Plan de mejoramiento del servicio municipal de cementerios del Municipio de Buenos Aires en el año 2021

EL municipio de Buenos Aires cuenta con dos cementerios públicos; uno ubicado en la zona norte del casco urbano del municipio y el otro ubicado en la zona rural, en la comarca del Menco. También existe un pequeño cementerio familiar privado en una finca al noreste del casco urbano, en el cual la familia le da mantenimiento cumplen con las normas requeridas y explicitas por el MINSA y la municipalidad. Actualmente el cementerio ubicado en el casco urbano, está deteriorado y además de esto no cuenta con personas permanente, que velen por el bienestar, el buen funcionamiento y la limpieza de este, así como también por las noches cuente con la seguridad adecuada puesto que muchos pobladores ponen adornos de alto valor a sus deudos en sus sepulturas y por las noches entran y hacen males a estas. Este es un estudio de investigación sobre el plan de mejoramiento del servicio del cementerio municipal de Buenos Aires, a lo largo del año 2021. Explica las características del municipio, la problemática del mal funcionamiento del servicio de cementerio, la mala infraestructura que este tiene, así como también abarcamos un plan de mejoramiento a dicho cementerio. Exponemos el presupuesto para las mejoras a la infraestructura del cementerio y el pago de los trabajadores a contratar garantizando la estabilidad laboral de estos. Se abarca un poco del impacto socioeconómico que tendrá durante la ejecución y la finalización de este. Se aborda las diferentes capacitaciones que se van a impartir a las personas a contratar de manera permanente. Consta de 4 capítulos en donde se desarrolla diferentes aspectos para mejorar el cementerio municipal, y brindar una mejor atención a la municipalida

ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status and scoring of several established biomarkers, including ER, PGR, HER2 and KI67. However, this is a task that can also be facilitated by computational pathology image analysis methods. The research in computational pathology has recently made numerous substantial advances, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients. The primary purpose of the data set was to facilitate the ACROBAT WSI registration challenge, aiming at accurately aligning H&E and IHC images. For research in the area of image registration, automatic quantitative feedback on registration algorithm performance remains available through the ACROBAT challenge website, based on more than 37,000 manually annotated landmark pairs from 13 annotators. Beyond registration, this data set has the potential to enable many different avenues of computational pathology research, including stain-guided learning, virtual staining, unsupervised pre-training, artefact detection and stain-independent models

The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods

Image Processing, Machine Learning and Visualization for Tissue Analysis

Knowledge discovery for understanding mechanisms of disease requires the integration of multiple sources of data collected at various magnifications and by different imaging techniques. Using spatial information, we can build maps of tissue and cells in which it is possible to extract, e.g., measurements of cell morphology, protein expression, and gene expression. These measurements reveal knowledge about cells such as their identity, origin, density, structural organization, activity, and interactions with other cells and cell communities. Knowledge that can be correlated with survival and drug effectiveness. This thesis presents multidisciplinary projects that include a variety of methods for image and data analysis applied to images coming from fluorescence- and brightfield microscopy. In brightfield images, the number of proteins that can be observed in the same tissue section is limited. To overcome this, we identified protein expression coming from consecutive tissue sections and fused images using registration to quantify protein co-expression. Here, the main challenge was to build a framework handling very large images with a combination of rigid and non-rigid image registration.  Using multiplex fluorescence microscopy techniques, many different molecular markers can be used in parallel, and here we approached the challenge to decipher cell classes based on marker combinations. We used ensembles of machine learning models to perform cell classification, both increasing performance over a single model and to get a measure of confidence of the predictions.  We also used resulting cell classes and locations as input to a graph neural network to learn cell neighborhoods that may be correlated with disease. Finally, the work leading to this thesis included the creation of an interactive visualization tool, TissUUmaps. Whole slide tissue images are often enormous and can be associated with large numbers of data points, creating challenges which call for advanced methods in processing and visualization. We built TissUUmaps so that it could visualize millions of data points from in situ sequencing experiments and enable contextual study of gene expression directly in the tissue at cellular and sub-cellular resolution. We also used TissUUmaps for interactive image registration, overlay of regions of interest, and visualization of tissue and corresponding cancer grades produced by deep learning methods.   The aforementioned methods and tools together provide the framework for analysing and visualizing vast and complex spatial tissue structures. These developments in understanding the spatial information of tissue in different diseases pave the way for new discoveries and improving the treatment for patients

