Computational Perspective Research Articles

Probing RNA structures and functions by solvent accessibility: an overview from experimental and computational perspectives.

Characterizing RNA structures and functions have mostly been focused on 2D, secondary and 3D, tertiary structures. Recent advances in experimental and computational techniques for probing or predicting RNA solvent accessibility make this 1D representation of tertiary structures an increasingly attractive feature to explore. Here, we provide a survey of these recent developments, which indicate the emergence of solvent accessibility as a simple 1D property, adding to secondary and tertiary structures for investigating complex structure–function relations of RNAs.

Rating of Modern Color Image Cryptography: A Next-Generation Computing Perspective

Issues such as inefficient encryption architectures, nonstandard formats of image datasets, weak randomness of chaos-based Pseudorandom Number Generators (PRNGs), omitted S-boxes, and unconvincing security metrics leading to increased computational time and inadequate security level of chaos and Deoxyribonucleic Acid- (DNA-) based image encryption schemes need careful examination towards the development of more stable encryption schemes in terms of efficiency and reasonable security. A new taxonomy of image encryption based on chaotic systems, hyperchaotic systems, and DNA is propounded to assess the impact of these issues on the performance and security metrics. The primary emphasis of this research is to study various recent encryption architectures centered on a variety of confusion and diffusion methods. It is aimed at assessing the performance and security of various ciphers using a cipher rating criterion that categorizes ciphers into different classes. The parameters that are included in the rating criteria are information entropy, chi-squared goodness of fit test for histogram uniformity analysis, encryption efficiency, key space, differential attacks (Number of Pixels Change Rate and Universal Average Changing Intensity), key sensitivity analysis, encryption time, randomness tests such as NIST-R (a statistical suite for validating the randomness designed by the National Institute of Standards and Technology), correlation coefficient analysis, contrast analysis, energy analysis, homogeneity analysis, Mean Absolute Error, peak signal-to-noise ratio, and robustness to noise and occlusion attacks.

Polymorphism, Structure, and Nucleation of Cholesterol·H2O at Aqueous Interfaces and in Pathological Media: Revisited from a Computational Perspective.

We revisit the important issues of polymorphism, structure, and nucleation of cholesterol·H2O using first-principles calculations based on dispersion-augmented density functional theory. For the lesser known monoclinic polymorph, we obtain a fully extended H-bonded network in a structure akin to that of hexagonal ice. We show that the energy of the monoclinic and triclinic polymorphs is similar, strongly suggesting that kinetic and environmental effects play a significant role in determining polymorph nucleation. Furthermore, we find evidence in support of various O–H···O bonding motifs in both polymorphs that may result in hydroxyl disorder. We have been able to explain, via computation, why a single cholesterol bilayer in hydrated membranes always crystallizes in the monoclinic polymorph. We rationalize what we believe is a single-crystal to single-crystal transformation of the monoclinic form on increased interlayer growth beyond that of a single cholesterol bilayer, interleaved by a water bilayer. We show that the ice-like structure is also relevant to the related cholestanol·2H2O and stigmasterol·H2O crystals. The structure of stigmasterol hydrate both as a trilayer film at the air–water interface and as a macroscopic crystal further assists us in understanding the polymorphic and thermal behavior of cholesterol·H2O. Finally, we posit a possible role for one of the sterol esters in the crystallization of cholesterol·H2O in pathological environments, based on a composite of a crystalline bilayer of cholesteryl palmitate bound epitaxially as a nucleating agent to the monoclinic cholesterol·H2O form.

How Working Memory and Reinforcement Learning Are Intertwined: A Cognitive, Neural, and Computational Perspective.

Reinforcement learning and working memory are two core processes of human cognition and are often considered cognitively, neuroscientifically, and algorithmically distinct. Here, we show that the brain networks that support them actually overlap significantly and that they are less distinct cognitive processes than often assumed. We review literature demonstrating the benefits of considering each process to explain properties of the other and highlight recent work investigating their more complex interactions. We discuss how future research in both computational and cognitive sciences can benefit from one another, suggesting that a key missing piece for artificial agents to learn to behave with more human-like efficiency is taking working memory's role in learning seriously. This review highlights the risks of neglecting the interplay between different processes when studying human behavior (in particular when considering individual differences). We emphasize the importance of investigating these dynamics to build a comprehensive understanding of human cognition.

