Dual-path convolutional neural network using micro-FTIR imaging to predict breast cancer subtypes and biomarkers levels: estrogen receptor, progesterone receptor, HER2 and Ki67

Breast cancer molecular subtypes classification plays an import role to sort patients with divergent prognosis. The biomarkers used are Estrogen Receptor (ER), Progesterone Receptor (PR), HER2, and Ki67. Based on these biomarkers expression levels, subtypes are classified as Luminal A (LA), Luminal B (LB), HER2 subtype, and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is used to classify subtypes, although interlaboratory and interobserver variations can affect its accuracy, besides being a time-consuming technique. The Fourier transform infrared micro-spectroscopy may be coupled with deep learning for cancer evaluation, where there is still a lack of studies for subtypes and biomarker levels prediction. This study presents a novel 2D deep learning approach to achieve these predictions. Sixty micro-FTIR images of 320x320 pixels were collected from a human breast biopsies microarray. Data were clustered by K-means, preprocessed and 32x32 patches were generated using a fully automated approach. CaReNet-V2, a novel convolutional neural network, was developed to classify breast cancer (CA) vs adjacent tissue (AT) and molecular subtypes, and to predict biomarkers level. The clustering method enabled to remove non-tissue pixels. Test accuracies for CA vs AT and subtype were above 0.84. The model enabled the prediction of ER, PR, and HER2 levels, where borderline values showed lower performance (minimum accuracy of 0.54). Ki67 percentage regression demonstrated a mean error of 3.6%. Thus, CaReNet-V2 is a potential technique for breast cancer biopsies evaluation, standing out as a screening analysis technique and helping to prioritize patients.Comment: 32 pages, 3 figures, 6 table

One-dimensional convolutional neural network model for breast cancer subtypes classification and biochemical content evaluation using micro-FTIR hyperspectral images

Breast cancer treatment still remains a challenge, where molecular subtypes classification plays a crucial role in selecting appropriate and specific therapy. The four subtypes are Luminal A (LA), Luminal B (LB), HER2 subtype, and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is the gold-standard evaluation, although interobserver variations are reported and molecular signatures identification is time-consuming. Fourier transform infrared micro-spectroscopy with machine learning approaches have been used to evaluate cancer samples, presenting biochemical-related explainability. However, this explainability is harder when using deep learning. This study created a 1D deep learning tool for breast cancer subtype evaluation and biochemical contribution. Sixty hyperspectral images were acquired from a human breast cancer microarray. K-Means clustering was applied to select tissue and paraffin spectra. CaReNet-V1, a novel 1D convolutional neural network, was developed to classify breast cancer (CA) and adjacent tissue (AT), and molecular subtypes. A 1D adaptation of Grad-CAM was applied to assess the biochemical impact to the classifications. CaReNet-V1 effectively classified CA and AT (test accuracy of 0.89), as well as HER2 and TNBC subtypes (0.83 and 0.86), with greater difficulty for LA and LB (0.74 and 0.68). The model enabled the evaluation of the most contributing wavenumbers to the predictions, providing a direct relationship with the biochemical content. Therefore, CaReNet-V1 and hyperspectral images is a potential approach for breast cancer biopsies assessment, providing additional information to the pathology report. Biochemical content impact feature may be used for other studies, such as treatment efficacy evaluation and development new diagnostics and therapeutic methods.Comment: 23 pages, 5 figures, 2 table

Intervención en Medio Piura

Mediante la actuación que aquí planteamos pretendemos mejorar notoriamente las condiciones de habitabilidad, de desarrollo personal y económicas de Medio Piura en la ciudad de Piura, Perú, por medio de toda una serie de actuaciones y en colaboración con la Universidad de Piura. Medio Piura está formado por una serie de doce asentamientos dispersos de pequeña escala al norte de la ciudad. Estas actuaciones tienen la pretensión de poder ser implantadas en el contexto y entorno reales y llevarse a cabo de una forma eficaz y barata, lo cual es uno de los pilares básicos de la disciplina. Para ello se desarrollarán proyectos de actuación a tres escalas distintas, siendo éstas: territorial, de emplazamiento y de caserío. Se busca la solución de ciertos problemas básicos a lo largo de todo el Medio Piura, sin embargo, gran parte de los esfuerzos se centrarán en un punto concreto entre dos caseríos que hemos considerado especialmente desfavorables. Pero, a su vez, con las potencialidades oportunas para convertirse en un motor impulsor de toda su zona de influencia. Este proyecto pretende ser un precedente en el entorno a partir del cual se comience a desarrollar, mejorar y consolidar todo el territorio circundante, llegando como culmen a la formación de uno o varios caseríos totalmente consolidados en el ámbito lineal de Medio Piura y con unas buenas condiciones de habitabilidad, conexión y desarrollo

DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning approach for thoracic aorta segmentation and aneurysm prediction using computed tomography scans

Thoracic aortic aneurysm (TAA) is a fatal disease which potentially leads to dissection or rupture through progressive enlargement of the aorta. It is usually asymptomatic and screening recommendation are limited. The gold-standard evaluation is performed by computed tomography angiography (CTA) and radiologists time-consuming assessment. Scans for other indications could help on this screening, however if acquired without contrast enhancement or with low dose protocol, it can make the clinical evaluation difficult, besides increasing the scans quantity for the radiologists. In this study, it was selected 587 unique CT scans including control and TAA patients, acquired with low and standard dose protocols, with or without contrast enhancement. A novel segmentation model, DeepVox, exhibited dice score coefficients of 0.932 and 0.897 for development and test sets, respectively, with faster training speed in comparison to models reported in the literature. The novel TAA classification model, SAVE-CT, presented accuracies of 0.930 and 0.922 for development and test sets, respectively, using only the binary segmentation mask from DeepVox as input, without hand-engineered features. These two models together are a potential approach for TAA screening, as they can handle variable number of slices as input, handling thoracic and thoracoabdominal sequences, in a fully automated contrast- and dose-independent evaluation. This may assist to decrease TAA mortality and prioritize the evaluation queue of patients for radiologists.Comment: 23 pages, 4 figures, 7 table

Avaliação da associação do biosilicato® ao laser de Nd:YAG para o tratamento de cárie/ Nd:YAG biosilicate® association assessment for the treatment of cárie

A cárie é uma doença infecciosa, transmissível, crônica, multifatorial e de lenta progressão. Ultimamente, há a busca pelo desenvolvimento de tratamentos minimamente invasivos das lesões cariosas com preservação estética. Uma estratégia interessante é o tratamento com Biomateriais e Laserterapia, pois permitiria uma melhor absorção e aproveitamento do Biosilicato® (BS) pelo dente lesionado com intuito de regeneração. Este estudo objetivou a investigação de métodos de aderência de BS ao dente cariado, assim como o desenvolvimento de metodologia para melhor interação do BS com o laser de Nd:YAG. O estudo foi conduzido em 2 fases experimentais. Na primeira, adotou-se o modelo de cárie química em dentina radicular bovina e, após, foi feito o tratamento com as partículas de BS em diferentes veículos de aplicação (água destilada, silicone e gel dental), com posterior avaliação composicional e morfológica, que mostraram a efetividade do tratamento de cárie com o BS veiculado em água destilada. Na segunda fase, a dentina cariada foi tratada com BS, associado ou não à irradiação laser e diferentes fotoabsorvedores, e avaliaram-se a morfologia e temperatura intrapulpar decorrentes dos tratamentos. A irradiação laser após a aplicação do BS, com utilização de carvão como fotoabsorvedor, possibilitou o recobrimento dos túbulos dentinários de forma uniforme, com aspecto de derretimento e recristalização do BS, proveniente do aquecimento promovido pelo laser. Ademais, a temperatura intrapulpar, monitorada durante a etapa de irradiação não apresentou variação superior a 5,6 ºC, que é considerada crítica para a vitalidade da polpa. Em conclusão, o tratamento de BS combinado ao laser de Nd:YAG se mostrou promissor ao tratamento da cárie

Heavy quarkonium: progress, puzzles, and opportunities

A golden age for heavy quarkonium physics dawned a decade ago, initiated by the confluence of exciting advances in quantum chromodynamics (QCD) and an explosion of related experimental activity. The early years of this period were chronicled in the Quarkonium Working Group (QWG) CERN Yellow Report (YR) in 2004, which presented a comprehensive review of the status of the field at that time and provided specific recommendations for further progress. However, the broad spectrum of subsequent breakthroughs, surprises, and continuing puzzles could only be partially anticipated. Since the release of the YR, the BESII program concluded only to give birth to BESIII; the $B$-factories and CLEO-c flourished; quarkonium production and polarization measurements at HERA and the Tevatron matured; and heavy-ion collisions at RHIC have opened a window on the deconfinement regime. All these experiments leave legacies of quality, precision, and unsolved mysteries for quarkonium physics, and therefore beg for continuing investigations. The plethora of newly-found quarkonium-like states unleashed a flood of theoretical investigations into new forms of matter such as quark-gluon hybrids, mesonic molecules, and tetraquarks. Measurements of the spectroscopy, decays, production, and in-medium behavior of c\bar{c}, b\bar{b}, and b\bar{c} bound states have been shown to validate some theoretical approaches to QCD and highlight lack of quantitative success for others. The intriguing details of quarkonium suppression in heavy-ion collisions that have emerged from RHIC have elevated the importance of separating hot- and cold-nuclear-matter effects in quark-gluon plasma studies. This review systematically addresses all these matters and concludes by prioritizing directions for ongoing and future efforts.Comment: 182 pages, 112 figures. Editors: N. Brambilla, S. Eidelman, B. K. Heltsley, R. Vogt. Section Coordinators: G. T. Bodwin, E. Eichten, A. D. Frawley, A. B. Meyer, R. E. Mitchell, V. Papadimitriou, P. Petreczky, A. A. Petrov, P. Robbe, A. Vair

Avaliação de subtipos moleculares de câncer de mama utilizando inteligência artificial em imagens hiperespectrais por micro-FTI

