The Beltrami Flow over Manifolds

In many medical computer vision tasks, the relevant data is attached to a specific tissue such as the cortex or the colon. This situation calls for regularization techniques which are defined over non flat surfaces. We introduce in this paper the Beltrami flow over manifolds. This new regularization technique overcomes the over-smoothing of the L_2 flow and the staircasing effects of the L_1 flow, that were recently suggested via the harmonic map methods. The key of our approach is first to clarify the link between the intrinsic Polyakov action and the implicit Harmonic energy functional and then use the geometrical understanding of the Beltrami Flow to generalize it to images on explicitly and implicitly defined non flat surfaces. It is shown that once again the Beltrami flow interpolates between the L_2 and L_1 flows on non-flat surfaces. The implementation scheme of this flow is presented and various experimental results obtained on a set of various real images illustrate the performances of the approach as well as the differences with the harmonic map flows. This extension of the Beltrami flow to the case of non flat surfaces opens new perspectives in the regularization of noisy data defined on manifolds

Analyse des groupes de gènes co-exprimés : un outil automatique pour l'interprétation des expériences de biopuces (version étendue)

National audienceLa technologie des biopuces permet de mesurer les niveaux d'expression de milliers de gènes dans différentes conditions biologiques générant ainsi des masses de données à analyser. De nos jours, l'interprétation de ces volumineux jeux de donnés à la lumière des différentes sources d'informations est l'un des principaux défis dans la bio-informatique. Nous avons développé une nouvelle méthode appelée AGGC (Analyse des Groupes de Gènes Co-exprimés) qui permet de constituer de manière automatique des groupes de gènes à la fois fonctionnellement riches, i.e. qui partagent les mêmes annotations fonctionnelles, et co-exprimés. AGGC intègre l'information issue des biopuces, i.e. les profils d'expression des gènes, avec les annotations fonctionnelles des gènes obtenues à partir des sources d'informations génomiques comme Gene Ontology. Les expérimentations menées avec cette méthode ont permis de mettre en évidence les principaux groupes de gènes fonctionnellement riches et co-exprimés dans des expériences de biopuces. Programme et informations annexes : http://keia.i3s.unice.fr/?Implementations:CGGA

Co-expressed Gene Groups Analysis (CGGA): An Automatic Tool for the Interpretation of Microarray Experiments

International audienceMicroarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of this large amount of data using different sources of information. We have developed a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co-expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology. By applying CGGA to well-known microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments. CGGA program is available at http://www.i3s.unice.fr/~rmartine/CGG

Co-expressed Gene Groups Analysis (CGGA): An Automatic Tool for the Interpretation of Microarray Experiments

International audienceMicroarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of this large amount of data using different sources of information. We have developed a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co-expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology. By applying CGGA to well-known microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments. CGGA program is available at http://www.i3s.unice.fr/~rmartine/CGG

Plan de responsabilidad social para la mejora de la compañía Alkosto

Anexo A E-Book de ética para la empresa Alkosto Link : https://editor-storage.reedsy.com/books/135368/exports/success/9355e084-0fb9- 4e34-9b07-28b4fe345e33/202004092354-codigo-de-etica-alkosto.zip Anexo B Diagnostico por observaciónLa empresa seleccionada es una cadena de tiendas en Colombia llamada Alkosto, se caracteriza por ser líder en el sector comercial compitiendo con sus precios bajos, además de comercializar todo tipo de elementos básicos para la satisfacción de una gran variedad de necesidades, por esto vemos una gran oportunidad en expandirnos enfocándonos para el año 2022 en tener mayor presencia a nivel nacional llevando a cada uno de nuestros clientes la mejor experiencia de compra para así fidelizarlos, esto iniciando desde varias de nuestras estrategias de marketing interno y externo, donde deseamos involucrar nuestros clientes y a su vez mejorar nuestra visibilidad mediante la potencialización de marketing digital y redes sociales que a su vez nos ayudara a disminuir publicidad física que genera desechos e impacto ambiental siendo eficaces en nuestro proceso de responsabilidad social, adicional buscamos generar inclusión y reconociendo a todas las personas que interactúen de manera directa o indirecta con la compañía impulsando una mejor calidad de vida, en aspectos económicos y ambientales mediante planes de acción que nos lleven a impactar positivamente y cumpliendo protocolos establecidos para nuestra excelencia, igualmente siempre enfocándonos en incentivar y organizar nuestro personal mediante las políticas de la compañía y cumplimiento estricto de normas para garantizar la calidad de nuestros productos, el buen funcionamiento de todos los almacenes y generando valores corporativos al personal encargado en todas las áreas, para alcanzar la excelencia en conjunto como compañía y transmitirla mediante la prestación de nuestros servicios logrando la satisfacción del cliente.The selected company is a chain of stores in Colombia called Alkosto, it is characterized by being a leader in the commercial sector competing with its low prices, in addition to marketing all kinds of basic elements to satisfy a wide variety of needs, for this we see a great opportunity to expand by focusing by 2022 on having a greater presence at the national level, bringing to each of our clients the best shopping experience in order to retain them, this starting from several of our internal and external marketing strategies, where we want to involve our clients and in turn improve our visibility through the enhancement of digital marketing and social networks that in turn will help us to reduce physical advertising that generates waste and environmental impact, being effective in our process of social responsibility, additionally we seek to generate inclusion and recognizing all people they interact man was direct or indirect with the company promoting a better quality of life, in economic and environmental aspects through action plans that lead us to positively impact and complying with established protocols for our excellence, also always focusing on incentivizing and organizing our personnel through the policies of the company and strict compliance with standards to guarantee the quality of our products, the proper functioning of all warehouses and generating corporate values for the personnel in charge in all areas, to achieve overall excellence as a company and transmit it through the provision of our services achieving customer satisfaction

An Innovative AI-based primer design tool for precise and accurate detection of SARS-CoV-2 variants of concern

As the COVID-19 pandemic winds down, it leaves behind the serious concern that future, even more disruptive pandemics may eventually surface. One of the crucial steps in handling the SARS-CoV-2 pandemic was being able to detect the presence of the virus in an accurate and timely manner, to then develop policies counteracting the spread. Nevertheless, as the pandemic evolved, new variants with potentially dangerous mutations appeared. Faced by these developments, it becomes clear that there is a need for fast and reliable techniques to create highly specific molecular tests, able to uniquely identify VOCs. Using an automated pipeline built around evolutionary algorithms, we designed primer sets for SARS-CoV-2 (main lineage) and for VOC, B.1.1.7 (Alpha) and B.1.1.529 (Omicron). Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets for the main lineage and each variant in a matter of hours. Preliminary in-silico validation showed that the sequences in the primer sets featured high accuracy. A pilot test in a laboratory setting confirmed the results: the developed primers were favorably compared against existing commercial versions for the main lineage, and the specific versions for the VOCs B.1.1.7 and B.1.1.529 were clinically tested successfully

Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network’s behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics

Association between convalescent plasma treatment and mortality in COVID-19: a collaborative systematic review and meta-analysis of randomized clinical trials.

Funder: laura and john arnold foundationBACKGROUND: Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, https://doi.org/10.17605/OSF.IO/GEHFX ). METHODS: In this collaborative systematic review and meta-analysis, clinical trial registries (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform), the Cochrane COVID-19 register, the LOVE database, and PubMed were searched until April 8, 2021. Investigators of trials registered by March 1, 2021, without published results were contacted via email. Eligible were ongoing, discontinued and completed randomized clinical trials that compared convalescent plasma with placebo or no treatment in COVID-19 patients, regardless of setting or treatment schedule. Aggregated mortality data were extracted from publications or provided by investigators of unpublished trials and combined using the Hartung-Knapp-Sidik-Jonkman random effects model. We investigated the contribution of unpublished trials to the overall evidence. RESULTS: A total of 16,477 patients were included in 33 trials (20 unpublished with 3190 patients, 13 published with 13,287 patients). 32 trials enrolled only hospitalized patients (including 3 with only intensive care unit patients). Risk of bias was low for 29/33 trials. Of 8495 patients who received convalescent plasma, 1997 died (23%), and of 7982 control patients, 1952 died (24%). The combined risk ratio for all-cause mortality was 0.97 (95% confidence interval: 0.92; 1.02) with between-study heterogeneity not beyond chance (I2 = 0%). The RECOVERY trial had 69.8% and the unpublished evidence 25.3% of the weight in the meta-analysis. CONCLUSIONS: Convalescent plasma treatment of patients with COVID-19 did not reduce all-cause mortality. These results provide strong evidence that convalescent plasma treatment for patients with COVID-19 should not be used outside of randomized trials. Evidence synthesis from collaborations among trial investigators can inform both evidence generation and evidence application in patient care

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta