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Pistachio genomes provide insights into nut tree domestication and ZW sex chromosome evolution

Plant Communications 2023年第3期4卷 80-98页

作者： Salih Kafkas Xiaokai Ma Xingtan Zhang Hayat Topc¸u Rafael Navajas-Pérez Ching Man Wai Haibao Tang Xuming Xu Mortaza Khodaeiaminjan Murat Güney Aibibula Paizila Harun Karcı Xiaodan Zhang Jing Lin Han Lin Roberto de la Herrán Carmelo Ruiz Rejón Jerson Alexander García-Zea Francisca Robles Coral del Val Muñoz Agnes Hotz-Wagenblatt Xiangjia Jack Min HakanÖzkan Elmira Ziya Motalebipour Hatice Gozel Nergiz C¸oban Nesibe Ebru Kafkas Andrej Kilian HuaXing Huang Xuanrui Lv Kunpeng Liu Qilin Hu Ewelina Jacygrad William Palmer Richard Michelmore Ray Ming Department of Horticulture Faculty of AgricultureUniversity of C¸ukurovaAdana 01330Turkey Center for Genomics and Biotechnology Haixia Institute of Science and TechnologySchool of Future TechnologyFujian Agriculture and Forestry UniversityFuzhouChina Key Laboratory of Orchid Conservation and Utilization of National Forestry and Grassland Administration Fujian Agriculture and Forestry UniversityFuzhouChina Departamento de Genética Facultad de CienciasCampus de Fuentenueva s/n18071 GranadaSpain Department of Plant Biology University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems College of the Environment and EcologyXiamen UniversityXiamen 361102China Department of Computer Science University of GranadaGranadaSpain Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI Institute) 18014 GranadaSpain German Cancer Research Center Omics IT and Data Management Core FacilityHeidelbergGermany Department of Biological Sciences Youngstown State UniversityYoungstownOH 44555USA Department of Field Crops Faculty of AgricultureUniversity of C¸ukurovaAdana 01330Turkey Pistachio Research Institute SxahinbeyGaziantep 27060Turkey Diversity Arrays Technology University of CanberraCanberraACTAustralia Genome Center University of California Davis451 Health Sciences DriveDavisCA 95616USA

Pistachio is a nut crop domesticated in the Fertile Crescent and a dioecious species with ZW sex *** sequenced the genomes of Pistacia vera cultivar(cv.)Siirt,the female parent,and *** ***,the male *** chromosome-level reference genomes of pistachio were generated,and Z and W chromosomes were *** ZW chromosomes originated from an autosome following the first inversion,which occurred approximately 8.18 *** inversion events in the W chromosome led to the formation of a 12.7-Mb(22.8%of the W chromosome)non-recombining *** W-specific sequences contain several genes of interest that may have played a pivotal role in sex determination and contributed to the initiation and evolution of a ZW sex chromosome system in *** W-specific genes,including defA,defA-like,DYT1,two PTEN1,and two tandem duplications of six VPS13A paralogs,are strong candidates for sex determination or *** history analysis of resequenced genomes suggest that cultivated pistachio underwent severe domestication bottlenecks approximately 7640 years ago,dating the domestication event close to the archeological record of pistachio domestication in *** identified 390,211,and 290 potential selective sweeps in 3 cultivar subgroups that underlie agronomic traits such as nut development and quality,grafting success,flowering time shift,and drought *** findings have improved our understanding of the genomic basis of sex determination/differentiation and horticulturally important traits and will accelerate the improvement of pistachio cultivars and rootstocks.

关键词： Pistacia vera pistachio sequencing reference genome sex chromosome domestication

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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Auction Theory and Game Theory Based Pricing of Edge Computing Resources: A Survey

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IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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A Review of Machine Learning Algorithms for Detection of Breast Cancer

A Review of Machine Learning Algorithms for Detection of Bre...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Abhay Tale Aditya Barhate Prateek Verma Palash Gourshettiwar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. The advancement in the applied technique of Machine Learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. This review paper provides insights into how different ML algorithms including the Support Vector Machine (SVM), Convolutional Neural network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. These algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. The paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. This review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice

关键词： Support vector machines Machine learning algorithms Sensitivity Reviews Precision medicine Ubiquitous computing Breast cancer Vectors Convolutional neural networks Random forests

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A Review of Machine Learning Algorithms in Diabetes Management

A Review of Machine Learning Algorithms in Diabetes Manageme...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Unnati Gayaki Subodh Daronde Abhay Tale Aditya Barhate Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University)) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare.

关键词： Deep learning Support vector machines Ethics Machine learning algorithms Reviews Neural networks Medical services Diabetes Convolutional neural networks Long short term memory

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Improving the Quality of Diabetic data with Large Language Model-driven Cleaning Techniques

Improving the Quality of Diabetic Data with Large Language M...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Divya Biradar Rahul Dattangire Ruchika Vaidya NagaSuryaShivani Inti Computer Science University of Texas at Arlington Arlington Texas USA Data Engineering Independent Researcher Houston Texas USA Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Computer and Information Science University of Texas at Arlington Arlington Texas USA

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The “Diabetes” data set from Kaggle is divided into implementing the combined approach data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach’s effective impact in correcting and consistent data within the healthcare dataset.