Breast cancer is the most incident cancer worldwide. The evaluation of molecular subtypes and their biomarkers plays an essential role in prognosis. The biomarkers used are Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal growth factor Receptor-type 2 (HER2), and Ki67. Based on these, subtypes are classified as Luminal A (LA), Luminal B (LB), HER2 subtype, and Triple-Negative Breast Cancer (TNBC). The gold standard for this analysis is histology and immunohistochemistry, semi-quantitative techniques that present inter-laboratory and inter-observer variations. The Fourier Transform Infrared micro-spectroscopy (micro-FTIR), which provides hyperspectral images with biochemical information of biological tissues, is applied together with artificial intelligence (AI) for cancer evaluation. In this thesis, twenty samples of two breast cancer cell lines, BT-474 and SK-BR-3, were used to define the optimal number of co-added scans for machine learning (ML) techniques. Linear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB) models were used. Sixty hyperspectral images of 320x320 pixels were collected from thirty patients of a human breast biopsies microarray, each containing a breast cancer (CA) and an adjacent tissue (AT) core. Automated methods based on K-Means clustering were developed for data organization and pre-processing to one-dimensional (1D) and two-dimensional (2D) data. The dataset was used to train two new deep learning models for breast cancer subtype evaluation: CaReNet-V1, a 1D Convolutional Neural Network (CNN); and CaReNet-V2, a 2D CNN. All ML models achieved similar performances with the b256_064 (256 background scans and 64 sample scans), b256_128, and b128_128 groups, where the best accuracy of 0.995 was presented by the XGB model. The b256_064 was established as the ideal among the three due to the shortest acquisition time. The K-Means-based method enabled fully automated preprocessing and organization, improving data quality and optimizing CNN training. CaReNet-V1 effectively classified CA and AT (individual spectra test accuracy of 0.89), as well as HER2 and TNBC subtypes (0.83 and 0.86), with greater difficulty for LA and LB (0.74 and 0.68). The model enabled the evaluation of the most contributing wavenumbers to the predictions, providing a direct relationship with the biochemical content of the samples. CaReNet-V2 demonstrated better performance than 1D, with test accuracies above 0.84, and enabled the prediction of ER, PR, and HER2 levels, where borderline values showed lower performance (minimum accuracy of 0.54). The Ki67 percentage regression demonstrated an absolute mean error of 3.6%. On the other hand, its impact evaluation by wavenumber was inferior to 1D. Thus, this study indicates image-based AI techniques using micro-FTIR as potential providers of additional information to pathological reports, also serving as patient biopsy screening techniques.O câncer de mama é o mais incidente no mundo. A avaliação do subtipo molecular e seus biomarcadores tem um papel fundamental para o prognóstico. Os biomarcadores utilizados são os Receptores de Estrogênio (ER), de Progesterona (PR), de tipo 2 do fator de Crescimento Epidérmico Humano (HER2), e Ki67. Com base nestes, os subtipos são classificados como Luminal A (LA), Luminal B (LB), subtipo HER2 e Triplo-Negativo (TNBC). O padrão-ouro desta análise é a histologia e imuno-histoquímica, técnicas semiquantitativas que apresentam variações inter-laboratorial e inter-observador. A técnica de micro-espectroscopia no Infravermelho por Transformada de Fourier (FTIR), que fornece imagens hiperspectrais com informações bioquímicas de tecidos biológicos, é aplicada em conjunto de inteligência artificial (IA) para avaliação de cânceres. Nesta tese, foram utilizadas vinte amostras de duas linhagens celulares de câncer de mama, BT-474 e SK-BR-3, para definição do número ótimo de varreduras co-adicionadas para técnicas de aprendizado de máquina (ML). Foram utilizados os modelos de Análise Discriminante Linear (LDA), Análise Discriminante por Mínimos Quadrados Parciais (PLS-DA), K-Vizinhos Mais Próximos (KNN), Máquinas de Vetores de Suporte (SVM), Floresta Aleatória (RF) e Aumento de Gradiente Extremo (XGB). Sessenta imagens hiperespectrais de 320x320 pixels foram coletadas de trinta pacientes de biópsias humanas de mama em um microarranjo, cada qual contendo um núcleo de Câncer de mama (CA) e um de Tecido Adjacente (AT). Foram desenvolvidos métodos automatizados para organização e pré-processamento dos dados em unidimensionais (1D) e bidimensionais (2D) baseados em agrupamento K-Médias. Os dados foram utilizados para treinamento de dois novos modelos de aprendizado profundo para avaliação de subtipo de câncer de mama: CaReNet-V1, Rede Neural Convolucional (CNN) 1D; e CaReNet-V2, CNN 2D. Todos os modelos de ML alcançaram desempenhos semelhantes com os grupos b256_064 (256 varreduras de fundo e 64 varreduras de amostra), b256_128 e b128_128, onde a melhor acurácia de 0.995 foi apresentada pelo modelo XGB. O b256_064 foi estabelecido como o ideal dentre os três devido ao menor tempo de aquisição. O método baseado em K-Médias possibilitou o pré-processamento e organização totalmente automatizado, melhorando a qualidade dos dados e otimizando o treinamento das CNN. A CaReNet-V1 classificou com eficácia CA e AT (acurácia de teste dos espectros individuais de 0,89), além dos subtipos HER2 e TNBC (0,83 e 0,86), apresentando maiores dificuldades para LA e LB (0,74 e 0,68). O modelo possibilitou a avaliação dos números de onda que mais contribuíram para as predições, fornecendo uma relação direta com o conteúdo bioquímico das amostras. A CaReNet-V2 demonstrou melhor desempenho que a 1D, com acurácias de teste acima de 0,84, e possibilitou a predição dos níveis de ER, PR e HER2, onde os valores limítrofes apresentaram menor desempenho (acurácia mínima de 0,54). A regressão da porcentagem de Ki67 demonstrou erro médio absoluto de 3,6%. Por outro lado, sua avaliação de impacto por número de onda foi inferior ao 1D. Assim, este estudo aponta as técnicas de IA por imagens por micro-FTIR como potenciais para prover informações adicionais aos relatórios patológicos, servindo ainda como técnicas de triagem de pacientes

