La Ecografía de Partes Blandas como Técnica Diagnóstica de los Quistes Poplíteos

Se presentan 39 casos de quistes poplíteos estudiados mediante ultrasonografía durante los últimos 5 años en el Hospital Clínico de Valencia. Se discute acerca de la utilidad de la ecografía de partes blandas en el diagnóstico de las patologías del hueco poplíteo, haciendo hincapié en sus ventajas respecto a otras técnicas más costosas e invasivas.The authors report 39 cases of popliteal's cysts diagnosed with ultrasonography along the last five years in the Hospital Clínico of Valencia. They argue about the usefulness of ultrasonography of soft-tissues in the diagnosis of popliteal pathologies, urging on his advantages over other techniques more aggresive and expensive

Region-based layout analysis of music score images

The Layout Analysis (LA) stage is of vital importance to the correct performance of an Optical Music Recognition (OMR) system. It identifies the regions of interest, such as staves or lyrics, which must then be processed in order to transcribe their content. Despite the existence of modern approaches based on deep learning, an exhaustive study of LA in OMR has not yet been carried out with regard to the performance of different models, their generalization to different domains or, more importantly, their impact on subsequent stages of the pipeline. This work focuses on filling this gap in the literature by means of an experimental study of different neural architectures, music document types, and evaluation scenarios. The need for training data has also led to a proposal for a new semi-synthetic data-generation technique that enables the efficient applicability of LA approaches in real scenarios. Our results show that: (i) the choice of the model and its performance are crucial for the entire transcription process; (ii) the metrics commonly used to evaluate the LA stage do not always correlate with the final performance of the OMR system, and (iii) the proposed data-generation technique enables state-of-the-art results to be achieved with a limited set of labeled data.This paper is part of the I+D+i PID2020-118447RA-I00 (MultiScore) project funded by MCIN/AEI/10.13039/501100011033, Spain and the GV/2020/030, Spain project funded by the Generalitat Valenciana, Spain. The first and third authors acknowledge support from the “Programa I+D+i de la Generalitat Valenciana, Spain ” through grants ACIF/2019/042 and ACIF/2021/356, respectively

A Generic Parallel Pattern Interface for Stream and Data Processing

Current parallel programming frameworks aid developers to a great extent in implementing applications that exploit parallel hardware resources. Nevertheless, developers require additional expertise to properly use and tune them to operate efficiently on specific parallel platforms. On the other hand, porting applications between different parallel programming models and platforms is not straightforward and demands considerable efforts and specific knowledge. Apart from that, the lack of high-level parallel pattern abstractions, in those frameworks, further increases the complexity in developing parallel applications. To pave the way in this direction, this paper proposes GRPPI, a generic and reusable parallel pattern interface for both stream processing and data-intensive C++ applications. GRPPI accommodates a layer between developers and existing parallel programming frameworks targeting multi-core processors, such as C++ threads, OpenMP and Intel TBB, and accelerators, as CUDA Thrust. Furthermore, thanks to its high-level C++ application programming interface and pattern composability features, GRPPI allows users to easily expose parallelism via standalone patterns or patterns compositions matching in sequential applications. We evaluate this interface using an image processing use case and demonstrate its benefits from the usability, flexibility, and performance points of view. Furthermore, we analyze the impact of using stream and data pattern compositions on CPUs, GPUs and heterogeneous configurations.This work has been partially supported by the EU project ICT 644235 “REPHRASE: REfactoring Parallel Heterogeneous Resource-aware Applications” and the Spanish “Ministerio de Economía y Competitividad” under the grant TIN2016-79673-P “Towards Unification of HPC and Big Data Paradigms.

Paving the way towards high-level parallel pattern interfaces for data stream processing

The emergence of the Internet of Things (IoT) data stream applications has posed a number of new challenges to existing infrastructures, processing engines, and programming models. In this sense, high-level interfaces, encapsulating algorithmic aspects in pattern-based constructions, have considerably reduced the development and parallelization efforts of this type of applications. An example of parallel pattern interface is GrPPI, a C++ generic high-level library that acts as a layer between developers and existing parallel programming frameworks, such as C++ threads, OpenMP and Intel TBB. In this paper, we complement the basic patterns supported by GrPPI with the new stream operators Split-Join and Window, and the advanced parallel patterns Stream-Pool, Windowed-Farm and Stream-Iterator for the aforementioned back ends. Thanks to these new stream operators, complex compositions among streaming patterns can be expressed. On the other hand, the collection of advanced patterns allows users to tackle some domain-specific applications, ranging from the evolutionary to the real-time computing areas, where compositions of basic patterns are not capable of fully mimicking the algorithmic behavior of their original sequential codes. The experimental evaluation of the new advanced patterns and the stream operators on a set of domain-specific use-cases, using different back ends and pattern-specific parameters, reports considerable performance gains with respect to the sequential versions. Additionally, we demonstrate the benefits of the GrPPI pattern interface from the usability, flexibility and readability points of view.This work was partially supported by the EU project ICT 644235 “RePhrase: REfactoring Parallel Heterogeneous Resource-Aware Applications” and the project TIN2013-41350-P “Scalable Data Management Techniques for High-End Computing Systems” from the Ministerio de Economía y Competitividad, Spai

Detecting semantic violations of lock-free data structures through C++ contracts

The use of synchronization mechanisms in multithreaded applications is essential on shared-memory multi-core architectures. However, debugging parallel applications to avoid potential failures, such as data races or deadlocks, can be challenging. Race detectors are key to spot such concurrency bugs; nevertheless, if lock-free data structures are used, these may emit a significant number of false positives. In this paper, we present a framework for semantic violation detection of lock-free data structures which makes use of contracts, a novel feature of the upcoming C++20, and a customized version of the ThreadSanitizer race detector. We evaluate the detection accuracy of the framework in terms of false positives and false negatives leveraging some synthetic benchmarks which make use of the SPSC and MPMC lock-free queue structures from the Boost C++ library. Thanks to this framework, we are able to check the correct use of lock-free data structures, thus reducing the number of false positives.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness through Project Grant TIN2016-79637-P (BigHPC - Towards Unification of HPC and Big Data Paradigms) and the European Commission through Grant No. 801091 (ASPIDE - Exascale programmIng models for extreme data processing)

An adaptive offline implementation selector for heterogeneous parallel platforms

Heterogeneous Parallel Platforms, Comprising Multiple Processing Units And Architectures, Have Become A Cornerstone In Improving The Overall Performance And Energy Efficiency Of Scientific And Engineering Applications. Nevertheless, Taking Full Advantage Of Their Resources Comes Along With A Variety Of Difficulties: Developers Require Technical Expertise In Using Different Parallel Programming Frameworks And Previous Knowledge About The Algorithms Used Underneath By The Application. To Alleviate This Burden, We Present An Adaptive Offline Implementation Selector That Allows Users To Better Exploit Resources Provided By Heterogeneous Platforms. Specifically, This Framework Selects, At Compile Time, The Tuple Device-Implementation That Delivers The Best Performance On A Given Platform. The User Interface Of The Framework Leverages Two C&#43 &#43 Language Features: Attributes And Concepts. To Evaluate The Benefits Of This Framework, We Analyse The Global Performance And Convergence Of The Selector Using Two Different Use Cases. The Experimental Results Demonstrate That The Proposed Framework Allows Users Enhancing Performance While Minimizing Efforts To Tune Applications Targeted To Heterogeneous Platforms. Furthermore, We Also Demonstrate That Our Framework Delivers Comparable Performance Figures With Respect To Other Approaches.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been partially supported by the Spanish ‘Ministerio de Economía y Competitividad’ under the project grant TIN2016-79637-P ‘Towards Unification of High Performance Computing (HPC) and Big Data Paradigms’ and the EU Projects ICT 644235 ‘RePhrase: REfactoring Parallel Heterogeneous Resource-Aware Applications’ and the FP7 609666 ‘Repara: Reengineering and Enabling Performance And poweR of Applications’

Effects of oral prednisone administration on serum cystatin C in dogs

ANTECEDENTES La administración oral de glucocorticoides altera la concentración sérica de cistatina C (sCysC) en humanos. OBJETIVO Determinar si la administración oral de prednisona altera la sCysC en perros sin enfermedad renal preexistente. Los animales Se incluyeron cuarenta y seis perros: 10 perros diagnosticados con meningitis arterial esteroide sensible (SRMA; grupo A), 20 perros diagnosticados de hiperadrenocorticismo hipofisario-dependiente (PDH; grupo B) y 16 perros control sanos (grupo C). Metodos Estudio observacional retrospectivo. A los perros diagnosticados con SRMA se les administró prednisona 4 mg / kg / 24 h PO 7 días, reduciendo la dosis a 2 mg / kg / 24 h 7 días antes del retiro de la medicación. En el grupo A, el muestreo se realizó en los días 0, 7, 14 y un control final en el día 21. Se recogieron muestras de sangre y orina en los 3 grupos, y en el grupo A, el muestreo se realizó en todos los puntos de tiempo (días 1, 7). , 14, y 21). RESULTADOS En el grupo A, sCysC fue significativamente mayor en el día 7 en comparación con el grupo de control (0,4 ± 0,04 mg / l vs. 0,18 ± 0,03 mg / l medio ± SEM respectivamente P 0.05). Los perros con PDH incluidos en el grupo B no tuvieron diferencias significativas en sCysC (0.22 ± 0.03 mg / L) en comparación con el control (P> 0.05). CONCLUSIONES Y IMPORTANCIA CLÍNICA La administración oral de prednisona, a diferencia de la producción alterada de glucocorticoides endógenos, aumenta la sCysC en perros de una manera dependiente de la dosis.BACKGROUND Oral administration of glucocorticoid alters serum cystatin C (sCysC) concentration in humans. OBJECTIVE To determine if oral administration of prednisone alters sCysC in dogs without pre‐existing renal disease. ANIMALS Forty six dogs were included: 10 dogs diagnosed with steroid responsive meningitis arteritis (SRMA; group A), 20 dogs diagnosed of pituitary‐dependent hyperadrenocorticism (PDH; group B), and 16 healthy control dogs (group C). METHODS Retrospective observational study. SRMA diagnosed dogs were administered prednisone 4 mg/kg/24 h PO 7 days, reducing the dose to 2 mg/kg/24 h 7 days before medication withdrawal. In group A, sampling was performed at days 0, 7, 14 and a final control at day 21. Blood and urine samples were collected in the 3 groups, and in group A, sampling was performed at all time points (days 1, 7, 14, and 21). RESULTS In group A, sCysC was significantly higher at day 7 compared to the control group (0.4 ± 0.04 mg/L vs. 0.18 ± 0.03 mg/L mean ± SEM respectively P 0.05). Dogs with PDH included in group B did not have significant differences in sCysC (0.22 ± 0.03 mg/L) compared to control (P > 0.05). CONCLUSIONS AND CLINICAL IMPORTANCE Oral administration of prednisone unlike altered endogenous glucocorticoid production, increases sCysC in dogs in a dose‐dependent fashion.• Ministerio de Economía y Competitividad. Beca Juan de la Cierva IJCI-2014-19428, para Beatriz Macías GarcíapeerReviewe

