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There is evidence to show that electro-acupuncture (EA) has a promotive effect on both lipolysis and thermogenesis, and that these mechanisms underlie the anti-obesity effect of EA. The sympathetic nervous system (SNS) is known to play a role in thermogenesis. Additionally, obesity is characterized by a chronic low-grade inflammatory state. Based on these findings, the aim of the present study is to investigate the potential neuro-immune mechanisms underlying the therapeutic effect of EA in obesity. In the experiment, we used a high fat diet (HFD) rats model to study the effect of EA in reducing body weight. EA increases the activity of sympathetic nerves in inguinal white adipose tissue (iWAT), especially in the HFD group. Compared to HFD rats, EA can decrease sympathetic associated macrophage (SAM) and the level of norepinephrine transporter protein (Slc6a2). The relative uncoupling protein 1 expression shows EA increases thermogenesis in iWAT, and increases β3 receptors. Interestingly, injecting β antagonist in iWAT increases Slc6a2 protein levels. Additionally, the SNS-macrophage cross-talk response to EA showed in iWAT but not in epididymis white adipose tissue. The results of the present study indicate that EA exerts its anti-obesity effect via three mechanisms: (1) inhibition of SAMs and the norepinephrine transporter protein SlC6a2, (2) promoting SNS activity and thermogenesis, and (3) regulating immunologic balance.

Vehicle space headway, also called spacing, is an important and basic traffic parameter. Traditional space headway calculation methods are facing the problems of large errors and high costs. This paper presents a novel algorithm based on measurement point pairs (MPPs) to estimate the real-time microcosmic vehicle space headway from single images in existing traffic surveillance videos and images without any additional equipment. First, the camera is calibrated with road markings to obtain the relationship between the image coordinates and the world coordinates. Second, vehicle pairs of two successive vehicles in the image are established, measurement points on each vehicle are selected by video intelligence analysis technologies, and their world coordinates are calculated by camera calibration results. Finally, the measurement points of the preceding and following vehicles are matched to obtain the MPPs, followed by the calculation of the weighted space headway. By using the measurement point information, one of the most difficult problems in image distance measurement, the lack of height information, is solved. The main factors causing estimation errors are fully addressed and the range and trend of errors under certain conditions are given by virtual simulation. Two real-world experiments are used to prove the accuracy and usability of the MPP in common video scenes: the simulation experiment indicates that the MPP algorithm achieves a high accuracy with estimation error less than ±0.1 m and the relative error within 1.1%; the application experiment shows that the MPP-based calculation is more accurate and stable than the state-of-the-art distance measurement algorithm and that the convenience of the proposed MPP algorithm is higher than that of traditional methods of space headway estimation.

Abstract Chronic myeloid leukemia (CML) is a clonal disease characterized by the presence of the constitutively active tyrosine kinase BCR-ABL oncoprotein. Although BCR-ABL is crucially important for pathogenesis and treatment response, it is thought that some additional factors might be involved in the regulation of these processes. Aberrant expression of long noncoding RNAs (lncRNAs) has recently been identified to be involved in various diseases including cancer, suggesting that lncRNAs may play a role in BCR-ABL-mediated CML. In this study, we found that nuclear-enriched abundant transcript 1 (NEAT1), a lncRNA essential for the formation of nuclear body paraspeckles, is significantly repressed in primary CML cells. NEAT1 expression could be restored by inhibiting BCR-ABL expression or its kinase activity in K562 cells. We also demonstrated that NEAT1 is regulated by c-Myc. Knockdown of NEAT1 could promote imatinib (IM)-induced apoptosis, and we demonstrated that the NEAT1-binding paraspeckle protein splicing factor proline/glutamine-rich (SFPQ) is required for NEAT1-mediated apoptosis in K562 cells. RNA-seq analysis revealed that SFPQ regulates cell growth and death pathway-related genes, confirming its function in IM-induced apoptosis. Collectively, these results assign a biological function to the NEAT1 lncRNA in CML apoptosis and may lead to fuller understanding of the molecular events leading to CML.

