Discovery Techniques Research Articles

Differentiation of Cystic Fibrosis-Related Pathogens by Volatile Organic Compound Analysis with Secondary Electrospray Ionization Mass Spectrometry.

Identifying and differentiating bacteria based on their emitted volatile organic compounds (VOCs) opens vast opportunities for rapid diagnostics. Secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) is an ideal technique for VOC-biomarker discovery because of its speed, sensitivity towards polar molecules and compound characterization possibilities. Here, an in vitro SESI-HRMS workflow to find biomarkers for cystic fibrosis (CF)-related pathogens P. aeruginosa, S. pneumoniae, S. aureus, H. influenzae, E. coli and S. maltophilia is described. From 180 headspace samples, the six pathogens are distinguishable in the first three principal components and predictive analysis with a support vector machine algorithm using leave-one-out cross-validation exhibited perfect accuracy scores for the differentiation between the groups. Additionally, 94 distinctive features were found by recursive feature elimination and further characterized by SESI-MS/MS, which yielded 33 putatively identified biomarkers. In conclusion, the six pathogens can be distinguished in vitro based on their VOC profiles as well as the herein reported putative biomarkers. In the future, these putative biomarkers might be helpful for pathogen detection in vivo based on breath samples from patients with CF.

Construction of a multiepitope vaccine candidate against Fasciola hepatica: an in silico design using various immunogenic excretory/secretory antigens

ABSTRACT Background Fasciola hepatica is an important pathogen that causes liver fluke disease in definitive hosts such as livestock animals and humans. Various excretory/secretory products have been used in serological diagnosis and vaccination studies targeting fasciolosis. There are no commercial vaccines against fasciolosis yet. Bioinformatic analysis based on computational methods have lower cost and provide faster output compared to conventional vaccine antigen discovery techniques. The aim of this study was to predict B- and T-cell specific epitopes of four excretory/secretory antigens (Kunitz-type serine protease inhibitor, cathepsin L1, helminth defense molecule, and glutathione S-transferase) of Fasciola hepatica and to construct a multiepitope vaccine candidate against fasciolosis. Methods and Results Initially, nonallergic and the highest antigenic B- and T- cell epitopes were selected and then, physico-chemical parameters, secondary and tertiary structures of designed multiepitope vaccine candidate were predicted. Tertiary structure was refined and validated using online bioinformatic tools. Linear and discontinuous B-cell epitopes and disulfide bonds were determined. Finally, molecular docking analysis for MHC-I and MHC-II receptors was performed. Conclusion This multi-epitope vaccine candidate antigen, with high immunological properties, can be considered as a promising vaccine candidate for animal experiments and wet lab studies.

Leveraging Unsupervised Machine Learning to Discover Patterns in Linguistic Health Summaries for Eldercare.

The Center for Eldercare and Rehabilitation Technology, at University of Missouri, has researched the use of smart, unobtrusive sensors for older adult residents' health monitoring and alerting in aging-in-place communities for many years. Sensors placed in the apartments of older adult residents generate a deluge of daily data that is automatically aggregated, analyzed, and summarized to aid in health awareness, clinical care, and research for healthy aging. When anomalies or concerning trends are detected within the data, the sensor information is converted into linguistic health messages using fuzzy computational techniques, so as to make it understandable to the clinicians. Sensor data are analyzed at the individual level, therefore, through this study we aim to discover various combinations of patterns of anomalies happening together and recurrently in the older adult's population using these text summaries. Leveraging various computational text data processing techniques, we are able to extract relevant analytical features from the health messages. These features are transformed into a transactional encoding, then processed with frequent pattern mining techniques for association rule discovery. At individual level analysis, resident ID 3027 was considered as an exemplar to describe the analysis. Seven combinations of anomalies/rules/associations were discovered in this resident, out of which rule group three showed an increased recurrence during the COVID lockdown of facility. At the population level, a total of 38 associations were discovered that highlight the health patterns, and we continue to explore the health conditions associated with them. Ultimately, our goal is to correlate the combinations of anomalies with certain health conditions, which can then be leveraged for predictive analytics and preventative care. This will improve the current clinical care systems for older adult residents in smart sensor, aging-in-place communities.

