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heatmap</title> <meta name="description" content="Search results for: heatmap"> <meta name="keywords" content="heatmap"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="heatmap"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 13</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: heatmap</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13</span> Performance Analysis with the Combination of Visualization and Classification Technique for Medical Chatbot</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shajida%20M.">Shajida M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sakthiyadharshini%20N.%20P."> Sakthiyadharshini N. P.</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamalesh%20S."> Kamalesh S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Aswitha%20B."> Aswitha B.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural Language Processing (NLP) continues to play a strategic part in complaint discovery and medicine discovery during the current epidemic. This abstract provides an overview of performance analysis with a combination of visualization and classification techniques of NLP for a medical chatbot. Sentiment analysis is an important aspect of NLP that is used to determine the emotional tone behind a piece of text. This technique has been applied to various domains, including medical chatbots. In this, we have compared the combination of the decision tree with heatmap and Naïve Bayes with Word Cloud. The performance of the chatbot was evaluated using accuracy, and the results indicate that the combination of visualization and classification techniques significantly improves the chatbot's performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentimental%20analysis" title="sentimental analysis">sentimental analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20chatbot" title=" medical chatbot"> medical chatbot</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=heatmap" title=" heatmap"> heatmap</a>, <a href="https://publications.waset.org/abstracts/search?q=na%C3%AFve%20bayes" title=" naïve bayes"> naïve bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20cloud" title=" word cloud"> word cloud</a> </p> <a href="https://publications.waset.org/abstracts/165924/performance-analysis-with-the-combination-of-visualization-and-classification-technique-for-medical-chatbot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165924.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">74</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12</span> Using Machine Learning Techniques to Extract Useful Information from Dark Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nigar%20Hussain">Nigar Hussain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is a subset of big data. Dark data means those data in which we fail to use for future decisions. There are many issues in existing work, but some need powerful tools for utilizing dark data. It needs sufficient techniques to deal with dark data. That enables users to exploit their excellence, adaptability, speed, less time utilization, execution, and accessibility. Another issue is the way to utilize dark data to extract helpful information to settle on better choices. In this paper, we proposed upgrade strategies to remove the dark side from dark data. Using a supervised model and machine learning techniques, we utilized dark data and achieved an F1 score of 89.48%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=dark%20data" title=" dark data"> dark data</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=heatmap" title=" heatmap"> heatmap</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/191942/using-machine-learning-techniques-to-extract-useful-information-from-dark-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191942.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">28</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11</span> Deep Neural Network Approach for Navigation of Autonomous Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mayank%20Raj">Mayank Raj</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20G.%20Narendra"> V. G. Narendra </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20vehicles" title="autonomous vehicles">autonomous vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a> </p> <a href="https://publications.waset.org/abstracts/130762/deep-neural-network-approach-for-navigation-of-autonomous-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130762.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10</span> Analysis of the Volatile Organic Compounds of Tillandsia Flowers by HS-SPME/GC-MS</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexandre%20Gonzalez">Alexandre Gonzalez</a>, <a href="https://publications.waset.org/abstracts/search?q=Zohra%20Benfodda"> Zohra Benfodda</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20B%C3%A9nim%C3%A9lis"> David Bénimélis</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Xavier%20Fontaine"> Jean-Xavier Fontaine</a>, <a href="https://publications.waset.org/abstracts/search?q=Roland%20Molini%C3%A9"> Roland Molinié</a>, <a href="https://publications.waset.org/abstracts/search?q=Patrick%20Meffre"> Patrick Meffre</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Volatile organic compounds (VOCs) emitted by flowers play an important role in plant ecology. However, the Tillandsia genus has been scarcely studied according to the VOCs emitted by flowers. Tillandsia are epiphytic flowering plants belonging to the Bromeliaceae family. The VOCs composition of twelve unscented and two faint-scented Tillandsia species was studied. The headspace solid phase microextraction coupled with gas chromatography combined with mass spectrometry method was used to explore the chemical diversity of the VOCs. This study allowed the identification of 65 VOCs among the fourteen species, and between six to twenty-five compounds were identified in each of the species. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tillandsia" title="tillandsia">tillandsia</a>, <a href="https://publications.waset.org/abstracts/search?q=headspace%20solid%20phase%20microextraction%20%28HS-SPME%29" title=" headspace solid phase microextraction (HS-SPME)"> headspace solid phase microextraction (HS-SPME)</a>, <a href="https://publications.waset.org/abstracts/search?q=gas%20chromatography-mass%20spectrometry%20%28GC-MS%29" title=" gas chromatography-mass spectrometry (GC-MS)"> gas chromatography-mass spectrometry (GC-MS)</a>, <a href="https://publications.waset.org/abstracts/search?q=scentless%20flowers" title=" scentless flowers"> scentless flowers</a>, <a href="https://publications.waset.org/abstracts/search?q=volatile%20organic%20compounds%20%28VOCs%29" title=" volatile organic compounds (VOCs)"> volatile organic compounds (VOCs)</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA%20analysis" title=" PCA analysis"> PCA analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=heatmap" title=" heatmap"> heatmap</a> </p> <a href="https://publications.waset.org/abstracts/152016/analysis-of-the-volatile-organic-compounds-of-tillandsia-flowers-by-hs-spmegc-ms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152016.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">124</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9</span> Exploring Relationship between Attention and Consciousness</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aarushi%20Agarwal">Aarushi Agarwal</a>, <a href="https://publications.waset.org/abstracts/search?q=Tara%20Singh"> Tara Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Anju%20Lata%20%20Singh"> Anju Lata Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Trayambak%20Tiwari"> Trayambak Tiwari</a>, <a href="https://publications.waset.org/abstracts/search?q=Indramani%20Lal%20Singh"> Indramani Lal Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The existing interdependent relationship between attention and consciousness has been put to debate since long. To testify the nature, dual-task paradigm has been used to simultaneously manipulate awareness and attention. With central discrimination task which is attentional demanding, participants also perform simple discrimination task in the periphery in near absence of attention. Individual-based analysis of performance accuracy in single and dual condition showed and above chance level performance i.e. more than 80%. In order to widen the understanding of extent of discrimination carried in near absence of attention, natural image and its geometric equivalent shape were presented in the periphery; synthetic objects accounted to lower level of performance than natural objects in dual condition. The gaze plot and heatmap indicate that peripheral performance do not necessarily involve saccade every time, verifying the discrimination in the periphery was in near absence of attention. Thus our studies show an interdependent nature of attention and awareness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attention" title="attention">attention</a>, <a href="https://publications.waset.org/abstracts/search?q=awareness" title=" awareness"> awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=dual%20task%20paradigm" title=" dual task paradigm"> dual task paradigm</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20and%20geometric%20images" title=" natural and geometric images"> natural and geometric images</a> </p> <a href="https://publications.waset.org/abstracts/83385/exploring-relationship-between-attention-and-consciousness" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83385.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">518</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> Applying Massively Parallel Sequencing to Forensic Soil Bacterial Profiling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hui%20Li">Hui Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Xueying%20Zhao"> Xueying Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Ke%20Ma"> Ke Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu%20Cao"> Yu Cao</a>, <a href="https://publications.waset.org/abstracts/search?q=Fan%20Yang"> Fan Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Qingwen%20Xu"> Qingwen Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenbin%20Liu"> Wenbin Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Soil can often link a person or item to a crime scene, which makes it a valuable evidence in forensic casework. Several techniques have been utilized in forensic soil discrimination in previous studies. Because soil contains a vast number of microbiomes, the analyse of soil microbiomes is expected to be a potential way to characterise soil evidence. In this study, we applied massively parallel sequencing (MPS) to soil bacterial profiling on the Ion Torrent Personal Genome Machine (PGM). Soils from different regions were collected repeatedly. V-region 3 and 4 of Bacterial 16S rRNA gene were detected by MPS. Operational taxonomic units (OTU, 97%) were used to analyse soil bacteria. Several bioinformatics methods (PCoA, NMDS, Metastats, LEfse, and Heatmap) were applied in bacterial profiles. Our results demonstrate that MPS can provide a more detailed picture of