Abstract Stem cell memory T (TSCM) and central memory T (TCM) cells can rapidly differentiate into effector memory (TEM) and terminal effector (TEF) T cells, and have the most potential for immunotherapy. In this study, we found that the frequency of TSCM and TCM cells in the CD8+ population dramatically decreased together with increases in TEM and TEF cells, particularly in younger patients with acute myeloid leukemia (AML) (

Abstract Background Patients with metabolic syndrome (MetS) are at increased risk of asymptomatic hyperuricemia (i.e., elevated serum uric acid (SUA) level without gout) and cardiovascular disease. We conducted a cross-sectional study to examine associations between SUA levels and coronary flow reserve and urate deposits in carotid arteries in patients with asymptomatic hyperuricemia and MetS. Methods Adults aged ≥40 years with MetS and SUA levels ≥6.5 mg/dl, but no gout, were eligible. Using a stress myocardial perfusion positron emission tomography (PET), we assessed myocardial blood flow (MBF) at rest and stress and calculated coronary flow reserve (CFR). CFR

We present an efficient approach for real-time continuous human action recognition with depth sensors. Instead of using the powerful but quite complex deep neural networks, our approach uses a lightweight discriminative frame-level descriptor, which is called the context pose (CP). The objective is to make our approach realistic in mobile depth sensor applications. CP integrates the context of the motion within a depth video. CP can increase the discriminative power of the frames and enable more frames to represent actions. CP is incorporated with a new part-based random decision forest (PRDF) method. The PRDF is designed to automatically select the optimal combination of body parts to represent and distinguish each action. We evaluate our approach on three classical single-action benchmark datasets. The experiments show that our approach has 200 frames/s and wins superior performances to the existing frame-level descriptors and classifiers in terms of accuracy.

Soil bacterial communities play crucial roles in ecosystem functions and biogeochemical cycles of fundamental elements and are sensitive to environmental changes. However, the response of soil bacterial communities to chronosequence in tropical ecosystems is still poorly understood. This study characterized the structures and co-occurrence patterns of soil bacterial communities in rhizosphere and bulk soils along a chronosequence of teak plantations and adjacent native grassland as control. Stand ages significantly shifted the structure of soil bacterial communities but had no significant impact on bacterial community diversity. Bacterial community diversity in bulk soils was significantly higher than that in rhizosphere soils. The number of nodes and edges in the bacterial co-occurrence network first increased and then decreased with the chronosequence. The number of strongly positive correlations per network was much higher than negative correlations. Available potassium, total potassium, and available phosphorus were significant factors influencing the structure of the bacterial community in bulk soils. In contrast, urease, total potassium, pH, and total phosphorus were significant factors affecting the structure of the bacterial community in the rhizosphere soils. These results indicate that available nutrients in the soil are the main drivers regulating soil bacterial community variation along a teak plantation chronosequence.

The present study emphasizes the effect of withering time set at 4 ± 0.5 h (WT4), 6 ± 0.5 h (WT6), 8 ± 0.5 h (WT8), 10 ± 0.5 h (WT10), and 12 ± 0.5 h (WT12) on the sensory qualities, chemical components, and nutritional characteristics of black tea. The sensory evaluation revealed high total quality scores at WT8 and WT10. Polysaccharides, amino acids, and soluble sugars significantly increased with an increase in withering time, and an apparent peak value was obtained at WT10. However, polyphenols, flavonoids, glycosides, organic acids, catechins, alkanoids, and theaflavins decreased with an increase in withering time. With an increase in withering time, the content of aromatic substances showed a trend of increasing first and then decreasing. The peaks of alcohols, aldehydes, and acids appeared at 10 ± 0.5 h, 10 ± 0.5 h, and 8 ± 0.5 h, respectively. The content of esters, ketones, and hydrocarbons showed a downward trend with an increase in withering time. Aroma analysis revealed that withering time could not exceed 10 ± 0.5 h. Black tea withered up to WT10 showed enhanced inhibition of α-glucosidase and α-amylase activity with good sensorial attributes. Glucose uptake inhibition capacity increased up 6 ± 0.5 h and then decreased, while antioxidant capacity decreased with an increase in withering time. The overall results show that the 8 ± 0.5 h to 10 ± 0.5 h withering time could improve black tea quality and nutritional characteristics.

To reduce the workload and misjudgment of manually discriminating microseismic events and blasts in mines, an artificial intelligence model called PSO-ELM, based on the extreme learning machine (ELM) optimized by the particle swarm optimization (PSO) algorithm, was applied in this study. Firstly, based on the difference between microseismic events and mine blasts and previous research results, 22 seismic parameters were selected as the discrimination feature parameters and their correlation was analyzed. Secondly, 1600 events were randomly selected from the database of the microseismic monitoring system in Fankou Lead-Zinc Mine to form a sample dataset. Then, the optimal discrimination model was established by investigating the model parameters. Finally, the performance of the model was tested using the sample dataset, and it was compared with the performance of the original ELM model and other commonly used intelligent discrimination models. The results indicate that the discrimination performance of PSO-ELM is the best. The values of the six evaluation indicators are close to the optimal value, which shows that PSO-ELM has great potential for discriminating microseismic events and blasts. The research results obtained can provide a new method for discriminating microseismic events and blasts, and it is of great significance to ensure the safe and smooth operation of mines.

Abstract Background Gastrointestinal motility disorder has been demonstrated to be regulated by acupuncture treatment. The mechanisms underlying the effects of acupuncture stimulation of abdominal and lower limb acupoints on gastrointestinal motility have been thoroughly studied; however, the physiology underlying the effects of acupuncture on the forelimbs to mediate gastrointestinal motility requires further exploration. The aim of this study was to determine whether electroacupuncture (EA) at LI11 promotes jejunal motility, whether the parasympathetic pathway participates in this effect, and if so, which somatic afferent nerve fibres are involved. Methods A manometric balloon was used to observe jejunal motility. The effects and mechanisms of EA at LI11 were explored in male Sprague-Dawley rats with or without drug administration (propranolol, clenbuterol, acetylcholine, and atropine) and with or without vagotomy. Three types of male mice (β1β2 receptor-knockout [β1β2 −/−] mice, M2M3 receptor-knockout [M2M3 −/−] mice and wild-type [WT] mice) were also studied by using different EA intensities (1, 2, 4, 6, and 8 mA). A total of 72 rats and 56 mice were included in the study. Results EA at LI11 increased the contractile amplitude of jejunal motility in the majority of both rats and mice. However, EA at LI11 did not enhance jejunal motility in rats administered atropine, rats that underwent vagotomy, and M2M3 −‍‍/− mice (at all intensities). In WT mice, EA at LI11 significantly increased jejunal motility at all intensities except 1 mA, and a plateau was reached at intensities greater than 4 mA. Conclusion Our results suggest that EA at LI11 promotes jejunal motility primarily by exciting the parasympathetic pathway, and that Aδ-fibres and C-fibres may play important roles in the process.

Collision electron spectroscopy (CES) which is based on probe diagnosis and allows for the identification of the impurity gas in pure inert gas has provided a new method for gas analysis. Due to the higher electron temperature in the positive column, the negative glow region is frequently selected as the optimization for gas analysis in DC discharge operated at a relatively high gas pressure. In this paper, the effect of ambipolar diffusion of fast electrons, and the importance of ambipolar diffusion considered as a deep influence on the resolution of fast penning electrons in the positive column, have been verified by designed experiment. It was found that the increase in plasma density has an energy loss effect on fast electrons, resulting a smooth effect on fast electron detection at pressures of about few hundred Pascals.

The development of efficient electrocatalyst to produce molecular hydrogen from water is receiving considerable attention, in an effort to decrease our reliance on fossil fuels. The prevention of the aggregation of active sites during material synthesis, in order to increase charge transport properties of electrocatalysts, is needed. We have designed, synthesized, and studied a Ag2S/reduced graphene oxide (rGO) electrochemical catalyst (for hydrogen evolution) from water. The Ag2S nanocrystals were synthesized by the solvothermal method in which the rGO was added. The addition of the rGO resulted in the formation of smaller Ag2S nanocrystals, which consequently increased the electrical conductivity of the composite catalyst. The composite catalyst showed a higher electrochemical catalytic activity than the one with an absence of rGO. At a current density of 10 mA/cm2, a low overpotential of 120 mV was obtained. A Tafel slope of 49.1 mV/dec suggests a Volmer–Herovsky mechanism for the composite catalyst. These results may provide a novel strategy for developing hydrogen evolution reaction (HER) electrocatalysts, via the combining of a nano-semiconductor catalyst with a 2D material.

