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Computation, Volume 12, Issue 9 (September 2024) – 6 articles

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29 pages, 10764 KiB

Open AccessArticle

In Silico Drug Screening for Hepatitis C Virus Using QSAR-ML and Molecular Docking with Rho-Associated Protein Kinase 1 (ROCK1) Inhibitors

by Joshua R. De Borja and Heherson S. Cabrera

Computation 2024, 12(9), 175; https://doi.org/10.3390/computation12090175 (registering DOI) - 31 Aug 2024

Abstract

The enzyme ROCK1 plays a pivotal role in the disruption of the tight junction protein CLDN1, a downstream effector influencing various cellular functions such as cell migration, adhesion, and polarity. Elevated levels of ROCK1 pose challenges in HCV, where CLDN1 serves as a crucial entry factor for viral infections. This study integrates a drug screening protocol, employing a combination of quantitative structure–activity relationship machine learning (QSAR-ML) techniques; absorption, distribution, metabolism, and excretion (ADME) predictions; and molecular docking. This integrated approach allows for the effective screening of specific compounds, using their calculated features and properties as guidelines for selecting drug-like candidates targeting ROCK1 inhibition in HCV treatment. The QSAR-ML model, validated with scores of 0.54 (R²), 0.15 (RMSE), and 0.71 (CCC), demonstrates its predictive capabilities. The ADME-Docking study’s final results highlight notable compounds from ZINC15, specifically ZINC000071318464, ZINC000073170040, ZINC000058568630, ZINC000058591055, and ZINC000058574949. These compounds exhibit the best ranking Vina scores for protein–ligand binding with the crystal structure of ROCK1 at the C2 pocket site. The generated features and calculated pIC₅₀ bioactivity of these compounds provide valuable insights, facilitating the identification of structurally similar candidates in the ongoing exploration of drugs for ROCK1 inhibition. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)

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18 pages, 6919 KiB

Open AccessArticle

FPGA-Based Numerical Simulation of the Chaotic Synchronization of Chua Circuits

by Leonardo Rentería, Margarita Mayacela, Klever Torres, Wladimir Ramírez, Rolando Donoso and Rodrigo Acosta

Computation 2024, 12(9), 174; https://doi.org/10.3390/computation12090174 (registering DOI) - 31 Aug 2024

Abstract

The objective of this work was to design and implement a system based on reconfigurable hardware as a study tool for the synchronization of chaotic circuits. Mathematical models were established for one circuit, two synchronized, and multiple synchronized Chua circuits. An ordinary differential equation solver was developed applying Euler’s method using the Verilog hardware description language and synthesized on a Spartan 3E FPGA (Field-Programmable Gate Array) equipped with a 32-bit RISC processor, 64 MB of DDR SDRAM, and 4 Mb of PROM. With a step size of 0.005 and a total of 10,000 iterations, the state equations for one and three Chua circuits were solved at a time of 0.2 ms and a frequency of 50 Mhz. The logical resources used by the system did not exceed 4%. To verify the operation, a numerical simulation was carried out using the Octave V9.1.0 calculation software on an Intel(R) Core i7-9750H CPU 2.59 GHz computer, obtaining the same results but in a time of 493 ms and 3.177 s for one and three circuits, respectively. Full article

(This article belongs to the Section Computational Engineering)

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22 pages, 13050 KiB

Open AccessArticle

A Deep Learning Model for Detecting Fake Medical Images to Mitigate Financial Insurance Fraud

by Muhammad Asad Arshed, Shahzad Mumtaz, Ștefan Cristian Gherghina, Neelam Urooj, Saeed Ahmed and Christine Dewi

Computation 2024, 12(9), 173; https://doi.org/10.3390/computation12090173 - 29 Aug 2024

