A unifying framework for seed sensitivity and its application to subset seeds

We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem -- a set of target alignments, an associated probability distribution, and a seed model -- that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which we propose an efficient automaton construction. Experimental results confirm that sensitive subset seeds can be efficiently designed using our approach, and can then be used in similarity search producing better results than ordinary spaced seeds

On subset seeds for protein alignment

We apply the concept of subset seeds proposed in [1] to similarity search in protein sequences. The main question studied is the design of efficient seed alphabets to construct seeds with optimal sensitivity/selectivity trade-offs. We propose several different design methods and use them to construct several alphabets. We then perform a comparative analysis of seeds built over those alphabets and compare them with the standard BLASTP seeding method [2], [3], as well as with the family of vector seeds proposed in [4]. While the formalism of subset seeds is less expressive (but less costly to implement) than the cumulative principle used in BLASTP and vector seeds, our seeds show a similar or even better performance than BLASTP on Bernoulli models of proteins compatible with the common BLOSUM62 matrix. Finally, we perform a large-scale benchmarking of our seeds against several main databases of protein alignments. Here again, the results show a comparable or better performance of our seeds vs. BLASTP.Comment: IEEE/ACM Transactions on Computational Biology and Bioinformatics (2009

Providing security and fault tolerance in P2P connections between clouds for mHealth services

