Bioactive polyketides and meroterpenoids from the mangrove-derived fungus Talaromyces flavus TGGP35.

Six new polyketides, which includes three new lactones (talarotones A-C) (1-3), one new polyketide (talarotide A) (4), two new polyenes (talaroyenes A, B) (5, 6), together with one new meroterpenoid (talaropenoid A) (7) and 13 known compounds (8-20) were isolated from the mangrove-derived fungus Talaromyces flavus TGGP35. The structure and configuration of the compounds 1-7 were elucidated from the data obtained from HR-ESI-MS, IR, 1D/2D NMR spectroscopy, Mo2 (OAc)4-induced electronic circular dichroism (ECD), CD spectroscopy, and modified Mosher's method. Compounds 5 and 20 displayed antioxidant activity with IC50 values of 0.40 and 1.36 mM, respectively. Compounds 3, 6, 11, 16, and 17 displayed cytotoxic activity against human cancer cells Hela, A549, and had IC50 values ranging from 28.89 to 62.23 µM. Compounds 7, 10-12, and 14-18 exhibited moderate or potent anti-insect activity against newly hatched larvae of Helicoverpa armigera Hubner, with IC50 values in the range 50-200 µg/mL. Compound 18 showed antibacterial activity against Ralstonia solanacearum with the MIC value of 50 µg/mL.

Enantioselective Synthesis of 1,2-Benzothiazine 1-Imines via RuII /Chiral Carboxylic Acid-Catalyzed C-H Alkylation/Cyclization.

Sulfondiimines are diaza-analogues of sulfones with a chiral sulfur center. Compared to sulfones and sulfoximines, their synthesis and transformations have so far been studied to a lesser extent. Here, we report the enantioselective synthesis of 1,2-benzothiazine 1-imines, i.e., cyclic sulfondiimine derivatives from sulfondiimines and sulfoxonium ylides via C-H alkylation/cyclization reactions. The combination of [Ru(p-cymene)Cl2 ]2 and a newly developed chiral spiro carboxylic acid is key to achieving high enantioselectivity.

HiAM: A Hierarchical Attention based Model for knowledge graph multi-hop reasoning.

Learning to reason in large-scale knowledge graphs has attracted much attention from research communities recently. This paper targets a practical task of multi-hop reasoning in knowledge graphs, which can be applied in various downstream tasks such as question answering, and recommender systems. A key challenge in multi-hop reasoning is to synthesize structural information (e.g., paths) in knowledge graphs to perform deeper reasoning. Existing methods usually focus on connection paths between each entity pair. However, these methods ignore predecessor paths before connection paths and regard entities and relations within every single path as equally important. With our observations, predecessor paths before connection paths can provide more accurate semantic representations. Furthermore, entities and relations in a single path contribute variously to the right answers. To this end, we propose a novel model HiAM (Hierarchical Attention based Model) for knowledge graph multi-hop reasoning. HiAM makes use of predecessor paths to provide more accurate semantics for entities and explores the effects of different granularities. Firstly, we extract predecessor paths of head entities and connection paths between each entity pair. Then, a hierarchical attention mechanism is designed to capture the information of different granularities, including entity/relation-level and path-level features. Finally, multi-granularity features are fused together to predict the right answers. We go one step further to select the most significant path as the explanation for predicted answers. Comprehensive experimental results demonstrate that our method achieves competitive performance compared with the baselines on three benchmark datasets.

Rhodium(III)/Chiral Carboxylic Acid Catalyzed Enantioselective C(sp3)-H Alkylation of 8-Ethylquinolines with α,ß-Unsaturated Carbonyl Compounds.

The enantioselective C-H alkylation of 8-ethylquinolines with enones or acrolein using a RhIII catalyst and a chiral carboxylic acid is described. Under mild reaction conditions, a binaphthyl-based chiral carboxylic acid enables the enantioselective cleavage of the 8-ethylquinoline C(sp3)-H bond. The obtained results demonstrate the utility of the combination of a high-valent group 9 metal catalyst and a chiral carboxylic acid for the enantioselective C(sp3)-H activation and the subsequent C-C bond formation.

Detecting glaucoma based on spectral domain optical coherence tomography imaging of peripapillary retinal nerve fiber layer: a comparison study between hand-crafted features and deep learning model.

PURPOSE: To develop a deep learning (DL) model for automated detection of glaucoma and to compare diagnostic capability against hand-craft features (HCFs) based on spectral domain optical coherence tomography (SD-OCT) peripapillary retinal nerve fiber layer (pRNFL) images. METHODS: A DL model with pre-trained convolutional neural network (CNN) based was trained using a retrospective training set of 1501 pRNFL OCT images, which included 690 images from 153 glaucoma patients and 811 images from 394 normal subjects. The DL model was further tested in an independent test set of 50 images from 50 glaucoma patients and 52 images from 52 normal subjects. A customized software was used to extract and measure HCFs including pRNFL thickness in average and four different sectors. Area under the receiver operator characteristics (AROC) curves was calculated to compare the diagnostic capability between DL model and hand-crafted pRNFL parameters. RESULTS: In this study, the DL model achieved an AROC of 0.99 [CI: 0.97 to 1.00] which was significantly larger than the AROC values of all other HCFs (AROCs 0.661 with 95% CI 0.549 to 0.772 for temporal sector, AROCs 0.696 with 95% CI 0.549 to 0.799 for nasal sector, AROCs 0.913 with 95% CI 0.855 to 0.970 for superior sector, AROCs 0.938 with 95% CI 0.894 to 0.982 for inferior sector, and AROCs 0.895 with 95% CI 0.832 to 0.957 for average). CONCLUSION: Our study demonstrated that DL models based on pre-trained CNN are capable of identifying glaucoma with high sensitivity and specificity based on SD-OCT pRNFL images.

NaI-mediated divergent synthesis of isatins and isoindigoes: a new protocol enabled by an oxidation relay strategy.

A new approach for the synthesis of isatins and isoindigoes by an inexpensive and environmentally friendly NaI-mediated transformation is disclosed. The selectivity could be switched by simply varying the solvent, and isatins (using THF) and isoindigoes (using DMSO) could be obtained in moderate to excellent yields.

KI-catalyzed C-S bond formation via an oxidation relay strategy: efficient access to various α-thio-ß-dicarbonyl compounds.

An efficient and practical methodology to obtain α-thio-ß-dicarbonyl compounds was presented under alkaline conditions via potassium iodide (KI) catalysis; various symmetrical/unsymmetrical 1,3-dicarbonyl compounds were obtained under an aerobic atmosphere in moderate to excellent yields, with good functional group tolerance. Notably, a widely used anti-inflammatory drug butazodine could be modified with our protocol, even on a gram scale.

On Deep Learning for Trust-Aware Recommendations in Social Networks.

With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.

Toward Risk Reduction for Mobile Service Composition.

The advances in mobile technologies enable us to consume or even provide services through powerful mobile devices anytime and anywhere. Services running on mobile devices within limited range can be composed to coordinate together through wireless communication technologies and perform complex tasks. However, the mobility of users and devices in mobile environment imposes high risk on the execution of the tasks. This paper targets reducing this risk by constructing a dependable service composition after considering the mobility of both service requesters and providers. It first proposes a risk model and clarifies the risk of mobile service composition; and then proposes a service composition approach by modifying the simulated annealing algorithm. Our objective is to form a service composition by selecting mobile services under the mobility model and to ensure the service composition have the best quality of service and the lowest risk. The experimental results demonstrate that our approach can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.