Currently, quantifying effective diffusivity in vivo is limited by the stringent assumptions, such as homogeneity, simplified geometry, steady state, etc., so that practitioners suffer from rough approximations and draw misleading conclusions or interpretations. Here, we report a general diffusivity quantification for biomolecules in reaction-diffusion systems with unprecedented accuracy and robustness. We demonstrate the first accurate measurement of morphogen effective diffusivity in highly dynamic and heterogeneous developing tissue with geometric constraints/irregularities. We find that identical morphogen molecules exhibit remarkable inter-tissue diffusivity differences of up to an order of magnitude. The robustness of a morphogen gradient and receptor influence on the gradient profile can be also evaluated by this new method we call Ds-SiFi. Ds-SiFi is further demonstrated to measure the diffusion coefficient of biomolecules intracellularly, in both the cytosol and plasma membrane. The novel insights provided by Ds-SiFi can transform our mechanistic understanding of diffusion-related processes.

Biological pattern formation ensures that tissues and organs develop in the correct place and orientation within the body. A great deal has been learned about cell and tissue staining techniques, and today's microscopes can capture digital images. A light microscope is an essential tool in biology and medicine. Analyzing the generated images will involve the creation of unique analytical techniques. Digital images of the material before and after deformation can be compared to assess how much strain and displacement the material responds. Furthermore, this article proposes Development Biology Patterns using Digital Image Technology (DBP-DIT) to cell image data in 2D, 3D, and time sequences. Engineered materials with high stiffness may now be characterized via digital image correlation. The proposed method of analyzing the mechanical characteristics of skin under various situations, such as one direction of stress and temperatures in the hundreds of degrees Celsius, is achievable using digital image correlation. A DBP-DIT approach to biological tissue modeling is based on digital image correlation (DIC) measurements to forecast the displacement field under unknown loading scenarios without presupposing a particular constitutive model form or owning knowledge of the material microstructure. A data-driven approach to modeling biological materials can be more successful than classical constitutive modeling if adequate data coverage and advice from partial physics constraints are available. The proposed procedures include a wide range of biological objectives, experimental designs, and laboratory preferences. The experimental results show that the proposed DBP-DIT achieves a high accuracy ratio of 99,3%, a sensitivity ratio of 98.7%, a specificity ratio of 98.6%, a probability index of 97.8%, a balanced classification ratio of 97.5%, and a low error rate of 38.6%.

Reactive oxygen species (ROS) contribute to cellular redox environment and serve as signaling molecules. Excessive ROS can lead to oxidative stress that are involved in a broad spectrum of physiological and pathological conditions. Stem cells have unique ROS regulation while cancer cells frequently show a constitutive oxidative stress that is associated with the invasive phenotype. Antioxidants have been proposed to forestall tumor progression while targeted oxidants have been used to destroy tumor cells. However, the delicate beneficial range of ROS levels for stem cells and tumor cells under distinct contexts remains elusive. Here, we used Drosophila midgut intestinal stem cell (ISCs) as an in vivo model system to tackle this question. The ROS levels of ISCs remained low in comparison to that of differentiated cells and increased with ageing, which was accompanied by elevated proliferation of ISCs in aged Drosophila. Neither upregulation nor downregulation of ROS levels significantly affected ISCs, implicating an intrinsic homeostatic range of ROS in ISCs. Interestingly, we observed similar moderately elevated ROS levels in both tumor-like ISCs induced by Notch (N) depletion and extracellular matrix (ECM)-deprived ISCs induced by β-integrin (mys) depletion. Elevated ROS levels further promoted the proliferation of tumor-like ISCs while reduced ROS levels suppressed the hyperproliferation phenotype; on the other hand, further increased ROS facilitated the survival of ECM-deprived ISCs while reduced ROS exacerbated the loss of ECM-deprived ISCs. However, N- and mys-depleted ISCs, which resembled metastatic tumor cells, harbored even higher ROS levels and were subjected to more severe cell loss, which could be partially prevented by ectopic supply of antioxidant enzymes, implicating a delicate pro-surviving and proliferating range of ROS levels for ISCs. Taken together, our results revealed stem cells can differentially respond to distinct ROS levels under various conditions and suggested that the antioxidant-based intervention of stem cells and tumors should be formulated with caution according to the specific situations.

