Improved genome annotation of Brassica oleracea highlights the importance of alternative splicing

Brassica oleracea has been developed into many important crops,including cabbage,kale,cauliflower,broccoli and so on.The genome and gene annotation of cabbage(cultivar JZS),a representative morphotype of B.oleracea,has been widely used as a common reference in biological research.Although its genome assembly has been updated twice,the current gene annotation still lacks information on untranslated regions(UTRs)and alternative splicing(AS).Here,we constructed a high-quality gene annotation(JZSv3)using a full-length transcriptome acquired by nanopore sequencing,yielding a total of 59452 genes and 75684 transcripts.Additionally,we re-analyzed the previously reported transcriptome data related to the development of different tissues and cold response using JZSv3 as a reference,and found that 3843 out of 11908 differentially expressed genes(DEGs)underwent AS during the development of different tissues and 309 out of 903 cold-related genes underwent AS in response to cold stress.Meanwhile,we also identified many AS genes,including BolLHCB5 and BolHSP70,that displayed distinct expression patterns within variant transcripts of the same gene,highlighting the importance of JZSv3 as a pivotal reference for AS analysis.Overall,JZSv3 provides a valuable resource for exploring gene function,especially for obtaining a deeper understanding of AS regulation mechanisms.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Meibomian glands segmentation in infrared images with limited annotation

●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble annotations.The viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,respectively.Furthermore,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for training.This model maintains an exceptionally high level of segmentation accuracy while substantially reducing training costs.It holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.

The History of the Former Han Dynasty:A Critical Translation with Annotations Translated by Homer Hasenpflug Dubs

Translation is an important medium of cultural communication.It is not a mere transfer of two languages,but the interaction of two cultures.Cultural misreading,which results from cultural discrepancy and translator’s subjectivity,truly reflects where the blockade and conflict in the cultural communication is.Cultural misreading is an objective phenomenon that exists in the entire process of translation.This paper intends to make a comprehensive analysis and discussion on The History of the Former Han Dynasty:a Critical Translation with Annotations translated by Homer Hasenpflug Dubs.As for the reasons of cultural misreading,this paper divides them into three types—language,thinking habit,traditional culture.It is to be hoped that this paper will draw more attention from the translation circle to the phenomena,and make contribution to the development of literary translation.

A Study on Annotations Under the Theory of Paratext in Translation

This paper mainly studies the basic types of annotation and the analysis of its effective functional usage,so as to pay more attention to annotation in the translation of poetry and Fu.The annotation of this study belongs to the category of paratext.Annotation is attributed to the paratext,undertakes its special function,enriches and perfects the paratext system.

Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

Every day,websites and personal archives create more and more photos.The size of these archives is immeasurable.The comfort of use of these huge digital image gatherings donates to their admiration.However,not all of these folders deliver relevant indexing information.From the outcomes,it is dif-ficult to discover data that the user can be absorbed in.Therefore,in order to determine the significance of the data,it is important to identify the contents in an informative manner.Image annotation can be one of the greatest problematic domains in multimedia research and computer vision.Hence,in this paper,Adap-tive Convolutional Deep Learning Model(ACDLM)is developed for automatic image annotation.Initially,the databases are collected from the open-source system which consists of some labelled images(for training phase)and some unlabeled images{Corel 5 K,MSRC v2}.After that,the images are sent to the pre-processing step such as colour space quantization and texture color class map.The pre-processed images are sent to the segmentation approach for efficient labelling technique using J-image segmentation(JSEG).Thefinal step is an auto-matic annotation using ACDLM which is a combination of Convolutional Neural Network(CNN)and Honey Badger Algorithm(HBA).Based on the proposed classifier,the unlabeled images are labelled.The proposed methodology is imple-mented in MATLAB and performance is evaluated by performance metrics such as accuracy,precision,recall and F1_Measure.With the assistance of the pro-posed methodology,the unlabeled images are labelled.

ASE-ERNIE:一种基于ERNIE的中文指令动作序列生成方法

中文结构灵活多样,同样的语义有多种表达方式,在中文指令过长或存在多组动作的情况下,家庭服务机器人很难正确理解并做出响应。此外,家庭环境的中文指令解析研究在数据方面仍然稀缺。现有的方法不能准确提取出指令中包含的全部动作序列。由此提出一种基于ERNIE方法的动作序列提取(ASE-ERNIE)模型来提取中文指令中的可执行动作序列,该模型通过动作类型和参数之间的关系提取指令信息,将动作序列的提取工作转化为序列标注任务。在指令中存在多组动作并且存在多个参数的情况下,依然能够提取出全部动作序列。通过实验验证了所提方法的有效性,动作序列提取任务的F 1值达到了80.37%。

