A Method for Automatically Generating Join Queries Based on Relations-Attributes Distance Matrix over Data Lakes

Techniques for identifying joinable or unionable tables in data lakes can yield valuable information for data scientists. However, more than half of their working time is spent familiarizing themselves with the metadata and correlations of datasets. Simplifying the use of information in data lakes is crucial for enhancing their utilization. The existing solution of integrating correlated relations into a single large data table via full disjunction requires integration updating when either data or metadata changes, complicating data maintenance. This paper proposes a method for automatically generating join queries based on the distance matrix of relations and attributes in data lakes. The distance matrix only requires updating when metadata changes, simplifying data maintenance. Experimental results demonstrate that once the distance matrix is generated, the time required to generate the join queries is negligible. Compared to the existing solution, the time cost for executing join queries over correlated tables is nearly identical to that of selection queries over integrated tables. The results of these two queries are also the same, showcasing the effectiveness and efficiency of our method

PLDANet: Reasonable Combination of PCA and LDA Convolutional Networks

Integrating deep learning with traditional machine learning methods is an intriguing research direction. For example, PCANet and LDANet adopts Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (LDA) to learn convolutional kernels separately. It is not reasonable to adopt LDA to learn filter kernels in each convolutional layer, local features of images from different classes may be similar, such as background areas. Therefore, it is meaningful to adopt LDA to learn filter kernels only when all the patches carry information from the whole image. However, to our knowledge, there are no existing works that study how to combine PCA and LDA to learn convolutional kernels to achieve the best performance. In this paper, we propose the convolutional coverage theory. Furthermore, we propose the PLDANet model which adopts PCA and LDA reasonably in different convolutional layers based on the coverage theory. The experimental study has shown the effectiveness of the proposed PLDANet model

SODs involved in the hormone mediated regulation of H2O2 content in Kandelia obovata root tissues under cadmium stress.

Cadmium (Cd) pollution in mangrove wetlands has received increasing attention as urbanization expands rapidly. As a dominant mangrove species, Kandelia obovata is highly tolerant to Cd toxicity. Plant hormones and superoxide dismutase (SODs) play critical roles in the response to heavy metal stress in K. obovata roots. Although theirs important influence have been reported, the regulation mechanism between SODs and plant hormones in Cd detoxification by K. obovata roots remains limited. Here, we investigated relationships among SOD, plant hormones, and Cd tolerance in K. obovata roots exposed to Cd. We found that Cd was retained in the epidermis and exodermis of roots, and the epidermis and exodermis had highest hydrogen peroxide (H2O2) content and SOD activity. Similarly, SOD isozymes also exhibited distinct activity in the different parts of root. Overexpressed KoCSD3 and KoFSD2 individually in Nicotiana benthamiana revealed that different SOD members contributed to H2O2 content regulation by promote the activity of downstream antioxidant enzymes under Cd treatment. In addition, assays on the effects of hormones showed that increased endogenous indole-3-acetic acid (IAA) was observed in the cortex and stele, whereas the abscisic acid (ABA) content was enhanced in the epidermis and exodermis in roots during Cd treatment. The results of exogenous hormones treatment indicated that KoFSD2 upregulated under ABA and IAA treatment, but KoCSD3 only induced by ABA stimulation. Taken together, our results reveal the relationship between SODs and plant hormones, which expands the knowledge base regarding KoSODs response to plant hormones and mediating H2O2 concentration under Cd stress

SODs involved in the hormone mediated regulation of H 2 O 2 content in Kandelia obovata root tissues under cadmium stress

Abstract(#br)Cadmium (Cd) pollution in mangrove wetlands has received increasing attention as urbanization expands rapidly. As a dominant mangrove species, Kandelia obovata is highly tolerant to Cd toxicity. Plant hormones and superoxide dismutase (SODs) play critical roles in the response to heavy metal stress in K. obovata roots. Although theirs important influence have been reported, the regulation mechanism between SODs and plant hormones in Cd detoxification by K. obovata roots remains limited. Here, we investigated relationships among SOD, plant hormones, and Cd tolerance in K. obovata roots exposed to Cd. We found that Cd was retained in the epidermis and exodermis of roots, and the epidermis and exodermis had highest hydrogen peroxide (H 2 O 2 ) content and SOD activity. Similarly, SOD isozymes also exhibited distinct activity in the different parts of root. Overexpressed KoCSD3 and KoFSD2 individually in Nicotiana benthamiana revealed that different SOD members contributed to H 2 O 2 content regulation by promote the activity of downstream antioxidant enzymes under Cd treatment. In addition, assays on the effects of hormones showed that increased endogenous indole-3-acetic acid (IAA) was observed in the cortex and stele, whereas the abscisic acid (ABA) content was enhanced in the epidermis and exodermis in roots during Cd treatment. The results of exogenous hormones treatment indicated that KoFSD2 upregulated under ABA and IAA treatment, but KoCSD3 only induced by ABA stimulation. Taken together, our results reveal the relationship between SODs and plant hormones, which expands the knowledge base regarding KoSODs response to plant hormones and mediating H 2 O 2 concentration under Cd stress

