Principle and Applications of Thermoelectric Generators: A Review.

For an extensive and sustainable deployment of technological ecosystems such as the Internet of Things, it is a must to leverage the free energy available in the environment to power the autonomous sensors. Among the different alternatives, thermal energy harvesters based on thermoelectric generators (TEGs) are an attractive solution for those scenarios in which a gradient of temperature is present. In such a context, this article reviews the operating principle of TEGs and then the applications proposed in the literature in the last years.

A framework for block-wise missing data in multi-omics.

High-throughput technologies have generated vast amounts of omic data. It is a consensus that the integration of diverse omics sources improves predictive models and biomarker discovery. However, managing multiple omics data poses challenges such as data heterogeneity, noise, high-dimensionality and missing data, especially in block-wise patterns.

Exploring functional conservation in silico: a new machine learning approach to RNA-editing.

Around 50 years ago, molecular biology opened the path to understand changes in forms, adaptations, complexity, or the basis of human diseases through myriads of reports on gene birth, gene duplication, gene expression regulation, and splicing regulation, among other relevant mechanisms behind gene function. Here, with the advent of big data and artificial intelligence (AI), we focus on an elusive and intriguing mechanism of gene function regulation, RNA editing, in which a single nucleotide from an RNA molecule is changed, with a remarkable impact in the increase of the complexity of the transcriptome and proteome. We present a new generation approach to assess the functional conservation of the RNA-editing targeting mechanism using two AI learning algorithms, random forest (RF) and bidirectional long short-term memory (biLSTM) neural networks with an attention layer.

A Tutorial on Mechanical Sensors in the 70th Anniversary of the Piezoresistive Effect.

An outstanding event related to the understanding of the physics of mechanical sensors occurred and was announced in 1954, exactly seventy years ago. This event was the discovery of the piezoresistive effect, which led to the development of semiconductor strain gauges with a sensitivity much higher than that obtained before in conventional metallic strain gauges. In turn, this motivated the subsequent development of the earliest micromachined silicon devices and the corresponding MEMS devices.

Two Proposals of a Simple Analog Conditioning Circuit for Remote Resistive Sensors with a Three-Wire Connection.

This article proposes and experimentally characterizes two implementations of a novel front-end circuit for three-wire connected resistive sensors with a wire-resistance compensation. The first implementation relies on two twin diodes, whereas the second on a switch; in both cases, those devices are non-remote (i.e.

A Front-End Circuit for Two-Wire Connected Resistive Sensors with a Wire-Resistance Compensation.

In this article, a novel front-end circuit for remote two-wire resistive sensors that is insensitive to the wire resistances is proposed and experimentally characterized. The circuit relies on an OpAmp-based current source with a square-wave excitation, two twin diodes in the feedback path, and a low-pass filter at the output. Using such a circuit topology, the output is a DC voltage proportional to the sensor resistance and independent of the wire resistances.

A fast non-parametric test of association for multiple traits.

The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations to assess significance, which hinders the analysis of large datasets.

Single-cell profiling of lncRNA expression during Ebola virus infection in rhesus macaques.

Long non-coding RNAs (lncRNAs) are involved in numerous biological processes and are pivotal mediators of the immune response, yet little is known about their properties at the single-cell level. Here, we generate a multi-tissue bulk RNAseq dataset from Ebola virus (EBOV) infected and not-infected rhesus macaques and identified 3979 novel lncRNAs. To profile lncRNA expression dynamics in immune circulating single-cells during EBOV infection, we design a metric, Upsilon, to estimate cell-type specificity.

The landscape of expression and alternative splicing variation across human traits.

Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare.

A mixture of four dietary fibres ameliorates adiposity and improves metabolic profile and intestinal health in cafeteria-fed obese rats: an integrative multi-omics approach.

The aim of this study was to assess the effects of a mixture of four dietary fibers on obese rats. Four groups of male Wistar rats were fed with either standard chow (STD) or cafeteria diet (CAF) and were orally supplemented with either fibre mixture (2 g kg of body weight) (STD+F or CAF+F groups) or vehicle (STD+VH or CAF+VH groups). We studied a wide number of biometric, biochemical, transcriptomic, metagenomic and metabolomic variables and applied an integrative multivariate approach based on multiple factor analysis and Pearson's correlation analysis.

BPF-Based Thermal Sensor Circuit for On-Chip Testing of RF Circuits.

A new sensor topology meant to extract figures of merit of radio-frequency analog integrated circuits (RF-ICs) was experimentally validated. Implemented in a standard 0.35 μm complementary metal-oxide-semiconductor (CMOS) technology, it comprised two blocks: a single metal-oxide-semiconductor (MOS) transistor acting as temperature transducer, which was placed near the circuit to monitor, and an active band-pass filter amplifier.

Identification and analysis of splicing quantitative trait loci across multiple tissues in the human genome.

