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Tumor homing peptides (THPs) have a distinctive capacity to specifically attach to tumor cells, providing a promising approach for targeted cancer treatment and detection. Although THPs have the potential for significant impact, their detection by conventional methods is both time-consuming and expensive. To tackle this issue, we provide LLM4THP, an innovative computational approach that utilizes large language models (LLMs) to quickly and effectively detect THPs. LLM4THP utilizes two protein LLMs, ESM2 and Prot_T5_XL_UniRef50, to encode peptide sequences. This allows for the capture of complex patterns and relationships within the peptide data. In addition, we utilize inherent sequence characteristics such as Amino Acid Composition (AAC), Pseudo Amino Acid Composition (PAAC), Amphiphilic Pseudo Amino Acid Composition (APAAC), and Composition, Transition, and Distribution (CTD) to improve the representation of peptides. The RDKitDescriptors feature representation approach transforms peptide sequences into molecular objects and computes chemical characteristics, resulting in enhanced THP identification. The LLM4THP ensemble strategy incorporates various features into a two-layer learning architecture. The first layer consists of LightGBM, XGBoost, Random Forest, and Extremely Randomized Trees, which generate a set of meta results. The second layer utilizes Logistic Regression to further refine the identification of sequences as either THP or non-THP. LLM4THP exhibits exceptional performance compared to the most advanced methods, showcasing enhancements in accuracy, Matthew's correlation coefficient, F1 score, area under the curve, and average precision. The source code and dataset can be accessed at the following URL: https://github.com/abcair/LLM4THP. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has underscored the critical need for precise diagnostic methods to distinguish between similar respiratory infections, such as COVID-19 and Mycoplasma pneumoniae (MP). Identifying key biomarkers and utilizing machine learning techniques, such as random forest analysis, can significantly improve diagnostic accuracy. We conducted a retrospective analysis of clinical and laboratory data from 214 patients with acute respiratory infections, collected between October 2022 and October 2023 at the Second Hospital of Nanping. The study population was categorized into three groups: COVID-19 positive (n = 52), MP positive (n = 140), and co-infected (n = 22). Key biomarkers, including C-reactive protein (CRP), procalcitonin (PCT), interleukin- 6 (IL-6), and white blood cell (WBC) counts, were evaluated. Correlation analyses were conducted to assess relationships between biomarkers within each group. The random forest analysis was applied to evaluate the discriminative power of these biomarkers. The random forest model demonstrated high classification performance, with area under the ROC curve (AUC) scores of 0.86 (95% CI: 0.70–0.97) for COVID-19, 0.79 (95% CI: 0.64–0.92) for MP, 0.69 (95% CI: 0.50–0.87) for co-infections, and 0.90 (95% CI: 0.83–0.95) for the micro-average ROC. Additionally, the precision-recall curve for the random forest classifier showed a micro-average AUC of 0.80 (95% CI: 0.69–0.91). Confusion matrices highlighted the model’s accuracy (0.77) and biomarker relationships. The SHAP feature importance analysis indicated that age (0.27), CRP (0.25), IL6 (0.14), and PCT (0.14) were the most significant predictors. The integration of computational methods, particularly random forest analysis, in evaluating clinical and biomarker data presents a promising approach for enhancing diagnostic processes for infectious diseases. Our findings support the use of specific biomarkers in differentiating between COVID-19 and MP, potentially leading to more targeted and effective diagnostic strategies. This study underscores the potential of machine learning techniques in improving disease classification in the era of precision medicine. [ABSTRACT FROM AUTHOR]

Computation of the solution of the nonlinear Caputo fractional differential equation is essential for using q , which is the order of the derivative, as a parameter. The value of q can be determined to enhance the mathematical model in question using the data. The numerical methods available in the literature provide only the local existence of the solution. However, the interval of existence is known and guaranteed by the natural upper and lower solutions of the nonlinear differential equations. In this work, we develop monotone iterates, together with lower and upper solutions that converge uniformly, monotonically, and quadratically to the unique solution of the Caputo nonlinear fractional differential equation over its entire interval of existence. The nonlinear function is assumed to be the sum of convex and concave functions. The method is referred to as the generalized quasilinearization method. We provide a Caputo fractional logistic equation as an example whose interval of existence is [ 0 , ∞). [ABSTRACT FROM AUTHOR]

Drug combination therapy, involving the use of two or more drugs, has been widely employed to treat complex diseases such as cancer. It enhances therapeutic efficacy, reduces drug resistance, and minimizes side effects. However, traditional methods to identify effective drug combinations are time-consuming, costly, and less efficient than computational methods. Therefore, developing computational approaches to predict drug combinations has become increasingly important.In this paper, we developed the Random Walk with Restart for Drug Combination (RWRDC) model to predict effective drug combinations for cancer therapy. The RWRDC model offers a quantitative mathematical method for predicting the potential effective drug combinations. Cross-validation results indicate that the RWRDC model outperforms other predictive models, particularly in breast, colorectal, and lung cancer predictions across various performance metrics. We have theoretically proven the convergence of its algorithm and provided an explanation for the algorithm's rationality. A targeted case study on breast cancer further highlights the capability of RWRDC to identify effective drug combinations. These findings highlight our model as a novel and effective tool for discovering potential effective drug combinations, offering new possibilities in therapy. Additionally, the graph-based framework of RWRDC holds potential for predicting drug combinations in other complex diseases, expanding its utility in the medical field.

Computation of the solution of the nonlinear Caputo fractional differential equation is essential for using q, which is the order of the derivative, as a parameter. The value of q can be determined to enhance the mathematical model in question using the data. The numerical methods available in the literature provide only the local existence of the solution. However, the interval of existence is known and guaranteed by the natural upper and lower solutions of the nonlinear differential equations. In this work, we develop monotone iterates, together with lower and upper solutions that converge uniformly, monotonically, and quadratically to the unique solution of the Caputo nonlinear fractional differential equation over its entire interval of existence. The nonlinear function is assumed to be the sum of convex and concave functions. The method is referred to as the generalized quasilinearization method. We provide a Caputo fractional logistic equation as an example whose interval of existence is [0,∞).

Lithium–sulfur (Li–S) batteries, as one of the most promising “post‐Li‐ion” energy storage devices, encounter several intrinsic challenges: polysulfide dissolution and shuttle effect, poor sulfur utilization, lithiation‐induced sulfur expansion, and lithium dendritic growth. These challenges must be resolved, and the associated mechanisms must be completely understood before the practical applications of Li–S batteries. Despite significant progress in enhancing battery capacities, experimental studies on the working mechanisms of Li–S batteries remain challenging. Alternatively, computational methods are proven useful for understanding Li–S electrochemistry and designing high‐performance Li–S batteries. This review presents recent advances in computational methods (density functional theory, molecular dynamics simulations, and finite element analysis) for Li–S batteries, compares their advantages, and summarizes their favorable applications in addressing the challenges of Li–S batteries. Computational methods should find more applications in the development of Li–S batteries. [ABSTRACT FROM AUTHOR]

Due to importance of cell voltage and caustic current efficiency (CCE) in chlor-alkali industry, the necessity of accurate approach for prediction these parameters has become evident. In the current work, an extreme learning machine (ELM) approach is used to this end. Determination of the statistical qualities including R2 and different types of error reveals the fact that ELM method is suitable tool for calculation of CCE and cell voltage. The determined R2 values for CCE and cell voltage are equal to 1. Furthermore, RMSE values are 0.00002 and 1.3 × 10−6 for cell voltage and CCE, respectively. On the other hand, different graphical methods confirmed this acclaim. Moreover a sensitivity analysis is used to show effect of brine concentration, current density, operating temperature, electrolyte velocity, run time and pH on cell voltage and CCE. This analysis concluded to the fact that brine concentration and current density have the most effects on CCE and Cell voltage, respectively. [ABSTRACT FROM AUTHOR]

Although an increasing amount of aggressive and polarized tweets about climate change are being observed, little is known about how they spread on Twitter. This study focuses on how different types of network gatekeepers use aggressive styles and how the styles affect their propagation. The current study employed a computational method and identified 951 influential accounts from 7.25 million tweets about climate change in 2019 and 2020. We analyzed their use of aggression and politicized cues, and the relationship with the volume of retweets. Results showed that even though aggressive tweets were a small portion of the overall tweets about climate change, aggressive tweets were more likely to be politicized and retweeted. Specifically, aggressive tweets from politicians received the most retweets and news media amplified the aggression. The findings of this study build upon the current knowledge of the use of aggression online and provide practical implications for environmental communicators. [ABSTRACT FROM AUTHOR]

A novel weak-form plane frame quadrature element (WPFQE) is developed. A complex structure can be divided into several non-homogeneous (or homogeneous) elements with large size. The Chebyshev–Lobatto differential quadrature dealing with differentiation and the Gauss–Lobatto quadrature dealing with integration have the same integration points including the ends of each element. Based on the energy variational principle, the diagonal mass matrix and positive definite real symmetric stiffness matrix for the element can be obtained. The unknown quantities at the internal quadrature points of the element can be represented by those at the endpoints of the element based on the principle of static equal effect anterior to the development of global matrices, which significantly reduces the sizes of modified element matrices while maintaining sufficient accuracy, thereby obtaining the small-sized global matrices. Assembly of elements can be performed efficiently just as that in the finite element (FE) method. In the case study, the static and dynamic performances of a three-span high-pier rigid-frame bridge with variable cross-section are studied by applying the proposed method. The results indicate that the orders of structural matrices and calculation time obtained by the WPFQE method are much smaller than those of the FE method. [ABSTRACT FROM AUTHOR]

In this study, we computationally extract the bright, singular and dark optical solitons for the cubic–quartic nonlinear Schrödinger equation by using a superior version of Adomian's approach, the improved Adomian decomposition method. Indeed, this reveals approximate closed-form solutions of various forms of Schrödinger equations, after splitting the complex-valued solution as a linear combination of two real-valued functions. Moreover, for comparison purposes, some realistic cases of fixed parameter values have been considered for the numerical examination. Notably, the extraction of soliton numerically without the aid of an exact solution stands out to be a remarkable take-home of this study. In addition, some plots and the error tables are reported in the end, where the proposed approach has been shown to enjoy ideal computational accuracy. [ABSTRACT FROM AUTHOR]

The experimentally obtained material microstructure can be used to calculate a material's properties and identify microstructure–property relationships. The key procedure to enable this is to interpret the observed microstructure accurately. This work reports on a newly developed computational method to serve such a purpose. The method is based on cubic spline interpolation and a simple search algorithm. Parameterisation was accomplished via the comparison between its preliminary statistical results and the information in a phase diagram. The method was applied to analyse the quenched microstructure of multicomponent and multiphase metallic-oxide materials. The importance of adequate parameterisation is demonstrated. The results provide a good explanation for the experimentally measured electric conductance behaviour. Further application of the method to the deformation of materials is discussed. The algorithms are directly available for the analysis of the three-dimensional microstructure of materials. [ABSTRACT FROM AUTHOR]

Finding the optimum conductor's pattern for unequally spaced grounding grids can be achieved by using empirical formulas or optimization techniques. Empirical formulas give an approximate solution depending on human experience and never cover the entire search space. On the other hand, the optimization techniques generally require a significant effort and time. To overcome the previous issues, this paper presents a closed-form solution based on optimized results. The main motivation behind this formula is the comprehensive study carried out by the authors to find optimum location of grounding grid conductors over different areas. In that work, particle swarm optimization technique (PSO) has been adapted with combined integration matrix method (CIMM) to facilitate the study. In this paper, the results obtained previously were summarized in the proposed formula. The proposed formula gives the optimum design of unequally grounding grids by specifying the conductor's locations as percentage of the side length for square and rectangular shapes. As long as the side length and the number of conductors are given, the location of each conductor as a percentage of the side length can be obtained. The effect of adding driven rods and the stratified soil conditions on the results of the optimized design were investigated. The validity of the proposed closed-form solution was verified experimentally. [ABSTRACT FROM AUTHOR]

• A random forest model is proposed for cell-type-specific cohesin-mediated chromatin loops prediction. • Chromatin state is responsible for cell-type-specificity of chromatin loops. • Chromatin state combines with the binding of key factors can predict cell-type-specific loops accurately. Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation. [ABSTRACT FROM AUTHOR]

Abstract Microbial biochemistry is central to the pathophysiology of inflammatory bowel diseases (IBD). Improved knowledge of microbial metabolites and their immunomodulatory roles is thus necessary for diagnosis and management. Here, we systematically analyzed the chemical, ecological, and epidemiological properties of ~82k metabolic features in 546 Integrative Human Microbiome Project (iHMP/HMP2) metabolomes, using a newly developed methodology for bioactive compound prioritization from microbial communities. This suggested >1000 metabolic features as potentially bioactive in IBD and associated ~43% of prevalent, unannotated features with at least one well-characterized metabolite, thereby providing initial information for further characterization of a significant portion of the fecal metabolome. Prioritized features included known IBD-linked chemical families such as bile acids and short-chain fatty acids, and less-explored bilirubin, polyamine, and vitamin derivatives, and other microbial products. One of these, nicotinamide riboside, reduced colitis scores in DSS-treated mice. The method, MACARRoN, is generalizable with the potential to improve microbial community characterization and provide therapeutic candidates.

In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing. [ABSTRACT FROM AUTHOR]

Microbial biochemistry is central to the pathophysiology of inflammatory bowel diseases (IBD). Improved knowledge of microbial metabolites and their immunomodulatory roles is thus necessary for diagnosis and management. Here, we systematically analyzed the chemical, ecological, and epidemiological properties of ~82k metabolic features in 546 Integrative Human Microbiome Project (iHMP/HMP2) metabolomes, using a newly developed methodology for bioactive compound prioritization from microbial communities. This suggested >1000 metabolic features as potentially bioactive in IBD and associated ~43% of prevalent, unannotated features with at least one well-characterized metabolite, thereby providing initial information for further characterization of a significant portion of the fecal metabolome. Prioritized features included known IBD-linked chemical families such as bile acids and short-chain fatty acids, and less-explored bilirubin, polyamine, and vitamin derivatives, and other microbial products. One of these, nicotinamide riboside, reduced colitis scores in DSS-treated mice. The method, MACARRoN, is generalizable with the potential to improve microbial community characterization and provide therapeutic candidates. Synopsis: MACARRoN combines ecological and epidemiological metrics to prioritize metabolic features with potential bioactivity in a phenotype of interest. Its application to inflammatory bowel disease (IBD) prioritizes both previously IBD-linked and less-explored microbiome-associated metabolites. Metabolic features that covary among conditions tend to be closer in mass and chemically homogeneous, pointing to underlying biochemical relationships. MACARRoN integrates metabolic feature prevalence, covariance, abundance with respect to an identified metabolite, and extent of perturbation to prioritize actionable, phenotype-associated features from untargeted metabolomics data. MACARRoN prioritized ~1,000 chemically diverse, stool-derived features as IBD-associated, including known IBD biomarkers and several unannotated features that covary with them. Newly IBD-linked metabolic features included microbial polyamines, nicotinamide riboside (shown here to reduce colitis in DSS-treated mice), and novel putatively microbially-derived bilirubin compounds. MACARRoN combines ecological and epidemiological metrics to prioritize metabolic features with potential bioactivity in a phenotype of interest. Its application to inflammatory bowel disease (IBD) prioritizes both previously IBD-linked and less-explored microbiome-associated metabolites. [ABSTRACT FROM AUTHOR]

This study evaluates the corrosion inhibition capabilities of two novel hydrazone derivatives, (E)-2-(5-methoxy-2-methyl-1H-indol-3-yl)-N′-(4-methylbenzylidene)acetohydrazide (MeHDZ) and (E)-N′-benzylidene-2-(5-methoxy-2-methyl-1H-indol-3-yl)acetohydrazide (HHDZ), on carbon steel in a 15 wt.% HCl solution. A comprehensive suite of analytical techniques, including gravimetric analysis, potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), and scanning electron microscopy (SEM), demonstrates their significant inhibition efficiency. At an optimal concentration of 5 × 10−3 mol/L, MeHDZ and HHDZ achieve remarkable inhibition efficiencies of 98% and 94%, respectively. EIS measurements reveal a dramatic reduction in effective double-layer capacitance (from 236.2 to 52.8 and 75.3 µF/cm2), strongly suggesting inhibitor adsorption on the steel surface. This effect is further corroborated by an increase in polarization resistance and a significant decrease in corrosion current density at optimal concentrations. Moreover, these inhibitors demonstrate sustained corrosion mitigation over extended exposure durations and maintain effectiveness even under elevated temperatures, highlighting their potential for diverse operational conditions. The adsorption process of these inhibitors aligns well with the Langmuir adsorption isotherm, implying physicochemical interactions at the carbon steel surface. Density functional tight-binding (DFTB) calculations and molecular dynamics simulations provide insights into the inhibitor-surface interaction mechanism, further elucidating the potential of these hydrazone derivatives as highly effective corrosion inhibitors in acidic environments. [ABSTRACT FROM AUTHOR]

Due to inherent operating constraints, wireless sensor networks (WSNs) need help assuring network security. This problem is caused by worms entering the networks, which can spread uncontrollably to nearby nodes from a single node infected with computer viruses, worms, trojans, and other malicious software, which can compromise the network’s integrity and functionality. This article discusses a fractional SE1E2IR{\mathsf{S}}{{\mathsf{E}}}_{1}{{\mathsf{E}}}_{2}{\mathsf{I}}{\mathsf{R}} model to explain worm propagation in WSNs. For capturing the dynamics of the virus, we use the Mittag–Leffler kernel and the Atangana–Baleanu (AB) Caputo operator. Besides other characteristics of the problem, the properties of superposition and Lipschitzness of the AB Caputo derivatives are studied. Standard numerical methods were employed to approximate the Atangana–Baleanu–Caputto fractional derivative, and a detailed analysis is presented. To illustrate our analytical conclusions, we ran numerical simulations.

Context: The search for highly efficient adsorbent materials remains a significant requirement in the field of adsorption for wastewater treatment. Computational study can highly contribute to the identification of efficient material. In this work, we propose a computational approach to study the adsorption of four cationic basic dyes, basic blue 26 (BB26), basic green 1 (BG1), basic yellow 2 (BY2), and basic red 1 (BR1), onto two models of graphene oxide as adsorbents. The main objectives of this study are the assessment of the adsorption capacity of the graphene oxide towards basic dyes and the evaluation of the environmental and temperature effects on the adsorption capacity. Quantum theory of atoms in molecules (QTAIM) analysis has been used to understand the interactions between the dyes and graphene oxides. In addition, adsorption free energies of the dyes onto graphene oxides are calculated in gas and solvent phases for temperatures varying from 200 to 400 K. As a result, the adsorption free energy varies linearly depending on the temperature, highlighting the importance of temperature effects in the adsorption processes. Furthermore, the results indicate that the environment (through the solvation) considerably affects the calculated adsorption free energies. Overall, the results show that the two models of graphene oxide used in this work are efficient for removing dyes from wastewater. Methods: We have optimized the complexes formed by the interaction of dyes with graphene oxides at the PW6B95-D3/def2-SVP level of theory. The SMD solvation model realizes the implicit solvation, and water is used as the solvent. Calculations are performed using the Gaussian 16 suite of program. QTAIM analysis is performed using the AIMAll program. Gibbs free energies as function of temperature are calculated using the TEMPO program. [ABSTRACT FROM AUTHOR]

