526 research outputs found
Gas Dynamics in the Barred Seyfert Galaxy NGC4151 - II. High Resolution HI Study
We present sensitive, high angular resolution (6" x 5") 21-cm observations of
the neutral hydrogen in the nearby barred Seyfert galaxy, NGC4151. These HI
observations, obtained using the VLA in B-configuration, are the highest
resolution to date of this galaxy, and reveal hitherto unprecedented detail in
the distribution and kinematics of the HI on sub-kiloparsec scales. A complete
analysis and discussion of the HI data are presented and the global properties
of the galaxy are related to the bar dynamics presented in Paper I.Comment: 13 pages including 9 figures and 3 tables; accepted for publication
in MNRA
Botulinum neurotoxin serotype F is a zinc endopeptidase specific for VAMP/synaptobrevin
Botulinum neurotoxin serotype F contains the zinc binding motif of zinc endopeptidases. Atomic adsorption analysis of highly purified toxin preparation revealed the presence of one atom of zinc per molecule of toxin, which could be removed with EDTA or o-phenanthroline. The light chain of the neurotoxin was shown to have a zinc-dependent protease activity specific for VAMP/synaptobrevin, an integral membrane protein of synaptic vesicles. Both isoforms of rat VAMP were cleaved at the same site corresponding to the single Gln-Lys peptide bond present in their sequences. This proteolytic activity was inhibited by EDTA, o-phenanthroline, and captopril as well as by VAMP peptides spanning the cleavage site
Digital Memories Based Mobile User Authentication for IoT
The increasing number of devices within the IoT is raising concerns over the efficiency and exploitability of existing authentication methods. The weaknesses of such methods, in particular passwords, are well documented. Although alternative methods have been proposed, they often rely on users being able to accurately recall complex and often unmemorable information. With the profusion of separate online accounts, this can often be a difficult task. The emerging digital memories concept involves the creation of a repository of memories specific to individuals. We believe this abundance of personal data can be utilised as a form of authentication. In this paper, we propose our digital memories based two-factor authentication mechanism, and also present our promising evaluation results.
Keywords—Digital memories, authentication, IoT, securit
The Open Access Advantage Revisited
This paper is a revision of one that appeared in 2008, incorporating the many developments and changes that have happened since then.published_or_final_versio
Statistical analysis driven optimized deep learning system for intrusion detection
Attackers have developed ever more sophisticated and intelligent ways to hack
information and communication technology systems. The extent of damage an
individual hacker can carry out upon infiltrating a system is well understood.
A potentially catastrophic scenario can be envisaged where a nation-state
intercepting encrypted financial data gets hacked. Thus, intelligent
cybersecurity systems have become inevitably important for improved protection
against malicious threats. However, as malware attacks continue to dramatically
increase in volume and complexity, it has become ever more challenging for
traditional analytic tools to detect and mitigate threat. Furthermore, a huge
amount of data produced by large networks has made the recognition task even
more complicated and challenging. In this work, we propose an innovative
statistical analysis driven optimized deep learning system for intrusion
detection. The proposed intrusion detection system (IDS) extracts optimized and
more correlated features using big data visualization and statistical analysis
methods (human-in-the-loop), followed by a deep autoencoder for potential
threat detection. Specifically, a pre-processing module eliminates the outliers
and converts categorical variables into one-hot-encoded vectors. The feature
extraction module discard features with null values and selects the most
significant features as input to the deep autoencoder model (trained in a
greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for
Cybersecurity is used as a benchmark to evaluate the feasibility and
effectiveness of the proposed architecture. Simulation results demonstrate the
potential of our proposed system and its outperformance as compared to existing
state-of-the-art methods and recently published novel approaches. Ongoing work
includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired
Cognitive Systems (BICS 2018
Purification of anthrax-toxin components by high-performance anion-exchange, gel-filtration and hydrophobic-interaction chromatography
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