Genetic characterization and genome-wide association mapping for stem rust resistance in spring bread wheat

BACKGROUND: Emerging wheat stem rust races have become a major threat to global wheat production. Finding additional loci responsible for resistance to these races and incorporating them into currently cultivated varieties is the most economic and environmentally sound strategy to combat this problem. Thus, this study was aimed at characterizing the genetic diversity and identifying the genetic loci conferring resistance to the stem rust of wheat. To accomplish this, 245 elite lines introduced from the International Center for Agricultural Research in the Dry Areas (ICARDA) were evaluated under natural stem rust pressure in the field at the Debre Zeit Agricultural Research Center, Ethiopia. The single nucleotide polymorphisms (SNP) marker data was retrieved from a 15 K SNP wheat array. A mixed linear model was used to investigate the association between SNP markers and the best linear unbiased prediction (BLUP) values of the stem rust coefficient of infection (CI). RESULTS: Phenotypic analysis revealed that 46% of the lines had a coefficient of infection (CI) in a range of 0 to 19. Genome-wide average values of 0.38, 0.20, and 0.71 were recorded for Nei’s gene diversity, polymorphism information content, and major allele frequency, respectively. A total of 46 marker-trait associations (MTAs) encompassed within eleven quantitative trait loci (QTL) were detected on chromosomes 1B, 3A, 3B, 4A, 4B, and 5A for CI. Two major QTLs with –log(10) (p) ≥ 4 (EWYP1B.1 and EWYP1B.2) were discovered on chromosome 1B. CONCLUSIONS: This study identified several novel markers associated with stem rust resistance in wheat with the potential to facilitate durable rust resistance development through marker-assisted selection. It is recommended that the resistant wheat genotypes identified in this study be used in the national wheat breeding programs to improve stem rust resistance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12863-022-01030-4

Development and Application of Coupled Optimization-Watershed Models for Selection and Placement of Best Management Practices in the Mackinaw River Watershed

Agricultural non-point source (NPS) pollution remains to be one of the biggest challenges in the Midwest due to extensive farming practices and the use of fertilizers to increase agricultural productivity. Excess sediment and nutrient loadings such as nitrogen and phosphorous are major causes of non-point source pollution in rivers and streams. The Mackinaw River watershed, which is one of the tributary watersheds of the larger Illinois River basin, covers a total drainage area of about 2950 square kilometers. Since 1990, this watershed has been one of The Nature Conservancy’s (TNC) conservation sites, considered to be an area of biological significance in the Upper Mississippi River basin. The Mackinaw River watershed plan prepared by The Nature Conservancy in 1998 indicates that altered hydrology and sedimentation are the primary threats to the Mackinaw River. The plan provided recommendations to improve river hydrology and reduce sediment yields through changes in the landscape. Conservation practices serve as crucial control measures in reducing NPS pollutants from agricultural watersheds. The 2008 Farm Bill provided more than $7 billion for promoting agricultural production and environmental quality by supporting implementation of structural or non-structural management practices under its Environmental Quality Incentives Program (EQIP) (Cowan and Johnson, 2008). Successful implementation of such programs, however, requires sound watershed management plans. Watershed management plans involving implementation of best management practices (BMPs) can help reduce pollution from agricultural sources. BMPs are structural or non-structural control measures that can be implemented in watersheds to control pollutant loads at their source or their transport to receiving water bodies. Implementation of these BMPs should focus on critical source areas that may contribute large amounts of pollutant loads. Identifying areas for the placement of BMPs should take into account both ecological benefits and associated implementation costs. The objectives of this study include (1) developing watershed models for Mackinaw River and two of its tributary watersheds, namely Bray Creek and Frog Alley, to simulate streamflows and water quality constituent loads, and (2) developing a coupled optimization-watershed model for cost-effective selection and placement of BMPs in Bray Creek and Frog Alley watersheds to reduce nonpoint source pollutants such as sediment and nutrient loads to the streams. An integrated modeling approach that involves interfacing a simulation model with an optimization algorithm has been employed to develop the coupled optimization-watershed model. Such integrated modeling approaches have been demonstrated in solving complex, realistic problems in the areas of watershed management, reservoir operations, groundwater monitoring design, and others. In this study, the coupled optimization-watershed model was developed by interfacing a watershed model known as the Soil and Water Assessment Tool (SWAT) with Non-dominated Sorting Genetic Algorithm II (NSGA-II), a multiobjective optimization algorithm. Figure 1 shows the location map of the Mackinaw River watershed.published or submitted for publicationis peer reviewe

Decision Support Model for Generating Optimal Alternative Scenarios of Watershed Best Management Practices

Surface and subsurface agricultural runoff has been the main cause of water quality problems in Lake Decatur, which is the major s ource of public water supply for the City of Decatur and the Village of Mt. Zion, serving a total population of more than 80,000. The lake has a watershed area of 925 square miles and was created by building a dam on the Sangamon River in 1922 with a modification in 1956 to incr ease its capacity. Extensive siltation is another critical issue, causing loss of significant storage volume. Nearly 90 percent of the Lake Decatur watershed is cropland, of which corn and soyb ean account for 44 and 39 percent, respectively. The watershed is extensively tile -drained to lower the water tabl e, creating favorable conditions for agricultural production. Hydr ologic and water quality monitoring has been conducted from 1993 to 2008 by the Illinois State Wate r Survey (ISWS) with support fr om the City of Decatur in an effort to alleviate the wa ter quality problem in Lake Decatur through watershed management alternatives. Additional waters hed monitoring was carried out from 2005 to 2008 by ISWS for a United States Environmental Protection Agency (USEPA) targeted watershed study with the goal of addressing economic and environmental as pects of nutrient management in the Upper Sangamon River watershed. The Illinois Environmental Protection Agency (IEPA) added Lake Decatur to the Illinois 2004 Section 303(d) list as impaired for nitr ogen-nitrate and total phosphorus (IEPA, 2004). Consequently, a Total Maximum Daily Load (TMDL) assessment was completed for the Sangamon River/Lake Decatur watershed in 20 07 and was approved by the USEPA. The TMDL study provided an overview of implementation alte rnatives that reduce nitrate and phosphorous loads, including nutrient manage ment, conservation tillage, conser vation buffers, and restriction of livestock. In addition, practices that limit losses from private sewage discharges and sedimentation were also proposed to reduce phosphorus loading (IEPA, 2007). Most cropland in the Lake Decatur watershed has been extensively tile-drained and therefore, the effectiveness of surface water-based best management practices (BMPs) for reducing nitrate may be limited. Specific placement areas for implementation of thes e alternatives have not been identified, which is the focus of this study. Two tributary watersheds of Lake Decatur we re identified for developing alternative implementation scenarios of selected BMPs that are designed to reduce nonpoint source pollutants (NPS) from agricultural sources. The watersheds are Big/Long Creek and Big Ditch watersheds, as illustrated in Figure 1. The Big/Long Creek watershed is located in the downstream portion of the Lake Decatur watershed, draining directly into the lake. In contrast, the Big Ditch watershed is located about 50 miles fr om the lake in the nort heastern edge of the Lake Decatur watershed. Both are agricultu rally dominated watersheds and their areas considered in this study correspond to the drainage areas of ISWS monito ring stations, which are close to the respective watershed outlets. The objective of this research was to evaluate the water quality benefits of selected BMPs at a watershed scale, generati ng alternative scenarios for implementation in Big Ditch and Big/Long Creek watersheds. This was accomp lished through the development of decision support models (DSMs) for each watershed. The DSMs were developed based on an integrated modeling approach, coupling a watershed si mulation model known as the Soil and Water Assessment Tool (SWAT) with an Archived-B ased Micro-Genetic Algorithm 2 (AMGA2) - a multi-objective optimization algorithm. Such integrated modeling approach, which involves interfacing a simulation model with an optimization algorithm, ha s been extensively applied to solve complex problems in watershed manageme nt (Bekele et al., 2013; Bekele et al., 2011), reservoir operations (Nicklow and Mays, 2000), groundwater monitoring design (Reed and Minsker, 2004), and others. The DSM was design ed to generate cost-effective implementation scenarios of selected conventional and newly em erging BMPs that include nutrient management, cover crops, perennial crops, constructed wetlan ds, drainage water management, bioreactors, saturated buffers, and filter strips. It is capable of providing optimal BMP placement scenarios that result in maximum re duction of NPS pollutants for a prescribed level of BMP implementation. BMP scenarios that strike a ba lance between NPS reducti on and total cost of implementation are identified as best tradeoff so lutions and are recommended for preparation of watershed implementation plans.published or submitted for publicationis peer reviewedOpe

Hydrologic and Hydraulic Modeling of the Cache River for Evaluating Alternative Restoration Measures

The Cache River basin, located in southern Illinois, has characteristics unique to the State of Illinois and nation, with diverse physical, chemical, and biological features that produced a great diversity of natural communities. Because of these unique characteristics, the Cache River basin contains some high quality bottomland hardwood forests and wetlands that have been recognized nationally and internationally. Changes in land-use practices and hydraulic modifications during the last century have significantly threatened the ecological integrity of some of these valuable habitats and wetlands. Therefore one of the key goals of resource managers working in the area is to restore the Cache River’s natural hydrologic character to a level that can sustain a viable ecology throughout the river corridor. An essential component of the restoration effort for the Cache River has been the development of detailed hydrologic and hydraulic models to determine water levels associated with proposed restoration measures. These models assist the Cache River Wetlands Joint Venture Partnership (JVP) in the decision-making process of selecting and implementing restoration projects that improve the hydrologic conditions for the natural ecosystem. Hydrologic and hydraulic modeling also allow the JVP to satisfy all regulatory requirements and ensure that natural, agricultural, and social resources are not damaged by flooding induced by modifications to the river system. Hydrologic and hydraulic models were developed by the Illinois State Water Survey for the Lower and Upper Cache River and are published in two reports (Demissie et al., 2008, 2010) located on the ISWS website http://www.isws.illinois.edu/pubs/search.asp (Contract Reports 2008-01 and 2010-06). This presentation summarizes the results for some of the modeling scenarios

Household Readiness to Care for Mild and Asymptomatic COVID-19 Cases at Home, Southwest Ethiopia: A Community-based Cross-Sectional Study

BACKGROUND: Corona virus disease (COVID-19) continued with its notorious effects overwhelming health institutions. Thus, home-based identification and care for asymptomatic and mild cases of COVID-19 has been recommended. Therefore, the objective of this study was to assess the level of household readiness for caring asymptomatic and mild cases of COVID-19 at home.  METHODS: A community-based cross-sectional study was conducted from March-June 2021 on randomly selected 778 households. Data entry and analysis were carried out using EpiData and SPSS version 25, respectively. Multivariable logistic regression was modeled to identify independent predictors of community readiness.  RESULTS: Overall readiness of the community was very low (43.8%). Factors positively affecting household readiness were male household heads (AOR = 1.6; 95%CI: 1.05, 2.45), primary (AOR=2.0; CI:.62, 1.59) and higher (AOR = 1.90; 95%CI: 1.04, 3.45) educational level of the respondents, number of rooms within household (AOR = 1.22; CI: 1.03, 1.46), having additionally house (AOR = 2.61; CI: 1.35, 5.03), availability of single use eating utensils (AOR = 2.76; 95%CI: 1.66, 4.56), availability of community water supply (AOR = 8.21; 95% CI: 5.02, 13.43), and community participation and engagement (AOR = 2.81; 95% CI: 1.93, 4.08) in accessing transport, water and sanitation. CONCLUSIONS: The community was less prepared in terms of housing, infection prevention, water and sanitation. Considering alternative options including universal coverage of vaccine is important; designed behavioral change communications can enhance community participation and engagement in improving access to transport, water and sanitation to reduce risk of infections

Reference soil groups map of Ethiopia based on legacy data and machine learning-technique: EthioSoilGrids 1.0

