Computational Techniques Research Articles

Structural and functional consequences of the cardiomyopathy-associated p.R157C mutation in the C-terminal palindromic motif of human αB-crystallin.

αB-crystallin, a small heat shock protein, is crucial for maintaining lenticular transparency and prevents protein aggregation as a molecular chaperone in various tissues. Mutations in αB-crystallin can lead to diseases such as cataracts, cardiomyopathy, and neurodegenerative disorders. This study explores the effects of the p.R157C mutation in the C-terminal domain, near the IXI motif, which is associated with cardiomyopathy. The mutant protein was generated through site-directed mutagenesis, expressed in bacterial systems, and purified by ion-exchange chromatography. Biophysical and computational techniques revealed significant alterations in secondary structure, oligomerization, and conformational stability. The mutation also enhanced chaperone activity and promoted amyloid fibril formation. These alterations may disrupt the interactions of the p.R157C mutant αB-crystallin with cardiac proteins such as desmin and calcineurin, potentially contributing to cardiomyopathy. These findings offer mechanistic insights into αB-crystallin-related cardiomyopathy, shedding light on its pathological role and potential therapeutic targets.

Fast alternating minimization algorithm for coded aperture snapshot spectral imaging based on sparsity and deep image priors

Coded aperture snapshot spectral imaging is a computational imaging technique used to reconstruct three-dimensional hyperspectral images from one or several two-dimensional projection measurements. However, fewer projection measurements or more spectral channels lead to a severely ill-posed problem, in which case regularization methods have to be applied. In order to significantly improve the accuracy of reconstruction, this paper proposes a fast alternating minimization algorithm based on the sparsity and deep image priors of natural images. By integrating deep image prior into the principle of compressive sensing reconstruction, the proposed algorithm can achieve state-of-the-art results without any training dataset. Extensive experiments show that the Fama-SDIP method significantly outperforms prevailing methods on simulation and real HSI datasets.

Privacy-preserving and verifiable spectral graph analysis in the cloud

Resorting to cloud computing for spectral graph analysis on large-scale graph data is becoming increasingly popular. However, given the intrusive and opaque natures of cloud services, privacy, and misbehaving cloud that returns incorrect results have raised serious concerns. Current schemes are proposed for privacy alone under the semi-honest model, while disregarding the realistic threat posed by the misbehaving cloud that might skip computationally intensive operations for economic gain. Additionally, existing verifiable computation techniques prove inadequate for the specialized requirements of spectral graph analysis, either due to compatibility issues with privacy-preserving protocols or the excessive computational burden they impose on resource-constrained users. To tackle the above two issues in a holistic solution, we present, tailor, and evaluate PVG, a privacy-preserving and verifiable framework for spectral graph analytics in the cloud for the first time. PVG concentrates on the eigendecomposition process, and provides strong privacy for graph data while enabling users to validate the accuracy of the outcomes yielded by the cloud. For this, we first design a new additive publicly verifiable computation algorithm, APVC, that can verify the accuracy of the result of the core operation (matrix multiplication) in eigendecomposition returned by cloud servers. We then propose three secure and verifiable functions for eigendecomposition based on APVC and lightweight cryptography. Extensive experiments on three manually generated and two real-world social graph datasets indicate that PVG’s accuracy is consistent with plaintext, with practically affordable performance superior to prior art.

Retraction: In Silico design of a multi-epitope vaccine for Human Parechovirus: Integrating immunoinformatics and computational techniques.

Retraction: In Silico design of a multi-epitope vaccine for Human Parechovirus: Integrating immunoinformatics and computational techniques.

Enhanced Exploration of Protein Conformational Space through Integration of Ultra-Coarse-Grained Models to Multiscale Workflows.

Computational techniques such as all-atom (AA) molecular dynamics (MD) simulations and coarse-grained (CG) models have been essential to study various biological problems over a wide range of scales. While AA simulations provide detailed insights, they are computationally expensive for capturing dynamics over longer length and time scales. CG approaches, particularly ultra-coarse-grained (UCG) models as considered in this study, have addressed this limitation by simplifying molecular representations, enabling the study of larger systems and longer time scales. This work focuses on the development of UCG models of proteins and their integration into the Multiscale Machine-Learned Modeling Infrastructure (MuMMI) to efficiently sample protein conformations, exemplified by the RAS-RBDCRD protein complex. By employing a combination of essential dynamics coarse graining (EDCG) and heterogeneous elastic network modeling (hENM) with anharmonic modifications, we developed UCG models based on the fluctuations observed in the higher resolution Martini CG simulations. These models allow the accurate sampling of protein configurations and long-range conformational changes. The incorporation of an implicit membrane model further enhanced the exploration of protein-membrane dynamics. Additionally, a novel machine-learning-based backmapping approach was developed to convert UCG structures to Martini CG representations, resulting in improved prediction accuracy. Finally, the integration of UCG models into MuMMI significantly enhances the exploration of protein configurations, offering critical insights into the role of protein dynamics in biological processes.

An efficient computational technique to solve second order Lane-Emden-Fowler type equations arising in physical phenomena

Abstract This article focuses on finding numerical solutions of high accuracy and low computational cost for second order Lane-Emden-Fowler type equations. The significance of these nonlinear equations is rooted in their ability to model important physical, astrophysical, and biological phenomena. The proposed study uses cubic spline approximations for discretization along with half-step grid points to develop a fourth order implicit numerical method capable of solving highly non-linear and singular equations with high accuracy. A comprehensive convergence analysis shows the claimed fourth order convergence of the scheme which is confirmed by graphical and computational results found for seven test problems involving various initial and boundary conditions.

Boost of Chiroptical Responses through the Enhancement of Electron Transition Dipole Moment.

The development of robust chiroptical systems is crucial for advancing optoelectronic technologies. Spirobifluorenes (SBFs) have proven to be versatile scaffolds for the construction of a wide variety of robust axially chiral compounds presenting strong chiroptical responses. Herein, we present a strategy for boosting the chiroptical responses via enhancing electric transition dipole moment (ETDM) of interacting chromophores in C2-symmetric SBFs presenting exciton coupling. To experimentally validate this approach, we peripherally extended the SBF scaffold through Knoevenagel condensation with the aim of increasing the ETDM of the featured chromophores. The influence of the functionalization on the ETDM was investigated using a combination of computational modeling and experimental techniques, enabling a detailed examination of the relationship between molecular structure and chiroptical properties. Our results demonstrate that peripheral functionalization of SBFs significantly enhances the electric transition dipole moment (ETDM) of the constituent chromophores, with each incremental increase in ETDM resulting in a five-fold enhancement of the g-factor, thereby substantially amplifying the overall chiroptical activity. This straightforward strategy underscores the importance of the ETDM of interacting chromophores in fine-tuning the optoelectronic properties and provides a promising avenue for the rational design of high-performance chiroptical materials.

Multi-biometric authentication system for enhancing the security levels in cloud computing using deep learning algorithm

In recent years, cloud computing has surged in popularity, offering vast computational resources in a scalable, cost-efficient manner. Despite its benefits, security concerns persist, prompting many companies to adopt cloud computing despite the associated risks. To address challenges in password management and the efficacy of authentication systems, biometric authentication has garnered significant attention. As the imperative for personal data security intensifies, multi-biometric fusion-based identification systems emerge as a promising solution to bolster performance accuracy. This paper introduces a novel computational multimodal biometric recognition technique aimed at autonomously authenticating facial, iris, and fingerprint images using advanced deep learning methodologies. By integrating features using Fusion-Based Feature Extraction (Weighted Sum Rule), and classification using Deep Cross-Modal Retrieval (DCMR), this approach produces robust representations of facial, iris, and fingerprint characteristics by generating OTP (One-Time Password) to enhance authentication in the cloud environment. The efficacy of the proposed approach is evaluated by comparing its performance against established classifiers such as Support Vector Machines (SVM), Random Forests, Decision Trees, and K-Nearest Neighbors (KNN), utilizing metrics including recognition rate, precision, recall, and F-measure. Results demonstrate a recognition rate of 99.2%, surpassing alternative models considered. These findings highlight the potential of advanced deep learning methodologies within cloud computing environments to enhance multimodal biometric authentication systems. This approach utilizes Biometric-as-a-Service (BaaS) to streamline complexity and computational overhead, facilitating broader implementation of robust biometric security measures in cloud-based ecosystems.

Fractional Poisson process for modeling extreme values in financial data using the ABC methodology in parameter estimation

In this research, we develop a Bayesian approach to estimate the parameters defining the fractional Poisson process in the context of extreme values and with a limited sample size, utilizing the approximate Bayesian computation (ABC) technique. Furthermore, a test was developed, wherein the null hypothesis was set as the presence of long memory in the fractional Poisson process. A simulation study was conducted to demonstrate the enhanced efficacy for parameter estimation in small samples in comparison to the asymptotic approach. Additionally, the study presented an application to real financial time series data, with the aim of characterizing the phenomenon of extreme data. This resulted in the delivery of a new approach to characterize large losses in stock markets and other phenomena related to the fractional Poisson process.

Applications of the Shapley Value to Financial Problems

Managing risk, matching resources efficiently, and ensuring fair allocation are fundamental challenges in both finance and decision-making processes. In many scenarios, participants contribute unequally to collective outcomes, raising the question of how to distribute costs, benefits, or opportunities in a justifiable and optimal manner. This paper applies the Shapley value—a solution concept from cooperative game theory—as a principled tool in the following two specific financial settings: first, in tax cooperation games; and second, in assignment markets. In tax cooperation games, we use the Shapley value to determine the equitable tax burden distribution among three firms, A, B, and C, which operate in two countries, Italy and Poland. Our model ensures that countries participating in coalitions face a lower degree of tax evasion compared to non-members, and that cooperating firms benefit from discounted tax liabilities. This structure incentivizes coalition formation and reveals the economic advantage of joint participation. In assignment markets, we use the Shapley value to find the optimal pairing in a four-buyers and four-sellers housing market. Our findings show that the Shapley value provides a rigorous framework for capturing the relative importance of participants in the coalition, leading to more balanced tax allocations and fairer market transactions. Our theoretical insights with computational techniques highlights the Shapley value’s effectiveness in addressing complex allocation challenges across financial management domains.

Mechanistic Insights into Thiazolidinones as Anticholinesterase Agents: 3D QSAR Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Studies for Alzheimer’s Therapy

Alertness toward multitarget, including acetylcholinesterase and butyrylcholinesterase, is crucial in addressing Alzheimer’s disease (AD). This study aims to explore the cholinesterase inhibitory potential of 37 thiazolidinone derivatives through computational techniques. In this study, in silico tools such as pharmacophore modeling, atom-based 3D QSAR study, molecular docking, binding free energy determination, molecular dynamics and density functional theory (DFT) studies were conducted by the software Schrodinger. Molecular docking against acetylcholinesterase (6O4W) displayed compound a6, with outstanding docking, complemented by MMGBSA for binding energy evaluation. Meanwhile, molecular dynamics (MD) simulations validated the stability of ligand-enzyme complexes and confirmed the stability of the a6/6O4W and a6/6EYF complexes. In DFT studies, based on the alpha_HOMO and alpha_LUMO energies, compound a6 was found to be electronically stable. Pharmacophore modeling and QSAR analysis identified structural features essential for inhibition. Pharmacophore modeling identified the five-feature hypotheses AAHHR_4 as the key structural feature for optimal inhibition. 3D-QSAR contour plot analysis was performed for the best compounds (in vitro and in silico studies) and compared with the weak compounds (in vitro studies). These findings emphasize the potential of thiazolidinone derivatives as potent acetylcholinesterase inhibitors. PASS tool predicted that all compounds would have anti-Alzheimer potential. Assessments of promiscuity showed that all thiazolidinones would be specific rather than promiscuous. These findings highlight the significance of computational studies in identifying promising compounds for treating AD.

ADME Properties in Drug Delivery

In recent decades, the identification of thousands of lead compounds, through development of analytical, synthetic, and computational techniques has occurred [...]

Evaluation of Antihyperglycemic Effect of Pectolinarigenin in Experimentally Induced Type 2 Diabetes Mellitus in Rats

Background: Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disease of insulin resistance with insufficient insulin secretion, in addition to chronic inflammation and impaired glucose metabolism. Finding new therapeutic uses of drugs that can target multiple pathways will serve an important role in disease management. The possible pharmacological effects of pectolinarigenin merit further investigation for its antidiabetic activity. Aim: Evaluation of the therapeutic impact of pectolinarigenin on the major molecular targets linked to T2DM via computational and experimental techniques. Methods: The interaction of pectolinarigenin with key T2DM targets was explored in silico using molecular docking simulations of Peroxisome Proliferator-Activated Receptor (PPAR), Dipeptidyl Peptidase 4 (DPP4) and Sulphonylurea Receptor 1 (SUR1). Animal Experimental Design: Animals (Sprague-Dawley male rats) were divided into five categories: normal, disease control, reference standard (glibenclamide), and pectolinarigenin at doses of 20 and 50 mg/kg, respectively (oral experiment). Different metabolic and biochemical parameters were measured over five weeks, including serum glucose fasting, insulin sensitivity, oxidative stress, inflammatory markers, lipid profiles, and HbA1c levels. Results: Docking analysis showed significant binding affinities of pectolinarigenin with diverse T2DM-associated targets, indicating its potential for multi-target therapy. Furthermore, treatment with pectolinarigenin improved glucose tolerance, markedly decreased fasting blood glucose levels, and increased insulin sensitivity in vivo compared to the disease control and reference standard groups. Compared to rats who received 50 mg/kg, rats who received 50 mg/kg and rats who lost a significant amount of weight had improved lipid profiles, maintained weight better, and had improved antioxidant defences and inflammation markers, as well as significantly lower HbA1c levels. These findings highlight the compound’s effectiveness for glycemic control and prevention of diabetes-related complications. Conclusion: Pectolinarigenin exhibited significant antidiabetic activity in both computational and experimental strategies, enhancing metabolic markers and alleviating inflammation. These findings underscore its potential as a therapeutic candidate for the treatment of diabetes. Additional research, including clinical trials, is needed to prove its effectiveness in people. Major Findings: Pectolinarigenin markedly enhanced glucose tolerance, insulin sensitivity, lipid profiles and oxidative stress parameters, with the 50 mg/kg dose-related effects being the most evident. This information further describes its potential for use as a novel antidiabetic agent and prevention of diabetes-related complications.

Leveraging Instabilities in Multifunctional Soft Materials: A Cutting Edge Review

Soft materials, such as elastomers and polymeric gels, exhibit exceptional deformability under applied loads but are susceptible to mechanical and morphological instabilities because of their low elastic modulus. Traditionally viewed as structural limitations, these instabilities are now harnessed as design characteristics to create multifunctional soft materials with adaptive properties. Leveraging phenomena such as buckling and wrinkling, researchers have enabled rapid actuation, energy harvesting, and adaptive responses in applications ranging from biomedical devices to soft robotics. This review addresses critical challenges in utilizing these instabilities, including precise control over complex interactions between mechanical, electrical, and chemical properties and overcoming nonlinearity and field‐induced variability. Computational modeling methods, machine learning, and experimental techniques used to study and characterize instability behavior are outlined. Applications such as rapid shape changes in biomedical implants, tunable adhesion surfaces in microfluidics, and high‐speed actuation in soft robotics highlight their transformative potential. This review identifies research gaps in understanding multiphysics interactions and suggests future directions to enhance the predictability, control, and scalability of instability‐driven behaviors in soft smart materials, driving innovation in next‐generation multifunctional devices.

Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithm

Abstract. Initialized ensemble simulations can help identify the physical drivers and assess the probabilities of weather and climate extremes based on a given initial state. However, the significant computational burden of complex climate models makes it challenging to quantitatively investigate extreme events with probabilities below a few percent. A possible solution to overcome this problem is to use rare event algorithms, i.e. computational techniques originally developed in statistical physics that increase the sampling efficiency of rare events in numerical simulations. Here, we apply a rare event algorithm to ensemble simulations with the intermediate-complexity coupled climate model PlaSim-LSG to study extremes of pan-Arctic sea ice area reduction under pre-industrial greenhouse gas conditions. We construct four pairs of control and rare event algorithm ensemble simulations, each starting from four different initial winter sea ice states. The rare event simulations produce sea ice lows with probabilities of 2 orders of magnitude smaller than feasible with the control ensembles and drastically increase the number of extremes compared to direct sampling. We find that for a given probability level, the amplitude of negative late-summer sea ice area anomalies strongly depends on the baseline winter sea ice thickness but hardly on the baseline winter sea ice area. Finally, we investigate the physical processes in two trajectories leading to sea ice lows with conditional probabilities of less than 0.001 %. In both cases, negative late-summer pan-Arctic sea ice area anomalies are preceded by negative spring sea ice thickness anomalies. These are related to enhanced surface downward longwave radiative and sensible heat fluxes in an anomalously moist, cloudy and warm atmosphere. During summer, extreme sea ice area reduction is favoured by enhanced open-water-formation efficiency, anomalously strong downward solar radiation and the sea ice–albedo feedback. This work highlights that the most extreme summer sea ice conditions result from the combined effects of preconditioning and weather variability, emphasizing the need for thoughtful ensemble design when turning to real applications.

GC-MS profiling and computational analysis of Balanites aegyptiaca phytoconstituents for antidiabetic activity: insights from network pharmacology and molecular docking.

This study investigates the antidiabetic potential of the extracts and subsequent phytochemicals of Balanites aegyptiaca (BA). The approaches used were GC-MS for phytochemical characterization, network pharmacology for target identification, in vitro studies, and computational techniques for the antidiabetic activity. Network pharmacology revealed genes-associated disease targets, i.e., IL-6, PPARα, GCG, and GCK, and their signaling pathways that were modulated by the identified phytocompounds. In vitro assays demonstrated substantial antioxidant activity of n-hexane and ethyl acetate extracts and were found to be comparable to ascorbic acid. The anti-inflammatory potential using the egg albumin method was found to be best for n-hexane extract in comparison to aspirin. The in vitro antidiabetic activity using α-amylase inhibition method was most pronounced in the methanol extract and was found to be comparable to acarbose. Molecular docking and molecular dynamics simulations (100ns) identified stable interactions between BA-derived compounds and target proteins. In silico pharmacokinetic investigations revealed that the heptadecanoic acid and ragaglitazar exhibited the LD50 of 900 and 1600mg/kg, vouching for the substantial safety. Molecular dynamics simulations confirmed the greater stability of protein-ligand complexes and also inferred about the possible ligand-protein interactions. The BA phytocompounds inherit huge potential in the management of diabetes, with promising antioxidant, anti-inflammatory, and antidiabetic effects.

Precision, intelligence, and a new paradigm for chemical research.

Chemists have long struggled to precisely regulate and create substances, often relying on trial-and-error methods that are inefficient for complex, high-dimensional research challenges. However, recent advancements in computational and experimental techniques, particularly those with artificial intelligence (AI), are providing new avenues for precision and intelligent chemistry. This perspective highlights the synergistic integration of accurate theoretical simulations, advanced experimental characterization, and AI-driven models, creating a closed-loop system to accelerate chemical discovery and material design. At the core of this framework is an iterative process: precise computational and experimental data lead to advanced intelligent models, which guide the design of optimized reaction parameters or chemical components, and direct robotic platforms that perform reproducible, high-throughput experiments. These experimental data, in turn, provide continuous feedback to refine intelligent models, ultimately enabling precise control of reaction conditions and material properties. To fully realize this vision, we advocate the development of key infrastructures: a multidisciplinary, multimodal, and standardized AI-ready chemical database as a data foundation; a knowledge and logic-enhanced large chemical model for intelligent prediction and design; distributed, full-process robotic laboratories for automated experimentation; and a cloud platform for resource sharing and collaboration. Together, these components constitute a vision for robotic chemist cloud facilities, which will empower researchers with unparalleled capabilities to seamlessly integrate precision and intelligence. This integrated approach promises to accelerate discovery and represents a paradigm shift in chemical research.

High-speed 4D fluorescence light field tomography of whole freely moving organisms

Volumetric fluorescence imaging techniques, such as confocal, multiphoton, light sheet, and light field microscopy, have become indispensable tools across a wide range of cellular, developmental, and neurobiological applications. However, it is difficult to scale such techniques to the large 3D fields of view (FOV), volume rates, and synchronicity requirements for high-resolution 4D imaging of freely behaving organisms. Here, we present reflective Fourier light field computed tomography (ReFLeCT), a high-speed volumetric fluorescence computational imaging technique. ReFLeCT synchronously captures entire tomograms of multiple unrestrained, unanesthetized model organisms across multi-millimeter 3D FOVs at 120 volumes per second. In particular, we applied ReFLeCT to reconstruct 4D videos of fluorescently labeled zebrafish and Drosophila larvae, enabling us to study their heartbeat, fin and tail motion, gaze, jaw motion, and muscle contractions with nearly isotropic 3D resolution while they are freely moving. To our knowledge, as a novel approach for snapshot tomographic capture, ReFLeCT is a major advance toward bridging the gap between current volumetric fluorescence microscopy techniques and macroscopic behavioral imaging.

Probing the Molecular Interactions of Piperidine Alkaloid Lobeline with Ovalbumin: Anti-Aggregation and Anti-Glycation Potential.

Lobeline (LOB) is a bioactive alkaloid known for its neuroprotective and cognitive-enhancing properties, particularly in mitigating oxidative stress and inflammation associated with neurodegenerative diseases. While LOB has been studied for its pharmacological roles in CNS disorders, substance abuse treatment, and smoking cessation, its molecular interactions with proteins and potential antiamyloid, antiglycation, and antioxidant properties remain largely unexplored. This study addresses this gap by analyzing LOB interactions with ovalbumin (OVA) through advanced biophysical and computational techniques to elucidate its therapeutic potential and inform drug development. Fluorescence spectroscopy confirmed a static quenching mechanism and binding constant (Kb) of 105 M-1 with a monopreferential binding site in OVA by LOB. Isothermal titration calorimetry (ITC) indicated an exothermic, entropy-driven interaction, while differential scanning calorimetry (DSC) demonstrated LOB-induced stabilization of OVA. Synchronous fluorescence and red edge excitation shift highlighted LOB's effect on Trp and Tyr residues, while circular dichroism (CD) spectroscopy indicated LOB-induced conformational changes in OVA. Molecular modeling (docking and dynamic simulation) corroborated the experimental findings. Turbidity, thioflavin T (ThT), nile red (NR), 8-anilinonaphthalene-1-sulfonic acid (ANS), CD, and morphological studies confirmed OVA fibril formation, and the aggregation was shown to be inhibited by ligand LOB in vitro. Additionally, d-fructose was used to glycate the OVA protein, creating a model to examine the antiglycation and antioxidant effects of LOB. These aspects are essential for advancing targeted drug design and delivery systems, as they provide vital insights into modern biophysical and biochemical research.

Cultural Beliefs and Participatory AI: Unlocking Untapped Catalysts for Climate Action

This review paper examines two underutilized yet transformative drivers in addressing the climate crisis: (1) the role of cultural belief systems in fostering large-scale behavioral shifts toward sustainability, and (2) the use of participatory artificial intelligence (AI) methods to mitigate natural disaster risks, such as flooding. Despite their potential, both areas remain largely untapped. The first driver stems from persistent inertia in behavioral change, prompting the 2023 IPCC Report to call for an ‘inner transition’—a cultural shift in which deeply held values shape socio-ecological behavior, encouraging individuals to move away from business-as-usual lifestyles. However, the mechanisms behind such a transition remain unclear, and empirical support for this approach is still emerging. The second driver highlights the untapped potential of advanced computational techniques in developing intelligent solutions for worsening ecological crises. AI development is often expert-driven, disconnected from societal needs and lived realities. To bridge this gap, inclusive technology co-design—engaging all societal groups, especially those most affected by climate change—is crucial. Additionally, effective mechanisms for networking, amplifying, and scaling these efforts are essential. This paper proposes an integrated, multi-method framework that unites both drivers, offering a novel approach to accelerating progress in climate action.