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The multi-directionality of polished surface topography (PST) is a key index to characterize the polished surface quality and surface integrity, and its prediction before actual machining is of great significance to the toolpath planning, verification, and optimization. However, most related works reported thus far focus mainly on the material removal modeling but few on the prediction of multi-directionality. Hence, this paper proposes a fast method to precisely predict the multi-directionality of the PST for the pad-polishing process of freeform surfaces. With this method, a pressure distribution model and a material removal profile (MRP) model are first established, in which the MRP is founded subject to the quadratic function distribution. Then, to avoid the time-consuming integral operation in the MRP model, an artificial neural network is developed to fit the quadratic function of MRP. With this model, a multi-directionality prediction algorithm is further proposed based on the angular spectrum of the PST. Simulation and experimental studies have shown that the proposed method can predict the multi-directionality of the PST with very high accuracy and efficiency for various freeform surface polishing, showing great application potential in promoting the efficiency of toolpath planning and optimization to obtain isotropic PST.

Industrial robot provides a promising alternative in free-surface milling. However, due to its low stiffness, it is difficult to guarantee the machining quality. While existing research considers mainly the influence of robot posture on stiffness, the workpiece setup's influence is equally important. In this study, to ensure the overall workpiece's robot stiffness meets the requirement of stiffness threshold in robotic milling, a method for simultaneously optimizing the robot posture and the workpiece setup is proposed. Firstly, to evaluate the robot stiffness during machining, this work presents a new stiffness index considering the robot's rotational deformation. And then, an optimization model is established to optimize both the robot redundancy and the workpiece setup, considering the constraints of joint limitation, singularity-free and collision-free. Moreover, for complex free-surface, to obtain the minimum number of posture changes of the robot and workpiece under the premise of meeting the limit of stiffness threshold, this work constructs a minimum set covering problem, which is solved by a clustering algorithm and a greedy algorithm. Finally, simulations and experimental studies are conducted to validate the effectiveness of the proposed robot stiffness index and the proposed optimization method, showing that the robot stiffness is improved during a milling process of the entire workpiece.

Virtual assembly technology is of great significance to training of mechanical engineers. However, existing force rendering models for virtual assembly are not accurate enough with less consideration of geometrical properties of the parts, which may lead to unpractical perceptions during assembly. This paper presents a novel force rendering model for virtual assembly, which takes into account parts’ geometrical properties according to the new generation of geometrical product specification (GPS). More specifically, skin model shapes of parts for force rendering are constructed and then the contact between the mating surfaces is analyzed. Based on which, the axial frictional and radial contact resistances are calculated and rendered. To verify the proposed model, two comparative case studies are designed using a shaft-bushing assembly as the example. The results have shown that with the proposed approach, the rendered assembly force is more realistic and can reflect different tolerances more precisely.

β-cell dedifferentiation is important in the pathogenesis of type 2 diabetes, and the relationship between lysosome membrane proteins and pancreatic β-cell dedifferentiation is still unknown. Sidt2 is a lysosomal membrane protein identified in our previous study. After Sidt2 elimination, mice exhibited a diabetic phenotype. In the present study, we found that Sidt2 also can be considered a new insulin granule membrane protein. Sidt2 deficiency resulted in islet dysfunction and inhibited insulin secretion. The absence of Sidt2 also led to increased β cell dedifferentiation in mice. Further experiments showed that defects in insulin secretion caused dedifferentiation in the absence of Sid2. In summary, our study has found a new dedifferentiation regulatory mechanism mediated by a lysosomal membrane protein. This new secretory regulation pathway will enrich our understanding of the regulatory mechanisms involved in β cell dedifferentiation and provide new insights into the pathogenesis of diabetes.

Fifteen undescribed eudesmane-type sesquiterpenes, named atramacronoids D-R, along with fourteen known analogues were isolated from the rhizomes of Atractylodes macrocephala. The structures of atramacronoids D-R were elucidated based on extensive spectroscopic data analysis, Snatzke's rule, electronic circular dichroism (ECD) calculations, and X-ray crystallographic analysis. Notably, of the undescribed isolates, atramacronoids D and E are the first example of eudesmanolactam-phenol and eudesmanolactam-ethyl hybrids obtained from plants, respectively. A pair of enantiomers, (+)- and (-)-atramacronoids F, was successfully resolved by chiral-phase HPLC. Atramacronoid D exhibited weak cytotoxicity against SGC-7901 cells. Atramacronoid E significantly promoted the proliferation of LPS-induced IEC-6 cells.

When using a force feedback device to interact with a virtual world, the effect of immersion is highly dependent on its performance of gravity compensation. In this work, an active and passive combined gravity compensation approach is presented for a horizontally mounted 6-DOF hybrid force feedback device (HFFD-6). Both active and passive methods are analyzed with simulation and corresponding parameters in the passive approach are then optimized. And to evaluate the performance of the gravity compensation approach, physical experiments are also conducted to measure the gravity compensation errors in the workspace. Moreover, comparison studies are conducted to illustrate the superiority of the proposed approach in terms of output force capability. These experiments have demonstrated that the proposed approach is feasible to achieve gravity compensation and improve the output force capability of the device.

Rapid simulation of grinding surface topography at different machining conditions can assist users to determine appropriate grinding parameters. Most simulation methods reported so far are time-consuming and can only simulate the surface topography on a micro-scale, which are certainly not effective and intuitive enough. Hence, in this work, a novel GPU-based prediction and simulation method of the grinding surface topography is proposed. With the method, an artificial neural network (ANN) model is first developed to model the grinding surface roughness and fractal dimension. Then, the Cook-Torrance illumination model is introduced to render the color and glossiness of the grinding surface, and a normal map generation method is proposed to render the rugged detail features of grinding surface topography. Finally, a GPU-based prediction and simulation algorithm is developed. A performance evaluation experiment of the ANN model and a comparative experiment between the simulated and actual grinding surface topography are conducted. The experimental results show that the proposed method can achieve fast prediction and photorealistic simulation of the grinding surface topography for the whole part surface, which provides a more intuitive and effective way to evaluate the surface quality under given processing parameters.

A mixed-ligand strategy has been used to construct stable luminescent coordination polymers (CPs). An ultra-stable Zn(ii) coordination polymer, [Zn(H3tpb)(Hbtc)]n (1) was hydrothermally synthesized by employing a new tripodal pyrazole ligand H3tpb and a classical carboxylic ligand H3btc (H3tpb = 1,3,5-tris(pyrazolyl)benzene, H3btc = 1,3,5-benzenetricarboxylic acid). Complex 1 exhibits a 2D sql network. Importantly, 1 not only possesses excellent thermal stability but also shows superior chemical stability in terms of water resistance, acid/base aqueous solutions tolerance (pH = 2-12), and organic solvents resistance. This excellent structural stability was further illustrated from the perspective of thermal decomposition kinetics. The luminescence properties were investigated, showing that complex 1 displays high sensitivity and selectivity for detecting Fe3+ and Cr2O72- ions in aqueous solutions via luminescence quenching effects.

In this paper, an additive manufacturing prepared porous stainless steel felt (AM-PSSF) is proposed as a novel catalyst support for hydrogen production via methanol steam reforming (MSR). In the method, 316 L stainless steel powder with diameter of 15–63 μm is processed by the additive manufacturing technology of selective laser melting (SLM). To accomplish the preparation, the reforming chamber where the AM-PSSF is embedded is firstly divided into an all-hexahedron mesh. Then, the triply periodic minimal surface (TPMS) unit with mathematical form, high interconnectivity and large specific surface area is mapped into the hexahedrons based on shape function, forming the fully connected three-dimensional (3D) micro pore structure of the AM-PSSF. By correlating the mathematical parameter and the porosity of the TPMS unit, and taking into account the SLM process, the porosity of the AM-PSSF is well controlled. Based on the designed 3D pore structure model, the AM-PSSF is produced using standard SLM process. The application of the AM-PSSF as catalyst support for hydrogen production through MSR indicates that: 1) both the naked and catalyst-coated AM-PSSF have the characteristics of high porosity, large specific surface area and high connectivity; 2) the MSR hydrogen production performance of the AM-PSSF is better than that of the commercial stainless steel fiber sintered felt. The feasibility of AM-PSSF as catalyst support for MSR hydrogen production may pave a better way to balance different requirements for catalyst support, thanks to the excellent controllability provided by AM on both the external shape and the internal pore structure, and to the produced rough surface morphology that benefits the catalyst adhesion strength. In addition, catalyst support with pore structures that are more accommodated with the flow field and the reaction rate of MSR reaction may be prepared in future, since the entire catalyst support structure, from macro scale to micro scale, is under control.

Detecting and locating surface defects in textured materials is a crucial but challenging problem due to factors such as texture variations and lack of adequate defective samples prior to testing. In this paper we present a novel unsupervised method for automatically detecting defects in fabrics based on a deep convolutional generative adversarial network (DCGAN). The proposed method extends the standard DCGAN, which consists of a discriminator and a generator, by introducing a new encoder component. With the assistance of this encoder, our model can reconstruct a given query image such that no defects but only normal textures will be preserved in the reconstruction. Therefore, when subtracting the reconstruction from the original image, a residual map can be created to highlight potential defective regions. Besides, our model generates a likelihood map for the image under inspection where each pixel value indicates the probability of occurrence of defects at that location. The residual map and the likelihood map are then synthesized together to form an enhanced fusion map. Typically, the fusion map exhibits uniform gray levels over defect-free regions but distinct deviations over defective areas, which can be further thresholded to produce a binarized segmentation result. Our model can be unsupervisedly trained by feeding with a set of small-sized image patches picked from a few defect-free examples. The training is divided into several successively performed stages, each under an individual training strategy. The performance of the proposed method has been extensively evaluated by a variety of real fabric samples. The experimental results in comparison with other methods demonstrate its effectiveness in fabric defect detection.

Polishing is one of the key processes for surface finishing. It can achieve mirror-effect on surface by removing the tool masks caused by milling. Since the process is now automated, tilted polishing disk is applied more and more widely, and the prediction of material removal map is vital for polishing trajectory planning to reduce scrap rate and shorten production cycle. This work proposed an improved material removal model of tilted elastic polishing disk by considering the geometric features of contact region, contact pressure, and velocity distribution while polishing. More specifically, based on the FEM simulation results, the contact region and the pressure distribution are discussed in three diverse types according to the geometric features of surface at contact point. The material removal map is then generated by numerical convolution of this model and trajectory planned, with which the issues of material removal, such as over-polishing, under-polishing, and waviness, can be graphically illustrated. To validate the approach, case studies on both planar and freeform surfaces are conducted. Comparative study reveals that the predicted result by the approach agrees with the actual polishing very well. Moreover, according to the generated material removal map, process optimization can be carried out to adjust process parameters, such as feedrate, interval, and step, and hence, mirror-effect surface quality can be achieved.

A new parametric method for the design of Voronoi-based lattice porous structures is proposed in this paper. First, the functional relationship between the porosity p, the number of seed points n, and the beam radius r is established. The design space is then divided into individual unit cells, and the seed points and beam radius values of each unit cell are calculated. In each unit cell, Voronoi tessellation is used to generate uniformly distributed seed points. Finally in the design space, all seed points are Voronoi tessellated globally, and the edges are cylindered with corresponding beam radius values. With this design method, not only can the lattice structures with uniform or graded distribution of porosity be generated, but the customized lattice structures can also be generated according to the porosity of each unit cell. Through the analysis of model data, it is verified that the uniform distribution of seed points in this paper is stable and the porosity of the model is consistent with the design value. The distribution of pore spheres between pores is used to illustrate that the lattice porous structures designed in this paper is globally controllable and locally uniform.

This work presents a novel milling strategy for complex pocket machining by integrating radius-varying trochoidal (RVTR) toolpath with contour parallel (CP) toolpath. Based on a quantitative analysis on the fluctuation of material removal rates (MRR), the proposed strategy is able to precisely identify critical regions from complex pocket geometries, and then by integrating flexible trochoidal radius with adaptive trochoidal step, the proposed approach is able to integrate the RVTR toolpath into CP toolpath under a consistent transition of material removal rate. Moreover, by applying RVTR toolpath, the cutting force can be maintained constantly when machining critical regions. Comparing with the trochoidal milling function available with current mainstream CAM software, experimental investigation has shown that the proposed RVTR-CP toolpath integration strategy offers a better machining condition with minimized fluctuation of cutting forces. Moreover, the total length of toolpath is decreased considerably and hence the machining efficiency is greatly improved.

Parameterisation-based methods for planning tool paths on mesh surfaces have been developing for years. The issue of existing mapping deformation which results in machining error has not been suffi...

Haptic rendering in virtual environment provides a powerful training and validation tool for assembly of bolted joints that require accurate assembly forces. This work proposes a staged haptic rendering approach for virtual assembly (VA) of bolted joints. Firstly, by analyzing the stress condition during the actual assembly process, four consecutive stages, namely navigation stage, transition stage, linearity stage, and yield stage, are identified. Then, the force rendering model is set up. Moreover, a prototype VA system is developed to implement and test the approach. Two groups of experiments on a two-stage gear reducer are conducted to verify the feasibility of the approach and evaluate the prototype’s performance. The results have shown that the force calculated by the proposed approach is consistent with the actual assembly and the evaluators are highly positive on the immersion and the guiding ability of the VA process with the haptic rendering provided.

Major depressive disorder (MDD) is a chronic disease that adversely affects mood and cognition. In this study, we randomly divided the rats into control group (C), model group (M), fluoxetine group (F), and acupuncture group (A), used open-field test to ascertain whether acupuncture affects chronic restraint stress (CRS) induced depression-like behaviors of rats, and explored the antidepressant mechanism of acupuncture at the molecular level of transcriptome in the frontal cortex of CRS rats by RNA-sequencing (RNA-seq). According to differentially expressed genes (DEG) analysis, we identified 134, 46, and 89 response genes differentially expressed in C versus M, F versus M, and A versus M, respectively. Through Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, we identified the gene sets involved in extracellular space, inflammatory response, Toll-like receptor signaling pathway, chemokine signaling pathway, and TNF signaling pathway. In this study, RNA-seq technology was used to investigate the frontal cortex genome-wide transcriptomes in depression rats under CRS, which suggested that the antidepressant effect of acupuncture is effective and has a multitarget characteristic, which may be related to amino acid metabolism and inflammatory pathways, especially the Toll-like receptor signaling pathway, TNF signaling pathway, and NF-kappa B signaling pathway.

Virtual offline robot teaching provides a flexible and more economic robot programming solution to belt lapping. Due to the elasticity of the components, the applied force directly influences the polishing result, and the corresponding reaction force sensed by the user affects his or her decision on next operation. However, most of the simulation works reported so far focus mainly on the machining effect of belt lapping but not the contacting force. Therefore, this paper proposes a novel force feedback model for virtual robot teaching of belt lapping. By analyzing the force conditions at different process stages, three kinds of forces, namely natural force, colliding force, and resistance force, are defined to facilitate users with continuous force feedback during a virtual lapping process. To validate the model, a comparative study is done between the actual force measured during the belt lapping process and that simulated by the proposed force model. Moreover, evaluators are involved to conduct the virtual lapping of a mechanical part using the prototype developed. Both the quantitative and the evaluation experiments have validated that the force model is able to provide users with realistic force feedback and better ergonomic feelings of immersion. Moreover, they are also positive on the flexibility and efficiency of using the prototype for virtual robot teaching of belt lapping processes.

Part modeling and assembly modeling are two important aspects of product modeling, which is crucial to manufacturing industrial completeness. Although the two aspects are greatly integrated, they are separated at the process level in current CAD systems. As a result, the design intent expressed at part modeling stage has to be expressed again at assembly modeling stage, through tedious and error-prone manual selection of geometric entities and assignment of mating constraints. To alleviate the problems, an approach to integrate part modeling and assembly modeling from the perspective of process is proposed. In this method, the previously developed concept of assembly feature pair (AFP), and the recently generalized and formalized phenomenon of structure pre-mapping (SPM) are introduced; AFP can be constructed in a mathematical way, and is utilized to describe the design intent existed in designers’ brain systematically; SPM is realized by associating AFPs into part solid models; in this way, the design intent is stored in part solid models at part modeling stage, thus bridging the gaps between part modeling and assembly modeling at process level, and providing heuristics for assembly modeling automation. On this basis, the natural relationships between part modeling and assembly modeling at process level are revealed, paving the way to formalize part modeling and assembly modeling from the perspective of process in a unified framework. Finally, an implementation sample is demonstrated to validate the feasibility of the proposed approach. The proposed integration theoretical system at process level may provide the potential to facilitate the paradigm shift in product modeling to make it simpler and integrate more downstream applications.

Background Both type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) are common age-associated disorders and T2DM patients show an increased risk to suffer from AD, however, there is currently no marker to identify who in T2DM populations will develop AD. Since glycogen synthase kinase-3β (GSK-3β) activity, ApoE genotypes and olfactory function are involved in both T2DM and AD pathogenesis, we investigate whether alterations of these factors can identify cognitive impairment in T2DM patients. Methods The cognitive ability was evaluated using Minimum Mental State Examination (MMSE) and Clinical Dementia Rating (CDR), and the mild cognitive impairment (MCI) was diagnosed by Petersen's criteria. GSK-3β activity in platelet, ApoE genotypes in leucocytes and the olfactory function were detected by Western/dot blotting, the amplification refractory mutation system (ARMS) PCR and the Connecticut Chemosensory Clinical Research Center (CCCRC) test, respectively. The odds ratio (OR) and 95% confidence intervals (95% CI) of the biomarkers for MCI diagnosis were calculated by logistic regression. The diagnostic capability of the biomarkers was evaluated by receiver operating characteristics (ROC) analyses. Findings We recruited 694 T2DM patients from Jan. 2012 to May. 2015 in 5 hospitals (Wuhan), and 646 of them met the inclusion criteria and were included in this study. 345 patients in 2 hospitals were assigned to the training set, and 301 patients in another 3 hospitals assigned to the validation set. Patients in each set were randomly divided into two groups: T2DM without MCI (termed T2DM-nMCI) or with MCI (termed T2DM-MCI). There were no significant differences for sex, T2DM years, hypertension, hyperlipidemia, coronary disease, complications, insulin treatment, HbA1c, ApoE ε2, ApoE ε3, tGSK3β and pS9GSK3β between the two groups. Compared with the T2DM-nMCI group, T2DM-MCI group showed lower MMSE score with older age, ApoE ε4 allele, higher olfactory score and higher rGSK-3β (ratio of total GSK-3β to Ser9-phosphorylated GSK-3β) in the training set and the validation set. The OR values of age, ApoE ε4 gene, olfactory score and rGSK-3β were 1.09, 2.09, 1.51, 10.08 in the training set, and 1.06, 2.67, 1.47, 7.19 in the validation set, respectively. The diagnostic accuracy of age, ApoE ε4 gene, olfactory score and rGSK-3β were 0.76, 0.72, 0.66, 0.79 in the training set, and 0.70, 0.68, 0.73, 0.79 in the validation set, respectively. These four combined biomarkers had the area under the curve (AUC) of 82% and 86%, diagnostic accuracy of 83% and 81% in the training set and the validation set, respectively. Interpretation Aging, activation of peripheral circulating GSK-3β, expression of ApoE ε4 and increase of olfactory score are diagnostic for the mild cognitive impairment in T2DM patients, and combination of these biomarkers can improve the diagnostic accuracy., Highlights • ApoE ε4 gene, platelet GSK-3β activation, olfactory dysfunction and aging are non-invasive, affordable and accessible biomarkers for diagnosing mild cognitive impairment in type 2 diabetes mellitus patients, and the combination of these non-invasive, affordable and accessible biomarkers can improve the accuracy of the diagnosis. Epidemiological studies show that type 2 diabetes mellitus is an independent risk factor of Alzheimer disease, and a large proportion of diabetic patients will develop Alzheimer disease, but no early diagnostic tool to identify them. We find that ApoE ε4 gene, platelet GSK-3β activation, olfactory dysfunction and aging are early markers for dementia in type 2 diabetes patients, and combination of these non-invasive markers can improve the diagnostic accuracy. These findings shed light on the early identification in type 2 diabetes population who will develop Alzheimer disease and thus enable early intervention to this currently incurable neurodegenerative disorder.

