Large-conductance calcium-activated potassium channels mediate lipopolysaccharide-induced activation of murine microglia.

Large-conductance calcium-activated potassium (BK) channels are ubiquitously expressed in most cell types where they regulate many cellular, organ, and organismal functions. Although BK currents have been recorded specifically in activated murine and human microglia, it is not yet clear whether and how the function of this channel is related to microglia activation. Here, using patch-clamping, Griess reaction, ELISA, immunocytochemistry, and immunoblotting approaches, we show that specific inhibition of the BK channel with paxilline (10 µm) or siRNA-mediated knockdown of its expression significantly suppresses lipopolysaccharide (LPS)-induced (100 ng/ml) BV-2 and primary mouse microglial cell activation. We found that membrane BK current is activated by LPS at a very early stage through Toll-like receptor 4 (TLR4), leading to nuclear translocation of NF-κB and to production of inflammatory cytokines. Furthermore, we noted that BK channels are also expressed intracellularly, and their nuclear expression significantly increases in late stages of LPS-mediated microglia activation, possibly contributing to production of nitric oxide, tumor necrosis factor-α, and interleukin-6. Of note, a specific TLR4 inhibitor suppressed BK channel expression, whereas an NF-κB inhibitor did not. Taken together, our findings indicate that BK channels participate in both the early and the late stages of LPS-stimulated murine microglia activation involving both membrane-associated and nuclear BK channels.

Interrelationship between myoglobin oxidation and lipid oxidation during the processing of Cantonese sausage with d-sodium erythorbate.

BACKGROUND: Pork is used as raw material to produce Cantonese sausage, and 0.5 or 1 g kg-1 of d-sodium erythorbate is added to the pork meat. In this study the myoglobin oxidation rate, relative metmyoglobin content, heme iron content, redness, pH, free radical content and thiobarbituric acid reactive substance (TBARS) value were measured at different processing times and different content of d-sodium erythorbate. RESULTS: It was found that d-sodium erythorbate significantly reduced the free radical content and myoglobin and lipid oxidation rates and increased heme iron levels. When d-sodium erythorbate was added to the sausage, the absorption peak of myoglobin porphyrin shifted left, migrating from 414 to 405 nm. At 72 h, with an increase in the d-sodium erythorbate content, a significant negative correlation was identified between heme iron and the degree of redness (P < 0.01). CONCLUSION: During sausage processing, there are strong correlations among TBARS values, free radical content, metmyoglobin levels, heme iron levels, a* and pH at the same d-sodium erythorbate level. At the same processing time, adding d-sodium erythorbate can slow the rate of myoglobin and lipid oxidation and prevent the discoloration of sausage. © 2019 Society of Chemical Industry.

Co-Immobilization of Enzymes and Magnetic Nanoparticles by Metal-Nucleotide Hydrogelnanofibers for Improving Stability and Recycling.

In this paper we report a facile method for preparing co-immobilized enzyme and magnetic nanoparticles (MNPs) using metal coordinated hydrogel nanofibers. Candida rugosa lipase (CRL) was selected as guest protein. For good aqueous dispersity, low price and other unique properties, citric acid-modified magnetic iron oxide nanoparticles (CA-Fe3O4 NPs) have been widely used for immobilizing enzymes. As a result, the relative activity of CA-Fe3O4@Zn/AMP nanofiber-immobilized CRL increased by 8-fold at pH 10.0 and nearly 1-fold in a 50 °C water bath after 30 min, compared to free CRL. Moreover, the immobilized CRL had excellent long-term storage stability (nearly 80% releative activity after storage for 13 days). This work indicated that metal-nucleotide nanofibers could efficiently co-immobilize enzymes and MNPs simultaneously, and improve the stability of biocatalysts.

Nanoparticle-mediated TRPV1 channel blockade amplifies cancer thermo-immunotherapy via heat shock factor 1 modulation.

The survival of malignant tumors is highly dependent on their intrinsic self-defense pathways such as heat shock protein (HSP) during cancer therapy. However, precisely dismantling self-defenses to amplify antitumor potency remains unexplored. Herein, we demonstrate that nanoparticle-mediated transient receptor potential vanilloid member 1 (TRPV1) channel blockade potentiates thermo-immunotherapy via suppressing heat shock factor 1 (HSF1)-mediated dual self-defense pathways. TRPV1 blockade inhibits hyperthermia-induced calcium influx and subsequent nuclear translocation of HSF1, which selectively suppresses stressfully overexpressed HSP70 for enhancing thermotherapeutic efficacy against a variety of primary, metastatic and recurrent tumor models. Particularly, the suppression of HSF1 translocation further restrains the transforming growth factor ß (TGFß) pathway to degrade the tumor stroma, which improves the infiltration of antitumor therapeutics (e.g. anti-PD-L1 antibody) and immune cells into highly fibrotic and immunosuppressive pancreatic cancers. As a result, TRPV1 blockade retrieves thermo-immunotherapy with tumor-eradicable and immune memory effects. The nanoparticle-mediated TRPV1 blockade represents as an effective approach to dismantle self-defenses for potent cancer therapy.

