Automated Network Assembly of Mechanistic Literature for Informed Evidence Identification to Support Cancer Risk Assessment

BACKGROUND: Mechanistic data is increasingly used in hazard identification of chemicals. However, the volume of data is large, challenging the efficient identification and clustering of relevant data. OBJECTIVES: We investigated whether evidence identification for hazard assessment can become more efficient and informed through an automated...

Seeking Windows of Opportunity to Shape Lifelong Immune Health A Network-Based Strategy to Predict and Prioritize Markers of Early Life Immune Modulation

A healthy immune status is strongly conditioned during early life stages. Insights into the molecular drivers of early life immune development and function are prerequisite to identify strategies to enhance immune health. Even though several starting points for targeted immune modulation have been identified and are being developed into...

Appetite ratings of foods are predictable with an in vitro advanced gastrointestinal model in combination with an in silico artificial neural network

The expected increase of global obesity prevalence makes it necessary to have information about the effects of meal intakes on the feeling of appetite. Because human clinical studies are time and cost intensive, there is a need for a reliable alternative. The aim of this study was to develop and evaluate an in vitro-in silico technology to...

Feed-forward neural network model for hunger and satiety related VAS score prediction

Background: An artificial neural network approach was chosen to model the outcome of the complex signaling pathways in the gastro-intestinal tract and other peripheral organs that eventually produce the satiety feeling in the brain upon feeding. Methods: A multilayer feed-forward neural network was trained with sets of experimental data relating...

A new flexible plug and play scheme for modeling, simulating, and predicting gastric emptying

In-silico models that attempt to capture and describe the physiological behavior of biological organisms, including humans, are intrinsically complex and time consuming to build and simulate in a computing environment. The level of detail of description incorporated in the model depends on the knowledge of the system's behavior at that level....

Pre-processing liquid chromatography/high-resolution mass spectrometry data: Extracting pure mass spectra by deconvolution from the invariance of isotopic distribution

RATIONALE Mass spectra obtained by deconvolution of liquid chromatography/high-resolution mass spectrometry (LC/HRMS) data can be impaired by non-informative mass-over-charge (m/z) channels. This impairment of mass spectra can have significant negative influence on further post-processing, like quantification and identification. METHODS A metric...

Exploiting multiple mahalanobis distance metrics to screen outliers from analog product manufacturing test responses

Stringent quality requirements on final electronic products are continuously forcing semiconductor industries, especially the automobile industry, to insert additional reliability tests in their production flow. The problem with linear regressions models for outlier identification in analog and RF devices is that they do not generally account...

The onset of type 2 diabetes: Proposal for a multi-scale model

Background: Type 2 diabetes mellitus (T2D) is a common age-related disease, and is a major health concern, particularly in developed countries where the population is aging, including Europe. The multi-scale immune system simulator for the onset of type 2 diabetes (MISSION-T2D) is a European Union-funded project that aims to develop and validate...

Instrument and process independent binning and baseline correction methods for liquid chromatography-high resolution-mass spectrometry deconvolution

Setting appropriate bin sizes to aggregate hyphenated high-resolution mass spectrometry data, belonging to similar mass over charge (m/. z) channels, is vital to metabolite quantification and further identification. In a high-resolution mass spectrometer when mass accuracy (ppm) varies as a function of molecular mass, which usually is the case...

A robust metric for screening outliers from analogue product manufacturing tests responses

Mahalanobis distance is one of the commonly used multivariate metrics for finely segregating defective devices from non-defective ones. An associated problem with this approach is the estimation of a robust mean and a covariance matrix. In the absence of such robust estimates, especially in the presence of outliers to test-response measurements,...

Multivariate model for test response analysis

A systematic approach to construct an effective multivariate test response model for capturing manufacturing defects in electronic products is described. The effectiveness of the model is demonstrated by its capability in reducing the number of test-points, while achieving the maximal coverage attainable by the specific test method on an...

Block-level Bayesian diagnosis of analogue electronic circuits

Daily experience with product designers, test and diagnosis engineers it is realized that the depth of interaction among them, ought be high for sucessfull diagnosis of analogue circuits. With this knowledge in mind, a responsibility was undertaken to choose a popular diagnostic method and define a systematic procedure that binds together the...