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Nanofat, as a derivative of adipose tissue, has gradually become a research hotspot in beauty and regenerative medicine. However, the nanofat preparation method has not yet been standardized; it remains unknown whether the aperture of the connector has any influence on the transplantation effect.Adipose tissue was mechanically emulsified into nanofat tissue through different connector apertures (1.0, 1.5, and 2.0 mm). Cell survival and apoptosis were measured using the volume of oil droplets, glucose transportation test, flow cytometry, cell counting kit-8 (CCK-8), wound healing assay, transwell migration assay, and fluorescence staining. The expression of adiponectin, GluT4, and PPAR-γ in nanofat-derived stem cells (NFSCs) was detected using quantitative real-time polymerase chain reaction (qRT-PCR).The fineness of nanofat tissue texture decreased with an increase in the aperture connector. The amounts of glucose transferred in the three groups (1, 1.5, and 2 mm) were 4.7 ± 0.894, 6.1 ± 1.026, and 6.9 ± 0.868 mmol/L, respectively. Flow cytometric analysis showed that the proportion of NFSCs in the 2.0 mm group was the highest (91.267±1.210%). Cell proliferation and migration abilities were stronger in the 1.5 and 2.0 mm groups. The numbers of late apoptotic and dead cells in the 2.0 mm group were significantly fewer than those in the two other groups. Expression levels of lipid-related genes were as follows: adiponectinGluT4PPAR-γ in each component.As nanofat is emulsified, the use of larger aperture connectors (2.0 mm) appeared to decrease the degree of adipocyte lysis and increase the biological activity of adipose tissue.

Human activity recognition (HAR) has become a hot research topic, due to its wide application prospect. Fusion-based methods can be used to complement single sensing modality methods. This paper presents the simultaneous utilization of skeleton data and inertial signals—which are captured at the same time using a Kinect depth camera and ten wearable inertial sensors —within a fusion framework, in order to achieve more robust human action recognition, compared to situations where each sensing modality is used individually. Skeleton data captured by the Kinect depth camera are transformed into a weighted front-view skeleton motion map (WF-SMM), a weighted multi-view skeleton motion map (WM-SMM), and a 3D weighted skeleton motion map (3DW-SMM), which are then fed as inputs into a convolutional neural network. Meanwhile, the inertial data are transformed into 2D inertial images, then fed into a 2D dilated convolutional neural network. Two types of fusion are considered: decision-level fusion and feature-level fusion. Experiments were conducted using the publicly available Changzhou University Multimodal Human Action Data set (CZU-MHAD), in which simultaneous skeleton sequence and inertial signals were captured for a total of 22 actions. The results obtained indicate that both the decision- and feature-level fusion approaches generate higher recognition accuracies, compared to the approaches where each sensing modality is used individually. The highest accuracy (of 98.90%) was obtained with the decision-level fusion approach using 3DW-SMM. In addition, some experiments are conducted on the continuous action streams generated based on the CZU-MHAD with different score threshold. And the highest f1 score 82.05 % is obtained with the threshold of 0.4.

Summary Background Human adipose mesenchymal stem cells (hADSCs) show poor survival after transplantation, limiting their clinical application. Tissue regeneration resulting from stem cell treatment may be caused by attenuation of hypoxia-inducible factor-1α (HIF-1α). In this study, we constructed hADSCs stably expressing HIF-1α and investigated the potential effects of HIF-1α expression in the ischemic microenvironment on mitochondrial apoptosis and survival of hADSCs, and studied the mechanisms involved. Method Apoptosis was induced by an ischemic microenvironment in vitro. ADSCs with stable HIF-1α expression were established. Cell survival and apoptosis were observed by CCK-8 assay, western blotting, flow cytometry, and fluorescence staining. ADSCs were subcutaneously transplanted into nude mice in the location where a hypoxia ischemic microenvironment was simulated in vivo. After 1, 3, and 7 d, mitochondrial apoptotic proteins were evaluated by immunohistochemistry and immunofluorescence staining. Results Exogenous HIF-1α downregulated mitochondrial reactive oxygen species, cytochrome c, caspase-9, and caspase-3, but inhibited mitochondrial membrane potential depolarization and increased the Bcl-2/bax ratio. HIF-1α prevented apoptosis and promoted vascular endothelial growth factor (VEGF) secretion as demonstrated by enzyme-linked immunosorbent assay (ELISA), terminal deoxyribonucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) staining, and flow cytometry analysis. HIF-1α enhanced the survival of transplanted ADSCs in nude mice. Conclusion We have shown that through inhibition of the mitochondria-mediated apoptotic pathway and promotion of VEGF secretion in hADSCs in an ischemic microenvironment, HIF-1α may potentially be applied in clinical therapy and as an alternative strategy for improving hADSC therapy.

Diabetic nephropathy (DN) is the major life-threatening complication of diabetes, and oxidative stress takes part in its initiation and development. This study was performed to evaluate the effects of carotenoids from Sporidiobolus pararoseus (CSP) on the renal function and oxidative stress status of mice with streptozotocin (STZ)-induced DN. The results indicated that CSP significantly attenuated symptoms of STZ-induced DN shown by decreased fasting blood glucose, reduced urine volume, urine albumin, serum creatinine and serum urea nitrogen, and improved kidney histological morphology. Furthermore, biochemical analysis of serum and kidney revealed a marked increase in oxidative stress of DN mice as evidenced by reduced total antioxidant capacity (T-AOC), decreased activity of antioxidant enzyme -superoxide dismutase (SOD) and increased level of malondialdehyde (MDA). However, treatment with CSP improved oxidative stress status in DN mice as compared with the mice in model group. Exploration of the potential mechanism validated that CSP ameliorated the oxidative stress status in DN mice by activating the expressions of Nrf2, NQO-1, HO-1, GST and CAT in kidney. These data revealed that CSP may retard the progression of DN by ameliorating renal function, improving the oxidative stress status and activating the Nrf2/ARE pathway.

Network traffic classification technology plays an important role in network security management. However, the inherent limitations of traditional methods have become increasingly obvious, and they cannot address existing traffic classification tasks. Very recently, neural architecture search (NAS) has aroused widespread interest as a tool to automate the manual architecture construction process. To this end, this paper proposes NAS based on multiobjective evolutionary algorithms (MOEAs) to classify malicious network traffic. The main purpose is to simplify the search space by reducing the spatial ratio and number of channels of the model. In addition, the search strategy is changed in the effective search space, and the utilized strategies include EAs with the nondominated sorting genetic algorithm with the elite retention strategy (NSGA-II), strength Pareto evolutionary algorithm (SPEA-II) and multiobjective particle swarm optimization (MOPSO) to solve the formulated multiobjective NAS. Through comprehensive comparison of the population convergence times, model accuracies, Pareto optimality sets, model complexities and running speeds of the strategies, it is concluded that the model based on NSGA-II search has the best performance. The experimental results of the current machine learning algorithms and artificial learning methods based on the network are compared, showing that our method achieved better classification performance on two public datasets with a lower computational complexity, as mainly measured by FLOPs. Our approach is able to achieve 99.806% and 99.369% F1-score with 11.501 MB and 4.718 MB FLOPs on both IDS2012 and ISCX VPN dataset respectively.

Human activity recognition (HAR) based on wearable sensors is a promising research direction. The resources of handheld terminals and wearable devices limit the performance of recognition and require lightweight architectures. With the development of deep learning, the neural architecture search (NAS) has emerged in an attempt to minimize human intervention. We propose an approach for using NAS to search for models suitable for HAR tasks, namely, HARNAS. The multi-objective search algorithm NSGA-II is used as the search strategy of HARNAS. To make a trade-off between the performance and computation speed of a model, the F1 score and the number of floating-point operations (FLOPs) are selected, resulting in a bi-objective problem. However, the computation speed of a model not only depends on the complexity, but is also related to the memory access cost (MAC). Therefore, we expand the bi-objective search to a tri-objective strategy. We use the Opportunity dataset as the basis for most experiments and also evaluate the portability of the model on the UniMiB-SHAR dataset. The experimental results show that HARNAS designed without manual adjustments can achieve better performance than the best model tweaked by humans. HARNAS obtained an F1 score of 92.16% and parameters of 0.32 MB on the Opportunity dataset.

The cell wall of carotenoids producing yeast Sporidiobolus pararoseus was broken through five different methods: acid-heating method, dimethyl sulfoxide (DMSO) method, enzymatic method, hig...

In type 2 diabetes mellitus (T2DM), hyperglycemia promotes oxidative stress and eventually leads to diabetic nephropathy (DN). Sporidiobolus pararoseus is reported to exhibit enhanced anti-oxidation properties. However, its role in DN remains obscure. This study aimed to determine the antioxidative effects of a Sporidiobolus pararoseus wall-broken powder (SPP) supplement on DN and investigate the possible underlying mechanisms. A model of T2DM was successfully established, and C57BL/6J male mice were fed a high-fat diet for 4 weeks and then injected with streptozotocin (100 mg per kg per day) for three consecutive days. After eight weeks of intervention, SPP strongly lowered fasting glucose levels, serum creatinine, serum urea nitrogen, urinary albumin and reduced glomerular hypertrophy and mesangial matrix expansion. In addition, SPP increased the activities of SOD, T-AOC, CAT, and GST and decreased the amount of MDA. Furthermore, it was revealed that SPP significantly abrogated oxidative stress not only by activating the Nrf2 gene but also by activating two Nrf2-targeted antioxidative genes (NQO-1 and HO-1) compared with metformin hydrochloride, which is widely accepted as a diabetes drug. Our study showed that SPP has antioxidant properties and delays the progression of DN; the underlying mechanism may be associated with activation of the Nrf2/ARE pathway.

Diabetic nephropathy (DN) is the major life-threatening complication of diabetes, and oxidative stress takes part in its initiation and development. This study was performed to evaluate the effects of carotenoids from

Human pose estimation is a fundamental yet challenging task in computer vision. Human pose estimation from a single image is a challenging problem due to the limited information of 2D images and the large variations in configuration and appearance of body parts. Recent works has largely improved the result of human pose estimation because of the development of convolutional neural network. However, there still exists many difficult cases, such as occluded keypoints, complex background and scale variations of human body keypoints, which cannot be well dealt with. In this paper, we design a novel scale-aware network with attentional selection that extracts multi-scale semantic information and meaningful features. Specifically, we propose a Feature Pyramid Supervision Module (FPSM), which can improve the estimation accuracy of scale variations. Meanwhile, a Spatial and Channel Attention Module (SCAM) is designed for recalibrating the spatial and channel features. Based on the proposed algorithm, we achieve state-of-the-art result on LSP dataset and make competitive performance on MPII Human Pose dataset.

Skeleton-based action recognition is a branch of action recognition which uses dynamic skeletons as input. Recent research based on graph convolutional networks (GCN) has achieved remarkable performance in this area. However, feature extraction and fusion at different physical scales have not been well studied. To solve these issues, we propose a novel MultiScale Adaptive Graph Convolutional Network (MSGCN) which contains a Multi-Scale Graph Convolutional Module and a MultiScale Selective Fusion Module. Extensive experiments on NTU- RGBD dataset demonstrate the effectiveness of our method, our method achieved competitive performance on NTU-RGBD dataset.

Human activity recognition (HAR) is a popular and challenging research topic, driven by a variety of applications. More recently, with significant progress in the development of deep learning networks for classification tasks, many researchers have made use of such models to recognise human activities in a sensor-based manner, which have achieved good performance. However, sensor-based HAR still faces challenges, in particular, recognising similar activities that only have a different sequentiality and similarly classifying activities with large inter-personal variability. This means that some human activities have large intra-class scatter and small inter-class separation. To deal with this problem, we introduce a margin mechanism to enhance the discriminative power of deep learning networks. We modified four kinds of common neural networks with our margin mechanism to test the effectiveness of our proposed method. The experimental results demonstrate that the margin-based models outperform the unmodified models on the OPPORTUNITY, UniMiB-SHAR, and PAMAP2 datasets. We also extend our research to the problem of open-set human activity recognition and evaluate the proposed method&rsquo, s performance in recognising new human activities.

The present study focuses on the kinematic analysis of a PPPR spatial serial mechanism with a large number of geometric errors. The study is implemented in three steps: (1) development of a map between the end-effector position error and geometric source errors within the serial mechanism kinematic chains using homogeneous transformation matrix; (2) selection of geometric errors which have significant effects on end-effector positioning accuracy by sensitivity analysis; (3) kinematic analysis of the serial mechanism within which the geometric errors are modelled as interval variables. The computational algorithms are presented for positioning accuracy analysis and workspace analysis in consideration of geometric errors. The analysis results show that the key factors which have significant effects on end-effector position error can be identified efficiently, and the uncertain workspace can also be calculated efficiently.

The cell wall of carotenoids producing yeast

Currently available suspension kinematics and compliance (K&C) model is mainly used for qualitative analysis rather than quantitative analysis. In order to reproduce actual suspension K&C characteristic, all the suspension components need to take into account as much details as possible. Suspension stiffness non-linearity, suspension components friction and kinematic joint clearances are examples of how the component models can be made more specific. This paper proposes a modeling technique that can not only reliably represent the hysteretic characteristic but also analyze the effect of joint clearances. The suspension air spring model and bushing model are composed of nonlinear elastic force and Berg's friction force. The suspension supporting components are modeled as flexible bodies. The suspension parameter identification and model validation are also proposed. The test procedure of a new K&C test rig based on non-contact binocular vision system is introduced and the simulation results are compared with the experimental results. Results from tests show good agreement between the proposed suspension model and experimental data. Effects of the suspension components friction and clearance joints are investigated. This type of study can provide useful information on the vehicle design, analysis and optimization.

In the actual situation, datasets with large amounts of data cannot directly obtain. Therefore, ensuring the accuracy and speed of the detection algorithm under small sample specific datasets is a key challenge for building target detection. This paper improved the algorithm based on Yolov2, expand small sample data sets to achieve accurate detection of targets