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

Understanding the Influence of Rendering Parameters in Synthetic Datasets for Neural Semantic Segmentation Tasks

Cursos e Congresos , C-155[Abstract] Deep neural networks are well known for demanding large amounts of training data, motivating the appearance of multiple synthetic datasets covering multiple domains. However, synthetic datasets have not yet outperformed real data for autonomous driving applications, particularly for semantic segmentation tasks. Thus, a deeper comprehension about how the parameters involved in synthetic data generation could help in creating better synthetic datasets. This work provides a summary review of prior research covering how image noise, camera noise and rendering photorealism could affect learning tasks. Furthermore, we presents novel experiments aimed at advancing our understanding around generating synthetic data for autonomous driving neural networks aimed at semantic segmentationXunta de Galicia; ED431F 2021/11This work has been supported by the Spanish Ministry of Science and Innovation (AEI/PID2020-115734RB-C22). We also want to acknowledge Side Effects Software Inc. for their support to this work. J.A. Iglesias-Guitian also acknowledges the UDC-Inditex InTalent programme, the Ministry of Science and Innovation (AEI/RYC2018-025385-I) and Xunta de Galicia (ED431F 2021/11). CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS

Modelos de Negocio en las Empresas de Biotecnología: Análisis Comparativo entre España y los Países Líderes

Biotechnology is still an emergent industry in Spain with no business models well defined yet. However, in Canada, a leading country for this industry, three models can be already depicted, two of them clearly dominant. Our empirical fieldwork, based on practically the whole population of Dedicated Biotechnological Firms in Spain, is intended to figure out the significant disparities encountered in the Spanish biotechnology firms in comparison to their Canadian counterparts, in most of the indicators and variables shaping the business models. Our study concludes the Spanish biotechnological industry overwhelmingly compiles services-based companies with low to medium innovative levels. This profile diverges from the most commonly displayed by this industry in Canada.En el emergente sector de la biotecnología en España, los modelos de negocio todavía no están definidos. No obstante, en un país considerado un referente para el sector como es Canadá, despuntan ya con cierta claridad dos modelos dominantes. Nuestro trabajo empírico corroborará las significativas diferencias que separan el tejido empresarial biotecnológico español de su homónimo canadiense, en la gran mayoría de variables que definen los modelos de negocio más implantados. Nuestro trabajo concluye que el sector biotecnológico español avanza por un camino distinto y divergente del marcado en Canadá, uno de los países que se sitúan a la vanguardia en esta industria.Biotechnology is still an emergent industry in Spain with no business models well defined yet. However, in Canada, a leading country for this industry, three models can be already depicted, two of them clearly dominant. Our empirical fieldwork, based on practically the whole population of Dedicated Biotechnological Firms in Spain, is intended to figure out the significant disparities encountered in the Spanish biotechnology firms in comparison to their Canadian counterparts, in most of the indicators and variables shaping the business models. Our study concludes the Spanish biotechnological industry overwhelmingly compiles services-based companies with low to medium innovative levels. This profile diverges from the most commonly displayed by this industry in Canada

Transport infrastructure management based on LiDAR synthetic data: a deep learning approach with a ROADSENSE simulator

