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The present study aimed to analyze the effects of acetylation-related lncRNAs in non-small-cell lung cancer (NSCLC). A total of 399 differentially expressed lncRNAs (DElncRNAs) have been identified between 497 NSCLC tissues and 54 normal tissues in the TCGA database, and 105 of which were correlated with acetylation regulators. By using univariate cox regression analysis and combining it with clinical prognosis information, 12 prognostic-related lncRNAs were selected for the subsequent analysis. The NSCLC patients were divided into two subgroups (cluster 1 and cluster 2) by clustering software, and immunocyte infiltration analysis, microenvironmental analysis, and clinical relevance analysis were performed between the two subgroups. A risk model was also built to further assess the prognosis value of prognostic-related lncRNAs in NSCLC patients. We found that AC099850.3 was significantly higher in both cluster 1 and high-risk subgroups, which may serve as a potential biomarker for the prognosis of NSCLC patients. Then, based on ceRNA competition mechanisms, the pathway enrichment of 105 acetylation-related lncRNAs was conducted by GO and KEGG analyses. We found the acetylation-related lncRNAs were primarily enriched in MAPK and EGFR signaling pathways, which were closely associated with NSCLC development. Finally, we validated the expression levels of AC099850.3 in NSCLC tissues and adjacent non-cancerous tissues and confirmed that AC099850.3 was significantly highly expressed in NSCLC tissues and cells. These results may provide clues for our understanding of the role of acetylation-related lncRNAs and valuable information for future clinical diagnosis and prognosis in NSCLC patients.

Background and Purpose: Platelet-to-neutrophil ratio (PNR) was suggested to be an independent protective predictor for 90-days outcomes in acute ischemic stroke (AIS) patients in previous studies. This study aims to investigate the association between PNR and outcomes of AIS in intravenous thrombolysis (IVT) group. Methods: Data on acute ischemic stroke patients who received intravenous thrombolysis from April 2015 to March 2019 were collected. We defined the PNR value at admission as pre-IVT PNR and after IVT within 24 h was defined as post-IVT PNR. Clinical outcome indicators included early neurological deterioration (END), hemorrhagic transformation (HT), delayed neurological deterioration (DND), and poor 3-month outcome (3m-mRS >2). Results: A total of 581 patients were enrolled in the final analysis. The age was 61(53-69) years, and 423(72.8%) were males. Post-IVT PNR was independently associated with hemorrhagic transformation (OR = 0.974; 95%CI = 0.956-0.992; P=0.006), early neurological deterioration (OR = 0.939; 95%CI = 0.913-0.966; P = 0.01), delayed neurological deterioration (OR = 0.949; 95%CI = 0.912- 0.988; P = 0.011), and poor outcome (OR = 0.962; 95%CI = 0.948-0.976; P2) between patients at high and low levels for platelet-to-neutrophil ratio (PNR) showed statistical differences (p Conclusions: Post-IVT PNR was independently associated with early neurological deterioration, hemorrhagic transformation, delayed neurological deterioration, and poor 3-month outcome. Lower PNR can predict a worse outcome.

The softmax function has been widely used in deep neural networks (DNNs), and studies on efficient hardware accelerators for DNN have also attracted tremendous attention. However, it is very challenging to design efficient hardware architectures for softmax because of the expensive exponentiation and division calculations in it. In this brief, the softmax function is firstly simplified by exploring algorithmic strength reductions. Afterwards, a hardware-friendly and precision-adjustable calculation method for softmax is proposed, which can meet different precision requirements in various deep learning (DL) tasks. Based on the above innovations, an efficient architecture for softmax is presented. By tuning the parameter $P$ , the system accuracy and complexity can be adjusted dynamically to achieve a good tradeoff between them. The proposed design is coded using hardware description language (HDL) and evaluated on two platforms, Xilinx Zynq-7000 ZC706 development board and TSMC 28-nm CMOS technology, respectively. Hardware implementation results show that our architecture significantly outperforms other works in speed and area, and that by adjusting $P$ , the accuracy can be further increased with little hardware overhead.

Weakly supervised object localization is a basic research in the field of computer vision. In this paper, a hierarchical saliency mapping network for object localization is proposed and designed to avoid missing detailed information of potential object. Based on the classical convolution network, we remove the fully connected part and add multiple information extraction branches. The network extracts information from convolution layers of different scales to generate Hierarchical Saliency Map. Hierarchical Saliency Maps that include Hierarchical-Class Activation Map and Hierarchical-Spatial Pyramid Saliency Map fuse deep-level features and low-level features to locate object. The datasets used for testing are Caltech-UCSD Birds 200, Caltech101 and ImageNet. Compared with Class Activation Map and Spatial Pyramid Saliency Map, the localization accuracy has been improved. This method can be used for fine-grained classification, object tracking and other fields.

Brain-inspired computing is considered to be an effective solution to the bottleneck of the von Neumann system, and artificial nerve synaptic devices are a key part of brain-like computing. Here, we propose an artificial nerve synapse device based on graphene and ion gel gated transistors, which uses graphene as the channel and ion gel as the gate electrolyte. The weight of the nerve synapse can be adjusted by the gate voltage, and the artificial nerve synaptic device can successfully realize the important synaptic functions of the nerve synapse such as excitatory postsynaptic membrane current, inhibitory postsynaptic membrane current, enhancement, inhibition and impulse alienation. These results indicate that graphene ion gel artificial nerve synapses have good application prospects in cranial nerve-like computing and flexible circuits.

To achieve higher accuracy in machine learning tasks, very deep convolutional neural networks (CNNs) are designed recently. However, the large memory access of deep CNNs will lead to high power consumption. A variety of hardware-friendly compression methods have been proposed to reduce the data transfer bandwidth by exploiting the sparsity of feature maps. Most of them focus on designing a specialized encoding format to increase the compression ratio. Differently, we observe and exploit the sparsity distinction between activations in earlier and later layers to improve the compression ratio. We propose a novel hardware-friendly transform-based method named 1D-Discrete Cosine Transform on Channel dimension with Masks (DCT-CM), which intelligently combines DCT, masks, and a coding format to compress activations. The proposed algorithm achieves an average compression ratio of 2.9x (53% higher than the state-of-the-art transform-based feature map compression works) during inference on ResNet-50 with an 8-bit quantization scheme., Accepted by IEEE International Symposium on Circuits and Systems(ISCAS) 2021

The docking principle based on flux pinning is detailed and flux pinned docking mechanism (FPDM) is designed. In order to perform the space docking experiment on earth readily and cheaply, the simulated docking experimental platform (SDEP) with a FPDM is first designed and established. By performing the single docking experiment on the SDEP, the dynamic docking time response of FPDM is directly extracted by using the high-speed camera and the real-time tracking software TEMA. The analytical result of docking time response can assess whether the collision between two spacecraft modules of FPDM happens or not. Furthermore, the docking frequency response can be extracted based on Fast Fourier transformation. Based on the docking frequency response and the half-power method, the dynamic connection characteristics (connection stiffness, damping ratio, and damping coefficient) of FPDM can be calculated and analyzed, which estimates its performance. Based on the single docking experiment, the multiple docking experiments can be carried out to validate the reusable feasibility and to acquire connection characteristics of every time docking. The experimental demonstration system is designed and established in order to support future space docking for CubeSat sized spacecraft based on flux pinning and to accumulate some useful experimental datum.

Early reports indicate that metformin, a clinical drug administered to treat type 2 diabetes mellitus (T2DM), was found to be associated with a better prognosis of cancer. The objective of this study was retrospectively analyzed the effect of metformin on the outcomes of Chinese breast cancer patients with T2DM. A total of 3757 primary invasive breast cancer patients who underwent surgery from January 2010 to December 2013 were enrolled. According to the medication treatment, all the patients were divided as non-diabetes group, metformin group and insulin group. The follow-up data for disease-free survival (DFS) and overall survival (OS) were obtained from 3553 patients (median follow up of 85 months) and estimated with the Kaplan–Meier method followed by a log-rank test. Multivariate Cox proportional hazards regression model was applied. The results showed that there was a significant survival difference among non-diabetes group, metformin group and insulin group, 5-year DFS was 85.8%, 96.1%, 73.0%, and 5-year OS was 87.3%, 97.1%, 73.3% respectively (P

The conversion of water energy into sustainable electricity has attracted more and more attention. Conventional water energy harvesting technologies are limited by manufacturing materials and output performance. The triboelectric nanogenerator (TENG) shows good performance in mechanical energy harvesting, while it has a relatively low short-circuit current and large inherent impedance. A novel high DC current output salt battery (SB) hybrid and enhanced by TENG (DC-SBHET) is proposed in this paper. The high open-circuit voltage generated by TENG can accelerate the velocity of mobile ions in the SB. The combination of TENG and SB can successfully increase the DC short- circuit current up to 18.1mA. Nano-foam electrodes were also chosen to further increase the battery performance.

Electronic skin (e-skin) is a kind of artificial skin, which has great application value in the fields of sensors, signal monitoring and human-machine interaction. This paper proposes a new type of self-powered flexible hybrid wearable e-skin system based on the combined working mechanism of Single-Electrode mode Triboelectric Nanogenerator (SE-TENG) and Piezoelectric Nanogenerator (PENG). The electrode and piezoelectric material are designed into a serpentine grid structure, which greatly improves the stretchability. The open circuit voltage (V OC ) is ~100V, the short circuit current (I SC ) is ~10µA, and the power density is as high as 1mW / cm2. The system can not only be used as a self-powered sensor to detect physical signals in real time, but also can achieve high-precision positioning functions, which has extremely broad application prospects in the field of artificial intelligence.

In the Internet of things, a large number of sensors are needed, triboelectric nanogenerators(TENG)has been proved to be the best solution for energy supply. However, the traditional friction nano-generator has large voltage, small current, large internal resistance and small output power. Therefore, we report a gel-based Solid Ion Channel Battery (SICB) that can greatly improve the output performance compared to traditional triboelectric nanogenerators. Based on the traditional TENG, using PTFE(Polytetrafluoroethylene) and Cu as the friction layer, the ion gel was affixed to the back of copper, and different sodium chloride concentrations were experimentally tested. Finally, it was found that the SICB can achieve 260V open circuit voltage and 0.09mA short circuit current.

Chronic spontaneous urticaria (CSU) is a common skin disease which symptom is local pruritus and pain. In medicine, researchers take a certain point that the brain is the control center of CSU, but in previous experiments, the researchers found that cerebellum also had a certain effect on CSU. In order to find out the influence of CSU in the brain and cerebellum, we collected the brain resting-state fMRI data from 40 healthy controls and 32 CSU patients and used DPABI to preprocess. We calculated the entropy values of five scales by using multiscale entropy (MSE) and the average entropy values of two groups’ BOLD signals; 15 regions with significant differences were found which not only had a more detailed impact in the brain but also had an impact in the cerebellum, such as precentral gyrus, lenticular putamen, and vermis of cerebellum. In addition, we found that compared with the healthy controls, the entropy values of CSU patients showed two trends which need further study. The advantage of our experiment is that the multiscale entropy value is used to get more influence regions of CSU in the brain and cerebellum. The results of this paper may provide some help for the pathological study of CSU.

In this paper, we propose the Napping Guard attack which can deanonymize hidden services in a stealthy way. The key insight of our method is utilizing a design flaw of hidden service's requests to build a simplex covert channel, which can send message from a malicious guard relay to the collusive Client-OP. With the help of this covert channel, the guard relay delivers the actual IP address of the hidden service to the collusive Client-OP. Considering the Client-OP knows the onion address of hidden service, the adversary is able to deanonymize the hidden service through correlating the actual IP address and onion address on Client-OP. In particular, compared with previous attacks, our covert channel utilizes latency signal instead of traffic signal, and eliminates the dependency of malicious Rend-Point, so as to achieve a better concealment and lower cost. Our experiment shows that the covert channel is reliable that has the precision and recall about 99.35% and 99.19%. In addition, we also propose a mitigation of Napping Guard attack, and report the design flaw to the Tor project.

Introduction: The role of Platelet-to-lymphocyte ratio (PLR) in outcomes of acute ischemic stroke, especially before and after intravenous thrombolysis with recombinant tissue plasminogen activator (rtPA), has not been elucidated. Thus, the aim of this study was to evaluate the effect of PLR before and after rtPA on clinical outcomes.Methods: A total of 582 consecutive patients who had acute ischemic stroke diagnosed and received intravenous thrombolysis with rtPA were included in this study. We collected demographics, vascular risk factors, previous history of drugs and other clinical information for all patients. Specifically, blood samples for PLR values were collected on admission and at 24 hours after stroke. Multivariate logistic regression analysis was used to assess the association between PLR with the risk of poor outcome (mRS≥3), death and hemorrhagic transformation (HT). Results: Of 582 patients, 191 (32.8%) had a poor outcome, 40 (6.9%) died and 82 (14.1%) had HT. After adjustment for potential confounders, multivariate logistic regression analysis showed that higher PLR at 24h after rtPA was independently associated with an increased risk of poor outcome (OR=1.004; 95% CI:1.001-1.007; P=0.009) and the occurrence of death (OR=1.009; 95% CI:1.004-1.013; P0.05).Conclusions: We found that PLR at 24h after rtPA can predict the risk of poor outcome and death in acute ischemic stroke patients, but PLR on admission cannot.

