An architecture for certification-aware service discovery

Service-orientation is an emerging paradigm for building complex systems based on loosely coupled components, deployed and consumed over the network. Despite the original intent of the paradigm, its current instantiations are limited to a single trust domain (e.g., a single organization). Also, some of the key promises of service-orientation - such as the dynamic orchestration of externally provided software services, using runtime service discovery and deployment - are still unachieved. One of the main reasons for this is the trust gap that normally arises when software services, offered by previously unknown providers, are to be selected at run-time, without any human intervention. To close this gap, the concept of machine-readable security certificates (called asserts) has been recently introduced, which paves the way to automated processing about security properties of services. Similarly to current security certification schemes, the assessment of the security properties of a service is delegated to an independent third party (certification authority), who issues a corresponding assert, bound to the service. In this paper, we propose an architecture, which exploits the assert concept to realise a certification-aware service discovery framework. The architecture supports the discovery of single services based on certified security properties (in additional to the usual functional properties), as well as the dynamic synthesis of service compositions, that satisfy the given security properties. The architecture is extensible, thus allowing for a range of domain specific matchmaking components, to cover dimensions related to, e.g., performance, cost and other non-functional characteristics

Automated Mapping of Vulnerability Advisories onto their Fix Commits in Open Source Repositories

The lack of comprehensive sources of accurate vulnerability data represents a critical obstacle to studying and understanding software vulnerabilities (and their corrections). In this paper, we present an approach that combines heuristics stemming from practical experience and machine-learning (ML) - specifically, natural language processing (NLP) - to address this problem. Our method consists of three phases. First, an advisory record containing key information about a vulnerability is extracted from an advisory (expressed in natural language). Second, using heuristics, a subset of candidate fix commits is obtained from the source code repository of the affected project by filtering out commits that are known to be irrelevant for the task at hand. Finally, for each such candidate commit, our method builds a numerical feature vector reflecting the characteristics of the commit that are relevant to predicting its match with the advisory at hand. The feature vectors are then exploited for building a final ranked list of candidate fixing commits. The score attributed by the ML model to each feature is kept visible to the users, allowing them to interpret of the predictions. We evaluated our approach using a prototype implementation named Prospector on a manually curated data set that comprises 2,391 known fix commits corresponding to 1,248 public vulnerability advisories. When considering the top-10 commits in the ranked results, our implementation could successfully identify at least one fix commit for up to 84.03% of the vulnerabilities (with a fix commit on the first position for 65.06% of the vulnerabilities). In conclusion, our method reduces considerably the effort needed to search OSS repositories for the commits that fix known vulnerabilities

Src family kinases as therapeutic targets in advanced solid tumors. What we have learned so far

Src is the prototypal member of Src Family tyrosine Kinases (SFKs), a large non-receptor kinase class that controls multiple signaling pathways in animal cells. SFKs activation is necessary for the mitogenic signal from many growth factors, but also for the acquisition of migratory and invasive phenotype. Indeed, oncogenic activation of SFKs has been demonstrated to play an important role in solid cancers; promoting tumor growth and formation of distant metastases. Several drugs targeting SFKs have been developed and tested in preclinical models and many of them have successfully reached clinical use in hematologic cancers. Although in solid tumors SFKs inhibitors have consistently confirmed their ability in blocking cancer cell progression in several experimental models; their utilization in clinical trials has unveiled unexpected complications against an effective utilization in patients. In this review, we summarize basic molecular mechanisms involving SFKs in cancer spreading and metastasization; and discuss preclinical and clinical data highlighting the main challenges for their future application as therapeutic targets in solid cancer progression

Routine blood analysis greatly reduces the false-negative rate of RT-PCR testing for COVID-19

