Additive Main Effects and Multiplicative Interactions (AMMI) and genotype by environment interaction (GGE) biplot analyses aid selection of high yielding and adapted finger millet varieties

Background and justification: Lack of stable high yielding cultivars is one of the major bottlenecks for production and productivity of finger millets in Ethiopia. Identification of adaptable, stable and high yielding genotypes under varying environmental conditions prior to release as a cultivar is the first and foremost steps for plant breedingr and this has direct bearing on the adoption of the variety, its productivity and total production of the crop.Objective: The major objectives of the present study were to (i) assess the stability and yield performance of advanced finger millet genotypes  evaluated in multiple environments, and (ii) identify stable high yieldingcandidate cultivar (s) for possible release using different statistical tools.Material and methods: A total of 30 advanced finger millet genotypes were evaluated against two standard checks (Gute and Taddese) across four locations (Arsi Negele, Assosa, Bako and Gute) in 2012 and 2013 main cropping seasons. The trial was arranged in a randomized complete block design (RCBD) replicated three times.Summary result and application of the study: Additive Main effect and Multiplicative Interaction (AMMI), Genotype and Genotype by Environment interaction (GGE) biplot analysis and, Eberhart and Russellmodel revealed that Acc. 203544 is stable high yielding (3.16 ton ha-1) with a yield advantage of 13.7% over the best standard check, Gute (2.78 ton ha-1), and thus should be recommended for possible release with wider environmental adaptability. Acc. 242111 (3.08 ton ha-1), Acc. BKFM0051 (3.07 ton ha-1) and Acc.229738 (2.99 ton ha-1) were also high yielding, but showed narrow stability and thus should be recommended for verification and possible release for specific environments.Key words/phrases: Additive main effect and multiplicative interaction (AMMI), Finger millet (Eleusine coracana subsp. coracana), Genotype by Environment Interaction (GEI

Birth data accessibility via primary care health records to classify health status in a multi-ethnic population of children: an observational study

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Sweetpotato silage manual for smallholder farmers.

This sweetpotato silage manual is made to benefit farmers and business entrepreneurs that would want to engage in sweetpotato silage making, marketing and use. It is based on practical experiences that were gained from studies and experiments conducted in Uganda by the International Potato Centre (CIP) and the International Livestock Research Institute (ILRI) in collaboration with other partners between 2014 and 2016. The manual focused on the piggery enterprise and covers five key aspects: importance of sweetpotato in small-scale farming systems, sweetpotato establishment and management, pig production in Uganda, sweetpotato as a feed resource for pigs, sweetpotato silage production. It is expected that the manual will give the reader incites for sweetpotato production and making sweetpotato silage to address feeding constraints

Continuous Glucose Monitoring and Tight Glycaemic Control in Critically Ill Patients

