Tomato Management Practices and Diseases Occurrence in Mwea West Sub County

Tomato is an important crop in Mwea West Sub County, Kirinyaga County, Kenya. A survey was carried out in the area to investigate tomato management practices, diseases and pests that hinder tomato production. The study endeavoured to establish farmers’ knowledge on fusarium wilt disease and root-knot nematodes and the methods used to control them. Data was collected from two hundred and eighteen randomly selected small holder producers who were equally distributed in the study area. Data collected included tomato management practices, diseases and pests that hinder production. Majority (85.3%) of the respondents were males while a few (14.7%) were female. The respondents (71.6%) indicated that tomato was the most important crop grown for income generation in the area. Most important varieties grown were cultivars, Safari, Kilele F1, Prosta F1 and Rio- Grande. Most important diseases affecting tomato crop were; early blight (Alternaria solani) and late blight (Phytophthora infestans), fungal wilts (Fusarium sp. Verticillium sp. Rhizoctonia sp.) and bacterial wilt (Ralstonia solanacearum). Plant parasitic nematodes and pests (thrips, aphids, spider mites) were also reported in the study area. There was a significant(P<0.05) association between the following variables; type of land owner and major use of land, type of land owner and cropping system, source of agricultural information and whether or not to apply pesticides into the soil. Farmers were quite knowledgeable about tomato farming as they had access to information from various sources; however there are still major gaps in knowledge especially on diseases and pests. Keywords: Tomato, diseases, pests, nematodes, managemen

A pseudo-likelihood method for estimating misclassification probabilities in competing-risks settings when true event data are partially observed

Outcome misclassification occurs frequently in binary-outcome studies and can result in biased estimation of quantities such as the incidence, prevalence, cause-specific hazards, cumulative incidence functions etc. A number of remedies have been proposed to address the potential misclassification of the outcomes in such data. The majority of these remedies lie in the estimation of misclassification probabilities, which are in turn used to adjust analyses for outcome misclassification. A number of authors advocate using a gold-standard procedure on a sample internal to the study to learn about the extent of the misclassification. With this type of internal validation, the problem of quantifying the misclassification also becomes a missing data problem as, by design, the true outcomes are only ascertained on a subset of the entire study sample. Although, the process of estimating misclassification probabilities appears simple conceptually, the estimation methods proposed so far have several methodological and practical shortcomings. Most methods rely on missing outcome data to be missing completely at random (MCAR), a rather stringent assumption which is unlikely to hold in practice. Some of the existing methods also tend to be computationally-intensive. To address these issues, we propose a computationally-efficient, easy-to-implement, pseudo-likelihood estimator of the misclassification probabilities under a missing at random (MAR) assumption, in studies with an available internal validation sample. We present the estimator through the lens of studies with competing-risks outcomes, though the estimator extends beyond this setting. We describe the consistency and asymptotic distributional properties of the resulting estimator, and derive a closed-form estimator of its variance. The finite-sample performance of this estimator is evaluated via simulations. Using data from a real-world study with competing risks outcomes, we illustrate how the proposed method can be used to estimate misclassification probabilities. We also show how the estimated misclassification probabilities can be used in an external study to adjust for possible misclassification bias when modeling cumulative incidence functions

Heritability of Malaria in Africa

BACKGROUND: While many individual genes have been identified that confer protection against malaria, the overall impact of host genetics on malarial risk remains unknown. METHODS AND FINDINGS: We have used pedigree-based genetic variance component analysis to determine the relative contributions of genetic and other factors to the variability in incidence of malaria and other infectious diseases in two cohorts of children living on the coast of Kenya. In the first, we monitored the incidence of mild clinical malaria and other febrile diseases through active surveillance of 640 children 10 y old or younger, living in 77 different households for an average of 2.7 y. In the second, we recorded hospital admissions with malaria and other infectious diseases in a birth cohort of 2,914 children for an average of 4.1 y. Mean annual incidence rates for mild and hospital-admitted malaria were 1.6 and 0.054 episodes per person per year, respectively. Twenty-four percent and 25% of the total variation in these outcomes was explained by additively acting host genes, and household explained a further 29% and 14%, respectively. The haemoglobin S gene explained only 2% of the total variation. For nonmalarial infections, additive genetics explained 39% and 13% of the variability in fevers and hospital-admitted infections, while household explained a further 9% and 30%, respectively. CONCLUSION: Genetic and unidentified household factors each accounted for around one quarter of the total variability in malaria incidence in our study population. The genetic effect was well beyond that explained by the anticipated effects of the haemoglobinopathies alone, suggesting the existence of many protective genes, each individually resulting in small population effects. While studying these genes may well provide insights into pathogenesis and resistance in human malaria, identifying and tackling the household effects must be the more efficient route to reducing the burden of disease in malaria-endemic areas