Mass Spectrometry (MS/MS) and Proteomics: A High-Performance Bioanalytical Technique for the Peptide Drug Discovery

Drug discovery research focuses on the Rational Drug Design (RDD) concepts, and the major obstacles in the drug discovery process are the lack of target specificity and selectivity. The realization of higher target selectivity of peptide drugs has promoted peptide research. Rapid growth in genomics along with recombinant DNA (rDNA) technology and gene expression studies stimulated the peptide research. The promising use of peptide therapeutics demands sensitive and selective quantification methods. Protein sequencing and proteomic investigations can be successfully accomplished through the Mass Spectroscopy (MS) based methods. Mass spectroscopy-based soft ionization methods, namely, electrospray ionization (ESI) and Matrix-Assisted Laser Desorption/Ionization (MALDI), offer high-throughput sequencing to provide the characterization (sequence and structure) of intact proteins/peptides. The advent of tandem Mass Spectrometry (MS/MS) along with data acquisition methods are the basis for the evolution of peptide therapeutics research. The evolution of data science helped in developing computational proteomics, which assists in the quantitative determination of protein samples. This review narrates the role of mass spectrometry in the peptide drug discovery particularly in the sequence characterization along with the latest developments, such as computational proteomics.

Computational Docking Technique for Drug Discovery: A Review

Computational and experimental techniques are two complimentary approaches that have important roles in drug discovery and development. Earlier time and cost of bringing a new drug in market bears a question as it takes seven to twelve years and $ 1.2 billion are often cited. Furthermore, five out of forty thousand compounds tested in animals reach human testing and only one of five compounds reaching clinical studies is approved. This accounts for a large input in terms of time, money and human and other resources. Therefore, new approaches are needed to facilitate, expedite and streamline drug discovery and development, save time, money and resources. Among many computational tools, molecular docking is one of the important means that can be used in drug discovery. It provides the information regarding the binding affinities between small molecules (ligands) and macromolecular receptor targets (proteins). Various approaches, methodology are cited in various literatures for describing the cost, time effect with success of drug discovery task. In this review, introduction of the available molecular docking methods, with simple methodology of docking and examples of drug design and discovery through computational docking methods is discussed and emphasis is made on various examples of sampling algorithms, scoring functions with their relevant characterstics with summary on type of ligand binding with receptors.

Smart Competence Management Using Business Analytics with Fuzzy Predicates

Organizations consider human capital as one of their most important assets. Experts in the field have focused on the research and development of human talent management skills. At present, companies are giving high importance to the management of this intangible resource. Management by competencies and skills is basic in the selection and development of the most valuable asset the organization has: its human capital. A conceptual framework of the intelligent management of human capital and some more advanced knowledge discovery techniques are presented in this paper. A methodology for smart detection of core competencies based on fuzzy logic predicates and business analytics is proposed. The proposed methodology allows: (1) the evaluation of the importance of competencies, (2) the identification of competencies achievement level of each employee, (3) the identification of competencies with difficulties, (4) the identification of competencies that have influence on others, and (5) a hierarchization of the competencies to select the most appropriated for the employee recruitment plan. Furthermore, an analysis is proposed using knowledge discovery, which allows one to identify which competences have influence on a specific one. All of the above is useful to build an ideal profile for a position. A case study was carried out in order to show the implementation and interpretation of our proposal.