An Improved Contextual Advertising Matching Approach based on Wikipedia Knowledge

The current boom of the Web is associated with the revenues originated from Web advertising. As one prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant commercial textual ads within the content of a Web page, so as to provide a better user experience and thereby increase the revenues of Web site owners and an advertising platform. Therefore, in contextual advertising, the relevance of selected ads with a Web page is essential. However, some problems, such as homonymy and polysemy, low intersection of keywords and context mismatch, can lead to the selection of irrelevant textual ads for a Web page, making that a simple keyword matching technique generally gives poor accuracy. To overcome these problems and thus to improve the relevance of contextual ads, in this paper we propose a novel Wikipedia-based matching technique which, using selective matching strategies, selects a certain amount of relevant articles from Wikipedia as an intermediate semantic reference model for matching Web pages and textual ads. We call this technique SIWI: Selective Wikipedia Matching, which, instead of using the whole Wikipedia articles, only matches the most relevant articles for a page (or a textual ad), resulting in the effective improvement of the overall matching performance. An experimental evaluation is conducted, which runs over a set of real textual ads, a set of Web pages from the Internet and a dataset of more than 260 000 articles from Wikipedia. The experimental results show that our method performs better than existing matching strategies, which can deal with the matching over the large dataset of Wikipedia articles efficiently, and achieve a satisfactory contextual advertising effect

A Method for Automatically Generating Join Queries Based on Relations-Attributes Distance Matrix over Data Lakes

Techniques for identifying joinable or unionable tables in data lakes can yield valuable information for data scientists. However, more than half of their working time is spent familiarizing themselves with the metadata and correlations of datasets. Simplifying the use of information in data lakes is crucial for enhancing their utilization. The existing solution of integrating correlated relations into a single large data table via full disjunction requires integration updating when either data or metadata changes, complicating data maintenance. This paper proposes a method for automatically generating join queries based on the distance matrix of relations and attributes in data lakes. The distance matrix only requires updating when metadata changes, simplifying data maintenance. Experimental results demonstrate that once the distance matrix is generated, the time required to generate the join queries is negligible. Compared to the existing solution, the time cost for executing join queries over correlated tables is nearly identical to that of selection queries over integrated tables. The results of these two queries are also the same, showcasing the effectiveness and efficiency of our method

CT-based radiomics signature of visceral adipose tissue for prediction of disease progression in patients with Crohn's disease: a multicentre cohort studyResearch in context

Summary: Background: Visceral adipose tissue (VAT) is involved in the pathogenesis of Crohn's disease (CD). However, data describing its effects on CD progression remain scarce. We developed and validated a VAT-radiomics model (RM) using computed tomography (CT) images to predict disease progression in patients with CD and compared it with a subcutaneous adipose tissue (SAT)-RM. Methods: This retrospective study included 256 patients with CD (training, n = 156; test, n = 100) who underwent baseline CT examinations from June 19, 2015 to June 14, 2020 at three tertiary referral centres (The First Affiliated Hospital of Sun Yat-Sen University, The First Affiliated Hospital of Shantou University Medical College, and The First People's Hospital of Foshan City) in China. Disease progression referred to the development of penetrating or stricturing diseases or the requirement for CD-related surgeries during follow-up. A total of 1130 radiomics features were extracted from VAT on CT in the training cohort, and a machine-learning–based VAT-RM was developed to predict disease progression using selected reproducible features and validated in an external test cohort. Using the same modeling methodology, a SAT-RM was developed and compared with the VAT-RM. Findings: The VAT-RM exhibited satisfactory performance for predicting disease progression in total test cohort (the area under the ROC curve [AUC] = 0.850, 95% confidence Interval [CI] 0.764–0.913, P < 0.001) and in test cohorts 1 (AUC = 0.820, 95% CI 0.687–0.914, P < 0.001) and 2 (AUC = 0.871, 95% CI 0.744–0.949, P < 0.001). No significant differences in AUC were observed between test cohorts 1 and 2 (P = 0.673), suggesting considerable efficacy and robustness of the VAT-RM. In the total test cohort, the AUC of the VAT-RM for predicting disease progression was higher than that of SAT-RM (AUC = 0.786, 95% CI 0.692–0.861, P < 0.001). On multivariate Cox regression analysis, the VAT-RM (hazard ratio [HR] = 9.285, P = 0.005) was the most important independent predictor, followed by the SAT-RM (HR = 3.280, P = 0.060). Decision curve analysis further confirmed the better net benefit of the VAT-RM than the SAT-RM. Moreover, the SAT-RM failed to significantly improve predictive efficacy after it was added to the VAT-RM (integrated discrimination improvement = 0.031, P = 0.102). Interpretation: Our results suggest that VAT is an important determinant of disease progression in patients with CD. Our VAT-RM allows the accurate identification of high-risk patients prone to disease progression and offers notable advantages over SAT-RM. Funding: This study was supported by the National Natural Science Foundation of China, Guangdong Basic and Applied Basic Research Foundation, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Nature Science Foundation of Shenzhen, and Young S&T Talent Training Program of Guangdong Provincial Association for S&T. Translation: For the Chinese translation of the abstract see Supplementary Materials section