Search

Background: A suspected food-poisoning outbreak occurred in a residential school in Mirzapur, India, in February, 2017. Objective: We investigated the outbreak to find out the epidemiology and to identify the risk factors. Methods: A descriptive study followed by retrospective-cohort study was done to investigate the outbreak. Cases (defined as ≥3 or more loose stools in 24 h, abdominal pain, or vomiting with onset between February 1 and 4, 2017) were searched by reviewing sick/patient registers from school and nearby health facilities. Cases were also searched through active surveillance by visiting school hostels. Stool samples were sent for microbiological testing. Food sources and food handlers were also assessed. Results: Among 468 students, 204 cases were identified (44% attack rate) without any mortality. The median age was found to be 14 years (range: 10–18 years) and 59% were male. Relative risk with consumption of curd, apple, and panjiri (sweetened wheat flour) was found to be 15.4, 2.5, and 3.7, respectively. All these three food items were served as prasad, a religious offering. Only consumption of sweetened curd (adjusted odds ratio = 36.1, 95% confidence interval = 12.1–107.8) was significantly associated with gastroenteritis. No microorganism was isolated from two tested stool samples. Curd from the vendor was prepared from nonpasteurized milk. There were no illnesses among food-handlers. Conclusions: This outbreak of acute gastroenteritis in a residential school was associated with consumption of curd, likely contaminated with preformed toxins. We recommend implementation of the food safety and standards authority of India regulations.

Abstract We report multi-differential measurements of strange hadron production ranging from mid- to target-rapidity in Au+Au collisions at a center-of-momentum energy per nucleon pair of s NN $$ \sqrt{s_{\textrm{NN}}} $$ = 3 GeV with the STAR experiment at RHIC. K S 0 $$ {K}_S^0 $$ meson and Λ hyperon yields are measured via their weak decay channels. Collision centrality and rapidity dependences of the transverse momentum spectra and particle ratios are presented. Particle mass and centrality dependence of the average transverse momenta of Λ and K S 0 $$ {K}_S^0 $$ are compared with other strange particles, providing evidence of the development of hadronic rescattering in such collisions. The 4π yields of each of these strange hadrons show a consistent centrality dependence. Discussions on radial flow, the strange hadron production mechanism, and properties of the medium created in such collisions are presented together with results from hadronic transport and thermal model calculations.

On the basis of historical influenza and COVID-19 forecasts, we found that more than 3 forecast models are needed to ensure robust ensemble accuracy. Additional models can improve ensemble performance, but with diminishing accuracy returns. This understanding will assist with the design of current and future collaborative infectious disease forecasting efforts.

Abstract This open-label, non-comparative, 2:1 randomized, phase II trial (NCT03275506) in women with stage IIIC/IV high-grade serous carcinoma (HGSC) for whom upfront complete resection was unachievable assessed whether adding pembrolizumab (200 mg every 3 weeks) to standard-of-care carboplatin plus paclitaxel yielded a complete resection rate (CRR) of at least 50%. Postoperatively patients continued assigned treatment for a maximum of 2 years. Postoperative bevacizumab was optional. The primary endpoint was independently assessed CRR at interval debulking surgery. Secondary endpoints were Completeness of Cytoreduction Index (CCI) and peritoneal cancer index (PCI) scores, objective and best response rates, progression-free survival, overall survival, safety, postoperative morbidity, and pathological complete response. The CRR in 61 pembrolizumab-treated patients was 74% (one-sided 95% CI = 63%), exceeding the prespecified ≥50% threshold and meeting the primary objective. The CRR without pembrolizumab was 70% (one-sided 95% CI = 54%). In the remaining patients CCI scores were ≥3 in 27% of the standard-of-care group and 18% of the investigational group and CC1 in 3% of the investigational group. PCI score decreased by a mean of 9.6 in the standard-of-care group and 10.2 in the investigational group. Objective response rates were 60% and 72%, respectively, and best overall response rates were 83% and 90%, respectively. Progression-free survival was similar with the two regimens (median 20.8 versus 19.4 months in the standard-of-care versus investigational arms, respectively) but overall survival favored pembrolizumab-containing therapy (median 35.3 versus 49.8 months, respectively). The most common grade ≥3 adverse events with pembrolizumab-containing therapy were anemia during neoadjuvant therapy and infection/fever postoperatively. Pembrolizumab was discontinued prematurely because of adverse events in 23% of pembrolizumab-treated patients. Combining pembrolizumab with neoadjuvant chemotherapy is feasible for HGSC considered not completely resectable; observed activity in some subgroups justifies further evaluation to improve understanding of the role of immunotherapy in HGSC.

Abstract Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021–22 and 12 out of 18 models in 2022–23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021–22 and the 5th most accurate in the 2022–23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

Abstract Intensified production of stocker‐size tilapia can be accomplished using the biofloc technology production system. Stocker‐size tilapia (ca. 10–200 g/fish) are raised for subsequent stocking for grow‐out to market size. The relationship between stocking rate and the production of stocker‐size hybrid tilapia (Oreochromis aureus × O. niloticus) was evaluated in two studies in outdoor mixotrophic biofloc tanks. Hybrid tilapia were stocked in 18.6‐m2 (15.7‐m3) tanks at 50–150 fish/m2 (59–178 fish/m3) in 25 fish/m2 (30 fish/m3) increments in Study 1 and grown for 141–143 days. Stocking rates evaluated in 4.7‐m2 (3.6‐m3) tanks during Study 2 over 78–79 days ranged from 100 to 300 fish/m2 (132–396 fish/m3) in 25 fish/m2 (33 fish/m3) increments. Linear regression models best described growth to stocker size in both studies. Fish yield increased linearly with stocking rate and ranged from 20.1 to 38.3 kg/m3. Individual weight at harvest was inversely related to stocking rate and ranged from 389.8 to 163.4 g/fish and 155.2 to 81.4 g/fish in Studies 1 and 2, respectively. Partial budget analyses suggested positive net changes in profit with incremental increases in stocking rate for producing a 75‐g stocker. Water quality concentrations were impacted directly by daily feed ration and total feed fed, which increased linearly with stocking rate. In Study 2, whole‐body dry matter, protein, protein retention, and lipid were unaffected by stocking rate, whereas whole‐body ash increased with stocking rate. Whole‐body energy retention significantly decreased with stocking rate. The outdoor mixotrophic biofloc production system is well suited for intensive production of hybrid tilapia stockers. However, additional replicated research and economic analysis are needed to evaluate stocking rate and culture duration to produce specific‐sized stocker hybrid tilapia.

This publication is about the life cycle and damage of cottony cushion scale.

This publication is a brief description of the life cycle and damage of the broadwinged katydid.

The chiral magnetic effect (CME) is a phenomenon that arises from the QCD anomaly in the presence of an external magnetic field. The experimental search for its evidence has been one of the key goals of the physics program of the Relativistic Heavy-Ion Collider. The STAR Collaboration has previously presented the results of a blind analysis of isobar collisions (_{44}^{96}Ru+_{44}^{96}Ru, _{40}^{96}Zr+_{40}^{96}Zr) in the search for the CME. The isobar ratio (Y) of CME-sensitive observable, charge separation scaled by elliptic anisotropy, is close to but systematically larger than the inverse multiplicity ratio, the naive background baseline. This indicates the potential existence of a CME signal and the presence of remaining nonflow background due to two- and three-particle correlations, which are different between the isobars. In this postblind analysis, we estimate the contributions from those nonflow correlations as a background baseline to Y, utilizing the isobar data as well as Heavy Ion Jet Interaction Generator simulations. This baseline is found consistent with the isobar ratio measurement, and an upper limit of 10% at 95% confidence level is extracted for the CME fraction in the charge separation measurement in isobar collisions at sqrt[s_{NN}]=200 GeV.

Abstract Past studies have focused on the importance of hydrological variables in analyzing landslide initiation condition. Even though precipitation is the main driver of shallow landslides and debris flows, use of only rainfall-based parameters has shown some limitations. Soil moisture has been used widely to improve threshold detection capabilities. Since soil moisture directly reflects the wetness status of the ground, it can be used to identify pore pressure fluctuations more effectively. This study used rainfall and soil moisture simultaneously to capture landslide initiation conditions in detail. Results showed that continued rainfall on the day of landslide leaded to a sudden increase in soil moisture, and that soil moisture increments (∆SM) were positive in 155 out of 170 landslide cases (91%). Two simple thresholds (daily precipitation over 40 mm, ∆SM over 0) and daily precipitation (P), Antecedent Precipitation Index (API), ∆SM-based three-dimensional threshold planes having 5%, 20% probability levels were applied and compared. With respect to false alarms (FA), P-based threshold was most effective among the single thresholds (FA ranging from 24 to 28 from September 2016 to December 2019 at five validation locations). Combining P- and ∆SM-based thresholds, FA reduced without compromising the detection accuracy (2 to 3 reduction in FA). Additionally combining three-dimensional threshold with 20% probability level, FA reduced significantly (ranging from 12 to 16), at the cost of two detection failures. These findings demonstrate the need for combining precipitation and soil moisture to determine landslide thresholds.

Phytoremediation of contaminated soil is an environmentally-friendly approach to minimize the impacts of nutrients and heavy metals on an ecosystem. Hence, selecting appropriate plants with phytoextraction potential is paramount to remediatie contaminated soils. This study aimed to investigate the nutrient and metal contents of four natural aquatic plants, including Cyperus rotundus, Eleocharis dulcis, Typha angustifolia, and Schoenoplectus grossus. They were grown in the meadow of a small reservoir in Sri Lanka to assess their phytoextraction ability using plant and soil samples collected at 32 sampling points in the meadow. Their biological concentration (roots/soil), accumulation (shoots/soil), and translocation (shoots/roots) factors were determined to assess element mobility and phytoextraction ability. Total K, Na, Mg, Ca, Zn, Cu, Fe, Mn, As, Pb, and Cd contents of plants and soil samples were measured using an Inductivity Couple Plasma Optical Emission Spectrophotometer. ANCOVA was used as a statistical test to assess the best plant type in terms of nutrient and metal absorption. Plant shoots exhibited significantly greater values for P, Na, Mg, Zn, Cd, and Fe than their roots. Their biological concentration, accumulation and translocation factors were not different among the four plant species. However, these values were >1 for all the species, indicating their potential to be used as hyperaccumulators. T. angustifolia, with its high potential for nutrient and metal accumulation and the highest aesthetic appeal, was selected as the best overall wetland species for phytoremediation purposes.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

This study evaluates statistical downscaling techniques using different metrics and compares climate change signals and extreme precipitation and temperature changes under future climate change scenarios in the Bosque watershed, North-Central Texas. The study utilizes observed gridded Daymet data to assess the effectiveness of statistical downscaling techniques. It involves comparing the mean, the 90th percentile, 10th percentile, wet day frequency, and Cumulative Distribution Function (CDF) of climate model simulations before and after downscaling and the Daymet data during the historical period (1981–2005). Furthermore, the study analyzes changes in climate change signals, extreme precipitation, and temperature values under both near-future (2031–2060) and far-future (2070–2099) climate scenarios. The Ratio Delta method (DeltaSD) and Equi-Distant Quantile Mapping (EDQM) statistical downscaling techniques adjust the mean annual, the wet days frequency, the 90th and 10th percentiles, and the CDF of Global Climate Models (GCMs) simulations of historical precipitation and temperature. The downscaling techniques influenced the climate change signal and changes in extreme values in the future climate. When examining future climate projections produced using the DeltaSD method, we observe a more pronounced reduction in precipitation, while simulations generated through EDQM exhibit a higher frequency of heavy precipitation events (R10mm, R20mm) and consecutive dry days (CDD). It's worth noting that the uncertainties associated with the statistical downscaling techniques are relatively small and not statistically significant (≤0.05). In contrast, substantial and significant uncertainties arise from the choice of emission scenarios and the selection of driving GCMs. Across most climate change scenarios, there is a consistent trend towards increased temperatures and extreme temperature indices. The trend of extreme temperature indices shows variation following the choice of emission scenarios where a significant change in temperature extremes was detected under the RCP8.5 emission scenario.

