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An Intelligent Prediction Model for Target Protein Identification in Hepatic Carcinoma Using Novel Graph Theory and ANN Model

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Computer Modeling in Engineering & Sciences 2022年第10期133卷 31-46页

作者： G.Naveen Sundar Stalin Selvaraj D.Narmadha K.Martin Sagayam a.amir anton jone Ayman A.Aly Dac-Nhuong Le Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatore641114India Centre for Nanotechnology&Advanced Biomaterials(CeNTAB) School of Chemical&BiotechnologySASTRA Deemed to be UniversityThanjavur613401India Department of ECE Karunya Institute of Technology and SciencesCoimbatore641114India Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Faculty of Information Technology Duy Tan UniversityDanang550000Vietnam

Hepatocellular carcinoma(HCC)is one major cause of cancer-related mortality around the ***,at advanced stages of HCC,systematic treatment options are currently *** a result,new pharmacological targetsmust be discovered regularly,and then tailored medicines against HCC must be *** this research,we used biomarkers of HCC to collect the protein interaction network related to ***,DC(Degree Centrality)was employed to assess the importance of each *** an improved Graph Coloring algorithm was used to rank the target proteins according to the interaction with the primary target protein after assessing the top ranked proteins related to ***,physio-chemical proteins are used to evaluate the outcome of the top ranked *** proposed graph theory and machine learning techniques have been compared with six existing *** the proposed approach,16 proteins have been identified as potential therapeutic drug targets for Hepatic *** is observable that the proposed method gives remarkable performance than the existing centrality measures in terms of Accuracy,Precision,Recall,Sensitivity,Specificity and F-measure.

关键词： Drug target detection hepatic carcinoma degree centrality graph coloring artificial neural network model

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Efficient Cloud Image Retrieval System Using Weighted-Inverted Index and Database Filtering Algorithms

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Journal of Electronic Science and Technology 2017年第2期15卷 161-168页

作者： Shuo-Fu Yen Jiann-jone Chen Yao-Hong Tsai Electrical Engineering Department Taiwan University of Science and Technology Information Management Department Hsuan Chuang University

With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scale image/video databases is the most important retrieval control target. In this paper, a cloud based content-based image retrieval （CBIR） scheme is presented. Database-categorizing based on weighted-inverted index （DCWII） and database f＇dtering algorithm （DFA） is used to speed up the features matching process. In the DCWII, the weights are assigned to discrete cosine transform （DCT） coefficients histograms and the database is categorized by weighted features. In addition, the DFA filters out the irrelevant image in the database to reduce unnecessary computation loading for features matching. Experiments show that the proposed CBIR scheme outperforms previous work in the precision-recall performance and maintains mean average precision （mAP） about 0.678 in the large-scale database comprising one million images. Our scheme also can reduce about 50% to 85% retrieval time by pre-filtering the database, which helps to improve the efficiency of retrieval systems.

关键词： Index Terms-Content-based image retrieval cloud computing MPEG-7.

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Relationship between Granite and Eclogite on the Southern Margin of the Qilian Mountains: Evidence from Zircon SHRIMP Agesof the Aolaoshan Granite

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Acta Geologica Sinica(English Edition) 2002年第1期76卷 118-125页

作者：吴才来 jone WOODEN YANG Jingsui Trevor IRELAND LIOU June Guang Wan Yusheng Shi Rendeng Institute of Geology CAGS Beijing 100037 Department of Geology Stanford University Stanford CA 94305-2115 USA

Zircon SHRIMP ages of the Aolaoshan granite on the south margin of the QilianMts. range from 445 + - 15.3 to 496 + - 7.6 Ma (averaging 473 Ma), belonging to the EarlyOrdovician. Geochemically, the granite is similar to I-type granite and, tectonically, was formed inan island-arc environment based on relevant diagrams for structural discriminations. Consideringalso the regional geology, the authors suggest that the granite is part of an ultrahigh-pressurebelt on the south margin of the Qilian Mts. and that its formation bears a close relationship tothis belt.