Generator based approach to analyze mutations in genomic datasets

In contrast to the conventional approach of directly comparing genomic sequences using sequence alignment tools, we propose a computational approach that performs comparisons between sequence generators. These sequence generators are learned via a data-driven approach that empirically computes the state machine generating the genomic sequence of interest. As the state machine based generator of the sequence is independent of the sequence length, it provides us with an efficient method to compute the statistical distance between large sets of genomic sequences. Moreover, our technique provides a fast and efficient method to cluster large datasets of genomic sequences, characterize their temporal and spatial evolution in a continuous manner, get insights into the locality sensitive information about the sequences without any need for alignment. Furthermore, we show that the technique can be used to detect local regions with mutation activity, which can then be applied to aid alignment techniques for the fast discovery of mutations. To demonstrate the efficacy of our technique on real genomic data, we cluster different strains of SARS-CoV-2 viral sequences, characterize their evolution and identify regions of the viral sequence with mutations.

Stress Driven Discovery of Natural Products From Actinobacteria with Anti-Oxidant and Cytotoxic Activities Including Docking and ADMET Properties.

Elicitation through abiotic stress, including chemical elicitors like heavy metals, is a new technique for drug discovery. In this research, the effect of heavy metals on actinobacteria Streptomyces sp. SH-1312 for secondary metabolite production, with strong pharmacological activity, along with pharmacokinetics profile, was firstly investigated. The optimum metal stress conditions consisted of actinobacteria strain Streptomyces sp. SH-1312 with addition of mix metals (Co2+ + Zn2+) ions at 0.5 mM in Gause’s medium. Under these conditions, the stress metabolite anhydromevalonolactone (MVL) was produced, which was absent in the normal culture of strain and other metals combinations. Furthermore, the stress metabolite was also evaluated for its anti-oxidant and cytotoxic activities. The compound exhibited remarkable anti-oxidant activities, recording the IC50 value of 19.65 ± 5.7 µg/mL in DPPH, IC50 of 15.49 ± 4.8 against NO free radicals, the IC50 value of 19.65 ± 5.22 µg/mL against scavenging ability, and IC50 value of 19.38 ± 7.11 µg/mL for iron chelation capacity and the cytotoxic activities against PC3 cell lines were recorded with IC50 values of 35.81 ± 4.2 µg/mL after 24 h, 23.29 ± 3.8 µg/mL at 48 h, and 16.25 ± 6.5 µg/mL after 72 h. Further mechanistic studies have revealed that the compound MVL has shown its pharmacological efficacy by upregulation of P53 and BAX while downregulation of BCL-2 expression, indicating that MVL is following apoptosis in varying degrees. To better understand the pharmacological properties of MVL, in this work, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) were also evaluated. During ADMET predictions, MVL has displayed a safer profile in case of hepatotoxicity, cytochrome inhibition and also displayed as non-cardiotoxic. The compound MVL showed good binding energy in the molecular docking studies, and the results revealed that MVL bind in the active region of the target protein of P53 and BAX. This work triumphantly announced a prodigious effect of heavy metals on actinobacteria with fringe benefits as a key tool of MVL production with a strong pharmacological and pharmacokinetic profile.

A deep learning ensemble approach to prioritize antiviral drugs against novel coronavirus SARS-CoV-2 for COVID-19 drug repurposing

The alarming pandemic situation of Coronavirus infectious disease COVID-19, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has become a critical threat to public health. The unexpected outbreak and unrealistic progression of COVID-19 have generated an utmost need to realize promising therapeutic strategies to fight the pandemic. Drug repurposing-an efficient drug discovery technique from approved drugs is an emerging tactic to face the immediate global challenge. It​ offers a time-efficient and cost-effective way to find potential therapeutic agents for the disease. Artificial Intelligence-empowered deep learning models enable the rapid identification of potentially repurposable drug candidates against diseases. This study presents a deep learning ensemble model to prioritize clinically validated anti-viral drugs for their potential efficacy against SARS-CoV-2. The method integrates the similarities of drug chemical structures and virus genome sequences to generate feature vectors. The best combination of features is retrieved by the convolutional neural network in a deep learning manner. The extracted deep features are classified by the extreme gradient boosting classifier to infer potential virus–drug associations. The method could achieve an AUC of 0.8897 with 0.8571 prediction accuracy and 0.8394 sensitivity under the fivefold cross-validation. The experimental results and case studies demonstrate the suggested deep learning ensemble system yields competitive results compared with the state-of-the-art approaches. The top-ranked drugs are released for further wet-lab researches.

