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The incipient fault is difficult to detect because of its small amplitude and insignificant impact, however, ignoring such fault may cause irreversible damage to the system. In this paper, a hierarchical latent variable extraction and multisegment probability density analysis method is proposed to detect the incipient fault. Firstly, three data subspaces are constructed, which are named dominant, intermediate and residual spaces, and key latent variables which contain more offline variance or online variation information will be retained. Afterwards, the expanded data distribution interval and multiple data segments are constructed for the probability density estimation. Based on the improved symmetric divergence index, the distribution distance between the online data and offline modeling data can be evaluated, which has achieved 95.3% and 86.8% average detection rates for the faults in numerical case and Tennessee Eastman process. Finally, a real multiphase flow facility is used to demonstrate the effectiveness of the proposed method.

We studied the value of circulating tumor DNA (ctDNA) in predicting early postoperative tumor recurrence and monitoring tumor burden in patients with hepatocellular carcinoma (HCC). Plasma‐free DNA, germline DNA, and tissue DNA were isolated from 41 patients with HCC. Serial ctDNAs were analyzed by next‐generation sequencing before and after operation. Whole‐exome sequencing was used to detect the DNA of HCC and adjacent tissues. In total, 47 gene mutations were identified in the ctDNA of the 41 patients analyzed before surgery. ctDNA was detected in 63.4% and 46% of the patient plasma pre‐ and postoperation, respectively. The preoperative ctDNA positivity rate was significantly lower in the nonrecurrence group than in the recurrence group. With a median follow‐up of 17.7 months, nine patients (22%) experienced tumor recurrence. ctDNA positivity at two time‐points was associated with significantly shorter recurrence‐free survival (RFS). Tumors with NRAS, NEF2L2, and MET mutations had significantly shorter times to recurrence than those without mutations and showed high recurrence prediction performance by machine learning. Multivariate analyses showed that the median variant allele frequency (VAF) of mutations in preoperative ctDNA was a strong independent predictor of RFS. ctDNA is a real‐time monitoring indicator that can accurately reflect tumor burden. The median VAF of baseline ctDNA is a strong independent predictor of RFS in individuals with HCC., Here, we studied the value of circulating tumor DNA (ctDNA), in combination with germline and tissue DNA by whole‐exome sequencing in patients withhepatocellular carcinoma (HCC). Our analysis demonstrated that ctDNA may serve as a real‐time monitoring indicator by accurately reflecting tumor burden, and as a strong independent predictor of recurrence‐free survival in HCC.

In real‐world applications, large amounts of data from multiple sources come in the form of streams. This makes multi‐view feature learning cost much time when new instances rise incrementally. Dealing with these growing multi‐view data becomes a challenging problem. Some single‐view methods focus on processing the data dynamically, but they are not suitable for multi‐view data. Some online multi‐view methods are proposed to tackle it, but they ignore the influence of uncorrelated items in each view. Therefore, in this study, the authors propose a new algorithm, called Incremental Multi‐view Correlated Feature Learning (IMCFL) based on non‐negative matrix factorisation, to learn the common feature across views. By separating uncorrelated items of new instances and constructing incremental joint learning of correlated and uncorrelated features, the proposed IMCFL can eliminate the influence of uncorrelated information in the individual view and improve the effectiveness of incremental multi‐view common feature learning. Extensive experiments on real‐world datasets confirm its superiority by comparing it with other state‐of‐the‐art incremental and non‐incremental methods.

With the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, the importance of vaccines in epidemic prevention and public health has become even more obvious than ever. However, the emergence of multiple severe acute respiratory syndrome coronavirus 2 variants worldwide has raised concerns about the effectiveness of current COVID-19 vaccines. Here, we review the characteristics of COVID-19 vaccine candidates in five platforms and the latest clinical trial results of them. In addition, we further discuss future directions for the research and development of the next generation of COVID-19 vaccines. We also summarize the serious adverse events reported recently after the large-scale vaccination with the current COVID-19 vaccines, including the thromboembolism caused by the AstraZeneca and Johnson & Johnson vaccines.

China is rich in primary energy resources. Due to specific terrain conditions, different types of problems may occur in the process of development. When the coal seams are located under the oil layers, due to the physical characteristics of oil, the exploitation of coal seams will cause losses to the oil layers, and the volatile oil may also cause threats to coal mining. To analyze the fracture development and leakage characteristics of overburden strata for this type of geological conditions, we have carried out physical simulation experiments and performed a further numerical simulation to study and verify the evolution of fractures. Based on numerical simulation results, methods have been proposed that may effectively reduce the air leakage in the working face and control the parameters, including viscous resistance, distance, and the construction period of the blocking. In this paper, based on the 30100 face in the Nanliang coal mine as an example, we have proved that the proposed measures can provide good theoretical guidance for the prevention of spontaneous combustion of residual coal and control of water in coal mine production. The developed method offers good economic benefits for safe production in coal mines.

While offering high-precision control of neural circuits, optogenetics is hampered by the necessity to implant fiber-optic waveguides in order to deliver photons to genetically engineered light-gated neurons in the brain. Unlike laser light, X-rays freely pass biological barriers. Here we show that radioluminescent Gd2(WO4)3:Eu nanoparticles, which absorb external X-rays energy and then downconvert it into optical photons with wavelengths of ∼610 nm, can be used for the transcranial stimulation of cortical neurons expressing red-shifted, ∼590-630 nm, channelrhodopsin ReaChR, thereby promoting optogenetic neural control to the practical implementation of minimally invasive wireless deep brain stimulation.

This work presents a novel process to manufacture advanced Germanium-On-Insulator with integrated Silicon Nitride (GOIN) stripes as light waveguide for Ge photonic devices. Through the integration of GOIN stripes, larger tensile strain could be imposed to the bonded Ge layer which may could be used to tailor the bandgap of Ge material for short wave length infrared application. The successful fabrication of advanced GOIN substrate makes the opportunity for monolithic integration of high-performance Ge-based high mobility transistors with photonic components where silicon nitride is the waveguide with low optical loss.

Jujube–cotton intercropping system is a common efficient practice in China, especially in Xinjiang, Northwest China. While setting up two jujube–cotton intercropping systems, we planted two or four rows of cotton between two rows of jujube to study the formation of yield and factors influencing yield of 1-year jujube on intercropping cotton. The results indicated that photosynthetic/physiological characteristics of cotton in different planting patterns were significantly different only from the squaring stage to prophase of the boll opening stage, and the soil plant analyzer development value were not different all the time. Under the influence of jujube shading, the net photosynthetic rate, stomatal conductance, transpiration rate, and yield of cotton in a four-row cotton intercropping pattern (Int-4) and a two-row cotton intercropping pattern (Int-2) were significantly decreased compared with those of one-row cotton (Sole) and the intercellular CO2 concentration increased. There was no significant difference between Int-2 and Int-4 in above photosynthetic/physiological characteristics. The higher planting density makes the harvested number and boll number of Int-4 superior. Compared to the Int-2, the yield of Int-4 increased significantly by 10.50%, and the economic benefits from high to low were Int-4 > Int-2 > Sole. Therefore, we concluded that an appropriate increase of cotton planting density is beneficial to the high yield of cotton in a jujube–cotton intercropping system, because it is conducive to increasing the income of farmers.

Wireless sensor networks (WSNs) is associated with a new exemplification of a real-time embedded system used for various applications that make the traditional infrastructure-based network seem infeasible. Due to imbalanced energy consumption among nodes, WSN has challenges with better utilization of energy and system enhancement. Clustering has been a better approach in this sequence. Therefore, this paper will utilize a fuzzy logic-based clustering protocol (unequal clustering) with multi-hop transmission for load balancing, energy consumption minimization, and network lifetime prolongation. The protocol forms unequal clusters with cluster head (CH) being selected by fuzzy logic with competition radius. Node distance to the base station, concentration, and residual energy are input variables. The simulation and results section displays the outperformance of the proposed protocol, where the low-energy adaptive clustering hierarchy (LEACH), energy-aware multi-hop multi-path hierarchical (EAMMH), energy-aware unequal clustering fuzzy (EAUCF), and two-tier distributed fuzzy logic-based protocol (TTDFP) for efficient data aggregation in multi-hop wireless sensor networks algorithms.

Eddy current based approaches have been investigated for a wide range of inspection applications. Dodd-Deeds model and the truncated region eigenfunction expansion (TREE) method are widely applied in various occasions, mostly for the cases that the sample is relatively larger than the radius of the sensor coil. The TREE method converts the integral expressions to the summation of many terms in the truncated region. In a recent work, the impedance of the co-axial air-cored sensor due to a plate of finite radius was calculated by the modified Dodd-Deeds analytical approach proposed by authors. In this paper, combining the modified analytical solution and the TREE method, a new finite region eigenfunction expansion (FREE) method is proposed. This method involves modifying its initial summation point from the first zero of the Bessel function to a value related to the radius of the plate, therefore makes it suitable for plate with finite dimensions. Experiments and simulations have been carried out and compared for the verification of the proposed method. Further, the planar size measurements of the metallic circular plate can be achieved by utilising the measured peak frequency feature.

Background Low-moisture foods (LMFs) are generally considered “lower risk” in terms of food safety, however, the frequent foodborne illnesses involved in the consumption of LMFs has heightened the public concern. The low aw environments also offer considerable protection against microorganisms. Meanwhile, the relatively high contamination risk of mycotoxins in low-moisture foods is a challenge for the food industry. Thermal decontamination techniques usually destroy heat-sensitive nutrients and lower product quality, and they are not adequate for mycotoxins detoxification. Therefore, developing non-thermal decontamination technologies to improve the safety of LMFs is of great interest in both of economics and public health. Scope and approach This review discusses the effects of non-thermal decontamination technologies, including ultraviolet and pulsed light, ionizing irradiation, cold plasma, and ozone, on microbial population and mycotoxin contents on LMFs. Furthermore, the working principles, applications, factors affecting processing efficacy and limitations of these decontamination technologies are summarized. The further research opportunities in commercialization are identified and discussed. Key findings and conclusions Non-thermal decontamination technologies like UV and pulsed light, ionizing irradiation, cold plasma, and ozone have high potential as promising technologies for microbial inactivation and mycotoxin degradation for improving the safety and quality of LMFs. The operating conditions of the treatment, food property, species of microorganisms and mycotoxins are the major determinants affecting the processing efficacy. Further studies are recommended to evaluate the degradant toxicology and its interaction with food components, and particularly scaling-up the technology for commercial applications should be given more attention.

Metallic wires are used in many industrial applications, where the diameter and conductivity are key parameters to be measured. In eddy currents testing, the inductance of the sensor is affected by both the diameter and conductivity of sample wire. Consequently, wire diameter and conductivity cannot be determined from a single inductance measurement. In this paper, a novel combined inductive and capacitive sensor is designed, in which the diameter can be inversed from the capacitance measurement of the sensor. Based on the diameter value, the inductance measurement can be used to accurately determine the conductivity of the wires using a look-up table approach. In addition, a model for a wire inside of the capacitive sensor is derived. Measurements have been carried out for 16 samples and the results have verified the proposed approach. This technique could contribute to real-time in-situ quality control of wire + arc additive manufacture processes.

Ultra-high-purity silicon tetrachloride (SiCl4) is demanded as an electronic-grade chemical to meet the stringent requirements of the rapidly developing semiconductor industry. The high requirement for ultra-high-purity SiCl4 has created the need for a high-efficient process for reducing energy consumption as well as satisfying product quality. In this paper, a mass of production technology of ultra-high-purity SiCl4 was successfully developed through chlorination reaction in the ultraviolet (UV)-based photo microreactor coupled with the distillation process. The influences of key operational parameters, including temperature, pressure, UV wavelength and light intensity on the product quality, especially for hydrogen-containing impurities, were quantified by the infrared transmittance of Fourier transform infrared spectroscopy (FT-IR) at 2185 cm−1 and 2160 cm−1 indicating that characteristic vibrational modes of Si H bonds, as well as the operating conditions of distillation were also investigated as key factors for metal impurities removing. The advanced intensification of SiCl4 manufactured by the integration of photo microreactor and distillation achieves the products with superior specifications higher than the standard commercial products.

Summary Background Previous clinical and animal studies suggested that medium-chain triglycerides (MCT) might be an alternative energy substrate for the brain and might benefit patients with Alzheimer's disease (AD), but the clinical evidence is not substantial or totally convincing. Objective To investigate the effects of MCT on cognitive ability in patients with mild to moderate AD and explore the changes in peripheral blood metabolomics. Methods A double-blind, randomized, placebo-controlled crossover study was undertaken in 53 mild to moderate AD patients. Participants were randomized between two sequences (placebo followed by MCT or MCT followed by placebo) and took MCT jelly or placebo jelly (canola oil) by mouth three times daily (total daily fat dose: 17.3 g MCT, or 19.7 g canola oil) for 30 days per phase. The primary outcome was cognition as measured by the Alzheimer's Disease Assessment Scale–Cognitive Subscale, Chinese version (ADAS-Cog-C). The secondary outcome was self-care as measured by the activities of daily living scale (ADL) and changes in plasma metabolites. Results This study showed a significant (p 0.05). The concentrations of TC, HDL-C, β-hydroxybutyrate and acetoacetate were significantly higher in the MCT group than in the placebo group (p Conclusions MCT had positive effects on cognitive ability in mild to moderate AD patients with APOE4−/−. These effects of MCT might be related to the metabolism of LysoPC, oleic acid, linoleic acid and palmitic acid, in addition to the ketogenic effect. Study ID number ChiCTR-IOR-16009737. Registry website WHO ICTRP Search Portal – http://apps.who.int/trialsearch/Default.aspx .

The technique of recognising text in natural scene pictures is widely used in social production. For the existing identification methods, it is difficult to accurately identify in complex environments. The accuracy of the detection determines the efficiency of the identification. A text detection method based on Multiscale Connectionist Text Proposal Network is proposed. The Multiscale-Region Proposal Network regresses and classifies the extracted region to obtain the final candidate region. Taking a large number of commodity image samples as a dataset, the multi-scale joint text proposal network is used to detect and locate the text content area in the image. The experimental results show that the proposed algorithm improves the detection accuracy in complex environments.

The recently increasing interest in using super-elastic shape memory alloys (SMAs) for structural vibration control creates the need for a suitable mechanical model that considers the material’s close correlation with the strain amplitude and the strain rate. This paper proposes an improved Graesser-Cozzarelli (G-C) model for predicting the hysteretic responses of the super-elastic SMAs at various strain rates and strain amplitudes. A series of uniaxial tensile tests of super-elastic SMA wires are conducted first, and the effects of the strain amplitude and the strain rate on the characteristic parameters are quantified. Then, based on the original G-C model and experimental test results, an improved version is proposed. The revised model considers the nonlinear hardening behaviour of martensite and divides the complete hysteretic curve into two stages: a loading stage and an unloading stage. Finally, numerical simulations are conducted to evaluate the model’s performance in predicting the hysteresis responses at various strain amplitudes and strain rates. High accuracy is realized in all comparisons with the experimental results; thus, the improved G-C model is highly suitable for the simulation of super-elastic SMA-based dampers in applications for vibration control.

Adaptive optics—optical coherence tomography (AO-OCT) is a non-invasive technique for imaging retinal vascular and structural features at cellular-level resolution. Whereas retinal blood vessel density is an important biomarker for ocular diseases, particularly glaucoma, automated blood vessel segmentation tools in AO-OCT have not yet been explored. One reason for this is that AO-OCT allows for variable input axial dimensions, which are not well accommodated by 2D-2D or 3D-3D segmentation tools. We propose a novel bidirectional long short-term memory (LSTM)-based network for 3D-2D segmentation of blood vessels within AO-OCT volumes. This technique incorporates inter-slice connectivity and allows for variable input slice numbers. We compare this proposed model to a standard 2D UNet segmentation network considering only volume projections. Furthermore, we expanded the proposed LSTM-based network with an additional UNet to evaluate how it refines network performance. We trained, validated, and tested these architectures in 177 AO-OCT volumes collected from 18 control and glaucoma subjects. The LSTM-UNet has statistically significant improvement (p &lt, 0.05) in AUC (0.88) and recall (0.80) compared to UNet alone (0.83 and 0.70, respectively). LSTM-based approaches had longer evaluation times than the UNet alone. This study shows that a bidirectional convolutional LSTM module improves standard automated vessel segmentation in AO-OCT volumes, although with higher time cost.

SLAM is a key technology in the field of mobile robot. However, it is difficult to meet the requirements of positioning accuracy by using single sensor to locate and navigate for a mobile robot. Multi-sensor information fusion technology has become an important method to solve the problem of mobile robot positioning and navigation. By establishing information fusion model, building sensor experimental platform such as laser radar, ultrasonic ranging module and monocular camera, based on the laser RBPF-SLAM algorithm, a SLAM algorithm based on multi-sensor information fusion is proposed. The mobile robot can locate and construct road sign map at the same time. The laser RBPF-SLAM positioning and monocular visual positioning are advanced by using the information fusion model Information fusion under the maximum posterior probability criterion. Through experiments, the accuracy, effectiveness and practicability of the proposed method are verified.

Intrahepatic cholangiocarcinoma (ICC) remains a highly heterogeneous disease with poor prognosis. Tumor-infiltrating lymphocytes were predictive in various cancers, but their prognostic value in ICC is less clear. A total of 168 ICC patients who had received liver resection were enrolled and assigned to the derivation cohort. Sixteen immune markers in tumor and peritumor regions were examined by immunohistochemistry. A least absolute shrinkage and selection operator model was used to identify prognostic markers and to establish an immune signature for ICC (ISICC ). An ISICC -applied prediction model was built and validated in another independent dataset. Five immune features, including CD3peritumor (P) , CD57P , CD45RAP , CD66bintratumoral (T) and PD-L1P , were identified and integrated into an individualized ISICC for each patient. Seven prognostic predictors, including total bilirubin, tumor numbers, CEA, CA19-9, GGT, HBsAg and ISICC , were integrated into the final model. The C-index of the ISICC -applied prediction model was 0.719 (95% CI, 0.660-0.777) in the derivation cohort and 0.667 (95% CI, 0.581-0.732) in the validation cohort. Compared with the conventional staging systems, the new model presented better homogeneity and a lower Akaike information criteria value in ICC. The ISICC -applied prediction model may provide a better prediction performance for the overall survival of patients with resectable ICC in clinical practice.

Background Mixed evidence challenges preoperative alpha-fetoprotein (AFP) as an independent prognostic factor for patients with hepatocellular carcinoma (HCC) after hepatectomy. Results Daily post-operative decrease of AFP by 9% as compared to the preoperative level (A09) were selected as the Cut-off. The Kaplan-Meier curve showed that A09 was significantly different for OS (P=0.043) and RFS (P=0.03). A decrease in risk by 54% was observed for OS and 32% for RFS in the at-risk population (A09>9%). A better concordance was observed after adding A09 into TNM and BCLC staging systems. Moreover, a consistent concordance was observed in the internal (FDZS5:0.63; FDZS3:0.608) and external (FDZS5:0.85; FDZS3:0.762) validation cohorts, suggesting its prognostic value in HCC population with elevated AFP. Conclusions Decrease in perioperative serum AFP rather than preoperative AFP is an independent prognostic factor for HCC patients after hepatectomy. Cut-off A09 significantly discriminates overall and recurrence-free survival and could be interpret into TNM and BCLC staging systems to improve the stratification power for HCC patients with elevated AFP. Methods Kaplan-Meier curve depicted the differences of overall survival (OS) and recurrence-free survival (RFS). Nomogram and concordance were employed to evaluate the superiority of the current staging system.

The effects of ultrasound-enhanced hot air drying on the drying characteristics, microstructure and bioactive profile of germinated highland barley seeds (GHB) were studied. GHB was dried by hot air at 55 &deg, C and 70 &deg, C and ultrasonic intensities of 125.1 W/dm2 and 180.2 W/dm2, respectively. The results showed that when the drying temperature was 55 &deg, C or 70 &deg, C, the sonicated groups could shorten the drying time by 17.4&ndash, 26.1% or 18.8&ndash, 31.3%, respectively. Ultrasound drying at 125.1 W/dm2 and 55 &deg, C could mostly increase the content of organic selenium and the rehydration rate, improve the color and maintain the original structure of GHB. Compared with hot air drying alone, the phenolic content did not increase due to ultrasound-enhanced hot air drying. Therefore, drying at an ultrasonic intensity of 125.1 W/dm2 and a temperature of 55 &deg, C could effectively shorten the drying time, and enhance the quality of GHB.

The drying kinetics and detailed phytochemical profile of broccoli under microwave drying (500 and 900 W) and air-borne ultrasound (125.2 and 180.1 W/dm2) assisted air drying at 70 °C were compared...

Background: Whether microvascular invasion (MVI) adversely influences oncological outcomes for intrahepatic cholangiocarcinoma (ICC) patients remains unclear. The purpose of this study was to determine the impact of MVI on postoperative survival and establish a new predictive model for MVI before surgical intervention in patients with ICC. Methods: In this two-center retrospective study, 556 and 31 consecutive patients who underwent curative liver resection for ICC at ZSH and XJFH were analyzed, respectively. Propensity score matching (PSM) and Cox regression analyses were used to explore the prognostic role of MVI on the OS and DFS. Multivariate logistic regression was used to identify the relative risk factors of MVI, which were incorporated into the nomogram. Results: After PSM, 50 MVI cases matched with 172 non-MVI cases, and no bias was observed between the two groups (propensity score, 0.118 (0.099, 0.203) vs. 0.115 (0.059, 0.174), p=0.251). The multivariate Cox analysis showed that MVI was negatively associated with OS (HR 1.635, 95% CI 1.405-1.993, p=0.04) and DFS (HR 1.596, 95% CI 1.077-2.366, p=0.02). The independent factors associated with MVI were ALT, AFP, tumor maximal diameter, and tumor capsule. The nomogram that incorporated these variables achieved good concordance indexes for predicting MVI. Patients with a cutoff score of 168 were considered to have different risks of the presence of MVI preoperatively. Conclusions: The presence of MVI was an adverse prognostic factor for ICC patients. Using the nomogram model, the risk of an individual patient harboring MVI was determined, which led to a rational therapeutic choice.

In order to study the gas occurrence regularity and the main controlling factors of Panxi Mine, gas parameters of the mine were determined through the combination of field survey and experiment, an...

Objective There is increasing neuroimaging evidence that type 2 diabetes patients with retinal microvascular complications show abnormal brain functional and structural architecture and are at an increased risk of cognitive decline and dementia. However, changes in the topological properties of the functional brain connectome in diabetic retinopathy (DR) patients remain unknown. The aim of this study was to explore the topological organization of the brain connectome in DR patients using graph theory approaches. Methods Thirty-five DR patients (18 males and 17 females) and 38 healthy controls (HCs) (18 males and 20 females), matched for age, sex, and education, underwent resting-state magnetic resonance imaging scans. Graph theory analysis was performed to investigate the topological properties of brain functional connectome at both global and nodal levels. Results Both DR and HC groups showed high-efficiency small-world network in their brain functional networks. Notably, the DR group showed reduction in the clustering coefficient (P=0.0572) and local efficiency (P=0.0151). Furthermore, the DR group showed reduced nodal centralities in the default-mode network (DMN) and increased nodal centralities in the visual network (VN) (P

Intrahepatic cholangiocarcinoma (ICC) is a rare malignancy with poor prognosis. The evaluation of recurrence risk after liver resection is of great importance for ICCs. We aimed to assess the prognostic value of intra- and peritumoral immune infiltrations and to establish a novel histopathology-related immunoscore (HRI) associated with ICC recurrence. A total of 280 ICC patients who received curative resection between February 2005 and July 2011 were enrolled in our study. Patients were randomly assigned to the derivation cohort (n = 176) or the validation cohort (n = 104). Sixteen immune biomarkers in both intra- and peritumoral tissues were examined by immunohistochemistry. The least absolute shrinkage and selection operator (LASSO) Cox model was used to establish the HRI score. Cox regression analysis was used for multivariate analysis. Nine recurrence-related immune features were identified and integrated into the HRI score. The HRI score was used to categorize patients into low-risk and high-risk groups using the X-tile software. Kaplan–Meier analysis presented that the HRI score showed good stratification between low-risk and high-risk groups in both the derivation cohort (P

In this study, we designed an experimental system of surface potential during coal adsorption of gas to examine the characteristics of surface potential in this process. The results suggested that ...

OBJECTIVE To investigate inhibitory effect of semen litchi drug serum on proliferation of human hepatoma HepG2 cells and its effect on the expression of vascular endothelial growth factor (VEGF) and matrix metalloproteinase-9 (MMP-9). STUDY DESIGN An experimental study. PLACE AND DURATION OF STUDY College of Pharmacy, Guangxi University of Chinese Medicine, China, from June 2017 to January 2018. METHODOLOGY Semen litchi drug serum with concentrations of 0 mg/kg (control group), 3 g/kg (low dose group), 6 g/kg (medium dose group) and 12 g/kg (high dose group) was used to act on HepG2 cells at the logarithmic phase. Inhibitory effect of semen litchi drug serum on cell growth, expression of VEGF and MMP-9 mRNA and protein was detected. RESULTS Inhibitory effect of semen litchi drug serum on the proliferation of HepG2 cells significantly increased with the increase of drug concentration, which was dose-time dependent. Expression levels of VEGF and MMP-9 mRNA in HepG2 cells after 48 hours of treatment by semen litchi low-dose group, medium-dose group, and high-dose group were lower than those in control group (all p

Purpose: To develop and validate a decision aid to help make individualized estimates of tumor recurrence for patients with resected combined hepatocellular cholangiocarcinoma (CHC). Patients and methods: Risk factors of recurrence were identified in the derivation cohort of 208 patients who underwent liver resection between 1995 and 2014 at Zhongshan Hospital to develop a prediction score. The model was subsequently validated in an external cohort of 101 CHC patients using the C concordance statistic and net reclassification index (NRI). Results: On multivariate analysis, five independent predictors associated with tumor recurrence were identified, including sex, γ-glutamyl transferase, macrovascular invasion, hilar lymphoid metastasis and adjuvant transcatheter arterial chemoembolization. The prediction score was constructed using these 5 variables, with scores ranging from 0 to 5. A patient with a score of 0 had a predicted 1- and 5-year recurrence risk of 11.1% and 22.2%, respectively. In the validation cohort, the NRIs of prediction score vs American Joint Committee on Cancer 7th TNM staging system at 1-year and 5-year were 0.185 (95% CI, 0.090-0.279, P

In this paper, a novel method named distributed independent component–principal component regression (distributed ICPCR) is proposed to monitor the large-scale non-Gaussian process. First, multiple...

Plaque characteristics play pivotal roles in ischemic events, but stenosis severity does not accurately reflect carotid plaque volume due to expansive remodeling in some patients with low-grade stenosis (LGS). This study aimed to assess the safety, efficacy, and durability of carotid endarterectomy (CEA) for symptomatic LGS.Study participants comprised 61 consecutive patients who underwent CEA for symptomatic carotid stenosis. Patients were divided into an LGS group (50%, n = 17) and a non-LGS group (≥50%, n = 44). Patient characteristics and short-term (within 30 days of CEA) and long-term outcomes were compared between groups for selective usage of internal shunt and known complications of CEA.Magnetic resonance imaging-detected intraplaque hemorrhage was more significant in LGS than in non-LGS (P = 0.04). For short-term outcomes, no symptomatic infarcts, hyperperfusion syndrome, or acute myocardial infarction was confirmed in either group. Internal shunts were used in 4 LGS (23.5%) and 6 non-LGS (13.6%). Asymptomatic diffusion-weighted imaging-positive lesions were confirmed in 2 LGS patients (11.8%) and 5 non-LGS patients (11.4%), neck hematoma in 1 LGS patient, and transient cranial nerve palsy in 1 LGS patient and 2 non-LGS patients, with no significant differences apparent between groups. For long-term outcomes, 5 non-LGS patients showed restenosis (P = 0.17). Hemorrhagic stroke was not observed in either group. No significant differences were seen for infarct in the ipsilateral carotid territory, any ischemic stroke, AMI, or mortality.CEA represents a safe and feasible therapeutic option for a subset of patients with symptomatic LGS.

Tomato spotted wilt virus is a wide-spread plant disease in the world. It can threaten thousands of plants with a persistent and propagative manner. Early disease detection is expected to be able to control the disease spread, to facilitate management practice, and further to guarantee accompanying economic benefits. Hyperspectral imaging, a powerful remote sensing tool, has been widely applied in different science fields, especially in plant science domain. Rich spectral information makes disease detection possible before visible disease symptoms showing up. In the paper, a new hyperspectral analysis proximal sensing method based on generative adversarial nets (GAN) is proposed, named as outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). It is an all-in-one method, which integrates the tasks of plant segmentation, spectrum classification and image classification. The model focuses on image pixels, which can effectively visualize potential plant disease positions, and keep experts’ attention on these diseased pixels. Meanwhile, this new model can improve the performances of classic spectrum band selection methods, including the maximum variance principle component analysis (MVPCA), fast density-peak-based clustering, and similarity-based unsupervised band selection. Selecting spectrum wavebands reasonably is an important preprocessing step in spectroscopy/hyperspectral analysis applications, which can reduce the computation time for potential in-field applications, affect the prediction results and make the hyperspectral analysis results explainable. In the experiment, the hyperspectral reflectance imaging system covers the spectral range from 395 nm to 1005 nm. The proprosed model makes use of 83 bands to do the analysis. The plant level classification accuracy gets 96.25% before visible symptoms shows up. The pixel prediction false positive rate in healthy plants gets as low as 1.47%. Combining the OR-AC-GAN with three existing band selection algorithms, the performance of these band selection models can be significantly improved. Among them, MVPCA can leverage only 8 spectrum bands to get the same plant level classification accuracy as OR-AC-GAN, and the pixel prediction false positive rate in healthy plants is 1.57%, which is also comparable to OR-AC-GAN. This new model can be potentially transferred to other plant diseases detection applications. Its property to boost the performance of existing band selection methods can also accelerate the in-field applications of hyperspectral imaging technology.

With the continuous expansion of the scale of the power grid and the gradual maturity of the application of drones, drones with the advantages of flexibility and convenience can overcome terrain constraints while reducing manpower and safety risks and can be well qualified for the power inspection work under the large-scale development of the power grid and improve the inspection efficiency. However, the massive amount of data collected by UAV power line inspection relies on the human judgment through the naked eyes of staff, and the recognition efficiency is very low and the accuracy rate is subjectively influenced. In recent years, the gradual introduction of traditional machine learning and deep learning has led to significant improvements in inspection efficiency and accuracy. The autonomous obstacle avoidance technology based on the inherent electromagnetic field characteristics of power lines makes it possible for UAVs to cruise autonomously. Based on a systematic summary of the achieved results, this paper briefly reviews the overall development of both power component detection and autonomous cruise in UAV power line inspection. At the same time, the current problems and future research trends are described, expecting to provide references for further research in this field.

Background Hereditary hearing loss (HHL) is the most common sensory deficit, which highly afflicts humans. With gene sequencing technology development, more variants will be identified and support genetic diagnoses, which is difficult for human experts to diagnose. This study aims to develop a machine learning-based genetic diagnosis model of HHL-related variants of GJB2, SLC26A4 and MT-RNR1. Methods This case-control study included 1898 subjects, among which 1354 were HHL patients and 544 were carriers. Risk assessment models were established based on variants at 144 sites in three genes related to HHL by building six machine learning (ML) models. We compared the ML models with the genetic risk score (GRS) and expert interpretation (EI) to verify the clinical performance. Findings Among the six ML models, the support vector machine (SVM) showed the best performance. For the prediction of HHL-related gene sites in subjects with variants, the area under the receiver operating characteristic (AUC) of the SVM model was 0.803 (0.680–0.814) in the 10-fold stratified cross-validation and 0.751 (0.635–0.779) in external validation. The predicted results were better than both EI and GRS. Furthermore, 11 sites were identified as the smallest feature set that can be accurately predicted. Interpretation The developed SVM model has great potential to be an efficient and effective tool for HHL prediction when high throughput sequencing data are available. Funding This study was supported by the National Key Research and Development Program (2017YFC1001800).

Multiple sclerosis (MS) is a demyelinating disease of the central nervous system, predominating within young adults. Cognitive disorders are common in MS and have are associated with several Magnetic Resonance Imaging (MRI) markers, especially brain atrophy. Many have found the symbol digit modalities test (SDMT) to be the most sensitive individual cognitive measure relevant to MS. However, the relationship between SDMT and regional brain cortex thickness in young adults with relapsing-remitting multiple sclerosis (YA-RRMS) has been little explored. The purpose of this study was to investigate the association between the SDMT and regional cortex thickness in YA-RRMS by FreeSurfer, which is an automatic brain structure segmentation method.Twenty-eight YA-RRMS patients (18-35 years old) were enrolled in the present study. Informed consent and information including gender, age, disease duration, number of relapses, annual relapse rate was collected from all patients. Clinical cognitive evaluations (SDMT and auditory verbal learning test (AVLT)) and daily performance: activities of daily living (ADL) were assessed in the present study. MRI scans were performed at the Institute of Neurosurgery of Tiantan Hospital. Twenty-eight matched healthy controls (HC) MRI data were obtained from Tiantan Hospital database. Data on thirty-four points of bilateral cortical structure thickness using statistically defined brain regions-of-interest from FreeSurfer were obtained from all participants.Patients with RRMS exhibited extensively thinner cerebellar cortex compared with HC. SDMT scores were significantly correlated with AVLT subentries (IM, immediate memory; DRM, delayed recall memory; LTRM, long-term recognition memory) in YA-RRMS patients (P 0.05). SDMT was strongly correlated with regional cortex thickness differences of the right temporal pole (r = 0.68) and bilateral parahippocampal areas (right r = 0.62; left r = 0.60), and moderately correlated with regional cortex thickness differences including the left superior temporal and right insula (r = 0.57 and 0.56, respectively) in YA-RRMS patients.The present study has shown the SDMT is strongly correlated with selected cortex regions including the bilateral parahippocampal area and the right temporal pole which are involved in geometric structures processing.

Highlights • Ultrasonic energy was input in both contact and airborne modes to dry blackberry. • Ultrasonic influences on heat and mass transfer were simulated numerically. • Shrinkage, input ultrasonic heat and temperature-dependent diffusivity were added. • Contact sonication was more capable to accelerate drying and save energy. • Contact sonication better preserved bioactives and flavors in blackberry., This study aimed at investigating the performances of air drying of blackberries assisted by airborne ultrasound and contact ultrasound. The drying experiments were conducted in a self-designed dryer coupled with a 20-kHz ultrasound probe. A numerical model for unsteady heat and mass transfer considering temperature dependent diffusivity, shrinkage pattern and input ultrasonic energies were applied to explore the drying mechanism, while the energy consumption and quality were analyzed experimentally. Generally, both airborne ultrasound and contact ultrasound accelerated the drying process, reduced the energy consumption and enhanced the retentions of blackberry anthocyanins and organic acids in comparison to air drying alone. At the same input ultrasound intensity level, blackberries received more ultrasound energies under contact sonication (0.299 W) than airborne sonication (0.245 W), thus avoiding the attenuation of ultrasonic energies by air. The modeling results revealed that contact ultrasound was more capable than airborne ultrasound to intensify the inner moisture diffusion and heat conduction, as well as surface exchange of heat and moisture with air. During air drying, contact ultrasound treatment eliminated the gradients of temperature and moisture inside blackberry easier than airborne ultrasound, leading to more homogenous distributions. Moreover, the total energy consumption under air drying with contact ultrasound assistance was 27.0% lower than that with airborne ultrasound assistance. Besides, blackberries dehydrated by contact ultrasound contained more anthocyanins and organic acids than those dried by airborne ultrasound, implying a higher quality. Overall, direct contact sonication can well benefit blackberry drying in both energy and quality aspects.

Partial least squares (PLS) and linear regression methods are widely utilized for quality-related fault detection in industrial processes. Standard PLS decomposes the process variables into principal and residual parts. However, as the principal part still contains many components unrelated to quality, if these components were not removed it could cause many false alarms. Besides, although these components do not affect product quality, they have a great impact on process safety and information about other faults. Removing and discarding these components will lead to a reduction in the detection rate of faults, unrelated to quality. To overcome the drawbacks of Standard PLS, a novel method, MI-PLS (mutual information PLS), is proposed in this paper. The proposed MI-PLS algorithm utilizes mutual information to divide the process variables into selected and residual components, and then uses singular value decomposition (SVD) to further decompose the selected part into quality-related and quality-unrelated components, subsequently constructing quality-related monitoring statistics. To ensure that there is no information loss and that the proposed MI-PLS can be used in quality-related and quality-unrelated fault detection, a principal component analysis (PCA) model is performed on the residual component to obtain its score matrix, which is combined with the quality-unrelated part to obtain the total quality-unrelated monitoring statistics. Finally, the proposed method is applied on a numerical example and Tennessee Eastman process. The proposed MI-PLS has a lower computational load and more robust performance compared with T-PLS and PCR.

Oysters are an essential species in the Chesapeake Bay living ecosystem. Oysters are filter feeders and considered the vacuum cleaners of the Chesapeake Bay that can considerably improve the Bay's water quality. Many oyster restoration programs have been initiated in the past decades and continued to date. Advancements in robotics and artificial intelligence have opened new opportunities for aquaculture. Drone-like ROVs with high maneuverability are getting more affordable and, if equipped with proper sensory devices, can monitor the oysters. This work presents our efforts for videography of the Chesapeake bay bottom using an ROV, constructing a database of oysters, implementing Mask R-CNN for detecting oysters, and counting their number in a video by tracking them., Comment: 7 pages, 9 figures, Submitted to ICRA 2021

The power supply mode of microelectronic equipment is still mainly battery. The difficulty in replacing the batteries affects the life time of these devices. Radio frequency energy harvesting (RFEH) technology provides a new way for the power supply of low-power microelectronic equipment. This paper designs a RFEH system which harvests 2.4 GHz radio frequency signal. The system is mainly composed of miniature patch antenna, impedance matching circuit, rectifier booster circuit, and storage circuit. The ADS simulation tool is used to verify the feasibility of the system design. When the load resistance is 100 KΩ and the input power is between −10 and 5 dBm, the energy conversion efficiency is above 30%. Continuous power supply for low-power microelectronic equipment can be realized.

Commercialized RGB-D and LiDAR cameras have been widely applied in food and agricultural engineering fields. However, the general-use 3D cameras cannot ensure high resolution 3D imaging because of their imaging principles. The accuracy of their generated height map ranges from 1cm – 10cm in the actual measurements, which cannot meet the requirement of accurate object height estimation in the food processing lines, especially for visualizing sub-centimeter food product shape variations. This research customized an active 3D scanning system, which utilized the line scanning method to ensure the scanning speed compared to the dot scanning and to minimize the cross talk in the frame scanning. Different from some industrial level 3D line scanning cameras, the system avoided synchronizing the imaging acquisition and processing line conveyor movement. Instead, to implement the 3D imaging for a static object, a programmable rotational mirror was integrated in the system to reflect the line laser for covering the scanning of the entire field of view. In our experimental setup, for a 5-cm calibration phantom, the standard derivation of height estimation from the system is 0.15mm in a 1m photographic distance, which is superior to commercialized RGB-D cameras including Intel RealSense and Kinect. The customized system can also automatically generate well-registered color and depth image pairs and its idea can easily fit into different food and agricultural applications.

Access control encryption (ACE) is a useful concept introduced by Damgard, Haagh and Orlandi in TCC 2016, which not only protects the data privacy but also controls the information flow. However, their DDH-based scheme suffered from the ciphertext revealing attack (CRA) introduced by Badertscher, Matt and Maurer in Asiacrypt 2017 and just satisfied chosen plaintext attack (CPA) security. Badertscher, Matt and Maurer strengthened the security model to chosen ciphertext attack (CCA) security and constructed a CCA secure scheme under Naor-Yung paradigm. However, they did not indicate how to fix the DDH-based scheme proposed by Damgard, Haagh and Orlandi. Their CCA secure scheme is inefficient and complicated due to the non-interactive zero knowledge proofs (NIZKs) of a very complicated relation. And their scheme is constructed just for limited communication policies.

Identity-Based Cryptography (IBC) is a useful tool for the security of IoT devices, but securely deploying this cryptographic technique to the IoT systems is quite challenging. For instance, a leakage of the master secret key will result in the leakage of all IoT devices’ private keys. SM9 is the only approved IBC algorithm standard in China. It is critical to have mechanisms to protect the SM9 master secret keys. In this work, to reduce the risk of the master secret key leakage, we propose a (t, n)-threshold distributed private key generation scheme for SM9 with some techniques from multiparty computation. Our scheme is compatible with all the three SM9 sub-algorithms (i.e., the encryption, signature and key agreement). It is also provably secure and completely eliminates the single point of failures in SM9 that is concerned by the industry. The experimental analysis indicates that the proposed scheme is efficient, e.g., up to 1 million private key generation requests can be handled per day.

Eddy current testing (ECT) is an effective technique for evaluating depth of metal surface defects. However, in practice, evaluation primarily relies on the experience of an operator and is often carried out by manual inspection. In this article, we address the challenges of automatic depth evaluation of metal surface defects by virtual of state-of-the-art deep learning (DL) techniques. The main contributions are threefold. First, a highly integrated portable ECT device is developed, taking the advantage of an advanced field-programmable gate array (Zynq-7020 system on chip) and provides fast data acquisition and in-phase/quadrature demodulation. Second, a dataset, termed metal defects of different depths by ECT (MDDECT), is constructed using the ECT device by human operators and made openly available. It contains 48000 scans from 18 defects of different depths and liftoffs. Third, the depth evaluation problem is formulated as a time series classification problem, and various state-of-the-art 1-D residual convolutional neural networks are trained and evaluated on the MDDECT dataset. A 38-layer 1-D ResNeXt achieves an accuracy of 93.58% in discriminating the surface defects in a stainless steel sheet with depths from 0.3 to 2.0 mm in the resolution of 0.1 mm. In addition, the results show that the trained ResNeXt1D-38 model is immune to liftoff signals.

Anonymous signature is a cryptographic tool where the signature of a message can hide its identity of the signer as long as part of the signature is hidden from the verifier. It gives a handy protection of signer identity and is useful in key exchange, auctions and voting.

Titanium alloy is widely used in the area of aerospace and aviation due to its excellent properties. Eddy current testing (ECT) is among the most extensively used non-destructive techniques for titanium alloy material inspection. However, most previous research has focused on inspecting defects far from the edge of the material. It is a challenging task for edge crack detection because of edge effect. This study aims to investigate the influences of sensor parameters on edge effect and defect detection capability, and in the meantime, optimize sensor parameters to improve the capability of edge defect detection. The simulation method for edge effect evaluation is proposed including the 2k factorial design used for factor screening, and the regression model is fitted and validated for sensor design and optimization for edge defect detection. A simulation scheme is designed to investigate the defect detection capability. An approach comprehensively analyzing the influence of coil parameters on edge effect and defect detection capability is applied to determine the optimal coil parameters for edge defect detection.