Enhancer of Shoot Regeneration 2(ESR2)regulates pollen maturation and vitality in watermelon(Citrullus lanatus)

Watermelon(Citrullus lanatus)holds global significance as a fruit with high economic and nutritional value.Exploring the regulatory network of watermelon male reproductive development is crucial for developing male sterile materials and facilitating cross-breeding.Despite its importance,there is a lack of research on the regulation mechanism of male reproductive development in watermelon.In this study,we identified that ClESR2,a VIIIb subclass member in the APETALA2/Ethylene Responsive Factor(AP2/ERF)superfamily,was a key factor in pollen development.RNA insitu hybridization confirmed significantClESR2 expression in the tapetum and pollen during the later stage of anther development.The pollens of transgenic plants showed major defects in morphology and vitality at the late development stage.The RNA-seq and protein interaction assay confirmed that ClESR2 regulates pollen morphology and fertility by interacting with key genes involved in pollen development at both transcriptional and protein levels.These suggest that Enhancer of Shoot Regeneration 2(ESR2)plays an important role in pollen maturation and vitality.This study helps understand the male reproductive development of watermelon,providing a theoretical foundation for developing male sterile materials.

A survey on deep learning for textual emotion analysis in social networks

Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.

Malignant Proliferating Trichilemmal Tumor in the Right Post Auricular Region

OBJECTIVE To report a case of a malignant proliferating trichilemmal tumor (PTT) in the right postauricular region, and to describe the clinical and histopathologic findings. METHODS Interventional case report and literature review. RESULTS A 46-year-old woman presented with a 15-year history of a nodule of 30×30×10 mm in diameter in the right postauricular region. It was diagnosed as a sebaceous cyst. A local mass excision was performed. Histopathologic examination revealed proliferation of the outer hair sheath epithelium with multiple central areas of trichilemmal kera-tinization. The presence of marked cellular atypia and frequent mitoses indicated a malignant transformation. A second operation employing an enlarged excision was conducted followed by a histopathologic examination showing that there was no malignant tumor remaining. Two weeks after the second operation, 50 cGy of regional prophylactic radiotherapy was applied. The patient was well after 26 months of follow-up and neither recurrences nor metastases were observed. CONCLUSION Malignant PTT is a rare skin neoplasm, with its diagnosis depending on a histopathologic examination. An extend excision is the main treatment after diagnosis.

Study on the optimization of transformation systems in watermelon

To date,the genetic transformation system of watermelon has remained inefficient.In this study,the genetic transformation system of watermelon mediated by Agrobacterium tumefaciens was optimized,including different seedling ages,strains of Agrobacterium tumefaciens,concentrations of acetosyringone infected solution,co-culture time,selection pressure of antibiotics timentin and glufosinate,and concentrations of hormones 6-benzylaminopurine,indoleacetic acid and naphthylacetic acid in the corresponding culture medium.Our results suggested that cotyledons would be used as explants,disinfected with 6%sodium hypochlorite for 12 min,cultured for 3 d,and then infected with Agrobacterium inoculum(Agrobacterium EHA105)containing 200μM acetosyringone and 0.02 of final OD_(600).The explants differentiated into adventitious shoots in the medium with 1.5 mg/L 6-benzylaminopurine and 200μM timentin.Positive adventitious shoots were obtained through further screening by 1.4 mg/L herbicide glufosinate-ammonium,and were induced by 0.1 mg/L naphthalene acetic acid into independent plants.Our system improves the genetic transformation efficiency of watermelon and provides a technical basis for continuous acquisition of watermelon transgenic plants.

Deep Broad Learning for Emotion Classification in Textual Conversations

Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual conversations.It is becoming one of the most important tasks for natural language processing in recent years.However,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual context.To address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely DBL.It aims to provide a more effective solution to capture local contextual information(i.e.,utterance-level)in an utterance,as well as global contextual information(i.e.,speaker-level)in a conversation,based on Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(Bi-LSTM),and broad learning.Extensive experiments have been conducted on three public textual conversation datasets,which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion classification.In addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.

CNN-Based Broad Learning for Cross-Domain Emotion Classification

Cross-domain emotion classification aims to leverage useful information in a source domain to help predict emotion polarity in a target domain in a unsupervised or semi-supervised manner.Due to the domain discrepancy,an emotion classifier trained on source domain may not work well on target domain.Many researchers have focused on traditional cross-domain sentiment classification,which is coarse-grained emotion classification.However,the problem of emotion classification for cross-domain is rarely involved.In this paper,we propose a method,called convolutional neural network(CNN)based broad learning,for cross-domain emotion classification by combining the strength of CNN and broad learning.We first utilized CNN to extract domain-invariant and domain-specific features simultaneously,so as to train two more efficient classifiers by employing broad learning.Then,to take advantage of these two classifiers,we designed a co-training model to boost together for them.Finally,we conducted comparative experiments on four datasets for verifying the effectiveness of our proposed method.The experimental results show that the proposed method can improve the performance of emotion classification more effectively than those baseline methods.

p-Norm Broad Learning for Negative Emotion Classification in Social Networks

Negative emotion classification refers to the automatic classification of negative emotion of texts in social networks.Most existing methods are based on deep learning models,facing challenges such as complex structures and too many hyperparameters.To meet these challenges,in this paper,we propose a method for negative emotion classification utilizing a Robustly Optimized BERT Pretraining Approach(RoBERTa)and p-norm Broad Learning(p-BL).Specifically,there are mainly three contributions in this paper.Firstly,we fine-tune the RoBERTa to adapt it to the task of negative emotion classification.Then,we employ the fine-tuned RoBERTa to extract features of original texts and generate sentence vectors.Secondly,we adopt p-BL to construct a classifier and then predict negative emotions of texts using the classifier.Compared with deep learning models,p-BL has advantages such as a simple structure that is only 3-layer and fewer parameters to be trained.Moreover,it can suppress the adverse effects of more outliers and noise in data by flexibly changing the value of p.Thirdly,we conduct extensive experiments on the public datasets,and the experimental results show that our proposed method outperforms the baseline methods on the tested datasets.