Local Global Relational Network for Facial Action Units Recognition

Many existing facial action units (AUs) recognition approaches often enhance the AU representation by combining local features from multiple independent branches, each corresponding to a different AU. However, such multi-branch combination-based methods usually neglect potential mutual assistance and exclusion relationship between AU branches or simply employ a pre-defined and fixed knowledge-graph as a prior. In addition, extracting features from pre-defined AU regions of regular shapes limits the representation ability. In this paper, we propose a novel Local Global Relational Network (LGRNet) for facial AU recognition. LGRNet mainly consists of two novel structures, i.e., a skip-BiLSTM module which models the latent mutual assistance and exclusion relationship among local AU features from multiple branches to enhance the feature robustness, and a feature fusion&refining module which explores the complementarity between local AUs and the whole face in order to refine the local AU features to improve the discriminability. Experiments on the BP4D and DISFA AU datasets show that the proposed approach outperforms the state-of-the-art methods by a large margin

Inhibiting Phase Transfer of Protein Nanoparticles by Surface Camouflage-A Versatile and Efficient Protein Encapsulation Strategy

Engineering a system with a high mass fraction of active ingredients, especially water-soluble proteins, is still an ongoing challenge. In this work, we developed a versatile surface camouflage strategy that can engineer systems with an ultrahigh mass fraction of proteins. By formulating protein molecules into nanoparticles, the demand of molecular modification was transformed into a surface camouflage of protein nanoparticles. Thanks to electrostatic attractions and van der Waals interactions, we camouflaged the surface of protein nanoparticles through the adsorption of carrier materials. The adsorption of carrier materials successfully inhibited the phase transfer of insulin, albumin, β-lactoglobulin, and ovalbumin nanoparticles. As a result, the obtained microcomposites featured with a record of protein encapsulation efficiencies near 100% and a record of protein mass fraction of 77%. After the encapsulation in microcomposites, the insulin revealed a hypoglycemic effect for at least 14 d with one single injection, while that of insulin solution was only ∼4 h.Peer reviewe

A Bacterial Enzyme Catalyzing Double Reduction of a β,δ-Diketo Ester with Unprecedented Stereoselectivity

Various ketoreductases exclusively participate in all common biological events, and they are a class of important biocatalysts for the production of chiral alcohols. While many types of ketoreductase have been extensively studied and their functions, properties and utilities have been well known, the capability of stereoselectively reducing two carbonyl groups in the same diketohexanoate ester molecule to form a dihydroxy product by a single ketoreductase has not been evidently characterized. Here we show that a unique and novel enzyme, diketoreductase, was cloned from Acinetobacter baylyi, heterogeneously expressed in _Escherichia coli_ and purified to homogeneity. The diketoreductase is up to 78% homologous to bacterial 3-hydroxyacyl coenzyme-A reductases. However, recombinant diketoreductase does not reduce HMG-CoA, showing that the inference of function of enzymes like the diketoreductase based on sequence homology may be in error. The enzyme directly converts a β,δ-diketo ester to the corresponding dihydroxy ester. More remarkably, our results demonstrate that the recombinant enzyme possesses unprecedented stereoselectivity with both diastereomeric and enantiomeric excesses of greater than 99%. This new enzyme is of immediate value in developing a practical biocatalytic route to the side chains of statin drugs, such as Lipitor®

A Bacterial Enzyme Catalyzing Double Reduction of a β,δ-Diketo Ester with Unprecedented Stereoselectivity

Various ketoreductases exclusively participate in all common biological events, and they are a class of important biocatalysts for the production of chiral alcohols. While many types of ketoreductase have been extensively studied and their functions, properties and utilities have been well known, the capability of stereoselectively reducing two carbonyl groups in the same diketohexanoate ester molecule to form a dihydroxy product by a single ketoreductase has not been evidently characterized. Here we show that a unique and novel enzyme, diketoreductase, was cloned from Acinetobacter baylyi, heterogeneously expressed in _Escherichia coli_ and purified to homogeneity. The diketoreductase is up to 78% homologous to bacterial 3-hydroxyacyl coenzyme-A reductases. However, recombinant diketoreductase does not reduce HMG-CoA, showing that the inference of function of enzymes like the diketoreductase based on sequence homology may be in error. The enzyme directly converts a β,δ-diketo ester to the corresponding dihydroxy ester. More remarkably, our results demonstrate that the recombinant enzyme possesses unprecedented stereoselectivity with both diastereomeric and enantiomeric excesses of greater than 99%. This new enzyme is of immediate value in developing a practical biocatalytic route to the side chains of statin drugs, such as Lipitor®

Investigation of interaction between MXene nanosheets and human plasma and protein corona composition

The composition of protein corona affects the behavior and fate of nanoparticles in biological systems, which strongly relates to the intrinsic properties of nanoparticles and proteins. Here, three types of MXene Ti3C2Tx nanosheets are prepared by different etching methods, and certain physicochemical characteristics of the nanosheets before and after exposure to human plasma (HP) are characterized. The Ti3C2Tx nanosheets with protein coronas suffer more easily from aggregation than pristine Ti3C2Tx. The composition of protein coronas by LC-MS/MS-based label-free proteomic analysis reveals a high overlap of protein types and functions but a significant difference in relative protein abundance for the three Ti3C2Tx. Immunoglobulins and coagulation proteins are highly enriched while albumin is depleted in the coronas compared with their abundance in original HP. The random forest classification model predicts that the main driving forces for the adsorption of HP proteins on Ti3C2Tx are hydrogen bonding, steric hindrance, and hydrophobic interaction. This study provides insights into the colloidal stability of Ti3C2Tx nanosheets and their interaction with human plasma proteins

