Knowledge-based gene expression classification via matrix factorization

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.Siemens AG, MunichDFG (Graduate College 638)DAAD (PPP Luso - Alem˜a and PPP Hispano - Alemanas

Effect of Level of Surface Spoilage on the Nutritive Value of Maize Silage Diets

This study determined the effect of surface spoilage in the diet on feed intake and nutrient digestibilities using growing steers fed whole-plant maize silage-based diets. A bunker silo, 0.9 m in depth, and a 2.7 m diameter AgBag were filled with alternating loads of chopped forage. After 90 days, the bunker was sealed with a sheet of polyethylene, and this silage was designated “spoiled”. The silage in the AgBag was designated “normal”. The four diets contained 90% silage and 10% supplement (dry matter basis), and the proportions of silage in the diets were A) 100% normal, B) 75% normal: 25% spoiled; C) 50% normal: 50% spoiled; and D) 25% normal: 75% spoiled. Feed intake decreased linearly as the proportion of spoiled silage increased from 0 to 75%. Steers consuming the normal silage diet had the highest nutrient digestibilities. Spoiled silage also had negative associative effects on nutrient digestibilities, and the integrity of the forage mat in the rumen was partially destroyed

Role of computed tomography coronary angiography in the management of coronary anomalies

Congenital abnormalities of coronary arteries are an uncommon cause of sudden cardiac death and are difficult to detect at coronary angiography. We describe two patients with acute coronary syndrome and non-occlusive coronary artery disease in which a 64-multidetector computed tomography (MDCT) coronary angiography showed the presence of a malignant coronary anomaly. Sixty-four-MDCT with the possibility of 3D reconstructions allows for easier diagnosis of coronary anomalies and provides essential details necessary for operative intervention

Contrasting gene expression patterns in grain of high and low asparagine wheat genotypes in response to sulphur supply

Background: Free asparagine is the precursor for acrylamide formation during cooking and processing of grains, tubers, beans and other crop products. In wheat grain, free asparagine, free glutamine and total free amino acids accumulate to high levels in response to sulphur deficiency. In this study, RNA-seq data were acquired for the embryo and endosperm of two genotypes of bread wheat, Spark and SR3, growing under conditions of sulphur sufficiency and deficiency, and sampled at 14 and 21 days post anthesis (dpa). The aim was to provide new knowledge and understanding of the genetic control of asparagine accumulation and breakdown in wheat grain. Results: There were clear differences in gene expression patterns between the genotypes. Sulphur responses were greater at 21 dpa than 14 dpa, and more evident in SR3 than Spark. TaASN2 was the most highly expressed asparagine synthetase gene in the grain, with expression in the embryo much higher than in the endosperm, and higher in Spark than SR3 during early development. There was a trend for genes encoding enzymes of nitrogen assimilation to be more highly expressed in Spark than SR3 when sulphur was supplied. TaASN2 expression in the embryo of SR3 increased in response to sulphur deficiency at 21 dpa, although this was not observed in Spark. This increase in TaASN2 expression was accompanied by an increase in glutamine synthetase gene expression and a decrease in asparaginase gene expression. Asparagine synthetase and asparaginase gene expression in the endosperm responded in the opposite way. Genes encoding regulatory protein kinases, SnRK1 and GCN2, both implicated in regulating asparagine synthetase gene expression, also responded to sulphur deficiency. Genes encoding bZIP transcription factors, including Opaque2/bZIP9, SPA/bZIP25 and BLZ1/OHP1/bZIP63, all of which contain SnRK1 target sites, were also expressed. Homeologues of many genes showed differential expression patterns and responses, including TaASN2. Conclusions: Data on the genetic control of free asparagine accumulation in wheat grain and its response to sulphur supply showed grain asparagine levels to be determined in the embryo, and identified genes encoding signalling and metabolic proteins involved in asparagine metabolism that respond to sulphur availability

Dietary Fat Patterns and Outcomes in Acute Pancreatitis in Spain

Background/Objective: Evidence from basic and clinical studies suggests that unsaturated fatty acids (UFAs) might be relevant mediators of the development of complications in acute pancreatitis (AP). Objective: The aim of this study was to analyze outcomes in patients with AP from regions in Spain with different patterns of dietary fat intake. Materials and Methods: A retrospective analysis was performed with data from 1,655 patients with AP from a Spanish prospective cohort study and regional nutritional data from a Spanish cross-sectional study. Nutritional data considered in the study concern the total lipid consumption, detailing total saturated fatty acids, UFAs and monounsaturated fatty acids (MUFAs) consumption derived from regional data and not from the patient prospective cohort. Two multivariable analysis models were used: (1) a model with the Charlson comorbidity index, sex, alcoholic etiology, and recurrent AP; (2) a model that included these variables plus obesity. Results: In multivariable analysis, patients from regions with high UFA intake had a significantly increased frequency of local complications, persistent organ failure (POF), mortality, and moderate-to-severe disease in the model without obesity and a higher frequency of POF in the model with obesity. Patients from regions with high MUFA intake had significantly more local complications and moderate-to-severe disease; this significance remained for moderate-to-severe disease when obesity was added to the model. Conclusions: Differences in dietary fat patterns could be associated with different outcomes in AP, and dietary fat patterns may be a pre-morbid factor that determines the severity of AP. UFAs, and particulary MUFAs, may influence the pathogenesis of the severity of AP

Six Centuries of Upper Indus Basin Streamflow Variability and Its Climatic Drivers

Our understanding of the full range of natural variability in streamflow, including how modern flow compares to the past, is poorly understood for the Upper Indus Basin because of short instrumental gauge records. To help address this challenge, we use Hierarchical Bayesian Regression with partial pooling to develop six centuries long (1394–2008 CE) streamflow reconstructions at three Upper Indus Basin gauges (Doyian, Gilgit, and Kachora), concurrently demonstrating that Hierarchical Bayesian Regression can be used to reconstruct short records with interspersed missing data. At one gauge (Partab Bridge), with a longer instrumental record (47 years), we develop reconstructions using both Bayesian regression and the more conventionally used principal components regression. The reconstructions produced by principal components regression and Bayesian regression at Partab Bridge are nearly identical and yield comparable reconstruction skill statistics, highlighting that the resulting tree ring reconstruction of streamflow is not dependent on the choice of statistical method. Reconstructions at all four reconstructions indicate that flow levels in the 1990s were higher than mean flow for the past six centuries. While streamflow appears most sensitive to accumulated winter (January–March) precipitation and summer (May–September) temperature, with warm summers contributing to high flow through increased melt of snow and glaciers, shifts in winter precipitation and summer temperatures cannot explain the anomalously high flow during the 1990s. Regardless, the sensitivity of streamflow to summer temperatures suggests that projected warming may increase streamflow in coming decades, though long-term water risk will additionally depend on changes in snowfall and glacial mass balance