Pan-African metamorphic and magmatic rocks of the Khanka Massif, NE China: Further evidence regarding their affinity

The Khanka Massif is a crustal block located along the eastern margin of the Central Asian Orogenic Belt (CAOB) and bordered to the east by Late Jurassic-Early Cretaceous circum-Pacific accretionary complexes of the Eastern Asian continental margin. It consists of graphite-, sillimanite- and cordierite-bearing gneisses, carbonates and felsic paragneisses, in association with various orthogneisses. Metamorphic zircons from a sillimanite gneiss from the Hutou complex yield a weighted mean 206Pb/ 238U age of 490 ± 4 Ma, whereas detrital zircons from the same sample give ages from 934-610 Ma. Magmatic zircon cores in two garnet-bearing granite gneiss samples, also collected from the Hutou complex, yield weighted mean 206Pb/ 238U ages of 522 ± 5 Ma and 515 ± 8 Ma, whereas their metamorphic rims record 206Pb/ 238U ages of 510-500 Ma. These data indicate that the Hutou complex in the Khanka Massif records early Palaeozoic magmatic and metamorphic events, identical in age to those in the Mashan Complex of the Jiamusi Massif to the west. The older zircon populations in the sillimanite gneiss indicate derivation from Neoproterozoic sources, as do similar rocks in the Jiamusi Massif. These data confirm that the Khanka Massif has a close affinity with other major components of the CAOB to the west of the Dun-Mi Fault. Based on these results and previously published data, the Khanka Massif is therefore confirmed as having formed a single crustal entity with the Jiamusi (and possibly the Bureya) massif since Neoproterozoic time. Copyright © Cambridge University Press 2010.published_or_final_versio

Design optimization considering variable thermal mass, insulation, absorptance of solar radiation, and glazing ratio using a prediction model and genetic algorithm

This paper presents the optimization of building envelope design to minimize thermal load and improve thermal comfort for a two-star green building in Wuhan, China. The thermal load of the building before optimization is 36% lower than a typical energy-efficient building of the same size. A total of 19 continuous design variables, including different concrete thicknesses, insulation thicknesses, absorbance of solar radiation for each exterior wall/roof and different window-to-wall ratios for each façade, are considered for optimization. The thermal load and annual discomfort degree hours are selected as the objective functions for optimization. Two prediction models, multi-linear regression (MLR) model and an artificial neural network (ANN) model, are developed to predict the building thermal performance and adopted as fitness functions for a multi-objective genetic algorithm (GA) to find the optimal design solutions. As compared to the original design, the optimal design generated by the MLRGA approach helps to reduce the thermal load and discomfort level by 18.2% and 22.4%, while the reductions are 17.0% and 22.2% respectively, using the ANNGA approach. Finally, four objective functions using cooling load, heating load, summer discomfort degree hours, and winter discomfort degree hours for optimization are conducted, but the results are no better than the two-objective-function optimization approach

Development of Building Thermal Load and Discomfort Degree Hour Prediction Models Using Data Mining Approaches

Thermal load and indoor comfort level are two important building performance indicators, rapid predictions of which can help significantly reduce the computation time during design optimization. In this paper, a three-step approach is used to develop and evaluate prediction models. Firstly, the Latin Hypercube Sampling Method (LHSM) is used to generate a representative 19-dimensional design database and DesignBuilder is then used to obtain the thermal load and discomfort degree hours through simulation. Secondly, samples from the database are used to develop and validate seven prediction models, using data mining approaches including multilinear regression (MLR), chi-square automatic interaction detector (CHAID), exhaustive CHAID (ECHAID), back-propagation neural network (BPNN), radial basis function network (RBFN), classification and regression trees (CART), and support vector machines (SVM). It is found that the MLR and BPNN models outperform the others in the prediction of thermal load with average absolute error of less than 1.19%, and the BPNN model is the best at predicting discomfort degree hour with 0.62% average absolute error. Finally, two hybrid models—MLR (MLR + BPNN) and MLR-BPNN—are developed. The MLR-BPNN models are found to be the best prediction models, with average absolute error of 0.82% in thermal load and 0.59% in discomfort degree hour

Creating an animal model of tendinopathy by inducing chondrogenic differentiation with kartogenin

Previous animal studies have shown that long term rat treadmill running induces over-use tendinopathy, which manifests as proteoglycan accumulation and chondrocytes-like cells within the affected tendons. Creating this animal model of tendinopathy by long term treadmill running is however time-consuming, costly and may vary among animals. In this study, we used a new approach to develop an animal model of tendinopathy using kartogenin (KGN), a bio-compound that can stimulate endogenous stem/progenitor cells to differentiate into chondrocytes. KGN-beads were fabricated and implanted into rat Achilles tendons. Five weeks after implantation, chondrocytes and proteoglycan accumulation were found at the KGN implanted site. Vascularity as well as disorganization in collagen fibers were also present in the same site along with increased expression of the chondrocyte specific marker, collagen type II (Col. II). In vitro studies confirmed that KGN was released continuously from KGN-alginate in vivo beads and induced chondrogenic differentiation of tendon stem/progenitor cells (TSCs) suggesting that chondrogenesis after KGN-bead implantation into the rat tendons is likely due to the aberrant differentiation of TSCs into chondrocytes. Taken together, our results showed that KGN-alginate beads can be used to create a rat model of tendinopathy, which, at least in part, reproduces the features of over-use tendinopathy model created by long term treadmill running. This model is mechanistic (stem cell differentiation), highly reproducible and precise in creating localized tendinopathic lesions. It is expected that this model will be useful to evaluate the effects of various topical treatments such as NSAIDs and platelet-rich plasma (PRP) for the treatment of tendinopathy. Copyright

Inactivation Kinetics of beta-N-Acetyl-D-glucosaminidase from Green Crab (Scylla serrata) in Dioxane Solution

Natural Science Foundation of China [40576066, 30500102]; Program for Innovative Research Team in Science and Technology in Fujian Province Universitybeta-N-Acetyl-D-glucosaminidase (NAGase, EC.3.2.1.52), which catalyzes the cleavage of N-acetylglucosamine polymers, is a composition of chitinase and cooperates with endochitinase and exo-chitinase to disintegrate chitin into N-acetylglucosamine (NAG). In this investigation, A NAGase from green crab (Scylla serrata) was purified and the effects of dioxane on the enzyme activity for the hydrolysis of p-Nitrophenyl-N-acetyl-beta-D-glucosaminide (pNP-NAG) were studied. The results show that appropriate concentrations of dioxane can lead to reversible inactivation of the enzyme and the inactivation is classified as mixed type. The value of IC(50), the dioxane (inactivator) concentration leading to 50% activity lost, is estimated to be 0.68%. The kinetics of inactivation of NAGase in the appropriate concentrations of dioxane solution has been studied using the kinetic method of the substrate reaction. The rate constants of inactivation have been determined. The results showed that k(+0) is much larger than k'(+0), indicating the free enzyme molecule is more fragile than the enzyme-substrate complex in the dioxane solution. It is suggested that the presence of the substrate offers marked protection of this enzyme against inactivation by dioxane

CD4 T lymphocyte autophagy is upregulated in the salivary glands of primary Sjögren’s syndrome patients and correlates with focus score and disease activity

Background: Primary Sjögren’s syndrome (pSS) is a common chronic autoimmune disease characterized by lymphocytic infiltration of exocrine glands and peripheral lymphocyte perturbation. In the current study, we aimed to investigate the possible pathogenic implication of autophagy in T lymphocytes in patients with pSS. Methods: Thirty consecutive pSS patients were recruited together with 20 patients affected by sicca syndrome a nd/or chronic sialoadenitis and 30 healthy controls. Disease activity and damage were evaluated according to SS disease activity index, EULAR SS disease activity index, and SS disease damage index. T lymphocytes were analyzed for the expression of autophagy-specific markers by biochemical, molecular, and histological assays in peripheral blood and labial gland biopsies. Serum interleukin (IL)-23 and IL-21 levels were quantified by enzyme-linked immunosorbent assay. Results: Our study provides evidence for the first time that autophagy is upregulated in CD4+ T lymphocyte salivary glands from pSS patients. Furthermore, a statistically significant correlation was detected between lymphocyte autophagy levels, disease activity, and damage indexes. We also found a positive correlation between autophagy enhancement and the increased salivary gland expression of IL-21 and IL-23, providing a further link between innate and adaptive immune responses in pSS. Conclusions: These findings suggest that CD4+ T lymphocyte autophagy could play a key role in pSS pathogenesis. Additionally, our data highlight the potential exploitation of T cell autophagy as a biomarker of disease activity and provide new ground to verify the therapeutic implications of autophagy as an innovative drug target in pSS

Tear proteomic analysis of Sjogren syndrome patients with dry eye syndrome by two-dimensional-nano-liquid chromatography coupled with tandem mass spectrometry

We examined the tear film proteome of patients with Sjögren's syndrome (SS) and dry eye syndrome (group A), patients with dry eye symptoms (group B) and normal volunteers (group C). Tear samples were pooled from 8 subjects from each group and were subjected to two-dimensional-nano-liquid chromatography coupled with tandem mass spectrometry (2D-nano-LC-MS/MS). The tear breakup time for group A was significantly reduced compared with group B and C (P < 0.001). Group A (Schirmer I test, 2.13 +/- 2.38 mm/5 min) had markedly lower tear volume than group B (5.94 +/- 4.75 mm/5 min) and C (14.44 +/- 6.57 mm/5 min) (P < 0.001). Group A had significantly higher normalized tear protein content (1.8291 +/- 0.2241 mu g/mm) than group B (1.0839 +/- 0.1120 mu g/mm) (P = 0.001) and C (0.2028 +/- 0.0177 mu g/mm) (P = 0.001). The 2D-nano-LC-MS/MS analysis identified a total of 435 proteins, including 182 (54.8%),247 (74.4%) and 278 (83.7%) in group A, B, and C, respectively, with 56 (16.7%) proteins including defensin alpha 1, clusterin and lactotransferrin unique to group A. In conclusion, dry eye syndrome in SS patients is associated with an altered proteomic profile with dysregulated expression of proteins involved in a variety of important cellular process including inflammation, immunity, and oxidative stress