川东南丁山地区燕山期—喜马拉雅期差异构造-热演化与页岩气保存

中国的海相富有机质页岩经历了多期构造改造，其含气性具有明显的差异。页岩气在不同构造演化阶段的保存条件是揭示页岩气差异富集机理的关键科学问题之一，开展构造-热演化研究可以明确其热演化史和构造隆升-剥蚀过程，为其评价提供演化格架。研究以丁山地区下古生界页岩为对象，联合磷灰石裂变径迹、磷灰石（U-Th）/He和锆石（U-Th）/He等多个古温标反演热演化史，结合镜质体反射率重建的最高古地温剖面，对丁山地区燕山期以来的差异构造隆升过程和剥蚀量进行了恢复，并在此基础上结合流体包裹体分析对丁山地区龙马溪组页岩的压力演化过程进行了模拟；根据页岩在埋藏—抬升过程中的温、压演化特征，定量表征了不同抬升阶段页岩含气量的变化，建立了龙马溪组页岩"埋藏—生烃—抬升"的演化格架。分析表明，丁山地区在燕山期和喜马拉雅期经历了不同的构造隆升过程。燕山期表现为"早期快速隆升—晚期缓慢隆升"的分段隆升，具有自NW向SE递进隆升且隆升幅度逐渐增大的特征；喜马拉雅期表现为整体快速隆升。燕山期是丁山地区产生差异构造隆升的主要时期。受这种差异构造隆升-剥蚀作用的影响，龙马溪组页岩的降温、降压过程和页岩气的散失过程具有明显的差异。燕山期的差异构造隆升是造成丁山地区龙马溪组页岩含气性呈平面分带的主要原因。     

n- and p-Channel field-effect transistors based on diquinoxalinoTTF derivatives

Three new quinoxalinoTTF derivatives with methyl, trifluoromethyl and fluoro groups were synthesized and characterized by UV–vis absorption spectroscopy, differential scanning calorimetry, X-ray single crystal analysis, X-ray diffraction, and field-effect transistor (FET) characteristics. All of them have π-stacking structures in the single crystals. The quinoxalinoTTF derivative with trifluoromethyl groups exhibited an n-type FET, which is a rare example of n-channel FETs based on TTF derivatives. The highest electron mobility is 0.01 cm² V^?1 s. The FET polarity was converted to p-channel from n-channel by replacing the trifluoromethyl groups with methyl groups. The hole mobility is as high as 0.2 cm² V^?1 s. In contrast, the fluoro substituted derivative did not show FET properties due to the poorly ordered molecular arrangement.     

川东南丁山地区燕山期—喜马拉雅期差异构造-热演化与页岩气保存

中国的海相富有机质页岩经历了多期构造改造，其含气性具有明显的差异。页岩气在不同构造演化阶段的保存条件是揭示页岩气差异富集机理的关键科学问题之一，开展构造-热演化研究可以明确其热演化史和构造隆升-剥蚀过程，为其评价提供演化格架。研究以丁山地区下古生界页岩为对象，联合磷灰石裂变径迹、磷灰石（U-Th）/He和锆石（U-Th）/He等多个古温标反演热演化史，结合镜质体反射率重建的最高古地温剖面，对丁山地区燕山期以来的差异构造隆升过程和剥蚀量进行了恢复，并在此基础上结合流体包裹体分析对丁山地区龙马溪组页岩的压力演化过程进行了模拟；根据页岩在埋藏—抬升过程中的温、压演化特征，定量表征了不同抬升阶段页岩含气量的变化，建立了龙马溪组页岩"埋藏—生烃—抬升"的演化格架。分析表明，丁山地区在燕山期和喜马拉雅期经历了不同的构造隆升过程。燕山期表现为"早期快速隆升—晚期缓慢隆升"的分段隆升，具有自NW向SE递进隆升且隆升幅度逐渐增大的特征；喜马拉雅期表现为整体快速隆升。燕山期是丁山地区产生差异构造隆升的主要时期。受这种差异构造隆升-剥蚀作用的影响，龙马溪组页岩的降温、降压过程和页岩气的散失过程具有明显的差异。燕山期的差异构造隆升是造成丁山地区龙马溪组页岩含气性呈平面分带的主要原因。     

