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Background Hypertension (HTN) is a common adverse event of the vascular endothelial growth factor pathway inhibitor apatinib. This study was conducted to evaluate the association of apatinib-induced HTN with clinical outcomes in patients with advanced non-small-cell lung cancer (NSCLC). Methods We retrospectively analyzed 110 consecutive patients with advanced NSCLC who were treated with apatinib from August 2014 to January 2018. All patients were classified as normotensive or hypertensive based on blood pressure measurements after initiating therapy. Therapeutic response, progression-free survival (PFS), and overall survival (OS) were evaluated. Univariate and multivariate analyses were performed using the Cox proportional hazards method. Results A total of 46 patients (42%) were diagnosed with HTN. The median PFS for the hypertensive and normotensive groups were 5.6 months and 4.2 months, respectively (P=0.0027). The median OS times for the hypertensive and normotensive groups were 9.9 months and 7.8 months, respectively (P=0.005). Thirty percent of patients who experienced HTN showed partial response to apatinib as compared with 6.3% of non-hypertensive patients (P=0.002). HTN was independently associated with improved PFS and OS on both univariate and multivariate analyses. Conclusion Apatinib-induced HTN may be an inexpensive, valid, and easily measurable biomarker for apatinib antitumor efficacy in patients with advanced NSCLC.

Given the input/output constraints and cross couplings of supply chain (SC) nodes, model predictive control (MPC) is efficient to seek the optimal solutions to the problems posed by interacting nodes to satisfy customer demands. In supply chain applications, due to the growing spatial distribution and interactions between the supply network elements, the information flow management becomes a challenging yet significant task. To reduce numerical complexity while maintaining implementability, a distributed MPC strategy is proposed. The scheme aims at finding the Nash equilibrium where the controller of each subsystem communicates with other ones in the presence of noncooperative interaction and strong coupled inputs due to the ordering decisions. Extensive numerical simulations verify that the strategy outperforms conventional policies in terms of substantially reduced SC operating cost.

Shen-Cun Fang,1,2 Wen Huang,2 Ying-Ming Zhang,1 Hai-Tao Zhang,1 Wei-Ping Xie2 1Department of Respiratory Medicine, Nanjing Chest Hospital, Nanjing, Jiangsu, China; 2Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China Background: Hypertension (HTN) is a common adverse event of the vascular endothelial growth factor pathway inhibitor apatinib. This study was conducted to evaluate the association of apatinib-induced HTN with clinical outcomes in patients with advanced non-small-cell lung cancer (NSCLC). Methods: We retrospectively analyzed 110 consecutive patients with advanced NSCLC who were treated with apatinib from August 2014 to January 2018. All patients were classified as normotensive or hypertensive based on blood pressure measurements after initiating therapy. Therapeutic response, progression-free survival (PFS), and overall survival (OS) were evaluated. Univariate and multivariate analyses were performed using the Cox proportional hazards method. Results: A total of 46 patients (42%) were diagnosed with HTN. The median PFS for the hypertensive and normotensive groups were 5.6 months and 4.2 months, respectively (P=0.0027). The median OS times for the hypertensive and normotensive groups were 9.9 months and 7.8 months, respectively (P=0.005). Thirty percent of patients who experienced HTN showed partial response to apatinib as compared with 6.3% of non-hypertensive patients (P=0.002). HTN was independently associated with improved PFS and OS on both univariate and multivariate analyses. Conclusion: Apatinib-induced HTN may be an inexpensive, valid, and easily measurable biomarker for apatinib antitumor efficacy in patients with advanced NSCLC. Keywords: biomarker, hypertension, apatinib, non-small-cell lung cancer, clinical outcomes

For portable devices with limited resources, it is often difficult to deploy deep networks due to the prohibitive computational overhead. Numerous approaches have been proposed to quantize weights and/or activations to speed up the inference. Loss-aware quantization has been proposed to directly formulate the impact of weight quantization on the model's final loss. However, we discover that, under certain circumstances, such a method may not converge and end up oscillating. To tackle this issue, we introduce a novel loss-aware quantization algorithm to efficiently compress deep networks with low bit-width model weights. We provide a more accurate estimation of gradients by leveraging the Taylor expansion to compensate for the quantization error, which leads to better convergence behavior. Our theoretical analysis indicates that the gradient mismatch issue can be fixed by the newly introduced quantization error compensation term. Experimental results for both linear models and convolutional networks verify the effectiveness of our proposed method.

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This article proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It achieves high randomness (diversity) by sampling its parameters randomly from a parameter pool, and selecting a subset of features randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 34 classification and regression datasets. Remarkably, BoostForest has only one hyper-parameter (the number of BoostTrees), which can be easily specified. Our code is publicly available, and the proposed ensemble learning framework can also be used to combine many other base learners.

Sparse Bayesian learning (SBL) is a popular machine learning approach with a superior generalization capability due to the sparsity of its adopted model. However, it entails a matrix inversion at each iteration, hindering its practical applications with large-scale data sets. To overcome this bottleneck, we propose an efficient SBL algorithm with O(n²) computational complexity per iteration based on a Gaussian-scale mixture prior model. By specifying two different hyperpriors, the proposed efficient SBL algorithm can meet two different requirements, such as high efficiency and high sparsity. A surrogate function is introduced herein to approximate the posterior density of model parameters and thereby to avoid matrix inversions. Using a data-dependent term, a joint cost function with separate penalty terms is reformulated in a joint space of model parameters and hyperparameters. The resulting nonconvex optimization problem is solved using a block coordinate descent method in a majorization-minimization framework. Finally, the results of extensive experiments for sparse signal recovery and sparse image reconstruction on benchmark problems are elaborated to substantiate the effectiveness and superiority of the proposed approach in terms of computational time and estimation error.

This paper proposes a distributed energy-based landing allocation method of multiple unmanned aerial vehicles (UAVs) on multiple unmanned surface vessels (USVs). First, an optimization function is established, which consists of the limited energy battery, the flight distance and the flight speed. Then, a distributed landing allocation scheme is designed to minimize the energy consumption difference. Finally, real lake experiments of multiple M-100UAVs and HUSTER-30 USVs are conducted to verify the effectiveness of the proposed method.

Objective To investigate the effects of serine protease inhibitor 3n (SerpinA3n) in a neonatal mouse model of asthma. Methods The study utilized a neonatal mouse ovalbumin (OVA) sensitization model of asthma. Wild type (WT) and SerpinA3n-/- mice were randomly divided into WT/SerpinA3n-/- + saline, WT/SerpinA3n-/- + OVA, WT/SerpinA3n-/- + OVA + rSerpinA3n (recombinant mouse SerpinA3n protein), and WT/SerpinA3n-/- + OVA + DEX (dexamethasone, positive control) groups followed by hematoxylin-eosin (HE) staining, Masson’s trichrome stainings, Sircol soluble collagen assay, quantitative real time polymerase chain reaction (qRT-PCR), Western Blot and enzyme linked immunosorbent assay (ELISA). Results OVA-induced neonatal mice showed the increases in airway hyper-reactivity with the up-regulated total cells, eosinophil, lymphocyte and neutrophil in bronchoalveolar lavage fluid (BALF), which was much higher in WT + OVA + rSerpinA3n group (P Conclusion SerpinA3n knockout can attenuate airway hyper-reactivity, mitigate inflammatory responses and reduce collagen deposition in lung tissues of neonatal mice with asthma.

