Diagnosing Patients Combining Principal Components Analysis and Case Based Reasoning

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Use of social networks for academic purposes: a case study

Purpose This paper aims to analyse various aspects of an academic social network: the profile of users, the reasons for its use, its perceived benefits and the use of other social media for scholarly purposes. Design/methodology/approach The authors examined the profiles of the users of an academic social network. The users were affiliated with 12 universities. The following were recorded for each user: sex, the number of documents uploaded, the number of followers, and the number of people being followed. In addition, a survey was sent to the individuals who had an email address in their profile. Findings Half of the users of the social network were academics and a third were PhD students. Social sciences scholars accounted for nearly half of all users. Academics used the service to get in touch with other scholars, disseminate research results and follow other scholars. Other widely employed social media included citation indexes, document creation, edition and sharing tools and communication tools. Users complained about the lack of support for the utilisation of these tools. Research limitations/implications The results are based on a single case study. Originality/value This study provides new insights on the impact of social media in academic contexts by analysing the user profiles and benefits of a social network service that is specifically targeted at the academic community.

Promoting the Creation, Share and Use of OERs through Open Repositories and Social Networks

In this paper we discuss and analyze the process of using a learning object repository and building a social network on the top of it, including aspects related to open source technologies, promoting the use of the repository by means of social networks and helping learners to develop their own learning paths.

A Secure and Anonymous Cooperative Sensing Protocol for Cognitive Radio Networks

Spectrum is an essential resource for the provision of mobile services. In order to control and delimit its use, governmental agencies set up regulatory policies. Unfortunately, such policies have led to a deficiency of spectrum as only few frequency bands are left unlicensed, and these are used for the majority of new emerging wireless applications. One promising way to alleviate the spectrum shortage problem is adopting a spectrum sharing paradigm in which frequency bands are used opportunistically. Cognitive radio is the key technology to enable this shift of paradigm.Cognitive radio networks are self-organized systems in which devices cooperate to use those spectrum ranges that are not occupied by licensed users. They carry out spectrum sensing in order to detect vacant channels that can be used for communication. Even though spectrum sensing is an active area of research, an important issue remains unsolved: the secure authentication of sensing reports. Not providing security enables the input of false data in the system thus empowering false results. This paper presents a distributed protocol based on wireless physical layer security, symmetric cryptography and one-way functions that allows determining a final sensing decision from multiple sources in a quick and secure way, as well as it preserves users¿ privacy.

A Secure Cooperative Sensing Protocol for Cognitive Radio Networks

Cognitive radio networks sense spectrum occupancyand manage themselves to operate in unused bands without disturbing licensed users. Spectrum sensing is more accurate if jointly performed by several reliable nodes. Even though cooperative sensing is an active area of research, the secureauthentication of local sensing reports remains unsolved, thus empowering false results. This paper presents a distributed protocol based on digital signatures and hash functions, and ananalysis of its security features. The system allows determining a final sensing decision from multiple sources in a quick and secure way.

Structural changes induced by daily music listening in the recovering brain after middle cerebral artery stroke: a voxel-based morphometry study

Music is a highly complex and versatile stimulus for the brain that engages many temporal, frontal, parietal, cerebellar, and subcortical areas involved in auditory, cognitive, emotional, and motor processing. Regular musical activities have been shown to effectively enhance the structure and function of many brain areas, making music a potential tool also in neurological rehabilitation. In our previous randomized controlled study, we found that listening to music on a daily basis can improve cognitive recovery and improve mood after an acute middle cerebral artery stroke. Extending this study, a voxel-based morphometry (VBM) analysis utilizing cost function masking was performed on the acute and 6-month post-stroke stage structural magnetic resonance imaging data of the patients (n = 49) who either listened to their favorite music [music group (MG), n = 16] or verbal material [audio book group (ABG), n = 18] or did not receive any listening material [control group (CG), n = 15] during the 6-month recovery period. Although all groups showed significant gray matter volume (GMV) increases from the acute to the 6-month stage, there was a specific network of frontal areas [left and right superior frontal gyrus (SFG), right medial SFG] and limbic areas [left ventral/subgenual anterior cingulate cortex (SACC) and right ventral striatum (VS)] in patients with left hemisphere damage in which the GMV increases were larger in the MG than in the ABG and in the CG. Moreover, the GM reorganization in the frontal areas correlated with enhanced recovery of verbal memory, focused attention, and language skills, whereas the GM reorganization in the SACC correlated with reduced negative mood. This study adds on previous results, showing that music listening after stroke not only enhances behavioral recovery, but also induces fine-grained neuroanatomical changes in the recovering brain.

Dendritic cells exposed to MVA-based HIV-1 vaccine induce highly functional HIV-1-specific CD8+ T cell responses in HIV-1-infected individuals

