EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs

Producción CientíficaIterative stencil computations are widely used in numerical simulations. They present a high degree of parallelism, high locality and mostly-coalesced memory access patterns. Therefore, GPUs are good candidates to speed up their computa- tion. However, the development of stencil programs that can work with huge grids in distributed systems with multiple GPUs is not straightforward, since it requires solv- ing problems related to the partition of the grid across nodes and devices, and the synchronization and data movement across remote GPUs. In this work, we present EPSILOD, a high-productivity parallel programming skeleton for iterative stencil computations on distributed multi-GPUs, of the same or different vendors that sup- ports any type of n-dimensional geometric stencils of any order. It uses an abstract specification of the stencil pattern (neighbors and weights) to internally derive the data partition, synchronizations and communications. Computation is split to better overlap with communications. This paper describes the underlying architecture of EPSILOD, its main components, and presents an experimental evaluation to show the benefits of our approach, including a comparison with another state-of-the-art solution. The experimental results show that EPSILOD is faster and shows good strong and weak scalability for platforms with both homogeneous and heterogene- ous types of GPUJunta de Castilla y León, Ministerio de Economía, Industria y Competitividad, y Fondo Europeo de Desarrollo Regional (FEDER): Proyecto PCAS (TIN2017-88614-R) y Proyecto PROPHET-2 (VA226P20).Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación y “European Union NextGenerationEU/PRTR” : (MCIN/ AEI/10.13039/501100011033) - grant TED2021-130367B-I00CTE-POWER and Minotauro and the technical support provided by Barcelona Supercomputing Center (RES-IM-2021-2-0005, RES-IM-2021-3-0024, RES- IM-2022-1-0014).Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Towards In-Vitro Point of Care devices for in-situ diagnosis

Electronic and optoelectronic systems and subsystems play an important role to develop In-Vitro Diagnostics (IVDs) systems for healthcare, clinical, agro-food, environmental, pharmaceutical research or drug control, among many other applications. Although significant advantages have been described for label-free biosensing technology, still a limitednumber of compact devices for monitoring IVD in-situ have been already developed. In this paper a discussion about the current trends for developing Point-of-Care devices will be analyzed, as well as the future challenges for in-situ In-vitro diagnostic systems

Paleo-watertable definition using cave ferromanganese stromatolites and associated cave-wall notches (Sierra de Arnero, Spain)

The steeply-dipping-dolostone-hosted caves of the Sierra de Arnero (N Spain) contain low-gradient relict canyons with up to ten mapped levels of ferromanganese stromatolites and associated wall notches over a vertical range of 85 m, the highest occurring ~ 460 m above base level. Despite a plausible speleogenetic contribution by pyrite oxidation, and the irregular cave-wall mesomorphologies suggestive of hypogenic speleogenesis, the Arnero relict caves are dominantly epigenic, as indicated by the conduit pattern and the abundant allogenic sediments. Allogenic input declined over time due to a piracy-related decrease in the drainage area of allogenic streams, explaining the large size of the relict Arnero caves relative to the limited present-day outcrop area of the karstified carbonates. Allogenic-sediment input also explains the observed change from watertable canyons to phreatic conduits in the paleo-downstream direction. Stromatolites and notches arguably formed in cave-stream passages at the watertable. The best-defined paleo-watertables show an overall slope of 1.7°, consistent with the present-day relief of the watertable, with higher-slope segments caused by barriers related to sulfide mineralization. The formation of watertable stromatolites favored wall notching by the combined effect of enhanced acidity by Mn–Fe oxidation and shielding of cave floors against erosion. Abrasive bedload further contributed to notch formation by promoting lateral mechanical erosion and protecting passage floors. The irregular wallrock erosional forms of Arnero caves are related partly to paragenesis and partly to the porous nature of the host dolostones, which favored irregular dissolution near passage walls, generating friable halos. Subsequent mechanical erosion contributed to generate spongework patterns. The dolostone porosity also contributes to explain the paradox that virtually all Arnero caves are developed in dolostone despite being less soluble than adjacent limestone. U-series dating of carbonate speleothems and paleomagnetic data from ferromanganese stromatolites and clastic sediments indicate that the paleo-watertables recorded ~ 320 m above the present-day watertable formed during the Matuyama Chron but prior to ~ 1.5 Ma, implying long-term base-level-lowering rates from ~ 125 to ~ 213 m/Ma. To our knowledge, this is the first attempt of paleomagnetic dating of cave ferromanganese stromatolites. These deposits are excellent geomagnetic recorders and offer a direct way to delineate and date paleo-watertables, especially in caves developed in dolostone.Financial support was provided by grants ICT-Soplao-53.5.00.12.00 (IGME - Provincial Government of Cantabria - Turismo del Nansa) and CGL2012-38481 (Ministerio de Economía y Competitividad - European Regional Development Fund

Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach

Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)-YNet model for the segmentation step. This architecture includes networks to model each radiologist's noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose "for presentation" mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82±0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of 0.76±0.17. An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist's label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.This research was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed by nomination to Valencian technological innovation centres under project expedient IMDEEA/2021/100. It was also supported by grants from Instituto de Salud Carlos III FEDER (PI17/00047).S

A deep learning framework to classify breast density with noisy labels regularization

Background and objective: Breast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervised learning algorithms have been developed with this objective in mind, however, the performance of these algorithms depends on the quality of the ground-truth information which is usually labeled by expert readers. These labels are noisy approximations of the ground truth, as there is often intra- and inter-reader variability among labels. Thus, it is crucial to provide a reliable method to obtain digital mammograms matching BI-RADS categories. This paper presents RegL (Labels Regularizer), a methodology that includes different image pre-processes to allow both a correct breast segmentation and the enhancement of image quality through an intensity adjustment, thus allowing the use of deep learning to classify the mammograms into BI-RADS categories. The Confusion Matrix (CM) - CNN network used implements an architecture that models each radiologist's noisy label. The final methodology pipeline was determined after comparing the performance of image pre-processes combined with different DL architectures. Methods: A multi-center study composed of 1395 women whose mammograms were classified into the four BI-RADS categories by three experienced radiologists is presented. A total of 892 mammograms were used as the training corpus, 224 formed the validation corpus, and 279 the test corpus. Results: The combination of five networks implementing the RegL methodology achieved the best results among all the models in the test set. The ensemble model obtained an accuracy of (0.85) and a kappa index of 0.71. Conclusions: The proposed methodology has a similar performance to the experienced radiologists in the classification of digital mammograms into BI-RADS categories. This suggests that the pre-processing steps and modelling of each radiologist's label allows for a better estimation of the unknown ground truth labels.This work was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed nominatively to Valencian technological innovation centres under project expedient IMAMCN/2021/1.S

Biosynthesis of antioxidant xanthan gum by Xanthomonas campestris using substrates added with moist olive pomace

Moist olive pomace (MOP) is a high moisture content by-product of the olive oil industry. Managing this recalcitrant residue (transport, storage, and drying) is a priority demanding investment in finding alternative valorisation routes. In this context, the biosynthesis of xanthan gum (XG) incorporating MOP in the substrate (0.0 %, 5.0 %, 10.0 %, 15.0 %, 20.0 %, 25.0 %, 30.0 % and 50.0 %) to induce bacterial stress was attempted. XG biosynthesis yield was quantified, and the product was characterised by structural analysis (FTIR), thermal behaviour (TG), rheology and antioxidant capacity. Relative to the control (sample with no added MOP), a significant increase in XG biosynthesis was found for concentrations up to 30.0 % MOP. In particular, for XG produced with 15 % MOP, a 50.91 % (p < 0.0001) increase was achieved, together with 395.78 % for viscosity. In general, XG produced with MOP presence showed antioxidant activity, a value-added property, especially for applications in the food, pharmaceutical and cosmetic areas. The results indicated that the stress imposed by the MOP induced a microbial response leading to XG production increase, structural and viscosity modifications, and antioxidant properties incorporation. Overall, this work points out a new MOP application contributing to the sustainability of the olive oil productive chain from a biobased circular economy perspective.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and UIDP/00690/2020), and SusTEC (LA/P/0007/2021). Project OleaChain “Skills for sustainability and innovation in the value chain of traditional olive groves in the Northern Inland of Portugal” (NORTE-06-3559-FSE-000188) for P.J.L. Crugeira and A.I.G. Rodrigues contracts. FCT for the PhD research grant of H.H.S. Almeida (SFRH/BD/148124/2019). National funding by FCT, P.I., through the institutional scientific employment program contract of A. Santamaria-Echart.info:eu-repo/semantics/publishedVersio

