Progranulin is Neurotrophic In Vivo and Protects against a Mutant TDP-43 Induced Axonopathy

Mislocalization, aberrant processing and aggregation of TAR DNA-binding protein 43 (TDP-43) is found in the neurons affected by two related diseases, amyotrophic lateral sclerosis (ALS) and frontotemporal lobe dementia (FTLD). These TDP-43 abnormalities are seen when TDP-43 is mutated, such as in familial ALS, but also in FTLD, caused by null mutations in the progranulin gene. They are also found in many patients with sporadic ALS and FTLD, conditions in which only wild type TDP-43 is present. The common pathological hallmarks and symptomatic cross over between the two diseases suggest that TDP-43 and progranulin may be mechanistically linked. In this study we aimed to address this link by establishing whether overexpression of mutant TDP-43 or knock-down of progranulin in zebrafish embryos results in motor neuron phenotypes and whether human progranulin is neuroprotective against such phenotypes. Mutant TDP-43 (A315T mutation) induced a motor axonopathy characterized by short axonal outgrowth and aberrant branching, similar, but more severe, than that induced by mutant SOD1. Knockdown of the two zebrafish progranulin genes, grna and grnb, produced a substantial decrease in axonal length, with knockdown of grna alone producing a greater decrease in axonal length than grnb. Progranulin overexpression rescued the axonopathy induced by progranulin knockdown. Interestingly, progranulin also rescued the mutant TDP-43 induced axonopathy, whilst it failed to affect the mutant SOD1-induced phenotype. TDP-43 was found to be nuclear in all conditions described. The findings described here demonstrate that progranulin is neuroprotective in vivo and may have therapeutic potential for at least some forms of motor neuron degeneration

Hearing loss and congenital CMV infection: a systematic review

BACKGROUND AND OBJECTIVE: Hearing loss caused by congenital cytomegalovirus (cCMV) infection was first observed in 1964. Today cCMV is the most common cause of nonhereditary sensorineural hearing loss in childhood. Our objective was to provide an overview of the prevalence of cCMV-related hearing loss, to better define the nature of cCMV-associated hearing loss, and to investigate the importance of cCMV infection in hearing-impaired children. METHODS: Two reviewers independently used Medline and manual searches of references from eligible studies and review articles to select cohort studies on children with cCMV infection with audiological follow-up and extracted data on population characteristics and hearing outcomes. RESULTS: Thirty-seven studies were included: 10 population-based natural history studies, 14 longitudinal cohort studies, and 13 retrospective studies. The prevalence of cCMV in developed countries is 0.58% (95% confidence interval, 0.41–0.79). Among these newborns 12.6% (95% confidence interval, 10.2–16.5) will experience hearing loss: 1 out of 3 symptomatic children and 1 out of 10 asymptomatic children. Among symptomatic children, the majority have bilateral loss; among asymp- tomatic children, unilateral loss predominates. In both groups the hear- ing loss is mainly severe to profound. Hearing loss can have a delayed onset, and it is unstable, with fluctuations and progression. Among hearing-impaired children, cCMV is the causative agent in 10% to 20%. Despite strict selection criteria, some heterogeneity was found between selected studies. CONCLUSIONS: This systematic review underscores the importance of cCMV as a cause of sensorineural hearing loss in childhood

Assessing the added value of context during stress detection from wearable data

Background Insomnia, eating disorders, heart problems and even strokes are just some of the illnesses that reveal the negative impact of stress overload on health and well-being. Early detection of stress is therefore of utmost importance. Whereas the gold-standard for detecting stress is by means of questionnaires, more recent work uses wearable sensors to find continuous and qualitative physical markers of stress. As some physiological stress responses, e.g. increased heart rate or sweating and chills, might also occur when doing sports, a more profound approach is needed for stress detection than purely considering physiological data. Methods In this paper, we analyse the added value of context information during stress detection from wearable data. We do so by comparing the performance of models trained purely on physiological data and models trained on physiological and context data. We consider the user's activity and hours of sleep as context information, where we compare the influence of user-given context versus machine learning derived context. Results Context-aware models reach higher accuracy and lower standard deviations in comparison to the baseline (physiological) models. We also observe higher accuracy and improved weighted F1 score when incorporating machine learning predicted, instead of user-given, activities as context information. Conclusions In this paper we show that considering context information when performing stress detection from wearables leads to better performance. We also show that it is possible to move away from human labeling and rely only on the wearables for both physiology and context

Hierarchical pattern matching for anomaly detection in time series

As companies rely on an ever increasing number of connected devices for their day to day operations, a need arises for automated anomaly detectors to constantly observe crucial device metrics in real time to prevent downtime and data loss. As production environments tend to monitor a huge amount of these metrics, it prevents current state-of-the-art techniques to be deployed as the required computational resources is too high. This paper proposes a lightweight anomaly detection method that can be deployed in these environments without a reduction in accuracy. The approach works fully online, and does not require an extensive history set to be kept in memory. The method is benchmarked on the publicly available Numenta dataset, as well as a network monitoring dataset from different environments provided by a network management solution vendor. These benchmarks show the proposed technique to be very competitive with the current state-of-the-art and exceeding it in production applicability