Currently, MVA virus vectors carrying HIV-1 genes are being developed as HIV-1/AIDS prophylactic/therapeutic vaccines. Nevertheless, little is known about the impact of these vectors on human dendritic cells (DC) and their capacity to present HIV-1 antigens to human HIV-specific T cells. This study aimed to characterize the interaction of MVA and MVA expressing the HIV-1 genes Env-Gag-Pol-Nef of clade B (referred to as MVA-B) in human monocyte-derived dendritic cells (MDDC) and the subsequent processes of HIV-1 antigen presentation and activation of memory HIV-1-specific T lymphocytes. For these purposes, we performed ex vivo assays with MDDC and autologous lymphocytes from asymptomatic HIV-infected patients. Infection of MDDC with MVA-B or MVA, at the optimal dose of 0.3 PFU/MDDC, induced by itself a moderate degree of maturation of MDDC, involving secretion of cytokines and chemokines (IL1-ra, IL-7, TNF-α, IL-6, IL-12, IL-15, IL-8, MCP-1, MIP-1α, MIP-1β, RANTES, IP-10, MIG, and IFN-α). MDDC infected with MVA or MVA-B and following a period of 48 h or 72 h of maturation were able to migrate toward CCL19 or CCL21 chemokine gradients. MVA-B infection induced apoptosis of the infected cells and the resulting apoptotic bodies were engulfed by the uninfected MDDC, which cross-presented HIV-1 antigens to autologous CD8+ T lymphocytes. MVA-B-infected MDDC co-cultured with autologous T lymphocytes induced a highly functional HIV-specific CD8+ T cell response including proliferation, secretion of IFN-γ, IL-2, TNF-α, MIP-1β, MIP-1α, RANTES and IL-6, and strong cytotoxic activity against autologous HIV-1-infected CD4+ T lymphocytes. These results evidence the adjuvant role of the vector itself (MVA) and support the clinical development of prophylactic and therapeutic anti-HIV vaccines based on MVA-B.

Dendritic cells exposed to MVA-based HIV-1 vaccine induce highly functional HIV-1-specific CD8+ T cell responses in HIV-1-infected individuals

Currently, MVA virus vectors carrying HIV-1 genes are being developed as HIV-1/AIDS prophylactic/therapeutic vaccines. Nevertheless, little is known about the impact of these vectors on human dendritic cells (DC) and their capacity to present HIV-1 antigens to human HIV-specific T cells. This study aimed to characterize the interaction of MVA and MVA expressing the HIV-1 genes Env-Gag-Pol-Nef of clade B (referred to as MVA-B) in human monocyte-derived dendritic cells (MDDC) and the subsequent processes of HIV-1 antigen presentation and activation of memory HIV-1-specific T lymphocytes. For these purposes, we performed ex vivo assays with MDDC and autologous lymphocytes from asymptomatic HIV-infected patients. Infection of MDDC with MVA-B or MVA, at the optimal dose of 0.3 PFU/MDDC, induced by itself a moderate degree of maturation of MDDC, involving secretion of cytokines and chemokines (IL1-ra, IL-7, TNF-α, IL-6, IL-12, IL-15, IL-8, MCP-1, MIP-1α, MIP-1β, RANTES, IP-10, MIG, and IFN-α). MDDC infected with MVA or MVA-B and following a period of 48 h or 72 h of maturation were able to migrate toward CCL19 or CCL21 chemokine gradients. MVA-B infection induced apoptosis of the infected cells and the resulting apoptotic bodies were engulfed by the uninfected MDDC, which cross-presented HIV-1 antigens to autologous CD8+ T lymphocytes. MVA-B-infected MDDC co-cultured with autologous T lymphocytes induced a highly functional HIV-specific CD8+ T cell response including proliferation, secretion of IFN-γ, IL-2, TNF-α, MIP-1β, MIP-1α, RANTES and IL-6, and strong cytotoxic activity against autologous HIV-1-infected CD4+ T lymphocytes. These results evidence the adjuvant role of the vector itself (MVA) and support the clinical development of prophylactic and therapeutic anti-HIV vaccines based on MVA-B.

Use of social networks for academic purposes: a case study

Purpose- This paper aims to analyse various aspects of an academic social network: the profile of users, the reasons for its use, its perceived benefits and the use of other social media for scholarly purposes. Design/methodology/approach- The authors examined the profiles of the users of an academic social network. The users were affiliated with 12 universities. The following were recorded for each user: sex, the number of documents uploaded, the number of followers, and the number of people being followed. In addition, a survey was sent to the individuals who had an email address in their profile. Findings- Half of the users of the social network were academics and a third were PhD students. Social sciences scholars accounted for nearly half of all users. Academics used the service to get in touch with other scholars, disseminate research results and follow other scholars. Other widely employed social media included citation indexes, document creation, edition and sharing tools and communication tools. Users complained about the lack of support for the utilisation of these tools. Research limitations/implications- The results are based on a single case study. Originality/value- This study provides new insights on the impact of social media in academic contexts by analysing the user profiles and benefits of a social network service that is specifically targeted at the academic community.

Social network extraction and analysis based on multimodal dyadic interaction

Social interactions are a very important component in people"s lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Times" Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The links" weights are a measure of the"influence" a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network.

Statistical mechanics of multi-edge networks

Statistical properties of binary complex networks are well understood and recently many attempts have been made to extend this knowledge to weighted ones. There are, however, subtle yet important considerations to be made regarding the nature of the weights used in this generalization. Weights can be either continuous or discrete magnitudes, and in the latter case, they can additionally have undistinguishable or distinguishable nature. This fact has not been addressed in the literature insofar and has deep implications on the network statistics. In this work we face this problem introducing multiedge networks as graphs where multiple (distinguishable) connections between nodes are considered. We develop a statistical mechanics framework where it is possible to get information about the most relevant observables given a large spectrum of linear and nonlinear constraints including those depending both on the number of multiedges per link and their binary projection. The latter case is particularly interesting as we show that binary projections can be understood from multiedge processes. The implications of these results are important as many real-agent-based problems mapped onto graphs require this treatment for a proper characterization of their collective behavior.