PHOTOMATCH: AN OPEN-SOURCE MULTI-VIEW and MULTI-MODAL FEATURE MATCHING TOOL for PHOTOGRAMMETRIC APPLICATIONS

Automatic feature matching is a crucial step in Structure-from-Motion (SfM) applications for 3D reconstruction purposes. From an historical perspective we can say now that SIFT was the enabling technology that made SfM a successful and fully automated pipeline. SIFT was the ancestor of a wealth of detector/descriptor methods that are now available. Various research activities have tried to benchmark detector/descriptors operators, but a clear outcome is difficult to be drawn. This paper presents an ISPRS Scientific Initiative aimed at providing the community with an educational open-source tool (called PhotoMatch) for tie point extractions and image matching. Several enhancement and decolorization methods can be initially applied to an image dataset in order to improve the successive feature extraction steps. Then different detector/descriptor combinations are possible, coupled with different matching strategies and quality control metrics. Examples and results show the implemented functionality of PhotoMatch which has also a tutorial for shortly explaining the implemented methods

Multiplatform characterization of dynamic changes in breast milk during lactation

The multicomponent analysis of human breast milk (BM) by metabolic profiling is a new area of study applied to determining milk composition, and is capable of associating BM composition with maternal characteristics, and subsequent infant health outcomes. A multiplatform approach combining HPLC‐MS and ultra‐performance LC‐MS, GC‐MS, CE‐MS, and 1H NMR spectroscopy was used to comprehensively characterize metabolic profiles from seventy BM samples. A total of 710 metabolites spanning multiple molecular classes were defined. The utility of the individual and combined analytical platforms was explored in relation to numbers of metabolites identified, as well as the reproducibility of the methods. The greatest number of metabolites was identified by the single phase HPLC‐MS method, while CE‐MS uniquely profiled amino acids in detail and NMR was the most reproducible, whereas GC‐MS targeted volatile compounds and short chain fatty acids. Dynamic changes in BM composition were characterized over the first 3 months of lactation. Metabolites identified as altering in abundance over lactation included fucose, di‐ and triacylglycerols, and short chain fatty acids, known to be important for infant immunological, neurological, and gastrointestinal development, as well as being an important source of energy. This extensive metabolic coverage of the dynamic BM metabolome provides a baseline for investigating the impact of maternal characteristics, as well as establishing the impact of environmental and dietary factors on the composition of BM, with a focus on the downstream health consequences this may have for infants

DEVELOPMENT OF AN ALL-PURPOSE FREE PHOTOGRAMMETRIC TOOL

Photogrammetry is currently facing some challenges and changes mainly related to automation, ubiquitous processing and variety of applications. Within an ISPRS Scientific Initiative a team of researchers from USAL, UCLM, FBK and UNIBO have developed an open photogrammetric tool, called GRAPHOS (inteGRAted PHOtogrammetric Suite). GRAPHOS allows to obtain dense and metric 3D point clouds from terrestrial and UAV images. It encloses robust photogrammetric and computer vision algorithms with the following aims: (i) increase automation, allowing to get dense 3D point clouds through a friendly and easy-to-use interface; (ii) increase flexibility, working with any type of images, scenarios and cameras; (iii) improve quality, guaranteeing high accuracy and resolution; (iv) preserve photogrammetric reliability and repeatability. Last but not least, GRAPHOS has also an educational component reinforced with some didactical explanations about algorithms and their performance. The developments were carried out at different levels: GUI realization, image pre-processing, photogrammetric processing with weight parameters, dataset creation and system evaluation. The paper will present in detail the developments of GRAPHOS with all its photogrammetric components and the evaluation analyses based on various image datasets. GRAPHOS is distributed for free for research and educational needs

Profiling of polar ionogenic metabolites in Polish wines by capillary electrophoresis-mass spectrometry

The composition of wine is determined by a complex interaction between environmental factors, genetic factors (i.e., grape varieties), and winemaking practices (including technology and storage). Metabolomics using NMR spectroscopy, GC-MS, and/or LC-MS has shown to be a useful approach for assessing the origin, authenticity, and quality of various wines. Nonetheless, the use of additional analytical techniques with complementary separation mechanisms may aid in the deeper understanding of wine's metabolic processes. In this study, we demonstrate that CE-MS is a very suitable approach for the efficient profiling of polar ionogenic metabolites in wines. Without using any sample preparation or derivatization, wine was analyzed using a 10-min CE-MS workflow with interday RSD values for 31 polar and charged metabolites below 3.8% and 23% for migration times and peak areas, respectively. The utility of this workflow for the global profiling of polar ionogenic metabolites in wine was evaluated by analyzing different cool-climate Polish wine samples.Analytical BioScience

Quantification of inaccurate diagnosis of COPD in primary care medicine: An analysis of the COACH clinical audit

Background: Inaccurate diagnosis in COPD is a current problem with relevant consequences in terms of inefficient health care, which has not been thoroughly studied in primary care medicine. The aim of the present study was to evaluate the degree of inaccurate diagnosis in Primary Care in Spain and study the determinants associated with it. Methods: The Community Assessment of COPD Health Care (COACH) study is a national, observational, randomized, non-interventional, national clinical audit aimed at evaluating clinical practice for patients with COPD in primary care medicine in Spain. For the present analysis, a correct diagnosis was evaluated based on previous exposure and airway obstruction with and without the presence of symptoms. The association of patient-level and center-level variables with inaccurate diagnosis was studied using multivariate multilevel binomial logistic regression models. Results: During the study 4,307 cases from 63 centers were audited. The rate of inaccurate diagnosis was 82.4% (inter-regional range from 76.8% to 90.2%). Patient-related interventions associated with inaccurate diagnosis were related to active smoking, lung function evaluation, and specific therapeutic interventions. Center-level variables related to the availability of certain complementary tests and different aspects of the resources available were also associated with an inaccurate diagnosis. Conclusions: The prevalence data for the inaccurate diagnosis of COPD in primary care medicine in Spain establishes a point of reference in the clinical management of COPD. The descriptors of the variables associated with this inaccurate diagnosis can be used to identify cases and centers in which inaccurate diagnosis is occurring considerably, thus allowing for improvement

Metabolomic profile of neuroendocrine tumors (NETs) identifies methionine, porphyrin and tryptophan metabolism as key dysregulated pathways associated with patient survival

Objective: Metabolic profiling is a valuable tool to characterize tumor biology but remains largely unexplored in neuroendocrine tumors (NETs). Our aim was to comprehensively assess the metabolomic profile of NETs and identify novel prognostic biomarkers and dysregulated molecular pathways.Design and Methods: Multiplatform untargeted metabolomic profiling (GC-MS, CE-MS, and LC-MS) was performed in plasma from 77 patients with G1-2 extra-pancreatic NETs enrolled in the AXINET trial (NCT01744249) (study cohort) and from 68 non-cancer individuals (control). The prognostic value of each differential metabolite (n = 155) in NET patients (P < .05) was analyzed by univariate and multivariate analyses adjusted for multiple testing and other confounding factors. Related pathways were explored by Metabolite Set Enrichment Analysis (MSEA) and Metabolite Pathway Analysis (MPA).Results: Thirty-four metabolites were significantly associated with progression-free survival (PFS) (n = 16) and/or overall survival (OS) (n = 27). Thirteen metabolites remained significant independent prognostic factors in multivariate analysis, 3 of them with a significant impact on both PFS and OS. Unsupervised clustering of these 3 metabolites stratified patients in 3 distinct prognostic groups (1-year PFS of 71.1%, 47.7%, and 15.4% (P = .012); 5-year OS of 69.7%, 32.5%, and 27.7% (P = .003), respectively). The MSEA and MPA of the 13-metablolite signature identified methionine, porphyrin, and tryptophan metabolisms as the 3 most relevant dysregulated pathways associated with the prognosis of NETs.Conclusions: We identified a metabolomic signature that improves prognostic stratification of NET patients beyond classical prognostic factors for clinical decisions. The enriched metabolic pathways identified reveal novel tumor vulnerabilities that may foster the development of new therapeutic strategies for these patients

Assessing trace-element mobility in Algeciras Bay (Spain) sediments by acid and complexing screening

Acetic acid (HOAc) and diethylenetriaminepentaacetic acid (DTPA) single extraction agents were evaluated as screening methods to estimate the mobility of some trace elements in coastal sediments from Algeciras Bay. Sediments’ total metal concentrations of most heavy metals were found to be high around the areas impacted by anthropogenic activities such a sewage, atmospheric deposition and industrial activities, with notable values for As, Ni, Cr, Pb and Cd. The order of significant extraction efficiencies obtained with DTPA, were as follows: Pb (25.65%), Cu (19.78%), Cd (14.80%) and Zn (11.25%), while those obtained with HOAc were: Mn (33.00%), Tl (24.88%), Pb (18.99%), Cd (13.59%) and Co (11.78%). The comparison between the risk assessment codes (RAC) and the percent metal extractable fractions provided results of serious concern. Very high risk values of Cu, Zn, Cd and Pb extracts in DTPA were observed near the metallurgical industry, with Mn and Tl in HOAc extracts showing high risk values near the same industrial area and harbour activities. Sediments’ total metal concentrations were compared with the Low Alert-Level (LAL) sediment quality guidelines, where Co, Pb, Zn and Ni in both extractants and Cd and Cu in DTPA as well as Tl extracted in HOAc exceeded the LAL values respectively. The Spearman Rank test showed positive correlations between Co, Cu, Ni and Zn in DTPA extracts and their corresponding total metal concentrations, with Co, Cr, Fe, Ni, Tl and Zn in HOAc and total concentrations showing positive correlations. Furthermore, higher positive correlations were found between both extraction methods for Co (q= 0.797), Cu (q = 0.777), Ni (q = 0.789) and Zn (q = 0.942), indicating comparable potential extraction efficiencies between these extractants for these metals in the sediment studied

Comprehensive Plasma Metabolomic Profile of Patients with Advanced Neuroendocrine Tumors (NETs). Diagnostic and Biological Relevance

Purpose: High-throughput “-omic” technologies have enabled the detailed analysis of metabolic networks in several cancers, but NETs have not been explored to date. We aim to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. Methods: Plasma samples from 77 NETs and 68 controls were profiled by GC−MS, CE−MS and LC−MS untargeted metabolomics. OPLS-DA was performed to evaluate metabolomic differences. Related pathways were explored using Metaboanalyst 4.0. Finally, ROC and OPLS-DA analyses were performed to select metabolites with biomarker potential. Results: We identified 155 differential compounds between NETs and controls. We have detected an increase of bile acids, sugars, oxidized lipids and oxidized products from arachidonic acid and a decrease of carnitine levels in NETs. MPA/MSEA identified 32 enriched metabolic pathways in NETs related with the TCA cycle and amino acid metabolism. Finally, OPLS-DA and ROC analysis revealed 48 metabolites with diagnostic potential. Conclusions: This study provides, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use. A reduced set of metabolites of high diagnostic accuracy has been identified. Additionally, new enriched metabolic pathways annotated may open innovative avenues of clinical research

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe