Retroviral vector-mediated transfer of the galactocerebrosidase (GALC) cDNA leads to overexpression and transfer of GALC activity to neighboring cells

Galactocerebrosidase (GALC) is responsible for the lysosomal catabolism of certain galactolipids, including galactosylceramide and psychosine. Patients with GALC deficiency have an autosomal recessive disorder known as globoid cell leukodystrophy (GLD) or Krabbe disease. Storage of undegraded glycolipids results in defective myelin and the characteristic globoid cells observed on pathological examination of the central and peripheral nervous systems. Most patients have the infantile form of GLD, although older individuals are also diagnosed. Recently the human, mouse, and canine GALC genes were cloned, and mutations causing GLD have been identified. We now describe the construction of a vector containing human GALC cDNA (MFG-GALC), and the transduction of cultured skin fibroblasts from molecularly characterized Krabbe disease patients, as well as rat brain astrocytes and human CD34(+) hematopoietic cells, using retrovirus produced by the psi-CRIP amphotropic packaging cell line. The transduced fibroblasts showed extremely high GALC activity (up to 20,000 times pretreatment levels, about 100 times normal). GALC was secreted into the media and was taken up by untransduced fibroblasts from the same or a different patient. Mannose-6-phosphate receptor-mediated uptake was only partially responsible for the efficient transfer of GALC to neighboring cells. Additional studies confirmed the presence of normal GALC cDNA and mRNA in the transduced cells. The GALC produced by the transduced cells and donated to neighboring untransduced cells was localized to lysosomes as demonstrated by the normal metabolism of [14C]stearic acid-labeled galactosylceramide produced from endocytosed [14C]sulfatide.     

Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms

When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most effective explanatory variables in predicting pregnancy outcome.     

The relation between exposure to violence and social information processing among incarcerated adolescents.

Combining evidence from social learning theory with reports of the association between community violence exposure and aggressive behavior development, the authors examined the link between specific characteristics of violence exposure and social information-processing mechanisms (N. R. Crick & K. A. Dodge, 1994; K. A. Dodge, 1980, 1986) in a sample of highly aggressive, incarcerated adolescent boys (N?=?110). Results demonstrated that victimization by severe violence was significantly related to approval of aggression as a social response, problems with the interpretation of social cues, and maladaptive social goals. Witnessing severe violence, in contrast, was related to perceived positive outcomes for the use of aggression. These data suggest the importance of examining the severity and modality of exposure to community violence for understanding patterns of social–cognitive functioning among adolescents exposed to violence. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Numerical modeling of splashing and air entrapment in high-pressure die casting

High-pressure die casting (HPDC) is one of the most important manufacturing processes. Air porosity in HPDC parts has many serious effects upon the casting quality. A 3D single-phase code based on the SOLA-VOF algorithm is used for the continuous phase advection during mold filling. In this research, a computational model based on concentration transport equation is used for calculation of air porosity distribution and a mixed VOF-Lagrange algorithm is developed in order to model splashing in HPDC. Finally, Schmid’s experimental tests are used to verify the modelling results and the comparison between the experimental data and simulation results has shown a good agreement.     

Salt-dependent renal effects of an angiotensin II antagonist in healthy subjects

This study was designed to evaluate in healthy volunteers the renal hemodynamic and tubular effects of the orally active angiotensin II receptor antagonist losartan (DuP 753 or MK 954). Losartan or a placebo was administered to 23 subjects maintained on a high-sodium (200 mmol/d) or a low-sodium (50 mmol/d) diet in a randomized, double-blind, crossover study. The two 6-day diet periods were separated by a 5-day washout period. On day 6, the subjects were water loaded, and blood pressure, renal hemodynamics, and urinary electrolyte excretion were measured for 6 hours after a single 100-mg oral dose of losartan (n = 16) or placebo (n = 7). Losartan induced no significant changes in blood pressure, glomerular filtration rate, or renal blood flow in these water-loaded subjects, whatever the sodium diet. In subjects on a low-salt diet, losartan markedly increased urinary sodium excretion from 115 +/- 9 to 207 +/- 21 mumol/min (P < .05). The fractional excretion of endogenous lithium was unchanged, suggesting no effect of losartan on the early proximal tubule in our experimental conditions. Losartan also increased urine flow rate (from 10.5 +/- 0.4 to 13.1 +/- 0.6 mL/min, P < .05); urinary potassium excretion (from 117 +/- 6.9 to 155 +/- 11 mumol/min); and the excretion of chloride, magnesium, calcium, and phosphate. In subjects on a high-salt diet, similar effects of losartan were observed, but the changes induced by the angiotensin II antagonist did not reach statistical significance. In addition, losartan demonstrated significant uricosuric properties with both sodium diets.(ABSTRACT TRUNCATED AT 250 WORDS)     

Short communication: Prediction of retention pay-off using a machine learning algorithm

Replacement decisions have a major effect on dairy farm profitability. Dynamic programming (DP) has been widely studied to find the optimal replacement policies in dairy cattle. However, DP models are computationally intensive and might not be practical for daily decision making. Hence, the ability of applying machine learning on a prerun DP model to provide fast and accurate predictions of nonlinear and intercorrelated variables makes it an ideal methodology. Milk class (1 to 5), lactation number (1 to 9), month in milk (1 to 20), and month of pregnancy (0 to 9) were used to describe all cows in a herd in a DP model. Twenty-seven scenarios based on all combinations of 3 levels (base, 20% above, and 20% below) of milk production, milk price, and replacement cost were solved with the DP model, resulting in a data set of 122,716 records, each with a calculated retention pay-off (RPO). Then, a machine learning model tree algorithm was used to mimic the evaluated RPO with DP. The correlation coefficient factor was used to observe the concordance of RPO evaluated by DP and RPO predicted by the model tree. The obtained correlation coefficient was 0.991, with a corresponding value of 0.11 for relative absolute error. At least 100 instances were required per model constraint, resulting in 204 total equations (models). When these models were used for binary classification of positive and negative RPO, error rates were 1% false negatives and 9% false positives. Applying this trained model from simulated data for prediction of RPO for 102 actual replacement records from the University of Wisconsin-Madison dairy herd resulted in a 0.994 correlation with 0.10 relative absolute error rate. Overall results showed that model tree has a potential to be used in conjunction with DP to assist farmers in their replacement decisions.