximum, butterflies crossed Beringia and diversified in the Palaeotropics. Our results also reveal that most butterfly species are specialists that feed on only one larval host plant family. However, generalist butterflies that consume two or more plant families usually feed on closely related plants

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

Development of Risk Prediction Equations for Incident Chronic Kidney Disease

IMPORTANCE ‐ Early identification of individuals at elevated risk of developing chronic kidney disease  could improve clinical care through enhanced surveillance and better management of underlying health  conditions.  OBJECTIVE – To develop assessment tools to identify individuals at increased risk of chronic kidney  disease, defined by reduced estimated glomerular filtration rate (eGFR).  DESIGN, SETTING, AND PARTICIPANTS – Individual level data analysis of 34 multinational cohorts from  the CKD Prognosis Consortium including 5,222,711 individuals from 28 countries. Data were collected  from April, 1970 through January, 2017. A two‐stage analysis was performed, with each study first  analyzed individually and summarized overall using a weighted average. Since clinical variables were  often differentially available by diabetes status, models were developed separately within participants  with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external  cohorts (N=2,253,540). EXPOSURE Demographic and clinical factors.  MAIN OUTCOMES AND MEASURES – Incident eGFR <60 ml/min/1.73 m2.  RESULTS – In 4,441,084 participants without diabetes (mean age, 54 years, 38% female), there were  660,856 incident cases of reduced eGFR during a mean follow‐up of 4.2 years. In 781,627 participants  with diabetes (mean age, 62 years, 13% female), there were 313,646 incident cases during a mean follow‐up of 3.9 years. Equations for the 5‐year risk of reduced eGFR included age, sex, ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, BMI, and albuminuria. For participants  with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction  between the two. The risk equations had a median C statistic for the 5‐year predicted probability of  0.845 (25th – 75th percentile, 0.789‐0.890) in the cohorts without diabetes and 0.801 (25th – 75th percentile, 0.750‐0.819) in the cohorts with diabetes. Calibration analysis showed that 9 out of 13 (69%) study populations had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was  similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 out of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25. CONCLUSIONS AND RELEVANCE – Equations for predicting risk of incident chronic kidney disease developed in over 5 million people from 34 multinational cohorts demonstrated high discrimination and  variable calibration in diverse populations