MapReduce Based Crow Search Adopted Partitional Clustering Algorithms For Handling Large Scale Data

Cluster analysis is the prominent data mining technique in knowledge discovery and it discovers the hidden patterns from the data. The K-Means, K-Modes and K-Prototypes are partition based clustering algorithms and these algorithms select the initial centroids randomly. Because of its random selection of initial centroids, these algorithms provide the local optima in solutions. To solve these issues, the strategy of Crow Search algorithm is employed with these algorithms to obtain the global optimum solution. With the advances in information technology, the size of data increased in a drastic manner from terabytes to petabytes. To make proposed algorithms suitable to handle these voluminous data, the phenomena of parallel implementation of these clustering algorithms with Hadoop Mapreduce framework. The proposed algorithms are experimented with large scale data and the results are compared in terms of cluster evaluation measures and computation time with the number of nodes.

Web Service Discovery Approaches for Dynamic Mobile Environment

Mobile web service (MWS) discovery is taking a new direction due to the explosion of users accessing mobile services using diverse mobile devices, coupled with the persistent changes in a dynamic mobile environment (DME). This leads to renewed adoption of lightweight solutions for the identification of the most suitable web services that correspond with the service requests. Contemporary mobile web service discovery approaches are plagued with performance and accuracy problems and are rarely compatible with the DME. The objective of this systematic literature review is to develop a more rigorous understanding and identify recent research trends in mobile web service discovery techniques in a dynamic mobile environment. This review followed the systematic literature review (SLR) guidelines. Essential information was extracted from the 76 relevant articles in line with the formulated questions and finally reported after in-depth analysis. The results of this study discuss the critical contributions and limitations of the proposed approaches.

Adoption of Image-Driven Machine Learning for Microstructure Characterization and Materials Design: A Perspective

The recent surge in the adoption of machine learning techniques for materials design, discovery, and characterization has resulted in increased interest in and application of image-driven machine learning (IDML) approaches. In this work, we review the application of IDML to the field of materials characterization. A hierarchy of six action steps is defined that compartmentalizes a problem statement into well-defined modules. The studies reviewed in this work are analyzed through the decisions adopted in them at each of these steps. Such a review permits a granular assessment of the field, for example, the impact of IDML on materials characterization at the nanoscale, the number of images in a typical dataset required to train a semantic segmentation model on electron microscopy images, the prevalence of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.

Detecting communities using social network analysis in online learning environments: Systematic literature review

AbstractUncovering community structure has made a significant advancement in explaining, analyzing, and forecasting behaviors and dynamics of networks related to different fields in sociology, criminology, biology, medicine, communication, economics, and academia. Detecting and clustering communities is a powerful step toward identifying the structural properties and the behavioral patterns in social networks. Recently, online learning has been progressively adopted by a lot of educational practices which raise many questions about assessing the learners' engagement, collaboration, and behaviors in the new emerging learning communities. This systematic literature review aims to assess the use of community detection techniques in analyzing the network's structure in online learning environments. It provides a comprehensive overview of the existing research that adopted those techniques with identifying the educational objectives behind their application as well as suggesting possible future research directions. Our analysis covered 65 studies that found in the literature and applied different community discovery techniques on various types of online learning environments to analyze their users' interactions patterns. Our review revealed the potential of this field in improving educational practices and decisions and in utilizing the massive amount of data generated from interacting with those environments. Finally, we highlighted the need to include automated community discovery techniques in online learning environments to facilitate and enhance their use as well as we stressed on the urge for further advance research to uncover a lot of hidden opportunities.This article is categorized under: Algorithmic Development > Statistics Algorithmic Development > Web Mining Application Areas > Education and Learning