the soil microbiomes and the composition of soil bacterial components from different region was individualistic. In conclusion, the utility of soil bacterial profiling via MPS of the 16S rRNA gene has potential value in characterising soil evidences and associating them with their place of origin, which can play an important role in forensic science in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bacterial%20profiling" title="bacterial profiling">bacterial profiling</a>, <a href="https://publications.waset.org/abstracts/search?q=forensic" title=" forensic"> forensic</a>, <a href="https://publications.waset.org/abstracts/search?q=massively%20parallel%20sequencing" title=" massively parallel sequencing"> massively parallel sequencing</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20evidence" title=" soil evidence"> soil evidence</a> </p> <a href="https://publications.waset.org/abstracts/80561/applying-massively-parallel-sequencing-to-forensic-soil-bacterial-profiling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80561.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">563</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> Spatiotemporal Neural Network for Video-Based Pose Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bin%20Ji">Bin Ji</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai%20Xu"> Kai Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Shunyu%20Yao"> Shunyu Yao</a>, <a href="https://publications.waset.org/abstracts/search?q=Jingjing%20Liu"> Jingjing Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ye%20Pan"> Ye Pan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20long%20short-term%20memory" title="convolutional long short-term memory">convolutional long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20pose%20estimation" title=" human pose estimation"> human pose estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=spatiotemporal%20series" title=" spatiotemporal series"> spatiotemporal series</a> </p> <a href="https://publications.waset.org/abstracts/129867/spatiotemporal-neural-network-for-video-based-pose-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129867.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> C-eXpress: A Web-Based Analysis Platform for Comparative Functional Genomics and Proteomics in Human Cancer Cell Line, NCI-60 as an Example</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chi-Ching%20Lee">Chi-Ching Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Po-Jung%20Huang"> Po-Jung Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Kuo-Yang%20Huang"> Kuo-Yang Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Petrus%20Tang"> Petrus Tang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Recent advances in high-throughput research technologies such as new-generation sequencing and multi-dimensional liquid chromatography makes it possible to dissect the complete transcriptome and proteome in a single run for the first time. However, it is almost impossible for many laboratories to handle and analysis these “BIG” data without the support from a bioinformatics team. We aimed to provide a web-based analysis platform for users with only limited knowledge on bio-computing to study the functional genomics and proteomics. Method: We use NCI-60 as an example dataset to demonstrate the power of the web-based analysis platform and data delivering system: C-eXpress takes a simple text file that contain the standard NCBI gene or protein ID and expression levels (rpkm or fold) as input file to generate a distribution map of gene/protein expression levels in a heatmap diagram organized by color gradients. The diagram is hyper-linked to a dynamic html table that allows the users to filter the datasets based on various gene features. A dynamic summary chart is generated automatically after each filtering process. Results: We implemented an integrated database that contain pre-defined annotations such as gene/protein properties (ID, name, length, MW, pI); pathways based on KEGG and GO biological process; subcellular localization based on GO cellular component; functional classification based on GO molecular function, kinase, peptidase and transporter. Multiple ways of sorting of column and rows is also provided for comparative analysis and visualization of multiple samples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer" title="cancer">cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=visualization" title=" visualization"> visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=database" title=" database"> database</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20annotation" title=" functional annotation"> functional annotation</a> </p> <a href="https://publications.waset.org/abstracts/16079/c-express-a-web-based-analysis-platform-for-comparative-functional-genomics-and-proteomics-in-human-cancer-cell-line-nci-60-as-an-example" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16079.