关键词： Adaptation models Large language models Noise Medical services Cleaning data models Diabetes Safety data mining Reliability

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Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease

Informatics and Health

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Informatics and Health 2024年第2期1卷 70-81页

作者： Temidayo Oluwatosin Omotehinwa David Opeoluwa Oyewola Ervin Gubin Moung Department of Mathematics and Computer Science Federal University of Health SciencesP.M.B.145OtukpoNigeria Department of Mathematics and Statistics Federal University KashereP.M.B.0182GombeNigeria Data Technologies and Applications(DaTA)Research Group Faculty of Computing and InformaticsUniversiti Malaysia SabahKota Kinabalu 88400SabahMalaysia

Background:Coronary heart disease(CHD)remains a prominent cause of mortality globally,necessitating early and accurate detection *** diagnostic approaches can be invasive,costly,and time-consuming,necessitating the need for more efficient *** aimed to optimize the Light Gradient-Boosting Machine(LightGBM)algorithm to enhance its performance and accuracy in the early detection of CHD,providing a reliable,cost-effective,and non-invasive diagnostic ***:The Framingham Heart Study(FHS)dataset publicly available on Kaggle was used in this *** Imputations by Chained Equations(MICE)were applied separately to the training and testing sets to handle missing ***-SMOTE(Synthetic Minority Over-sampling Technique)was used on the training set to balance the *** LightGBM algorithm was selected for its efficiency in classification tasks,and Bayesian Optimization with Tree-structured Parzen Estimator(TPE)was employed to fine-tune its *** optimized LightGBM model was trained and evaluated using metrics such as accuracy,precision,and AUC-ROC on the test set,with cross-validation to ensure robustness and ***:The optimized LightGBM model showed significant improvement in early CHD *** baseline LightGBM model with dropped missing values had an accuracy of 0.8333,sensitivity of 0.1081,precision of 0.3429,F1 score of 0.1644,and AUC of *** MICE imputation,performance improved to an accuracy of 0.9399,sensitivity of 0.6693,precision of 0.9043,F1 score of 0.7692,and AUC of *** combined approach of Borderline-SMOTE,MICE imputation,and TPE for LightGBM achieved an accuracy of 0.9882,sensitivity of 0.9370,precision of 0.9835,F1 score of 0.9597,and AUC of 0.9963,indicating a highly effective and robust ***:The optimized model demonstrated outstanding performance in early CHD *** study’s strengths include its comprehensive approach to addres

关键词： Clinical decision making Coronary heart disease Light gradient-boosting machine Machine learning MICE Tree-structured Parzen estimator

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SepLL: Separating Latent Class Labels from Weak Supervision Noise

arXiv

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arXiv 2022年

作者： Stephan, Andreas Kougia, Vasiliki Roth, Benjamin Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria UniVie Doctoral School Computer Science Vienna Austria Faculty of Philological and Cultural Studies University of Vienna Vienna Austria

In the weakly supervised learning paradigm, labeling functions automatically assign heuristic, often noisy, labels to data samples. In this work, we provide a method for learning from weak labels by separating two types of complementary information associated with the labeling functions: information related to the target label and information specific to one labeling function only. Both types of information are reflected to different degrees by all labeled instances. In contrast to previous works that aimed at correcting or removing wrongly labeled instances, we learn a branched deep model that uses all data as-is, but splits the labeling function information in the latent space. Specifically, we propose the end-to-end model SepLL which extends a transformer classifier by introducing a latent space for labeling function specific and task-specific information. The learning signal is only given by the labeling functions matches, no pre-processing or label model is required for our method. Notably, the task prediction is made from the latent layer without any direct task signal. Experiments on Wrench text classification tasks show that our model is competitive with the state-of-the-art, and yields a new best average performance. © 2022, CC BY.

关键词： Classification (of information)

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The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

arXiv

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arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the Software Defined network (SDN) and network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： network security

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Multi-chain Storage Scheme Based on Hash Calendar and Recursive Zero-knowledge Proof 2

Multi-chain Storage Scheme Based on Hash Calendar and Recurs...

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2nd International Conference on computer science and Blockchain, CCSB 2022

作者： Wang, Chao Yao, Zhongyuan Zhang, Yong Guo, Shangkun Zhao, Lindong Si, Xueming Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China Zhongyuan University of Technology The Frontier Information Technology Research Institute China Shanghai Jiao Tong University Department of Computer Science and Engineer Shanghai China

ISBN: (纸本)9798350331882

Blockchain technology is currently a research hotspot in the computer field. However, with the rapid development of blockchain technology, poor storage scalability limits its further application. In response to this problem, this paper proposes a multi-chain storage scheme based on hash calendar and recursive zero-knowledge proof, which consists of two parts: off-chain and on-chain. The off-chain part uses hash calendar technology to store account information, and through recursive zero-knowledge proof of account state transitions, ensures the verifiability of off-chain state transitions and improves the security of the solution;and sets up proof generators and transactions The two roles of the executor improve the efficiency of transaction execution. A dynamically addable main and sub-chain array is designed on the chain. The sub-chains store the brief information of transactions and the world state. The main chain is responsible for verifying the correctness of off-chain state transitions. On-chain and off-chain collaborative storage reduces the space occupied by data., to realize the storage expansion of the blockchain. Finally, simulation experiments show that this scheme can effectively realize the storage expansion of blockchain. © 2022 IEEE.

关键词： Blockchain

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