Left ventricular diverticulum: A case report and review of the literature.

Left ventricular diverticulum is a rare congenital left ventricular outpouchings. The disease is often diagnosed during childhood because it is frequently associated with midline thoracoabdominal defects and other congenital cardiac anomalies. Most cases are asymptomatic, often discovered incidentally. Some complications have been reported including infarction, arrhythmia, heart failure. The most severe complication is rupture of the diverticulum, which can cause a patient's death. Therefore, this congenital defect should be detected early to assess potential risks for appropriate treatment. In this article, we report a case of a 3-month-old boy with left ventricular diverticulum diagnosed with Doppler ultrasound and cardiac MSCT. Complete resection was undertaken. The patient remained asymptomatic with good heart function 2 months after surgery.

A Bayesian belief data mining approach applied to rice and shrimp aquaculture.

In many parts of the world, conditions for small scale agriculture are worsening, creating challenges in achieving consistent yields. The use of automated decision support tools, such as Bayesian Belief Networks (BBNs), can assist producers to respond to these factors. This paper describes a decision support system developed to assist farmers on the Mekong Delta, Vietnam, who grow both rice and shrimp crops in the same pond, based on an existing BBN. The BBN was previously developed in collaboration with local farmers and extension officers to represent their collective perceptions and understanding of their farming system and the risks to production that they face. This BBN can be used to provide insight into the probable consequences of farming decisions, given prevailing environmental conditions, however, it does not provide direct guidance on the optimal decision given those decisions. In this paper, the BBN is analysed using a novel, temporally-inspired data mining approach to systematically determine the agricultural decisions that farmers perceive as optimal at distinct periods in the growing and harvesting cycle, given the prevailing agricultural conditions. Using a novel form of data mining that combines with visual analytics, the results of this analysis allow the farmer to input the environmental conditions in a given growing period. They then receive recommendations that represent the collective view of the expert knowledge encoded in the BBN allowing them to maximise the probability of successful crops. Encoding the results of the data mining/inspection approach into the mobile Decision Support System helps farmers access explicit recommendations from the collective local farming community as to the optimal farming decisions, given the prevailing environmental conditions.

Effects of common full-sib families on accuracy of genomic prediction for tagging weight in striped catfish Pangasianodon hypophthalmus.

Common full-sib families (c 2 ) make up a substantial proportion of total phenotypic variation in traits of commercial importance in aquaculture species and omission or inclusion of the c 2 resulted in possible changes in genetic parameter estimates and re-ranking of estimated breeding values. However, the impacts of common full-sib families on accuracy of genomic prediction for commercial traits of economic importance are not well known in many species, including aquatic animals. This research explored the impacts of common full-sib families on accuracy of genomic prediction for tagging weight in a population of striped catfish comprising 11,918 fish traced back to the base population (four generations), in which 560 individuals had genotype records of 14,154 SNPs. Our single step genomic best linear unbiased prediction (ssGLBUP) showed that the accuracy of genomic prediction for tagging weight was reduced by 96.5%-130.3% when the common full-sib families were included in statistical models. The reduction in the prediction accuracy was to a smaller extent in multivariate analysis than in univariate models. Imputation of missing genotypes somewhat reduced the upward biases in the prediction accuracy for tagging weight. It is therefore suggested that genomic evaluation models for traits recorded during the early phase of growth development should account for the common full-sib families to minimise possible biases in the accuracy of genomic prediction and hence, selection response.