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">618</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Relative Entropy Used to Determine the Divergence of Cells in Single Cell RNA Sequence Data Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=An%20Chengrui">An Chengrui</a>, <a href="https://publications.waset.org/abstracts/search?q=Yin%20Zi"> Yin Zi</a>, <a href="https://publications.waset.org/abstracts/search?q=Wu%20Bingbing"> Wu Bingbing</a>, <a href="https://publications.waset.org/abstracts/search?q=Ma%20Yuanzhu"> Ma Yuanzhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jin%20Kaixiu"> Jin Kaixiu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen%20Xiao"> Chen Xiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Ouyang%20Hongwei"> Ouyang Hongwei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Single cell RNA sequence (scRNA-seq) is one of the effective tools to study transcriptomics of biological processes. Recently, similarity measurement of cells is Euclidian distance or its derivatives. However, the process of scRNA-seq is a multi-variate Bernoulli event model, thus we hypothesize that it would be more efficient when the divergence between cells is valued with relative entropy than Euclidian distance. In this study, we compared the performances of Euclidian distance, Spearman correlation distance and Relative Entropy using scRNA-seq data of the early, medial and late stage of limb development generated in our lab. Relative Entropy is better than other methods according to cluster potential test. Furthermore, we developed KL-SNE, an algorithm modifying t-SNE whose definition of divergence between cells Euclidian distance to Kullback–Leibler divergence. Results showed that KL-SNE was more effective to dissect cell heterogeneity than t-SNE, indicating the better performance of relative entropy than Euclidian distance. Specifically, the chondrocyte expressing Comp was clustered together with KL-SNE but not with t-SNE. Surprisingly, cells in early stage were surrounded by cells in medial stage in the processing of KL-SNE while medial cells neighbored to late stage with the process of t-SNE. This results parallel to Heatmap which showed cells in medial stage were more heterogenic than cells in other stages. In addition, we also found that results of KL-SNE tend to follow Gaussian distribution compared with those of the t-SNE, which could also be verified with the analysis of scRNA-seq data from another study on human embryo development. Therefore, it is also an effective way to convert non-Gaussian distribution to Gaussian distribution and facilitate the subsequent statistic possesses. Thus, relative entropy is potentially a better way to determine the divergence of cells in scRNA-seq data analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Single%20cell%20RNA%20sequence" title="Single cell RNA sequence">Single cell RNA sequence</a>, <a href="https://publications.waset.org/abstracts/search?q=Similarity%20measurement" title=" Similarity measurement"> Similarity measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=Relative%20Entropy" title=" Relative Entropy"> Relative Entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=KL-SNE" title=" KL-SNE"> KL-SNE</a>, <a href="https://publications.waset.org/abstracts/search?q=t-SNE" title=" t-SNE"> t-SNE</a> </p> <a href="https://publications.waset.org/abstracts/63441/relative-entropy-used-to-determine-the-divergence-of-cells-in-single-cell-rna-sequence-data-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63441.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">340</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Efficacy and Safety of Updated Target Therapies for Treatment of Platinum-Resistant Recurrent Ovarian Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Hang%20Leung">John Hang Leung</a>, <a href="https://publications.waset.org/abstracts/search?q=Shyh-Yau%20Wang"> Shyh-Yau Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hei-Tung%20Yip"> Hei-Tung Yip</a>, <a href="https://publications.waset.org/abstracts/search?q=Fion"> Fion</a>, <a href="https://publications.waset.org/abstracts/search?q=Ho%20Tsung-chin"> Ho Tsung-chin</a>, <a href="https://publications.waset.org/abstracts/search?q=Agnes%20LF%20Chan"> Agnes LF Chan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Objectives: Platinum-resistant ovarian cancer has a short overall survival of 9–12 months and limited treatment options. The combination of immunotherapy and targeted therapy appears to be a promising treatment option for patients with ovarian cancer, particularly to patients with platinum-resistant recurrent ovarian cancer (PRrOC). However, there are no direct head-to-head clinical trials comparing their efficacy and toxicity. We, therefore, used a network to directly and indirectly compare seven newer immunotherapies or targeted therapies combined with chemotherapy in platinum-resistant relapsed ovarian cancer, including antibody-drug conjugates, PD-1 (Programmed death-1) and PD-L1 (Programmed death-ligand 1), PARP (Poly ADP-ribose polymerase) inhibitors, TKIs (Tyrosine kinase inhibitors), and antiangiogenic agents. Methods: We searched PubMed (Public/Publisher MEDLINE), EMBASE (Excerpta Medica Database), and the Cochrane Library electronic databases for phase II and III trials involving PRrOC patients treated with immunotherapy or targeted therapy plus chemotherapy. The quality of included trials was assessed using the GRADE method. The primary outcomes compared were progression-free survival, the secondary outcomes were overall survival and safety. Results: Seven randomized controlled trials involving a total of 2058 PRrOC patients were included in this analysis. Bevacizumab plus chemotherapy showed statistically significant differences in PFS (Progression-free survival) but not OS (Overall survival) for all interested targets and immunotherapy regimens; however, according to the heatmap analysis, bevacizumab plus chemotherapy had a statistically significant risk of ≥grade 3 SAEs (Severe adverse effects), particularly hematological severe adverse events (neutropenia, anemia, leukopenia, and thrombocytopenia). Conclusions: Bevacizumab plus chemotherapy resulted in better PFS as compared with all interested regimens for the treatment of PRrOC. However, statistical differences in SAEs as bevacizumab plus chemotherapy is associated with a greater risk for hematological SAE. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=platinum-resistant%20recurrent%20ovarian%20cancer" title="platinum-resistant recurrent ovarian cancer">platinum-resistant recurrent ovarian cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20meta-analysis" title=" network meta-analysis"> network meta-analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=immune%20checkpoint%20inhibitors" title=" immune checkpoint inhibitors"> immune checkpoint inhibitors</a>, <a href="https://publications.waset.org/abstracts/search?q=target%20therapy" title=" target therapy"> target therapy</a>, <a href="https://publications.waset.org/abstracts/search?q=antiangiogenic%20agents" title=" antiangiogenic agents"> antiangiogenic agents</a> </p> <a href="https://publications.waset.org/abstracts/163813/efficacy-and-safety-of-updated-target-therapies-for-treatment-of-platinum-resistant-recurrent-ovarian-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163813.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">79</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Comparing Remote Sensing and in Situ Analyses of Test Wheat Plants as Means for Optimizing Data Collection in Precision Agriculture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Endalkachew%20Abebe%20Kebede">Endalkachew Abebe Kebede</a>, <a href="https://publications.waset.org/abstracts/search?q=Bojin%20Bojinov"> Bojin Bojinov</a>, <a href="https://publications.waset.org/abstracts/search?q=Andon%20Vasilev%20Andonov"> Andon Vasilev Andonov</a>, <a href="https://publications.waset.org/abstracts/search?q=Orhan%20Dengiz"> Orhan Dengiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remote sensing has a potential application in assessing and monitoring the plants' biophysical properties using the spectral responses of plants and soils within the electromagnetic spectrum. However, only a few reports compare the performance of different remote sensing sensors against in-situ field spectral measurement. The current study assessed the potential applications of open data source satellite images (Sentinel 2 and Landsat 9) in estimating the biophysical properties of the wheat crop on a study farm found in the village of OvchaMogila. A Landsat 9 (30 m resolution) and Sentinel-2 (10 m resolution) satellite images with less than 10% cloud cover have been extracted from the open data sources for the period of December 2021 to April 2022. An Unmanned Aerial Vehicle (UAV) has been used to capture the spectral response of plant leaves. In addition, SpectraVue 710s Leaf Spectrometer was used to measure the spectral response of the crop in April at five different locations within the same field. The ten most common vegetation indices have been selected and calculated based on the reflectance wavelength range of remote sensing tools used. The soil samples have been collected in eight different locations within the farm plot. The different physicochemical properties of the soil (pH, texture, N, P₂O₅, and K₂O) have been analyzed in the laboratory. The finer resolution images from the UAV and the Leaf Spectrometer have been used to validate the satellite images. The performance of different sensors has been compared based on the measured leaf spectral response and the extracted vegetation indices using the five sampling points. A scatter plot with the coefficient of determination (R2) and Root Mean Square Error (RMSE) and the correlation (r) matrix prepared using the corr and heatmap python libraries have been used for comparing the performance of Sentinel 2 and Landsat 9 VIs compared to the drone and SpectraVue 710s spectrophotometer. The soil analysis revealed the study farm plot is slightly alkaline (8.4 to 8.52). The soil texture of the study farm is dominantly Clay and Clay Loam.The vegetation indices (VIs) increased linearly with the growth of the plant. Both the scatter plot and the correlation matrix showed that Sentinel 2 vegetation indices have a relatively better correlation with the vegetation indices of the Buteo dronecompared to the Landsat 9. The Landsat 9 vegetation indices somewhat align better with the leaf spectrometer. Generally, the Sentinel 2 showed a better performance than the Landsat 9. Further study with enough field spectral sampling and repeated UAV imaging is required to improve the quality of the current study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landsat%209" title="landsat 9">landsat 9</a>, <a href="https://publications.waset.org/abstracts/search?q=leaf%20spectrometer" title=" leaf spectrometer"> leaf spectrometer</a>, <a href="https://publications.waset.org/abstracts/search?q=sentinel%202" title=" sentinel 2"> sentinel 2</a>, <a href="https://publications.waset.org/abstracts/search?q=UAV" title=" UAV"> UAV</a> </p> <a href="https://publications.waset.org/abstracts/152005/comparing-remote-sensing-and-in-situ-analyses-of-test-wheat-plants-as-means-for-optimizing-data-collection-in-precision-agriculture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152005.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">107</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Differential Expression Analysis of Busseola fusca Larval Transcriptome in Response to Cry1Ab Toxin Challenge</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bianca%20Peterson">Bianca Peterson</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomasz%20J.%20Sa%C5%84ko"> Tomasz J. Sańko</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20C.