Deciphering the competitive inhibition of dihydropteroate synthase by 8 marcaptoguanine analogs: enhanced potency in phenylsulfonyl fragments

The emergence of sulfa-drug resistance and reduced efficacy of pterin-based analogs towards Dihydropteroate synthase (DHPS) inhibition dictate a pressing need of developing novel antimicrobial agents for immune-compromised patients. Recently, a series of 8-Marcaptoguanin (8-MG) derivatives synthesized for 6-Hydroxymethyl-7,8-dihydropterin pyrophosphokinase (experimental KD ∼ 100–.0.36) showed remarkable homology with the pteroic-acid and serve as a template for product antagonism in DHPS. The present work integrates ligand-based drug discovery techniques with structure-based docking, enhanced MD simulation, and MM/PBSA techniques to demonstrate the essential features of 8-MG analogs which make it a potent inhibitor for DHPS. The delicate balance in hydrophilic, hydrophobic substitutions on the 8-MG core is the crucial signature for DHPS inhibition. It is found that the dynamic interactions of active compounds are mainly dominated by consistent hydrogen bonding network with Asp 96, Asn 115, Asp 185, Ser 222, Arg 255 and π-π stacking, π-cation interactions with Phe 190, Lys 221. Further, two new 8-MG compounds containing N-phenylacetamide (compound S1, ΔGbind-eff = –62.03 kJ/mol) and phenylsulfonyl (compound S3, ΔGbind-eff = −71.29 kJ/mol) fragments were found to be the most potent inhibitor of DHPS, which stabilize the flexible pABA binding loop, thereby increasing their binding affinity. MM/PBSA calculation shows electrostatic energy contribution to be the principal component in stabilizing the inhibitors in the binding pocket. This fact is further confirmed by the higher energy barrier obtained in umbrella sampling for this class of inhibitors.

Data Mining to Assess Organizational Transparency across Technology Processes: An Approach from IT Governance and Knowledge Management

Information quality and organizational transparency are relevant issues for corporate governance and sustainability of companies, as they contribute to reducing information asymmetry, decreasing risks, and improving the conduct of decision-makers, ensuring an ethical standard of organizational control. This work uses the COBIT framework of IT governance, knowledge management, and machine learning techniques to evaluate organizational transparency considering the maturity levels of technology processes applied in 285 companies of southern Brazil. Data mining techniques have been methodologically applied to analyze the 37 processes in four different domains: Planning and organization, acquisition and implementation, delivery and support, and monitoring. Four learning techniques for knowledge discovery have been used to build a computational model that allowed us to evaluate the organizational transparency level. The results evidence the importance of IT performance monitoring and assessment, and internal control processes in enabling organizations to improve their levels of transparency. These processes depend directly on the establishment of IT strategic plans and quality management, as well as IT risk and project management, therefore an improvement in the maturity of these processes implies an increase in the levels of organizational transparency and their reputational, financial, and accountability impact.

Industrial applications of cold-adapted enzymes: challenges, innovations and future perspective.

Extreme cold environments are potential reservoirs of microorganisms producing unique and novel enzymes in response to environmental stress conditions. Such cold-adapted enzymes prove to be valuable tools in industrial biotechnology to meet the increasing demand for efficient biocatalysts. The inherent properties like high catalytic activity at low temperature, high specific activity and low activation energy make the cold-adapted enzymes well suited for application in various industries. The interest in this group of enzymes is expanding as they are the preferred alternatives to harsh chemical synthesis owing to their biodegradable and non-toxic nature. Irrespective of the multitude of applications, the use of cold-adapted enzymes at the industrial level is still limited. The current review presents the unique adaptive features and the role of cold-adapted enzymes in major industries like food, detergents, molecular biology and bioremediation. The review highlights the significance of omics technology i.e., metagenomics, metatranscriptomics and metaproteomics in enzyme bioprospection from extreme environments. It further points out the challenges in using cold-adapted enzymes at the industrial level and the innovations associated with novel enzyme prospection strategies. Documentations on cold-adapted enzymes and their applications are abundant; however, reports on the role of omics tools in exploring cold-adapted enzymes are still scarce. So, the review covers the aspect concerning the novel techniques for enzyme discovery from nature.