[EN] The mobile health (mHealth) and electronic health (eHealth) systems are useful to maintain a correct administration of health information and services. However, it is mandatory to ensure a secure data transmission and in case of a node failure, the system should not fall down. This fact is important because several vital systems could depend on this infrastructure. On the other hand, a cloud does not have infinite computational and storage resources in its infrastructure or would not provide all type of services. For this reason, it is important to establish an interrelation between clouds using communication protocols in order to provide scalability, efficiency, higher service availability and flexibility which allow the use of services, computing and storage resources of other clouds. In this paper, we propose the architecture and its secure protocol that allows exchanging information, data, services, computing and storage resources between all interconnected mHealth clouds. The system is based on a hierarchic architecture of two layers composed by nodes with different roles. The routing algorithm used to establish the connectivity between the nodes is the shortest path first (SPF), but it can be easily changed by any other one. Our architecture is highly scalable and allows adding new nodes and mHealth clouds easily, while it tries to maintain the load of the cloud balanced. Our protocol design includes node discovery, authentication and fault tolerance. We show the protocol operation and the secure system design. Finally we provide the performance results in a controlled test bench.Lloret, J.; Sendra, S.; Jimenez, JM.; Parra-Boronat, L. (2016). Providing security and fault tolerance in P2P connections between clouds for mHealth services. Peer-to-Peer Networking and Applications. 9(5):876-893. doi:10.1007/s12083-015-0378-3S87689395The Fifty-eighth World Health Assembly, Resolutions and Decisions. Document: A58/21. Available at: http://www.who.int/healthacademy/media/WHA58-28-en.pdf . [Last access: Dec. 30, 2014]World Health organization. Topics of eHealth. In WHO website. Available at: http://www.who.int/topics/eHealth/en/ . [Last access: Dec. 30, 2014]Pickup JC, Freeman SC, Sutton AJ (2011) Glycaemic control in type 1 diabetes during real time continuous glucose monitoring compared with self monitoring of blood glucose: meta-analysis of randomised controlled trials using individual patient data. BMJ 343:d3805Promotional Material Digital health: working in partnership. Department of Health. UK. (2014) Available at: https://www.gov.uk/government/publications/digital-health-working-in-partnership/digital-health-working-in-partnerships#digital-health---harnessing-technology-for-patient-benefit . [Last access: Dec. 30, 2014]eHealth for a Healthier Europe!– opportunities for a better use of healthcare resources. Available at: https://joinup.ec.europa.eu/sites/default/files/files_epractice/sites/eHealth%20for%20a%20Healthier%20Europe %20-%20Opportunities%20for%20a%20better%20use%20of%20healthcare%20resources.pdf. [Last access: Dec. 30, 2014]Adibi S (2012) Link technologies and BlackBerry mobile health (mHealth) solutions: a review. IEEE Trans Inf Technol Biomed 16(4):586–597Chiarini G, Ray P, Akter S, Masella C, Ganz A (2013) mHealth technologies for chronic diseases and elders: a systematic review. IEEE J Sel Areas Commun 31(9):6–18Lopes IM, Silva BM, Rodrigues JJ, Lloret J, Proenca ML (2011) A mobile health monitoring solution for weight control. In proceedings of the 2011 International Conference on Wireless Communications and Signal Processing (WCSP 2011), Nanjing, pp 1–5Lopes IM, Silva BM, Rodrigues JJPC, Lloret J (2012) Performance evaluation of cooperation mechanisms for m-health applications. In proceedings of the 2012 I.E. Global Communications Conference (GLOBECOM 2012), AnaheimKyriacou EC, Pattichis CS, Pattichis MS (2009) An overview of recent health care support systems for eEmergency and mHealth applications. In proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2009), Hilton Minneapolis, pp 1246–1249Nkosi MT, Mekuria F (2010) Cloud computing for enhanced mobile health applications. In proceedings of the 2010 I.E. Second International Conference on Cloud Computing Technology and Science (CloudCom 2010), Indianapolis, pp 629–633Sultan N (2014) Making use of cloud computing for healthcare provision: opportunities and challenges. Int J Inf Manag 34(2):177–184Pandey S, Voorsluys W, Niu S, Khandoker A, Buyya R (2012) An autonomic cloud environment for hosting ECG data analysis services. Futur Gener Comput Syst 28(1):147–154Xia H, Asif I, Zhao X (2013) Cloud-ECG for real time ECG monitoring and analysis. Comput Methods Prog Biomed 110(3):253–259Bourouis A, Feham M, Bouchachia A (2012) A new architecture of a ubiquitous health monitoring system: a prototype of cloud mobile health monitoring system. arXiv preprint. Reference: arXiv:1205.6910Chen KR, Lin YL, Huang MS (2011) A mobile biomedical device by novel antenna technology for cloud computing resource toward pervasive healthcare. In proceedings of the 11th International Conference on Bioinformatics and Bioengineering (BIBE 2011), Taichung, pp 133–136Lacuesta R, Lloret J, Sendra S, Peñalver L (2014), Spontaneous ad hoc mobile cloud computing network. Sci World J (Article ID 232419): 1–19Ghafoor KZ, Bakar KA, Mohammed MA, Lloret J (2013) Vehicular cloud computing: trends and challenges (Chapter 14). In Mobile Networks and Cloud computing Convergence for Progressive Services and Applications. IGI Global. pp. 262–274. DOI: 10.4018/978-1-4666-4781-7.ch014Wan J, Zhang D, Zhao S, Yang LT, Lloret J (2014) Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges and solutions. IEEE Commun Mag 52(8):106–113. doi: 10.1109/MCOM.2014.6871677Rodrigues JJPC, Zhou L, Mendes LDP, Lin K, Lloret J (2012) Distributed media-aware flow scheduling in cloud computing environment. Comput Commun 35(15):1819–1827Dutta R, Annappa B (2014) Protection of data in unsecured public cloud environment with open, vulnerable networks using threshold-based secret sharing. Netw Protoc Algoritm 6(1):58–75Modares H, Lloret J, Moravejosharieh A, Salleh R (2013) Security in mobile cloud computing (Chapter 5). In Mobile Networks and Cloud computing Convergence for Progressive Services and Applications. IGI Global. pp. 79–91Mehmood A, Song H, Lloret J (2014) Multi-agent based framework for secure and reliable communication among open clouds. Netw Protoc Algoritm 6(4):60–76Mendes LDP, Rodrigues JJPC, Lloret J, Sendra S (2014) Cross-layer dynamic admission control for cloud-based multimedia sensor networks. IEEE Syst J 8(1):235–246Xiong J, Li F, Ma J, Liu X, Yao Z, Chen PS (2014) A full lifecycle privacy protection scheme for sensitive data in cloud computing. Peer-to-Peer Netw Appl 1–13Yang H, Kim H, Mtonga K (2014) An efficient privacy-preserving authentication scheme with adaptive key evolution in remote health monitoring system. Peer-to-Peer Netw Appl 1–11Silva BM, Rodrigues JJ, Canelo F, Lopes IM, Lloret J (2014) Towards a cooperative security system for mobile-health applications. Electron Commer Re 1–27Flynn D, Gregory P, Makki H, Gabbay M (2009) Expectations and experiences of eHealth in primary care: a qualitative practice-based investigation. Int J Med Inform 78(9):588–604Thampi SM (2010) Survey of search and replication schemes in unstructured P2P networks. Netw Protoc Algoritm 2(1):93–131Khan SM, Mallesh N, Nambiar A, Wright M (2010) The dynamics of salsa: a robust structured P2P system. Netw Protoc Algoritm 2(4):40–60Garcia M, Hammoumi M, Canovas A, Lloret J (2011) Controlling P2P file-sharing networks’ traffic. Netw Protoc Algoritm 3(4):54–92Lloret J, Garcia M, Tomas J, Rodrigues JJPC (2014) Architecture and protocol for InterCloud communication. Inf Sci 258:434–451Chowdhury CR (2014) A survey of cloud based health care system. Int J Innov Res Comput Commun Eng 2(8):5477–5481Ghosh R, Papapanagiotou I, Boloor KA (2014) Survey on research initiatives for healthcare clouds. Cloud Computing Applications for Quality Health Care Delivery. IGI Global 1–18Donahue S (2010) Can cloud computing help fix health care? Cloudbook J 1(6):1–6Deng M, Petkovic M, Nalin M, Baroni IA (2011) Home healthcare system in the cloud--addressing security and privacy challenges. In proceedings of the 2011 I.E. International Conference on Cloud Computing (CLOUD 2011), Washington, pp 549–556Wang X, Gui Q, Liu B, Chen Y, Jin Z (2013) Leveraging mobile cloud for telemedicine: a performance study in medical monitoring. In proceedings of the 39th Annual Northeast Bioengineering Conference (NEBEC 2013), Syracuse, pp 49–50Alamri A (2012) Cloud-based e-health multimedia framework for heterogeneous network. In proceedings of the 2012 I.E. International Conference on Multimedia and Expo Workshops (ICMEW 2012), Melbourne, pp 447–452Constantinescu L, Kim J, Feng DD (2012) Sparkmed: a framework for dynamic integration of multimedia medical data into distributed m-health systems. IEEE Trans Inf Technol Biomed 16(1):40–52Botts N, Thoms B, Noamani A, Horan TA (2010) Cloud computing architectures for the underserved: public health cyberinfrastructures through a network of healthatms. In proceedings of the 43rd Hawaii International Conference on System Sciences (HICSS 2010), Honolulu, pp 1–10Fan L, Buchanan W, Thummler C, Lo O, Khedim A, Uthmani O, Lawson A, Bell D (2011) DACAR platform for eHealth services cloud. In proceedings of the 2011 I.E. International Conference on Cloud Computing (CLOUD 2011), Washington, pp 219–226Ruiz-Zafra A, Benghazi K, Noguera M, Garrido JL (2013) Zappa: An Open Mobile Platform to Build Cloud-Based m-Health Systems. In proceedings of the 4th International Symposium on Ambient Intelligence (ISAmI 2013), Salamanca, pp 87–94Nijon S, Dickerson RF, Asare P, Li Q, Hong D, Stankovic JA, Hu P, Shen G, Jiang X (2013) Auditeur: a mobile-cloud service platform for acoustic event detection on smartphones. In Proceeding of the 11th annual international conference on Mobile systems, applications, and services. ACM, Taipei, pp 403–416Lloret J, Diaz JR, Boronat F, Jiménez JM (2006) A fault-tolerant P2P-based protocol for logical networks interconnection. In proceedings of the International Conference on Networking and Services (ICNS’06), Silicon ValleyLloret J, Palau C, Boronat F, Tomas J (2008) Improving networks using group-based topologies. Comput Commun 31(14):3438–3450Lloret J, Boronat Segui F, Palau C, Esteve M (2005) Two levels SPF-based system to interconnect partially decentralized P2P file sharing networks. In proceedings of the Joint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services.(ICAS-ICNS 2005), Papeete, p 39Cramer C, Kutzner K, Fuhrmann T (2004) Bootstrapping locality-aware P2P networkS. In proceedings of the 12th IEEE International Conference on Networks (ICON 2004), Singapore, pp 357–361FIPS 180-1 - Secure Hash Standard, SHA-1. National Institute of Standards and Technology. http://www.itl.nist.gov/fipspubs/fip180-1.htm [Last access: Dec. 30, 2014]Eastlake D., Jones P., US Secure Hash Algorithm 1 (SHA1),(2001). In IETF website, Available at: http://www.ietf.org/rfc/rfc3174.txt [Last access: March 20, 2015]Lacuesta R, Lloret J, Garcia M, Peñalver L (2011) Two secure and energy-saving spontaneous Ad-Hoc protocol for wireless mesh client networks. J Netw Comput Appl 3(2):492–50

