Enhancing Genetic Gains in Grain Yield and Efficiency of Testing Sites of Early-Maturing Maize Hybrids under Contrasting Environments.

The major challenges of maize production and productivity in Sub-Saharan Africa (SSA) include Striga hermonthica infestation, recurrent drought, and low soil nitrogen (low N). This study assessed the following: (i) accelerated genetic advancements in grain yield and other measured traits of early-maturing maize hybrids, (ii) ideal test environments for selecting early-maturing multiple-stress tolerant hybrids, and (iii) high-yielding and stable hybrids across multiple-stress and non-stress environments. Fifty-four hybrids developed during three periods of genetic enhancement (2008-2010, 2011-2013, and 2014-2016) were evaluated in Nigeria, The Republic of Benin, and Ghana under multiple stressors (Striga infestation, managed drought, and Low N) and non-stress environments from 2017 to 2019. Under multiple-stress and non-stress environments, annual genetic gains from selection in grain yield of 84.72 kg ha-1 (4.05%) and 61 kg ha-1 (1.56%), respectively, were recorded. Three mega-environments were identified across 14 stress environments. Abuja was identified as an ideal test environment for selecting superior hybrids. The hybrid TZdEI 352 × TZEI 355 developed during period 3 was the most outstanding under multiple-stress and non-stress environments. On-farm testing of this hybrid is required to verify its superior performance for commercialization in SSA. Considerable progress has been made in the genetic improvement of early-maturing maize hybrids for tolerance of multiple stressors and high yield. The identified core testing sites of this study could be used to enhance the testing and selection of promising hybrids.

Cardiovascular disease behavioural risk factors in rural interventions: cross-sectional study.

This study aims to (1) assess the distribution of variables within the population and the prevalence of cardiovascular disease (CVD) behavioural risk factors in patients, (2) identify target risk factor(s) for behaviour modification intervention, and (3) develop an analytical model to define cluster(s) of risk factors which could help make any generic intervention more targeted to the local patient population. Study patients with at least one CVD behavioural risk factor living in a rural region of the Scottish Highlands. The study used the STROBE methodology for cross-sectional studies. Demographic and clinical data of patients (n = 2025) in NHS Highlands hospital were collected at the point of admission for PCI between 04.01.2016 and 31.12.2019. Collected data distributions were analysed by CVD behavioural risk factors for prevalence, associations, and direction of associations. Cluster definition was measured by assignment of a unit score each for the overall level of prevalence and significance of associations, and general logistics modelling for direction and significance of the risk. The mean (SD) age was 69.47(± 10.93) years [95% CI (68.99-69.94)]. The key risk factors were hyperlipidaemia, hypertension, and elevated body mass index (BMI). Approximately 40% of the population have multiple risk factor counts of two. Analytical measures revealed a population risk factor cluster with elevated BMI [77.5% (1570/2025)] that is mostly either hyperlipidaemic [9.43%, co-eff. (17), P = 0.007] or hypertensive [22.72%, co-eff. (17), P = 0.99] as key risk factor clusters. Carefully modelled analyses revealed clustered risk associated with elevated BMI. This information would support a strategy for targeting risk factor clusters in novel interventions to improve implementation efficiency. Exposure to and outcome of an elevated BMI is linked more to the population's socio-economic outcomes rather than to regional rurality or urbanity.

Diallel analysis of soybean (Glycine max L.) for biomass yield and root characteristics under low phosphorus soil conditions in Western Ethiopia.

Combining ability studies under low soil P conditions provides useful information on the inheritance of important traits to improve soybean for low P tolerance. The study aimed at determining the combining ability and gene actions of biomass yield and root traits in soybean under low phosphorus conditions. Nine parental genotypes and their 36 half diallel F2/F3 progenies were evaluated at two locations in Ethiopia on soils of low P availability. Highly significant (P<0.01) general combining ability (GCA) were found for all the traits and specific combining ability (SCA) for root dry weight and root fresh weight; while the SCA effects of all the rest of the traits were significant (P<0.05). The higher relative contributions of GCA over SCA revealed the preponderance of additive gene action in the inheritance of biomass yield, root dry weight, biomass dry weight, root volume, and root fresh weight with respective relative GCA:SCA contributions of (60.6, 39.4), (50.4, 49.6), (54.9, 45.1), (51.1, 48.9), and (52.1, 47.9); while the narrow-sense heritability was high (34.3%) only for biomass yield. Hardee-1 displayed significant (P<0.05) and positive GCA effects for most of the studied traits, and several crosses involving this parent showed superior performances. The traits i.e., biomass yield, biomass dry weight, root volume and root fresh weight showed highly (P<0.001) correlation with grain yield. Thus, breeding programs aiming to improve soybean for biomass yield and root traits under low-P condition can use Hardee-1 as a parent.