In order to evaluate the comprehensive benefits of forest ecological economy, a model based on discrete particle swarm optimization is proposed. First of all, the discrete particle swarm optimization algorithm is defined and introduced into the evaluation parameters of forest ecological and economic benefits for internal processing, and the evaluation model of forest ecological and economic benefits is constructed. Optimize the system evaluation of the model, continuously develop the internal model structure adjustment operation, and finally obtain the experimental data needed by the system. The experimental results show that the model based on the discrete particle swarm optimization algorithm has better convergence, better optimization and robustness, and has higher practical application value.

Aberrant mitophagy has been implicated in a broad spectrum of disorders. PINK1, Parkin, and ubiquitin have pivotal roles in priming mitophagy. However, the entire regulatory landscape and the precise control mechanisms of mitophagy remain to be elucidated. Here, we uncover fundamental mitophagy regulation involving PINK1 and a non-canonical role of the mitochondrial Tu translation elongation factor (TUFm). The mitochondrion-cytosol dual-localized TUFm interacts with PINK1 biochemically and genetically, which is an evolutionarily conserved Parkin-independent route toward mitophagy. A PINK1-dependent TUFm phosphoswitch at Ser222 determines conversion from activating to suppressing mitophagy. PINK1 modulates differential translocation of TUFm because p-S222-TUFm is restricted predominantly to the cytosol, where it inhibits mitophagy by impeding Atg5-Atg12 formation. The self-antagonizing feature of PINK1/TUFm is critical for the robustness of mitophagy regulation, achieved by the unique kinetic parameters of p-S222-TUFm, p-S65-ubiquitin, and their common kinase PINK1. Our findings provide new mechanistic insights into mitophagy and mitophagy-associated disorders.

How complex interactions of genetic, environmental factors and aging jointly contribute to dopaminergic degeneration in Parkinson's disease (PD) is largely unclear. Here, we applied frequent gene co-expression analysis on human patient substantia nigra-specific microarray datasets to identify potential novel disease-related genes. In vivo Drosophila studies validated two of 32 candidate genes, a chromatin-remodeling factor SMARCA4 and a biliverdin reductase BLVRA. Inhibition of SMARCA4 was able to prevent aging-dependent dopaminergic degeneration not only caused by overexpression of BLVRA but also in four most common Drosophila PD models. Furthermore, down-regulation of SMARCA4 specifically in the dopaminergic neurons prevented shortening of life span caused by α-synuclein and LRRK2. Mechanistically, aberrant SMARCA4 and BLVRA converged on elevated ERK-ETS activity, attenuation of which by either genetic or pharmacological manipulation effectively suppressed dopaminergic degeneration in Drosophila in vivo. Down-regulation of SMARCA4 or drug inhibition of MEK/ERK also mitigated mitochondrial defects in PINK1 (a PD-associated gene)-deficient human cells. Our findings underscore the important role of epigenetic regulators and implicate a common signaling axis for therapeutic intervention in normal aging and a broad range of age-related disorders including PD.

目的 构建基于支持向量机(Support Vector Machine,SVM)的妊娠期糖尿病(Gestational Diabetes Mellitus,GDM)预测模型.方法 选择2018年1～12月在福建省立医院南院定期产检及分娩的产妇115例,其中妊娠期糖尿病产妇50例为观察组,随机抽取同期正常产妇65人为对照组,收集产妇的一般资料和孕早期(8～12 W)的血常规、凝血功能和生化指标检测资料,将这些变量采用皮尔森相关系数分析,找出纳入预测模型分析的变量.结果 特征变量与GDM的皮尔森相关系数绝对值最大的前5个变量分别是甘油三酯、活化部分凝血活酶时间、抗凝血酶Ⅲ、孕前BMI和孕次,且这五个变量观察组和正常组孕妇之间的差异均有统计学意义(P值均小于0.01),使用SVM算法将这五个变量纳入预测模型分析,对GDM的预测有78.3％的准确率和84.6％的精确率.结论 使用SVM算法对预测早孕期孕妇患有GDM具有重要的临床意义.

Long non-coding RNAs (lncRNAs) are non-protein coding transcripts that are involved in a broad range of biological processes. Here, we examine the functional role of lncRNAs in feather regeneration. RNA-seq profiling of the regenerating feather blastema revealed that Wnt signaling is among the most active pathways during feather regeneration, with Wnt ligands and their inhibitors showing distinct expression patterns. Co-expression analysis identified hundreds of lncRNAs with similar expression patterns to either the Wnt ligands (the Lwnt group) or their downstream target genes (the Twnt group). Among these, we randomly picked two lncRNAs in the Lwnt group and three lncRNAs in the Twnt group to validate their expression and function. Members in the Twnt group regulated feather regeneration and axis formation, whereas members in the Lwnt group showed no obvious phenotype. Further analysis confirmed that the three Twnt group members inhibit Wnt signal transduction and, at the same time, are downstream target genes of this pathway. Our results suggest that the feather regeneration model can be utilized to systematically annotate the functions of lncRNAs in the chicken genome.

Objective To investigate the new etiology of preeclampsia and fetal growth restriction by testing the placental gene expression difference among the two kinds of diseases and normal pregnancy by microarray.Methods RMA was used to normalization the initial data.SAM of the Stanford University was used to test significance.And then enrichment gene list was analyzed in significant genes.Results A total of 111 differently-expressed genes,which were highly related to secretion,transport,regulation of hormone,were identified in placental tissue of preeclampsia,and 63 differently-expressed genes were found in placental tissue of fetal growth restriction,including 3 genes related to hormones.Conclusion There are different hormone-related genes expression levels in preeclampsia and fetal growth restriction to the normal pregnant women,which suggests that there are certain correlation with preeclampsia and fetal growth restriction in the hormone-related genes expression levels.

介绍了用C语言编程来实现牛顿迭代法求解油脂氢化反应速率常数的方法,并通过具体算例给出了计算程序和计算结果,与已有的解析计算方法结果一致。Turbo C编程计算操作简便,精确度可靠,对有效控制油脂选择性氢化和研究氢化反应机理有一定的指导意义。

Gene Co-expression Network (GCN) analysis has been widely used for gene function and disease biomarker discovery. In this study, we present a workflow for identifying GCN associated with colon cancer metastasis. The workflow includes dense network discovery from weighted GCN followed by network activity analysis using a mutual information-based approach to identify gene networks related to metastasis. Our findings suggest several genomic regions as genetic aberrations related to colon cancer malignancy including chr11q13, 20q13, 8q24 and 14q22-23. Our work also demonstrates a novel way of interpreting gene co-expression analysis results besides functional relationships and the effectiveness of the mutual information based network analysis in detecting subtle changes between different disease states.

本文提出了一种通过pearson相关系数建立的邻接矩阵中,利用基于单亲遗传算法寻找最大团的基因芯片的筛选方法。通过对实际数据的模拟证明该算法具有良好的应用价值。

In this paper, six known PD marker genes as anchor genes and apply frequent gene co-expression analysis to identify genes that frequently co-express with these anchor genes in multiple brain gene expression profiles was studied. Gene set enrichment analysis on these genes demonstrated significant relevance with neurodegenerative diseases and metabolism. These genes will be further screened for experimental validation using transgenic Drosophila models.

本文给出了一种地脉动并行计算的单亲遗传任务分配算法。算法采用特殊的变异算子,可以加快收敛的速度。对该算法的时间复杂度、空间复杂度和效果进行分析,结果表明该算法具有较好的收敛速度与运行效率。