关键词： zircon SHRIMP age Aolaoshan granite ultrahigh-pressure metamorphic belt south margin of the Qilian Mts

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Photosynthetic and Molecular Markers of CO_2-mediated Photosynthetic Downregulation in Nodulated Alfalfa

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Journal of Integrative Plant Biology 2013年第8期55卷 721-734页

作者： Alvaro Sanz-Sez Gorka Erice Iker Aranjuelo Ricardo Aroca Juan Manuel Ruíz-Lozano jone Aguirreolea Juan Jos Irigoyen Manuel Sanchez-Diaz Department of Plant Biology University of NavarraPamplonaE-31008Spain Department of Plant Biology University of BarcelonaBarcelonaE-8028Spain Department of Plant Biology University of Illinois at Urbana-Champaign61801USA Agrobiotechnology Institute Public University of NavarraMutilva BajaE-31192Spain Department of Soil Microbiology and Symbiotic Systems Zaidin Experimental Station(CSIC)GranadaE-18008.Spain

Elevated CO2 leads to a decrease in potential net photosynthesis in long-term experiments and thus to a reduction in potential growth. This process is known as photosynthetic downregulation. There is no agreement on the definition of which parameters are the most sensitive for detecting CO2 acclimation. In order to investigate the most sensitive photosynthetic and molecular markers of CO2 acclimation, the effects of elevated CO2, and associated elevated temperature were analyzed in alfalfa plants inoculated with different Sinorhizobium meliloti strains. Plants （Medicago sativa L. cv. Aragon） were grown in summer or autumn in temperature gradient greenhouses （TGG）. At the end of the experiment, all plants showed acclimation in both seasons, especially under elevated summer temperatures. This was probably due to the lower nitrogen （N） availability caused by decreased N2-fixation under higher temperatures. Photosynthesis measured at growth CO2 concentration, rubisco in vitro activity and maximum rate of carboxylation were the most sensitive parameters for detecting downregulation. Severe acclimation was also related with decreases in leaf nitrogen content associated with declines in rubisco content （large and small subunits） and activity that resulted in a drop in photosynthesis. Despite the sensitivity of rubisco content as a marker of acclimation, it was not coordinated with gene expression, possibly due to a lag between gene transcription and protein translation.

关键词： Carbon dioxide Medicago sativa (alfalfa) photosynthetic downregulation RbcL and RbcS Sinorhizobium meliloti.

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Estimation of flea beetle damage in the field using a multistage deep learning-based solution

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture 2024年第3期13卷 18-31页

作者： Arantza Bereciartua-Pérez María Monzón Daniel Múgica Greta De Both Jeroen Baert Brittany Hedges Nicole Fox jone Echazarra Ramón Navarra-Mestre TECNALIA Basque Research and Technology Alliance(BRTA)Parque Tecnológico de BizkaiaAstondo Bidea.Edificio 70048160 DerioBizkaiaSpain BASF SE Speyererstrasse 267117 LimburgerhofGermany BASF Belgium(BBCC-Innovation Center Gent) Technologiepark-Zwijnaarde 1019052 GentBelgium BASF Canada Inc 51028 Quarry Park Blvd SECalgaryAlberta T2C 4P5Canada

Estimation of damage in plants is a key issue for crop ***,experts in the field manually assess the *** is a time-consuming task that can be automated thanks to the latest technology in computer vision(CV).The use of image-based systems and recently deep learning-based systems have provided good results in several agricultural *** image-based applications outperform expert evaluation in controlled environments,and now they are being progressively included in non-controlled field applications.A novel solution based on deep learning techniques in combination with image processingmethods is proposed to tackle the estimate of plant damage in the *** proposed solution is a two-stage *** a first stage,the single plants in the plots are detected by an object detection YOLO based *** a regression model is applied to estimate the damage of each individual *** solution has been developed and validated in oilseed rape plants to estimate the damage caused by flea *** crop detection model achieves a mean precision average of 91%with a mAP@0.50 of 0.99 and a mAP@0.95 of 0.91 for oilseed rape *** regression model to estimate up to 60%of damage degree in single plants achieves a MAE of 7.11,and R2 of 0.46 in comparison with manual evaluations done plant by plant by *** are deployed in a docker,and with a REST API communication protocol they can be inferred directly for images acquired in the field from a mobile device.

关键词： Convolutional neural networks Deep learning Plant phenotyping Damage estimation Plant crop detection and identification

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