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Zoological Systematics 2024年第1期49卷 99-100页

The current journal is mainly focused in zoological systematics.According to Systematics Agenda 2000(1994),systematics is the science built on the following tasks:Taxonomy-the science of discovering,describing,and classifying species or groups of species(together termed taxa);Phylogenetic analysisthe discovery of the evolutionary relationships among a group of species;and Classification-the grouping of species,ultimately on the basis of evolutionary relationships.

关键词： relationships classify describing

A novel molecular classification method for osteosarcoma based on tumor cell differentiation trajectories

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Bone Research 2023年第1期11卷 148-162页

作者： Hao Zhang Ting Wang Haiyi Gong Runyi Jiang Wang Zhou Haitao Sun Runzhi Huang Yao Wang Zhipeng Wu Wei Xu Zhenxi Li Quan Huang Xiaopan Cai Zaijun Lin Jinbo Hu Qi Jia Chen Ye Haifeng Wei Jianru Xiao Department of Orthopedic Oncology Shanghai Changzheng HospitalNaval Military Medical UniversityShanghai200003China Musculoskeletal Laboratory Institute of BiotechnologyUniversity of Shanghai for Science and TechnologyShanghai200093China Department of Orthopedics Naval Medical Center of CPLANaval Medical UniversityShanghai200052China Peking-Tsinghua Center for Life Sciences Tsinghua UniversityBeijing100084China Division of Spine Department of OrthopedicsTongji Hospital Affiliated to Tongji University School of MedicineShanghai200065China

Subclassification of tumors based on molecular features may facilitate therapeutic choice and increase the response rate of cancer patients.However,the highly complex cell origin involved in osteosarcoma(OS)limits the utility of traditional bulk RNA sequencing for OS subclassification.Single-cell RNA sequencing(sc RNA-seq)holds great promise for identifying cell heterogeneity.However,this technique has rarely been used in the study of tumor subclassification.By analyzing sc RNA-seq data for six conventional OS and nine cancellous bone(CB)samples,we identified 29 clusters in OS and CB samples and discovered three differentiation trajectories from the cancer stem cell(CSC)-like subset,which allowed us to classify OS samples into three groups.The classification model was further examined using the TARGET dataset.Each subgroup of OS had different prognoses and possible drug sensitivities,and OS cells in the three differentiation branches showed distinct interactions with other clusters in the OS microenvironment.In addition,we verified the classification model through IHC staining in 138 OS samples,revealing a worse prognosis for Group B patients.Furthermore,we describe the novel transcriptional program of CSCs and highlight the activation of EZH2 in CSCs of OS.These findings provide a novel subclassification method based on sc RNA-seq and shed new light on the molecular features of CSCs in OS and may serve as valuable references for precision treatment for and therapeutic development in OS.

关键词： osteosarcoma holds classify

Validating neural networks for spectroscopic classification on a universal synthetic dataset

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npj Computational Materials 2023年第1期9卷 1325-1336页

作者： Jan Schuetzke Nathan J.Szymanski Markus Reischl Institute for Automation and Applied Informatics Karlsruhe Institute of TechnologyKarlsruhe 76131Germany Department of Materials Science&Engineering Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA Department of Materials Science&Engineering UC BerkeleyBerkeleyCA 94720USA

To aid the development of machine learning models for automated spectroscopic data classification,we created a universal synthetic dataset for the validation of their performance.The dataset mimics the characteristic appearance of experimental measurements from techniques such as X-ray diffraction,nuclear magnetic resonance,and Raman spectroscopy among others.We applied eight neural network architectures to classify artificial spectra,evaluating their ability to handle common experimental artifacts.While all models achieved over 98%accuracy on the synthetic dataset,misclassifications occurred when spectra had overlapping peaks or intensities.We found that non-linear activation functions,specifically ReLU in the fully-connected layers,were crucial for distinguishing between these classes,while adding more sophisticated components,such as residual blocks or normalization layers,provided no performance benefit.Based on these findings,we summarize key design principles for neural networks in spectroscopic data classification and publicly share all scripts used in this study.