Up-to-date digital soil resource information and its comprehensive understanding are crucial to supporting crop production and sustainable agricultural development. Generating such information through conventional approaches consumes time and resources, and is difficult for developing countries. In Ethiopia, the soil resource map that was in use is qualitative, dated (since 1984), and small scaled (1 : 2 M), which limit its practical applicability. Yet, a large legacy soil profile dataset accumulated over time and the emerging machine-learning modeling approaches can help in generating a high-quality quantitative digital soil map that can provide better soil information. Thus, a group of researchers formed a Coalition of the Willing for soil and agronomy data-sharing and collated about 20 000 soil profile data and stored them in a central database. The data were cleaned and harmonized using the latest soil profile data template and 14 681 profile data were prepared for modeling. Random forest was used to develop a continuous quantitative digital map of 18 World Reference Base (WRB) soil groups at 250 m resolution by integrating environmental covariates representing major soil-forming factors. The map was validated by experts through a rigorous process involving senior soil specialists or pedologists checking the map based on purposely selected district-level geographic windows across Ethiopia. The map is expected to be of tremendous value for soil management and other land-based development planning, given its improved spatial resolution and quantitative digital representation.</p

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016

Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

BACKGROUND: Measurement of changes in health across locations is useful to compare and contrast changing epidemiological patterns against health system performance and identify specific needs for resource allocation in research, policy development, and programme decision making. Using the Global Burden of Diseases, Injuries, and Risk Factors Study 2016, we drew from two widely used summary measures to monitor such changes in population health: disability-adjusted life-years (DALYs) and healthy life expectancy (HALE). We used these measures to track trends and benchmark progress compared with expected trends on the basis of the Socio-demographic Index (SDI). METHODS: We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2016. We calculated DALYs by summing years of life lost and years of life lived with disability for each location, age group, sex, and year. We estimated HALE using age-specific death rates and years of life lived with disability per capita. We explored how DALYs and HALE differed from expected trends when compared with the SDI: the geometric mean of income per person, educational attainment in the population older than age 15 years, and total fertility rate. FINDINGS: The highest globally observed HALE at birth for both women and men was in Singapore, at 75·2 years (95% uncertainty interval 71·9-78·6) for females and 72·0 years (68·8-75·1) for males. The lowest for females was in the Central African Republic (45·6 years [42·0-49·5]) and for males was in Lesotho (41·5 years [39·0-44·0]). From 1990 to 2016, global HALE increased by an average of 6·24 years (5·97-6·48) for both sexes combined. Global HALE increased by 6·04 years (5·74-6·27) for males and 6·49 years (6·08-6·77) for females, whereas HALE at age 65 years increased by 1·78 years (1·61-1·93) for males and 1·96 years (1·69-2·13) for females. Total global DALYs remained largely unchanged from 1990 to 2016 (-2·3% [-5·9 to 0·9]), with decreases in communicable, maternal, neonatal, and nutritional (CMNN) disease DALYs offset by increased DALYs due to non-communicable diseases (NCDs). The exemplars, calculated as the five lowest ratios of observed to expected age-standardised DALY rates in 2016, were Nicaragua, Costa Rica, the Maldives, Peru, and Israel. The leading three causes of DALYs globally were ischaemic heart disease, cerebrovascular disease, and lower respiratory infections, comprising 16·1% of all DALYs. Total DALYs and age-standardised DALY rates due to most CMNN causes decreased from 1990 to 2016. Conversely, the total DALY burden rose for most NCDs; however, age-standardised DALY rates due to NCDs declined globally. INTERPRETATION: At a global level, DALYs and HALE continue to show improvements. At the same time, we observe that many populations are facing growing functional health loss. Rising SDI was associated with increases in cumulative years of life lived with disability and decreases in CMNN DALYs offset by increased NCD DALYs. Relative compression of morbidity highlights the importance of continued health interventions, which has changed in most locations in pace with the gross domestic product per person, education, and family planning. The analysis of DALYs and HALE and their relationship to SDI represents a robust framework with which to benchmark location-specific health performance. Country-specific drivers of disease burden, particularly for causes with higher-than-expected DALYs, should inform health policies, health system improvement initiatives, targeted prevention efforts, and development assistance for health, including financial and research investments for all countries, regardless of their level of sociodemographic development. The presence of countries that substantially outperform others suggests the need for increased scrutiny for proven examples of best practices, which can help to extend gains, whereas the presence of underperforming countries suggests the need for devotion of extra attention to health systems that need more robust support. FUNDING: Bill & Melinda Gates Foundation