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Article: Morphometric genetic analysis： population differentiation of Apis cerana cerana in Longxi Mountain

Zhu Xiangjie, Fujian Agriculture and Forestry University, Fuzhou(China), College of Bee Science / Zhou Bingfeng, Fujian Agriculture and Forestry University, Fuzhou(China), College of Bee Science / Wu Xianda, Fujian Agriculture and Forestry University, Fuzhou(China), College of Bee Science

Journal of Fujian Agricultural and Forestry University

Feb.2009 , Issue (1)

Abstract: 对龙栖山国家级自然保护区及其周边地区的中华蜜蜂31个形态指标进行测定，发现6个形态指标存在显著差异．与周边地区人工饲养的中华蜜蜂相比，龙栖山野生中华蜜蜂的翅长、蜡镜长、蜡镜间距分别为8．6655、1．1568、0．2083mm，分别减少0．0728、0．0382、0．0730mm；翅脉角D7为95．6°，增大1．8°；翅脉角N23和J16分别为83．3°和101．2°，分别减小4．0°和3．2°（P〈0．05）．对采用判别式分析得到的样点重心的判别函数值进行聚类分析的结果显示， ... ...

Abstract	对龙栖山国家级自然保护区及其周边地区的中华蜜蜂31个形态指标进行测定，发现6个形态指标存在显著差异．与周边地区人工饲养的中华蜜蜂相比，龙栖山野生中华蜜蜂的翅长、蜡镜长、蜡镜间距分别为8．6655、1．1568、0．2083mm，分别减少0．0728、0．0382、0．0730mm；翅脉角D7为95．6°，增大1．8°；翅脉角N23和J16分别为83．3°和101．2°，分别减小4．0°和3．2°（P〈0．05）．对采用判别式分析得到的样点重心的判别函数值进行聚类分析的结果显示，龙栖山中华蜜蜂已从周边地区的中华蜜蜂种群中分化，形成独立的种群．[著者文摘]
Keywords	APIS CERANA ; POPULATION CENSUSES ; APIS CERANA ; RECENSEMENT DE LA POPULATION ; APIS CERANA ; CENSOS DE POBLACION ; http://www.fao.org/aos/agrovoc#c_29808 ; http://www.fao.org/aos/agrovoc#c_28745
Language	zho
Document type	Article
ISSN	1671-5470
Database	AGRIS - International Information System for the Agricultural Sciences and Technology

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Article ; Online: Functional divergences of natural variations of TaNAM-A1 in controlling leaf senescence during wheat grain filling.

Zhou, Longxi / Chang, Guowei / Shen, Chuncai / Teng, Wan / He, Xue / Zhao, Xueqiang / Jing, Yanfu / Huang, Zhixiong / Tong, Yiping

Journal of integrative plant biology

2024

Abstract: Leaf senescence is an essential physiological process related to grain yield potential and nutritional quality. Green leaf duration (GLD) after anthesis directly reflects the leaf senescence process and exhibits large genotypic differences in common ... ...

Abstract	Leaf senescence is an essential physiological process related to grain yield potential and nutritional quality. Green leaf duration (GLD) after anthesis directly reflects the leaf senescence process and exhibits large genotypic differences in common wheat; however, the underlying gene regulatory mechanism is still lacking. Here, we identified TaNAM-A1 as the causal gene of the major loci qGLD-6A for GLD during grain filling by map-based cloning. Transgenic assays and TILLING mutant analyses demonstrated that TaNAM-A1 played a critical role in regulating leaf senescence, and also affected spike length and grain size. Furthermore, the functional divergences among the three haplotypes of TaNAM-A1 were systematically evaluated. Wheat varieties with TaNAM-A1d (containing two mutations in the coding DNA sequence of TaNAM-A1) exhibited a longer GLD and superior yield-related traits compared to those with the wild type TaNAM-A1a. All three haplotypes were functional in activating the expression of genes involved in macromolecule degradation and mineral nutrient remobilization, with TaNAM-A1a showing the strongest activity and TaNAM-A1d the weakest. TaNAM-A1 also modulated the expression of the senescence-related transcription factors TaNAC-S-7A and TaNAC016-3A. TaNAC016-3A enhanced the transcriptional activation ability of TaNAM-A1a by protein-protein interaction, thereby promoting the senescence process. Our study offers new insights into the fine-tuning of the leaf functional period and grain yield formation for wheat breeding under various geographical climatic conditions.
Language	English
Publishing date	2024-04-24
Publishing country	China (Republic : 1949- )
Document type	Journal Article
ZDB-ID	2130095-1
ISSN	1744-7909 ; 1672-9072
ISSN (online)	1744-7909
ISSN	1672-9072
DOI	10.1111/jipb.13658
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A unified method to revoke the private data of patients in intelligent healthcare with audit to forget.

Zhou, Juexiao / Li, Haoyang / Liao, Xingyu / Zhang, Bin / He, Wenjia / Li, Zhongxiao / Zhou, Longxi / Gao, Xin

Nature communications

2023 Volume 14, Issue 1, Page(s) 6255

Abstract: Revoking personal private data is one of the basic human rights. However, such right is often overlooked or infringed upon due to the increasing collection and use of patient data for model training. In order to secure patients' right to be forgotten, we ...

Abstract	Revoking personal private data is one of the basic human rights. However, such right is often overlooked or infringed upon due to the increasing collection and use of patient data for model training. In order to secure patients' right to be forgotten, we proposed a solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing an approach called knowledge purification. To implement our solution, we developed an audit to forget software (AFS), which is able to evaluate and revoke patients' private data from pre-trained deep learning models. Here, we show the usability of AFS and its application potential in real-world intelligent healthcare to enhance privacy protection and data revocation rights.
MeSH term(s)	Humans ; Computer Security ; Privacy ; Confidentiality ; Software ; Delivery of Health Care
Language	English
Publishing date	2023-10-06
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-41703-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data.

Zhou, Juexiao / Chen, Siyuan / Wu, Yulian / Li, Haoyang / Zhang, Bin / Zhou, Longxi / Hu, Yan / Xiang, Zihang / Li, Zhongxiao / Chen, Ningning / Han, Wenkai / Xu, Chencheng / Wang, Di / Gao, Xin

Science advances

2024 Volume 10, Issue 5, Page(s) eadh8601

Abstract: Modern machine learning models toward various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Here, we proposed a secure and privacy-preserving machine learning method (PPML-Omics) by ... ...

Abstract	Modern machine learning models toward various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Here, we proposed a secure and privacy-preserving machine learning method (PPML-Omics) by designing a decentralized differential private federated learning algorithm. We applied PPML-Omics to analyze data from three sequencing technologies and addressed the privacy concern in three major tasks of omic data under three representative deep learning models. We examined privacy breaches in depth through privacy attack experiments and demonstrated that PPML-Omics could protect patients' privacy. In each of these applications, PPML-Omics was able to outperform methods of comparison under the same level of privacy guarantee, demonstrating the versatility of the method in simultaneously balancing the privacy-preserving capability and utility in omic data analysis. Furthermore, we gave the theoretical proof of the privacy-preserving capability of PPML-Omics, suggesting the first mathematically guaranteed method with robust and generalizable empirical performance in protecting patients' privacy in omic data.
MeSH term(s)	Humans ; Privacy ; Algorithms ; Data Analysis ; Machine Learning ; Technology
Language	English
Publishing date	2024-01-31
Publishing country	United States
Document type	Journal Article
ZDB-ID	2810933-8
ISSN	2375-2548 ; 2375-2548
ISSN (online)	2375-2548
ISSN	2375-2548
DOI	10.1126/sciadv.adh8601
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI.

Zhou, Juexiao / Zhou, Longxi / Wang, Di / Xu, Xiaopeng / Li, Haoyang / Chu, Yuetan / Han, Wenkai / Gao, Xin

Computers in biology and medicine

2023 Volume 169, Page(s) 107861

Abstract: Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging. However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and ...