Coal mining and environmental sustainability in South Africa: do institutions matter?

This study investigates the effects of coal mining on environmental sustainability in South Africa in relation to the moderating role of institutions. To achieve the study's objectives, the fully modified least square (FMOLS), dynamic least squares (DOLS), canonical cointegrating regression (CCR), Bayer-Hanck cointegration and Toda-Yamamoto causality test are employed for the period 1984-2018. Results from the study show that coal mining contributes to environmental degradation in South Africa, while its interaction with institutional quality mitigates the severity of this negative impact. Furthermore, there is evidence that economic growth has a bidirectional causality with ecological footprint and coal mining, while institutional quality also Granger causes ecological footprint. To promote a sustainable environment, there is a need for the government and the institutions to form the foundation for the shift toward environmental sustainability, with particular attention paid to the development and implementation of greenhouse policy.

Spatial impact of foreign direct investment on ecological footprint in Africa.

This study examines the spatial impact of FDI on ecological footprint of 31 African countries. In achieving this, the study uses the Driscoll-Kraay (1998) random effect model, fixed-effect instrumental variable regression, and the spatial Durbin model. There are three main important findings from this empirical study. First, FDI has a nonlinear impact on ecological footprint in Africa. At the initial stage, FDI reduces ecological footprint up to a threshold of $404.75-$669.96 million, before the impact increases ecological degradation. This result is robust to the instrumental regression model. Second, the results further reveal a significant spatial spillover of FDI on ecological footprint in Africa. Third, the empirical results provide evidence of both direct and spillover effects of environmental degradation determinant in Africa. This denotes that environmental quality of a particular country influences the environmental quality of other neighbouring countries. While it is important to attract significant amount of foreign investment to Africa, this study recommends that African governments need to improve their environmental regulations and laws to achieve transfer of energy-saving technology from foreign investors.

Analysis of Apps With a Medication List Functionality for Older Adults With Heart Failure Using the Mobile App Rating Scale and the IMS Institute for Healthcare Informatics Functionality Score: Evaluation Study.

BACKGROUND: Managing the care of older adults with heart failure (HF) largely centers on medication management. Because of frequent medication or dosing changes, an app that supports these older adults in keeping an up-to-date list of medications could be advantageous. During the COVID-19 pandemic, HF outpatient consultations are taking place virtually or by telephone. An app with the capability to share a patient's medication list with health care professionals before consultation could support clinical efficiency, for example, by reducing consultation time. However, the influence of apps on maintaining an up-to-date medication history for older adults with HF in Ireland remains largely unexplored. OBJECTIVE: The aims of this review are twofold: to review apps with a medication list functionality and to assess the quality of the apps included in the review using the Mobile App Rating Scale (MARS) and the IMS Institute for Healthcare Informatics functionality scale. METHODS: A systematic search of apps was conducted in June 2019 using the Google Play Store and iTunes App Store. The MARS was used independently by 4 researchers to assess the quality of the apps using an Android phone and an iPad. Apps were also evaluated using the IMS Institute for Healthcare Informatics functionality score. RESULTS: Google Play and iTunes App store searches identified 483 potential apps (292 from Google Play and 191 from iTunes App stores). A total of 6 apps (3 across both stores) met the inclusion criteria. Of the 6 apps, 4 achieved an acceptable MARS score (3/5). The Medisafe app had the highest overall MARS score (4/5), and the Medication List & Medical Records app had the lowest overall score (2.5/5). On average, the apps had 8 functions based on the IMS functionality criteria (range 5-11). A total of 2 apps achieved the maximum score for number of features (11 features) according to the IMS Institute for Healthcare Informatics functionality score, and 2 scored the lowest (5 features). Peer-reviewed publications were identified for 3 of the apps. CONCLUSIONS: The quality of current apps with medication list functionality varies according to their technical aspects. Most of the apps reviewed have an acceptable MARS objective quality (ie, the overall quality of an app). However, subjective quality (ie, satisfaction with the apps) was poor. Only 3 apps are based on scientific evidence and have been tested previously. A total of 2 apps featured all the IMS Institute for Healthcare Informatics functionalities, and half did not provide clear instructions on how to enter medication data, did not display vital parameter data in an easy-to-understand format, and did not guide users on how or when to take their medication.

Digital Technology Interventions for Risk Factor Modification in Patients With Cardiovascular Disease: Systematic Review and Meta-analysis.

