High post-anthesis temperature effects on 3 bread wheat (Triticum aestivum L.) grain 4 transcriptome during early grain-filling

Background: High post-anthesis (p.a) temperatures reduce mature grain weights in wheat and other cereals. However, the causes of this reduction are not entirely known. Control of grain expansion by the maternally derived pericarp of the grain has previously been suggested, although this interaction has not been investigated under high p.a. temperatures. Down-regulation of pericarp localised genes that regulate cell wall expansion under high p.a. temperatures may limit expansion of the encapsulated endosperm due to a loss of plasticity in the pericarp,reducing mature grain weight. Here the effect of high p.a. temperatures on the transcriptome of the pericarp and endosperm of the wheat grain during early grain-filling was investigated via RNA-Seq and is discussed alongside grain moisture dynamics during early grain development and mature grain weight. Results: High p.a. temperatures applied from 6-days after anthesis (daa) and until 18daa reduced the grain’s ability to accumulate water, with total grain moisture and percentage grain moisture content being significantly reduced from 14daa onwards. Mature grain weight was also significantly reduced by the same high p.a. temperatures applied from 6daa for 4-days or more, in a separate experiment. Comparison of our RNA-Seq data from whole grains, with existing data sets from isolated pericarp and endosperm tissues enabled the identification of subsets of genes whose expression was significantly affected by high p.a. temperature and predominantly expressed in either tissue. Hierarchical clustering and gene ontology analysis resulted in the identification of a number of genes implicated in the regulation of cell wall expansion, predominantly expressed in the pericarp and significantly down26 regulated under high p.a. temperatures, including endoglucanase, xyloglucan endotransglycosylases and a β27 expansin. An over-representation of genes involved in the ‘cuticle development’ functional pathway that were expressed in the pericarp and affected by high p.a. temperatures was also observed. Conclusions: High p.a. temperature induced down-regulation of genes involved in regulating pericarp cell wall expansion. This concomitant down-regulation with a reduction in total grain moisture content and grain weight following the same treatment period, adds support to the theory that high p.a. temperatures may cause a reduction in mature grain weight as result of decreased pericarp cell wall expansion

Process and machine system development for the forming of miniature/micro sheet metal products

This paper reports on the current development of the process for the forming of thin sheet-metal micro-parts (t < 50µm) and the corresponding machine system which is part of the research and technological development of an EU funded integrated project - MASMICRO ("Integration of Manufacturing Systems for the Mass-Manufacture of Miniature/Micro-Products" (/www.masmicro.net/). The process development started with qualification of the fundamentals related to the forming of thin sheet-metals in industrial environment, for which a testing machine and several sets of the testing tools were developed. The process was further optimised, followed by new tool designs. Based on the experience gained during the process development, a new forming press which is suitable for industrial, mass-customised production, has been designed

A Finite Element based Deep Learning solver for parametric PDEs

We introduce a dynamic Deep Learning (DL) architecture based on the Finite Element Method (FEM) to solve linear parametric Partial Differential Equations(PDEs). The connections between neurons in the architecture mimic the Finite Element connectivity graph when applying mesh refinements. We select and discuss several losses employing preconditioners and different norms to enhance convergence. For simplicity, we implement the resulting Deep-FEM in one spatial domain (1D), although its extension to 2D and 3D problems is straightforward. Extensive numerical experiments show in general good approximations for both symmetric positive definite (SPD) and indefinite problems in parametric and non-parametric problems. However, in some cases, lack of convexity prevents us from obtaining high-accuracy solutions.This work has received funding from: the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 777778 (MATHROCKS); the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra program POCTEFA 2014-2020 Project PIXIL (EFA362/19); the Spanish Ministry of Science and Innovation projects with references PID2019-108111RB-I00 (FEDER/AEI) and PDC2021-121093-I00, the "BCAM Severo Ochoa" accreditation of excellence (SEV-2017-0718); and the Basque Government through the BERC 2018-2021 program, the three Elkartek projects 3KIA (KK-2020/00049), EXPERTIA (KK-2021/00048), and SIGZE (KK-2021/00095), and the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education

