Volatile Organic Compounds (VOCs) Diversity in the Orchid Himantoglossum robertianum (Loisel.) P. Delforge from Sardinia (Italy)

Volatile Organic Compounds (VOCs) are produced by plants to address a variety of physiological and ecological tasks (among others, stress resistance, and pollinator attraction). Genetics is a key factor in determining plants’ VOCs content and emission, nevertheless, environment strongly influences VOCs profiles in plants. Orchids are a widespread group of plants that colonize diverse environments and rely on complex and refined pollination mechanisms to reproduce. Orchids VOCs are rarely studied and discussed in relation to growing conditions. In the present study, we compare the volatile profiles of inflorescences of Himantoglossum robertianum (Loisel.) P. Delforge sampled in six ecologically diverse populations on Sardinia Island (Italy). The essential oils obtained by steam distillation were characterized by GC‐FID and GC‐MS analysis. A total of 79 compounds were detected, belonging to the chemical classes of saturated hydrocarbons, esters, alcohols, ketones, unsaturated hydrocarbons, sesquiterpenes, oxygenated terpenes, terpenes, acids, and aldehydes. Multivariate statistics separated H. robertianum populations based on their chemical profiles. Differences were positively linked to the distance separating populations and reflected climatological features of the sampling sites. Interestingly, our results differed from those available in the literature, pointing out the high variability of VOCs profiles in this food‐deceptive orchid

Secure Mobile IPv6 for Mobile Networks based on the 3GPP IP Multimedia Subsystem

The rapid spread of new radio access technologies and the consequent service opportunities have stimulated thetechnical and scientific community to investigate future evolution scenarios for 3rd Generation networks (3G), generically referred to as Beyond-3G or 4G. They are going to be characterized by ever stronger requirements for security, as well as the capability for the final users to experience continuous connectivity and uninterrupted services of IP applications as they move about from one access network to another. Key issues are: i) securityprovision for applications exchanging data in diverse wireless networks; ii) seamless mobility (handoff) between different coverage domains and, in case, access technologies. Since many proposals are based on the use of the Mobile IPv6 protocol, in this paper we analyze the security threats emerging from some Mobile IPv6 mechanisms for mobility management, and we propose a solution against such threats, under the assumption that both end users (mobile or not) are attached to a Mobile IPv6-enabled 3GPP IP Multimedia Subsystem network

INCB84344-201: Ponatinib and steroids in frontline therapy for unfit patients with Ph+ acute lymphoblastic leukemia

Tyrosine kinase inhibitors have improved survival for patients with Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukemia (ALL). However, prognosis for old or unfit patients remains poor. In the INCB84344-201 (formerly GIMEMA LAL 1811) prospective, multicenter, phase 2 trial, we tested the efficacy and safety of ponatinib plus prednisone in newly diagnosed patients with Ph+ ALL 6560 years, or unfit for intensive chemotherapy and stem cell transplantation. Forty-four patients received oral ponatinib 45 mg/d for 48 weeks (core phase), with prednisone tapered to 60 mg/m2/d from days-14-29. Prophylactic intrathecal chemotherapy was administered monthly. Median age was 66.5 years (range, 26-85). The primary endpoint (complete hematologic response [CHR] at 24 weeks) was reached in 38/44 patients (86.4%); complete molecular response (CMR) in 18/44 patients (40.9%) at 24 weeks. 61.4% of patients completed the core phase. As of 24 April 2020, median event-free survival was 14.31 months (95% CI 9.30-22.31). Median overall survival and duration of CHR were not reached; median duration of CMR was 11.6 months. Most common treatment-emergent adverse events (TEAEs) were rash (36.4%), asthenia (22.7%), alanine transaminase increase (15.9%), erythema (15.9%), and \u3b3-glutamyltransferase increase (15.9%). Cardiac and vascular TEAEs occurred in 29.5% (grade 653, 18.2%) and 27.3% (grade 653, 15.9%), respectively. Dose reductions, interruptions, and discontinuations due to TEAEs occurred in 43.2%, 43.2%, and 27.3% of patients, respectively; 5 patients had fatal TEAEs. Ponatinib and prednisone showed efficacy in unfit patients with Ph+ ALL; however, a lower ponatinib dose may be more appropriate in this population. This trial was registered at www.clinicaltrials.gov as #NCT01641107

Effectiveness of Video-Classification in??Android Malware Detection Through API-Streams and CNN-LSTM Autoencoders

The outbreak of the COVID-19 pandemic has forced worldwide employees to massive use of their mobile devices to access corporate systems. This new scenario has made mobile devices more susceptible to malicious applications, which are yearly developed to conduct several hostile activities. Concerned about this fact, many Deep Learning (DL) based solutions have been proposed, in the last decade, by considering both static and dynamic approaches. However, static solutions are adversely affected by obfuscation techniques and polymorphic applications, while dynamic ones cannot reduce the damages caused during applications execution. To this purpose, the following paper aims to propose a novel approach called API-Streams to minimize damages at Run-time. Therefore, we investigate several Video-Classification tasks through CNN-LSTM Autoencoders (CNN-LSTM-AEs). More precisely, we combine the capability of AEs in finding compact features with the classification abilities of Deep Neural Networks (DNNs), and we show that the proposed approach achieves an average accuracy of 98% in the presence of several unbalanced training datasets. Finally, we use the t-Stochastic Neighbor Embedded (t-SNE) representation technique to investigate the abilities of the employed AE to cluster data into their respective classes by limiting their overlapping