BIM模型三维出图关键技术研究与应用

为解决BIM模型三维正向设计出图问题,基于MicroStation三维设计平台,通过二次开发方式系统研究成图及标注关键技术。通过提取剖切位置处模型切面、剖视范围内三维模型,实现剖面图底图绘制;通过将材料属性信息和剖面图关联,并配合预定义填充符号库,实现剖面图自动填充;通过将自定义数据结构在模型和图纸之间关联,实现图纸与模型联动更新;通过修改剖视图中前视范围内不可见边显示样式,实现二维剖视图中不可见边虚线显示;研发二、三维图面高程、坡比、桩号等快速标注、图框快速调用工具,实现快速成图。实例应用结果表明,该方法可明显提升基于BIM模型的二维图纸生成效率,适用直线型、折线型等不同剖面位置线图纸批量剖切,满足结构模型、地形模型、地质模型等同时剖切需求、不同出图比例图纸剖切要求、模型修改后图纸批量更新需求,研究方法和软件开发思路可为同类项目提供参考。

基于Transformer的司法文书命名实体识别方法

命名实体识别是自然语言处理领域的关键任务之一,是实现下游任务的基础。目前针对司法领域的相关研究相对较少,司法系统的信息化和智能化转型仍有许多问题亟需解决。相比其他领域的文本,司法文书存在专业性强、语料资源少等局限,导致现有的司法文书识别结果较低。因此,从以下3方面开展研究:首先,提出了一种多标签层级迭代的文本标注方式,可以对原始司法文书文本进行自动化标注,同时有效地提升司法文书命名实体识别任务的实体识别效果;其次,提出了一种交融式的Transformer神经网络模型,对汉字固有属性的深层特征进行了充分利用,用于对司法文书进行命名实体识别;最后,对所提出的标注方法和模型与其他神经网络模型进行了对比实验。所提出的文本标注方式可以较为准确地实现司法文书的标注任务;同时,所提出的模型在通用数据集中相对于对照模型有较大的提高,并在司法领域数据集中取得了良好的效果。

人工智能囊胚形态评估数据集构建与质控专家共识

囊胚形态人工智能(AI)评估是AI医疗器械发展的新兴方向,也是AI在辅助生殖领域的重要应用。AI在新领域应用的起步阶段,数据集的构建与质控对产品质量有重要影响。目前,囊胚形态学AI评估在数据采集、标注、质控等方面尚未形成统一的规范。在参考AI医疗器械、辅助生殖医疗器械现有国家行业标准的基础上,本文以囊胚形态AI评估数据集为主题,对数据集构建与质控要求进行了探讨,对数据集质量特性进行了解析,旨在指导数据集制造责任方加强数据集全生命周期管理,更好地为产品研发、测试、临床试验等环节提供质量保障,助力产业发展。

基于自监督的主动标签清洗

主动标签清洗利用主动学习来进行标签噪声处理,以降低人工标注成本。现有的主动标签清洗方法仍然存在人工额外标注成本较高的问题,即挑选出的可疑样本中正确样本所占比例较高。为了缓解这一问题,提出了一种基于核心集的自监督主动标签清洗方法。首先利用自监督任务进行表征学习,随后将数据映射到特征空间中,并利用贪婪的K-Center集合覆盖方法挑选出可疑样本,最后根据不确定性筛选出标签噪声样本进行重标注。并同时考虑到了样本的代表性与不确定性,能够有效降低可疑样本中正确样本的比例。在含有不同比例标签噪声的公开数据集上的实验结果表明,在各迭代轮次中明显地降低了人工额外标注成本,同时也在一定程度上缓解了冷启动问题。此外,还通过消融实验证明了方法中自监督核心集采样模块和不确定性预测模块的有效性。

基于可调场景语义标注范围的家庭室内语义地图构建

针对家庭室内环境语义地图建图速度较慢和在门口场景语义标注易出现错误等问题,提出一种基于可调场景语义标注范围的家庭室内语义地图构建方法。首先根据YOLOv5s识别的物体大小赋予相应的场景置信度,基于该场景置信度设置阈值使得语义标注范围限制在机器人当前所在区域,确保场景切换时语义标注范围不会立即改变。然后基于人工势场虚拟力“引力斥力”原理,实现语义标注范围的扩大或缩小。最后结合阈值和动态语义标注范围,避免在门口场景中出现语义标注错误。实验结果表明:与Places205-VGG16神经网络建立家庭室内语义地图相比,所提方法平均效率和平均精准率分别提升了11.0%和7.8%,在家庭室内环境中具有一定的优越性。

