Significance This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

Most existing event extraction (EE) methods merely extract event arguments within the sentence scope. However, such sentence-level EE methods struggle to handle soaring amounts of documents from emerging applications, such as finance, legislation, health, etc., where event arguments always scatter across different sentences, and even multiple such event mentions frequently co-exist in the same document. To address these challenges, we propose a novel end-to-end model, Doc2EDAG, which can generate an entity-based directed acyclic graph to fulfill the document-level EE (DEE) effectively. Moreover, we reformalize a DEE task with the no-trigger-words design to ease document-level event labeling. To demonstrate the effectiveness of Doc2EDAG, we build a large-scale real-world dataset consisting of Chinese financial announcements with the challenges mentioned above. Extensive experiments with comprehensive analyses illustrate the superiority of Doc2EDAG over state-of-the-art methods. Data and codes can be found at https://github.com/ dolphin-zs/Doc2EDAG.

Selective isolation of mono-and multi-phosphorylated peptides is important for understanding how a graded protein kinase or phosphatase signal can precisely modulate the on and off states of signal transduction pathways. Here we report that metal ions at exposed octahedral sites of nano-ferrites, including Fe 3 O 4 , NiFe 2 O 4 , ZnFe 2 O 4 and NiZnFe 2 O 4 , have distinctly selective coordination abilities with mono-and multi-phosphopeptides. Due to their intrinsic magnetic properties and high surface area to volume ratios, these nanoparticles enable the rapid isolation of mono-and multi-phosphopeptides by an external magnetic field. Model phosphoprotein a-casein and two synthesized mono-and di-phosphopeptides have been chosen for proof-of-principle demonstrations, and these nanoparticles have also been applied to phosphoproteome profiling of zebrafish eggs. It is shown that NiZnFe 2 O 4 is highly selective for multi-phosphopeptides. In contrast, Fe 3 O 4 , NiFe 2 O 4 and ZnFe 2 O 4 can bind with both mono-and multi-phosphopeptides with relatively stronger affinity towards monophosphopeptides.

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 multi-model ensemble forecast that combined predictions from dozens of different research 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-week horizon 3-5 times larger than when predicting at a 1-week 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. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Concentrated formic acid is among the most effective solvents for protein solubilization. Unfortunately, this acid also presents a risk of inducing chemical modifications thereby limiting its use in proteomics. Previous reports have supported the esterification of serine and threonine residues (O-formylation) for peptides incubated in formic acid. However as shown here, exposure of histone H4 to 80% formic (1 h, 20(o) C) induces N-formylation of two independent lysine residues. Furthermore, incubating a mixture of Escherichia coli proteins in formic acid demonstrates a clear preference toward lysine modification over reactions at serine/threonine. N-formylation accounts for 84% of the 225 uniquely identified formylation sites. To prevent formylation, we provide a detailed investigation of reaction conditions (temperature, time, acid concentration) that define the parameters permitting the use of concentrated formic acid in a proteomics workflow for MS characterization. Proteins can be maintained in 80% formic acid for extended periods (24 h) without inducing modification, so long as the temperature is maintained at or below -20(o) C.