Enantioselective Nickel-Catalyzed Reductive anti-Arylative Annulation of Alkyne-Tethered Malononitriles to Construct Quaternary Stereocenters.

A nickel-catalyzed reductive desymmetrizing annulation of alkyne-tethered malononitriles and (hetero)aryl iodides is reported for the access of cyclohexenones containing an α-all-carbon quaternary stereocenter. The use of a nickel catalyst derived from an electron-rich phosphinooxazoline ligand combined with iron powder as a reductant is crucial to the success of this transformation.

Towards artificial general intelligence via a multimodal foundation model.

The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI".

Variational Context: Exploiting Visual and Textual Context for Grounding Referring Expressions.

We focus on grounding (i.e., localizing or linking) referring expressions in images, e.g., "largest elephant standing behind baby elephant". This is a general yet challenging vision-language task since it does not only require the localization of objects, but also the multimodal comprehension of context - visual attributes (e.g., "largest", "baby") and relationships (e.g., "behind") that help to distinguish the referent from other objects, especially those of the same category. Due to the exponential complexity involved in modeling the context associated with multiple image regions, existing work oversimplifies this task to pairwise region modeling by multiple instance learning. In this paper, we propose a variational Bayesian method, called Variational Context, to solve the problem of complex context modeling in referring expression grounding. Specifically, our framework exploits the reciprocal relation between the referent and context, i.e., either of them influences estimation of the posterior distribution of the other, and thereby the search space of context can be greatly reduced. In addition to reciprocity, our framework considers the semantic information of context, i.e., the referring expression can be reproduced based on the estimated context. We also extend the model to unsupervised setting where no annotation for the referent is available. Extensive experiments on various benchmarks show consistent improvement over state-of-the-art methods in both supervised and unsupervised settings.

Zero and Few Shot Learning With Semantic Feature Synthesis and Competitive Learning.

Zero-shot learning (ZSL) is made possible by learning a projection function between a feature space and a semantic space (e.g., an attribute space). Key to ZSL is thus to learn a projection that is robust against the often large domain gap between the seen and unseen class domains. In this work, this is achieved by unseen class data synthesis and robust projection function learning. Specifically, a novel semantic data synthesis strategy is proposed, by which semantic class prototypes (e.g., attribute vectors) are used to simply perturb seen class data for generating unseen class ones. As in any data synthesis/hallucination approach, there are ambiguities and uncertainties on how well the synthesised data can capture the targeted unseen class data distribution. To cope with this, the second contribution of this work is a novel projection learning model termed competitive bidirectional projection learning (BPL) designed to best utilise the ambiguous synthesised data. Specifically, we assume that each synthesised data point can belong to any unseen class; and the most likely two class candidates are exploited to learn a robust projection function in a competitive fashion. As a third contribution, we show that the proposed ZSL model can be easily extended to few-shot learning (FSL) by again exploiting semantic (class prototype guided) feature synthesis and competitive BPL. Extensive experiments show that our model achieves the state-of-the-art results on both problems.

Enantioselective Assembly of Cycloenones with a Nitrile-Containing All-Carbon Quaternary Center from Malononitriles Enabled by Ni Catalysis.

Chiral nitriles are valuable molecules in modern organic synthesis and drug discovery. Selectively differentiating the two nitrile groups of widely available malononitrile derivatives is a straightforward yet underdeveloped route to construct enantioenriched nitriles. Here we report an enantioselective nickel-catalyzed desymmetrization of malononitriles for the generation of nitrile-containing all-carbon quaternary stereocenters. This protocol involves a nickel-catalyzed addition of aryl boronic acids to alkynes, followed by a selective nitrile insertion, providing unprecedented access to enantioenriched 5-7-membered α-cyano-cycloenones with a fully substituted olefin from a broad range of substrates. The synthetic utility of these nitrile products is demonstrated by gram-scale synthesis and conversion to several useful functional groups.

Enantioselective Synthesis of ß-Quaternary Carbon-Containing Chromanes and 3,4-Dihydropyrans via Cu-Catalyzed Intramolecular C-O Bond Formation.

