ConSLAM: Periodically Collected Real-World Construction Dataset for SLAM

International audienc

Non-invasive diagnostic tests for Helicobacter pylori infection

BACKGROUND: Helicobacter pylori (H pylori) infection has been implicated in a number of malignancies and non-malignant conditions including peptic ulcers, non-ulcer dyspepsia, recurrent peptic ulcer bleeding, unexplained iron deficiency anaemia, idiopathic thrombocytopaenia purpura, and colorectal adenomas. The confirmatory diagnosis of H pylori is by endoscopic biopsy, followed by histopathological examination using haemotoxylin and eosin (H & E) stain or special stains such as Giemsa stain and Warthin-Starry stain. Special stains are more accurate than H & E stain. There is significant uncertainty about the diagnostic accuracy of non-invasive tests for diagnosis of H pylori. OBJECTIVES: To compare the diagnostic accuracy of urea breath test, serology, and stool antigen test, used alone or in combination, for diagnosis of H pylori infection in symptomatic and asymptomatic people, so that eradication therapy for H pylori can be started. SEARCH METHODS: We searched MEDLINE, Embase, the Science Citation Index and the National Institute for Health Research Health Technology Assessment Database on 4 March 2016. We screened references in the included studies to identify additional studies. We also conducted citation searches of relevant studies, most recently on 4 December 2016. We did not restrict studies by language or publication status, or whether data were collected prospectively or retrospectively. SELECTION CRITERIA: We included diagnostic accuracy studies that evaluated at least one of the index tests (urea breath test using isotopes such as13C or14C, serology and stool antigen test) against the reference standard (histopathological examination using H & E stain, special stains or immunohistochemical stain) in people suspected of having H pylori infection. DATA COLLECTION AND ANALYSIS: Two review authors independently screened the references to identify relevant studies and independently extracted data. We assessed the methodological quality of studies using the QUADAS-2 tool. We performed meta-analysis by using the hierarchical summary receiver operating characteristic (HSROC) model to estimate and compare SROC curves. Where appropriate, we used bivariate or univariate logistic regression models to estimate summary sensitivities and specificities. MAIN RESULTS: We included 101 studies involving 11,003 participants, of which 5839 participants (53.1%) had H pylori infection. The prevalence of H pylori infection in the studies ranged from 15.2% to 94.7%, with a median prevalence of 53.7% (interquartile range 42.0% to 66.5%). Most of the studies (57%) included participants with dyspepsia and 53 studies excluded participants who recently had proton pump inhibitors or antibiotics.There was at least an unclear risk of bias or unclear applicability concern for each study.Of the 101 studies, 15 compared the accuracy of two index tests and two studies compared the accuracy of three index tests. Thirty-four studies (4242 participants) evaluated serology; 29 studies (2988 participants) evaluated stool antigen test; 34 studies (3139 participants) evaluated urea breath test-13C; 21 studies (1810 participants) evaluated urea breath test-14C; and two studies (127 participants) evaluated urea breath test but did not report the isotope used. The thresholds used to define test positivity and the staining techniques used for histopathological examination (reference standard) varied between studies. Due to sparse data for each threshold reported, it was not possible to identify the best threshold for each test.Using data from 99 studies in an indirect test comparison, there was statistical evidence of a difference in diagnostic accuracy between urea breath test-13C, urea breath test-14C, serology and stool antigen test (P = 0.024). The diagnostic odds ratios for urea breath test-13C, urea breath test-14C, serology, and stool antigen test were 153 (95% confidence interval (CI) 73.7 to 316), 105 (95% CI 74.0 to 150), 47.4 (95% CI 25.5 to 88.1) and 45.1 (95% CI 24.2 to 84.1). The sensitivity (95% CI) estimated at a fixed specificity of 0.90 (median from studies across the four tests), was 0.94 (95% CI 0.89 to 0.97) for urea breath test-13C, 0.92 (95% CI 0.89 to 0.94) for urea breath test-14C, 0.84 (95% CI 0.74 to 0.91) for serology, and 0.83 (95% CI 0.73 to 0.90) for stool antigen test. This implies that on average, given a specificity of 0.90 and prevalence of 53.7% (median specificity and prevalence in the studies), out of 1000 people tested for H pylori infection, there will be 46 false positives (people without H pylori infection who will be diagnosed as having H pylori infection). In this hypothetical cohort, urea breath test-13C, urea breath test-14C, serology, and stool antigen test will give 30 (95% CI 15 to 58), 42 (95% CI 30 to 58), 86 (95% CI 50 to 140), and 89 (95% CI 52 to 146) false negatives respectively (people with H pylori infection for whom the diagnosis of H pylori will be missed).Direct comparisons were based on few head-to-head studies. The ratios of diagnostic odds ratios (DORs) were 0.68 (95% CI 0.12 to 3.70; P = 0.56) for urea breath test-13C versus serology (seven studies), and 0.88 (95% CI 0.14 to 5.56; P = 0.84) for urea breath test-13C versus stool antigen test (seven studies). The 95% CIs of these estimates overlap with those of the ratios of DORs from the indirect comparison. Data were limited or unavailable for meta-analysis of other direct comparisons. AUTHORS' CONCLUSIONS: In people without a history of gastrectomy and those who have not recently had antibiotics or proton ,pump inhibitors, urea breath tests had high diagnostic accuracy while serology and stool antigen tests were less accurate for diagnosis of Helicobacter pylori infection.This is based on an indirect test comparison (with potential for bias due to confounding), as evidence from direct comparisons was limited or unavailable. The thresholds used for these tests were highly variable and we were unable to identify specific thresholds that might be useful in clinical practice.We need further comparative studies of high methodological quality to obtain more reliable evidence of relative accuracy between the tests. Such studies should be conducted prospectively in a representative spectrum of participants and clearly reported to ensure low risk of bias. Most importantly, studies should prespecify and clearly report thresholds used, and should avoid inappropriate exclusions

ConSLAM: Periodically Collected Real-World Construction Dataset for SLAM and Progress Monitoring

Hand-held scanners are progressively adopted to workflows on con- struction sites. Yet, they suffer from accuracy problems, preventing them from deployment for demanding use cases. In this paper, we present a real-world dataset collected periodically on a construction site to measure the accuracy of SLAM algorithms that mobile scanners utilize. The dataset contains time-synchronised and spatially registered images and LiDAR scans, inertial data and professional ground-truth scans. To the best of our knowledge, this is the first publicly available dataset which reflects the periodic need of scanning construction sites with the aim of accurate progress monitoring using a hand-held scanner.BP, GeoSLAM, Laing O’Rourke, Topcon, Trimble, EU Horizon 2020 BIM2TWIN: Optimal Construction Management & Production Control project under an agreement No. 958398

ConSLAM: Construction Data Set for SLAM

International audienceThis paper presents a dataset collected periodically on a construction site. The dataset aims to evaluate the performance of SLAM algorithms used by mobile scanners or autonomous robots. It includes ground-truth scans of a construction site collected using a terrestrial laser scanner along with five sequences of spatially registered and time-synchronized images, LiDAR scans and inertial data coming from our prototypical hand-held scanner. We also recover the ground-truth trajectory of the mobile scanner by registering the sequential LiDAR scans to the ground-truth scans and show how to use a popular software package to measure the accuracy of SLAM algorithms against our trajectory automatically. To the best of our knowledge, this is the first publicly accessible dataset consisting of periodically collected sequential data on a construction site

Vehicle fuzzy driving based on DGPS and vision

Trabajo presentado en la 9th IFSA World Congress and 20th NAFIPS International Conference, celebrada en Vancouver (Canadá), del 25 al 28 de julio de 2001This document presents a fuzzy control application in the unmanned driving field. Two electric cars have been conveniently instrumented in order to transform them in platforms for automatic driving experiments. Onboard speed and steering fuzzy controllers are the core of the guiding system. Navigation is essentially DGPS-based providing obstacles detection and avoidance by means of artificial vision in a reactive manner.Peer reviewe