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Abstract

Artificial Intelligence and Deepfake Technologies have brought a new dimension to the generation of fake data, making it easier and faster than ever before—this fake data could include text, images, sounds, videos, etc. This has brought new challenges that require the faster development of tools and techniques to avoid fraudulent activities at pace and scale. Our focus in this research study is to empirically evaluate the use and effectiveness of deep learning models such as Convolutional Neural Networks (CNNs) and Patch-based Neural Networks in the context of successful identification of real and fake images. We chose the healthcare domain as a potential case study where the fake medical data generation approach could be used to make false insurance claims. For this purpose, we obtained publicly available skin cancer data and used recently introduced stable diffusion approaches—a more effective technique than prior approaches such as Generative Adversarial Network (GAN)—to generate fake skin cancer images. To the best of our knowledge, and based on the literature review, this is one of the few research studies that uses images generated using stable diffusion along with real image data. As part of the exploratory analysis, we analyzed histograms of fake and real images using individual color channels and averaged across training and testing datasets. The histogram analysis demonstrated a clear change by shifting the mean and overall distribution of both real and fake images (more prominent in blue and green) in the training data whereas, in the test data, both means were different from the training data, so it appears to be non-trivial to set a threshold which could give better predictive capability. We also conducted a user study to observe where the naked eye could identify any patterns for classifying real and fake images, and the accuracy of the test data was observed to be 68%. The adoption of deep learning predictive approaches (i.e., patch-based and CNN-based) has demonstrated similar accuracy (~100%) in training and validation subsets of the data, and the same was observed for the test subset with and without StratifiedKFold (k = 3). Our analysis has demonstrated that state-of-the-art exploratory and deep-learning approaches are effective enough to detect images generated from stable diffusion vs. real images. Full article

(This article belongs to the Special Issue Computational Medical Image Analysis—2nd Edition)

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12 pages, 6660 KiB

Open AccessArticle

Amide–π Interactions in the Structural Stability of Proteins: Role in the Oligomeric Phycocyanins

by Luka M. Breberina, Mario V. Zlatović, Srđan Đ. Stojanović and Milan R. Nikolić

Computation 2024, 12(9), 172; https://doi.org/10.3390/computation12090172 - 27 Aug 2024

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Abstract

This study investigates the influences and environmental preferences of amide–π interactions, a relatively unexplored class of charge-free interactions, in oligomeric phycocyanins. In a data set of 20 proteins, we observed 2086 amide–π interactions, all of which were part of the protein backbone. Phe and Tyr residues were found to be involved in amide–π interactions more frequently than Trp or His. The most favorable amide–π interactions occurred within a pair distance range of 5–7 Å, with a distinct angle preference for T-shaped ring arrangements. Multiple interaction patterns suggest that approximately 76% of the total interacting residues participate in multiple amide–π interactions. Our ab initio calculations revealed that most amide–π interactions have energy from 0 to −2 kcal/mol. Stabilization centers of phycocyanins showed that all residues in amide–π interactions play a crucial role in locating one or more such centers. Around 78% of the total interacting residues in the dataset contribute to creating hot-spot regions. Notably, the amide–π interacting residues were found to be highly evolutionarily conserved. These findings enhance our understanding of the structural stability and potential for protein engineering of phycocyanins used as bioactive natural colorants in various industries, including food and pharmaceuticals. Full article

(This article belongs to the Section Computational Chemistry)

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22 pages, 2140 KiB

Open AccessArticle

Synthesis of Self-Checking Circuits for Train Route Traffic Control at Intermediate Stations with Control of Calculations Based on Weight-Based Sum Codes

by Dmitry V. Efanov, Artyom V. Pashukov, Evgenii M. Mikhailiuta, Valery V. Khóroshev, Ruslan B. Abdullaev, Dmitry G. Plotnikov, Aushra V. Banite, Alexander V. Leksashov, Dmitry N. Khomutov, Dilshod Kh. Baratov and Davron Kh. Ruziev

Computation 2024, 12(9), 171; https://doi.org/10.3390/computation12090171 - 26 Aug 2024

