Search Results (14)

Learnability in Automated Driving (LiAD) is a neglected research topic, especially when considering the unpredictable and intricate ways humans learn to interact and use automated driving systems (ADS) over the sequence of time. Moreover, there is a scarcity of publications dedicated to LiAD (specifically extended learnability methods) to guide the scientific paradigm. As a result, this generates scientific discord and, thus, leaves many facets of long-term learning effects associated with automated driving in dire need of significant research courtesy. This, we believe, is a constraint to knowledge discovery on quality interaction design phenomena. In a sense, it is imperative to abstract knowledge on how long-term effects and learning effects may affect (negatively and positively) users’ learning and mental models. As well as induce changeable behavioural configurations and performances. In view of that, it may be imperative to examine operational concepts that may help researchers envision future scenarios with automation by assessing users’ learning ability, how they learn and what they learn over the sequence of time. As well as constructing a theory of effects (from micro, meso and macro perspectives), which may help profile ergonomic quality design aspects that stand the test of time. As a result, we reviewed the literature on learnability, which we mined for LiAD knowledge discovery from the experience perspective of long-term learning effects. Therefore, the paper offers the reader the resulting discussion points formulated under the Learnability Engineering Life Cycle. For instance, firstly, contextualisation of LiAD with emphasis on extended LiAD. Secondly, conceptualisation and operationalisation of the operational mechanics of LiAD as a concept in ergonomic quality engineering (with an introduction of Concepts for Applying Learnability Engineering (CALE) research based on LiAD knowledge discovery). Thirdly, the systemisation of implementable long-term research strategies towards comprehending behaviour modification associated with extended LiAD. As the vehicle industry revolutionises at a rapid pace towards automation and artificially intelligent (AI) systems, this knowledge is useful for illuminating and instructing quality interaction strategies and Quality Automated Driving (QAD). Full article

Currently, discovering subsequence anomalies in time series remains one of the most topical research problems. A subsequence anomaly refers to successive points in time that are collectively abnormal, although each point is not necessarily an outlier. Among numerous approaches to discovering subsequence anomalies, the discord concept is considered one of the best. A time series discord is intuitively defined as a subsequence of a given length that is maximally far away from its non-overlapping nearest neighbor. Recently introduced, the MERLIN algorithm discovers time series discords of every possible length in a specified range, thereby eliminating the need to set even that sole parameter to discover discords in a time series. However, MERLIN is serial, and its parallelization could increase the performance of discord discovery. In this article, we introduce a novel parallelization scheme for GPUs called PALMAD, parallel arbitrary length MERLIN-based anomaly discovery. As opposed to its serial predecessor, PALMAD employs recurrent formulas we have derived to avoid redundant calculations, and advanced data structures for the efficient implementation of parallel processing. Experimental evaluation over real-world and synthetic time series shows that our algorithm outperforms parallel analogs. We also apply PALMAD to discover anomalies in a real-world time series, employing our proposed discord heatmap technique to illustrate the results. Full article

Identifying patients likely to develop breast cancer recurrence remains a challenge. Thus, the discovery of biomarkers capable of diagnosing recurrence is of the utmost importance. MiRNAs are small, non-coding RNA molecules which are known to regulate genetic expression and have previously demonstrated relevance as biomarkers in malignancy. To perform a systematic review evaluating the role of miRNAs in predicting breast cancer recurrence. A formal systematic search of PubMed, Scopus, Web of Science, and Cochrane databases was performed. This search was performed according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) checklist. A total of 19 studies involving 2287 patients were included. These studies identified 44 miRNAs which predicted breast cancer recurrence. Results from nine studies assessed miRNAs in tumour tissues (47.4%), eight studies included circulating miRNAs (42.1%), and two studies assessed both tumour and circulating miRNAs (10.5%). Increased expression of 25 miRNAs were identified in patients who developed recurrence, and decreased expression of 14 miRNAs. Interestingly, five miRNAs (miR-17-5p, miR-93-5p, miR-130a-3p, miR-155, and miR-375) had discordant expression levels, with previous studies indicating both increased and reduced expression levels of these biomarkers predicting recurrence. MiRNA expression patterns have the ability to predict breast cancer recurrence. These findings may be used in future translational research studies to identify patients with breast cancer recurrence to improve oncological and survival outcomes for our prospective patients. Full article

Circular RNAs (circRNAs) have gained recent attraction due to their functional versatility and particular structure connected to human diseases. Current investigations are focused on the interplay between their ability to sponge smaller species of RNAs, such as microRNAs (miRNAs), thus influencing their regulatory activity on gene expression and protein templates. Therefore, their reported implication in various biological processes axis has resulted in an accumulating number of studies. While the testing and annotation methods of novel circular transcripts are still under development, there is still a plethora of transcript candidates suitable for investigation in human disease. The discordance in the literature regarding the approaches used in circRNAs quantification and validation methods, especially regarding qRT-PCR, the current golden standard procedure, leads to high result variability and undermines the replicability of the studies. Therefore, our study will offer several valuable insights into bioinformatic data for experimental design for circRNA investigation and in vitro aspects. Specifically, we will highlight key aspects such as circRNA database annotation divergent primer design and several processing steps, such as RNAse R treatment optimization and circRNA enrichment assessment. Additionally, we will provide insights into the exploration of circRNA-miRNA interactions, a prerequisite for further functional investigations. With this, we aim to contribute to the methodological consensus in a currently expanding field with possible implications for assessing therapeutic targets and biomarker discovery. Full article

Currently, big sensor data arise in a wide spectrum of Industry 4.0, Internet of Things, and Smart City applications. In such subject domains, sensors tend to have a high frequency and produce massive time series in a relatively short time interval. The data collected from the sensors are subject to mining in order to make strategic decisions. In the article, we consider the problem of choosing a Time Series Database Management System (TSDBMS) to provide efficient storing and mining of big sensor data. We overview InfluxDB, OpenTSDB, and TimescaleDB, which are among the most popular state-of-the-art TSDBMSs, and represent different categories of such systems, namely native, add-ons over NoSQL systems, and add-ons over relational DBMSs (RDBMSs), respectively. Our overview shows that, at present, TSDBMSs offer a modest built-in toolset to mine big sensor data. This leads to the use of third-party mining systems and unwanted overhead costs due to exporting data outside a TSDBMS, data conversion, and so on. We propose an approach to managing and mining sensor data inside RDBMSs that exploits the Matrix Profile concept. A Matrix Profile is a data structure that annotates a time series through the index of and the distance to the nearest neighbor of each subsequence of the time series and serves as a basis to discover motifs, anomalies, and other time-series data mining primitives. This approach is implemented as a PostgreSQL extension that allows an application programmer both to compute matrix profiles and mining primitives and to represent them as relational tables. Experimental case studies show that our approach surpasses the above-mentioned out-of-TSDBMS competitors in terms of performance since it assumes that sensor data are mined inside a TSDBMS at no significant overhead costs. Full article

After 26 years of discovery of the determinant survival motor neuron 1 and the modifier survival motor neuron 2 genes (SMN1 and SMN2, respectively), three SMN-dependent specific therapies are already approved by FDA and EMA and, as a consequence, worldwide SMA patients are currently under clinical investigation and treatment. Bi-allelic pathogenic variants (mostly deletions) in SMN1 should be detected in SMA patients to confirm the disease. Determination of SMN2 copy number has been historically employed to correlate with the phenotype, predict disease evolution, stratify patients for clinical trials and to define those eligible for treatment. In view that discordant genotype-phenotype correlations are present in SMA, besides technical issues with detection of SMN2 copy number, we have hypothesized that copy number determination is only the tip of the iceberg and that more deepen studies of variants, sequencing and structures of the SMN2 genes are necessary for a better understanding of the disease as well as to investigate possible influences in treatment responses. Here, we highlight the importance of a comprehensive approach of SMN1 and SMN2 genetics with the perspective to apply for better prediction of SMA in positive neonatal screening cases and early diagnosis to start treatments. Full article