This work proposes a novel adaptive trochoidal (TR) toolpath model for complex pocket machining. The proposed model is able to adjust its TR radius, while simultaneously offers an adaptive trochoidal step for generation of each toolpath cycle. With the varying radius, complex regions can be machined by a single strip of TR toolpath. Moreover, the step of each TR cycle can be adjusted with reference to the varied radius so as to maintain a constant radial depth of cut, and hence more stable material removal rate among all TR cycles can be achieved. For the pocket machining cases with complex inner and outer boundaries, experimental study has shown that the proposed TR toolpath can effectively minimise the fluctuation of cutting forces, and achieve better toolpath efficiency.

Condyloma acuminatum (CA) is a condition caused by the highly contagious human papillomavirus (HPV), characterized by warts that undergo abnormal cell proliferation. One of the important regulators of cell proliferation is microRNAs (miRNAs). In this study, we aimed to investigate the expression profile of miR-99b in HPV positive CA samples and normal skin. We found significantly lower miR-99b levels in CA samples than in normal skin. Therefore, we investigated the role of miR-99b in regulating the proliferation of primary cultured human epidermal keratinocytes, and found that forced expression of miR-99b inhibited proliferation and induced G1-phase arrest. Based on conserved sequences in 3'UTR for miR-99b binding, we identified the insulin-like growth factor-1 receptor (IGF-1R) gene as a direct target for miR-99b. Further, we confirmed the binding site for miR-99b in the IGF-1R 3'UTR by mutation using a luciferase reporter assay that showed decrease in luciferase activity in the presence of miR-99b in the construct with the wild-type 3'UTR, but not in the construct with the mutant 3'UTR. Moreover, miR-99b over-expression could down-regulate IGF-1R expression, and could repress the PI3K-AKT signaling pathway. Lastly, over-expression of IGF-1R reversed the inhibitory effect of miR-99b on keratinocyte proliferation. Taken together, our results suggest that IGF-1R levels may be modulated by miR-99b in CA: downregulation of miR-99b with concomitant upregulation of its target gene IGF-1R may over-induce the PI3K-AKT signaling pathway, leading to deregulated cell proliferation in CA.

Because of industrial robots’ relatively low stiffness, many research efforts have been performed to improve the robot stiffness by optimizing the robot posture. For freeform surfaces with large curvature, however, the expected high stiffness posture may undergo excessive changes that exceed the robot joint speed limit. Therefore, the stiffness optimization may not achieve the expected results in actual machining owing to the limitation of robot kinematics and conventional toolpath pattern. To address this problem, a region-based toolpath generation method is proposed to improve robot stiffness in this study for robotic milling of freeform surfaces. To provide the possibility of higher stiffness robot posture, not only the redundant degree of freedom (DOF) of the robot but also the orientation of tool axis during machining is optimized. Under the influence of surface curvature and position, the change of high stiffness posture has regionality. A surface subdivision method is proposed to divide the surface into multiple sub-regions, so that actual robot posture with better stiffness can be obtained. For each sub-region, the feed direction of toolpath is optimized to further enhance robot stiffness. Simulations and experimental studies are conducted, and show that the proposed toolpath generation method can improve the robot stiffness in freeform surface machining.

The degree of toolpath redundancy is a critical concern when looking for an appropriate toolpath strategy for free-form surface machining. Hence, quantitative analysis of toolpath redundancy is important to CAM applications. In this work, a novel approach for prediction of toolpath redundancy for free-form surface machining is proposed. Firstly, a general mathematical model to represent toolpath redundancy rate is proposed based on the analysis of local toolpath intervals and their difference from the optimal values. And then, taking the most widely used iso-planar machining as case study, the steep-wall features that bring in the variation of surface slope rates alone machining strips are identified as the main cause of the generation of toolpath redundancy, so a method to automatic recognising steep-wall features from free-form surface is developed. At last, based on the steep-wall feature segmentation, an algorithm is presented to quantitatively predict the toolpath redundancy rate for free-form surfac...

Cyclovirobuxinum D (CVB-D), an alkaloid isolated from the Chinese medicinal plant Buxus microphylla, has been found to be effective to treat cardiac insufficiency, arrhythmias and coronary heart disease. In the present study, we investigated the effects of CVB-D on the inflammatory responses in lipopolysaccharide (LPS)-stimulated murine macrophages in vitro and the underlying mechanisms.Murine macrophage cell line RAW264.7 cells were incubated in the presence of LPS (0.1 μg/mL) for 24 h. The cell viability was measured using MTT assay. The release of NO and cytokines were detected using the Griess test and ELISA, respectively. The mRNA and protein levels were determined using RT-PCR and Western blot, respectively. Reporter gene assays were used to analyze the transcriptional activity of NF-κB.Treatment of RAW264.7 cells with CVB-D (25-300 μmol/L) did not affect the cell viability. Pretreatment with CVB-D (50, 100 and 200 μmol/L) concentration-dependently decreased NO release and iNOS expression in LPS-treated RAW264.7 cells (its IC50 value in inhibition of NO production was 144 μmol/L). CVB-D also concentration-dependently inhibited the secretion and mRNA expression of IL-1β and IL-6 in LPS-treated RAW264.7 cells. Furthermore, CVB-D remarkably inhibited the phosphorylation of STAT1 and STAT3, as well as JAK2 in LPS-treated RAW264.7 cells, but did not affect the activation of NF-κB and MAPKs pathways. Pretreatment with the JAK2 specific inhibitor AG490 (30 μmol/L) produced similar effects on NO release and iNOS expression in LPS-treated RAW264.7 cells.CVB-D exerts anti-inflammatory effects in LPS-stimulated murine macrophages in vitro at least in part by blocking the JAK-STAT signaling pathway. The anti-inflammatory actions of CVB-D may contribute to its cardioprotection.

Collaborative product design on heterogeneous computer-aided design CAD platforms has shown its significance to today’s global manufacturing industries. This article proposes a novel collaboration framework for supporting distributed collaborative design with heterogeneous CAD resources. The unique feature of the framework is that, instead of directly accessing heterogeneous CAD resources, a so-called co-proxy model is defined and created to work as the replication agent representing original CAD resources during collaborative sessions. The co-proxy model is designed to represent corresponding CAD resources with both structured polygonal graphics for visualisation and a set of embedded virtual topological elements VTE, so as to support collaborative design manipulations such as virtual assembly and annotation. Moreover, the co-proxy model comes with an external link mechanism, with which the powerful modelling and editing functionalities from heterogeneous CAD systems can be reasonably integrated into the distributed collaboration framework. The proposed framework together with its enabling technologies has been implemented into a prototype system named as Co-DMU, which is generally applicable for feature-based CAD modelling systems with automation interfaces provided.

Computer-aided design CAD has been a de facto industry standard approach for common product design. Analysing the manufacturing factors of the product at its design stage can greatly reduce its cost at the later stages of product life cycle. This article presents an integrated approach of disassembly constraint generation, based on which an object-oriented prototype is designed and developed. The approach is implemented as an embedded application on commercial CAD software platform so as to provide a tight and seamless integration to CAD process. It utilises the CAD model information directly and allows designers to evaluate the on-going design for its disassembly properties. Moreover, since the modules and objects are well encapsulated, it is possible for further disassembly planning applications to build on top of or integrate to it. The case study on a widely used shaft assembly demonstrated the effectiveness of the integrated approach.