A copper-catalyzed efficient enantioselective construction of chiral quaternary carbon-containing chromanes and 3,4-dihydropyrans is reported. The desymmetric C-O coupling is enabled by a chiral dimethylcyclohexane-1,2-diamine ligand and provides the desired products in good yields with high enantioselectivities. This method presents a broad substrate scope and is applicable to diversely substituted aryl bromides and alkenyl bromides. The application is demonstrated by a gram-scale synthesis and derivatization of the products toward valuable building blocks.

Synthesis of Sultams and Cyclic N-Sulfonyl Ketimines via Iron-Catalyzed Intramolecular Aliphatic C-H Amidation.

Cyclic sulfonamides (sultams) play a unique role in drug discovery and synthetic chemistry. A direct synthesis of sultams by an intramolecular C(sp3)-H amidation reaction using an iron complex in situ derived from Fe(ClO4)2 and aminopyridine ligand is reported. This strategy features a readily available catalyst and tolerates a broad variety of substrates as demonstrated by 22 examples (up to 89% yield). A one-pot iron-catalyzed amidation/oxidation procedure for the synthesis of cyclic N-sulfonyl ketimines is also realized with up to 92% yield (eight examples). The synthetic utility of the method is validated by a gram-scale reaction and derivatization of the products to ring-fused sultams.

Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation.

Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept and 2) how to annotate an image with the optimal number of class labels. To address the first issue, we propose a novel multi-scale deep model for extracting rich and discriminative features capable of representing a wide range of visual concepts. Specifically, a novel two-branch deep neural network architecture is proposed, which comprises a very deep main network branch and a companion feature fusion network branch designed for fusing the multi-scale features computed from the main branch. The deep model is also made multi-modal by taking noisy user-provided tags as model input to complement the image input. For tackling the second issue, we introduce a label quantity prediction auxiliary task to the main label prediction task to explicitly estimate the optimal label number for a given image. Extensive experiments are carried out on two large-scale image annotation benchmark datasets, and the results show that our method significantly outperforms the state of the art.

Large-Scale Sparse Learning From Noisy Tags for Semantic Segmentation.

In this paper, we present a large-scale sparse learning (LSSL) approach to solve the challenging task of semantic segmentation of images with noisy tags. Different from the traditional strongly supervised methods that exploit pixel-level labels for semantic segmentation, we make use of much weaker supervision (i.e., noisy tags of images) and then formulate the task of semantic segmentation as a weakly supervised learning (WSL) problem from the view point of noise reduction of superpixel labels. By learning the data manifolds, we transform the WSL problem into an LSSL problem. Based on nonlinear approximation and dimension reduction techniques, a linear-time-complexity algorithm is developed to solve the LSSL problem efficiently. We further extend the LSSL approach to visual feature refinement for semantic segmentation. The experiments demonstrate that the proposed LSSL approach can achieve promising results in semantic segmentation of images with noisy tags.

A highly enantioselective access to chiral chromanones and thiochromanones via copper-catalyzed asymmetric conjugated reduction of chromones and thiochromones.

The chromanone scaffold is a privileged structure in heterocyclic chemistry and drug discovery. A highly efficient copper-catalyzed asymmetric conjugated reduction of chromones is developed to give chiral chromanones with good yields (80-99%) and excellent ee values (94->99% ee). Particularly noteworthy is that chiral thiochromanones are also constructed using this method in 74-87% yields with 96-97% ee. The established asymmetric synthesis paves the way for their further pharmaceutical studies.

Iridium-Catalyzed Asymmetric Addition of Thiophenols to Oxabenzonorbornadienes.

A highly efficient asymmetric ring addition reaction of oxabenzonorbornadienes with thiophenols using an iridium/(S)-xyl-binap catalyst is developed. This catalyst system overcomes catalyst poisoning and background reactions and allows the formation of exclusive thiol addition products in high yields (up to 97% yield) with excellent enantioselectivities (up to 98% ee). Particularly noteworthy is that no competitive ring-opened side products are observed. X-ray crystal structure analysis confirmed the adduct is solely in the exo-configuration.

CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction.

MOTIVATION: Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information. METHOD: We present a novel network-based protein homology detection method, CMsearch, based on cross-modal learning. Instead of exploring a single network built from the mixture of sequence and structure space information, CMsearch builds two separate networks to represent the sequence space and the structure space. It then learns sequence-structure correlation by simultaneously taking sequence information, structure information, sequence space information and structure space information into consideration. RESULTS: We tested CMsearch on two challenging tasks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 proteins. Our results demonstrate that CMsearch is insensitive to the similarity metrics used to define the sequence and the structure spaces. By using HMM-HMM alignment as the sequence similarity metric, CMsearch clearly outperforms state-of-the-art homology detection methods and the CASP-winning template-based protein structure prediction methods. AVAILABILITY AND IMPLEMENTATION: Our program is freely available for download from http://sfb.kaust.edu.sa/Pages/Software.aspx CONTACT: : xin.gao@kaust.edu.sa SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Palladium/Zinc Co-Catalyzed syn-Stereoselectively Asymmetric Ring-Opening Reaction of Oxabenzonorbornadienes with Phenols.

A new palladium/zinc co-catalyst system associated with chiral (R)-Difluorphos for asymmetric ring-opening reaction of oxabenzonorbornadienes with phenols is reported. This catalyst system allows the formation of cis-2-aryloxy-1,2-dihydronaphthalen-1-ol products in good yields (up to 95 % yield) with excellent enantioselectivities (up to 99 % ee). The cis-configuration of the product has been confirmed by X-ray crystal structure analysis. To the best of our knowledge, it represents the first example in ring-opening reactions of bicycloalkenes with heteronucleophiles in a syn-stereoselective manner.

Semantic sparse recoding of visual content for image applications.

This paper presents a new semantic sparse recoding method to generate more descriptive and robust representation of visual content for image applications. Although the visual bag-of-words (BOW) representation has been reported to achieve promising results in different image applications, its visual codebook is completely learnt from low-level visual features using quantization techniques and thus the so-called semantic gap remains unbridgeable. To handle such challenging issue, we utilize the annotations (predicted by algorithms or shared by users) of all the images to improve the original visual BOW representation. This is further formulated as a sparse coding problem so that the noise issue induced by the inaccurate quantization of visual features can also be handled to some extent. By developing an efficient sparse coding algorithm, we successfully generate a new visual BOW representation for image applications. Since such sparse coding has actually incorporated the high-level semantic information into the original visual codebook, we thus consider it as semantic sparse recoding of the visual content. Finally, we apply our semantic sparse recoding method to automatic image annotation and social image classification. The experimental results on several benchmark datasets show the promising performance of our semantic sparse recoding method in these two image applications.

STAT3 and beta-catenin signaling pathway may affect GSK-3beta expression in hepatocellular carcinoma.

BACKGROUND/AIMS: To study the correlation and significance of beta-catenin, STAT3 and GSK-3beta signaling pathway in hepatocellular carcinoma (HCC). METHODOLOGY: The HCC cell line HepG2 was transfected with small interfering RNA (siRNA) directed against 8-catenin or STAT3. After 72 and 96h, protein was extracted and the protein expression of beta-catenin, STAT3, and GSK-3beta was detected by Western blot analysis. RESULTS: After siRNA directed against beta-catenin was transfected into HepG2 cells, beta-catenin protein expression was decreased at 72 and 96h, GSK-3beta and p-GSK-3beta protein expression increased gradually at 72 and 96h, and STAT3 protein expression showed no change following transfection. After siRNA directed against STAT3 was transfected into HepG2 cells, STAT3 protein expression was decreased at 72 and 96h and beta-catenin, GSK-3beta and p-GSK-3beta protein expression all increased at 72h and decreased at 96 h after transfection. CONCLUSION: In HCC, the beta-catenin signaling pathway may regulate GSK-3beta protein expression and the STAT3 signaling pathway may regulate beta-catenin and GSK-3beta protein expression, thereby playing key roles during HCC genesis and development.

Contextual kernel and spectral methods for learning the semantics of images.

This paper presents contextual kernel and spectral methods for learning the semantics of images that allow us to automatically annotate an image with keywords. First, to exploit the context of visual words within images for automatic image annotation, we define a novel spatial string kernel to quantify the similarity between images. Specifically, we represent each image as a 2-D sequence of visual words and measure the similarity between two 2-D sequences using the shared occurrences of s -length 1-D subsequences by decomposing each 2-D sequence into two orthogonal 1-D sequences. Based on our proposed spatial string kernel, we further formulate automatic image annotation as a contextual keyword propagation problem, which can be solved very efficiently by linear programming. Unlike the traditional relevance models that treat each keyword independently, the proposed contextual kernel method for keyword propagation takes into account the semantic context of annotation keywords and propagates multiple keywords simultaneously. Significantly, this type of semantic context can also be incorporated into spectral embedding for refining the annotations of images predicted by keyword propagation. Experiments on three standard image datasets demonstrate that our contextual kernel and spectral methods can achieve significantly better results than the state of the art.