Feasibility, acceptability, effect, and cost of integrating counseling and testing for HIV within family planning services in Kenya

FRONTIERS supported the Division of Reproductive Health and the National AIDS and STI Control Program of the Kenya Ministry of Health to design, implement, and compare two models of integrating counseling and testing (CT) for HIV within family planning (FP) services in terms of their feasibility, acceptability, cost, and effect on the voluntary use of CT, as well as the quality of FP services. The study demonstrated that both models were feasible and acceptable to providers and to clients as means of integrating and linking HIV prevention counseling, condom promotion, and counseling and testing with FP services, and are effective in increasing quality of care and service utilization. Drawing from the lessons learned, the report outlines a number of key programmatic recommendations for institutionalizing and scaling up this approach. Lessons from this study were presented at several national and international workshops and conferences

Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower- and middle-income countries:A multicountry analysis of survey data

BackgroundCardiovascular diseases are leading causes of death, globally, and health systems that deliver quality clinical care are needed to manage an increasing number of people with risk factors for these diseases. Indicators of preparedness of countries to manage cardiovascular disease risk factors (CVDRFs) are regularly collected by ministries of health and global health agencies. We aimed to assess whether these indicators are associated with patient receipt of quality clinical care.Methods and findingsWe did a secondary analysis of cross-sectional, nationally representative, individual-patient data from 187,552 people with hypertension (mean age 48.1 years, 53.5% female) living in 43 low- and middle-income countries (LMICs) and 40,795 people with diabetes (mean age 52.2 years, 57.7% female) living in 28 LMICs on progress through cascades of care (condition diagnosed, treated, or controlled) for diabetes or hypertension, to indicate outcomes of provision of quality clinical care. Data were extracted from national-level World Health Organization (WHO) Stepwise Approach to Surveillance (STEPS), or other similar household surveys, conducted between July 2005 and November 2016. We used mixed-effects logistic regression to estimate associations between each quality clinical care outcome and indicators of country development (gross domestic product [GDP] per capita or Human Development Index [HDI]); national capacity for the prevention and control of noncommunicable diseases ('NCD readiness indicators' from surveys done by WHO); health system finance (domestic government expenditure on health [as percentage of GDP], private, and out-of-pocket expenditure on health [both as percentage of current]); and health service readiness (number of physicians, nurses, or hospital beds per 1,000 people) and performance (neonatal mortality rate). All models were adjusted for individual-level predictors including age, sex, and education. In an exploratory analysis, we tested whether national-level data on facility preparedness for diabetes were positively associated with outcomes. Associations were inconsistent between indicators and quality clinical care outcomes. For hypertension, GDP and HDI were both positively associated with each outcome. Of the 33 relationships tested between NCD readiness indicators and outcomes, only two showed a significant positive association: presence of guidelines with being diagnosed (odds ratio [OR], 1.86 [95% CI 1.08-3.21], p = 0.03) and availability of funding with being controlled (OR, 2.26 [95% CI 1.09-4.69], p = 0.03). Hospital beds (OR, 1.14 [95% CI 1.02-1.27], p = 0.02), nurses/midwives (OR, 1.24 [95% CI 1.06-1.44], p = 0.006), and physicians (OR, 1.21 [95% CI 1.11-1.32], p ConclusionIn this study, we observed that indicators of country preparedness to deal with CVDRFs are poor proxies for quality clinical care received by patients for hypertension and diabetes. The major implication is that assessments of countries' preparedness to manage CVDRFs should not rely on proxies; rather, it should involve direct assessment of quality clinical care

Multi-Site Benchmark Classification of Major Depressive Disorder Using Machine Learning on Cortical and Subcortical Measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible

Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex

The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects