Levels of fluoride in bottled soft drinks marketed in Addis Ababa, Ethiopia

The main objective of this study was to determine the levels of fluoride in soft drinks (Coca Cola, Pepsi, Mirinda, Fanta and Sprite) marketed and widely consumed in Addis Ababa, Ethiopia. Three glass-bottled and three plastic-bottled soft drink samples from each brand were purchased randomly from Arat Kilo, Shiromeda and Shola supermarkets, kiosk and tea houses in Addis Ababa, Ethiopia. Levels of fluoride in the soft drink samples were determined by fluoride ion selective electrode. The method was validated by spiking test which provided percentage recoveries of fluoride in the soft drinks in the range 91-96%. The mean fluoride concentration (mg/L) in the glass-bottled and plastic-bottled soft drink samples, respectively, were: Coca Cola (0.03±0.01, 0.06±0.01), Pepsi (0.23±0.01, 0.10±0.01), Mirinda (0.21±0.02, 0.09±0.01), Fanta (0.03±0.01, 0.05±0.01) and Sprite (0.04±0.01, 0.27±0.01). Pearson correlation showed that the levels of fluoride in the soft drinks were found to correlate positively with each other, which indicates similar source of main component (the water used for dilution). The low levels of fluoride in the soft drinks may not impose health risk in the adults but excessive consumption of soft drinks regularly by the children may result in dental fluorosis.               KEY WORDS: Fluoride, Coca cola, Pepsi, Mirinda, Fanta, Sprite Bull. Chem. Soc. Ethiop. 2019, 33(2), 203-213.DOI: https://dx.doi.org/10.4314/bcse.v33i2.

Correlation studies and path coefficient analysis for seed yield and yield components in Ethiopian coriander accessions

Coriander (Coriandrum sativum L.) is an annual spice herb that belongs to the family Umbelliferae. Even though Ethiopia is a centre of primary diversity for the crop, the current knowledge about its biology, variety development and agronomy is neither complete nor conclusive under Ethiopian conditions. To contribute to filling some of the existing gaps, a field experiment was conducted during the main rainy season of 2007-2008 at Wondo Genet and Kokate, southern Ethiopia. Data for 15 agronomic and quality traits were measured and statistically tested. Moreof the traits were found having high correlation coefficients at genotypic level than the phenotypic level, demonstrating intrinsic associations among the traits. Seeds plant-1 and thousand seeds weight were associatedsignificantly and positively with seed yield plant-1 at phenotypic and genotypic levels. Essential oil and fatty oil contents were negatively associated with most of the trait studied. Path analysis revealed that days to end 50% flowering, longest basal leaf length, plant height, days to 50% maturity and seeds umbellet-1 exerted positive direct effect on seed yield plant-1, indicating that selection using these traits would be effective in improving seed yield in coriander.Key Words: Coriandrum sativum, essential oil, Ethiopia, fatty oi

Novel Expressed Sequence Tag-Derived and Other Genomic Simple Sequence Repeat Markers Revealed Genetic Diversity in Ethiopian Finger Millet Landrace Populations and Cultivars

Finger millet (Eleusine coracana (L.) Geartn.) is a self-pollinating amphidiploid crop cultivated with minimal input for food and feed, as well as a source of income for small-scale farmers. To efficiently assess its genetic diversity for conservation and use in breeding programs, polymorphic DNA markers that represent its complex tetraploid genome have to be developed and used. In this study, 13 new expressed sequence tag-derived simple sequence repeat (EST-SSR) markers were developed based on publicly available finger millet ESTs. Using 10 polymorphic SSR markers (3 genomic and 7 novel EST-derived), the genetic diversity of 55 landrace accessions and 5 cultivars of finger millet representing its major growing areas in Ethiopia was assessed. In total, 26 alleles were detected across the 10 loci, and the average observed number of alleles per locus was 5.6. The polymorphic information content (PIC) of the loci ranged from 0.045 (Elco-48) to 0.71 (UGEP-66). The level of genetic diversity did not differ much between the accessions with the mean gene diversity estimates ranging only from 0.44 (accession 216054) to 0.68 (accession 237443). Similarly, a narrow range of variation was recorded at the level of regional states ranging from 0.54 (Oromia) to 0.59 (Amhara and Tigray). Interestingly, the average gene diversity of the landrace accessions (0.57) was similar to that of the cultivars (0.58). The analysis of molecular variance (AMOVA) revealed significant genetic variation both within and among accessions. The variation among the accessions accounted for 18.8% of the total variation (FST = 0.19; P < 0.001). Similarly, significant genetic variation was obtained among the geographic regions, accounting for 6.9% of the total variation (P < 0.001). The results of the cluster, principal coordinate, and population structure analyses suggest a poor correlation between the genetic makeups of finger millet landrace populations and their geographic regions of origin, which in turn suggests strong gene flow between populations within and across geographic regions. This study contributed novel EST-SSR markers for their various applications, and those that were monomorphic should be tested in more diverse finger millet genetic resources

Inter simple sequence repeat (ISSR) analysis of Ethiopian white lupine (Lupinus albus L.)

White lupine (Lupinus albus L.) collected from two zones (West Gojjam and Awi) of Amhara region and one zone (Metekel) of Benishangul - Gumuz regional state of Ethiopia were studied using inter simple sequence repeat (ISSR) markers in an attempt to assess the genetic diversity. Four ISSR primers of which three were dinucleotide repeats and one, a penta nucleotide repeat amplified a total of 39 clear and reproducible bands. Both unweighted pair- group method with arithmetic average (UPGMA) phenograms and a neighbor joining (NJ) trees were constructed for the individuals and populations using Jaccard’s similarity coefficient. The dendrogram clearly indicated four distinct groups/populations based on the area of origin. The principal coordinates (PCO) analysis also recovered UPGMA and neighbor joining tree groups, although Amhara region white lupine were intermixed with each other. The genetic diversity among white lupine population considered in the present study indicated that Merawi was the highest (0.223) followed by Addis Kidam, Sekela and Wembera with genetic diversity of 0.198, 0.189 and 0.167, respectively. Generally, Amhara region white lupine (0.203) population shows higher genetic diversity than white lupine population of B-Gumuz region (0.167). Analysis of molecular variance (AMOVA) in both grouping and without grouping revealed larger genetic diversity within the populations (74.6%) than among populations (25.4%). Shannon’s diversity index also confirmed the existence of higher genetic diversity in Amhara region lupine populations than in Benishangul-Gumuz. Furthermore AMOVA demonstrated highly significant (P = 0.00) genetic differences among populations within groups, among groups and within populations. Of the total variation, 64.64% was attributable to within populations, 27.23% to among groups and the least, 8.13% to among populations within groups. Generally, on the basis of samples of 39 bands in the four populations, ISSR was able to reveal moderate to high levels of genetic diversity within and among Ethiopian white lupine population.Keywords: Amhara, Benishangul - Gumuz, Ethiopia, genetic diversity, ISSR, white lupine.Abbreviation: ISSR, Inter simple sequence repeats; UPGMA, unweighted pair- group method with arithmetic average; NJ, neighbor joining; PCO, principal coordinates; AMOVA, analysis of molecular variance; RAPD, random amplified polymorphic DNA; AFLP, amplified fragment length polymorphism

Evaluating surgical skills from kinematic data using convolutional neural networks

The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming. Thus, automating surgical skills evaluation is a very important step towards improving surgical practice. In this paper, we designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by extracting patterns in the surgeon motions performed in robotic surgery. The proposed method is validated on the JIGSAWS dataset and achieved very competitive results with 100% accuracy on the suturing and needle passing tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate its black-box effect using class activation map. This feature allows our method to automatically highlight which parts of the surgical task influenced the skill prediction and can be used to explain the classification and to provide personalized feedback to the trainee.Comment: Accepted at MICCAI 201

Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions

PURPOSE: Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. METHODS: The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. RESULTS: Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. CONCLUSION: ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical robotics. Current devices possess no intelligence whatsoever and are merely advanced and expensive instruments

Spatiotemporal variability of soil moisture over Ethiopia and its teleconnections with remote and local drivers

Soil moisture is one of the essential climate variables with a potential impact on local climate variability. Despite the importance of soil moisture, studies on soil moisture characteristics in Ethiopia are less documented. In this study, the spatiotemporal variability of Ethiopian soil moisture (SM) has been characterized, and its local and remote influential driving factors are investigated. An empirical orthogonal function (EOF) and KMeans clustering algorithm have been employed to classify the large domain into homogeneous zones. Complex maximum covariance analysis (CMCA) is applied to evaluate the covariability between SM and selected local and remote variables such as rainfall (RF), evapotranspiration (ET), and sea surface temperature (SST). Inter-comparison among SM datasets highlight that the FLDAS dataset better depicts the country’s SM spatial and temporal distribution (i.e., a correlation coefficient r=0.95 , rmsd=0.04m3m−3 with observations). Results also indicate that regions located in northeastern Ethiopia are drier irrespective of the season (JJAS, MAM, and OND) considered. In contrast, the western part of the country consistently depicted a wetter condition in all seasons. During summer (JJAS), the soil moisture variability is characterized by a strong east–west spatial contrast. The highest and lowest soil moisture values were observed across the country’s central western and eastern parts, respectively. Furthermore, analyses indicate that interannual variability of SM is dictated substantially by RF, though the impact on some regions is weaker. It is also found that ET likely drives the SM in the eastern part of Ethiopia due to a higher atmospheric moisture demand that ultimately invokes changes in surface humidity and rainfall. A composite analysis based on the extreme five wettest and driest SM years revealed a similar spatial distribution of wet SM with positive anomalies of RF across the country and ET over the southern regions. Remote SSTs are also found to have a significant influence on SM distribution. In particular, equatorial central Pacific and western Indian oceans SST anomalies are predominant factors for spatiotemporal SM variations over the country. Major global oceanic indices: Oceanic Nino Index (ONI), Indian Ocean Dipole (IOD), Pacific warm pool (PACWARMPOOL), and Pacific Decadal Oscillations (PDO) are found to be closely associated with the SM anomalies in various parts of the country. The associationship between these remote SST anomalies and local soil moisture is via large-scale atmospheric circulations that are linked to regional factors such as precipitation and temperature anomalies.publishedVersio

Coupled Impacts of Soil Acidification and Climate Change on Future Crop Suitability in Ethiopia

Agricultural sustainability faces challenges in the changing climate, particularly for rain-fed systems like those in Ethiopia. This study examines the combined impacts of climate change and soil acidity on future crop potential, focusing on Ethiopia as a case study. The EcoCrop crop suitability model was parameterized and run for four key food crops in Ethiopia (teff, maize, barley and common wheat), under current and mid-century climate conditions. To assess the impacts of soil acidification on crop suitability, a simulation study was conducted by lowering the soil pH values by 0.5, 1.0 and 1.5 and re-running the suitability model, comparing the changes in the area suitable for each crop. Our evaluation of the model, by comparing the modeled suitable areas with reference data, indicated that there was a good fit for all the four crops. Using default soil pH values, we project that there will be no significant changes in the suitability of maize, barley and wheat and an increase in the suitability of teff by the mid-century, as influenced by projected increases in rainfall in the country. Our results demonstrate a direct relationship between the lowering of soil pH and increasing losses in the area suitable for all crops, but especially for teff, barley and wheat. We conclude that soil acidification can have a strong impact on crop suitability in Ethiopia under climate change, and precautionary measures to avoid soil acidification should be a key element in the design of climate change adaptation strategies

Are Healthcare Choices Predictable? The Impact of Discrete Choice Experiment Designs and Models

© 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research Background: Lack of evidence about the external validity of discrete choice experiments (DCEs) is one of the barriers that inhibit greater use of DCEs in healthcare decision making. Objectives: To determine whether the number of alternatives in a DCE choice task should reflect the actual decision context, and how complex the choice model needs to be to be able to predict real-world healthcare choices. Methods: Six DCEs were used, which varied in (1) medical condition (involving choices for influenza vaccination or colorectal cancer screening) and (2) the number of alternatives per choice task. For each medical condition, 1200 respondents were randomized to one of the DCE formats. The data were analyzed in a systematic way using random-utility-maximization choice processes. Results: Irrespective of the number of alternatives per choice task, the choice for influenza vaccination and colorectal cancer screening was correctly predicted by DCE at an aggregate level, if scale and preference heterogeneity were taken into account. At an individual level, 3 alternatives per choice task and the use of a heteroskedastic error component model plus observed preference heterogeneity seemed to be most promising (correctly predicting >93% of choices). Conclusions: Our study shows that DCEs are able to predict choices—mimicking real-world decisions—if at least scale and preference heterogeneity are taken into account. Patient characteristics (eg, numeracy, decision-making style, and general attitude for and experience with the health intervention) seem to play a crucial role. Further research is needed to determine whether this result remains in other contexts

Stay-green expression in early generation sorghum [Sorghum bicolor (L.) Moench] QTL introgression lines

Reduced leaf senescence (stay-green) has been demonstrated to improve tolerance of post-Xowering moisture stress in grain sorghum. A number of quantitative trait loci (QTLs) associated with staygreen have been identiWed in sorghum, to facilitate transfer of this trait into adapted genetic backgrounds. This study reports initial evaluations, in both well watered and post-Xowering stress environments, following partial introgression (BC2F3/BC1F4 generations) of four stable stay-green QTLs (StgB, Stg1, Stg3 and Stg4) from donor parent B35 to senescent variety R 16. The majority of the introgression lines had higher leaf chlorophyll levels at Xowering (a distinctive trait of the donor parent) and a greater percentage green leaf area during the latter part of grain Wlling, than did R 16, indicating that the stay-green QTLs were expressed phenotypically in the R 16 background. None of the QTL introgression lines achieved the same level of stay-green as B35, however. Maintenance of a greater relative green leaf area during the latter half of grain Wlling was related to a greater relative grain yield in two of three post-Xowering moisture deWcit environments in which the materials were evaluated (r2 = 0.34 in 2004–2005 and r2 = 0.76 in 2005–2006), as was a direct measure of leaf chlorophyll in one of the post-Xowering stress environments in which this was measured (r2 = 0.42, P < 0.05). Thus the study provided useful evidence that the marker-assisted backcross transfer of staygreen QTLs from B35 into an adapted, but senescent background has the potential to enhance tolerance of post-Xowering drought stress in sorghum