Epigenetic mechanisms may contribute to idiopathic scoliosis (IS). We identified 8 monozygotic twin pairs with IS, 6 discordant (Cobb angle difference > 10°) and 2 concordant (Cobb angle difference ≤ 2°). Genome-wide methylation in blood was measured with the Infinium HumanMethylation EPIC Beadchip. We tested for differences in methylation and methylation variability between discordant twins and tested the association between methylation and curve severity in all twins. Differentially methylated region (DMR) analyses identified gene promoter regions. Methylation at cg12959265 (chr. 7 DPY19L1) was less variable in cases (false discovery rate (FDR) = 0.0791). We identified four probes (false discovery rate, FDR < 0.10); cg02477677 (chr. 17, RARA gene), cg12922161 (chr. 2 LOC150622 gene), cg08826461 (chr. 2), and cg16382077 (chr. 7) associated with curve severity. We identified 57 DMRs where hyper- or hypo-methylation was consistent across the region and 28 DMRs with a consistent association with curve severity. Among DMRs, 21 were correlated with bone methylation. Prioritization of regions based on methylation concordance in bone identified promoter regions for WNT10A (WNT signaling), NPY (regulator of bone and energy homeostasis), and others predicted to be relevant for bone formation/remodeling. These regions may aid in understanding the complex interplay between genetics, environment, and IS. Full article

Epigenetics is a mechanism underlying cardiovascular disease. It is unknown whether DNA hydroxymethylation is prospectively associated with the risk for cardiovascular death independent of germline and common environment. Male twin pairs middle-aged in 1969–1973 and discordant for cardiovascular death through December 31, 2014, were included. Hydroxymethylation was quantified in buffy coat DNA collected in 1986–1987. The 1893 differentially hydroxymethylated regions (DhMRs) were identified after controlling for blood leukocyte subtypes and age among 12 monozygotic (MZ) pairs (Benjamini–Hochberg False Discovery Rate < 0.01), of which the 102 DhMRs were confirmed with directionally consistent log₂-fold changes and p < 0.01 among additional 7 MZ pairs. These signature 102 DhMRs, independent of the germline, were located on all chromosomes except for chromosome 21 and the Y chromosome, mainly within/overlapped with intergenic regions and introns, and predominantly hyper-hydroxymethylated. A binary linear classifier predicting cardiovascular death among 19 dizygotic pairs was identified and equivalent to that generated from MZ via the 2D transformation. Computational bioinformatics discovered pathways, phenotypes, and DNA motifs for these DhMRs or their subtypes, suggesting that hydroxymethylation was a pathophysiological mechanism underlying cardiovascular death that might be influenced by genetic factors and warranted further investigations of mechanisms of these signature regions in vivo and in vitro. Full article

Although the burden of histoplasmosis in patients with advanced HIV has been the focus of detailed estimations, knowledge about invasive fungal infections in patients living with HIV in an Amazonian context is somewhat scattered. Our goal was thus to adopt a broader view integrating all invasive fungal infections diagnosed over a decade in French Guiana. All patients hospitalized at Cayenne hospital from 1 January 2009 to 31 December 2018 with a proven diagnosis of invasive fungal infection were included (N = 227). Histoplasmosis was the most common (48.2%), followed by Cryptococcus infection (26.3%), and pneumocystosis (12.5%). For cryptococcal infection, there was a discordance between the actual diagnosis of cryptococcal meningitis n = (26) and the isolated presence of antigen in the serum (n = 46). Among the latter when the information was available (n = 34), 21(65.6%) were treated with antifungals but not coded as cryptococcocosis. Most fungal infections were simultaneous to the discovery of HIV (38%) and were the AIDS-defining event (66%). The proportion of major invasive fungal infections appeared to remain stable over the course of the study, with a clear predominance of documented H. capsulatum infections. Until now, the focus of attention has been histoplasmosis, but such attention should not overshadow other less-studied invasive fungal infections. Full article

