The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling

Effects of adiponectin on breast cancer cell growth and signaling

Obesity is a risk factor for postmenopausal breast cancer. Adiponectin/Acrp30 is lower in obese individuals and may be negatively regulating breast cancer growth. Here we determined that five breast cancer cell lines, MDA-MB-231, MDA-MB-361, MCF-7, T47D, and SK-BR-3, expressed one or both of the Acrp30 receptors. In addition, we found that the addition of Acrp30 to MCF-7, T47D, and SK-BR-3 cell lines inhibited growth. Oestrogen receptor (ER) positive MCF-7 and T47D cells were inhibited at lower Acrp30 concentrations than ER-negative SK-BR-3 cells. Growth inhibition may be related to apoptosis since PARP cleavage was increased by Acrp30 in the ER-positive cell lines. To investigate the role of ER in the response of breast cancer cells to Acrp30, we established the MDA-ERα7 cell line by insertion of ER-α into ER-α-negative MDA-MB-231 cells. This line readily formed tumours in athymic mice and was responsive to oestradiol in vivo. In vitro, MDA-ERα7 cells were growth inhibited by globular Acrp30 while the parental cells were not. This inhibition appeared to be due to blockage of JNK2 signalling. These results provide information on how obesity may influence breast cancer cell proliferation and establish a new model to examine interactions between ER and Acrp30

Analysis of Ultrasonic Waves in Arbitrarily Oriented or Rotating Anisotropic Thin Materials

Much work has been performed modeling thin anisotropic materials for use in nondestructive testing with plane wave excitation and detection [1–5]. There has been interest in the development of dispersion curve inversion and group velocity inversion procedures [6–8]. The general approach in many of these techniques is to align the excitation and detection along a known crystal axis, and analyze the detected signal to determine elastic constants, bond quality, thickness, or any number of other parameters [9–12]. Unfortunately, most of these approaches will fail to perform adequately when the target’s crystal axis is rotating with respect to the excitation axis, or when the coordinate frame of the crystal axis is unknown.</p

Analysis of Dispersive Ultrasonic Signals by the Ridges of the Analytic Wavelet Transform

In many applications, material parameters are extracted by comparing theoretical models of ultrasonic propagation with experimental measurements. A particular challenge arises in the case of thin materials, for which the ultrasonic signal is dispersive and multiple propagation modes are commonly present. The purpose of this paper is to provide an overview of the applicability of the analytic wavelet transform technique to the analysis of the propagation of dispersive ultrasonic waves. Ridges in the modulus of the transform determine regions in the time and frequency domain with high concentrations of acoustic energy; hence they are the natural candidates for the characterization and reconstruction of the ultrasonic signal. This approach results in a time-frequency representation of the ultrasonic signal that is extremely useful in the characterization of thin coatings or thin plates. The fundamentals and experimental results are presented to show the usefulness of the proposed technique.</p

Nondestructive Determination of Thickness and Elastic Modulus of Plasma Spray Coatings Using Laser Ultrasonics

Plasma sprayed coatings are widely used to protect parts from aggressive environments. In applications such as land-based gas turbines, Thermal barrier Coatings (TBCs) are utilized to protect the turbine components from very high operating or firing temperatures [1]. The TBCs are commonly applied by standard air plasma spray process, which is an open-loop operation with no feedback about the coating conditions during deposition. Unfortunately, on-line variations of the spray conditions, such as the continuous wearing of the torch hardware, can adversely affect the coating quality and create significant part-to-part variations. The standard method of evaluating coatings is destructive in nature; hence these tests cannot be performed on each produced part [2]. As a result, coated parts may not have the consistent quality and durability needed for many applications [3].</p