BACKGROUND: Approximately 50% of cardiovascular disease (CVD) cases are attributable to lifestyle risk factors. Despite widespread education, personal knowledge, and efficacy, many individuals fail to adequately modify these risk factors, even after a cardiovascular event. Digital technology interventions have been suggested as a viable equivalent and potential alternative to conventional cardiac rehabilitation care centers. However, little is known about the clinical effectiveness of these technologies in bringing about behavioral changes in patients with CVD at an individual level. OBJECTIVE: The aim of this study is to identify and measure the effectiveness of digital technology (eg, mobile phones, the internet, software applications, wearables, etc) interventions in randomized controlled trials (RCTs) and determine which behavior change constructs are effective at achieving risk factor modification in patients with CVD. METHODS: This study is a systematic review and meta-analysis of RCTs designed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) statement standard. Mixed data from studies extracted from selected research databases and filtered for RCTs only were analyzed using quantitative methods. Outcome hypothesis testing was set at 95% CI and P=.05 for statistical significance. RESULTS: Digital interventions were delivered using devices such as cell phones, smartphones, personal computers, and wearables coupled with technologies such as the internet, SMS, software applications, and mobile sensors. Behavioral change constructs such as cognition, follow-up, goal setting, record keeping, perceived benefit, persuasion, socialization, personalization, rewards and incentives, support, and self-management were used. The meta-analyzed effect estimates (mean difference [MD]; standard mean difference [SMD]; and risk ratio [RR]) calculated for outcomes showed benefits in total cholesterol SMD at -0.29 [-0.44, -0.15], P<.001; high-density lipoprotein SMD at -0.09 [-0.19, 0.00], P=.05; low-density lipoprotein SMD at -0.18 [-0.33, -0.04], P=.01; physical activity (PA) SMD at 0.23 [0.11, 0.36], P<.001; physical inactivity (sedentary) RR at 0.54 [0.39, 0.75], P<.001; and diet (food intake) RR at 0.79 [0.66, 0.94], P=.007. Initial effect estimates showed no significant benefit in body mass index (BMI) MD at -0.37 [-1.20, 0.46], P=.38; diastolic blood pressure (BP) SMD at -0.06 [-0.20, 0.08], P=.43; systolic BP SMD at -0.03 [-0.18, 0.13], P=.74; Hemoglobin A1C blood sugar (HbA1c) RR at 1.04 [0.40, 2.70], P=.94; alcohol intake SMD at -0.16 [-1.43, 1.10], P=.80; smoking RR at 0.87 [0.67, 1.13], P=.30; and medication adherence RR at 1.10 [1.00, 1.22], P=.06. CONCLUSIONS: Digital interventions may improve healthy behavioral factors (PA, healthy diet, and medication adherence) and are even more potent when used to treat multiple behavioral outcomes (eg, medication adherence plus). However, they did not appear to reduce unhealthy behavioral factors (smoking, alcohol intake, and unhealthy diet) and clinical outcomes (BMI, triglycerides, diastolic and systolic BP, and HbA1c).

Genetic diversity and population structure of early and extra-early maturing maize germplasm adapted to sub-Saharan Africa.

BACKGROUND: Assessment and effective utilization of genetic diversity in breeding programs is crucial for sustainable genetic improvement and rapid adaptation to changing breeding objectives. During the past two decades, the commercialization of the early and extra-early maturing cultivars has contributed to rapid expansion of maize into different agro-ecologies of sub-Saharan Africa (SSA) where maize has become an important component of the agricultural economy and played a vital role in food and nutritional security. The present study aimed at understanding the population structure and genetic variability among 439 early and extra-early maize inbred lines developed from three narrow-based and twenty-seven broad-based populations by the International Iinstitute of Tropical Agriculture Maize Improvement Program (IITA-MIP). These inbreds were genotyped using 9642 DArTseq-based single nucleotide polymorphism (SNP) markers distributed uniformly throughout the maize genome. RESULTS: About 40.8% SNP markers were found highly informative and exhibited polymorphic information content (PIC) greater than 0.25. The minor allele frequency and PIC ranged from 0.015 to 0.500 and 0.029 to 0.375, respectively. The STRUCTURE, neighbour-joining phylogenetic tree and principal coordinate analysis (PCoA) grouped the inbred lines into four major classes generally consistent with the selection history, ancestry and kernel colour of the inbreds but indicated a complex pattern of the genetic structure. The pattern of grouping of the lines based on the STRUCTURE analysis was in concordance with the results of the PCoA and suggested greater number of sub-populations (K = 10). Generally, the classification of the inbred lines into heterotic groups based on SNP markers was reasonably reliable and in agreement with defined heterotic groups of previously identified testers based on combining ability studies. CONCLUSIONS: Complete understanding of potential heterotic groups would be difficult to portray by depending solely on molecular markers. Therefore, planned crosses involving representative testers from opposing heterotic groups would be required to refine the existing heterotic groups. It is anticipated that the present set of inbreds could contribute new beneficial alleles for population improvement, development of hybrids and lines with potential to strengthen future breeding programs. Results of this study would help breeders in formulating breeding strategies for genetic enhancement and sustainable maize production in SSA.