Abstract We report a new measurement of the production of electrons from open heavy-flavor hadron decays (HFEs) at mid-rapidity (|y| < 0.7) in Au+Au collisions at s NN $$ \sqrt{s_{\textrm{NN}}} $$ = 200 GeV. Invariant yields of HFEs are measured for the transverse momentum range of 3.5 < p T < 9 GeV/c in various configurations of the collision geometry. The HFE yields in head-on Au+Au collisions are suppressed by approximately a factor of 2 compared to that in p + p collisions scaled by the average number of binary collisions, indicating strong interactions between heavy quarks and the hot and dense medium created in heavy-ion collisions. Comparison of these results with models provides additional tests of theoretical calculations of heavy quark energy loss in the quark-gluon plasma.

This publication is a brief description of the life cycle and damage of the orangedog. It is one publication in a series of quick guides to citrus pests. Each fact sheet is designed to be a quick reference of citrus pests for both residential and commercial industry audiences.

Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent.

Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract

Abstract Large-scale single-cell ‘omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.

Abstract Partons traversing the strongly interacting medium produced in heavy-ion collisions are expected to lose energy depending on their color charge and mass. We measure the nuclear modification factors for charm- and bottom-decay electrons, defined as the ratio of yields, divided by the number of binary nucleon–nucleon collisions, in $$\sqrt{s_{\textrm{NN}}}=200$$ s NN = 200 GeV Au+Au collisions to p+p collisions ( $$R_{\textrm{AA}}$$ R AA ), or in central to peripheral Au+Au collisions ( $$R_{\textrm{CP}}$$ R CP ). We find the bottom-decay electron $$R_{\textrm{AA}}$$ R AA and $$R_{\textrm{CP}}$$ R CP to be significantly higher than those of charm-decay electrons. Model calculations including mass-dependent parton energy loss in a strongly coupled medium are consistent with the measured data. These observations provide evidence of mass ordering of charm and bottom quark energy loss when traversing through the strongly coupled medium created in heavy-ion collisions.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain. Characterizing cellular diversity is necessary to understand the function of different cell types in the brain. This Consensus View provides a guide to the cellular and spatial approaches used in cell type surveys by the BRAIN Initiative Cell Census Network (BICCN), as well as information on accessing and using the BICCN data and its extensive resources.

Abstract Turtles can be unintentionally hooked during recreational and commercial fishing operations. While hook ingestion is not always fatal, ingested hooks can harm or kill turtles by damaging soft tissues or affecting digestion. Detecting ingested hooks and other metallic debris in the field is a challenge. As such, we tested the efficacy of a handheld metal detector in detecting ingested fish hooks and incidental metallic debris in freshwater turtles in Georgia, USA. We captured 426 individual freshwater turtles representing 4 species and radiographed 106 yellow‐bellied sliders (Trachemys scripta scripta). Of 37 turtles that tested positive with a handheld metal detector, radiographs revealed 17 (46%) had ingested at least one hook, 15 (40%) had ingested an unidentifiable metal object or objects, and 5 (14%) showed no clear signs of hooks or other metallic objects. All 69 turtles that tested negative in the field and were radiographed showed no signs of metal ingestion. Accordingly, our proportion of false positives was 14% (5 of 37), false negatives (metal not detected in field but on follow‐up radiograph) were 0% (0 of 69), and our overall accuracy at detecting ingested metal with a handheld metal detector was 95% (101 of 106 radiographed turtles). Thus, handheld metal detectors may be a useful tool for detecting ingested hooks and other metallic objects in freshwater turtles.

