Machine learning based data mining for Milky Way filamentary structures reconstruction

We present an innovative method called FilExSeC (Filaments Extraction, Selection and Classification), a data mining tool developed to investigate the possibility to refine and optimize the shape reconstruction of filamentary structures detected with a consolidated method based on the flux derivative analysis, through the column-density maps computed from Herschel infrared Galactic Plane Survey (Hi-GAL) observations of the Galactic plane. The present methodology is based on a feature extraction module followed by a machine learning model (Random Forest) dedicated to select features and to classify the pixels of the input images. From tests on both simulations and real observations the method appears reliable and robust with respect to the variability of shape and distribution of filaments. In the cases of highly defined filament structures, the presented method is able to bridge the gaps among the detected fragments, thus improving their shape reconstruction. From a preliminary "a posteriori" analysis of derived filament physical parameters, the method appears potentially able to add a sufficient contribution to complete and refine the filament reconstruction.Comment: Proceeding of WIRN 2015 Conference, May 20-22, Vietri sul Mare, Salerno, Italy. Published in Smart Innovation, Systems and Technology, Springer, ISSN 2190-3018, 9 pages, 4 figure

Imaging-based indices combining disease severity and time from disease onset to predict COVID-19 mortality: A cohort study

Background: COVID-19 prognostic factors include age, sex, comorbidities, laboratory and imaging findings, and time from symptom onset to seeking care. Purpose: The study aim was to evaluate indices combining disease severity measures and time from disease onset to predict mortality of COVID-19 patients admitted to the emergency department (ED). Materials and methods: All consecutive COVID-19 patients who underwent both computed tomography (CT) and chest X-ray (CXR) at ED presentation between 27/02/2020 and 13/03/2020 were included. CT visual score of disease extension and CXR Radiographic Assessment of Lung Edema (RALE) score were collected. The CT- and CXR-based scores, C-reactive protein (CRP), and oxygen saturation levels (sO2) were separately combined with time from symptom onset to ED presentation to obtain severity/time indices. Multivariable regression age- and sex-adjusted models without and with severity/time indices were compared. For CXR-RALE, the models were tested in a validation cohort. Results: Of the 308 included patients, 55 (17.9%) died. In multivariable logistic age- and sex-adjusted models for death at 30 days, severity/time indices showed good discrimination ability, higher for imaging than for laboratory measures (AUCCT = 0.92, AUCCXR = 0.90, AUCCRP = 0.88, AUCsO2 = 0.88). AUCCXR was lower in the validation cohort (0.79). The models including severity/time indices performed slightly better than models including measures of disease severity not combined with time and those including the Charlson Comorbidity Index, except for CRP-based models. Conclusion: Time from symptom onset to ED admission is a strong prognostic factor and provides added value to the interpretation of imaging and laboratory findings at ED presentation

T Helper Lymphocyte and Mast Cell Immunohistochemical Pattern in Nonceliac Gluten Sensitivity

Background and Aims. Nonceliac gluten sensitivity (NCGS) is a gluten-related emerging condition. Since few data about NCGS histopathology is available, we assessed the markers of lymphocyte and innate immunity activation. Materials and Methods. We retrieved duodenal biopsy samples of patients with NCGS diagnosis according to the Salerno criteria. We selected specimens of positive (seropositive celiac disease/Marsh 1-2 stage) and negative (normal microscopic picture) controls. Immunohistochemistry for CD3 (intraepithelial lymphocytes-IELs), CD4 (T helper lymphocytes), CD8 (T cytotoxic lymphocytes), and CD1a/CD117 (Langerhans/mast cells) was performed. ANOVA plus Bonferroni’s tests were used for statistical analysis. Results. Twenty NCGS, 16 celiac disease, and 16 negative controls were selected. CD3 in NCGS were higher than negative controls and lower than celiac disease (18.5 ± 6.4, 11.9 ± 2.8, and 40.8 ± 8.1 IELs/100 enterocytes; p<0.001). CD4 were lower in NCGS than controls and celiac disease (31.0 ± 22.1, 72.5 ± 29.5, and 103.7 ± 15.7 cells/mm2; p<0.001). CD8 in NCGS were similar to negative controls, but lower than celiac disease (14.0 ± 7.4 and 34.0 ± 7.1 IELs/100 enterocytes, p<0.001). CD117 were higher in NCGS than celiac disease and negative controls (145.8 ± 49.9, 121.3 ± 13.1, and 113.5 ± 23.4 cells/mm2; p=0.009). Conclusions. The combination of CD4 and CD117, as well as IEL characterization, may be useful to support a clinical diagnosis of NCGS

Perioperative anaphylactic risk score for risk-oriented premedication

Basing on the current knowledge, this paper is aimed to review the core characteristics of the most relevant therapeutic agents (steroids and antihistamines), administered to prevent perioperative anaphylaxis. Moreover, the Authors propose the validation of a Global Anaphylactic Risk Score, built up by recording the individual scores related to the most relevant anaphylaxis parameters (i.e. medical history, symptoms and medication for asthma, rhinitis and urticaria etc) and by adding them on all together; the score could be used in the preoperative phase to evaluate the global anaphylactic risk and to prescribe risk-oriented premedication protocols

ISO -LWS two-colour diagram of young stellar objects

We present a [60-100] versus [100-170]μm two-colour diagram for a sample of 61 young stellar objects (YSOs) observed with the Long Wavelength Spectrometer (LWS) on-board the Infrared Space Observatory (ISO). The sample consists of 17 Class 0 sources, 15 Class I, nine Bright Class I (Lbol>104Lsolar) and 20 Class II (14 Herbig Ae/Be stars and six T Tauri stars). We find that each class occupies a well-defined region in our diagram with colour temperatures increasing from Class 0 to Class II. Therefore the [60-100] versus [100-170] two-colour diagram is a powerful and simple tool to derive from future (e.g. with the Herschel Space Observatory) photometric surveys the evolutionary status of YSOs. The advantage over other tools already developed is that photometry at other wavelengths is not required: three flux measurements are enough to derive the evolutionary status of a source. As an example we use the colours of the YSO IRAS 18148-0440 to classify it as Class I. The main limitation of this work is the low spatial resolution of the LWS which, for some objects, causes a high uncertainty in the measured fluxes due to background emission or to source confusion inside the LWS beam

An individual patient-data meta-analysis of metronomic oral vinorelbine in metastatic non-small cell lung cancer

MOV is an active and well-tolerated chemotherapy in metastatic NSCLC and is a manageable therapy in frail patients

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms

SIMPLE SUMMARY: Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome, particularly for the intermediate domains of adenocarcinomas and large-cell neuroendocrine carcinomas. Moreover, subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. The aim of this study was to design and evaluate an objective and reproducible approach to the grading of lung NENs, potentially extendable to other NENs, by exploring a completely new perspective of interpreting the well-recognised proliferation marker Ki-67. We designed an automated pipeline to harvest quantitative information from the spatial distribution of Ki-67-positive cells, analysing its heterogeneity in the entire extent of tumour tissue—which currently represents the main weakness of Ki-67—and employed machine learning techniques to predict prognosis based on this information. Demonstrating the efficacy of the proposed framework would hint at a possible path for the future of grading and classification of NENs. ABSTRACT: Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs