Preliminary results of an attempt to provide soil moisture datasets in order to verify numerical weather prediction models

In the recent years, there has been a significant growth in the recognition of the soil moisture importance in large-scale hydrology and climate modelling. Soil moisture is a lower boundary condition, which rules the partitioning of energy in terms of sensible and latent heat flux. Wrong estimations of soil moisture lead to wrong simulation of the surface layer evolution and hence precipitations and cloud cover forecasts could be consequently affected. This is true for largescale medium-range weather forecasts as well as for local-scale short-range weather forecasts, particularly in those situations in which local convection is well developed. Unfortunately, despite the importance of this physical parameter there are only few soilmoisture data sets sparse in time and in space around in the world. Due to this scarcity of soil moisture observations, we developed an alternative method to provide soilmoisture datasets in order to verify numericalw eather prediction models. In this paper are presented the preliminary results of an attempt to verify soil moisture fields predicted by a mesoscale model. The data for the comparison were provided by the simulations of the diagnostic land surface scheme LSPM (Land Surface Process Model), widely used at the Piedmont Regional Weather Service for agro-meteorological purposes. To this end, LSPM was initialized and driven by Synop observations, while the surface (vegetation and soil) parameter values were initialized by ECOCLIMAP global dataset at 1km2 resolution

JKarma: A Highly-Modular Framework for Pattern-Based Change Detection on Evolving Data

Pattern-based change detection (PBCD) describes a class of change detection algorithms for evolving data. Contrary to conventional solutions, PBCD seeks changes exhibited by the patterns over time and therefore works on an abstract form of the data, which prevents the search for changes on the raw data. Moreover, PBCD provides arguments on the validity of the results because patterns mirror changes occurred with any form of evidence. However, the existing solutions differ on data representation, mining algorithm and change identification strategy, which we can deem as main modules of a general architecture, so that any PBCD task could be designed by accommodating custom implementations for those modules. This is what we propose in this paper through jKarma, a highly-modular framework for designing and performing PBCD

Giant acquired tracheocele in a syndromic child: Case report and review of the literature

We report a case of acquired tracheocele in a child with multiple congenital anomalies of the face, limbs, kidneys, and heart, to share our experience with international scientific community, considering the rarity of the disease especially in the pediatric population. Patientâs history reported a tracheotomy at one month of life that was closed at 3 years old. Ten years later, the patient come to us for an anterior cervical mass swelling during respiratory effort and chronic productive cough. The diagnosis was made by high resolution Computed Tomography scan of the neck and chest and an elective surgical resection of the lesion under general anesthesia was done

Overview of the first HyMeX Special Observation Period over Italy: observations and model results

Abstract. The Special Observation Period (SOP1), part of the HyMeX campaign (Hydrological cycle in the Mediterranean Experiments, 5 September–6 November 2012), was dedicated to heavy precipitation events and flash floods in the western Mediterranean, and three Italian hydro-meteorological monitoring sites were identified: Liguria–Tuscany, northeastern Italy and central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models, including hydrological models and marine models, allowed an unprecedented monitoring and analysis of high-impact weather events around the Italian hydro-meteorological sites. This activity has seen strong collaboration between the Italian scientific and operational communities. In this paper an overview of the Italian organization during SOP1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in northeastern Italy, IOP13 (15–16 October 2012) in central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems, including the hydrological impacts. The usefulness of having different weather forecast operational chains characterized by different numerical weather prediction models and/or different model set up or initial conditions is finally shown for one of the events (IOP19)

Determinants of frontline tyrosine kinase inhibitor choice for patients with chronic-phase chronic myeloid leukemia: A study from the Registro Italiano LMC and Campus CML

BackgroundImatinib, dasatinib, and nilotinib are tyrosine kinase inhibitors (TKIs) approved in Italy for frontline treatment of chronic-phase chronic myeloid leukemia (CP-CML). The choice of TKI is based on a combined evaluation of the patient's and the disease characteristics. The aim of this study was to analyze the use of frontline TKI therapy in an unselected cohort of Italian patients with CP-CML to correlate the choice with the patient's features. MethodsA total of 1967 patients with CP-CML diagnosed between 2012 and 2019 at 36 centers throughout Italy were retrospectively evaluated; 1089 patients (55.4%) received imatinib and 878 patients (44.6%) received a second-generation (2G) TKI. ResultsSecond-generation TKIs were chosen for most patients aged <45 years (69.2%), whereas imatinib was used in 76.7% of patients aged >65 years (p < .001). There was a predominant use of imatinib in intermediate/high European long-term survival risk patients (60.0%/66.0% vs. 49.7% in low-risk patients) and a limited use of 2G-TKIs in patients with comorbidities such as hypertension, diabetes, chronic obstructive pulmonary disease, previous neoplasms, ischemic heart disease, or stroke and in those with >3 concomitant drugs. We observed a greater use of imatinib (61.1%) in patients diagnosed in 2018-2019 compared to 2012-2017 (53.2%; p = .002). In multivariable analysis, factors correlated with imatinib use were age > 65 years, spleen size, the presence of comorbidities, and & GE;3 concomitant medications. ConclusionsThis observational study of almost 2000 cases of CML shows that imatinib is the frontline drug of choice in 55% of Italian patients with CP-CML, with 2G-TKIs prevalently used in younger patients and in those with no concomitant clinical conditions. Introduction of the generic formulation in 2018 seems to have fostered imatinib use

Analyzing Microblogging Posts for Tracking Collective Emotional Trajectories

The technologies of communication, such as forums and instant messaging, available in the social media platforms open to the possibility to convey and express emotions and feelings, besides to facilitate interaction. Emotions and social relationships are often connected, indeed, emotions and feelings can make the users favorable or reluctant to socialize, as well, experiences of socialization can influence the behaviors. Being personal, emotions and feelings can be crucial in the dynamics of social communities, perhaps more than other elements, such as events and multimedia items, because the individuals tend to interact with the users with who have particular affinity or with who share sensations. In this paper we introduce the problem of tracking users who share emotional behavior with other users. The proposed method relies on a cyberspace based on emotional words extracted from social media posts. It builds emotional trajectories as sequences of points of the cyberspace characterized by highly similar emotions. We show the viability of the method on Twitter data and provide a quantitative evaluation and qualitative considerations

Mining spatio-temporal patterns of periodic changes in climate data

The climate changes have attracted always interest because they may have great impact on the life on Earth and living beings. Computational solutions may be useful both for the prediction of the climate changes and for their characterization, perhaps in association with other phenomena. Due to the cyclic and seasonal nature of many climate processes, studying their repeatability may be relevant and, in many cases, determinant. In this paper, we investigate the task of determining changes of the weather conditions, which are periodically repeated over time and space. We introduce the spatio-temporal patterns of periodic changes and propose a computational solution to discover them. These patterns allows us to represent spatial regions with same periodic changes. The method works on a grid-based data representation and relies on a time-windows analysis model to detect periodic changes in the grid cells. Then, the cells with same changes are selected to form a spatial region of interest. The usefulness of the method is demonstrated on a real-world dataset collecting weather conditions