Editorial Forward

With the availability of next generation sequencing technology, there is a tremendous need for development of novel tools, algorithms and methodologies for extracting useful information and knowledge from exponentially growing data.  This need has catalyzed active research in the overlapping fields of Machine Learning (ML) and Artificial Intelligence (AI). First issue of IJCB is bringing some very good research articles with a detailed view of the cutting edge machine learning algorithms

Doctor of Philosophy

dissertationThe bituminous sand deposits of Utah are estimated to contain 25 - 29 billion barrels of oil in place and are the largest petroleum resource of this type in the United States. There are six major deposits of commercial importance* some of them potentially amenable to surface mining techniques. In this investigation an experimental program was conducted to determine the feasibility of an aboveground fluidized bed thermal process rfor the recovery of - a synthetic crude from the minable bituminous sand deposits of Utah. A continuous bench-scale, fluidized bed reactor, designed for a maximum throughput capacity of 2.25 kilograms of feed sand per hour, was developed for this investigation. Bituminous sands of distinctly different origin were processed, that is, (i) the Sunnyside bituminous sand, a deposit of fresh water origin having a bitumen content of 8.5 percent by weight, and (ii) the Tarsand Triangle sand, a deposit of marine origin having a bitumen content of 4.5 percent by weight. The effects of the following variables on the synthetic liquid yield and on the liquid quality were studied: Reactor Temperature: 698 - 898 K Solids Retention Time: 20.4 - 31.4 minutes Particle Size of Feed Sand: 162 - 507.5 microns The maximum liquid yield for the Sunnyside sand, 70 weight percent of the bitumen fed, was obtained at 773 K and a solids retention time of 20.4 minutes for a feed sand particle size of 358.5 microns. The remaining 30 weight percent of the bitumen was converted to coke and light hydrocarbon gases. Increasing the solids retention time lowered the liquid yield and shifted the-temperature for maximum liquid yield to a lower value. The physical properties and chemical nature of the synthetic liquid obtained were correlated with the reactor temperature. The synthetic liquid obtained was paraffinic and contained a low percentage of heteroatoms. A mechanism for the thermal cracking of the bitumen has been developed to explain the results obtained. Extrapolation of the data to a solids retention time of 16 minutes predicts a yield of 80 weight percent synthetic liquid, 8 weight percent light hydrocarbon gases (C-j - C4 ), and 12 weight percent coke. The thermal processing of Tarsand Triangle sand was studied as a function reactor temperature in the range 723 - 898 K. It was found that the liquid yield was lower than that obtained with the Sunnyside feed. The maximum liquid yield of 51 weight percent based on bitumen fed was obtained at 798 K and a retention time of 27.2 minutes. Despite the differences in the origin of the feed sand and the operating temperature range, the yield of coke (19 - 22 wt %) was comparable to Sunnyside coke yields. The liquid product was more aromatic than the Sunnyside liquid product

Implementing GIS to improve hospital efficiency in natural disasters

© Authors 2018. CC BY 4.0 License. Over the past decades, the number of natural disasters has been growing around the world. In addition to damaging communities and infrastructures, unexpected disasters also affect service providers such as hospitals and health centers. Markedly, hospital safety from disasters is a challenge in all countries. With disaster damage to health systems resulting in human tragedy, huge economic losses, devastating blows to developmental goals, and shaken social confidence. Ensuring that hospitals and health facilities are safe and secure from disasters depend on implementing an appropriate method to mitigate adverse impacts on hospitals during incidents. Thus, disaster management becomes even more significant, as the health sector has been particularly vulnerable to damages. So, it is crucial to develop appropriate mitigation and adoption method for healthcare facilities, to withstand the natural disasters such as earthquakes and floods. A comprehensive disaster plan is required to ensure a prompt disaster response and coordinated management of a multi causality incident. The aim of this research is to systemically and critically review the importance of hospitals in disaster events and this research attempts to reach a basic understanding to mitigate the risk of disasters in hospitals and improve the continuity of health services during or after disaster events. For this study, secondary information was retrieved from the literature review and document review on sudden-onset natural disasters in different parts of the world was collected. This study found some challenges and deliverables for disaster managers that could mitigate the risk of a natural disaster's impact on a hospital. Accordingly, this research will evaluate the importance of disaster management for hospitals and the challenges that need to be considered during the disaster response

A Multi-class Machine Learning Framework to Predict Ampicillin-Sulbactam Resistance of Acinetobacter baumannii

