Intestinal microbiota influences non-intestinal related autoimmune diseases

Indexación: Scopus.The human body is colonized by millions of microorganisms named microbiota that interact with our tissues in a cooperative and non-pathogenic manner. These microorganisms are present in the skin, gut, nasal, oral cavities, and genital tract. In fact, it has been described that the microbiota contributes to balancing the immune system to maintain host homeostasis. The gut is a vital organ where microbiota can influence and determine the function of cells of the immune system and contributes to preserve the wellbeing of the individual. Several articles have emphasized the connection between intestinal autoimmune diseases, such as Crohn's disease with dysbiosis or an imbalance in the microbiota composition in the gut. However, little is known about the role of the microbiota in autoimmune pathologies affecting other tissues than the intestine. This article focuses on what is known about the role that gut microbiota can play in the pathogenesis of non-intestinal autoimmune diseases, such as Grave's diseases, multiple sclerosis, type-1 diabetes, systemic lupus erythematosus, psoriasis, schizophrenia, and autism spectrum disorders. Furthermore, we discuss as to how metabolites derived from bacteria could be used as potential therapies for non-intestinal autoimmune diseases. © 2018 Opazo, Ortega-Rocha, Coronado-Arrázola, Bonifaz, Boudin, Neunlist, Bueno, Kalergis and Riedel.https://www.frontiersin.org/articles/10.3389/fmicb.2018.00432/ful

Applying MILP-based algorithms to automated job-shop scheduling problems in aircraft-part manufacturing

This work presents efficient algorithms based on Mixed-Integer Linear Programming (MILP) for complex job-shop scheduling problems raised in Automated Manufacturing Systems. The aim of this work is to find alternative solution approaches of production and transportation operations in a multi-product multistage production process that can be used to solve industrial-scale problems with reasonable computational effort. The MILP model developed must take into account; dissimilar recipes, single unit per production stage, re-entrant flows, sequence- dependent free transferring times and load transfer movements in a single automated material-handling device. In addition, logical-based strategies are proposed to iteratively find and improve the solutions generated over time. These approaches were tested in different real-world problems appeared in the surfacetreatment process of metal components in aircraft manufacturing industry.Sociedad Argentina de Informática e Investigación Operativ

Applying MILP-based algorithms to automated job-shop scheduling problems in aircraft-part manufacturing

This work presents efficient algorithms based on Mixed-Integer Linear Programming (MILP) for complex job-shop scheduling problems raised in Automated Manufacturing Systems. The aim of this work is to find alternative solution approaches of production and transportation operations in a multi-product multistage production process that can be used to solve industrial-scale problems with reasonable computational effort. The MILP model developed must take into account; dissimilar recipes, single unit per production stage, re-entrant flows, sequence- dependent free transferring times and load transfer movements in a single automated material-handling device. In addition, logical-based strategies are proposed to iteratively find and improve the solutions generated over time. These approaches were tested in different real-world problems appeared in the surfacetreatment process of metal components in aircraft manufacturing industry.Sociedad Argentina de Informática e Investigación Operativ

Applying MILP-based algorithms to automated job-shop scheduling problems in aircraft-part manufacturing

This work presents efficient algorithms based on Mixed-Integer Linear Programming (MILP) for complex job-shop scheduling problems raised in Automated Manufacturing Systems. The aim of this work is to find alternative solution approaches of production and transportation operations in a multi-product multistage production process that can be used to solve industrial-scale problems with reasonable computational effort. The MILP model developed must take into account; dissimilar recipes, single unit per production stage, re-entrant flows, sequence- dependent free transferring times and load transfer movements in a single automated material-handling device. In addition, logical-based strategies are proposed to iteratively find and improve the solutions generated over time. These approaches were tested in different real-world problems appeared in the surfacetreatment process of metal components in aircraft manufacturing industry.Sociedad Argentina de Informática e Investigación Operativ

Understanding mountain soils : a contribution from mountain areas to the International Year of Soils 2015

A volcanic tuff rock known as cangahua (Ecuador) or tepetate (Mexico) is found throughout the Andes. Problems have arisen as the layers of light but fragile soil that once covered the tuff have been lost for both natural (environmental) reasons and because of over-cultivation. When the soil is gone, the tuff is impermeable and sterile. Now, a project in Ecuador has determined that the tuff itself can be reclaimed and is supporting a programme that sends bulldozers to the tuff regions to break up the rock and create a new fertile soil

FTIR-ATR Spectroscopy Combined with Multivariate Regression Modeling as a Preliminary Approach for Carotenoids Determination in Cucurbita spp

Quantitative analysis of carotenoids has been extensively reported using UV\u2010Vis spectrophotometry and chromatography, instrumental techniques that require complex extraction protocols with organic solvents. Fourier transform infrared spectroscopy (FTIR) is a potential alternative for simplifying the analysis of food constituents. In this work, the application of FTIR with attenuated total reflectance (ATR) was evaluated for the determination of total carotenoid content (TCC) in Cucurbita spp. samples. Sixty\u2010three samples, belonging to different cultivars of butternut squash (C. moschata) and pumpkin (C. maxima), were selected and analyzed with FTIR\u2010 ATR (attenuated total reflectance). Three different preparation protocols for samples were followed: homogenization (A), freeze\u2010drying (B), and solvent extraction (C). The recorded spectra were used to develop regression models by Partial Least Squares (PLS), using data from TCC, determined by UV\u2010Vis spectrophotometry. The PLS regression model obtained with the FTIR data from the freeze\u2010dried samples, using the spectral range 920\u20133000 cm 121, had the best figures of merit (R2CAL of 0.95, R2PRED of 0.93 and RPD of 3.78), being reliable for future application in agriculture. This approach for carotenoid determination in pumpkin and squash avoids the use of organic solvents. Moreover, these results are a rationale for further exploring this technique for the assessment of specific carotenoids in food matrices

Mediterranean-type diet and brain structural change from 73 to 76 years in a Scottish cohort

STUDY FUNDING The data were collected by a Research into Ageing programme grant; research continues as part of the Age UK–funded Disconnected Mind project. The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1), with funding from the BBSRC and Medical Research Council. Imaging and image analysis was performed at the Brain Research Imaging Centre (sbirc.ed.ac.uk/), Edinburgh, supported by the Scottish Funding Council SINAPSE Collaboration. Derivation of mean cortical thickness measures was funded by the Scottish Funding Council’s Postdoctoral and Early Career Researchers Exchange Fund awarded by SINAPSE to David Alexander Dickie. L.C.A.C. acknowledges funding from the Scottish Government's Rural and Environment Science and Analytical Services (RESAS) division.Peer reviewedPublisher PD

Domain adaptation with cyclegan for change detection in the amazon forest

Deep learning classification models require large amounts of labeled training data to perform properly, but the production of reference data for most Earth observation applications is a labor intensive, costly process. In that sense, transfer learning is an option to mitigate the demand for labeled data. In many remote sensing applications, however, the accuracy of a deep learning-based classification model trained with a specific dataset drops significantly when it is tested on a different dataset, even after fine-tuning. In general, this behavior can be credited to the domain shift phenomenon. In remote sensing applications, domain shift can be associated with changes in the environmental conditions during the acquisition of new data, variations of objects' appearances, geographical variability and different sensor properties, among other aspects. In recent years, deep learning-based domain adaptation techniques have been used to alleviate the domain shift problem. Recent improvements in domain adaptation technology rely on techniques based on Generative Adversarial Networks (GANs), such as the Cycle-Consistent Generative Adversarial Network (CycleGAN), which adapts images across different domains by learning nonlinear mapping functions between the domains. In this work, we exploit the CycleGAN approach for domain adaptation in a particular change detection application, namely, deforestation detection in the Amazon forest. Experimental results indicate that the proposed approach is capable of alleviating the effects associated with domain shift in the context of the target application. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Turnover time of fluorescent dissolved organic matter in the dark global ocean

Research articleMarine dissolved organic matter (DOM) is one of the largest reservoirs of reduced carbon on Earth. In the dark ocean (4200 m), most of this carbon is refractory DOM. This refractory DOM, largely produced during microbial mineralization of organic matter, includes humic-like substances generated in situ and detectable by fluorescence spectroscopy. Here we show two ubiquitous humic-like fluorophores with turnover times of 435±41 and 610±55 years, which persist significantly longer than the B350 years that the dark global ocean takes to renew. In parallel, decay of a tyrosine-like fluorophore with a turnover time of 379±103 years is also detected. We propose the use of DOM fluorescence to study the cycling of resistant DOM that is preserved at centennial timescales and could represent a mechanism of carbon sequestration (humic-like fraction) and the decaying DOM injected into the dark global ocean, where it decreases at centennial timescales (tyrosine-like fraction).Versión del editor10,015

Demonstrating the Potential of Using Bio-Based Sustainable Polyester Blends for Bone Tissue Engineering Applications

YesHealthcare applications are known to have a considerable environmental impact and the use of bio-based polymers has emerged as a powerful approach to reduce the carbon footprint in the sector. This research aims to explore the suitability of using a new sustainable polyester blend (Floreon™) as a scaffold directed to aid in musculoskeletal applications. Musculoskeletal problems arise from a wide range of diseases and injuries related to bones and joints. Specifically, bone injuries may result from trauma, cancer, or long-term infections and they are currently considered a major global problem in both developed and developing countries. In this work we have manufactured a series of 3D-printed constructs from a novel biopolymer blend using fused deposition modelling (FDM), and we have modified these materials using a bioceramic (wollastonite, 15% w/w). We have evaluated their performance in vitro using human dermal fibroblasts and rat mesenchymal stromal cells. The new sustainable blend is biocompatible, showing no differences in cell metabolic activity when compared to PLA controls for periods 1-18 days. FloreonTM blend has proven to be a promising material to be used in bone tissue regeneration as it shows an impact strength in the same range of that shown by native bone (just under 10 kJ/m2) and supports an improvement in osteogenic activity when modified with wollastonite.We would like to acknowledge the Medical Research Council in the UK (MRC) for funding this research throughout a MRC Proximity to Discovery award (P2D) with grant number MC_PC_16084. We would also like to acknowledge CONACYT for funding DH RamosRodriguez’s work