Advanced Computational Tools Research Articles

Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases

The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.

Chemometric and Machine Learning Analysis of Lithium Concentration and Solvation Behavior in Li-Ion Battery Electrolytes

The demand for batteries is rapidly growing across a range of technologies. The increasingly diverse use cases for batteries require various capabilities, particularly requirements for high energy densities, that are currently unmet by traditional Li-ion batteries. Electrolyte stability proves to be a bottleneck for battery advancement towards energy dense chemistries beyond Li-ion, including metal anodes. In situ spectroscopy tools, such as Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, and X-ray spectroscopy, have provided insight into critical molecular-level interactions in batteries during cycling. These in situ tools have yielded continuous improvement of electrolyte properties. Spectroscopy datasets, however, contain many nuances that challenge meaningful human understanding. Artificial intelligence and chemometric tools can be coupled with in situ spectroscopy to find relevant interpretations of spectral datasets and elucidate complex molecular phenomena. In this research, an analysis was performed on FTIR spectroscopy data from an electrolyte composed of LiPF6 in ethylene carbonate (EC) and ethyl methyl carbonate (EMC) to discern solvation behavior using principal component analysis (PCA) and a convolutional neural network (CNN). PCA pinpointed exact band locations of solvation shifting behavior in the IR spectra and improved understanding of the relationship between lithium concentrations and peak changes. The CNN was trained with spectral datasets of electrolytes with known lithium concentrations and then could predict lithium concentrations from spectral datasets with extraordinarily high accuracy. Additionally, the CNN interpreted FTIR spectral datasets from a graphite half-cell with EC/EMC/LiPF6 electrolyte and accurately determined the lithium concentration in the bulk electrolyte. The CNN also observed lithium depletion events (up to 10% lithium depletion) in the graphite anode during fast-charging cycles of the galvanostatic intermittent titration technique. This research breaks new ground on using advanced computational tools for in situ spectroscopic analysis of battery electrolytes and demonstrates an improved understanding of complex molecular-level phenomena in electrolytes.

Struct2Graph: a graph attention network for structure based predictions of protein–protein interactions

BackgroundDevelopment of new methods for analysis of protein–protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains.ResultsIn this study, we address this problem and describe a PPI analysis based on a graph attention network, named Struct2Graph, for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a fivefold cross validation average accuracy of 99.42%. Moreover, Struct2Graph can potentially identify residues that likely contribute to the formation of the protein–protein complex. The identification of important residues is tested for two different interaction types: (a) Proteins with multiple ligands competing for the same binding area, (b) Dynamic protein–protein adhesion interaction. Struct2Graph identifies interacting residues with 30% sensitivity, 89% specificity, and 87% accuracy.ConclusionsIn this manuscript, we address the problem of prediction of PPIs using a first of its kind, 3D-structure-based graph attention network (code available at https://github.com/baranwa2/Struct2Graph). Furthermore, the novel mutual attention mechanism provides insights into likely interaction sites through its unsupervised knowledge selection process. This study demonstrates that a relatively low-dimensional feature embedding learned from graph structures of individual proteins outperforms other modern machine learning classifiers based on global protein features. In addition, through the analysis of single amino acid variations, the attention mechanism shows preference for disease-causing residue variations over benign polymorphisms, demonstrating that it is not limited to interface residues.

Use of aerodynamically favorable tapered form in contemporary supertall buildings

Today, supertall buildings can be constructed in unusual forms as a pragmatic reflection of advances in construction techniques and engineering technologies, together with advanced computational design tools for architectural design. As with many other buildings, architectural and practical principles play a crucial role in the form of a supertall building, where aerodynamic behavior shaped by wind-induced excitations also becomes a critical design input. Various methods are used to meet the functional needs of these towers and reduce excitations, including aerodynamic modification methods directly related to the building form. Tapered forms are one of the most frequently used and most effective methods in today's skyscrapers, which significantly affect architectural design. To date, no study has been conducted in the literature that provides an understanding of the interrelationships between tapered building forms and main planning criteria, considering the aerodynamic design concerns of the tapering effect in supertall buildings (≥300 m). This important issue is explored in this article with data gathered from 41 supertall case studies, considering location, function, structural system, and structural material as well as the aerodynamic taper effect. The main findings of the study highlighted the following: (1) Asia was where tapered towers were most favored, with a wider margin in all regions; (2) mixed-use was the most preferred function in selected supertall buildings with tapered form; (3) outriggered frame systems were mainly used; (4) tapered supertall cases were mostly built in composite; (5) the sample group included 17 cases that used the tapering effect with aerodynamic design concerns, some of which were accompanied by corner modifications. It is believed that this study will be a basic guide for design and construction professionals including architectural and structural designers, and contractors.

Identification of Key Drivers of Cost and Environmental Impact for Biomass-Derived Fuel for Advanced Multimode Engines Based on Techno-Economic and Life Cycle Analysis

Early stage research and development are needed to accelerate the introduction of advanced biofuel and engine technologies. Under the Co-Optima initiative, the U.S. Department of Energy is leveraging capabilities from its nine national laboratories and more than 35 university and industry partners including advanced computational tools, process design, data analysis, and economic and sustainability modeling tools to simultaneously design fuels and engines capable of running efficiently in an affordable, scalable, and sustainable way. In this work, we conducted techno-economic analysis (TEA) and life cycle assessment (LCA) to understand the cost, technology development, and environmental impacts of producing selected bioblendstocks for advanced engines such as multimode (MM) type engines at the commercial scale. We assessed 12 biofuel production pathways from renewable lignocellulosic biomass feedstocks using different conversion technologies (biochemical, thermochemical, or hybrid) to produce target co-optimized biofuels. TEA and LCA were used to evaluate 19 metrics across technology readiness, economic viability, and environmental impact and for each ranked on a set of criteria as favorable, neutral, unfavorable, or unknown. We found that most bioblendstocks presented in this study showed favorable economic metrics, while the technology readiness metrics were mostly neutral. The economic viability results showed potentially competitive target costs of less than $4 per gasoline gallon equivalent (GGE) for six candidates and less than $2.5/GGE for methanol. We identified 10 MM bioblendstock candidates with synergistic blending performance and with the potential to reduce greenhouse gas (GHG) emissions by 60% or more compared to petroleum-derived gasoline. The analysis presented here also provides insights into major economic and sustainability drivers of the production process and potential availability of the feedstocks for producing each MM bioblendstock.

The effect of different sampling method integrated in NSGA II optimization on performance and emission of diesel/hydrogen dual-fuel CI engine

Recent advances in computational tools and techniques have enabled engineers to tackle single-step and iterative complex computer-aided engineering (CAE) problems. They have also helped develop CAE optimization techniques capable of driving design parameters towards regions where selected system characteristics can be further improved. The present work was conducted in two steps; In the first step, the effects of adding hydrogen to diesel fuel in a turbocharged direct injection (TDI) compression ignition (CI) Audi engine was analyzed through 1D simulation in AVL BOOST. With no significant modifications to the engine, the analysis was carried out at 1900 rpm and 2500 rpm under 0.8 MPa nominal loading conditions. Hydrogen was injected with air at 30 L/min through the intake manifold before the turbocharger. The present work considered all the engine components that are thermodynamically involved in the combustion process. The second analysis step coupled the simulated model with modeFRONTIER to study the optimum parameter values for engine performance and emission and determined the effects of various input variables on these parameters. Optimization was carried out by adopting hybrid algorithms to design experiments and the non-dominated sorting genetic algorithm II (NSGA_II). The response level predictive capability of the Taguchi and SOBOL methods increases the convergence speed of the NSGA_II. The results from the first analysis step indicated that the hydrogen diesel blend reduced brake specific fuel consumption (BSFC), engine power and torque, CO2 and NOx emissions, and increased entropy. The results from the second step suggested that in multivariable optimization, the peak pressure and NOx emissions were affected mainly, by combustion initiation time and the hydrogen added to the diesel fuel, respectively. Finally, it was found that hydrogen injection at 20 mg/min resulted in the best engine performance and emission.

Media framing and expression of anti-China sentiment in COVID-19-related news discourse: An analysis using deep learning methods

This study focuses on news content related to China and COVID-19 during the COVID-19 pandemic and investigates how media frame, affected the emergence of anti-China sentiments through a case study of Japanese online news discourse. We collected large-scale digital trace data including online news and comments during the COVID-19 pandemic. By employing deep learning-based sentiment classifications, we were able to measure the extent of anti-China sentiments expressed through comments during the pandemic's different phases and on different types of news content. Our results provide empirical evidence that the news media's negative depictions of China and coverage related to political and international relations issues increased as the prevalence of COVID-19 in Japan increased. Importantly, since this coverage can prompt the expression of anti-China sentiment, we argue that the framing used by the media can provide discursive contexts that escalate COVID-19 issues into a broader expression of anti-China sentiment. This study not only identifies the impact of media frames on the expression of anti-China sentiment but also contributes to the development of methods for detecting public opinion and measuring the framing effect with big data and advanced computational tools.

