16 research outputs found
Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection
Fabric defect segmentation is integral to textile quality control. Despite
this, the scarcity of high-quality annotated data and the diversity of fabric
defects present significant challenges to the application of deep learning in
this field. These factors limit the generalization and segmentation performance
of existing models, impeding their ability to handle the complexity of diverse
fabric types and defects. To overcome these obstacles, this study introduces an
innovative method to infuse specialized knowledge of fabric defects into the
Segment Anything Model (SAM), a large-scale visual model. By introducing and
training a unique set of fabric defect-related parameters, this approach
seamlessly integrates domain-specific knowledge into SAM without the need for
extensive modifications to the pre-existing model parameters. The revamped SAM
model leverages generalized image understanding learned from large-scale
natural image datasets while incorporating fabric defect-specific knowledge,
ensuring its proficiency in fabric defect segmentation tasks. The experimental
results reveal a significant improvement in the model's segmentation
performance, attributable to this novel amalgamation of generic and
fabric-specific knowledge. When benchmarking against popular existing
segmentation models across three datasets, our proposed model demonstrates a
substantial leap in performance. Its impressive results in cross-dataset
comparisons and few-shot learning experiments further demonstrate its potential
for practical applications in textile quality control.Comment: 13 pages,4 figures, 3 table
Adaptive Locality Preserving Regression
This paper proposes a novel discriminative regression method, called adaptive
locality preserving regression (ALPR) for classification. In particular, ALPR
aims to learn a more flexible and discriminative projection that not only
preserves the intrinsic structure of data, but also possesses the properties of
feature selection and interpretability. To this end, we introduce a target
learning technique to adaptively learn a more discriminative and flexible
target matrix rather than the pre-defined strict zero-one label matrix for
regression. Then a locality preserving constraint regularized by the adaptive
learned weights is further introduced to guide the projection learning, which
is beneficial to learn a more discriminative projection and avoid overfitting.
Moreover, we replace the conventional `Frobenius norm' with the special l21
norm to constrain the projection, which enables the method to adaptively select
the most important features from the original high-dimensional data for feature
extraction. In this way, the negative influence of the redundant features and
noises residing in the original data can be greatly eliminated. Besides, the
proposed method has good interpretability for features owing to the
row-sparsity property of the l21 norm. Extensive experiments conducted on the
synthetic database with manifold structure and many real-world databases prove
the effectiveness of the proposed method.Comment: The paper has been accepted by IEEE Transactions on Circuits and
Systems for Video Technology (TCSVT), and the code can be available at
https://drive.google.com/file/d/1iNzONkRByIaUhXwdEhOkkh_0d2AAXNE8/vie
PlantQTL-GE: a database system for identifying candidate genes in rice and Arabidopsis by gene expression and QTL information
We have designed and implemented a web-based database system, called PlantQTL-GE, to facilitate quantitatine traits locus (QTL) based candidate gene identification and gene function analysis. We collected a large number of genes, gene expression information in microarray data and expressed sequence tags (ESTs) and genetic markers from multiple sources of Oryza sativa and Arabidopsis thaliana. The system integrates these diverse data sources and has a uniform web interface for easy access. It supports QTL queries specifying QTL marker intervals or genomic loci, and displays, on rice or Arabidopsis genome, known genes, microarray data, ESTs and candidate genes and similar putative genes in the other plant. Candidate genes in QTL intervals are further annotated based on matching ESTs, microarray gene expression data and cis-elements in regulatory sequences. The system is freely available at
Background modelling using discriminative motion representation
Robustness is an important factor for background modelling on various scenarios. Current pixel‐based adaptive segmentation method cannot effectively tackle diverse objects simultaneously. To address this problem, in this study, a background modelling method using discriminative motion representation is proposed. Instead of simple usage of intensity to construct the background model, the proposed method extracts a new local descriptor which uses a weighted combination of differential excitations for each pixel to enhance the discriminability of pixels. On the basis of this background model, different categories of objects can be quickly identified by a simple but effective classification rule and accurately be represented in background model by a smart selection of updating strategies. Therefore, the authors’ background modelling method can generate complete representation for static objects and decrease false detection caused by dynamic background or illumination variations. Extensive experiments have been conducted to demonstrate that the proposed method obtains more advantages of foreground detection than the state‐of‐the‐art methods. In addition, the proposed method provides a computational efficient algorithm for foreground detection tasks
