Direct Comparisons of 2D and 3D Dental Microwear Proxies in Extant Herbivorous and Carnivorous Mammals

The analysis of dental microwear is commonly used by paleontologists and anthropologists to clarify the diets of extinct species, including herbivorous and carnivorous mammals. Currently, there are numerous methods employed to quantify dental microwear, varying in the types of microscopes used, magnifications, and the characterization of wear in both two dimensions and three dimensions. Results from dental microwear studies utilizing different methods are not directly comparable and human quantification of wear features (e.g., pits and scratches) introduces interobserver error, with higher error being produced by less experienced individuals. Dental microwear texture analysis (DMTA), which analyzes microwear features in three dimensions, alleviates some of the problems surrounding two-dimensional microwear methods by reducing observer bias. Here, we assess the accuracy and comparability within and between 2D and 3D dental microwear analyses in herbivorous and carnivorous mammals at the same magnification. Specifically, we compare observer-generated 2D microwear data from photosimulations of the identical scanned areas of DMTA in extant African bovids and carnivorans using a scanning white light confocal microscope at 100x magnification. Using this magnification, dental microwear features quantified in 2D were able to separate grazing and frugivorous bovids using scratch frequency; however, DMTA variables were better able to discriminate between disparate dietary niches in both carnivorous and herbivorous mammals. Further, results demonstrate significant interobserver differences in 2D microwear data, with the microwear index remaining the least variable between experienced observers, consistent with prior research. Overall, our results highlight the importance of reducing observer error and analyzing dental microwear in three dimensions in order to consistently interpret diets accurately.     

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.     

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).     

Depicting the Spatial Distribution of Proteins in Human Tumor Tissue Combining SELDI and MALDI Imaging and Immunohistochemistry

Carcinoma tissue consists of not only tumor cells but also fibroblasts, endothelial cells or vascular structures, and inflammatory cells forming the supportive tumor stroma. Therefore, the spatial distribution of proteins that promote growth and proliferation in these complex functional units is of high interest. Matrix-assisted laser desorption/ionization imaging mass spectrometry is a newly developed technique that generates spatially resolved profiles of protein signals directly from thin tissue sections. Surface-enhanced laser desorption/ionization mass spectrometry (MS)combined with tissue microdissection allows analysis of defined parts of the tissue with a higher sensitivity and a broader mass range. Nevertheless, both MS-based techniques have a limited spatial resolution. IHC is a technique that allows a resolution down to the subcellular level. However, the detection and measurement of a specific protein expression level is possible only by semiquantitative methods. Moreover, prior knowledge about the identity of the proteins of interest is necessary. In this study, we combined all three techniques to gain highest spatial resolution, sensitivity, and quantitative information. We used frozen tissue from head and neck tumors and chose two exemplary proteins (HNP1–3 and S100A8) to highlight the advantages and disadvantages of each technique. It could be shown that the combination of these three techniques results in congruent but also synergetic data. (J Histochem Cytochem 58:929–937, 2010)     

Carbon flows in eutrophic Lake Rotsee: a <Superscript>13</Superscript>C-labelling experiment

The microbial segment of food webs plays a crucial role in lacustrine food-web functioning and carbon transfer, thereby influencing carbon storage and CO₂ emission and uptake in freshwater environments. Variability in microbial carbon processing (autotrophic and heterotrophic production and respiration based on glucose) with depth was investigated in eutrophic, methane-rich Lake Rotsee, Switzerland. In June 2011, ¹³C-labelling experiments were carried out at six depth intervals in the water column under ambient light as well as dark conditions to evaluate the relative importance of (chemo)autotrophic, mixotrophic and heterotrophic production. Label incorporation rates of phospholipid-derived fatty acid (PLFA) biomarkers allowed us to differentiate between microbial producers and calculate group-specific production. We conclude that at 6 m, net primary production (NPP) rates were highest, dominated by algal photoautotrophic production. At 10 m —the base of the oxycline— a distinct low-light community was able to fix inorganic carbon, while in the hypolimnion, heterotrophic production prevailed. At 2 m depth, high label incorporation into POC could only be traced to nonspecific PLFA, which prevented definite identification, but suggests cyanobacteria as dominating organisms. There was also depth zonation in extracellular carbon release and heterotrophic bacterial growth on recently fixed carbon. Large differences were observed between concentrations and label incorporation of POC and biomarkers, with large pools of inactive biomass settling in the hypolimnion, suggesting late-/post-bloom conditions. Net primary production (115 mmol C m^?2 d^?1) reached highest values in the epilimnion and was higher than glucose-based production (3.3 mmol C m^?2 d^?1, highest rates in the hypolimnion) and respiration (5.9 mmol C m^?2 d^?1, highest rates in the epilimnion). Hence, eutrophic Lake Rotsee was net autotrophic during our experiments, potentially storing large amounts of carbon.     

Schwannoma-Derived Growth Factor Interacts with the Epidermal Growth Factor Receptor

Abstract: Schwannoma-derived growth factor (SDGF) is a potent mitogen and neuronal differentiation factor. Because of its relationship to epidermal growth factor (EGF) and the heregulins, it was asked if SDGF interacts with the EGF receptor or HER2/neu. SDGF binds to and causes the phosphorylation on tyrosine of the EGF receptor but not HER2/neu.