Identification of QTLs for grain yield and other traits in tropical maize under Striga infestation.

Striga is an important biotic factor limiting maize production in sub-Saharan Africa and can cause yield losses as high as 100%. Marker-assisted selection (MAS) approaches hold a great potential for improving Striga resistance but requires identification and use of markers associated with Striga resistance for adequate genetic gains from selection. However, there is no report on the discovery of quantitative trait loci (QTL) for resistance to Striga in maize under artificial field infestation. In the present study, 198 BC1S1 families obtained from a cross involving TZEEI 29 (Striga resistant inbred line) and TZEEI 23 (Striga susceptible inbred line) plus the two parental lines were screened under artificial Striga-infested conditions at two Striga-endemic locations in Nigeria in 2018, to identify QTL associated with Striga resistance indicator traits, including grain yield, ears per plant, Striga damage and number of emerged Striga plants. Genetic map was constructed using 1,386 DArTseq markers distributed across the 10 maize chromosomes, covering 2076 cM of the total genome with a mean spacing of 0.11 cM between the markers. Using composite interval mapping (CIM), fourteen QTL were identified for key Striga resistance/tolerance indicator traits: 3 QTL for grain yield, 4 for ears per plant and 7 for Striga damage at 10 weeks after planting (WAP), across environments. Putative candidate genes which encode major transcription factor families WRKY, bHLH, AP2-EREBPs, MYB, and bZIP involved in plant defense signaling were detected for Striga resistance/tolerance indicator traits. The QTL detected in the present study would be useful for rapid transfer of Striga resistance/tolerance genes into Striga susceptible but high yielding maize genotypes using MAS approaches after validation. Further studies on validation of the QTL in different genetic backgrounds and in different environments would help verify their reproducibility and effective use in breeding for Striga resistance/tolerance.

Genome-wide association study of Striga resistance in early maturing white tropical maize inbred lines.

BACKGROUND: Striga hermonthica (Benth.) parasitism militates against increased maize production and productivity in savannas of sub-Saharan Africa (SSA). Identification of Striga resistance genes is important in developing genotypes with durable resistance. So far, there is only one report on the existence of QTL for Striga resistance on chromosome 6 of maize. The objective of this study was to identify genomic regions significantly associated with grain yield and other agronomic traits under artificial Striga field infestation. A panel of 132 early-maturing maize inbreds were phenotyped for key agronomic traits under Striga-infested and Striga-free conditions. The inbred lines were also genotyped using 47,440 DArTseq markers from which 7224 markers were retained for population structure analysis and genome-wide association study (GWAS). RESULTS: The inbred lines were grouped into two major clusters based on structure analysis as well as the neighbor-joining hierarchical clustering. A total of 24 SNPs significantly associated with grain yield, Striga damage at 8 and 10 weeks after planting (WAP), ears per plant and ear aspect under Striga infestation were detected. Under Striga-free conditions, 11 SNPs significantly associated with grain yield, number of ears per plant and ear aspect were identified. Three markers physically located close to the putative genes GRMZM2G164743 (bin 10.05), GRMZM2G060216 (bin 3.06) and GRMZM2G103085 (bin 5.07) were detected, linked to grain yield, Striga damage at 8 and 10 WAP and number of ears per plant under Striga infestation, explaining 9 to 42% of the phenotypic variance. Furthermore, the S9_154,978,426 locus on chromosome 9 was found at 2.61 Mb close to the ZmCCD1 gene known to be associated with the reduction of strigolactone production in the maize roots. CONCLUSIONS: Presented in this study is the first report of the identification of significant loci on chromosomes 9 and 10 of maize that are closely linked to ZmCCD1 and amt5 genes, respectively and may be related to plant defense mechanisms against Striga parasitism. After validation, the identified loci could be targets for breeders for marker-assisted selection (MAS) to accelerate genetic enhancement of maize for Striga resistance in the tropics, particularly in SSA, where the parasitic weed is endemic.