300 research outputs found
Isolation, characterization, and expression analysis of [beta]-1, 3-glucanase genes from strawberry plants
Plant beta-1, 3-glucanases are pathogenesis-related proteins, which are implicated in plant defense responses against pathogen infection. As an initial step in understanding the roles of beta-1, 3-glucanases in the strawberry plant defense system, genome walking, and 3\u27 and 5\u27 RACE were performed to isolate beta-1, 3-glucanase genomic and cDNA clones. In addition, real time PCR was performed to determine the expression levels of two of the isolated beta-1, 3-glucanase genes in healthy and fungal infected plants. Two genomic clones, FaBG2-1 and FaBG2-2, and a cDNA clone, FaBG2-3, encoding three different beta-1, 3-glucanases, were isolated. FaBG2-1 was comprised of two exons and one intron. The first exon of FaBG2-1 encodes the major part of a signal peptide. Results of Southern blotting analysis indicated that the strawberry genome contains several copies of FaBG2-1 or related genes. FaBG2-2 appears to be an intronless gene and does not encode a signal peptide. FaBG2-3, like FaBG2-1, also encodes a signal peptide, but is different from FaBG2-1 in 3\u27 and 5\u27 non-coding regions. The proteins encoded by these three genes share a high degree of sequence homology to plant class II beta-1, 3-glucanases. The expression of FaBG2-1 and FaBG2-3 in strawberry plants infected with Colletotrichum fragariae and Colletotrichum acutatum, two important strawberry fungal pathogens, were examined. High levels of induction of both genes were observed in plants infected with C. fragariae, whereas lower levels of induction were observed in plants infected with C. acutatum. Moreover, the expression of FaBG2-3 was much greater than FaBG2-1 in both the uninfected and the infected plants. The expressions of FaBG2-1 and FaBG2-3 in leaves, crowns, and roots were examined at different time points during a 7 month growth period. Different organs showed different expression patterns for the two genes. Furthermore, the total beta-1, 3-glucanase activity and isozyme pattern were analyzed. The isozyme patterns were different between the uninfected and the infected plants. Also, the differences were observed between young plants and older plants. This research shows that beta-1, 3-glucanase in strawberry plant may play roles in plant defense and plant development
The Style and the Theme of Loss in Hemingway’ s Hills Like White Elephants
Hemingway’ s Short Story Hills Like White Elephants presents a simple story between an American man and a young woman. Under the simple plot lies strong conflict between protagonists. Through probing into its language techniques, repetition, documentary style, and girl’s loss of unborn child, her love, and her future, this paper aims to give an in-depth analysis of its style and theme of loss.Key words: Repetition; Documentary style; Los
Fast Fourier Intrinsic Network
We address the problem of decomposing an image into albedo and shading. We
propose the Fast Fourier Intrinsic Network, FFI-Net in short, that operates in
the spectral domain, splitting the input into several spectral bands. Weights
in FFI-Net are optimized in the spectral domain, allowing faster convergence to
a lower error. FFI-Net is lightweight and does not need auxiliary networks for
training. The network is trained end-to-end with a novel spectral loss which
measures the global distance between the network prediction and corresponding
ground truth. FFI-Net achieves state-of-the-art performance on MPI-Sintel, MIT
Intrinsic, and IIW datasets.Comment: WACV 2021 - camera read
Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond
Recently, fine-tuning pre-trained code models such as CodeBERT on downstream
tasks has achieved great success in many software testing and analysis tasks.
While effective and prevalent, fine-tuning the pre-trained parameters incurs a
large computational cost. In this paper, we conduct an extensive experimental
study to explore what happens to layer-wise pre-trained representations and
their encoded code knowledge during fine-tuning. We then propose efficient
alternatives to fine-tune the large pre-trained code model based on the above
findings. Our experimental study shows that (1) lexical, syntactic and
structural properties of source code are encoded in the lower, intermediate,
and higher layers, respectively, while the semantic property spans across the
entire model. (2) The process of fine-tuning preserves most of the code
properties. Specifically, the basic code properties captured by lower and
intermediate layers are still preserved during fine-tuning. Furthermore, we
find that only the representations of the top two layers change most during
fine-tuning for various downstream tasks. (3) Based on the above findings, we
propose Telly to efficiently fine-tune pre-trained code models via layer
freezing. The extensive experimental results on five various downstream tasks
demonstrate that training parameters and the corresponding time cost are
greatly reduced, while performances are similar or better. Replication package
including source code, datasets, and online Appendix is available at:
\url{https://github.com/DeepSoftwareAnalytics/Telly}.Comment: Accepted by ISSTA 2023 (The 32nd ACM SIGSOFT International Symposium
on Software Testing and Analysis
Enhancing Semantic Code Search with Multimodal Contrastive Learning and Soft Data Augmentation
Code search aims to retrieve the most semantically relevant code snippet for
a given natural language query. Recently, large-scale code pre-trained models
such as CodeBERT and GraphCodeBERT learn generic representations of source code
and have achieved substantial improvement on code search task. However, the
high-quality sequence-level representations of code snippets have not been
sufficiently explored. In this paper, we propose a new approach with multimodal
contrastive learning and soft data augmentation for code search. Multimodal
contrastive learning is used to pull together the representations of code-query
pairs and push apart the unpaired code snippets and queries. Moreover, data
augmentation is critical in contrastive learning for learning high-quality
representations. However, only semantic-preserving augmentations for source
code are considered in existing work. In this work, we propose to do soft data
augmentation by dynamically masking and replacing some tokens in code sequences
to generate code snippets that are similar but not necessarily
semantic-preserving as positive samples for paired queries. We conduct
extensive experiments to evaluate the effectiveness of our approach on a
large-scale dataset with six programming languages. The experimental results
show that our approach significantly outperforms the state-of-the-art methods.
We also adapt our techniques to several pre-trained models such as RoBERTa and
CodeBERT, and significantly boost their performance on the code search task
Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach
Graph Neural Networks (GNNs) are popular machine learning methods for
modeling graph data. A lot of GNNs perform well on homophily graphs while
having unsatisfactory performance on heterophily graphs. Recently, some
researchers turn their attention to designing GNNs for heterophily graphs by
adjusting the message passing mechanism or enlarging the receptive field of the
message passing. Different from existing works that mitigate the issues of
heterophily from model design perspective, we propose to study heterophily
graphs from an orthogonal perspective by rewiring the graph structure to reduce
heterophily and making the traditional GNNs perform better. Through
comprehensive empirical studies and analysis, we verify the potential of the
rewiring methods. To fully exploit its potential, we propose a method named
Deep Heterophily Graph Rewiring (DHGR) to rewire graphs by adding homophilic
edges and pruning heterophilic edges. The detailed way of rewiring is
determined by comparing the similarity of label/feature-distribution of node
neighbors. Besides, we design a scalable implementation for DHGR to guarantee
high efficiency. DHRG can be easily used as a plug-in module, i.e., a graph
pre-processing step, for any GNNs, including both GNN for homophily and
heterophily, to boost their performance on the node classification task. To the
best of our knowledge, it is the first work studying graph rewiring for
heterophily graphs. Extensive experiments on 11 public graph datasets
demonstrate the superiority of our proposed methods.Comment: 11 page
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