A systems-based approach for detecting molecular interactions across tissues.

Current high-throughput gene expression experiments have a straightforward design of examining the gene expression of one group or condition relative to that of another. The data is typically analyzed as if they represent strictly intracellular events, and often treats genes as coming from a homogeneous population. Although intracellular events are crucial to nearly all biological processes, cell-cell interactions are often just as important, especially when gene expression data is generated from heterogeneous cell populations, such as from whole tissues. Cell-cell molecular interactions are generally lost in the available analytical procedures and as a result, are not examined experimentally, at least not accurately or with efficiency. Most importantly, this imposes major limitations when studying gene expression changes in multiple samples that interact with one another. In order to addresses the limitations of current techniques, we have developed a novel systems-based approach that expands the traditional analysis of gene expression in two stages. This includes a novel sequence-based meta-analytic tool, AbsIDconvert, that allows for conversion of annotated features using an interval tree for storing and querying absolute genomic coordinates for comparison of multi-scale macro-molecule identifiers across platforms and/or organisms. In addition, a systems-based heuristic algorithm is developed to find intercellular interactions between two sets of genes, potentially from different tissues by utilizing location information of each gene along with the information available in the secondary databases in the form of interactions, pathways and signaling. AbsIDconvert is shown to provide a high accuracy in identifier conversion as compared to other available methodologies (typically at an average rate of 84%) while maintaining a higher efficiency (O(n*log(n)). Our intercellular interaction approach and underlying visualization shows promise in allowing researchers to uncover novel signaling pathways in an intercellular fashion that to this point has not been possible

Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods

Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situation responding to a situation. The presented study aimed to focus on the analysis of the individual’s affective state. The results reflect the excitation expressed by the subjects’ statements collected with psychological questionnaires. The research group consisted of 49 participants (22 women and 25 men). The measurement protocol included acquiring the electrodermal activity signal, cardiac signals, and accelerometric signals in three axes. Subjective measurements were acquired for affective state using the JAWS questionnaires, for cognitive skills the DST, and for verbal fluency the VFT. The physiological and psychological data were subjected to statistical analysis and then to a machine learning process using different features selection methods (JMI or PCA). The highest accuracy of the kNN classifier was achieved in combination with the JMI method (81.63%) concerning the division complying with the JAWS test results. The classification sensitivity and specificity were 85.71% and 71.43%

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.https://doi.org/10.3390/e1809027

On the structure differences of short fragments and amino acids in proteins with and without disulfide bonds

Of the 20 standard amino acids, cysteines are the only amino acids that have a reactive sulphur atom, thus enabling two cysteines to form strong covalent bonds known as disulfide bonds. Even though almost all proteins have cysteines, not all of them have disulfide bonds. Disulfide bonds provide structural stability to proteins and hence are an important constraint in determining the structure of a protein. As a result, disulfide bonds are used to study various protein properties, one of them being protein folding. Protein structure prediction is the problem of predicting the three-dimensional structure of a protein from its one-dimensional amino acid sequence. Ab initio methods are a group of methods that attempt to solve this problem from first principles, using only basic physico-chemical properties of proteins. These methods use structure libraries of short amino acid fragments in the process of predicting the structure of a protein. The protein structures from which these structure libraries are created are not classified in any other way apart from being non-redundant. In this thesis, we investigate the structural dissimilarities of short amino acid fragments when occurring in proteins with disulfide bonds and when occurring in those proteins without disulfide bonds. We are interested in this because, as mentioned earlier, the protein structures from which the structure libraries of ab initio methods are created, are not classified in any form. This means that any significant structural difference in amino acids and short fragments when occurring in proteins with and without disulfide bonds would remain unnoticed as these structure libraries have both fragments from proteins with disulfide bonds and without disulfide bonds together. Our investigation of structural dissimilarities of amino acids and short fragments is done in four phases. In phase one, by statistically analysing the phi and psi backbone dihedral angle distributions we show that these fragments have significantly different structures in terms of dihedral angles when occurring in proteins with and without disulfide bonds. In phase two, using directional statistics we investigate how structurally different are the 20 different amino acids and the short fragments when occurring in proteins with and without disulfide bonds. In phase three of our work, we investigate the differences in secondary structure preference of the 20 amino acids in proteins with and without disulfide bonds. In phase four, we further investigate and show that there are significant differences within the same secondary structure region of amino acids when they occur in proteins with and without disulfide bonds. Finally, we present the design and implementation details of a dihedral angle and secondary structure database of short amino acid fragments (DASSD) that is publicly available. Thus, in this thesis we show previously unknown significant structure differences in terms of backbone dihedral angles and secondary structures in amino acids and short fragments when they occur in proteins with and without disulfide bonds

Artificial Intelligence and Ambient Intelligence

This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.