We built a named entity recognition system on Esports News. We established an ontology for Esports-related entities, collected and annotated corpus from 80 articles on four different Esports titles. We also trained a CRF and a BERT-based entity recognizer, built a basic DOTA2 knowledge base, and an entity linker that links mentions of entities to articles in Liquipedia (the Esports Wikipedia), and a naive web app which serves as a demo of this entire proof-of-concept system. We achieved an over 61% overall entity-level F1-score on the test set for the NER task.

Designing hardware accelerators for deep neural networks (DNNs) has been much desired. Nonetheless, most of these existing accelerators are built for either convolutional neural networks (CNNs) or recurrent neural networks (RNNs). Recently, the Transformer model is replacing the RNN in the natural language processing (NLP) area. However, because of intensive matrix computations and complicated data flow being involved, the hardware design for the Transformer model has never been reported. In this paper, we propose the first hardware accelerator for two key components, i.e., the multi-head attention (MHA) ResBlock and the position-wise feed-forward network (FFN) ResBlock, which are the two most complex layers in the Transformer. Firstly, an efficient method is introduced to partition the huge matrices in the Transformer, allowing the two ResBlocks to share most of the hardware resources. Secondly, the computation flow is well designed to ensure the high hardware utilization of the systolic array, which is the biggest module in our design. Thirdly, complicated nonlinear functions are highly optimized to further reduce the hardware complexity and also the latency of the entire system. Our design is coded using hardware description language (HDL) and evaluated on a Xilinx FPGA. Compared with the implementation on GPU with the same setting, the proposed design demonstrates a speed-up of 14.6x in the MHA ResBlock, and 3.4x in the FFN ResBlock, respectively. Therefore, this work lays a good foundation for building efficient hardware accelerators for multiple Transformer networks., 6 pages, 8 figures. This work has been accepted by IEEE SOCC (System-on-chip Conference) 2020, and peresnted by Siyuan Lu in SOCC2020. It also received the Best Paper Award in the Methdology Track in this conference

In a website fingerprinting attack, an eavesdropper analyses the traffic between the Tor user and entry node of the Tor network to infer which websites the user has visited. Some recent work apply deep learning algorithms, however, most of them do not fully exploit the packet timing information. In this work, we propose a novel website fingerprinting attack based on a two-channel Temporal Convolutional Networks model that extracts features from both the packet sequences and packet timing information. Our attack is proved to perform better compared to the state-of-the-art attacks. Experiment results also show that the timing information is very useful for classification. Furthermore, we collect our own traffic traces between client and entry node, and transform them into three extraction layers: TCP, TLS and Tor cell layer, and meanwhile record Tor’s cell log at the entry node. The experimental results show that the data of the cell layer is the most divisible among the three layers. Based on the experimental results, we conclude that the adversary at the entry node has an advantage over the one who just listens to traffic between client and entry node.

Training the neural networks on chip, which enables the local privacy data to be stored and processed at edge platforms, is earning vital importance with the explosive growth of Internet of Things (IoT). Although the on-chip training has been widely investigated in previous arts, there are few works related to the on-chip learning of Few-Shot Learning (FSL), an emerging topic which explores effective learning with only a small number of samples. In this paper, we explore the effectiveness of quantization, a mainstream compression method that helps reduce the memory footprint and computational resource requirements of a full-precision neural network to enable the on-chip deployment of FSL. We first perform extensive experiments on quantization of three mainstream meta-learning-based FSL networks, MAML, Meta-SGD, and Reptile, for both training and testing stages. Experimental results show that the 16-bit quantized training and testing models can be achieved with negligible losses on MAML and Meta-SGD. Then a comprehensive analysis is presented which demonstrates that a most favorable trade-off between accuracy, computational complexity, and model size can be achieved using the Meta-SGD model. This paves the way for the deployment of FSL system on the resource-constrained platforms.

