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Comment inMulti-omic Analyses Reveal Complex Interactions Between HCV and Hepatocytes Demonstrating That the Red Queen Is Up and Running. [Gastroenterology. 2019]; International audience; BACKGROUND & AIMS:The mechanisms of hepatitis C virus (HCV) infection, liver disease progression, and hepatocarcinogenesis are only partially understood. We performed genomic, proteomic, and metabolomic analyses of HCV-infected cells and chimeric mice to learn more about these processes.METHODS:Huh7.5.1dif (hepatocyte-like cells) were infected with culture-derived HCV and used in RNA sequencing, proteomic, metabolomic, and integrative genomic analyses. uPA/SCID (urokinase-type plasminogen activator/severe combined immunodeficiency) mice were injected with serum from HCV-infected patients; 8 weeks later, liver tissues were collected and analyzed by RNA sequencing and proteomics. Using differential expression, gene set enrichment analyses, and protein interaction mapping, we identified pathways that changed in response to HCV infection. We validated our findings in studies of liver tissues from 216 patients with HCV infection and early-stage cirrhosis and paired biopsy specimens from 99 patients with hepatocellular carcinoma, including 17 patients with histologic features of steatohepatitis. Cirrhotic liver tissues from patients with HCV infection were classified into 2 groups based on relative peroxisome function; outcomes assessed included Child-Pugh class, development of hepatocellular carcinoma, survival, and steatohepatitis. Hepatocellular carcinomas were classified according to steatohepatitis; the outcome was relative peroxisomal function.RESULTS:We quantified 21,950 messenger RNAs (mRNAs) and 8297 proteins in HCV-infected cells. Upon HCV infection of hepatocyte-like cells and chimeric mice, we observed significant changes in levels of mRNAs and proteins involved in metabolism and hepatocarcinogenesis. HCV infection of hepatocyte-like cells significantly increased levels of the mRNAs, but not proteins, that regulate the innate immune response; we believe this was due to the inhibition of translation in these cells. HCV infection of hepatocyte-like cells increased glucose consumption and metabolism and the STAT3 signaling pathway and reduced peroxisome function. Peroxisomes mediate β-oxidation of very long-chain fatty acids; we found intracellular accumulation of very long-chain fatty acids in HCV-infected cells, which is also observed in patients with fatty liver disease. Cells in livers from HCV-infected mice had significant reductions in levels of the mRNAs and proteins associated with peroxisome function, indicating perturbation of peroxisomes. We found that defects in peroxisome function were associated with outcomes and features of HCV-associated cirrhosis, fatty liver disease, and hepatocellular carcinoma in patients.CONCLUSIONS:We performed combined transcriptome, proteome, and metabolome analyses of liver tissues from HCV-infected hepatocyte-like cells and HCV-infected mice. We found that HCV infection increases glucose metabolism and the STAT3 signaling pathway and thereby reduces peroxisome function; alterations in the expression levels of peroxisome genes were associated with outcomes of patients with liver diseases. These findings provide insights into liver disease pathogenesis and might be used to identify new therapeutic targets.

Several new algorithms for camera-based fall detection have been proposed, with the aim of reliably alerting caregivers about older persons' falls at home. These algorithms have been evaluated almost exclusively using brief segments of video data captured in artificial environments under optimal conditions and with falls simulated by actors. By contrast, we collected real-life video data recorded over several months at seven older persons' residences. Here, we report on our fall-detection algorithm based on the state-of-the-art, and we present an analysis of the real-life video data. The performance of our detection algorithm was compared with the performance of three previously reported algorithms that used a publicly available simulation data set. All four algorithms produced similar results when using the simulated data. However, the performance of our algorithm degraded drastically when evaluating falls in the real-life data. The false alarm rate was especially high, showing that some challenges still need to be met to make the system sufficiently robust to deploy in real-world situations. We conclude that using more realistic data sets that include longer video recordings and a broad range of activities are essential to reveal weaknesses in fall-detection algorithms.

