Uncertainty Analysis of Carbon Ablation in the VKI Plasmatron

International audienc

Antiproliferative activity of extracts of Euphorbia tirucalli L (Euphorbiaceae) from three regions of Brazil

Purpose: To investigate Euphorbia tirucalli extract for probable geographic variations in its antiproliferative activity.Methods: The aerial parts of E. tirucalli were collected in the Brazilian states of Mato Grosso, Rio de Janeiro, Pará, Minas Gerais and Santa Catarina. The 70 % ethanol extract was obtained according to the procedure described in Brazilian Homeopathic Pharmacopeia. The antiproliferative activity of extracts, in concentrations of 62, 125, 250, and 500 μg mL-1, was tested against leukemia (HL-60), lymphoma (Daudi) and melanoma (B16F10) cell lines using methyl thiazol tetrazolium assay (MTT). Phytochemical analysis were carried out using High-performance liquid chromatography-diode array (HPLC-UV-DAD) and electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI(-) FT-ICR MS) assays.Results: There was significant regional variability in the cytotoxicity of E. tirucalli extracts in a dosedependent manner. The extracts had similar activity towards leukemia cell line HL-60, decreasing cell viability to about 60 – 70 %. The extract showed the presence of ellagitannins, flavonoids, veracylglucan, and acid triterpenes as the major compounds.Conclusion: While the results support the ethnopharmacological use of E. tirucalli throughout Brazil, regional quantitative differences found in some classes of secondary metabolites may explain the variations observed in antitumor activity.Keywords: Aveloz, Cancer, Cytotoxicity, Antiproliferative, Ethnopharmacological, Traditional medicin

Producción y calidad poscosecha de tomate (Solanum lycopersicum L.) larga vida sometido a la aplicación de ácido giberélico

Tomate-Solanum lycopersicu

A Comparison of Machine Learning Methods for Cross-Domain Few-Shot Learning

We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, Food101, ISIC and ChestX) and using two feature extractors: a ResNet10 model trained on a subset of ImageNet known as miniImageNet and a ResNet152 model trained on the ILSVRC 2012 subset of ImageNet. Commonly used machine learning algorithms including logistic regression, support vector machines, random forests, nearest neighbour classification, naïve Bayes, and linear and quadratic discriminant analysis were evaluated on the extracted feature vectors. We also evaluated classification accuracy when subjecting the feature vectors to normalisation using p-norms. Algorithms originally developed for the classification of gene expression data—the nearest shrunken centroid algorithm and LDA ensembles obtained with random projections—were also included in the experiments, in addition to a cosine similarity classifier that has recently proved popular in few-shot learning. The results enable us to identify algorithms, normalisation methods and pre-trained feature extractors that perform well in cross-domain few-shot learning. We show that the cosine similarity classifier and ℓ² -regularised 1-vs-rest logistic regression are generally the best-performing algorithms. We also show that algorithms such as LDA yield consistently higher accuracy when applied to ℓ² -normalised feature vectors. In addition, all classifiers generally perform better when extracting feature vectors using the ResNet152 model instead of the ResNet10 model

Mixed integer programming in production planning with backlogging and setup carryover : modeling and algorithms

This paper proposes a mixed integer programming formulation for modeling the capacitated multi-level lot sizing problem with both backlogging and setup carryover. Based on the model formulation, a progressive time-oriented decomposition heuristic framework is then proposed, where improvement and construction heuristics are effectively combined, therefore efficiently avoiding the weaknesses associated with the one-time decisions made by other classical time-oriented decomposition algorithms. Computational results show that the proposed optimization framework provides competitive solutions within a reasonable time

Deep Learning Techniques for Geospatial Data Analysis

Consumer electronic devices such as mobile handsets, goods tagged with RFID labels, location and position sensors are continuously generating a vast amount of location enriched data called geospatial data. Conventionally such geospatial data is used for military applications. In recent times, many useful civilian applications have been designed and deployed around such geospatial data. For example, a recommendation system to suggest restaurants or places of attraction to a tourist visiting a particular locality. At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather reporting. Typically such applications are leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes Classifiers, Support Vector Machines, and decision trees. Recent advances in the field of deep-learning showed that Neural Network-based techniques outperform conventional techniques and provide effective solutions for many geospatial data analysis tasks such as object recognition, image classification, and scene understanding. The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data. The chapter is organized as below: (i) A brief overview of deep learning algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques for RFID data analytics.Comment: This is a pre-print of the following chapter: Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam, {\em Deep Learning Techniques for Geospatial Data Analysis}, published in {\bf Machine Learning Paradigms}, edited by George A. TsihrintzisLakhmi C. Jain, 2020, publisher Springer, Cham reproduced with permission of publisher Springer, Cha

Progesterone Receptor induces bcl-x expression through intragenic binding sites favoring RNA Polymerase II elongation

Steroid receptors were classically described for regulating transcription by binding to target gene promoters. However, genome-wide studies reveal that steroid receptors-binding sites are mainly located at intragenic regions. To determine the role of these sites, we examined the effect of pro- gestins on the transcription of the bcl-x gene, where only intragenic progesterone receptor-binding sites (PRbs) were identified. We found that in response to hormone treatment, the PR is recruited to these sites along with two histone acetyltransferases CREB-binding protein (CBP) and GCN5, leading to an increase in histone H3 and H4 acetylation and to the binding of the SWI/SNF complex. Concomitant, a more relaxed chromatin was detected along bcl-x gene mainly in the regions sur- rounding the intragenic PRbs. PR also mediated the recruitment of the positive elongation factor pTEFb, favoring RNA polymerase II (Pol II) elongation activity. Together these events promoted the re-dis- tribution of the active Pol II toward the 30-end of the gene and a decrease in the ratio between proximal and distal transcription. These results suggest a novel mechanism by which PR regulates gene ex- pression by facilitating the proper passage of the polymerase along hormone-dependent genes.Fil: Bertucci, Paola Yanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Nacht, Ana Silvina. Universitat Pompeu Fabra; España. Centro de Regulación Genómica; EspañaFil: Alló, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Rocha Viegas, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Fisiología, Biología Molecular y Celular; ArgentinaFil: Ballaré, Cecilia. Universitat Pompeu Fabra; España. Centro de Regulación Genómica; EspañaFil: Soronellas, Daniel. Centro de Regulación Genómica; España. Universitat Pompeu Fabra; EspañaFil: Castellano, Giancarlo. Centro de Regulación Genómica; España. Universitat Pompeu Fabra; EspañaFil: Zaurin, Roser. Centro de Regulación Genómica; España. Universitat Pompeu Fabra; EspañaFil: Kornblihtt, Alberto Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Fisiología, Biología Molecular y Celular; ArgentinaFil: Beato, Miguel. Centro de Regulación Genómica; España. Universitat Pompeu Fabra; EspañaFil: Vicent, Guillermo. Centro de Regulación Genómica; España. Universitat Pompeu Fabra; EspañaFil: Pecci, Adali. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentin