Impact of utilization of hepatitis C positive organs in liver transplant:Analysis of united network for organ sharing database

BACKGROUND The utility of hepatitis C virus(HCV)organs has increased after the Food and Drug Administration approval of direct acting anti-viral(DAA)medications for the HCV treatment.The efficacy of DAA in treating HCV is nearly 100%.AIM To analyze the United Network for Organ Sharing(UNOS)database to compare the survival rates between the hepatitis C positive donors and negative recipients and hepatitis C negative donors and recipients.METHODS We analyzed the adult patients in UNOS database who underwent deceased donor liver transplant from January 2014 to December 2017.The primary endpoint was to compare the survival rates among the four groups with different hepatitis C donor and recipient status:(Group 1)Both donor and recipient negative for HCV(Group 2)Negative donor and positive recipient for HCV(Group 3)Positive donor and negative recipient for HCV(Group 4)Both positive donor and recipient for HCV.SAS 9.4 software was used for the data analysis.Kaplan Meier log rank test was used to analyze the estimated survival rates among the four groups.RESULTS A total of 24512 patients were included:Group 1:16436,Group 2:6174,Group 3:253 and Group 4:1649.The 1-year(Group 1:91.8%,Group 2:92.12%,Group 3:87%,Group 4:92.8%),2-year(Group 1:88.4%,Group 2:88.1%,Group 3:84.3%,Group 4:87.5%),3-year(Group 1:84.9%,Group 2:84.3%,Group 3:75.9%,Group 4:83.2%)survival rates showed no statistical significance among the four groups.Kaplan Meier log rank test did not show any statistical significance difference in the estimated survival rates between Group 3 vs all the other groups.CONCLUSION The survival rates in hepatitis C positive donors and negative recipients are similar as compared to both hepatitis C negative donors and recipients.This could be due to the use of DAA therapy with cure rates of nearly 100%.This study supports the use of hepatitis C positive organs in the selected group of recipients with and without HCV infection.Further long-term studies are needed to further validate these findings.

Radar Quantitative Precipitation Estimation Based on the Gated Recurrent Unit Neural Network and Echo-Top Data

The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.

Security System in United Storage Network and Its Implementation

With development of networked storage and its applications, united storage network (USN) combined with network attached storage (NAS) and storage area network (SAN) has emerged. It has such advantages as high performance, low cost, good connectivity, etc. However the security issue has been complicated because USN responds to block I/O and file I/O requests simultaneously. In this paper, a security system module is developed to prevent many types of attacks against USN based on NAS head. The module not only uses effective authentication to prevent unauthorized access to the system data, but also checks the data integrity. Experimental results show that the security module can not only resist remote attacks and attacks from those who has physical access to the USN, but can also be seamlessly integrated into underlying file systems, with little influence on their performance.

Research on the Security of the United Storage Network Based on NAS

A multi-user view file system (MUVFS) and a security scheme are developed to improve the security of the united storage network (USN) that integrates a network attached storage (NAS) and a storage area network (SAN). The MUVFS offers a storage volume view for each authorized user who can access only the data in his own storage volume, the security scheme enables all users to encrypt and decrypt the data of their own storage view at client-side, and the USN server needs only to check the users’ identities and the data’s integrity. Experiments were performed to compare the sequential read, write and read/write rates of NFS+MUVFS+secure_module with those of NFS. The results indicate that the security of the USN is improved greatly with little influence on the system performance when the MUVFS and the security scheme are integrated into it.

Inferior outcomes of liver transplantation for hepatocellular carcinoma during early-COVID-19 pandemic in the United States

BACKGROUND Early in the coronavirus disease 2019(COVID-19)pandemic,there was a significant impact on routine medical care in the United States,including in fields of transplantation and oncology.AIM To analyze the impact and outcomes of early COVID-19 pandemic on liver transplantation(LT)for hepatocellular carcinoma(HCC)in the United States.METHODS WHO declared COVID-19 as a pandemic on March 11,2020.We retrospectively analyzed data from the United Network for Organ Sharing(UNOS)database regarding adult LT with confirmed HCC on explant in 2019 and 2020.We defined pre-COVID period from March 11 to September 11,2019,and early-COVID period as from March 11 to September 11,2020.RESULTS Overall,23.5%fewer LT for HCC were performed during the COVID period(518 vs 675,P<0.05).This decrease was most pronounced in the months of March-April 2020 with a rebound in numbers seen from May-July 2020.Among LT recipients for HCC,concurrent diagnosis of non-alcoholic steatohepatitis significantly increased(23 vs 16%)and alcoholic liver disease(ALD)significantly decreased(18 vs 22%)during the COVID period.Recipient age,gender,BMI,and MELD score were statistically similar between two groups,while waiting list time decreased during the COVID period(279 days vs 300 days,P=0.041).Among pathological characteristics of HCC,vascular invasion was more prominent during COVID period(P<0.01),while other features were the same.While the donor age and other characteristics remained same,the distance between donor and recipient hospitals was significantly increased(P<0.01)and donor risk index was significantly higher(1.68 vs 1.59,P<0.01)during COVID period.Among outcomes,90-day overall and graft survival were the same,but 180-day overall and graft were significantly inferior during COVID period(94.7 vs 97.0%,P=0.048).On multivariable Coxhazard regression analysis,COVID period emerged as a significant risk factor of post-transplant mortality(Hazard ratio 1.85;95%CI:1.28-2.68,P=0.001).CONCLUSION During COVID period,there was a significant decrease in LTs performed for HCC.While early postoperative outcomes of LT for HCC were same,the overall and graft survival of LTs for HCC after 180 days were significantly inferior.