BETHSY 9.1b test calculation with TRACE using 3D vessel component

Recently, several advanced multidimensional computational tools for simulating reactor system behavior during real and hypothetical transient scenarios were developed. One of such advanced, best-estimate reactor systems codes is TRAC/RELAP Advanced Computational Engine (TRACE), developed by the U.S. Nuclear Regulatory Commission. The advanced TRACE comes with a graphical user interface called SNAP (Symbolic Nuclear Analysis Package). It is intended for pre- and post-processing, running codes, RELAP5 to TRACE input deck conversion, input deck database generation etc. The TRACE code is still not fully development and it will have all the capabilities of RELAP5. The purpose of the present study was therefore to assess the 3D capability of the TRACE on BETHSY 9.1b test. The TRACE input deck was semi-converted (using SNAP and manual corrections) from the RELAP5 input deck. The 3D fluid dynamics within reactor vessel was modeled and compared to 1D fluid dynamics. The TRACE 3D calculation was compared both to TRACE 1D calculation and RELAP5 calculation. Namely, the geometry used in TRACE is basically the same, what gives very good basis for the comparison of the codes. The only exception is 3D reactor vessel model in case of TRACE 3D calculation. The TRACE V5.0 Patch 1 and RELAP5/MOD3.3 Patch 4 were used for calculations. The BETHSY 9.1b test (International Standard Problem no. 27 or ISP-27) was 5.08 cm equivalent diameter cold leg break without high pressure safety injection and with delayed ultimate procedure. BETHSY facility was a 3-loop replica of a 900 MWe FRAMATOME pressurized water reactor. In general, all presented code calculations were in good agreement with the BETHSY 9.1b test. The TRACE 1D calculation results are comparable to RELAP5 calculated results. For some parameters they are better, this is mostly due to better tuning of the break flow, what influences timing of the transient. When comparing TRACE 1D and TRACE 3D calculation, the latter is slightly better. One reason for comparable results is already good agreement of 1D calculations and there was not much space to further improve the results. The other reason may be that in the facility the phenomena were mostly one dimensional (for example, external downcomer was used for reactor vessel modeling). However, when 3D behavior of the heater rod temperatures was investigated, the advantage of three dimensional treatment was clearly demonstrated.

Dietary Alterations in Impaired Mitochondrial Dynamics Due to Neurodegeneration.

Alzheimer’s disease is still an incurable disease with significant social and economic impact globally. Nevertheless, newly FDA-approved drugs and non-pharmacological techniques may offer efficient disease treatments. Furthermore, it is widely accepted that early diagnosis or even prognosis of Alzheimer’s disease using advanced computational tools could offer a compelling alternative way of management. In addition, several studies have presented an insight into the role of mitochondrial dynamics in Alzheimer’s development. In combination with diverse dietary and obesity-related diseases, mitochondrial bioenergetics may be linked to neurodegeneration. Considering the probabilistic expectations of Alzheimer’s disease development or progression due to specific risk factors or biomarkers, we designed a Bayesian model to formulate the impact of diet-induced obesity with an impaired mitochondrial function and altered behavior. The applied probabilities are based on clinical trials globally and are continuously subject to updating and redefinition. The proposed multiparametric model combines various data types based on uniform probabilities. The program simulates all the variables with a uniform distribution in a sample of 1000 patients. First, the program initializes the variable age (30–95) and the four different diet types (“HFO_diet,” “Starvation,” “HL_diet,” “CR”) along with the factors that are related to prodromal or mixed AD (ATP, MFN1, MFN2, DRP1, FIS1, Diabetes, Oxidative_Stress, Hypertension, Obesity, Depression, and Physical_activity). Besides the known proteins related to mitochondrial dynamics, our model includes risk factors like Age, Hypertension, Oxidative Stress, Obesity, Depression, and Physical Activity, which are associated with Prodromal Alzheimer’s. The outcome is the disease progression probability corresponding to a random individual ID related to diet choices and mitochondrial dynamics parameters. The proposed model and the programming code are adjustable to different parameters and values. The program is coded and executed in Python and is fully and freely available for research purposes and testing the correlation between diet type and Alzheimer’s disease progression regarding various risk factors and biomarkers.

