Autophagy Inhibition by Chloroquine Sensitizes Cervical Cancer SiHa Cells to CPT Treatment

自噬诱导是肿瘤细胞对化疗药物抵抗性的原因之一,该研究探讨溶酶体抑制剂氯喹对喜树碱(camptothecin,CPT)诱导的宫颈癌细胞Si Ha死亡的增敏效果。CPT和/或氯喹处理宫颈癌Si Ha细胞,MTT法检测细胞增殖,DAPI和TUNEL染色观察细胞凋亡,Western blot和免疫荧光检测自噬及凋亡相关蛋白。结果发现,CPT处理后,Si Ha细胞MAP1LC3B荧光点和LC3II(microtubuleassociated protein light chain 3II)蛋白水平增加,p62荧光点和蛋白质水平则减少;而采用氯喹特异抑制自噬后,可明显提高CPT诱导的细胞凋亡、caspase-9的激活和PARP(poly ADP-ribose polymerase)的切割,而全长caspase-2水平显著下降。以上结果提示,氯喹可通过抑制细胞自噬而增强宫颈癌细胞株Si Ha对CPT诱导细胞凋亡的敏感性。The autophagy induction is one of the reasons for the resistance of tumor cells to chemotherapy drugs. In this study, the enhanced sensitivity of cervical cancer Si Ha cells to camptothecin(CPT)-induced cell death by chloroquine(a lysosome inhibitor) was investigated. The cell viability was detected by MTT assay, meanwhile, apoptosis was observed by DAPI and TUNEL, autophagy related proteins and apoptosis proteins were analyzed by immunofluorescence(IF) staining and Western blot in Si Ha cells after CPT treatment alone or combined with chloroquine. The results found that in Si Ha cells with CPT treatment the autophagy related protein LC3 foci and microtubule-associated protein light chain 3II(LC3II) protein level was increased, but p62 foci and protein level was decreased. When autophagy was inhibited by chloroquine, the CPT-induced apoptosis was obviously enhanced, and caspase-9 was activated and PARP was cleaved, but full length caspase-2 was decreased. Taken together, these results indicated that the inhibition of autophagy by chloroquine could sensitize cervical cancer Si Ha cells to CPT inducing cell apoptosis.山西省自然科学基金(批准号:2014021037-9);; 山西医科大学汾阳学院博士启动基金(批准号:1301);山西医科大学汾阳学院科研项目基金(批准号:1422)资助的课题~

中国的税制改革(中)

本部分主要介绍并评价了中国个人所得税制,认为中国现行个人所得税具有最高边际税率较高、基本免征额相对较高、税级较宽的特点,与OECd国家明显不同的是:对劳动所得分类课征、没有基于家庭的常规性税收减免、按月计征等,建议中国进一步强化个人所得税制的累进性;按照相同的累进税率表对所有个人劳动所得进行课税;继续沿用现行以个人为纳税单位,但辅之以特别税收宽免和抵免来实现总收入相同但配偶双方收入构成不同的家庭间税负的均等化;实行准二元所得税制,对资本所得适用较低的比例税率;按年计征个人所得税

中国的税制改革(上)

本文对中国的税收制度以及所推行的税制改革进行了分析。文章着重阐述了列入中国当前改革日程的税收以及税收问题,包括对消费课征的税收(特别是营业税与增值税的整合)、环境相关税收、个人所得税、中央政府与地方政府的财政关系以及财产税等问题。虽然一国的税收制度及其改革进程深受其文化、传统及法律制度的影响,但本文仍全面论述了税制设计的一般问题,包括对中国税制如何进行设计以更有利于增长、更加简洁、更加透明、更少扭曲和更加公平。本文对上述税种均作了详细讨论并提出了未来可能的改革方向

