Privacy-preserving model learning on a blockchain network-of-networks.

ObjectiveTo facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.Materials and methodsWe propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.ResultsHierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.DiscussionHierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.ConclusionWe demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction

Mechanisms underlying sequence-independent beta-sheet formation

We investigate the formation of beta-sheet structures in proteins without taking into account specific sequence-dependent hydrophobic interactions. To accomplish this, we introduce a model which explicitly incorporates both solvation effects and the angular dependence (on the protein backbone) of hydrogen bond formation. The thermodynamics of this model is studied by comparing the restricted partition functions obtained by "unfreezing" successively larger segments of the native beta-sheet structure. Our results suggest that solvation dynamics together with the aforementioned angular dependence gives rise to a generic cooperativity in this class of systems; this result explains why pathological aggregates involving beta-sheet cores can form from many different proteins. Our work provides the foundation for the construction of phenomenological models to investigate the competition between native folding and non-specific aggregation.Comment: 20 pages, 5 figures, Revtex4, simulation mpeg movie available at http://www-physics.ucsd.edu/~guochin/Images/sheet1.mp

Direct Acyclic Graph based Ledger for Internet of Things: Performance and Security Analysis

Direct Acyclic Graph (DAG)-based ledger and the corresponding consensus algorithm has been identified as a promising technology for Internet of Things (IoT). Compared with Proof-of-Work (PoW) and Proof-of-Stake (PoS) that have been widely used in blockchain, the consensus mechanism designed on DAG structure (simply called as DAG consensus) can overcome some shortcomings such as high resource consumption, high transaction fee, low transaction throughput and long confirmation delay. However, the theoretic analysis on the DAG consensus is an untapped venue to be explored. To this end, based on one of the most typical DAG consensuses, Tangle, we investigate the impact of network load on the performance and security of the DAG-based ledger. Considering unsteady network load, we first propose a Markov chain model to capture the behavior of DAG consensus process under dynamic load conditions. The key performance metrics, i.e., cumulative weight and confirmation delay are analysed based on the proposed model. Then, we leverage a stochastic model to analyse the probability of a successful double-spending attack in different network load regimes. The results can provide an insightful understanding of DAG consensus process, e.g., how the network load affects the confirmation delay and the probability of a successful attack. Meanwhile, we also demonstrate the trade-off between security level and confirmation delay, which can act as a guidance for practical deployment of DAG-based ledgers.Comment: accepted by IEEE Transactions on Networkin

Local order and magnetic field effects on the electronic properties of disordered binary alloys in the Quantum Site Percolation limit

Electronic properties of disordered binary alloys are studied via the calculation of the average Density of States (DOS) in two and three dimensions. We propose a new approximate scheme that allows for the inclusion of local order effects in finite geometries and extrapolates the behavior of infinite systems following `finite-size scaling' ideas. We particularly investigate the limit of the Quantum Site Percolation regime described by a tight-binding Hamiltonian. This limit was chosen to probe the role of short range order (SRO) properties under extreme conditions. The method is numerically highly efficient and asymptotically exact in important limits, predicting the correct DOS structure as a function of the SRO parameters. Magnetic field effects can also be included in our model to study the interplay of local order and the shifted quantum interference driven by the field. The average DOS is highly sensitive to changes in the SRO properties, and striking effects are observed when a magnetic field is applied near the segregated regime. The new effects observed are twofold: there is a reduction of the band width and the formation of a gap in the middle of the band, both as a consequence of destructive interference of electronic paths and the loss of coherence for particular values of the magnetic field. The above phenomena are periodic in the magnetic flux. For other limits that imply strong localization, the magnetic field produces minor changes in the structure of the average DOS.Comment: 13 pages, 9 figures, 31 references, RevTex preprint, submitted to Phys. Rev.

Prevalence of Panton-valentine gene in Staphylococcus aureus isolated from clinical samples and healthy carriers in Gorgan city, north of Iran

Aim. Staphylococcus aureus (S. aureus) is a nosocomial and community acquired pathogen. S. aureus is a pathogen that causes several types of disease from skin infections to systemic diseases that is because of having several virulence factors such as enzymes, toxins, superantigens and Panton-Valentine leukocidin (pvl). pvl is a bi-component leukotoxin that destroy PMNs and monocytes and causes furunculosis, abscesses and necrotizing soft tissue infections in patients without any risk factors for such infections. The goal of this study was determine the prevalence of pvl gene in S. aureus isolated from patients and healthy carriers in Gorgan city, north of Iran. Methods. One hundred seventy isolates of S. aureus, 95 from patients and 75 healthy carriers, were collected during one year. After identification and purification, DNA extraction was done by phenol-chloroform method. Amplification of pvl gene was done by specific primer and polymerase chain reaction method. Results. Among the 170 isolates of S. aureus, 20 contained pvl gene. The frequency of isolates contained pvl gene in MRSA and MSSA isolates were 21.6, 19.3, which was not statistically significant. The frequency of these genes was not related to age, sex and source of isolation from patients. Conclusion. The frequency of pvl gene in this region were much higher than expected. © Copyright 2016 Edizioni Minerva Medica

Generalized isotropic Lipkin-Meshkov-Glick models: ground state entanglement and quantum entropies

We introduce a new class of generalized isotropic Lipkin-Meshkov-Glick models with su$(m+1)$ spin and long-range non-constant interactions, whose non-degenerate ground state is a Dicke state of su$(m+1)$ type. We evaluate in closed form the reduced density matrix of a block of $L$ spins when the whole system is in its ground state, and study the corresponding von Neumann and R\'enyi entanglement entropies in the thermodynamic limit. We show that both of these entropies scale as $a\log L$ when $L$ tends to infinity, where the coefficient $a$ is equal to $(m-k)/2$ in the ground state phase with $k$ vanishing su$(m+1)$ magnon densities. In particular, our results show that none of these generalized Lipkin-Meshkov-Glick models are critical, since when $L\to\infty$ their R\'enyi entropy $R_q$ becomes independent of the parameter $q$. We have also computed the Tsallis entanglement entropy of the ground state of these generalized su$(m+1)$ Lipkin-Meshkov-Glick models, finding that it can be made extensive by an appropriate choice of its parameter only when $m-k\ge3$. Finally, in the su$(3)$ case we construct in detail the phase diagram of the ground state in parameter space, showing that it is determined in a simple way by the weights of the fundamental representation of su$(3)$. This is also true in the su$(m+1)$ case; for instance, we prove that the region for which all the magnon densities are non-vanishing is an $(m+1)$-simplex in $\mathbf R^m$ whose vertices are the weights of the fundamental representation of su$(m+1)$.Comment: Typeset with LaTeX, 32 pages, 3 figures. Final version with corrections and additional reference