A Review of Synthetic-Aperture Radar Image Formation Algorithms and Implementations: A Computational Perspective

Designing synthetic-aperture radar image formation systems can be challenging due to the numerous options of algorithms and devices that can be used. There are many SAR image formation algorithms, such as backprojection, matched-filter, polar format, Range–Doppler and chirp scaling algorithms. Each algorithm presents its own advantages and disadvantages considering efficiency and image quality; thus, we aim to introduce some of the most common SAR image formation algorithms and compare them based on these two aspects. Depending on the requisites of each individual system and implementation, there are many device options to choose from, for instance, FPGAs, GPUs, CPUs, many-core CPUs, and microcontrollers. We present a review of the state of the art of SAR imaging systems implementations. We also compare such implementations in terms of power consumption, execution time, and image quality for the different algorithms used.

Probing the biomolecular (DNA/BSA) interaction by new Pd(II) complex via in-depth experimental and computational perspectives: synthesis, characterization, cytotoxicity, and DFT approach

Scientists should not forget that the rate of death as a result of cancer is far more than that of other diseases like influenza or coronavirus (COVID-19), so the research in this field is of cardinal significance. Therefore, a new and hydrophilic palladium(II) complex of the general formula [Pd(bpy)(proli-dtc)]NO3, in which bpy and proli-dtc are 2,2'-bipyridine and pyrroline dithiocarbamate ligands, respectively, was synthesized and characterized utilizing spectral and analytical procedures. Density functional theory (DFT) calculation was also performed with B3LYP method in the gas phase. The DFT and spectral analysis specified that the Pd(II) atom is found in a square-planar geometry. HOMO/LUMO analysis, quantum chemical parameters and MEP surface of the complex were investigated to acquire an intuition about the nature of the compound. Partition coefficient and water solubility determination showed that both lipophilicity and hydrophilicity of the compound are more than cisplatin. The 50% inhibition concentration (IC50) value was evaluated against K562 cancer cells, the obtained result has revealed a promising cytotoxic effect. DNA and BSA binding of the complex were explored through multi-spectroscopic (UV–Vis, fluorescence, FRET, and CD) and non-spectroscopic (gel electrophoresis, viscosity and docking simulation) techniques. The obtained findings demonstrated that the complex strongly interacts with CT-DNA by hydrophobic interactions and possesses medium interaction with BSA via hydrogen bond and van der Waals forces, thus BSA could efficiently carry out complex transportation. Furthermore, the results of docking simulation agree well with the experimental findings. In conclusion, the new Pd(II) complex has cytotoxic activity and could interact with DNA and BSA effectively.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s13738-022-02519-6.

Spectroscopy and laser cooling of SiBr+: A computational perspective

In the previous work, the SiBr+ cation is suggested as a candidate molecule for laser cooling, and in this work, on the basis of high-level ab initio calculation, the possibility of SiBr+ cation laser cooling is studied by considering the core-valence electrons correlations and spin–orbit coupling (SOC) effect. The potential energy curves of 12 Λ-S states and 36 Ω states considering SOC were obtained by using the multi-reference configuration interaction method and Davidson correction. Our computed results show that the Franck-Condon factor f00 for SiBr+ is only 0.619, which cannot guarantee that the number of the leaked photons is enough small, and more pumping laser beams are needed. Based on the calculated molecular properties, we discuss in detail the feasibility of laser cooling of SiBr+ and the results indicate that it is a challenge for laser cooling of SiBr+ .

Computing Optimized Path Integrals for Knapsack Feasibility

A generating function technique for solving integer programs via the evaluation of complex path integrals is discussed from a theoretical and computational perspective. Applying the method to knapsack feasibility problems, it is demonstrated how the presented numerical integration algorithm benefits from a preoptimized path of integration. After discussing the algorithmic setup in detail, a numerical study is implemented to evaluate the computational performance of the preoptimized integration method, and the algorithmic parameters are tuned to a set of knapsack instances. The goal is to highlight the method’s computational advantage for hard knapsack instances. Summary of Contribution: A method for evaluating the feasibility of knapsack problems is discussed and connected to the existing theory on generating function techniques for computational integer optimization. Specifically, the number of solutions to knapsack instances is computed using numerical quadrature of complex path integrals. The choice of the path of integration is identified as an important degree of freedom, and it is shown that preoptimizing the path improves the computational performance for hard instances significantly. The scope of this work is to give a self-contained presentation of the mathematical theory, introduce and discuss path optimization as a presolving routine, and demonstrate the computational performance of the overall approach with the help of a detailed numerical study. The main goal is to highlight the computational advantage of the new algorithm for hard knapsack instances.