Table_1_Weighted gene co-expression network analysis identifies dysregulated B-cell receptor signaling pathway and novel genes in pulmonary arterial hypertension.XLSX
BackgroundPulmonary arterial hypertension (PAH) is a devastating cardio-pulmonary vascular disease in which chronic elevated pulmonary arterial pressure and pulmonary vascular remodeling lead to right ventricular failure and premature death. However, the exact molecular mechanism causing PAH remains unclear.MethodsRNA sequencing was used to analyze the transcriptional profiling of controls and rats treated with monocrotaline (MCT) for 1, 2, 3, and 4 weeks. Weighted gene co-expression network analysis (WGCNA) was employed to identify the key modules associated with the severity of PAH. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the potential biological processes and pathways of key modules. Real-time PCR and western blot analysis were used to validate the gene expression. The hub genes were validated by an independent dataset obtained from the Gene Expression Omnibus database.ResultsA total of 26 gene modules were identified by WGCNA. Of these modules, two modules showed the highest correlation with the severity of PAH and were recognized as the key modules. GO analysis of key modules showed the dysregulated inflammation and immunity, particularly B-cell-mediated humoral immunity in MCT-induced PAH. KEGG pathway analysis showed the significant enrichment of the B-cell receptor signaling pathway in the key modules. Pathview analysis revealed the dysregulation of the B-cell receptor signaling pathway in detail. Moreover, a series of humoral immune response-associated genes, such as BTK, BAFFR, and TNFSF4, were found to be differentially expressed in PAH. Additionally, five genes, including BANK1, FOXF1, TLE1, CLEC4A1, and CLEC4A3, were identified and validated as the hub genes.ConclusionThis study identified the dysregulated B-cell receptor signaling pathway, as well as novel genes associated with humoral immune response in MCT-induced PAH, thereby providing a novel insight into the molecular mechanisms underlying inflammation and immunity and therapeutic targets for PAH.</p
Data_Sheet_1_Weighted gene co-expression network analysis identifies dysregulated B-cell receptor signaling pathway and novel genes in pulmonary arterial hypertension.PDF
BackgroundPulmonary arterial hypertension (PAH) is a devastating cardio-pulmonary vascular disease in which chronic elevated pulmonary arterial pressure and pulmonary vascular remodeling lead to right ventricular failure and premature death. However, the exact molecular mechanism causing PAH remains unclear.MethodsRNA sequencing was used to analyze the transcriptional profiling of controls and rats treated with monocrotaline (MCT) for 1, 2, 3, and 4 weeks. Weighted gene co-expression network analysis (WGCNA) was employed to identify the key modules associated with the severity of PAH. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the potential biological processes and pathways of key modules. Real-time PCR and western blot analysis were used to validate the gene expression. The hub genes were validated by an independent dataset obtained from the Gene Expression Omnibus database.ResultsA total of 26 gene modules were identified by WGCNA. Of these modules, two modules showed the highest correlation with the severity of PAH and were recognized as the key modules. GO analysis of key modules showed the dysregulated inflammation and immunity, particularly B-cell-mediated humoral immunity in MCT-induced PAH. KEGG pathway analysis showed the significant enrichment of the B-cell receptor signaling pathway in the key modules. Pathview analysis revealed the dysregulation of the B-cell receptor signaling pathway in detail. Moreover, a series of humoral immune response-associated genes, such as BTK, BAFFR, and TNFSF4, were found to be differentially expressed in PAH. Additionally, five genes, including BANK1, FOXF1, TLE1, CLEC4A1, and CLEC4A3, were identified and validated as the hub genes.ConclusionThis study identified the dysregulated B-cell receptor signaling pathway, as well as novel genes associated with humoral immune response in MCT-induced PAH, thereby providing a novel insight into the molecular mechanisms underlying inflammation and immunity and therapeutic targets for PAH.</p
Data_Sheet_2_Weighted gene co-expression network analysis identifies dysregulated B-cell receptor signaling pathway and novel genes in pulmonary arterial hypertension.PDF
