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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.

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 aimed to detect the Eimeria spp in cattle by using microscopic and PCR methods. One hundred fecal samples of cattle from the slaughter houses & rural areas of Al-Diwaniyah province were collected. Some cattle shows clinical sign of diarrhea while others have no clinical signs. Age of cattle under study range from 6 months to one year or more. According to our results, the percentage of Eimeria spp was 78% after using direct smear examination under microscopic for oocysts. According on our results, the infection rate was higher (91.6%) in ≤ 6 month animal age than others, while according to animals sex, the infection rate was higher in females (87.7%) compared to males. Depending on PCR results, the prevalence of Eimeria spp. was found 89%. [ABSTRACT FROM AUTHOR]

This study were conducted in Al-Diwaniya province, in south Iraq during the period from February to July 2019 to determine the rate of infection of Cryptosporidium parvum in domestic chicken, study the effect of some epidemiological factors such as sex and months on the rate of infection, addition to the molecular identification of Cryptococcus parvum by amplification HSP70 gene by conventional PCR. Number of collected fecal sample was 210 from domestic chicken and stained by Ziehl-Neelsen stain. The results of the microscopic examination showed that 108(51.4%) out of 210 fecal samples were infected with Cryptosporidium spp. The statistical analysis founded no marked difference in prevalence of infection between sexes. Significant difference was recorded between infection rate during the months of the study and higher prevalence of infection rate was observed in March 11.9%, while lowest infection rate was observed in July 5.23%. and June 5.23%. Genomic DNA was extracted from 108 fecal samples and HSP70 gene for C. parvum was amplified by PCR. PCR technique is showed that out of 108 fecal samples 21.3% were positive for Cryptosporidium parvum. [ABSTRACT FROM AUTHOR]

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.

Clayey soils could be exposed to thermal cycles in some engineering applications such as geothermal piles and nuclear waste storages. These temperature changes could affect the consolidation behaviour of these soils. In this context, this paper investigates the impact of temperature on the consolidation behaviour of saturated compacted clays. A temperature-controlled oedometric cell has been developed to perform constant rate of strain (CRS) consolidation tests for different strain rates (0.002 – 0.02%/min) within a temperature range of 5 °C – 70 °C. Two different compacted saturated clays were used in these tests. The results indicated that the compression and swelling indices for both materials changed with temperature and strain rate. The preconsolidation pressure of both clays decreased as the temperature increased, while it decreased as the strain rate decreased. The creep index increased as the temperature increased for both clays. These variations were completely dependent on the nature of the clays. [ABSTRACT FROM AUTHOR]

Toxocara (T.) canis is a nematode parasite of canines; belong to the Ascarididae family, which accidentally infected humans. Puppies expel the eggs with the feces from the fourth week of the life cycle. This study is the first study in Iraq for detection seroprevalence in stray dogs and extended from January to September 2017. Our study was aimed to investigate the seroprevalence of T. canis infection in stray dogs from different areas in the Al-Diwaniya province, Iraq to detection of specific IgG antibodies to T. canis compared to Conventional PCR technique with the effect of the risk factor. One hundred of the blood sample and one hundred of a faecal sample of same dogs after shooting were studied using indirect ELISA test and PCR. The result revealed that 71% of the dogs had a seropositive result for this parasite by ELISA test. Dog age is an important factor and affects seroprevalence, were shown that positive rate in adult dogs was more 83.05% than the young dogs 53.65%, while no significant between dogs according to sex. PCR technique showed 58% of dogs were positive for internal transcribed spacer 1 (ITS1) ribosomal RNA. The sensitivity and specificity of ELISA test was 79 and 40% respectively. [ABSTRACT FROM AUTHOR]

Diploid organisms have two copies of each gene, called alleles, that can be separately transcribed. The RNA abundance associated to any particular allele is known as allele-specific expression (ASE). When two alleles have polymorphisms in transcribed regions, ASE can be studied using RNA-seq read count data. ASE has characteristics different from the regular RNA-seq expression: ASE cannot be assessed for every gene, measures of ASE can be biased towards one of the alleles (reference allele), and ASE provides two measures of expression for a single gene for each biological samples with leads to additional complications for single-gene models. We present statistical methods for modeling ASE and detecting genes with differential allelic expression. We propose a hierarchical, overdispersed, count regression model to deal with ASE counts. The model accommodates gene-specific overdispersion, has an internal measure of the reference allele bias, and uses random effects to model the gene-specific regression parameters. Fully Bayesian inference is obtained using the fbseq package that implements a parallel strategy to make the computational times reasonable. Simulation and real data analysis suggest the proposed model is a practical and powerful tool for the study of differential ASE.