Acinetobacter baumannii is a serious pathogen responsible for many of the hospital-acquired infections. The emergence of multi-drug and pan-drug resistant strains of A. baumannii has been a growing concern. Ampicillin-sulbactam combination has proven to be effective in treatment of several resistant strains. However, strains resistant to ampicillin-sulbactam combination have also emerged necessitating other combination therapy. Rapid and accurate identification of the phenotype of the organism is essential for starting the right treatment. To this end, genome-based approaches have garnered much attention. In this work, we report a multi-class machine-learning based approach to predict the ampicillin-sulbactam resistance phenotype and MIC of Acinetobacter baumannii based on the presence/absence of AMR genes in the genome of strains isolated in the USA region. Our model achieves an accuracy of about 94% indicating that the gene presence/absence itself can capture the resistance phenotype.  Further, we show that our model, built based on the USA strains, does not predict reliably the AMR phenotypes of Indian isolates pointing to the need for building machine learning models from region-specific data

Detection and Prevention Against Poisoning Attacks in Federated Learning

This paper proposes and investigates a new approach for detecting and preventing several different types of poisoning attacks from affecting a centralized Federated Learning model via average accuracy deviation detection (AADD). By comparing each client's accuracy to all clients' average accuracy, AADD detect clients with an accuracy deviation. The implementation is further able to blacklist clients that are considered poisoned, securing the global model from being affected by the poisoned nodes. The proposed implementation shows promising results in detecting poisoned clients and preventing the global model's accuracy from deteriorating

β-Catenin gets an honorable discharge

Wnt signaling component is removed from active duty by exosomes

LIPOPREDICT: Bacterial lipoprotein prediction server

Bacterial lipoproteins have many important functions owing to their essential nature and roles in pathogenesis and represent a class of possible vaccine candidates. The prediction of bacterial lipoproteins from sequence is thus an important task for computational vaccinology. A Support Vector Machines (SVM) based module for predicting bacterial lipoproteins, LIPOPREDICT, has been developed. The best performing sequence model were generated using selected dipeptide composition, which gave 97% accuracy of prediction. The results obtained were compared very well with those of previously developed methods

A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli

Motivation: Inclusion body formation has been a major deterrent for overexpression studies since a large number of proteins form insoluble inclusion bodies when overexpressed in Escherichia coli. The formation of inclusion bodies is known to be an outcome of improper protein folding; thus the composition and arrangement of amino acids in the proteins would be a major influencing factor in deciding its aggregation propensity. There is a significant need for a prediction algorithm that would enable the rational identification of both mutants and also the ideal protein candidates for mutations that would confer higher solubility-on-overexpression instead of the presently used trial-and-error procedures. Results: Six physicochemical properties together with residue and dipeptide-compositions have been used to develop a support vector machine-based classifier to predict the overexpression status in E.coli. The prediction accuracy is ~72% suggesting that it performs reasonably well in predicting the propensity of a protein to be soluble or to form inclusion bodies. The algorithm could also correctly predict the change in solubility for most of the point mutations reported in literature. This algorithm can be a useful tool in screening protein libraries to identify soluble variants of proteins

Functional measurement of a supplementary teaching system based on augmented reality technology for the course “building mechanical services and utilities” in architecture

Background and Objective:The advancement of information technology in the field of portable technologies has made it possible to develop omnipresent learning. Mobile learning (learning everywhere) is a new learning environment in which the learner is placed in a real-world scenario, with access to online resources, through portable tools and wireless networks. On the other hand, augmented reality has helped to complement human sensory perceptions of the environment by positioning them in the middle of the real world and the virtual world and creating an environment in which virtual components are combined in a dynamic interaction with the real environment. Portable augmented reality technology is a great tool for adding content to field visits by adding virtual components and information to a specific physical location. Such a tool can change the student-centered and inactive educational process into a student-centered and active process by creating a self-sufficient learning situation for students. The learning environment resulting from the combination of the real world and the virtual world is effective in creating a valid learning environment for students. Numerous studies have examined the application of augmented reality technologies in various educational fields such as engineering, medicine, ecology, science, art, history, etc. This study has used a tool based on augmented reality technology to enhance the efficiency of regular visits in teaching technical courses in the field of architecture. Methods: This study is applied utilizing a quantitative research method.  Participants included 73 students in the mechanical engineering course divided into experimental groups (38) and control group (35) after an initial theoretical training and administering pre-tests. The instruments in this study were tests and questionnaires. The experiment took place over a three-week period creating an active learning environment. Findings: The results of the study show that the application of the AR supplementary teaching tool contributes to enhance the students’ learning through the field visits and it is more effective than field visits in order to provide the satisfaction of learning approach and higher scientific validity from the students’ point of view. Conclusion: The use of AR technology and the focus on important points in field visits have made the teaching and learning process more efficient and enjoyable for students. From the students' point of view, the knowledge credibility of the activity designed for the experimental group was higher than the activity designed for the control group. The combination of building information in a simple and understandable software caused valid and superior knowledge.   ===================================================================================== COPYRIGHTS  ©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================