Alternative splicing (AS) is a fundamental step in eukaryotic mRNA biogenesis. Here, we develop an efficient and reproducible pipeline for the discovery of genetic variants that affect AS (splicing QTLs, sQTLs). We use it to analyze the GTEx dataset, generating a comprehensive catalog of sQTLs in the human genome.

The impact of sex on gene expression across human tissues.

Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release).

Correction to: Enhancing SVM for survival data using local invariances and weighting.

An amendment to this paper has been published and can be accessed via the original article.

A limited set of transcriptional programs define major cell types.

We have produced RNA sequencing data for 53 primary cells from different locations in the human body. The clustering of these primary cells reveals that most cells in the human body share a few broad transcriptional programs, which define five major cell types: epithelial, endothelial, mesenchymal, neural, and blood cells. These act as basic components of many tissues and organs.

Enhancing SVM for survival data using local invariances and weighting.

Background: The necessity to analyze medium-throughput data in epidemiological studies with small sample size, particularly when studying biomedical data may hinder the use of classical statistical methods. Support vector machines (SVM) models can be successfully applied in this setting because they are a powerful tool to analyze data with large number of predictors and limited sample size, especially when handling binary outcomes. However, biomedical research often involves analysis of time-to-event outcomes and has to account for censoring.

A Compact Thévenin Model for a Rectenna and Its Application to an RF Harvester with MPPT.

This paper proposes a compact Thévenin model for a rectenna. This model is then applied to design a high-efficiency radio frequency harvester with a maximum power point tracker (MPPT). The rectenna under study consists of an L-matching network and a half-wave rectifier.

Seat Occupancy Detection Based on a Low-Power Microcontroller and a Single FSR.

This paper proposes a microcontroller-based measurement system to detect and confirm the presence of a subject in a chair. The system relies on a single Force Sensing Resistor (FSR), which is arranged in the seat of the chair, that undergoes a sudden resistance change when a subject/object is seated/placed over the chair. In order to distinguish between a subject and an inanimate object, the system also monitors small-signal variations of the FSR resistance caused by respiration.

SVM-RFE: selection and visualization of the most relevant features through non-linear kernels.

Background: Support vector machines (SVM) are a powerful tool to analyze data with a number of predictors approximately equal or larger than the number of observations. However, originally, application of SVM to analyze biomedical data was limited because SVM was not designed to evaluate importance of predictor variables. Creating predictor models based on only the most relevant variables is essential in biomedical research.

Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk.

Coding variants represent many of the strongest associations between genotype and phenotype; however, they exhibit inter-individual differences in effect, termed 'variable penetrance'. Here, we study how cis-regulatory variation modifies the penetrance of coding variants. Using functional genomic and genetic data from the Genotype-Tissue Expression Project (GTEx), we observed that in the general population, purifying selection has depleted haplotype combinations predicted to increase pathogenic coding variant penetrance.

Measuring Dynamic Signals with Direct Sensor-to-Microcontroller Interfaces Applied to a Magnetoresistive Sensor.

This paper evaluates the performance of direct interface circuits (DIC), where the sensor is directly connected to a microcontroller, when a resistive sensor subjected to dynamic changes is measured. The theoretical analysis provides guidelines for the selection of the components taking into account both the desired resolution and the bandwidth of the input signal. Such an analysis reveals that there is a trade-off between the sampling frequency and the resolution of the measurement, and this depends on the selected value of the capacitor that forms the RC circuit together with the sensor resistance.

Discovery of Cancer Driver Long Noncoding RNAs across 1112 Tumour Genomes: New Candidates and Distinguishing Features.

Long noncoding RNAs (lncRNAs) represent a vast unexplored genetic space that may hold missing drivers of tumourigenesis, but few such "driver lncRNAs" are known. Until now, they have been discovered through changes in expression, leading to problems in distinguishing between causative roles and passenger effects. We here present a different approach for driver lncRNA discovery using mutational patterns in tumour DNA.

Extreme genomic erosion after recurrent demographic bottlenecks in the highly endangered Iberian lynx.

Background: Genomic studies of endangered species provide insights into their evolution and demographic history, reveal patterns of genomic erosion that might limit their viability, and offer tools for their effective conservation. The Iberian lynx (Lynx pardinus) is the most endangered felid and a unique example of a species on the brink of extinction.

Results: We generate the first annotated draft of the Iberian lynx genome and carry out genome-based analyses of lynx demography, evolution, and population genetics.

Inferring differentially expressed pathways using kernel maximum mean discrepancy-based test.

Background: Pathway expression is multivariate in nature. Thus, from a statistical perspective, to detect differentially expressed pathways between two conditions, methods for inferring differences between mean vectors need to be applied. Maximum mean discrepancy (MMD) is a statistical test to determine whether two samples are from the same distribution, its implementation being greatly simplified using the kernel method.