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Introduzione - More equal than others? Il principio di eguaglianza nel diritto internazionale ed europeo
No full textIntroduzione sul tema del principio di eguaglianza nel diritto internazionale ed europe
Paid Sick Leave and Employee Absences
No full textThis paper studies the response of sickness absences to changes in the replacement rate for sick leave. In June 2008, a national law modified both the strength of monitoring and the monetary cost of sick leaves for public sector employees in Italy. Focusing on the National Health Service, which accounts for 21 per cent of the Italian public administration, first, I show that absences largely decreased following the reform. Second, using a difference‐in‐differences strategy that exploits variation in changes to the replacement rate for sick leave, I estimate that a 1 per cent point decrease in the replacement rate reduces absences by 1 per cent
Detailed Structural Characterization of the Lipooligosaccharide from the Extracellular Membrane Vesicles of Shewanella vesiculosa HM13
No full textBacterial extracellular membrane vesicles (EMVs) are membrane-bound particles released during cell growth by a variety of microorganisms, among which are cold-adapted bacteria. Shewanella vesiculosa HM13, a cold-adapted Gram-negative bacterium isolated from the intestine of a horse mackerel, is able to produce a large amount of EMVs. S. vesiculosa HM13 has been found to include a cargo protein, P49, in the EMVs, but the entire mechanism in which P49 is preferentially included in the vesicles has still not been completely deciphered. Given these premises, and since the structural study of the components of the EMVs is crucial for deciphering the P49 transport mechanism, in this study the complete characterization of the lipooligosaccharide (LOS) isolated from the cells and from the EMVs of S. vesiculosa HM13 grown at 18 °C is reported. Both lipid A and core oligosaccharide have been characterized by chemical and spectroscopic methods
Genomic coamplification of CDK4/MDM2/FRS2 is associated with very poor prognosis and atypical clinical features in neuroblastoma patients
No full textNeuroblastoma (NB) is the most common extracranial malignant tumor of childhood and is characterized by a broad heterogeneity in clinical presentation and evolution. Recent advances in pangenomic analysis of NB have revealed different recurrent chromosomal aberrations. Indeed, it is now well established that the overall genomic profile is important for treatment stratification. In previous studies, 11 genes were shown to be recurrently amplified (ODC1, ALK, GREB1, NTSR2, LIN28B, MDM2, CDK4, MYEOV, CCND1, TERT, and MYC) besides MYCN, with poor survival of NB patients harboring these amplifications being suggested. Genomic profiles of 628 NB samples analyzed by array-comparative genome hybridization (a-CGH) were re-examined to identify gene amplifications other them MYCN amplification. Clinical data were retrospectively collected. We additionally evaluated the association of FRS2 gene expression with NB patient outcome using the public R2 Platform. We found eight NB samples with high grade amplification of one or two loci on chromosome arm 12q. The regional amplifications were located on bands 12q13.3-q14.1 and 12q15-q21.1 involving the genes CDK4, MDM2, and the potential oncogenic gene FRS2. The CDK4, MDM2, and FRS2 loci were coamplified in 8/8 samples. The 12q amplifications were associated with very poor prognosis and atypical clinical features of NB patients. Further functional and clinical investigations are needed to confirm or refute these associations
On the construction of some fractional stochastic Gompertz models
No full textThe aim of this paper is the construction of stochastic versions for some fractional Gompertz curves. To do this, we first study a class of linear fractional-integral stochastic equations, proving existence and uniqueness of a Gaussian solution. Such kinds of equations are then used to construct fractional stochastic Gompertz models. Finally, a new fractional Gompertz model, based on the previous two, is introduced and a stochastic version of it is provided
Machine learning prediction of oncology drug targets based on protein and network properties
No full textBackground: The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. Results: We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an Area Under the Curve (AUC) of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates. Conclusions: We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions
An integrative information aqueduct to close the gaps between global satellite observation of water cycle and local sustainable management of water resources (iAqueduct).
No full textThe past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration and soil moisture, often at tens of kilometre scales. Whilst these data effectively characterise water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in-situ Earth Observations (EO): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (iAqueduct) is proposed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. iAqueduct aims to accomplish these goals by combining medium resolution (10m – 1km) Copernicus satellite data with high resolution (cm) Unmanned Aerial System (UAS) and in-situ observations. It is to note that iAqueduct is in the progress and this paper provides a general overview of its concept framework and introduces some preliminary results