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

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

Texture analysis in gel electrophoresis images using an integrative kernel-based approach

[Abstract] Texture information could be used in proteomics to improve the quality of the image analysis of proteins separated on a gel. In order to evaluate the best technique to identify relevant textures, we use several different kernel-based machine learning techniques to classify proteins in 2-DE images into spot and noise. We evaluate the classification accuracy of each of these techniques with proteins extracted from ten 2-DE images of different types of tissues and different experimental conditions. We found that the best classification model was FSMKL, a data integration method using multiple kernel learning, which achieved AUROC values above 95% while using a reduced number of features. This technique allows us to increment the interpretability of the complex combinations of textures and to weight the importance of each particular feature in the final model. In particular the Inverse Difference Moment exhibited the highest discriminating power. A higher value can be associated with an homogeneous structure as this feature describes the homogeneity; the larger the value, the more symmetric. The final model is performed by the combination of different groups of textural features. Here we demonstrated the feasibility of combining different groups of textures in 2-DE image analysis for spot detection.Instituto de Salud Carlos III; PI13/00280United Kingdom. Medical Research Council; G10000427, MC_UU_12013/8Galicia. Consellería de Economía e Industria; 10SIN105004P

Multiomic features associated with mucosal healing and inflammation in paediatric Crohn's disease

Background The gastrointestinal microbiota has an important role in mucosal immune homoeostasis and may contribute to maintaining mucosal healing in Crohn's disease (CD). Aim To identify changes in the microbiota, metabolome and protease activity associated with mucosal healing in established paediatric CD. Methods Twenty‐five participants aged 3‐18 years with CD, disease duration of over 6 months, and maintenance treatment with biological therapy were recruited. They were divided into a low calprotectin group (faecal calprotectin 100 μg/g, “mucosal inflammation,” n = 11). 16S gene‐based metataxonomics, 1H‐NMR spectroscopy‐based metabolic profiling and protease activity assays were performed on stool samples. Results Relative abundance of Dialister species was six times greater in the low calprotectin group (q = 0.00999). Alpha and beta diversity, total protease activity and inferred metagenomic profiles did not differ between groups. Pentanoate (valerate) and lysine were principal discriminators in a machine‐learning model which differentiated high and low calprotectin samples using NMR spectra (R2 0.87, Q2 0.41). Mean relative concentration of pentanoate was 1.35‐times greater in the low calprotectin group (95% CI 1.03‐1.68, P = 0.036) and was positively correlated with Dialister. Mean relative concentration of lysine was 1.54‐times greater in the high calprotectin group (95% CI 1.05‐2.03, P = 0.028). Conclusions This multiomic study identified an increase in Dialister species and pentanoate, and a decrease in lysine, in patients with “mucosal healing.” It supports further investigation of these as potential novel therapeutic targets in CD

The Mitochondrial Genome Is a “Genetic Sanctuary” during the Oncogenic Process

Since Otto Warburg linked mitochondrial physiology and oncogenesis in the 1930s, a number of studies have focused on the analysis of the genetic basis for the presence of aerobic glycolysis in cancer cells. However, little or no evidence exists today to indicate that mtDNA mutations are directly responsible for the initiation of tumor onset. Based on a model of gliomagenesis in the mouse, we aimed to explore whether or not mtDNA mutations are associated with the initiation of tumor formation, maintenance and aggressiveness. We reproduced the different molecular events that lead from tumor initiation to progression in the mouse glioma. In human gliomas, most of the genetic alterations that have been previously identified result in the aberrant activation of different signaling pathways and deregulation of the cell cycle. Our data indicates that mitochondrial dysfunction is associated with reactive oxygen species (ROS) generation, leading to increased nuclear DNA (nDNA) mutagenesis, but maintaining the integrity of the mitochondrial genome. In addition, mutational stability has been observed in entire mtDNA of human gliomas; this is in full agreement with the results obtained in the cancer mouse model. We use this model as a paradigm of oncogenic transformation due to the fact that mutations commonly found in gliomas appear to be the most common molecular alterations leading to tumor development in most types of human cancer. Our results indicate that the mtDNA genome is kept by the cell as a “genetic sanctuary” during tumor development in the mouse and humans. This is compatible with the hypothesis that the mtDNA molecule plays an essential role in the control of the cellular adaptive survival response to tumor-induced oxidative stress. The integrity of mtDNA seems to be a necessary element for responding to the increased ROS production associated with the oncogenic process

Metabolic Profiling Reveals a Dependency of Human Metastatic Breast Cancer on Mitochondrial Serine and One-Carbon Unit Metabolism.

Breast cancer is the most common cancer among American women and a major cause of mortality. To identify metabolic pathways as potential targets to treat metastatic breast cancer, we performed metabolomics profiling on the breast cancer cell line MDA-MB-231 and its tissue-tropic metastatic subclones. Here, we report that these subclones with increased metastatic potential display an altered metabolic profile compared with the parental population. In particular, the mitochondrial serine and one-carbon (1C) unit pathway is upregulated in metastatic subclones. Mechanistically, the mitochondrial serine and 1C unit pathway drives the faster proliferation of subclones through enhanced de novo purine biosynthesis. Inhibition of the first rate-limiting enzyme of the mitochondrial serine and 1C unit pathway, serine hydroxymethyltransferase (SHMT2), potently suppresses proliferation of metastatic subclones in culture and impairs growth of lung metastatic subclones at both primary and metastatic sites in mice. Some human breast cancers exhibit a significant association between the expression of genes in the mitochondrial serine and 1C unit pathway with disease outcome and higher expression of SHMT2 in metastatic tumor tissue compared with primary tumors. In addition to breast cancer, a few other cancer types, such as adrenocortical carcinoma and kidney chromophobe cell carcinoma, also display increased SHMT2 expression during disease progression. Together, these results suggest that mitochondrial serine and 1C unit metabolism plays an important role in promoting cancer progression, particularly in late-stage cancer. IMPLICATIONS: This study identifies mitochondrial serine and 1C unit metabolism as an important pathway during the progression of a subset of human breast cancers

Moderate-High Disease Activity in Patients with Recent-Onset Psoriatic Arthritis-Multivariable Prediction Model Based on Machine Learning

The aim was to identify patient- and disease-related characteristics predicting moderate-to-high disease activity in recent-onset psoriatic arthritis (PsA). We performed a multicenter observational prospective study (2-year follow-up, regular annual visits) in patients aged ≥18 years who fulfilled the CASPAR criteria and had less than 2 years since the onset of symptoms. The moderate-to-high activity of PsA was defined as DAPSA > 14. We trained a logistic regression model and random forest-type and XGBoost machine learning algorithms to analyze the association between the outcome measure and the variables selected in the bivariate analysis. The sample comprised 158 patients. At the first follow-up visit, 20.8% of the patients who attended the clinic had a moderate-to-severe disease. This percentage rose to 21.2% on the second visit. The variables predicting moderate-high activity were the PsAID score, tender joint count, level of physical activity, and sex. The mean values of the measures of validity of the machine learning algorithms were all high, especially sensitivity (98%; 95% CI: 86.89-100.00). PsAID was the most important variable in the prediction algorithms, reinforcing the convenience of its inclusion in daily clinical practice. Strategies that focus on the needs of women with PsA should be considered

Galaxy clusters and groups in the ALHAMBRA Survey

We present a catalogue of 348 galaxy clusters and groups with $0.2<z<1.2$ selected in the 2.78 $deg^2$ ALHAMBRA Survey. The high precision of our photometric redshifts, close to $1\%$, and the wide spread of the seven ALHAMBRA pointings ensure that this catalogue has better mass sensitivity and is less affected by cosmic variance than comparable samples. The detection has been carried out with the Bayesian Cluster Finder (BCF), whose performance has been checked in ALHAMBRA-like light-cone mock catalogues. Great care has been taken to ensure that the observable properties of the mocks photometry accurately correspond to those of real catalogues. From our simulations, we expect to detect galaxy clusters and groups with both $70\%$ completeness and purity down to dark matter halo masses of $M_h\sim3\times10^{13}\rm M_{\odot}$ for $z<0.85$. Cluster redshifts are expected to be recovered with $\sim0.6\%$ precision for $z<1$. We also expect to measure cluster masses with $\sigma_{M_h|M^*_{CL}}\sim0.25-0.35\, dex$ precision down to $\sim3\times10^{13}\rm M_{\odot}$, masses which are $50\%$ smaller than those reached by similar work. We have compared these detections with previous optical, spectroscopic and X-rays work, finding an excellent agreement with the rates reported from the simulations. We have also explored the overall properties of these detections such as the presence of a colour-magnitude relation, the evolution of the photometric blue fraction and the clustering of these sources in the different ALHAMBRA fields. Despite the small numbers, we observe tentative evidence that, for a fixed stellar mass, the environment is playing a crucial role at lower redshifts (z$<$0.5).Comment: Accepted for publication in MNRAS. Catalogues and figures available online and under the following link: http://bascaso.net46.net/ALHAMBRA_clusters.htm

Achievements of EU funded project BFIRST on BIPV technology

EU funded “Building-integrated fibre reinforced solar technology” (BFIRST) project (Grant Agreement number 296016) was launched in 2012 by a consortium of EU companies, research institutes and universities, led by Tecnalia. The project, which will end in early 2017, is focused on the design, development, fabrication and demonstration of a set of standardized multifunctional photovoltaic products for building integration using an innovative manufacturing solution based on glass fibre-reinforced composite materials. This novel encapsulation technology is the basis for a wide range of new BIPV (building-integrated photovoltaic) products with enhanced building integration possibilities. The resulting modules present advanced characteristics in terms of flexibility of design, adaptability to non-planar geometries, structural properties and lightweight, among others. They provide additional advantages related to cost reduction in transport, manipulation, assembly and installatio