Abstract	Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging. However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection without the demand to modify the existing model structures or to share any private data. Here, we proposed PPPML-HMI, a novel open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis. To our best knowledge, personalization and privacy protection were discussed simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing the novel cyclic secure aggregation with the homomorphic encryption algorithm. To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans. Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the strong privacy-preserving capability of PPPML-HMI. By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we demonstrated the strong generalizability of PPPML-HMI in privacy-preserving federated learning on heterogeneous medical images.
MeSH term(s)	Humans ; Privacy ; Algorithms ; COVID-19 ; Hospitals ; Learning
Language	English
Publishing date	2023-12-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107861
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Ribosomal frameshifting at normal codon repeats recodes functional chimeric proteins in human.

Ren, Guiping / Gu, Xiaoqian / Zhang, Lu / Gong, Shimin / Song, Shuang / Chen, Shunkai / Chen, Zhenjing / Wang, Xiaoyan / Li, Zhanbiao / Zhou, Yingshui / Li, Longxi / Yang, Jiao / Lai, Fan / Dang, Yunkun

Nucleic acids research

2024 Volume 52, Issue 5, Page(s) 2463–2479

Abstract: Ribosomal frameshifting refers to the process that ribosomes slip into +1 or -1 reading frame, thus produce chimeric trans-frame proteins. In viruses and bacteria, programmed ribosomal frameshifting can produce essential trans-frame proteins for viral ... ...

Abstract	Ribosomal frameshifting refers to the process that ribosomes slip into +1 or -1 reading frame, thus produce chimeric trans-frame proteins. In viruses and bacteria, programmed ribosomal frameshifting can produce essential trans-frame proteins for viral replication or regulation of other biological processes. In humans, however, functional trans-frame protein derived from ribosomal frameshifting is scarcely documented. Combining multiple assays, we show that short codon repeats could act as cis-acting elements that stimulate ribosomal frameshifting in humans, abbreviated as CRFS hereafter. Using proteomic analyses, we identified many putative CRFS events from 32 normal human tissues supported by trans-frame peptides positioned at codon repeats. Finally, we show a CRFS-derived trans-frame protein (HDAC1-FS) functions by antagonizing the activities of HDAC1, thus affecting cell migration and apoptosis. These data suggest a novel type of translational recoding associated with codon repeats, which may expand the coding capacity of mRNA and diversify the regulation in human.
MeSH term(s)	Humans ; Frameshifting, Ribosomal ; Proteomics ; Codon/genetics ; Codon/metabolism ; Ribosomes/metabolism ; Recombinant Fusion Proteins/metabolism ; Protein Biosynthesis
Chemical Substances	Codon ; Recombinant Fusion Proteins
Language	English
Publishing date	2024-01-28
Publishing country	England
Document type	Journal Article
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkae035
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Implicit Perception of Differences between NLP-Produced and Human-Produced Language in the Mentalizing Network.

Wei, Zhengde / Chen, Ying / Zhao, Qian / Zhang, Pengyu / Zhou, Longxi / Ren, Jiecheng / Piao, Yi / Qiu, Bensheng / Xie, Xing / Wang, Suiping / Liu, Jia / Zhang, Daren / Kadosh, Roi Cohen / Zhang, Xiaochu

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

2023 Volume 10, Issue 12, Page(s) e2203990

Abstract: Natural language processing (NLP) is central to the communication with machines and among ourselves, and NLP research field has long sought to produce human-quality language. Identification of informative criteria for measuring NLP-produced language ... ...

Abstract	Natural language processing (NLP) is central to the communication with machines and among ourselves, and NLP research field has long sought to produce human-quality language. Identification of informative criteria for measuring NLP-produced language quality will support development of ever-better NLP tools. The authors hypothesize that mentalizing network neural activity may be used to distinguish NLP-produced language from human-produced language, even for cases where human judges cannot subjectively distinguish the language source. Using the social chatbots Google Meena in English and Microsoft XiaoIce in Chinese to generate NLP-produced language, behavioral tests which reveal that variance of personality perceived from chatbot chats is larger than for human chats are conducted, suggesting that chatbot language usage patterns are not stable. Using an identity rating task with functional magnetic resonance imaging, neuroimaging analyses which reveal distinct patterns of brain activity in the mentalizing network including the DMPFC and rTPJ in response to chatbot versus human chats that cannot be distinguished subjectively are conducted. This study illustrates a promising empirical basis for measuring the quality of NLP-produced language: adding a judge's implicit perception as an additional criterion.
MeSH term(s)	Humans ; Natural Language Processing ; Mentalization ; Software ; Magnetic Resonance Imaging ; Perception
Language	English
Publishing date	2023-02-07
Publishing country	Germany
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2808093-2
ISSN	2198-3844 ; 2198-3844
ISSN (online)	2198-3844
ISSN	2198-3844
DOI	10.1002/advs.202203990
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: SkinGPT-4