The chemo-mechanical mechanism of the maturity evolution of kerogen

&nbsp; 当前，油气资源的供需紧张已成为制约我国经济发展、制造产业升级的关键矛盾，高对外依存度使我国工业发展时刻可能陷入&ldquo;卡脖子&rdquo;困境，能源系统的安全是关系工业主动脉的重大战略问题。另一现状是，我国的页岩油气储量位居世界前列，可利用空间巨大，突破非常规油气的开发问题，对推进能源自由意义重大。因此，页岩油气高效开发的相关问题研究十分重要。干酪根作为地球上分布最广、含量最丰富的有机碳存在形式，是页岩油气至关重要的来源。其结构研究是评价储层油气潜力的重要基础。干酪根成熟度是资源富集储层评价的重要指标，但常见成熟度指数受到多种地质因素的制约。而我国储层普遍存在干酪根成熟发育不充分的问题，原位转化技术是页岩油气开发的重点方向。因此，厘清干酪根演化规律，发展新的成熟度评价指标，建立干酪根演化动力学模型十分必要。另外，干酪根作为沉积岩中的主要有机组分，其力学性能对储层物性有显著影响，开展干酪根的力学响应研究对储层的压裂设计有重要意义。 &nbsp; 基于以上背景，本文针对干酪根的结构构建、成油成气机理、热演化过程及力学性能几个方面开展研究。主要关注两个关键科学问题：干酪根生烃化学-力学耦合效应及成熟度演化，采用实验观测、跨尺度模拟及理论分析，针对深部页岩样品展开研究工作。 &nbsp; 针对干酪根结构认知不清晰的问题，通过一系列物理化学实验，厘清了干酪根的类型及其包含的结构信息，构建了具有统计意义的矿区干酪根二维大分子模型，结合分子力学和分子动力学 (molecular dynamics, MD) 的退火算法，建立能量最小化的三维干酪根大分子模型。使用反应力场的分子动力学 (reactive force field molecular dynamics, ReaxFF-MD) 厘清了温度和升温速率对干酪根热解的影响，提出了干酪根热解典型反应机理。通过混合 MD 和力偏倚蒙特卡罗的计算方法，实现了在实验温度下计算机时间尺度内的干酪根热解反应的发生，为实验温度下的热解模拟提供了一种可靠方法。 &nbsp; 基于热演化过程中有机大分子结构的变化，提出了一种干酪根成熟度指数&ldquo;分子成熟度指数 (molecular maturity index, MMI)&rdquo;。厘清了 MMI 与传统成熟度评价指标镜质体反射率的关系，建立了随 MMI 升高的失重率变化规律。通过 MMI 引入了新的转换率，得到了活化能与 MMI 间的函数关系，建立了干酪根成熟度演化的活化能随成熟度变化的动力学模型 (kinetic model of the maturity evolution, MEKM)，描述了时间-温度-成熟度之间的关系。MEKM 模型形式简单，便于工程应用，为人工催熟储层的温度和时间选择提供了理论模型。 &nbsp; 通过热解-气相色谱/质谱联用实验，厘清温度与干酪根热解产物种类和相对含量的关系。基于提出的干酪根热解典型反应机理，结合密度泛函计算，将热解产物反演接入干酪根大分子热解位点，提出了干酪根重构的物理力学反演法 (physico-mechanical inversion method, PMIM)。通过物理力学反演法建立了一组具有更多矿区特征干酪根分子，分子量符合高斯分布，为进一步研究干酪根的性质提供了模型基础。基于建立的分子群，揭示了不同压强情况下的干酪根孔隙率变化规律，阐述了温度和孔隙尺寸对结构中甲烷分子吸附的影响。 &nbsp; 基于物理力学反演法，以构造的干酪根分子群为基础，建立干酪根纳米尺度的团聚体。通过 Reaxff-MD 计算不同的应变率下干酪根的力学响应，厘清了应变率对干酪根性能的影响。考虑化学键与非化学键的变化，得到了干酪根力学行为与微观机制间的关系。建立了可描述不同应变率下干酪根力学行为的可压缩超弹性-黏弹性本构关系，得到了干酪根团聚体的力学参数，有助于从微观角度理解页岩储层的力学行为。 &nbsp; 本文研究了干酪根的结构性质，从分子层面探索了干酪根的成油成气机理、成熟度演化规律及力学行为的微观机制，并建立了相关的理论模型，为人工原位催熟开发提供理论指导。</p

The time-temperature-maturity relationship: A chemical kinetic model of kerogen evolution based on a developed molecule-maturity index

Kerogen maturity is an important indicator for evaluating source rocks. We propose a kerogen maturity index (molecule-maturity index, MMI) based on the changes in the molecular structure of organic matter during thermal evolution. The MMI has a positive correlation with vitrinite reflectance. Molecular dynamics simulations indicate that the weight loss is with a positive linear correlation with the MMI, demonstrating the ability of the index to reflect the hydrocarbon generation of organic matter. In addition, a new conversion is introduced by the MMI to analyze the kinetics of kerogen thermal evolution. The activation energy is expressed as a continuous function of the MMI linking by the new conversion. The activation energy was calculated at the isoconversional points of previous conversion, which was not defined as a continuous function before. A linear relationship between the activation energy of kerogen is observed, and a new chemical kinetic model of kerogen thermal maturity is established based on the MMI (kinetic model of the maturity evolution, MEKM). The MEKM equation has a simple form and is convenient for engineering applications. Our findings provide insights into the kinetics of kerogen thermal maturation and guide the choice of temperature and time for artificial maturity.</p

Realization of Self-Rotating Droplets Based on Liquid Metal

Owing to their fascinating characteristics, liquid metals (LMs) have attracted increasing attention from the scientific community, and are a potential material for various applications. A novel phenomenon is reported in which an acid droplet spontaneously rotates on the surface of an LM. The experimental results show that this phenomenon originates from the collective motion of bubbles generated by the chemical reactions between the droplet and the LM. The angular velocity of the droplet rotation is on the order of 10(1) rad s(-1), which is much higher than that driven by other mechanisms. Under different conditions, the period of the droplet differs, and it increases with the pH and radius of the acid droplet. The theoretical results indicate the dominant factors and the characterized angular velocity, which agree well with the experimental data. This phenomenon demonstrate that the general particles can also induce special spatial-temporal patterns, and opens up a new field for the application of LMs

Predicting the components and types of kerogen in shale by combining machine learning with NMR spectra

This study aims to develop a new method that combines machine learning with nuclear magnetic resonance (NMR) spectra to predict the kemgen components and types. Kerogen is the primary hydrocarbon source of shale oil/gas, and nearly half of the hydrocarbons in shale are adsorbed in kemgen. The adsorption and hydrocarbon generation capacity of kerogen is directly related to its types, molecular components, and structures. Fruitful researches studying kerogen at the molecular level have been conducted. Unfortunately, these methods are complicated, time-consuming, and labor-intensive. Our method has the advantages of high-throughput prediction, high accuracy, and time savings compared with the existing methods. Additionally, this method simplifies the operations from repetitive trial and error. This study proposes a solution to convert non-uniform two-dimensional (2D) graph into a uniform one-dimensional (1D) matrix, which makes 2D graph data available for machine learning models. An automatic labeling platform is constructed that annotated over 22,000 groups of organic matter molecules and their NMR spectra. The results show that the carbon, hydrogen, and oxygen element prediction accuracy reach 96.1%, 94.8%, and 81.7%, respectively. In addition, the accuracy of the three kerogen types is approximately 90% in total. These results reflect the excellent performance of the machine learning method. Therefore, our work provides an automated and intelligent prediction and analysis method, which is a powerful and superior tool in kerogen studies at the molecular level

Defining kerogen maturity from orbital hybridization by machine learning

Kerogen is the primary material for oil and gas. Its maturity is used to determine the potential for hydrocarbon generation. Nowadays, kerogen maturity is mainly measured experimentally and characterized by its chemical composition. The fundamental reason for the change in its chemical composition during the maturation is the breaking and recombination of chemical bonds, manifested by the transformation in atomic hybridization based on quantum mechanics. While traditional methods are time-consuming and labor-intensive, machine learning technique has been introduced to clarify the relationship between hybridization and maturity. A kerogen maturity prediction model based on hybridization is constructed. The average error of the predicted values is only 4.91%, and more than 87% of the test samples have an error of less than 10%. The results demonstrate that the model can accurately predict the maturity of kerogen. As the evolution of kerogen maturity increases the proportion of sp(2) hybridized carbons, the orbital hybridization maturity index (OrbHMI) is proposed. The chemical changes in the thermal evolution and pyrolysis mechanism of kerogen can be explained and understood more essentially by OrbHMI. The results provide a basis for guiding artificial maturation and pave a promising path toward studying the kerogen structure and predicting hydrocarbon generating potential