Deep Learning for Fashion Style Generation.

In this article, we work on generating fashion style images with deep neural network algorithms. Given a garment image, and single or multiple style images (e.g., flower, blue and white porcelain), it is a challenge to generate a synthesized clothing image with single or mix-and-match styles due to the need to preserve global clothing contents with coverable styles, to achieve high fidelity of local details, and to conform different styles with specific areas. To address this challenge, we propose a fashion style generator (FashionG) framework for the single-style generation and a spatially constrained FashionG (SC-FashionG) framework for mix-and-match style generation. Both FashionG and SC-FashionG are end-to-end feedforward neural networks that consist of a generator for image transformation and a discriminator for preserving content and style globally and locally. Specifically, a global-based loss is calculated based on full images, which can preserve the global clothing form and design. A patch-based loss is calculated based on image patches, which can preserve detailed local style patterns. We develop an alternating patch-global optimization methodology to minimize these losses. Compared with FashionG, SC-FashionG employs an additional spatial constraint to ensure that each style is blended only onto a specific area of the clothing image. Extensive experiments demonstrate the effectiveness of both single-style and mix-and-match style generations.

Effect of Multiple Freeze-Thaw Cycles on Lipid Degradation and Lipid Oxidation of Grass Carp Surimi Containing Different Amounts of Pork Back Fat.

Fresh grass carp was used to produce surimi samples that were supplemented with 50 g/kg, 100 g/kg, or 150 g/kg pork back fat. The lipid composition, lipase activity, lipid oxidation index, and lipoxygenase activity of samples subjected to repeated freeze-thaw process were determined to assess the effects of the added fat on lipolysis and lipid oxidation of grass carp surimi. Freeze-thaw treatment increased free fatty acid content, mainly due to the decomposition of phospholipids and some neutral lipids by lipase. With repeated freeze-thaw treatment, the levels of free fatty acids and phospholipids were correlated with the lipid oxidation indexes and lipoxygenase activity, indicating that lipid degradation can promote lipid oxidation. In the same freeze-thaw cycle, surimi products with high fat content are more vulnerable to oxidative damage, neutral lipids are the main source of free fatty acids in the early stage of freeze-thaw, and phospholipids are the main source of free fatty acids in the late stage.

The Prognostic Value of Early Detection of Minimal Residual Disease as Defined by Flow Cytometry and Gene Mutation Clearance for Myelodysplastic Syndrome Patients After Myeloablative Allogeneic Hematopoietic Stem-Cell Transplantation.

High relapse incidence remains a major problem for myelodysplastic syndrome (MDS) patients who have received an allogeneic hematopoietic stem-cell transplantation (allo-HSCT). We retrospectively analyzed the correlations between clinical outcomes and minimal residual disease (MRD) by using mutations (MUT) and flow cytometry (FCM) analysis of 115 MDS patients with allo-HSCT. We divided 115 MDS patients into four groups based on molecular genetics and FCM MRD results at day 30 post-HSCT. There were significant differences in the 2-year progression-free survival (PFS) between the FCMhigh MUTpos and FCMlow MUTneg groups (20% vs 79%, P < 0.001). In addition, by univariate analysis, we found that an IPSS-R score ≥4 pre-HSCT (HR, 5.061; P=0.007), DNMT3A mutations (HR, 2.291; P=0.052), TP53 mutations (HR, 3.946; P=0.011), and poor and very poor revised International Prognostic Scoring System (IPSS-R) cytogenetic risk (HR, 4.906; P < 0.001) were poor risk factors for PFS. In multivariate analysis, we found that an IPSS-R score ≥ 4 pre-HSCT (HR, 4.488; P=0.015), DNMT3A mutations (HR, 2.385; P=0.049), positive FCM MRD combined with persistence gene mutations at day 30 (HR, 5.198; P=0.013) were independent risk factors for disease progression. In conclusion, our data indicated that monitoring MRD by FCM combined with gene mutation clearance at day 30 could help in the prediction of disease progression for MDS patients after transplantation.

