Pan-Cancer Analysis Identifies MNX1 and Associated Antisense Transcripts as Biomarkers for Cancer

The identification of diagnostic and prognostic biomarkers is a major objective in improving clinical outcomes in cancer, which has been facilitated by the availability of high-throughput gene expression data. A growing interest in non-coding genomic regions has identified dysregulation of long non-coding RNAs (lncRNAs) in several malignancies, suggesting a potential use as biomarkers. In this study, we leveraged data from large-scale sequencing projects to uncover the expression patterns of the MNX1 gene and its associated lncRNAs MNX1-AS1 and MNX1-AS2 in solid tumours. Despite many reports describing MNX1 overexpression in several cancers, limited studies exist on MNX1-AS1 and MNX1-AS2 and their potential as biomarkers. By employing clustering methods to visualise multi-gene relationships, we identified a discriminative power of the three genes in distinguishing tumour vs. normal samples in several cancers of the gastrointestinal tract and reproductive systems, as well as in discerning oesophageal and testicular cancer histological subtypes. Notably, the expressions of MNX1 and its antisenses also correlated with clinical features and endpoints, uncovering previously unreported associations. This work highlights the advantages of using combinatory expression patterns of non-coding transcripts of differentially expressed genes as clinical evaluators and identifies MNX1, MNX1-AS1, and MNX1-AS2 expressions as robust candidate biomarkers for clinical applicationsD.R. is the recipient of a Kidscan funded PhD studentship and partly supported by Brunel University Londo

Identification of DC thermal steady-state differential inductance of ferrite power inductors

In this paper, we propose a method for the identification of the differential inductance of saturable ferrite inductors adopted in DC–DC converters, considering the influence of the operating temperature. The inductor temperature rise is caused mainly by its losses, neglecting the heating contribution by the other components forming the converter layout. When the ohmic losses caused by the average current represent the principal portion of the inductor power losses, the steady-state temperature of the component can be related to the average current value. Under this assumption, usual for saturable inductors in DC–DC converters, the presented experimental setup and characterization method allow identifying a DC thermal steady-state differential inductance profile of a ferrite inductor. The curve is obtained from experimental measurements of the inductor voltage and current waveforms, at different average current values, that lead the component to operate from the linear region of the magnetization curve up to the saturation. The obtained inductance profile can be adopted to simulate the current waveform of a saturable inductor in a DC–DC converter, providing accurate results under a wide range of switching frequency, input voltage, duty cycle, and out-put current values

Remote Sensing and the Earth

A text book on remote sensing, as part of the earth resources Skylab programs, is presented. The fundamentals of remote sensing and its application to agriculture, land use, geology, water and marine resources, and environmental monitoring are summarized

Data-Driven Constraint Handling in Multi-Objective Inductor Design

This paper analyses the multi-objective design of an inductor for a DC-DC buck converter. The core volume and total losses are the two competing objectives, which should be minimised while satisfying the design constraints on the required differential inductance profile and the maximum overheating. The multi-objective optimisation problem is solved by means of a population-based metaheuristic algorithm based on Artificial Immune Systems (AIS). Despite its effectiveness in finding the Pareto front, the algorithm requires the evaluation of many candidate solutions before converging. In the case of the inductor design problem, the evaluation of a configuration is time-consuming. In fact, a non-linear iterative technique (fixed point) is needed to obtain the differential inductance profile of the configuration, as it may operate in conditions of partial saturation. However, many configurations evaluated during an optimisation do not comply with the design constraint, resulting in expensive and unnecessary calculations. Therefore, this paper proposes the adoption of a data-driven surrogate model in a pre-selection phase of the optimisation. The adopted model should classify newly generated configurations as compliant or not with the design constraint. Configurations classified as unfeasible are disregarded, thus avoiding the computational burden of their complete evaluation. Interesting results have been obtained, both in terms of avoided configuration evaluations and the quality of the Pareto front found by the optimisation procedure

Anisotropy of losses in grain-oriented Fe-Si

Comprehensive assessment of the magnetic behavior of grain-oriented steel (GO) Fe-Si sheets, going beyond the conventional characterization at power frequencies along the rolling direction (RD), can be the source of much needed information for the optimal design of transformers and efficient rotating machines. However, the quasi-monocrystal character of the material is conducive, besides an obviously strong anisotropic response, to a dependence of the measured properties on the sample geometry whenever the field is applied along a direction different from the rolling and the transverse (TD) directions. In this work, we show that the energy losses, measured from 1 to 300 Hz on GO sheets cut along directions ranging from 0° to 90° with respect to RD, can be interpreted in terms of linear composition of the same quantities measured along RD and TD. This feature, which applies to both the DC and AC properties, resides on the sample geometry-independent character of the RD and TD magnetization and on the loss separation principle. This amounts to state that, as substantiated by magneto-optical observations, the very same domain wall mechanisms making the magnetization to evolve in the RD and TD sheets, respectively, independently combine and operate in due proportions in all the other cases. By relying on these concepts, which overcome the limitations inherent to the semi-empirical models of the literature, we can consistently describe the magnetic losses as a function of cutting angle and stacking fashion of GO strips at different peak polarization levels and different frequencies

Full Micromagnetic Numerical Simulations of Thermal Fluctuations

Thermal fluctuations for fine ferromagnetic particles are studied with the full micromagnetic analysis based on numerical integration of the spatially discretized Langevin-Landau-Lifshitz equation. These results can be used as a basis for the formulation of a standard problem to test the implementation of thermal fluctuations in numerical micromagnetic codes. To this end an example of micromagnetic analysis of thermal fluctuations in an ellipsoidal magnetic nanoparticle is presented