基于Yolov5的交通信号灯智能识别程序开发

交通信号检测是智能汽车识别交通环境的一项重要辅助技术,现有的算法能够解决单一交叉口环境下的信号检测问题,但需要在十字路口的复杂交通环境中提高算法的精度和干扰可靠性。文章以one-stage目标检测算法Yolov5的应用为研究基础,实现多场景下的交通信号灯自动检测与识别,使用Labeling进行图片标注,通过镜像、裁剪、反转、等运行增强数据集,不断地调参实验与迭代模型训练,目标检测精度达到80%。

吕才《阴阳书·历注》与唐历——具注历源起新说

唐太宗曾命吕才等人编修《阴阳书》,此书“历注”二卷被作为注历书长期使用,可以视作最早的注历书。梳理敦煌吐鲁番出土具注历与日本藏《大唐阴阳书》抄本后发现:一、《大唐阴阳书》抄本即《阴阳书》卷三十二、三十三“历注”部分,与出土唐至宋初具注历历注顺序、类目相同,内容相关;二、今存最早的具注历为唐高宗显庆三年具注历,晚于《阴阳书》,具注历阴阳历注内容应系从后者抄出。因此推断,具注历的产生与《阴阳书》有关,时间不早于太宗贞观十五年。

基于多层优选卷积的水声信号样本自动标注方法

针对深度学习在水声研究领域的应用中面临大数据量要求和现有样本量限制的问题,本文提出了一种多层优选卷积网络模型。通过基于相似度的优选方法选出最佳卷积核,以提取更具代表性的特征。利用探索层特征融合策略,叠加多层卷积输出,获取更全面的特征信息。采用约减策略优化模型,有效缩短运算时间。通过优选、特征融合和注意力机制,有效解决此类问题。实验结果表明,该模型在数据集上取得的最好的标注准确率为高基线模型1.12%;同时运行时间减少了43.5%。因此,该模型的使用提高了水声信号标注的准确率和效率。

安乐哲《中庸》英译本中注释的哲学建构

对安乐哲(Roger T.Ames)《中庸》英译本中的注释进行了研究,发现译者运用了形音型、比照型、解释型和评价型4类注释,藉此阐明了译者源文本翻译的哲学基础,彰显了其术语翻译的中国哲学特色,凸显了中国古典哲学的现代意义,体现了《中庸》对中国古典哲学发展的重要性,并展示了丰硕的儒学研究成果。

领域大语言模型下的古籍词性标注应用研究

[目的/意义]大语言模型的发展为古籍文本挖掘带来了新的思路,将大语言模型与古籍数字化、智能化相结合是新时代古籍工作的必经之路。[方法/过程]文章使用《左传》词性标注语料,通过数据清洗和预处理,构建了一批高质量的词性标注指令数据,在此基础上,分别使用500、1 000、2 000、5 000条数据对大语言模型进行指令微调,并在另外1 000条数据上进行性能测试。[结果/结论]实验结果表明,“荀子”系列模型在古籍文本词性标注任务上性能优于通用领域模型,在微调数据量达到5 000时,Xunzi-Baichuan2-7B模型表现出了最优性能,F1值达到81.67%。

基于特征图网络和多种生物信息预测关键蛋白质的深度学习框架

针对生物实验识别关键蛋白质费时费力,使用计算方法预测关键蛋白质无法有效整合生物信息的问题,提出一个深度学习框架.首先利用网络拓扑结构、基因表达数据和GO(gene ontology)注释数据构建加权蛋白质相互作用网络;然后分别使用特征图网络和双向长短期记忆细胞从亚细胞定位数据、蛋白质复合物数据和基因表达数据中提取特征向量;最后将这些特征向量输入到任务学习层预测关键蛋白质.实验结果表明,相比于现有的计算方法,该方法预测性能更好.

《甬言稽诂》的校注特色和价值

《甬言稽诂》是近人应钟撰写的一部考释宁波方言的专著,用文言写成,征引丰富,考证详尽,观点或是或非,不好裁择。《〈甬言稽诂〉校注及研究》一书对其进行了校注,探赜发微,正讹辨误,多有创获。今就“校注”的特色和价值略作分析和评述,便于读者更好地了解《甬言稽诂》,更好地传承方言文化。

杜甫“江州妹”小考及其它

杜甫诗“长葛书难得,江州涕不禁”之“江州”乃指其妹,旧注以为这个“江州妹”即《乾元中寓居同谷县作歌七首》其四中提到的“韦氏妹”。然“韦氏妹”所嫁之地钟离(今安徽凤阳)距江州遥远,其《五盘》曰:“故乡有弟妹,流落随丘墟。”这个故乡的妹妹与“韦氏妹”显然并非一人,令杜甫“涕不禁”的“江州妹”应是另有其人。明确杜甫有个“江州妹”之后,便需要重新解读杜诗中的“九江”“庐山”“柴桑”等语词之涵义。杜诗旧注中此类失误不止一处。