Visible-Light-Triggered Difluoroacetylation/Cyclization of Chromone-Tethered Alkenes Enabling Synthesis of Tetrahydroxanthones.

Hydroxanthones have attracted considerable attention due to their significance in organic and biological chemistry, yet their synthesis remains a great challenge. In this study, a series of chromone-tethered alkenes are designed, and a radical cyclization reaction of these chromone derivatives has been achieved under photoredox conditions. The reaction uses bromodifluoroacetamides or bromodifluoroacetates as coupling partners, affording a broad range of functionalized tetrahydroxanthone products with up to 85% yields. The reaction is triggered via the generation of difluoroacetate radicals or alkene radical cations with fac-Ir(ppy)3 or 2,3,5,6-tetrakis(carbazol-9-yl)-1,4-dicyanobenzene as a photocatalyst. This approach offers access to various tetrahydroxanthone derivatives from readily available starting materials and enriches the research content of heteroarene-tethered alkenes.

Genetic structure and triazole resistance among Aspergillus fumigatus populations from remote and undeveloped regions in Eastern Himalaya.

Aspergillus fumigatus is a ubiquitous mold and a common human fungal pathogen. Recent molecular population genetic and epidemiological analyses have revealed evidence for long-distance gene flow and high genetic diversity within most local populations of A. fumigatus. However, little is known about the impact of regional landscape factors in shaping the population diversity patterns of this species. Here we sampled extensively and investigated the population structure of A. fumigatus from soils in the Three Parallel Rivers (TPR) region in Eastern Himalaya. This region is remote, undeveloped and sparsely populated, bordered by glaciated peaks more than 6,000 m above sea level, and contained three rivers separated by tall mountains over very short horizontal distances. A total of 358 A. fumigatus strains from 19 sites along the three rivers were isolated and analyzed at nine loci containing short tandem repeats. Our analyses revealed that mountain barriers, elevation differences, and drainage systems all contributed low but statistically significant genetic variations to the total A. fumigatus population in this region. We found abundant novel alleles and genotypes in the TPR population of A. fumigatus and significant genetic differentiation between this population and those from other parts of Yunnan and the globe. Surprisingly, despite limited human presence in this region, about 7% of the A. fumigatus isolates were resistant to at least one of the two medical triazoles commonly used for treating aspergillosis. Our results call for greater surveillance of this and other human fungal pathogens in the environment. IMPORTANCE The extreme habitat fragmentation and substantial environmental heterogeneity in the TPR region have long known to contribute to geographically shaped genetic structure and local adaptation in several plant and animal species. However, there have been limited studies of fungi in this region. Aspergillus fumigatus is a ubiquitous pathogen capable of long-distance dispersal and growth in diverse environments. In this study, using A. fumigatus as a model, we investigated how localized landscape features contribute to genetic variations in fungal populations. Our results revealed that elevation and drainage isolation rather than direct physical distances significantly impacted genetic exchange and diversity among the local A. fumigatus populations. Interestingly, within each local population, we found high allelic and genotypic diversities, and with evidence ~7% of all isolates being resistant to two medical triazoles, itraconazole and voriconazole. Given the high frequency of ARAF found in mostly natural soils of sparsely populated sites in the TPR region, close monitoring of their dynamics in nature and their effects on human health is needed.

Electrochemical Annulation of Indole-Tethered Alkynes Enabling Synthesis of Exocyclic Alkenyl Tetrahydrocarbazoles.

An electrochemical sulfonylation-triggered cyclization reaction of indole-tethered terminal alkynes with sulfinates as sulfonyl sources has been developed, which affords exocyclic alkenyl tetrahydrocarbazoles in good chemical yields. This reaction features convenient operation and tolerates a wide scope of substrates with a variety of electronically and sterically diverse substituents. Furthermore, high E-stereoselectivity is observed for this reaction, which provides an efficient method for the preparation of functionalized tetrahydrocarbazole derivatives.

Natural and Artificial Dynamics in Graphs: Concept, Progress, and Future.

Graph structures have attracted much research attention for carrying complex relational information. Based on graphs, many algorithms and tools are proposed and developed for dealing with real-world tasks such as recommendation, fraud detection, molecule design, etc. In this paper, we first discuss three topics of graph research, i.e., graph mining, graph representations, and graph neural networks (GNNs). Then, we introduce the definitions of natural dynamics and artificial dynamics in graphs, and the related works of natural and artificial dynamics about how they boost the aforementioned graph research topics, where we also discuss the current limitation and future opportunities.

Dynamic transfer learning with progressive meta-task scheduler.

Dynamic transfer learning refers to the knowledge transfer from a static source task with adequate label information to a dynamic target task with little or no label information. However, most existing theoretical studies and practical algorithms of dynamic transfer learning assume that the target task is continuously evolving over time. This strong assumption is often violated in real world applications, e.g., the target distribution is suddenly changing at some time stamp. To solve this problem, in this paper, we propose a novel meta-learning framework L2S based on a progressive meta-task scheduler for dynamic transfer learning. The crucial idea of L2S is to incrementally learn to schedule the meta-pairs of tasks and then learn the optimal model initialization from those meta-pairs of tasks for fast adaptation to the newest target task. The effectiveness of our L2S framework is verified both theoretically and empirically.

Arylsulfonylation-Triggered Cyclization of 3-Alkenyl Indoles Enabling Synthesis of Tetrahydro-γ-carboline.

A visible-light promoted cyclization reaction of 3-alkenyl indole derivatives with arylsulfonyl chlorides as coupling partners has been developed, which afforded functionalized tetrahydro-γ-carbolines in good chemical yields. The reaction used 3-alkenyl indoles as radical acceptors and proceeded via the sequence of sulfonylation and intramolecular cyclization. The reaction was carried out under mild conditions and tolerated a wide range of substrates, which provides an efficient strategy for the synthesis of tetrahydro-γ-carboline derivatives.

Visible-Light-Irradiated Cascade Reaction of Indole-Tethered Alkenes to Access Tetracyclic Tetrahydro-γ-carbolines.

A series of indole-derived alkenes have been designed and applied in a photocatalytic cascade reaction with bromodifluoroacetate esters, affording an unknown type of tetracyclic tetrahydro-γ-carboline derivative in up to 90% yields. Mechanistic studies suggest that the reaction proceeds with tetrahydro-γ-carboline as a key intermediate. The reaction tolerates a diverse pool of substrates, which provides an efficient method for the construction of tetracyclic tetrahydro-γ-carboline compounds.

Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative.

This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL). We focus on the following question: How to construct contrastive views for hypergraphs via augmentations? We provide the solutions in two folds. First, guided by domain knowledge, we fabricate two schemes to augment hyperedges with higher-order relations encoded, and adopt three vertex augmentation strategies from graph-structured data. Second, in search of more effective views in a data-driven manner, we for the first time propose a hypergraph generative model to generate augmented views, and then an end-to-end differentiable pipeline to jointly learn hypergraph augmentations and model parameters. Our technical innovations are reflected in designing both fabricated and generative augmentations of hypergraphs. The experimental findings include: (i) Among fabricated augmentations in HyperGCL, augmenting hyperedges provides the most numerical gains, implying that higher-order information in structures is usually more downstream-relevant; (ii) Generative augmentations do better in preserving higher-order information to further benefit generalizability; (iii) HyperGCL also boosts robustness and fairness in hypergraph representation learning. Codes are released at https://github.com/weitianxin/HyperGCL.

A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes.

Many statistical learning models hold an assumption that the training data and the future unlabeled data are drawn from the same distribution. However, this assumption is difficult to fulfill in real-world scenarios and creates barriers in reusing existing labels from similar application domains. Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time. Despite recent advances in exploring deep learning models with visual analytics tools, little work has explored the issue of explaining and diagnosing the knowledge transfer process between deep learning models. In this paper, we present a visual analytics framework for the multi-level exploration of the transfer learning processes when training deep neural networks. Our framework establishes a multi-aspect design to explain how the learned knowledge from the existing model is transferred into the new learning task when training deep neural networks. Based on a comprehensive requirement and task analysis, we employ descriptive visualization with performance measures and detailed inspections of model behaviors from the statistical, instance, feature, and model structure levels. We demonstrate our framework through two case studies on image classification by fine-tuning AlexNets to illustrate how analysts can utilize our framework.

Misc-GAN: A Multi-scale Generative Model for Graphs.

Characterizing and modeling the distribution of a particular family of graphs are essential for the studying real-world networks in a broad spectrum of disciplines, ranging from market-basket analysis to biology, from social science to neuroscience. However, it is unclear how to model these complex graph organizations and learn generative models from an observed graph. The key challenges stem from the non-unique, high-dimensional nature of graphs, as well as graph community structures at different granularity levels. In this paper, we propose a multi-scale graph generative model named Misc-GAN, which models the underlying distribution of graph structures at different levels of granularity, and then "transfers" such hierarchical distribution from the graphs in the domain of interest, to a unique graph representation. The empirical results on seven real data sets demonstrate the effectiveness of the proposed framework.

