Development of a molecular method for the rapid screening and identification of the three functionally relevant polymorphisms in the human TAS2R38 receptor gene in studies of sensitivity to the bitter taste of PROP

The objective of this work was to develop a rapid screening method to identify the three single nucleotide polymorphisms (SNPs) in the TAS2R38 gene, with the aim of providing a significant contribution to studies designed to assess sensitivity to the bitter taste of 6-n-propylthiouracil (PROP). Specifically, the objective of this study was to characterize the TAS2R38 gene haplotypes in a group of 60 subjects with variable sensitivity to PROP and preliminarily genotyped for the rs2274333 allele (A/G) of carbonic anhydrase isoform VI gene (CA6). The molecular characterization of the TAS2R38 gene was conducted using the PCR-restriction fragment length polymorphism technique after creating artificial restriction sites upstream or downstream of the SNPs, as none of the three polymorphisms contributes to the formation of a restriction site for a specific endonuclease. The results indicate that the method described in this paper could be a valid and simple experimental strategy to identify genetic differences related to taste sensitivity to bitter taste, and could be applied as a nutrigenetics test in studies aimed at understanding people’s eating behaviors

Optimal fault resolution in geodetic inversion of coseismic data

With the continued growth in availability of DInSAR and GPS data, space based geodesy has been widely applied to image the coseismic displacement field and to retrieve the static dislocation over the fault plane for almost all the significant earthquakes of the past two decades. This is performed by linear data inversion over a set of subfaults, generally characterized by a constant and predefined or manually adjusted dimensions. In this paper we propose a new algorithm to automatically retrieve an optimized fault subdivision in the linear inversion of coseismic geodetic data. The code iteratively keeps the parameter resolution close to a predefined high value. We first discuss the rationale supporting our algorithm and, after a detailed description of its implementation, we analyze the advantages of its introduction in the data inversion. The algorithm was tested against an exhaustive range of synthetic and real datasets and fault mechanisms. Among them, we present the results for the Mw 6.2, 2009 L’Aquila (Central Italy) earthquake and compare the new and previously published slip distributions showing the disappearance of misleading slip pattern and the increased resolution for shallower zones

Cross-Talk Effects in the Uncertainty Estimation of Multiplexed Data Acquisition Systems

This paper deals with the analysis of multi-channel data-acquisition systems with the aim of identifying and combining the main uncertainty contributions according to the GUM framework. Particular attention has been paid towards cross-talk effect, which could be an important uncertainty contribution in multiplexed data-acquisition systems. The uncertainty analysis is described for three commercial data acquisition devices highlighting that cross-talk specifications are often not suitable for a reliable uncertainty estimation in operating conditions. For this reason, an experimental set-up has been arranged to fully characterize the inter-channel effects of the investigated devices. The obtained results have highlighted that a proper characterization of a data-acquisition system is effective in estimating the actual performance at the frequency of interest and in the operating conditions for the source resistance and the input-channel configuration. Eventually, a customized procedure has been proposed that is effective in correcting the cross-talk effects also in very severe conditions of inter-channel disturbance

Checking and Enforcing Security through Opacity in Healthcare Applications

The Internet of Things (IoT) is a paradigm that can tremendously revolutionize health care thus benefiting both hospitals, doctors and patients. In this context, protecting the IoT in health care against interference, including service attacks and malwares, is challenging. Opacity is a confidentiality property capturing a system's ability to keep a subset of its behavior hidden from passive observers. In this work, we seek to introduce an IoT-based heart attack detection system, that could be life-saving for patients without risking their need for privacy through the verification and enforcement of opacity. Our main contributions are the use of a tool to verify opacity in three of its forms, so as to detect privacy leaks in our system. Furthermore, we develop an efficient, Symbolic Observation Graph (SOG)-based algorithm for enforcing opacity

Towards the prediction of the quality of experience from facial expression and gaze direction

