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Denoising is an indispensable step in seismic data processing. Deep-learning-based seismic data denoising has been recently attracting attentions due to its outstanding performance. In this letter, we investigate the architecture of deep Convolutional Networks (ConvNets) for seismic data denoising. The untrained ConvNets are served as a generative network to a single seismic data profile with Gaussian noise. Starting with random initialized parameters, the generative networks with various handcrafted architectures have different ability to map the seismic data at iterations and can separate the Gaussian noise as residuals. For the purpose of exploring the ability of Gaussian noise separation, the depth, width, and skip connection as the main components of generative network are assembled as various architectures to fit Gaussian noise, clean, and noisy seismic data, respectively. Then, the favorable network architecture with high and low impedance (an ability to hinder data reconstruction) to noise and seismic data is adopted as prior model to seismic data denoising task. Furthermore, a stopping criterion is designed for the data fitting process to obtain the latent clean seismic data automatically. The proposed method does not need data sets for training and it makes use of network architecture as prior. Extensive experiments both on synthetic and field data demonstrate the effectiveness of the selected ConvNet and the advantages are evaluated by comparing the denoising results with f-x multi-channel singular spectrum analysis (MSSA) and state-of-the-art unsupervised neural network (NN)-based method.

Due to the constraints of natural environments, acquired prestack seismic data is usually not complete, which seriously affects subsequent seismic data processing. With the progress of deep learning, many neural networks with different structures have been applied to missing seismic data interpolation. Among them, U-net can efficiently interpolate the regularly and irregularly missing seismic traces with small gap. While for consecutively missing seismic traces with big gap, the interpolation results for low amplitude missing components need to be further improved. In this letter, we analyze the variation of interpolation results for consecutively missing seismic traces during the traditional U-net training process, and find that U-net tends to only interpolate the high amplitude missing components. Meanwhile, due to the distribution difference between low and high amplitude seismic data, one U-net model is insufficient to interpolate both high and low amplitude missing components with a wide amplitude range. To improve the interpolation results of single U-net, we propose a multistage training process to train multiple U-net models. Each U-net model focuses on interpolating different missing components with a small amplitude range. In this way, more accurate interpolation results for low amplitude missing components can be obtained. Comparison experiments conducted on synthetic and field seismic data show that, under the same number of training epochs, the proposed training process can produce more accurate interpolation results comparing with traditional single U-net.

Fault detection is a crucial task in seismic structure interpretation. Convolutional neural network (CNN)-based methods, in general, require large amount of labeled data for network training. One way to build the labeled data is to create synthetic seismic images with corresponding fault labels. However, it is hard to ensure that the synthetic data have the same fault feature distributions as the field data, which may lead to inaccurate and unreliable prediction results. Another way is to manually label the faults, which is time-consuming and subjective. In this letter, we propose that using knowledge distillation (KD) to improve the performance of fault detection by integrating the features from large number of synthetic samples and a small number of field samples. We distill knowledge from an ensemble of two teacher CNNs to train a student CNN (applied to final target) for seismic fault detection. In our work, one segmentation teacher CNN is trained on synthetic samples with known ground truth fault labels and another classification teacher CNN is trained on field samples with manually picked labels. Then, a classification student network is trained on samples generated by voting the results from two teacher models. The student CNN learns not only the general fault characteristics in the synthetic data but also the specific fault features of the target field data. Test on the field data shows that the student CNN highlights seismic fault more accurately with higher resolution than the teacher CNNs.

This paper discusses the key technologies for the fusion of Radio Frequency Identification (RFID) technology and wireless sensor networks through a fusion approach; meanwhile, it addresses the problem of energy imbalance infusion networks. This paper introduces the network energy consumption calculation methods under direct transmission and minimum energy multi-hop routing protocols, and designs an improved network structure model to improve the communication scale. The RFID reading and writing system based on Zig Bee technology is developed, and focuses on the hardware interface circuit design of the fusion system, the development of compatible communication protocols, the anti-collision algorithm, and the transmission performance of the system under two outdoor environments. The design of the environmental collection terminal uses relevant sensors to collect the transport environment data in the carriage. And Wireless Sensor Network (WSN) combined with General Packet Radio Service (GPRS) technology realizes the function of the main aggregation node of wireless sensors. The method achieves data aggregation and data transmission, and the development of the application platform of the monitoring system uses software technology. Based on the browser/server (B/S) architecture, the development of the monitoring system is finally realized. Finally, the cold chain transportation simulation experiment was conducted to test the system, and the results showed that the hardware of the cold chain coordination monitoring system can work normally and the software design of the monitoring system can meet the basic requirements of the system for the third-party logistics enterprises. The efficiency of our results is improved by about 10% compared to other studies, and their accuracy is improved to a higher degree, which makes our model more practical.

Residual magnetic error remains after standard levelling process. The weak non-geological effect, manifesting itself as streaky noise along flight lines, creates a challenge for airborne geophysical data processing and interpretation. Microleveling is the process to eliminate this residual noise and is now a standard areogeophysical data processing step. In this paper, we propose a two-step procedure for single aerogeophysical data microleveling: a deep convolutional network is first adopted as approximator to map the original data into a low-level part with nature geological structures and a corrugated residual which still contains high-level detail geological structures; second, the mixture of Gaussian robust principal component analysis (MoG-RPCA) is then used to separate the weak energy fine structures from the residual. The final microleveling result is the addition of low-level structures from deep convolutional network and fine structures from MoG-RPCA. The deep convolutional network does not need dataset for training and the handcrafted network serves as prior (deep image prior) to capture the low-level nature geological structures in the areogeophysical data. Experiments on synthetic data and field data demonstrate that the combination of deep convolutional network and MoG-RPCA is an effective framework for single areogeophysical data microleveling.

Aim: We investigated the efficacy and safety of hepatic artery infusion chemotherapy (HAIC) combined with anti-PD-1 immunotherapy and tyrosine kinase inhibitors (TKIs) for advanced hepatocellular carcinoma (HCC). Method: This retrospective study included HCC patients treated with HAIC, TKIs and anti-PD-1 antibodies between May 2019 and November 2020 in our hospital. Primary end points were progression-free survival and safety. Results: Twenty-seven advanced HCC patients were analyzed. The median follow-up was 12.9 months (range: 4.0–24.0 months) and the median progression-free survival was 10.6 months. The objective response rate and disease control rate were 63.0 and 92.6%, respectively. No treatment-related deaths occurred. Conclusion: In patients with advanced HCC, treatment with HAIC, anti-PD-1 antibodies and oral TKIs was effective and safe.

Depressive symptoms and abnormal glycolipid metabolisms are common in patients with Parkinson’s disease (PD), but their relationship has not been fully reported. It is not clear whether glycolipid impairments lead to poor cognitive and motor function, and aggravate depressive symptoms. Therefore, we aimed to explore the relationships between glycolipid variables, cognition, motor and depressive symptoms in PD patients cross-sectionally. Two hundred ten PD patients were recruited. Glycolipid parameters and Uric acid (UA) were measured. Depressive symptoms, cognitive function and motor symptoms were assessed using the Hamilton Depression Rating Scale-17 (HAMD-17), the Montreal Cognitive Assessment (MOCA) and the Movement Disorder Society Unified Parkinson’s Disease Rating Scale Part-III (UPDRS-III). Depressive PD patients had significantly worse motor symptoms and higher levels of fasting plasma glucose (FPG) than those in non-depressive patients (F = 24.145, P

This paper proposes a 7-dimensional ( 7D ) Cellular Neural Network (CNN) based offset quadrature amplitude modulation filter bank multicarrier (OQAM-FBMC) encryption scheme for seven core fiber. The chaotic sequences generated by 7D CNN are applied to produce the masking vectors to encrypt the phase, carrier frequency, and time. In order to verify the performance of the encryption scheme, 70 Gb/s (7×10 Gb/s) encrypted OQAM-FBMC signal transmission over 2 km 7 core fiber is experimentally demonstrated. The key space of 7D CNN can reach 101575 and the scrambling degree can be maintained at 100% regardless of the number of symbols. The experimental results also show that when some keys are compromised, the system's bit error rate (BER) can still reach above 0.46, which effectively ensures the security of the system. Due to its good performance in security, the proposed scheme has important application prospects in future optical access network.

Here a 1×5 microfluidic optical switch using double drives is proposed. It has a simple structure and ignorable polarization dependent loss. First, the switch with the straight waveguide structure ...

This study aimed to expand our understanding of metformin (Met) in inhibiting hepatocellular carcinoma (HCC) progression and to investigate its underlying mechanism. Met was administrated to HCC cells at 5, 10, and 20 μM, after which the cell phenotype was evaluated. RNA-seq cluster analysis was used to screen for target genes modulated by Met. Luciferase activity and ChIP assays were performed to detect the effect of FOXO3 on the transcriptional activation of NLRP3. We evaluated the effect of Met and FOXO3 and on the growth of HCC cells in vivo. Met inhibited HCC cell proliferation and promoted apoptosis. Met also induced pyroptosis of HCC cells. FOXO3 was significantly upregulated by Met treatment, and FOXO3 activated transcription of NLRP3. Cells after Met treatment together with FOXO3 knockdown have a stronger colony formation and migration ability but a lower apoptosis rate compared to the Met treatment alone group. In vivo, Met inhibited HCC tumor growth. The tumors in Met treatment and FOXO3 knockdown group grew faster than in Met treatment group. Thus, Met attenuates HCC cell development by inducing apoptosis and pyroptosis. This effect of metformin is partially dependent on FOXO3 which can activate the transcription of NLRP3.

This prospective non-randomized, multicenter clinical trial was performed to investigate efficacy and safety of 131I-labeled metuximab in adjuvant treatment of unresectable hepatocellular carcinoma. Methods: Patients were assigned to treatment with transcatheter arterial chemoembolization (TACE) combined with 131I-metuximab or TACE alone. The primary outcome was overall tumor recurrence. The secondary outcomes were safety and overall survival. Results: The median time to tumor recurrence was 6 months in the TACE+131I-metuximab group (n = 160) and 3 months in the TACE group (n = 160) (hazard ratio, 0.55; 95% confidence interval, 0.43 to 0.70; P < 0.001). The median overall survival was 28 months in the TACE+131I-metuximab group and 19 months in the TACE group (hazard ratio, 0.62; 95% confidence interval, 0.47 to 0.82; P = 0.001). Conclusion: TACE+131I-metuximab showed a greater anti-recurrence benefit, significantly improved the 5-year survival of patients with advanced hepatocellular carcinoma, and was well tolerated by patients.

Background In patients with traumatic brain injury (TBI) combined with long bone fracture, the fracture healing is always faster than that of patients with single fracture, which is characterized by more callus growth at the fracture site and even ectopic ossification. Exosomes are nanoscale membrane vesicles secreted by cells, which contain cell-specific proteins, miRNAs, and mRNAs. Methods In this study, we used exosomes as the entry point to explore the mechanism of brain trauma promoting fracture healing. We established a model of tibia fracture with TBI in mice to observe the callus growth and expression of osteogenic factors at the fracture site. Blood samples of model mice were further collected, exosomes in plasma were extracted by ultra-centrifugation method, and then identified and acted on osteoblasts cultured in vitro. The effects of exosomes on osteoblast differentiation at the cell, protein and gene levels were investigated by Western Blot and q-PCR, respectively. Furthermore, miRNA sequencing of exosomes was performed to identify a pattern of miRNAs that were present at increased or decreased levels. Results The results suggested that plasma exosomes after TBI had the ability to promote the proliferation and differentiation of osteoblasts, which might be due to the increased expression of osteoblast-related miRNA in exosomes. They were transmitted to the osteoblasts at the fracture site, so as to achieve the role of promoting osteogenic differentiation. Conclusion The TBI-derived exosomes may have potential applications for promoting fracture healing in future. The translational potential of this article Plasma exosomes early after TBI have the ability to promote osteoblast proliferation and differentiation. The mechanism may be achieved by miRNA in exosomes. Plasma exosomes may be used as breakthrough clinical treatment for delayed or non-union fractures.

The field of environmental health has begun to examine the effects of higher-order chemical combinations. The current literature lacks studies exploring associations between multiple organic chemical mixtures and cardiometabolic diseases (CVDs). This study aimed to evaluate associations between urinary phenols, parabens metabolites, and total and individual CVDs among a nationally representative sample of adults in the US. This cross-sectional study analyzed 7 urinary chemicals detected among the general population from the 2005–2016 National Health and Nutrition Examination Survey (NHANES, n=10,428). Multivariate logistic regression and weighted quantile sum (WQS) regression were applied to examine relationships between phenols and parabens metabolites, alone and in combination, and total and individual CVDs prevalence. Compared with the lowest quartile, URBPA (OR: 1.52; 95% CI: 1.20–1.91; P=0.001) levels in the highest quartile were independently associated with increased total CVD. The WQS index of phenols and parabens mixtures were independently correlated with total CVD (adjusted odds ratios [OR]: 1.16; 95% confidence interval [CI]:1.06–1.28; P=0.002), angina (adjusted OR: 1.30; 95% CI: 1.07–1.59; P=0.009), and heart attack (adjusted OR: 1.30; 95% CI: 1.12–1.51, P

Summary Background Inhibition of vascular endothelial growth factor receptor (VEGFR) has shown antitumour activity in advanced hepatocellular carcinoma, but few studies of VEGFR inhibitors have been done in populations with a high prevalence of hepatitis B virus infection. The aim of this study was to evaluate the efficacy and safety of apatinib in patients with pretreated advanced hepatocellular carcinoma. Methods AHELP was a randomised, double-blind, placebo-controlled, phase 3 trial done at 31 hospitals in China, in patients (aged ≥18 years) with advanced hepatocellular carcinoma who had previously been refractory or intolerant to at least one line of systemic chemotherapy or targeted therapy. Patients were randomly assigned (2:1) to receive apatinib 750 mg or placebo orally once daily in 28-day treatment cycles. Group allocation was done with a central randomisation system, with a block size of six, and was stratified by Eastern Cooperative Oncology Group performance status, previous sorafenib treatment, and presence of vascular invasion or extrahepatic metastasis. The primary endpoint was overall survival, which was defined as time from randomisation to death from any cause, and was analysed in patients who were randomly assigned and received at least one dose of the study drug. Safety analyses were done in patients who received at least one dose of the study treatment and had post-dose safety assessments. This trial is registered with ClinicalTrials.gov , NCT02329860 . Findings Between April 1, 2014, and May 3, 2017, 400 eligible patients were randomly assigned to receive apatinib (n=267) or placebo (n=133). Seven patients (six in the apatinib group and one in the placebo group) did not receive study treatment and were excluded from efficacy analyses. Overall survival was significantly improved in the apatinib group compared with the placebo group (median 8·7 months [95% CI 7·5‒9·8] vs 6·8 months [5·7‒9·1]; hazard ratio 0·785 [95% CI 0·617‒0·998], p=0·048). 387 patients (257 in the apatinib group and 130 in the placebo group) had a safety assessment after study treatment and were included in safety analyses. The most common treatment-related adverse events of grade 3 or 4 were hypertension (71 [28%] patients in the apatinib group vs three [2%] in the placebo group), hand–foot syndrome (46 [18%] vs none), and decreased platelet count (34 [13%] vs one [1%]). 24 (9%) patients in the apatinib group and 13 (10%) in the placebo group died due to adverse events, but none of these deaths were deemed to be related to treatment by investigators. Interpretation Apatinib significantly improved overall survival in patients with pretreated advanced hepatocellular carcinoma compared with placebo, with a manageable safety profile. Funding Jiangsu Hengrui Medicine.

BackgroundThe United States Centers for Disease Control and Prevention (CDC)-sanctioned prevention strategies have included frequent handwashing with soap and water, covering the mouth and nose with a mask when around others, cleaning and disinfecting maintaining a distance of at least 6 feet from others, etc. Although many of these recommendations are based upon observation and past infection control practices, it is important to combine and explore public data sets to identify predictors of infection, morbidity and mortality to develop more finely honed interventions, based on sociodemographic factors.MethodCross-sectional study of both states in the US and counties in NY state.ResultsPopulation density was found to be significantly associated with state-level coronavirus infection and mortality rate (b = 0.49, 95% confidence interval (CI): 0.34, 0.64, P ConclusionPopulation density was the only significant predictor of mortality across states in the USA. Lower mean age, lower median household incomes and more densely populated states were at higher risk of COVID-19 infection. Population density was not found to be a significant independent variable compared to minority status and socioeconomic factors in the New York epicenter. Meanwhile, public ridership was found to be a significant factor associated with incidence in New York counties.

In this article, we focus on a wireless-powered body area network in which the simultaneous wireless information and power transfer (SWIPT) technique is adopted. We consider two scenarios based on whether sensor nodes (SNs) are equipped with battery. For the first time, energy consumption minimization with throughput heterogeneity (ECM-TH) problem is addressed for both scenarios. For the battery-free scenario, a low-complexity time allocation scheme is proposed. This scheme solves the ECM-TH problem based on a hybrid method of gradient descent and bisection search algorithms. Consequently, compared with the interior-point method, our scheme has a lower computational complexity for the same energy consumption performance of the network. For the battery-assisted scenario, the nonconvex ECM-TH problem is first transformed into a convex optimization problem by introducing auxiliary variables. Then, a joint time and power allocation scheme based on the Lagrange dual subgradient method is proposed to solve it. Compared with the battery-free scenario, energy consumption and outage probability are both decreased in the battery-assisted scenario. Moreover, we address a special case wherein the feasible set of the above-mentioned ECM-TH problems may be empty owing to poor channel conditions or high throughput requirements of SNs.

Chillers play essential roles in the heating, ventilation and air conditioning (HVAC) systems to ensure the required thermal comfort. To reduce the operational risk such as faulty operation or ener...

Objective This study aimed to introduce and evaluate a new embolization technique for the right gastric artery (RGA) during percutaneous implantation of a port-catheter system for hepatic arterial infusion chemotherapy (HAIC). Methods From January 2013 to January 2017, 159 patients with unresectable advanced liver cancer underwent percutaneous implantation of a port-catheter system. In 86 of these patients (56 men; aged 28–88 years; mean: 60.6 ​± ​12.0 years), in whom the RGA was obvious on arteriography, embolization of RGA was attempted using microcoils to protect the gastric mucosa during HAIC. In the first phase (first three years), antegrade embolization of the RGA using a 2.7 Fr microcatheter was performed in 55 patients. In the second phase (next two years), embolization of the RGA was attempted by combining antegrade embolization and retrograde embolization through the left gastric artery (LGA) in 31 patients. The success rates and the incidence of acute gastroduodenal mucosal toxicity (AGMT) in these two groups were compared. Results The total success rate of the RGA embolization was 70.9%. The success rate was 83.9% in 31 patients who underwent combined antegrade and retrograde embolization, which was significantly higher than that of antegrade embolization alone (63.6%) performed in 55 patients (p ​= ​0.047). No complications related to embolization of RGA were documented. The incidence of AGMT was 29.1% (16/55) in patients in the first phase, which was significantly higher than that in the patients in the second phase (9.7%, 3/31) (p ​= ​0.037). Conclusion A combination of retrograde embolization via LGA could increase the success rates of RGA embolization and reduce the incidence of AGMT after HAIC.

HIGHLIGHTSA deep learning algorithm with an improved lightweight network was used to identify apple fruit.Multiscale pooling was used to reduce the image size and enrich the features.Compound scaling was used to scale the basic network.The optimal compound scaling coefficient for apple identification was obtained with the WOA algorithm.The proposed method achieved an average recognition precision rate of 94.43% and a speed of 0.051s.ABSTRACT. Accurate fruit identification is the basis for automating the operation of orchard production. To better apply the identification model in mobile devices so that venue becomes a less restrictive factor for application, this study proposes an apple fruit identification method based on an improved lightweight network named “MobileNetV3-Small.” The whale optimization algorithm was introduced to improve the model by obtaining an optimal compound-scaling coefficient for the MobileNetV3-Small network. A multiscale pooling approach was used for fruit recognition, comprising operations such as lossless scaling and feature extraction on sample images. The obtained images were then inputted into the model for recognition and classification. The experimental process was conducted on an apple data set. The test results show that the multiclass average precision of apple recognition using this model was 94.43% and the running time of recognition was 0.051 s per image. Both indicators outperformed the control network models of “MobileNetV3-Small,” ResNet-50, and VGG-19. This model is 14.63% more accurate and 1.95 times quicker on average in identification than the next best model. These findings indicate that the method can realize high-efficiency and high-precision recognition of apples with high stability and portability, which lays a good foundation for the mechanization of repetitive operations such as orchard yield estimation, fruit labeling, and fruit picking. Keywords: Apple recognition, Compound scaling, Deep learning algorithm, Lightweight network, Yield estimation.

Purpose: This first-in-human study (NCT02947152) evaluated the safety, tolerability, pharmacokinetics, and preliminary efficacy of HKT288, a first-in-class CDH6-targeting antibody-drug conjugate (ADC). Experimental Design: HKT288 was administered intravenously (IV) every 3 weeks until patients experienced unacceptable toxicity or progressive disease (PD). The starting dose of 0.3 mg/kg was determined based on the highest nonseverely toxic dose in monkeys, which was 2 mg/kg IV weekly. Based on preclinical toxicology, skin, eyes, bone marrow, and liver were expected targets of toxicity. Results: Nine patients were enrolled: 5 with renal cell carcinoma and 4 with epithelial ovarian cancer. The best overall response on the 0.3 mg/kg cohort in patients with measurable disease was RECIST v1.1 stable disease in 3 patients and PD in 2 patients. The most frequent adverse events (AEs) regardless of causality were pyrexia (44.4%), constipation (44.4%), fatigue (33.3%), and vomiting (33.3%). Three suspected-related neurologic AEs (Grade 2) were reported on the 0.75 mg/kg cohort: seizure in 1 patient and another patient with aphasia and encephalopathy. Further studies were unable to identify the underlying mechanism of the neurologic AEs, and the study was terminated early. Conclusions: Preclinical toxicology did not predict the neurotoxicity observed with HKT288, and a comprehensive assessment performed post hoc did not identify the mechanism of toxicity. The development of further CDH6-targeting ADCs should be pursued with caution.

The conventional control methods of variable air volume (VAV) air conditioning systems usually assume that the indoor air is well mixed, and consider each building zone as one node with homogeneous temperature distribution. The average temperature is subsequently used as the controlled parameter in the VAV cascade control process, which might cause uneven temperature distribution and unsatisfactory thermal comfort. This paper presents a coupled simulation of computational fluid dynamics (CFD) and building energy simulation (BES) for the VAV system in an office building located in Shanghai for the purpose of simulating the building, the VAV control system, and indoor thermal environment simultaneously. An external interface is developed to integrate the CFD and BES models based on quasi-dynamic coupling approach. Based upon the developed co-simulation platform, the novel VAV control method is further proposed by fusing information from multiple sensors. By adding two temperature sensors to constrain the thermal comfort of the occupied zone, the supply air temperature setpoint of the VAV terminal unit can be reset in real time. The novel control method is embedded into the co-simulation platform and compared with the conventional VAV control approach. The results illustrate that the temperature distribution under the proposed method is more uniform. At most times of the typical test day, the air diffusion performance indexes (ADPIs) for the proposed method are above 80%, while the ADPIs for the conventional control method are between 60% and 80%. Due to multi-sensor information fusion, the proposed VAV control approach has better ability to ensure the indoor thermal comfort.

A novel tunable vector-sum filtering power divider (FPD) with arbitrary output phase difference is proposed. It is constructed by a tunable FPD [which has reconfigurable in-/out-of-phase output and arbitrary power division ratios (PDRs)] on the left and a wideband branch-line coupler (BLC) on the right. The output wave from the left FPD forms two orthogonal vectors at each output port of the BLC. The orthogonal vectors are summed up and result in a new vector with phase that is purely determined by the amplitude ratio of the orthogonal vectors. As a result, the phase difference between the new vectors at the outputs of the BLC can be continuously tuned by adjusting the PDR of the left FPD. Detailed theoretical analysis has been given. It shows that the phase difference between the output ports can be flexibly tuned from 0 °C to 360 °C within the full frequency tuning range.

Various faults may occur in the air conditioning systems due to improper installation and poor maintenance. Various fault detection and diagnosis methods have been developed, which need lots of data to evaluate the protocols. However, experimental data is usually not sufficient. The FDD protocols especially machine learning based can easily overfit the limited experiment data. It may be not satisfied for the real applications because of wider range of operation. The machine learning enhanced inverse modeling method is presented to generate the simulation data under various conditions of different scenarios. The clustering algorithm is used to classify the training data reasonably balancing the weights of different conditions. The particle swarm optimization (PSO) is developed to obtain the global optimal estimation of model parameters under wider operation conditions. The experimental data of both variable and constant speed systems are used to validate clustering-PSO enhanced algorithm, which shows acceptable capability and accuracy of prediction.

Herein, we developed an unmodified hexagonal boron nitride (h-BN) photoelectrochemical (PEC) biosensing platform with a low background signal and high sensitivity based on CuS quantum dots (QDs)/Co3O4 polyhedra-driven multiple signal amplifications. The prepared porous h-BN nanosheets with large specific surface areas, as the photoelectric substrate material, can provide extensive active reaction sites. Meanwhile, the CuS QDs/Co3O4 polyhedra were synthesized by the zeolitic imidazolate framework (ZIF-67) and utilized as a multiple signal amplifier, which can not only drive the p-n semiconductor quenching effect to compete with the h-BN photoelectrode for the consumption of electron donors and exciting light but also trigger a mimetic enzymatic catalytic precipitation effect to inhibit electron transfer. The quenching ability and peroxidase-like activity of CuS QDs/Co3O4 polyhedra were evaluated to prove its superiority, and the possible mechanisms of electron transfer and enzymatic catalytic were further analyzed in detail. The developed PEC biosensing platform for the chlorpyrifos assay presented outstanding performance with a wide linear range from 1 × 10-1 to 1 × 107 ng mL-1 and a low detection limit of 0.34 pg mL-1 and exhibited excellent selectivity, reproducibility, and stability. In addition, the CuS QDs/Co3O4 polyhedra-activated h-BN PEC biosensing platform may exhibit universality for various analytes via replacing the corresponding target aptamer sequence. This work provides a remarkable inspiration and valuable reference for the development of the PEC biosensor, and the signal amplifier-enabled unmodified PEC biosensing platform strategy has a bright application in early safety warning, bioanalysis and clinical diagnosis.

BACKGROUND Transarterial chemoembolization (TACE) and hepatic arterial infusion chemotherapy (HAIC) have shown promising local benefits for advanced hepatocellular carcinoma (HCC). S-1, a composite preparation of a 5-fluorouracil prodrug, has proven to be a convenient oral chemotherapeutic agent with definite efficacy against advanced HCC. AIM To evaluate the efficacy and safety of TACE followed by HAIC with or without oral S-1 for treating advanced HCC. METHODS In this single-center, open-label, prospective, randomized controlled trial, 117 participants with advanced HCC were randomized to receive TACE followed by oxaliplatin-based HAIC either with (TACE/HAIC + S-1, n = 56) or without (TACE/HAIC, n = 61) oral S-1 between December 2013 and September 2017. Two participants were excluded from final analysis for withdrawing consent. The primary endpoint was progression-free survival (PFS) and secondary endpoints included overall survival (OS), objective response rate, disease control rate and safety. RESULTS In total, 115 participants (100 males and 15 females; mean age, 57.7 years ± 11.9) were analyzed. The median PFS and OS were 5.0 mo (0.4–58.6 mo) (95% confidence interval (CI): 3.82 to 6.18) vs 4.4 mo (1.1–54.4 mo) (95%CI: 2.54 to 6.26; P = 0.585) and 8.4 mo (0.4–58.6 mo) (95%CI: 6.88 to 9.92) vs 8.3 mo (1.4–54.4 m) (95%CI: 5.71 to 10.96; P = 0.985) in the TACE/HAIC + S-1 and TACE/HAIC groups, respectively. The objective response rate and disease control rate were 30.9% vs 18.4% and 72.7% vs 56.7% in the TACE/HAIC + S-1 and TACE/HAIC groups, respectively. Grade 3/4 adverse events had a similar frequency in both treatment groups. CONCLUSION No improvements in tumor response rates, PFS or OS were observed with the addition of S-1 to TACE/HAIC in advanced HCC. Both treatment regimens had a similar safety profile.

BACKGROUND There is little evidence of combining sorafenib with hepatic arterial infusion chemotherapy (HAIC) after transarterial chemoembolization (TACE) for intermediate and advanced hepatocellular carcinoma (HCC). It is important to identify that patients with intermediate and advanced HCC are most likely to benefit from this combination therapy. AIM To investigate the safety and clinical outcomes of sorafenib combined with HAIC with folinic acid, 5-fluorouracil (5-FU), and oxaliplatin (FOLFOX) after TACE for intermediate and advanced HCC. METHODS This prospective phase II study enrolled patients with intermediate and advanced HCC who underwent treatment with sorafenib combined with TACE-HAIC. All patients initially received the standard 400 mg dose of sorafenib twice daily before TACE-HAIC. Participants at our institute with intermediate and advanced HCC underwent routine TACE. Then, the catheter used for embolization was kept in place in the hepatic artery, and oxaliplatin was intra-arterially administered for 6 h, followed by 5-FU for 18 h, and folinic acid was intravenously administered for 2 h. The primary endpoints were safety, as evaluated by the Common Terminology and Criteria for Adverse Events version 4.0, and 12-mo progression-free survival (PFS), as analyzed by the Kaplan-Meier method. As secondary endpoints, the objective response rate (ORR) was evaluated by the modified Response Evaluation Criteria for Solid Tumors, and survival time [overall survival (OS)] was analyzed by the Kaplan-Meier method. RESULTS Sixty-six participants at our institute with intermediate and advanced HCC were enrolled in this prospective study (mean age, 53.3 ± 11.7 years). Approximately 56.1% of participants had Barcelona Clinic Liver Cancer (BCLC) stage C disease, and 43.9% had BCLC stage B disease. The ORR was 42.4%. The disease control rate was 87.9%. The grade 3-4 toxicities consisted of thrombocytopenia (4.5%), neutropenia (3.0%), and elevated aspartate aminotransferase (12.2%). Hand-foot skin reaction was also observed (40.9%). The median PFS was 13.1 mo (13.5 mo in the BCLC stage B participants and 9.4 mo in the BCLC stage C participants). The 6-mo, 12-mo, and 24-mo PFS rates were 75.0%, 54.7%, and 30.0%, respectively. The median OS was 21.8 mo. CONCLUSION Sorafenib combined with HAIC (FOLFOX) after TACE may be a feasible treatment choice for intermediate and advanced HCC because this treatment met the prespecified endpoint of a 6-mo PFS rate exceeding 50% and had good patient tolerance. Prospective randomized controlled trials are needed to confirm the effect of this combination therapy.

Pulmonary fibrosis (PF) is a kind of interstitial lung disease with the features of progressive and often fatal dyspnea. Tetrandrine (TET) is the major active constituent of Chinese herbal Stephania tetrandra S. Moore, which has already applied clinically to treat rheumatism, lung cancer, and silicosis. In this work, a tetrandrine-hydroxypropyl-β-cyclodextrin inclusion compound (TET-HP-β-CD) was developed for the treatment of pulmonary fibrosis via inhalation administration. TET-HP-β-CD was prepared by the freeze-drying method and identified using the cascade impactor, differential scanning calorimetry (DSC), X-ray diffraction (XRD), and Fourier transform infrared spectrum (FT-IR). A bleomycin-induced pulmonary fibrosis rat model was used to assess the effects of inhaled TET and TET-HP-β-CD. Animal survival, hydroxyproline content in the lungs, and lung histology were detected. The results showed that inhalation of TET-HP-β-CD alleviated inflammation and fibrosis, limited the accumulation of hydroxyproline in the lungs, regulated protein expression in PF development, and improved postoperative survival. Moreover, nebulized delivery of TET-HP-β-CD accumulated chiefly in the lungs and limited systemic distribution compared with intravenous administration. The present results indicated that inhalation of TET-HP-β-CD is an attractive candidate for the treatment of pulmonary fibrosis.

Owing to its powerful adaptability and robustness to the weak light, the infrared camera equipped on the unmanned aerial vehicle is more and more applied for aerial photography. Nowadays, how to ma...

Convex edges are located on convex surfaces of hidden geometry spaces, resulting in shorter geometrical distance than observed. Convex edges reveal the close relationships between two connected nodes and have a key role in many research fields and applications, including social, trust, and protein–protein interaction networks. We first quantitatively define convex edges using hidden geometry space defined by Ricci curvature and show these to be capable of predicting highly interacted edges in social networks. From empirical studies of social networks, we find that convex edges often occur on two connected nodes with similar popularity and allow for frenemy (or versatile node) identification, and that almost all nodes maintain an upper bound of convex edges. These findings present further evidence of the limited attention in an attention economy from a different perspective. Using three well-known synthetic networks, we show that convex edges can reflect the formation of a network from scatter (concave) to aggregation (convex). Taken together, our results show that convex edges are a novel mechanism to investigate social networks from different disciplines and offer insights into many network phenomena.

Objective: To further explore the relationship between the blood urea nitrogen to creatinine (BUN/Cr) ratio and the prognosis of patients with acute heart failure (AHF), a two-part study consisting of a prospective cohort study and meta-analysis were conducted. Methods: A total of 509 hospitalized patients with AHF were enrolled and followed up. Cox proportional hazards regression was used to analyze the relationship between the BUN/Cr ratio and the long-term prognosis of patients with AHF. Meta-analysis was also conducted regarding the topic by searching PubMed and Embase for relevant studies published up to October 2019. Results: During a median follow-up of 2.8 years, 197 (42.6%) deaths occurred. The cumulative survival rate of patients with a BUN/Cr ratio in the bottom quartile was significantly lower than in the other 3 groups (log-rank test: p = 0.003). In multivariate Cox regression models, the mortality rate of AHF patients with a BUN/Cr ratio in the bottom quartile was significantly higher than in the top quartile (adjusted HR 1.52; 95% CI 1.03–2.24). For the meta-analysis, we included 8 studies with 4,700 patients, consisting of 7 studies from the database and our cohort study. The pooled analysis showed that the highest BUN/Cr ratio category was associated with an 77% higher all-cause mortality than the lowest category (pooled HR 1.77; 95% CI 1.52–2.07). Conclusions: Elevated BUN/Cr ratio is associated with poor prognosis in patients with AFH and is an independent predictor of all-cause mortality.

Human chromosome classification is essential to the clinical diagnosis of cytogenetical diseases such as genetic disorders and cancer. This process, however, is time-consuming and requires specialist knowledge. Considerable efforts have been made to automat the process. Recently, methods based on Convolutional Neural Networks achieved state-of-the-art results on the chromosome classification task. Many studies used karyotype images in performance evaluation, few studies have reported the results of human chromosome classification on microscopical images. This paper proposes a novel method to classify single chromosome images into one of 24 types. In the proposed method an extended ResNet was first devised to extract features of single chromosome images. A label feature vector was then extracted for each of 24 chromosome types based on a validation dataset. Hausdorff distance between feature vector of an input image and each of 24 label feature vectors were calculated, and the label feature vector that has minimum hausdorff distance to the feature vector of the input image was selected as the potential label of the input image. To finally allocate the single chromosomes from a same microscopical image into one of 24 types, a Label Redistribution strategy was used to shrink the label space and to increase the efficiency of chromosome classification. Experiments were implemented with 90,624 single chromosome images, 644 of which were randomly picked to form a testing set in advance. The classification accuracy on microscopical images using our proposed method achieved an accuracy of 94.72%.

Introduction: The benefits of combining transarterial chemoembolization (TACE) and sorafenib (TACE-S) over TACE alone for treatment of unresectable hepatocellular carcinoma (HCC) remain controversial. Yet, such populations are heterogeneous in terms of baseline characteristics. Objective: To investigate the predictors of survival benefits from added sorafenib and identify the potential candidates for TACE-S. Methods: This multicenter observational study was conducted in 17 Chinese tertiary hospitals for patients with unresectable, liver-confined HCC. Eligible patients with performance status score of ≤1 and Child-Pugh score of ≤7 were treated with TACE or TACE-S. Interactions between treatment and baseline variables were evaluated to find indicators for survival benefits, based on which the patients were stratified. Multivariate models adjusted for baseline characteristics or propensity score were used to compare overall survival (OS) and time to tumor progression (TTP). Results: From January 2009 to December 2015, 1,719 consecutive patients received TACE (n = 1,406) or TACE-S (n = 313). Although TACE-S compared with TACE improved TTP (adjusted hazard ratio [HR] 0.75, p = 0.008), no difference in OS was observed (adjusted HR 0.87, p = 0.090). Nevertheless, the tumor burden (sum of maximum diameter of largest tumor [cm] and tumor number) and albumin-bilirubin (ALBI) score independently predicted the survival benefits from added sorafenib (interaction p< 0.001). For patients with either moderate tumor burden (7–13) or low ALBI score (no more than –2.8) defined as candidates, TACE-S prolonged OS (adjusted HR 0.73, p = 0.003) and TTP (adjusted HR 0.72, p = 0.014) compared to TACE alone, whereas its superiority disappeared in non-candidates. Conclusions: Not all unresectable HCC patients but those with moderate tumor burden or low ALBI score achieve survival benefits from TACE-S compared with TACE alone. Future randomized controlled trials focusing on the subset are warranted.

A dual-band dual-polarized base station antenna for the fifth-generation (5G) mobile system is presented in this paper. The proposed antenna covers the frequency bands from 3.3 to 3.8 GHz (the lower band) and from 4.8 to 5.0 GHz (the upper band) with good isolation between its ports (≥ 20 dB). It consists of two double-oval-shaped dipoles, two double-oval-shaped feeding lines and a cavity reflector. In this design, parts of the dipole antenna structure are used as the feeding lines and it is found that using one arm of the dipole to feed the whole antenna can improve impedance matching. The dual-band performance is achieved by integrating a small oval-shaped loop within the large oval-shaped loop without increasing the size of the radiating patch. The size of the radiating patch is only 0.26λ0 × 0.26λ0 (λ0 is the free-space wavelength at 3.3 GHz). The cavity reflector improves the gain performance and reduces the overall size of the antenna, which is only 0.66 λ0 × 0.66λ0 × 0.2λ0. The antenna has an average realized gain of 7.56 dBi in the lower band and 7.42 dBi in the higher band. Meanwhile, for both bands, the radiation pattern is stable, and the half power beamwidth is within 65° ± 5°. Both simulated and measured results demonstrate that the antenna is a very good candidate for 5G mobile base stations.

The traditional energy system has the disadvantages of high energy consumption and high emission. Through multiple energy networks interconnection and flexible energy conversion, the regional integrated energy system (RIES) can satisfy various energy demands and achieve clean and efficient energy utilization. However, the demand and supply relationship of RIES is complex. Without a reasonable energy supply structure, it’s difficult for RIES to give full play to the economic and efficient operation. At the same time, the multi-energy trading and service mechanisms are still unclear. The promotion of RIES brings challenges and opportunities to energy sector. In order to address the issues in the promotion of RIES, a day-ahead energy trading strategy of RIES considering energy cascade utilization is proposed in this paper. Firstly, the relationship between energy suppliers (ESs), integrated energy service provider (IESP) and energy users (EUs) is clarified. And their benefits are clarified in the energy trading strategy. Furthermore, the day-ahead energy trading model, which is a bi-level optimization model is built. In the upper model, the IESP purchases electric energy and natural gas from ESs at wholesale prices and set energy retail prices. According to energy cascade utilization theory, the IESP make decisions on the energy retail plan and energy scheduling. In the lower model, optimal energy consuming plan are given for EUs. In this model, energy grades of energy production, conversion and storage devices output are classified and the energy grade demands of different energy loads are divided. And the energy cascade utilization trading model is simulated based on actual data. The simulation results show that the trading strategy is feasible and the application of energy cascade utilization theory can further improve the energy utilization efficiency of RIES.

Huang-Lian Jie-Du decoction (HLJDD), a famous traditional Chinese prescription constituted by Rhizoma Coptidis, Radix Scutellariae, Cortex Phellodendri and Fructus Gradeniae, has notable characteristics of dissipating heat and detoxification, interfering with tumors, hepatic diseases, metabolic disorders, inflammatory or allergic processes, cerebral diseases and microbial infections. Based on the wide clinical applications, accumulating investigations about HLJDD focused on several aspects: (1) chemical analysis to explore the underlying substrates responsible for the therapeutic effects; (2) further determination of pharmacological actions and the possible mechanisms of the whole prescription and of those representative ingredients to provide scientific evidence for traditional clinical applications and to demonstrate the intriguing molecular targets for specific pathological processes; (3) pharmacokinetic feature studies of single or all components of HLJDD to reveal the chemical basis and synergistic actions contributing to the pharmacological and clinically therapeutic effects. In this review, we summarized the main achievements of phytochemical, pharmacological and pharmacokinetic profiles of HLJDD and its herbal or pharmacologically active chemicals, as well as our understanding which further reveals the significance of HLJDD clinically.

PurposeWe investigated the movement characteristics of pancreas and the clinical accuracy of tracking pancreas with the Synchrony Respiratory Tracking System (SRTS) during the CyberKnife treatment. These data provide a clinical data basis for the expansion margins of pancreatic tumor target.Methods and MaterialsForty-two patients with pancreatic cancer treated by CyberKnife were retrospectively studied. The pancreatic displacement calculated from the x-ray images collected during the time interval between two consecutive movements constituted a data set.ResultsThe total mean motion amplitudes and standard deviations of pancreatic tumors in SI, LR, AP, and radial directions were 3.66 ± 1.71 mm, 0.97 ± 0.62 mm, 1.52 ± 1.02 mm, and 1.36 ± 0.49 mm, respectively. The overall mean correlation errors and standard deviations were 0.82 ± 0.46 mm, 0.47 ± 0.33 mm, 0.41 ± 0.24 mm, and 0.98 ± 0.37 mm, respectively. The overall mean prediction errors and standard deviations were 0.57 ± 0.14 mm, 0.62 ± 0.28 mm, 0.39 ± 0.17 mm, and 1.58 ± 0.36 mm, respectively. The correlation errors and prediction errors of pancreatic tumors at different anatomical positions in SI, LR, and AP directions were statistically significant (p < 0.05).ConclusionsThe tumor motion amplitude, the tumor location, and the treatment time are the main factors affecting the tracking accuracy. The pancreatic tumors at different anatomical locations should be treated differently to ensure sufficient dose coverage of the pancreatic target area.

Few-mode fiber (FMF), a mode multiplex technique, has been a candidate to provide high transmission capability in next-generation elastic optical networks (EONs), where the probabilistic shaping (PS) technology is widely used to approach Shannon limit. In this paper, we investigate a fast and accurate method of modulation format recognition (MFR) of received signals based on a transfer learning network (TLN) in PS-based FMF-EONs. TLN can apply the feature extraction ability of convolutional neural networks to the analysis of the constellations. We conduct experiments to demonstrate the effectiveness of the proposed scheme in FMF transmissions. Six modulation formats, including 16QAM, PS-16QAM, 32QAM, PS-32QAM, 64QAM and PS-64QAM, and four propagating modes, including LP01, LP11a, LP11b and LP21, are involved. In addition, comparisons of TLN with different structures of convolutional neural networks backbones are presented. In the experiment, the iterations of the TLN are one-tenth that of conventional deep learning network (DLN), and the TLN overcomes the problem of overfitting and requires less data than that of DLN. The experimental results show that the TLN is an efficient and feasible method for MFR in the PS-based FMF communication system.

A completely non-blocking M × N electrically controlled optofluidic matrix switch that uses a 1 × 3 optical switch with a V-shaped microchannel as the switching unit is proposed. Its light paths and output ports are selected by a micro-actuator matrix and a control circuit. There are few reports of optofluidic matrix switches. Here the given electrostatic micro-actuator and the basic switch structure provide an effective feasible manner for the matrix switch due to the simple and compact structure as well as the operation style. The impacts of microchannels and intersecting waveguides on the switch performance are discussed, and multiple optimization schemes are proposed to reduce the insertion loss efficiently and significantly. The research results indicate that the M × N matrix switch has the advantages of good matrix controllability, simple structure, wide waveband (400–1700 nm), negligible polarization-dependent loss, small insertion loss, and low cross talk. For 1550 nm wavelength, the insertion loss of a 2 × 6 matrix switch is about 0.17–0.55 dB, and the maximum cross talk is less than − 26.8 d B . In addition, the performance parameters of a 4 × 8 matrix switch are given and compared with other reported matrix switches. The proposed M × N matrix switch solves the problem of large insertion loss of general optical matrix switches and can be expanded to a large-scale matrix switch. Moreover, the design of multiple output ports has more flexible applications in systems with multiple branch optical paths and network nodes.

Aldehydes have been shown to be potentially carcinogenic, mutagenic, and cardiotoxic to humans. Dietary fiber reduces exposure to certain environmental pollutants and has been widely used to improve various metabolic disorders. However, the effects of dietary fiber on serum concentrations of aldehydes remain unexplored. Data was collected from the National Health and Nutrition Examination Survey (NHANES) 2013–2014. Generalized linear regression and restricted cubic spline models were performed to elucidate the association of dietary fiber intake with the serum concentration of aldehydes. After fully adjusting for age, sex, education level, race, smoking status, alcohol use, diabetes, hypertension, body mass index, energy intake, poverty-income ratio, and physical activity, dietary fiber intake had a strong negative association with serum levels of isopentanaldehyde and propanaldehyde and a positive association with serum levels of benzaldehyde. The estimated increases in the mean log2-unit (ng/mL) of aldehydes for each fold increase in dietary fiber were −0.140 (95% confidence interval [CI]: −0.195 to −0.085) for isopentanaldehyde, −0.060 (95% CI: −0.099 to −0.015) for propanaldehyde, and 0.084 (95% CI: 0.017 to 0.150) for benzaldehyde, respectively. No significant association was observed between dietary fiber intake and the concentration of any other aldehydes. These results demonstrate that dietary fiber reduces the concentration of certain aldehydes in serum.

Background: The FK506-binding protein 4 ( FKBP4 ), a tumor-related gene, plays a vital role in tumorigenesis and cancer progression. The study is aimed to clarify the effect of FKBP4 in lung adenocarcinoma (LUAD). Methods: Relying on The Cancer Genome Atlas (TCGA) cohort, the FKBP4 expression difference between LUAD tissues and non-tumor tissues was first detected, and verified with public tissue microarrays (TMAs), clinical LUAD specimen cohort and Gene Expression Omnibus (GEO) cohort. Then, logistic regression analysis and chi-square test were applied to detect the correlation between FKBP4 expression and clinicopathological parameters. Kaplan-Meier survival analysis and Cox regression model were utilized to evaluate the effect of FKBP4 expression on survival. Signaling pathways related to LUAD were obtained via employing Gene Set Enrichment Analysis (GSEA). Results: The FKBP4 expression level in LUAD samples was dramatically higher than that in non-tumor samples. High FKBP4 expression in LUAD is associated with gender, pathological stage, T classification, lymph node metastasis and distant metastasis. The Kaplan-Meier curve indicated a poor prognosis for LUAD patients with high FKBP4 expression. Multivariate analysis suggested that the high FKBP4 expression was a vital independent predictor of poor overall survival (OS). GSEA showed that a total of 15 signaling pathways were enriched in samples with high FKBP4 expression phenotype. Conclusions: FKBP4 may be an oncogene in LUAD, and is promised to become a prognostic indicator and therapeutic target for LUAD.

Background Elderly patients with COVID-19 were shown to have a high case-fatality rate. We aimed to explore the risk factors associated with death in patients over 70 years old (yr). Methods In this retrospective study, we enrolled consecutively hospitalized patients over 70 yr with COVID-19 between January 20 and February 15, 2020 in Renmin Hospital of Wuhan University. Epidemiological, demographic, and clinical data were collected. Clinical subtypes, including mild, moderate, severe, and critical types, were used to evaluate the severity of disease. Patients were classified into two groups: survivor and non-survivor groups. Clinical data were compared between the two groups. Univariable and multivariable Cox regression methods were used to explore the risk factors. Results A total of 147 patients were enrolled. The case-fatality rate was 28.6%. Multivariable Cox proportional hazard regression showed that clinical subtypes, including the severe type (HR = 2.983, 95% CI: 1.231–7.226, P = 0.016) and the critical type (HR = 3.267, 95%CI: 1.009–10.576, P = 0.048), were associated with increasing risk of death when compared with the general type. Blood urea nitrogen greater than 9.5 mmol/L (HR = 2.805, 95% CI: 1.141–6.892, P = 0.025) on admission was an independent risk factor for death among laboratory findings. Conclusion The patients over 70 yr with COVID-19 had a high case-fatality rate. The risk factors, including clinical subtypes and blood urea nitrogen greater than 9.5 mmol/L, could help physicians to identify elderly patients with poor clinical outcomes at an early stage.

Semblance velocity analysis plays an indispensable role in seismic data processing. In order to avoid the huge time-cost when performed manually, some deep learning methods are proposed for automatic velocity picking from semblance. However, the application of existing deep learning methods is still restricted by the shortage of labels in practice. To solve this problem, we take semblance velocity analysis as a point-to-point regression problem at each time sample. A time window on semblance which can extract the block corresponding to a time-velocity (t-v) pair and the reference velocity curve (RVC) which can transform semblance randomly are employed together to augment the labeled data. We divide the development of data augmentation strategy into three progressive modes. The datasets from three modes are prepared for training designed attention neural network. The field experiments show that the attention neural network can produce reasonable results and the data augmentation strategy can effectively improve the velocity picking accuracy.

PurposeThis study aimed to evaluate the efficacy and safety of trans-arterial chemoembolization (TACE) followed by stereotactic body radiation therapy (SBRT) in treating Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC) not amenable to resection and radiofrequency ablation (RFA).MethodsFrom February 2012 to January 2017, a total of 57 BCLC stage B HCC patients who were unsuitable candidates for resection and RFA treated with TACE combined with CyberKnife SBRT were included in this retrospective study. Patients underwent TACE for a median of two times (1–5 times) before SBRT. SBRT prescription doses ranged from 30 Gy to 50 Gy in 3–5 fractions.ResultsThe median follow-up time was 42 months. The objective response rate (CR + PR) was 85.9%, and the disease control rate (CR + PR + SD) was 96.5%. The local control (LC) rates were 91.1% and 84.3% at 1 and 2 years, respectively. The 1-, 2-, 3-year overall survival (OS) and the median survival time were 73.2%, 51.4%, 32.4% and 26.6 months, respectively. The 1-, 2-, and 3-year progression-free survival (PFS) were 34.2%, 21.6%, and 9%, respectively, with a median PFS time of 9.7 months. A subgroup analysis was conducted in 32 patients with AFP ≥ 200 ng/ml before TACE. OS was significantly prolonged in those with AFP that decreased by more than 75% than those with AFP that decreased by less than 75% (P = 0.018) after SBRT. The treatment was well tolerated with only one patient (1.8%) developed grade 3 gastrointestinal toxicity, and another patient developed non-classical RILD. In multivariate analysis, tumor length ≥ 10 cm and AFP ≥ 200 ng/ml were independent poor prognostic factors for OS.ConclusionThe combination of TACE and Cyberknife SBRT showed optimal efficacy with acceptable toxicity for BCLC stage B HCC.

Ting Yin,1 Iokfai Cheang,1 Xu Zhu,1 Shengen Liao,1 Haifeng Zhang,2 Xinli Li1 1Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, People’s Republic of China; 2Department of Cardiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, 215002, People’s Republic of ChinaCorrespondence: Haifeng ZhangDepartment of Cardiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, 215002, People’s Republic of ChinaEmail haifeng_zhang@163.comShengen LiaoDepartment of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, People’s Republic of ChinaEmail shengenliao@163.comPurpose: The optimal blood pressure (BP) targets in terms of mortality risk after stroke remain unclear. This study aimed to assess the relationship between BP and mortality in stroke survivors.Patients and Methods: We included 1696 participants with self-reported history of stroke aged 18 years and older from the National Health and Nutrition Examination Survey (NHANES) 1999– 2014 and NHANES III with public-use linked mortality files from 2015. Baseline systolic BP (SBP) and diastolic BP (DBP) levels were obtained by taking the average of 3 measures. Cox proportional hazard models and restricted cubic splines were conducted to explore the relationship between BP and all-cause mortality.Results: During a median follow-up period of 5.6 years, 888 deaths occurred. After fully adjusting for confounding factors, SBP displayed a J-curve relationship (nadir 135 mm Hg), while DBP exhibited a reverse J-curve relationship (nadir 73 mm Hg) with the risk of all-cause mortality. However, the J-curve or reverse J-curve pattern between blood pressure and mortality appeared to be limited to individuals with an age > 65 years, identifying a nadir of SBP/DBP of 142/73 mm Hg. The risk of mortality followed a linear relationship for SBP and DBP in stroke survivors aged ≤ 65 years, with risks increasing with higher SBP and lower DBP.Conclusion: In this cross-sectional study that used national survey data, these data suggest a strong J-curve or reverse J-curve relationship between blood pressure and risk of all-cause mortality, whereas the pattern appears to be limited to individuals with an age > 65 years, with a nadir at 142/73 mmHg. However, missing data on stroke type and stroke treatment limits the generalizability. Future prospective studies are needed to determine preferential blood pressure target in patients after stroke.Keywords: blood pressure, J-curve, stroke, age, mortality

Environmental health has begun to examine the effects of higher-order chemical combinations. The current literature lacks studies exploring associations between multiple organic chemicals mixture and cardiometabolic disease (CVD). This study aimed to evaluate associations between urinary phenol and paraben metabolites and total and individual CVD among a nationally representative sample of adults in the US. This cross-sectional study analyzed 7 urinary chemicals detected among the general population from the 2005–2016 National Health and Nutrition Examination Survey (NHANES, n = 10,428). Multivariate logistic regression and weighted quantile sum (WQS) regression were applied to examine relationships between phenol and paraben metabolites, alone and combined, and total and individual CVD prevalence. WQS regression showed that phenol and paraben indices were independently correlated with total CVD (adjusted odds ratios [OR]: 1.16; 95% confidence interval [CI]:1.06–1.28; P = 0.002), angina (adjusted OR: 1.30; 95% CI: 1.07–1.59; P = 0.009), and heart attack (adjusted OR: 1.30; 95% CI: 1.12–1.51, P

This study aimed to evaluate the clinical outcomes, toxicity, and prognostic factors of SBRT combined with gemcitabine plus capecitabine (GEM-CAP) in treating locally advanced pancreatic cancer (LAPC). A total of 56 patients with LAPC treated with SBRT combined with GEM-CAP were reviewed from October 2010 to October 2016. The median total prescription dose at five fractions was 40 Gy (30–50 Gy). The patients were subjected to two cycles of GEM-CAP before SBRT. GEM-CAP chemotherapy was then offered for four cycles or until disease tolerance or progression. The primary endpoints included overall survival (OS) and progression-free survival (PFS). The median OS and PFS from the date of diagnosis was 19 (95% CI 14.6–23.4) and 12 months (95% CI 8.34–15.66), respectively. The 1-year and 2-year survival rates were 82.1% and 35.7%, whereas the 1-year and 2-year PFS rates were 48.2% and 14.3%, respectively. The median carbohydrate antigen 19-9-determined PFS time was 11 months (95% CI 5.77–16.24). Multivariate analysis demonstrated that tumor diameter, lymph node metastasis, pre-treatment CA19-9 level, and post-treatment CA19-9 decline were independent prognostic factors (p

Accurate refrigerant charge fault estimation is important to ensure the efficient operation of air conditioning systems. This paper presents a novel hybrid model based refrigerant charge estimation approach. Firstly, an improved gray box model is presented, which integrates the key characteristic variables of subcooling temperature, superheat temperature, quality and pressure drop. Secondly, three extra variables having highest maximal information coefficients with the prediction residual are used to extend the gray box, and the robust machine learning model is developed using the gradient boosting decision tree algorithm. Then, a hybrid model is presented by combining the improved gray box and machine learning models. Finally, the prediction and generalization capacities of the proposed models under various operation conditions are validated using the experimental data. The results show that the hybrid charge fault estimation model has the best performance. Its overall prediction and generalization MREs are 2.53% and 3.09%, respectively.

Broadband power line communication (BPLC) and television white space (TVWS) are regarded as promising candidates for indoor broadband applications of the Internet of Things. However, they share the access to the very high frequency (VHF) band, which could cause harmful interference and performance degradation to each other. In this paper, a TVWS regulated BPLC system is proposed for point-to-multipoint downlink communication, which integrates the requirement of primary user sensing and the permissible transmission power spectral density (PSD) for TVWS users into the BPLC standard, regarding VHF band access. This integration guarantees minimum interference level between TVWS and BPLC and allows higher transmission PSD for BPLC users in VHF, and hence higher capacity and coverage for BPLC.

Background The methylation of microtubule-associated protein tau (MAPT) was first described in patients with Alzheimer's disease. In this study, we aim to determine if MAPT promoter CpG island is hypermethylated and whether this signature could work as a prognostic marker for patients with stage II colorectal cancer (CRC). Methods MAPT methylation level and CpG island methylator phenotype (CIMP) status were examined. The prognostic value of MAPT methylation was analyzed using Cox regression analysis. Results Amongst stage II CRC patients (n=107), hypermethylation of MAPT promoter CpG island was seen in 23.4% of them. MAPT methylation was much more frequent in patients with age ≥60 compared to age