Abstract 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.

The deconfined quark-gluon plasma (QGP) created in relativistic heavy-ion collisions enables the exploration of the fundamental properties of matter under extreme conditions. Noncentral collisions can produce strong magnetic fields on the order of 10^{18} G, which offers a probe into the electrical conductivity of the QGP. In particular, quarks and antiquarks carry opposite charges and receive contrary electromagnetic forces that alter their momenta. This phenomenon can be manifested in the collective motion of final-state particles, specifically in the rapidity-odd directed flow, denoted as v_{1}(y). Here, we present the charge-dependent measurements of dv_{1}/dy near midrapidities for π^{±}, K^{±}, and p(p[over ¯]) in Au+Au and isobar (_{44}^{96}Ru+_{44}^{96}Ru and _{40}^{96}Zr+_{40}^{96}Zr) collisions at sqrt[s_{NN}]=200 GeV, and in Au+Au collisions at 27 GeV, recorded by the STAR detector at the Relativistic Heavy Ion Collider. The combined dependence of the v_{1} signal on collision system, particle species, and collision centrality can be qualitatively and semiquantitatively understood as several effects on constituent quarks. While the results in central events can be explained by the u and d quarks transported from initial-state nuclei, those in peripheral events reveal the impacts of the electromagnetic field on the QGP. Our data put valuable constraints on the electrical conductivity of the QGP in theoretical calculations.

Study region: Addis Ababa, capital city of Ethiopia. Addis Ababa’s surface water reservoirs and groundwater infrastructures are in the Akaki watershed of the Awash Basin. Study focus: This study integrates a hydrological model, socioeconomic data, and different water management scenarios to investigate the current and future water supply and demand. Baseline water supply and water demand data are obtained from Addis Ababa Water and Sewerage Authority. The Soil and Water Assessment Tool (SWAT) model was used to simulate reservoir sedimentation and water quantity in the Akaki watershed. The city’s water demand projection is based on the standard public water demand requirement and projected population. The study considered five scenarios to assess the gaps between water supply and demand. New hydrological insights for the region: Simulation results showed that the area upstream of the existing reservoirs has the potential to supply 651,452 m3/day of surface water; however, currently, the reservoirs are supplying only 224,658 m3/day of water. With an optimistic scenario of planned reservoirs and groundwater boreholes becoming operational by 2029, water supply will only meet demands through 2035. Afterwards, the gap between water supply and demand will increase, due to an increase in population. Therefore, additional water supply projects, effective water use, and maintaining critical hydrological ecosystem services are required to bridge the gap between water supply and demand.

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

[This corrects the article DOI: 10.1371/journal.pcbi.1008618.].

Women with colorectal cancer (CRC) have survival advantages over men, yet the underlying mechanisms are unclear. T cell infiltration within the CRC tumor microenvironment (TME) correlates strongly with survival. We hypothesized that women with CRC have increased T cell infiltration and differential gene expression in the TME compared to men. Tissue microarrays comprising primary tumor, tumor infiltrated lymph nodes, and uninvolved colon were created from CRC patients. Proportions of CD4 positive (CD4+) and CD8 positive (CD8+) T cells were identified using immunohistochemistry. TME immune- and cancer-related genetic expression from primary and metastatic CRC tumor were also evaluated via the NanoStringIO360 panel and The Cancer Genome Atlas Project database. CD4+ was higher in tumor samples from women compared to men (22.04% vs. 10.26%, p=0.002) and also in lymph node samples (39.54% vs. 8.56%, p=0.001). CD8+ was increased in uninvolved colon from women compared to men (59.40% vs. 43.61%, p=0.015), and in stage I/II tumors compared to III/IV in all patients (37.01% vs. 23.91%, p=0.009). Top CD8+ tertile patients survived longer compared to the bottom (43.9 months vs. 25.3 months, p=0.007). Differential gene expression was observed in pathways related to Treg function, T cell activity, and T cell exhaustion, amongst several others, in women compared to men. Thus, significant sexual dimorphism exists in the TME that could contribute to survival advantages observed in female patients with CRC.