Rhesus macaque (Macaca mulatta) is widely applied in animal model construction of infertility, spermatogonia stem cell transplantation and male reproductive diseases. In this review, we describe the seasonal changes of the reproductive system in rhesus macaques, the regular pattern of spermatogenesis and spermatozoa maturation, and the differentiation of spermatogonia and spermatocytes. The duration of the M. mulatta spermatogenesis is approximately 10 days and seminiferous epithelium cycles mainly consist of 12 stages, which provide a suitable model for reproductive studies in non-human primates. Here, we summarize the features of gonadal development and sperm maturation in the rhesus monkeys, which provide important information in the studies of reproductive biology. Rhesus macaque is an excellent animal model in spermatogonia stem cell transplantation. We discuss the applications and progresses of assisted reproductive technologies in sperm liquefaction, semen cryopreservation and spermatogonia stem cell transplantation of rhesus macaques. Besides, we sort out recent proteomic analyses of male reproductive systems and semen samples in rhesus macaques. This review mainly focuses on male reproductive biology and application studies using M. mulatta, which would promote the development of new therapeutic interventions on assisted reproduction and reproductive disease studies in the future.

In this paper, we study an event-triggered output synchronization problem for linear heterogenous MASs under a network with a directed topology requiring only intermittent output communication. Based on a two-step design manner, we design a distributed control law together with a periodic event-triggered mechanism where the triggering condition is periodically monitored such that Zeno behavior is ruled out. We convert the resulted closed-loop system into a linear system with time-varying delay. Sufficient conditions are achieved for the stabilization of the time-delay system as well as the output synchronization of the linear MASs.

Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People’s Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan–Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.

One new 14-membered ring cyclopeptide alkaloid, plenane A (

Coronavirus Disease 2019 (COVID-19) has become a world-wide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods for clinicians to promptly identify high-risk patients. Here we developed a risk score using clinical data from 1,479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital in Wuhan (validation cohort 1) and 432 inpatients from the Third People’s Hospital Shenzhen (validation cohort 2). The risk score is based on three biomarkers readily available in routine blood samples and can be easily translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 days in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients upon admission can clearly be differenciated into low, medium or high risk, with an AUC score of 0.9551. In summary, a simple risk score was validated to predict death in patients infected with COVID-19 and was validated in independent cohorts.

Background The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia. Methods A total of 2460 RT-PCR tested SARS-CoV-2-positive patients (1250 men and 1210 women; mean age, 57.7 ± 14.0 years (age range, 11–93 years) were retrospectively identified from Huoshenshan Hospital in Wuhan from February 11 to March 16, 2020. Basic clinical characteristics were reviewed. The uAI Intelligent Assistant Analysis System was used to assess the CT scans. Results CT scans of 2215 patients (90%) showed multiple lesions of which 36 (1%) and 50 patients (2%) had left and right lung infections, respectively (> 50% of each affected lung’s volume), while 27 (1%) had total lung infection (> 50% of the total volume of both lungs). Overall, 298 (12%), 778 (32%) and 1300 (53%) patients exhibited pure ground glass opacities (GGOs), GGOs with sub-solid lesions and GGOs with both sub-solid and solid lesions, respectively. Moreover, 2305 (94%) and 71 (3%) patients presented primarily with GGOs and sub-solid lesions, respectively. Elderly patients (≥ 60 years) were more likely to exhibit sub-solid lesions. The generalized linear mixed model showed that the dorsal segment of the right lower lobe was the favoured site of COVID-19 pneumonia. Conclusion Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients.

This paper has developed a vision-based control approach for autonomous tracking and landing on a motional Unmanned surface vessel (USV) with an unmanned aerial vehicle (UAV). First, a novel three-stage visual detection method is proposed to estimate the relative 3D position between UAV and USV, where the USV stays in a suitable view of the camera of the UAV. Then a PID controller is designed to regulate the attitude of the quadrotor and the movement of the vessel, which enables the successful landing procedure of the UAV on a motional USV. Finally, lake experiments are conducted to verify the effectiveness of the proposed algorithm.

Three new phomalone derivatives, phomalichenones E-G (

Since the outbreak of Corona Virus Disease 2019 (COVID-19) in Hubei province, the epidemic scale has increased rapidly, and no effective antiviral drug therapy has been identified yet. This study aimed to evaluate the adjuvant efficacy of Natural Herbal Medicine (NHM) combined with Western medicine in the treatment of COVID-19. We performed a retrospective, 1:1 matched, case-control study of the first cohort of hospitalized COVID-19-confirmed cases (January 17, 2020 to January 28, 2020). A total of 22 of the 36 confirmed patients were included in this study, split into two groups of 11: the NHM group (NHM combined standard Western medicine treatment) and control group (standard Western medicine treatment alone). All patients received appropriate supportive care and regular clinical and laboratory monitoring. Main evaluation indicators included improvement of clinical symptoms such as fever, cough and diarrhea after hospitalization; pathogen nucleic acid test result of respiratory tract and fecal specimens of the patient after hospitalization, and change of chest CT examination after hospitalization. The duration of fever in the NHM group ([Formula: see text] days) was significantly shorter than that in the control group ([Formula: see text] days) ([Formula: see text]). During the whole hospitalization period, the number of cases with diarrhea in the NHM group (two cases) was less than that in the control group (eight cases) ([Formula: see text]). Compared with the control group ([Formula: see text]), the duration for improvement (DI) of chest CT in the NHM group ([Formula: see text]) was significantly shorter ([Formula: see text]). Our results suggest that NHM could improve the clinical symptoms of COVID-19 patients and may be effective in treating COVID-19; thus, a larger, prospective, randomized, controlled clinical trial should be conducted to further evaluate the adjuvant efficacy of NHM in the treatment of COVID-19.

The sudden increase in COVID-19 cases is putting high pressure on healthcare services worldwide. At this stage, fast, accurate and early clinical assessment of the disease severity is vital. To support decision making and logistical planning in healthcare systems, this study leverages a database of blood samples from 485 infected patients in the region of Wuhan, China, to identify crucial predictive biomarkers of disease mortality. For this purpose, machine learning tools selected three biomarkers that predict the mortality of individual patients more than 10 days in advance with more than 90% accuracy: lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP). In particular, relatively high levels of LDH alone seem to play a crucial role in distinguishing the vast majority of cases that require immediate medical attention. This finding is consistent with current medical knowledge that high LDH levels are associated with tissue breakdown occurring in various diseases, including pulmonary disorders such as pneumonia. Overall, this Article suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate. Early and accurate clinical assessment of disease severity in COVID-19 patients is essential for planning the allocation of scarce hospital resources. An explainable machine learning tool trained on blood sample data from 485 patients from Wuhan selected three biomarkers for predicting mortality of individual patients with high accuracy.

Maturation arrest is a common cause of male infertility which has caused worldwide concern, and its pathophysiological process remains further elucidation. Our study aimed to identify genetic characteristics of maturation arrest by comparing gene expression between maturation arrest and normal samples using microarray technology. A total of 6,373 genes were identified differentially expressed (p .05, fold change 2.0 or-2.0) and 1,594 genes were selected as statistically significant after Bonferroni correction, including 419 up-regulated and 1,175 down-regulated genes. Microarray data were validated by quantitative reverse transcriptase-polymerase chain reaction. Bioinformation analysis was performed to explore genetic function of statistically significant genes. Gene Ontology results showed the statistically significant genes enriched in sexual reproduction, spermatogenesis and male gamete generation. Reactome pathway analysis highlighted the olfactory signalling pathway, fertilisation, developmental biology, etc. One module and eight hub genes were found to be involved in ubiquitin-mediated proteolysis and may affect as indicators of spermatogenic process through protein-protein interaction analysis. Our study provided a comprehensive genetic characteristic of differential expressed genes in testicular tissues from maturation arrest patients and speculated several genes as potential indicators of disease.

As a cell-penetrating peptide, polyarginine is widely used in drug delivery systems based on its membrane permeation ability. Previously, we developed the mPEG-PLA-b-polyarginine(R15) triblock copolymer, which exhibited a high siRNA delivery efficiency both in vitro and in vivo. As a continued effort, here the amphiphilic diblock polymer PCL-R15 was synthesized as a simplified model to further elucidate the structure-activity relationship of arginine-based amphiphilic polymers as siRNA delivery systems, and the cellular trafficking mechanisms of the PCL-R15/siRNA nanoplexes were investigated to understand the interaction patterns between the nanoplexes and cells. Compared to the R15/siRNA complexes, the introduction of PCL moiety was found to result in the stronger interactions with cells and the enhanced transfection efficiency after the formation of condensed nanoplexes. Caveolae-mediated endocytosis and clathrin-mediated endocytosis were major routes for the internalization of PCL-R15/siRNA nanoplexes. The intracellular release of siRNA from nanoplexes was confirmed by fluorescence resonance energy transfer assay. It was also noticed that the internalized PCL-R15/siRNA nanoplexes were transported through digestive routes and trapped in lysosomes, which may be the bottleneck for efficient siRNA delivery of PCL-R15/siRNA nanoplexes. This study investigated the relationship between the polymer structure of PCL-R15 and the cellular interaction patterns, which may render implications on the rational design of polyarginine-based siRNA delivery systems.

Background Heparin-binding protein (HBP), a potent inducer of increased vascular permeability, is a potentially useful biomarker for predicting outcomes in patients with postoperative myocardial injury-related cardiogenic shock (MIRCS). We aimed to evaluate and validate HBP as a prognostic biomarker for postoperative MIRCS. Methods We performed a case-control study in 792 patients undergoing cardiac surgery from January 1, 2016, to August 1, 2019, including 172 patients with postoperative MIRCS and 620 age- and sex-matched controls. The association between HBP and MIRCS was determined by multivariate logistic regression analysis. Receiver operating characteristic curves (ROCs) with area under the curve (AUC) were performed to calculate the cut-off value, sensitivity and specificity. The association between HBP and cardiac troponin T (cTnT) was determined by multivariable linear regression analysis. Blood samples were drawn from the coronary sinus and arterial line of the cardiopulmonary bypass (CPB) before aortic cross-clamping (time point 1) and 5 min after aortic declamping (time point 2). Results Before aortic cross-clamping, coronary sinus HBP (HBPCS1) showed no differences between the two groups. However, after declamping, the MIRCS group had a significantly higher sinus HBP level (HBPCS2) than did the control group. HBPCS2 predicted MIRCS with an AUC of 0.85 (95% CI: 0.81–0.89, cut-off: 220 ng/ml, sensitivity: 92% and specificity: 70%). After adjusting for confounding factors, we found that HBP was an independent risk factor for MIRCS (OR: 7.65, 95% CI: 4.86–12.06, P 0, P Conclusions Elevated levels of coronary sinus HBP were useful biomarkers for predicting MIRCS after cardiac surgery.

The sudden increase of COVID-19 cases is putting a high pressure on healthcare services worldwide. At the current stage, fast, accurate and early clinical assessment of the disease severity is vital. To support decision making and logistical planning in healthcare systems, this study leverages a database of blood samples from 404 infected patients in the region of Wuhan, China to identify crucial predictive biomarkers of disease severity. For this purpose, machine learning tools selected three biomarkers that predict the survival of individual patients with more than 90% accuracy: lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP). In particular, relatively high levels of LDH alone seem to play a crucial role in distinguishing the vast majority of cases that require immediate medical attention. This finding is consistent with current medical knowledge that high LDH levels are associated with tissue breakdown occurring in various diseases, including pulmonary disorders such as pneumonia. Overall, this paper suggests a simple and operable formula to quickly predict patients at the highest risk, allowing them to be prioritised and potentially reducing the mortality rate.FundingNone.

An air-stable heterometallic Bi-Pt complex with the formula [BiPt(SAc)5]n (1; SAc = thioacetate) was synthesized. The crystal structure, natural bond orbital (NBO) and local orbital locator (LOL) analyses, localized orbital bonding analysis (LOBA), and X-ray absorption fine structure (XAFS) measurements were used to confirm the existence of Bi-Pt bonding and an ionic cage of O atoms surrounding the Bi ion. From the cyclic voltammetry (CV) and controlled potential electrolysis (CPE) experiments, 1 in tetrahydrofuran reduced CO2 to CO, with a faradaic efficiency (FE) of 92% and a turnover frequency (TOF) of 8 s-1 after 30 min of CPE at -0.79 V vs. NHE. The proposed mechanism includes an energetically favored pathway via the ionic cage, which is supported by the results of DFT calculations and reflectance infrared spectroelectrochemistry data.