Viewed by 349

Abstract

When synthesizing systems for railway interlocking, it is recommended to use automated models to implement the logic of railway automation and remote control units. Finite-state machines (FSMs) can be implemented on any hardware component. When using relay technology, the functional safety of electrical interlocking is achieved by using uncontrolled (safety) relays with a high coefficient of asymmetry of failures in types 1 → 0 and 0 → 1. When using programmable components, the use of backup and diverse protection methods is required. This paper presents a flexible approach to synthesizing FSMs for railway automation and remote control units that offer both individual and route-based control. Unlike existing solutions, this proposal considers the pre-failure states of railway automation and remote control units during the finite-state machine synthesis stage. This enables the implementation of self-checking and self-diagnostic modules to manage automation units. By increasing the number of states for individual devices and considering the states of interconnected objects, the transition graphs can be expanded. This expansion allows for the synthesis of the transition graph of the control subsystem and other systems. The authors used a field-programmable gate array (FPGA) to implement a finite-state machine. In this case, the proposal is to encode the states of a finite-state machine using weight-based sum codes in the residue class ring based on a given modulus. The best coverage of errors occurring at the outputs of the logic converter in the structure of the FSM can be ensured by selecting the weighting coefficients and the value of the module. This paper presents an example of synthesizing an FPGA-based FSM using state encoding through modular weight-based sum codes. The operation of the synthesized device was modeled. It was found to operate according to the same algorithm as the real devices. When synthesizing self-checking and self-controlled train control devices, it is recommended to consider the solutions proposed in this paper. Full article

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21 pages, 6177 KiB

Open AccessArticle

Statistical Synthesis and Analysis of Functionally Deterministic Signal Processing Techniques for Multi-Antenna Direction Finder Operation

by Semen Zhyla, Eduard Tserne, Yevhenii Volkov, Sergey Shevchuk, Oleg Gribsky, Dmytro Vlasenko, Volodymyr Kosharskyi and Danyil Kovalchuk

Computation 2024, 12(9), 170; https://doi.org/10.3390/computation12090170 - 23 Aug 2024

Viewed by 285

Abstract

This manuscript focuses on the process of measuring the angular positions of radio sources using radio engineering systems. This study aims to improve the accuracy of measuring the angular positions of sources that radiate functionally determined signals and to expand the range of the unambiguous operation angles for multi-antenna radio direction finders. To achieve this goal, the following tasks were addressed: (1) defining the models of signals, noise, and their statistical characteristics, (2) developing the theoretical foundations of statistical optimization methods for measuring the angular positions of radio sources in multi-antenna radio direction finders, (3) optimizing the structures of radio direction finders with different configurations, (4) analyzing the accuracy and range of the unambiguous measurement angles in the developed methods, and (5) conducting experimental measurements to confirm the main results. The methods used are based on the statistical theory of optimization for remote sensing and radar systems. For the specified type of signals, given by functionally deterministic models, a likelihood function was constructed, and its maxima were determined for different multi-antenna direction finder configurations. The results of statistical synthesis were verified through simulation modeling and experiments. The primary approach to improving measurement accuracy and expanding the range of unambiguous angles involves combining antennas with different spatial characteristics and optimally integrating classical radio direction-finding methods. The following results were obtained: (1) theoretical studies and simulation modeling confirmed the existence of a contradiction between high resolution and the width of the range of the unambiguous measurements in two-antenna radio direction finders, (2) an improved signal processing method was developed for a four-antenna radio direction finder with a pair of high-gain and a pair of low-gain antennas, and (3) to achieve maximum direction-finding accuracy within the unambiguous measurement range, a new signal processing method was synthesized for a six-element radio receiver, combining processing in two amplitude direction finders and one phase direction finder. This work provides a foundation for further theoretical studies, highlights the specifics of combining engineering measurements in direction-finding systems, and offers examples of rapid verification of new methods through computer modeling and experimental measurements. Full article

(This article belongs to the Special Issue Integrated Computer Technologies in Mechanical Engineering—Synergetic Engineering III)

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