Image Processing, Machine Learning and Visualization for Tissue Analysis

Knowledge discovery for understanding mechanisms of disease requires the integration of multiple sources of data collected at various magnifications and by different imaging techniques. Using spatial information, we can build maps of tissue and cells in which it is possible to extract, e.g., measurements of cell morphology, protein expression, and gene expression. These measurements reveal knowledge about cells such as their identity, origin, density, structural organization, activity, and interactions with other cells and cell communities. Knowledge that can be correlated with survival and drug effectiveness. This thesis presents multidisciplinary projects that include a variety of methods for image and data analysis applied to images coming from fluorescence- and brightfield microscopy. In brightfield images, the number of proteins that can be observed in the same tissue section is limited. To overcome this, we identified protein expression coming from consecutive tissue sections and fused images using registration to quantify protein co-expression. Here, the main challenge was to build a framework handling very large images with a combination of rigid and non-rigid image registration.  Using multiplex fluorescence microscopy techniques, many different molecular markers can be used in parallel, and here we approached the challenge to decipher cell classes based on marker combinations. We used ensembles of machine learning models to perform cell classification, both increasing performance over a single model and to get a measure of confidence of the predictions.  We also used resulting cell classes and locations as input to a graph neural network to learn cell neighborhoods that may be correlated with disease. Finally, the work leading to this thesis included the creation of an interactive visualization tool, TissUUmaps. Whole slide tissue images are often enormous and can be associated with large numbers of data points, creating challenges which call for advanced methods in processing and visualization. We built TissUUmaps so that it could visualize millions of data points from in situ sequencing experiments and enable contextual study of gene expression directly in the tissue at cellular and sub-cellular resolution. We also used TissUUmaps for interactive image registration, overlay of regions of interest, and visualization of tissue and corresponding cancer grades produced by deep learning methods.   The aforementioned methods and tools together provide the framework for analysing and visualizing vast and complex spatial tissue structures. These developments in understanding the spatial information of tissue in different diseases pave the way for new discoveries and improving the treatment for patients

TissUUmaps : interactive visualization of large-scale spatial gene expression and tissue morphology data

Motivation: Visual assessment of scanned tissue samples and associated molecular markers, such as gene expression, requires easy interactive inspection at multiple resolutions. This requires smart handling of image pyramids and efficient distribution of different types of data across several levels of detail. Results: We present TissUUmaps, enabling fast visualization and exploration of millions of data points overlaying a tissue sample. TissUUmaps can be used both as a web service or locally in any computer, and regions of interest as well as local statistics can be extracted and shared among users

Diseño e implementación de equipo regenerador de tejidos blandos basado en diodos emisores de luz y láser diodo

El objetivo del presente estudio es diseñar e implementar un equipo regenerador de tejidos blandos basado en diodos emisores de luz y diodo láser, con potencia luminosa regulable entre 5-100mW, en pasos de 5mW; con frecuencia de pulsación del haz de luz, generado por una onda cuadrada simétrica, variable entre 1-100Hz, en pasos de 1Hz; y tiempo de aplicación entre 1 y 15 minutos, con pasos de 1min; de esta de manera se puede controlar la energía luminosa total emitida por los diodos emisores de luz o diodo láser que contribuya en la investigación de una posible solución a la necesidad de acelerar el proceso regenerativo de los tejidos blandos. En la presente tesis se usará una longitud de onda entre 630 – 660nm, luz roja, debido a que en este rango de longitudes de onda se ha demostrado científicamente que se acelera el proceso de regeneración de las células. Se abarcan temas específicos como conocimientos teóricos y prácticos acerca del fundamento clínico de la regeneración celular. Para la implementación del equipo se hizo uso de un microcontrolador, que permitió controlar la energía luminosa emitida por los diodos emisores de luz y el láser diodo hacia los tejidos blandos, controlando la potencia, la frecuencia y el tiempo del tratamiento, además de ayudarnos a implementar un equipo interactivo con el usuario que permita su fácil uso, con una pantalla LCD y un teclado matricial. Se implementó el equipo siguiendo algunas consideraciones teóricas de la norma de seguridad eléctrica para equipos médicos, IEC60601 y la norma IEC 601-2-22, para equipos médicos eléctricos con requerimientos particulares para la seguridad del diagnóstico y terapia de equipos con láser. Se realizaron pruebas eléctricas y se verificó que el margen de error de 10% permitido por la norma no fuese superado. Las pruebas se realizaron a la corriente entregada para la potencia luminosa deseada, frecuencia y tiempo. Los resultados en potencia luminosa fueron: un error mínimo de 0.15% y máximo de 2.744% para los diodos emisores de luz, y un error mínimo de 2.338% y máximo de 8.371% para el diodo láser. Los resultados en frecuencia fueron: un error mínimo de 0.417% y máximo de 0.625%. Por último, el resultado en tiempo fue de un error del 0.01%. Se verifica que se cumplen las normas básicas para la implementación del equipo. Además se observa que el equipo deberá pasar muchas más pruebas para ser comercializado, ya que requiere experimentación clínica lo cual no es parte de esta tesis. Finalmente se realizó una tabulación del costo del proyecto.Tesi

Automated identification of the mouse brain’s spatial compartments from in situ sequencing data

Background Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape. With the advent of in situ sequencing technologies, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments. This also opens up for new approaches to identifying tissue compartments. Results Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that computationally defined anatomical compartments are highly reproducible across individuals and have the potential to replace manual annotation based on cell and tissue morphology.  Conclusion Mapping the brain based on molecular information means that we can create detailed atlases independent of angle at sectioning or variations between individuals.Gabriele Partel and Markus M. Hilscher contributed equally to this work.</p