Memory-Based Monte Carlo Integration for Solving Partial Differential Equations Using Neural Networks

Monte Carlo integration is a widely used quadrature rule to solve Partial Differential Equations with neural networks due to its ability to guarantee overfitting-free solutions and high-dimensional scalability. However, this stochastic method produces noisy losses and gradients during training, which hinders a proper convergence diagnosis. Typically, this is overcome using an immense (disproportionate) amount of integration points, which deteriorates the training performance. This work proposes a memory-based Monte Carlo integration method that produces accurate integral approximations without requiring the high computational costs of processing large samples during training

A Deep Double Ritz Method (D2RM) for solving Partial Differential Equations using Neural Networks

Residual minimization is a widely used technique for solving Partial Differential Equations in variational form. It minimizes the dual norm of the residual, which naturally yields a saddle-point (min–max) problem over the so-called trial and test spaces. In the context of neural networks, we can address this min–max approach by employing one network to seek the trial minimum, while another network seeks the test maximizers. However, the resulting method is numerically unstable as we approach the trial solution. To overcome this, we reformulate the residual minimization as an equivalent minimization of a Ritz functional fed by optimal test functions computed from another Ritz functional minimization. We call the resulting scheme the Deep Double Ritz Method (DRM), which combines two neural networks for approximating trial functions and optimal test functions along a nested double Ritz minimization strategy. Numerical results on different diffusion and convection problems support the robustness of our method, up to the approximation properties of the networks and the training capacity of the optimizers

Multicore Methods to accelerate Ship Power System Simulations

Two methods to partition and parallelize the simulation of large-scale shipboard power systems on multicore computes are demonstrated. The first method is node tearing, which is used for offline simulation. The second is the state-space nodal method, which is used for real-time simulation. Both methods are benchmarked against MATLAB/Simulink 2012b for speed and accuracy The simulation model is a notional shipboard power system having characteristics of AC-radial, 450 V, 60 Hz, three-phase, delta-ungrounded power system. The parallel simulation results show speedups in excess of one order of magnitude and general agreement in accuracy.Center for Electromechanic

GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest

<p>Abstract</p> <p>Background</p> <p>Microarray data are often used for patient classification and gene selection. An appropriate tool for end users and biomedical researchers should combine user friendliness with statistical rigor, including carefully avoiding selection biases and allowing analysis of multiple solutions, together with access to additional functional information of selected genes. Methodologically, such a tool would be of greater use if it incorporates state-of-the-art computational approaches and makes source code available.</p> <p>Results</p> <p>We have developed GeneSrF, a web-based tool, and varSelRF, an R package, that implement, in the context of patient classification, a validated method for selecting very small sets of genes while preserving classification accuracy. Computation is parallelized, allowing to take advantage of multicore CPUs and clusters of workstations. Output includes bootstrapped estimates of prediction error rate, and assessments of the stability of the solutions. Clickable tables link to additional information for each gene (GO terms, PubMed citations, KEGG pathways), and output can be sent to PaLS for examination of PubMed references, GO terms, KEGG and and Reactome pathways characteristic of sets of genes selected for class prediction. The full source code is available, allowing to extend the software. The web-based application is available from <url>http://genesrf2.bioinfo.cnio.es</url>. All source code is available from Bioinformatics.org or The Launchpad. The R package is also available from CRAN.</p> <p>Conclusion</p> <p>varSelRF and GeneSrF implement a validated method for gene selection including bootstrap estimates of classification error rate. They are valuable tools for applied biomedical researchers, specially for exploratory work with microarray data. Because of the underlying technology used (combination of parallelization with web-based application) they are also of methodological interest to bioinformaticians and biostatisticians.</p

Caracterización clínica, funcional y hemodinámica de la población con hipertensión pulmonar arterial evaluada en el Instituto Nacional del Tórax

Pulmonary Arterial Hypertension is a rare, progressive and devastating disease with severe consequences in quality of life and survival. Aim: A clinical, functional and hemodynamic assessment of patients with pulmonary arterial hypertension and categorization according to severity. Material and methods: Prospective registry of patients with arterial pulmonary hypertension, hemodynamically defined. Clinical evaluation was performed using World Health Organization functional score (I to IV) and Borg dyspnea scale. Six minute walking test, echocardiography and right heart catheterization were used for functional and hemodynamic assessment. Intravenous Adenosine was used to assess vascular reactivity during the hemodynamic evaluation. Results: Twenty nine patients were included (25 women, age range 16-72 years). Pulmonary hypertension was idiopathic in 11, associated to connective tissue disease in seven, associated to congenital heart disease in nine and associated to chronic thromboembolism in two. The mean lapse of symptoms before assessment was 2.9 years and 100% had dyspnea (Borg 5.1). Functional class I, II, III and IV was observed in 0, 5, 21 and 3 patients respectively. Six minutes walking test was 378±113 m. Mean pulmonary pressure was 59.4±12.2 mmHg, cardiac index was 2.57±0.88 and pulmonary vascular resistance index: 1798.4±855 (dyne.sec)/cm5. Nine patients had a mean pulmonary arterial pressure >55 mmHg and a cardiac index <2.1, considered as bad prognosis criteria. Adenosine test was positive in 17%. Conclusions: This group of patients with Pulmonary Arterial Hypertension was mainly conformed by young females, with a moderate to severe disease.http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0034-98872006000500007&nrm=is

High post-anthesis temperature effects on bread wheat (Triticum aestivum L.) grain transcriptome during early grain-filling

Background: High post-anthesis (p.a) temperatures significantly reduce mature grain weight in wheat and other cereals. However, the mechanisms through which this reduction occurs are not entirely known. It has been suggested that the pericarp may control grain expansion and weight potential, but this interaction has not been investigated under high p.a. temperatures. Disreregulation, caused by high p.a. temperatures, of pericarp-localised genes involved in cell wall expansion may limit the expansion of the endosperm and contribute to a reduction in mature grain size. Here the effect of high p.a. temperature on the transcriptome of the outer-pericarp and endosperm of the wheat grain during early grain-filling was investigated via RNA-Seq and is discussed in the context of grain moisture dynamics during early grain development and of mature grain weight Results: High p.a. temperatures applied from 6-days after anthesis (daa) and until 18daa reduced the ability of the grain to accumulate water, with total grain moisture and percentage moisture content of the grain being significantly reduced from 14daa onwards. High p.a. temperatures applied from 6daa and for a minimum of 4-daysalso significantly reduced mature grain weight. Comparison of our RNA-Seq data from whole grains, with existing data sets from isolated outer-pericarp and endosperm tissues enabled the identification of subsets of genes whose expression was significantly affected by high p.a. temperature and predominantly expressed in either tissue. Hierarchical clustering and gene ontology analysis resulted in the identification of a number of genes implicated in the regulation of cell wall expansion, predominantly expressed in the outer-pericarp and significantly down-regulated under high p.a. temperatures; these included endoglucanase, xyloglucan endotransglycosylases and a β-expansin. An over-representation of genes involved in the ‘cuticle development’ functional pathway expressed in the outer-pericarp and affected by high p.a. temperatures was also observed. Conclusions: High p.a. temperatures induced down regulation of genes involved in the control of pericarp cell wall expansion and occurred concomitantly to a reduction in the potential for grain moisture accumulation, which is the driving force for endosperm cell volume enlargement and an important determinant of final grain sink capacity. This suggests that high p.a. temperatures impairs the coordination of the development of the different grain tissues, resulting in reduced expansion of the maternal layers and therefore, reduce mature grain weight