A simple and accurate evaluation method of broken rock zone thickness (BRZT), which is usually used to describe the broken rock zone (BRZ), is meaningful, due to its ability to provide a reference for the roadway stability evaluation and support design. To create a relationship between various geological variables and the broken rock zone thickness (BRZT), the multiple linear regression (MLR), artificial neural network (ANN), Gaussian process (GP) and particle swarm optimization algorithm (PSO)-GP method were utilized, and the corresponding intelligence models were developed based on the database collected from various mines in China. Four variables including embedding depth (ED), drift span (DS), surrounding rock mass strength (RMS) and joint index (JI) were selected to train the intelligence model, while broken rock zone thickness (BRZT) is chosen as the output variable, and the k-fold cross-validation method was applied in the training process. After training, three validation metrics including variance account for (VAF), determination coefficient (R2) and root mean squared error (RMSE) were applied to describe the predictive performance of these developed models. After comparing performance based on a ranking method, the obtained results show that the PSO-GP model provides the best predictive performance in estimating broken rock zone thickness (BRZT). In addition, the sensitive effect of collected variables on broken rock zone thickness (BRZT) can be listed as JI, ED, DS and RMS, and JI was found to be the most sensitive factor.

Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation.

This experimental investigation was conducted to research the properties of polypropylene (PP) fiber-reinforced cemented paste backfill (CPB). The unconfined compressive strength (UCS) of the fiber-reinforced CPB showed a significant improvement with average UCS increase ratios of 141.07%, 57.62% and 63.17% at 3, 7 and 28 days, respectively. The macroscopic failure mode and SEM analysis indicated that fibers prevented the formation of large tensile and shear cracks during the pull-out and pull-off failure modes. A linear fitting function for the UCS at a curing time of 3 days and two polynomial fitting functions for the UCS at curing times of 7 and 28 days were established to characterize the relationship between the UCS of the fiber-reinforced and unreinforced CPB. Moreover, based on composite mechanics, nonlinear models related to the UCS and fiber reinforcement index were obtained. The estimated functions containing the fiber reinforcement index λ, which consists of the fiber content and aspect ratio of fiber, could evaluate the UCS. Furthermore, the fiber reinforcement index λ quantifies the enhancement by the fibers. Both estimation results indicated that the UCS values were estimated accurately at curing times of 3, 7 and 28 days in this study. Additionally, the estimation models could be used to guide the strength design of fiber-reinforced CPB. Besides this, the results showed that fiber-reinforced CPB can be used more widely in mine backfills and meets the requirements of controlled low-strength material (CLSM) for broader applications.

Target location is the basic application of a multistatic sonar system. Determining the position/velocity vector of a target from the related sonar observations is a nonlinear estimation problem. The presence of possible sensor position uncertainties turns this problem into a more challenging hybrid parameter estimation problem. Conventional gradient-based iterative estimators suffer from the problems of initialization difficulties and local convergence. Even if there is no problem with initialization and convergence, a large computational cost is required in most cases. In view of these drawbacks, we develop a computationally efficient non-iterative position/velocity estimator. The main numerical computation involved is the weighted least squares optimization, which makes the estimator computationally efficient. Parameter transformation, model linearization and two-stage processing are exploited to prevent the estimator from iterative computation. Through performance analysis and experimental verification, we find that the proposed estimator reaches the hybrid Cramér−Rao bound and has linear computational complexity.

Following publication of the original article [1], it has come to our attention that we did not appropriately convert the units for CRP from the National Health and Nutrition Examination Survey (NHANES) from mg/dL to mg/L. In this study, NHANES was considered the general population cohort for this study; interestingly, the correction resulted in changes to a broader range of CRP values in NHANES from 0.01 to 18.0 mg/dL to 0.10 to 180.0 mg/L.

In view of problem that static plan management method in text-form is not accurate enough to guide rescue work when coal mine accident happened, and it is hard to formulate emergency plan feasibly, a emergency plan management system of coal mine based on case-based reasoning was designed. According to problems of mismatch of feature attribution between actual cases and history cases, differences similarity algorithm was proposed which was combined with nearest neighborhood matching method in case-based reasoning. This algorithm can retrieve the highest similarity historical cases from library plans, and then improve the efficiency of rescue program generating. For the problem that corresponding plan becomes difficult to find since the case base growing, a hierarchical search strategy was presented. The practical application results show that the emergency plan management system of coal mine based on case-based reasoning is better to meet requirements of timeliness and pertinence of coal mine emergency rescue compared with the traditional coal mine emergency plan.