Automatic Recognition of Tea Diseases Based on Deep Learning

With the rapid development of intelligent agriculture and precision agriculture, computer image processing technology has been widely used to solve various problems in the agricultural field. In particular, the advantages of convolutional neural networks (CNNs) in image classification have also been widely used in the automatic recognition and classification of plant diseases. In this paper, a deep convolutional neural network named LeafNet capable of recognizing the seven types of diseases from tea leaf disease images was established, with an accuracy of up to 90.23%, aiming to provide timely and accurate diagnostic services in the remote and topographic tea plantation in China. At the same time, the traditional machine learning algorithm is applied for comparative analysis, which extracts the dense scale-invariant feature transform (DSIFT) of the image and constructs the bag of visual word (BOVW) model to express the image based on the DSIFT descriptor. The support vector machines (SVMs) and multilayer perceptron (MLP) were used to identify tea leaf diseases, with an accuracy of 60.91 and 70.94%, respectively

A hybrid representation based simile component extraction

Simile, a special type of metaphor, can help people to express their ideas more clearly. Simile component extraction is to extract tenors and vehicles from sentences. This task has a realistic significance since it is useful for building cognitive knowledge base. With the development of deep neural networks, researchers begin to apply neural models to component extraction. Simile components should be in cross-domain. According to our observations, words in cross-domain always have different concepts. Thus, concept is important when identifying whether two words are simile components or not. However, existing models do not integrate concept into their models. It is difficult for these models to identify the concept of a word. What’s more, corpus about simile component extraction is limited. There are a number of rare words or unseen words, and the representations of these words are always not proper enough. Exiting models can hardly extract simile components accurately when there are low-frequency words in sentences. To solve these problems, we propose a hybrid representation-based component extraction (HRCE) model. Each word in HRCE is represented in three different levels: word level, concept level and character level. Concept representations (representations in concept level) can help HRCE to identify the words in cross-domain more accurately. Moreover, with the help of character representations (representations in character levels), HRCE can represent the meaning of a word more properly since words are consisted of characters and these characters can partly represent the meaning of words. We conduct experiments to compare the performance between HRCE and existing models. The experiment results show that HRCE significantly outperforms current models

SeDR: Segment Representation Learning for Long Documents Dense Retrieval

Recently, Dense Retrieval (DR) has become a promising solution to document retrieval, where document representations are used to perform effective and efficient semantic search. However, DR remains challenging on long documents, due to the quadratic complexity of its Transformer-based encoder and the finite capacity of a low-dimension embedding. Current DR models use suboptimal strategies such as truncating or splitting-and-pooling to long documents leading to poor utilization of whole document information. In this work, to tackle this problem, we propose Segment representation learning for long documents Dense Retrieval (SeDR). In SeDR, Segment-Interaction Transformer is proposed to encode long documents into document-aware and segment-sensitive representations, while it holds the complexity of splitting-and-pooling and outperforms other segment-interaction patterns on DR. Since GPU memory requirements for long document encoding causes insufficient negatives for DR training, Late-Cache Negative is further proposed to provide additional cache negatives for optimizing representation learning. Experiments on MS MARCO and TREC-DL datasets show that SeDR achieves superior performance among DR models, and confirm the effectiveness of SeDR on long document retrieval

Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval

Open-domain table question answering aims to provide answers to a question by retrieving and extracting information from a large collection of tables. Existing studies of open-domain table QA either directly adopt text retrieval methods or consider the table structure only in the encoding layer for table retrieval, which may cause syntactical and structural information loss during table scoring. To address this issue, we propose a syntax- and structure-aware retrieval method for the open-domain table QA task. It provides syntactical representations for the question and uses the structural header and value representations for the tables to avoid the loss of fine-grained syntactical and structural information. Then, a syntactical-to-structural aggregator is used to obtain the matching score between the question and a candidate table by mimicking the human retrieval process. Experimental results show that our method achieves the state-of-the-art on the NQ-tables dataset and overwhelms strong baselines on a newly curated open-domain Text-to-SQL dataset.Comment: IJCNLP-AACL 202

UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

Object detection with transformers (DETR) reaches competitive performance with Faster R-CNN via a transformer encoder-decoder architecture. Inspired by the great success of pre-training transformers in natural language processing, we propose a pretext task named random query patch detection to Unsupervisedly Pre-train DETR (UP-DETR) for object detection. Specifically, we randomly crop patches from the given image and then feed them as queries to the decoder. The model is pre-trained to detect these query patches from the original image. During the pre-training, we address two critical issues: multi-task learning and multi-query localization. (1) To trade off classification and localization preferences in the pretext task, we freeze the CNN backbone and propose a patch feature reconstruction branch which is jointly optimized with patch detection. (2) To perform multi-query localization, we introduce UP-DETR from single-query patch and extend it to multi-query patches with object query shuffle and attention mask. In our experiments, UP-DETR significantly boosts the performance of DETR with faster convergence and higher average precision on object detection, one-shot detection and panoptic segmentation. Code and pre-training models: https://github.com/dddzg/up-detr.Comment: Accepted by CVPR 202

Differential miRNA expression in Rehmannia glutinosa plants subjected to continuous cropping

<p>Abstract</p> <p>Background</p> <p>The productivity of the medicinally significant perennial herb <it>Rehmannia glutinosa </it>is severely affected after the first year of cropping. While there is some information available describing the physiological and environmental causes of this yield decline, there is as yet no data regarding the changes in gene expression which occur when the species is continuously cropped.</p> <p>Results</p> <p>Using a massively parallel (Solexa) DNA sequencing platform, it was possible to identify and quantify the abundance of a large number of <it>R. glutinosa </it>miRNAs. We contrasted the miRNA content of first year crop plants with that of second year crop ones, and were able to show that of 89 conserved (belonging to 25 families) and six novel miRNAs (six families), 29 of the former and three of the latter were differentially expressed. The three novel miRNAs were predicted to target seven genes, and the 29 conserved ones 308 genes. The potential targets of 32 of these differentially expressed miRNAs involved in the main transcription regulation, plant development and signal transduction. A functional analysis of the differentially expressed miRNAs suggested that several of the proposed targets could be directly or indirectly responsible for the development of the tuberous root.</p> <p>Conclusion</p> <p>We have compared differential miRNAs expression in the first year crop (FP) <it>R. glutinosa </it>plants and second year crop (SP) ones. The outcome identifies some potential leads for understanding the molecular basis of the processes underlying the difficulty of maintaining the productivity of continuously cropped <it>R. glutinosa</it>.</p

Prospects for Bioethanol Production from Macroalgae

Macroalgae (mainly marine macroalgae, i.e. seaweeds) are considered as a very promising source for bioethanol production, because they have high carbohydrate contents, superior productivity, and wide adaptability. Macroalgae are generally grouped into three major categories: red, green, and brown algae. Each category has thousands of species, and each species possesses its unique cellular structure, biochemistry, and constitutes. Converting macroalgae to bioethanol involves pretreatment, saccharification, fermentation, and distillation; and the establishment of economic pretreatment methods is always the first key step for bioethanol production. In present, dilute-acid or alkali hydrolysis is typically used to treat macroalgal biomass. Macroalgae can be depolymerized under mild conditions as they have low lignin content. The resulting polysaccharides can be converted to ethanol through enzymatic hydrolysis, followed by adding bacteria, such as Saccharomyces cerevisiae and recombinant Escherichia coli KO11. Compared with the separate hydrolysis and fermentation process, the simultaneous saccharification and fermentation process often provided higher ethanol titer and conversion efficiency. However, the research on bioethanol production from macroalgae is still in its early stage due to both technical and economic barriers, significant amount of research and development work is needed prior to the commercialization of bioethanol manufacture from macroalgae.Citation: Chen, J., Bai, J., Li, H., Chang, C., and Fang, S. (2015). Prospects for Bioethanol Production from Macroalgae. Trends in Renewable Energy, 1(3), 185-197. DOI: 10.17737/tre.2015.1.3.001

Quantitative Proteomics of Chromochloris zofingiensis Reveals the Key Proteins Involved in Cell Growth and Bioactive Compound Biosynthesis

Glucose metabolism regulates cell growth and affects astaxanthin accumulation in the green algae Chromochloris zofingiensis. Hub gene functioning in this bioactive compound has been illustrated at the genome, transcriptome and metabolome level, but is rather limited from a proteome aspect. Microalgal cell produce an enhanced biomass (8-fold higher) but decreased lipid and astaxanthin content (~20% less) in the glucose condition compared to the control. Here, we investigate the proteomic response of C. zofingiensis grown with and without glucose using an LC-MS/MS-based Tandem Mass Tag (TMT) approach. The proteomic analysis demonstrated that glucose supplementation triggers the upregulation of 105 proteins and downregulation of 151 proteins. Thus, the carbon and energy flux might flow to cell growth, which increased the associated protein abundance, including DNA polymerase, translation initiation factor, 26S proteasome regulatory subunits, and the marker enzyme of the TCA cycle ribosomal protein. Moreover, the glucose supplement triggered the downregulation of proteins mainly involved in photosynthesis, chloroplasts, valine, leucine and isoleucine biosynthesis, 2-oxocarboxylic acid metabolism, and pantothenate and CoA biosynthesis pathways. This proteomic analysis is likely to provide new insights into algal growth and lipid or astaxanthin accumulation upon glucose supplementation, providing a foundation for further development of C. zofingiensis as oleaginous microalga for bioengineering applications