[The role and significance of Wnt/beta-catenin signaling pathway regulating the signaling molecules in hepatocellular carcinoma].

OBJECTIVE: To investigate the role and significance of Wnt/beta-catenin signaling pathway regulating GSK-3beta, STAT3, Smad3 and TERT in hepatocellular carcinoma (HCC). METHODS: The HCC cell line HepG2 was transfected with small interfering RNA (siRNA) directed against beta-catenin. Proteins were extracted and the expressions of beta-catenin, GSK-3beta, p-GSK-3beta, STAT3, Smad3 and TERT were detected by Western blot at 72 h and 96 h respectively after transfection. RESULTS: beta-catenin expression was inhibited at both time points and the expression at 96 h was higher than that at 72 h (t = 4.43, P < 0.05). Interestingly, GSK-3beta and p-GSK-3beta expressions increased gradually at 72 and 96 h (tGSK-3beta= 4.98, tp-GSK-3beta= 29.83, P < 0.05) respectively, and STAT3 expression showed no alteration after transfection (F = 0.49, P > 0.05). Smad3 expression was increased at 72 h (t = 10.67, P < 0.05) and decreased to normal at 96 h (t = 1.26, P < 0.05), while TERT expression decreased at 72 h (t = 4.18, P is less than 0.05) and increased to normal at 96 h (t = 1.26, P > 0.05). CONCLUSIONS: Wnt/beta-catenin signaling pathway is related to the expressions of GSK-3beta, Smad3 and TERT, but perhaps not related to STAT3 protein expression in HCC. It suggested that Wnt/beta-catenin signaling pathway might participate in HCC genesis and development through regulating the above three factors.

[The changes of interdigestive migrating motor complex in rats with acute liver failure].

OBJECTIVE: To observe the changes and characteristics of interdigestive migrating motor complex (MMC) in rat models of acute liver failure. METHODS: 30 rat models with acute liver failure were induced with D-galactosamine and another 30 normal rats were used as controls. The indexes of MMC recorded by multi-channel physiological recorder were compared. RESULTS: No significant differences found between the two groups in antral and duodenal MMC cycles and frequencies of duodenal and jejunal MMC III phase. Compared with normal controls, the MMC II phase in the acute liver failure rats was significantly prolonged (t=-3.97, -3.85, P<0.05), the MMC III duration of antrum and duodenum (u=-4.99, t=4.66, P<0.05) was shorter and the MMC III frequency of antrum (u=-4.73, P<0.05) was faster. In addition, the MMC cycle and MMC III phase of jejunum were significantly prolonged (u=-1.63, t=-4.94, P<0.05) and the MMC III phase duration was significantly shorter in the acute liver failure rats (t=5.10, P<0.05). CONCLUSION: Significantly prolonged MMC II phase characterized by migrating clustered contraction, shortened MMC III phase and extended jejunal MMC cycles were probably the major contributors to the gastrointestinal motility disorders in the rats with acute liver failure.

Generalized competitive learning of gaussian mixture models.

When fitting Gaussian mixtures to multivariate data, it is crucial to select the appropriate number of Gaussians, which is generally referred to as the model selection problem. Under regularization theory, we aim to solve this model selection problem through developing an entropy regularized likelihood (ERL) learning on Gaussian mixtures. We further present a gradient algorithm for this ERL learning. Through some theoretic analysis, we have shown a mechanism of generalized competitive learning that is inherent in the ERL learning, which can lead to automatic model selection on Gaussian mixtures and also make our ERL learning algorithm less sensitive to the initialization as compared to the standard expectation-maximization algorithm. The experiments on simulated data using our algorithm verified our theoretic analysis. Moreover, our ERL learning algorithm has been shown to outperform other competitive learning algorithms in the application of unsupervised image segmentation.