Dynamic-parinet (D-parinet) : indexing present and future trajectories in networks

While indexing historical trajectories is a hot topic in the field of moving objects (MO) databases for many years, only a few of them consider that the objects movements are constrained. DYNAMIC-PARINET (D-PATINET) is designed for capturing of trajectory data flow in multiple discrete small time interval efficiently and to predict a MO’s movement or the underlying network state at a future time. The cornerstone of D-PARINET is PARINET, an efficient index for historical trajectory data. The structure of PARINET is based on a combination of graph partitioning and a set of composite B+-tree local indexes tuned for a given query load and a given data distribution in the network space. D-PARINET studies continuous update of trajectory data and use interpolation to predict future MO movement in the network. PARINET and D-PARINET can easily be integrated into any RDBMS, which is an essential asset particularly for industrial or commercial applications. The experimental evaluation under an off-the-shelf DBMS using simulated traffic data shows that DPARINET is robust and significantly outperforms the R-tree based access methods

Knowledge Rich Natural Language Queries over Structured Biological Databases

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made

Magnetism and its microscopic origin in iron-based high-temperature superconductors

High-temperature superconductivity in the iron-based materials emerges from, or sometimes coexists with, their metallic or insulating parent compound states. This is surprising since these undoped states display dramatically different antiferromagnetic (AF) spin arrangements and N$\rm \acute{e}$el temperatures. Although there is general consensus that magnetic interactions are important for superconductivity, much is still unknown concerning the microscopic origin of the magnetic states. In this review, progress in this area is summarized, focusing on recent experimental and theoretical results and discussing their microscopic implications. It is concluded that the parent compounds are in a state that is more complex than implied by a simple Fermi surface nesting scenario, and a dual description including both itinerant and localized degrees of freedom is needed to properly describe these fascinating materials.Comment: 14 pages, 4 figures, Review article, accepted for publication in Nature Physic

Chenodeoxycholic acid rescues axonal degeneration in induced pluripotent stem cell-derived neurons from spastic paraplegia type 5 and cerebrotendinous xanthomatosis patients

Abstract Background Biallelic mutations in CYP27A1 and CYP7B1, two critical genes regulating cholesterol and bile acid metabolism, cause cerebrotendinous xanthomatosis (CTX) and hereditary spastic paraplegia type 5 (SPG5), respectively. These rare diseases are characterized by progressive degeneration of corticospinal motor neuron axons, yet the underlying pathogenic mechanisms and strategies to mitigate axonal degeneration remain elusive. Methods To generate induced pluripotent stem cell (iPSC)-based models for CTX and SPG5, we reprogrammed patient skin fibroblasts into iPSCs by transducing fibroblast cells with episomal vectors containing pluripotency factors. These patient-specific iPSCs, as well as control iPSCs, were differentiated into cortical projection neurons (PNs) and examined for biochemical alterations and disease-related phenotypes. Results CTX and SPG5 patient iPSC-derived cortical PNs recapitulated several disease-specific biochemical changes and axonal defects of both diseases. Notably, the bile acid chenodeoxycholic acid (CDCA) effectively mitigated the biochemical alterations and rescued axonal degeneration in patient iPSC-derived neurons. To further examine underlying disease mechanisms, we developed CYP7B1 knockout human embryonic stem cell (hESC) lines using CRISPR-cas9-mediated gene editing and, following differentiation, examined hESC-derived cortical PNs. Knockout of CYP7B1 resulted in similar axonal vesiculation and degeneration in human cortical PN axons, confirming a cause-effect relationship between gene deficiency and axonal degeneration. Interestingly, CYP7B1 deficiency led to impaired neurofilament expression and organization as well as axonal degeneration, which could be rescued with CDCA, establishing a new disease mechanism and therapeutic target to mitigate axonal degeneration. Conclusions Our data demonstrate disease-specific lipid disturbances and axonopathy mechanisms in human pluripotent stem cell-based neuronal models of CTX and SPG5 and identify CDCA, an established treatment of CTX, as a potential pharmacotherapy for SPG5. We propose this novel treatment strategy to rescue axonal degeneration in SPG5, a currently incurable condition