Edge and plane classification with a biomimetic iCub fingertip sensor

The exploration and interaction of humanoid robots with the environment through tactile sensing is an important task for achieving truly autonomous agents. Recently much research has been focused on the development of new technologies for tactile sensors and new methods for tactile exploration. Edge detection is one of the tasks required in robots and humanoids to explore and recognise objects. In this work we propose a method for edge and plane classification with a biomimetic iCub fingertip using a probabilistic approach. The iCub fingertip mounted on an xy-table robot is able to tap and collect the data from the surface and edge of a plastic wall. Using a maximum likelihood classifier the xy-table knows when the iCub fingertip has reached the edge of the object. The study presented here is also biologically inspired by the tactile exploration performed in animals

Edge and plane classification with a biomimetic iCub fingertip sensor

The exploration and interaction of humanoid robots with the environment through tactile sensing is an important task for achieving truly autonomous agents. Recently much research has been focused on the development of new technologies for tactile sensors and new methods for tactile exploration. Edge detection is one of the tasks required in robots and humanoids to explore and recognise objects. In this work we propose a method for edge and plane classification with a biomimetic iCub fingertip using a probabilistic approach. The iCub fingertip mounted on an xy-table robot is able to tap and collect the data from the surface and edge of a plastic wall. Using a maximum likelihood classifier the xy-table knows when the iCub fingertip has reached the edge of the object. The study presented here is also biologically inspired by the tactile exploration performed in animals

Zoonosis, cambio climático y sociedad

La sociedad contemporánea se enfrenta a uno de los retos más grandes de la historia humana, el calentamiento global, mismo que acarrea enormes consecuencias, tales como los disturbios climáticos, así como los patrones de las enfermedades de origen animal transmisibles al hombre. Precisamente ante este escenario las instituciones educativas de nivel superior deben dar cumplimiento a su responsabilidad y ser las generadoras de alternativas de solución mediante el trabajo especializado de investigación; y para ello, la pesquisa científica es la mejor de las alternativas a nuestro alcance para comprender y encarar estos desafíos.Universidad Autónoma del Estado de México y Ediciones y Gráficos Eón, S.A. de C.V

Cognitive model for visual SLAM

In mobile robotics, visual perception in unknown environments consists mainly of two tasks: generation of a map and localization of the agent within it, using images as input. This problem is commonly called Visual Simultaneous Localization and Mapping ( Visual SLA M). This project aims of increasing the capacities of spatial reasoning in robotics systems based on cognitive vision. Baycsian formulation of visual mapping is extended to consider geometric properties for increasing of scene understand- ing. In this search, this work presents a framework that covers the computa- tional and algorithmic perspectives in Cognitive Vision. proposed by Marr. A part of the work is devoted to develop the representational framework for describing the visual phenomenon in monocular SLAM, unifying concepts about the existent parametrization and extending the analysis to geometric properties and relations in the scene. This yields a novel strategy for aug- mented mapping with high lcvellandmarks based on planar surfaces. After, we explore the computational level of visual SLAM through a sym- bolic framework for describing the elements required for spatial reasoning, involved in visual SLAM. An ontology for spatial reasoning is proposed upon visual SLAM context. An axiomatic set in visual scenario is related to prop- erties in projective geometry, assuring a scheme for semantic attachment. Finally, a novel approach to solve visual SLAM based on spatial reasoning is presented, focusing on the algorithmic level. The model of latent geomet- ric constraints is presented as a non-parametric Baycsian extension of visual SLAM. The generative model produces multiples scenarios with different visual conditions. Although this thesis is focused on solving visual SLAM, the proposed ap- proach can be conceived as a methodology for the design of cognitive dynamic systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The Potential of Resveratrol to Act as a Caloric Restriction Mimetic Appears to Be Limited: Insights from Studies in Mice

Caloric restriction (CR) has been shown repeatedly to prolong the lifespan in laboratory animals, with its benefits dependent on molecular targets forming part of the nutrient signaling network, including the NAD-dependent deacetylase silent mating type information regulation 2 homologue 1 (SIRT1). It has been hypothesized that the stilbene resveratrol (RSV) may counteract age- and obesity-related diseases similarly to CR. In yeast and worms, RSV-promoted longevity also depended on SIRT1. While it remains unclear whether RSV can prolong lifespans in mammals, some studies in rodents supplemented with RSV have reported lowered body weight (BW) and fat mass, improved insulin sensitivity, lowered cholesterol levels, increased fitness, and mitochondrial biogenesis. Molecular mechanisms possibly leading to such changes include altered gene transcription and activation of SIRT1, AMP-activated kinase (AMPK), and peroxisome proliferator–activated receptor gamma coactivator 1-alpha (PPARGC1A). However, some mouse models did not benefit from RSV treatment to the same extent as others. We conducted a literature search on PubMed (15 April, 2020) for trials directly comparing RSV application to CR feeding in mice. In most studies retrieved by this systematic PubMed search, mice supplemented with RSV did not show significant reductions of BW, glucose, or insulin. Moreover, in some of these studies, RSV and CR treatments affected molecular targets differently and/or findings on RSV and CR impacts varied between trials. We discuss those RSV-induced changes in gene transcription hypothesized to partly counteract age-related alterations. Although there may be a moderate effect of RSV supplementation on parameters such as insulin sensitivity toward a more CR-like profile in mice, data are inconsistent. Likewise, RSV supplementation trials in humans report controversial findings. While we consider that RSV may, under certain circumstances, moderately mimic some aspects of CR, current evidence does not fully support its use to prevent or treat age- or obesity-related diseases.The project was funded by the German Research Foundation Deutsche Forschungsgemeinschaft (project number 274521263).Peer reviewe

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)