A novel pineal night-specific ATPase encoded by the Wilson disease gene

We have identified a pineal night-specific ATPase (PINA), a novel splice variant of the ATP7B gene disrupted in Wilson disease (WD). PINA expression exhibits a dramatic diurnal rhythm in both pineal gland and retina with 100-fold greater expression at night than at day. PINA is expressed in pinealocytes and a subset of photoreceptors in adult rats and is transiently expressed in the retinal pigment epithelium and the ciliary body during retinal development. Nocturnal pineal expression of PINA is under the control of a suprachiasmatic nucleus clock mediated by superior cervical ganglion innervation of the pineal. In vitro, PINA expression in pineal cells can be stimulated by agents activating the cAMP signal transduction pathway. PINA is able to restore copper transport activity in Saccharomyces cerevisiae deficient in the homologous copper-transporting ATPase CCC2, suggesting that this protein may function as a copper transporter in rat pinealocytes. These studies suggest a potential role of rhythmic copper metabolism in pineal and/or retina circadian function.     

特种纸技术发展现状

主要介绍了近年来特种纸及相关产业的技术进步和发展,包括纤维原料、工艺和装备、新产品和应用等。此外,还介绍了日本造纸行业在纳米纤维素纤维的制备及应用产业化方面取得的引人注目进展。     

四川盆地深层海相页岩气赋存机理与勘探潜力

深层—超深层页岩气是四川盆地天然气增储上产的战略接替领域。基于志留系页岩气的大量勘探开发实践、实验测试资料和前瞻性研究成果,探讨了深层海相页岩气的赋存状态与聚集机制,指出了四川盆地深层页岩气的有利勘探区。页岩的纳米孔喉系统决定了其内部聚集的天然气呈现吸附态,主要以单分子层在微孔—介孔中吸附聚集。页岩吸附气量除受控于孔、缝的比表面积,还受TOC含量、温度、压力和含水性的影响。深层高温条件下,页岩储层的最大吸附气量较低,随着温度降低页岩的吸附能力增强,最大吸附气量也逐渐增加。页岩气在深层超压条件下主要以游离气赋存,呈超临界状态高密度聚集,游离气含量受储集空间及孔喉结构、埋藏深度、地层温度和压力、超临界流体性质以及页岩含水性等诸多因素综合影响。在抬升过程中,因构造改造的强度、时间和方式不同,页岩气的赋存相态转化及散失机制不同。其中,抬升幅度小且改造强度弱时,页岩气层仍保持深层"游离气为主,超压富气"的特性;抬升至中—浅层时,受断裂开启、剥蚀露头和页岩自封闭性降低等游离气散失机制影响,页岩气层的含气量和游离气量降低、吸附气占比增加,因此,远离剥蚀区、大断裂带的深埋藏区是深层页岩气的最有利富集区。四川盆地内部构造相对稳定,深层海相页岩气普遍保持着"早期滞留,超压富气"的成藏特征,优选宜宾—泸州地区、綦江—涪陵地区、永川—大足地区和垫江—梁平地区为有利勘探区。     

Perturbation analysis for the normalized Laplacian matrices in the multiway spectral clustering method

In this paper, we present a perturbation analysis for the matrices in the multiway normalized cut spectral clustering method based on the matrix perturbation theory. The analytical results show that the eigenvalues and the eigenspaces of the normalized Laplacian matrices are continuous. Therefore, clustering algorithms can be designed according to the special properties of the normalized Laplacian matrices in the ideal case and the method can be extended to the general case based on the continuity of the eigenvalues and the eigenspaces of the normalized Laplacian matrices. The numerical results are consistent with the theoretical results.     

Non-unique cluster numbers determination methods based on stability in spectral clustering

Recently, a large amount of work has been devoted to the study of spectral clustering—a simple yet powerful method for finding structure in a data set using spectral properties of an associated pairwise similarity matrix. Most of the existing spectral clustering algorithms estimate only one cluster number or estimate non-unique cluster numbers based on eigengap criterion. However, the number of clusters not always exists one, and eigengap criterion lacks theoretical justification. In this paper, we propose non-unique cluster numbers determination methods based on stability in spectral clustering (NCNDBS). We first utilize the multiway normalized cut spectral clustering algorithm to cluster data set for a candidate cluster number $k$ . Then the ratio value of the multiway normalized cut criterion of the obtained clusters and the sum of the leading eigenvalues (descending sort) of the stochastic transition matrix is chosen as a standard to decide whether the $k$ is a reasonable cluster number. At last, by varying the scaling parameter in the Gaussian function, we judge whether the reasonable cluster number $k$ is also a stability one. By three stages, we can determine non-unique cluster numbers of a data set. The Lumpability theorem concluded by Meil $\breve{a}$ and Xu provides a theoretical base for our methods. NCNDBS can estimate non-unique cluster numbers of the data set successfully by illustrative experiments.