In this paper we investigate on the potentials to implicitly estimate the Quality of Experience (QoE) of a user of video streaming services by acquiring a video of her face and monitoring her facial expression and gaze direction. To this, we conducted a crowdsourcing test in which participants were asked to watch and rate the quality when watching 20 videos subject to different impairments, while their face was recorded with their PC's webcam. The following features were then considered: the Action Units (AU) that represent the facial expression, and the position of the eyes' pupil. These features were then used, together with the respective QoE values provided by the participants, to train three machine learning classifiers, namely, Support Vector Machine with quadratic kernel, RUSBoost trees and bagged trees. We considered two prediction models: only the AU features are considered or together with the position of the eyes' pupils. The RUSBoost trees achieved the best results in terms of accuracy, sensitivity and area under the curve scores. In particular, when all the features were considered, the achieved accuracy is of 44.7%, 59.4% and 75.3% when using the 5-level, 3level and 2-level quality scales, respectively. Whereas these results are not satisfactory yet, these represent a promising basis

Using a distributed Shapley-value based approach to ensure navigability in a social network of smart objects

The huge number of nodes that is expected to join the Internet of Things in the short term will add major scalability issues to several procedures. A recent promising approach to these issues is based on social networking solutions to allow objects to autonomously establish social relationships. Every object in the resulting Social IoT (SIoT) exchanges data with its friend objects in a distributed manner to avoid the need for centralized solutions to implement major functionalities, such as: node discovery, information search and trustworthiness management. However, the number and types of established friendship affects network navigability. This paper addresses this issue proposing an efficient, distributed and dynamic strategy for the objects to select the right friends for the benefit of the overall network connectivity. The proposed friendship selection model relies on a Shapley-value based algorithm mapping the friendship selection process in the SIoT onto the coalition formation problem in a corresponding cooperative game. The obtained results show that the proposed solution is able to ensure global navigability, measured in terms of average path length among two nodes in the network, by means of a distributed and wise selection of the number of friend objects a node has to handle

Bandwidth and accuracy-aware state estimation for smart grids using software defined networks

Smart grid (SG) will be one of the major application domains that will present severe pressures on future communication networks due to the expected huge number of devices that will be connected to it and that will impose stringent quality transmission requirements. To address this challenge, there is a need for a joint management of both monitoring and communication systems, so as to achieve a flexible and adaptive management of the SG services. This is the issue addressed in this paper, which provides the following major contributions. We define a new strategy to optimize the accuracy of the state estimation (SE) of the electric grid based on available network bandwidth resources and the sensing intelligent electronic devices (IEDs) installed in the field. In particular, we focus on phasor measurement units (PMUs) as measurement devices. We propose the use of the software defined networks (SDN) technologies to manage the available network bandwidth, which is then assigned by the controller to the forwarding devices to allow for the flowing of the data streams generated by the PMUs, by considering an optimization routine to maximize the accuracy of the resulting SE. Additionally, the use of SDN allows for adding and removing PMUs from the monitoring architecture without any manual intervention. We also provide the details of our implementation of the SDN solution, which is used to make simulations with an IEEE 14-bus test network in order to show performance in terms of bandwidth management and estimation accuracy

development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images

Abstract An established deep neural network (DNN) based on transfer learning and a newly designed DNN were tested to predict the grade of meningiomas from magnetic resonance (MR) images in dogs and to determine the accuracy of classification of using pre- and post-contrast T1-weighted (T1W), and T2-weighted (T2W) MR images. The images were randomly assigned to a training set, a validation set and a test set, comprising 60%, 10% and 30% of images, respectively. The combination of DNN and MR sequence displaying the highest discriminating accuracy was used to develop an image classifier to predict the grading of new cases. The algorithm based on transfer learning using the established DNN did not provide satisfactory results, whereas the newly designed DNN had high classification accuracy. On the basis of classification accuracy, an image classifier built on the newly designed DNN using post-contrast T1W images was developed. This image classifier correctly predicted the grading of 8 out of 10 images not included in the data set