Effective classification of android malware families through dynamic features and neural networks

Due to their open nature and popularity, Android-based devices have attracted several end-users around the World and are one of the main targets for attackers. Because of the reasons given above, it is necessary to build tools that can reliably detect zero-day malware on these devices. At the moment, many of the frameworks that have been proposed to detect malware applications leverage Machine Learning (ML) techniques. However, an essential requirement to build these frameworks consists of using very large and sophisticated datasets for model construction and training purposes. Their success, indeed, strongly depends on the choice of the right features used for building a classification model providing adequate generalisation capability. Furthermore, the creation of a training dataset that well represents the malware properties and behaviour is one of the most critical challenges in malware analysis. Therefore, the main aim of this paper is proposing a new dataset called Unisa Malware Dataset (UMD) available on http://antlab.di.unisa.it/malware/, which is based on the extraction of static and dynamic features characterising the malware activities. Additionally, we will show some experiments concerning common ML tools to demonstrate how it is possible to build efficient ML-based malware classification frameworks using the proposed dataset

Space-Based Augmentation for Integrity Improvement

In air navigation the use of current GPS SPS service as support to the equipped Navigation systems is in use only during less restrictive flight phase (i.e. en-route phase over oceanic areas). The satellite positioning could be in use as support to air navigation if it will satisfy the restrictive performance (stated by ICAO) parameters (RNP) that are issued with regard to flight phase in terms of availability, continuity, accuracy and integrity. Integrity is the parameter more difficult to be satisfied on vehicles with high dynamics in safety critical flight phase, such as approach and landing phases. In order to solve the GPS gap in civil air application, a solution could be provided by integration of current constellation with further satellites. The purpose of this work is to analyze the improvement provided by a augmented GPS constellation in terms of coverage and integrity. The considered space-based augmentation is composed by geostationary and geosynchronous satellites. A simulation software has been developed in MATLAB® environment in order to study the integration of existent and feasible constellations. The used indicators to evaluate coverage performance are the VSN (Visible Satellites Number) and the probability that integrity is available to be computed in autonomous

Algorithms for GNSS Positioning in Difficult Scenario

Satellite navigation is critical in signal-degraded environments such as urban canyons and mountainous area, where many GNSS signals are blocked by natural and artificial obstacles or are strongly degraded. Hence standalone GPS is often unable to guarantee a continuous and accurate positioning. A suitable approach could be the integration of several GNSS. Multi-constellation system guarantees an improved satellite availability with respect to GPS standalone, providing a positioning enhancement in terms of accuracy, continuity and integrity. Currently the ideal candidate for supplement GPS in a multi-constellation approach is the Russian GLONASS. The main purposes of this work are the performance assessment of a GNSS multi-constellation relative to GPS stand-alone and the comparison of Least Squares and Kalman Filter

Privacy-preserving malware detection in Android-based IoT devices through federated Markov chains

The continuous emergence of new and sophisticated malware specifically targeting Android-based Internet of Things devices is causing significant security hazards and is consequently fostering the need for effective detection models and strategies able to work with these hardware-constrained devices. In addition, since such models are often trained on confidential application data, many involved subjects are reluctant to share their data for this purpose. Accordingly, several Federated Learning-based solutions are emerging, which rely on the capabilities of Machine Learning models in malware detection/classification without sharing user data. However, Federated Learning methods are often adversely affected by non-independent and identically distributed data in terms of both the required training time and classification results. Therefore, a promising solution could be to overcome the Federated Learning-related issues by preserving the privacy of end-user data. In this direction, the capabilities of Markov chains and associative rules are extended within a federated environment to face malware classification tasks in the IoT scenario. The presented approach, evaluated on several malware families, has achieved an average accuracy of 99% in the presence of centralized and decentralized unbalanced training/testing data by overcoming the most common state-of-the-art approaches. Also, its runtime performance is comparable with centralized ones by considering several non independent and identically distributed dataset partitions, splitting criteria, and clients, respectively

Space-Based Augmentation for Integrity Improvement

In air navigation use of current GPS SPS service as support to the equipped Navigation systems is in use only during less restrictive flight phase (i.e. en-route phase over oceanic areas). Satellite positioning could be in use as support to air navigation if it will satisfy the restrictive performance (stated by ICAO) parameters (RNP) that are issued with regard to flight phase in terms of availability, continuity, accuracy and integrity. Integrity is the parameter more difficult to be satisfied on vehicles with high dynamics in safety critical flight phase, such as approach and landing phases. In order to solve the GPS gap in civil air application, a solution could be provided by integration of current constellation with further satellites. The purpose of this work is to analyze the improvement provided by a augmented GPS constellation in terms of coverage and integrity. The considered space-based augmentation is composed by geostationary and geosynchronous satellites. A simulation software has been developed in MATLAB® environment in order to study the integration of existent and feasible constellations. The used indicators to evaluate coverage performance are the VSN and the probabilit

Ionospheric models comparison for single-frequency GNSS positioning

Broadcast Ionospheric Correction Algorithms (ICA) are used in Global Navigation Satellite System (GNSS) single frequency receivers positioning to compute the satellite signal delay due to the propagation through ionosphere. In this paper two ionospheric models are considered: Klobuchar model used by the U.S. Global Positioning System (GPS) and NeQuick model adopted by the European Galileo system. The goal of this paper is to investigate the validity and efficiency of the examined models by analyzing their performances in single frequency GNSS receiver