Abstract Riparian buffers can improve water quality, but watershed‐scale evaluations of riparian buffering opportunities are rare. A landscape discretization tool called riparian catchments, part of the Agricultural Conservation Planning Framework (ACPF) version 3, was applied to evaluate functional riparian settings for 32 headwater watersheds representing three major land resource areas (MLRAs) in Iowa. Riparian settings of 250‐m length were classified based on height above channel and upslope contributing area to show where to place buffers primarily designed to intercept runoff, treat nitrate in shallow groundwater, and/or protect streambanks. Riparian zones found below small riparian catchments were common, typically occupying >50% of streambank lengths in MLRA 103 (northern Iowa) and MLRA 108 (southeast Iowa). In these settings, narrow (6–10 m wide) buffers provide a buffer/contributing area ratio of >0.02 to filter surface runoff, while providing streambank protection. This similarity occurred despite these two MLRAs having contrasting landscapes. Whereas the narrow buffers suggested are associated with ditches and flat terrain in MLRA 103, they occur below short slopes along streams that have well dissected the watersheds in MLRA 108. In MLRA 104 of east‐central Iowa, headwater alluvial streams often had broad low‐lying riparian zones, where wide buffers (>25 m) may be placed to help mitigate nitrate transport in shallow groundwater. The ACPF riparian catchments approach enabled cross‐watershed analyses of riparian settings, while providing spatial data to inform watershed‐scale riparian planning efforts.

Process-based agroecosystem models are powerful tools to assess performance of managed landscapes, but their ability to accurately represent reality is limited by the types of input data they can use. Ensuring these models can represent cropping field heterogeneity and environmental impact is important, especially given the growing interest in using agroecosystem models to quantify ecosystem services from best management practices and land use change. We posited that augmenting process-based agroecosystem models with additional field-specific information such as topography, hydrologic processes, or independent indicators of yield could help limit simulation artifacts that obscure mechanisms driving observed variations. To test this, we augmented the agroecosystem model Agricultural Production Systems Simulator (APSIM) with field-specific topography and satellite imagery in a simulation framework we call Foresite. We used Foresite to optimize APSIM yield predictions to match those created from a machine learning model built on remotely sensed indicators of hydrology and plant productivity. Using these improved subfield yield predictions to guide APSIM optimization, total ${\text{N}}{{\text{O}}_3}^ - {\text{N}}$ loss estimates increased by 39% in maize and 20% in soybeans when summed across all years. In addition, we found a disproportionate total amount of leaching in the lowest yielding field areas vs the highest yielding areas in maize (42% vs 15%) and a similar effect in soybeans (31% vs 20%). Overall, we found that augmenting process-based models with now-common subfield remotely sensed data significantly increased values of predicted nutrient loss from fields, indicating opportunities to improve field-scale agroecosystem simulations, particularly if used to calculate nutrient credits in ecosystem service markets.

We investigated differences in the volume of the pancreas in cystic fibrosis (CF) patients with and without diabetes using MRI to study the natural history of CFrelated diabetes (CFRD). We investigated 29 pancreas-insufficient adult CF patients, 13 with CFRD and 16 without diabetes. Patients with CFRD were receiving insulin therapy at the time of study. None of the non-diabetic CF patients had evidence of impaired glucose tolerance. Pancreas volume was estimated by MRI scans using T1 weighted fatsuppression sequences and assessed by an examiner who was unaware of the patients' diabetes status. Pancreas volume of CF patients was measured and subsequently compared with that of non-CF age-matched Type 1 diabetes (T1DM) patients and healthy controls previously investigated. The two CF groups were matched for age and gender. There were no differences in spirometry values, body mass index or pancreatic exocrine function. The pancreas was visible by MRI in only 3 of 13 (23.1%) patients with CFRD and in 5 of 16 (31.3%) patients without diabetes (p-value50.7). In total, the pancreas was not detected by MRI as an anatomical entity in 21 of 29 (72.4%) CF patients, irrespective of their diabetes status. When comparing the four study groups, the pancreas was significantly smaller in CF patients than in T1DM patients and healthy controls. [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance imaging (MRI) has been shown to be a useful tool to evaluate the volume of the pancreas. There is currently no information about the size of the spleen in cystic fibrosis (CF) patients. Patients and methods: We investigated 51 adult volunteers: 28 pancreatic insufficient CF patients [13 with CF-related diabetes (CFRD) and 15 non-diabetic] and 23 male non-CF patients [12 with type 1 diabetes mellitus (T1DM) and 11 healthy control subjects]. Patients with known liver cirrhosis or portal hypertension were excluded. The size of the spleen was measured in all subjects by an investigator unaware of patients' clinical status. For comparison of spleen size in the four study groups only male CF patients were included. For CF patients, spleen size was compared with forced expiratory volume in 1 s (FEV1), body mass index (BMI), total number of days of intravenous (IV) antibiotic treatment for pulmonary exacerbations in year previous to study, levels of circulating white blood cells, glycosylated haemoglobin A1c (HbA1c), and exocrine function of the pancreas, as assessed by daily requirement of oral lipase. Results: Amongst the four study groups, spleen size was greatest in the male non-diabetic CF patients (P = 0.01). For CF patients, spleen size was greater in male compared to female patients (P = 0.012). For patients with CFRD, there was an inverse correlation between the spleen size and HbA1c (r = -0.59, P = 0.04) and the daily intake of supplementary lipase (r = -0.63, P = 0.02). The size of the spleen in patients with CFRD, but not in CF patients without CFRD, inversely correlated with the days of IV antibiotic treatment received in the year previous to the study (r = -0.67, P = 0.012). There was no correlation between spleen size and BMI, FEV1 and white blood cell counts in any group. Conclusions: On MRI, the spleen size was greatest in male non-diabetic CF patients in comparison with other groups. The size of the spleen in CFRD patients was smaller when diabetes was poorly controlled, when exocrine pancreatic function was greatly impaired and in those with greater need for IV antibiotics in the year prior to the study. [ABSTRACT FROM AUTHOR]

Data collected on adult cystic fibrosis (CF) patients and entered onto the CF database in the south and west regions of England were analysed for the year 2001. FEV1 was taken as a marker of lung disease severity. Data on 371 patients (158 female) mean age 24.7 years (range 16.0–48.9) were assessed. FEV1 was reduced in CF patients infected with Pseudomonas aeruginosa (Pa) (mean 62.6% predicted) compared with those without (mean FEV1 77.8%, P < 0.00001). The reduction was noted irrespective of age group. FEV1 progressively reduced with the increasing need for high-intensity treatment (P < 0.00001) and with the diagnosis of diabetes mellitus (P = 0.03). FEV1 correlated with body mass index (BMI) (r = +0.42, P, 0.0001). Genetic profile and poverty and deprivation score did not affect the value of FEV1. Chronic infection with Pa mainly in young adults, treatment intensity, diabetes mellitus and reduced body mass index are associated with reduced FEV1 in adult patients with cystic fibrosis in the south and west regions of England. In this relatively affluent area, scores of poverty and deprivation were not associated with the decline in lung function tests. [ABSTRACT FROM AUTHOR]

Interest in modeling contemporary crime trends, a task that has historically been considered valuable to the public, researchers, and policymakers, is resurging. Advancements in criminology have made it clear that understanding crime trends necessarily involves understanding trends in how likely individuals are to report crimes to the police, as well as how likely the police are to accurately record those crimes. In this paper, we use dynamic linear models to simultaneously model the time series for several crime types in order to gain insight into trends in crime and crime reporting. We analyze crime data from Chicago spanning 2007 through 2016 and show how correlations in the way crime trends evolve may contain information about drivers of crime and crime reporting. We provide evidence of substantial differences in the relationships between the trends of crimes of different types depending on whether crimes are violent or nonviolent and whether or not crimes are tracked in the FBI's Uniform Crime Report.

Remote sensing observations that vary in response to plant growth and senescence can be used to monitor crop development within and across growing seasons. Identifying when crops reach specific growth stages can improve harvest yield prediction and quantify climate change. Using the Level 2 vegetation optical depth (VOD) product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite, we retrospectively estimate the timing of a key crop development stage in the United States Corn Belt. We employ nonlinear curves nested within a hierarchical modeling framework to extract the timing of the third reproductive development stage of corn (R3) as well as other new agronomic signals from SMOS VOD. We compare our estimates of the timing of R3 to United States Department of Agriculture (USDA) survey data for the years 2011, 2012, and 2013. We find that 87%, 70%, and 37%, respectively, of our model estimates of R3 timing agree with USDA district-level observations. We postulate that since the satellite estimates can be directly linked to a physiological state (the maximum amount of plant water, or water contained within plant tissue per ground area) it is more accurate than the USDA data which is based upon visual observations from roadways. Consequently, SMOS VOD could be used to replace, at a finer resolution than the district-level USDA reports, the R3 data that has not been reported by the USDA since 2013. We hypothesize the other model parameters contain new information about soil and crop management and crop productivity that are not routinely collected by any federal or state agency in the Corn Belt.