01_1098 A new geochemical--sequence stratigraphic model for the Mahakam Delta and Makassar slope,

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GenCrispr Cas9-C-NLS 双链 nuclease 说明书

GenCrispr Cas9-C-NLS 双链 nuclease 说明书

GenCrispr Cas9-C-NLS Nuclease Cat. No. Z03385 Version 03152016I Description. (1)II Kit Contents. ......................................................................... .... .... (1)III Key Features. .... .... .... .... .... . (2)IV Quality control analysis (2)V Utilities of p roduct....................................................... . (2)VI Storage (2)VII Diluent Compatibility (2)VIII Activity test (2)IX References (3)I DESCRIPTIONCas9 nuclease is an RNA-guided endonuclease that can catalyze cleavage of double stranded DNA. This kind of targeted nuclease is a powerful tool for genome editing with high precision. Cas9 protein forms a very stable ribonucleoprotein (RNP) complex with the guide RNA (gRNA) component of the CRISPR/Cas9 system. The Cas9 RNP complex can localize to the nucleus immediately upon entering the cell with the addition of a nuclear localization signal (NLS). There is no requirement for transcription and translation compared with mRNA or plasmid systems. Additionally, the Cas9 RNP complex is rapidly cleared from the cell minimizing the chance of off-target cleavage when compared to other systems (Kim, et al. 2014). This DNA-free system avoids the risk of inserting foreign DNA into the genome, which can be quite useful for gene editing-based disease therapy. GenScript has developed a Cas9-C-NLS nuclease which contains a nuclear localization sequence (NLS) on the C-terminus of the protein to meet all the researchers’ requirements (e.g. in vitro cleavage assay, RNP complex transfection, and micro injection).Product Source: GenCrispr Cas9-C-NLS is produced by expression in an E. coli strain carrying a plasmid encoding the Cas9 gene from Streptococcus pyogenes with a C terminal nuclear localization signal (NLS).II KIT CONTENTSIII KEY FEATURESDNA-free: no external DNA added to systemHigh cleavage efficiency: NLS ensures the entry of Cas9 protein into nucleiLow off target: transient expression of Cas9 nucleaseTime-saving: no need for transcription and translationIV Quality Control AnalysisHigh Protein purity: GenCrispr Cas9 is > 95% pure as determined by SDS-PAGE using Coomassie Blue detection.Low Endotoxin: Endotoxin level is <0.1eu/ug test by gel-clot method: limit test.Non-specific DNase activity: A 20 ul reaction in Cas9 reaction buffer containing 100 ng linearized pUC57 plasmid and 0.1 ug GenCrispr Cas9 incubated for 16 h at 37℃. No DNA degradation is determined by agarose gel electrophoresis.Non-specific RNase activity: A 10 ul reaction in Cas9 reaction buffer containing 1800 ng total RNA and 0.1 ug of GenCrispr Cas9 incubated for 2 h at 37℃. No RNA degradation as determined by Agarose gel electrophoresis.High Bioactivity: 20 nM GenCrispr Cas9 incubated for 1 hour at 37℃ result in 90% digestion of the substrate DNA as determined by agarose gel electrophoresis.V Utilities of Product1. Screening for highly efficient and specific targeting gRNAs by in vitr o DNA cleavage using Cas9Nuclease, S. pyrogenes.2. In vivo gene editing when combined with a specific gRNA by electroporation or injection.VI STORAGEGenCrispr Cas9-C-NLS nuclease is supplied with 1X storage buffer (10 mM Tris, 300 mM NaCl, 0.1 mM EDTA, 1 mM DTT, 50% Glycerol PH 7.4 @ 25°C) and recommended to be stored at -20°C.VII Diluent CompatibilityDiluent Buffer B: 300 mM NaCl, 10 mM Tris-HCl, 0.1 mM EDTA, 1 mM DTT, 500 μg/ml BSA and 50% glycerol. (pH 7.4 @ 25°C).VIII Activity testCas9 site-specific digestion:GenScript used in vitro digestion of a linearized plasmid to determine the activity of the Cas9 nuclease. It is a sensitive assay for GenCrispr Cas9 quality control. The linearized plasmid containing the target site: (CATCATTGGAAAACGTTCTT)can be digested with gRNA:(CAUCAUUGGAAAACGUUCUUGUUUUAGAGCUAGAAAUAGCAAGUUAAAAUAAGGCUAGUCCGU UAUCAACUUGAAAAAGUGGCACCGAGUCGGUGCUUUUUUUU)and GenCrispr Cas9. Two cleavage DNA fragments (812 bp and 1898 bp) are determined by agarose gel electrophoresis. A 20 µl reaction in 1xCas9 Nuclease Reaction Buffer containing 160 ng linearized plasmid, 40 nM gRNA and 20 nM GenCrispr Cas9 for 2 hour at 37°C results in 90% digestion of linearized plasmid as determined by agarose gel electrophoresis.In vitro DNA cleavage assay with GenCrispr Cas9-C-NLS nucleaseReactions were set up according to recommended conditions, and cleavage products were resolved on a 1% agarose gel. Input DNA is EcoRV-linearized pUC57 plasmid DNAIX References1. Jinek et al. A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity. (2012)Science 337 (6096) 816-821 (2012).2. Larson, M. H., et al. CRISPR interference (CRISPRi) for sequence-specific control of gene expression.NatureProtocols. 8, (11), 2180-2196 (2013).3. Ran, F. A., et al. Genome engineering using the CRISPR-Cas9 system. Nature Protocols. 8, (11), 2281-2308(2013).4. Kim, S., Kim, D., Cho, S.W., Kim, J., Kim, J.S, (2014) Highly efficient RNA-guided genome editing in human cellsvia delivery of purified Cas9 ribonucleoprotein. Genome Res. 24(6), 1012-1019.Note:1. This is a basic protocol. The reagent concentrations, conditions, and parameters may need to be optimized.2. 1000 nM is equal to 160 ng/ul.GenScript US860 Centennial Ave., Piscataway, NJ 08854 Tel: 732-885-9188, 732-885-9688Fax: 732-210-0262, 732-885-5878Email: *********************Web: For Research Use Only.。

基于深度学习的油页岩CT图像有机质识别分割方法研究

基于深度学习的油页岩CT图像有机质识别分割方法研究

第54卷 第4期2023年7月太原理工大学学报J O U R N A L O F T A I Y U A N U N I V E R S I T Y O F T E C HN O L O G YV o l .54N o .4 J u l .2023引文格式:王欣,杨栋,黄旭东.基于深度学习的油页岩C T 图像有机质识别分割方法研究[J ].太原理工大学学报,2023,54(4):663-672.WA N G X i n ,Y A N G D o n g ,HU A N G X u d o n g .D e e p l e a r n i n g -b a s e d s e g m e n t a t i o n m e t h o d f o r o r ga n i c m a t t e r i d e n -t i f i c a t i o n i n o i l s h a l e C T i m a g e s [J ].J o u r n a l o f T a i y u a n U n i v e r s i t y o f T e c h n o l o g y,2023,54(4):663-672.收稿日期:2023-02-11;修回日期:2023-04-10基金项目:国家重点研发计划项目(2019Y F A 0705501) 第一作者:王欣(1996-),硕士生,(E -m a i l )184********@163.c o m通信作者:杨栋(1970-),博士,教授,主要从事原位改性流体化采矿相关理论与技术研究,(E -m a i l )y a n g d o n g @t yu t .e d u .c n 基于深度学习的油页岩C T 图像有机质识别分割方法研究王 欣,杨 栋,黄旭东(太原理工大学原位改性采矿教育部重点实验室,太原030024)摘 要:ʌ目的ɔ油页岩中有机质的密度远低于其他岩石基质,因此,在C T 图像中有机质的灰度值往往接近于孔隙裂隙的灰度值,从而在图像中表现为灰度值差异不明显,有机质和岩石的边界模糊等问题㊂ʌ方法ɔ为了精准识别分割出油页岩C T 图像中的有机质,对深度学习领域的图像分割方法进行研究,并自主搭建了描述有机质分割的OM -U n e t 语义分割网络架构㊂通过在传统U n e t 模型中引入混合空洞卷积模块㊁由粗到精的部署策略和轻量化自适应特征融合模块,利用卷积神经网络识别分割油页岩C T 图像中的有机质,并结合M I o U 等评价指标对其分割效果进行评估㊂ʌ结果ɔOM -U n e t 模型的M I o U 为80.66%,相较于三相分割方法㊁U n e t ㊁C B AM -U n e t ㊁D e e p-L a b V 3㊁H D C -U n e t 和L A F F -U n e t 模型分别增加了8.01%㊁17.68%㊁9.5%㊁2.54%㊁2.83%和9.13%.OM -U n e t 模型的M P A 为89.16%,相较于三相分割方法㊁U n e t ㊁C B AM -U n e t ㊁D e e p -L a b V 3㊁H D C -U n e t 和L A F F -U n e t 模型分别增加了12.85%㊁20.62%㊁15.82%㊁8.81%㊁9.55%和15.34%.ʌ结论ɔ该结果证明OM -U n e t 模型可有效提高油页岩有机质分割的准确性,更加精确地确定有机质体积百分比㊁有机质团数量随温度或者热解条件的变化规律,为油页岩原位开发提供基础理论数据㊂关键词:深度学习;油页岩;有机质;混合空洞卷积;语义分割中图分类号:T D 83 文献标识码:AD O I :10.16355/j .c n k i .i s s n 1007-9432t yu t .2023.04.010 文章编号:1007-9432(2023)04-0663-10D e e p L e a r n i n g -b a s e d S e g m e n t a t i o n M e t h o d f o r O r g a n i c M a t t e r I d e n t i f i c a t i o n i n O i l S h a l e C T I m a ge s W A N G X i n ,Y A N G D o n g ,H U A N G X u d o n g(K e y L a b o r a t o r y o f I n -s i t u P r o p e r t y I m p r o v i n g M i n i n g o f M i n i s t r y o f E d u c a t i o n ,T a i yu a n U n i v e r s i t y o f T e c h n o l o g y ,T a i yu a n 030024,C h i n a )A b s t r a c t :ʌP u r p o s e s ɔT h e d e n s i t y o f o r g a n i c m a t t e r i n o i l s h a l e i s m u c h l o w e r t h a n t h a t o f o t h e r r o c k m a t r i x ,s o t h e g r a y v a l u e o f o r g a n i c m a t t e r i n C T i m a ge s i s of t e n c l o s e t h a t o f p o r e f r a c t u r e s ,w h i c h r e s u l t s i n p r o b l e m s s u c h a s i n c o n s p i c u o u s d i f f e r e n c e i ng r a y va l u e a n db l u r r e d b o u n d a r y b e t w e e n o r g a n ic m a t t e r a nd r o c k i n t he i m a g e s .I n o r d e r t o a c c u r a t e l y i d e n t if y th e o r -g a n i c m a t t e r i n t h e s e g m e n t e d o i l s h a l e C T i m a g e s ,t h e i m a g e s e gm e n t a t i o n m e t h o d s i n t h e f i e l d Copyright ©博看网. All Rights Reserved.o f d e e p l e a r n i n g a r e s t u d i e d,a n d t h e OM-U n e t s e m a n t i c s e g m e n t a t i o n n e t w o r k a r c h i t e c t u r e s d e-s c r i b i n g t h e o r g a n i c m a t t e r s e g m e n t a t i o n i s b u i l t i n d e p e n d e n t l y.ʌM e t h o d sɔB y i n t r o d u c i n g a h y-b r i d n u l l c o n v o l u t i o n m o d u l e,a c o a r s e-t o-f i n e d e p l o y m e n t s t r a t e g y,a n d a l i g h t w e i g h t a d a p t i v e f e a t u r e f u s i o n m o d u l e i n t o t h e t r a d i t i o n a l U n e t m o d e l,t h e c o n v o l u t i o n a l n e u r a l n e t w o r k i s u s e d t o i d e n t i f y a n d s e g m e n t o r g a n i c m a t t e r i n o i l s h a l e C T i m a g e s,a n d i t s s e g m e n t a t i o n e f f e c t i s e v a l-u a t e d b y c o m b i n i n g M I o U a n d o t h e r e v a l u a t i o n i n d e x e s.ʌF i n d i n g sɔT h e M I o U o f t h e OM-U n e t m o d e l i s80.66%,w h i c h i s h i g h e r t h a n t h a t o f t h e t h r e e-p h a s e s e g m e n t a t i o n m e t h o d s,U n e t,C B AM-U n e t,D e e p L a b V3,H D C-U n e t,a n d L A F F-U n e t m o d e l s b y8.01%,17.68%,9.5%,2.54%,2.83%,a n d9.13%,r e s p e c t i v e l y.T h e M P A o f OM-U n e t m o d e l i s89.16%,w h i c h i sh i g h e r t h a n t h a t o f t h e t h r e e-p h a s e s e g m e n t a t i o n m e t h o d,U n e t,C B AM-U n e t,D e e p L a b V3,H D C-U n e t,a n d L A F F-U n e t m o d e l s b y12.85%,20.62%,15.82%,8.81%,9.55%,a n d15.34%,r e s p e c t i v e l y.ʌC o n c l u s i o n sɔT h e r e s u l t s d e m o n s t r a t e t h a t t h e OM-U n e t m o d e l c a n e f f e c-t i v e l y i m p r o v e t h e a c c u r a c y o f o i l s h a l e o r g a n i c m a t t e r p a r t i t i o n i n g,m o r e a c c u r a t e l y d e t e r m i n e t h e v a r i a t i o n p a t t e r n s o f o r g a n i c m a t t e r v o l u m e p e r c e n t a g e a n d o r g a n i c m a t t e r c l u s t e r n u m b e r w i t h t e m p e r a t u r e o r p y r o l y s i s c o n d i t i o n s,a n d p r o v i d e b a s i c t h e o r e t i c a l d a t a f o r i n s i t u o i l s h a l e d e v e l o p m e n t.K e y w o r d s:d e e p l e a r n i n g;o i l s h a l e;o r g a n i c m a t t e r;h y b r i d h o l e c o n v o l u t i o n;s e m a n t i c s e g-m e n t a t i o n油页岩是一种含有固态干酪根的细粒沉积岩[1-2]㊂在隔绝氧气的环境下加热油页岩可以使油页岩中的固态干酪根发生热解反应生成页岩油和气[3]㊂油页岩在受热时会发生热破裂,油页岩内部的有机质会发生热解并形成孔隙㊂油页岩内部的孔隙和裂隙是油气运移的通道,因此,对油页岩有机质和孔裂隙分布和演化特征的研究具有十分重要的意义[4]㊂大部分研究者基于C T数字岩芯利用传统的二值化分割方法研究油页岩孔裂隙的演化特征[5]㊂赵静等[6]利用C T扫描研究分析了不同温度下油页岩样内部孔裂隙的演化特征,R A B B A N I e t a l[7]通过C T扫描研究了油页岩的孔隙变化特征㊂但很少有人针对油页岩有机质的分布和随温度的演化特征进行研究㊂HU A N G e t a l[8]结合热重分析研究了不同蒸汽温度热解后油页岩内部的有机质残留量和分布情况以及热解产物孔隙裂隙分布情况㊂该研究在一定程度上分割了有机质与孔裂隙,但通过灰度差异进行图像分割,总体上存在如下缺点:1)分割过程繁琐复杂,耗时较长,效率较低,在整张C T图片上盲目遍历和密集推理会浪费计算资源和时间;2)有机质和孔裂隙灰度值的高度相似性,容易导致有机质与孔裂隙灰度值重合部分被误分割为有机质区域,存在边界值分割模糊问题㊂在高精度的显微C T扫描图像中,油页岩内部能够观察到较大的有机质空间分布㊂但较小的孔隙裂隙的灰度值会随不同温度的热解条件而变化,由于有机质和孔裂隙灰度值相近,在一定温度下会和有机质的灰度值接近或重合㊂如果只通过C T灰度图的灰度值大小获得有机质的分布情况和体积百分数会出现较大的误差,因此,在不同热解温度下,精准识别分割有机质和孔裂隙边界值是一个亟待解决的问题㊂王云艳等[9]提出一种改进型D e e p L a b V3图像语义分割算法,解决了下采样特征细节信息丢失导致图像边缘分割有偏差的问题㊂刘帅等[10]基于C T 实验提出了一种改进的U N e t++模型算法用于图像分割㊂这些方法提高了图像分割的准确性,并在一定程度上解决了边界值模糊不清的问题,这为油页岩C T图像有机质的分割提供了新的思路㊂本文基于深度学习的油页岩C T图像有机质识别分割方法,建立了基于U n e t的用于识别油页岩C T数字岩芯中有机质的OM-U n e t方法,对油页岩样品在8个温度高温热解下的油页岩C T图像有机质进行语义分割图像处理,再运用蒙版阈值分割方法进一步去除有机质中已经热解为孔裂隙的区域,从而实现有机质和孔隙的语义分割,便于后续观察同一样品在不同温度下油页岩有机质的迁移情况㊂1油页岩C T图像有机质语义分割流程油页岩C T图像有机质分割流程如图1所示㊂466太原理工大学学报第54卷Copyright©博看网. All Rights Reserved.其油页岩C T 图像有机质分割包括以下步骤:①数据采集,在不同温度下热解油页岩岩芯样品并进行C T 扫描,之后选取部分扫描图像作为数据集;②数据集预处理,包括对样品C T 图像进行图像增强㊁格式转换㊁等大小裁剪等操作,之后通过l a b e l m e 工具标记有机质生成J s o n 文件并转换为掩膜图像;③将数据集划分为训练集㊁验证集和测试集;④搭建OM -U n e t 模型并将训练集输入模型训练模型;⑤将验证集输入模型进行验证,若达到较高精度,则选择模型中分割效果最优权重,保存最终模型并预测分割结果,反之则继续训练模型;⑥将测试集输入验证过的模型中,获得分割结果并评估分割效果的精准度;⑦通过蒙版阈值分割方法进一步去除有机质中已经热解为孔裂隙的区域㊂数据集预处理及数据集标注图像增强格式转换等大小裁剪labelme 工具标记油页岩CT 图像数据采集划分数据集搭建OM-Unet 模型验证模型并评估模型训练模型达到较高精度不成立保存模型权重、获得最终模型成立测试模型预测油页岩有机质分割结果图1 油页岩C T 图像有机质分割流程F i g .1 O r g a n i c m a t t e r s e g m e n t a t i o n p r o c e s s o f o i l s h a l e C T i m a ge 2 构建油页岩C T 图像数据集2.1 数据集构建取采自新疆巴里坤的圆柱形油页岩样品3个,2个用于热解实验,1个用于对比参照进行验证,如图2所示㊂利用油页岩高温蒸汽热解实验系统将试件加热到设定温度并保持一定时间,即选取8个温度点(25ħ㊁200ħ㊁300ħ㊁350ħ㊁400ħ㊁450ħ㊁500ħ㊁550ħ)对同一试件依次加热,之后采用N a n o V o x e l -3000高分辨X 射线三维C T 检测系统对不同蒸汽温度热解后的油页岩样品进行C T 扫描,获得油页岩C T 图像㊂由于本文的油页岩有机质C T 图像像素分辨率高,能够真实清晰反映油页岩内部有机质聚集团的分布情况,从而保证了最终的预测模型具有准确性以及良好的鲁棒性㊂每个温度点均获得1500张C T 图像,共获得45000张C T图像㊂从2个用于热解实验的油页岩样品的C T 图像的不同加热蒸汽温度下各选取100张,共2000张C T 图像全部作为油页岩CT 图像的初始数据集用于训练模型,以避免模型的过拟合现象,减少噪声的影响㊂图2 圆柱形油页岩样品F i g .2 C y l i n d r i c a l o i l s h a l e s a m pl e 2.2 数据集预处理及数据集标注由于油页岩C T 图像灰度图过暗,无法直接使用l a b e l m e 工具对其进行手工标注,因此,首先使用对数调整函数和强度调整函数对数据集进行图像增强操作并将其t i f 格式批量转换成j p g 格式,之后进行等大小裁剪操作㊂为了便于手工标记,将1500ˑ1500大小的C T 图像等大小裁剪成4块,即每块C T 图像大小为750ˑ750,最后再使用l a b e l m e 工具进行标记,生成J s o n 文件并转换为掩膜图像,其过程如图3所示㊂其中,油页岩有机质聚集团区域的像素的标签颜色标记为红色,背景区域以及孔裂隙区域皆标记为黑色㊂图像增强原图标注图掩膜图图3 对加热300ħ下的油页岩有机质C T 图像进行预处理及标注F i g .3 C T i m a g e p r e p r o c e s s i n g a n d l a b e l i n g of o i l s h a l e o r ga n i c m a t t e r h e a t e d a t 300ħ2.3 划分数据集将经过预处理的数据集进行l a b e l m e 标注并按照不同温度分组,最后将其按照8ʒ1ʒ1划分为训练集㊁验证集和测试集,将训练集输入OM -U n e t 模型,增强边界信息的特征提取㊂测试集用于评价训练模型的泛化能力,验证集用于评估训练模型的效果,便于后续对模型调参,选择出效果最好的模型㊂3 构建OM -U n e t 模型3.1 U n e t 神经网络U n e t 神经网络是一个有影响力的最初用于微观的生物医学领域的图像分割网络[11]㊂由于U n e t网络可以从较少数据集㊁小物体和多目标中学习相关特征,而油页岩C T 图像细观分析中仅需考虑有机质㊁孔裂隙以及固体基质的分布特征,因此本文基566 第4期 王 欣,等:基于深度学习的油页岩C T 图像有机质识别分割方法研究Copyright ©博看网. All Rights Reserved.于U n e t[12]网络并对其改进,分割油页岩有机质和孔裂隙㊂U N e t 模型采用端到端对称结构,使网络能够检索浅层信息,左侧为收缩路径(编码器模块),右侧为扩展路径(解码器模块),编码器通常是一个预训练的分类网络,解码器的任务是将编码器学习到的较低分辨率从语义上投影到较高分辨率像素空间,以获得密集分类㊂U N e t 简单地将编码器的特征图拼接至每个阶段解码器的上采样特征图,从而形成一个梯形结构㊂如图4所示在收缩路径中,卷积块由3ˑ3卷积组成卷积层,R e L U 函数,以及批次标准化(B N )执行4次,信道数分别为64㊁128㊁256和512.此外,对特征图进行了2ˑ2最大池化运算,使图像的大小变为原始图像的1/256.相应地,在扩展路径中,上采样采用转置卷积,由2ˑ2上采样操作和2ˑ2卷积组成的2ˑ2上卷积操作重复4次㊂在每一层实现扩展路径和收缩路径之间的跳跃连接,复制收缩路径生成的特征,并帮助网络检索因池化操作而丢失的空间信息㊂通过跳跃连接的架构,在每个阶段都允许解码器学习在编码器池化中丢失的相关特征㊂此外,还进行了两次3ˑ3卷积运算,通过网络的最后一层全连接层1ˑ1卷积和激活函数得出分割结果㊂图4是一个典型的U N e t 框架[13]㊂conv 3×3,ReLU copy and crop max pool 2×2up -conv 2×2conv 1×12830325122561001021045121 024******* 024*********512136138140256256280282284200198196256128128128568×568570×570572×572392×392390×390388×388386×386输出图像1286464264641输入图像图4 典型的U N e t 框架F i g .4 T y pi c a l U N e t f r a m e w o r k U n e t 的编解码器架构中的池化操作和带步长的卷积容易产生相当大的细粒度信息丢失,缺乏全局多尺度特征交互,容易造成空间信息的丢失㊂对于有机质与孔裂隙的分割,应用U n e t 编解码器架构很容易导致有机质边缘特征的缺失,以及对有机质与孔裂隙混淆区域的错误检测㊂因此,为了解决以上问题,需要花费成本在编码器上避免分辨率下降,或者在解码器中使用不同的机制,恢复在编码器中降低分辨率时丢失的信息㊂3.2 空洞卷积模块空洞卷积(d i l a t e d c o n v o l u t i o n ,D C )也可称之为扩张卷积,其具体操作为在卷积核中插入空洞,扩大感受野[14]㊂假设空洞卷积扩张率为a ,原始卷积核大小为k ,由于空洞卷积在常规卷积核中填充0,则空洞卷积核的大小K 的计算如下:K =k +(k -1)(a -1).(1)其中,a =1的常规卷积与a =2的空洞卷积的具体示意图如图5所示㊂a =1a =2图5 常规卷积与空洞卷积的具体示意图F i g .5 S p e c i f i c s c h e m a t i c d i a gr a m o f c o n v e n t i o n a l c o n v o l u t i o n a n d d i l a t e d c o n v o l u t i o n针对油页岩有机质的特点,本文在U n e t 神经网络的编码器中加入了混合空洞卷积[15],设置3ˑ3的卷积核,扩张率为1㊁2㊁4,则感受野分别为3ˑ3㊁7ˑ7㊁15ˑ15,扩大感受野的同时避免了分辨率下降,填充0不增加额外的参数降低了计算量,叠加多个不同扩张率的空洞卷积获取了多尺度上下文信息,提高了膨胀率,且保持了像素的相对空间位置不变㊂3.3 轻量化自适应特征融合模块L A F F为了提升模型性能并减少计算复杂度,在模型解码器上采样的位置,使用了轻量化自适应特征融合模块(l i g h t w e i gh t a t t e n t i o n a l f e a t u r e f u s i o n ,L A F F ).HU e t a l [16]在混合域注意机制的基础上[17]提出了一种轻量级的自适应特征融合模块L A F F ,可将其应用于编码器结构网络㊂当解码器从编码器学习到新一级的分辨率特征图时,采用L A F F 来过滤基于特征图的空间域和通道域的基本特征㊂L A F F 使用简化的结构和大的卷积膨胀率在图像中执行轻量级和远程语义建模,依次进行通道轴特征重构和空间轴特征重构,促进网络关注关键特征,抑制不必要特征㊂首先,进行通道轴特征重构㊂输入特征映射FɪRH ˑW ˑC沿通道轴重建以进行特征表示,如图6(a)中所示,其过程如下:F C =σ(c o n v (k c ,d c )☉G A P (F ) F ).(2)式中:c o n v (k c ,d c )指内核大小和膨胀率的卷积;☉表示卷积核对特征映射的滤波器响应; 是哈达马666太原理工大学学报 第54卷Copyright ©博看网. All Rights Reserved.积;σ是非线性激活函数S i gm o i d 函数;F C ɪRH ˑW ˑC是重建的通道轴特征㊂其次,将重建的通道轴特征F C 进行空间轴特征重构㊂在上采样之前,将F C 再次沿空间轴重建,从而进行空间特征重建F s ɪR H ˑW ˑC ,如图6(b )所示,其过程如下式:F S =σ(c o n v (k S ,d S )☉[G A P (F C ),GM P (F C )]) F .(3)经过通道轴特征重构和空间轴特征重构,L A F F 的注意力权重可选择较少的特征,但检索性能大部分保持不变㊂因此,L A F F 可以在加强特征提取网络的上采样前自适应地对融合后的特征图进行特征映射质量的重新标定㊂Sigmoidconv 1D GAP HWC输入特征FH WC F CSigmoidconv 2DGMPGAPHW C输入特征FH WCF C(a )通道轴特征重构,GAP 为全局平均池。

应用地球化学元素丰度数据手册-原版

应用地球化学元素丰度数据手册-原版

应用地球化学元素丰度数据手册迟清华鄢明才编著地质出版社·北京·1内容提要本书汇编了国内外不同研究者提出的火成岩、沉积岩、变质岩、土壤、水系沉积物、泛滥平原沉积物、浅海沉积物和大陆地壳的化学组成与元素丰度,同时列出了勘查地球化学和环境地球化学研究中常用的中国主要地球化学标准物质的标准值,所提供内容均为地球化学工作者所必须了解的各种重要地质介质的地球化学基础数据。

本书供从事地球化学、岩石学、勘查地球化学、生态环境与农业地球化学、地质样品分析测试、矿产勘查、基础地质等领域的研究者阅读,也可供地球科学其它领域的研究者使用。

图书在版编目(CIP)数据应用地球化学元素丰度数据手册/迟清华,鄢明才编著. -北京:地质出版社,2007.12ISBN 978-7-116-05536-0Ⅰ. 应… Ⅱ. ①迟…②鄢…Ⅲ. 地球化学丰度-化学元素-数据-手册Ⅳ. P595-62中国版本图书馆CIP数据核字(2007)第185917号责任编辑:王永奉陈军中责任校对:李玫出版发行:地质出版社社址邮编:北京市海淀区学院路31号,100083电话:(010)82324508(邮购部)网址:电子邮箱:zbs@传真:(010)82310759印刷:北京地大彩印厂开本:889mm×1194mm 1/16印张:10.25字数:260千字印数:1-3000册版次:2007年12月北京第1版•第1次印刷定价:28.00元书号:ISBN 978-7-116-05536-0(如对本书有建议或意见,敬请致电本社;如本社有印装问题,本社负责调换)2关于应用地球化学元素丰度数据手册(代序)地球化学元素丰度数据,即地壳五个圈内多种元素在各种介质、各种尺度内含量的统计数据。

它是应用地球化学研究解决资源与环境问题上重要的资料。

将这些数据资料汇编在一起将使研究人员节省不少查找文献的劳动与时间。

这本小册子就是按照这样的想法编汇的。

r8s使用指南

r8s使用指南

r8s使用指南中国科学院植物研究所张金龙编译zhangjl@前言r8s是美国加利福尼亚大学戴维斯分校的进化生物学家Mike Sanderson编写的用于估算进化树分化时间的软件,在进化生物学、分子生物地理学等学科有着广泛的应用,已经成为估算分化时间不可或缺的软件之一。

该软件中的一些方法如NPRS和PL是软件作者最先提出的,目前在同类的其他软件中还难以实现。

R8s的运行平台为MacOS和Linux,在国内应用的还不多,也难以找到中文的练习资料和说明。

本文基于当前版本r8s 1.7.1,参照其说明书,介绍该软件在Linux下的安装和操作,并对其模块的功能和选项进行简要的说明。

译者于北京香山2010年1月23日目录一r8s下载与安装 (1)下载 (1)安装 (1)1 在MacOS上 (1)2 在Linux上(以Ubuntu 9.0为例) (1)(1)下载源程序 (1)(2)解压缩 (1)(3) 源代码的编译 (1)注:g77编译器的安装 (1)3 Windows用户 (2)二程序运行 (2)1 在Linux中(Ubuntu linux 或PHYLIS) (2)2 在WindowXP中运行 (3)程序运行模式 (3)1 交互模式 (3)2 批处理模式 (3)三命令行说明 (4)blformat命令: 进化树的基本信息 (4)mrca命令为节点定名 (5)fixage命令:设定节点的分化时间 (5)constrain命令:限定节点的分化时间 (5)divtime 命令分化时间估算 (5)showage 显示分化时间和分化速率: (6)describe 显示进化树及树的说明 (6)set 命令 (7)calibrate 时间校对 (7)profile 从多个树中提取某个节点的信息 (7)rrlike 检验进化速率 (7)四数据处理过程中的建议 (7)关于进化模型的说明 (7)局部进化时间模型localmodel (7)对于获得时间的建议 (8)关于时间估算的bootstrap的方法 (8)改错 (8)五实例分析 (8)附录命令参考 (11)blformat (11)calibrate (11)cleartrees (11)collapse (11)constrain (11)describe (11)divtime (11)execute (12)fixage (12)localmodel (12)mrca (12)profile (12)prune (12)quit (12)reroot (12)rrlike (12)set (12)showage (13)unfixage (13)mrp (13)bd (14)一r8s下载与安装下载r8s的下载网址/r8s//r8s/r8s1.71.dist.tar.Z安装1 在MacOS上在MacOS上运行,在UNIX shell中运行已经编译好的可执行文件即可。

同轴共聚结构微流控芯片中HMX液滴生成的理论模拟与实验验证

同轴共聚结构微流控芯片中HMX液滴生成的理论模拟与实验验证

第38卷 第4期 2023年12月 西 南 科 技 大 学 学 报 JournalofSouthwestUniversityofScienceandTechnology Vol.38No.4 Dec.2023DOI:10.20036/j.cnki.1671 8755.2023.04.008收稿日期:2022-12-16;修回日期:2023-02-27基金项目:环境友好能源材料国家重点实验室自主课题资助(21fksy21)作者简介:第一作者,李扬(1997—),男,硕士研究生,E mail:326358971@qq.com;通信作者,李兆乾(1978—),男,副研究员,研究方向为含能材料,Email:lizhaoqian@swust.edu.cn同轴共聚结构微流控芯片中HMX液滴生成的理论模拟与实验验证李 扬1 杨凌枫1 谢育辛1 任俊铭1 孙凯欣2 裴重华1 李兆乾1(1.西南科技大学环境友好能源材料国家重点实验室 四川绵阳 621010;2.北京化工大学机电工程学院微纳制造实验室 北京 100029)摘要:微流控技术可为球形炸药颗粒的制备提供新的方法,但球形液滴的形成过程尚待深入研究。

以奥克托今(HMX)为研究对象,基于COMSOLMultiphysics5.5的两相流模块建立HMX球形液滴生成模型,采用数值模拟方法分别模拟出入口流速、接触角等因素对HMX液滴生成过程、大小以及生成时间的影响。

结果表明:出入口流速的变化不仅影响生成液滴的大小还影响液滴的生成速率;接触角的变化主要影响生成液滴的大小。

通过配置相应的高速摄影系统观察微流控芯片中HMX液滴的生成过程,发现理论模拟结果与实验结果的规律吻合较好,表明建模方法可以很好地反映实验过程。

HMX液滴形成过程的模拟研究结果可为球形炸药的制备提供理论支撑。

关键词:微流控技术 HMX 球形炸药 数值模拟中图分类号:TJ55 文献标志码:A 文章编号:1671-8755(2023)04-0054-09TheoreticalSimulationandExperimentofHMXDropletFormationinCoaxialMicrofluidicChipLIYang1,YANGLingfeng1,XIEYuxin1,RENJunming1,SUNKaixin2,PEIChonghua1,LIZhaoqian1(1.StateKeyLaboratoryofEnvironment friendlyEnergyMaterials,SouthwestUniversityofScienceandTechnology,Mianyang621010,Sichuan,China;2.Micro nanoManufacturingLaboratory,SchoolofMechanicalandElectricalEngineering,BeijingUniversityofChemicalTechnology,Beijing100029,China)Abstract:Microfluidictechnologycanprovideanewmethodforthepreparationofsphericalexplosiveparticles,buttheformationprocessofsphericaldropletsremainstobefurtherstudied.Basedonthetwo phaseflowmoduleofCOMSOLMultiphysics5.5,asphericaloctogen(HMX)dropletformationmodelwasestablishedwithHMXastheresearchobject.Theeffectsofinletandoutletflowrates,contactan gles,andotherfactorsontheformationprocess,size,andtimeofHMXdropletsweresimulatedbyusingnumericalsimulationmethods.Theresultsshowthatthechangeofinletandoutletflowratenotonlyaf fectsthesizeofthedropletsbutalsoaffectstheformationrateofthedroplets;Thechangeincontactanglemainlyaffectsthesizeofthegenerateddroplets.Byconfiguringacorrespondinghigh speedphotographysystemtoobservetheformationprocessofHMXdropletsinmicrofluidicchips,itwasfoundthatthetheo reticalsimulationresultsmeetwellwiththeexperimentalresults,indicatingthatthemodelingmethodcanwellreflecttheexperimentalprocess.ThesimulationresultsofHMXdropletformationprocesscanprovidetheoreticalsupportforthepreparationofsphericalexplosives.Keywords:Microfluidictechnology;HMX;Numericalsimulation;Sphericalexplosive 奥克托今(HMX)是目前综合性能最好的单质猛炸药,已成为常规武器、核武器、推进剂和发射药中潜在的重要组分,但较高的撞击感度阻碍了其实际应用。

Protein Sequences

Protein Sequences

Digital Signal Processing Techniques in the Analysis ofDNA/RNA and Protein SequencesbyIvan V.Baji´cSubmitted to the Department of Electronic Engineeringin partial ful…llment of the requirements for the degree ofBachelor of Science in Engineering(Electronic)at theUNIVERSITY OF NATALDecember1998c°University of Natal1998Signature of Author...................................................................Department of Electronic Engineering26October1998 Certi…ed by...........................................................................Professor Anthony D.BroadhurstChairman of the School of Electrical and Electronic EngineeringResearch Head Accepted by...........................................................................Digital Signal Processing Techniques in the Analysis of DNA/RNA andProtein SequencesbyIvan V.Baji´cSubmitted to the Department of Electronic Engineeringon26October1998,in partial ful…llment of therequirements for the degree ofBachelor of Science in Engineering(Electronic)AbstractThis report investigates the possibility of using signal processing techniques in the analysis of biological sequences:DNA,RNA and proteins.The starting point in the investigation was the Resonant Recog-nition Model(RRM)and the method proposed here builds on the foundations set by RRM.However, some fundamental modi…cations to RRM had to be madein order to make it suitable for the analysis of a greater variety of sequences.The concepts were tested on the problem of promoter recognition. During the course of the project,several methods for extracting the features from the spectra of bio-logical sequences and several types of classi…ers were tested.For promoter classi…cation the suitable choices were found to be Principal Component Analysis(PCA)feature extraction and General Regres-sion Neural Network(GRNN)classi…er.Promoter recognition system designed around this classi…er showed very good performance in comparison to other promoter recognition tools.Results indicate that signal processing methods may be very suitable for analyzing biological sequences.Research Head:Professor Anthony D.BroadhurstTitle:Chairman of the School of Electrical and Electronic Engineering2Contents1Introduction51.1Structure and function of DNA (6)1.2The role of promoters in the synthesis of proteins (7)1.3Promoter recognition (8)2The Resonant Recognition Model(RRM)102.1Application of the RRM to promoter characterization (12)2.2Guidelines for development of a signal processing based method for biological sequenceanalysis (13)3Spectral analysis of DNA/RNA and protein sequences173.1Problem de...nition. (18)3.2Reduction of spectral resolution (18)3.3An example (20)4Promoter classi…cation234.1Selection of features for the input space (23)4.1.1Principal Component Analysis(PCA)feature extraction (24)4.1.2Canonical Discriminant Analysis(CDA)feature extraction (26)4.2Arti...cial Neural Network(ANN)classi...er (29)4.2.1Generalized Regression Neural Network(GRNN) (29)4.3The choice of feature extraction method (37)5Promoter recognition385.1Basic promoter recognition system (38)5.2Improved promoter recognition system (41)5.3The experiment (45)36Conclusion and future work497Appendix1-Glossary54 8Appendix2-Typical data record from the EMBL genetic sequence database559Appendix3-Some…gures from Chapter4589.1Structure of the space of PSDVs from group1 (58)9.2Characteristics of the classi...er with PCA feature extraction.. (61)10Appendix4-Test sequences used in Chapter564 11Appendix5-MATLAB program code654Chapter1IntroductionTowards the end of this century,the…elds of genetics and genetic engineering have experienced rapid development.Since the introduction of reliable techniques for experimental determination of deoxyri-bonucleic acid(DNA)sequences of various organisms,the number of such sequences available for the analysis has increased greatly.This has prompted the need for the development of methods for the analysis of such sequences and extraction of information encoded in them.This new science that deals with the extraction and utilization of information contained in biological sequences(DNA,RNA and proteins)has become known as bioinformatics.It draws its inspiration from various natural sciences such as biological,physical,mathematical and computational.At this point,we need to explain what is meant by’DNA sequence analysis’.Just as electrical circuit analysis involves determination of voltages and currents throughout the circuit,DNA sequence analysis involves determination of the function of speci…c portions of the DNA sequence.It has been found that some regions(segments)of the DNA sequence are responsible for some speci…c tasks within the overall function of the DNA.A group of DNA segments(sub-sequences)that perform the same function within the DNA is called a functional group.Several functional groups have been experimentally discovered so far(e.g.promoters,terminators,enhancers,attenuators,etc.).In this report we concentrate on development of a method for recognition and localization of sequences from a particular functional group-promoters.As will be seen,however,the method can be easily modi…ed for recognition of other functional groups.The rest of this chapter gives introductory details about the DNA and the function that promoters perform within it(material is presented at the level of the introductory text in genetics).Chapter 2presents a particular signal processing-based method for biological sequence analysis,the Resonant Recognition Model(RRM),and investigates its applicability to the problem of promoter recognition.In5Chapter3an alternative method is proposed and Chapters4and5are devoted to the investigation of the ways in which it can be used for promoter recognition.Appendices include the glossary of genetics terminology used throughout the report,some…gures and tables related to the…rst…ve chapters and the supporting MATLAB program code.1.1Structure and function of DNADNA molecule has a form of a double helix[1],with each strand(helix)represented by a sequence composed of four nucleotides:adenine(A),thymine(T),guanine(G),cytosine(C).The two helices are bonded to each other with a hydrogen bond between each nucleotide pair.The only bindings possible between the two helices,due to the physical shape of nucleotide molecules,are A-T and G-C.Hence, if we know the sequence of one of the helices,we also know the sequence of the other(for example,if a portion of one of the DNA helices is AGATC,the corresponding portion of the other helix of that DNA is TCTAG).It is widely accepted belief that the function of the DNA is determined mainly by the sequence of the nucleotides from which it is composed,which is why methods for DNA analysis are usually termed’DNA sequence analysis’methods.Due to the one-to-one correspondence between the sequences of the two helices,as described above,most of these methods analyze the sequence of only one of the helices.DNA molecule itself is not directly involved in the functioning of the cell-in eukaryotic(non-bacterial) organisms it is located within the nucleus of the cell and isolated from most of the chemical processes taking place in the cytoplasm of the cell.Proteins are the main’workhorse’of the cell and of the organism as a whole.They account for more than half of the dry weight of most cells and they perform almost all of the biochemical functions of an organism[1]:structural support,storage,transport of other substances,signalling between di¤erent parts of an organism and defense against alien substances.(Of course,each speci…c protein has only one function,e.g.hemoglobin,a protein present in red blood cells, transports oxygen around the organism;lysozyme,a protein present in tears,protects the surface of the eyes by destroying speci…c molecules on the surface of many bacteria,etc.).But the DNA sequence itself determines the structure of proteins that the cell is able to synthesize,which is why the information it contains is so important for the functioning of any organism.Promoters,as parts of the DNA sequence, are crucial in the synthesis of proteins.61.2The role of promoters in the synthesis of proteinsTo explain the role of promoters in the synthesis of proteins,we will look into this process in more detail. The process essentially consists of two steps[1]:transcription and translation.In eukaryotic cells transcription takes place in the nucleus of the cell(prokaryotic(bacterial)cells do not have nucleus,so transcription takes place in the cytoplasm,but the process is otherwise very similar to the eukaryotic transcription;in further text we concentrate on the eukaryotic case).During this process a messenger ribonucleic acid(mRNA)is synthesized as a replica of a portion of one strand of the DNA.The process of transcription of a DNA sequence x DNA(n);n=0;1;:::;N¡1of length N into a mRNA sequence x mRNA(n);n=0;1;:::;N¡1of the same length can be described as follows:;n=0;1;:::;N¡1(1.1) x mRNA(n)=8<:x DNA(n);x DNA(n)2f A;G;C gU;x DNA(n)=TThus,the information contained in the DNA sequence is exactly transcribed(bijective mapping)into a mRNA sequence,the only di¤erence between the two sequences being that each T in DNA is replacedmRNA.by a very similar nucleotide uracil(U)inFigure1-1:Illustration of the transcription processIllustration of the physical process of transcription is depicted in Figure1-1.The…gure shows how a mRNA chain is created by copying one coding region-a portion of a DNA sequence containing the information of how to synthesize a particular protein.Coding region is bounded by the promoter(region where transcription starts)and the terminator(region where transcription stops).A particular molecule, called RNA polymerase,enables the transcription.RNA polymerase recognizes the promoter and binds to it,thereby starting the process of transcription.As it continues to move along the DNA strand it attracts the free nucleotides‡oating around in the nucleus and binds them together according to the sequence determined by the DNA.When it reaches the terminator it detaches itself from the DNA and releases the newly created mRNA chain that is a replica(in the sense of(1.1))of the particular coding region.The role of a promoter in this process is to show the RNA polymerase exactly where to start7the transcription.Obviously,recognition of the correct position for the start of transcription by RNA polymerase is of crucial importance-unless the transcription starts at the correct point,wrong protein would be synthesized,which may ultimately result in the malfunctioning of the whole organism.This newly created mRNA molecule travels from the nucleus into the cytoplasm where translation takes place.This process is analogous to that of a transcription,but this time a chain of amino-acids (instead of nucleotides)is created according to the mRNA sequence.This chain of amino-acids(also called a polypeptide)represents the protein.The process of translation will not be discussed here,since promoters do not play a signi…cant role in it.Details on translation can be found in[1].1.3Promoter recognitionSince promoters are not a part of a coding region,they do not in‡uence the structure of a subsequently synthesized protein,and therefore it is coding regions and not promoters that are of primary interest in the study of genetics.However,the task of locating coding regions in the DNA sequence is not an easy one.Since coding regions are responsible for encoding a great variety of proteins,it should be expected that there is a great structural variety among them,so the knowledge of a structure of several particular coding regions would not help us to recognize the others.In other words,it would be hard to make some sort of generalization in this case.Fortunately,however,we know that each coding region is preceded by a promoter,so the search for a coding region may be reduced to the search for a promoter. As we have seen above,promoter sequence contains some unique information that enables the RNA polymerase molecule to recognize it.Thus,although the number of di¤erent promoters is large,it would be reasonable to expect that they all share some structural similarity that enables their recognition.This is the main reason that sparked the interest in the problem of computational promoter recognition,that is,the automated(computer-based)recognition and localization of promoters within the DNA sequence.It is still not known exactly what type of similarity is shared by promoters,and many approaches to the problem(arising from many theories about what this similarity may be)have been developed[3]. They range from homology-based methods,to the ones based on Generalized Hidden Markov Models (GHMM)and Arti…cial Neural Networks(ANN).A list of some of the currently used programs based on these methods is shown in Table1:1,along with their accuracies determined experimentally in[3].More details about the methods used in these programs can be found in[3].Here we will simply explain the information given in the table.The…rst row(TP)gives the so called’sensitivity’or the number of true positives(TP)-the number of correctly recognized promoters on a test segment of the DNA sequence.For example,the…rst program(Audic)recognized5out of24promoters,or24%(second row).Third row(FP)gives the so called’speci…city’or the number of false positives-the number of8Table1.1:Programs currently used for promoter recognition and their accuraciessequences on the same segment of DNA that are recognized by the program as promoters,but are not really promoters.For example,Audic recognized33false positives at a rate of1false positive per1004 nucleotides(1=1004-last row).As we can see from the table,by the time this survey was completed(March1997),promoter recognition was still in its embryonic stage,and the situation has not changed much since then[4].The best sensitivity was achieved by a ANN-based program NNPP(54%),but at the same time this program had the worst speci…city(72false positives at a rate of1=460).This trade-o¤between sensitivity and speci…city can be noticed throughout the table(e.g.the best speci…city was achieved by P’Scan,only6 false positives at a rate1=5520,but this program had the worst sensitivity,only13%).The type of behavior seen in the table above suggests that,perhaps,the input space is not su¢ciently good for the purpose of promoter recognition.It appears that in the input space described by the sequence itself(e.g.AGTTC...)promoters overlap with other types of sequences(i.e.there are non-promoter sequences in the neighborhood of many promoter sequences)so the more promoters we are able to recognize,the more false positives we get.What we need is a way to transform the input space (i.e.the sequence)into a space in which promoters will emerge as a distinct class i.e.they will occupy a su¢ciently convex region not occupied by non-promoter sequences.Spectral analysis may provide this transformation and a possible method is presented in the following chapter.It is based on signal processing and performs the extraction of information from a biological sequence in the normalized spatial frequency domain.9Chapter2The Resonant Recognition Model (RRM)The Resonant Recognition Model(RRM)evolved from the Information Spectrum Method(ISM)[5] developed in the attempt to utilize methods of signal processing in the analysis of biological sequences-DNA/RNA and proteins.Details of the theory and applications of RRM can be found in[6]and[7].In this section we describe the procedure used by the RRM to extract relevant information from biological sequences.Suppose we have a set of M biological sequences S=f s i g;i=1;2;:::;M with the same biological function(e.g.promoters).RRM postulates that in this case,this common biological function corresponds to a speci…c frequency f RRM in the normalized spatial frequency domain that can be determined in the following way:Step1.Substitute each of the nucleotides in each of the sequences s i with a value of its Electron-Ion Interaction Potential(EIIP)-a quantity that relates some of the physical properties of nucleotides and amino-acids to biological properties of organic molecules[8].Values of EIIP for the four nucleotides are given in Table2:1(from[6]).Thus,a set S n=f y i g;i=1;2;:::;M of M numerical sequences is obtained.Step2.Perform the detrending operation(removal of the mean value-DC component)from eachTable2.1:Values of EIIP for the four nucleotides10of the sequences y i .Thus,a set of M detrended numerical sequences S nd =f x i g ;i =1;2;:::;M is obtained.This step is necessary since all the values of EIIP are positive so each of the sequences y i has a strong DC component,which is not important for the RRM analysis,but may cause errors due to spectral leakage later in the process.Step 3.Let the longest sequence in S nd be x i 0of length N .Perform the zero-padding operation onall sequences from S nd nf x i 0g by adding zeros to the end of each of the sequences up to the length N .Thus,a set of zero-padded sequences S (0)nd =f x (0)i g ;i =1;2;:::;M ,each of length N ,is obtained.Note that x (0)i 0=x i 0since the longest sequence is not zero-padded.Also,if there are several sequences in S nd of the same (largest)length N ,then neither of these are zero-padded.Step 4.A spectrum of each of the sequences from S (0)nd is found as X (0)i =DF T (x (0)i )whereDF T denotes the Discrete Fourier Transform.The distance between elements of x (0)iis assumed to be constant (since,indeed,distance between nucleotides in the DNA sequence is very nearly constant [6])and normalized to d =1.Thus,each of the spectra X (0)i are de…ned on a normalized spatial frequency interval [0;1),with non-redundant components in the interval [0;0:5].(This is analogous to the case where a signal is sampled with a sampling frequency of 1).Step 5.A cross-spectrum (or consensus spectrum)of sequences from S (0)nd is found asX c (j )=M Y i =1¯¯¯X (0)i (j )¯¯¯;j =0;1;:::;N ¡1(2.1)and a signal-to-noise ratio (SNR)of the consensus spectrum is found as a magnitude of the largest frequency component relative to the mean value of the spectrum [6].Step 6.The largest frequency component in the consensus spectrum is considered to be signi…cant if the value of SNR is at least 20[6].Signi…cant frequency component is the characteristic RRM frequency (f RRM )for the entire group of biological sequences having the same biological function as those in S ,since it is the strongest frequency component common to all of the biological sequences from that particular functional group.Apart from being an interesting approach to the analysis of biological sequences,RRM also o¤ers some physical explanation of the selective interactions between biological macromolecules,based on their structure.RRM proposes that these selective interactions (that is,recognition of a target molecule by another molecule,e.g.recognition of a promoter by RNA polymerase)are caused by resonant electromag-netic energy exchange -hence the name ’Resonant Recognition Model’.According to RRM,charge that is being transferred along the backbone of a macromolecule travels through the changing electric …eld described by a sequence of EIIPs,causing the radiation of some small amount of electromagnetic energy at particular frequencies that can be recognized by other molecules.If there is a frequency component11common to the radiation patterns of two molecules,then they can recognize each other and interact.So far,RRM has had some success in the design of new proteins with desired biological functions[9]and [10],but there have also been some problems in its application,as will be seen later in the text.In the following section we apply the RRM procedure in the attempt to characterize a group of human promoters by a common RRM frequency(f RRM).If we were able to…nd a single strong frequency component common to all promoter sequences,the search for promoters within the large segment of a DNA sequence would be signi…cantly simpli…ed.2.1Application of the RRM to promoter characterizationIn this section we examine the applicability of the RRM to the problem of promoter recognition,in the similar way as it was done in[11].For this purpose,set H of41promoters was arbitrarily extracted from the EMBL and GenBank databases(public databases that contain segments of DNA sequences examined and documented so far).Three subsets were arbitrarily chosen from set H.Sets E and F are distinct subsets of H(i.e.E\F=?and E[F=H),while G was created by combining some promoters from E with some promoters from F.RRM procedure,as described in the previous section was applied to each of the sets E,F,G and H.Results are summarized in Table2:2and resulting normalized consensus spectra are shown in Figure2-1.Results show that each of the four sets considered is characterized by a di¤erent RRM frequency. In each case SNR is much greater than20and therefore each of the frequencies may be considered as a characteristic RRM frequency for all promoters,which contradicts the fundamental hypothesis of the RRM that each group of biological sequences with the same biological function is characterized by a single frequency.Also,each of the obtained frequencies is signi…cantly di¤erent from the characteristic frequency for promoters quoted in[6]as0:34375.Reason for these discrepancies is suspected to be zero-padding in step3of the RRM procedure and is discussed in more detail in[12]and in the following section.Here it will be su¢cient to say that zero-padding results in occurrence of some frequency components in the spectrum of the zero-padded sequence whose existence is not guaranteed by the original sequence(this phenomenon,represented by the creation of sidelobes between the original frequency components,is called spectral leakage).These newly introduced frequency components enter the process of cross-spectral multiplication in step5of the RRM procedure and become candidates to be recognized as a characteristic RRM frequencies.This is probably the reason why di¤erent characteristic frequencies were obtained for the four sets of promoters examined in this section.12Table2.2:Results of RRM analysis of set H of41promoters and three of its subsetsFigure2-1:RRM consensus spectra for promoters from(a)set E;(b)set F;(c)set G;(d)set H.2.2Guidelines for development of a signal processing basedmethod for biological sequence analysisIn the previous section we saw that RRM,in its current form,cannot be applied to the problem of promoter recognition,since it is not possible to characterize promoters by a single frequency compo-13nent in the normalized spatial frequency domain.This does not mean that the concept of resonant recognition between macromolecules is wrong.Rather,it seems that signal processing aspects in the RRM are incorrectly applied in the form of zero-padding.In this section we present basic guidelines for development of a method for spectral analysis of biological sequences that attempts to avoid problems associated with the RRM that were demonstrated in the previous section.In order to do this,we must …rst examine the essence of the problem we are faced with.Consider two sequences x1and x2of lengths N1and N2,respectively,such that N1<N2.Let X1=DF T(x1)and X2=DF T(x2).Now,if we consider the elements of x1and x2to be equidistant, with normalized distance d=1,then X1and X2are de…ned on the following two sets of points, respectivelyS1=f k=N1j k=0;1;:::;N1¡1g(2.2)S2=f k=N2j k=0;1;:::;N2¡1gwhich,if N2is not a multiple of N1,do not have any points in common(apart from0).Equivalently,we say that X1and X2have di¤erent resolutions:1=N1and1=N2,respectively.Therefore,direct comparison of X1and X2is not possible.As an example,spectra of sequences x1=0;1;3;2and x2=1;2;3;2;1;2;0 are shown in Figure2-2.Figure shows the complete spectra on interval[0;1).Non-redundant components are,of course,lo-cated in the interval[0;0:5].Since the sequences are of di¤erent lengths(4and7),their spectra X1and X2 are de…ned on two di¤erent sets of points S1=f0;1=4;2=4;3=4g and S1=f0;1=7;2=7;3=7;4=7;5=7;6=7g, respectively.Our problem is how to compare these two spectra and see to what extent are they similar.Obviously,we have to modify sets S1and S2in some way.RRM proposes zero-padding as a way to increase the length of x1up to the length of x2in order to make sets S1and S2equal.This provides a14way of interpolation in the frequency domain and increases the computational resolution of the spectrum of x1to1=N2[13].However,due to the Uncertainty Principle of Fourier analysis[14],physical resolution of the spectrum of x1is limited by its original length and remains1=N1[13].This e¤ectively means that we cannot discover any new information about the spectrum of the signal by performing the zero-padding operation-interpolation is done on the wrong curve[13](unless the signal indeed happens to be zero outside of its original domain of de…nition,which is,in case of biological sequences,physically virtually never possible[12]).We therefore conclude that zero-padding introduces an error into the spectrum of the signal.This error can often be large enough to make zero-padding unsuitable as a way to enable the comparison of spectra of di¤erent resolutions.Hence,if this comparison cannot be made by increasing the resolution of the spectrum with lower resolution(X1),it seems natural to attempt to enable the comparison by reducing the resolution of the spectrum with higher resolution(X2).This reduction in resolution may be achieved in the following way.First,physical resolution of X1is limited to1=N1,which means that it is limited to a set of intervalsI1=f[a¡12N1;a+12N1]j a2S1g.We can therefore take as the estimate of the power of x1in anyparticular frequency interval from I1the square of the amplitude of the frequency component of x1that belongs to that interval(e.g.for the signal x1given above,estimate of its power in frequency interval [0:125;0:375]would be(3:16)2=4¼2:5,since component of X1at point0:25in the normalized frequency domain has an amplitude of3:16-refer to Figure2-2;note:division by4above is necessary since the power of the frequency component X1(k)is j X1(k)j2=N1-see[15]).Equivalently,we estimate the power of x2in any particular frequency interval from I1as the sum of squares amplitudes of frequency components of x2that belong to that interval(e.g.for the signal x2given above,estimate of its power in frequency interval[0:125;0:375]would be(3:36)2=7+(2:21)2=7¼2:31,since the components of X2that belong to[0:125;0:375]have amplitudes of3:36and2:21-refer to Figure2-2).Performing this process for all intervals from I1for signals x1and x2given above,we arrive at the following two graphs shown in Figure2-3.From the…gure,we see that estimated power of signals represented by sequences x1and x2is di¤erent in intervals[0;0:125]and[0:375;0:625],but is fairly similar in intervals[0:125;0:375]and [0:425;0:875],which is what might have been expected from the graphs in Figure2-2.On the other hand,consider what happens when x1is zero-padded up to length7(Figure2-4).Now the region of the greatest similarity between the spectra of x1and x2is actually closer to0:5.Also, note that the cross-spectral multiplication(2.1)would in this case give non-zero values at all7points in the spectrum,even at3=7¼0:43and4=7¼0:57,although in the original spectrum of x1in Figure 2-2there is no evidence that x1contains any power in the interval[0:4;0:6].This does not happen if we multiply the spectra shown in Figure2-3.Comparing the spectra of sequences of di¤erent lengths by reducing the resolution of the spectrum15。

《地质学报》格式注意事项参考

《地质学报》格式注意事项参考

《地质学报》修改注意事项参考文献的引用1、请作者一一核对文献,前后一一对应:文中(含图、表中)提到的文献,一定要能在参考文献表中找到;文献表中列出的文献,一定要在文中(含图、表中)提及(非正式出版物可在正文中用 ❖ 等符号,在参考文献前用注释) 。

2、在正文及其图表中:如文献两个作者或两个作者以上,用“等”或“et al.”举例说明:××××(Pearce, 1983; Whalen et al., 1987; 宋彪等,2002)。

3、参考文献表下面几篇参考文献格式供作者参考(红色表示需特别注意的),参考文献的作者需全部例出。

西洋人姓写全,名缩写。

如:Hu Ruizhong, Bi Xianwu, Zhou Meifu, Peng Jiantang, Su Wenchao, Liu Shen, Qi Huawen. 2008. Uranium metallogenesis in South China and its relationship to crustal extension during the Cretaceous to Tertiary. Economic Geology, 103 (3): 583~598.余心起, 舒良树, 颜铁增, 俞云文, 祖辅平, 王彬. 2004.江山—广丰地区早白垩世晚期玄武岩的岩石地球化学及其构造意义.地球化学,33 (5): 465~476.Gilder S A, Keller G R, Luo M, Goodell P C. 1991. Eastern Asia and the Western Pacific Timing and spatial distribution of rifting in China. Tectonophysics, 197 (2~4): 225~243.4、格式请按参考:(1)第一作者,第二作者,...第N作者.年份.文章名称. 刊物名称,卷号(期号):起始页~终止页.(2)第一作者,第二作者,...第N作者.年份.书名. 出版地:出版社,起始页~终止页.(3)第一作者,第二作者,...第N作者.年份.文章名称. 见:XXX, XXX,...,(等). 主编. 书名. 出版地:出版社,起始页~ 终止页.(4)按著者姓氏拉丁字母顺序排列参考文献表(第一作者相同的,按年代先后排列).(5)文章文献中,题名只有首词和其中的专有名词的首字母大写及冒号后的单词的首字母大写,其余词均不用大写;而书名、期刊名、出版社名等的所有实词的首字母均须大写正文修改细节说明1) 正文和图、表中所有的:“ppm”改为“×10-6”;“ppb”、改为“×10-9”;“ppt”改为“×10-12”;正文、图中、表格中“wt%”均改为“%”。

Illumina cBot自动克隆扩增系统说明书

Illumina cBot自动克隆扩增系统说明书

The Best Next-Gen Sequencing Workflow Just Got BettercBot is a revolutionary automated clonal amplification system at the core of the Illumina sequencing workflow (Figure 1, upper panel). cBot replaces a lab full of equipment with a single compact device, deliver-ing unsurpassed efficiency and ease of use for the highest quality sequencing results.With cBot, hands-on time is reduced to less than 10 minutes, com-pared to more than six hours of hands-on effort for emulsion PCR methods. The process of creating sequencing templates is complete in about four hours, compared to more than 24 hours for emulsion PCR-based protocols (Figure 1, lower panel).Breakthrough System for Cluster GenerationThe Illumina sequencing workflow is based on three simple steps: libraries are prepared from virtually any nucleic acid sample, amplified to produce clonal clusters, and sequenced using massively parallel synthesis. The cBot clonal amplification system has innovative features that eliminate user intervention, reduce potential failure points, and increase sequencing productivity.TruSeq Cluster Generation reagents are packaged in ready-to-use96-well plates, completely removing reagent preparation errors, potential sources of contamination, and decreasing storage require-ments. cBot features a single unique, plate-piercing manifold for intervention-free runs. Cluster generation occurs within the sealed, eight-channel Illumina flow cell, bypassing the frequent handling and contamination issues inherent to emulsion PCR-based protocols. cBot is capable of processing > 96 samples within a single flow cell, resulting in substantial cost savings without incremental effort and wasted reagents. Innovative instrument features ensure seamless operation for your sequencing workflow (Figure 2).Better Results with Less EffortcBot software enhancements and user interaction features ensure high productivity:• Integrated 8-inch touch screen provides simplified operation in a small, lab-friendly footprint• On-screen, step-by-step instructions with embedded multimedia help enable user operation with no prior training • Real-time progress indicators provide at-a-glance monitoring • Remote monitoring allows a single user to manage multiple systems from any web browser or phone• Status emails are sent when the run is complete or when intervention is requiredcBot Cluster Generation ProcessPrior to sequencing, single-molecule DNA templates are bridge amplified to form clonal clusters inside the flow cell. (Figure 3).cBotFully automated clonal cluster generation for Illumina sequencing.Illumina cBot Highlights• Fast, Efficient Workflow:Amplify > 96 samples in ~4–5 hours with < 10 minutes ofhands-on time• Easiest to Use:Pre-packaged 96-well TruSeq™ reagents, and simple touch screen interface simplifies operation• Innovative System Design:Real-time fluidic monitoring, integrated system sensors and remote monitoring ensure robust instrument operation• Highest Quality Results:Improved chemistry generates higher density clusters and sequencing accuracy LibraryPreparation SequencingCluster GenerationEight-channel flow cell reduces risk of contamination and eliminates the needfor extra equipment Manifold clamps for leak-free connections and superior thermal contactTouch screen monitor simplifies operation and provides real-timeImmobilization of Single-Molecule DNA TemplatesHundreds of millions of templates are hybridized to a lawn of oligo-nucleotides immobilized on the flow cell surface. The templates are copied from the hybridized primers by 3’ extension using a high-fidelity DNA polymerase to prevent misincorporation errors. The original templates are denatured, leaving the copies immobilized on the flow cell surface.Isothermal Bridge AmplificationImmobilized DNA template copies are amplified by isothermal bridge amplification. The templates loop over to hybridize to adjacent lawn oligonucleotides. DNA polymerase copies the templates from the hybridized oligonucleotides, forming dsDNA bridges, which are dena-tured to form two ssDNA strands. These two strands loop over and hybridize to adjacent oligonucleotides and are extended again to form two new dsDNA loops. The process is repeated on each template by cycles of isothermal denaturation and amplification to create millions of individual, dense clonal clusters containing ~2,000 molecules. Linearization, Blocking, and Primer HybridizationEach cluster of dsDNA bridges is denatured, and the reverse strand is removed by specific base cleavage, leaving the forward DNA strand. The 3’-ends of the DNA strands and flow cell-bound oligonucleotides are blocked to prevent interference with the sequencing reaction. The sequencing primer is hybridized to the complementary sequence on the Illumina adapter on unbound ends of the templates in the clusters. The flow cell now contains >200 million clusters with ~1,000 mol-ecules/cluster, and is ready for sequencing.SummaryIllumina sequencing with cBot automated cluster generation sets the new standard for simplified next- generation sequencing. Ready-to-use reagents, smart instrumentation improvements, and new cluster generation chemistry offers significant advantages over emulsion PCR-based workflows and promotes even higher data density and sequencing accuracy. By streamlining the critical clonal amplification step in the next-generation sequencing workflow, Illumina continues to accelerate your landmark discoveries and publications.Ordering InformationDescriptioncBotCatalog No.HiSeq System Genome AnalyzercBot Instrument Includes cBot, flow cell adapter plate,one year warranty, user manualSY-301-2002cBot Flow Cell Manifold (Optional)SY-301-2014TruSeq Single-Read Cluster Generation Kits include flow cell,reagent plate, manifold, user instructionsGD-401-3001GD-300-2001TruSeq Paired-End Cluster Generation Kits include flow cell,reagent plate, manifold, PE reagents, user instructionsPE-401-3001PE-300-2001Illumina, Inc. •9885TowneCentreDrive,SanDiego,CA92121USA•1.800.809.4566toll-free•1.858.202.4566tel•************************• For research use only© 2011 Illumina, Inc. All rights reserved.Illumina, illuminaDx, BeadArray, BeadXpress, cBot, CSPro, DASL, Eco, Genetic Energy, GAIIx, Genome Analyzer, GenomeStudio, GoldenGate, HiScan, HiSeq, Infinium, iSelect, MiSeq, Nextera, Sentrix, Solexa, TruSeq, VeraCode, the pumpkin orange color, and the Genetic Energy streaming bases design are trademarks or registered trademarks of Illumina, Inc. All other brands and names contained herein are the property of their respective owners. Pub. No. 770-2009-032 Current as of 27 April 2011at the address below.Laser radiationDo not stare into the visible-light beam of the barcode scanner. The barcode scanner is a Class 2 laser product.SY-301-2002Instrument ConfigurationCE Marked and ETL Listed instrument, Installation, setup, and accessoriesInstrument Control ComputerMini-ITX Board with Celeron M Processor 1 GB RAM, 80 GB Hard Drive Windows Embedded OSIntegrated 8” Touch Screen Monitor Operating Environment Temperature: 22°C ± 3°CHumidity: Non-Condensing 20%–80%Altitude: Less than 2,000 m (6,500 ft)Air Quality: Pollution Degree Rating of II For Indoor Use Only LaserClass 2 Laser: 630 –650 nm DimensionsW×D×H: 38 cm × 62 cm × 40 cm Weight: 34 kg Crated Weight: 36 kg Power Requirements100−240V AC 50/60 Hz, 4A, 400 Watts。

基于ITS区序列的疑似野生双孢菇菌株的分子鉴定

基于ITS区序列的疑似野生双孢菇菌株的分子鉴定

图1 显示 , S区段是位于 5 0—70 b 间的序 列 , I T 0 5 p之 琼
脂糖凝胶 电泳检测条带单一 、 清晰 , 说明 I 通用引物适合 疑 S T
似野生双孢菇菌株的核糖 体基 因转 录间隔 区, 样本符合测序 的条件 。
C 3 ~ 0 p G T T C T G T C A A C A G G T C ;5 4 0 b : T C T A C A G G G G C A A A C 1
(. 1 塔里木大学食用 菌研究所 , 新疆阿拉尔 8 30 ; 4 3 0 2 塔里木盆地生物资源保护利用省部共建国家重点实验室培育基地 , . 新疆阿拉尔 8 30 ) 4 30
摘要 : 通过提取新疆南疆疑似野生双孢 菇基 因组 D A, N 采用通用 引物 IS T 1和 IS T 4扩增 IS区序列 , G n ak T 在 eB n 中进行 B A T, L S 对疑似菌株双孢菇进行初 步分 子鉴定 。结果表 明 : G n ak的核酸序列数 据库 中登陆 的、 在 eB n 与双孢 菇
收稿 日期 :0 1—1 9 21 0—1
退火 3 ,2℃延伸 1mn共 3 0s7 i, 0个循环;2℃延伸1 i。 7 0mn 12 4 IS区序列测定 . . T 将 P R产物送往上 海生工 生物工 C
基 金 项 目: 疆 生 产 建 设 兵 团 科 技 攻 关 项 目 (编 号 : 新
IS T 序列 相似度 为 9 % , 数十种之 多 , 中与登 录号为 E405 9 达 其 F634的双孢菇 同源性 达 10 , 0% 覆盖率 达 9% 。根 据 1 rN S区序列分析准则和双孢 菇的形态 特征 , D AI T 鉴定新疆南疆疑似野生双孢菇为双孢菇 (gru bpr ) A acs io s 。 i s u

液晶弹性体

液晶弹性体

3. Actuators based on LCEs
3.1. Actuators based on thermally actuated LCEs
Fig 3. Micrometer-sized nematic LCE actuators consisting of a pillar array. (a) Experimental setup used to prepare the responsive pillars. (b) Top view (under an optical microscope) of the pillar pattern obtained by the imprint in the nematic liquid crystal elastomer. (Inset) Zoom on the structure (pillar diameter=20mm)[1]. [1 ]Buguin A, Li M H, Silberzan P, et al. Journal of the American Chemical Society, 2006, 128(4): 1088-1089.
4. Summary
1.Introduction
Smart materials:
There is a group of materials capable of responding to external stimuli with mechanical deformation.
Fig 1. The diferent kinds of actuator materials both in natural and synthetic systems
3. Actuators based on LCEs

Finding community structure in networks using the eigenvectors of matrices

Finding community structure in networks using the eigenvectors of matrices
Finding community structure in networks using the eigenvectors of matrices
M. E. J. Newman
Department of Physics and Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109–1040
We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity” over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in neteasure that identifies those vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.

21世纪是生命科学的世纪20世纪后叶分子生物学的突破性...

21世纪是生命科学的世纪20世纪后叶分子生物学的突破性...

第一章绪论一简答题1. 21世纪是生命科学的世纪。

20世纪后叶分子生物学的突破性成就,使生命科学在自然科学中的位置起了革命性的变化。

试阐述分子生物学研究领域的三大基本原则,三大支撑学科和研究的三大主要领域?答案:(1)研究领域的三大基本原则:构成生物大分子的单体是相同的;生物遗传信息表达的中心法则相同;生物大分子单体的排列(核苷酸,氨基酸)导致了生物的特异性。

(2)三大支撑学科:细胞学,遗传学和生物化学。

(3)研究的三大主要领域:主要研究生物大分子结构与功能的相互关系,其中包括DNA和蛋白质之间的相互作用;激素和受体之间的相互作用;酶和底物之间的相互作用。

2. 分子生物学的概念是什么?答案:有人把它定义得很广:从分子的形式来研究生物现象的学科。

但是这个定义使分子生物学难以和生物化学区分开来。

另一个定义要严格一些,因此更加有用:从分子水平来研究基因结构和功能。

从分子角度来解释基因的结构和活性是本书的主要内容。

3 二十一世纪生物学的新热点及领域是什么?答案:结构生物学是当前分子生物学中的一个重要前沿学科,它是在分子层次上从结构角度特别是从三维结构的角度来研究和阐明当前生物学中各个前沿领域的重要学科问题,是一个包括生物学、物理学、化学和计算数学等多学科交叉的,以结构(特别是三维结构)测定为手段,以结构与功能关系研究为内容,以阐明生物学功能机制为目的的前沿学科。

这门学科的核心内容是蛋白质及其复合物、组装体和由此形成的细胞各类组分的三维结构、运动和相互作用,以及它们与正常生物学功能和异常病理现象的关系。

分子发育生物学也是当前分子生物学中的一个重要前沿学科。

人类基因组计划,被称为“21世纪生命科学的敲门砖”。

“人类基因组计划”以及“后基因组计划”的全面展开将进入从分子水平阐明生命活动本质的辉煌时代。

目前正迅速发展的生物信息学,被称为“21世纪生命科学迅速发展的推动力”。

尤应指出,建立在生物信息基础上的生物工程制药产业,在21世纪将逐步成为最为重要的新兴产业;从单基因病和多基因病研究现状可以看出,这两种疾病的诊断和治疗在21世纪将取得不同程度的重大进展;遗传信息的进化将成为分子生物学的中心内容”的观点认为,随着人类基因组和许多模式生物基因组序列的测定,通过比较研究,人类将在基因组上读到生物进化的历史,使人类对生物进化的认识从表面深入到本质;研究发育生物学的时机已经成熟。

Entropy changes in the clustering of galaxies in a

Entropy changes in the clustering of galaxies in a

Vol.3, No.1, 65-68 (2011)doi:10.4236/ns.2011.31009Natural ScienceEntropy changes in the clustering of galaxies in an expanding universeNaseer Iqbal1,2*, Mohammad Shafi Khan1, Tabasum Masood11Department of Physics, University of Kashmir, Srinagar, India; *Corresponding Author:2Interuniversity Centre for Astronomy and Astrophysics, Pune, India.Received 19 October 2010; revised 23 November 2010; accepted 26 November 2010.ABSTRACTIn the present work the approach-thermody- namics and statistical mechanics of gravitating systems is applied to study the entropy change in gravitational clustering of galaxies in an ex-panding universe. We derive analytically the expressions for gravitational entropy in terms of temperature T and average density n of the par-ticles (galaxies) in the given phase space cell. It is found that during the initial stage of cluster-ing of galaxies, the entropy decreases and fi-nally seems to be increasing when the system attains virial equilibrium. The entropy changes are studied for different range of measuring correlation parameter b. We attempt to provide a clearer account of this phenomena. The entropy results for a system consisting of extended mass (non-point mass) particles show a similar behaviour with that of point mass particles clustering gravitationally in an expanding uni-verse.Keywords:Gravitational Clustering; Thermodynamics; Entropy; Cosmology1. INTRODUCTIONGalaxy groups and clusters are the largest known gravitationally bound objects to have arisen thus far in the process of cosmic structure formation [1]. They form the densest part of the large scale structure of the uni-verse. In models for the gravitational formation of struc-ture with cold dark matter, the smallest structures col-lapse first and eventually build the largest structures; clusters of galaxies are then formed relatively. The clus-ters themselves are often associated with larger groups called super-clusters. Clusters of galaxies are the most recent and most massive objects to have arisen in the hiearchical structure formation of the universe and the study of clusters tells one about the way galaxies form and evolve. The average density n and the temperature T of a gravitating system discuss some thermal history of cluster formation. For a better larger understanding of this thermal history it is important to study the entropy change resulting during the clustering phenomena be-cause the entropy is the quantity most directly changed by increasing or decreasing thermal energy of intraclus-ter gas. The purpose of the present paper is to show how entropy of the universe changes with time in a system of galaxies clustering under the influence of gravitational interaction.Entropy is a measure of how disorganised a system is. It forms an important part of second law of thermody-namics [2,3]. The concept of entropy is generally not well understood. For erupting stars, colloiding galaxies, collapsing black holes - the cosmos is a surprisingly or-derly place. Supermassive black holes, dark matter and stars are some of the contributors to the overall entropy of the universe. The microscopic explanation of entropy has been challenged both from the experimental and theoretical point of view [11,12]. Entropy is a mathe-matical formula. Standard calculations have shown that the entropy of our universe is dominated by black holes, whose entropy is of the order of their area in planck units [13]. An analysis by Chas Egan of the Australian National University in Canberra indicates that the col-lective entropy of all the supermassive black holes at the centers of galaxies is about 100 times higher than previ-ously calculated. Statistical entropy is logrithmic of the number of microstates consistent with the observed macroscopic properties of a system hence a measure of uncertainty about its precise state. Statistical mechanics explains entropy as the amount of uncertainty which remains about a system after its observable macroscopic properties have been taken into account. For a given set of macroscopic quantities like temperature and volume, the entropy is a function of the probability that the sys-tem is in various quantumn states. The more states avail-able to the system with higher probability, the greater theAll Rights Reserved.N. Iqbal et al. / Natural Science 3 (2011) 65-6866 disorder and thus greater the entropy [2]. In real experi-ments, it is quite difficult to measure the entropy of a system. The technique for doing so is based on the thermodynamic definition of entropy. We discuss the applicability of statistical mechanics and thermodynam-ics for gravitating systems and explain in what sense the entropy change S – S 0 shows a changing behaviour with respect to the measuring correlation parameter b = 0 – 1.2. THERMODYNAMIC DESCRIPTION OF GALAXY CLUSTERSA system of many point particles which interacts by Newtonian gravity is always unstable. The basic insta-bilities which may occur involve the overall contraction (or expansion) of the system, and the formation of clus-ters within the system. The rates and forms of these in-stabilities are governed by the distribution of kinetic and potential energy and the momentum among the particles. For example, a finite spherical system which approxi-mately satisfies the viral theorem, contracts slowlycompared to the crossing time ~ ()12G ρ- due to the evaporation of high energy particles [3] and the lack of equipartition among particles of different masses [4]. We consider here a thermodynamic description for the sys-tem (universe). The universe is considered to be an infi-nite gas in which each gas molecule is treated to be agalaxy. The gravitational force is a binary interaction and as a result a number of particles cluster together. We use the same approximation of binary interaction for our universe (system) consisting of large number of galaxies clustering together under the influence of gravitational force. It is important to mention here that the characteri-zation of this clustering is a problem of current interest. The physical validity of the application of thermody-namics in the clustering of galaxies and galaxy clusters has been discussed on the basis of N-body computer simulation results [5]. Equations of state for internal energy U and pressure P are of the form [6]:(3122NTU =-)b (1) (1NTP V=-)b (2) b defines the measuring correlation parameter and is dimensionless, given by [8]()202,23W nb Gm n T r K Tτξ∞=-=⎰,rdr (3)W is the potential energy and K the kinetic energy ofthe particles in a system. n N V = is the average num-ber density of the system of particles each of mass m, T is the temperature, V the volume, G is the universalgravitational constant. (),,n T r ξ is the two particle correlation function and r is the inter-particle distance. An overall study of (),n T r ξ has already been dis-cussed by [7]. For an ideal gas behaviour b = 0 and for non-ideal gas system b varies between 0 and 1. Previ-ously some workers [7,8] have derived b in the form of:331nT b nT ββ--=+ (4) Eq.4 indicates that b has a specific dependence on the combination 3nT -.3. ENTROPY CALCULATIONSThermodynamics and statistical mechanics have been found to be equal tools in describing entropy of a system. Thermodynamic entropy is a non-conserved state func-tion that is of great importance in science. Historically the concept of entropy evolved in order to explain why some processes are spontaneous and others are not; sys-tems tend to progress in the direction of increasing en-tropy [9]. Following statistical mechanics and the work carried out by [10], the grand canonical partition func-tion is given by()3213212,1!N N N N mkT Z T V V nT N πβ--⎛⎫⎡=+ ⎪⎣Λ⎝⎭⎤⎦(5)where N! is due to the distinguishability of particles. Λrepresents the volume of a phase space cell. N is the number of paricles (galaxies) with point mass approxi-mation. The Helmholtz free energy is given by:ln N A T Z =- (6)Thermodynamic description of entropy can be calcu-lated as:,N VA S T ∂⎛⎫=- ⎪∂⎝⎭ (7)The use of Eq.5 and Eq.6 in Eq.7 gives()3120ln ln 13S S n T b b -⎛⎫-=-- ⎪ ⎪⎝⎭- (8) where S 0 is an arbitary constant. From Eq.4 we write()31bn b T β-=- (9)Using Eq.9, Eq.8 becomes as3203ln S S b bT ⎡⎤-=-+⎢⎣⎦⎥ (10)Again from Eq.4All Rights Reserved.N. Iqbal et al. / Natural Science 3 (2011) 65-68 6767()13221n b T b β-⎡⎤=⎢⎣⎦⎥ (11)with the help of Eq.11, Eq.10 becomes as()011ln ln 1322S S n b b b ⎡-=-+-+⎡⎤⎣⎦⎢⎥⎣⎦⎤ (12) This is the expression for entropy of a system consist-ing of point mass particles, but actually galaxies have extended structures, therefore the point mass concept is only an approximation. For extended mass structures we make use of softening parameter ε whose value is taken between 0.01 and 0.05 (in the units of total radius). Following the same procedure, Eq.8 becomes as()320ln ln 13N S S N T N b Nb V εε⎡⎤-=---⎢⎥⎣⎦(13)For extended structures of galaxies, Eq.4 gets modi-fied to()()331nT R b nT R εβαεβαε--=+ (14)where α is a constant, R is the radius of a cell in a phase space in which number of particles (galaxies) is N and volume is V . The relation between b and b ε is given by: ()11b b b εαα=+- (15) b ε represents the correlation energy for extended mass particles clustering gravitationally in an expanding uni-verse. The above Eq.10 and Eq.12 take the form respec-tively as;()()3203ln 111bT b S S b b ααα⎡⎤⎢⎥-=-+⎢⎥+-+-⎢⎥⎣⎦1 (16) ()()()120113ln ln 2111b b b S S n b b ααα⎡⎤-⎡⎤⎢⎥⎣⎦-=-++⎢⎥+-+-⎢⎥⎣⎦1 (17)where2R R εεεα⎛⎫⎛⎫=⎪ ⎪⎝⎭⎝⎭(18)If ε = 0, α = 1 the entropy equations for extended mass galaxies are exactly same with that of a system of point mass galaxies approximation. Eq.10, Eq.12, Eq.16and Eq.17 are used here to study the entropy changes inthe cosmological many body problem. Various entropy change results S – S 0 for both the point mass approxima-tion and of extended mass approximation of particles (galaxies) are shown in (Figures 1and2). The resultshave been calculated analytically for different values ofFigure 1. (Color online) Comparison of isothermal entropy changes for non-point and point mass particles (galaxies) for an infinite gravitating system as a function of average relative temperature T and the parameter b . For non-point mass ε = 0.03 and R = 0.06 (left panel), ε = 0.04 and R = 0.04 (right panel).All Rights Reserved.N. Iqbal et al. / Natural Science 3 (2011) 65-68 68Figure 2. (Color online) Comparison of equi-density entropy changes for non-point and point mass particles (galaxies) for an infinite gravitating system as a function of average relative density n and the parameter b. For non-point mass ε= 0.03 and R = 0.04.R (cell size) corresponding to different values of soften-ing parameter ε. We study the variations of entropy changes S – S0with the changing parameter b for differ-ent values of n and T. Some graphical variations for S – S0with b for different values of n = 0, 1, 100 and aver-age temperature T = 1, 10 and 100 and by fixing value of cell size R = 0.04 and 0.06 are shown. The graphical analysis can be repeated for different values of R and by fixing values of εfor different sets like 0.04 and 0.05. From both the figures shown in 1 and 2, the dashed line represents variation for point mass particles and the solid line represents variation for extended (non-point mass) particles (galaxies) clustering together. It has been ob-served that the nature of the variation remains more or less same except with some minor difference.4. RESULTSThe formula for entropy calculated in this paper has provided a convenient way to study the entropy changes in gravitational galaxy clusters in an expanding universe. Gravity changes things that we have witnessed in this research. Clustering of galaxies in an expanding universe, which is like that of a self gravitating gas increases the gases volume which increases the entropy, but it also increases the potential energy and thus decreases the kinetic energy as particles must work against the attrac-tive gravitational field. So we expect expanding gases to cool down, and therefore there is a probability that the entropy has to decrease which gets confirmed from our theoretical calculations as shown in Figures 1 and 2. Entropy has remained an important contributor to our understanding in cosmology. Everything from gravita-tional clustering to supernova are contributors to entropy budget of the universe. A new calculation and study of entropy results given by Eqs.10, 12, 16 and 17 shows that the entropy of the universe decreases first with the clustering rate of the particles and then gradually in-creases as the system attains viral equilibrium. The gravitational entropy in this paper furthermore suggests that the universe is different than scientists had thought.5. ACKNOWLEDGEMENTSWe are thankful to Interuniversity centre for Astronomy and Astro-physics Pune India for providing a warm hospitality and facilities during the course of this work.REFERENCES[1]Voit, G.M. (2005) Tracing cosmic evolution with clus-ters of galaxies. Reviews of Modern Physics, 77, 207- 248.[2]Rief, F. (1965)Fundamentals of statistical and thermalphysics. McGraw-Hill, Tokyo.[3]Spitzer, L. and Saslaw, W.C. (1966) On the evolution ofgalactic nuclei. Astrophysical Journal, 143, 400-420.doi:10.1086/148523[4]Saslaw, W.C. and De Youngs, D.S. (1971) On the equi-partition in galactic nuclei and gravitating systems. As-trophysical Journal, 170, 423-429.doi:10.1086/151229[5]Itoh, M., Inagaki, S. and Saslaw, W.C. (1993) Gravita-tional clustering of galaxies. Astrophysical Journal, 403,476-496.doi:10.1086/172219[6]Hill, T.L. (1956) Statistical mechanics: Principles andstatistical applications. McGraw-Hill, New York.[7]Iqbal, N., Ahmad, F. and Khan, M.S. (2006) Gravita-tional clustering of galaxies in an expanding universe.Journal of Astronomy and Astrophysics, 27, 373-379.doi:10.1007/BF02709363[8]Saslaw, W.C. and Hamilton, A.J.S. (1984) Thermody-namics and galaxy clustering. Astrophysical Journal, 276, 13-25.doi:10.1086/161589[9]Mcquarrie, D.A. and Simon, J.D. (1997) Physical chem-istry: A molecular approach. University Science Books,Sausalito.[10]Ahmad, F, Saslaw, W.C. and Bhat, N.I. (2002) Statisticalmechanics of cosmological many body problem. Astro-physical Journal, 571, 576-584.doi:10.1086/340095[11]Freud, P.G. (1970) Physics: A Contemporary Perspective.Taylor and Francis Group.[12]Khinchin, A.I. (1949) Mathamatical Foundation of statis-tical mechanics. Dover Publications, New York.[13]Frampton, P., Stephen, D.H., Kephar, T.W. and Reeb, D.(2009) Classical Quantum Gravity. 26, 145005.doi:10.1088/0264-9381/26/14/145005All Rights Reserved.。

棉花AP2

棉花AP2

作物学报ACTA AGRONOMICA SINICA 2024, 50(1): 126 137 / ISSN 0496-3490; CN 11-1809/S; CODEN TSHPA9E-mail:***************DOI: 10.3724/SP.J.1006.2024.34045棉花AP2/ERF转录因子GhTINY2负调控植株抗盐性的功能分析肖胜华1,2,**,*陆妍1,**李安子1覃耀斌1廖铭静1闭兆福1卓柑锋1朱永红2朱龙付2,*1 广西大学农学院 / 亚热带农业生物资源保护与利用国家重点实验室, 广西南宁 530000; 2华中农业大学 / 作物遗传改良国家重点实验室, 湖北武汉 430000摘要: 棉花属于相对耐盐作物, 但高盐胁迫同样会造成棉花产量和纤维品质的大幅下降。

深入挖掘抗盐基因并解析棉花响应盐胁迫的分子机理, 对加快棉花抗盐遗传改良育种进程具有重要意义。

本研究从棉花响应盐胁迫的转录组数据中鉴定到一个受盐诱导极显著下调表达的AP2/ERF转录因子GhTINY2, 并分析了GhTINY2超表达拟南芥的抗盐表型和各生理指标。

结果显示, 在盐胁迫下, GhTINY2超表达植株的种子萌发率显著下降; 脯氨酸、可溶性糖、叶绿素含量等均显著减少; 多个盐胁迫响应基因显著下调表达; 因而表现出更为严重的叶片萎蔫枯黄表型。

通过分析GhTINY2超表达拟南芥中的RNA-seq数据, 发现差异表达基因(DEGs)富集到叶绿素代谢、刺激响应等生物过程中,且DEGs均呈下调表达趋势。

此外, 在棉花中通过病毒诱导的基因沉默(VIGS)试验沉默GhTINY2后, TRV:GhTINY2植株在盐胁迫下叶绿素和脯氨酸含量显著增加, 从而增强了棉花的抗盐性。

综上, GhTINY2是棉花中一个负调控盐胁迫抗性的重要基因, 未来将有望通过现代基因工程技术利用GhTINY2创制耐盐棉花材料。

关键词:棉花; GhTINY2; 盐胁迫; 转录因子; 转基因Function analysis of an AP2/ERF transcription factor GhTINY2 in cotton nega-tively regulating salt toleranceXIAO Sheng-Hua1,2,**,*, LU Yan1,**, LI An-Zi1, QIN Yao-Bin1, LIAO Ming-Jing1, BI Zhao-Fu1, ZHUOGan-Feng1, ZHU Yong-Hong2, and ZHU Long-Fu2,*1 State Key Laboratory of Conservation and Utilization of Agro-Biological Resources in Subtropical Region / College of Agriculture, Guangxi Uni-versity, Nanning 530000, Guangxi, China; 2 State Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430000,Hubei, ChinaAbstract: Cotton is a relatively salt-tolerant crop, but high salt stress leads to a significant decline in cotton yield and fiber quality.Mining the genes involved in salt-tolerance and illuminating the molecular mechanisms that underlie this resistance is of greatimportance in cotton breeding programs. Here, we identified an AP2/ERF transcription factor GhTINY2 in the transcriptome da-tabase from cotton treated with salt, and the relative expression level of GhTINY2 was reduced by salt. Subsequently, thesalt-resistant phenotype and physiological indicators of the GhTINY2-overexpression Arabidopsis were analyzed. The resultsrevealed that the GhTINY2-overexpression Arabidopsis had a significant decrease in seed germination rate, the content ofproline, soluble sugar, and chlorophyll under salt stress, leading to more severe leaf wilting compared with WT. RNA-seq datafrom GhTINY2-transgenic Arabidopsis revealed that differentially expressed genes (DEGs) were enriched in a series of bio-logical processes, including chlorophyll metabolism and response to stimulus, and the relative expression level of these DEGs本研究由广西大学高层次人才科研启动基金项目(A3310051044)和广西大学农学院科研发展金项目(EE101711)资助。

薄壳山核桃CiSPL2基因克隆、亚细胞定位及表达分析

薄壳山核桃CiSPL2基因克隆、亚细胞定位及表达分析

薄壳山核桃CiSPL2基因克隆、亚细胞定位及表达分析王敏;席东;莫正海;张仕杰;朱灿灿【期刊名称】《南方农业学报》【年(卷),期】2024(55)1【摘要】【目的】克隆薄壳山核桃SQUAMOSA启动子结合蛋白(SPL)转录因子基因CiSPL2,并对其进行亚细胞定位及表达分析,为探究该基因在薄壳山核桃雌花发育过程的分子机制提供理论依据。

【方法】根据薄壳山核桃基因组数据,采用RT-PCR方法克隆CiSPL2基因编码区(CDS)序列,利用生物信息学软件分析其序列特征和蛋白理化性质,构建pCAMBIA1300-CiSPL2-GFP融合表达载体,瞬时转化烟草后观察荧光信号确定亚细胞定位情况。

基于转录组数据和实时荧光定量PCR检测CiSPL2基因在不同组织及不同雌花芽分化时期的表达模式。

【结果】薄壳山核桃CiSPL2基因CDS长度为1398 bp,共编码465个氨基酸残基,相对分子量为51.78 kD,理论等电点(p I)为8.28,不稳定系数为49.89,脂肪酸氨基酸指数为64.62,平均亲水性平均值(GRAVY)为-0.617,为不稳定的亲水性蛋白,含有SBP结构域(位于第183~257位氨基酸),亚细胞定位于细胞核。

CiSPL2蛋白的二级结构主要由α-螺旋(17.42%)、延伸链(13.55%)、β-转角(1.72%)和无规则卷曲(67.31%)组成。

薄壳山核桃CiSPL2蛋白与核桃JrSPL2蛋白氨基酸序列的相似性最高,为95.05%,其次是光皮桦BlSPL2蛋白,相似性为70.83%。

CiSPL2蛋白与核桃JrSPL2蛋白亲缘关系最近。

CiSPL2基因启动子中含有植物激素响应、干旱胁迫响应和分生组织表达元件。

CiSPL2基因在雌花和果实中的表达水平显著高于其他组织(P<0.05),在雌花芽分化各阶段均有表达,在雌花序形成期表达水平最高。

【结论】CiSPL2基因含有SPL转录因子家族典型的SBP保守结构域,推测其参与调控薄壳山核桃雌花芽分化和发育过程。

中科院海洋所测出牡蛎基因全序列

中科院海洋所测出牡蛎基因全序列

中科院海洋所测出牡蛎基因全序列
佚名
【期刊名称】《水产科技情报》
【年(卷),期】2012(39)6
【摘要】由中国科学院海洋研究所联合华大基因、美国新泽西州立大学等多家单
位完成的牡蛎对潮问带逆境适应机制的研究成果9月20日被《自然》(Nature)杂志以长篇论文形式在线发表。

【总页数】1页(P324-324)
【关键词】中科院海洋所;基因;牡蛎;中国科学院海洋研究所;全序列;《自然》;研究
成果;适应机制
【正文语种】中文
【中图分类】S968.321
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2.中科院海洋所等在贝类基因组演化研究中取得进展 [J],
3.中科院海洋所获得国际首个对虾基因组参考图谱 [J],
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A new geochemical–sequence stratigraphic model for the Mahakam Delta and Makassar slope,Kalimantan, Indonesia:DiscussionTobias H.D.Payenberg*and Andrew D.Miall** I N T R O D U C T I O NThe article by Peters et al.(2000)presents a new geochemical–sequence stratigraphic model for the middle Miocene and younger rocks in the Kutei basin, east Kalimantan,Indonesia.Peters et al.’s(2000)re-interpretation of the lowstand systems tract containing transported terrigenous source rocks helps to explain the recent offshore discoveries in the Makassar Strait. The work is primarily based on the geochemicalfinger-prints of oil condensatesfitted into a sequence strati-graphic model modified from a previous version by Snedden et al.(1996).It is this sequence stratigraphic framework with which we take issue in this article.The model lacks supportive data,it uses an outdated global cycle chart,and it implies eustatic origin of the middle Miocene and younger sequences in the Kutei basin.We realize that the sequence stratigraphic model is not the primary objective of Peters et al.(2000);however,the stratigraphic framework is an integral part of the geo-chemical–sequence stratigraphic model,and the model can only be as good as its stratigraphic framework.Copyright᭧2001.The American Association of Petroleum Geologists.All rights reserved.*Department of Geology,University of Toronto,22Russell Street,Toronto,Ontario, M5S3B1Canada;tobi@geology.utoronto.ca.**Department of Geology,University of Toronto,22Russell Street,Toronto,Ontario, M5S3B1,Canada;miall@quartz.geology.utoronto.ca.This article benefited from suggestions by AAPG associate editor John Lorenz.We acknowledge ongoing research support through an NSERC grant to Andrew Miall and a University of Toronto Fellowship to Tobi Payenberg.Manuscript received February22,2000;revised manuscript received November27, 2000;final acceptance December19,2000.L A C K O F D A T ATo explain the origin of the sequence stratigraphicmodel,Peters et al.(2000)state:“Standard sequence stratigraphic analysis allowed identification of se-quence bounding unconformities and maximumflood-ing surfaces.Seismic facies within sequences were cal-ibrated with available well(lithology)control.The sequences were dated using paleontological picks inwell ties and compared to available coastal onlap charts(e.g.,Haq et al.,1987)”(Peters et al.,2000,p.18).Although seismic interpretation is now a standard toolin stratigraphic analysis,the assignment of absoluteages as described in this quotation is not,or should notbe.The reader is left in the dark regarding what kindof paleontological data was used in the study.Also,noindication is given of the precision of the paleontolog-ical data set.The common practice of employing the global cy-cle chart of Haq et al.(1987)as a template for cor-relation and as a means for the identification of eustatically controlled sequence boundaries has beencriticized elsewhere(Miall,1992,1994)on the basis(1)that the data justifying the construction of the origi-nal chart have never been published and its validity istherefore unproven,and(2)that the accuracy and pre-cision of chronostratigraphic techniques do not permitrigorous dating and global correlation of sequence boundaries(see also Ricken,1991;Johnson,1992). Therefore,the proposition of the eustatic dominanceof global stratigraphy remains unproven.In theirfigure2,Peters et al.(2000)highlight sev-eral sequence boundaries they claim to have identifiedin their data set(Figure1).None of these surfaces cor-respond to a biozone boundary or to a log top.UnlessMobil has refined the paleontological data set,and sub-divided each biozone,a correlation of the sequence boundaries with the global cycle chart is highly errorprone.For example,in the case of the N16biozone,which spans about2.5m.y.,this error could be up to2.5m.y.,depending on the position of the sequence boundary.W H I C H C Y C L E O N W H I C H C Y C L E C H A R T?To date sequence boundaries,Peters et al.(2000)com-pare their data to the global cycle chart of Haq et al. (1987).Snedden et al.(1996)previously noted,how-ever,that the9.0Ma sequence boundary“has no coun-terpart on the global chart,suggesting local variationsContentsSearch ResultsSearch Index1098AAPG Bulletin,v.85,no.6(June2001),pp.1098–1101Pay enberg andMiall1099Figure 1.Sequence boundaries and ages used by Peters et al.(2000)and original global cycle chart data from Haq et al.(1987)and Vanderberghe and Hardenbol (1998).Although Peters et al.(2000)claim to be using the Haq et al.(1987)chart,one sequence boundary was added (9.0Ma),one omitted (4.2Ma),and one age was changed from 2.9Ma to 3.0Ma.The latest global cycle chart from Vanderberghe and Hardenbol (1998)shows 16sequence boundaries that have mostly different ages compared to the 12from Haq et al.(1987).If Peters et al.(2000)used the newest cycle chart,their timing of the sequence boundaries would change substantially.in relative sea level may be responsible”(Snedden et al.,1996,p.285).Peters et al.(2000)do not mention that the 4.2Ma sequence boundary from Haq et al.(1987)is not present in their data set,and they also have changed Haq et al.’s.(1987)2.9Ma boundary to 3.0Ma (Figure 1).If eustasy were responsible for the formation of the sequences in the Kutei basin,then all observed sequence boundaries would be synchronous globally and present in the geological record.This is not the case presented by Peters et al.(2000).Also unclear is why,if Peters et al.(2000)attribute sequence boundaries to eustatic control,they used the Haq et al.(1987)global cycle chart instead of Van-derberghe and Hardenbol’s (1998)new cycle chart de-rived from several European basins (de Graciansky et al.,1998).Figure 1shows the new cycle chart along-side the Haq et al.(1987)and Peters et al.(2000)charts.In addition to more sequence boundaries on the new chart,most of the dates have also been changed (Figure 1).Because Vanderberghe and Hardenbol(1998)propose a glacio-eustatic mechanism for the Neogene sequences,the same number of sequence boundaries having exactly the same dates should show in the data presented by Peters et al.(2000).E U S T A T I C O R I G I NDating of sequence boundaries using the global cycle chart(Haq et al.,1987)necessarily implies a eustatic origin of the stratigraphic sequences.Despite their brief tectonic review,Peters et al.(2000)fail to rec-ognize the importance of tectonism in the Kutei basin. The Kutei basin is a tectonically very active basin that has extraordinary subsidence rates.It accumulated14 km of sediments in some parts of the basin during the Tertiary(Chambers and Daley,1995).Since the mid-dle Miocene alone,it has accumulated more than4km offluvial,deltaic,and shelf deposits in deltaic cycles (Magnier et al.,1975).Paleocurrent analysis of middle Miocene rocks in the Mutiara area by Payenberg (1998)showed distributary channels diverging around growing anticlinal structures.Stratigraphic thinning also occurred across these anticlines,indicating the im-portance of tectonism during this time of deposition (seefigure1in Peters et al.[2000]for location).Peters et al.(2000)state that the offshore Ma-hakam Delta comprises two different tectonic phases: middle Miocene rocks experienced compressional fold-ing and thrusting,whereas upper Miocene and Plio-cene rocks are deposited in an extensional regime(Al-len and Chambers,1998).The two distinct tectonic regimes are separated by a sequence boundary,which “is a significant angular unconformity”(Peters et al., 2000).Regional compressional stress during the mid-dle Miocene increased from west to east and inverted earlier extensional faults(Allen and Chambers,1998), leading to shallower,less tight anticlines and thrusted anticlines toward the east.During this compressional time,the Mahakam River was incising into the growing anticlinal structures belonging to the Samarinda anti-clinorium.As the compressional stress moved from west to east,the Mahakam River also locked into the anticlines progressively from the west to the east(Allen and Chambers,1998;Payenberg,1998).At the end of the middle Miocene,the sedimentation in the present-day onshore part of the Mahakam Delta ceased,ex-posing the rocks to the north and south of the Ma-hakam River(Payenberg,1998).Only to the east of the Sanga-Sanga anticline has sedimentation continued intermittently until today.The prominent angular un-conformity dated as the10.5Ma unconformity by Pe-ters et al.(2000)is thus tectonic in origin and cannot be associated with the eustatic sea level drop inferred from the cycle chart,let alone dated using it.C O N C L U S I O N SThe new geochemical–sequence stratigraphic model uses a precision of the stratigraphic model that is not supported by data and thus cannot be verified.The biozone boundaries and log tops presented do not correspond to any of the sequence boundaries,cast-ing doubt on the precision of those sequence bound-aries.An outdated cycle chart was used,and the new chart shows more sequence boundaries of different ages.Most important,active,syndepositional tecton-ism was downplayed in the stratigraphic framework and eustasy inferred for the origin of the stratigraphic sequences observed in the Neogene of the Kutei basin.The assignment of absolute ages to sequence boundaries without supportive data is a practice that should be abandoned.Extrapolation of a stratigraphic synthesis based on this approach to other areas of the basin that have more precise data can lead to mis-interpretations.Because the cycle chart implies solely eustatic origins for sequence boundaries,its use in tectonically active basins should be abandoned.In the Kutei basin,more evidence exists for a tectonic origin of the sequences than for a eustatic origin,despite the Neogene being a time of active glacio-eustasy.R E F E R E N C E S C I T E DAllen,G.P.,and J.L.C.Chambers,1998,Sedimentation in the modern and Miocene Mahakam Delta:Jakarta,Indonesian Pe-troleum Association,236p.Chambers,J.L.C.,and T.E.Daley,1995,A tectonic model for the onshore Kutei basin,east Kalimantan,based upon an in-tegrated geological and geophysical interpretation:Jakarta, Proceedings of the Indonesian Petroleum Association,v.24, p.115–130.de Graciansky,P.-C.,J.Hardenbol,T.Jacquin,and P.R.Vail,eds., 1998,Mesozoic and Cenozoic sequence stratigraphy of Euro-pean basins:SEPM Special Publication60,786p.Haq,B.U.,J.Hardenbol,and P.R.Vail,1987,Chronology offluc-tuating sea levels since the Triassic(250million years ago to present):Science,v.235,p.1156–1167.Johnson,J.G.,1992,Belief and reality in biostratigraphic zonation: Newsletters in Stratigraphy,v.26,p.41–48.Magnier,P.,T.Oki,and L.Kartaadiputra,1975,The Mahakam Delta,Kalimantan,Indonesia:Proceedings of the9th World Petroleum Congress,v.2,p.239–250.1100Discussions and RepliesMiall,A.D.,1992,The Exxon global cycle chart:an event for every occasion?:Geology,v.20,p.787–790.Miall,A.D.,1994,Sequence stratigraphy and chronostratigraphy: problems of definition and precision in correlation,and their implications for global eustasy:Geoscience Canada,v.21, p.1–26.Payenberg,T.H.D.,1998,Paleocurrents and reservoir architecture of the middle Miocene channel deposits in Mutiarafield,Kutei basin,east Kalimantan,Indonesia:M.Sc.thesis,Queensland University of Technology,Brisbane,Australia,235p. Peters,K.E.,J.W.Snedden,A.Sulaeman,J.F.Sarg,and R.J.Enrico,2000,A new geochemical–sequence stratigraphic model for the Mahakam Delta and Makassar slope,Kaliman-tan,Indonesia:AAPG Bulletin v.84,p.12–44.Ricken,W.,1991,Time span assessment—an overview,in G.Ein-sele,W.Ricken,and A.Seilacher,eds.,Cycles and events in stratigraphy:Berlin,Springer-Verlag,p.773–794. Snedden,J.W.,J.F.Sarg,M.J.Clutson,M.Maas,T.E.Okon, M.H.Carter,B.S.Smith,T.H.Kolich,and M.Y.Mansor, 1996,Using sequence stratigraphic methods in high-sediment supply deltas:examples from the ancient Mahakam and Ra-jang-Lupar deltas:Jakarta,Proceedings of the Indonesian Pe-troleum Association,v.25,no.1,p.281–296. Vanderberghe,N..and J.Hardenbol,1998,Introduction to the Neogene,in P.-C.de Graciansky,J.Hardenbol,T.Jacquin, and P.R.Vail,eds.,Mesozoic and Cenozoic sequence stratig-raphy of European basins:SEPM Special Publication60, p.83–85.Pay enberg andMiall1101A new geochemical–sequence stratigraphic model for the Mahakam Delta and Makassar slope,Kalimantan, Indonesia:ReplyJohn W.Snedden*,J.F.(Rick)Sarg**,and Kenneth E.Peters**I N T R O D U C T I O NWe appreciate the opportunity to address the sequence stratigraphic issues raised in the discussion by Payen-berg and Miall.Although stratigraphy was a small part of the new geochemical model presented in Peters et al.(2000),we agree that it is an important framework element in study of source rocks,hydrocarbon kitch-ens,and basin history of the Kutei petroleum province.Payenberg and Miall identify three points of dis-agreement:supportive data,outdated global cycle chart,and eustatic origin of middle Miocene and younger sequences.In fact,their argument concerning supportive data pertains to the precision of biostrati-graphic age assignments and global cycle assignments discussed in the second point.For consistency,how-ever,we will address their concerns in the same order.S U P P O R T I V E D A T APayenberg and Miall argue that Peters et al.(2000) lacks supportive data for the stratigraphic framework of the Mahakam Delta.The concerns extend to a pre-vious article by Snedden et al.(1996).Copyright᭧2001.The American Association of Petroleum Geologists.All rights reserved.*ExxonMobil Exploration Company,P.O.Box4778,Houston,Texas,77210; john.w.snedden@.**ExxonMobil Exploration Company,P.O.Box4778,Houston,Texas,77210; sarg@;ken_peters@.Manuscript received December19,2000;final acceptance January11,2001.The focus of Peters et al.(2000)is organic geo-chemistry and the new model that reasonably explains a series of recent discoveries on the outer Mahakam shelf and slope.For editorial reasons,only limited space was available to discuss the stratigraphic meth-odology and background on the stratigraphic analysis that itself spanned two years of investigation,involving 500ft of core,4000km of two-dimensional(2-D)seis-mic data,logs and cuttings from40-plus wells,and pa-leontological and isotopic dating.Proprietary data in a two-volume company report could not be entirely shared with the readers of the Peters et al.(2000)or Snedden et al.(1996)articles because of continuing exploration efforts by Mobil and now ExxonMobil and its partners in the Mahakam slope and adjacent Ma-kassar Straits.More information has been approved for release,however,beginning with recent AAPG presen-tations and expanded abstracts(Snedden and Sarg, 1998a,b;2000).We intend to publish further docu-mentation of our framework as proprietary controls are relaxed.Our framework is a clear improvement on previ-ous publications that stressed lithostratigraphic picks (e.g.,Sujatmiko and Irawan,1984)or had limited ties to the Mahakam Delta slope/Makassar Straits region (e.g.,Duval et al.,1992).Miscorrelation of the pre–middle Miocene section as shown in Burrus et al. (1992)had significant impact on the calculated source maturation trends.Our stratigraphic correlations,il-lustrated by a seismic section in Peters et al.(2000), continue to be confirmed by new drilling.Payenberg and Miall point out that many of the sequence boundaries do not coincide with biozone boundaries and thus have uncertain ages.The biozones shown for reference are planktonic foram biochrono-zones;however,a more refined subdivision was made using a combination of nannofossils,dinoflagellates, and strontium-isotopic dating.Thus,a stratigraphic as-signment of a particular sequence boundary can be made with precision within the biozones shown for reference.The argument made by Payenberg and Miall con-cerning imprecision of biostratigraphic methods and the uncertainty of global synchronicity dates back to Miall(1991).Much progress has been made in the last ten years toward improving age resolution using a va-riety of biostratigraphic and isotopic dating techniques, especially in the Neogene(de Graciansky et al.,1998). In the Gulf of Mexico Basin,for example,age resolu-tion approaching100–200k.y.has been achieved (Wornardt et al.,1998).1102AAPG Bulletin,v.85,no.6(June2001),pp.1102–1105Snedden et al.1103W H I C H C Y C L E O N W H I C H C H A R TPayenberg and Miall point out the inconsistency be-tween the Mahakam stratigraphy of Peters et al.(2000)and the Haq et al.(1987)chart.Specifically,they list recognition of a possible new sequence boundary at 9.0Ma and lack of a sequence boundary at 4.2Ma in Sned-den et al.(1996).Most practitioners of sequence strati-graphic techniques understand that the Haq et al.(1987)chart was a composite of many different basins and that any single basin will not display all 119se-quence boundaries (213in the Triassic to Holocene chronostratigraphy of de Graciansky et al.[1998]).Experienced seismic interpreters also know that conventional 2-D seismic data has resolution limits.Where a sequence falls below one-quarter to one-half wavelength of the dominant seismic frequency,it is generally not observed (Sheriff,1977).This may be one explanation for the “missing”4.2Ma sequence in the Mahakam stratigraphy.The new 9.0sequence may actually be a fourth-order or high-frequency sequence,following the definition of Mitchum and Van Wagoner (1991).The fact that our framework differs somewhat from Haq et al.(1987)indicates that we are not force-fitting the Mahakam stratigraphy to the Haq et al.(1987)coastal onlap chart.Payenberg and Miall are unclear as to why we did not use the new European basins chronostratigraphy (de Graciansky et al.,1998).Unfortunately,this new set of chronostratigraphic charts was not available to us at the time of the submission of our article in 1998.Although the publication date of the European basins system is 1998,the actual SEPM volume was not in wide release until 1999,following submission of our manuscript.As Payenberg and Miall illustrate in their figure 1,use of the new chronostratigraphic charts changes tim-ing of the sequence boundaries emphasized in the stratigraphic framework of Peters et al.(2000).This is due,however,to a recalibration of all biostratigraphic,isotope stratigraphic,and sequence stratigraphic en-tries to the new Gradstein et al.(1995)and Berggren et al.(1995)time scales in the European basins chrono-stratigraphy (de Graciansky et al.,1998).The sequence boundaries of Peters et al.(2000)have not changed their biozone position.Thus,relative timing and strati-graphic position of the sequences and sequence bound-aries remain unchanged.In fact,one of the key products from the new European basins chronostratigraphy is the elimination of absolute age dates for sequences and sequence-boundary names.For example,instead of the 10.5Ma sequence boundary,one should refer to the Ser-ravallian 4(Ser 4)sequence boundary (Figure 1).Payenberg and Miall unduly focus on absolute ages for sequence boundaries in their figure 1,which are prone to change with evolution of the Mesozoic and Cenozoic time e of the newly calibrated European basins chronostratigraphy should eliminate this problem.The sequence boundaries are keyed to biozone position and thus to the chronologic scale,not directly to the time scale as implied byPayenbergFigure 1.Translation chart for sequences and sequenceboundaries used in Peters et al.(2000)and new European ba-sins chronostratigraphy of de Graciansky et al.(1998).Changes in the ages of the se-quence boundaries are primar-ily due to a change in the geo-chronologic scale,not biozone e of European ba-sins nomenclature (e.g.,Ser 1)for sequences and sequence boundary names is an obvious improvement over absolute ages,which are prone to change with evolution of the time scales.and Miall.Figure1shows our translation of the ear-lier published Mahakam stratigraphic cycles to this new chronostratigraphy.E U S T A T I C O R I G I N OF S T R A T IG R A PHI CS E Q U E N C E SPayenberg and Miall point out that Peters et al. (2000)ignore tectonics as a factor in formation of the stratigraphic sequences in the Kutei basin.Ap-parently Payenberg and Miall did fully appreciate our explanation of how tectonic processes forced deltaic depocenters and shelf margin positions to shift within and between sequences.Our interpretation of the tectonic styles in our study area is based on a large set of regional seismic lines and extends much farther seaward of the area discussed by Chambers and Daley(1995).Furthermore,we have presented arti-cles elsewhere suggesting that the Mahakam strati-graphic sequences are strongly influenced by synse-dimentary tectonics(Snedden and Sarg,1998a, 2000).Payenberg and Miall also suggest that the Mio-cene and younger stratigraphy cannot be related to or is unlikely to be influenced by eustatic processes. This opinion ignores the well-established fact that the Miocene and younger time frame is clearly an ice-house period in earth rge ice sheets existed in Antarctica as far back as the Oligocene(Abreu and Anderson,1998).L arge,globally correlative glacio-eustaticfluctuations clearly occurred during the Mio-cene period in discussion(e.g.,Miller et al.,1996). These relatively high-frequency eustatic changes worked in combination with tectonic forces related to plate collision and formation of the central Borneo Highlands(early Miocene)and later uplift of the Meratus Mountains closer to the Kutei basin(middle Miocene).The observed results are pronounced an-gular truncations seen in Sarawak(Langhian2[Lan 2]sequence boundary)and offshore Mahakam(Ser 4sequence boundary).Our comparison of the two basins(discussed in Snedden and Sarg[1998a, 2000])demonstrates the utility of sequence strati-graphic techniques in high sedimentation,tectoni-cally active basins such as Sarawak and Kutei.Finally,we should point out that rigorous anal-ysis,drilling,and sequence dating continues to con-firm the causal link between coastal plain and slope sequence boundaries formed during global sea level falls(e.g.,Miller et al.,1996;Eberli,2000).C O N C L U S I O N SThe inquiry by Payenberg and Miall has allowed us to further elaborate the stratigraphy of the offshore Kutei basin,beyond what was discussed in the geochemically focused article by Peters et al.(2000).Updating the previous stratigraphy using the new global chronostra-tigraphy is also another benefit of this discussion and reply(Figure1).Of course,we do not agree with their rather bleak assessment of the supportive data,cycle chart issues,and implied eustatic origin of the strati-graphic sequences in our study area.Many improve-ments in stratigraphic resolution,global chronostratig-raphy,and the sequence stratigraphic approach itself have been made since concerns were raised by Miall (1991).The validity of the global stratigraphic frame-work continues to be supported by rigorous analysis of outcrops and deep-sea cores(Miller et al.,1996; Thiede and Myhre,1996;Franseen et al.,1998;Eberli, 2000).We expect,and look forward to,further discus-sions of global chronostratigraphy and sequence stra-tigraphy with these authors in the future.R E F E R E N C E S C I T E DAbreu,V.S.,and J.B.Anderson,1998,Glacial eustasy during the Cenozoic:sequence stratigraphic implications:AAPG,v.82, p.1385–1400.Berggren,W.A.,D.V.Kent,C.C.Swisher,and M.-P.Aubry,1995,A revised Cenozoic geochronology and chronostratigraphy,inW.A.Berggren,D.V.Kent,and J.Hardenbol,eds.,Geochro-nology,time scales,and global stratigraphic correlations:SEPM Special Publication54,p.129–212.Burrus,J.E.,E.Brosse,G.C.de Janvry,Y.Grosjean,and J.L.Oudin, 1992,Basin modeling in the Mahakam Delta based upon the integrated2D Temispack:Proceedings of the Indonesian Petro-leum Association Twenty-First Annual Convention,v.1,p.23–43.Chambers,J.L.C.,and T.E.Daley,1995,A tectonic model for the onshore Kutei basin,east Kalimantan,based upon an integrated geological and geophysical interpretation:Proceedings of the Indonesian Petroleum Association Twenty-Fourth Annual Con-vention,v.1,p.111–117.de Graciansky,P.-C.,J.Hardenbol,T.Jacquin,and P.R.Vail,1998, Mesozoic and Cenozoic sequence stratigraphy of European ba-sins:SEPM Special Publication60,786p.Duval,B.C.,C.de Janvry,and B.L oiret,1992,Detailed geoscience reinterpretation of Indonesia’s Mahakam Delta scores:Oil& Gas Journal,v.90,no.22,p.67–72.Eberli,G.P.,2000,The record of Neogene sea-level changes in the prograding carbonates along the Bahamas Transect—Leg166 synthesis,in P.K.Swart,G.P.Eberli,M J.Malone,and J.F.Sarg,eds.,Proceedings of the Ocean Drilling Program,scientific results:v.166,p.167–176.Franseen,E.K.,R.H.Goldstein,and M.R.Farr,1998,Quantitative controls on location and architecture of carbonate depositional1104Discussions and Repliessequences:upper Miocene,Cabo De Gata region,SE Spain: Journal of Sedimentary Research,v.68,p.283–298. Gradstein,F.M.,F.P.Agterberg,J.G.Ogg,J.Hardenbol,P.van Veen,J.Thierry,and Z.Huang,1995,A Triassic,Jurassic,and Cretaceous time scale,in W.A.Berggren,D.V.Kent,and J.Hardenbol,eds.,Geochronology,time scales,and global strati-graphic correlations:SEPM Special Publication54,p.129–212. Haq,B.U.,J.Hardenbol,and P.R.Vail,1987,The chronology of fluctuating sea level since the Triassic:Science,v.235,p.1156–1167.Miall,A.D.,1991,Stratigraphic sequences and their chronostrati-graphic correlation:Journal of Sedimentary Petrology,v.61, p.497–505.Miller,K.G.,G.S.Mountain,L eg150Shipboard Party,and Mem-bers of the New Jersey Coastal Plain Drilling Project,1996, Drilling and dating New Jersey Oligocene–Miocene sequences: ice volume,global sea level,and Exxon records:Science,v.271, p.1092–1095.Mitchum,R.M.,and J.C.Van Wagoner,1991,High-frequency sequences and their stacking patterns:sequence-stratigraphic evidence of high-frequency eustatic cycles:Sedimentary Geol-ogy,v.70,p.131–160.Peters,K.E.,J.W.Snedden,A.Sulaeman,J.F.Sarg,and R.J.Enrico, 2000,A new geochemical–sequence stratigraphic model for the Mahakam Delta and Makassar slope,Kalimantan,Indonesia: AAPG Bulletin,v.84,p.12–44.Sheriff,R.E.,1977,L imitations on resolution of seismic reflections and geologic detail derivable from them,in C.E.Payton,ed., Seismic stratigraphy—applications to hydrocarbon exploration: AAPG Memoir26,p.3–14.Snedden,J.W.,and J.F.Sarg,1998a,L arge scale synsedimentary tectonic control upon stratigraphic sequences in two petroleum provinces of Borneo(abs.):AAPG Annual Convention Ex-tended Abstracts,v.2,p.A615.Snedden,J.W.,and J.F.Sarg,1998b,Reducing reservoir and source rock risk in deepwater plays:examples from Southeast Asia (abs.):AAPG Bulletin,v.82,no.10,p.1968.Snedden,J.W.,and J.F.Sarg,2000,Synsedimentary tectonic control upon Miocene stratigraphic sequences in two petroleum prov-inces of Borneo(abs.):AAPG Bulletin,v.84,p.1494. Snedden,J.W.,J.F.Sarg,M.J.Clutson,M.Maas,T.E.Okon,M.H.Carter,B.S.,Smith,T.H.Kolich,and M.Y.Mansor,1996, Using sequence stratigraphic methods in high-sediment supply deltas:examples from the ancient Mahakam and Rajang-Lupar deltas:Proceedings of the Indonesian Petroleum Association Twenty-Fifth Silver Anniversary Convention,v.1,p.281–295. Sujatmiko,S.A.,A.Salim,and B.S.Irawan,1984,Geology of the Tunu gasfield:Jakarta,Proceedings of the Indonesia Petroleum Association Thirteenth Annual Convention,v.1,p.341–363. Thiede,J.,and A.M.Myhre,1996,The paleogeographic history of the North Atlantic–Arctic gateways:synthesis of the Leg151 drilling results,in J.Thiede,A.M.Myhre,J.V.Firth,G.L.Johnson,and W.F.Ruddiman,eds.,Proceedings of the Ocean Drilling Program,scientific results,North Atlantic–Arctic gate-ways:v.151B,p.645–658.Wornardt Jr.,W.W.,B.Shaffer,and P.R.Vail,1998,Revision of the late Miocene,Pliocene,and Pleistocene sequence cycles: Houston Geological Society Bulletin,v.41,p.30–31.Snedden et al.1105。

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