Endovascular treatment (EVT) has been widely used for treating acute ischemic stroke (AIS). However, the safety and efficacy of treating AIS with tirofiban combined with EVT remain controversial. Therefore, we conducted a meta-analysis to evaluate this treatment. Randomized controlled trials and cohort studies that compared treatment with tirofiban combined with EVT and EVT alone were included in our meta-analysis. Those published from inception to March 31, 2020, were searched using the PubMed, Web of Science, Embase, and Cochrane Library databases. Safety was assessed based on symptomatic intracranial hemorrhage (sICH) incidence and 3-month mortality. Efficacy was assessed based on modified Rankin Scale (mRS) scores at 3 months post-EVT and recanalization rates. Data were analyzed using either the random-effects or fixed-effects model based on the heterogeneity of studies. In total, one RCT, six prospective studies, and four retrospective studies (2387 AIS cases) were assessed. Our meta-analysis showed that tirofiban combined with EVT did not increase sICH risk (RR, 1.06; 95%CI, 0.79 to 1.42; P = 0.72) and 3-month mortality (RR, 0.87; 95%CI, 0.74 to 1.04; P = 0.12). Recanalization rates were not significantly different between patients treated with tirofiban combined with EVT and those treated with EVT alone (RR, 1.04; 95%CI, 1.00 to 1.08; P = 0.07), but tirofiban combined with EVT was significantly associated with favorable functional outcomes (mRS score, 0-2) in AIS patients (RR, 1.13; 95%CI, 1.02 to 1.25; P = 0.02). Tirofiban combined with EVT appears to be safe and potentially effective in treating AIS.

This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images (restoration of rich details in hazy image). We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor. NH-Haze is the first realistic nonhomogeneous haze dataset that provides ground truth images. The nonhomogeneous haze has been produced using a professional haze generator that imitates the real conditions of haze scenes. 168 participants registered in the challenge and 27 teams competed in the final testing phase. The proposed solutions gauge the state-of-the-art in image dehazing., CVPR Workshops Proceedings 2020

Collaborative filtering is one of the most successful and extensive methods used by recommender systems for predicting the preferences of users. However, traditional collaborative filtering only uses rating information to model the user, the data sparsity problem and the cold start problem will severely reduce the recommendation performance. To overcome these problems, we propose two neural network models to improve recommendations. The first one called TDAE uses a denoising autoencoder to integrate the ratings and the explicit trust relationships between users in the social networks in order to model the preferences of users more accurately. However, the explicit trust information is very sparse, which limits the performance of this model. Therefore, we propose a second method called TDAE++ for extracting the implicit trust relationships between users with similarity measures, where we employ both the explicit and implicit trust information together to improve the quality of recommendations. Finally, we inject the trust information into both the input and the hidden layer in order to fuse these two types of different information to learn more reliable semantic representations of users. Comprehensive experiments based on three popular data sets verify that our proposed models perform better than other state-of-the-art approaches in common recommendation tasks.

With the increasing popularity of platform economics, supply chain members are facing strategic decisions regarding whether to introduce online marketplace platforms in addition to their existing brick-and-mortar sales channels. Our study investigates this problem by quantitatively modelling and evaluating four different channel structures defined by whether the manufacturer, the retailer, neither or both choose to adopt the platform to enhance their sales. It is shown that the introduction of a marketplace platform is beneficial for supply chain members only if the degree of competition among different channels is low. Moreover, modes MR, MN and NN are shown as possible equilibrium channel structures depending on the degree of channel competition and the platform fee rate, and it is found that the Nash and Stackelberg equilibriums coincide, indicating that there is no first-mover advantage in platform introduction. Some extended cases are explored by incorporating the endogenous platform fee rate, platform spillover effect, and asymmetric market base, and the robustness of the results is analysed.

The construction of direct Z-scheme heterojunctions with high photocatalytic degradation ability is a theme of importance in both environmental and materials sciences, but still retains many unresolved challenges. In this article, we report the construction of Z-scheme CuInS2/Bi2MoO6 heterostructure by in-situ hydrothermal reactions, demonstrating superior photocatalytic activity towards the degradation of tetracycline under visible light, compared to their individual components: that is to say 8 and 2.5 times those of CuInS2 and Bi2MoO6, respectively. The photocatalytic performance of CuInS2/Bi2MoO6 heterostructure is mainly ascribed to the effective charge transfer at the interface through the construction of a direct Z-scheme heterojunction, combined with a ternary sulfide semiconductor absorbing light in the useful region of the solar spectrum. This photocatalyst provides new insights on the fundamental aspects governing the mechanisms responsible for multicomponent photodegradation, while constituting already a promising candidate for practical environmental applications.

Morphology of electron transport layers (ETLs) has an important influence on the device architecture and electronic processes of mesostructured solar cells. In this work, we thoroughly investigated the effect of the interspace of TiO2 nanorod (NR) arrays on the photovoltaic performance of the perovskite solar cells (PSCs). Along with the interspace in TiO2-NR arrays increasing, the thickness as well as the crystal size of perovskite capping layer are reduced accordingly, and the filling of perovskite in the channel becomes incomplete. Electrochemical impedance spectroscopy measurements reveal that this variation of perovskite absorber layer, induced by interspace of TiO2 NR arrays, causes the change of charge recombination process at the TiO2/perovskite interface, suggesting that a balance between capping layer and the perovskite filling is critical to obtain high charge collection efficiency of PSCs. A power conversion efficiency of 10.3% could be achieved through careful optimization of interspace in TiO2-NR arrays. Our research will shed light on the morphology control of ETLs with 1D structure for heterojunction solar cells fabricated by solution-deposited method.

