Wearable Devices Based on Bioimpedance Test in Heart Failure: Clinical Relevance: Systematic Review.

Background: Heart failure (HF) represents a frequent cause of hospital admission, with fluid overload directly contributing to decompensations. Bioimpedance (BI), a physical parameter linked to tissue hydration status, holds promise in monitoring congestion and improving prognosis. This systematic review aimed to assess the clinical relevance of BI-based wearable devices for HF fluid monitoring.

A Pix2Pix Architecture for Complete Offline Handwritten Text Normalization.

In the realm of offline handwritten text recognition, numerous normalization algorithms have been developed over the years to serve as preprocessing steps prior to applying automatic recognition models to handwritten text scanned images. These algorithms have demonstrated effectiveness in enhancing the overall performance of recognition architectures. However, many of these methods rely heavily on heuristic strategies that are not seamlessly integrated with the recognition architecture itself.

Training Scientific Communication Skills on Medical Imaging within the Virtual World Second Life: Perception of Biomedical Engineering Students.

Second Life is a multi-user virtual world platform which enables online learning through immersive activities. This study evaluates the perception of third-year biomedical engineering students about learning activities complementary to a biomedical imaging course carried out within Second Life and focused on training in the public presentation of scientific content to their peers. Between 2015 and 2017, students gave oral presentations on medical imaging topics selected from the proposals of their classmates.

Reliability Study of Inertial Sensors LIS2DH12 Compared to ActiGraph GT9X: Based on Free Code.

The study’s purpose was to assess the reliability of the LIS2DH12 in two different positions, using the commercial sensor Actigraph GT9X as a reference instrument. Five participants completed two gait tests on a treadmill. Firstly, both sensors were worn on the wrist and around the thigh.

Citation Classics in Consumer Neuroscience, Neuromarketing and Neuroaesthetics: Identification and Conceptual Analysis.

Neuromarketing, consumer neuroscience and neuroaesthetics are a broad research area of neuroscience with an extensive background in scientific publications. Thus, the present study aims to identify the highly cited papers (HCPs) in this research field, to deliver a summary of the academic work produced during the last decade in this area, and to show patterns, features, and trends that define the past, present, and future of this specific area of knowledge. The HCPs show a perspective of those documents that, historically, have attracted great interest from a research community and that could be considered as the basis of the research field.

Direct solid sampling for speciation of Zn and ZnO nanoparticles in cosmetics by graphite furnace atomic absorption spectrometry.

The application of nanoparticles (NPs) in science and technology is a fast growing field. Therefore, reliable and straightforward analytical methods are required for their fast determination in different types of samples. This work investigates a method that enables the determination of ZnO NPs, discriminating them from ionic zinc in cosmetic samples.

Sentiment Analysis and Emotion Understanding during the COVID-19 Pandemic in Spain and Its Impact on Digital Ecosystems.

COVID-19 has changed our lives forever. The world we knew until now has been transformed and nowadays we live in a completely new scenario in a perpetual restructuring transition, in which the way we live, relate, and communicate with others has been altered permanently. Within this context, risk communication is playing a decisive role when informing, transmitting, and channeling the flow of information in society.

Facilitating the design of HL7 domain models through a model-driven solution.

Background And Goal: Health information systems are increasingly sophisticated and developing them is a challenge for software developers. Software engineers usually make use of UML as a standard model language that allows defining health information system entities and their relations. However, working with health system requires learning HL7 standards, that defines and manages standards related to health information systems.

Method for Detecting Abnormal Activity in a Group of Mobile Robots.

The range of attacks implemented on wireless networks is quite wide. To avoid or reduce the likelihood of an attack, it is necessary to use various defense mechanisms. Existing protection mechanisms are not always suitable for robotic systems and may not fully provide the necessary level of security.

Graph coloring for extracting discriminative genes in cancer data.

Background And Objective: The major difficulty of the analysis of the input gene expression data in a microarray-based approach for an automated diagnosis of cancer is the large number of genes (high dimensionality) with many irrelevant genes (noise) compared to the very small number of samples. This research study tackles the dimensionality reduction challenge in this area.

Methods: This research study introduces a dimension-reduction technique termed graph coloring approach (GCA) for microarray data-based cancer classification based on analyzing the absolute correlation between gene-gene pairs and partitioning genes into several hubs using graph coloring.

WARCProcessor: An Integrative Tool for Building and Management of Web Spam Corpora.

In this work we present the design and implementation of WARCProcessor, a novel multiplatform integrative tool aimed to build scientific datasets to facilitate experimentation in web spam research. The developed application allows the user to specify multiple criteria that change the way in which new corpora are generated whilst reducing the number of repetitive and error prone tasks related with existing corpus maintenance. For this goal, WARCProcessor supports up to six commonly used data sources for web spam research, being able to store output corpus in standard WARC format together with complementary metadata files.

RUbioSeq+: A multiplatform application that executes parallelized pipelines to analyse next-generation sequencing data.

Background And Objective: To facilitate routine analysis and to improve the reproducibility of the results, next-generation sequencing (NGS) analysis requires intuitive, efficient and integrated data processing pipelines.

Methods: We have selected well-established software to construct a suite of automated and parallelized workflows to analyse NGS data for DNA-seq (single-nucleotide variants (SNVs) and indels), CNA-seq, bisulfite-seq and ChIP-seq experiments.

Results: Here, we present RUbioSeq+, an updated and extended version of RUbioSeq, a multiplatform application that incorporates a suite of automated and parallelized workflows to analyse NGS data.

Improving the text classification using clustering and a novel HMM to reduce the dimensionality.

