Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximum Likelihood Estimation of a Low-Rank Probability Mass Tensor From Partial Observations

We consider the problem of estimating the Probability Mass Function (PMF) of a discrete random vector (RV) from partial observations, namely when some elements in each observed realization may be missing. Since the PMF takes the form of a multi-way tensor, under certain model assumptions the problem becomes closely associated with tensor factorization. Indeed, in recent studies it was shown that a low-rank PMF tensor can be fully recovered (under some mild conditions) by applying a low-rank (approximate) joint factorization to all estimated joint PMFs of subsets of fixed cardinality larger than two (e.g., triplets). The joint factorization is based on a Least Squares (LS) fit to the estimated lower-order sub-tensors. In this letter we take a different estimation approach by fitting the partial factorization directly to the observed partial data in the sense of Kullback-Leibler divergence (KLD). Consequently, we avoid the need for particular selection and direct estimation of sub-tensors of a particular order, as we inherently apply proper weighting to all the available partial data. We show that our approach essentially attains the Maximum Likelihood estimate of the full PMF tensor (under the low-rank model) and therefore enjoys its well-known properties of consistency and asymptotic efficiency. In addition, based on the Bayesian model interpretation of the low-rank model, we propose an Estimation-Maximization (EM) based approach, which is computationally cheap per iteration. Simulation results demonstrate the advantages of our proposed KLD-based hybrid approach (combining alternating-directions minimization with EM) over LS fitting of sub-tensors.
Recognition of Composite Motions based on sEMG via Deep Learning

Surface electromyography(sEMG) is a reliable physiological electrical signal, which represents real-time human motion intents. And the EMG-based motion recognition has the characteristics of convenient operation, non-invasion and noninterference, which has broad application prospects. This paper focuses on composite motion(including multiple actions) recognition, such as sign language motions and handwritten motions. We proposed a novel method for composite motion recognition by using deep learning. To begin with, we defined a novel data structure called sEMG image and established convolution Neural Network designed for sEMG images. In order to reduce the demand of training data, we proposed to pre-train the network by MNIST data set based on the thought of transfer learning. To verify the methods that we proposed, we acquired and preprocessed the surface EMG signals of composite motions, including handwritten number motions and sign language motions. From the results, it can be concluded that deep learning methods perform better than traditional methods, including support vector machine(SVM) and Dynamic Time Warping(DTW). Especially in different sizes handwritten number recognition experiments, the deep learning methods is still very excellent, while accuracies of traditional methods are greatly reduced. In addition, we discovered that transfer learning can help ConvNet to quickly converge and reduce the demand for data.
Signed Network Community Mining Based on Fine-grained Signed Stochastic Block Model

The reasoning of the stochastic block model mainly includes model learning and model selection, which respectively determine the block number K and the estimated parameters. The fine-grained stochastic block model (fg-SBM) can reduce the time complexity of parameter estimation, but it cannot be directly applied to signed networks. To this end, an improved model called fine-grained signed stochastic block model (fgs-SBM) is presented for signed network community mining. At the same time, in order to further reduce the time complexity of the signed network community mining algorithm, we choose to apply the block-wise SBM learning(BLOS) algorithm to integrate minimum message length (MML )and expectation maximization (EM) algorithm to realize parallel learning of parameter estimation and model selection. By comparing with the most advanced methods in synthetic networks and real networks, the advantages of this method in community mining of exploratory networks are illustrated.
Evolutionary Compression of Deep Neural Networks for Biomedical Image Segmentation

Biomedical image segmentation is lately dominated by deep neural networks (DNNs) due to their surpassing expert-level performance. However, the existing DNN models for biomedical image segmentation are generally highly parameterized, which severely impede their deployment on real-time platforms and portable devices. To tackle this difficulty, we propose an evolutionary compression method (ECDNN) to automatically discover efficient DNN architectures for biomedical image segmentation. Different from the existing studies, ECDNN can optimize network loss and number of parameters simultaneously during the evolution, and search for a set of Pareto-optimal solutions in a single run, which is useful for quantifying the tradeoff in satisfying different objectives, and flexible for compressing DNN when preference information is uncertain. In particular, a set of novel genetic operators is proposed for automatically identifying less important filters over the whole network. Moreover, a pruning operator is designed for eliminating convolutional filters from layers involved in feature map concatenation, which is commonly adopted in DNN architectures for capturing multi-level features from biomedical images. Experiments carried out on compressing DNN for retinal vessel and neuronal membrane segmentation tasks show that ECDNN can not only improve the performance without any retraining but also discover efficient network architectures that well maintain the performance. The superiority of the proposed method is further validated by comparison with the state-of-the-art methods.
Allografts in orthopaedic practice

Biology of musculoskeletal allografts immunology, bone and tissue banking indications and uses of allograft bone in general orthopaedics the use of fresh osteochondral allografts to replace traumatic joint defects allograft reconstruction after tumour surgery in the pelvis revision arthroplasty using allograft bone allograft reconstruction in total knee arthroplasty soft tissue allograft reconstruction - the knee.
Corticosteroid-Induced Psychosis After a Single Transforaminal Epidural Steroid Injection.

INTRODUCTION Neuropsychiatric symptoms are a well-described side effect of systemic corticosteroid therapy and can range from mild to severe. CASE PRESENTATION We describe a case of substance-induced psychosis following epidural injection of 10 mg dexamethasone. Three days after the procedure, the patient developed symptoms including anger, hostility, insomnia, paranoia, and delusions. Symptoms resolved between 7 and 17 days. DISCUSSION In the past 50 years, there have been several case reports of severe neuropsychiatric effects following intraarticular or other interventional pain injections with various corticosteroids. More recent reviews have identified possible risk factors, including corticosteroid dose, patient age, sex, and history of neuropsychiatric disorder, among others, although these conclusions are not duplicated across all studies. CONCLUSION Recommendations for practice include patient and family education on possible adverse effects of corticosteroid administration, utilization of minimum effective doses for interventional procedures, and the consideration of close follow-up and multidisciplinary coordination, especially in high-risk patients.
Outage Analysis of NOMA Exploited Coordinated Direct and Relay assisted Uplink Transmission

An uplink Non-orthogonal multiple access exploiting coordinated direct and relay aided communication protocol named as UNOMA is proposed, where a user having better channel experience directly communicates with the base station and a user having comparatively bad channel experience communicates by the assistance of a half-duplex decode-and-forward relay. Under the considerations of perfect and imperfect successive interference cancellations (SICs), the outage probability (OP) of UNOMA is analysed over Rayleigh fading channels. The effect of different physical parameters on the OP is investigated. To justify the analytical results, Monte-Carlo simulations are provided that verifies the correctness under both imperfect and perfect SICs. Finally, the OP of the UNOMA is compared with the traditional uplink orthogonal multiple access (UOMA) scheme that demonstrates the advantage of UNOMA over UOMA conditioned on the system parameters.
Analysis and Comparison of Three Typical Resonant DC-DC Converters

HF transformer isolated resonant converters are well-suited for high-power DC-DC systems. Investigation of three typical series and parallel resonant (SPR) topologies are presented. Expressions of each circuits are derived to exhibit their natural voltage and current characteristics based on the approximate analysis approach. Design criteria incorporated with the soft switching and gain requirements are developed for key parameters for each topology. Soft simulation results for three converters are presented in this paper. Simulation result shows that the CLLC-type leads to the highest efficiency for full-load range, but the LCL-type shows better economic performance. It's recommended that the LCL-type could be a good alternative for the CLLC-type in some special applications.
Roles of Extracellular HSPs as Biomarkers in Immune Surveillance and Immune Evasion

Extracellular heat shock proteins (ex-HSPs) have been found in exosomes, oncosomes, membrane surfaces, as well as free HSP in cancer and various pathological conditions, also known as alarmins. Such ex-HSPs include HSP90 (α, β, Gp96, Trap1), HSP70, and large and small HSPs. Production of HSPs is coordinately induced by heat shock factor 1 (HSF1) and hypoxia-inducible factor 1 (HIF-1), while matrix metalloproteinase 3 (MMP-3) and heterochromatin protein 1 are novel inducers of HSPs. Oncosomes released by tumor cells are a major aspect of the resistance-associated secretory phenotype (RASP) by which immune evasion can be established. The concepts of RASP are: (i) releases of ex-HSP and HSP-rich oncosomes are essential in RASP, by which molecular co-transfer of HSPs with oncogenic factors to recipient cells can promote cancer progression and resistance against stresses such as hypoxia, radiation, drugs, and immune systems; (ii) RASP of tumor cells can eject anticancer drugs, targeted therapeutics, and immune checkpoint inhibitors with oncosomes; (iii) cytotoxic lipids can be also released from tumor cells as RASP. ex-HSP and membrane-surface HSP (mHSP) play immunostimulatory roles recognized by CD91+ scavenger receptor expressed by endothelial cells-1 (SREC-1)+ Toll-like receptors (TLRs)+ antigen-presenting cells, leading to antigen cross-presentation and T cell cross-priming, as well as by CD94+ natural killer cells, leading to tumor cytolysis. On the other hand, ex-HSP/CD91 signaling in cancer cells promotes cancer progression. HSPs in body fluids are potential biomarkers detectable by liquid biopsies in cancers and tissue-damaged diseases. HSP-based vaccines, inhibitors, and RNAi therapeutics are also reviewed.
Sexual risk behaviors in the internet age: the case of Chinese men who have sex with men

ABSTRACT This cross-sectional study examined a sample of sexually active Chinese MSM recruited online (N = 403) to determine the associations of MSM social life (both online and in-person) and sexual identity stigma with three types of sexual risk behaviors in the past 12 months, including condom use %, number of anal intercourse partners, and number of partners engaged in condomless anal intercourse (CAI). Hierarchical regression analyses were conducted. More frequent use of social media was associated with higher numbers of anal intercourse partners as well as more condom use in past 12 months, but not number of CAI partners. More active in-person MSM social life was associated with higher numbers of partners in anal intercourse as well as CAI. Both perceived and enacted sexual identity stigma associated with higher numbers of CAI partners; perceived stigma was also linked to less condom use. In conclusion, social life and minority stress are relevant factors of sexual risk among MSM in China in the uptrend of internet use. The internet may be an important and promising platform for HIV prevention, and intervention efforts should consider online-based designs to promote safe sex and reduce sexual minority stigma.
Adaptive Neural Network Control and Optimal Path Planning of UAV Surveillance System With Energy Consumption Prediction

A surveillance system is one of the most interesting research topics for an unmanned aerial vehicle (UAV). However, the problem of planning an energy-efficient path for the surveillance purpose while anticipating disturbances and predicting energy consumptions during the path tracking is still a challenging problem in recent years. The optimal path planning and the disturbance rejection control for a UAV surveillance system are investigated in this paper. A trained and tested energy consumption regression model is used to be the cost function of an optimal path planning scheme, which is designed from a clustered 3D real pilot flight pattern with the proposed K-agglomerative clustering method, and is processed via A-star and set-based particle-swarm-optimization (S-PSO) algorithm with adaptive weights. Moreover, an online adaptive neural network (ANN) controller with varied learning rates is designed to ensure the control stability while having a reliably fast disturbance rejection response. The effectiveness of the proposed framework is verified by numerical simulations and experimental results. By applying the proposed optimal path planning scheme, the energy consumption of the optimal path is only 72.3397 Wh while the average consumed energy of real pilot flight data is 96.593Wh. In addition, the proposed ANN control improves average root-mean-square error (RMSE) of horizontal and vertical tracking performance by 49.083% and 37.50% in comparison with a proportional-integral-differential (PID) control and a fuzzy control under the occurrence of external disturbances. According to all of the results, the combination of the proposed optimal path planning scheme and ANN controller can achieve an energy-efficient UAV surveillance systems with fast disturbance rejection response.
The genetic structure of Aedes aegypti populations is driven by boat traffic in the Peruvian Amazon

In the Americas, as in much of the rest of the world, the dengue virus vector Aedes aegypti is found in close association with human habitations, often leading to high population densities of mosquitoes in urban settings. In the Peruvian Amazon, this vector has been expanding to rural communities over the last 10–15 years, but to date, the population genetic structure of Ae. aegypti in this region has not been characterized. To investigate the relationship between Ae. aegypti gene flow and human transportation networks, we characterized mosquito population structure using a panel of 8 microsatellite markers and linked results to various potential mechanisms for long-distance dispersal. Adult and immature Ae. aegypti (>20 individuals per site) were collected from Iquitos city and from six neighboring riverine communities, i.e., Nauta, Indiana, Mazan, Barrio Florida, Tamshiaco, and Aucayo. FST statistics indicate significant, but low to moderate differentiation for the majority of study site pairs. Population structure of Ae. aegypti is not correlated with the geographic distance between towns, suggesting that human transportation networks provide a reasonable explanation for the high levels of population mixing. Our results indicate that Ae. aegypti gene flow among sub-populations is greatest between locations with heavy boat traffic, such as Iquitos-Tamshiaco and Iquitos-Indiana-Mazan, and lowest between locations with little or no boat/road traffic between them such as Barrio Florida-Iquitos. Bayesian clustering analysis showed ancestral admixture among three genetic clusters; no single cluster was exclusive to any site. Our results are consistent with the hypothesis that human transportation networks, particularly riverways, are responsible for the geographic spread of Ae. aegypti in the Peruvian Amazon. Our findings are applicable to other regions of the world characterized by networks of urban islands connected by fluvial transport routes.
Association of RAGE rs1800625 Polymorphism and Cancer Risk: A Meta-Analysis of 18 Case-Control Studies

Background Accumulating evidence suggests that the rs1800625 polymorphism in RAGE promoter region might be associated with cancer risk; however, data from different studies show conflicting results. Here, a meta-analysis was conducted to evaluate the associations between RAGE rs1800625 polymorphism and cancer risk. Material/Methods We searched Embase (Excerpt Medica Database), PubMed, and CNKI (Chinese National Knowledge Infrastructure) databases until March 15, 2019 to identify potential studies for the meta-analysis. Results Eighteen eligible studies were included in the current meta-analysis, representing 6246 cases and 6819 controls. Pooled analysis showed positive correlation between the RAGE rs1800625 polymorphism and susceptibility of cancer in recessive genetic model [CC versus TC+TT: odds ratio (OR)=1.397, 95% confidence interval (CI): 1.031–1.894, P=0.031]. Subgroup analysis revealed this association in the Asian, but not Caucasian population, and this correlation was not detected in either breast or lung cancer. Sensitivity analysis indicated unstable results, which should be interpreted with caution. No publication bias was observed. Conclusions In conclusion, the RAGE rs1800625 polymorphism was associated with increased overall cancer risk in Asians in recessive genetic model. However, large-scale and well-designed studies in different populations and diverse cancer types are needed for a precise conclusion.
Software–Defined End–to–End Evaluation Platform for Quality of Service in Non–Standalone 5G Systems

The fifth generation of mobile communication (5G) will support a much broader spectrum of quality of service (QoS) profiles than its predecessor long–term evolution (LTE). These improvements are enabled by new features such as software–defined end–to–end network slicing, which concern both the core network and the air interface. However, the current vision for the actual rollout is a smooth transition from LTE: First, the new radio (NR) base stations are integrated into the existing LTE network infrastructure in a configuration called non–standalone mode. At a later stage, the core network will be replaced by a complete and standalone 5G network. The long transition period using the previous generation’s core will pose challenges in delivering some of the promised advantages of 5G networks. This work addresses these shortcomings of non–standalone operation. We propose a development and evaluation platform capable of integrating QoS and investigate ways of enabling 5G performance at a much earlier stage of the generation transition. In the experimental setup, an LTE air interface is augmented by a software–defined radio (SDR) based NR link at 28 GHz, while end–to–end network slicing functionalities are implemented via software–defined networking at the core as well as the radio access network. First evaluations prove that the proposed system is capable of reliably ensuring QoS.
miR‐200c overexpression inhibits the invasion and tumorigenicity of epithelial ovarian cancer cells by suppressing lncRNA HOTAIR in mice

Epithelial ovarian cancer (EOC) is a common ovarian cancer in gynecological cancers today. It has been found that microRNAs and long‐chain noncoding RNA (lncRNA) regulate the gene transcriptional expression in cells. However, it is not well understood that the upstream and downstream regulatory molecules of lncRNA HOX antisense intergenic RNA (HOTAIR). The effects of miR‐200c overexpression on the invasion and nude mouse tumorigenicity, as well as lncRNA HOTAIR and snail expression of EOC SKOV3 cells, should be further explored. The expression of miR‐200c and lncRNA HOTAIR was detected by reverse transcription PCR (RT‐PCR) in EOC SKOV3 cells. The whole miR‐200c gene fragment was cloned into a lentiviral plasmid vector. The miR‐200c expression in transducted SKOV3 cells with reconstructed miR‐200c lentivirus was significantly higher than the negative control (P < .01). The lentivirus‐miR‐200c‐SKOV3 cells show that the invasion ability was significantly decreased compared with the negative control (P < .01). The nude mouse tumorigenicity was significantly decreased compared with that of the control group (P < .01). The snail protein expression in lentivirus‐miR‐200c‐SKOV3 xenograft tumor was significantly decreased compared with the negative control lentivirus‐SKOV3 group (P < .05). The miR‐200c overexpression significantly decreased the expressions of lncRNA HOTAIR and snail, but increased E‐cadherin expression in the lentivirus‐miR‐200c transducted SKOV3 cells of xenograft tumor, compared with the negative control (P < .05). The miR‐200c overexpression in SKOV3 cells with transducted lentivirus‐miR‐200c can inhibit lncRNA HOTAIR expression, decrease snail, increase E‐cadherin and significantly reduce the invasion and tumorigenicity of EOC SKOV3 cells. These results suggest that the miR‐200c and lncRNA HOTAIR could be effective therapeutic targets for human epithelial ovarian cancer treatment.
Construction of two-input logic gates using Transcriptional Interference

Transcriptional Interference (TI) has been shown to regulate gene expression at the DNA level via different molecular mechanisms. The obstacles present on the DNA that a transcribing RNA Polymerase might encounter, e.g. a DNA-bound protein or another RNA Polymerase, can result in TI causing termination of transcription, thus reducing gene expression. However, the potential of TI as a new strategy to engineer complex gene expression modules has not been fully explored yet. Here we created a series of two-input devices using the presence of a roadblocking protein using both experimental and mathematical modeling approaches. We explore how multiple characteristics affect the response of genetic devices engineered to act like either AND, OR, or Single Input logic gates. We show that the dissociation constant of the roadblocking protein, inducer activation of promoter and operator sites, and distance between tandem promoters tune gate behavior. This work highlights the potential of rationally creating different types of genetic responses using the same transcription factors in subtly different genetic architectures.
Boosting Performance of Na-S Batteries Using Sulfur-Doped Ti3C2Tx MXene Nanosheets with a Strong Affinity to Sodium Polysulfides.

Sodium-sulfur batteries using abundant elements offer an attractive alternative to currently used batteries, but they need better sulfur host materials to compete with lithium-ion batteries in capacity and cyclability. We report an in situ sulfur-doping strategy to functionalize MXene nanosheets by introducing heteroatomic sulfur into the MXene structure form the MAX phase precursor. By employing the vacuum freeze-drying method, a three-dimensional (3D) wrinkled MXene nanoarchitecture with the high specific surface area was prepared. The tailor-made wrinkled sulfur-doped MXene (S-Ti3C2Tx) nanosheets were applied as an electrode host material in room temperature sodium-sulfur batteries. The S-Ti3C2Tx matrix shows high polarity with sodium polysulfides, restricting the diffusion of sodium polysulfides. The MXene/sulfur electrode can achieve high areal sulfur loading up to 4.5 mg cm-2 as well as good electrochemical performance (reversible capacity of 577 mAh g-1 at 2 C after 500 cycles).