Effective Instance Matching for Heterogeneous Structured Data

One main problem towards the effective usage of structured data is instance matching, where the goal is to find instance representations referring to the same real-world thing. In this book we investigate how to effectively match Heterogeneous structured data. We evaluate our approaches against the latest baselines. The results show advances beyond the state-of-the-art

Supervised Typing of Big Graphs using Semantic Embeddings

We propose a supervised algorithm for generating type embeddings in the same semantic vector space as a given set of entity embeddings. The algorithm is agnostic to the derivation of the underlying entity embeddings. It does not require any manual feature engineering, generalizes well to hundreds of types and achieves near-linear scaling on Big Graphs containing many millions of triples and instances by virtue of an incremental execution. We demonstrate the utility of the embeddings on a type recommendation task, outperforming a non-parametric feature-agnostic baseline while achieving 15x speedup and near-constant memory usage on a full partition of DBpedia. Using state-of-the-art visualization, we illustrate the agreement of our extensionally derived DBpedia type embeddings with the manually curated domain ontology. Finally, we use the embeddings to probabilistically cluster about 4 million DBpedia instances into 415 types in the DBpedia ontology.Comment: 6 pages, to be published in Semantic Big Data Workshop at ACM, SIGMOD 2017; extended version in preparation for Open Journal of Semantic Web (OJSW

RGBT Salient Object Detection: A Large-scale Dataset and Benchmark

Salient object detection in complex scenes and environments is a challenging research topic. Most works focus on RGB-based salient object detection, which limits its performance of real-life applications when confronted with adverse conditions such as dark environments and complex backgrounds. Taking advantage of RGB and thermal infrared images becomes a new research direction for detecting salient object in complex scenes recently, as thermal infrared spectrum imaging provides the complementary information and has been applied to many computer vision tasks. However, current research for RGBT salient object detection is limited by the lack of a large-scale dataset and comprehensive benchmark. This work contributes such a RGBT image dataset named VT5000, including 5000 spatially aligned RGBT image pairs with ground truth annotations. VT5000 has 11 challenges collected in different scenes and environments for exploring the robustness of algorithms. With this dataset, we propose a powerful baseline approach, which extracts multi-level features within each modality and aggregates these features of all modalities with the attention mechanism, for accurate RGBT salient object detection. Extensive experiments show that the proposed baseline approach outperforms the state-of-the-art methods on VT5000 dataset and other two public datasets. In addition, we carry out a comprehensive analysis of different algorithms of RGBT salient object detection on VT5000 dataset, and then make several valuable conclusions and provide some potential research directions for RGBT salient object detection.Comment: 12 pages, 10 figures https://github.com/lz118/RGBT-Salient-Object-Detectio

Alterations of microbiota and metabolites in the feces of calves with diarrhea associated with rotavirus and coronavirus infections

The changes in the composition of intestinal microbiota and metabolites have been linked to digestive disorders in calves, especially neonatal calf diarrhea. Bovine rotavirus (BRV) and bovine coronavirus (BCoV) are known to be the primary culprits behind neonatal calf diarrhea. In this study, we analyzed changes in the fecal microbiota and metabolites of calves with neonatal diarrhea associated with BRV and BCoV infection using high-throughput 16S rRNA sequencing and metabolomics technology. The microbial diversity in the feces of calves infected with BRV and BCoV with diarrhea decreased significantly, and the composition changed significantly. The significant increase of Fusobacterium and the reductions of some bacteria genera, including Faecalibacterium, Bifidobacterium, Ruminococcus, Subdoligranulum, Parabacteroides, Collinsella, and Olsenella, etc., were closely related to diarrhea associated with BRV and BCoV infection. Metabolites in the feces of BRV and BCoV-infected calves with diarrhea were significantly changed. Phosphatidylcholine [PC; 16:1(9 Z)/16:1(9 Z)], lysophosphatidylethanolamine (LysoPE; 0:0/22:0), lysophosphatidylcholine (LysoPC; P-16:0) and LysoPE (0:0/18:0) were significantly higher in the feces of BRV-infected calves with diarrhea. In contrast, some others, such as desthiobiotin, were significantly lower. BRV infection affects glycerophospholipid metabolism and biotin metabolism in calves. Two differential metabolites were significantly increased, and 67 differential metabolites were significantly reduced in the feces of BCoV-infected calves with diarrhea. Seven significantly reduced metabolites, including deoxythymidylic acid (DTMP), dihydrobiopterin, dihydroneopterin triphosphate, cortexolone, cortisol, pantetheine, and pregnenolone sulfate, were enriched in the folate biosynthesis, pantothenate and CoA biosynthesis, pyrimidine metabolism, and steroid hormone biosynthesis pathway. The decrease in these metabolites was closely associated with increased harmful bacteria and reduced commensal bacteria. The content of short-chain fatty acids (SCFAs) such as acetic acid and propionic acid in the feces of BRV and BCoV-infected calves with diarrhea was lower than that of healthy calves, which was associated with the depletion of SCFAs-producing bacteria such as Parabacteroides, Fournierella, and Collinsella. The present study showed that BRV and BCoV infections changed the composition of the calf fecal microbiota and were associated with changes in fecal metabolites. This study lays the foundation for further revealing the roles of intestinal microbiota in neonatal calf diarrhea associated with BRV and BCoV infection

Efficacy and safety of neoadjuvant immunotherapy in resectable esophageal or gastroesophageal junction carcinoma: A pooled analysis of prospective clinical trials

Neoadjuvant chemoradiotherapy (NCRT) plus radical esophagectomy is currently the standard treatment for resectable esophageal or gastroesophageal junction (GEJ) carcinoma. The aim of this study is to evaluate the efficacy and safety of neoadjuvant immunotherapy in resectable esophageal or GEJ carcinoma. Prospective clinical trials investigating efficacy and/or safety of neoadjuvant immunotherapy with immune checkpoint inhibitors (ICIs) followed by radical esophagectomy in patients with newly diagnosed resectable esophageal or GEJ carcinoma were identified through literature search. Quality assessment was performed by using the Newcastle–Ottawa scale. Preliminary treatment outcomes of pathologically complete response (pCR, ypT0N0) and grade 3-4 adverse effects (AEs) were pooled together and then compared with standard NCRT of the historical control CROSS study by Chi-square (χ2) test. A two-sided P value < 0.05 was considered statistically significant. A total of 17 eligible non-randomized trials with 455 participants were included into analysis. The most common primary endpoint was pCR (n = 7, 41%), and the median sample size and follow-up period was 23 patients and 7.9 months, respectively. For patients receiving neoadjuvant immunotherapy, the overall pCR, R0 resection, and grade 3-4 AE rates were 33.2%, 95.5%, and 35.1%, respectively. For esophageal squamous cell carcinoma (ESCC) and adenocarcinoma (EAC), neoadjuvant immunochemoradiotherapy showed no significant improvement in pCR rate than NCRT (ESCC, 50% vs 48.7%, P = 0.9; EAC, 32.6% vs 23.1%, P = 0.22). Grade 3-4 AEs were the most common in patients with neoadjuvant immunochemoradiotherapy, significantly higher than immunochemotherapy (46.7% vs 32.8%, P = 0.04) and NCRT (46.7% vs 18.1%, P < 0.0001). In conclusion, for patients with resectable esophageal or GEJ carcinoma, the addition of ICIs to standard NCRT could not improve pCR rate in both ESCC and EAC, but significantly increased the risk of severe AEs. Large-scale phase 3 randomized trials were urgently needed to further confirm the survival benefit and safety profile of neoadjuvant immunotherapy