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Biochemical mechanisms and molecular interactions of vitamins in cancer therapy

Cancer Pathogenesis and Therapy 2025年第1期3卷 3-15页

作者： Abdullahi T.Aborode Isreal A.Onifade Mercy M.Olorunshola Gladys O.Adenikinju Ibude J.Aruorivwooghene Adeboboye C.Femi Osasere Jude-Kelly Osayawe Abraham Osinuga Ebenezer A.Omojowolo Adekunle F.Adeoye Segun Olapade Ibroadhfrimed O.Acdoelaku Ogundepo D.Moyinoluwa Oluwatosin M.Adeyemo Godfred Y.Scott Ruth A.Ogbonna Emmanuel A.Fajemisin Omama Ehtasham Soyemi Toluwalashe Adetolase A.Bakre Ridwan O.Adesola Seto C.Ogunleye Nnenna R.Anyanwu Terungwa H.lorkula Department of Chemistry Mississippi State UniversityStarkvilleMS 39759USA Department of Biology University at AlbanyAlbanyNY 12222USA Department of Biological Sciences State University of New York at BinghantonBinghamtonNY 13902USA Department of Biological and Environmental Sciences University of Rhode IslandKingstonRI 02881USA Department of Biological Sciences University of Kansas Medical CenterKansasKS 66103USA Department of Microbiology Federal University of TechnologyAkure 340110Nigeria Department of Chemistry and Biochemistry Brigham Young UniversityProvoUT 84602USA 8 Department of Chemical and Biomolecular Engineering University of Nbraska-LincolnLincolnNE 68588USA9 Department of Chemistry University of AlbanyState University of New YorkAlbanyNY 12222USA Department of Mathematics and Statistics Georgia State UniversityAtlantaGA 30302USA Department of Chemistry University of LouisvilleLouisvilleKY 40208USA Department of Biochemistry Federal University of TechnologyAkure 340110Nigeria Deparment of Medical Diagnostics Kwame Nkrumah University of Science and TechnologyKumasi AK385Ghana Department of Research and Development Nasarawa State AIDS and STI Control ProgramNasarawaLafia 96210Nigeria Department of Integrative Biomedical Science University of Cape TownCape'Town 7701South Africa Department of Medicine and Surgery Karachi Medical and Dental CollegeKarachi 74700Pakistan Department of Medicine Lagos State University College of MedicineLagos 10010Nigeria Department of Veterinary Medicine Faculty of Veterinary MedicineUniversity of lbadanlbadan 200005Nigeria Faculty of Pharmaceutical Sciences University of JosPlateauJos 930003Nigeria

Recently,the potential role of vitamins in cancer therapy has attracted considerable research ***,the reported findings are inconsistent,with limited information on the biochemical and molecular interactions of different vitamins in various cancer ***,the presence of vitamin receptors in tumor cells suggests that vitamins play a significant role in the molecular and biochemical interactions in ***,studies on the efficacy of vitamin supplementation and dosage levels on tumor progression and mortality risk have yielded inconsistent ***,molecular and biochemical investigations have reported the function of vitamins in the proliferation,growth,and invasiveness of tumor cells,as well as in cell cycle arrest and inflammatory ***,different vitamins may regulate the cancer microenvironment by activating various molecular *** significantly affect immunological function,antioxidant defense,inflammation,and epigenetic control,and can improve treatment outcomes by affecting cell behavior and combating stress and DNA ***,further research is necessary to confirm the efficacy of vitamins,establish ideal dosages,and develop effective cancer prevention and treatment *** supplementation plans guided by medical knowledge are crucial to achieving optimal results in clinical and preclinical *** this review,we critically evaluated the effects of different vitamins on the risk and development of ***,we examined the potential of vitamin supplements to enhance the efficacy of drug therapy and counteract resistance mechanisms that often arise during cancer treatment.

关键词： Vitamins Cancer Biochemical Molecular interactions Therapy

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Epileptic Seizure Detection Based on Bandwidth Features of EEG Signals

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Procedia Computer Science 2019年 161卷 568-576页

作者： Diah P. Wulandari Nomala G.P. Putriz Yoyon K. Suprapto Santi W. Purnami Anda I. Juniani Wardah R. Islamiyah Dept. of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Statistics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Design and Manufacture Study Program Politeknik Perkapalan Negeri Surabaya Indonesia Dept. of Neurology Universitas Airlangga Surabaya Indonesia

Detection of seizure from EEG signals may help neurologist analyze the condition of epileptic patients. Variance in types of epileptic seizures provides challenge in recognizing the pattern of seizure signals from normal ones. In this paper, we discuss the classification of seizure and non-seizure conditions in epilepsy based on bandwidth features of EEG signals. These features were extracted using Empirical Mode Decomposition (EMD). This method decomposes signal into eight Intrinsic Mode Functions (IMFs) and its residue. The IMFs is then analyzed by Hilbert transform to obtain amplitude modulation bandwidth (BAM) and frequency modulation bandwidth (BFM). These features are fed as input to Support Vector Machine (SVM). We propose to combine the first four IMFs to extract bandwidth features and we implement this method on two data sets, public data set that contains signals from both extracranial and intracranial EEG, and data set OF Rumah Sakit Universitas Airlangga (RSUA) that contains signals from extracranial EEG only. The results showed that in general, the values of accuracy and specificity using combined features of first four IMFs outperformed those using features of single IMF, for all SVM kernel functions. The best accuracy and specificity on public data set were 97.3% and 98.25% respectively using RBF kernel, while the best sensitivity was gained using polynomial kernel based on only IMF1 bandwidth features by 99.0%. The experiments on RSUA data showed that the best accuracy was 96.5% and the best specificity was 99.38% when using Mexican Hat kernel based on combined features of first four IMFs, while the best sensitivity was 94% when using polynomial kernel based on only IMF1 bandwidth features.

关键词： EEG Empirical Mode Decomposition epilepsy machine learning

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A Middleware for OSCAR and Wireless Sensor Network Environments

A Middleware for OSCAR and Wireless Sensor Network Environme...

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IEEE International Symposium on High Performance Computing Systems and Applications (HPCS)

作者： D. J. Ferreira M.A.R. Dantas A. R. Pinto C. Montez Martius Rodriguez Departament of Informatics and Statistics (INE) University of Santa Catarina (UFSC) Florianopolis Brazil Graduation Program in Electrical Engineering University of Santa Catarina (UFSC) Florianopolis Brazil Fluminense Federal University (UFF) Niteroi Rio de Janeiro

There has been a steady increase in the use of mobile computing, especially with appliances such as sensors. The main challenge in this scenario is to increase network sensor lifetime. Cluster computing integration with wireless sensor networks can represent an interesting answer for high-performance computing to monitor several environments. Computational resources available from cluster configurations are cost effective components to improve several classes of applications in many organizations. Scientific, industrial and commercial applications are more relying on cluster performance. OSCAR is a useful open software approach to manage cluster of workstations. In this article, we present a middleware design and implementation that integrates an OSCAR cluster configuration with a wireless sensor network environment.

关键词： Middleware Wireless sensor networks Application software Distributed computing statistics Computer networks Workstations Space technology Batteries Prototypes

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Combining Hierarchical Inference in Ontologies with Heterogeneous Data Sources Improves Gene Function Prediction

Combining Hierarchical Inference in Ontologies with Heteroge...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoyu Jiang Naoki Nariai Martin Steffen Simon Kasif David Gold Eric D. Kolaczyk Department of Mathematics and Statistics Boston University Boston MA USA Bioinformatics Program Boston University Boston MA USA Department of Biomedical Engineering Boston University Boston MA USA Boston University Boston MA US

The study of gene function is critical in various genomic and proteomic fields. Due to the availability of tremendous amounts of different types of protein data, integrating these datasets to predict function has become a significant opportunity in computational biology. In this paper, to predict protein function we (i) develop a novel Bayesian framework combining relational,hierarchical and structural information with improvement in data usage efficiency over similar methods, and (ii) propose to use it in conjunction with an integrative protein-protein association network, STRING (Search Tool for the Retrieval of INteracting Genes/proteins), which combines information from seven different sources. At the heart of our work is accomplishing protein data integration in a concerted fashion with respect to algorithm and data source. Method performance is assessed by a 5-fold cross-validation in yeast on selected terms from the Molecular Function ontology in the Gene Ontology database. Results show that our combined use of the proposed computational framework and the protein network from STRING offers substantial improvements in prediction. The benefits of using an aggressively integrative network, such as STRING, may derive from the fact that although it is likely that the ultimate gene interaction matrix (including but not limited to protein-protein, genetic, or regulatory interactions) will be sparse, presently it is still known only incompletely in most organisms, and thus the use of multiple distinct data sources is rewarded.

关键词： Ontologies Protein engineering Genomics Bioinformatics Proteomics Computational biology Bayesian methods Information retrieval Heart Fungi

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Optimizing Neural Networks for Academic Performance Classification Using Feature Selection and Resampling Approach

Mendel

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Mendel 2023年第2期29卷 261-272页

作者： Supriyadi, Didi Purwanto, Purwanto Warsito, Budi Doctorate Program of Information Systems School of Postgraduate Studies Universitas Diponegoro Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Diponegoro Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Universitas Diponegoro Semarang Indonesia Department of Information System Faculty of Informatics Institut Teknologi Telkom Purwokerto Banyumas Indonesia

The features present in large datasets significantly affect the performance of machine learning models. Redundant and irrelevant features will be rejected and cause a decrease in machine learning model performance. This paper proposes HyFeS-ROS-ANN: Hybrid Feature Selection and Resampling combination method for binary classification using artificial neural network multilayer perceptron (MLP). The first stage of this approach is to use a combination of two feature selection methods to select essential features that are highly correlated with model performance. The second stage of this approach is to use a combination of resampling methods to handle unbalanced data classes. Both approaches are applied to the academic performance classification model using the MLP neural network. This research dataset is obtained using three-dimensional (3D) frameworks such as the Big Five Personality to determine the Personality that affects academic performance from the student dimension, the Family Influence Scale (FIS), which measures factors that affect academic performance from the family dimension, and Higher Education Institutions Service Quality (HEISQUAL) to measure service quality and its influence on academic performance from the Education institution dimension. Previous research shows that the CoR-ANN algorithm has a model accuracy rate of 94%. The research results based on the dataset show that our proposed method can improve accuracy by selecting more relevant and essential features in improving model performance. The results show that the features are reduced from 135 to 108, while the HyFS-ROS-ANN model for binary classification accuracy increases to 100%. © 2023, Brno University of Technology. All rights reserved.

关键词： Feature Selection

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Monitoring the navy's strategic reserve of ozone-depleting substances

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NAVAL ENGINEERS JOURNAL 2002年第4期114卷 95-104页

作者： Smith, DE Breslin, DA Toms, GS Norton, P DENNIS E. SMITH PH.D. is currently a statistical consultant for GEO-CENTERS INC. He received a B.A. in Mathematics (magna cum laude)from Wabash College and an M.S. in mathematics an M.S. in statistics and a Ph.D. in statistics from the University of Wisconsin. From November 1973 until February 1999 he was president and principal statistician at Desmatics Inc. where he participated in a number of studies involving the application of statistical methods to various technical problems in the Navy's environmental-protection and auxiliary-machinery programs. Dr. Smith was instrumental in developing the statistical procedures underlying the Navy's ODS reserve monitoring plan. DAVID A. BRESLIN P.E. is the director of technical operations for the Naval Surface Warfare Center and chairs the ASNE/SNAME Joint Committee on Environmental Engineering. He received a master of science in aerospace engineering from Virginia Tech a master of engineering administration in industrial and systems engineering from Virginia Tech and a bachelor of engineering in mechanical engineering from Stevens Institute of Technology. From 1993 to 1997 he was the program manager of the Naval Sea Systems Command's CFC and Halon Elimination Program and played a critical role in establishing the Navy's strategic reserve of ozone-depleting substances. Because of his many efforts he received a Stratospheric Ozone Protection Award in 1995 from the U.S. Environmental Protection Agency in recognition of “exceptional contributions to global environmental protection”. GREGORY S. TOMS P.E. is the CFC &Halon Elimination Program Manager for the Naval Sea Systems Command (NAVSEA). He received a master of science degree in mechanical engineering from the University of Maryland and a B.S. degree in mechanical engineering from West Virginia University. From 1984 to 1997 he was employed at the Naval Surface Warfare Center Carderock Division Annapolis Detachment (formerly David Taylor Research Center)where he worked on the development of advanced cent

The domestic production of the most powerful ozone-depleting substances (ODSs) has permanently ceased and the abundant supplies of a number of refrigerants, fire-fighting agents, and solvents, once taken for granted, are now a thing of the past. The Navy has adopted a successful strategy for addressing the threat posed by the unavailability of ODSs. That strategy includes relying on a strategic reserve of ODSs. Considering that the strategic reserve will play a critical role in sustained Fleet operations into the middle of the century, it is important to record the history of the reserve and to document lessons learned in order to educate future generations that may face similar challenges. This is the third of three papers that cover all aspects of the Navy's strategic reserve of ODSs. Breslin (1999) documented the history of the Navy's effort to size, establish, and monitor a reserve designed to support Fleet operations until the middle of the century. Breslin, Smith, and Toms (2000) addressed in detail the process used to estimate the required size of the reserve, including the survey method, statistical analyses, and sizing algorithms. This paper discusses the Navy's plan for monitoring the Navy's strategic reserves of ODSs and describes the methods used in the monitoring process. In particular it discusses the underlying assumptions and statistical techniques associated with the Navy's prediction of the expected drawdown of the reserves. A comparison of actual data, collected since the strategic reserves began operations in 1995, with the predicted reserve drawdown, is included in the paper.

关键词： Marine engineering

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Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

Exploring Low Rate Dos Attack Detection Methods: A Bibliomet...

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International Conference on Data and Software engineering (ICODSE)

作者： Anteng Widodo Adian Fatchur Rochim Budi Warsito Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Department of Information System Faculty of Engineering Muria Kudus University Kudus Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future.

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A statistical method for estimating the life expectancy of certain shipboard equipment

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NAVAL ENGINEERS JOURNAL 2002年第1期114卷 27-32页

作者： Smith, DE Breslin, DA Toms, GS DENNIS E. SMITH PH.D. is a statistical consultant for Geo-Centers Inc. He received a B.A. in mathe-matics (magna cum laude) from Wabash College and an M.S. in mathematics an M.S. in statistics and a Ph.D. in statistics from the University of Wisconsin. From November 1973 until February 1999 he was President and Principal Statistician at Desmatics Inc. where he participated in a number of studies involving the application of statistical methods to various technical problems in the Navy's environmental-protection and auxiliary-machinery programs. Dr. Smith was instrumental in developing the statistical procedures underlying the Navy's ODS reserve monitoring plan. DAVID A. BRESLIN P.E. is the Director of Technical Operations for the Naval Surface Warfare Center and is the chairperson of ASNE's Committee on Environmental Engineering. He received a master of science degree in aerospace engineering from Virginia Tech a master of engineering administration in indus-trial & systems engineering from Virginia Tech and a bachelor of engineering in mechanical engineering from Stevens Institute of Technology. From 1993 to 1997 he was the program manager of the Naval Sea Systems Command's CFC & Halon Elimination Program and played a critical role in establishing the Navy's strategic reserve of ozone-depleting substances. Because of his many efforts he received a Stratospheric Ozone Protection Award in 1995 from the U.S. Environmental Protection Agency in recogni-tion of “exceptional contributions to global environmental protection”. GREGORY S. TOMS P.E. is the CFC & Halon Elimination Program Manager for the Naval Sea Systems Command (NAVSEA). He received a master of science degree in mechanical engineering from the University of Maryland and a B.S. degree in mechanical engineering from West Virginia University. From 1984 to 1997 he was employed at the Naval Surface Warfare Center Carderock Division Annapolis Detachment (formerly David Taylor Research Center) where he worked on the development of advanced cent

