Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

Sulf1 has ligand-dependent effects on canonical and non-canonical Wnt signalling

Wnt signalling plays essential roles during embryonic development and is known to be mis-regulated in human disease. There are many molecular mechanisms that ensure tight regulation of Wnt activity. One such regulator is the heparan-sulfate-specific 6-O-endosulfatase Sulf1. Sulf1 acts extracellularly to modify the structure of heparan sulfate chains to affect the bio-availability of Wnt ligands. Sulf1 could, therefore, influence the formation of Wnt signalling complexes to modulate the activation of both canonical and non-canonical pathways. In this study, we use well-established assays in Xenopus to investigate the ability of Sulf1 to modify canonical and non-canonical Wnt signalling. In addition, we model the ability of Sulf1 to influence morphogen gradients using fluorescently tagged Wnt ligands in ectodermal explants. We show that Sulf1 overexpression has ligand-specific effects on Wnt signalling: it affects membrane accumulation and extracellular levels of tagged Wnt8a and Wnt11b ligands differently, and inhibits the activity of canonical Wnt8a but enhances the activity of non-canonical Wnt11b

Search for Gravitational Waves Associated with 39 Gamma-Ray Bursts Using Data from the Second, Third, and Fourth LIGO Runs

We present the results of a search for short-duration gravitational-wave bursts associated with 39 gamma-ray bursts (GRBs) detected by gamma-ray satellite experiments during LIGO's S2, S3, and S4 science runs. The search involves calculating the crosscorrelation between two interferometer data streams surrounding the GRB trigger time. We search for associated gravitational radiation from single GRBs, and also apply statistical tests to search for a gravitational-wave signature associated with the whole sample. For the sample examined, we find no evidence for the association of gravitational radiation with GRBs, either on a single-GRB basis or on a statistical basis. Simulating gravitational-wave bursts with sine-gaussian waveforms, we set upper limits on the root-sum-square of the gravitational-wave strain amplitude of such waveforms at the times of the GRB triggers. We also demonstrate how a sample of several GRBs can be used collectively to set constraints on population models. The small number of GRBs and the significant change in sensitivity of the detectors over the three runs, however, limits the usefulness of a population study for the S2, S3, and S4 runs. Finally, we discuss prospects for the search sensitivity for the ongoing S5 run, and beyond for the next generation of detectors.Comment: 24 pages, 10 figures, 14 tables; minor changes to text and Fig. 2; accepted by Phys. Rev.

The chicken left right organizer has nonmotile cilia which are lost in a stage-dependent manner in the talpid3 ciliopathy

Motile cilia are an essential component of the mouse, zebrafish, and Xenopus laevis Left Right Organizers, generating nodal flow and allowing the reception and transduction of mechanosensory signals. Nonmotile primary cilia are also an important component of the Left Right Organizer's chemosensory mechanism. It has been proposed in the chicken that signaling in Hensen's node, the Left Right Organizer of the chicken, is independent of cilia, based on a lack of evidence of motile cilia or nodal flow. It is speculated that the talpid(3) chicken mutant, which has normal left–right patterning despite lacking cilia at many stages of development, is proof of this hypothesis. Here, we examine the evidence for cilia in Hensen's node and find that although cilia are present; they are likely to be immotile and incapable of generating nodal flow. Furthermore, we find that early planar cell polarity patterning and ciliogenesis is normal in early talpid(3) chicken embryos. We conclude that patterning and development of the early talpid(3) chicken is normal, but not necessarily independent of cilia. Although it appears that Hensen's node does not require motile cilia or the generation of motile flow, there may remain a requirement for cilia in the transduction of SHH signaling. RESULTS: FOXJ1 is expressed at low levels in the chicken node incompatible with motile cilia generation. Short cilia are present in the mesodermal cells of the chicken node. Talpid(3) chicken embryos have normal VANGL2 localization early in development. Talpid(3) chicken embryos have primary cilia early in development

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