Advanced Computational Tools Research Articles

Bibliometric Analysis of Neural Network in improving Computational Fluid Dynamics Education

The integration of neural networks into computational fluid dynamics (CFD) has led to significant advancements in fluid dynamics simulations, enhancing accuracy and effectiveness. This convergence presents a promising avenue for studying fluid systems and has the potential to revolutionize our understanding of their complex behaviours. To foster collaboration and drive research in this field, a comprehensive bibliometric review was conducted, examining the intersection of fluid dynamics education and neural networks. From a pool of 999 articles gathered from the Google Scholar database, 779 articles published between 2018 and 2023 were selected using the Publishing or Perish (PoP) software. These articles were analysed and categorized using VOSviewer, revealing three distinct clusters representing the primary research topics in computational fluid dynamics and neural networks. The findings of this study provide valuable insights into the current state of research in this emerging field, offering a comprehensive overview of key themes and their relationships. These insights can guide researchers and practitioners in identifying gaps, exploring collaborations, and advancing interdisciplinary approaches. Moving forward, further exploration of the identified clusters and associated keywords can deepen understanding of specific research topics, while investigations into methodologies and techniques employed in the articles can contribute to the development of advanced computational tools and frameworks for fluid dynamics simulations. Collaborative efforts and interdisciplinary approaches hold great potential for advancing our knowledge of fluid systems and driving progress in this exciting field.

Integrated design optimization framework for green lightweight concrete

The increasing global concern for the environment and advancements in computational tools and techniques are transforming the way building structures are designed and constructed. The current economic climate and growth of the Architecture, Engineering, and Construction (AEC) industry demand the creation of environmentally sustainable and efficient buildings, starting from the design stage. Despite the AEC industry being a thriving sector in many countries, it is vulnerable to environmental degradation due to the impact of building materials. This paper presents a decision support framework for the optimal structural design of green concrete buildings using recycled plastic aggregates and lightweight concrete. The framework considers uncertainties such as design safety, architectural requirements, material efficacy, cost-effectiveness, and environmental impact. The approach evaluates four concrete aggregate materials with varying characteristics: Natural Weight Aggregate, Lightweight Volcanic Aggregate, and Plastic-Based Green Lightweight Aggregates. The proposed approach is demonstrated through a prototype model, which shows cost savings of up to 5% per floor compared to the conventional concrete mix. The results provide quantitative evidence of the direct financial benefits of using green concrete in lieu of the optimization framework, which is expected to encourage construction professionals to use environmentally friendly concrete and take part in greening the construction industry.

Multiple criteria decision analytic methods in management with T-spherical fuzzy information.

With a focus on T-spherical fuzzy (T-SF) sets, the aim of this paper is to create a split-new appraisal mechanism and an innovative decision analytic method for use with multiple-criteria assessment and selection in uncertain situations. The T-SF frame is the latest recent advancement in fuzzy settings and uses four facets (consisting of membership grades of positivity, neutrality, negativity, and refusal) to elucidate complex uncertainties, thereby evidently reducing information loss, in anticipation of fully manifesting indistinct and equivocal information. This paper adds to the body of knowledge regarding multiple criteria choice modeling by raising T-SF correlation-oriented measurements connected to the fixed and displaced ideal/anti-ideal benchmarks and by creating an approachable appraisal mechanism for advancing a T-SF decision analytic methodology. Consider, in particular, the performance ratings of available options in terms of judging criteria under the T-SF type of uncertainties. This research gives correlation-oriented measurements focusing on two varieties of maximum and square root functions in T-SF situations, which serve as a solid foundation for an efficacious appraisal mechanism from two views of anchored judgments corresponding to the fixed and displaced benchmarks. The T-SF Minkowski distance index is generated to integrate the outranking and outranked identifiers relying on correlation-oriented measurements for figuring out the local outranking and outranked indices. The T-SF decision analytic procedures are constructed using a new appraisal significance index that is founded on certain valuable insights of correlation-oriented maximizing and minimizing indices as well as global outranking and outranked indices. Additionally, a concrete location selection dilemma is dealt with in this research to showcase the applicability and efficiency of the suggested T-SF decision analytic methodology. Sensitivity analyses and comparative studies are carried out to investigate substantial modifications in pertinent parameters and to confirm the robustness of the predominance relationships among the available options. The suggested approaches are adaptable, flexible, and reliable, according to the application outcomes and comparison findings. This research provides four scientific contributions: (1) the utilization of T-SF correlation coefficients as the basis for prioritization analysis involving multiple criteria assessments, (2) the evolution of the T-SF Minkowski distance index to model outranking decision-making processes, (3) the creation of a reliable appraisal mechanism based on T-SF correlation-oriented measurements for intelligent decision support, and (4) the advancement of computational tools and procedures (e.g., correlation-oriented maximizing and minimizing indices, global outranking and outranked indices, and appraisal significance indices) to perform the decision analytic procedure in T-SF settings. In terms of managerial implications, the solution findings support the employment of the fixed ideal/anti-ideal benchmarking mechanism, as its measurements and indices are easy to operate and suitably sensitive. Next, in practical implementations of the T-SF decision analytic procedure, it is advised to utilize the T-SF Manhattan distance index for calculating convenience. Finally, the T-SF decision analytic techniques offer fundamental ideas and measurements appropriate for manipulating T-SF information in complex decision situations, thereby increasing the application potential in the area of decision-making with information uncertainty.

Modeling, simulation, and trade‐off analysis for multirobot, multioperator surveillance

AbstractAs unmanned vehicles become smaller and more autonomous, it is becoming feasible to use them in large groups with comparatively few human operators. Design and analysis of such distributed systems are complicated by the many interactions among agents and phenomena of human behavior. In particular, human susceptibility to fatigue and cognitive overload can introduce errors and uncertainty into the system. In this paper, we demonstrate how advanced computational tools can help to overcome these engineering difficulties by optimizing multirobot, multioperator surveillance systems for cost, speed, accuracy, and stealth according to diverse user preferences in multiple case studies. The tool developed is a graphical user interface that returns the optimal number and mix of diverse agent types as a function of the user's trade‐off preferences. System performance prediction relies on a multiagent simulation with submodels for human operators, fixed‐wing unmanned aerial vehicles (UAVs), quadrotor UAVs, and flapping wing UAVs combined in different numbers.

Advances in computational frameworks in the fight against TB: The way forward.

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Transformer-based language models for mental health issues: A survey

Early identification and prevention of mental health stresses and their outcomes has become of urgent importance worldwide. To this purpose, artificial intelligence provides a body of advanced computational tools that can effectively support decision-making clinical processes by modeling and analyzing the presence of a variety of mental health issues, particularly when these can be detected in text data. In this regard, Transformer-based language models (TLMs) have demonstrated exceptional efficacy in a number of NLP tasks also in the health domain. To the best of our knowledge, the use of TLMs for specifically addressing mental health issues has not been deeply investigated so far. In this paper, we aim to fill this gap in the literature by providing the first survey of methods using TLMs for text-based identification of mental health issues.

Predictive Modeling of Metabolomics data for the Identification of Biomarkers in Chronic Kidney Disease

Chronic kidney disease is a specific type of Kidney Disease in which, a gradual loss of Kidney Function over a period of 3-30 months is noticeable. Early detection is imperative to prevent this catastrophic event and initiate treatment that may mitigate renal injury. Metabolomics data in Chronic Kidney Disease carries a lot of information about biomarkers. However, it is not clear which of these biomarkers are significant, biostatistical analysis of metabolomics data might provide the clues. In this work, an attempt has been made to find novel biomarkers that may be responsible for causing Chronic Kidney Disease by employing bioinformatics and advanced computational tools. The Chronic Kidney Disease data of the patients (in stages 3 and 4) was selected and data was segregated based on renal and cardiovascular parameters. The study consisted 441 patients and 293 metabolites. Subsequently the identification of top metabolites (as biomarkers) was carried out using statistical methods like t-test, Principal component analysis and partial least square analysis. Nine biomarkers were identified from these statistical analyses i.e Galacturonic acid, p-cresol, L-serine, L-glutamine, Lactose, 2-O-Glycerol-.alpha.-d-galactopyranoside, hexa-TMS, Butanoic acid, 2,4-bis[(trimethylsilyl)oxy]-, trimethylsilyl ester, Pseudo uridine penta-tms and Myo-inositol. The Reactivity of identified metabolites was confirmed by using quantum chemistry calculations in Gaussian software. Heat Map was constructed to find out the variations in concentrations of biomarkers in healthy and CKD patients and the showed the higher concentrations of L-serine, Galacturonic acid , L-glutamine and lower concentrations of Pseudo uridine penta-tms, Butanoic acid, 2,4-bis[(trimethylsilyl)oxy]-, trimethylsilyl ester , 2-O-Glycerol-.alpha.-d-galactopyranoside, hexa-TMS , Myo-inositol , p-cresol , Lactose in Death Patients. The biological significance of identified top metabolites has been evaluated by identifying the metabolic pathways in which the metabolites are involved. The metabolites which were found to be toxic are pseudouridine, L-glutamine, and galactouronic acid as per the previous reported literature. The Variations in concentration of these metabolites are responsible for the Death of patient with Chronic Kidney Disease.

Static and vibration analysis of reinforced cement concrete cellular beams: A three-dimensional study

This paper presents an analysis of Reinforced Cement Concrete (RCC) cellular beams, focusing on both static and dynamic aspects. The study investigates the behavior of RCC cellular beams under varying loads, including dead weight, superimposed loads, and environmental factors like wind. It addresses the essential characteristics of dynamic problems compared to static loading scenarios, emphasizing the influence of time-varying loads on structural response. The research methodology involves the design and analysis of a simply supported RCC beam with circular openings, utilizing advanced computational tools such as RISA 3D®. The study outlines the process of modeling, including the design of circular openings and reinforcement details. It also discusses the theoretical background, including the strut and tie model of concrete, which is essential for understanding the structural behavior. The paper presents the results of static and vibrational analysis, including stress distribution, mode shapes, and fundamental frequencies of vibration. The analysis evaluates the performance of RCC cellular beams, considering factors such as stress concentrations, principal stresses, shear stresses, and Von Mises stresses.

Benchmarking AlphaFold2 on peptide structure prediction

Recent advancements in computational tools have allowed protein structure prediction with high accuracy. Computational prediction methods have been used for modeling many soluble and membrane proteins, but the performance of these methods in modeling peptide structures has not yet been systematically investigated. We benchmarked the accuracy of AlphaFold2 in predicting 588 peptide structures between 10 and 40 amino acids using experimentally determined NMR structures as reference. Our results showed AlphaFold2 predicts α-helical, β-hairpin, and disulfide-rich peptides with high accuracy. AlphaFold2 performed at least as well if not better than alternative methods developed specifically for peptide structure prediction. AlphaFold2 showed several shortcomings in predicting Φ/Ψ angles, disulfide bond patterns, and the lowest RMSD structures failed to correlate with lowest pLDDT ranked structures. In summary, computation can be a powerful tool to predict peptide structures, but additional steps may be necessary to analyze and validate the results.

Breathomics profiling of metabolic pathways affected by major depression: Possibilities and limitations.

Major depressive disorder (MDD) is one of the most common psychiatric disorders with multifactorial etiologies. Metabolomics has recently emerged as a particularly potential quantitative tool that provides a multi-parametric signature specific to several mechanisms underlying the heterogeneous pathophysiology of MDD. The main purpose of the present study was to investigate possibilities and limitations of breath-based metabolomics, breathomics patterns to discriminate MDD patients from healthy controls (HCs) and identify the altered metabolic pathways in MDD. Breath samples were collected in Tedlar bags at awakening, 30 and 60 min after awakening from 26 patients with MDD and 25 HCs. The non-targeted breathomics analysis was carried out by proton transfer reaction mass spectrometry. The univariate analysis was first performed by T-test to rank potential biomarkers. The metabolomic pathway analysis and hierarchical clustering analysis (HCA) were performed to group the significant metabolites involved in the same metabolic pathways or networks. Moreover, a support vector machine (SVM) predictive model was built to identify the potential metabolites in the altered pathways and clusters. The accuracy of the SVM model was evaluated by receiver operating characteristics (ROC) analysis. A total of 23 differential exhaled breath metabolites were significantly altered in patients with MDD compared with HCs and mapped in five significant metabolic pathways including aminoacyl-tRNA biosynthesis (p = 0.0055), branched chain amino acids valine, leucine and isoleucine biosynthesis (p = 0.0060), glycolysis and gluconeogenesis (p = 0.0067), nicotinate and nicotinamide metabolism (p = 0.0213) and pyruvate metabolism (p = 0.0440). Moreover, the SVM predictive model showed that butylamine (p = 0.0005, pFDR=0.0006), 3-methylpyridine (p = 0.0002, pFDR = 0.0012), endogenous aliphatic ethanol isotope (p = 0.0073, pFDR = 0.0174), valeric acid (p = 0.005, pFDR = 0.0162) and isoprene (p = 0.038, pFDR = 0.045) were potential metabolites within identified clusters with HCA and altered pathways, and discriminated between patients with MDD and non-depressed ones with high sensitivity (0.88), specificity (0.96) and area under curve of ROC (0.96). According to the results of this study, the non-targeted breathomics analysis with high-throughput sensitive analytical technologies coupled to advanced computational tools approaches offer completely new insights into peripheral biochemical changes in MDD.

Deep learning for MYC binding site recognition.

Motivation: The definition of the genome distribution of the Myc transcription factor is extremely important since it may help predict its transcriptional activity particularly in the context of cancer. Myc is among the most powerful oncogenes involved in the occurrence and development of more than 80% of different types of pediatric and adult cancers. Myc regulates thousands of genes which can be in part different, depending on the type of tissues and tumours. Myc distribution along the genome has been determined experimentally through chromatin immunoprecipitation This approach, although powerful, is very time consuming and cannot be routinely applied to tumours of individual patients. Thus, it becomes of paramount importance to develop in silico tools that can effectively and rapidly predict its distribution on a given cell genome. New advanced computational tools (DeeperBind) can then be successfully employed to determine the function of Myc in a specific tumour, and may help to devise new directions and approaches to experiments first and personalized and more effective therapeutic treatments for a single patient later on. Results: The use of DeeperBind with DeepRAM on Colab platform (Google) can effectively predict the binding sites for the MYC factor with an accuracy above 0.96 AUC, when trained with multiple cell lines. The analysis of the filters in DeeperBind trained models shows, besides the consensus sequence CACGTG classically associated to the MYC factor, also the other consensus sequences G/C box or TGGGA, respectively bound by the SP1 and MIZ-1 transcription factors, which are known to mediate the MYC repressive response. Overall, our findings suggest a stronger synergy between the machine learning tools as DeeperBind and biological experiments, which may reduce the time consuming experiments by providing a direction to guide them.

Abstract B011: Network propagation of the kinase activity signatures of therapy-resistant tumors reveals novel druggable targets in BRAF(V600E)-mutated colorectal cancer

Abstract Despite combination therapies targeting BRAF and EGFR, metastatic colorectal cancer (mCRC) with a BRAF(V600E) mutation confers poor prognosis with early development of drug resistance in a majority of patients. Treatment with a current standard of care (encorafenib plus panitumumab) temporarily suppresses tumor growth, but fails to promote regression and leads to eventual disease progression. Consequently, there is a need to better characterize the compensatory resistance pathways that are activated in response to treatment. To identify these parallel resistance mechanisms and novel druggable targets, we analyzed the effects of combination treatment with encorafenib (a BRAF-inhibitor) and panitumumab (an anti-EGFR antibody) in BRAF(V600E) mCRC patient-derived xenograft (PDX) tumor samples via a high throughput kinase activity mapping (HT-KAM) platform and advanced computational tools. HT-KAM uses libraries of peptides as combinatorial sensors that identify and measure the activity of >160 kinases involved in oncogenic pathways (Coppé et al 2019). As these key kinases provide an informative yet filtered view of proteome-wide signaling effects, we developed a network-based workflow to further analyze our HT-KAM results. Network propagation methods were applied to integrate this data into ReactomeFI, a well-validated gene/protein interaction network. Significant genes from the network propagation results were clustered and enriched for pathway analysis using additional scripts, and results were visualized using Cytoscape. Based on HT-KAM results comparing the kinase signatures of PDX tumors treated with encorafenib + panitumumab versus vehicle, EGFR was confirmed to be downregulated. Moreover, several kinases previously identified as (re-)activated upon treatment and progression were significantly upregulated, including kinases in the RAF–MEK–ERK cascade as well as AKTs and downstream effectors. In addition, STK11, a tumor suppressor also known as LKB1, was significantly downregulated, while the activity of several kinases involved in cell surface signaling were upregulated, including PKA and PKCs. Network propagation of this data and enrichment of network subclusters resulted in a broad range of pathways implicated in persistent cell survival. Among top hits, signaling networks orchestrated by GPCRs, MTOR, WNT and beta-catenin were significantly enriched, suggesting that multiple interacting pathways coordinate the response and resistance to BRAF/EGFR joint inhibition. Our workflow implementing network propagation and enrichment analysis of HT-KAM data pinpoints essential pathways that contain druggable targets. We are in the process of validating these dependencies in PDX models and translating these mechanisms into novel combinatorial targeted therapy interventions designed to overcome drug resistance in BRAF(V600E) mCRC. Citation Format: Yeonjoo Hwang, Mehdi Bouhaddou, Christina Moelders, Denise P. Muñoz, Chloe E. Atreya, Jean-Philippe Coppé. Network propagation of the kinase activity signatures of therapy-resistant tumors reveals novel druggable targets in BRAF(V600E)-mutated colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr B011.

Plenty of Planning, Scanty Guidance: Evaluating the Implementation Degree of the General Master Plan in the City of Tampere, Finland

Digitalizing cities has become increasingly complex and difficult to control despite advanced computational tools. The comprehension of emergent, dynamic agent–pattern interaction is limited. Studies show that the implementation of large-scale plans occasionally fails to meet expectations due to uncertainty in urban actor processes and institutions. Theories of complexity and resilience reflecting urban unpredictability and non-equilibrium enable understanding and planning methods for guiding actors. We explored empirically via close reading and spatial analyses the ability of the traditional master planning instrument to steer the actor allocation in Tampere, Finland. The plan apparently failed to appropriately guide the actors, who formed self-organizing patterns colliding with the planning aims enabled by deviations and lower-level planning instruments. The planning mode was either enabling or reactive. We recognized three types of self-organization: single-point attraction, emergent type, and location-based self-organization. Self-organization was the major force behind urban transition. Only certain large-scale projects in the city center somewhat complied with the planning aims, however through negotiations. We proposed planning solutions encouraging and guiding self-organizing patterns by recognizing complexity in strategies, and with loose plans, constant monitoring, correcting, and experimenting in planning. The results participate in building more general knowledge of planning considering self-organizing urban dynamics and provide applications for urban planning.

A Tour of Soft Atomic Motions: Chemical Pressure Quadrupoles Across Transition Metal–Main Group 1:2 Structure Types

The rational design of materials requires an understanding of the relationships between structure and properties. In some fields, advances in computational tools and modeling have mapped out many of these connections. In the realm of intermetallics, however, principles for such relationships are still needed. Recently, we illustrated how the DFT–Chemical Pressure (CP) analysis can link geometrical motifs within crystalline arrangements to the phonon band structures of intermetallics and thus the physical properties related to vibrations and atomic motion. The key indicators here are quadrupolar CP features─distributions in which positive and negative interatomic pressures are oriented perpendicular to one another. These quadrupoles then create low energy paths of motion for the atoms that can be linked to various structural phenomena, such as anisotropic vibrations and incommensurate modulations. In this article, we survey the presence of CP quadrupoles in a collection of TE2 structures (T = transition metal and E = main group element) and the roles they play in the behavior observed for these compounds. We begin by developing a metric for CP quadrupolar character, which can be calculated from the output of CP analysis. After compiling these metrics for 17 TE2 structure types, we delve deeper into four systems detected as exhibiting atomic sites with quadrupolar character: (1) the Co–Sn binary system, where the quadrupoles on the Co atoms in CuAl2-type CoSn2 direct the features of its superstructure in CoSn3; (2) arsenopyrite-type CoSb2, in which the highly quadrupolar CP distributions on the Sb atoms correlate with the motions followed during the phase transition to its high-temperature marcasite-type form; (3) the CrSi2 structure, in which the expansion of the lattice is predicted to open a helical path of potential soft motion analogous to that underlying the Nowotny Chimney Ladder series; and (4) the CdAs2 type, whose CP features anticipate the ability to incorporate Li atoms as guests. Together, these case studies highlight the potential for CP quadrupoles to link local atomic arrangements with a variety of emergent properties in intermetallic phases.

A stepwise docking and molecular dynamics approach for enzymatic biolubricant production using Lipase Eversa® Transform as a biocatalyst

Biolubricants have gained a notable market share in the last decade due to the increased interest in replacing petroleum-based lubricants in various applications. Biolubricants are produced by using renewable raw materials such as vegetable oils, and enzymes such as lipases . The latter can be used as catalysts for biolubricant synthesis, which renders these processes environmentally friendly. Among the several innovations enabled by enzymatic engineering tools, the lipase Eversa® Transform 2.0 stands out as a genetically-modified and low-cost enzymatic formulation that has shown high activity in the production of biodiesel , fatty acids, and biolubricants. In addition to enzymatic engineering, advanced computational tools, such as docking and molecular dynamics, can also be powerful allies in the development of competitive products that impart low negative impact in the environment. These simulation tools enable the elucidation and understanding of reaction mechanisms, cut reagent costs, and inform choices regarding materials and conditions to be used in related industrial processes. Thus being, this work proposed the analysis and modeling of the synthesis reactions of biolubricants derived from oleic acid via the use of n-octanol and isoamyl alcohol, with lipase Eversa® Transform 2.0. Molecular dockings showed that the predominant interactions between the ligands and the amino acid residues that make up the active site of the enzyme and its surroundings were of Van der Waals and hydrophobic nature. Through simulations via molecular dynamics, it was found that these interactions were responsible for the stability of the lipase-ligand complex, given the low number of hydrogen bonds formed during the simulations. Furthermore, the low variation in the position of the lipase-ligand complexes comparatively to their initial conformations over the production stages attested that the chosen docking poses were adequate to describe the ligand conformations in the region of the active sites of the enzyme. These results also provide a structural view of the interactions between lipase Eversa® and the fatty esters used as lubricants, and confirm the potential in the use of these alcohols in this process. Finally, an in vitro validation showed improved results of esterification activity of the lipase with isoamyl alcohol. This hinted that the substrate that was in closer conformation and that showed shorter and more stable hydrogen bonds with the active sites of the enzyme demonstrated better use potential for the biolubricant production system that has been proposed. • The synthesis of biolubricant was evaluated through docking and molecular dynamics studies. • Most interactions of the lipase-ligand complex are Van der Waals or hydrophobic. • The interactions provided the stability of the lipase-ligand complex. • Docking positions exposed the conformations of the ligands in the lipase active site.

Crop Proteomics under Abiotic Stress: From Data to Insights.

Food security is a major challenge in the present world due to erratic weather and climatic changes. Environmental stress negatively affects plant growth and development which leads to reduced crop yields. Technological advancements have caused remarkable improvements in crop-breeding programs. Proteins have an indispensable role in developing stress resilience and tolerance in crops. Genomic and biotechnological advancements have made the process of crop improvement more accurate and targeted. Proteomic studies provide the information required for such targeted approaches. The crosstalk among cellular components is being analyzed by subcellular proteomics. Additionally, the functional diversity of proteins is being unraveled by post-translational modifications during abiotic stress. The exploration of precise cellular responses and the networking among different cellular organelles help in the prediction of signaling pathways and protein-protein interactions. High-throughput mass-spectrometry-based protein studies are now possible due to incremental advancements in mass-spectrometry techniques, sample protocols, and bioinformatic tools as well as the increasing availability of plant genome sequence information for multiple species. In this review, the key role of proteomic analysis in identifying the abiotic-stress-responsive mechanisms in various crops was summarized. The development and availability of advanced computational tools were discussed in detail. The highly variable protein responses among different crops have provided a wide avenue for molecular-marker-assisted genetic buildup studies to develop smart, high-yielding, and stress-tolerant varieties to cope with food-security challenges.

Conventional and Omics Approaches for Understanding the Abiotic Stress Response in Cereal Crops-An Updated Overview.

Cereals have evolved various tolerance mechanisms to cope with abiotic stress. Understanding the abiotic stress response mechanism of cereal crops at the molecular level offers a path to high-yielding and stress-tolerant cultivars to sustain food and nutritional security. In this regard, enormous progress has been made in the omics field in the areas of genomics, transcriptomics, and proteomics. Omics approaches generate a massive amount of data, and adequate advancements in computational tools have been achieved for effective analysis. The combination of integrated omics and bioinformatics approaches has been recognized as vital to generating insights into genome-wide stress-regulation mechanisms. In this review, we have described the self-driven drought, heat, and salt stress-responsive mechanisms that are highlighted by the integration of stress-manipulating components, including transcription factors, co-expressed genes, proteins, etc. This review also provides a comprehensive catalog of available online omics resources for cereal crops and their effective utilization. Thus, the details provided in the review will enable us to choose the appropriate tools and techniques to reduce the negative impacts and limit the failures in the intensive crop improvement study.

Effect of substrate orientation and anisotropic strength on corrosion pits

The complexities of pitting corrosion and its associated pit morphologies are critical for predicting and preventing pit-related failure in materials. With the advances in computational tools, modeling of pitting corrosion has lately gained momentum. We recently developed a phase field model for 3-D pit morphology, and in the current work, the model is utilized to gain insight into the role of substrate orientation and anisotropic strength on crystallographic pits. The influence of these parameters on pit morphology and corrosion metrics such as pit volume, current, pit area, current density, and pit depth are studied. The crystallographic pit morphology changes with substrate orientation but not significantly with anisotropic strength. The anisotropic strength strongly influences the corrosion metrics whereas most corrosion metrics remain unaffected by substrate orientation.

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.