Population-based incidence of lymphoid neoplasms : Twenty years of epidemiological data in the Girona province, Spain

Background: The aim of this study was to describe incidence patterns of lymphoid neoplasms in the Girona province (Spain) (1996-2015), and to predict the number of cases in Spain during 2020. Methods: Data were extracted from the Girona cancer registry. Incident cases were classified using the ICD-O-3, third revision, and grouped according to the WHO 2008 classification scheme. Age-adjusted incidence rates to the European standard population (ASRE) were estimated and incidence trends were modeled using Joinpoint. Results: 4367 lymphoid neoplasms were diagnosed in the Girona province. The ASRE for overall lymphoma was 37.1 (95% CI: 36.0; 38.2), with a marked male predominance in almost all subtypes. During 1996-2015, incidence trends remained stable for broader lymphoma categories. According to our predictions, 17,950 new cases of LNs will be diagnosed in Spain in 2020. Conclusions: This 'real-world' data will provide valuable information to better inform etiological hypotheses and plan future health-care services

Diversity of Acari and Collembola along a pollution gradient in soils of a pre-pyrenean forest ecosystem

Mites and springtails are important members of soil mesofauna and have been proven to be good bioindicators of airborne pollutants. We studied the surrounding area of a steel mill located in a mountain valley of North Spain. Previous studies had documented the existence of a pollution gradient in this area due to the emissions of the factory, thus providing an interesting site to investigate the potential effects of pollutants (heavy metals and nitrogen) on soil biodiversity. The density of Acari and Collembola significantly decreased with the increase in concentration of Cr, Mn, Zn, Cd and Pb. Mites appeared to be more sensitive to heavy metal pollution than springtails. Likewise, the density of these microarthropoda was lower in those soils exhibiting higher nitrogen content. The species composition of the community of Acari and Collembola changed according to heavy metal pollution. Significant differences in abundance, species richness and diversity were observed between the communities of the sampling sites. Some species were exclusive of the less polluted sites, while other appeared in the most contaminated ones. This different response of soil mesofauna to pollutants suggests that some mite or springtail species could be used as bioindicators of heavy metal pollution

TWEAK promotes peritoneal inflammation

Peritoneal dialysis (PD) is complicated by peritonitis episodes that cause loss of mesothelium and eventually sclerosing peritonitis. An improved understanding of the molecular contributors to peritoneal injury and defense may increase the therapeutic armamentarium to optimize peritoneal defenses while minimizing peritoneal injury. There is no information on the expression and function of the cytokine TWEAK and its receptor Fn14 during peritoneal injury. Fn14 expression and soluble TWEAK levels were measured in human PD peritoneal effluent cells or fluids with or without peritonitis. Fn14 expression was also analyzed in peritoneal biopsies from PD patients. Actions of intraperitoneal TWEAK were studied in mice in vivo. sTWEAK levels were increased in peritoneal effluent in PD peritonitis. Effluent sTWEAK levels correlated with the number of peritoneal macrophages (r = 0.491, p = 0.002). Potential TWEAK targets that express the receptor Fn14 include mesothelial cells and macrophages, as demonstrated by flow cytometry of peritoneal effluents and by analysis of peritoneal biopsies. Peritoneal biopsy Fn14 correlated with mesothelial injury, fibrosis and inflammation, suggesting a potential deleterious effect of TWEAK/Fn14. In this regard, intraperitoneal TWEAK administration to mice promoted peritoneal inflammation characterized by increased peritoneal effluent MCP-1, Fn14 and Gr1+ macrophages, increased mesothelial Fn14, MCP-1 and CCL21 expression and submesothelial tissue macrophage recruitment. Taken together these data suggest that the TWEAK/Fn14 system may promote inflammation and tissue injury during peritonitis and PD.This work was supported by FIS PS09/00447, PI08/1564, PI10/00234, MS12/03262, FEDER funds ISCIII-RETIC REDinREN/RD06/0016, RD12/0021, Comunidad de Madrid (Fibroteam S2010/BMD-2321, S2010/BMD-2378). Programa Intensificación Actividad Investigadora (ISCIII/Agencia Laı´n-Entralgo/CM) to AO, Programa Estabilizacio´n Investigadores to LB-C, Miguel Servet to ABS, Sara Borrell to BS, MDSN. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Risk of breast cancer and residential proximity to industrial installations: New findings from a multicase-control study (MCC-Spain)