By considering the importance of water and its purity, herein, a powerful adsorbent has been developed for the adsorption of two toxic contaminants that commonly exist in water, viz., divalent mercury and hexavalent chromium. The efficient adsorbent, CNTs–PLA–Pd, was prepared by covalent grafting polylactic acid to carbon nanotubes and subsequent deposition of palladium nanoparticles. The CNTs–PLA–Pd could adsorb Hg(II), and Cr(VI) entirely exists in water. The adsorption rate for Hg(II) and Cr(VI) was rapid at initial stage, followed by gradual decrease, and attained the equilibrium. The Hg(II) and Cr(VI) adsorption was perceived within 50 min and 80 min, respectively with CNTs–PLA–Pd,. Further, experimental data for Hg(II) and Cr(VI) adsorption was analyzed, and kinetic parameters were estimated using pseudo–first and second–order models. The adsorption process of Hg(II) and Cr(VI) followed the pseudo–second–order kinetics, and the rate–limiting step in the adsorption was chemisorption. The Weber−Morris intraparticle pore diffusion model revealed that the Hg(II) and Cr(VI) adsorption over CNTs–PLA–Pd occurs through multiple phases. The experimental equilibrium parameters for the Hg(II) and Cr(VI) adsorption were estimated by Langmuir, Freundlich, and Temkin isotherms models. All three models were well suited and demonstrated that Hg(II) and Cr(VI) adsorption over CNTs–PLA–Pd transpires through monolayer molecular covering and chemisorption.

The objective was to parameterize a modified water cloud model using crop coefficients (A and B). These crop coefficients were derived from Landsat-8 and Sentinel-2 data. Whereas the coefficients C and D are of soil parameters. The water cloud model was modified using crop coefficients by minimizing the RMSE between observed VVσ0 and Sentinel-1 based simulated VVσ0. The comparison with observed and simulated VV polarized σ0 showed low RMSE (0.81 dB) and strong R2 of 0.98 for NDVI-EVI combination. However, based on other possible combinations of vegetation indices VVσ0 and simulated VVσ0 do not show a good statistical agreement. It was observed that the errors in crop coefficients (A and B) are sensitive to errors in initial vegetation/canopy descriptor parameters.

This is an identification guide for citrus whitefly and citrus whitefly damage.

The relationship between government intervention and cooperative development has always been a source of controversy in the developing world. This paper aims to examine the rationale and successful conditions of government intervention to promote cooperative development in poor areas of rural China. In the context of the “targeted poverty alleviation” program (2015–2020), a government-led campaign covering all poverty-stricken villages in west China, cooperative development was listed by the central government as a criterion for evaluating successful intervention at the county government level. Accordingly, the central questions of this paper are: why is government intervention necessary to initiate a process of cooperative development in poor areas of China; and under what conditions can government intervention be successful, leading to sustainable cooperative development? Bearing in mind the complexity of government intervention with mixed results, both successful and failed, the above questions are addressed through a case study of XM Beekeeping Cooperative, representing one type of successful government intervention in poverty-stricken and ethnic-minority-dominated regions of China. Overall, government intervention is crucial in building cooperative ecosystems in poor regions of China. However, government intervention is not invariable because the approaches can be modified to accommodate the effect of the intervention.