关键词： spectroscopic adding classify

Automated classification of big X-ray diffraction data using deep learning models

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npj Computational Materials 2023年第1期9卷 169-180页

作者： Jerardo E.Salgado Samuel Lerman Zhaotong Du Chenliang Xu Niaz Abdolrahim Materials Science program University of RochesterRochesterNew York 14627USA Department of Computer Science University of RochesterRochesterNew York 14627USA Department of Mechanical Engineering University of RochesterRochesterNew York 14627USA Laboratory for Laser Energetics University of RochesterRochesterNew York 14627USA

In current in situ X-ray diffraction(XRD)techniques,data generation surpasses human analytical capabilities,potentially leading to the loss of insights.Automated techniques require human intervention,and lack the performance and adaptability required for material exploration.Given the critical need for high-throughput automated XRD pattern analysis,we present a generalized deep learning model to classify a diverse set of materials’crystal systems and space groups.In our approach,we generate training data with a holistic representation of patterns that emerge from varying experimental conditions and crystal properties.We also employ an expedited learning technique to refine our model’s expertise to experimental conditions.In addition,we optimize model architecture to elicit classification based on Bragg’s Law and use evaluation data to interpret our model’s decision-making.We evaluate our models using experimental data,materials unseen in training,and altered cubic crystals,where we observe state-of-the-art performance and even greater advances in space group classification.

关键词： Automate classify cubic

Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network

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World Journal of Gastroenterology 2022年第22期28卷 2457-2467页

作者： Gao-Shuang Liu Pei-Yun Huang Min-Li Wen Shuai-Shuai Zhuang Jie Hua Xiao-Pu He Department of Gastroenterology Nanjing BenQ Medical CenterThe Affiliated BenQ Hospital of Nanjing Medical UniversityNanjing 210000Jiangsu ProvinceChina Department of Geriatric Gastroenterology The First Affiliated Hospital of Nanjing Medical UniversityNanjing 210000Jiangsu ProvinceChina School of Computer Science and Engineering Southeast UniversityNanjing 211102Jiangsu ProvinceChina Department of Gastroenterology The First Affiliated Hospital of Nanjing Medical UniversityNanjing 210000Jiangsu ProvinceChina

BACKGROUND A convolutional neural network(CNN) is a deep learning algorithm based on the principle of human brain visual cortex processing and image recognition.AIM To automatically identify the invasion depth and origin of esophageal lesions based on a CNN.METHODS A total of 1670 white-light images were used to train and validate the CNN system.The method proposed in this paper included the following two parts:(1)Location module,an object detection network,locating the classified main image feature regions of the image for subsequent classification tasks;and(2) Classification module,a traditional classification CNN,classifying the images cut out by the object detection network.RESULTS The CNN system proposed in this study achieved an overall accuracy of 82.49%,sensitivity of 80.23%,and specificity of 90.56%.In this study,after follow-up pathology,726 patients were compared for endoscopic pathology.The misdiagnosis rate of endoscopic diagnosis in the lesion invasion range was approximately 9.5%;41 patients showed no lesion invasion to the muscularis propria,but 36 of them pathologically showed invasion to the superficial muscularis propria.The patients with invasion of the tunica adventitia were all treated by surgery with an accuracy rate of 100%.For the examination of submucosal lesions,the accuracy of endoscopic ultrasonography(EUS) was approximately 99.3%.Results of this study showed that EUS had a high accuracy rate for the origin of submucosal lesions,whereas the misdiagnosis rate was slightly high in the evaluation of the invasion scope of lesions.Misdiagnosis could be due to different operating and diagnostic levels of endoscopists,unclear ultrasound probes,and unclear lesions.CONCLUSION This study is the first to recognize esophageal EUS images through deep learning,which can automatically identify the invasion depth and lesion origin of submucosal tumors and classify such tumors,thereby achieving good accuracy.In future studies,this method can provide guidance and help to clinical endoscopists.

关键词： Endoscopic ultrasonography Convolutional neural network Esophageal lesion Automatically classify Identify

Survey on Segmentation and Classification Techniques of Satellite Images by Deep Learning Algorithm

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Computers, Materials & Continua 2023年第6期75卷 4973-4984页

作者： Atheer Joudah Souheyl Mallat Mounir Zrigui Department of Computer Sciences University of MonastirMonastir1001Tunisia Research Laboratory in Algebra Numbers Theory and Intelligent SystemsMonastir1001Tunisia

This survey paper aims to show methods to analyze and classify field satellite images using deep learning and machine learning algorithms.Users of deep learning-based Convolutional Neural Network(CNN)technology to harvest fields from satellite images or generate zones of interest were among the planned application scenarios(ROI).Using machine learning,the satellite image is placed on the input image,segmented,and then tagged.In contem-porary categorization,field size ratio,Local Binary Pattern(LBP)histograms,and color data are taken into account.Field satellite image localization has several practical applications,including pest management,scene analysis,and field tracking.The relationship between satellite images in a specific area,or contextual information,is essential to comprehending the field in its whole.

