Distinguishing cause from effect - many deficits associated with developmental dyslexia may be a consequence of reduced and suboptimal reading experience

The cause of developmental dyslexia is still unknown despite decades of intense research. Many causal explanations have been proposed, based on the range of impairments displayed by affected individuals. Here we draw attention to the fact that many of these impairments are also shown by illiterate individuals who have not received any or very little reading instruction. We suggest that this fact may not be coincidental and that the performance differences of both illiterates and individuals with dyslexia compared to literate controls are, to a substantial extent, secondary consequences of either reduced or suboptimal reading experience or a combination of both. The search for the primary causes of reading impairments will make progress if the consequences of quantitative and qualitative differences in reading experience are better taken into account and not mistaken for the causes of reading disorders. We close by providing four recommendations for future research

Continued Beneficial Effects of Burosumab in Adults with X-Linked Hypophosphatemia:Results from a 24-Week Treatment Continuation Period After a 24-Week Double-Blind Placebo-Controlled Period

Burosumab, a fully human monoclonal antibody to FGF23, is the only approved treatment for X-linked hypophosphatemia (XLH), a rare genetic disorder characterized by renal phosphate wasting and substantial cumulative musculoskeletal morbidity. During an initial 24-week randomized, controlled trial, 134 adults with XLH received burosumab 1 mg/kg (n = 68) or placebo (n = 66) every 4 weeks. After 24 weeks, all subjects received open-label burosumab until week 48. This report describes the efficacy and safety of burosumab during the open-label treatment period. From weeks 24-48, serum phosphorus concentrations remained normal in 83.8% of participants who received burosumab throughout and were normalized in 89.4% who received burosumab after placebo. By week 48, 63.1% of baseline fractures/pseudofractures healed fully with burosumab, compared with 35.2% with burosumab after placebo. In both groups, burosumab was associated with clinically significant and sustained improvement from baseline to week 48 in scores for patient-reported outcomes of stiffness, pain, physical function, and total distance walked in 6 min. Rates of adverse events were similar for burosumab and placebo. There were no fatal adverse events or treatment-related serious adverse events. Nephrocalcinosis scores did not change from baseline by more than one grade at either week 24 or 48. These data demonstrate that in participants with XLH, continued treatment with burosumab is well tolerated and leads to sustained correction of serum phosphorus levels, continued healing of fractures and pseudofractures, and sustained improvement in key musculoskeletal impairments

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

Geschichte Griechenlands von dem ende des peloponnesischen krieges bis zu dem regierungsantritte Alexanders des Grossen.

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