© 2019, Springer Nature Singapore Pte Ltd. A novel way to detect food intake events using a wearable accelerometer is presented in this paper. The accelerometer is mounted on wearable glasses and used to capture the movements of the head. During meals, a person’s chewing motion is clearly visible in the time domain of the captured accelerometer signal. Features are extracted from this signal and a forward feature selection algorithm is used to determine the optimal set of features. Support Vector Machine and Random Forest classifiers are then used to automatically classify between epochs of chewing and non-chewing. Data was collected from 5 volunteers. The Support Vector Machine approach with linear kernel performs best with a detection accuracy of 73.98% ± 3.99. ispartof: pages:73-77 ispartof: IFMBE Proceedings vol:64 pages:73-77 ispartof: International Conference on Biomedical and Health Informatics (ICBHI) location:PEOPLES R CHINA, Haikou date:8 Oct - 10 Oct 2015 status: published

Chronic hepatitis B and D infections are major causes of liver disease and hepatocellular carcinoma worldwide. Efficient therapeutic approaches for cure are absent. Sharing the same envelope proteins, hepatitis B virus and hepatitis delta virus use the sodium/taurocholate cotransporting polypeptide (a bile acid transporter) as a receptor to enter hepatocytes. However, the detailed mechanisms of the viral entry process are still poorly understood. Here, we established a high-throughput infectious cell culture model enabling functional genomics of hepatitis delta virus entry and infection. Using a targeted RNA interference entry screen, we identified glypican 5 as a common host cell entry factor for hepatitis B and delta viruses. Conclusion: These findings advance our understanding of virus cell entry and open new avenues for curative therapies. As glypicans have been shown to play a role in the control of cell division and growth regulation, virus–glypican 5 interactions may also play a role in the pathogenesis of virus-induced liver disease and cancer. (Hepatology 2016;63:35–48)

In both research and clinical settings a fall incident is commonly defined as unintentionally coming to rest on the ground, floor, or other lower level. Such a fall incident is experienced at least once a year by approximately one in three adults older than 65. This number increases with age and frailty level. Yearly, 37.3 million fall incidents have physiological consequences. Additionally, psychological consequences, such as loss of mobility and independence often restrict the ability of older adults to perform daily activities. By detecting when a person has an elevated fall risk, and taking preventive measures when necessary, the number of fall incidents can be reduced. The first part of this chapter will therefore focus on automatic fall risk assessment techniques. Following this, the second part focuses on fall detection systems. As not all fall incidents can be prevented these systems can reduce the consequences of a fall incident by ensuring that timely aid is given.

Fall incidents are an important health hazard for older adults. Automatic fall detection systems can reduce the consequences of a fall incident by assuring that timely aid is given. The development of these systems is therefore getting a lot of research attention. Real-life data which can help evaluate the results of this research is however sparse. Moreover, research groups that have this type of data are not at liberty to share it. Most research groups thus use simulated datasets. These simulation datasets, however, often do not incorporate the challenges the fall detection system will face when implemented in real-life. In this Letter, a more realistic simulation dataset is presented to fill this gap between real-life data and currently available datasets. It was recorded while re-enacting real-life falls recorded during previous studies. It incorporates the challenges faced by fall detection algorithms in real life. A fall detection algorithm from Debard et al. was evaluated on this dataset. This evaluation showed that the dataset possesses extra challenges compared with other publicly available datasets. In this Letter, the dataset is discussed as well as the results of this preliminary evaluation of the fall detection algorithm. The dataset can be downloaded from www.kuleuven.be/advise/datasets.

Due to the rapidly aging population, developing automated home care systems is a very important step in taking care of elderly people. This will enable us to automatically monitor the health of senior citizens in their own living environment and prevent problems before they happen. One of the challenging tasks is to actively monitor walking habits of elderlies, who alternate between the use of different walking aids, and to combine this with automated fall risk assessment systems. We propose a camera based system that uses object categorization techniques to robustly detect walking aids, like a walker, in order to improve the classification of the fall risk. By automatically integrating the application specific scenery knowledge like camera position and used walker type, we succeed in detecting walking aids within a single frame with an accuracy of 68% for trajectory A and 38% for trajectory B. Furthermore, compared to current state of the art detection systems, we use a rather limited set of training data to achieve this accuracy and thus create a system that is easily adaptable for other applications. Moreover, we applied spatial constraints between detections to optimize the object detection output and to limit the amount of false positive detections. Finally, we evaluate the output on a walking sequence base, leading up to a 92.3% correct classification rate of walking sequences. It can be noted that adapting this approach to other walking aids, like a walking cane, is quite straightforward and opens up the door for many future applications. ispartof: pages:5040-5045 ispartof: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society vol:2015 pages:5040-5045 ispartof: Annual International Conference of the IEEE Engineering in Medicine and Biology Society location:Milan, Italy date:25 Aug - 29 Aug 2015 status: published