Experimental, DFT study, and in silico molecular docking investigations of dichlorodiphenyltrichloroethane against human estrogen receptor alpha

Advanced computational tools allowed to study a pure commercial sample of dichlorodiphenyltrichloroethane (DDT) prepared in liquid phase in KBr pellets and characterized using FT-IR and GC-MS followed by the application of DDT for molecular docking against human estrogen receptor alpha. The compound was modelled using GaussView software. Using Veda 04 program, the theoretical vibrational energy distributions and experimental vibrational frequencies were compared. Interestingly, C1 and C2�possess the highest atomic charge density distribution (ACDD)�of -0.284e and -0.283e while C21 and C11 have lowest ACDD of -0.064e and -0.063e in a relative manner, since the deactivating power of chlorine atoms decreases charge densities of the bonded carbon. The highest intramolecular interacting perturbation energy is 1121.92 kJ mol�1 occurs between ?*C19�C21 donor orbital and ?*C14�C16 acceptor orbital while the least intramolecular interaction occurs in the lone pair of LPC26 and the�sigma nonbonding (??C1�Cl24) NBO orbitals with E(2) of 32.21�kJ mol�1. Steric interaction was the only interaction found within the complex after the docking.

The reproducibility of COVID-19 data analysis: paradoxes, pitfalls, and future challenges.

In the midst of the COVID-19 experience, we learned an important scientific lesson: knowledge acquisition and information quality in medicine depends more on "data quality" rather than "data quantity." The large number of COVID-19 reports, published in a very short time, demonstrated that the most advanced statistical and computational tools cannot properly overcome the poor quality of acquired data. The main evidence for this observation comes from the poor reproducibility of results. Indeed, understanding the data generation process is fundamental when investigating scientific questions such as prevalence, immunity, transmissibility, and susceptibility. Most of COVID-19 studies are case reports based on "non probability" sampling and do not adhere to the general principles of controlled experimental designs. Such collected data suffers from many limitations when used to derive clinical conclusions. These include confounding factors, measurement errors and bias selection effects. Each of these elements represents a source of uncertainty, which is often ignored or assumed to provide an unbiased random contribution. Inference retrieved from large data in medicine is also affected by data protection policies that, while protecting patients' privacy, are likely to reduce consistently usefulness of big data in achieving fundamental goals such as effective and efficient data-integration. This limits the degree of generalizability of scientific studies and leads to paradoxical and conflicting conclusions. We provide such examples from assessing the role of risks factors. In conclusion, new paradigms and new designs schemes are needed in order to reach inferential conclusions that are meaningful and informative when dealing with data collected during emergencies like COVID-19.

Experimental, DFT study, and in silico molecular docking investigations of dichlorodiphenyltrichloroethane against human estrogen receptor alpha

Advanced computational tools allowed to study a pure commercial sample of dichlorodiphenyltrichloroethane (DDT) prepared in liquid phase in KBr pellets and characterized using FT-IR and GC-MS followed by the application of DDT for molecular docking against human estrogen receptor alpha. The compound was modelled using GaussView software. Using Veda 04 program, the theoretical vibrational energy distributions and experimental vibrational frequencies were compared. Interestingly, C1 and C2�possess the highest atomic charge density distribution (ACDD)�of -0.284e and -0.283e while C21 and C11 have lowest ACDD of -0.064e and -0.063e in a relative manner, since the deactivating power of chlorine atoms decreases charge densities of the bonded carbon. The highest intramolecular interacting perturbation energy is 1121.92 kJ mol�1 occurs between ?*C19�C21 donor orbital and ?*C14�C16 acceptor orbital while the least intramolecular interaction occurs in the lone pair of LPC26 and the�sigma nonbonding (??C1�Cl24) NBO orbitals with E(2) of 32.21�kJ mol�1. Steric interaction was the only interaction found within the complex after the docking.

Use of the LunAero Open-Source Hardware Platform to Enhance the Accuracy and Precision of Traditional Nocturnal Migration Bird Counts.

