Analysis of extracellular vesicle microRNA profiles reveals distinct blood and lymphatic endothelial cell origins.

Extracellular vesicles (EVs) are crucial mediators of cell-to-cell communication in physiological and pathological conditions. Specifically, EVs released from the vasculature into blood were found to be quantitatively and qualitatively different in diseases compared to healthy states. However, our understanding of EVs derived from the lymphatic system is still scarce.

On the Validation of Protein Force Fields Based on Structural Criteria.

Molecular dynamics simulations depend critically on the quality of the force field used to describe the interatomic interactions and the extent to which it has been validated for use in a specific application. Using a curated test set of 52 high-resolution structures, 39 derived from X-ray diffraction and 13 solved using NMR, we consider the extent to which different parameter sets of the GROMOS protein force field can be distinguished based on comparing a range of structural criteria, including the number of backbone hydrogen bonds, the number of native hydrogen bonds, polar and nonpolar solvent-accessible surface area, radius of gyration, the prevalence of secondary structure elements, -coupling constants, nuclear Overhauser effect (NOE) intensities, positional root-mean-square deviations (RMSD), and the distribution of backbone ϕ and ψ dihedral angles. It is shown that while statistically significant differences between the average values of individual metrics could be detected, these were in general small.

Identification of Activating Mutations in the Transmembrane and Extracellular Domains of EGFR.

The epidermal growth factor receptor (EGFR) is frequently mutated in human cancer, most notably non-small-cell lung cancer and glioblastoma. While many frequently occurring EGFR mutations are known to confer constitutive EGFR activation, the situation is less clear for rarely detected variants. In fact, more than 1000 distinct EGFR mutations are listed in the Catalogue of Somatic Mutations in Cancer (COSMIC), but for most of them, the functional consequence is unknown.

Investigating Explanatory Factors of Machine Learning Models for Plant Classification.

Recent progress in machine learning and deep learning has enabled the implementation of plant and crop detection using systematic inspection of the leaf shapes and other morphological characters for identification systems for precision farming. However, the models used for this approach tend to become black-box models, in the sense that it is difficult to trace characters that are the base for the classification. The interpretability is therefore limited and the explanatory factors may not be based on reasonable visible characters.

Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models.

Simplicity renders shake flasks ideal for strain selection and substrate optimization in biotechnology. Uncertainty during initial experiments may, however, cause adverse growth conditions and mislead conclusions. Using growth models for online predictions of future biomass (BM) and the arrival of critical events like low dissolved oxygen (DO) levels or when to harvest is hence important to optimize protocols.

PhosphoFlowSeq - A High-throughput Kinase Activity Assay for Screening Drug Resistance Mutations in EGFR.

Drug resistance poses a major challenge for targeted cancer therapy. To be able to functionally screen large randomly mutated target gene libraries for drug resistance mutations, we developed a biochemically defined high-throughput assay termed PhosphoFlowSeq. Instead of selecting for proliferation or resistance to apoptosis, PhosphoFlowSeq directly analyzes the enzymatic activities of randomly mutated kinases, thereby reducing the dependency on the signaling network in the host cell.

PCprophet: a framework for protein complex prediction and differential analysis using proteomic data.

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein-protein interaction inference.

Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning.

Visual characteristics are among the most important features for characterizing the phenotype of biological organisms. Color and geometric properties define population phenotype and allow assessing diversity and adaptation to environmental conditions. To analyze geometric properties classical morphometrics relies on biologically relevant landmarks which are manually assigned to digital images.

Root traits of European Vicia faba cultivars-Using machine learning to explore adaptations to agroclimatic conditions.

Faba bean (Vicia faba L.) is an important source of protein, but breeding for increased yield stability and stress tolerance is hampered by the scarcity of phenotyping information. Because comparisons of cultivars adapted to different agroclimatic zones improve our understanding of stress tolerance mechanisms, the root architecture and morphology of 16 European faba bean cultivars were studied at maturity.

Detecting and correcting systematic variation in large-scale RNA sequencing data.

High-throughput RNA sequencing (RNA-seq) enables comprehensive scans of entire transcriptomes, but best practices for analyzing RNA-seq data have not been fully defined, particularly for data collected with multiple sequencing platforms or at multiple sites. Here we used standardized RNA samples with built-in controls to examine sources of error in large-scale RNA-seq studies and their impact on the detection of differentially expressed genes (DEGs). Analysis of variations in guanine-cytosine content, gene coverage, sequencing error rate and insert size allowed identification of decreased reproducibility across sites.

Bacterial protein signals are associated with Crohn's disease.

Objective: No Crohn's disease (CD) molecular maker has advanced to clinical use, and independent lines of evidence support a central role of the gut microbial community in CD. Here we explore the feasibility of extracting bacterial protein signals relevant to CD, by interrogating myriads of intestinal bacterial proteomes from a small number of patients and healthy controls.

Design: We first developed and validated a workflow-including extraction of microbial communities, two-dimensional difference gel electrophoresis (2D-DIGE), and LC-MS/MS-to discover protein signals from CD-associated gut microbial communities.

An analysis of single amino acid repeats as use case for application specific background models.

Background: Sequence analysis aims to identify biologically relevant signals against a backdrop of functionally meaningless variation. Increasingly, it is recognized that the quality of the background model directly affects the performance of analyses. State-of-the-art approaches rely on classical sequence models that are adapted to the studied dataset.

The impact of quantitative optimization of hybridization conditions on gene expression analysis.

Background: With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols.

Results: As shown in this study, suboptimal conditions can have considerable impact on biologically relevant observations.

RNA interference in ageing research--a mini-review.

Background: The search for genetic mechanisms affecting life-span and ageing represents an important part of ageing research, especially since the discovery of single-gene mutations with dramatic effects on these traits. Due to its relative ease of use and its power to specifically target arbitrary genes, RNA interference (RNAi) has rapidly been adopted as a technique for silencing gene expression. The feasibility of genome-wide RNAi screens potentially much simplifies the identification of novel ageing-related genes.

Model-based probe set optimization for high-performance microarrays.

A major challenge in microarray design is the selection of highly specific oligonucleotide probes for all targeted genes of interest, while maintaining thermodynamic uniformity at the hybridization temperature. We introduce a novel microarray design framework (Thermodynamic Model-based Oligo Design Optimizer, TherMODO) that for the first time incorporates a number of advanced modelling features: (i) A model of position-dependent labelling effects that is quantitatively derived from experiment. (ii) Multi-state thermodynamic hybridization models of probe binding behaviour, including potential cross-hybridization reactions.

The transcriptome of syncytia induced by the cyst nematode Heterodera schachtii in Arabidopsis roots.

Arabidopsis thaliana is a host for the sugar beet cyst nematode Heterodera schachtii. Juvenile nematodes invade the roots and induce the development of a syncytium, which functions as a feeding site for the nematode. Here, we report on the transcriptome of syncytia induced in the roots of Arabidopsis.

Gene expression changes in long term expanded human neural progenitor cells passaged by chopping lead to loss of neurogenic potential in vivo.

Numerous cell culture protocols have been described for the proliferation of multipotent human neural progenitor cells (HNPCs). The mitogen combinations used to expand HNPCs vary, and it is not clear to what extent this may affect the subsequent differentiation of these cells. In this study human foetal cortical tissue was cultured in the presence of either EGF, or FGF-2, or a combination of both using a unique chopping method in which cell to cell contact is maintained.

Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation.

This paper proposes the use of variational Kalman filtering as an inference technique for adaptive classification in a brain computer interface (BCI). The proposed algorithm translates electroencephalogram segments adaptively into probabilities of cognitive states. It, thus, allows for nonstationarities in the joint process over cognitive state and generated EEG which may occur during a consecutive number of trials.

Cognitive tasks for driving a brain-computer interfacing system: a pilot study.

Different cognitive tasks were investigated for use with a brain-computer interface (BCI). The main aim was to evaluate which two of several candidate tasks lead to patterns of electroencephalographic (EEG) activity that could be differentiated most reliably and, therefore, produce the highest communication rate. An optimal signal processing method was also sought to enhance differentiation of EEG profiles across tasks.