In text classification problems, the representation of a document has a strong impact on the performance of learning systems. The high dimensionality of the classical structured representations can lead to burdensome computations due to the great size of real-world data. Consequently, there is a need for reducing the quantity of handled information to improve the classification process.

Classifying patients in peritoneal dialysis by mass spectrometry-based profiling.

Protein equalization with dithiothreitol, protein depletion with acetonitrile and the entire proteome were assessed in conjunction with matrix assisted laser desorption ionization time of flight mass spectrometry-based profiling for a fast and effective classification of patients with renal insufficiency. Two case groups were recruited as proof of concept, patients with chronic glomerulonephritis and diabetic nephropathy. Two key tools were used to develop this approach: protein concentration with centrifugal concentrator tubes with 10 KDa cut-off membranes and chemical assisted protein equalization with dithiothreitol or chemical assisted protein depletion with acetonitrile.

A methodological approach based on gold-nanoparticles followed by matrix assisted laser desorption ionization time of flight mass spectrometry for the analysis of urine profiling of knee osteoarthritis.

The aim of this work is to develop a nanoparticle-based methodology to find out biomarkers of diagnostic for knee osteoarthritis, KOA, through the use of matrix assisted laser desorption ionization time-of-flight-based mass spectrometry profiling. Urine samples used for this study were obtained from KOA patients (42 patients), patients with prosthesis (58 patients), and controls (36 individuals) with no history of joint disease. Gold-nano particle MALDI-based urine profiling was optimized and then applied over the 136 individuals.

A linear-RBF multikernel SVM to classify big text corpora.

Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility.

Using the eServices platform for detecting behavior patterns deviation in the elderly assisted living: a case study.

World's aging population is rising and the elderly are increasingly isolated socially and geographically. As a consequence, in many situations, they need assistance that is not granted in time. In this paper, we present a solution that follows the CRISP-DM methodology to detect the elderly's behavior pattern deviations that may indicate possible risk situations.

A distributed multiagent system architecture for body area networks applied to healthcare monitoring.

In the last years the area of health monitoring has grown significantly, attracting the attention of both academia and commercial sectors. At the same time, the availability of new biomedical sensors and suitable network protocols has led to the appearance of a new generation of wireless sensor networks, the so-called wireless body area networks. Nowadays, these networks are routinely used for continuous monitoring of vital parameters, movement, and the surrounding environment of people, but the large volume of data generated in different locations represents a major obstacle for the appropriate design, development, and deployment of more elaborated intelligent systems.

A mobile Virtual Butler to bridge the gap between users and ambient assisted living: a Smart Home case study.

Ambient Intelligence promises to transform current spaces into electronic environments that are responsive, assistive and sensitive to human presence. Those electronic environments will be fully populated with dozens, hundreds or even thousands of connected devices that share information and thus become intelligent. That massive wave of electronic devices will also invade everyday objects, turning them into smart entities, keeping their native features and characteristics while seamlessly promoting them to a new class of thinking and reasoning everyday objects.

A ubiquitous and low-cost solution for movement monitoring and accident detection based on sensor fusion.

The low average birth rate in developed countries and the increase in life expectancy have lead society to face for the first time an ageing situation. This situation associated with the World's economic crisis (which started in 2008) forces the need of equating better and more efficient ways of providing more quality of life for the elderly. In this context, the solution presented in this work proposes to tackle the problem of monitoring the elderly in a way that is not restrictive for the life of the monitored, avoiding the need for premature nursing home admissions.

Sequential depletion coupled to C18 sequential extraction as a rapid tool for human serum multiple profiling.

Sequential chemical depletion of serum coupled to C18 sequential extraction of peptides as a rapid tool for human serum multiple profiling is herein presented. The methodology comprises depletion with DTT and then with ACN; the extract thus obtained is then summited to fast protein digestion using ultrasonic energy. The pool of peptides is subsequently concentrated using C18-based Zip-tips and the peptides are sequentially extracted using different concentrations of ACN.

Proteomics analysis of the peritoneal dialysate effluent reveals the presence of calcium-regulation proteins and acute inflammatory response.

Background: Peritoneal dialysis (PD) is a form of renal replacement used for advanced chronic kidney disease. PD effluent holds a great potential for biomarker discovery for diagnosis and prognosis. In this study a novel approach to unravelling the proteome of PD effluent based-on dithiothreitol depletion followed by 2D-SDS-PAGE and protein identification using tandem mass spectrometry is proposed.

Study of query expansion techniques and their application in the biomedical information retrieval.

Information Retrieval focuses on finding documents whose content matches with a user query from a large document collection. As formulating well-designed queries is difficult for most users, it is necessary to use query expansion to retrieve relevant information. Query expansion techniques are widely applied for improving the efficiency of the textual information retrieval systems.

Precipitation of phosphate minerals by microorganisms isolated from a fixed-biofilm reactor used for the treatment of domestic wastewater.

The ability of bacteria isolated from a fixed-film bioreactor to precipitate phosphate crystals for the treatment of domestic wastewater in both artificial and natural media was studied. When this was demonstrated in artificial solid media for crystal formation, precipitation took place rapidly, and crystal formation began 3 days after inoculation. The percentage of phosphate-forming bacteria was slightly higher than 75%.

Unobstructive Body Area Networks (BAN) for efficient movement monitoring.

The technological advances in medical sensors, low-power microelectronics and miniaturization, wireless communications and networks have enabled the appearance of a new generation of wireless sensor networks: the so-called wireless body area networks (WBAN). These networks can be used for continuous monitoring of vital parameters, movement, and the surrounding environment. The data gathered by these networks contributes to improve users' quality of life and allows the creation of a knowledge database by using learning techniques, useful to infer abnormal behaviour.