Non-invasive assessment of liver fibrosis in chronic liver diseases:Implementation in clinical practice and decisional algorithms

Chronic hepatitis B and C together with alcoholic and non-alcoholic fatty liver diseases represent the major causes of progressive liver disease that can eventually evolve into cirrhosis and its end-stage complications,including decompensation,bleeding and liver cancer.Formation and accumulation of fibrosis in the liver is the common pathway that leads to an evolutive liver disease.Precise definition of liver fibrosis stage is essential for management of the patient in clinical practice since the presence of bridging fibrosis represents a strong indication for antiviral therapy for chronic viral hepatitis,while cirrhosis requires a specif ic follow-up including screening for esophageal varices and hepatocellular carcinoma.Liver biopsy has always represented the standard of reference for assessment of hepatic fibrosis but it has some limitations being invasive,costly and prone to sampling errors.Recently,blood markers and instrumental methods have been proposed for the non-invasive assessment of liver fibrosis.However,there are still some doubts as to their implementation in clinical practice and a real consensus on how and when to use them is not still available.This is due to an unsatisfactory accuracy for some of them,and to an incomplete validation for others.Some studies suggest that performance of non-invasive methods for liver fibrosis assessment may increase when they are combined.Combination algorithms of non-invasive methods for assessing liver fibrosis may represent a rational and reliable approach to implement non-invasive assessment of liver fibrosis in clinical practice and to reduce rather than abolish liver biopsies.

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

Fast combination of scheduling chains under resource and time constraints

Scheduling chain combination is the core of chain-based scheduling algorithms, the speed of which determines the overall performance of corresponding scheduling algorithm. However, backtracking is used in general combination algorithms to traverse the whole search space which may introduce redundant operations, so performance of the combination algorithm is generally poor. A fast scheduling chain combination algorithm which avoids redundant operations by skipping “incompatible” steps of scheduling chains and using a stack to remember the scheduling state is presented in this paper to overcome the problem. Experimental results showed that it can improve the performance of scheduling algorithms by up to 15 times. By further omitting unnecessary operations, a fast algorithm of minimum combination length prediction is developed, which can improve the speed by up to 10 times.

Combining TDLAS and multi-fusion algorithms for methane gas concentration detection

High-precision methane gas detection is of great importance in industrial safety, energy production and environmental protection, etc. However, in the existing measurement techniques, the methane gas concentration information is susceptible to noise, which leads to its useful signal being drowned by noise. A fusion algorithm of variational modal decomposition(VMD) and improved wavelet threshold filtering is proposed, which is used in combination with tunable diode laser absorption spectroscopy(TDLAS) to implement a non-contact, high-resolution methane gas concentration detection. The fusion algorithm can perform noise reduction and further segmentation of the methane gas detection signal. And the simulation and experiment verify the effectiveness of the fusion algorithm, and the experimental results show that for the detection of air containing 10 ppm, 30 ppm, 60 ppm, 80 ppm, and 99 ppm methane, the errors are 12.75%, 8.18%, 3.37%, 2.46%, and 1.78%, respectively.

Fibrosis assessment using Fibro Meter combined to first generation tests in hepatitis C

AIMTo evaluate the performance of FibroMeterVirus3G combined to the first generation tests aspartate aminotransferase-to-platelet ratio index (APRI) or Forns index to assess significant fibrosis in chronic hepatitis C (CHC). METHODSFirst generation tests APRI or Forns were initially applied in a derivation population from Rio de Janeiro in Brazil considering cut-offs previously reported in the literature to evaluate significant fibrosis. FibroMeterVirus3G was sequentially applied to unclassified cases from APRI or Forns. Accuracy of non-invasive combination of tests, APRI plus FibroMeterVirus3G and Forns plus FibroMeterVirus3G was evaluated in the Brazilian derivation population. APRI plus FibroMeterVirus3G combination was validated in a population of CHC patients from Angers in France. All patients were submitted to liver biopsy staged according to METAVIR score by experienced hepatopathologists. Significant fibrosis was considered as METAVIR F ≥ 2. The fibrosis stage classification was used as the reference for accuracy evaluation of non-invasive combination of tests. Blood samples for the calculation of serum tests were collected on the same day of biopsy procedure or within a maximum 3 mo interval and stored at -70 °C. RESULTSSeven hundred and sixty CHC patients were included (222 in the derivation population and 538 in the validation group). In the derivation population, the FibroMeterVirus3G AUROC was similar to APRI AUROC (0.855 vs 0.815, P = 0.06) but higher than Forns AUROC (0.769, P Virus3G cut-off to discriminate significant fibrosis was 0.61 (80% diagnostic accuracy; 75% in the validation population, P = 0.134). The sequential combination of APRI or Forns with FibroMeterVirus3G in derivation population presented similar performance compared to FibroMeterVirus3G used alone (79% vs 78% vs 80%, respectively, P = 0.791). Unclassified cases of significant fibrosis after applying APRI and Forns corresponded to 49% and 54%, respectively, of the total sample. However, the combination of APRI or Forns with FibroMeterVirus3G allowed 73% and 77%, respectively, of these unclassified cases to be correctly evaluated. Moreover, this combination resulted in a reduction of FibroMeterVirus3G requirement in approximately 50% of the entire sample. The stepwise combination of APRI and FibroMeterVirus3G applied to the validation population correctly identified 74% of patients with severe fibrosis (F ≥ 3). CONCLUSIONThe stepwise combination of APRI or Forns with FibroMeterVirus3G may represent an accurate lower cost alternative when evaluating significant fibrosis, with no need for liver biopsy.

A Hybrid Multi-Objective Evolutionary Algorithm for Optimal Groundwater Management under Variable Density Conditions

In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search （TS） based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm （GA） with a TS based MOEA, the niched Pareto tabu search （NPTS）, which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources.

Task scheduling for multi-electro-magnetic detection satellite with a combined algorithm

Task scheduling for electro-magnetic detection satellite is a typical combinatorial optimization problem. The count of constraints that need to be taken into account is of large scale. An algorithm combined integer programming with constraint programming is presented. This algorithm is deployed in this problem through two steps. The first step is to decompose the original problem into master and sub-problem using the logic-based Benders decomposition; then a circus combines master and sub-problem solving process together, and the connection between them is general Benders cut. This hybrid algorithm is tested by a set of derived experiments. The result is compared with corresponding outcomes generated by the strength Pareto evolutionary algorithm and the pure constraint programming solver GECODE, which is an open source software. These tests and comparisons yield promising effect.

LEARNING ALGORITHM OF STAGE CONTROL NBP NETWORK

This letter analyzes the reasons why the known Neural Back Promulgation (NBP)network learning algorithm has slower speed and greater sample error. Based on the analysis and experiment, the training group descending Enhanced Combination Algorithm (ECA) is proposed.The analysis of the generalized property and sample error shows that the ECA can heighten the study speed and reduce individual error.

Reliability analysis of k-out-of-n system with load-sharing and failure propagation effect

In complex systems,functional dependency and physical dependency may have a coupling effect.In this paper,the reliability of a k-out-of-n system is analyzed considering load-sharing effect and failure mechanism(FM)propagation.Three types of FMs are considered and an accumulative damage model is proposed to illustrate the system behavior of the k-out-of-n system and the coupling effect between load-sharing effect and FM propagation effect.A combinational algorithm based on Binary decision diagram(BDD)and Monte-Carlo simulation is presented to evaluate the complex system behavior and reliability of the k-out-of-n system.A current stabilizing system that consists of a 3-out-of-6 subsystem with FM propagation effect is presented as a case to illustrate the complex behavior and to verify the applicability of the proposed method.Due to the coupling effect change,the main mechanism and failure mode will be changed,and the system lifetime is shortened.Reasons are analyzed and results show that different sensitivity factors of three different FMs lead to the change of the development rate,thus changing the failure scenario.Neglecting the coupling effect may lead to an incomplete and ineffective measuring and monitoring plan.Design strategies must be adopted to make the FM propagation insensitive to load-sharing effect.

Application of Interval Algorithm in Rural Power Network Planning

Rural power network planning is a complicated nonlinear optimized combination problem which based on load forecasting results, and its actual load is affected by many uncertain factors, which influenced optimization results of rural power network planning. To solve the problems, the interval algorithm was used to modify the initial search method of uncertainty load mathematics model in rural network planning. Meanwhile, the genetic/tabu search combination algorithm was adopted to optimize the initialized network. The sample analysis results showed that compared with the certainty planning, the improved method was suitable for urban medium-voltage distribution network planning with consideration of uncertainty load and the planning results conformed to the reality.

Combined Approach for Nonuniformity Correction in Infrared Focal Plane Array

A new algorithm of nonuniformity correction for infrared focal plane array(IRFPA) is reported,which is a combined algorithm based on both the two-point correction and artificial neural networks correction. The combined algorithm is calibrated by two-point correction,and the calibrated correction coefficients are automatically modified by BP algorithm. So it is not only calibrated,but also real-time processed. In adaptive nonuniformity correction algorithm,the phenomena ghost artifact and target fade-out are avoided by edge extraction. In order to get intensified image,the modified median filters are adopted. The simulated data indicates the proposed scheme is an effective algorithm.

RSMD-repeat searcher and motif detector

The functionality of a gene or a protein depends on codon repeats occurring in it.As a consequence of their vitality in protein function and apparent involvement in causing diseases,an interest in these repeats has developed in recent years.The analysis of genomic and proteomic sequences to identify such repeats requires some algorithmic support from informatics level.Here,we proposed an offline stand-alone toolkit Repeat Searcher and Motif Detector（RSMD）,which uncovers and employs few novel approaches in identification of sequence repeats and motifs to understand their functionality in sequence level and their disease causing tendency.The tool offers various features such as identifying motifs,repeats and identification of disease causing repeats.RSMD was designed to provide an easily understandable graphical user interface（GUI）,for the tool will be predominantly accessed by biologists and various researchers in all platforms of life science.GUI was developed using the scripting language Perl and its graphical module PerlTK.RSMD covers algorithmic foundations of computational biology by combining theory with practice.

EQUILIBRIUM ALGORITHMS WITH NONMONOTONE LINE SEARCH TECHNIQUE FOR SOLVING THE TRAFFIC ASSIGNMENT PROBLEMS

This paper presents a unified bination algorithms （such as FrankWolfe problems. Global convergence results are framework of the nonmonotone convex comAlgorithm） for solving the traffic assignment established under mild conditions. The line search procedure used in our algorithm includes the nonmonotone Armijo rule, the non- monotone Goldstein rule and the nonmonotone Wolfe rule as special cases. So, the new algorithm can be viewed as a generalization of the regular convex combination algorithm.

Parallel Critical Path Tracing——A Fault Simulation Algorithm for Combinational Circuits

Critical path tracing,a fault simulation method for gate-level combinational circuits,is extended to theparallel critical path tracing for functional block-level combinational circuits.If the word length of the hostcomputer is m,then the parallel critical path tracing will be approximately m times faster than the originalone.

A Complete Critical Path Algorithm for Test Generation of Combinational Circuits

It is known that critical path test generation method is not a complete algorithm for combinational circuits with reconvergent-fanout.In order to make it a complete algorithm,we put forward a reconvergent-fanout- oriented technique,the principal critical path algorithm,propagating the critical value back to primary inputs along a single path,the principal critical path,and allowing multiple path sensitization if needed.Relationship among test patterns is also discussed to accelerate test generation.

Blind equalization of underwater acoustic channel using a combined constant modulus algorithm

A kind of Combined Constant Modulus Algorithm (CCMA) is presented to compensate the defects of the Constant Modulus Algorithm (CMA) and the Sign Error CMA (SECMA). And CCMA is applied to the equalization of the underwater acoustic channel (UWAC). Based on the decision of the equalizer’s output, its iteration process switches between