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

The experimentally obtained material microstructure can be used to calculate a material’s properties and identify microstructure–property relationships. The key procedure to enable this is to interpret the observed microstructure accurately. This work reports on a newly developed computational method to serve such a purpose. The method is based on cubic spline interpolation and a simple search algorithm. Parameterisation was accomplished via the comparison between its preliminary statistical results and the information in a phase diagram. The method was applied to analyse the quenched microstructure of multicomponent and multiphase metallic-oxide materials. The importance of adequate parameterisation is demonstrated. The results provide a good explanation for the experimentally measured electric conductance behaviour. Further application of the method to the deformation of materials is discussed. The algorithms are directly available for the analysis of the three-dimensional microstructure of materials.

In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing.

Drug-induced Behavioral Signature Analysis (DBSA), is a machine learning (ML) method for in silico screening of compounds, inspired by analytical methods quantifying gene enrichment in genomic analyses. When applied to behavioral data it can identify drugs that can potentially reverse in vivo behavioral symptoms in animal models of human disease and suggest new hypotheses for drug discovery and repurposing. We present a proof-of-concept study aiming to assess Drug-induced Behavioral Signature Analysis (DBSA) as a systematic approach for drug discovery for rare disorders. We applied Drug-induced Behavioral Signature Analysis to high-content behavioral data obtained with SmartCube®, an automated in vivo phenotyping platform. The therapeutic potential of several dozen approved drugs was assessed for phenotypic reversal of the behavioral profile of a Huntington's Disease (HD) murine model, the Q175 heterozygous knock-in mice. The in silico Drug-induced Behavioral Signature Analysis predictions were enriched for drugs known to be effective in the symptomatic treatment of Huntington's Disease, including bupropion, modafinil, methylphenidate, and several SSRIs, as well as the atypical antidepressant tianeptine. To validate the method, we tested acute and chronic effects of tianeptine (20 mg/kg, i. p.) in vivo, using Q175 mice and wild type controls. In both experiments, tianeptine significantly rescued the behavioral phenotype assessed with the SmartCube® platform. Our target-agnostic method thus showed promise for identification of symptomatic relief treatments for rare disorders, providing an alternative method for hypothesis generation and drug discovery for disorders with huge disease burden and unmet medical needs. [ABSTRACT FROM AUTHOR]

Simple Summary: Histones are a group of proteins that are essential for chromatin function. Histone coding genes have been found to be mutated in a number of cancers but how these mutations arise and whether they contribute to cancer biology remains largely unknown. Answering these questions is difficult because histone proteins are encoded by large families of genes that are thought to be redundant. Here, we overcome these limitations using a large dataset of whole exome sequencing data from cancer patients representing 68 different cancer types. We find that specific cancer types exhibit disproportionate susceptibilities to accruing mutations in histone genes versus other genes. We delineate factors influencing the probability of mutations accumulation in histone genes and detect new histone gene drivers. Histones play a critical role in chromatin function but are susceptible to mutagenesis. In fact, numerous mutations have been observed in several cancer types, and a few of them have been associated with carcinogenesis. Histones are peculiar, as they are encoded by a large number of genes, and the majority of them are clustered in three regions of the human genome. In addition, their replication and expression are tightly regulated in a cell. Understanding the etiology of cancer mutations in histone genes is impeded by their functional and sequence redundancy, their unusual genomic organization, and the necessity to be rapidly produced during cell division. Here, we collected a large data set of histone gene mutations in cancer and used it to investigate their distribution over 96 human histone genes and 68 different cancer types. This analysis allowed us to delineate the factors influencing the probability of mutation accumulation in histone genes and to detect new histone gene drivers. Although no significant difference in observed mutation rates between different histone types was detected for the majority of cancer types, several cancers demonstrated an excess or depletion of mutations in histone genes. As a consequence, we identified seven new histone genes as potential cancer-specific drivers. Interestingly, mutations were found to be distributed unevenly in several histone genes encoding the same protein, pointing to different factors at play, which are specific to histone function and genomic organization. Our study also elucidated mutational processes operating in genomic regions harboring histone genes, highlighting POLE as a factor of potential interest. [ABSTRACT FROM AUTHOR]