Critically ill patients often exhibit abnormal glycaemia that can lead to severe complications and potentially death. In critically ill adults, hyperglycaemia is a common problem that has been associated with increased morbidity and mortality. In contrast, critically ill infants often suffer from hypoglycaemia, which may cause seizures and permanent brain injury. Further complicating the matter, both of these conditions are diagnosed by blood glucose (BG) measurements, often taken several hours apart, and, as a result, these conditions can remain poorly managed or go completely undetected. Emerging ‘continuous’ glucose monitoring (CGM) devices with 1-5 minute measurement intervals have the potential to resolve many issues associated with conventional intermittent BG monitoring. The objective of this research was to investigate and develop methods and models to optimise the clinical use of CGM devices in critically ill patients. For critically ill adults, an in-silico study was conducted to quantify the potential benefits of introducing CGM devices into the intensive care unit (ICU). Mathematical models of CGM error characteristics were implemented with existing, clinically validated, models of the insulin-glucose regulatory system, to simulate the behaviour of CGM devices in critically ill patients. An alarm algorithm was also incorporated to provide a warning at the onset of predicted hypoglycaemia, allowing a virtual dextrose intervention to be administered as a preventative measure. The results of the in-silico study showed a potential reduction in nurse workload of approximately 75% and a significant reduction in hypoglycaemia, while also providing insight into the optimal rescue dose size and resulting dynamics of glucose recovery. During 2012, ten patients were recruited into a pilot clinical trial of CGM devices in critical care with a primary goal of assessing the reliability of CGM devices in this environment, with a specific interest in the effects of CGM device type and sensor site on sensor glucose (SG) data. Results showed the mean absolute relative difference of SG data across the cohort was between 12-24% and CGM devices were capable of monitoring some patients with a high degree of accuracy. However, certain illnesses, drugs and therapies can potentially affect sensor performance, and one particular set of results suggested severe oedema may have affected sensor performance. A novel and first of its kind metric, the Trend Compass was developed and used to assesses trend accuracy of SG in a mathematically precise fashion without approximation, and, importantly, does so independent of glucose level or sensor bias, unlike any other such metrics. In this analysis, the trend accuracy between CGM devices was typically good. A recent hypothesis suggesting that glucose complexity is associated with mortality was also investigated using the clinical CGM data. The results showed that complexity results from detrended fluctuation analysis (DFA) were influenced far more by CGM device type than patient outcome. In addition, the location of CGM sensors had no significant effect on complexity results in this data set. Thus, while this emerging analytical method has shown positive results in the literature, this analysis indicates that those results may be misleading given the impact of technology outweighing that of physiology. This particular result helps to further delineate the range of potential applications and insight that CGM devices might offer in this clinical scenario. In critically ill infants, CGM devices were used to investigate hypoglycaemia during the first 48 hours after birth. More than 50 CGM data sets were obtained from several studies of CGM in infants at risk of hypoglycaemia at the Waikato hospital neonatal ICU (NICU). In light of concerns regarding CGM accuracy, particularly during the first few hours of monitoring and/or at low BG levels, an alternative, novel calibration scheme was developed to increase the reliability of SG data. The recalibration algorithm maximised the value of very accurate calibration BG measurements from a blood gas analyser (BGA), by forcing SG data to pass through these calibration BG measurements. Recalibration increased all metrics of hypoglycaemia (number, duration, severity and hypoglycaemic index) as the factory CGM calibration was found to be reporting higher values at low BG levels due to its least squares calibration approach based on the assumption of a less accurate calibration glucose meter. Thus, this research defined new calibration methods to directly optimise the use of CGM devices in this clinical environment, where accurate reference BG measurements are available. Furthermore, this work showed that metrics such as duration or area under curve were far more robust to error than the typically used counted-incidence metrics, indicating how clinical assessment may have to change when using these devices. The impact of errors in calibration measurements on metrics used to classify hypoglycaemia was also assessed. Across the cohort, measurement error, particularly measurement bias, had a larger effect on hypoglycaemia metrics than delays in entering calibration measurements. However, for patients with highly variable glycaemia, timing error can have a significantly larger impact on output SG data than measurement error. Unusual episodes of hypoglycaemia could be successfully identified using a stochastic model, based on kernel density estimation, providing another level of information to aid decision making when assessing hypoglycaemia. Using the developed algorithms/tools, with CGM data from 161 infants, the incidence of hypoglycaemia was assessed and compared to results determined using BG measurements alone. Results from BG measurements showed that ~17% of BG measurements identified hypoglycaemia and over 80% of episodes occurred in the first day after birth. However, with concurrent BG and SG data available, the SG data consistently identified hypoglycaemia at a higher rate suggesting the BG measurements were not capturing some episodes. Duration of hypoglycaemia in SG data varied from 0-10+%, but was typically in the range 4-6%. Hypoglycaemia occurred most frequently on the first day after birth and an optimal measurement protocol for at risk infants would likely involve CGM for the first week after birth with frequent intermittent BG measurements for the first day. Overall, CGM devices have the potential to increase the understanding of certain glycaemic abnormalities and aid in the diagnosis/treatment of other conditions in critically ill patients. This research has used a range of prospective and retrospective clinical studies to develop methods to further optimise the use of CGM devices within the critically ill clinical environment, as well as delineating where they are less useful or less robust. These latter results clearly define areas where clinical practice needs to adapt when using these devices, as well as areas where device makers could target technological improvements for best effect. Although further investigations are required before these devices are regularly implemented in day-to-day clinical practice, as an observational tool they are capable of providing useful information that is not currently available with conventional intermittent BG monitoring

Inheritance and Association of Quantitative Traits in Finger Millet (Eleusine coracana Subsp. Coracana) Landraces Collected from Eastern and South Eastern Africa

One hundred forty four finger millet landraces were collected from different regions of Ethiopia and some introduced from Eastern and south Eastern African (Kenya, Eritrea, Zambia and Zimbabwe) were planted with six improved varieties in RCBD design at Gute and Arsi Negele during 2011 cropping season to assess variability, heritability, genetic advance and association of quantitative traits. The analysis of variance indicated that the mean square due to location and genotype were highly significant (P#0.01) for all quantitative traits except ear weight for the latter case. Phenotypic coefficient of variation was higher than the corresponding genotypic and genotype by environment coefficient of variations for all traits. This implies, beside the genetic factors, environmental factors have high contributions for the variations observed. The higher heritability coupled with higher genetic advance noted for ear weight (71.14%), lodging index (53.49), finger length (41.94%), thousand grain weights (28.88) and grain yield per plant (26.34) indicated that the ease of phenotype based selection for the improvement of those traits. About 68.4% of the total traits association showed positive correlation. As obtained from path coefficient analysis, the higher and positive direct effect of productive tiller per plant (0.356) and thousand grain weight (0.285) and the positive direct effect of finger length, finger number, ear weight, number of grain per spikelet and culm diameter on grain yield indicated that any genetic improvement on those traits has positive contribution to improve productivity of finger millet

Mobile-based and open-source case detection and infectious disease outbreak management systems: a review [version 1; peer review: awaiting peer review]

In this paper we perform a rapid review of existing mobile-based, open-source systems for infectious disease outbreak data collection and management. Our inclusion criteria were designed to match the PANDORA-ID-NET consortium’s goals for capacity building in sub-Saharan Africa, and to reflect the lessons learned from the 2014–16 West African Ebola outbreak. We found eight candidate systems that satisfy some or most of these criteria, but only one (SORMAS) fulfils all of them. In addition, we outline a number of desirable features that are not currently present in most outbreak management systems

Nature and dynamics of climate variability in the uganda cattle corridor

The study was conducted in the districts of Nakaseke and Nakasongola stratified into four farming systems of crop dominancy, pastoralists, mixed crop and livestock and fishing. The study was guided by two research questions: (1) how do community residents perceive climate change/variability? (2) What is the trend and nature of climate variability and how does it compare with people’s perceptions? Ninety eight percent (98%) of the respondents reported that the routine patterns of weather and climate had changed in the last 5 to 10 years and it has become less predictable with sunshine hours being extended and rainfall amounts being reduced. This compared well with the analyzed secondary data. Over 78% respondents perceived climate change and variability to be caused by tree cutting other than the known scientific reasons like increase in industrial fumes or increased fossil fuel use. Climate data showed that over the period 1961 to 2010 the number of dry spells within a rainfall season had increased with the most significant increase observed in the first rainfall season of March to May as compared to the season of September to November. The first dry season of June/July to August is short while the second dry season of December to February is long during the study period. The two rainfall seasons of March to May and September to November seem to be merging into one major season from May to November. Temperature data shows a significant increasing trend in mean annual temperatures with the most increase observed in the mean annual minimum temperatures than the maximum temperatures.Key words: Climate variability, community perceptions, Uganda cattle corridor, dry spells

Mothers of Soldiers in Wartime: A National News Narrative

National news media represent mothers of US combat soldiers in the Iraq War as archetypal good mothers, that is, mothers who continue their maternal work even after their children are deployed. However, not all mothers are depicted as the archetypal patriotic mother, i.e., a good mother who is also stoic and silent about the war and her child\u27s role in it. Mothers of soldiers are portrayed as good mothers who sometimes also voice their attitudes about the war effort. The maternal attitudes ranged from complete support for the war to opposition to the war but support for the soldiers. The findings suggest a picture of wartime motherhood that is more nuanced than the historical image of the patriotic mother suggests