La ganadería ante escenarios complejos.

La calidad de las contribuciones, producto de la pluma de especialistas en los temas tratados, el presente es un libro que esperamos, basándonos en la importancia de los temas tratados, sea de utilidad y abone a la reflexión de los estudiosos de la ganadería mexicana y, por supuesto, en beneficio de las familias ganaderas y de los consumidores de sus productos.este libro refleja en muchos sentidos la situación de la ganadería mexicana, a la que se le están demandando mayor producción y productividad, que los procesos productivos tengan la menor huella ecológicposible, que los alimentos sean inocuos, que se abatan costos de producción y, cada vez aumentan las presiones de diversos grupos para, que se incluyan los protocolos de bienestar animal, solamente por citar algunos de los retos que tiene. Algunas de estas demandas son complementarias, otras se contraponen, lo que hace valiosos a los estudios que desde las ciencias sociales se realizan y, desde diversas ópticas, se hagan propuestas de política pública balanceadas que consideren lo mejor de cada enfoque, pero sin desechar por completo los antagónicos.Universidad Autónoma Chaping

Exploring stream parallel patterns in distributed MPI environments

In recent years, the large volumes of stream data and the near real-time requirements of data streaming applications have exacerbated the need for new scalable algorithms and programming interfaces for distributed and shared-memory platforms. To contribute in this direction, this paper presents a new distributed MPI back end for GrPPI, a C++ high-level generic interface of data-intensive and stream processing parallel patterns. This back end, as a new execution policy, supports distributed and hybrid (distributed+shared-memory) parallel executions of the Pipeline and Farm patterns, where the hybrid mode combines the MPI policy with a GrPPI shared-memory one. These patterns internally leverage distributed queues, which can be configured to use two-sided or one-sided MPI primitives to communicate items among nodes. A detailed analysis of the GrPPI MPI execution policy reports considerable benefits from the programmability, flexibility and readability points of view. The experimental evaluation of two different streaming applications with different distributed and shared-memory scenarios reports considerable performance gains with respect to the sequential versions at the expense of negligible GrPPI overheads.This work was partially supported by the EU project No. 801091 "ASPIDE: Exascale programming models for extreme data process ing"; and the project TIN2013-41350-P "Scalable Data Management Techniques for High-End Computing Systems" from the Ministerio de Economía y Competitividad , Spain

Domain Adaptation for Document Image Binarization via Domain Classification

Binarization represents a key role in many document image analysis workflows. The current state of the art considers the use of supervised learning, and specifically deep neural networks. However, it is very difficult for the same model to work successfully in a number of document styles, since the set of potential domains is very heterogeneous. We study a multi-source domain adaptation strategy for binarization. Within this scenario, we look into a novel hypothesis where a specialized binarization model must be selected to be used over a target domain, instead of a single model that tries to generalize across multiple domains. The problem then boils down to, given several specialized models and a new target set, deciding which model to use. We propose here a simple way to address this question by using a domain classifier, that estimates which of the source models must be considered to binarize the new target domain. Our experiments on several datasets, including different text styles and music scores, show that our initial hypothesis is quite promising, yet the way to deal with the decision of which model to use still shows great room for improvement.This paper has been supported by Generalitat Valenciana through grant ACIF/2019/042 and project GV/2020/030, and Universidad de Alicante through project GRE19-04. The first two authors carried out this work as recipients of a grant from the Office for Educational Quality and Innovation of the University of Alicante, within the collaboration agreement with Banco de Santander S.A