Diseño y cálculo de centro de transformación en punta, con transformador trifásico reductor de 400 Kvas

El presente proyecto tiene por objeto el estudio técnico, diseño, cálculo y dimensionado de un centro de transformación en punta, con transformador trifásico reductor de 400 Kva'sTraballo fin de grao (UDC.EUP). Enxeñaría eléctrica. Curso 2012/201

Towards cloud-based parallel metaheuristics: A case study in computational biology with Differential Evolution and Spark

[Abstract] Many key problems in science and engineering can be formulated and solved using global optimization techniques. In the particular case of computational biology, the development of dynamic (kinetic) models is one of the current key issues. In this context, the problem of parameter estimation (model calibration) remains as a very challenging task. The complexity of the underlying models requires the use of efficient solvers to achieve adequate results in reasonable computation times. Metaheuristics have been the focus of great consideration as an efficient way of solving hard global optimization problems. Even so, in most realistic applications, metaheuristics require a very large computation time to obtain an acceptable result. Therefore, several parallel schemes have been proposed, most of them focused on traditional parallel programming interfaces and infrastructures. However, with the emergence of cloud computing, new programming models have been proposed to deal with large-scale data processing on clouds. In this paper we explore the applicability of these new models for global optimization problems using as a case study a set of challenging parameter estimation problems in systems biology. We have developed, using Spark, an island-based parallel version of Differential Evolution. Differential Evolution is a simple population-based metaheuristic that, at the same time, is very popular for being very efficient in real function global optimization. Several experiments were conducted both on a cluster and on the Microsoft Azure public cloud to evaluate the speedup and efficiency of the proposal, concluding that the Spark implementation achieves not only competitive speedup against the serial implementation, but also good scalability when the number of nodes grows. The results can be useful for those interested in using parallel metaheuristics for global optimization problems benefiting from the potential of new cloud programming models.Ministerio de Economía y Competitividad and FEDER; through the Project SYNBIOFACTORY; DPI2014-55276-C5-2-RMinisterio de Economía y Competitividad and FEDER; TIN2013-42148-PMinisterio de Economía y Competitividad and FEDER; TIN2016-75845-PXunta de Galicia; R2014/04

Methods for direct determination of mitomycin C in aqueous solutions and in urine

Stripping voltammetry (SV) is used to quantitatively determine concentrations of the anti-neoplastic drug mitomycin C (MMC) alone and in mixtures with 5-fluorouracil and cisplatin, both of which are used in combined chemotherapy with MMC. If the accumulation is performed at the potentials of MMC reduction (-0.35 V vs. SCE), reduced MMC is strongly adsorbed at the electrode. It is possible to prepare a MMC-modified electrode, which, after a washing step, is transferred to the background electrolyte to determine MMC by voltammetry. This procedure, which is termed transfer stripping voltammetry (TSV), helps to eliminate interferences and can be applied for a direct determination of MMC alone or in mixtures with other drugs in urine

Using the Cloud for Parameter Estimation Problems: Comparing Spark vs MPI with a Case-Study

Date of Conference: 14-17 May 2017. Conference Location: Madrid[Abstract] Systems biology is an emerging approach focused in generating new knowledge about complex biological systems by combining experimental data with mathematical modeling and advanced computational techniques. Many problems in this field are extremely challenging and require substantial supercomputing resources to be solved. This is the case of parameter estimation in large-scale nonlinear dynamic systems biology models. Recently, Cloud Computing has emerged as a new paradigm for on-demand delivery of computing resources. However, scientific computing community has been quite hesitant in using the Cloud, simply because traditional programming models do not fit well with the new paradigm, and the earliest cloud programming models do not allow most scientific computations being efficiently run in the Cloud. In this paper we explore and compare two distributed computing models: the MPI (message-passing interface) model, that is high-performance oriented, and the Spark model, which is throughput oriented but outperforms other cloud programming solutions adding improved support for iterative algorithms through in-memory computing. The performance of a very well known metaheuristic, the Differential Evolution algorithm, has been thoroughly assessed using a challenging parameter estimation problem from the domain of computational systems biology. The experiments have been carried out both in a local cluster and in the Microsoft Azure public cloud, allowing performance and cost evaluation for both infrastructures.Gobierno de España; DPI2014-55276-C5-2-RFondos Feder; TIN2016-75845-PXunta de Galicia; R2016/045Xunta de Galicia; GRC2013/05

Role of upstream stimulatory factor 2 in glutamate dehydrogenase gene transcription

Glutamate dehydrogenase (Gdh) plays a central role in ammonia detoxification by catalysing reversible oxidative deamination of L-glutamate into α-ketoglutarate using NAD+ or NADP+ as cofactor. To gain insight into transcriptional regulation of glud, the gene that codes for Gdh, we isolated and characterised the 5' flanking region of glud from gilthead sea bream (Sparus aurata). In addition, tissue distribution, the effect of starvation as well as short- and long-term refeeding on Gdh mRNA levels in the liver of S. aurata were also addressed. 5'-deletion analysis of glud promoter in transiently transfected HepG2 cells, electrophoretic mobility shift assays, chromatin immunoprecipitation (ChIP) and site-directed mutagenesis allowed us to identify upstream stimulatory factor 2 (Usf2) as a novel factor involved in the transcriptional regulation of glud. Analysis of tissue distribution of Gdh and Usf2 mRNA levels by reverse transcriptase-coupled quantitative real-time PCR (RT-qPCR) showed that Gdh is mainly expressed in the liver of S. aurata, while Usf2 displayed ubiquitous distribution. RT-qPCR and ChIP assays revealed that long-term starvation down-regulated the hepatic expression of Gdh and Usf2 to similar levels and reduced Usf2 binding to glud promoter, while refeeding resulted in a slow but gradial restoration of both Gdh and Usf2 mRNA abundance. Herein, we demonstrate that Usf2 transactivates S. aurata glud by binding to an E-box located in the proximal region of glud promoter. In addition, our findings provide evidence for a new regulatory mechanism involving Usf2 as a key factor in the nutritional regulation of glud transcription in the fish liver

Performance Evaluation of MPI, UPC and OpenMP on Multicore Architectures

This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: https://doi.org/10.1007/978-3-642-03770-2_24[Abstract] The current trend to multicore architectures underscores the need of parallelism. While new languages and alternatives for supporting more efficiently these systems are proposed, MPI faces this new challenge. Therefore, up-to-date performance evaluations of current options for programming multicore systems are needed. This paper evaluates MPI performance against Unified Parallel C (UPC) and OpenMP on multicore architectures. From the analysis of the results, it can be concluded that MPI is generally the best choice on multicore systems with both shared and hybrid shared/distributed memory, as it takes the highest advantage of data locality, the key factor for performance in these systems. Regarding UPC, although it exploits efficiently the data layout in memory, it suffers from remote shared memory accesses, whereas OpenMP usually lacks efficient data locality support and is restricted to shared memory systems, which limits its scalability.Gobierno de España; TIN2007-67537-C03-0

Neuroblastoma in Spain: Linking the national clinical database and epidemiological registries – A study by the Joint Action on Rare Cancers

Purpose: Linkage between clinical databases and population-based cancer registries may serve to evaluate Eu ropean Reference Networks’ (ERNs) activity, by monitoring the proportion of patients benefiting from these and their impact on survival at a population level. To test this, a study targeting neuroblastoma (Nb) was conducted in Spain by the European Joint Action on Rare Cancers. Material and methods: Subjects: Nb cases, incident 1999–2017, aged < 15 years. Linkage included: Spanish Neuroblastoma Clinical Database (NbCDB) (1217 cases); Spanish Registry of Childhood Tumours (RETI) (1514 cases); and 10 regional population-based registries (RPBCRs) which cover 33% of the childhood population (332 cases). Linkage was semiautomatic. We estimated completeness, incidence, contribution, deficit, and 5-year survival in the databases and specific subsets. Results: National completeness estimates for RETI and NbCDB were 91% and 72% respectively, using the Spanish RPBCRs on International Incidence of Childhood Cancer (https://iicc.iarc.fr/) as reference. RPBCRs’ specific contribution was 1.6%. Linkage required manual crossover in 54% of the semiautomatic matches. Five-year survival was 74% (0–14 years) and 90% (0–18 months). Conclusions: All three databases were incomplete as regards Spain as a whole and should therefore be combined to achieve full childhood cancer registration. A unique personal patient identifier could facilitate such linkage. Most children have access to Nb clinical trials. Consolidated interconnections between the national registry and clinical registries (including ERNs and paediatric oncology clinical groups) should be established to evaluate outcomes

Performance Evaluation of Unified Parallel C Collective Communications

This is a post-peer-review, pre-copyedit version. The final authenticated version is available online at: http://dx.doi.org/10.1109/HPCC.2009.88[Abstract] Unified Parallel C (UPC) is an extension of ANSI C designed for parallel programming. UPC collective primitives, which are part of the UPC standard, increase programming productivity while reducing the communication overhead. This paper presents an up-to-date performance evaluation of two publicly available UPC collective implementations on three scenarios: shared, distributed, and hybrid shared/distributed memory architectures. The characterization of the throughput of collective primitives is useful for increasing performance through the runtime selection of the appropriate primitive implementation, which depends on the message size and the memory architecture, as well as to detect inefficient implementations. In fact, based on the analysis of the UPC collectives performance, we proposed some optimizations for the current UPC collective libraries. We have also compared the performance of the UPC collective primitives and their MPI counterparts, showing that there is room for improvement. Finally, this paper concludes with an analysis of the influence of the performance of the UPC collectives on a representative communication-intensive application, showing that their optimization is highly important for UPC scalability.Ministerio de Ciencia e Innovación; TIN2007-67537-C03-02Xunta de Galicia; 3/2006 DOGA 13/12/200

Sequential Colocalization of ERa, PR, and AR Hormone Receptors Using Confocal Microscopy Enables New Insights into Normal Breast and Prostate Tissue and Cancers

Multiplex immunohistochemistry (mIHC) use markers staining different cell populations applying widefield optical microscopy. Resolution is low not resolving subcellular co-localization. We sought to colocalize markers at subcellular level with antibodies validated for clinical diagnosis, including the single secondary antibody (combination of anti-rabbit/mouse-antibodies) used for diagnostic IHC with any primary antibody, and confocal microscopy. We explore colocalization in the nucleus (ColNu) of nuclear hormone receptors (ERa, PR, and AR) along with the baseline marker p63 in paired samples of breast and prostate tissues. We established ColNu mIHCF as a reliable technique easily implemented in a hospital setting. In ERa+ breast cancer, we identified different colocalization patterns (nuclear or cytoplasmatic) with PR and AR on the luminal epithelium. A triple-negative breast-cancer case expressed membrane-only ERa. A PR-only case was double positive PR/p63. In normal prostate, we identified an ERa+/p63+/AR-negative distinct population. All prostate cancer cases characteristically expressed ERa on the apical membrane of the AR+ epithelium. We confirmed this using ERa IHC and needle-core biopsies. ColNu mIHCF is feasible and already revealed a new marker for prostate cancer and identified sub-patterns in breast cancer. It could be useful for pathology as well as for functional studies in normal prostate and breast tissues