To investigate the expression and prognostic significance of CD56 in the patients suffered from acute myeloid leukemia with AML/ETO mutation.The clinical data of 60 newly diagnosed AML patients with AML/ETO from January 2012 to December 2016 were analyzed retrospectively. The clinical characteristics and outcome between CD56There were significant differences in CR rate after one course chemotherapy, relapse rate within 1 year and gene mutations between the CD56 positive and negative patients (P＜0.05). The RFS and OS of CD56 positive patients were significantly lower than those of CD56 negative patients (P＜0.05).The expression of CD56 may be an important factor of poor prognosis in AML patients with AML/ETO.CD56表达对伴有AML/ETO的急性髓系白血病患者预后影响的分析.探讨 CD56表达对伴有AML/ETO的急性髓系白血病患者的预后影响.回顾性分析西安交通大学第一附属医院2012年1月至2016年12月60例伴有AML/ETO的初发AML患者临床资料，比较CD56CD56CD56

For traditional constant impedance control, the robot suffers from constant stiffness, poor flexibility, large wear and high energy consumption in the process of movement. To address these problems, a variable impedance control method based on reinforcement learning (RL) algorithm Deep Q Network (DQN) is proposed in this paper. Our method can optimize the reference trajectory and gain schedule simultaneously according to the completion of task and the complexity of surroundings. Simulation experiments show that, compared with the constant impedance control, the proposed algorithm can adjust impedance in real time while manipulator is executing the task, which implies a better compliance, less wear and less control energy.

Collision avoidance implies that extra motion in joint space must be taken, which might exert unexpected influences on the execution of the desired end-effector tasks. In this paper, a novel framework for generating collision-free trajectories while respecting task priorities is proposed. Firstly, a data-driven approach is applied to learn an efficient representation of the distance decision function of the system. The function is then working as the collision avoidance constraints in the inverse kinematics (IK) solver, which avoids the collision between manipulators. To eliminate undesired influences of the extra motion for collision avoidance on the execution of tasks, task constraints are proposed to control the task priorities, offering the system with the ability to trade off between collision avoidance and task execution. Furthermore, the overall framework is formulated as a QP (quadratic programming), therein guarantees a real time performance. Numerical simulations are conducted to demonstrate the effectiveness and efficiency of the presented method.

Drastic reduction in biodiversity has been a severe threat to ecosystems, which is exacerbated when losing few species leads to disastrous and even irreparable consequences. Therefore, revealing the mechanism underlining biodiversity loss is of uttermost importance. In this study, we show that abundant indirect interactions among mutualistic ecosystems are critical in determining species' status. Combining topological and ecological characteristics, we propose an indicator derived from a dynamic model to identify keystone species and quantify their influence, which outperforms widely-used indicators like degree in realistic and simulated networks. Furthermore, we demonstrate that networks with high modularity, heterogeneity, biodiversity, and less intimate interactions tend to have larger indirect effects, which are more amenable in predicting decline of biodiversity with the proposed indicator. These findings shed some light onto the influence of apposite biodiversities, paving the way from complex network theory to ecosystem protection and restoration.

In view of increasing concerns about climate change and global warming, various renewable energy sources are widely adopted in power systems to reduce greenhouse gas emissions. However, the widespread adoption of renewable energy sources exacerbates the problem of harmonics pollution. In order to improve power quality, it is urgent to develop a new approach for monitoring harmonics. By leveraging the structured sparsity and spatial sparsity of harmonic sources, this paper proposes a group sparse Bayesian learning method for solving harmonic state estimation problems. The proposed algorithm can employ a small number of measurements less than the number of state variables to pinpoint harmonic sources and to estimate the harmonic magnitudes and angles. Moreover, the proposed method achieves automatic hyperparameter adjustments and solves the system states in complex domain without splitting them into real and imaginary parts. Extensive results on a benchmark IEEE 14-bus system are presented to substantiate the superiority of the proposed method in terms of localization accuracy and harmonic magnitude and phase angle estimation.

One new indole-type alkaloid, α-L-rhamnopyranosyl-(1→6)-β-D- glucopyranosyl 6-methoxy-3-indolecarbonate (1), together with three known alkaloids (2–4), one aromatic acid (5) and five known saponins (6–10), was isolated from the roots of Clematis florida var. plena. Their structures were established by NMR spectroscopic analysis and acid hydrolysis. In in vivo anti-inflammatory activity, n-butanol extract was found to be potent against ear edema in mice, with inhibition rate of 48.7% at a dose of 800 mg/kg. Furthermore, compounds 8 and 9 obtained from the n-butanol extract exhibited significant anti-inflammatory activities with inhibition rates of 50.9% and 54.7% at a dose of 200 mg/kg.

Hysteresis modeling is interesting yet challenging for piezoelectric actuated systems, which are often used in micro/nano scale measurement and manufacturing equipments. However, due to its complexity, few efforts have been devoted to characterizing cross-coupling hysteresis effect of multiaxis piezoelectric micropositioning stages. To this end, a distributed Hammerstein model, composed of a cascaded connection of a static nonlinearity and a dynamic linearity, is proposed in this paper to approximate the nonlinear spatial/temporal cross-coupling effect. This model outperforms conventional piezo models, such as the Preisach model. Meanwhile, theoretical analysis is provided to guarantee the convergence of the proposed Hammerstein model. Finally, extensive experiments are conducted to verify the superiority of the proposed modeling method.

A distributed consensus control method is developed in this paper for second-order nonlinear multiagent systems with external stochastic disturbances. By utilizing the graph theory, the stochastic theory, the control technique, and the linear matrix inequality method, sufficient conditions are derived to guarantee the convergence to mean-square exponential consensus for strongly connected proximity networks and proximity networks with directed spanning trees, respectively. Particularly, using such a methodology, a detailed distributed consensus controller design procedure is provided for networked Euler–Lagrange systems, which are often used in mechanical engineering processes. Finally, the effectiveness of the proposed consensus control method is illustrated by numerical simulations on networked Euler–Lagrange systems.

Chatter is a typical kind of unstable dynamics often encountered in machining processes, which often results in overcut and rapid tool wear. Hence, chatter phenomenon worsens the surface quality and reduces productivity in milling systems as well. Recent years have witnessed a surging industrial demand of high quality and high efficiency machining. Specifically, for low radial immersion milling situation, large depth of cuts is inevitably needed so as to increase the machining efficiency. To fulfill such a task, this paper develops a robust active control method to mitigate the chatter dynamics of low radial immersion milling processes. The present approach increases the axial depth of cuts, and the improvement is inversely proportional to the radial immersion ratio. Finally, case studies are conducted to show the substantially enlarged stable region in the stability lobe diagram (SLD) spanned by spindle rotational speed and axial depth of cut. Thus, the method can be expected to improve the efficiency of milling processes. Note to Practitioners —This paper seeks to mitigate machining chatters that worsen the workpiece surface quality in high-speed machining processes. To overcome such unexpected effects, traditional passive schemes simply adjust the rotational speed and cutting depth, which do not seek assistance from external power sources. Hence, neither the damping nor stiffness of the system can be changed, and a high maximal material removal rate (MMRR) is arduous to obtain. By contrast, this paper develops a robust active control method to mitigate the chatter dynamics for low radial immersion milling. In the design stage of the milling spindle, two active magnetic bearings (AMBs) are embedded in the tool house as control actuators, which produce extra force to counteract the chatters. In real milling processes, the chatters are detected by the associated inductive displacement sensors around the AMBs, and an uncertainty model is accordingly established with low radial immersion. With the detected vibration displacements, robust active control signals are calculated online, and then fed into the power amplifiers to drive the two AMBs. By this means, a closed-loop active control system is thus established for milling processes, which substantially enlarges the stable region of the SLD spanned by rotational speed and cutting depth. As a result, the proposed active control scheme can be expected to increase the MMRR and, hence, to enhance the productivity of milling processes.

