Renal allograft procurement from living unrelated donors in Iran: What falls under the eclipse

Renal transplantation is the treatment of choice for end stage kidney disease.However,despite all the efforts to expand the donor pool,the shortage of donors is increasing and as a consequence,there has been a significant increase in the number of patients on transplant waiting lists globally.Societies worldwide have employed different methods to address this,each with specific ethical concerns surrounding them.Over three decades ago,a governmentally regulated program of kidney transplantation from living unrelated donors was introduced in Iran and since practiced which has been the subject of hot debate in the literature.Nevertheless,despite all these extensive discussions and publications,several key aspects of the program have still not been properly elucidated and addressed.In this article,the author aims to illuminate some dark corners related to this issue that have largely escaped the notice of ethicists.

A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

Production of Poly（3-hydroxybutyrate-co-3-hydroxyhexanoate） by Recombinant Pseudomonas stutzeri 1317 from Unrelated Carbon Sources

Synthetic biology promises to simplify the construction of metabolic pathways by assembling the de- tached modules of the whole pathway. This gives new approaches for the microbial production of industrial products such as polyhydroxyalkanoates （PHA）. In this study, to produce poly（3-hydroxybutyrate-co-3-hydroxyhexanoate） （PHBHHx） by Pseudomonas stutzeri 1317 from unrelated carbon sources such as glucose, the phaCl-phaZ-phaC2 operon of P. stutzeri 1317 was knocked out to generate the PHA deficient mutant P. stutzeri 1317LF. Then three modules containing phaCahAReBRe, phaCahBReGep and phaCAhPah were introduced into P. stutzeri 1317LF separately The shake flask results indicated that the precursor supply and PHA synthase activity were the vital factors for the PHBHHx accumulation of P. stutzeri 1317LF. Furthermore, the PHBHHx accumulation of the recombinants from different carbon resources were performed. The highest PHBHHx content was 23.7% （by mass） with 58.6% （by mole） 3HB fraction. These results provide basis for further improving the PHBHHx accumulation of P. stutzeri from unrelated carbon sources.

Design and Comparison of Simulated Annealing Algorithm and GRASP to Minimize Makespan in Single Machine Scheduling with Unrelated Parallel Machines

This paper discusses design and comparison of Simulated Annealing Algorithm and Greedy Randomized Adaptive Search Procedure (GRASP) to minimize the makespan in scheduling n single operation independent jobs on m unrelated parallel machines. This problem of minimizing the makespan in single machine scheduling problem with uniform parallel machines is NP hard. Hence, heuristic development for such problem is highly inevitable. In this paper, two different Meta-heuristics to minimize the makespan of the assumed problem are designed and they are compared in terms of their solutions. In the first phase, the simulated annealing algorithm is presented and then GRASP (Greedy Randomized Adaptive Search procedure) is presented to minimize the makespan in the single machine scheduling problem with unrelated parallel machines. It is found that the simulated annealing algorithm performs better than GRASP.

Two Stage Estimation and Its Covariance Matrix in Multivariate Seemingly Unrelated Regression System

Multivariate seemingly unrelated regression system is raised first and the two stage estimation and its covariance matrix are given. The results of the literatures[1-5] are extended in this paper.

IgG4-Unrelated Type 1 Auto Immune Pancreatitis Manifesting with Pancreatic Head Mass Mimicking Cancer

Auto immune pancreatitis (AIP) is an uncommon form of chronic pancreatitis that has been divided into type 1 and type 2 which have distinct histopathology and clinical features. Type 1 AIP seems to be the pancreatic manifestation of an IgG4-related systemic disease, characterized by elevated IgG4 serum levels, infiltration of IgG4-positive plasma cells and extrapancreatic lesions. When manifesting as a focal disease in the pancreas, it can be challenging to differentiate it from pancreatic cancer (PC). Because AIP is typically responsive to steroid therapy without the need for resection, differentiation between these two diseases is critical. We report the case of a 56-year-old man with initial suspect of PC, and final diagnosis of type 1 AIP according to the International Consensus Diagnostic Criteria (ICDC) without elevated levels of serum IgG4 or need for a histology sample. We take a review of literature in order to improve the diagnostic accuracy of AIP and describe clinical and imaging features to differentiate it from PC and avoid unnecessary surgery due to misdiagnosis.

Living related and living unrelated kidney transplantations:A systematic review and meta-analysis

AIM To compare the outcomes between related and unrelated kidney transplantations.METHODS Literature searches were performed following the Cochrane guidelines. We conducted a systematic review and a meta-analysis, which included 12 trials that investigated outcomes including the long-term(ten years), midterm(one to five years), and short-term(one year) graft survival rate as well as the acute rejection rate. Metaanalyses were performed using fixed and random-effects models, which included tests for publication bias and heterogeneity.RESULTS No difference in graft survival rate was detected in patients who underwent living related kidney transplantations compared to unrelated(P = 0.44) transplantations after ten years. There were no significant differences between the graft survival rate in living related and unrelated kidney transplantations after a short-and midterm follow-up(P = 0.35, P = 0.46). There were no significant differences between the acute rejection rate in living related and unrelated kidney transplantations(P = 0.06).CONCLUSION The long, mid and short term follow-up of living related and unrelated kidney transplantation showed no significant difference in graft survival rate. Also, acute rejection rate was not significantly different between groups.

Efficient Heuristic to Minimize Makespan in Single Machine Scheduling Problem with Unrelated Parallel Machines

This paper discusses an efficient heuristic to minimize the makespan of scheduling n independent jobs on m unrelated parallel machines. The problem of scheduling the jobs on the unrelated parallel machines is combinatorial in nature. Hence, the heuristic approach is inevitable for quicker solution. In this paper, a simple and efficient heuristic is designed to minimize the makespan of scheduling n independent jobs on m unrelated parallel machines. A mathematical model is also presented for this problem. A factorial experiment is used to compare the results of the proposed heuristic with that of a mathematical model by taking “Method” (Heuristic and Model) as the first factor and “Problem Size” (No. of machines X No. of Jobs: 2X5, 2X6, ……, 2X9, 3X5, 3X6, ……, 3X9, ……., 5X5, 5X6, …5X9) as the second factor. It is found that there is no significant difference between the results of the proposed heuristic and that of the mathematical model. Further, the mean percent error of the results obtained by the heuristic from the optimal results obtained by the model is 2.336 %. The heuristic gives optimal solution for 76.67 % of the problems.

The Pre－test Principal Components Estimator in the Two Seemingly Unrelated Regression System

For the two seemingly unrelated regression system, this paper proposed a new type of estimator called pre-test principal components estimator (PTPCE) and discussed some properties of PTPCE.

A Multi-Criteria Decision Making for the Unrelated Parallel Machines Scheduling Problem

In this paper, we propose a multi-criteria machine-schedules decision making method that can be applied to a produc-tion environment involving several unrelated parallel machines and we will focus on three objectives: minimizing makespan, total flow time, and total number of tardy jobs. The decision making method consists of three phases. In the first phase, a mathematical model of a single machine scheduling problem, of which the objective is a weighted sum of the three objectives, is constructed. Such a model will be repeatedly solved by the CPLEX in the proposed Multi-Objective Simulated Annealing (MOSA) algorithm. In the second phase, the MOSA that integrates job clustering method, job group scheduling method, and job group – machine assignment method, is employed to obtain a set of non-dominated group schedules. During this phase, CPLEX software and the bipartite weighted matching algorithm are used repeatedly as parts of the MOSA algorithm. In the last phase, the technique of data envelopment analysis is applied to determine the most preferable schedule. A practical example is then presented in order to demonstrate the applicability of the proposed decision making method.

Improved Estimation of Distribution Algorithm for Solving Unrelated Parallel Machine Scheduling Problem

Scheduling problem is a well-known combinatorial optimization problem.An effective improved estimation of distribution algorithm(IEDA) was proposed for minimizing the makespan of the unrelated parallel machine scheduling problem(UPMSP).Mathematical description was given for the UPMSP.The IEDA which was combined with variable neighborhood search(IEDA_VNS) was proposed to solve the UPMSP in order to improve local search ability.A new encoding method was designed for representing the feasible solutions of the UPMSP.More knowledge of the UPMSP were taken consideration in IEDA_ VNS for probability matrix which was based the processing time matrix.The simulation results show that the proposed IEDA_VNS can solve the problem effectively.

Unrelated Parallel-Machine Scheduling Problems with General Truncated Job-Dependent Learning Effect

In this paper, we consider scheduling problems with general truncated job-dependent learning effect on unrelated parallel-machine. The objective functions are to minimize total machine load, total completion (waiting) time, total absolute differences in completion (waiting) times respectively. If the number of machines is fixed, these problems can be solved in time respectively, where m is the number of machines and n is the number of jobs.

A Multi-Objective Hybrid Algorithm for the Casting Scheduling Problem with Unrelated Batch Processing Machine

The casting production process typically involves single jobs and small batches,with multiple constraints in the molding and smelting operations.To address the discrete optimization challenge of casting production scheduling,this paper presents a multi-objective batch scheduling model for molding and smelting operations on unrelated batch processing machines with incompatible job families and non-identical job sizes.The model aims to minimise the makespan,number of batches,and average vacancy rate of sandboxes.Based on the genetic algorithm,virus optimization algorithm,and two local search strategies,a hybrid algorithm(GA-VOA-BMS)has been designed to solve the model.The GA-VOA-BMS applies a novel Batch First Fit(BFF)heuristic for incompatible job families to improve the quality of the initial population,adopting the batch moving strategy and batch merging strategy to further enhance the quality of the solution and accelerate the convergence of the algorithm.The proposed algorithm was then compared with multi-objective swarm optimization algorithms,namely NSGA-ll,SPEA-l,and PESA-ll,to evaluate its effectiveness.The results of the performance comparison indicate that the proposed algorithm outperforms the others in terms of both qualityand stability.

Does digitalization mitigate regional inequalities?Evidence from China

Regional inequality significantly influences sustainable development and human well-being.In China,there exists pronounced regional disparities in economic and digital advancements;however,scant research delves into the interplay between them.By analyzing the economic development and digitalization gaps at regional and city levels in China,extending the original Cobb-Douglas production function,this study aims to evaluate the impact of digitalization on China's regional inequality using seemingly unrelated regression.The results indicate a greater emphasis on digital inequality compared to economic disparity,with variable coefficients of 0.59 for GDP per capita and 0.92 for the digitalization index over the past four years.However,GDP per capita demonstrates higher spatial concentration than digitalization.Notably,both disparities have shown a gradual reduction in recent years.The southeastern region of the Hu Huanyong Line exhibits superior levels and rates of economic and digital advancement in contrast to the northwestern region.While digitalization propels economic growth,it yields a nuanced impact on achieving balanced regional development,encompassing both positive and negative facets.Our study highlights that the marginal utility of advancing digitalization is more pronounced in less developed regions,but only if the government invests in the digital infrastructure and education in these areas.This study's methodology can be utilized for subsequent research,and our findings hold the potential to the government's regional investment and policy-making.

Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors,site quality,and aridity index

The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.

Bayes and empirical Bayes iteration estimators in two seemingly unrelated regression equations

For a system of two seemingly unrelated regression equations given by (?)(y_1 is an m×1 vector and y_2 is an n×1 vector,m≠n),employ- ing the covariance adjusted technique,we propose the parametric Bayes and empirical Bayes iteration estimator sequences for regression coefficients.We prove that both the covariance matrices converge monotonically and the Bayes iteration estimator squence is consistent as well.Based on the mean square error (MSE) criterion,we elaborate the su- periority of empirical Bayes iteration estimator over the Bayes estimator of single equation when the covariance matrix of errors is unknown.The results obtained in this paper further show the power of the covariance adiusted approach.

Successful transplantation of double unit umbilical-cord blood from unrelated donors in high risk leukemia with a long follow-up

Umbilical-cord blood (UCB) is a source of hematopoietic stem cells that has been successfully used for transplantation, primarily in children. But the low cell dose severely limits the application of unrelated umbilical-cord blood transplantation (UCBT) in adult patients, particularly in those with high body weight. We hypothesized that the combined transplantation of two partially matched UCB units would improve engraftment without crossed immunological rejection. Since May 2000, six adult patients with high risk leukemia have been transplanted in this study.

A NEW ESTIMATE OF REGRESSION COEFFICIENTS IN SEEMINGLY UNRELATED REGRESSION SYSTEM

For a seemingly unrelated regression system consisting of m equations, the information contained in all the equations is divided into sample information and additional information, and a new estimate of the regression coefficients is proposed by using successive superposition. More precisely, the following three problems are solved: (ⅰ) a statistic summarized the additional information is constructed, (ⅱ) a procedure which superposes the additional information on the sample information and a new estimate of regression coefficients are proposed, (ⅲ) some properties of the new estimate are established.

TWO-STAGE ESTIMATION FOR SEEMINGLY UNRELATED NONPARAMETRIC REGRESSION MODELS

This paper is concerned with the estimating problem of seemingly unrelated （SU） non- parametric regression models. The authors propose a new method to estimate the unknown functions, which is an extension of the two-stage procedure in the longitudinal data framework. The authors show the resulted estimators are asymptotically normal and more efficient than those based on only the individual regression equation. Some simulation studies are given in support of the asymptotic results. A real data from an ongoing environmental epidemiologie study are used to illustrate the proposed procedure.