Quantification of nocturnal migration of birds through moon watching is a technique ripe for modernization with superior computational power. In this paper, collected by a motorized telescope mount was data analyzed using both video observations by trained observers and modernized approaches using computer vision. The more advanced data extraction used the OpenCV library of computer vision tools to identify bird silhouettes by means of image stabilization and background subtraction. The silhouettes were sanitized and analyzed in sequence to produce stacked relationships between temporally close contours, discriminating birds from noise based on the assumption that birds migrate in stable paths. The flight ceiling of the birds was determined by extracting relevant correlation coefficient data from doppler radar co-located with the LunAero instrument in Norman, OK, USA using a method with low-computational overhead. The bird paths and flight ceiling were combined with lunar ephemera to provide input for the original method used for nocturnal migration quantification as well as an enhanced version of the same method with more advanced computational tools. We found that the manual quantification of migration activity detected 16,300 birds/km•h heading northwest from 110°, whereas the automated analysis reported a density of 43,794 birds/km•h heading northwest from 106.67°. Hence, there was agreement with regard to flight direction, but the automated method overestimated migration density by approximately three times. The reasons for the discrepancy between flight path detection appeared to be due to a substantial amount of noise in the video data as well as a tendency for the computer vision analysis to split single flight paths into two or more segments. The authors discuss ongoing innovations aimed at addressing these methodological challenges.

Computational lung modelling in respiratory medicine.

Computational modelling of the lungs is an active field of study that integrates computational advances with lung biophysics, biomechanics, physiology and medical imaging to promote individualized diagnosis, prognosis and therapy evaluation in lung diseases. The complex and hierarchical architecture of the lung offers a rich, but also challenging, research area demanding a cross-scale understanding of lung mechanics and advanced computational tools to effectively model lung biomechanics in both health and disease. Various approaches have been proposed to study different aspects of respiration, ranging from compartmental to discrete micromechanical and continuum representations of the lungs. This article reviews several developments in computational lung modelling and how they are integrated with preclinical and clinical data. We begin with a description of lung anatomy and how different tissue components across multiple length scales affect lung mechanics at the organ level. We then review common physiological and imaging data acquisition methods used to inform modelling efforts. Building on these reviews, we next present a selection of model-based paradigms that integrate data acquisitions with modelling to understand, simulate and predict lung dynamics in health and disease. Finally, we highlight possible future directions where computational modelling can improve our understanding of the structure–function relationship in the lung.

“3D computational simulation and experimental validation of structured materials: Case studies of tissue papers

The development and optimization of structured materials, such as tissue paper materials, benefit from modeling strategies that take into consideration its structural hierarchy at the fiber and the paper levels. The use of an innovative three-dimensional voxel approach to model both the fiber and the 3D paper structure were validated by comparison of the computational structures with the laboratory-made structures. The main goal of this work was to model tissue structures and obtain a computational implementation adapted for tissue products. The fibers were modeled in 3D according to their dimensions, and the structures produced by them were characterized using the Representative Elementary Volume (REV) and image analysis computational tools. This methodology made it possible to model the fibers according to their morphology, flexibility, and collapse, resulting in a tissue structure with thickness, porosity, relative bonding area, coverage, among other properties. The experimental design plan included the production and characterization of isotropic laboratory structures with basis weights of 20, 40, and 60 g/m2 with different eucalyptus fibers and beating degrees. With the aid of these advanced computational tools, mathematic models with predictive capacity for tissue properties such as softness, strength, and absorption can be developed.

Achievable accuracy of resonating nanomechanical systems for mass sensing of larger analytes in GDa range

Measurement of larger analytes such as many chemical and biological structures or viruses in gigadalton (GDa) range is a reminding fundamental task in analytical chemistry and life sciences, which can be possibly resolved with the resonating nanomechanical systems. Common approaches to mass sensing with these systems model the bound analyte as a point particle and assume the analyte does not change the vibrational mode shapes. However, for larger analytes their stiffness and size not only affect the resonant frequencies but also cause the significant changes in the vibrational mode shapes making their measurement highly challenging and still under-explored problem. Here, we develop a 3D model capable to accurately predict the resonant frequencies and vibrational mode shapes of the resonating nanomechanical systems with the bound analyte of arbitrary properties and size. Then, we examine in details the impact of analyte properties, size and its position of attachment on the resonant frequencies and vibrational mode shapes and, correspondingly, resolve a dispute over the achievable detection limits of the nanomechanical systems, especially for mass sensing of larger analytes. Furthermore, we identify three different sensing regimes, that is, the ultra-light, light and heavy, for which the effects of the analyte mass, stiffness, its size and the position of attachment on the accuracy of the determined mass can be separated. For the ultra-light regime (mass ratio < 10−3) the analyte stiffness and its size affect notably the resonant frequencies, the point mass approximation is inaccurate and the analyte can be identified by its mass and stiffness. For the light regime the point mass approximation is accurate, while for the heavy one (mass ratio > 2∙10−2) the mass effect dominates the frequency response and alters the vibrational mode shapes which, for the common approaches, yields the errors in the determined analyte mass. Finally, we also propose an easily accessible approach for the identification of larger analytes (in GDa range) that does not require the advanced computational tools or experimental setup, applicable to the majority of the clamped-clamped ends resonating nanomechanical systems.