Book Reviews

Book Reviews

ADOPS: Aspect Discovery OPinion Summarisation Methodology based on deep learning and subgroup discovery for generating explainable opinion summaries

Opinion summarisation is concerned with generating structured summaries of multiple opinions in order to provide insightful knowledge to end users. We present the Aspect Discovery for OPinion Summarisation (ADOPS) methodology, which is aimed at generating explainable and structured opinion summaries. ADOPS is built upon aspect-based sentiment analysis methods based on deep learning and Subgroup Discovery techniques. The resultant opinion summaries are presented as interesting rules, which summarise in explainable terms for humans the state of the opinion about the aspects of a specific entity. We annotate and release a new dataset of opinions about a single entity on the restaurant review domain for assessing the ADOPS methodology, and we call it ORCo. The results show that ADOPS is able to generate interesting rules with high values of support and confidence, which provide explainable and insightful knowledge about the state of the opinion of a certain entity.

Exoplanets with ELT-METIS

Direct imaging is a powerful exoplanet discovery technique that is complementary to other techniques and offers great promise in the era of 30 m class telescopes. Space-based transit surveys have revolutionized our understanding of the frequency of planets at small orbital radii around Sun-like stars. The next generation of extremely large ground-based telescopes will have the angular resolution and sensitivity to directly image planets with R < 4 R⊕ around the very nearest stars. Here, we predict yields from a direct imaging survey of a volume-limited sample of Sun-like stars with the Mid-Infrared ELT Imager and Spectrograph (METIS) instrument, planned for the 39 m European Southern Observatory Extremely Large Telescope (ELT) that is expected to be operational towards the end of the decade. Using Kepler occurrence rates, a sample of stars with spectral types A-K within 6.5 pc, and simulated contrast curves based on an advanced model of what is achievable from coronagraphic imaging with adaptive optics, we estimated the expected yield from METIS using Monte Carlo simulations. We find the METIS expected yield of planets in the N2 band (10.10−12.40 μm) is 1.14 planets, which is greater than comparable observations in the L (3.70−3.95 μm) and M (4.70−4.90 μm) bands. We also determined a 24.6% chance of detecting at least one Jovian planet in the background limited regime assuming a 1 h integration. We calculated the yield per star and estimate optimal observing revisit times to increase the yield. We also analyzed a northern hemisphere version of this survey and found there are additional targets worth considering. In conclusion, we present an observing strategy aimed to maximize the possible yield for limited telescope time, resulting in 1.48 expected planets in the N2 band.

Identification of 3-((1-(Benzyl(2-hydroxy-2-phenylethyl)amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic Acid as a Potential Inhibitor of Non-Nucleosidase Reverse Transcriptase Inhibitors through InSilico Ligand- and Structure-Based Approaches.

Non-nucleosidase reverse transcriptase inhibitors (NNRTIs) are highly promising agents for use in highly effective antiretroviral therapy. We implemented a rational approach for the identification of promising NNRTIs based on the validated ligand- and structure-based approaches. In view of our state-of-the-art techniques in drug design and discovery utilizing multiple modeling approaches, we report here, for the first time, quantitative pharmacophore modeling (HypoGen), docking, and in-house database screening approaches in the identification of potential NNRTIs. The validated pharmacophore model with three hydrophobic groups, one aromatic ring group, and a hydrogen-bond acceptor explains the interactions at the active site by the inhibitors. The model was implemented in pharmacophore-based virtual screening (in-house and commercially available databases) and molecular docking for prioritizing the potential compounds as NNRTI. The identified leads are in good corroboration with binding affinities and interactions as compared to standard ligands. The model can be utilized for designing and identifying the potential leads in the area of NNRTIs.