Social media, in general, and Facebook in particular, have been clearly identified as important platforms for the dissemination of mis- and disinformation and related problematic content. However, the patterns and processes of such dissemination are still not sufficiently understood. We detail a novel computational methodology that focusses on the identification of high-profile vectors of "fake news" and other problematic information in public Facebook spaces. The method enables examination of networks of content sharing that emerge between public pages and groups, and external sources, and the study of longitudinal dynamics of these networks as interests and allegiances shift and new developments (such as the COVID-19 pandemic or the US presidential elections) drive the emergence or decline of dominant themes. Through a case study of content captured between 2016 and 2021, we demonstrate how this methodology allows the development of a new and more comprehensive picture of the overall impact of "fake news," in all its forms, on contemporary societies. [ABSTRACT FROM AUTHOR]

A powerful feature of single-cell genomics is the possibility of identifying cell types from their molecular profiles. In particular, identifying novel rare cell types and their marker genes is a key potential of single-cell RNA sequencing. Standard clustering approaches perform well in identifying relatively abundant cell types, but tend to miss rarer cell types. Here, we have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a cluster-independent computational tool designed to select genes that are likely to be markers of rare cell types. Genes selected by CIARA are subsequently integrated with common clustering algorithms to single out groups of rare cell types. CIARA outperforms existing methods for rare cell type detection, and we use it to find previously uncharacterized rare populations of cells in a human gastrula and among mouse embryonic stem cells treated with retinoic acid. Moreover, CIARA can be applied more generally to any type of single-cell omic data, thus allowing the identification of rare cells across multiple data modalities. We provide implementations of CIARA in user-friendly packages available in R and Python. [ABSTRACT FROM AUTHOR]

Using two-dimensional cylindrically symmetric physical and mathematical model and an algorithm, a numerical investigation of the problem of irradiating a volumetric aluminum target with a single femtosecond laser pulse is carried out. The problem has a number of fundamental and practical applications related to the hardening effect of residual plastic deformations after the passage of a laser-induced shock wave, in particular, laser shock hardening technology, also known in the literature as laser forging, laser riveting, or laser peening. The axial symmetry of laser beam permits one to reduce the dimension of the problem from three to two and save considerable computational resources. Semiempirical equation of state of aluminum in the Mie–Grüneisen form is used with the adjustment of parameters according to the cold curve of the metal and the data of shock-wave experiments. The law of shock wave propagation and attenuation is investigated, and the stages of (1) single, (2) transient, and (3) hemispherical shock wave propagation are identified. The size and shape of the area on which the strengthening effect can be carried out by a single femtosecond laser pulse are described. [ABSTRACT FROM AUTHOR]

Tin oxide (SnO2) thin films was prepared by dip-coating technique at various bath temperatures (313, 333, 353 and 373 K) and annealed at 673 K in this study. And the obtained results were studied and correlated with the computational method. Scanning electron microscopy (SEM) investigation demonstrated that the prepared samples are spherical with agglomeration. The elemental analysis (EDAX) confirms the presence of Sn and O. Further, the SnO2 thin films microstructures are simulated, their thermodynamic and surface properties have been calculated. Micro-Raman spectra were recorded for the prepared samples. Micro-Raman results exhibit the first-order Raman mode E1gsub> (475 cm−1) indicating that the grown SnO2 belongs to the rutile structure. In addition, the envelope method used for studying optical characteristics of the thin films from the transmittance spectra. The semiconducting nature of the films has been noticed from linear I-V characteristics. Furthermore, the electrical conductivity studies suggest that the highest conductivity samples acquire the lowest activation energy and their values are also in the semiconducting range.

A computational method was developed for the prediction of handle value of the bed linen fabrics by using step-wise block regression method. The objective evaluation of fabric hand would contribute in engineering and developing the bed linen fabrics that offers maximum comfort while sleeping. The subjective evaluation of fabric hand restricts the engineering of the high-quality fabrics and the scientific understanding of the fabric properties. The expert’s opinion selected the four primary hand properties that are soft feeling, smoothness, fullness, and stiffness influencing the handle of the bed linen fabrics. Primary and total hand equations were developed using the 16 basic mechanical properties. The experimental total hand value was also calculated using existing hand equation for futon’s cloth and the statistical approach was followed to investigate the correlation between subjective, experimental, and computational total hand values. An excellent correlation of 0.889 was found between the subjective and computational hand values. The research concludes that the total hand value of the bed linen fabrics can be predicted with tolerable accuracy level.

The purpose of this research was to use develop a sentiment model using machine learning algorithms for discerning public response about the misinformation correction practices of Muslim clerics on YouTube.

This paper presents a computational method for the rapid evaluation of the design of a disk-type electrical machine with permanent magnets, which reduces the time of product development. [ABSTRACT FROM AUTHOR]

This paper focuses on solving a class of nonlinear optimal control problems by constructing novel hat functions based on fourth-order polynomials, namely, fourth-degree hat functions (FDHFs). The FDHFs are used in this method to approximate the state equations and cost function. In fact, the FDHFs enable us to turn the optimal control problem under consideration into a nonlinear optimal control problem with unknown coefficients that is easy to solve by any numerical method. The main advantages of the proposed method are its simplicity, ease of application, low computational expense, and avoidance of numerical integration. Several examples have been discussed to demonstrate the efficacy and applicability of the suggested method.