The traveling deliveryman problem under uncertainty: Fundamentals for flexible supply chains

In recent years, supply chain optimization has become a major topic in operational research. Huge efforts were made to outline effective methodologies both from computational and final solution quality perspective. However, besides being optimal, one major need of supply chains is flexibility and adaptability to disturbances. This research work aims at the definition of a thorough procedure for flexible supply chain design able to exploit deterministic and stochastic flexibility indices. The Traveling Deliveryman Problem was then selected as case study since it represents the most basic operational research problem. The adaptation of flexibility assessment methodologies, primarily conceived for unit operations, provides good results and allows to properly identify criticalities. In addition, it proves that the optimal solutions under uncertainty are confined to a limited subset among million possible alternatives according to the expected deviation nature. This procedure is then worth to be extended to more complex systems in future studies.

Flap Dynamics in Pepsin-Like Aspartic Proteases: A Computational Perspective Using Plasmepsin-II and BACE-1 as Model Systems.

The flexibility of β hairpin structure known as the flap plays a key role in catalytic activity and substrate intake in pepsin-like aspartic proteases. Most of these enzymes share structural and sequential similarity. In this study, we have used apo Plm-II and BACE-1 as model systems. In the apo form of the proteases, a conserved tyrosine residue in the flap region remains in a dynamic equilibrium between the normal and flipped states through rotation of the χ1 and χ2 angles. Independent MD simulations of Plm-II and BACE-1 remained stuck either in the normal or flipped state. Metadynamics simulations using side-chain torsion angles (χ1 and χ2 of tyrosine) as collective variables sampled the transition between the normal and flipped states. Qualitatively, the two states were predicted to be equally populated. The normal and flipped states were stabilized by H-bond interactions to a tryptophan residue and to the catalytic aspartate, respectively. Further, mutation of tyrosine to an amino-acid with smaller side-chain, such as alanine, reduced the flexibility of the flap and resulted in a flap collapse (flap loses flexibility and remains stuck in a particular state). This is in accordance with previous experimental studies, which showed that mutation to alanine resulted in loss of activity in pepsin-like aspartic proteases. Our results suggest that the ring flipping associated with the tyrosine side-chain is the key order parameter that governs flap dynamics and opening of the binding pocket in most pepsin-like aspartic proteases.

Incompleteness of moral choice and evolution towards fully autonomous AI

Nowadays, it is fashionable to add the attribute “with artificial intelligence” to all possible devices, platforms and machines. The problem of ethical decision-making, viewed from the perspective of computer, technical and natural sciences, lies only in the complexity of the topic. AI scientists and developers basically proceed from the Turing machine model, assuming that a machine can be constructed to resolve any problems (including ethical decision-making issues) that can mechanically calculate a particular function if this function can be put into an algorithm. Thus, ethical decision-making is conceived as an abstract concept whose manifestation does not depend on the particular physical organism in which the algorithm takes place, nor on what it is made of. Whether from photons, mechanical relays, quantum fluctuations, artificial neurons or human nerve cells. If in practice, a sufficiently complex algorithm is built, it will also exhibit sufficiently complex behavior that can be characterized as ethical in the full sense of the word. This article reflects the main argument that if a task requires some form of moral authority when it is performed by humans, its full automation, transferring the same task to autonomous machines, platforms, and AI algorithms, necessarily implies the transfer of moral competence. The question of what this competence should include presupposes empirical research and reassessing purely normative approaches in AI ethics.

Comparison of experimental phase diagrams and residue-level coarse-grained simulations of intrinsically disordered proteins

Liquid-liquid phase separation (LLPS) is an important mechanism that contributes to intracellular organization via the formation of biomolecular condensates. From a theoretical and computational perspective, the development of accurate and transferable coarse-grained models that allow us to elucidate the molecular mechanisms driving condensate formation in the cytoplasm and nucleoplasm with attainable computational cost is highly desirable.

