Interactions of marine sulfated glycans with antithrombin and platelet factor 4

The molecular interactions of sulfated glycans, such as heparin, with antithrombin (AT) and platelet factor 4 (PF4) are essential for certain biological events such as anticoagulation and heparin induced thrombocytopenia (HIT). In this study, a library including 84 sulfated glycans (polymers and oligomers) extracted from marine algae along with several animal-originated polysaccharides were subjected to a structure-activity relationship (SAR) study regarding their specific molecular interactions with AT and PF4 using surface plasmon resonance. In this SAR study, multiple characteristics were considered including different algal species, different methods of extraction, molecular weight, monosaccharide composition, sulfate content and pattern and branching vs. linear chains. These factors were found to influence the binding affinity of the studied glycans with AT. Many polysaccharides showed stronger binding than the low molecular weight heparin (e.g., enoxaparin). Fourteen polysaccharides with strong AT-binding affinities were selected to further investigate their binding affinity with PF4. Eleven of these polysaccharides showed strong binding to PF4. It was observed that the types of monosaccharides, molecular weight and branching are not very essential particularly when these polysaccharides are oversulfated. The sulfation levels and sulfation patterns are, on the other hand, the primary contribution to strong AT and PF4 interaction

MiR-34a-5p promotes the multi-drug resistance of osteosarcoma by targeting the CD117 gene.

An association has been reported between miR-34a-5p and several types of cancer. Specifically, in this study, using systematic observations of multi-drug sensitive (G-292 and MG63.2) and resistant (SJSA-1 and MNNG/HOS) osteosarcoma (OS) cell lines, we showed that miR-34a-5p promotes the multi-drug resistance of OS through the receptor tyrosine kinase CD117, a direct target of miR-34a-5p. Consistently, the siRNA-mediated repression of CD117 in G-292 and MG63.2 cells led to a similar phenotype that exhibited all of the miR-34a-5p mimic-triggered changes. In addition, the activity of the MEF2 signaling pathway was drastically altered by the forced changes in the miR-34a-5p or CD117 level in OS cells. Furthermore, si-CD117 suppressed the enhanced colony and sphere formation, which is in agreement with the characteristics of a cancer stem marker. Taken together, our data established CD117 as a direct target of miR-34-5p and demonstrated that this regulation interferes with several CD117-mediated effects on OS cells. In addition to providing new mechanistic insights, our results will provide an approach for diagnosing and chemotherapeutically treating OS

New Algorithms for Secure Outsourcing of Modular Exponentiations

With the rapid development in availability of cloud services, the techniques for securely outsourcing the prohibitively expensive computations to untrusted servers are getting more and more attentions in the scientific community. Exponentiations modulo a large prime have been considered the most expensive operation in discrete-logarithm based cryptographic protocols, and the computationally limited devices such as RFID tags or smartcard may be incapable to accomplish these operations. Therefore, it is meaningful to present an efficient method to securely outsource most of this work-load to (untrusted) cloud servers. In this paper, we propose a new secure outsourcing algorithm for (variable-exponent, variable-base) exponentiation modular a prime in the two untrusted program model. Compared with the state-of-the-art algorithm \cite{HL05}, the proposed algorithm is superior in both efficiency and checkability. We then utilize this algorithm as a subroutine to achieve outsource-secure Cramer-Shoup encryptions and Schnorr signatures. Besides, we propose the first outsource-secure and efficient algorithm for simultaneous modular exponentiations. Moreover, we formally prove that both the algorithms can achieve the desired security notions. We also provide the experimental evaluation that demonstrates the efficiency and effectiveness of the proposed outsourcing algorithms and schemes

Enabling Public Verifiability and Data Dynamics for Storage Security

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of client through the auditing of whether his data stored in the cloud is indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public verifiability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the Proof of Retrievability model \cite{Shacham:ASIACRYPT:2008} by manipulating the classic Merkle Hash Tree (MHT) construction for block tag authentication. Extensive security and performance analysis show that the proposed scheme is highly efficient and provably secure

Design of Cyber-Physical Systems Architecture for Prognostics and Health Management of High-speed Railway Transportation Systems

The high-speed railway (HSR) transportation system in China has been growing rapidly during the past decade. In 2016, the total length of HSR in China has reached to 22,000 kilometers, and there are over 2,000 pairs of high speed trains operating daily. With the advancement of design and manufacturing technologies, the reliability and construction costs have been improved significantly. However, there is still great need for reduction of their operation and maintenance costs. With such incentive, a pilot project has been launched to develop a prognostics and health management system for rolling stock to transform the maintenance paradigm from preventive to predictive maintenance. Considering the high task variety and big data environment in HSR real-time monitoring system, a cyberphysical system (CPS) architecture is proposed as the framework for its PHM system. This paper reviews the needs of predictive maintenance for the HSR system, and then present a concept design of the CPS-enabled smart operation and maintenance system

GANet: Goal Area Network for Motion Forecasting

Predicting the future motion of road participants is crucial for autonomous driving but is extremely challenging due to staggering motion uncertainty. Recently, most motion forecasting methods resort to the goal-based strategy, i.e., predicting endpoints of motion trajectories as conditions to regress the entire trajectories, so that the search space of solution can be reduced. However, accurate goal coordinates are hard to predict and evaluate. In addition, the point representation of the destination limits the utilization of a rich road context, leading to inaccurate prediction results in many cases. Goal area, i.e., the possible destination area, rather than goal coordinate, could provide a more soft constraint for searching potential trajectories by involving more tolerance and guidance. In view of this, we propose a new goal area-based framework, named Goal Area Network (GANet), for motion forecasting, which models goal areas rather than exact goal coordinates as preconditions for trajectory prediction, performing more robustly and accurately. Specifically, we propose a GoICrop (Goal Area of Interest) operator to effectively extract semantic lane features in goal areas and model actors' future interactions, which benefits a lot for future trajectory estimations. GANet ranks the 1st on the leaderboard of Argoverse Challenge among all public literature (till the paper submission), and its source codes will be released