Self-supervised pseudo-colorizing of masked cells

Self-supervised learning, which is strikingly referred to as the dark matter of intelligence, is gaining more attention in biomedical applications of deep learning. In this work, we introduce a novel self-supervision objective for the analysis of cells in biomedical microscopy images. We propose training deep learning models to pseudo-colorize masked cells. We use a physics-informed pseudo-spectral colormap that is well suited for colorizing cell topology. Our experiments reveal that approximating semantic segmentation by pseudo-colorization is beneficial for subsequent fine-tuning on cell detection. Inspired by the recent success of masked image modeling, we additionally mask out cell parts and train to reconstruct these parts to further enrich the learned representations. We compare our pre-training method with self-supervised frameworks including contrastive learning (SimCLR), masked autoencoders (MAEs), and edge-based self-supervision. We build upon our previous work and train hybrid models for cell detection, which contain both convolutional and vision transformer modules. Our pre-training method can outperform SimCLR, MAE-like masked image modeling, and edge-based self-supervision when pre-training on a diverse set of six fluorescence microscopy datasets. Code is available at: https://github.com/roydenwa/pseudo-colorize-masked-cells

Self-supervised pseudo-colorizing of masked cells

Self-supervised learning, which is strikingly referred to as the dark matter of intelligence, is gaining more attention in biomedical applications of deep learning. In this work, we introduce a novel self-supervision objective for the analysis of cells in biomedical microscopy images. We propose training deep learning models to pseudo-colorize masked cells. We use a physics-informed pseudo-spectral colormap that is well suited for colorizing cell topology. Our experiments reveal that approximating semantic segmentation by pseudo-colorization is beneficial for subsequent fine-tuning on cell detection. Inspired by the recent success of masked image modeling, we additionally mask out cell parts and train to reconstruct these parts to further enrich the learned representations. We compare our pre-training method with self-supervised frameworks including contrastive learning (SimCLR), masked autoencoders (MAEs), and edge-based self-supervision. We build upon our previous work and train hybrid models for cell detection, which contain both convolutional and vision transformer modules. Our pre-training method can outperform SimCLR, MAE-like masked image modeling, and edge-based self-supervision when pre-training on a diverse set of six fluorescence microscopy datasets. Code is available at: https://github.com/roydenwa/pseudo-colorize-masked-cell

Fuentes de financiamiento y su influencia en la rentabilidad de la micro y pequeña empresa del sector transporte del distrito de Huaraz - empresa “Transporte Renzo” - 2016

El presente proyecto de investigación tiene por finalidad Describir El financiamiento y su influencia en la rentabilidad de las micro y pequeñas empresas del sector transportes del Perú: Caso empresa “Transportes Renzo” de Huaraz, 2016. el objetivo general comienza a desarrollarse sobre la importancia económica y social, que actualmente tienen el financiamiento y su influencia en la rentabilidad, por intermedio de esta investigación se van a canalizar nuevas estrategias de desarrollo con respecto a la caracterización del financiamiento y rentabilidad de las micro y pequeñas empresas en el rubro, seguidamente se realizara el planeamiento del problema de investigación, así como también el enunciado del problema, los objetivos de estudio tanto el objetivo general como los específicos de la misma manera la justificación del problema su aporte, conocimiento y beneficio. Se presenta información sobre los antecedentes internacionales, nacionales, regionales y locales resaltando su importancia y su auge, en esta fase se incluye el proceso que han atravesado las Mypes en el Perú y el mundo, así como también teorías y definiciones conceptuales del financiamiento, rentabilidad y Mypes. En la metodología se presenta el tipo y nivel de investigación, el diseño de investigación, la población y la muestra, operacionalizacion de las variables, así como las técnicas e instrumentos a utilizar y el plan de análisis. Finamente se presenta las conclusiones que fueron fruto de la investigación, así como la bibliografía consultada y algunos anexos que se consideraron de vital importancia.Tesi

A negative feedback between anthropogenic ozone pollution and enhanced ocean emissions of iodine

Naturally emitted from the oceans, iodine compounds efficiently destroy atmospheric ozone and reduce its positive radiative forcing effects in the troposphere. Emissions of inorganic iodine have been experimentally shown to depend on the deposition to the oceans of tropospheric ozone, whose concentrations have significantly increased since 1850 as a result of human activities. A chemistry-climate model is used herein to quantify the current ocean emissions of inorganic iodine and assess the impact that the anthropogenic increase in tropospheric ozone has had on the natural cycle of iodine in the marine environment since pre-industrial times. Our results indicate that the human-driven enhancement of tropospheric ozone has doubled the oceanic inorganic iodine emissions following the reaction of ozone with iodide at the sea surface. The consequent build-up of atmospheric iodine, with maximum enhancements of up to 70% with respect to pre-industrial times in continental pollution outflow regions, has in turn accelerated the ozone chemical loss over the oceans with strong spatial patterns. We suggest that this ocean-atmosphere interaction represents a negative geochemical feedback loop by which current ocean emissions of iodine act as a natural buffer for ozone pollution and its radiative forcing in the global marine environment.Fil: Prados Roman, C.. Consejo Superior de Investigaciones Científicas. Instituto de Química Física; EspañaFil: Cuevas, Carlos Alberto. Consejo Superior de Investigaciones Científicas. Instituto de Química Física; EspañaFil: Fernandez, Rafael Pedro. Consejo Superior de Investigaciones Científicas. Instituto de Química Física; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Kinnison, Douglas E.. National Center For Atmospheric Research. Amospheric Chemistry División; Estados UnidosFil: Lamarque, Jean Francoise. National Center For Atmospheric Research. Amospheric Chemistry División; Estados UnidosFil: Saiz-lopez, Alfonso. Consejo Superior de Investigaciones Científicas. Instituto de Química Física; Españ

Vitiligo-like Depigmentation in a Patient Undergoing Treatment with Nivolumab for Advanced Renal-cell Carcinoma

Nivolumab is a fully human monoclonal antibody that targets the programmed cell death 1 (PD-1) immune checkpoint. It has been approved for its use in several types of advanced solid tumors, including melanoma, lung cancer, and renal cell carcinoma (RCC). The inhibition of PD-1 leads to an enhanced adaptive immune response against tumor cells through the activation of T-cells

Vitiligo-like Depigmentation in a Patient Undergoing Treatment with Nivolumab for Advanced Renal-cell Carcinoma

Nivolumab is a fully human monoclonal antibody that targets the programmed cell death 1 (PD-1) immune checkpoint. It has been approved for its use in several types of advanced solid tumors, including melanoma, lung cancer, and renal cell carcinoma (RCC). The inhibition of PD-1 leads to an enhanced adaptive immune response against tumor cells through the activation of T-cells

Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes

Crohn's disease; Microbiome; Ulcerative colitisEnfermedad de Crohn; Microbioma; Colitis ulcerosaMalaltia de Crohn; Microbioma; Colitis ulcerosaInflammatory bowel disease (IBD) is a chronic disease with unknown pathophysiological mechanisms. There is evidence of the role of microorganims in this disease development. Thanks to the open access to multiple omics data, it is possible to develop predictive models that are able to prognosticate the course and development of the disease. The interpretability of these models, and the study of the variables used, allows the identification of biological aspects of great importance in the development of the disease. In this work we generated a metagenomic signature with predictive capacity to identify IBD from fecal samples. Different Machine Learning models were trained, obtaining high performance measures. The predictive capacity of the identified signature was validated in two external cohorts. More precisely a cohort containing samples from patients suffering Ulcerative Colitis and another from patients suffering Crohn's Disease, the two major subtypes of IBD. The results obtained in this validation (AUC 0.74 and AUC = 0.76, respectively) show that our signature presents a generalization capacity in both subtypes. The study of the variables within the model, and a correlation study based on text mining, identified different genera that play an important and common role in the development of these two subtypes.CF-L's work was supported by the Collaborative Project in Genomic Data Integration (CICLOGEN) PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER)–A way to build Europe. JS's work was funded by the Ramón y Cajal grant (RYC2019-026576-I) funded by Ministry of Science and Innovation of the Spanish government. GL-C's work was supported by a grant from the Biotechnology and Biological Sciences Research Council (BBSRC grant BB/S006281/1) and open access publication fees were supported by Queen's University of Belfast UKRI block grant

Phenomenology of Supersymmetric Theories with and without R-Parity

We review supersymmetry models with and without R-parity. After briefly describing the Minimal Supersymetric Standard Model and its particle content we move to models where R-parity is broken, either spontaneously or explicitly. In this last case we consider the situation where R-parity is broken via bilinear terms in the superpotential. The radiative breaking of these models is described in the context of $b$--$\tau$ and $b$--$\tau$--$t$ unification. Finally we review the phenomenology of these R-parity violating models.Comment: 9 pages, 5 Figures in Postscript. Talk given at the EuroConference on Frontiers in Particle Astrophysics and Cosmology, San Feliu de Guixols, Spain, 30 September - 5 October, 200

Chemical interactions between ship-originated air pollutants and ocean-emitted halogens

Ocean-going ships supply products from one region to another and contribute to the world's economy. Ship exhaust contains many air pollutants and results in significant changes in marine atmospheric composition. The role of Reactive Halogen Species (RHS) in the troposphere has received increasing recognition and oceans are the largest contributors to their atmospheric burden. However, the impact of shipping emissions on RHS and that of RHS on ship-originated air pollutants have not been studied in detail. Here, an updated WRF-Chem model is utilized to explore the chemical interactions between ship emissions and oceanic RHS over the East Asia seas in summer. The emissions and resulting chemical transformations from shipping activities increase the level of NO and NO2 at the surface, increase O3 in the South China Sea, but decrease O3 in the East China Sea. Such changes in pollutants result in remarkable changes in the levels of RHS as well as in their partitioning. The abundant RHS, in turn, reshape the loadings of air pollutants and those of the oxidants with marked patterns along the ship tracks. We, therefore, suggest that these important chemical interactions of ship-originated emissions with RHS should be considered in the environmental policy assessments of the role of shipping emissions in air quality and climate.Fil: Li, Qinyi. Consejo Superior de Investigaciones Científicas. Instituto de Química Física; EspañaFil: Fernandez, Rafael P.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Interdisciplinario de Ciencias Básicas. - Universidad Nacional de Cuyo. Instituto Interdisciplinario de Ciencias Básicas; ArgentinaFil: Mahan, Anoop. No especifíca;Fil: Lopez, Ana Isabel. Universidad Tecnológica Nacional. Facultad Regional de Mendoza; ArgentinaFil: Shanshan, Wang. Key Laboratory Atmospheric Particle Pollution Research; ChinaFil: Puliafito, Salvador Enrique. Universidad Tecnológica Nacional. Facultad Regional de Mendoza; ArgentinaFil: Cuevas, Carlos A.. Consejo Superior de Investigaciones Científicas. Instituto de Química Física; EspañaFil: Saiz Lopez, Alfonso. Consejo Superior de Investigaciones Científicas. Instituto de Química Física; EspañaEGU General Assembly 2021AustriaEuropean Geosciences Unio