Breast cancer is the most frequent tumor in women worldwide, although well-established risk factors account for 53%-55% of cases. Therefore, other risk factors, including environmental exposures, may explain the remaining variation. Our objective was to assess the relationship between risk of breast cancer and residential proximity to industries, according to categories of industrial groups and specific pollutants released, in the context of a population-based multicase-control study of incident cancer carried out in Spain (MCC-Spain). Using the current residence of cases and controls, this study was restricted to small administrative divisions, including both breast cancer cases (452) and controls (1511) in the 10 geographical areas recruiting breast cancer cases. Distances were calculated from the respective woman's residences to the 116 industries located in the study area. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (95%CIs) for categories of distance (between 1 km and 3 km) to industrial plants, adjusting for matching variables and other confounders. Excess risk (OR; 95%CI) of breast cancer was found near industries overall (1.30; 1.00-1.69 at 3 km), particularly organic chemical industry (2.12; 1.20-3.76 at 2.5 km), food/beverage sector (1.87; 1.26-2.78 at 3 km), ceramic (4.71; 1.62-13.66 at 1.5 km), surface treatment with organic solvents (2.00; 1.23-3.24 at 3 km), and surface treatment of plastic and metals (1.51; 1.06-2.14 at 3 km). By pollutants, the excess risk (OR; 95%CI) was detected near industries releasing pesticides (2.09; 1.14-3.82 at 2 km), and dichloromethane (2.09; 1.28-3.40 at 3 km). Our results suggest a possible increased risk of breast cancer in women living near specific industrial plants and support the need for more detailed exposure assessment of certain agents released by these plants.The authors thank all those who took part in this study providing questionnaire data. The study was partially funded by the “Acción Transversal del Cáncer", approved on the Spanish Ministry Council on the 11th October 2007, by the Scientific Foundation of the Spanish Association Against Cancer (Fundación Científica de la Asociación Española Contra el Cáncer (AECC) – EVP-1178/14), by the Spain's Health Research Fund (Fondo de Investigación Sanitaria - FIS 12/01416), by the Instituto de Salud Carlos III-FEDER (PI08/1770, PI08/0533, PI08/1359 PS09/01286-León, PS09/00773-Cantabria, PS09/02078-Huelva, PS09/01903-Valencia, PS09/01662-Granada, PI11/01403, PI11/01889-FEDER, PI11/00226, PI11/01810, PI11/02213, PI12/00488, PI12/00265, PI12/01270, PI12/00715, PI12/00150, PI14/01219, PI14/00613, PI15/00069, PI15/00914, PI15/01032), by the ICGC International Cancer Genome Consortium CLL (The ICGC CLL-Genome Project is funded by Spanish Ministerio de Economía y Competitividad (MINECO) through the Instituto de Salud Carlos III (ISCIII) and Red Temática de Investigación del Cáncer (RTICC) del ISCIII (RD12/0036/0036)), by the Fundación Marqués de Valdecilla (API 10/09), by the Consejería de Salud of the Junta de Andalucía (PI-0571-2009, PI-0306-2011, salud201200057018tra), by the Junta de Castilla y León (LE22A10-2), by the Conselleria de Sanitat of the Generalitat Valenciana (AP_061/10), by the Regional Government of the Basque Country, by the Recercaixa (2010ACUP 00310), by the European Commission grants FOOD-CT-2006-036224-HIWATE, by the Spanish Association Against Cancer (AECC) Scientific Foundation, by the Catalan Government DURSI grant 2017SGR723, by the University of Oviedo, and by the Fundación Caja de Ahorros de Asturias. ISGlobal is a member of the CERCA Program, Generalitat de Catalunya.S