We have previously reported the molecular signature of murine pathogenic TH17 cells that induce experimental autoimmune encephalomyelitis (EAE) in animals. Here we show that human peripheral blood IFN-γ+IL-17+ (TH1/17) and IFN-γ−IL-17+ (TH17) CD4+ T cells display distinct transcriptional profiles in high-throughput transcription analyses. Compared to TH17 cells, TH1/17 cells have gene signatures with marked similarity to mouse pathogenic TH17 cells. Assessing 15 representative signature genes in patients with multiple sclerosis, we find that TH1/17 cells have elevated expression of CXCR3 and reduced expression of IFNG, CCL3, CLL4, GZMB, and IL10 compared to healthy controls. Moreover, higher expression of IL10 in TH17 cells is found in clinically stable vs. active patients. Our results define the molecular signature of human pro-inflammatory TH17 cells, which can be used to both identify pathogenic TH17 cells and to measure the effect of treatment on TH17 cells in human autoimmune diseases., CD4+ T cells secreting interleukin-17 (TH17) have diverse functions in modulating autoimmune diseases. Here the authors show via transcriptome analyses that a subset of human TH 17 co-expressing interferon-γ (TH1/17) has a molecular signature similar to “pathogenic” mouse TH 17 but distinct from “non-pathogenic” mouse TH 17.

It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82% and an average detection time of 9.64 days.

Although a lot of research effort has focused on the development of new technologies that support healthcare professionals, only a small fraction of these developments penetrate the healthcare market. The reasons for this are multi-factorial, but in our opinion a major problem is the lack of cooperation between engineers and care providers during the early research phase. An improved collaboration between engineers and care providers would ensure that the developed products better match the needs of the care providers and hence facilitate the marketing of the developed product. To improve this collaboration throughout the entire research phase, a new strategy is presented in this paper. An important step in this strategy is the incorporation of an engineering lab within a nursing facility, which is set-up during the project ‘Ingenieurs@WZC’.

Accurate, non-intrusive and straightforward techniques for gait quality analysis can provide important information concerning the fall risk of a person. For this purpose an algorithm was developed which can measure step length and step time using the Kinect depth image. The validity of the measured step length and time is determined using the GAITRite walkway as a ground truth. The results of this validation confirm that the Kinect is well-suited for determining general parameters of a walking sequence (a Spearmans Correlation Coefficient (SCC) of 0.94 for average step length and 0.75 for average step time per walk), but we furthermore show that determining accurate results for single steps is more difficult (SCC of 0.74 for step length and 0.43 for step time for each step), making it harder to measure more complex gait parameters such as e.g. gait symmetry. ispartof: pages:5920-5923 ispartof: Proc. of EMBC vol:2014 pages:5920-5923 ispartof: EMBC 2014 location:Chicago,Illinois date:Aug - Aug 2014 status: published

Objective Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure detection with the standard method of video electroencephalography monitoring. The goal of this paper is to propose a method for hypermotor seizure detection based on accelerometers that are attached to the extremities. Methods Supervised methods that are commonly used in literature need annotation of data and hence require expert (neurologist) interaction resulting in a substantial cost. In this paper an unsupervised method is proposed that uses extreme value statistics and seizure detection based on a model of normal behavior that is estimated using all recorded and unlabeled data. In this way the expensive interaction can be avoided. Results When applying this method to a labeled dataset, acquired from 7 patients, all hypermotor seizures are detected in 5 of the 7 patients with an average positive predictive value (PPV) of 53%. For evaluating the performance on an unlabeled dataset, seizure events are presented to the system as normal movement events. Since hypermotor seizures are rare compared to normal movements, the very few abnormal events have a negligible effect on the quality of the model. In this way, it was possible to evaluate the system for 3 of the 7 patients when 3% of the training set was composed of seizure events. This resulted in sensitivity scores of 80%, 22% and 90% and a PPV of 89%, 21% and 44% respectively. These scores are comparable with a state-of-the-art supervised machine learning based approach which requires a labeled dataset. Conclusions A person-dependent epileptic seizure detection method has been designed that requires little human interaction. In contrast to traditional machine learning approaches, the imbalance of the dataset does not cause substantial difficulties. publisher: Elsevier articletitle: Detecting rare events using extreme value statistics applied to epileptic convulsions in children journaltitle: Artificial Intelligence in Medicine articlelink: http://dx.doi.org/10.1016/j.artmed.2013.11.007 content_type: article copyright: Copyright © 2013 Elsevier B.V. All rights reserved. ispartof: Artificial Intelligence in Medicine vol:60 issue:2 pages:89-96 ispartof: location:Netherlands status: published

Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure monitoring with the standard method of video/EEG-monitoring. We propose a method for hypermotor seizure detection based on accelerometers attached to the extremities. From the acceleration signals, multiple temporal, frequency and wavelet based features are extracted. After determining the features with the highest discriminative power, we classify movement events in epileptic and non-epileptic movement. This classification is only based on a non-parametric estimate of the probability density function of normal movements. Such approach allows to build patientspecific models to classify movement data without the need for seizure data that is rarely available. If, in the test phase, the probability of a data point (event) is lower than a threshold, this event is considered to be an epileptic seizure, otherwise it is considered as a normal nocturnal movement event. The mean performance over seven patients gives a sensitivity of 95.24% and a Positive Predictive Value (PPV) of 60.04%. However, there is a noticeable inter-patient difference. ispartof: IEEE Journal of Biomedical and Health Informatics vol:18 issue:3 pages:1026-1033 ispartof: location:United States status: published

In reinforcement learning problems, an agent has the task of learning a good or optimal strategy from interaction with his environment. At the start of the learning task, the agent usually has very little information. Therefore, when faced with complex problems that have a large state space, learning a good strategy might be infeasible or too slow to work in practice. One way to overcome this problem, is the use of guidance to supply the agent with traces of “reasonable policies”. However, in a lot of cases it will be hard for the user to supply such a policy. In this paper, we will investigate the use of transfer learning in Relational Reinforcement Learning. The goal of transfer learning is to accelerate learning on a target task after training on a different, but related, source task. More specifically, we introduce an extension of the options framework to the relational setting and show how one can learn skills that can be transferred across similar, but different domains. We present experiments showing the possible benefits of using relational options for transfer learning. ispartof: pages:88-97 ispartof: Lecture Notes in Computer Science vol:4894 pages:88-97 ispartof: The 17th International Conference on Inductive Logic Programming (ILP) location:Corvallis, Oregon date:19 Jun - 21 Jun 2007 status: published

Recently, there has been an increasing interest in directed probabilistic logical models and a variety of languages for describing such models has been proposed. Although many authors provide high-level arguments to show that in principle models in their language can be learned from data, most of the proposed learning algorithms have not yet been studied in detail. We introduce an algorithm, generalized ordering-search, to learn both structure and conditional probability distributions (CPDs) of directed probabilistic logical models. The algorithm upgrades the ordering-search algorithm for Bayesian networks. We use relational probability trees as a representation for the CPDs. We present experiments on blocks world domains, a gene domain and the Cora dataset. ispartof: pages:40-42 ispartof: Lecture Notes in Computer Science vol:4455 pages:40-42 ispartof: Inductive Logic Programming location:Santiago de Compostella, Spain date:24 Aug - 27 Aug 2006 status: published

In complex games with a high branching factor, global alphabeta search is computationally infeasible. One way to overcome this problem is by using selective goal-directed search algorithms. These goal-directed searches can use relevancy zones to determine which part of the board influences the goal. In this paper, we propose a general method that uses these relevancy zones for searching for compound goals. A compound goal is constructed from less complex atomic goals, using the standard connectives. In contrast to other approaches that treat goals separately in the search phase, compound goal search obtains exact results. ispartof: pages:113-128 ispartof: Lecture Notes in Computer Science vol:3846 pages:113-128 ispartof: 4th International Conference on Computers and Games location:Ramat-Gan, Israel date:5 Jul - 7 Jul 2004 status: published

In this paper we report on using a relational state space in multi-agent reinforcement learning. There is growing evidence in the Reinforcement Learning research community that a relational representation of the state space has many benefits over a propositional one. Complex tasks as planning or information retrieval on the web can be represented more naturally in relational form. Yet, this relational structure has not been exploited for multi-agent reinforcement learning tasks and has only been studied in a single agent context so far. In this paper we explore the powerful possibilities of using Relational Reinforcement Learning (RRL) in complex multi-agent coordination tasks. More precisely, we consider an abstract multi-state coordination problem, which can be considered as a variation and extension of repeated stateless Dispersion Games. Our approach shows that RRL allows to represent a complex state space in a multi-agent environment more compactly and allows for fast convergence of learning agents, Moreover, with this technique, agents are able to make complex interactive models (in the sense of learning from an expert), to predict what other agents will do and generalize over this model. This enables to solve complex multi-agent planning tasks, in which agents need to be adaptive and learn, with more powerful tools. ispartof: pages:192-206 ispartof: Lecture Notes in Computer Science vol:3898 pages:192-206 ispartof: Learning and Adaptation in Multi Agent Systems 2005 location:Utrecht, The Netherlands date:25 Jul 2005 status: published