Evaluating epidemic forecasts in an interval format

For practical reasons, many forecasts of case, hospitalization and death counts in the context of the current COVID-19 pandemic are issued in the form of central predictive intervals at various levels. This is also the case for the forecasts collected in the COVID-19 Forecast Hub (https://covid19forecasthub.org/). Forecast evaluation metrics like the logarithmic score, which has been applied in several infectious disease forecasting challenges, are then not available as they require full predictive distributions. This article provides an overview of how established methods for the evaluation of quantile and interval forecasts can be applied to epidemic forecasts in this format. Specifically, we discuss the computation and interpretation of the weighted interval score, which is a proper score that approximates the continuous ranked probability score. It can be interpreted as a generalization of the absolute error to probabilistic forecasts and allows for a decomposition into a measure of sharpness and penalties for over- and underprediction

A review of how we assess denitrification in oyster habitats and proposed guidelines for future studies

Excess nitrogen (N) loading and resulting eutrophication plague coastal ecosystems globally. Much work is being done to remove N before it enters coastal receiving waters, yet these efforts are not enough. Novel techniques to remove N from within the coastal ecosystem are now being explored. One of these techniques involves using oysters and their habitats to remove N via denitrification. There is substantial interest in incorporating oyster-mediated enhancement of benthic denitrification into N management plans and trading schemes. Measuring denitrification, however, is expensive and time consuming. For large-scale adoption of oyster-mediated denitrification into nutrient management plans, we need an accurate model that can be applied across ecosystems. Despite significant effort to measure and report rates of denitrification in oyster habitats, we are unable to create such a model, due to methodological differences between studies, incomplete data reporting, and inconsistent measurements of environmental variables that may be used to predict denitrification. To make a model that can predict denitrification in oyster habitats a reality, a common sampling and reporting scheme is needed across studies. Here, we provide relevant background on how oysters may stimulate denitrification, and the importance of oyster-mediated denitrification in remediating excess N loading to coastal systems. We then summarize methods commonly used to measure denitrification in oyster habitats, discuss the importance of various environmental variables that may be useful for predicting denitrification, and present a set of guidelines for measuring denitrification in oyster habitats, allowing development of models to support incorporation of oyster-mediated denitrification into future policy decisions

Biomass Blending and Densification: Impacts on Feedstock Supply and Biochemical Conversion Performance

The success of lignocellulosic biofuels and biochemical industries depends on an economic and reliable supply of high‐quality biomass. However, research and development efforts have been historically focused on the utilization of agriculturally derived cellulosic feedstocks, without considerations of their low energy density, high variations in compositions and potential supply risks in terms of availability and affordability. This chapter demonstrated a strategy of feedstock blending and densification to address the supply chain challenges. Blending takes advantage of low‐cost feedstock to avoid the prohibitive costs incurred through reliance on a single feedstock resource, while densification produces feedstocks with increased bulk density and desirable feed handling properties, as well as reduced transportation cost. We also review recent research on the blending and densification dealing with various types of feedstocks with a focus on the impacts of these preprocessing steps on biochemical conversion, that is, various thermochemical pretreatment chemistries and enzymatic hydrolysis, into fermentable sugars for biofuel production

Nitrous Oxide Dynamics in the Siberian Arctic Ocean and Vulnerability to Climate Change

Nitrous oxide (N2O) is a strong greenhouse gas and stratospheric ozone-depleting substance. Around 20% of global emissions stem from the ocean, but current estimates and future projections are uncertain due to poor spatial coverage over large areas and limited understanding of drivers of N2O dynamics. Here, we focus on the extensive and particularly data-lean Arctic Ocean shelves north of Siberia that experience rapid warming and increasing input of land-derived nitrogen with permafrost thaw. We combine water column N2O measurements from two expeditions with on-board incubation of intact sediment cores to assess N2O dynamics and the impact of land-derived nitrogen. Elevated nitrogen concentrations in water column and sediments were observed near large river mouths. Concentrations of N2O were only weakly correlated with dissolved nitrogen and turbidity, reflecting particulate matter from rivers and coastal erosion, and correlations varied between river plumes. Surface water N2O concentrations were on average close to equilibrium with the atmosphere, but varied widely (N2O saturation 38%–180%), indicating strong local N2O sources and sinks. Water column N2O profiles and low sediment-water N2O fluxes do not support strong sedimentary sources or sinks. We suggest that N2O dynamics in the region are influenced by water column N2O consumption under aerobic conditions or in anoxic microsites of particles, and possibly also by water column N2O production. Changes in biogeochemical and physical conditions will likely alter N2O dynamics in the Siberian Arctic Ocean over the coming decades, in addition to reduced N2O solubility in a warmer ocean.publishedVersio

N6-Furfuryladenine is protective in Huntington’s disease models by signaling huntingtin phosphorylation

© 2018 National Academy of Sciences. All Rights Reserved. The huntingtin N17 domain is a modulator of mutant huntingtin toxicity and is hypophosphorylated in Huntington’s disease (HD). We conducted high-content analysis to find compounds that could restore N17 phosphorylation. One lead compound from this screen was N6-furfuryladenine (N6FFA). N6FFA was protective in HD model neurons, and N6FFA treatment of an HD mouse model corrects HD phenotypes and eliminates cortical mutant huntingtin inclusions. We show that N6FFA restores N17 phosphorylation levels by being salvaged to a triphosphate form by adenine phosphoribosyltransferase (APRT) and used as a phosphate donor by casein kinase 2 (CK2). N6FFA is a naturally occurring product of oxidative DNA damage. Phosphorylated huntingtin functionally redistributes and colocalizes with CK2, APRT, and N6FFA DNA ad-ducts at sites of induced DNA damage. We present a model in which this natural product compound is salvaged to provide a triphosphate substrate to signal huntingtin phosphorylation via CK2 during low-ATP stress under conditions of DNA damage, with protective effects in HD model systems

Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced?

Relative to standard red/green/blue (RGB) imaging systems, hyperspectral imaging systems offer superior capabilities but tend to be expensive and complex, requiring either a mechanically complex push-broom line scanning method, a tunable filter, or a large set of light emitting diodes (LEDs) to collect images in multiple wavelengths. This paper proposes a new methodology to support the design of a hypothesized system that uses three imaging modes—fluorescence, visible/near-infrared (VNIR) reflectance, and shortwave infrared (SWIR) reflectance—to capture narrow-band spectral data at only three to seven narrow wavelengths. Simulated annealing is applied to identify the optimal wavelengths for sparse spectral measurement with a cost function based on the accuracy provided by a weighted k-nearest neighbors (WKNN) classifier, a common and relatively robust machine learning classifier. Two separate classification approaches are presented, the first using a multi-layer perceptron (MLP) artificial neural network trained on sparse data from the three individual spectra and the second using a fusion of the data from all three spectra. The results are compared with those from four alternative classifiers based on common machine learning algorithms. To validate the proposed methodology, reflectance and fluorescence spectra in these three spectroscopic modes were collected from fish fillets and used to classify the fillets by species. Accuracies determined from the two classification approaches are compared with benchmark values derived by training the classifiers with the full resolution spectral data. The results of the single-layer classification study show accuracies ranging from ~68% for SWIR reflectance to ~90% for fluorescence with just seven wavelengths. The results of the fusion classification study show accuracies of about 95% with seven wavelengths and more than 90% even with just three wavelengths. Reducing the number of required wavelengths facilitates the creation of rapid and cost-effective spectral imaging systems that can be used for widespread analysis in food monitoring/food fraud, agricultural, and biomedical applications

A Randomized Controlled Multicenter US Food and Drug Administration Trial of the Safety and Efficacy of the Minerva Endometrial Ablation System: One-Year Follow-Up Results

AbstractStudy ObjectiveTo assess the safety and effectiveness of the Minerva Endometrial Ablation System for the treatment of heavy menstrual bleeding in premenopausal women.DesignMulticenter, randomized, controlled, international study (Canadian Task Force classification I).SettingThirteen academic and private medical centers.PatientsPremenopausal women (n = 153) suffering from heavy menstrual bleeding (PALM-COEIN: E, O).InterventionPatients were treated using the Minerva Endometrial Ablation System or rollerball ablation.Measurements and Main ResultsAt 1-year post-treatment, study success (alkaline hematin ≤80 mL) was observed in 93.1% of Minerva subjects and 80.4% of rollerball subjects with amenorrhea reported by 71.6% and 49% of subjects, respectively. The mean procedure times were 3.1 minutes for Minerva and 17.2 minutes for rollerball. There were no intraoperative adverse events and/or complications reported.ConclusionThe results of this multicenter randomized controlled trial demonstrate that at the 12-month follow-up, the Minerva procedure produces statistically significantly higher rates of success, amenorrhea, and patient satisfaction as well as a shorter procedure time when compared with the historic criterion standard of rollerball ablation. Safety results were excellent and similar for both procedures