The role of EVI1 gene quantification in AML patients with 11q23/MLL rearrangement after allogeneic hematopoietic stem cell transplantation.

It remains unclear about the role of the EVI1 gene in AML patients with 11q23/MLL rearrangement (MLL-r AML) undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT). We analyzed the clinical value of EVI1 gene quantification in 96 MLL-r AML patients. High EVI1 expression was found in 73% (70/96) of MLL-r AML patients, and EVI1-high MLL-r AML patients were characterized by high WBC counts (P = 0.046) and low platelet counts (P < 0.001) and commonly had t(6;11) (P = 0.032). In addition, a significant difference was observed in the SETD2 gene mutation between the EVI1 high and low groups (0% vs. 50%, P < 0.001). EVI1-high MLL-r AML patients had worse 2-year OS (49.8% vs. 79.7%, P = 0.01) and 2-year PFS (40.2% vs. 68.1%, P = 0.014) than EVI1-low patients. In 57 MLL-r AML patients undergoing allo-HSCT, poorer 2-year PFS (48.6% vs. 72.4%, P = 0.039) and higher CIR (33.2% vs. 11.1%, P = 0.035) were observed in the EVI1-high patients. Multivariate analysis revealed that pre-EVI1+ was the sole independent factor of high CIR (P = 0.035, HR = 4.97, 95% CI: 1.12-22.04). EVI1+ at 100 days post allo-HSCT was associated with a significantly higher 2-year CIR (P = 0.017). The quantification of the EVI1 gene could be used as an additional marker for early predicting relapse in allo-HSCT MLL-r AML patients.

Heterogeneous Recommendation via Deep Low-Rank Sparse Collective Factorization.

A real-world recommender usually adopts heterogeneous types of user feedbacks, for example, numerical ratings such as 5-star grades and binary ratings such as likes and dislikes. In this work, we focus on transferring knowledge from binary ratings to numerical ratings, facing a more serious data sparsity problem. Conventional Collective Factorization methods usually assume that there are shared user and item latent factors across multiple related domains, but may ignore the shared common knowledge of rating patterns. Furthermore, existing works may also fail to consider the hierarchical structures in the heterogeneous recommendation scenario (i.e., genre, sub-genre, detailed-category). To address these challenges, in this paper, we propose a novel Deep Low-rank Sparse Collective Factorization (DLSCF) framework for heterogeneous recommendation. Specifically, we adopt low-rank sparse decomposition to capture the common rating patterns in related domains while splitting the domain-specific patterns. We also factorize the model in multiple layers to capture the affiliation relation between latent categories and sub-categories. We propose both batch and Stochastic Gradient Descent (SGD) based optimization algorithms for solving DLSCF. Experimental results on MoviePilot, Netfilx, Flixter, MovieLens10M and MovieLens20M datasets demonstrate the effectiveness of the proposed algorithms, by comparing them with several state-of-the-art batch and SGD based approaches.

Deep Decision Tree Transfer Boosting.

Instance transfer approaches consider source and target data together during the training process, and borrow examples from the source domain to augment the training data, when there is limited or no label in the target domain. Among them, boosting-based transfer learning methods (e.g., TrAdaBoost) are most widely used. When dealing with more complex data, we may consider the more complex hypotheses (e.g., a decision tree with deeper layers). However, with the fixed and high complexity of the hypotheses, TrAdaBoost and its variants may face the overfitting problems. Even worse, in the transfer learning scenario, a decision tree with deep layers may overfit different distribution data in the source domain. In this paper, we propose a new instance transfer learning method, i.e., Deep Decision Tree Transfer Boosting (DTrBoost), whose weights are learned and assigned to base learners by minimizing the data-dependent learning bounds across both source and target domains in terms of the Rademacher complexities. This guarantees that we can learn decision trees with deep layers without overfitting. The theorem proof and experimental results indicate the effectiveness of our proposed method.

Learning Consensus Representation for Weak Style Classification.

Style classification (e.g., Baroque and Gothic architecture style) is grabbing increasing attention in many fields such as fashion, architecture, and manga. Most existing methods focus on extracting discriminative features from local patches or patterns. However, the spread out phenomenon in style classification has not been recognized yet. It means that visually less representative images in a style class are usually very diverse and easily getting misclassified. We name them weak style images. Another issue when employing multiple visual features towards effective weak style classification is lack of consensus among different features. That is, weights for different visual features in the local patch should have been allocated similar values. To address these issues, we propose a Consensus Style Centralizing Auto-Encoder (CSCAE) for learning robust style features representation, especially for weak style classification. First, we propose a Style Centralizing Auto-Encoder (SCAE) which centralizes weak style features in a progressive way. Then, based on SCAE, we propose both the non-linear and linear version CSCAE which adaptively allocate weights for different features during the progressive centralization process. Consensus constraints are added based on the assumption that the weights of different features of the same patch should be similar. Specifically, the proposed linear counterpart of CSCAE motivated by the "shared weights" idea as well as group sparsity improves both efficacy and efficiency. For evaluations, we experiment extensively on fashion, manga and architecture style classification problems. In addition, we collect a new dataset-Online Shopping, for fashion style classification, which will be publicly available for vision based fashion style research. Experiments demonstrate the effectiveness of the SCAE and CSCAE on both public and newly collected datasets when compared with the most recent state-of-the-art works.

[Somatic hybrids between Rorippa indica (Linn.) Hiern and Brassica napus L. through protoplast-fusion system].

High yield of protoplast isolation was achieved from hypocotyls of B. napus L. and leaves of Rorippa indica (Linn.) Hiern. The isolated protoplasts were used to establish an efficient protoplast-fusion system between the two cruciferous species by PEG-DMSO method and culturing with MS liquid medium. Ten somatic fusion hybrids between B. napus and R. indica were obtained. The enzyme combinations for isolating protoplast from B. napus L. and R. indica were 1% cellulase + 0.2% macerozyme + 3 mmol/L MES and 0.25% cellulase + 0.5% macerozyme + 5 mmol/L MES, respectively. Fusion percentage of 10.4% was obtained on the condition of 30% PEG + 0.3 mol/L glucose +50 mmol/L CaCl2.2H2O + 15% DMSO. Seeds plants obtained from protoplast fusion are new germplasm derived from R. indica.

Multicopper Laccase Mimicking Nanozymes with Nucleotides as Ligands.

Using nanomaterials to achieve functional enzyme mimics (nanozymes) is attractive for both applied and fundamental research. Laccases are multicopper oxidases highly important for biotechnology and environmental remediation. In this work, we report an exceptionally simple yet functional laccase mimic based on guanosine monophosphate (GMP) coordinated copper. It forms an amorphous metal-organic framework (MOF) material. The ratio of copper and GMP is 3:4 as determined by isothermal titration calorimetry. It has excellent laccase-like activity and converts a diverse range of phenol containing substrates such as hydroquinone, naphthol, catechol and epinephrine. Comparative work shows that the activity is originated from guanosine coordination instead of phosphate binding in GMP. Cu2+ is required and cannot be substituted by other metal ions. At the same mass concentration, the Cu/GMP nanozyme has a higher Vmax and similar Km compared to the protein laccase. To achieve the same catalytic efficiency, the cost of the Gu/GMP is ∼2400-fold lower than that of laccase. The Cu/GMP is much more stable at extreme pH, high salt, high temperature and for long-term storage. This is one of the first laccase-mimicking nanozymes, which will find important applications in analytical chemistry, environmental protection, and biotechnology.

Co-immobilization of multiple enzymes by metal coordinated nucleotide hydrogel nanofibers: improved stability and an enzyme cascade for glucose detection.

Preserving enzyme activity and promoting synergistic activity via co-localization of multiple enzymes are key topics in bionanotechnology, materials science, and analytical chemistry. This study reports a facile method for co-immobilizing multiple enzymes in metal coordinated hydrogel nanofibers. Specifically, four types of protein enzymes, including glucose oxidase, Candida rugosa lipase, α-amylase, and horseradish peroxidase, were respectively encapsulated in a gel nanofiber made of Zn(2+) and adenosine monophosphate (AMP) with a simple mixing step. Most enzymes achieved quantitative loading and retained full activity. At the same time, the entrapped enzymes were more stable against temperature variation (by 7.5 °C), protease attack, extreme pH (by 2-fold), and organic solvents. After storing for 15 days, the entrapped enzyme still retained 70% activity while the free enzyme nearly completely lost its activity. Compared to nanoparticles formed with AMP and lanthanide ions, the nanofiber gels allowed much higher enzyme activity. Finally, a highly sensitive and selective biosensor for glucose was prepared using the gel nanofiber to co-immobilize glucose oxidase and horseradish peroxidase for an enzyme cascade system. A detection limit of 0.3 µM glucose with excellent selectivity was achieved. This work indicates that metal coordinated materials using nucleotides are highly useful for interfacing with biomolecules.