Flexible uretero- renoscopy for intrarenal calculi. Initial experience of a single centre. Outcome analysis

Clinica Endoplus, Cluj Napoca, Romania, Al VI-lea Congres de Urologie, Dializă şi Transplant Renal din Republica Moldova cu participare internaţională (21-23 octombrie 2015)INTRODUCERE cu privire la rata de succes (stone free rate-SFR) şi rata Scopul acestui studiu a fost să evaluăm rezultatele clinicii complicaţiilor (RC). noastre în uretero-renoscopia flexibila pentru calculii renali.Introduction Aim of this study was to evaluate the outcome of flexible uretero- renoscopy treatment for renal stones of a single surgeon, with regard to primary stone-free rates (SFR) and complication rates (CR) in a single center

Эффективность лечения диабетической нейропатии с применением переходного магнитного поля

Spitalul Clinic al Ministerului Sănătăţii, Chişinău, Conferinţa Ştiinţifico-Practică „Medicina modernă, actualităţi şi perspective”, consacrată aniversării de 40 de ani ai Spitalului Clinic al Ministerului Sănătăţii, 27-28 mai, 2010, Chişinău, Republica MoldovaApplication of transient magnetic field combined with drug correction of blood sugar levels and dietotherapy in diabetic neuropathy showed a higher efficacy than monotherapy. The minor spectrum of contraindications and good tolerance to this physical factor gives magnetotherapy a large application in the treatment of diabetic neuropathy. Применение переходного магнитного поля в сочетании с медикаментозной коррекцией уровня сахара в крови и диетотерапией в диабетической невропатии показало более высокую эффективность по сравнению с монотерапией. Незначительный спектр противопоказаний и хорошая переносимость этого физического фактора дает возможность рекомендовать его к широкому применению в лечении диабетической невропатии

A versatile Cloud Computing environment to facilitate African-European partnership in research: EO AFRICA R&D Innovation Lab

The African Framework for Research, Innovation, Communities and Applications (EO AFRICA) is an ESA initiative in collaboration with the African Union Commission that aims to foster an African-European R&D partnership facilitating the sustainable adoption of Earth Observation and related space technologies in Africa. EO AFRICA R&D Facility is the flagship of EO AFRICA with the overarching goals of enabling an active research community and promoting creative and collaborative innovation processes by providing funding, advanced training, and computing resources. The Innovation Lab is a state-of-the-art Cloud Computing infrastructure provided by the Facility to 30 research projects of African-European research tandems and participants of the capacity development activities of the Space Academy. The Innovation Lab creates new opportunities for innovative research to develop EO algorithms and applications adapted to African challenges and needs, through interactive Virtual Research Environments (VREs) with ready-to-use research and EO analysis software, and facilitated access to a wide range of analysis-ready EO datasets by leveraging the host DIAS infrastructure. The Innovation Lab is a cloud-based, user-friendly, and versatile Platform as a service (PaaS) that allows the users to develop, test, run, and optimize their research code making full use of the Copernicus DIAS infrastructure and a tailor-made interactive computing environment for geospatial analysis. Co-located data and computing services enable fast data exploitation and analysis, which in turn facilitates the utilization of multi-spectral spatiotemporal big data and machine learning methods. Each user has direct access to all online EO data available on the host DIAS (CreoDIAS), especially for Africa, and if required, can also request archived data, which is automatically retrieved and made available within a short delay. The Innovation Lab also supports user-provided in-situ data and allows access to EO data on the Cloud (e.g., other DIASes, CNES PEPS, Copernicus Hub, etc.) through a unified and easy-to-use and open-source data access API (EODAG). Because all data access and analysis are performed on the server-side, the platform does not require a fast Internet connection, and it is adapted for low bandwidth access to enable active collaboration of African – European research tandems. As a minimum configuration, each user has access to computing units with four virtual CPUs, 32 GB RAM, 100 GB local SSD storage, and 1 TB network storage. To a limited extent and for specific needs (e.g., AI applications like Deep Learning), GPU-enabled computing units are also provided. The user interface of the Innovation Lab allows the use of interactive Jupyter notebooks through the JupyterLab environment, which is served by a JupyterHub deployment with improved security and scalability features. For advanced research code development purposes, the Innovation Lab features a web-based VS Code integrated development environment, which provides specialized tools for programming in different languages, such as Python and R. Code analytics tools are also available for benchmarking, code profiling, and memory/performance monitoring. For specific EO workflows that require exploiting desktop applications (e.g., ESA SNAP, QGIS) for pre-processing, analysis, or visualization purposes, the Innovation Lab provides a web-based remote desktop with ready-to-use EO desktop applications. The users can also customize their working environment by using standard package managers. As endorsed by the European Commission Open Science approach, data and code sharing and versioning are crucial to allow reuse and reproduction of the algorithms, workflows, and results. In this context, the Innovation Lab has tools integrated into its interactive development environment that provide direct access to code repositories and allow easy version control. Although public code repositories (e.g., Github) are advised for better visibility, the Innovation Lab also includes a dedicated code repository to support the users' particular needs (e.g., storage of sensitive information). The assets (e.g., files, folders) stored on the platform can be easily accessed and shared externally through the FileBrowser tool. Besides providing a state-of-the-art computing infrastructure, the Innovation Lab also includes other necessary services to ensure a comfortable virtual research experience. All research projects granted by the EO AFRICA R&D Facility receive dedicated technical support for the Innovation Lab facilities. Scientific support and advice from senior researchers and experts for developing geospatial computing workflows are also provided. Users are able to request support contacting a helpdesk via a dedicated ticketing and chat system. After a 6-month development and testing period, the Innovation Lab became operational in September 2021. The first field testing of the platform took place in November 2021 during a 3-day hackathon jointly organized by EO AFRICA R&D, GMES & Africa, and CURAT as part of the AfricaGIS 2021 conference. Forty participants utilized the platform to develop innovative solutions to food security and water resources challenges, such as the impact of the COVID-19 pandemic on agricultural production or linking the decrease in agricultural production to armed conflicts. The activity was successful and similar ones are expected to be organized during major GIS and EO conferences in Africa during the lifetime of the project. Thirty research projects of African-European research tandems granted by the Facility will utilize the Innovation Lab to develop innovative and open-source EO algorithms and applications, preferably as interactive notebooks, adapted to African solutions to African challenges in food security and water scarcity by leveraging cutting-edge cloud-based data access and computing infrastructure. The call for the first 15 research projects was published in November 2021, and the projects are expected to start using the Innovation Lab in February 2022. In parallel, the Innovation Lab provides the computing environment for the capacity development activities of the EO AFRICA R&D Facility, which are organized under the umbrella of EO AFRICA Space Academy. These capacity development activities include several MOOCs, webinars, online and face-to-face courses designed and tailored to improve the knowledge and skills of African researchers in the utilization of Cloud Computing technology to work with EO data. Selected participants of the capacity development activities will use the Innovation Lab during their training. Moreover, the instructors in the Facility use the Innovation Lab to develop the training materials for the Space Academy. Access to the Innovation Lab will also be granted to individual researchers and EO experts depending on the use case and resource availability. Application for access can be made easily through the EO AFRICA R&D web portal after becoming a member of the EO AFRICA Community.This study is funded by ESA Contract No. 4000133905/21/I-EF

Drexel University

We present a 3D matching framework based on a many-to-many matching algorithm that works with skeletal representations of 3D volumetric objects. We demonstrate the performance of this approach on a large database of 3D objects containing more than 1000 exemplars. The method is especially suited to matching objects with distinct part structure and is invariant to part articulation. Skeletal matching has an intuitive quality that helps in defining the search and visualizing the results. In particular, the matching algorithm produces a direct correspondence between two skeletons and their parts, which can be used for registration and juxtaposition. 1

Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night

Deep learning techniques have enabled the emergence of state-of-the-art models to address object detection tasks. However, these techniques are data-driven, delegating the accuracy to the training dataset which must resemble the images in the target task. The acquisition of a dataset involves annotating images, an arduous and expensive process, generally requiring time and manual effort. Thus, a challenging scenario arises when the target domain of application has no annotated dataset available, making tasks in such situation to lean on a training dataset of a different domain. Sharing this issue, object detection is a vital task for autonomous vehicles where the large amount of driving scenarios yields several domains of application requiring annotated data for the training process. In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented. For that, a model based on Generative Adversarial Networks (GANs) is explored to enable the generation of an artificial dataset with its respective annotations. The artificial dataset (fake dataset) is created translating images from day-time domain to night-time domain. The fake dataset, which comprises annotated images of only the target domain (night images), is then used to train the car detector model. Experimental results showed that the proposed method achieved significant and consistent improvements, including the increasing by more than 10% of the detection performance when compared to the training with only the available annotated data (i.e., day images).Comment: 8 pages, 8 figures, https://github.com/viniciusarruda/cross-domain-car-detection and accepted at IJCNN 201

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image samples of the problem (i.e., real world) for proper training. The acquisition of such real-world data sets is not always possible in the autonomous driving context, and sometimes their annotation is not feasible (e.g., takes too long or is too expensive). Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. It turns out that traffic sign detection is a problem in which these three issues are seen altogether. In this work, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the domain of interest, and (ii) templates of the traffic signs, i.e., templates synthetically created to illustrate the appearance of the category of a traffic sign. The effortlessly generated training database is shown to be effective for the training of a deep detector (such as Faster R-CNN) on German traffic signs, achieving 95.66% of mAP on average. In addition, the proposed method is able to detect traffic signs with an average precision, recall and F1-score of about 94%, 91% and 93%, respectively. The experiments surprisingly show that detectors can be trained with simple data generation methods and without problem domain data for the background, which is in the opposite direction of the common sense for deep learning

Arrays of Nano-Electromechanical Biosensors Functionalized by Microcontact Printing

The biofunctionalization of nanoelectromechanical structures is critical for the development of new classes of biosensors displaying improved performances and higher-level of integration. We propose a modified microcontact printing method for the functionalization and passivation of large arrays of nanocantilevers in a single, self-aligned step. Using fluorescence microscopy and resonant frequency measurements, we demonstrate (1) the bioactivity and the anti-fouling property of deposited antibodies and BSA molecules and (2) the preservation of the nanostructures' mechanical integrity.Comment: 20 pages, 5 figure