This study evaluates the corrosion inhibition capabilities of two novel hydrazone derivatives, (E)-2-(5-methoxy-2-methyl-1H-indol-3-yl)-N′-(4-methylbenzylidene)acetohydrazide (MeHDZ) and (E)-N′-benzylidene-2-(5-methoxy-2-methyl-1H-indol-3-yl)acetohydrazide (HHDZ), on carbon steel in a 15 wt.% HCl solution. A comprehensive suite of analytical techniques, including gravimetric analysis, potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), and scanning electron microscopy (SEM), demonstrates their significant inhibition efficiency. At an optimal concentration of 5 × 10−3 mol/L, MeHDZ and HHDZ achieve remarkable inhibition efficiencies of 98% and 94%, respectively. EIS measurements reveal a dramatic reduction in effective double-layer capacitance (from 236.2 to 52.8 and 75.3 µF/cm2), strongly suggesting inhibitor adsorption on the steel surface. This effect is further corroborated by an increase in polarization resistance and a significant decrease in corrosion current density at optimal concentrations. Moreover, these inhibitors demonstrate sustained corrosion mitigation over extended exposure durations and maintain effectiveness even under elevated temperatures, highlighting their potential for diverse operational conditions. The adsorption process of these inhibitors aligns well with the Langmuir adsorption isotherm, implying physicochemical interactions at the carbon steel surface. Density functional tight-binding (DFTB) calculations and molecular dynamics simulations provide insights into the inhibitor-surface interaction mechanism, further elucidating the potential of these hydrazone derivatives as highly effective corrosion inhibitors in acidic environments.

Uncovering conserved 3D protein–ligand binding patterns on the basis of functional groups (FGs) shared by a variety of small molecules can greatly expand our knowledge of protein–ligand interactions. Despite that conserved binding patterns for a few commonly used FGs have been reported in the literature, large-scale identification and evaluation of FG-based 3D binding motifs are still lacking. Here, we propose a computational method, Automatic FG-based Three-dimensional Motif Extractor (AFTME), for automatic mapping of 3D motifs to different FGs of a specific ligand. Applying our method to 233 naturally-occurring ligands, we define 481 FG-binding motifs that are highly conserved across different ligand-binding pockets. Systematic analysis further reveals four main classes of binding motifs corresponding to distinct sets of FGs. Combinations of FG-binding motifs facilitate the binding of proteins to a wide spectrum of ligands with various binding affinities. Finally, we show that our FG–motif map can be used to nominate FGs that potentially bind to specific drug targets, thus providing useful insights and guidance for rational design of small-molecule drugs.

Introduction: COVID-19 has spread out all around the world and seriously interrupted human activities. Being a newfound disease, not only many aspects of the disease are unknown, but also there is not an effective medication to cure the disease. Besides, designing a drug is a time-consuming process and needs large investment. Hence, drug repurposing techniques, employed to discover the hidden benefits of the existing drugs, maybe a useful option for treating COVID-19. Methods: The present study exploits the drug repositioning concepts and introduces some candidate drugs which may be effective in controlling COVID-19. The suggested method consists of three main steps. First, the required data such as the amino acid sequences of targets and drug-target interactions are extracted from the public databases. Second, the similarity score between the targets (protein/enzymes) and genome of SARS-COV-2 is computed using the proposed fuzzy logic-based method. Since the classical approaches yield outcomes which may not be useful for the real-world applications, the fuzzy technique can address the issue. Third, after ranking targets based on the obtained scores, the usefulness of drugs affecting them is examined for managing COVID-19. Results: The results indicate that antiviral medicines, designed for curing hepatitis C, may also cure COVID-19. According to the findings, ribavirin, simeprevir, danoprevir, and XTL-6865 may be helpful in controlling the disease. Conclusion: It can be concluded that the similarity-based drug repurposing techniques may be the most suitable option for managing emerging diseases such as COVID-19 and can be applied to a wide range of data. Also, fuzzy logic-based scoring methods can produce outcomes which are more consistent with the real-world biological applications than others.

It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures.