BackgroundPulmonary arterial hypertension (PAH) is a devastating cardio-pulmonary vascular disease in which chronic elevated pulmonary arterial pressure and pulmonary vascular remodeling lead to right ventricular failure and premature death. However, the exact molecular mechanism causing PAH remains unclear.MethodsRNA sequencing was used to analyze the transcriptional profiling of controls and rats treated with monocrotaline (MCT) for 1, 2, 3, and 4 weeks. Weighted gene co-expression network analysis (WGCNA) was employed to identify the key modules associated with the severity of PAH. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the potential biological processes and pathways of key modules. Real-time PCR and western blot analysis were used to validate the gene expression. The hub genes were validated by an independent dataset obtained from the Gene Expression Omnibus database.ResultsA total of 26 gene modules were identified by WGCNA. Of these modules, two modules showed the highest correlation with the severity of PAH and were recognized as the key modules. GO analysis of key modules showed the dysregulated inflammation and immunity, particularly B-cell-mediated humoral immunity in MCT-induced PAH. KEGG pathway analysis showed the significant enrichment of the B-cell receptor signaling pathway in the key modules. Pathview analysis revealed the dysregulation of the B-cell receptor signaling pathway in detail. Moreover, a series of humoral immune response-associated genes, such as BTK, BAFFR, and TNFSF4, were found to be differentially expressed in PAH. Additionally, five genes, including BANK1, FOXF1, TLE1, CLEC4A1, and CLEC4A3, were identified and validated as the hub genes.ConclusionThis study identified the dysregulated B-cell receptor signaling pathway, as well as novel genes associated with humoral immune response in MCT-induced PAH, thereby providing a novel insight into the molecular mechanisms underlying inflammation and immunity and therapeutic targets for PAH.</p
Table_2_Weighted gene co-expression network analysis identifies dysregulated B-cell receptor signaling pathway and novel genes in pulmonary arterial hypertension.XLSX
BackgroundPulmonary arterial hypertension (PAH) is a devastating cardio-pulmonary vascular disease in which chronic elevated pulmonary arterial pressure and pulmonary vascular remodeling lead to right ventricular failure and premature death. However, the exact molecular mechanism causing PAH remains unclear.MethodsRNA sequencing was used to analyze the transcriptional profiling of controls and rats treated with monocrotaline (MCT) for 1, 2, 3, and 4 weeks. Weighted gene co-expression network analysis (WGCNA) was employed to identify the key modules associated with the severity of PAH. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the potential biological processes and pathways of key modules. Real-time PCR and western blot analysis were used to validate the gene expression. The hub genes were validated by an independent dataset obtained from the Gene Expression Omnibus database.ResultsA total of 26 gene modules were identified by WGCNA. Of these modules, two modules showed the highest correlation with the severity of PAH and were recognized as the key modules. GO analysis of key modules showed the dysregulated inflammation and immunity, particularly B-cell-mediated humoral immunity in MCT-induced PAH. KEGG pathway analysis showed the significant enrichment of the B-cell receptor signaling pathway in the key modules. Pathview analysis revealed the dysregulation of the B-cell receptor signaling pathway in detail. Moreover, a series of humoral immune response-associated genes, such as BTK, BAFFR, and TNFSF4, were found to be differentially expressed in PAH. Additionally, five genes, including BANK1, FOXF1, TLE1, CLEC4A1, and CLEC4A3, were identified and validated as the hub genes.ConclusionThis study identified the dysregulated B-cell receptor signaling pathway, as well as novel genes associated with humoral immune response in MCT-induced PAH, thereby providing a novel insight into the molecular mechanisms underlying inflammation and immunity and therapeutic targets for PAH.</p
Table_3_Weighted gene co-expression network analysis identifies dysregulated B-cell receptor signaling pathway and novel genes in pulmonary arterial hypertension.xlsx
BackgroundPulmonary arterial hypertension (PAH) is a devastating cardio-pulmonary vascular disease in which chronic elevated pulmonary arterial pressure and pulmonary vascular remodeling lead to right ventricular failure and premature death. However, the exact molecular mechanism causing PAH remains unclear.MethodsRNA sequencing was used to analyze the transcriptional profiling of controls and rats treated with monocrotaline (MCT) for 1, 2, 3, and 4 weeks. Weighted gene co-expression network analysis (WGCNA) was employed to identify the key modules associated with the severity of PAH. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the potential biological processes and pathways of key modules. Real-time PCR and western blot analysis were used to validate the gene expression. The hub genes were validated by an independent dataset obtained from the Gene Expression Omnibus database.ResultsA total of 26 gene modules were identified by WGCNA. Of these modules, two modules showed the highest correlation with the severity of PAH and were recognized as the key modules. GO analysis of key modules showed the dysregulated inflammation and immunity, particularly B-cell-mediated humoral immunity in MCT-induced PAH. KEGG pathway analysis showed the significant enrichment of the B-cell receptor signaling pathway in the key modules. Pathview analysis revealed the dysregulation of the B-cell receptor signaling pathway in detail. Moreover, a series of humoral immune response-associated genes, such as BTK, BAFFR, and TNFSF4, were found to be differentially expressed in PAH. Additionally, five genes, including BANK1, FOXF1, TLE1, CLEC4A1, and CLEC4A3, were identified and validated as the hub genes.ConclusionThis study identified the dysregulated B-cell receptor signaling pathway, as well as novel genes associated with humoral immune response in MCT-induced PAH, thereby providing a novel insight into the molecular mechanisms underlying inflammation and immunity and therapeutic targets for PAH.</p