%20Bezuidenhout"> Carlos C. Bezuidenhout</a>, <a href="https://publications.waset.org/abstracts/search?q=Johnnie%20Van%20Den%20Berg"> Johnnie Van Den Berg</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Busseola fusca (Fuller) (Lepidoptera: Noctuidae), the maize stem borer, is a major pest in sub-Saharan Africa. It causes economic damage to maize and sorghum crops and has evolved non-recessive resistance to genetically modified (GM) maize expressing the Cry1Ab insecticidal toxin. Since B. fusca is a non-model organism, very little genomic information is publicly available, and is limited to some cytochrome c oxidase I, cytochrome b, and microsatellite data. The biology of B. fusca is well-described, but still poorly understood. This, in combination with its larval-specific behavior, may pose problems for limiting the spread of current resistant B. fusca populations or preventing resistance evolution in other susceptible populations. As part of on-going research into resistance evolution, B. fusca larvae were collected from Bt and non-Bt maize in South Africa, followed by RNA isolation (15 specimens) and sequencing on the Illumina HiSeq 2500 platform. Quality of reads was assessed with FastQC, after which Trimmomatic was used to trim adapters and remove low quality, short reads. Trinity was used for the de novo assembly, whereas TransRate was used for assembly quality assessment. Transcript identification employed BLAST (BLASTn, BLASTp, and tBLASTx comparisons), for which two libraries (nucleotide and protein) were created from 3.27 million lepidopteran sequences. Several transcripts that have previously been implicated in Cry toxin resistance was identified for B. fusca. These included aminopeptidase N, cadherin, alkaline phosphatase, ATP-binding cassette transporter proteins, and mitogen-activated protein kinase. MEGA7 was used to align these transcripts to reference sequences from Lepidoptera to detect mutations that might potentially be contributing to Cry toxin resistance in this pest. RSEM and Bioconductor were used to perform differential gene expression analysis on groups of B. fusca larvae challenged and unchallenged with the Cry1Ab toxin. Pairwise expression comparisons of transcripts that were at least 16-fold expressed at a false-discovery corrected statistical significance (p) ≤ 0.001 were extracted and visualized in a hierarchically clustered heatmap using R. A total of 329,194 transcripts with an N50 of 1,019 bp were generated from the over 167.5 million high-quality paired-end reads. Furthermore, 110 transcripts were over 10 kbp long, of which the largest one was 29,395 bp. BLAST comparisons resulted in identification of 157,099 (47.72%) transcripts, among which only 3,718 (2.37%) were identified as Cry toxin receptors from lepidopteran insects. According to transcript expression profiles, transcripts were grouped into three subclusters according to the similarity of their expression patterns. Several immune-related transcripts (pathogen recognition receptors, antimicrobial peptides, and inhibitors) were up-regulated in the larvae feeding on Bt maize, indicating an enhanced immune status in response to toxin exposure. Above all, extremely up-regulated arylphorin genes suggest that enhanced epithelial healing is one of the resistance mechanisms employed by B. fusca larvae against the Cry1Ab toxin. This study is the first to provide a resource base and some insights into a potential mechanism of Cry1Ab toxin resistance in B. fusca. Transcriptomic data generated in this study allows identification of genes that can be targeted by biotechnological improvements of GM crops. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epithelial%20healing" title="epithelial healing">epithelial healing</a>, <a href="https://publications.waset.org/abstracts/search?q=Lepidoptera" title=" Lepidoptera"> Lepidoptera</a>, <a href="https://publications.waset.org/abstracts/search?q=resistance" title=" resistance"> resistance</a>, <a href="https://publications.waset.org/abstracts/search?q=transcriptome" title=" transcriptome"> transcriptome</a> </p> <a href="https://publications.waset.org/abstracts/71571/differential-expression-analysis-of-busseola-fusca-larval-transcriptome-in-response-to-cry1ab-toxin-challenge" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71571.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">203</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Times2D: A Time-Frequency Method for Time Series Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Nematirad">Reza Nematirad</a>, <a href="https://publications.waset.org/abstracts/search?q=Anil%20Pahwa"> Anil Pahwa</a>, <a href="https://publications.waset.org/abstracts/search?q=Balasubramaniam%20Natarajan"> Balasubramaniam Natarajan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=derivative%20patterns" title="derivative patterns">derivative patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrogram" title=" spectrogram"> spectrogram</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20forecasting" title=" time series forecasting"> time series forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=times2D" title=" times2D"> times2D</a>, <a href="https://publications.waset.org/abstracts/search?q=2D%20representation" title=" 2D representation"> 2D representation</a> </p> <a href="https://publications.waset.org/abstracts/186575/times2d-a-time-frequency-method-for-time-series-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186575.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">42</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul 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