Search results for "STN"
showing 10 items of 739 documents
Tracking Moving Objects With a Catadioptric Sensor Using Particle Filter
2011
International audience; Visual tracking in video sequences is a widely developed topic in computer vision applications. However, the emergence of panoramic vision using catadioptric sensors has created the need for new approaches in order to track an object in this type of images. Indeed the non-linear resolution and the geometric distortions due to the insertion of the mirror, make tracking in catadioptric images a very challenging task. This paper describes particle filter for tracking moving object over time using a catadioptric sensor. In this work different problems due to the specificities of the catadioptric systems such as geometry are considered. The obtained results demonstrate an…
Secure and efficient verification for data aggregation in wireless sensor networks
2017
Summary The Internet of Things (IoT) concept is, and will be, one of the most interesting topics in the field of Information and Communications Technology. Covering a wide range of applications, wireless sensor networks (WSNs) can play an important role in IoT by seamless integration among thousands of sensors. The benefits of using WSN in IoT include the integrity, scalability, robustness, and easiness in deployment. In WSNs, data aggregation is a famous technique, which, on one hand, plays an essential role in energy preservation and, on the other hand, makes the network prone to different kinds of attacks. The detection of false data injection and impersonation attacks is one of the majo…
Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy)
2016
Debris flows are among the most hazardous phenomena in nature, requiring the preparation of suscep- tibility models in order to cope with this severe threat. The aim of this research was to verify whether a grid cell-based susceptibility model was capable of predicting the debris- flow initiation sites in the Giampilieri catchment (10 km2), which was hit by a storm on the 1st October 2009, resulting in more than one thousand landslides. This kind of event is to be considered as recurrent in the area as attested by historical data. Therefore, predictive models have been prepared by using forward stepwise binary logistic regression (BLR), a landslide inventory and a set of geo- environmental …
Building Robustness into Translational Research
2020
AbstractNonclinical studies form the basis for the decision whether to take a therapeutic candidate into the clinic. These studies need to exhibit translational robustness for both ethical and economic reasons. Key findings confirmed in multiple species have a greater chance to also occur in humans. Given the heterogeneity of patient populations, preclinical studies or at least programs comprising multiple studies need to reflect such heterogeneity, e.g., regarding strains, sex, age, and comorbidities of experimental animals. However, introducing such heterogeneity requires larger studies/programs to maintain statistical power in the face of greater variability. In addition to classic sourc…
Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition
2019
Abstract Background Ongoing EEG data are recorded as mixtures of stimulus-elicited EEG, spontaneous EEG and noises, which require advanced signal processing techniques for separation and analysis. Existing methods cannot simultaneously consider common and individual characteristics among/within subjects when extracting stimulus-elicited brain activities from ongoing EEG elicited by 512-s long modern tango music. New method Aiming to discover the commonly music-elicited brain activities among subjects, we provide a comprehensive framework based on fast double-coupled nonnegative tensor decomposition (FDC-NTD) algorithm. The proposed algorithm with a generalized model is capable of simultaneo…
A novel microglial subset plays a key role in myelinogenesis in developing brain.
2017
Microglia are resident macrophages of the central nervous system that contribute to homeostasis and neuroinflammation. Although known to play an important role in brain development, their exact function has not been fully described. Here, we show that in contrast to healthy adult and inflammation-activated cells, neonatal microglia show a unique myelinogenic and neurogenic phenotype. A CD11c(+) microglial subset that predominates in primary myelinating areas of the developing brain expresses genes for neuronal and glial survival, migration, and differentiation. These cells are the major source of insulin-like growth factor 1, and its selective depletion from CD11c(+) microglia leads to impa…
Large-scale identification of functional microRNA targeting reveals cooperative regulation of the hemostatic system.
2018
Essentials MicroRNAs (miRNAs) regulate the molecular networks controlling biological functions such as hemostasis. We utilized novel methods to analyze miRNA-mediated regulation of the hemostatic system. 52 specific miRNA interactions with 11 key hemostatic associated genes were identified. Functionality and drugability of miRNA-19b-3p against antithrombin were demonstrated in vivo. SUMMARY: Background microRNAs (miRNAs) confer robustness to complex molecular networks regulating biological functions. However, despite the involvement of miRNAs in almost all biological processes, and the importance of the hemostatic system for a multitude of actions in and beyond blood coagulation, the role o…
Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images -- A Cross-Site Robustness Assessment
2017
Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the…
Deep Brain Stimulation and L-DOPA Therapy: Concepts of Action and Clinical Applications in Parkinson's Disease.
2018
L-DOPA is still the most effective pharmacological therapy for the treatment of motor symptoms in Parkinson's disease (PD) almost four decades after it was first used. Deep brain stimulation (DBS) is a safe and highly effective treatment option in patients with PD. Even though a clear understanding of the mechanisms of both treatment methods is yet to be obtained, the combination of both treatments is the most effective standard evidenced-based therapy to date. Recent studies have demonstrated that DBS is a therapy option even in the early course of the disease, when first complications arise despite a rigorous adjustment of the pharmacological treatment. The unique feature of this therapeu…
Gut Microbiome Developmental Patterns in Early Life of Preterm Infants: Impacts of Feeding and Gender.
2015
Gut microbiota plays a key role in multiple aspects of human health and disease, particularly in early life. Distortions of the gut microbiota have been found to correlate with fatal diseases in preterm infants, however, developmental patterns of gut microbiome and factors affecting the colonization progress in preterm infants remain unclear. The purpose of this prospective longitudinal study was to explore day-to-day gut microbiome patterns in preterm infants during their first 30 days of life in the neonatal intensive care unit (NICU) and investigate potential factors related to the development of the infant gut microbiome. A total of 378 stool samples were collected daily from 29 stable/…