Background: Developing patient-centric baseline standards that enable the detection of clinically significant outlier gene products on a genome-scale remains an unaddressed challenge required for advancing personalized medicine beyond the small pools of subjects implied by “precision medicine”. This manuscript proposes a novel approach for reference standard development to evaluate the accuracy of single-subject analyses of transcriptomes and offers extensions into proteomes and metabolomes. In evaluation frameworks for which the distributional assumptions of statistical testing imperfectly model genome dynamics of gene products, artefacts and biases are confounded with authentic signals. Model confirmation biases escalate when studies use the same analytical methods in the discovery sets and reference standards. In such studies, replicated biases are confounded with measures of accuracy. We hypothesized that developing method-agnostic reference standards would reduce such replication biases. We propose to evaluate discovery methods with a reference standard derived from a consensus of analytical methods distinct from the discovery one to minimize statistical artefact biases. Our methods involve thresholding effect-size and expression-level filtering of results to improve consensus between analytical methods. We developed and released an R package “referenceNof1” to facilitate the construction of robust reference standards. Results: Since RNA-Seq data analysis methods often rely on binomial and negative binomial assumptions to non-parametric analyses, the differences create statistical noise and make the reference standards method dependent. In our experimental design, the accuracy of 30 distinct combinations of fold changes (FC) and expression counts (hereinafter “expression”) were determined for five types of RNA analyses in two different datasets. This design was applied to two distinct datasets: Breast cancer cell lines and a yeast study with isogenic biological replicates in two experimental conditions. Furthermore, the reference standard (RS) comprised all RNA analytical methods with the exception of the method testing accuracy. To mitigate biases towards a specific analytical method, the pairwise Jaccard Concordance Index between observed results of distinct analytical methods were calculated for optimization. Optimization through thresholding effect-size and expression-level reduced the greatest discordances between distinct methods’ analytical results and resulted in a 65% increase in concordance. Conclusions: We have demonstrated that comparing accuracies of different single-subject analysis methods for clinical optimization in transcriptomics requires a new evaluation framework. Reliable and robust reference standards, independent of the evaluated method, can be obtained under a limited number of parameter combinations: Fold change (FC) ranges thresholds, expression level cutoffs, and exclusion of the tested method from the RS development process. When applying anticonservative reference standard frameworks (e.g., using the same method for RS development and prediction), most of the concordant signal between prediction and Gold Standard (GS) cannot be confirmed by other methods, which we conclude as biased results. Statistical tests to determine DEGs from a single-subject study generate many biased results requiring subsequent filtering to increase reliability. Conventional single-subject studies pertain to one or a few patient’s measures over time and require a substantial conceptual framework extension to address the numerous measures in genome-wide analyses of gene products. The proposed referenceNof1 framework addresses some of the inherent challenges for improving transcriptome scale single-subject analyses by providing a robust approach to constructing reference standards. Full article

Vascular smooth muscle cells (VSMCs) are major components of blood vessels. They regulate physiological functions, such as vascular tone and blood flow. Under pathological conditions, VSMCs undergo a remodeling process known as phenotypic switching. During this process, VSMCs lose their contractility and acquire a synthetic phenotype, where they over-proliferate and migrate from the tunica media to the tunica interna, contributing to the occlusion of blood vessels. Since their discovery as effector proteins of cyclic adenosine 3′,5′-monophosphate (cAMP), exchange proteins activated by cAMP (EPACs) have been shown to play vital roles in a plethora of pathways in different cell systems. While extensive research to identify the role of EPAC in the vasculature has been conducted, much remains to be explored to resolve the reported discordance in EPAC’s effects. In this paper, we review the role of EPAC in VSMCs, namely its regulation of the vascular tone and phenotypic switching, with the likely involvement of reactive oxygen species (ROS) in the interplay between EPAC and its targets/effectors. Full article

Abundant evidence reveals that activation of the renin-angiotensin system promotes skeletal muscle atrophy in several conditions including congestive heart failure, chronic kidney disease, and prolonged mechanical ventilation. However, controversy exists about whether circulating angiotensin II (AngII) promotes skeletal muscle atrophy by direct or indirect effects; the centerpiece of this debate is the issue of whether skeletal muscle fibers express AngII type 1 receptors (AT1Rs). While some investigators assert that skeletal muscle expresses AT1Rs, others argue that skeletal muscle fibers do not contain AT1Rs. These discordant findings in the literature are likely the result of study design flaws and additional research using a rigorous experimental approach is required to resolve this issue. We tested the hypothesis that AT1Rs are expressed in both human and rat skeletal muscle fibers. Our premise was tested using a rigorous, multi-technique experimental design. First, we established both the location and abundance of AT1Rs on human and rat skeletal muscle fibers by means of an AngII ligand-binding assay. Second, using a new and highly selective AT1R antibody, we carried out Western blotting and determined the abundance of AT1R protein within isolated single muscle fibers from humans and rats. Finally, we confirmed the presence of AT1R mRNA in isolated single muscle fibers from rats. Our results support the hypothesis that AT1Rs are present in both human and rat skeletal muscle fibers. Moreover, our experiments provide the first evidence that AT1Rs are more abundant in fast, type II muscle fibers as compared with slow, type I fibers. Together, these discoveries provide the foundation for an improved understanding of the mechanism(s) responsible for AngII-induced skeletal muscle atrophy. Full article

Lysophosphatidylcholines are a group of bioactive lipids heavily investigated in the context of inflammation and atherosclerosis development. While present in plasma during physiological conditions, their concentration can drastically increase in certain inflammatory states. Lysophosphatidylcholines are widely regarded as potent pro-inflammatory and deleterious mediators, but an increasing number of more recent studies show multiple beneficial properties under various pathological conditions. Many of the discrepancies in the published studies are due to the investigation of different species or mixtures of lysophatidylcholines and the use of supra-physiological concentrations in the absence of serum or other carrier proteins. Furthermore, interpretation of the results is complicated by the rapid metabolism of lysophosphatidylcholine (LPC) in cells and tissues to pro-inflammatory lysophosphatidic acid. Interestingly, most of the recent studies, in contrast to older studies, found lower LPC plasma levels associated with unfavorable disease outcomes. Being the most abundant lysophospholipid in plasma, it is of utmost importance to understand its physiological functions and shed light on the discordant literature connected to its research. LPCs should be recognized as important homeostatic mediators involved in all stages of vascular inflammation. In this review, we want to point out potential pro- and anti-inflammatory activities of lysophospholipids in the vascular system and highlight recent discoveries about the effect of lysophosphatidylcholines on immune cells at the endothelial vascular interface. We will also look at their potential clinical application as biomarkers. Full article

Phosphorus (P) is an essential component for all living beings. Low P diets prompt phenotypic and molecular adaptations to maintain P homeostasis and increase P utilization (PU). Knowledge of the molecular mechanisms of PU is needed to enable targeted approaches to improve PU efficiency and thus lower P excretion in animal husbandry. In a previous population study, Japanese quail were subjected to a low P diet lacking mineral P and exogenous phytase. Individual PU was determined based on total P intake and excretion. A subset of 20 extreme siblings discordant for PU was selected to retrieve gene expression patterns of ileum (n = 10 per PU group). Sequencing reads have been successfully mapped to the current Coturnix japonica reference genome with an average mapping rate of 86%. In total, 640 genes were found to be differentially abundant between the low and high PU groups (false discovery rate ≤ 0.05). Transcriptional patterns suggest a link between improved PU and mitochondrial energy metabolism, accelerated cell proliferation of enterocytes, and gut integrity. In assessing indicators of the efficient use of macro- and micronutrients, further research on turnover and proliferation rates of intestinal cells could provide an approach to improve P efficiency in poultry species. Full article