A Review on Mechanistic Insight of Plant Derived Anticancer Bioactive Phytocompounds and Their Structure Activity Relationship.

Cancer is a disorder that rigorously affects the human population worldwide. There is a steady demand for new remedies to both treat and prevent this life-threatening sickness due to toxicities, drug resistance and therapeutic failures in current conventional therapies. Researchers around the world are drawing their attention towards compounds of natural origin. For decades, human beings have been using the flora of the world as a source of cancer chemotherapeutic agents. Currently, clinically approved anticancer compounds are vincristine, vinblastine, taxanes, and podophyllotoxin, all of which come from natural sources. With the triumph of these compounds that have been developed into staple drug products for most cancer therapies, new technologies are now appearing to search for novel biomolecules with anticancer activities. Ellipticine, camptothecin, combretastatin, curcumin, homoharringtonine and others are plant derived bioactive phytocompounds with potential anticancer properties. Researchers have improved the field further through the use of advanced analytical chemistry and computational tools of analysis. The investigation of new strategies for administration such as nanotechnology may enable the development of the phytocompounds as drug products. These technologies have enhanced the anticancer potential of plant-derived drugs with the aim of site-directed drug delivery, enhanced bioavailability, and reduced toxicity. This review discusses mechanistic insights into anticancer compounds of natural origins and their structural activity relationships that make them targets for anticancer treatments.

Immunoglobulin G N-glycan Biomarkers for Autoimmune Diseases: Current State and a Glycoinformatics Perspective.

The effective treatment of autoimmune disorders can greatly benefit from disease-specific biomarkers that are functionally involved in immune system regulation and can be collected through minimally invasive procedures. In this regard, human serum IgG N-glycans are promising for uncovering disease predisposition and monitoring progression, and for the identification of specific molecular targets for advanced therapies. In particular, the IgG N-glycome in diseased tissues is considered to be disease-dependent; thus, specific glycan structures may be involved in the pathophysiology of autoimmune diseases. This study provides a critical overview of the literature on human IgG N-glycomics, with a focus on the identification of disease-specific glycan alterations. In order to expedite the establishment of clinically-relevant N-glycan biomarkers, the employment of advanced computational tools for the interpretation of clinical data and their relationship with the underlying molecular mechanisms may be critical. Glycoinformatics tools, including artificial intelligence and systems glycobiology approaches, are reviewed for their potential to provide insight into patient stratification and disease etiology. Challenges in the integration of such glycoinformatics approaches in N-glycan biomarker research are critically discussed.

Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets.

Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.

Antimicrobial Compounds from Microorganisms.

Antimicrobial resistance is an exigent public health concern owing to the emergence of novel strains of human resistant pathogens and the concurrent rise in multi-drug resistance. An influx of new antimicrobials is urgently required to improve the treatment outcomes of infectious diseases and save lives. Plant metabolites and bioactive compounds from chemical synthesis have found their efficacy to be dwindling, despite some of them being developed as drugs and used to treat human infections for several decades. Microorganisms are considered untapped reservoirs for promising biomolecules with varying structural and functional antimicrobial activity. The advent of cost-effective and convenient model organisms, state-of-the-art molecular biology, omics technology, and machine learning has enhanced the bioprospecting of novel antimicrobial drugs and the identification of new drug targets. This review summarizes antimicrobial compounds isolated from microorganisms and reports on the modern tools and strategies for exploiting promising antimicrobial drug candidates. The investigation identified a plethora of novel compounds from microbial sources with excellent antimicrobial activity against disease-causing human pathogens. Researchers could maximize the use of novel model systems and advanced biomolecular and computational tools in exploiting lead antimicrobials, consequently ameliorating antimicrobial resistance.