Background: The COVID-19 outbreak is now a pandemic disease reaching as much as 210 countries worldwide with more than 2.5 million infected people and nearly 200.000 deaths. Amplification of viral RNA by RT-PCR represents the gold standard for confirmation of infection, yet it showed false-negative rates as large as 15-20% which may jeopardize the effect of the restrictive measures taken by governments. We previously showed that several hematological parameters were significantly different between COVID-19 positive and negative patients. Among them aspartate aminotransferase and lactate dehydrogenase had pre-dictive values as large as 90%. Thus a combination of RT-PCR and blood tests could reduce the false-negative rate of the genetic test. Methods: We retrospectively analyzed 24 patients showing multiple and inconsistent RT-PCR, test during their first hospitalization period, and compared the genetic tests results with their AST and LDH levels. Results: We showed that when considering the hematological parameters, the RT-PCR false-negative rates were reduced by almost 4-fold. Conclusions: The study represents a preliminary work aiming at the development of strategies that, by combining RT-PCR tests with routine blood tests, will lower or even abolish the rate of RT-PCR false-negative results and thus will identify, with high accuracy, patients infected by COVID-19. (www.actabiomedica.it)

The effect of quarantine due to Covid-19 pandemic on seizure frequency in 102 adult people with epilepsy from Apulia and Basilicata regions, Southern Italy

Objective: following the COVID-19 pandemic, a quarantine was imposed to all of regions Italy by 9th March until 3rd May 2020. We investigated the effect of COVID-19 infection and quarantine on seizure frequency in adult people with epilepsy (PwE) of Apulia and Basilicata regions, Southern Italy. Methods: This is an observational, retrospective study based on prospective data collection of 102 successive PWE. The frequency of seizures was evaluated during pre-quarantine (January- February), quarantine (March-April), and post-quarantine period (May-June), while PwE were divided into A) cases responding to treatment with ≤ 1 seizure per year; B) cases responding to treatment with 2-5 seizure per year; C) cases with drug-resistant epilepsy with ≤ 4 seizures per month; D) cases with drug-resistant epilepsy with 5-10 seizures per month. PwE underwent several self-report questionnaires regarding therapeutic compliance, mood, stress and sleep during quarantine period. Results: Approximately 50 % of PwE showed seizure frequency changes (22.55 % an increase and 27.45 % a reduction) during quarantine. Seizure frequency significantly (p < 0.05) increased in PwE responding to treatment with ≤ 1 seizure per year, while significantly (p < 0.05) reduced in PwE with drug-resistant epilepsy with 5-10 seizures per month. The data was not influenced by therapeutic adherence, sleep and depression. The analysis of anxiety showed a moderate level of anxiety in PwE responding to treatment with < 1 seizure per year, while moderate stress was perceived by all PwE. Seizure frequency changes were related to quarantine, but not to COVID-19 infection. In fact, unlike other regions of Italy, particularly Northern Italy, Apulia and Basilicata regions were less affected by COVID-19 infection, and almost all PwE recognized the quarantine as a stressful event. Emotional distress and anxiety due to social isolation, but also the relative reduction of triggers for epileptic seizures were the most important factors for changes in seizure frequency. Conclusions: Our study adds to the growing concern that the indirect effects of COVID-19 pandemic will far outstrip the direct consequences of the infection

Tau oligomers accumulation sensitizes prostate cancer cells to docetaxel treatment