Predictive constitutive equations that connect easy-to-measure transport properties (e.g., viscosity and conductivity) with system performance variables (e.g., power consumption and efficiency) are needed to design advanced thermal and electrical systems. In this work, we explore the use of fluorescent particle-streak analysis to directly measure the local velocity field of a pressure-driven flow, introducing a new Python package (FSVPy) to perform the analysis. Fluorescent streak velocimetry combines high-speed imaging with highly fluorescent particles to produce images that contain fluorescent streaks, whose length and intensity can be related to the local flow velocity. By capturing images throughout the sample volume, the three-dimensional velocity field can be quantified and reconstructed. We demonstrate this technique by characterizing the channel flow profiles of several non-Newtonian fluids: micellar Cetylpyridinium Chloride solution, Carbopol 940, and Polyethylene Glycol. We then explore more complex flows, where significant acceleration is created due to microscale features encountered within the flow. We demonstrate the ability of FSVPy to process streaks of various shapes and use the variable intensity along the streak to extract position-specific velocity measurements from individual images. Thus, we demonstrate that FSVPy is a flexible tool that can be used to extract local velocimetry measurements from a wide variety of fluids and flow conditions. [ABSTRACT FROM AUTHOR]

The high-speed atomic force microscopy (HS-AFM) is a unique and prominent method to observe structural dynamics of biomolecules at single molecule level at near-physiological condition. To achieve high temporal resolution, the probe tip scans the stage at high speed which can cause the so-called parachuting artifact in the HSAFM images. Here, we develop a computational method to detect and remove the parachuting artifact in HS-AFM images using the two-way scanning data. To merge the two-way scanning images, we employed a method to infer the piezo hysteresis effect and to align the forward- and backward-scanning images. We then tested our method for HS-AFM videos of actin filaments, molecular chaperone, and duplex DNA. Together, our method can remove the parachuting artifact from the raw HS-AFM video containing two-way scanning data and make the processed video free from the parachuting artifact. The method is general and fast so that it can easily be applied to any HSAFM videos with two-way scanning data. [ABSTRACT FROM AUTHOR]

In this paper, we are interested in finding a numerical solution of a linear BVP for a Fredholm IDE with a degenerate kernel subjected to impulsive actions. By Dzumabaev's parametrization method the original problem is reduced to a multipoint BVP for the system of Fredholm IDEs with additional parameters. For fixed parameters, the special Cauchy problem for the system of FIDEs on subintervals is obtained and by using a solution to this problem, a system of algebraic equations in parameters is constructed. An algorithm for solving the BVP and its computational implementation is developed. In the algorithm, Cauchy problems for ODEs and the calculation of definite integrals are the main auxiliary problems. By using various numerical methods for solving these auxiliary problems, the proposed algorithm can be implemented in different ways. The program codes were written to solve the problem and all calculations are performed on the Matlab 2018 software platform. [ABSTRACT FROM AUTHOR]

Computational methods of determining the operating pressure (Pwf) and flowrate (qsc) of gas wells are particularly important in the petroleum industry because it saves time and serves as a platform for petroleum engineers to perform complex sensitivity analysis on a production system. Sadly, methods to computationally determine the point of intersection between the IPR (Inflow Performance Relationship) and VLP (Vertical Lift Performance) curves are scarce in literature due to their complexity. This study seeks to determine dry gas wells' performance by predicting their bottomhole flowing pressure and corresponding gas flowrate, using the concept of Taylor series expansion. A computer program (TAYNOD) was developed to implement the proposed mathematical model. In addition, a graphical method of computing Pwf and qsc was employed to validate the developed prediction model. The operating pressure and flow rate results were painstakingly read from the charts to obtain the exact solution. The exact values were then contrasted with the results from the developed prediction model. Nevertheless, it was established that the predicted values of Pwf and qsc matched adequately with an R² of 0.937. [ABSTRACT FROM AUTHOR]

Based on molecular nanoelectronic knowledge, field-effect molecular electronic devices (such as molecular wires, molecular switches, and molecular memories) can be intelligent designed for use in nano-circuits. Therefore, in this study, the performance of a candidate field-effect molecular wire (of both isolated, M, and non-isolated, Au–M–Au, molecular systems) is studied, using density functional theory (DFT). In this regard, the external electric field (EF) on some structural, electronic and vibrational properties of this molecular wire is investigated. Also, the EF effect on current-voltage (I‒V) curves and molecular Joule-like coefficient of this molecular systems are studied theoretically. Based on the results of this study, it is expected that this molecular wire has acceptable performance in nanoelectronic circuits. [ABSTRACT FROM AUTHOR]

A computational method was developed for the prediction of handle value of the bed linen fabrics by using step-wise block regression method. The objective evaluation of fabric hand would contribute in engineering and developing the bed linen fabrics that offers maximum comfort while sleeping. The subjective evaluation of fabric hand restricts the engineering of the high-quality fabrics and the scientific understanding of the fabric properties. The expert's opinion selected the four primary hand properties that are soft feeling, smoothness, fullness, and stiffness influencing the handle of the bed linen fabrics. Primary and total hand equations were developed using the 16 basic mechanical properties. The experimental total hand value was also calculated using existing hand equation for futon's cloth and the statistical approach was followed to investigate the correlation between subjective, experimental, and computational total hand values. An excellent correlation of 0.889 was found between the subjective and computational hand values. The research concludes that the total hand value of the bed linen fabrics can be predicted with tolerable accuracy level. [ABSTRACT FROM AUTHOR]