Facing Erosion Identification in Railway Lines Using Pixel-wise Deep-based Approaches

Soil erosion is considered one of the most expensive natural hazards with a high impact on several infrastructure assets. Among them, railway lines are one of the most likely constructions for the appearance of erosion and, consequently, one of the most troublesome due to the maintenance costs, risks of derailments, and so on. Therefore, it is fundamental to identify and monitor erosion in railway lines to prevent major consequences. Currently, erosion identification is manually performed by humans using huge image sets, a time-consuming and slow task. Hence, automatic machine learning methods appear as an appealing alternative. A crucial step for automatic erosion identification is to create a good feature representation. Towards such objective, deep learning can learn data-driven features and classifiers. In this paper, we propose a novel deep learning-based framework capable of performing erosion identification in railway lines. Six techniques were evaluated and the best one, Dynamic Dilated ConvNet, was integrated into this framework that was then encapsulated into a new ArcGIS plugin to facilitate its use by non-programmer users. To analyze such techniques, we also propose a new dataset, composed of almost 2,000 high-resolution images

Bose-Einstein correlations of same-sign charged pions in the forward region in pp collisions at √s=7 TeV

Bose-Einstein correlations of same-sign charged pions, produced in protonproton collisions at a 7 TeV centre-of-mass energy, are studied using a data sample collected by the LHCb experiment. The signature for Bose-Einstein correlations is observed in the form of an enhancement of pairs of like-sign charged pions with small four-momentum difference squared. The charged-particle multiplicity dependence of the Bose-Einstein correlation parameters describing the correlation strength and the size of the emitting source is investigated, determining both the correlation radius and the chaoticity parameter. The measured correlation radius is found to increase as a function of increasing charged-particle multiplicity, while the chaoticity parameter is seen to decreas

Observation of an Excited Bc+ State

Using pp collision data corresponding to an integrated luminosity of 8.5 fb-1 recorded by the LHCb experiment at center-of-mass energies of s=7, 8, and 13 TeV, the observation of an excited Bc+ state in the Bc+π+π- invariant-mass spectrum is reported. The observed peak has a mass of 6841.2±0.6(stat)±0.1(syst)±0.8(Bc+) MeV/c2, where the last uncertainty is due to the limited knowledge of the Bc+ mass. It is consistent with expectations of the Bc∗(2S31)+ state reconstructed without the low-energy photon from the Bc∗(1S31)+→Bc+γ decay following Bc∗(2S31)+→Bc∗(1S31)+π+π-. A second state is seen with a global (local) statistical significance of 2.2σ (3.2σ) and a mass of 6872.1±1.3(stat)±0.1(syst)±0.8(Bc+) MeV/c2, and is consistent with the Bc(2S10)+ state. These mass measurements are the most precise to date

Measurement of the inelastic pp cross-section at a centre-of-mass energy of 13TeV

The cross-section for inelastic proton-proton collisions at a centre-of-mass energy of 13TeV is measured with the LHCb detector. The fiducial cross-section for inelastic interactions producing at least one prompt long-lived charged particle with momentum p > 2 GeV/c in the pseudorapidity range 2 < η < 5 is determined to be ϭ acc = 62:2 ± 0:2 ± 2:5mb. The first uncertainty is the intrinsic systematic uncertainty of the measurement, the second is due to the uncertainty on the integrated luminosity. The statistical uncertainty is negligible. Extrapolation to full phase space yields the total inelastic proton-proton cross-section ϭ inel = 75:4 ± 3:0 ± 4:5mb, where the first uncertainty is experimental and the second due to the extrapolation. An updated value of the inelastic cross-section at a centre-of-mass energy of 7TeV is also reported

Exome and Tissue-Associated Microbiota as Predictive Markers of Response to Neoadjuvant Treatment in Locally Advanced Rectal Cancer

The clinical and pathological responses to multimodal neoadjuvant therapy in locally advanced rectal cancers (LARCs) remain unpredictable, and robust biomarkers are still lacking. Recent studies have shown that tumors present somatic molecular alterations related to better treatment response, and it is also clear that tumor-associated bacteria are modulators of chemotherapy and immunotherapy efficacy, therefore having implications for long-term survivorship and a good potential as the biomarkers of outcome. Here, we performed whole exome sequencing and 16S ribosomal RNA (rRNA) amplicon sequencing from 44 pre-treatment LARC biopsies from Argentinian and Brazilian patients, treated with neoadjuvant chemoradiotherapy or total neoadjuvant treatment, searching for predictive biomarkers of response (responders, n = 17; non-responders, n = 27). In general, the somatic landscape of LARC was not capable to predict a response; however, a significant enrichment in mutational signature SBS5 was observed in non-responders (p = 0.0021), as well as the co-occurrence of APC and FAT4 mutations (p < 0.05). Microbiota studies revealed a similar alpha and beta diversity of bacteria between response groups. Yet, the linear discriminant analysis (LDA) of effect size indicated an enrichment of Hungatella, Flavonifractor, and Methanosphaera (LDA score ≥3) in the pre-treatment biopsies of responders, while non-responders had a higher abundance of Enhydrobacter, Paraprevotella (LDA score ≥3) and Finegoldia (LDA score ≥4). Altogether, the evaluation of these biomarkers in pre-treatment biopsies could eventually predict a neoadjuvant treatment response, while in post-treatment samples, it could help in guiding non-operative treatment strategies.Fil: Takenaka, Isabella Kuniko T. M.. No especifíca;Fil: Bartelli, Thais F.. No especifíca;Fil: Defelicibus, Alexandre. No especifíca;Fil: Sendoya, Juan Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Golubicki, Mariano. Gobierno de la Ciudad de Buenos Aires. Hospital de Gastroenterología "Dr. Carlos B. Udaondo"; ArgentinaFil: Robbio, Juan. No especifíca;Fil: Serpa, Marianna S.. No especifíca;Fil: Branco, Gabriela P.. No especifíca;Fil: Santos, Luana B. C.. No especifíca;Fil: Claro, Laura C. L.. No especifíca;Fil: Oliveira dos Santos, Gabriel. No especifíca;Fil: Kupper, Bruna E. C.. No especifíca;Fil: da Silva, Israel T.. No especifíca;Fil: Llera, Andrea Sabina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: de Mello, Celso A. L.. No especifíca;Fil: Riechelmann, Rachel P.. No especifíca;Fil: Dias Neto, Emmanuel. Universidade de Sao Paulo; BrasilFil: Iseas, Soledad. Gobierno de la Ciudad de Buenos Aires. Hospital de Gastroenterología "Dr. Carlos B. Udaondo"; ArgentinaFil: Aguiar, Samuel. No especifíca;Fil: Nunes, Diana Noronha. No especifíca

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types