Using multi-omics analysis to explore diagnostic tool and optimize drug therapy selection for patients with glioma based on cross-talk gene signature

Background:The heterogeneity of prognosis and treatment benefits among patients with gliomas is due to tumor microenvironment characteristics.However,biomarkers that reflect microenvironmental characteristics and predict the prognosis of gliomas are limited.Therefore,we aimed to develop a model that can effectively predict prognosis,differentiate microenvironment signatures,and optimize drug selection for patients with glioma.Materials and Methods:The CIBERSORT algorithm,bulk sequencing analysis,and single-cell RNA(scRNA)analysis were employed to identify significant cross-talk genes between M2 macrophages and cancer cells in glioma tissues.A predictive model was constructed based on cross-talk gene expression,and its effect on prognosis,recurrence prediction,and microenvironment characteristics was validated in multiple cohorts.The effect of the predictive model on drug selection was evaluated using the OncoPredict algorithm and relevant cellular biology experiments.Results:A high abundance of M2 macrophages in glioma tissues indicates poor prognosis,and cross-talk between macrophages and cancer cells plays a crucial role in shaping the tumor microenvironment.Eight genes involved in the cross-talk between macrophages and cancer cells were identified.Among them,periostin(POSTN),chitinase 3 like 1(CHI3L1),serum amyloid A1(SAA1),and matrix metallopeptidase 9(MMP9)were selected to construct a predictive model.The developed model demonstrated significant efficacy in distinguishing patient prognosis,recurrent cases,and characteristics of high inflammation,hypoxia,and immunosuppression.Furthermore,this model can serve as a valuable tool for guiding the use of trametinib.Conclusions:In summary,this study provides a comprehensive understanding of the interplay between M2 macrophages and cancer cells in glioma;utilizes a cross-talk gene signature to develop a predictive model that can predict the differentiation of patient prognosis,recurrence instances,and microenvironment characteristics;and aids in optimizing the application of trametinib in glioma patients.

Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs

Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it was limited by the purebred population.Compared to directly combining two uncorrelated purebred populations to extend the reference population size,it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction.In this study,we simulated purebred offspring(PAS and PBS)and crossbred offspring(CAB)base on real genotype data of two base purebred populations(PA and PB),to evaluate the performance of genomic selection on purebred while incorporating crossbred information.The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship(REL)was better than the other methods(individuals closet or farthest to the purebred population,CP/FP)in term of the prediction accuracy.Furthermore,the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA,which was similar to combine PA and PAS.Moreover,the rank correlation between the multiple of the increased relationship(MIR)and reliability improvement was 0.60-0.70.But for individuals with low correlation(Cor(Pi,PA or B),the reliability improvement was significantly lower than other individuals.Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only.The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

INTRASPECIFIC CROSSINGS OF UNDARIA PINNATIFIDA (HARV.) SUR.-A POSSIBLE TIME-SAVING WAY OF STRAIN SELECTION

Unicellular gametophytes of Undaria pinnatifida (Harv.) Sur. were isolated in Qingdao, P. R. China in April 1993 and in Tokushima, southem Japan in March 1995. Diferent intraspecific crossings by using unicellular male and female gametophytes were successfully undertaken in Sept. of 1995 in Qingdao.Sporophytes were transplanted to two different locations for open an cultivation. One was at Zhanqiao (ZQ) Bay where the water current was slower than that of another location - Taipingjiao(TPJ). A total of218 adult sporophytes were harvested on January 12 and 18 from TPJ and ZQ repectively. For eacncombination, 10 sporophytes un cultivated. Analysis of the mprpholgical characteristics of adult spprophytes indicated that the longest length between two bases of the serration of pinnate bldes (W2) is a morphological characteristic that can be transferred from the parent plant to the next generations regardless of environmental variations. There was evidence that W2 was apparently determined by sex-linked factors, i.e, by male parental grametophyte.. Sporophytes from certain crossing combinations showed more vigorous growth than those from other crossing combinations. It is therrfore possible to select grametophyte strains which can be used as parental gametophytes for the seeding production of sporophytes with more vigorous growth within shorter cultivation period. The morphology of hybrids from a Qingdao strain and a Tokushima cultivated strain resembled that of both parental plants in frond feaures (wrinkled or smooth) and W2. Sporophyll formation also varied with strains. The fact that adult sporophytes resulting from the same crossing combinations have identical morphological characteristics under the same environmental conditions indicates the possibility of a new way to select strains which are expected to be ideal for commerical production by purposely selecting, propagating, and seeding unicellular gametophytes for sporeing production through freeliving techniques of gametophytes.

Use of genomic selection and breeding simulation in cross prediction for improvement of yield and quality in wheat (Triticum aestivum L.)

In wheat breeding, it is a difficult task to select the most suitable parents for making crosses aimed at the improvement of both grain yield and grain quality. By quantitative genetics theory,the best cross should have high progeny mean and large genetic variance, and ideally yield and quality should be less negatively or positively correlated. Usefulness is built on population mean and genetic variance, which can be used to select the best crosses or populations to achieve the breeding objective. In this study, we first compared five models(RR-BLUP, Bayes A, Bayes B, Bayes ridge regression, and Bayes LASSO) for genomic selection(GS) with respect to prediction of usefulness of a biparental cross and two criteria for parental selection, using simulation. The two parental selection criteria were usefulness and midparent genomic estimated breeding value(GEBV). Marginal differences were observed among GS models. Parental selection with usefulness resulted in higher genetic gain than midparent GEBV. In a population of 57 wheat fixed lines genotyped with 7588 selected markers, usefulness of each biparental cross was calculated to evaluate the cross performance, a key target of breeding programs aimed at developing pure lines. It was observed that progeny mean was a major determinant of usefulness, but the usefulness ratings of quality traits were more influenced by their genetic variances in the progeny population. Near-zero or positive correlations between yield and major quality traits were found in some crosses, although they were negatively correlated in the population of parents. A selection index incorporating yield, extensibility, and maximum resistance was formed as a new trait and its usefulness for selecting the crosses with the best potential to improve yield and quality simultaneously was calculated. It was shown that applying the selection index improved both yield and quality while retaining more genetic variance in the selected progenies than the individual trait selection. It was concluded that combining genomic selection with simulation allows the prediction of cross performance in simulated progenies and thereby identifies candidate parents before crosses are made in the field for pure-line breeding programs.

Cross-Validation, Shrinkage and Variable Selection in Linear Regression Revisited

In deriving a regression model analysts often have to use variable selection, despite of problems introduced by data- dependent model building. Resampling approaches are proposed to handle some of the critical issues. In order to assess and compare several strategies, we will conduct a simulation study with 15 predictors and a complex correlation structure in the linear regression model. Using sample sizes of 100 and 400 and estimates of the residual variance corresponding to R2 of 0.50 and 0.71, we consider 4 scenarios with varying amount of information. We also consider two examples with 24 and 13 predictors, respectively. We will discuss the value of cross-validation, shrinkage and backward elimination (BE) with varying significance level. We will assess whether 2-step approaches using global or parameterwise shrinkage (PWSF) can improve selected models and will compare results to models derived with the LASSO procedure. Beside of MSE we will use model sparsity and further criteria for model assessment. The amount of information in the data has an influence on the selected models and the comparison of the procedures. None of the approaches was best in all scenarios. The performance of backward elimination with a suitably chosen significance level was not worse compared to the LASSO and BE models selected were much sparser, an important advantage for interpretation and transportability. Compared to global shrinkage, PWSF had better performance. Provided that the amount of information is not too small, we conclude that BE followed by PWSF is a suitable approach when variable selection is a key part of data analysis.

Green Rice Leafhopper Resistance Gene Transferring Through Backcrossing and CAPS Marker Assisted Selection

Grh2, a green rice leafhopper resistant gene from an indica cultivar DV85, was located on chromosome 11, and two RFLP markers C189 and G1465 were found to be linked to this gene. In order to transfer Grh2 into Taichung65, a japonica cultivar with elite characters, backcross method with Taichung65 as the recurrent parent was used and the two RFLP markers were converted into CAPS markers for marker assisted selection (MAS). In the BC6F3 population, both phenotypic evaluation and MAS were conducted to screen the resistant plants with Taichung65 background. The linkage distance between CAPS markers and Grh2 was calculated and the efficiency of MAS was analyzed.

Dynamic services selection algorithm in Web services composition supporting cross-enterprises collaboration

Based on the deficiency of time convergence and variability of Web services selection for services composition supporting cross-enterprises collaboration,an algorithm QCDSS(QoS constraints of dynamic Web services selection)to resolve dynamic Web services selection with QoS global optimal path,was proposed.The essence of the algorithm was that the problem of dynamic Web services selection with QoS global optimal path was transformed into a multi-objective services composition optimization problem with QoS constraints.The operations of the cross and mutation in genetic algorithm were brought into PSOA(particle swarm optimization algorithm),forming an improved algorithm(IPSOA)to solve the QoS global optimal problem.Theoretical analysis and experimental results indicate that the algorithm can better satisfy the time convergence requirement for Web services composition supporting cross-enterprises collaboration than the traditional algorithms.

基于Cross熵与改进麻雀搜索算法的图像分割模型

传统基于熵标准的图像分割法采用穷尽法搜索分割阈值,存在计算代价高、分割效率低的不足。针对这一问题,设计基于Cross熵与改进麻雀搜索算法的图像分割方法。为了提升标准麻雀搜索算法的寻优精度和寻优速率,利用反向学习机制进行种群初始化,改善初始种群结构,提升种群多样性和初始解质量。设计正余弦优化和惯性权重的发现者更新机制,提升发现者全局搜索能力。提出柯西混沌变异的追随者更新机制,结合混沌映射和柯西变异,避免算法产生局部最优。以Cross熵最小为标准评估个体适应度,利用改进麻雀搜索算法寻找图像分割最佳阈值,并实现图像分割。实验结果表明,改进算法在分割指标上表现优异,可以有效提升图像分割精度和分割效率。

User Selection Algorithms for Lowering Cross-Tier Interference in Heterogeneous Network

To lower the cross-tier intercell interference(ICI) between macrocell and microcell,three user selection algorithms for the heterogeneous network were proposed in this paper, assuming full knowledge of channelstate information at the transmitter. Algorithm 1 chooses microcell users whose interference channel matrix is parallel to that of a known user and targets at increasing user SINR. Algorithm 2 takes effect of chordal distance-channel norm balance on the system into account and predetermines the available user set from which it can choose service users. With comprehensive considerations to effect of interference signal and useful signal on system, Algorithm 3 set a weighting function as the objective function of user selection. Simulation results demonstrated that all three proposed algorithms could achieve user diversity gain while lowering cross-tier interference.

Pattern selection in a predation model with self and cross diffusion

In this paper, we present the amplitude equations for the excited modes in a cross-diffusive predator-prey model with zero-flux boundary conditions. From these equations, the stability of patterns towards uniform and inhomogenous perturbations is determined. Furthermore, we present novel numerical evidence of six typical turing patterns, and find that the model dynamics exhibits complex pattern replications： for μ1 〈μ ≤μ2, the steady state is the only stable solution of the model; for μ2 〈 μ ≤ μ4, by increasing the control parameter μ, the sequence Hπ-hexagons→ Hπ- hexagon-stripe mixtures → stripes → H0-hexagon-stripe mixtures → H0-hexagons is observed; for μ 〉 μ4, the stripe pattern emerges. This may enrich the pattern formation in the cross-diffusive predatorprey model.

基于遗传算法与Hardy-Cross方法的矿井通风机控制

针对矿井通风机系统控制的复杂问题,提出了一种遗传算法与Hardy-Cross方法相结合的矿井通风控制,强化了其在处理离散变量和非凸搜索空间中寻找全局最优解的能力。通过数学形式化定义问题并结合基尔霍夫定律,确立了通风机网络的优化模型。根据实际用风情况智能调节通风机的供风量,以实现能源节约和安全生产的目标。通过对某煤矿的通风案例深入研究和验证,风扇的配置在优化气流方面表现出了卓越性能。该研究不仅为矿山工程领域提供了新的优化思路和方法,也为通风机系统的管理提供了重要参考。

Genetic Parameters of Body Length and Response to Selection for Growth Across Four Generations of Artemia sinica

To investigate the genetic components of growth in the brine shrimp Artemia sinica, we estimated the genetic parameters of body length and the response to selection using a fully pedigreed population of A. sinica. The base population was generated from four wild founder populations. We tested 4160 offspring in 360 families over four generations for growth and survival performance. Across four generations, we produced full-and half-sib families with nested mating, where two dams were mated to the same sire. Individual body length was measured for each nauplius at day 20 post-hatching. Heritability of body length was estimated across four generations with the restricted maximum likelihood method. The heritability of body length in A. sinica was low(0.14 ± 0.05), and the common environmental effect was 0.14 ± 0.02. We estimated the response to selection for body length by calculating the difference in the mean breeding values between different generations. The accumulated genetic gain in body length was 278.94 μm after three generations of selection. This low response to selection was probably caused by the low heritability of body length, small sample size, and the low selection intensity(50%). The results suggest that A. sinica selective breeding programs must be changed to generate any substantial, sustainable genetic increases in body length. We suggest that optimal genetic gains could be achieved by introducing wild strains into the nuclear breeding population to increase genetic variation, and by increasing the size of the breeding population to allow for increased selection intensity.

Lowering Grain Amylose Content in Backcross Offsprings of indica Rice Variety 057 by Molecular Marker-Assisted Selection

To lower the amylose content （AC） of the indica rice restorer line 057 with high AC, backcrosses were made respectively by using four indica varieties （R367, 91499, Yanhui 559, Hui 527） as low AC donor parents and 057 as the recurrent parent. A molecular marker （PCR-Acc Ⅰ） was used to identify the genotypes （GG, TT and GT） of the waxy （Wx） gene. Plants with GT genotype were selected and used as female parent and crossed with 057 to advance generation. The ACs of rice grains harvested from plants with different Wx genotypes were measured and compared to analyze the efficiency of marker-assisted selection. The ACs of the rice grain, harvested from the plants of Wx genotypes GG, GT and TT, were higher than 20%, in the range of 17.7-28.5%, and less than 18%, respectively. The PCR-Acc Ⅰ marker could be used for efficiently lowering the AC of 057 through backcrossing, and there were some influence of parental genetic background on the AC of rice grains with the same Wx genotype.

Selection Criteria for Holstein Friesian and Crossbreed Dairy Cattle Objective Traits in Ethiopia

There was no research regarding selection criteria for the economically relevant dairy cattle objective traits in Ethiopia. Therefore, the goal of this paper was to determine the selection criteria for Holstein Friesian and crossbreed dairy cattle economically relevant traits. The research was based on 236 respondents of large, medium and small scale dairy farms from Dire-Dawa, Harar, Bishoftu, Holeta agricultural research center and Mekele. Data were analyzed using statistical analysis software and traits preferences were ranked by calculating index values with the principle of weighted average. For Holstein Friesian preducers, the most preferred breeds were pure Holstein Friesian, Holstein Friesian crossbreeds and local cattle breeds with an overall index value of 0.47, 0.36, and 0.13, respectively. Similarly for crossbreed producers, pure Holstein Friesian (0.46), Holstein Friesian crossbreed (0.37) and local cattle (0.15), respectively, were their main breed preferred. In the present study, both Holstein Friesian and crossbreed producers were used milk yield (0.61, and 0.64) and milk yield composition (0.39, and 0.34), as the main preferred selection criteria for milk composition, respectively. On the other situations, age at first calving (0.45, and 0.39), age at first service (0.38, and 0.37), and service per conception (0.08, and 0.17), were the preferred selection criteria for economically relevant reproduction traits both for Holstein Friesian and crossbreed, respectively.

Tabu Search Solution for Resource Confidence Considered Partner Selection Problem in Cross-Enterprise Project

Cross-enterprise project is the main implementation form in multi enterprises collaborative production environment. Minimizing the risk of failure and tardiness caused by the uncertainty of partner’s resources in partner selection is the key problem to ensure success in Cross-enterprise project. In this paper, considering the factors and constraints of sub-project processing times, precedence of sub-project and project due date, especially the resource confidence, a 0-1 integer programming model was presented with the objective to minimize the risk of failure and the tardiness of the project. A project scheduling algorithm was designed to search and evaluate selection solutions, and the project scheduling algorithm was embedded into a Tabu search algorithm to solve the model. Simulation experiments and comparisons with other algorithms showed that the proposed approach was possible to find the optimal solution with a faster speed and higher probability.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Corneal collagen cross-linking in patients with keratoconus from the Dresden protocol to customized solutions:theoretical basis

Keratoconus is an ectatic condition characterized by gradual corneal thinning,corneal protrusion,progressive irregular astigmatism,corneal fibrosis,and visual impairment.The therapeutic options regarding improvement of visual function include glasses or soft contact lenses correction for initial stages,gas-permeable rigid contact lenses,scleral lenses,implantation of intrastromal corneal ring or corneal transplants for most advanced stages.In keratoconus cases showing disease progression corneal collagen crosslinking(CXL)has been proven to be an effective,minimally invasive and safe procedure.CXL consists of a photochemical reaction of corneal collagen by riboflavin stimulation with ultraviolet A radiation,resulting in stromal crosslinks formation.The aim of this review is to carry out an examination of CXL methods based on theoretical basis and mathematical models,from the original Dresden protocol to the most recent developments in the technique,reporting the changes proposed in the last 15y and examining the advantages and disadvantages of the various treatment protocols.Finally,the limits of non-standardized methods and the perspectives offered by a customization of the treatment are highlighted.

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Turing pattern selection for a plant-wrack model with cross-diffusion

We investigate the Turing instability and pattern formation mechanism of a plant-wrack model with both self-diffusion and cross-diffusion terms.We first study the effect of self-diffusion on the stability of equilibrium.We then derive the conditions for the occurrence of the Turing patterns induced by cross-diffusion based on self-diffusion stability.Next,we analyze the pattern selection by using the amplitude equation and obtain the exact parameter ranges of different types of patterns,including stripe patterns,hexagonal patterns and mixed states.Finally,numerical simulations confirm the theoretical results.