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Prediction of key pathways in hepatocellular carcinoma (HCC): A machine learning approach using a sample pathway information matrix

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2025年 118卷 108481页

作者： Annadurai, Priyadharshini Isaac, Arnold Emerson Vellore Inst Technol Sch Bio Sci & Technol Dept Biosci Bioinformat Programming Lab Vellore 632014 Tamil Nadu India

Hepatocellular carcinoma (HCC) represents the most prevalent form of primary liver cancer, accounting for 75 % of all cases. Individuals with metabolic dysfunctions are at risk of developing significant symptoms, including cirrhosis. To address this, we proposed a novel method to find the signalling pathway based on the patient's gene expression. The objective includes examining the predictive biomarkers associated with cirrhosis-related HCC. The study combines gene expression and pathway enrichment data to find the biologically important pathways in disease progression. The differential gene expression analysis showed 58 upregulated and 62 downregulating differentially expressed genes. These DEGs were utilized to construct a protein-protein interaction network, and then the clustered genes were determined. Subsequently, pathway enrichment analysis was performed for the clustered genes and the gene-pathway interaction matrix was developed. The sample-pathway information matrix (SPIM) was obtained by multiplying the gene-expression and gene-pathway matrix. The key pathways were predicted from the SPIM using random forest model and we achieved 94 % of accuracy. The arachidonic acid metabolism was the most important pathway and genes involved in this pathway includes CYP2C9, CYP2C8, CYP2B6. These genes are well known for promoting metabolic disorders in the liver. Hence, our novel method proves that it could distinguish the samples and extract important pathways that are involved in differentiating the diseased samples based on the gene expression. Therefore, integrating gene expression and their enriched biological pathways may effectively help in identifying the key signalling pathways.

关键词： Hepatocellular carcinoma Chronic liver disease Cirrhosis Protein-protein interaction Machine learning Random forest

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A Method Combining Mobile Transmissometer and Lidar for High Precision Measurement of Visibility

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2024年第4期16卷 1页

作者： Li, Meng Wang, Guojin Civil Aviat Univ China Key Lab Operat Programming & Safety Technol Air Tr Tianjin 300300 Peoples R China

High precision measurement of visibility has wide applications in the fields of civil aviation and maritime transportation. However, for a long time, the non-uniform of the atmosphere and optical contamination under low visibility have always been important issues affecting the measurement accuracy of visibility. This paper proposes a novel method that combines a mobile transmissometer with Lidar, which can not only accurately measure visibility in non-uniform atmospheric environments, but also effectively suppress environmental contamination by using the method of single end sampling. In this method, the mobile transmissometer is coupled with the Lidar at its baseline end, thereby effectively improving the Lidar measurement accuracy with the high-precision measurement results of the mobile transmissometer. The experimental and simulation results demonstrate that this method has better performances of anti noise and optical contamination suppression, compared to traditional Lidar and atmospheric transmissometer. It can achieve high-precision visibility measurement in a large range of non-uniform atmospheric environments, and also exhibits good robustness in low visibility environments.

关键词： Pollution measurement Atmospheric measurements Laser radar Optical transmitters Length measurement Adaptive optics Optical variables measurement Visibility mobile transmissometer Lidar

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A high-precision measuring system for atmospheric visibility based on a multi-reflection cell

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OPTICAL REVIEW 2024年第1期31卷 8-16页

作者： Li, Meng Liu, Suyue Ma, Yuzhao Fu, Ning Univ China Key Lab Operat Programming & Safety Technol Air Tr Tianjin 300300 Peoples R China

Visibility high-precision measurement has a wide range of applications in the field of aviation and navigation. This paper proposes and designs a visibility measuring system based on a multi-reflection cell. In terms of the definition of the meteorological optical range (MOR), this system calculates the distance at which the beam is attenuated to 5% by means of multiple reflections with a long propagation distance, so as to obtain accurate atmospheric visibility. Different from the short-distance ring-down cavity with a length of 0.5 m, the length of the multi-reflection cell of this system can reach more than 10 m. Compared with the traditional transmissive method, the calculation results are more in line with the real situation of the atmospheric environment because the atmospheric non-uniformity and inversion parameters are considered. Experiments show that this method has better robustness and can stably measure atmospheric visibility under the condition of limited equipment space with an error of 5%.

关键词： Visibility Multi-reflection cell Meteorological optical range

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Investigating the binding affinity of andrographolide against human SARS-CoV-2 spike receptor-binding domain through docking and molecular dynamics simulations

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JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 2023年第22期41卷 13438-13453页

作者： Bhattarai, Anil Priyadharshini, Annadurai Emerson, Isaac Arnold Vellore Inst Technol VIT Sch Biosci & Technol Dept Biotechnol Bioinformat Programming Lab Vellore Tamil Nadu India Vellore Inst Technol VIT Sch Biosci & Technol Dept Biotechnol Bioinformat Programming Lab Vellore 632014 Tamil Nadu India

SARS-CoV-2 is a positive-sense single-stranded RNA virus that causes a deadly coronavirus disease (COVID-19) in humans. The infection of SARS-CoV-2 in humans involves a viral surface spike glycoprotein containing the receptor-binding domain (RBD). The interactions of SARS-CoV-2 with the host angiotensin-converting enzyme 2 (ACE2) receptor are mediated by RBD. It binds to the host ACE2 and influences viral replication and disease pathogenesis. Therefore, targeting the RBD to prevent SARSCoV-2 infections is of utmost importance. In this study, we used docking and molecular dynamics simulations to understand the binding effect of andrographolide on the SARS-CoV-2 spike protein. During docking, a strong binding affinity was observed between the ligand and the target receptor protein. MD results demonstrated higher conformational fluctuations in the ligand-free protein compared to the bound form. Several residues in the active sites make conformational rearrangements for the S protein to interact with the ligand. While RBD experiences conformational transition to gain more stability upon binding with the ligand. This binding is strengthened via several non-covalent interactions that make the complex structure more stable with higher binding affinity. Overall findings of the study may shed some valuable insights concerning the development of potential therapeutics in the strategies for COVID-19 prevention.

关键词： RBD S protein MD simulations protein-ligand complex binding energy

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Antibacterial activity of a novel compound isolated from Bacillus licheniformis for treating bacterial infections in fishes: An in-silico approach

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MOLECULAR AND CELLULAR BIOCHEMISTRY 2023年第12期478卷 2609-2620页

作者： Mondal, Haimanti Silvia, D. Delsy Gnana Emerson, I. Arnold Chandrasekaran, Natarajan Mukherjee, Amitava Thomas, John Vellore Inst Technol VIT Ctr Nanobiotechnol Vellore 632014 Tamil Nadu India Vellore Inst Technol VIT Sch Biosci & Technol Dept Biotechnol Bioinformat Programming Lab Vellore 632014 India

Aeromonas hydrophila is a fish pathogen which is widely associated with diseases related to freshwater fishes. Vibrio parahemolyticus is a major globally emerging marine pathogen. Seven novel compounds were extracted from the ethyl acetate extract of Bacillus licheniformis, a novel marine bacterium isolated from marine actinomycetes. The compounds were identified using Gas Chromatography-Mass Spectroscopy (GC-MS). Only one bioactive compound having potent antibacterial activity was virtually screened to understand its drug-like property according to Lipinski's rule. The core proteins, 3L6E and 3RYL from the pathogens, A. hydrophila and V. parahemolyticus were targeted for drug discovery. In the present in-silico approach, Phenol,2,4-Bis(1,1-Dimethylethyl) a potent bioactive compound present in Bacillus licheniformis was used to prevent the infection due to the two pathogens. Further, using this bioactive compound, molecular docking was done to block their specific target proteins. This bioactive compound satisfied all the five rules of Lipinski. Molecular docking result revealed the best binding efficacy of Phenol,2,4-Bis(1,1-Dimethylethyl) against 3L6E and 3RYL with - 4.24 kcal/mol and - 4.82 kcal/mol, respectively. Molecular dynamics (MD) simulations were also executed to determine the binding modes as well as the stability of the protein-ligand docking complexes in the dynamic structure. The in vitro toxicity analysis of this potent bioactive compound against Artemia salina was carried out, revealing the non-toxic nature of B. licheniformis ethyl acetate extract. Thus, the bioactive compound of B. licheniformis was found to be a potent antibacterial agent against A. hydrophila and V. parahemolyticus.