关键词： Identification satellite images classify deep learning machine learning

Automated GUI widgets classification

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Frontiers of Computer Science 2023年第1期17卷 233-235页

作者： Kabir Sulaiman SAID Liming NIE Yuanchang LIN Yaowen ZHENG Zuohua DING School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China School of Computer Science and Engineering Nanyang Technological UniversitySingap

1 Introduction Graphical User Interface(GUI)widgets classification entails classifying widgets into their appropriate domain-specific types(e.g.,CheckBox and EditText)[1,2].The widgets classification is essential as it supports several software engineering tasks,such as GUI design and testing[1,3].The ability to obtain better widget classification performance has become one of the keys to the success of these tasks.Researchers in recent years have proposed many techniques for improving widget classification performance[1,2,4].For example,Moran et al.[1]proposed a deep learning technique to classify GUI widgets into their domain-specific type.The authors used the deep learning algorithm,a Convolutional Neural Network(CNN)architecture,to classify the GUI widgets.Chen et al.[2]proposed combining text-based and non-text-based models to improve the overall performance of GUI widget detection while classifying the widgets with the ResNet50 model.

关键词： GUI User classify

Advanced Reinforcement Learning and Its Connections with Brain Neuroscience

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Research 2023年第4期2023卷 263-279页

作者： Chaoqiong Fan Li Yao Jiacai Zhang Zonglei Zhen Xia Wu School of Artificial Intelligence Beijing Normal UniversityBeijingChina Faculty of Psychology Beijing Normal UniversityBeijingChina

In recent years,brain science and neuroscience have greatly propelled the innovation of computer science.In particular,knowledge from the neurobiology and neuropsychology of the brain revolutionized the development of reinforcement learning(RL)by providing novel interpretable mechanisms of how the brain achieves intelligent and efficient decision making.Triggered by this,there has been a boom in research about advanced RL algorithms that are built upon the inspirations of brain neuroscience.In this work,to further strengthen the bidirectional link between the 2 communities and especially promote the research on modern RL technology,we provide a comprehensive survey of recent advances in the area of brain-inspired/related RL algorithms.We start with basis theories of RL,and present a concise introduction to brain neuroscience related to RL.Then,we classify these advanced RL methodologies into 3 categories according to different connections of the brain,i.e.,micro-neural activity,macro-brain structure,and cognitive function.Each category is further surveyed by presenting several modern RL algorithms along with their mathematical models,correlations with the brain,and open issues.Finally,we introduce several important applications of RL algorithms,followed by the discussions of challenges and opportunities for future research.

关键词： discussions classify category

Lipid-Based Intelligent Vehicle Capabilitized with Physical and Physiological Activation

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Research 2023年第2期2022卷 275-295页

作者： Fuxue Zhang Bozhang Xia Jiabei Sun Yufei Wang Jinjin Wang Fengfei Xu Junge Chen Mei Lu Xin Yao Peter Timashev Yuanyuan Zhang Meiwan Chen Jing Che Fangzhou Li Xing-Jie Liang CAS Key Laboratory for Biomedical Efects of Nanomaterials and Nanosafety CAS Center for Excellence in NanoscienceNational Center for Nanoscience and Technology of ChinaNo.11First North RoadZhongguancunBeijing 100190China Sino-Danish Center for Education and Research Sino-Danish Cllge of University of Chinese Academyof Sciences Beijing 100049China School of Nanoscience and Technology University of Chinese Academy of SciencesBeijing 100049China China National Institutes for Food and Drug Control Beijing 102629China Beijing Advanced Innovation Center for Biomedical Engineering School of Engineering MedicineBeihang UniversityBeijing 100083China Advanced Research Institute of Multidisciplinary Science School of Life ScienceSchool of Medical Technology(Institute of Engineering Medicine)Key Laboratory of Molecular Medicine and BiotherapyKey Laboratory of Medical Molecule Science and Pharmaceutics EngineeringBeijing Institute of TechnologyBeijing 100081China School of Chemical Sciences University of Chinese Academy of SciencesBeijing 100049China Laboratory of Clinical Smart Nanotechnologies Institute for Regenerative MedicineSechenov UniversityMoscowRussia State Key Laboratory of Quality Research in Chinese Medicine Institute of Chinese Medical SciencesUniversity of MacaoMacao SARChina

Intelligent drug delivery system based on “stimulus-response”mode emerging a promising perspective in next generation lipidbased nanoparticle.Here,we classify signal sources into physical and physiological stimulati... 详细信息

关键词： classify Lipid Intelligent