Yungas

Las Yungas proveen múltiples recursos forestales madereros y no madereros de importancia regional, tienen una rol clave como proveedor de servicios ecosistémicos y albergan una extraordinaria biodiversidad. Con más de 3 millones de hectáreas en la actualidad, el 30% de las Yungas ha sido transformado a otros usos de la tierra y los bosques remanentes han sido degradados por aprovechamientos forestales no sostenibles y por una ganadería extensiva no manejada adecuadamente. El tratamiento silvícola tradicionalmente utilizado en las Yungas ha sido de tipo selectivo de especies arbóreas de mayor valor económico, basado en diámetros mínimos de corta y extrayendo los mejores individuos (denominado floreo). El floreo intensivo aplicado en las Yungas disminuyó el valor económico de los rodales dejando bosques empobrecidos económica y ecológicamente. Para revertir esta degradación se han propuesto mejoras en técnicas de bajo impacto, incluyendo selección de árboles semilleros y protección de árboles futuro. Sin embargo, dado que la regeneración de especies arbóreas está severamente comprometida es necesario avanzar hacia una nueva silvicultura. Esta nueva silvicultura se basa en aplicar técnicas de retención variable, donde se realizan aprovechamientos intensos que generan claros para promover la regeneración de especies arbóreas heliófilas y mantener áreas de reserva para promover la regeneración de esciófitas. Este esquema requiere la intervención del rodal con tratamientos intermedios, ciclos de reentradas de al menos 40 años y una planificación cuidadosa de las vías de saca. En el caso que los rodales presenten ganadería, el ganado debería manejarse para no afectar la regeneración, disminuyendo la carga ganadera y excluyendo espacial o temporalmente la actividad silvopastoril en ciertas áreas. La nueva silvicultura debe estar enmarcada en una planificación más amplia a escala eco-regional, donde el manejo del bosque sea acorde a su aptitud para proveer determinados bienes y servicios. Para implementar la nueva silvicultura será necesario generar esquemas de pago por servicios ecosistémicos y nuevos mercados para productos madereros no convencionales y productos no madereros. Esta nueva silvicultura debe ser acompañada de lineamientos de manejo forestal que deben validarse en el marco de un programa de monitoreo regional y con la implementación de mecanismos que eviten prácticas ilegales.Fil: Politi, Natalia. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Rivera, Luis Osvaldo. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Balducci, Ezequiel Diego. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Salta-jujuy. Estacion Experimental Agropecuaria Yuto.; ArgentinaFil: Malizia, Lucio Ricardo. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Blundo, Cecilia Mabel. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Fornes, Luis Fernando. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Tucuman-Santiago del Estero. Estación Experimental Agropecuaria Famaillá; ArgentinaFil: Galarza, Martín. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Salta-jujuy. Estacion Experimental Agropecuaria Yuto. Agencia de Extension Rural Tartagal.; ArgentinaFil: Alcalde, Ana Sofía. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Aragón, Myriam Roxana. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Bardavid, Sofia. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Eliano, Pablo. Universidad Nacional de Jujuy; ArgentinaFil: Gómez, Daniela. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Jayat, Jorge Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - Tucumán. Unidad Ejecutora Lillo; ArgentinaFil: Lupo, Liliana Concepcion. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Malizia, Agustina. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Laboratorio de Investigaciones Ecológicas de las Yungas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Mangini, Gabriela Giselle. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Mayol, Eduardo. No especifíca;Fil: Mazzini, Flavia. Universidad Nacional de Jujuy. Facultad de Ciencias Agrarias; ArgentinaFil: Molineri, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Biodiversidad Neotropical. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Biodiversidad Neotropical. Instituto de Biodiversidad Neotropical; ArgentinaFil: Nuñez Montellano, Maria Gabriela. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Pacheco, Silvia. Fundación ProYungas; ArgentinaFil: Pero, Edgardo Javier Ignacio. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Biodiversidad Neotropical; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Ruggera, Román Alberto. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Sánchez Cuartielles, Estefanía. Gobierno de la Provincia de Jujuy. Ministerio de Ambiente; ArgentinaFil: Schaaf, Alejandro Alberto. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Speranza, Flavio. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Salta-jujuy. Estacion Experimental Agropecuaria Yuto.; ArgentinaFil: Tallei, Ever Denis. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Vivanco, Constanza Guadalupe. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Zelener, Noga. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; ArgentinaFil: Brown, Alejandro Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo; Argentin