Some Molecular and Cellular Stress Mechanisms Associated with Neurodegenerative Diseases and Atherosclerosis

Chronic stress is a combination of nonspecific adaptive reactions of the body to the influence of various adverse stress factors which disrupt its homeostasis, and it is also a corresponding state of the organism’s nervous system (or the body in general). We hypothesized that chronic stress may be one of the causes occurence of several molecular and cellular types of stress. We analyzed literary sources and considered most of these types of stress in our review article. We examined genes and mutations of nuclear and mitochondrial genomes and also molecular variants which lead to various types of stress. The end result of chronic stress can be metabolic disturbance in humans and animals, leading to accumulation of reactive oxygen species (ROS), oxidative stress, energy deficiency in cells (due to a decrease in ATP synthesis) and mitochondrial dysfunction. These changes can last for the lifetime and lead to severe pathologies, including neurodegenerative diseases and atherosclerosis. The analysis of literature allowed us to conclude that under the influence of chronic stress, metabolism in the human body can be disrupted, mutations of the mitochondrial and nuclear genome and dysfunction of cells and their compartments can occur. As a result of these processes, oxidative, genotoxic, and cellular stress can occur. Therefore, chronic stress can be one of the causes forthe occurrence and development of neurodegenerative diseases and atherosclerosis. In particular, chronic stress can play a large role in the occurrence and development of oxidative, genotoxic, and cellular types of stress.     

Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

New Insights into the Pathogenesis of Systemic Mastocytosis

Mastocytosis is a type of myeloid neoplasm characterized by the clonal, neoplastic proliferation of morphologically and immunophenotypically abnormal mast cells that infiltrate one or more organ systems. Systemic mastocytosis (SM) is a more aggressive variant of mastocytosis with extracutaneous involvement, which might be associated with multi-organ dysfunction or failure and shortened survival. Over 80% of patients with SM carry the KIT D816V mutation. However, the KIT D816V mutation serves as a weak oncogene and appears to be a late event in the pathogenesis of mastocytosis. The management of SM is highly individualized and was largely palliative for patients without a targeted form of therapy in past decades. Targeted therapy with midostaurin, a multiple kinase inhibitor that inhibits KIT, has demonstrated efficacy in patients with advanced SM. This led to the recent approval of midostaurin by the United States Food and Drug Administration and European Medicines Agency. However, the overall survival of patients treated with midostaurin remains unsatisfactory. The identification of genetic and epigenetic alterations and understanding their interactions and the molecular mechanisms involved in mastocytosis is necessary to develop rationally targeted therapeutic strategies. This review briefly summarizes recent developments in the understanding of SM pathogenesis and potential treatment strategies for patients with SM.     

盖子环替换及定点突变提高菜豆环氧化物水解酶对邻甲基苯基缩水甘油醚的催化性能

菜豆环氧化物水解酶1和2（PvEH1、PvEH2）能够动力学拆分外消旋邻甲基苯基缩水甘油醚（rac-oMGE），从而保留(R)-oMGE。基于对PvEH1和PvEH2结构的同源模拟和分析，发现二者分子中的盖子环差异较大，故本文选择盖子环作为研究目标。经融合聚合酶链式反应（FPCR），获得了PvEH2的盖子环区域被PvEH1对应区域替换的杂合酶Pv2Pv1。用全细胞酶E. coli/pv2pv1催化rac-oMGE，当(S)-oMGE刚好水解完全时，产物(S)-3-邻甲苯基-1,2-丙二醇（(S)-oTPD）的ee_p由PvEH2的58.3%提高至75.5%。为进一步提高酶的性质，在Pv2Pv1中选取11个氨基酸位点进行丙氨酸(A)突变，获得最优突变子E. coli/pv2pv1^K176A，活性为E. coli/pv2pv1（4.2U/g）的2.1倍，且当S构型的底物刚好完全水解时，(S)-oTPD的ee_p进一步提高为80.3%。分子对接分析发现，盖子环替换和K176位点突变为A，均使(R)-oMGE环氧环中的C_α更易受到酶中D101位点的攻击。利用E. coli/pv2pv1^K176A催化150mmol/L rac-oMGE水解制备(R)-oMGE（ee_s＞99%）和(S)-oTPD（ee_p=80.4%），二者的产率Y_S和Y_P分别为32.7%和60.1%，时空产率STY_S和STY_P为1.6g/(L·h)和3.3g/(L·h)。本实验为改善EH的催化性质提供了一种有效策略。     

The Genetic Basis of Primary Myelofibrosis and Its Clinical Relevance

Among classical BCR-ABL-negative myeloproliferative neoplasms (MPN), primary myelofibrosis (PMF) is the most aggressive subtype from a clinical standpoint, posing a great challenge to clinicians. Whilst the biological consequences of the three MPN driver gene mutations (JAK2, CALR, and MPL) have been well described, recent data has shed light on the complex and dynamic structure of PMF, that involves competing disease subclones, sequentially acquired genomic events, mostly in genes that are recurrently mutated in several myeloid neoplasms and in clonal hematopoiesis, and biological interactions between clonal hematopoietic stem cells and abnormal bone marrow niches. These observations may contribute to explain the wide heterogeneity in patients’ clinical presentation and prognosis, and support the recent effort to include molecular information in prognostic scoring systems used for therapeutic decision-making, leading to promising clinical translation. In this review, we aim to address the topic of PMF molecular genetics, focusing on four questions: (1) what is the role of mutations on disease pathogenesis? (2) what is their impact on patients’ clinical phenotype? (3) how do we integrate gene mutations in the risk stratification process? (4) how do we take advantage of molecular genetics when it comes to treatment decisions?     

Translational Read-Through Therapy of RPGR Nonsense Mutations

X-chromosomal retinitis pigmentosa (RP) frequently is caused by mutations in the retinitis pigmentosa GTPase regulator (RPGR) gene. We evaluated the potential of PTC124 (Ataluren, Translama^TM) treatment to promote ribosomal read-through of premature termination codons (PTC) in RPGR. Expression constructs in HEK293T cells showed that the efficacy of read-through reagents is higher for UGA than UAA PTCs. We identified the novel hemizygous nonsense mutation c.1154T > A, p.Leu385* ({"type":"entrez-nucleotide","attrs":{"text":"NM_000328.3","term_id":"1677500221"}}NM_000328.3) causing a UAA PTC in RPGR and generated patient-derived fibroblasts. Immunocytochemistry of serum-starved control fibroblasts showed the RPGR protein in a dot-like expression pattern along the primary cilium. In contrast, RPGR was no longer detectable at the primary cilium in patient-derived cells. Applying PTC124 restored RPGR at the cilium in approximately 8% of patient-derived cells. RT-PCR and Western blot assays verified the pathogenic mechanisms underlying the nonsense variant. Immunofluorescence stainings confirmed the successful PTC124 treatment. Our results showed for the first time that PTC124 induces read-through of PTCs in RPGR and restores the localization of the RPGR protein at the primary cilium in patient-derived cells. These results may provide a promising new treatment option for patients suffering from nonsense mutations in RPGR or other genetic diseases.     

A directional mutation operator for differential evolution algorithms

Differential evolution (DE) is widely studied in the past decade. In its mutation operator, the random variations are derived from the difference of two randomly selected different individuals. Difference vector plays an important role in evolution. It is observed that the best fitness found so far by DE cannot be improved in every generation. In this article, a directional mutation operator is proposed. It attempts to recognize good variation directions and increase the number of generations having fitness improvement. The idea is to construct a pool of difference vectors calculated when fitness is improved at a generation. The difference vector pool will guide the mutation search in the next generation once only. The directional mutation operator can be applied into any DE mutation strategy. The purpose is to speed up the convergence of DE and improve its performance. The proposed method is evaluated experimentally on CEC 2005 test set with dimension 30 and on CEC 2008 test set with dimensions 100 and 1000. It is demonstrated that the proposed method can result in a larger number of generations having fitness improvement than classic DE. It is combined with eleven DE algorithms as examples of how to combine with other algorithms. After its incorporation, the performance of most of these DE algorithms is significantly improved. Moreover, simulation results show that the directional mutation operator is helpful for balancing the exploration and exploitation capacity of the tested DE algorithms. Furthermore, the directional mutation operator modifications can save computational time compared to the original algorithms. The proposed approach is compared with the proximity based mutation operator as both are claimed to be applicable to any DE mutation strategy. The directional mutation operator is shown to be better than the proximity based mutation operator on the five variants in the DE family. Finally, the applications of two real world engineering optimization problems verify the usefulness of the proposed method.     

Evolution on neutral networks accelerates the ticking rate of the molecular clock

Large sets of genotypes give rise to the same phenotype, because phenotypic expression is highly redundant. Accordingly, a population can accept mutations without altering its phenotype, as long as the genotype mutates into another one on the same set. By linking every pair of genotypes that are mutually accessible through mutation, genotypes organize themselves into neutral networks (NNs). These networks are known to be heterogeneous and assortative, and these properties affect the evolutionary dynamics of the population. By studying the dynamics of populations on NNs with arbitrary topology, we analyse the effect of assortativity, of NN (phenotype) fitness and of network size. We find that the probability that the population leaves the network is smaller the longer the time spent on it. This progressive ‘phenotypic entrapment’ entails a systematic increase in the overdispersion of the process with time and an acceleration in the fixation rate of neutral mutations. We also quantify the variation of these effects with the size of the phenotype and with its fitness relative to that of neighbouring alternatives.     

基于变异测试的错误定位研究进展

随着软件规模和复杂度的不断提高，软件的质量问题成为了关注的焦点，如何高效地找出软件中的错误成为一个亟需解决的问题。错误定位是软件质量保证的重要途径之一，近年来已经成为软件工程中一个非常重要的研究课题。基于变异测试的错误定位通过比较原程序和对应变异体的差异来计算每条语句的怀疑度，再由怀疑度大小进行排序，程序员根据排序逐个检查找出错误语句。汇总近7年（2012-2018）国内外的基于变异测试的错误定位技术的研究成果，介绍了错误定位的基本方法，介绍基于变异测试的错误定位思想，从变异算子、变异体及等价变异体3个方面对已有的研究工作进行分类归纳和总结，探讨了基于变异测试的错误定位未来可能的研究方向、机遇和挑战。     

基于弱变异准则的测试用例约简方法

针对为数较多的测试用例增加了回归测试成本的问题，提出一种基于弱变异准则的测试用例约简方法。首先，基于弱变异准则获得测试用例和变异分支关系矩阵；然后，重复约简4种无效测试需求和子集测试用例；最后，结合人工鱼群算法选择当前最优测试用例，并且交替执行简化和测试用例选择操作直至覆盖所有测试需求。该方法针对6个经典程序与贪心算法和HGS算法相比，基于弱变异准则并且不改变或稍微改变变异评分的条件下，约简率分别提高了73.4%和8.2%，且耗时分别降低了25.3%和56.1%。实验结果表明，所提方法在回归测试中可有效约简测试用例，降低测试代价。