In the realm of transportation system management, various remote sensing techniques have proven instrumental in enhancing safety, mobility, and overall resilience. Among these techniques, Light Detection and Ranging (LiDAR) has emerged as a prevalent method for object detection, facilitating the comprehensive monitoring of environmental and infrastructure assets in transportation environments. Currently, the application of Artificial Intelligence (AI)-based methods, particularly in the domain of semantic segmentation of 3D LiDAR point clouds by Deep Learning (DL) models, is a powerful method for supporting the management of both infrastructure and vegetation in road environments. In this context, there is a lack of open labeled datasets that are suitable for training Deep Neural Networks (DNNs) in transportation scenarios, so, to fill this gap, we introduce ROADSENSE (Road and Scenic Environment Simulation), an open-access 3D scene simulator that generates synthetic datasets with labeled point clouds. We assess its functionality by adapting and training a state-of-the-art DL-based semantic classifier, PointNet++, with synthetic data generated by both ROADSENSE and the well-known HELIOS++ (HEildelberg LiDAR Operations Simulator). To evaluate the resulting trained models, we apply both DNNs on real point clouds and demonstrate their effectiveness in both roadway and forest environments. While the differences are minor, the best mean intersection over union (MIoU) values for highway and national roads are over 77%, which are obtained with the DNN trained on HELIOS++ point clouds, and the best classification performance in forested areas is over 92%, which is obtained with the model trained on ROADSENSE point clouds. This work contributes information on a valuable tool for advancing DL applications in transportation scenarios, offering insights and solutions for improved road and roadside management.Agencia Estatal de Investigación | Ref. PID2022-140662OB-I00Agencia Estatal de Investigación | Ref. PCI2020-120705-2-I0

Synthetic studies on alotamide A: construction of N‐demethylalotamide A

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGSeveral approaches to the synthesis of cyclodepsipeptide natural product alotamide A are described, eventually affording a very advanced N-demethylated analogue of the targeted natural product. The difficulties found in our endeavors on the synthesis of alotamide A have allowed us to gather some valuable information regarding the most convenient synthetic step for each key transformation. The intramolecular Csp2−Csp2 Stille cross-coupling and the macrolactam formation were found to be reliable protocols for the final construction of the alotamide A skeleton.Agencia Estatal de Investigación | Ref. PID2019-107855RB−I00Xunta de Galicia | Ref. ED431 C 2021/45Xunta de Galicia | Ref. ED431G/0

All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes

We introduce UrbanSyn, a photorealistic dataset acquired through semi-procedurally generated synthetic urban driving scenarios. Developed using high-quality geometry and materials, UrbanSyn provides pixel-level ground truth, including depth, semantic segmentation, and instance segmentation with object bounding boxes and occlusion degree. It complements GTAV and Synscapes datasets to form what we coin as the 'Three Musketeers'. We demonstrate the value of the Three Musketeers in unsupervised domain adaptation for image semantic segmentation. Results on real-world datasets, Cityscapes, Mapillary Vistas, and BDD100K, establish new benchmarks, largely attributed to UrbanSyn. We make UrbanSyn openly and freely accessible (www.urbansyn.org).Comment: The UrbanSyn Dataset is available in http://urbansyn.org

Herbivore corridors sustain genetic footprint in plant populations: a case for Spanish drove roads

Habitat fragmentation is one of the greatest threats to biodiversity conservation and ecosystem productivity mediated by direct human impact. Its consequences include genetic depauperation, comprising phenomena such as inbreeding depression or reduction in genetic diversity. While the capacity of wild and domestic herbivores to sustain long-distance seed dispersal has been proven, the impact of herbivore corridors in plant population genetics remains to be observed. We conducted this study in the Conquense Drove Road in Spain, where sustained use by livestock over centuries has involved transhumant herds passing twice a year en route to winter and summer pastures. We compared genetic diversity and inbreeding coefficients of Plantago lagopus populations along the drove road with populations in the surrounding agricultural matrix, at varying distances from human settlements. We observed significant differences in coefficients of inbreeding between the drove road and the agricultural matrix, as well as significant trends indicative of higher genetic diversity and population nestedness around human settlements. Trends for higher genetic diversity along drove roads may be present, although they were only marginally significant due to the available sample size. Our results illustrate a functional landscape with human settlements as dispersal hotspots, while the findings along the drove road confirm its role as a pollinator reservoir observed in other studies. Drove roads may possibly also function as linear structures that facilitate long-distance dispersal across the agricultural matrix, while local P. lagopus populations depend rather on short-distance seed dispersal. These results highlight the role of herbivore corridors for conserving the migration capacity of plants, and contribute towards understanding the role of seed dispersal and the spread of invasive species related to human activities.Peer reviewe