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Cycling and micro-mobility, in general, have been long promoted as sustainable and suitable modes of transport due to emission mitigation, congestion reduction and improvements to users' health and lifestyle. However, as most cities in the world have followed a car-centric development, their bicycle network is often highly fragmented, constituting the biggest barrier for attracting new users. This paper introduces a data-driven procedure based on micro-mobility geo-referenced data collected in the city of Rome (Italy). The aim is to identify corridors of high-density micro-mobility demand through an iterative clustering procedure and to evaluate potential growth scenarios of the bicycle network by locating the strategic missing links in the existing infrastructure to achieve a fully connected bicycle network that maximizes the overall usage of deployed bike lanes. The procedure has been applied to the city of Rome (Italy) adopting point data of the e-scooter sharing operator Dott.

Recent electric micromobility solutions can represent a sustainable transport alternative in urban environments. Indeed, these can be adopted as a substitute of car, especially for specific distance classes, as well as they can increase accessibility to transit services. Aiming to investigate the potential demand that can be moved from private cars to environment-friendly micromobility modes (e.g., e-scooters and e-bikes), a methodology based on exploiting data by probe vehicles is presented. To test its goodness, it is applied to the city of Rome (Italy) with challenging results.

The astrophysical journal / Supplement series 266(2), 37 (2023). doi:10.3847/1538-4365/acc181, We study the broadband emission of Mrk 501 using multiwavelength observations from 2017 to 2020 performed with a multitude of instruments, involving, among others, MAGIC, Fermi's Large Area Telescope (LAT), NuSTAR, Swift, GASP-WEBT, and the Owens Valley Radio Observatory. Mrk 501 showed an extremely low broadband activity, which may help to unravel its baseline emission. Nonetheless, significant flux variations are detected at all wave bands, with the highest occurring at X-rays and very-high-energy (VHE) $γ$-rays. A significant correlation (>3σ) between X-rays and VHE $γ$-rays is measured, supporting leptonic scenarios to explain the variable parts of the emission, also during low activity. This is further supported when we extend our data from 2008 to 2020, and identify, for the first time, significant correlations between the Swift X-Ray Telescope and Fermi-LAT. We additionally find correlations between high-energy γ-rays and radio, with the radio lagging by more than 100 days, placing the γ-ray emission zone upstream of the radio-bright regions in the jet. Furthermore, Mrk 501 showed a historically low activity in X-rays and VHE $γ$-rays from mid-2017 to mid-2019 with a stable VHE flux (>0.2 TeV) of 5% the emission of the Crab Nebula. The broadband spectral energy distribution (SED) of this 2 $γ$r long low state, the potential baseline emission of Mrk 501, can be characterized with one-zone leptonic models, and with (lepto)-hadronic models fulfilling neutrino flux constraints from IceCube. We explore the time evolution of the SED toward the low state, revealing that the stable baseline emission may be ascribed to a standing shock, and the variable emission to an additional expanding or traveling shock., Published by Institute of Physics Publ., London

The aim of this work is to establish inclusive guidelines on electroencephalography (EEG) applicable to all neonatal intensive care units (NICUs). Guidelines on ideal EEG monitoring for neonates are available, but there are significant barriers to their implementation in many centres around the world. These includebarriers due to limited resources regarding the availability of equipment and technical and interpretive round-the-clock personnel. On the other hand, despite its limitations, amplitude-integrated EEG (aEEG) (previously called Cerebral Function Monitor [CFM]) is a common alternative used in NICUs.The Italian Neonatal Seizure Collaborative Network (INNESCO), working with all national scientific societies interested in the field of neonatal clinical neurophysiology, performed a systematic literature review and promoted interdisciplinary discussions among experts (neonatologists, paediatric neurologists, neurophysiologists, technicians) between 2017 and 2020 with the aim of elaborating shared recommendations.A consensus statement on videoEEG (vEEG) and aEEG for the principal neonatal indications was established. The authors propose a flexible frame of recommendations based on the complementary use of vEEG and aEEG applicable to the various neonatal units with different levels of complexity according to local resources and specific patient features. Suggestions for promoting cooperation between neonatologists, paediatric neurologists, and neurophysiologists, organisational restructuring, and teleneurophysiology implementation are provided.(c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Biliary tract cancer (BTC) patients usually have poor prognosis. Whereas combination chemotherapy has been shown to improve survival in the frontline setting, second-line treatment is subject to a lot of debate in the scientific community. Recent data of the ABC-06 trial has provided slight evidence for the use of second-line chemotherapy after progression on cisplatin plus gemcitabine combination. In this study, mFOLFOX plus active symptom control (ASC) improved overall survival (OS) after progression on cisplatin-gemcitabine combination compared with ASC alone, with an increase in 6- and 12-month OS rate. Although genomic studies have paved the way for a new age in cancer management, the “Precision Medicine Era” in BTC is still limited to intrahepatic cholangiocarcinoma and primarily focused on isocitrate dehydrogenase (IDH) and fibroblast growth factor receptor (FGFR) targeted therapies. We herein review recent published data regarding the use of second-line treatment after failure of standard first-line therapies in BTC patients, with a particular focus on ongoing active and recruiting clinical trials.

Background: Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status. Methods: Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon two-sample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features. Results: Shape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1). Conclusions: Despite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading. © 2020 The Author(s).