Real-Time Predictive Condition Monitoring Using Multivariate Data.

This article presents an algorithmic framework for real-time condition monitoring and state forecasting using multivariate data demonstrated on thermal imagery data of a ship's engine. The proposed method aims to improve the accuracy, efficiency, and robustness of condition monitoring and state predictions by identifying the most informative sampling locations of high-dimensional datasets and extracting the underlying dynamics of the system. The method is based on a combination of Proper Orthogonal Decomposition (POD), Optimal Sampling Location (OSL), and Dynamic Mode Decomposition (DMD), allowing the identification of key features in the system's behavior and predicting future states.

Causality, machine learning and human insight.

Modern instruments generate BIG DATA that require information extraction before they can be used. A hybrid modelling framework for that is presented and illustrated. Its purpose is to convert meaningless data to meaningful information and to contribute to a theoretical, practical, and democratic basis for tomorrow's handling of BIG DATA in science and technology.

Corrigendum: Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding.

[This corrects the article DOI: 10.3389/fchem.2022.

Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding.

Hyperspectral imaging has recently gained increasing attention from academic and industrial world due to its capability of providing both spatial and physico-chemical information about the investigated objects. While this analytical approach is experiencing a substantial success and diffusion in very disparate scenarios, far less exploited is the possibility of collecting sequences of hyperspectral images over time for monitoring dynamic scenes. This trend is mainly justified by the fact that these so-called hyperspectral usually result in BIG DATA sets, requiring TBs of computer memory to be both stored and processed.

Genome-wide association mapping for milk fat composition and fine mapping of a QTL for de novo synthesis of milk fatty acids on bovine chromosome 13.

Background: Bovine milk is widely regarded as a nutritious food source for humans, although the effects of individual fatty acids on human health is a subject of debate. Based on the assumption that genomic selection offers potential to improve milk fat composition, there is strong interest to understand more about the genetic factors that influence the biosynthesis of bovine milk and the molecular mechanisms that regulate milk fat synthesis and secretion. For this reason, the work reported here aimed at identifying genetic variants that affect milk fatty acid composition in Norwegian Red cattle.

Frequency Analysis and Feature Reduction Method for Prediction of Cerebral Palsy in Young Infants.

The aim of this paper is to achieve a model for prediction of cerebral palsy based on motion data of young infants. The prediction is formulated as a classification problem to assign each of the infants to one of the healthy or with cerebral palsy groups. Unlike formerly proposed features that are mostly defined in the time domain, this study proposes a set of features derived from frequency analysis of infants' motions.

Frequency-based features for early cerebral palsy prediction.

In this paper we aim at predicting cerebral palsy, the most serious and lifelong motor function disorder in children, at an early age by analysing infants' motion data. An essential step for doing so is to extract informative features with high class separability. We propose a set of features derived from frequency analysis of the motion data.

High-throughput biochemical fingerprinting of Saccharomyces cerevisiae by Fourier transform infrared spectroscopy.

Single-channel optical density measurements of population growth are the dominant large scale phenotyping methodology for bridging the gene-function gap in yeast. However, a substantial amount of the genetic variation induced by single allele, single gene or double gene knock-out technologies fail to manifest in detectable growth phenotypes under conditions readily testable in the laboratory. Thus, new high-throughput phenotyping technologies capable of providing information about molecular level consequences of genetic variation are sorely needed.

150 years of the mass action law.

This year we celebrate the 150th anniversary of the law of mass action. This law is often assumed to have been "there" forever, but it has its own history, background, and a definite starting point. The law has had an impact on chemistry, biochemistry, biomathematics, and systems biology that is difficult to overestimate.

A computational pipeline for quantification of mouse myocardial stiffness parameters.

The mouse is an important model for theoretical-experimental cardiac research, and biophysically based whole organ models of the mouse heart are now within reach. However, the passive material properties of mouse myocardium have not been much studied. We present an experimental setup and associated computational pipeline to quantify these stiffness properties.

Emulating facial biomechanics using multivariate partial least squares surrogate models.

A detailed biomechanical model of the human face driven by a network of muscles is a useful tool in relating the muscle activities to facial deformations. However, lengthy computational times often hinder its applications in practical settings. The objective of this study is to replace precise but computationally demanding biomechanical model by a much faster multivariate meta-model (surrogate model), such that a significant speedup (to real-time interactive speed) can be achieved.

Estimating muscle activation patterns using a surrogate model of facial biomechanics.

Analyzing the muscle activities that drive the expressive facial gestures can be a useful tool in assessing one's emotional state of mind. Since the skin motion is much easier to measure in comparison to the actual electrical excitation signal of facial muscles, a biomechanical model of the human face driven by these muscles can be a useful tool in relating the geometric information to the muscle activity. However, long computational time often hinders its practicality.

Estimation of ion competition via correlated responsivity offset in linear ion trap mass spectrometry analysis: theory and practical use in the analysis of cyanobacterial hepatotoxin microcystin-LR in extracts of food additives.