Location Prediction for Tweets.

Geographic information provides an important insight into many data mining and social media systems. However, users are reluctant to provide such information due to various concerns, such as inconvenience, privacy, etc. In this paper, we aim to develop a deep learning based solution to predict geographic information for tweets. The current approaches bear two major limitations, including (a) hard to model the long term information and (b) hard to explain to the end users what the model learns. To address these issues, our proposed model embraces three key ideas. First, we introduce a multi-head self-attention model for text representation. Second, to further improve the result on informal language, we treat subword as a feature in our model. Lastly, the model is trained jointly with the city and country to incorporate the information coming from different labels. The experiment performed on W-NUT 2016 Geo-tagging shared task shows our proposed model is competitive with the state-of-the-art systems when using accuracy measurement, and in the meanwhile, leading to a better distance measure over the existing approaches.

Should patients with diabetes be encouraged to integrate social media into their care plan?

AIM: To evaluate the use of social media of individuals with diabetes mellitus (DM). MATERIALS & METHODS: Both web-based and in-clinic surveys were collected from individuals with DM. Descriptive and correlation analyses were employed to evaluate respondents' diabetes-specific social networking site behaviors. RESULTS: Forty-five patients with DM completed the web-based survey and 167, the clinic-based survey, of whom only 40 visited diabetes-specific social networking sites. Analysis of online survey data indicated that self-reported adherence to lifestyle recommendations was significantly correlated (p < 0.01) with visiting the sites. Clinic-based survey data found that patients who reported using DM-specific web sites monitored home glucose values more often and had better compliance with insulin administration (both p < 0.05) compared with nonusers. CONCLUSION: This study provides insight into why individuals visit DM-specific social networking sites. Certain self-management behaviors may improve as a result of visiting these sites. Further work is needed to explore how to leverage social media technology to assist patients with the management of DM.

RCLens: Interactive Rare Category Exploration and Identification.

Rare category identification is an important task in many application domains, ranging from network security, to financial fraud detection, to personalized medicine. These are all applications which require the discovery and characterization of sets of rare but structurally-similar data entities which are obscured within a larger but structurally different dataset. This paper introduces RCLens, a visual analytics system designed to support user-guided rare category exploration and identification. RCLens adopts a novel active learning-based algorithm to iteratively identify more accurate rare categories in response to user-provided feedback. The algorithm is tightly integrated with an interactive visualization-based interface which supports a novel and effective workflow for rare category identification. This paper (1) defines RCLens' underlying active-learning algorithm; (2) describes the visualization and interaction designs, including a discussion of how the designs support user-guided rare category identification; and (3) presents results from an evaluation demonstrating RCLens' ability to support the rare category identification process.

Towards Optimal Connectivity on Multi-layered Networks.

Networks are prevalent in many high impact domains. Moreover, cross-domain interactions are frequently observed in many applications, which naturally form the dependencies between different networks. Such kind of highly coupled network systems are referred to as multi-layered networks, and have been used to characterize various complex systems, including critical infrastructure networks, cyber-physical systems, collaboration platforms, biological systems and many more. Different from single-layered networks where the functionality of their nodes is mainly affected by within-layer connections, multi-layered networks are more vulnerable to disturbance as the impact can be amplified through cross-layer dependencies, leading to the cascade failure to the entire system. To manipulate the connectivity in multi-layered networks, some recent methods have been proposed based on two-layered networks with specific types of connectivity measures. In this paper, we address the above challenges in multiple dimensions. First, we propose a family of connectivity measures (SUBLINE) that unifies a wide range of classic network connectivity measures. Third, we reveal that the connectivity measures in SUBLINE family enjoy diminishing returns property, which guarantees a near-optimal solution with linear complexity for the connectivity optimization problem. Finally, we evaluate our proposed algorithm on real data sets to demonstrate its effectiveness and efficiency.

Generalized manifold-ranking-based image retrieval.

In this paper, we propose a general transductive learning framework named generalized manifold-ranking-based image retrieval (gMRBIR) for image retrieval. Comparing with an existing transductive learning method named MRBIR [12], our method could work well whether or not the query image is in the database; thus, it is more applicable for real applications. Given a query image, gMRBIR first initializes a pseudo seed vector based on neighborhood relationship and then spread its scores via manifold ranking to all the unlabeled images in the database. Furthermore, in gMRBIR, we also make use of relevance feedback and active learning to refine the retrieval result so that it converges to the query concept as fast as possible. Systematic experiments on a general-purpose image database consisting of 5,000 Corel images demonstrate the superiority of gMRBIR over state-of-the-art techniques.