Digital twins for prognostics of electro-hydraulic actuators: novel simplified fluid dynamic models for aerospace valves

In the design and development phases of electro-hydraulic actuators (EHAs) used for aircraft flight controls, it is often necessary to carry out accurate and high-fidelity fluid dynamics simulations to evaluate the system behaviour within its entire operating range and, if necessary, investigate its most critical issues. These high-fidelity simulations (nowadays achievable with different techniques and commercial software) generally become pretty expensive from a computational perspective. Therefore, especially in the preliminary design phases or implementing system health monitoring algorithms (in real-time), the need to adopt simplified models emerges definitely (albeit capable of guaranteeing the appropriate level of detail and accuracy). These simplified models are also essential for developing effective and reliable model-based prognostic strategies capable of performing early health assessments of EHA valves. This work proposes a new lumped-parameters simplified numerical model, which, despite having a very compact formulation and reduced computational costs, simulates the internal fluid dynamics of the valve, overcoming some critical issues typical of other models available in the literature. It evaluates valve performance as a function of spool position and environmental conditions (e.g. supply pressure), better-assessing flow rate feedback, internal leakages, and other operating conditions (e.g. spool fine adjustment, pressure supply variable, overpressure, or water hammer). The performance of this numerical model is evaluated comparing with other simplified models published in the literature. Moreover, it is validated with a high-fidelity digital twin that simulates the behaviour of the valve, taking into account the geometry of the spool, the properties of the hydraulic fluid, and the local internal fluid-dynamics (laminar or turbulent regime, cavitation, etc.).

A Computational Perspective on Molecular Recognition by Galectins.

This article presents an overview of recent computational studies dedicated to the analysis of binding between galectins and small-molecule ligands. We first present a summary of the most popular simulation techniques adopted for calculating binding poses and binding energies and then discuss relevant examples reported in the literature for the three main classes of galectins (dimeric, tandem, and chimera). We show that simulation of galectin-ligand interactions is a mature field that has proven invaluable for completing and unraveling experimental observations. Future perspectives to further improve the accuracy and cost-effectiveness of existing computational approaches will involve the development of new schemes to account for solvation and entropy effects, which represent the main current limitations to the accuracy of computational results.

Computing wave velocity of rock sample using rock chips and cuttings

Rock cuttings and chips, usually from boreholes, are considered as direct samples from the subsurface layers containing valuable information. They are rarely used in routine studies because of their imprecise depth, induced cracks during transfer, and saturation by mud fluid, however, mainly due to their small sizes. To investigate the latter, we computed P- and S-wave velocity of Ruhr and Bentheimer sandstones from 2D images using a 2D-to-3D procedure in Digital Rock Physics (DRP). We found that cuttings are problematic when: a. the size of their images is smaller than Representative Elementary Volume (REV) and b. their porosity is medium to high. To overcome these problems, we introduce two methods based on Differential Effective Medium equations (equation-based method) and Cross-Correlation Simulation (CCSIM) (simulation-based method). From both sample preparation and computational perspectives, these methods are easy to apply, inexpensive and fast relative to standard 3D DRP. Results on Bentheimer and Berea sandstone and Grosmont carbonate showed that both methods are valid and reliable. In the best cases, they could compute wave velocity of rock with a root mean square error (RMSE) less than 100 m/s, which are comparable or, in some cases, even better than 3D DRP. The size of cuttings images could also be up to 5 and 3 times smaller than REV for the equation- and simulation-based methods, respectively, which is promising for practical purposes in the petroleum industry.

The MAMe dataset: on the relevance of high resolution and variable shape image properties

The mostcommon approach in image classification tasks is to resize all images in the dataset to a unique shape, while reducing their resolution to a size that makes experimentation at scale easier. This practice has benefits from a computational perspective, but it entails negative side-effects on performance due to loss of information and image deformation. In this work we introduce the MAMe dataset, an image classification dataset with remarkable properties of high resolution and variable shape. The goal of MAMe is to provide a tool for studying the impact of such properties in image classification, while motivating research in the topic. The MAMe dataset contains thousands of artworks from three different museums, and proposes a classification task consisting on differentiating between 29 mediums (i.e., materials and techniques) supervised by art experts. After analyzing the novelty of MAMe in the context of the current image classification tasks, a thorough description of the task is provided, along with statistics of the dataset. Experiments are conducted to evaluate the impact of using high resolution images, variable shape inputs, as well as both properties at the same time. Results illustrate the positive impact in performance when using high resolution images, while highlighting the lack of solutions to exploit variable shapes. An additional experiment exposes the distinctiveness between the MAMe dataset and the prototypical ImageNet dataset, showing that performance improves due to information gain and resolution gain. Finally, the baselines are inspected using explainability methods and expert knowledge, in order to gain insights about the challenges that remain ahead.