Speech Separation Algorithm Using Gated Recurrent Network Based on Microphone Array

Speech separation is an active research topic that plays an important role in numerous applications,such as speaker recognition,hearing pros-thesis,and autonomous robots.Many algorithms have been put forward to improve separation performance.However,speech separation in reverberant noisy environment is still a challenging task.To address this,a novel speech separation algorithm using gate recurrent unit(GRU)network based on microphone array has been proposed in this paper.The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost.The proposed algorithm extracts the sub-band steered response power-phase transform(SRP-PHAT)weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position in formation.Since the GRU net work has the advantage of processing time series data with faster training speed and fewer training parameters,the GRU model is adopted to process the separation featuresof several sequential frames in the same sub-band to estimate the ideal Ratio Masking(IRM).The proposed algorithm decomposes the mixture signals into time-frequency(TF)units using gammatone filter bank in the frequency domain,and the target speech is reconstructed in the frequency domain by masking the mixture signal according to the estimated IRM.The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost.Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech sep-aration in noisy and reverberant environments,provides good performance in terms of speech quality and intelligibility,and has the generalization capacity to reverberate.

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

Reducing parameter space for neural network training

For neural networks(NNs)with rectified linear unit(ReLU)or binary activation functions,we show that their training can be accomplished in a reduced parameter space.Specifically,the weights in each neuron can be trained on the unit sphere,as opposed to the entire space,and the threshold can be trained in a bounded interval,as opposed to the real line.We show that the NNs in the reduced parameter space are mathematically equivalent to the standard NNs with parameters in the whole space.The reduced parameter space shall facilitate the optimization procedure for the network training,as the search space becomes(much)smaller.We demonstrate the improved training performance using numerical examples.

Ensembling Neural Networks for User’s Indoor Localization Using Magnetic Field Data from Smartphones

Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data lengths.The use of multifarioussmartphones cripples the performance of such approaches owing to the variability of the magnetic field data.In the same vein,smaller lengths of magnetic field data decrease the localization accuracy substantially.The current study proposes the use of multiple neural networks like deep neural network(DNN),long short term memory network(LSTM),and gated recurrent unit network(GRN)to perform indoor localization based on the embedded magnetic sensor of the smartphone.A voting scheme is introduced that takes predictions from neural networks into consideration to estimate the current location of the user.Contrary to conventional magnetic field-based localization approaches that rely on the magnetic field data intensity,this study utilizes the normalized magnetic field data for this purpose.Training of neural networks is carried out using Galaxy S8 data while the testing is performed with three devices,i.e.,LG G7,Galaxy S8,and LG Q6.Experiments are performed during different times of the day to analyze the impact of time variability.Results indicate that the proposed approach minimizes the impact of smartphone variability and elevates the localization accuracy.Performance comparison with three approaches reveals that the proposed approach outperforms them in mean,50%,and 75%error even using a lesser amount of magnetic field data than those of other approaches.

Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network

An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.

Modeling and optimum operating conditions for FCCU using artificial neural network

A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.

A novel algorithm for Ethernet-passive-optical-network dynamic bandwidth allocation based on threshold

The Ethemet passive optical network （EPON） is the next generation of broad-band network technique. A crucial issue in EPONs is the sharing of uplink bandwidth among optical network units （ONUs）. This article provides a novel dynamic bandwidth allocation algorithm, i.e. threshold dynamic bandwidth allocation （TDBA）, which is based on adaptive threshold, to increase resource utilization. The algorithm uses ONU data-transmitting rate to adjust optical line terminal （OLT） receiving data threshold from an ONU. Simulation results show that this algorithm can decrease average packet delay and increase network throughput in a l 0G EPON system.

A passive optical network based on optical code division multiplexing and time division multiple access technology

A passive optical network （PON） scheme based on optical code division multiplexing （OCDM） for the downstream traffics is proposed and analyzed in detail. In the PON, the downstream traffics are broadcasted by OCDM technology to guarantee the security, while the upstream traffics pass through the same optical fiber by the common time division multiple access （TDMA） technology to decrease the cost. This schemes are denoted as OCDM/TDMA-PON, which can be applied to an optical access network （OAN） with full services on demand, such as Internet protocol, video on demand, tele-presence and high quality audio. The proposed OCDM/TDMA-PON scheme combines advantages of PON, TDMA, and OCDM technology. Simulation results indicate that the designed scheme improves the OAN performance, and enhances flexibility and scalability of the system.

Optimization of Disjoints Using WDM-PON in an Optical Network

An optical network is a type of data communication network built with optical fibre technology. It utilizes optical fibre cables as the primary communication medium for converting data and passing data as light pulses between sender and receiver nodes. The major issue in optical networking is disjoints that occur in the network. A polynomial time algorithm Wavelength Division Multiplexing-Passive Optical Networking (WDM-PON) computes disjoints of an optical network and reduces the count of disjoints that occur in the network by separating Optical Network Units (ONU) into several virtual point-to-point connections. The Arrayed Waveguide Grating (AWG) filter is included in WDM-PON to avoid the traffic in the network thereby increasing the bandwidth capacity. In case of a failure or disjoint Ant Colony Optimization (ACO) algorithm is used to find the optimized shortest path for re-routing. For enhanced security, modified Rivert Shamir Adleman (RSA) algorithm encrypts the message during communication between the nodes. The efficiency is found to be improved in terms of delay in packet delivery, longer optical reach, optimized shortest path, packet error rate.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

Prediction of landslide displacement with dynamic features using intelligent approaches

Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorporated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.

Analysis of hepatitis C virus-positive organs in liver transplantation

The authors of this study note that in liver transplantation(LT),the survival rates of hepatitis C virus(HCV)-positive donors and HCV-negative receivers are compa-rable to those of HCV-negative donors and recipients.Direct-acting antiviral(DAA)therapies have nearly 100%effectiveness in treating HCV.Between 2006 and 2016,the percentages of HCV-positive patients on the waiting list and HCVpositive LT recipients fell by 8.2 percent and 7.6 percent,respectively.Records from April 1,2014,in which the donor and receiver were both at least 18 years old and had a positive HCV status,were the only ones eligible for the study.The analysis for this study was restricted to the first transplant recorded for each patient using a data element that documented the number of prior transplants for each recipient,although some recipients appeared multiple times in the data set.HCV-positive recipients or people with fulminant hepatic failure were the main beneficiaries of primary biliary cirrhosis among HCV-positive donors.However,there is still a reticence to use HCV-positive donor organs in HCV recipients due to clinical and ethical considerations.Similar survival rates between HCV-positive donors and recipients and HCV-negative donors and receivers illustrate the efficacy of these DAA regimens.

High-Speed EMU TCMS Design and LCC Technology Research

This paper introduces the high-speed electrical multiple unit （EMO） life cycle, including the design, manufacturing, testing, and maintenance stages. It also presents the train control and monitoring system （TCMS） software development platform, the TCMS testing and verification bench, the EMU driving simulation platform, and the EMU remote data transmittal and maintenance platform. All these platforms and benches combined together make up the EMU life cycle cost （LCC） system. Each platform facilitates EMU LCC management and is an important part of the system.

Synthesis of novel hierarchical porous polymers with a nanowire-interconnected network structure from core-shell polymer nanoobjects

Design and fabrication of the micro/nanostructures of the network units is a critical issue for porous nanonetwork structured materials. Significant progress has been attained in construction of the network units with zero-dimensional spherical shapes.However, owing to the limitations of synthetic methods, construction of porous building blocks in one dimension featuring high aspect ratios for porous nanonetwork structured polymer(PNSP) remains largely unexplored. Here we present the successful design and preparation of PNSP with a novel type of one-dimensional network unit, i.e., microporous heterogeneous nanowire. Well-defined core-shell polymer nanoobjects prepared from a gelable block copolymer, poly(3-(triethoxysilyl)propyl methacrylate)-block-polystyrene are employed as building blocks, and facilely transformed into PNSP via hypercrosslinking of polystyrene shell. The as-prepared PNSP exhibits unique three-dimensional hierarchical nanonetwork morphologies with large surface area. These findings could provide a new avenue for fabrication of unique well-defined PNSP, and thus generate valuable breakthroughs in many applications.