关键词： Phenol 2 4-Bis(1 1-Dimethylethyl) Bioactive compound Molecular docking Molecular dynamics and simulation Drug-like nature In vitro toxicity

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Construction and analysis of brain networks from different neuroimaging techniques

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INTERNATIONAL JOURNAL OF NEUROSCIENCE 2022年第8期132卷 745-766页

作者： Prajapati, Rutvi Emerson, Isaac Arnold Vellore Inst Technol Sch Biosci & Technol Dept Biotechnol Bioinformat Programming Lab Vellore 632014 Tamil Nadu India

Objectives The human brain is one of the most complicated biological structure in the entire universe. It is incredibly challenging to see how it functions, mainly during scatter-brain, and when diseases occur in human beings. The notable contribution from consultants, academic institutions, researchers, and others have contributed significant findings in the field of neuroscience and made substantial changes in different dimensions. The exploration of the brain is becoming an emerging area due to the rapid advancement of neuroimaging techniques. Methods Brain disorder is one such significant disease that is very difficult to diagnose, notably, which influence many people worldwide. Results In this review, we explore various tools and software to construc and analyze brain networks and different brain scanning methods. Further, this study addresses the research work related to brain networks, including various graph theory methods. More specifically, we present the different diagnostics techniques in brain disorders. Conclusion This review provides a detailed understanding of brain scans, tools, and brain network construction methods. Lastly, applications of complex network theory will contribute new insights about brain structure and function.

关键词： Neuroimaging brain scans graph theory brain network diagnosis

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Distributed saddle point problems: lower bounds, near-optimal and robust algorithms

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OPTIMIZATION METHODS & SOFTWARE 2025年

作者： Beznosikov, Aleksandr Samokhin, Valentin Gasnikov, Alexander RAS Lab Federated Learning Problems Ivannikov Inst Syst Programming Moscow Russia Moscow Inst Phys & Technol BRAIn Lab Moscow Russia Russian Presidential Acad Natl Econ & Publ Adm Ctr Appl Artificial Intelligence Moscow Russia Innopolis Univ Artificial Intelligence Ctr Innopolis Russia Artificial Intelligence Res Inst AGI Med Lab Moscow Russia RAS Dept Data Anal Neurosci Inst Informat Transmiss Problems Moscow Russia RAS Ivannikov Inst Syst Programming Artificial Intelligence Ctr Moscow Russia Moscow Inst Phys & Technol Lab Math Methods Optimizat Moscow Russia

This paper focuses on the distributed optimization of stochastic saddle point problems. The first part of the paper is devoted to lower bounds for the centralized and decentralized distributed methods for smooth (strongly) convex-(strongly) concave saddle point problems, as well as the near-optimal algorithms by which these bounds are achieved. Next, we present a new federated algorithm for centralized distributed saddle-point problems - Extra Step Local SGD. The theoretical analysis of the new method is carried out for strongly convex-strongly concave and non-convex-non-concave problems. In the experimental part of the paper, we show the effectiveness of our method in practice. In particular, we train GANs in a distributed manner.

关键词： Distributed optimization saddle point problems lower and upper bounds local methods convex optimization stochastic optimization

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Molecular dynamic study on PTEN frameshift mutations in breast cancer provide c2 domain as a potential biomarker

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JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 2022年第7期40卷 3132-3143页

作者： Karn, Rohit Emerson, Isaac Arnold VIT Sch Bio Sci & Technol Dept Biotechnol Bioinformat Programming Lab Vellore 632014 Tamil Nadu India

PTEN is a tumour suppressor gene known for regulating apoptosis, cell growth, and many other pathways. It is one of the most frequently mutated genes comprising the phosphatase domain (PD) and C terminal domain (C2). Direct therapeutic methods are not applicable for targeting PTEN because once gets mutated, it needs restoration. For mutant detection and restoration using PTEN mRNA there is a need to explore various mutations taking place in PTEN, identify their particular domains, and study their interactions within the cellular system. Here, we have tried to highlight a few such regions in the mutated PTEN of breast cancer patients. In this study, we have selected the top-most-occurring PTEN mutation in breast cancer and compared them to determine the specific properties of each mutation and its effect on functionality. Molecular dynamic simulation for 50 ns was performed on five structures to compare the structural behaviour of mutated PTEN in the system. Our finding suggests that frameshift mutations are more damaging and affect the c2 domain. Frameshift mutant fs_ACTT is the highest occurring as well as the most damaging mutation in all the compared structures. Docking study shows that substitution mutations D92H and R130Q causes loss of binding ability towards PIP2 in normal PTEN, interfering the dephosphorylation process. Overall, the C2 domain is more frequently mutated, and the amino acid residues in the C2 domain show more fluctuations compared to the other regions. Our study can provide the basis for selecting frequently mutated C2 domain as a potential therapeutic marker. Communicated by Ramaswamy H. Sarma

关键词： PTEN phosphatase domain C2 domain frameshift molecular dynamic simulation

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Database Creator for Mass Analysis of Peptides and Proteins, DC-MAPP: A Standalone Tool for Simplifying Manual Analysis of Mass Spectral Data to Identify Peptide/Protein Sequences

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JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023年第9期34卷 1962-1969页

作者： Pandeswari, Pandi Boomathi Isaac, Arnold Emerson Sabareesh, Varatharajan Vellore Inst Technol VIT Ctr Bioseparat Technol CBST Vellore 632014 Tamil Nadu India Univ Colorado Dept Med Anschutz Med Campus Aurora CO USA VIT Sch Bio Sci & Technol SBST Bioinformat Programming Lab Vellore 632014 Tamil Nadu India

Proteomic studiestypically involve the use of different typesof software for annotating experimental tandem mass spectrometricdata (MS/MS) and thereby simplifying the process of peptide and proteinidentification. For such annotations, these softwares calculate the m/z values of the peptide/protein precursorand fragment ions, for which a database of protein sequences mustbe provided as an input file. The calculated m/z values are stored as another database, which the userusually cannot view. Database Creator for Mass Analysis of Peptidesand Proteins (DC-MAPP) is a novel standalone software that can createcustom databases for "viewing" the calculated m/z values of precursor and fragment ions,prior to the database search. It contains three modules. Peptide/Proteinsequences as per user's choice can be entered as input to thefirst module for creating a custom database. In the second module, m/z values must be queried-in, which aresearched within the custom database to identify protein/peptide *** third module is suited for peptide mass fingerprinting, whichcan be used to analyze both ESI and MALDI mass spectral data. Thefeature of "viewing" the custom database can be helpfulnot only for better understanding the search engine processes, butalso for designing multiple reaction monitoring (MRM) methods. Post-translationalmodifications and protein isoforms can also be analyzed. Since, DC-MAPPrelies on the protein/peptide "sequences" for creatingcustom databases, it may not be applicable for the searches involvingspectral libraries. Python language was used for implementation, andthe graphical user interface was built with Page/Tcl, making thistool more user-friendly. It is freely available at https://***/DC-MAPP/.

关键词： MS Tandem Mass Spectrometry Proteomics Middle-DownProteomics Protein Sequencing Peptide Sequencing Databases

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Adaptive polarization-multiplexing foveated ghost imaging method based on deep learning

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OPTICS COMMUNICATIONS 2025年 577卷

作者： Shumigai, Vladimir S. Tuchin, Vladislav S. Moreva, Polina E. Starceva, Arina M. Ismagilov, Azat O. Nasedkin, Boris A. Tcypkin, Anton N. ITMO Univ Lab Quantum Proc & Measurements Kronverksky Pr 49 St Petersburg 197101 Russia ITMO Univ Inst Appl Comp Sci Kronverksky Pr 49 St Petersburg 197101 Russia Fac Informat Technol & Programming 49 Kronverksky Pr St Petersburg 197101 Russia

The challenges of remote sensing and area mapping have been recently addressed by using single-pixel imaging providing flexibility in modification and high sensitivity. This paper proposes a method for image reconstruction using polarization-multiplexing foveated ghost imaging and deep learning. The method suggested involves detection of noisy images and subsequent generation of modified foveated illumination patterns. In our proof- of-concept experiment, we used two polarization channels with independent illumination patterns and the YOLOv8 algorithm to accelerate the data acquisition rate. The images were reconstructed using a compressive sensing algorithm that employs the Moore-Penrose pseudoinverse for norm minimization. Two techniques were considered for detecting objects in noisy images: (i) processing the test image using filters, and (ii) training the model on a noisy dataset, with images simulating those produced by ghost imaging. According to the results, the second technique was selected for its efficient and accurate object detection in noisy images, while the first one failed to correctly detect target objects and required time-consuming filter application for each test image, significantly increasing the system's processing time. Using modified patterns and two polarization channels has been found to increase the data acquisition rate by 4.6 times. This makes the proposed method promising for high-speed demanding applications including remote sensing and object positioning.

关键词： Ghost imaging Illumination patterns Deep learning Polarization-multiplexing YOLOv8 Detection

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