Adulteration of food products is a widespread problem of great concern to society and dairy products are no exception to this. Due to new methods of adulteration being devised in order to circumvent existing detection methods, new detection methods must be developed to counter fraud. Bovine hard cheeses such as Asiago, Parmesan, and Romano are widely sold and consumed in pre-grated form for convenience. Due to being processed products, there is ample opportunity for the introduction of inexpensive adulterants and as such, there is concern regarding the authenticity of these products. An analytical method was developed using a simple organic extraction to verify the authenticity of bovine hard cheese products by examining the lipid profile of these cheeses via proton Nuclear Magnetic Resonance (NMR) spectroscopy. In this study, 52 samples of pre-grated hard cheese were analyzed as a market survey and a significant number of these samples were found to be adulterated with vegetable oils. This method is well suited to high throughput analysis of these products and relies on ratiometrics of the lipids in the samples themselves. Genuine cheeses were found to have a very consistent lipid profile from sample to sample, improving the power of this approach to detect vegetable oil adulteration. The method is purely ratiometric with no need for internal or external references, reducing sample preparation time and reducing the potential for the introduction of error.

C-arm cone-beam computed tomography (CBCT) is a valuable tool for three-dimensional navigation and mapping in the interventional radiology suite owing to its flexible gantry positioning, real-time three-dimensional volume acquisition, and reduced contrast and radiation use. Reports of CBCT-guided bone and lung interventions are relatively infrequent, however, possibly due in part to the lack of dedicated bone and lung reconstruction algorithms and concerns regarding insufficient lesion conspicuity. Two cases of an ad hoc intraprocedural CBCT sharpening reconstruction are presented in this article.

An efficient adsorbent, CNTs–PAMAM–Ag, was prepared by grafting fourth-generation aromatic poly(amidoamine) (PAMAM) to carbon nanotubes (CNTs) and successive deposition of Ag nanoparticles. The FT–IR, XRD, TEM and XPS results confirmed the successful grafting of PAMAM onto CNTs and deposition of Ag nanoparticles. The absorption efficiency of CNTs–PAMAM–Ag was evaluated by estimating the adsorption of two toxic contaminants in water, viz., Pb(II) and As(III). Using CNTs–PAMAM–Ag, about 99 and 76% of Pb(II) and As(III) adsorption, respectively, were attained within 15 min. The controlling mechanisms for Pb(II) and As(III) adsorption dynamics were revealed by applying pseudo-first and second-order kinetic models. The pseudo-second-order kinetic model followed the adsorption of Pb(II) and As(III). Therefore, the incidence of chemisorption through sharing or exchanging electrons between Pb(II) or As(III) ions and CNTs–PAMAM–Ag could be the rate-controlling step in the adsorption process. Further, the Weber–Morris intraparticle pore diffusion model was employed to find the reaction pathways and the rate-controlling step in the adsorption. It revealed that intraparticle diffusion was not a rate-controlling step in the adsorption of Pb(II) and As(III); instead, it was controlled by both intraparticle diffusion and the boundary layer effect. The adsorption equilibrium was evaluated using the Langmuir, Freundlich, and Temkin isotherm models. The kinetic data of Pb(II) and As(III) adsorption was adequately fitted to the Langmuir isotherm model compared to the Freundlich and Temkin models.

Due to increased concerns regarding unidentified impurities in delta-8 tetrahydrocannabinol (Δ-8 THC) consumer products, a study using Nuclear Magnetic Resonance (NMR), high performance liquid chromatography (HPLC), and mass spectrometry (MS) was conducted to further investigate these products. Ten Δ-8 THC products, including distillates and ready to use vaporizer cartridges, were analyzed. The results yield findings that the tested products contain several impurities in concentrations far beyond what is declared on certificates of analysis for these products. As Δ-8 THC is a synthetic product synthesized from cannabidiol (CBD), there are valid concerns regarding the presence of impurities in these products with unknown effects on the human body. Compounding this problem is apparent inadequate testing of these products by producers and independent laboratories.