Heterozygosity‐Fitness Correlations and Inbreeding Depression in Two Critically Endangered Mammals

The relation among inbreeding, heterozygosity, and fitness has been studied primarily among outbred populations, and little is known about these phenomena in endangered populations. Most researchers conclude that the relation between coefficient of inbreeding estimated from pedigrees and fitness traits (inbreeding‐fitness correlations) better reflects inbreeding depression than the relation between marker heterozygosity and fitness traits (heterozygosity‐fitness correlations). However, it has been suggested recently that heterozygosity‐fitness correlations should only be expected when inbreeding generates extensive identity disequilibrium (correlations in heterozygosity and homozygosity across loci throughout the genome). We tested this hypothesis in Mohor gazelle (Gazella dama mhorr) and Iberian lynx (Lynx pardinus). For Mohor gazelle, we calculated the inbreeding coefficient and measured heterozygosity at 17 microsatellite loci. For Iberian lynx, we measured heterozygosity at 36 microsatellite loci. In both species we estimated semen quality, a phenotypic trait directly related to fitness that is controlled by many loci and is affected by inbreeding depression. Both species showed evidence of extensive identity disequilibrium, and in both species heterozygosity was associated with semen quality. In the Iberian lynx the low proportion of normal sperm associated with low levels of heterozygosity was so extreme that it is likely to limit the fertility of males. In Mohor gazelle, although heterozygosity was associated with semen quality, inbreeding coefficient was not. This result suggests that when coefficient of inbreeding is calculated on the basis of a genealogy that begins after a long history of inbreeding, the coefficient of inbreeding fails to capture previous demographic information because it is a poor estimator of accumulated individual inbreeding. We conclude that among highly endangered species with extensive identity disequilibrium, examination of heterozygosity‐fitness correlations may be an effective way to detect inbreeding depression, whereas inbreeding‐fitness correlations may be poor indicators of inbreeding depression if the pedigree does not accurately reflect the history of inbreeding. Correlaciones Heterocigosidad‐ Adaptabilidad y Depresión Endogámica en Dos Especies de Mamíferos Críticamente en Peligro     

Is there a role for fetal interventions in gastroschisis management? – An updated comprehensive review

We conducted a comprehensive evidence-based review on the epidemiology and current standard of care of gastroschisis management as well as the pathophysiology, rationale and feasibility of fetal therapy as a viable alternative. Gastroschisis is a periumbilical abdominal wall defect characterized by abdominal viscera herniation in utero. It affects 4 in 10 000 live births, but the prevalence has steadily increased in recent years. Gastroschisis is typically diagnosed on routine second-trimester ultrasound. The overall prognosis is favorable, but complex gastroschisis, which accounts for about 10% to 15% of cases, is associated with a higher mortality, significant disease burden and higher healthcare costs due to long- and short-term complications. The current standard of care has yet to be established but generally involves continued fetal surveillance and multidisciplinary perinatal care. Postnatal surgical repair is achieved with primary closure, staged silo closure or sutureless repair. Experimental animal studies have demonstrated the feasibility of in utero closure, antiinflammatory therapy and prenatal regenerative therapy. However, reports of early preterm delivery and amnioinfusion trials have failed to show any benefit in humans. Further experimental studies and human trials are necessary to demonstrate the potential benefit of fetal therapy in gastroschisis.     

Spatiotemporal Associations of Reservoir Nutrient Characteristics and the Invasive,Harmful Alga Prymnesium parvum in West Texas

Golden alga (Prymnesium parvum) is a harmful alga that has caused ecological and economic harm in freshwater and marine systems worldwide. In inland systems of North America, toxic blooms have nearly eliminated fish populations in some systems. Modifying nutrient profiles through alterations to land or water use may be a viable alternative for golden alga control in reservoirs. The main objective of this study was to improve our understanding of the nutrient dynamics that influence golden alga bloom formation and toxicity in west Texas reservoirs. We examined eight sites in the Upper Colorado River basin, Texas: three impacted reservoirs that have experienced repeated golden alga blooms; two reference reservoirs where golden alga is present but nontoxic; and three confluence sites downstream of the impacted and reference sites. Total, inorganic, and organic nitrogen and phosphorus and their ratios were quantified monthly along with golden alga abundance and ichthyotoxicity between December 2010 and July 2011. Blooms persisted for several months at the impacted sites, which were characterized by high organic nitrogen and low inorganic nitrogen. At impacted sites, abundance was positively associated with inorganic phosphorus and bloom termination coincided with increases in inorganic nitrogen and decreases in inorganic phosphorus in late spring. Management of both inorganic and organic forms of nutrients may create conditions in reservoirs unfavorable to golden alga.     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.