Electrodynamic machine-learning-enhanced fault-tolerance of robotic free-form printing of complex mixtures

2018 ◽  
Vol 63 (5) ◽  
pp. 913-929 ◽  
Author(s):  
T. I. Zohdi
Keyword(s):  
Machine Learning ◽  
Fault Tolerance ◽  
Complex Mixtures ◽  
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scholarly journals A Threat-Hunting of UNSW-NB15 with Machine Learning Techniques to Achieve Resilience

2019 ◽  
Vol 9 (1S3) ◽  
pp. 488-494
Keyword(s):  
Machine Learning ◽  
Fault Tolerance ◽  
Critical Role ◽  
Cyber Attacks ◽  
Three Dimensions ◽  
Machine Learning Techniques ◽  
Advanced Persistent Threat ◽  
Learning Techniques

In order to focus on the mission and functions of the business of the organizations, cyber resiliency have to play a critical role against the adversaries’ target. The strategy recommended by NIST to reduce the suspect ability of cyber-attacks of the system with the three dimensions such as harden the target, limit the damage to the target and make the target resilient. The threats could be based on cyber and noncyber. The objective is to provide cyber resiliency on the Advanced Persistent Threat (APT), has born with the nature of sophisticated, stealthy, persistent towards target and highly adoptable to the environment. The challenge is to provide cyber resilience to the system from compromising tactics of the adversaries, uncertain in eradication of threat due to its persistent nature, recognizing its adapting ability. The cyber resiliency also links with other disciplines like safety, fault tolerance, privacy, resilience and survivability, reliability and security

scholarly journals Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra

2018 ◽  
Author(s):  
Simon Rogers ◽  
Cher Wei Ong ◽  
Joe Wandy ◽  
Madeleine Ernst ◽  
Lars Ridder ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Structural Information ◽  
Chemical Space ◽  
Building Blocks ◽  
Complex Mixtures ◽  
Machine Learning Classification ◽  
Chemical Structures ◽  
Automated Annotation ◽  
Classification Prediction

Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique to obtain structural information on complex mixtures. However, just knowing the molecular masses of the mixture's constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra? We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses termed Mass2Motifs that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases. Here, we describe how additional strategies, taking advantage of i) combinatorial in-silico matching of experimental mass features to substructures of candidate molecules, and ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations. To make annotated Mass2Motifs available to the community, we also present motifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an API. MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database we can more quickly gain insight in the constituents of complex mixtures. That will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks.

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