A new pregnane-type steroid, 2α,3β,16β-trihydroxy-5α-pregnane-20R-methacrylate (1), was isolated from the leaves of Melia azedarach, together with 17 known compounds. Their structures were elucidated by spectroscopic and mass-spectrometric analysis, including 1D, 2D NMR, ESI-MS, and HR-MS. Compound 1 was found to possess cytotoxicities against HepG2, K562, and SGC7901 cell lines, with IC50 values 18.6, 19.1, and 62.9 μM, respectively.

The cooling curves and the change of contraction/expansion during solidification and cooling were tested by using a self-made device which could achieve the one-dimensional contraction instead of three-dimensional contraction of the casting. Then, the effects of Al content (0, 1.1, 3, 5, 10, 12.9, 15, 17, 19, 22, 24 and 30 wt%) on the thermal contraction/expansion of the binary Mg–Al as-cast alloys during solidification were obtained. The results showed that expanding instead of contraction was present in Mg–Al alloys with the addition of 0–30 wt% Al during solidification. The values of expansion significantly increased at first and then decreased with the increase in Al content. And the maximum expansion ratio of 0.44% (maximum expansion value: 0.841 mm) was present in the Mg–15 wt% Al alloy. Contraction instead of expansion occurred once the temperature drops to the temperature corresponding to the expansion value in total, indicating the occurrence of a continuous expansion during the solidification process in mushy zone for the Mg alloys with Al addition of 5–30 wt%. The expansion value in total consisted of two parts: the expansions occurring in the liquid-phase zone and mushy zone. The expansion in liquid zone was present in every Mg–Al alloy, and it contributed to the most proportion of the total expansion value when the Al content in Mg–Al alloy was lower than 10 wt% or higher than 22 wt%. However, the total expansion value was mainly determined by the solidification behavior in mushy zone when the Al content was among 10–22 wt% in Mg–Al alloys.

Objective To compare arteriovenous flush with heparin and argatroban and observe its effect on platelet counts, biochemical index and the incidence of thrombocytopenia (HIT). Methods Retrospective analysis for 746 cardiovascular surgery patients, who were divided into heparin group (350 cases, 10 mg heparin dissolved in 250 ml of saline) and argatroban group (396 cases, 2.5 mg argatroban dissolved in 250 ml saline). Monitoring the changes of platelet count, hepatic function, renal function and blood coagulation index, hemorrhage and thrombosis incidence were monitored in two groups. HIT related 4Ts score, HIT incidence and complications were also recorded. Results There was no significant difference in basic clinical data between the two groups. There was no difference in platelet counts between the two groups before and after the operation, and the platelets of heparin group were significantly lower than that of the argatroban group on the first day and the 5th day after surgery. According to 4Ts score, 13 patients in heparin group got medium and high score of 4Ts, of whom 7 patients were positive for antibody (2.0%), and then standard anti-coagulation therapy was initiated for antibody positive patients. There were 5 patients in argatroban group getting medium and high score of 4Ts, and 2 were HIT antibody positive (0.5%). As such, the standard anti-coagulation therapy was also performed on these patients, and no bleeding event occurred. The total number of thrombosis events were more in the heparin group than in the argatroban group. There was no statistically significant difference between the two groups in hepatic, renal function index and coagulation index. Conclusions Postoperative routine use of arteriovenous flush with argatroban for anticoagulation has litte effect on platelet count, could reduce the incidence of HIT and does not increase the risk of bleeding. DOI: 10.11855/j.issn.0577-7402.2018.06.13