Asymptotic Stabilization of Delayed Linear Fractional-Order Systems Subject to State and Control Constraints

Studies have shown that fractional calculus can describe and characterize a practical system satisfactorily. Therefore, the stabilization of fractional-order systems is of great significance. The asymptotic stabilization problem of delayed linear fractional-order systems (DLFS) subject to state and control constraints is studied in this article. Firstly, the existence conditions for feedback controllers of DLFS subject to both state and control constraints are given. Furthermore, a sufficient condition for invariance of polyhedron set is established by using invariant set theory. A new Lyapunov function is constructed on the basis of the constraints, and some sufficient conditions for the asymptotic stability of DLFS are obtained. Then, the feedback controller and the corresponding solution algorithms are given to ensure the asymptotic stability under state and control input constraints. The proposed solution algorithm transforms the asymptotic stabilization problem into a linear/nonlinear programming (LP/NP) problem which is easy to solve from the perspective of computation. Finally, three numerical examples are offered to illustrate the effectiveness of the proposed method.

The structural aspects of neural dynamics and information flow.

Neurons have specialized structures that facilitate information transfer using electrical and chemical signals. Within the perspective of neural computation, the neuronal structure is an important prerequisite for the versatile computational capabilities of neurons resulting from the integration of diverse synaptic input patterns, complex interactions among the passive and active dendritic local currents, and the interplay between dendrite and soma to generate action potential output. For this, characterization of the relationship between the structure and neuronal spike dynamics could provide essential information about the cellular-level mechanism supporting neural computations. This work describes simulations and an information-theoretic analysis to investigate how specific neuronal structure affects neural dynamics and information processing. Correlation analysis on the Allen Cell Types Database reveals biologically relevant structural features that determine neural dynamics-eight highly correlated structural features are selected as the primary set for characterizing neuronal structures. These features are used to characterize biophysically realistic multi-compartment mathematical models for primary neurons in the direct and indirect hippocampal pathways consisting of the pyramidal cells of Cornu Ammonis 1 (CA1) and CA3 and the granule cell in the dentate gyrus (DG). Simulations reveal that the dynamics of these neurons vary depending on their specialized structures and are highly sensitive to structural modifications. Information-theoretic analysis confirms that structural factors are critical for versatile neural information processing at a single-cell and a neural circuit level; not only basic AND/OR but also linearly non-separable XOR functions can be explained within the information-theoretic framework. Providing quantitative information on the relationship between the structure and the dynamics/information flow of neurons, this work would help us understand the design and coding principles of biological neurons and may be beneficial for designing biologically plausible neuron models for artificial intelligence (AI) systems.

Role of Probiotics in the Management of COVID-19: A Computational Perspective.

Coronavirus disease 2019 (COVID-19) was declared a pandemic at the beginning of 2020, causing millions of deaths worldwide. Millions of vaccine doses have been administered worldwide; however, outbreaks continue. Probiotics are known to restore a stable gut microbiota by regulating innate and adaptive immunity within the gut, demonstrating the possibility that they may be used to combat COVID-19 because of several pieces of evidence suggesting that COVID-19 has an adverse impact on gut microbiota dysbiosis. Thus, probiotics and their metabolites with known antiviral properties may be used as an adjunctive treatment to combat COVID-19. Several clinical trials have revealed the efficacy of probiotics and their metabolites in treating patients with SARS-CoV-2. However, its molecular mechanism has not been unraveled. The availability of abundant data resources and computational methods has significantly changed research finding molecular insights between probiotics and COVID-19. This review highlights computational approaches involving microbiome-based approaches and ensemble-driven docking approaches, as well as a case study proving the effects of probiotic metabolites on SARS-CoV-2.

Training Data Selection for Machine Learning-Enhanced Monte Carlo Simulations in Structural Dynamics

The evaluation of structural response constitutes a fundamental task in the design of ground-excited structures. In this context, the Monte Carlo simulation is a powerful tool to estimate the response statistics of nonlinear systems, which cannot be represented analytically. Unfortunately, the number of samples which is required for estimations with high confidence increases disproportionally to obtain a reliable estimation of low-probability events. As a consequence, the Monte Carlo simulation becomes a non-realizable task from a computational perspective. We show that the application of machine learning algorithms significantly lowers the computational burden of the Monte Carlo method. We use artificial neural networks to predict structural response behavior using supervised learning. However, one shortcoming of supervised learning is the inability of a sufficiently accurate prediction when extrapolating to data the neural network has not seen yet. In this paper, neural networks predict the response of structures subjected to non-stationary ground excitations. In doing so, we propose a novel selection process for the training data to provide the required samples to reliably predict rare events. We, finally, prove that the new strategy results in a significant improvement of the prediction of the response statistics in the tail end of the distribution.