Three level neutral-point-clamped (3L-NPC) converter is a multilevel topology that is well-suited solution in high voltage, high frequency applications such as flexible AC transmission systems and electric vehicle drive systems. This paper is focused on a silicon carbon (SiC) based 3L-NPC for high frequency application which presents a higher switching loss and aging mismatch. In this paper, a new improved approach of the discontinuous pulse width modulation (DPWM) of 3L-NPC based on carrier disposition is presented. Based on the relationship between switching loss and power factor angle, an optimized DPWM method with minimum switching loss is proposed. The simulation and experimental results show how the optimized carrier disposition based discontinuous pulse-width modulation (OCD-DPWM) is achieved whereas the switching loss is reduced and the system efficiency is improved.

Bacteremia caused by Eggerthia catenaformis is a rarely reported pathogen in cancer patients. Herein, we report a case of bacteremia caused by E.catenaformis in a patient with gastric cancer in China. The patient was a 55-year-old man, diagnosed with gastric cancer more than one month ago, with intermittent fever at a maximum of 39.5 °C for more than half a month. He received symptomatic and supportive treatment after admission to our hospital; a rare anaerobic microorganism, E. catenaformis was isolated from an anaerobic blood culture sample taken from the patient. The antimicrobial susceptibility testing was performed after the isolates were successfully identified by MALDI-TOF MS and 16S rRNA sequencing. The patient was then successfully treated for the bacteremia with metronidazole. To the best of our knowledge, this is the first report of E. catenaformis bacteremia in a patient with cancer.

This systematic review and meta-analysis aimed to evaluate the correlations between the bone turnover markers (BTMs) and the bone mineral density (BMD) in patients treated for primary osteoporosis and to identify promising BTMs for the prediction of future BMD changes. The PubMed, EMBASE, Cochrane Library, and Web of Science databases were searched for relevant studies that investigated the relationship between the BTMs and the BMD changes in patients treated for osteoporosis. All significant correlation coefficients of the baseline BTMs or changes in BTMs from baseline with the BMD changes from baseline under different interventions from eligible studies were used for systematic review and the subgroup analyses. The correlations were analyzed in terms of bone sites, intervention, time duration of BTMs measurements, and time duration of BMD measurements. Twenty-two records reporting correlation coefficients and the corresponding p-values were included, 13 of which were enrolled in the further subgroup analyses. The combined results from the systematic review and meta-analyses indicated that the changes in osteocalcin (OC), procollagen type I N propeptide (PINP), and urine N-terminal crosslinking telopeptide of type I collagen (U-NTX), or the PINP at baseline tended to be useful in evaluating the long-term BMD changes after drug intervention.

With the development of more electric aircraft (MEA), the number of power electronic equipment and variable frequency electrical loads are increasing rapidly, which leads to challenges in terms of power quality of aircraft power system. In this paper, a harmonic current generator (HCG) for MEA power system is designed, which can realize high frequency harmonic current injection (up to 40 times of fundamental frequency, 32kHz for 800Hz power system). For the equivalent switching frequency is close to 1MHz, a GaN HFETs based cascaded Hbridge topology is selected and a frequency adaptive multiharmonic proportional resonance controller is introduced to achieve accurate injection of harmonic currents. To verify the analysis and injection performance of the proposed HCG, the simulation model based on PLECS is developed and tested under various operating conditions. To verify the performance of the proposed HCG, a 10kW experimental prototype is built and tested in the laboratory.

Recently, significant improvement has been achieved for hardware architecture design of deep neural networks (DNNs). However, the hardware implementation of one widely used softmax function in DNNs has not been much investigated, which involves expensive division and exponentiation units. This paper performs an efficient hardware implementation of softmax function. Mathematical transformations and linear fitting are used to simplify this function. Multiple algorithmic strength reduction strategies and fast addition methods are employed to optimize the architecture. By using these techniques, complicated logic units like multipliers are eliminated and the memory consumption is largely reduced while the accuracy loss is negligible. The proposed design is coded using hardware description language (HDL) and synthesized under the TSMC 28-nm CMOS technology. Synthesis results show that the architecture achieves a throughput of 6.976 G/s for 8-bit input data. The power efficiency of 463.04 Gb/(mm2• mW) is achieved and it costs only 0.015mm2 area resources. To the best of our knowledge, this is the first work on efficient hardware implementation for softmax in open literature.

Background Drug resistance of paclitaxel (TAX), the first-line chemotherapy drug for breast cancer, was reported to develop in 90% of patients with breast cancer, especially metastatic breast cancer. Investigating the mechanism of TAX resistance of breast cancer cells and developing the strategy improving its therapeutic efficiency are crucial to breast cancer cure. Methods and Results We here report an elegant nanoparticle (NP)-based technique that realizes efficient breast cancer treatment of TAX. Using lentiviral vector-mediated gene knockdown, we first demonstrated that TAX therapeutic efficiency was closely correlated with metadherin (MTDH) gene expression in breast cancer cell lines. This finding was also supported by efficacy of TAX treatment in breast cancer patients from our clinical studies. Specifically, TAX treatment became more effective when MTDH expression was decreased in MCF-7 cancer cells by the blocking nuclear factor-kappa B (NF-κB) pathway. Based on these findings, we subsequently synthesized a polymeric NP that could co-deliver MTDH-small interfering RNA (MTDH–siRNA) and TAX into the breast cancer tumors in tumor-bearing mice. The NPs were composed of a cationic copolymer, which wrapped TAX in the inside and adsorbed the negatively charged siRNA on their surface with high drug-loading efficiency and good stability. Conclusions NP-based co-delivery approach can effectively knock down the MTDH gene both in vitro and in vivo, which dramatically inhibits breast tumor growth, achieving effective TAX chemotherapy treatment without overt side effects. This study provides a potential therapeutic strategy for the treatment of a wide range of solid tumors highly expressing MTDH.

Background: The aim of this study was to investigate the association between body mass index (BMI) and prognosis of Chinese women with breast cancer. Patients and Methods: 3,380 primary breast cancer patients who underwent surgery from 2010 to 2012 were selected and classified as low BMI group (BMI Results: The high BMI group showed more aggressive pathological features. BMI was negatively associated with OS (hazard ratio (HR) 1.33, 95% confidence interval (CI) 1.06–1.66; p = 0.012) but not DFS (HR 1.15, 95% CI 0.94–1.40; p = 0.17). Furthermore, when stratified by age, BMI was significantly and negatively associated with OS (HR 1.43, 95% CI 1.05–1.95; p = 0.025) in patients above 50 years of age, but this effect was not detected in younger patients. Conclusion: BMI was an independent prognostic factor of OS in Chinese women with breast cancer, and age might be a mitigating factor. Among patients above 50 years of age, those with a high BMI were at greater risk of poor prognosis compared to individuals with a low BMI.

Based on the sample of listed family enterprises from 2008 to 2010 in China, this paper studies the familization degree of family enterprises and the optimal path to reduce the degree of familization of different types of family enterprises. From the perspective of ownership and management control, this paper finds that the way to introduce non-family member CEO has a significant improvement on the value of enterprises. Transfer of enterprises is not conducive to the improvement of enterprise value. Further research has found that the optimal path to reduce the degree of familization of directly listed family enterprises is the introduction of non-family member professional managers. Indirect listed family enterprises do not have the optimal path of reducing familization.

This paper concerns with the application of memristive bidirectional associative memory neural networks (MBAMNNs) for image encryption. Firstly, we propose a new model of MBAMNNs with leakage delays and probabilistic time-varying delays. Furthermore, we design a color image encryption algorithm based on this model. The algorithm shows high efficiency, and it has large key space. It can also resist strong atttacks including differential attack, exhaustive key search, etc. Finally, the effectiveness of the proposed algorithm is validated with numerical experiments.

With the rapid development of information technology, office automation is continuously improving. The data leakage problem resulting from insider threats is getting worse, legitimate users may abuse privileges or masquerade as other users, which may result in the loss of data. In this paper, a new data-centric approach is proposed to detect insider threat, which based on characterizing user behavior by extracting the features of user interaction behavior including keystroke dynamics and consecutive queries to model users' access patterns. Statistical learning algorithms are trained and tested from opening dataset to predict abnormal behavior patterns; experimental results indicate that the approach is very effective and accurate.

The high anonymity of Darknet makes it attractive to users who want to avoid Internet censorship and surveillance. As a result, in recent years, Darknet is abused for various illegal purposes. Undoubtedly, measurement and analysis towards the attributes of people in the Darknet can obtain a comprehensive characterization of dangerous users and help trace malicious users, reducing cybercrimes. However, it is still challenging to extract person attributes in Darknet scenario due to its anonymity and content sparsity. Therefore, in this paper, we propose a new person attribute extraction method consisting of three steps: block filtration, attribute candidate generation and attribute candidate verification. Experiments show that our extraction method performs better than traditional extraction methods. Using the extracted information as input, we measure and analyze the number of attributes, Top-K name entities, email domain name, etc. of people in Darknet, revealing the characteristics of the person attributes in the dark web pages.

The electron transport layer (ETL) plays an important role in planar heterojunction perovskite solar cell (PSCs), by affecting the light-harvesting, electron injection and transportation processes, and especially the crystallization of perovskite absorber. In this work, we utilized a commercial TKD-TiO2 nanoparticle with a small diameter of 6 nm for the first time to prepare a compact ETL by spin coating. The packing of small-size particles endowed TKD-TiO2 ETL an appropriate surface-wettability, which is beneficial to the crystallization of perovskite deposited via solution-processed method. The uniform and high-transmittance TKD-TiO2 films were successfully incorporated into PSCs as ETLs. Further careful optimization of ETL thickness gave birth to a highest power conversion efficiency of 11.0%, which was much higher than that of PSC using an ETL with the same thickness made by spray pyrolysis. This TKD-TiO2 provided a universal solar material suitable for the further large-scale production of PSCs. The excellent morphology and the convenient preparation method of TKD-TiO2 film gave it an extensive application in photovoltaic devices.

Prototyping and proper testing of the electrical control unit of more electric aircraft (MEA) under various load profiles and fault conditions is necessary to ensure the performance of the controller. In order to solve the problems such as complicated operation, high experiment cost and long development period in aircraft power system traditional test method, this paper presents a semi-physical real-time test platform for aircraft power system testing and presents results and design principles of a three-stage brushless excitation power system emulation using HIL simulation.

The structural and electronic properties of silicon carbide nanoribbons (SiC NRs) with edges passivated by hydrogen and halogens are calculated based on density functional theory. It is found that the band gap (Eg) values of armchair SiC NRs decrease as the atomic number of hydrogen and halogen elements increases. However the effect of edge functionalization on the Eg of zigzag SiC NRs is very small. This is because the energy levels of the conduction band minimum of armchair SiC NRs shift to lower energy by introducing hydrogen and halogen atoms, while the energies of the valence band maximum are barely affected. Our results provide a theoretical guideline to adjust the electronic properties of SiC NRs.

Competition is the essence of market economy, and how commercial Banks reform and develop and occupy a dominant position in competition is a common concern in the fields of finance and information science. By sorting out relevant results, it can provide reference for the development and research of commercial Banks. Starting from financial intelligence, this paper discusses the development evolution of commercial Banks from the perspective of competitive intelligence. Based on SCP analysis of the development dilemma of commercial Banks, this paper proposes the development path of competitive intelligence system of commercial Banks. Putting forward new ideas for the development of commercial Banks, combined with the needs of the industry, in-depth competition intelligence collection and processing work for the innovation and development of commercial Banks is of great significance.

The activity of CVT-E002, an aqueous extract containing mainly oligosaccharides and polysaccharides from North American ginseng (Panax quinquefolium), as an immunobooster on murine spleen cells and peritoneal macrophages, was studied in-vitro. CVT-E002 stimulated the proliferation of normal mouse spleen cells, of which the major responding subpopulation was identified as B lymphocytes. CVT-E002 also activated peritoneal exudate macrophages leading to enhanced interleukin-1 (IL-1), interleukin-6 (IL-6), tumour necrosis factor-α (TNF-α) and nitric oxide (NO) production. In addition, CVT-E002 stimulated in-vivo immunoglobulin G (IgG) production in treated mice. These results identify some of the immunomodulating activities of CVT-E002 and suggest its use clinically for the modulation of immune responses.

A patented aqueous extract from North American ginseng ( Panax quinquefolium ), containing mainly oligosaccharides and polysaccharides, is commercially available over the counter as COLD-FX (CVT-E002). This proprietary extract is used for the treatment of upper respiratory tract infections. Its in vitro stimulating effects on the immunoglobulin production by B lymphocytes and on natural immune responses by peritoneal exudates macrophages have been previously reported. Using C57 BL/6 mice, an ex vivo study was conducted to examine Con-A-induced splenocytic productions of interleukin-2 (IL-2) and interferon-gamma (IFN-γ) as markers of acquired immune responses. CVT-E002 (10–500 μg/ml) significantly increased Con-A-induced IL-2 and IFN-γ productions in spleen cells in a dose-dependent manner. Such response was seen by the ginseng extract originated from three different lots, suggesting consistency between the lots.