This paper reviews an inexpensive statistical-sampling methodology that can be used to determine the attrition rates of certain types of shipboard equipment. The methodology relies on surveys that are conducted in conjunction with regularly scheduled ship visits over a period of time and a statistical analysis of the survey results. The statistical technique allows for reasonably accurate estimates to be made of attrition rates. The specific example cited in this paper is related to galley-type refrigeration equipment, such as salad chillers, beverage dispensers, et cetera. In the case of galley-type refrigeration equipment, the Navy needs to know whether the Fleet's inventory of equipment relying on Class I Ozone-Depleting Substances (ODSs) will be replaced through natural attrition by the end of fiscal year (FY) 2005, at which time supplies of ODS-based refrigerants will no longer be available for use by ship galleys. This paper outlines the process used to gather data, the analysis of the data, and concludes that the Navy's inventory of galley equipment using Class I ODSs will be replaced through natural attrition by the end of FY05. The methodology reviewed by this paper has wider applications. For various types of shipboard equipment, the methodology can also be used to estimate with reasonable accuracy life expectancies, configurations, conditions, and other items of interest.

关键词： Ship equipment

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Additive Poisson regression via forced categorical covariates and generalized fused Lasso 27

Additive Poisson regression via forced categorical covariate...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Yamamura, Mariko Ohishi, Mineaki Yanagihara, Hirokazu Department of Statistics Radiation Effects Research Foundation 5-2 Hijiyama Park Minami-ku Hiroshima732-0815 Japan Center for Data-driven Science and Artificial Intelligence Tohoku University 41 Kawauchi Aoba-ku Miyagi Sendai980-8576 Japan Mathematics Program Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan

In this study, we use the log-linear link function and propose a generalized fused Lasso (GFL) Poisson regression model in which the nonlinear trend is discretely represented by categorical covariates in the additive model. We use the coordinate descent algorithm for the estimation and show that the optimal solution in a coordinate axis can be found explicitly. To demonstrate the proposed approach, we analyze Japanese crime data. Simulation results showed a fitness ratio for true fusion to be more than 90% in total, demonstrating the reliability of the estimates. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Additive Poisson regression Coordinate descent algorithm Generalized fused Lasso Spatio-temporal analysis

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VLSI Design of Tree-Based Inference for Low-Power Learning Applications

VLSI Design of Tree-Based Inference for Low-Power Learning A...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Brunno A. Abreu Mateus Grellert Sergio Bampi Graduate Program in Microelectronics (PGMICRO) INF-UFRGS Federal University of Rio Grande do Sul Porto Alegre Brazil Department of Informatics and Statistics − INE Federal University of Santa Catarina (UFSC) Florianópolis Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

The use of Machine Learning techniques in battery-powered devices has increased in recent years. Therefore, evaluating power-accuracy trade-offs of inference models has become important. This work explores decision trees architectures, analyzing the effects of model complexity and approximation in power and accuracy. By quantizing the inputs to limited widths, we increased the accuracy of the models in up to 8.7% compared to precise cases. In terms of power, the variation in model complexity was more significant than in input width, as we obtained reductions of up to 97% by decreasing the tree depth, compared to 88% when decreasing the width.

关键词： Complexity theory Hardware Integrated circuit modeling Training Analytical models Decision trees Quantization (signal)

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