Selective biosynthesis of a rhamnosyl nosiheptide by a novel bacterial rhamnosyltransferase

Abstract Nosiheptide (NOS) is a thiopeptide antibiotic produced by the bacterium Streptomyces actuosus. The hydroxyl group of 3‐hydroxypyridine in NOS has been identified as a promising site for modification, which we therefore aimed to rhamnosylate. After screening, Streptomyces sp. 147326 was found to regioselectively attach a rhamnosyl unit to the 3‐hydroxypyridine site in NOS, resulting in the formation of a derivative named NOS‐R at a productivity of 24.6%. In comparison with NOS, NOS‐R exhibited a 17.6‐fold increase in aqueous solubility and a new protective effect against MRSA infection in mice, while maintaining a similar in vitro activity. Subsequently, SrGT822 was identified as the rhamnosyltransferase in Streptomyces sp. 147326 responsible for the biosynthesis of NOS‐R using dTDP‐L‐rhamnose. SrGT822 demonstrated an optimal reaction pH of 10.0 and temperature of 55°C, which resulted in a NOS‐R yield of 74.9%. Based on the catalytic properties and evolutionary analysis, SrGT822 is anticipated to be a potential rhamnosyltransferase for use in the modification of various complex scaffolds

Structured Multi-modal Feature Embedding and Alignment for Image-Sentence Retrieval

The current state-of-the-art image-sentence retrieval methods implicitly align the visual-textual fragments, like regions in images and words in sentences, and adopt attention modules to highlight the relevance of cross-modal semantic correspondences. However, the retrieval performance remains unsatisfactory due to a lack of consistent representation in both semantics and structural spaces. In this work, we propose to address the above issue from two aspects: (i) constructing intrinsic structure (along with relations) among the fragments of respective modalities, e.g., "dog → play → ball" in semantic structure for an image, and (ii) seeking explicit inter-modal structural and semantic correspondence between the visual and textual modalities. In this paper, we propose a novel Structured Multi-modal Feature Embedding and Alignment (SMFEA) model for image-sentence retrieval. In order to jointly and explicitly learn the visual-textual embedding and the cross-modal alignment, SMFEA creates a novel multi-modal structured module with a shared context-aware referral tree. In particular, the relations of the visual and textual fragments are modeled by constructing Visual Context-aware Structured Tree encoder (VCS-Tree) and Textual Context-aware Structured Tree encoder (TCS-Tree) with shared labels, from which visual and textual features can be jointly learned and optimized. We utilize the multi-modal tree structure to explicitly align the heterogeneous image-sentence data by maximizing the semantic and structural similarity between corresponding inter-modal tree nodes. Extensive experiments on Microsoft COCO and Flickr30K benchmarks demonstrate the superiority of the proposed model in comparison to the state-of-the-art methods

Differentiated Relevances Embedding for Group-based Referring Expression Comprehension

Referring expression comprehension (REC) aims to locate a certain object in an image referred by a natural language expression. For joint understanding of regions and expressions, existing REC works typically target on modeling the cross-modal relevance in each region-expression pair within each single image. In this paper, we explore a new but general REC-related problem, named Group-based REC, where the regions and expressions can come from different subject-related images (images in the same group), e.g., sets of photo albums or video frames. Different from REC, Group-based REC involves differentiated cross-modal relevances within each group and across different groups, which, however, are neglected in the existing one-line paradigm. To this end, we propose a novel relevance-guided multi-group self-paced learning schema (termed RMSL), where the within-group region-expression pairs are adaptively assigned with different priorities according to their cross-modal relevances, and the bias of the group priority is balanced via an across-group relevance constraint simultaneously. In particular, based on the visual and textual semantic features, RMSL conducts an adaptive learning cycle upon triplet ranking, where (1) the target-negative region-expression pairs with low within-group relevances are used preferentially in model training to distinguish the primary semantics of the target objects, and (2) an across-group relevance regularization is integrated into model training to balance the bias of group priority. The relevances, the pairs, and the model parameters are alternatively updated upon a unified self-paced hinge loss

Design of a chimeric glycosyltransferase OleD for the site‐specific O‐monoglycosylation of 3‐hydroxypyridine in nosiheptide

Abstract To identify the potential role of the 3‐hydroxyl group of the pyridine ring in nosiheptide (NOS) for its antibacterial activity against Gram‐positive pathogens, enzymatic glycosylation was utilized to regio‐selectively create a monoglycosyl NOS derivative, NOS‐G. For this purpose, we selected OleD, a UDP glycosyltransferase from Streptomyces antibioticus that has a low productivity for NOS‐G. Activity of the enzyme was increased by swapping domains derived from OleI, both single and in combination. Activity enhancement was best in mutant OleD‐10 that contained four OleI domains. This chimer was engineered by site‐directed mutagenesis (single and in combination) to increase its activity further, whereby variants were screened using a newly‐established colorimetric assay. OleD‐10 with I117F and T118G substitutions (FG) had an increased NOS‐G productivity of 56%, approximately 70 times higher than that of wild‐type OleD. The reason for improved activity of FG towards NOS was structurally attributed to a closer distance (<3 Å) between NOS/sugar donor and the catalytic amino acid H25. The engineered enzyme allowed sufficient activity to demonstrate that the produced NOS‐G had enhanced stability and aqueous solubility compared to NOS. Using a murine MRSA infection model, it was established that NOS‐G resulted in partial protection within 20 h of administration and delayed the death of infected mice. We conclude that 3‐hydroxypyridine is a promising site for structural modification of NOS, which may pave the way for producing nosiheptide derivatives as a potential antibiotic for application in clinical treatment