► We present a totally developed monitoring system for elderly people. ► We address how to model elderly people at home and their context. ► The model consists of a simple automaton whose transitions ...are modeled by semantic web rules expressed in SWRL. ► An adaptive mechanism which takes routines of the subject into account is created to detect abnormal situations. ► The system is working now in real houses as a final product.
In this work, the process followed for the development of a specific Ambient Assisted Living system is presented. The proposed systems has been designed to monitor elders which live alone and want to keep living independently. The process covers all the phases in intelligent system development: requirement analysis, conceptual model specification, architectural design and evaluation. One of the main contributions of the proposed work is an exhaustive evaluation methodology that is integrated in the development process. A relevant characteristic of the evaluation process is that, from initial phases, commercial presentations of the products functionalities is possible. Another important contribution is related with the capability of the system to adapt its behavior to that of the monitored elder. The presented system is called Necesity. It has become a commercial product which is already working and giving service to elders in the South-East of Spain.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning ...of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn ).
We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices.
The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Genome-wide association studies have reported that, amongst other microglial genes, variants in TREM2 can profoundly increase the incidence of developing Alzheimer's disease (AD). We have ...investigated the role of TREM2 in primary microglial cultures from wild type mice by using siRNA to decrease Trem2 expression, and in parallel from knock-in mice heterozygous or homozygous for the Trem2 R47H AD risk variant. The prevailing phenotype of Trem2 R47H knock-in mice was decreased expression levels of Trem2 in microglia, which resulted in decreased density of microglia in the hippocampus. Overall, primary microglia with reduced Trem2 expression, either by siRNA or from the R47H knock-in mice, displayed a similar phenotype. Comparison of the effects of decreased Trem2 expression under conditions of lipopolysaccharide (LPS) pro-inflammatory or IL-4 anti-inflammatory stimulation revealed the importance of Trem2 in driving a number of the genes up-regulated in the anti-inflammatory phenotype. RNA-seq analysis showed that IL-4 induced the expression of a program of genes including Arg1 and Ap1b1 in microglia, which showed an attenuated response to IL-4 when Trem2 expression was decreased. Genes showing a similar expression profile to Arg1 were enriched for STAT6 transcription factor recognition elements in their promoter, and Trem2 knockdown decreased levels of STAT6. LPS-induced pro-inflammatory stimulation suppressed Trem2 expression, thus preventing TREM2's anti-inflammatory drive. Given that anti-inflammatory signaling is associated with tissue repair, understanding the signaling mechanisms downstream of Trem2 in coordinating the pro- and anti-inflammatory balance of microglia, particularly mediating effects of the IL-4-regulated anti-inflammatory pathway, has important implications for fighting neurodegenerative disease.
Recently, we reported oligoadenylate synthetase 1 (OAS1) contributed to the risk of Alzheimer's disease, by its enrichment in transcriptional networks expressed by microglia. However, the function of ...OAS1 within microglia was not known. Using genotyping from 1313 individuals with sporadic Alzheimer's disease and 1234 control individuals, we confirm the OAS1 variant, rs1131454, is associated with increased risk for Alzheimer's disease. The same OAS1 locus has been recently associated with severe coronavirus disease 2019 (COVID-19) outcomes, linking risk for both diseases. The single nucleotide polymorphisms rs1131454(A) and rs4766676(T) are associated with Alzheimer's disease, and rs10735079(A) and rs6489867(T) are associated with severe COVID-19, where the risk alleles are linked with decreased OAS1 expression. Analysing single-cell RNA-sequencing data of myeloid cells from Alzheimer's disease and COVID-19 patients, we identify co-expression networks containing interferon (IFN)-responsive genes, including OAS1, which are significantly upregulated with age and both diseases. In human induced pluripotent stem cell-derived microglia with lowered OAS1 expression, we show exaggerated production of TNF-α with IFN-γ stimulation, indicating OAS1 is required to limit the pro-inflammatory response of myeloid cells. Collectively, our data support a link between genetic risk for Alzheimer's disease and susceptibility to critical illness with COVID-19 centred on OAS1, a finding with potential implications for future treatments of Alzheimer's disease and COVID-19, and development of biomarkers to track disease progression.
Mesial temporal lobe epilepsy with hippocampal sclerosis represents the most common epilepsy syndrome in adult patients with medically intractable partial epilepsy. Mesial temporal lobe epilepsy is ...usually regarded as a polygenic and complex disorder, still poorly understood but probably caused and perpetuated by dysregulation of numerous biological networks and cellular functions. The study of gene expression changes by single nucleotide polymorphisms in regulatory elements (expression quantitative trait loci, eQTLs) has been shown to be a powerful complementary approach to the detection and understanding of risk loci by genome-wide association studies. We performed a whole (gene and exon-level) transcriptome analysis on cortical tissue samples (Brodmann areas 20 and 21) from 86 patients with mesial temporal lobe epilepsy with hippocampal sclerosis and 75 neurologically healthy controls. Genome-wide genotyping data from the same individuals (patients and controls) were analysed and paired with the transcriptome data. We report potential epilepsy-risk eQTLs, some of which are specific to tissue from patients with mesial temporal lobe epilepsy with hippocampal sclerosis. We also found large transcriptional and splicing deregulation in mesial temporal lobe epilepsy with hippocampal sclerosis tissue as well as gene networks involving neuronal and glial mechanisms that provide new insights into the cause and maintenance of the seizures. These data (available via the 'Seizubraineac' web-tool resource, www.seizubraineac.org) will facilitate the identification of new therapeutic targets and biomarkers as well as genetic risk variants that could influence epilepsy and pharmacoresistance.
There is growing evidence for the importance of 3' untranslated region (3'UTR) dependent regulatory processes. However, our current human 3'UTR catalogue is incomplete. Here, we develop a machine ...learning-based framework, leveraging both genomic and tissue-specific transcriptomic features to predict previously unannotated 3'UTRs. We identify unannotated 3'UTRs associated with 1,563 genes across 39 human tissues, with the greatest abundance found in the brain. These unannotated 3'UTRs are significantly enriched for RNA binding protein (RBP) motifs and exhibit high human lineage-specificity. We find that brain-specific unannotated 3'UTRs are enriched for the binding motifs of important neuronal RBPs such as TARDBP and RBFOX1, and their associated genes are involved in synaptic function. Our data is shared through an online resource F3UTER ( https://astx.shinyapps.io/F3UTER/ ). Overall, our data improves 3'UTR annotation and provides additional insights into the mRNA-RBP interactome in the human brain, with implications for our understanding of neurological and neurodevelopmental diseases.
Despite the wealth of genomic and transcriptomic data in Parkinson’s disease (PD), the initial molecular events are unknown. Using LD score regression analysis, we show significant enrichment in PD ...heritability within regulatory sites for LPS-activated monocytes and that TLR4 expression is highest within human substantia nigra, the most affected brain region, suggesting a role for TLR4 inflammatory responses. We then performed extended incubation of cells with physiological concentrations of small alpha-synuclein oligomers observing the development of a TLR4-dependent sensitized inflammatory response with time, including TNF-α production. ROS and cell death in primary neuronal cultures were significantly reduced by TLR4 antagonists revealing that an indirect inflammatory mechanism involving cytokines produced by glial cells makes a major contribution to neuronal death. Prolonged exposure to low levels of alpha-synuclein oligomers sensitizes TLR4 responsiveness in astrocytes and microglial, explaining how they become pro-inflammatory, and may be an early causative event in PD.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Anthozoans (e.g., corals, anemones) are an ecologically important and diverse group of marine metazoans that occur from shallow to deep waters worldwide. However, our understanding of the ...evolutionary relationships among the ~7,500 species within this class is hindered by the lack of phylogenetically informative markers that can be reliably sequenced across a diversity of taxa. We designed and tested 16,306 RNA baits to capture 720 ultraconserved element loci and 1,071 exon loci. Library preparation and target enrichment were performed on 33 taxa from all orders within the class Anthozoa. Following Illumina sequencing and Trinity assembly, we recovered 1,774 of 1,791 targeted loci. The mean number of loci recovered from each species was 638 ± 222, with more loci recovered from octocorals (783 ± 138 loci) than hexacorals (475 ± 187 loci). Parsimony informative sites ranged from 26 to 49% for alignments at differing hierarchical taxonomic levels (e.g., Anthozoa, Octocorallia, Hexacorallia). The per cent of variable sites within each of three genera (Acropora, Alcyonium, and Sinularia) for which multiple species were sequenced ranged from 4.7% to 30%. Maximum‐likelihood analyses recovered highly resolved trees with topologies matching those supported by other studies, including the monophyly of the order Scleractinia. Our results demonstrate the utility of this target‐enrichment approach to resolve phylogenetic relationships from relatively old to recent divergences. Redesigning the baits with improved affinities to capture loci within each subclass will provide a valuable toolset to address systematic questions, further our understanding of the timing of diversifications and help resolve long‐standing controversial relationships in the class Anthozoa.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their ...attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. In this paper, we propose two new multivariate feature ranking methods based on pairwise correlation and pairwise consistency, which have been applied for cancer gene expression and genotype-tissue expression classification tasks using public datasets. We statistically proved that the proposed methods outperform the state-of-the-art feature ranking methods Clustering Variation , Chi Squared , Correlation , Information Gain , ReliefF and Significance , as well as other feature selection methods for attribute subset evaluation based on correlation and consistency with the multi-objective evolutionary search strategy, and with the embedded feature selection methods C4.5 and LASSO . The proposed methods have been implemented on the WEKA platform for public use, making all the results reported in this paper repeatable and replicable.
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