Chemical characterization of atmospheric particulate matter in Friuli Venezia Giulia (NE Italy) by exploratory data analysis with multisite and multivariate approach

Abstract

The chemical composition of atmospheric particulate (PM10) in the Friuli Venezia Giulia (FVG) region (NE Italy) has been characterized for the first time with the help of exploratory data analysis (EDA) techniques (uni-, bi-, and multivariated, i.e., principal components analysis), molecular and elemental diagnostic ratios, and seasonal trends. Despite that the available analytical data was limited to the parameters routinely analyzed on PM10 by ARPA FVG (11 elements and 16 PAH congeners), the large number of samples and of measured chemical parameters, together with the applied techniques of data analysis, allowed us to extract useful latent information from the dataset, resulting in a greater knowledge of both regional and local features. Specifically, we succeeded in matching data patterns to the known pollution sources of some sampling stations, both industrial (two secondary fusion steelworks and one coke oven) and urban (traffic and domestic heating), and in defining the mainly urban or mainly industrial feature of some questionable sampling stations. This is of paramount importance to check for possible industrial inputs in urban stations, allowing policymakers to implement the most appropriate response.

A deep learning framework for segmentation and pose estimation of pedicle screw implants based on C-arm fluoroscopy

Abstract

Purpose

Pedicle screw fixation is a challenging procedure with a concerning rates of reoperation. After insertion of the screws is completed, the most common intraoperative verification approach is to acquire anterior–posterior and lateral radiographic images, based on which the surgeons try to visually assess the correctness of insertion. Given the limited accuracy of the existing verification techniques, we identified the need for an accurate and automated pedicle screw assessment system that can verify the screw insertion intraoperatively. For doing so, this paper offers a framework for automatic segmentation and pose estimation of pedicle screws based on deep learning principles.

Methods

Segmentation of pedicle screw X-ray projections was performed by a convolutional neural network. The network could isolate the input X-rays into three classes: screw head, screw shaft and background. Once all the screw shafts were segmented, knowledge about the spatial configuration of the acquired biplanar X-rays was used to identify the correspondence between the projections. Pose estimation was then performed to estimate the 6 degree-of-freedom pose of each screw. The performance of the proposed pose estimation method was tested on a porcine specimen.

Results

The developed machine learning framework was capable of segmenting the screw shafts with 93% and 83% accuracy when tested on synthetic X-rays and on clinically realistic X-rays, respectively. The pose estimation accuracy of this method was shown to be \(1.93^{\circ } \pm 0.64^{\circ }\) and \(1.92 \pm 0.55\,\hbox {mm}\) on clinically realistic X-rays.

Conclusions

The proposed system offers an accurate and fully automatic pedicle screw segmentation and pose assessment framework. Such a system can help to provide an intraoperative pedicle screw insertion assessment protocol with minimal interference with the existing surgical routines.

Oropharyngeal squamous cell carcinoma induces an innate systemic inflammation, affected by the size of the tumour and the lymph node spread

Clinical Otolaryngology, EarlyView.

Resonance frequency analysis in bone‐anchored hearing aids: Patient demographics and an approach to assess implant stability

Clinical Otolaryngology, EarlyView.

Physical outcome measures for conductive and mixed hearing loss treatment: A systematic review

Clinical Otolaryngology, EarlyView.

Gastrointestinal stromal tumours: ESMO–EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up†

Soft tissue and visceral sarcomas: ESMO–EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up†

Soft tissue sarcomas (STSs) gather over 80 histological entities, with even more molecular subsets, characterised by a low to very low incidence in all populations. The majority of sarcomas arise from the soft tissue (close to 75%), with ∼15% gastrointestinal stromal tumours (GISTs) and 10% bone sarcomas. These ESMO–EURACAN (European Society for Medical Oncology–European Reference Network for rare adult solid cancers) Clinical Practice Guidelines cover STSs, while GISTs are covered by dedicated ESMO–EURACAN Clinical Practice Guidelines [1]. Kaposi's sarcoma is not considered in the present document. Extraskeletal Ewing and Ewing-like sarcoma is covered by ESMO Clinical Practice Guidelines on bone sarcomas [2]. In general, the same principles for these tumours in children apply to adults. This is also the case for embryonal and alveolar rhabdomyosarcomas, which are exceedingly rare in adults. On the other hand, pleomorphic rhabdomyosarcoma is viewed as a high-grade, adult-type STS. Extraskeletal osteosarcoma is also a high-grade STS, whose clinical resemblance with osteosarcoma of bone is doubtful (prospective collection of data is encouraged to generate evidence on the therapeutic implications of such a diagnosis). Adult STS pathological subtypes occurring in adolescents should be managed the same way as in adult patients, though the same histotype might display clinical peculiarities when occurring at different ages.

A case of food-dependent exercise-induced angioedema

Publication date: Available online 26 May 2018
Source:The Journal of Allergy and Clinical Immunology: In Practice
Author(s): Eli Magen, Tinatin Chikovani

Eritema difuso e hiperqueratosis acral en un lactante

Publication date: Available online 26 May 2018
Source:Actas Dermo-Sifiliográficas
Author(s): A. Gómez-Zubiaur, I. Spanoudi-Kitrimi, A. Torrelo