Advancements in genome sequencing have facilitated whole-genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural ...networks (NNs), particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, an NN model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping. This study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single-nucleotide polymorphism (SNP) array, potentially useful in predicting traits that are difficult to define. GenoDrawing demonstrates proficiency in its task using a small dataset of shape-related SNPs. Results indicate that the use of SNPs associated with visual traits has substantial impact on the generated images, consistent with biological interpretation. While using substantial SNPs is crucial, incorporating additional, unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs. The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping, particularly beneficial for small to medium breeding companies to predict economically substantial heritable traits. Although GenoDrawing has limitations, it sets the groundwork for future research in image prediction from genomic markers. Future studies should focus on using stronger models for image reproduction, SNP information extraction, and dataset balance in terms of phenotypes for more precise outcomes.
Incomplete annotation has the greatest impact on brain-specific genes and consequently, brain-related disorders.
Growing evidence suggests that human gene annotation remains incomplete; however, it ...is unclear how this affects different tissues and our understanding of different disorders. Here, we detect previously unannotated transcription from Genotype-Tissue Expression RNA sequencing data across 41 human tissues. We connect this unannotated transcription to known genes, confirming that human gene annotation remains incomplete, even among well-studied genes including 63% of the Online Mendelian Inheritance in Man–morbid catalog and 317 neurodegeneration-associated genes. We find the greatest abundance of unannotated transcription in brain and genes highly expressed in brain are more likely to be reannotated. We explore examples of reannotated disease genes, such as
SNCA
, for which we experimentally validate a previously unidentified, brain-specific, potentially protein-coding exon. We release all tissue-specific transcriptomes through vizER:
http://rytenlab.com/browser/app/vizER
. We anticipate that this resource will facilitate more accurate genetic analysis, with the greatest impact on our understanding of Mendelian and complex neurogenetic disorders.
Measurement of gene expression levels and detection of eQTLs (expression quantitative trait loci) are difficult in tissues with limited sample availability, such as the brain. However, eQTL overlap ...between tissues might be high, which would allow for inference of eQTL functioning in the brain via eQTLs detected in readily accessible tissues, e.g. whole blood. Applying Stratified Linkage Disequilibrium Score Regression (SLDSR), we quantified the enrichment in polygenic signal of blood and brain eQTLs in genome-wide association studies (GWAS) of 11 complex traits. We looked at eQTLs discovered in 44 tissues by the Genotype-Tissue Expression (GTEx) consortium and two other large representative studies, and found no tissue-specific eQTL effects. Next, we integrated the GTEx eQTLs with regions associated with tissue-specific histone modifiers, and interrogated their effect on rheumatoid arthritis and schizophrenia. We observed substantially enriched effects of eQTLs located inside regions bearing modification H3K4me1 on schizophrenia, but not rheumatoid arthritis, and not tissue-specific. Finally, we extracted eQTLs associated with tissue-specific differentially expressed genes and determined their effects on rheumatoid arthritis and schizophrenia, these analysis revealed limited enrichment of eQTLs associated with gene specifically expressed in specific tissues. Our results pointed to strong enrichment of eQTLs in their effect on complex traits, without evidence for tissue-specific effects. Lack of tissue-specificity can be either due to a lack of statistical power or due to the true absence of tissue-specific effects. We conclude that eQTLs are strongly enriched in GWAS signal and that the enrichment is not specific to the eQTL discovery tissue. Until sample sizes for eQTL discovery grow sufficiently large, working with relatively accessible tissues as proxy for eQTL discovery is sensible and restricting lookups for GWAS hits to a specific tissue for which limited samples are available might not be advisable.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Agent-based electronic commerce is known to offer many advantages to users. However, very few studies have been devoted to deal with privacy issues in this domain. Privacy is of great concern and ...preserving users’ privacy plays a crucial role to promote their trust in agent-based technologies. In this paper, we focus on preference profiling, which is a well-known threat to users’ privacy. Specifically, we review strategies for customers’ agents to prevent seller agents from obtaining accurate preference profiles of the former group by using data mining techniques. We experimentally show the efficacy of each of these strategies and discuss their suitability in different situations. Our experimental results show that customers can improve their privacy notably with these strategies.
This paper proposes a general architecture for testing, validating and verifying
Ambient Intelligence
(
AmI
) environments:
AmISim
. The development of AmI is a very complex task because this ...technology must often adapt to contextual information as well as unpredictable behaviours and environmental features. The architecture presented deals with AmI applications in order to cover the different components of these kinds of systems: environment, users, context and adaptation. This architecture is the first one that is able to cover all these features, which are needed in a full AmI system. The paper shows that AmISim is able to cover a complete AmI system and to provide a framework which can test scenarios that would be impossible to test in real environments or even with previous simulation approaches. Simulated and real elements coexist in AmISim for a robust testing, validation and verification of the AmI systems, which provide an easier and less costly deployment.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The mainstream of research in Ambient Assisted Living (AAL) is devoted to developing intelligent systems for processing the data collected through artificial sensing. Besides, there are other ...elements that must be considered to foster the adoption of AAL solutions in real environments. In this paper we focus on the problem of designing interfaces among caregivers and AAL systems. We present an alert management tool that supports carers in their task of validating alarms raised by the system. It generates text-based explanations--obtained through an argumentation process--of the causes leading to alarm activation along with graphical sensor information and 3D models, thus offering complementary types of information. Moreover, a guideline to use the tool when validating alerts is also provided. Finally, the functionality of the proposed tool is demonstrated through two real cases of alert.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Neuropathological and experimental evidence suggests that the cell-to-cell transfer of α-synuclein has an important role in the pathogenesis of Parkinson’s disease (PD). However, the mechanism ...underlying this phenomenon is not fully understood. We undertook a small interfering RNA (siRNA), genome-wide screen to identify genes regulating the cell-to-cell transfer of α-synuclein. A genetically encoded reporter, GFP-2A-αSynuclein-RFP, suitable for separating donor and recipient cells, was transiently transfected into HEK cells stably overexpressing α-synuclein. We find that 38 genes regulate the transfer of α-synuclein-RFP, one of which is ITGA8, a candidate gene identified through a recent PD genome-wide association study (GWAS). Weighted gene co-expression network analysis (WGCNA) and weighted protein-protein network interaction analysis (WPPNIA) show that those hits cluster in networks that include known PD genes more frequently than expected by random chance. The findings expand our understanding of the mechanism of α-synuclein spread.
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•38 genes regulate the cell-to-cell transfer of α-synuclein•Those genes participate in the same networks as known Parkinson’s disease (PD) genes•Knockdown of selected genes affects mitochondrial ΔΨm similarly to recessive PD genes•The “prionoid” hypothesis likely has an important role in the pathogenesis of PD
Kara et al. undertook a high-throughput screen that identified 38 genes that regulate the cell-to-cell transfer of α-synuclein. Those genes participate in the same networks as known Parkinson’s disease Mendelian and risk genes. This work provides a framework for a gene-discovery strategy in complex diseases.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Ambient intelligence is a new information paradigm, where people are empowered through a digital environment that is "aware" of their presence and context and is sensitive, adaptive, and responsive ...to their needs. Hence, one of the important requirements for ambient intelligent environments (AIEs) is the ability to localize the whereabouts of the user in the AIE to address her/his needs. In order to protect user privacy, the use of cameras is not desirable in AIEs, and hence, there is a need to rely on nonintrusive sensors. There are various localization means that are available for outdoor spaces such as those which rely on satellite signals triangulation. However, these outdoor localization means cannot be used in indoor environments. The majority of nonintrusive and noncamera-based indoor localization systems require the installation of extra hardware such as ultrasound emitters/antennas, radio-frequency identification (RFID) antennas, etc. In this paper, we propose a novel indoor localization system that is based on WiFi signals which are free to receive, and they are available in abundance in the majority of domestic spaces. However, free WiFi signals are noisy and uncertain, and their strengths and availability are continuously changing. Hence, we present a fuzzy logic-based system which employs free available WiFi signals to localize a given user in AIEs. The proposed system receives WiFi signals from a large number of existing WiFi access points (up to 170 access points), where no prior knowledge of the access points locations and the environment is required. The system employs an incremental lifelong learning approach to adjust its behavior to the varying and changing WiFi signals to provide a zero-cost localization system which can provide high accuracy in real-world living spaces. We have compared our system in both simulated and real environments with other relevant techniques in the literature, and we have found that our system outperforms the other systems in the offline learning process, whereas our system was the only system which is capable of performing online learning and adaptation. The proposed system was tested in real-world spaces from a living lab intelligent apartment (iSpace) to a town center apartment to a block of offices. In all these experiments, our system has been highly accurate in detecting the user in the given AIEs, and the system was able to adapt its behavior to changes in the AIE or the WiFi signals. We envisage that the proposed system will play an important role in AIEs, especially for privacy concerned situations like elderly care scenarios.
•A proposal for modeling quality of context (QoC) for pervasive systems is presented.•The MLContext modeling language is extended to support QoC.•A Model-Driven approach is applied to generate ...software artefacts related to QoC.•The proposal has been validated for the COSMOS and SAMURAI context-aware frameworks.
Handling context is a crucial activity in context-aware systems. In building such systems, the creation of models helps developers to understand and reason on the context information. The quality of context information is required to achieve these systems behavior according to the requirements. Therefore, context models should not only represent the context information but also the quality of context (QoC). MLContext is a textual domain-specific language (DSL) designed to model context, which has been used to automatically generate software artifacts related to the context management for some context-aware frameworks. In this article we present an MLContext extension for modeling QoC. The QoC added features include constructs to express context situations (i.e. constraints on context information), quality requirements and quality levels. The new constructs have been mapped to code for two frameworks supporting QoC (COSMOS and SAMURAI). These mappings have been validated by means of a case study.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The definition of protocols between agents is not enough for guaranteeing the absence of undesirable communication in organizations and the presence of desirable ones in large multi-agent systems ...(MASs). This is a consequence of the complex system nature of MASs, which cause sophisticated behaviors to arise out of a multiplicity of relatively simple interactions among the independent agents composing them. With this motivation, this paper presents an approach for testing communication in MAS architectures. In this approach, designers are not only recommended to specify the desired communication protocols, but also the undesired patterns and organization structures in the agents’ communications, allowing designers to define robust communication structures. For this purpose, this work presents (1) a language to define such patterns; (2) a set of already defined desired and undesired patterns which usually appear in general MASs; (3) a tool that allows developers to automatically detect these patterns in logs of MAS executions; and (4) a guideline that takes developers through the testing of the communications in MASs. The current approach is experienced with a case study, and the results show that the application of the current approach and the suppression of detected undesired patterns improve the effectiveness and efficiency of the corresponding MAS.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
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