Responsivity is a conversion qualification of a measurement device given by the functional dependence between the input and output quantities. A concentration-response-dependent calibration curve represents the most simple experiment for the measurement of responsivity in mass spectrometry. The cyanobacterial hepatotoxin microcystin-LR content in complex biological matrices of food additives was chosen as a model example of a typical problem.

Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models.

Background: Statistical approaches to describing the behaviour, including the complex relationships between input parameters and model outputs, of nonlinear dynamic models (referred to as metamodelling) are gaining more and more acceptance as a means for sensitivity analysis and to reduce computational demand. Understanding such input-output maps is necessary for efficient model construction and validation. Multi-way metamodelling provides the opportunity to retain the block-wise structure of the temporal data typically generated by dynamic models throughout the analysis.

Mining for genotype-phenotype relations in Saccharomyces using partial least squares.

Background: Multivariate approaches are important due to their versatility and applications in many fields as it provides decisive advantages over univariate analysis in many ways. Genome wide association studies are rapidly emerging, but approaches in hand pay less attention to multivariate relation between genotype and phenotype. We introduce a methodology based on a BLAST approach for extracting information from genomic sequences and Soft- Thresholding Partial Least Squares (ST-PLS) for mapping genotype-phenotype relations.

Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models.

Background: Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically.

Perceptions of German prison officials while visiting American correctional facilities: an example of a fruitful international collaboration.

In June 2007, a group of 15 prison psychologists, social workers, wardens, and correctional administrators from across Germany visited the northeast United States for the purpose of conducting tours of various kinds of correctional facilities. The trip was organized through a collaborative effort from the authors over a period of 2 years. This article describes the correctional facilities visited and observations made by the Germans during their correctional facility tours and will focus on the similarities and differences between the German and American correctional systems.

Predicting the fatty acid composition of milk: a comparison of two Fourier transform infrared sampling techniques.

In the present study a novel approach for Fourier transform infrared (FT-IR) characterization of the fatty acid composition of milk based on dried film measurements has been presented and compared to a standard FT-IR approach based on liquid milk measurements. Two hundred and sixty-two (262) milk samples were obtained from a feeding experiment, and the samples were measured with FT-IR as dried films as well as liquid samples. Calibrations against the most abundant fatty acids, CLA (i.

RMieS-EMSC correction for infrared spectra of biological cells: extension using full Mie theory and GPU computing.

In the field of biomedical infrared spectroscopy it is often desirable to obtain spectra at the cellular level. Samples consisting of isolated single biological cells are particularly unsuited to such analysis since cells are strong scatterers of infrared radiation. Thus measured spectra consist of an absorption component often highly distorted by scattering effects.

A high-throughput microcultivation protocol for FTIR spectroscopic characterization and identification of fungi.

Characterization and identification of fungi in food industry is an important issue both for routine analysis and trouble-shooting incidences. Present microbial techniques for fungal characterization suffer from a low throughput and are time consuming. In this study we present a protocol for high-throughput microcultivation and spectral characterization of fungi by Fourier transform infrared spectroscopy.

Resonant Mie scattering (RMieS) correction of infrared spectra from highly scattering biological samples.

Infrared spectra of single biological cells often exhibit the 'dispersion artefact' observed as a sharp decrease in intensity on the high wavenumber side of absorption bands, in particular the Amide I band at approximately 1655 cm(-1), causing a downward shift of the true peak position. The presence of this effect makes any biochemical interpretation of the spectra unreliable. Recent theory has shed light on the origins of the 'dispersion artefact' which has been attributed to resonant Mie scattering (RMieS).

The genotype-phenotype relationship in multicellular pattern-generating models--the neglected role of pattern descriptors.

Background: A deep understanding of what causes the phenotypic variation arising from biological patterning processes, cannot be claimed before we are able to recreate this variation by mathematical models capable of generating genotype-phenotype maps in a causally cohesive way. However, the concept of pattern in a multicellular context implies that what matters is not the state of every single cell, but certain emergent qualities of the total cell aggregate. Thus, in order to set up a genotype-phenotype map in such a spatiotemporal pattern setting one is actually forced to establish new pattern descriptors and derive their relations to parameters of the original model.

Improved dynamic range of protein quantification in silver-stained gels by modelling gel images over time.

Silver staining is a commonly used protein stain to visualise proteins separated by 2-DE. Despite this, the technique suffers from a limited dynamic range, making the simultaneous quantification of high- and low-abundant proteins difficult. In this paper we take advantage of the fact that silver staining is not an end-point stain by photographing the gels during development.

An improved pixel-based approach for analyzing images in two-dimensional gel electrophoresis.

An improved pixel-based approach for analyzing 2-DE images is presented. The key feature of the method is to create a mask based on all gels in the experiment using image morphology, followed by multivariate analysis on the pixel level. The method reduces the impact of noise and background by identifying regions in the image where protein spots are present, but make no assumption on individual spot boundaries for isolated spots.