Medidas Físicas de Dirac

O presente trabalho tem como objetivo estudar sistemas dinâmicos que são caóticos do ponto de vista da dinâmica topológica, porém munidos de uma medida física de Dirac, isto é, medidas invariantes cuja bacia estatística de atração tem medida de Lebesgue positiva, suportadas em um ponto fixo. Neste sentido, provamos o teorema de Saghin-Sun-Vargas, que exibe uma deformação do fluxo linear no toro, cuja única medida ergódica é uma delta de Dirac com massa concentrada num ponto fixo; em particular, essa medida é física, e sua bacia estatística de atração coincide com o toro. Em seguida, estudamos os mapas com pontos fixos neutrais do intervalo e provamos o teorema sobre aplicações Maneville-Pomeau, que afirma o seguinte: existe uma medida infinita que é invariante e absolutamente contínua com respeito à medida de Lebesgue, e sua única medida física é uma delta de Dirac concentrada no ponto fixo com derivada igual a 1. Para finalizar, estudamos o teorema de Lai-Sang e Hu sobre difeomorfismos quase-Anosov; difeomorfismos que podem ser vistos como deformações de um anosov linear no toro no qual a direção instável no ponto fixo na origem fica indiferente.The work presented here has the objective to study dynamical systems that are chaotic from the point of view of topological dynamics, provided with a Dirac physical measure, that is, invariant measures whose statistical basin has positive Lebesgue measure and are supported in a fixed point. After, we prove the Saghin-Sun-Vargas theorem, exhibiting a perturbation of a linear flow in the torus, whose unique ergodic measure is a Dirac delta supported in a fixed point; in particular, we conclude that this measure is physical, and its statistical basin is the whole torus. From that point on, we study maps of the interval with neutral fixed points, and we prove a theorem about Maneville-Pomeau maps that states the following claim: that exists an infinite invariant measure that is absolutely continuous with respect to the Lebesgue measure, and its only physical measure is a Dirac delta supported in the fixed point whose derivative is equal to 1. We finish this work studying the Lai-Sang and Hu theorem about almost Anosov diffeomorphisms; these can be seen as a perturbation of an Anosov linear diffeomorphism in which the unstable direction in the fixed point at the origin is indifferent.177 f

Legal actions for plant-breeders’ rights in the comparative venezuelan laws

La naturaleza privada, la particularidad del objeto protegido por el Derecho de Obtentor y la falta de experiencia de las autoridades con poder de decisión, en varios países latinoamericanos, son circunstancias que exigen que las normas de observancia en esta área sean especialmente diseñadas para resguardar los derechos del titular de una variedad protegida. No basta que el legislador conceda derechos de exclusividad al obtentor de una nueva variedad vegetal; sino que igualmente debe otorgársele a este titular medios de defensa que les permitan hacer frente a cualquier acto que implique una explotación ilícita de su variedad. El presente trabajo hace referencia a las acciones administrativas y judiciales en algunos de los países de Suramérica, haciendo una breve alusión a [email protected] nature, particularity of the protected object by Plant Breeders’ Rights, and the lack of experience of decision-making authorities, in several Latin American countries, are circumstances requiring that compliance standards in this area are mainly designed to protect the exclusive rights of a plant variety. It is necessary the lawmaker grants exclusive rights to the breeder of a new plant variety, as well as defense means, which allow them to deal with any incident related to illicit uses. This paper offer administrative and legal actions in some South American countries, with a brief reference to Venezuela