Exploring the influence of meteorological conditions on the performance of a waste stabilization pond at high altitude with structural equation modeling

Algal photosynthesis plays a key role in the removal mechanisms of waste stabilization ponds (WSPs), which is indicated in the variations of three parameters, dissolved oxygen, pH, and chlorophyll a. These variations can be considerably affected by extreme climatic conditions at high altitude. To investigate these effects, three sampling campaigns were conducted in a high-altitude WSP in Cuenca (Ecuador). From the collected data, the first application of structure equation modeling (SEM) on a pond system was fitted to analyze the influence of high-altitude characteristics on pond performance, especially on the three indicators. Noticeably, air temperature appeared as the highest influencing factors as low temperature at high altitude can greatly decrease the growth rate of microorganisms. Strong wind and large diurnal variations of temperature, 7-20 degrees C, enhanced flow efficiency by improving mixing inside the ponds. Intense solar radiation brought both advantages and disadvantages as it boosted oxygen level during the day but promoted algal overgrowth causing oxygen depletion during the night. From these findings, the authors proposed insightful recommendations for future design, monitoring, and operation of high-altitude WSPs. Moreover, we also recommended SEM to pond engineers as an effective tool for better simulation of such complex systems like WSPs

Author Correction: The genetic legacy of continental scale admixture in Indian Austroasiatic speakers

This Article contains errors in the Methods section, under subsection ‘Samples collection and genotyping’

Budget impact analysis of medicines : updated systematic review and implications

This evaluation determines whether published studies to date meet the key characteristics identified for budget impact analyses (BIA) for medicines, accomplished through a systematic review and assessment against identified key characteristics. Studies from 2001 to 2015 on "budget impact analysis" with "drug" interventions were assessed, selected based on their titles/abstracts and full texts, with their characteristics checked according to key criteria. Out of 1984 studies, 92 were identified. Of these, 95% were published in Europe and the USA. 2012 saw the largest number of publications (16%) with a decline thereafter. 48% met up to 6 or 7 out of the 9 key characteristics. Only 22% stated no conflict of interest. The results indicate low adherence to the key characteristics that should be considered for BIAs and strong conflict of interest. This is an issue since BIAs can be of fundamental importance in managing the entry of new medicines including reimbursement decisions

An evaluation of purified Salmonella Typhi protein antigens for the serological diagnosis of acute typhoid fever.

OBJECTIVES: The diagnosis of typhoid fever is a challenge. Aiming to develop a typhoid diagnostic we measured antibody responses against Salmonella Typhi (S. Typhi) protein antigens and the Vi polysaccharide in a cohort of Bangladeshi febrile patients. METHODS: IgM against 12 purified antigens and the Vi polysaccharide was measured by ELISA in plasma from patients with confirmed typhoid fever (n = 32), other confirmed infections (n = 17), and healthy controls (n = 40). ELISAs with the most specific antigens were performed on plasma from 243 patients with undiagnosed febrile disease. RESULTS: IgM against the S. Typhi protein antigens correlated with each other (rho > 0.8), but not against Vi (rho 0.85, respectively. Applying a dynamic cut-off to patients with undiagnosed febrile disease suggested that 34-58% had an IgM response indicative of typhoid. CONCLUSIONS: We evaluated the diagnostic potential of several S. Typhi antigens; our assays give good sensitivity and specificity, but require further assessment in differing patient populations

SDSS-III: Massive Spectroscopic Surveys of the Distant Universe, the Milky Way Galaxy, and Extra-Solar Planetary Systems

Building on the legacy of the Sloan Digital Sky Survey (SDSS-I and II), SDSS-III is a program of four spectroscopic surveys on three scientific themes: dark energy and cosmological parameters, the history and structure of the Milky Way, and the population of giant planets around other stars. In keeping with SDSS tradition, SDSS-III will provide regular public releases of all its data, beginning with SDSS DR8 (which occurred in Jan 2011). This paper presents an overview of the four SDSS-III surveys. BOSS will measure redshifts of 1.5 million massive galaxies and Lya forest spectra of 150,000 quasars, using the BAO feature of large scale structure to obtain percent-level determinations of the distance scale and Hubble expansion rate at z<0.7 and at z~2.5. SEGUE-2, which is now completed, measured medium-resolution (R=1800) optical spectra of 118,000 stars in a variety of target categories, probing chemical evolution, stellar kinematics and substructure, and the mass profile of the dark matter halo from the solar neighborhood to distances of 100 kpc. APOGEE will obtain high-resolution (R~30,000), high signal-to-noise (S/N>100 per resolution element), H-band (1.51-1.70 micron) spectra of 10^5 evolved, late-type stars, measuring separate abundances for ~15 elements per star and creating the first high-precision spectroscopic survey of all Galactic stellar populations (bulge, bar, disks, halo) with a uniform set of stellar tracers and spectral diagnostics. MARVELS will monitor radial velocities of more than 8000 FGK stars with the sensitivity and cadence (10-40 m/s, ~24 visits per star) needed to detect giant planets with periods up to two years, providing an unprecedented data set for understanding the formation and dynamical evolution of giant planet systems. (Abridged)Comment: Revised to version published in The Astronomical Journa

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