Zhou, Juexiao / He, Xiaonan / Sun, Liyuan / Xu, Jiannan / Chen, Xiuying / Chu, Yuetan / Zhou, Longxi / Liao, Xingyu / Zhang, Bin / Gao, Xin

An Interactive Dermatology Diagnostic System with Visual Large Language Model

2023

Abstract: Skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases, impacting a considerable portion of the population. Nonetheless, the field of dermatology diagnosis faces three significant hurdles. ... ...

Abstract	Skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases, impacting a considerable portion of the population. Nonetheless, the field of dermatology diagnosis faces three significant hurdles. Firstly, there is a shortage of dermatologists accessible to diagnose patients, particularly in rural regions. Secondly, accurately interpreting skin disease images poses a considerable challenge. Lastly, generating patient-friendly diagnostic reports is usually a time-consuming and labor-intensive task for dermatologists. To tackle these challenges, we present SkinGPT-4, which is the world's first interactive dermatology diagnostic system powered by an advanced visual large language model. SkinGPT-4 leverages a fine-tuned version of MiniGPT-4, trained on an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors' notes. We designed a two-step training process to allow SkinGPT to express medical features in skin disease images with natural language and make accurate diagnoses of the types of skin diseases. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identifies the characteristics and categories of the skin conditions, performs in-depth analysis, and provides interactive treatment recommendations. Meanwhile, SkinGPT-4's local deployment capability and commitment to user privacy also render it an appealing choice for patients in search of a dependable and precise diagnosis of their skin ailments. To demonstrate the robustness of SkinGPT-4, we conducted quantitative evaluations on 150 real-life cases, which were independently reviewed by certified dermatologists, and showed that SkinGPT-4 could provide accurate diagnoses of skin diseases.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	610
Publishing date	2023-04-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI

Zhou, Juexiao / Zhou, Longxi / Wang, Di / Xu, Xiaopeng / Li, Haoyang / Chu, Yuetan / Han, Wenkai / Gao, Xin

2023

Abstract	Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging. However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data. In this paper, we proposed PPPML-HMI, an open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis. To our best knowledge, personalization and privacy protection were achieved simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing our novel cyclic secure aggregation with the homomorphic encryption algorithm. To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans. For the real-world task, PPPML-HMI achieved $\sim$5\% higher Dice score on average compared to conventional FL under the heterogeneous scenario. Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the solid privacy-preserving capability of PPPML-HMI. By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we further demonstrated the strong robustness of PPPML-HMI.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-02-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Audit to Forget

Zhou, Juexiao / Li, Haoyang / Liao, Xingyu / Zhang, Bin / He, Wenjia / Li, Zhongxiao / Zhou, Longxi / Gao, Xin

A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare

2023

Abstract: Revoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries. However, with the development of data science, machine learning and deep learning techniques, this ... ...

Abstract	Revoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries. However, with the development of data science, machine learning and deep learning techniques, this right is usually neglected or violated as more and more patients' data are being collected and used for model training, especially in intelligent healthcare, thus making intelligent healthcare a sector where technology must meet the law, regulations, and privacy principles to ensure that the innovation is for the common good. In order to secure patients' right to be forgotten, we proposed a novel solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing a new approach called knowledge purification. To implement our solution, we developed AFS, a unified open-source software, which is able to evaluate and revoke patients' private data from pre-trained deep learning models. We demonstrated the generality of AFS by applying it to four tasks on different datasets with various data sizes and architectures of deep learning networks. The software is publicly available at \url{https://github.com/JoshuaChou2018/AFS}.
Keywords	Computer Science - Machine Learning ; Computer Science - Cryptography and Security
Subject code	006
Publishing date	2023-02-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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