Identifying essential genes in bacterial metabolic networks with machine learning methods

Background  

Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.     

Detection of genetic variants affecting cattle behaviour and their impact on milk production: a genome‐wide association study

Behaviour traits of cattle have been reported to affect important production traits, such as meat quality and milk performance as well as reproduction and health. Genetic predisposition is, together with environmental stimuli, undoubtedly involved in the development of behaviour phenotypes. Underlying molecular mechanisms affecting behaviour in general and behaviour and productions traits in particular still have to be studied in detail. Therefore, we performed a genome‐wide association study in an F₂ Charolais × German Holstein cross‐breed population to identify genetic variants that affect behaviour‐related traits assessed in an open‐field and novel‐object test and analysed their putative impact on milk performance. Of 37 201 tested single nucleotide polymorphism (SNPs), four showed a genome‐wide and 37 a chromosome‐wide significant association with behaviour traits assessed in both tests. Nine of the SNPs that were associated with behaviour traits likewise showed a nominal significant association with milk performance traits. On chromosomes 14 and 29, six SNPs were identified to be associated with exploratory behaviour and inactivity during the novel‐object test as well as with milk yield traits. Least squares means for behaviour and milk performance traits for these SNPs revealed that genotypes associated with higher inactivity and less exploratory behaviour promote higher milk yields. Whether these results are due to molecular mechanisms simultaneously affecting behaviour and milk performance or due to a behaviour predisposition, which causes indirect effects on milk performance by influencing individual reactivity, needs further investigation.     

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples.

     

Toll-like receptor 4 deficiency: Smaller infarcts,but nogain in function

Backgound  

It has been reported that Toll-like receptor 4 (TLR4) deficiency reduces infarct size after myocardial ischemia/reperfusion (MI/R). However, measurement of MI/R injury was limited and did not include cardiac function. In a chronic closed-chest model we assessed whether cardiac function is preserved in TLR4-deficient mice (C3H/HeJ) following MI/R, and whether myocardial and systemic cytokine expression differed compared to wild type (WT).     

From Regional to National Clouds: TV Coverage in the Czech Republic

Media, and particularly TV media, have a great impact on the general public. In recent years, spatial patterns of information and the relevance of intangible geographies have become increasingly important. Gatekeeping plays a critical role in the selection of information that is transformed into media. Therefore, gatekeeping, through national media, also co-forms the generation of mental maps. In this paper, correspondence analysis (a statistical method) combined with cloud lines (a new visual analytics technique) is used to analyze how individual major regional events in one of the post-communist countries, the Czech Republic, penetrate into the media on a national scale. Although national news should minimize distortions about regions, this assumption has not been verified by our research. Impressions presented by the media of selected regions that were markedly influenced by one or several events in those regions demonstrate that gatekeepers, especially news reporters, functioned as a filter by selecting only a few specific, and in many cases, unusual events for dissemination.     

Klimacharakter und Pflanzendecke

Ohne Zusammenfassung