Purpose: Human tau is a highly dynamic, multifunctional protein expressed in different isoforms and conformers, known to modulate microtubule turnover. Tau oligomers are considered pathologic forms of the protein able to initiate specific protein accumulation diseases, called tauopathies. In our study, we investigated the potential association between autophagy and tau oligomers accumulation and its role in the response of prostate cancer cells to docetaxel. Methods: We evaluated in vitro the expression of tau oligomers in prostate cancer cell lines, PC3 and DU145, in presence of autophagy inhibitors and investigated the role of tau oligomers accumulation in resistance to docetaxel treatment. Results: Tau protein was basally expressed in prostate cancer lines as several monomeric and oligomeric forms. The pharmacologic inhibition of autophagy induced in cancer cells the accumulation of tau protein, with a prevalent expression of oligomeric forms. Immunofluorescence analysis of untreated cells revealed that tau was visible mainly in dividing cells where it was localized on the mitotic spindle. Inhibition of autophagy determined an evident upregulation of tau signal in dividing cells and the presence of aberrant monoastral mitotic spindles. The accumulation of tau oligomers was associated with DNA DSB and increased cytotoxic effect by docetaxel. Conclusions: Our data indicate that autophagy could exert a promoting role in cancer growth and during chemotherapy facilitating degradation of tau protein and thus blocking the antimitotic effect of accumulated tau oligomers. Thus, therapeutic strategies aimed at stimulating tau oligomers formation, such as autophagy inhibition, could be an effective adjuvant in cancer therapy

Overview of the Italian strong Motion database ITACA 1.0

The Italian Strong Motion Database, ITACA, was developed within projects 2 S6 and S4, funded in the framework of the agreements between the Italian Department of 3 Civil Protection (Dipartimento della Protezione Civile, DPC) and the Istituto Nazionale di 4 Geofisica e Vulcanologia (INGV), starting from 2005. The alpha version of the database 5 was released in 2007 and subsequently upgraded to version 1.0 after: (i) including the most 6 recent strongmotion data (from2005 to 2007) recorded in Italy, in addition to the 2008 Parma 7 earthquake, M 5.4, and the M 4.0, 2009 Abruzzo seismic events; (ii) processing the raw 8 strong motion data using an updated procedure; (iii) increasing the number of stations with a 9 measured shear wave velocity profile; (iv) improving the utilities to retrieve time series and 10 ground motion parameters; (v) implementing a tool for selecting time series in agreement 11 with design-response spectra; (vi) compiling detailed station reports containing miscella12 neous information such as photo, maps and site parameters; (vii) developing procedures for 13 the automatic generation of station reports and for the updating of the header files. After such 14 improvements, ITACA 1.0 was published at the web site http://itaca.mi.ingv.it, in 2010. It 15 presently contains 3,955 three-component waveforms, comprising the most complete cata16 logue of the Italian accelerometric records in the period 1972–2007 (3,562 records) and the 17 strongest events in the period 2008–2009. Records were mainly acquired by DPC through its 18 Accelerometric National Network (RAN) and, in few cases, by local networks and temporary 19 stations or networks. This paper introduces the published version of the Italian StrongMotion 20 database (ITACA version 1.0) together with main improvements and new functionalities

The Relevance of Discovering and Recovering the Biodiversity of Apulian Almond Germplasm by Means of Molecular and Phenotypic Markers

Almond cultivation has great traditional and economic relevance in Southern Italy, especially in the Apulia region, where almond trees feature an ample and ancient varietal richness. To contrast the loss of plant genetic erosion and to safeguard the available bioresources, as well as to reinforce the local production, the regional Re.Ge.Fru.P. project aimed to re-evaluate, identify, and characterize the Apulian almond germplasm that is still uncharacterized and not jet studied using a dual (genetic and morphological) approach. Collection was conducted in the regional territory of 187 among the most widespread and minor or marginalized genotypes that were molecularly fingerprinted by means of 18 nuclear microsatellites (simple sequence repeats, SSRs). The high number of scored alleles reflected the great level of diversification within the Apulian germplasm, as also confirmed by neighbor joining and structure analysis, that clearly distinguished different genotype clusters. The phenotypic characterization using 17 morphological and phenological descriptors mirrored the genetic results, revealing a high degree of variability. The morphological traits with the best discriminatory ability were nut ventral suture, shell softness and shape and petal color. This work emphasizes the importance of recovering the genetic variability of Apulian almond germplasm, and the need to promote added value and enhance the local agri-food economy

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15-20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative. Three different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub-datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation. We developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC from 0.75 to 0.78; and specificity from 0.92 to 0.96. ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions