Conductance fluctuations in graphene systems: The relevance of classical dynamics Phys. Rev. B 85 , 245448 附带code 摘要： Conductance fluctuations associated with transport through quantum-dot systems are currently understood to depend on the nature of the corresponding classical dynamics, i.e., integrable or chaotic. However, we find that in graphene quantum-dot systems, when a magnetic field is present, signatures of classical dynamics can disappear and universal scaling behaviors emerge. In particular, as the Fermi energy or the magnetic flux is varied, both regular oscillations and random fluctuations in the conductance can occur, with alternating transitions between the two. By carrying out a detailed analysis of two types of integrable (hexagonal and square) and one type of chaotic (stadium) graphene dot system, we uncover a universal scaling law among the critical Fermi energy, the critical magnetic flux, and the dot size. We develop a physical theory based on the emergence of edge states and the evolution of Landau levels (as in quantum Hall effect) to understand these experimentally testable behaviors. 更多内容参见 google citation or reasearch gate: https://scholar.google.com/citations?user=yC38Vb4AAAAJhl=en https://www.researchgate.net/profile/Lei_Ying4
全球临床试验研究项目计量分析报告 ClinicalTrials.gov is a registry and results database of publicly and privately supported clinical studies of human participants conducted around the world. ClinicalTrials.gov临床试验网 currently lists 137,539 studies 项目数量with locations in all 50 states and in 182 countries国家数量. ClinicalTrials.gov receives more than 95 million page views per month and 60,000 unique visitors daily 日访问量(as of February 2012). Contents Locations of Registered Studies Locations of Recruiting Studies Map of Studies Registered on ClinicalTrials.gov Types of Registered Studies Number of Registered Studies over Time Number of Registered Studies with Posted Results over Time Locations of Registered Studies This chart shows the distribution of locations for all studies registered on ClinicalTrials.gov. Total N = 137,539 studies (Data as of December 18, 2012) Legend for Recruiting Study location pie chart image Pie Color Location Non-U.S. Only (43%) U.S. Only (41%) Not Specified (9%) Both U.S. Non-U.S. (6%) Distribution of locations for all studies registered on ClinicalTrials.gov" Location Number of Registered Studies and Percentage of Total Non-U.S. Only 58,698 (43%) U.S. Only 56,983 (41%) Not Specified* 12,983 (9%) Both U.S. Non-U.S. 8,875 (6%) Total 137,539 * Not Specified: The location of the study was not provided by the Sponsor. (Data as of December 18, 2012) Locations of Recruiting Studies This chart shows the distribution of locations for recruiting studies registered on ClinicalTrials.gov. Total N = 29,400 studies (Data as of December 18, 2012) Legend for Recruiting Study location pie chart image Pie Color Location Non-U.S. Only (49%) U.S. Only (45%) Both U.S. Non-U.S. (6%) Distribution of locations for recruiting studies registered on ClinicalTrials.gov Location Number of Recruiting Studies and Percentage of Total Non-U.S. Only 14,378 (49%) U.S. Only 13,118 (45%) Both U.S. Non-U.S. 1,904 (6%) Total 29,400 (Data as of December 18, 2012) To Top Map of Studies Registered on ClinicalTrials.gov See Studies on a Map for an interactive map of studies that are registered on ClinicalTrials.gov. To Top Types of Registered Studies This table shows the number and types of studies that are registered and for which results are available on ClinicalTrials.gov. (Data as of December 18, 2012) Study and intervention types with number of registered studies and with number of studies having posted results Study and Intervention Type Number of Registered Studies and Percentage of Total Number of Studies With Posted Results and Percentage of Total*** Total 137,539 7,679 Interventional 111,598 (81%) 7,159 (93%) Type of Intervention* Drug or biologic 75,606 6,133 Behavioral, other 26,611 750 Surgical procedure 12,574 293 Device** 9,490 639 Observational 25,315 (18%) 520 (6%) Expanded Access 186 N/A * A study may include more than one type of intervention (that is, a single study may be counted more than once). Because of this, the sum of counts by type of intervention do not equal the total number of interventional studies. ** A total of 440 applicable device clinical trials were submitted as "delayed posting" under the Food and Drug Administration Amendments Act of 2007 (FDAAA). That is, the Responsible Party has indicated that such a trial includes a device not previously approved or cleared by the U.S. FDA for any use. These are not included in the count of trials with at least one device. *** Results are only required to be submitted for certain trials. For example, results submission is generally not required for trials of drugs, biologics, or devices that have not been approved by FDA; observational studies; and trials completed before 2008. N/A = not applicable To Top Number of Registered Studies Over Time The graph below shows the total number of studies registered on ClinicalTrials.gov since 2000, based on the First Received Date. The first version of ClinicalTrials.gov was made available to the public on February 29, 2000. (Data as of December 18, 2012) Key ICMJE: Indicates when the International Committee of Medical Journal Editors began requiring trial registration as a condition of publication under the Uniform Requirements for Manuscripts Submitted to Biomedical Journals (URM) (September 2005). FDAAA: Indicates when the expanded registration requirements of FDAAA began and were implemented on ClinicalTrials.gov (December 2007). Years with numbers of registered studies Year Total Number of Registered Studies 2000 5,647 2001 7,000 2002 8,598 2003 10,279 2004 12,081 2005 25,041 2006 35,984 2007 49,399 2008 66,448 2009 83,636 2010 101,356 2011 119,482 (Data as of December 18, 2012) To Top Number of Registered Studies With Posted Results Over Time The graph below shows the number of registered studies with results posted on ClinicalTrials.gov, based on the Results First Received Date. ClinicalTrials.gov launched its results database in September 2008, at which time sponsors or investigators were allowed to begin submitting results for their registered studies. The results database was developed to accommodate results submission requirements outlined in FDAAA. (Data as of December 18, 2012) Years with numbers of registered studies having posted results Year Total Number of Registered Studies With Posted Results 2009 1,795 2010 3,459 2011 5,742 (Data as of December 18, 2012) This page last reviewed in August 2012 To Top 数据来源： http://clinicaltrials.gov/ct2/resources/trends See Studies on Map Map of All Studies in ClinicalTrials.gov Click on the map below to show a more detailed map (when available) or search for studies (when map not available). Region Name Number of Studies World 137539 Africa 3094 Central America 1827 East Asia 11668 Japan 2555 Europe 36903 Middle East 5582 North America 72205 Canada 10420 Mexico 1772 United States 65858 North Asia 2530 Pacifica 3834 South America 4650 South Asia 2472 Southeast Asia 2763 Hints: Click on a link to show a map of that region Click on a link to search for studies in that region. Use the back button to return to this list and try another region. Studies with no locations are not included in the counts or on the map. Studies with multiple locations are included in each region containing locations. See Studies on Map Map of All Studies in ClinicalTrials.gov Click on the map below to show a more detailed map (when available) or search for studies (when map not available). Region Name Number of Studies World 137539 East Asia 11668 China 3434 Hong Kong 771 Korea, Republic of 3902 Mongolia 6 Taiwan 2836 Hints: Click on a link to show a map of that region Click on a link to search for studies in that region. Use the back button to return to this list and try another region. Studies with no locations are not included in the counts or on the map. Studies with multiple locations are included in each region containing locations. 数据来源： http://clinicaltrials.gov/ct2/search/map?map=ES
欢迎大家访问OpenPR主页： http://www.openpr.org.cn ， 并提出意见和建议！同时，OpenPR也期待您分享您的代码！ OpenPR, stands for Open Pattern Recognition project and is intended to be an open source platform for sharing algorithms of image processing, computer vision, natural language processing, pattern recognition, machine learning and the related fields. Code released by OpenPR is under BSD license, and can be freely used for education and academic research. OpenPR is currently supported by the National Laboratory of Pattern Recognition, Institution of Automation, Chinese Academy of Sciences. Thresholding program This is demo program on global thresholding for image of bright small objects, such as aircrafts in airports. the program include four method, otsu,2D-Tsallis,PSSIM, Smoothnees Method. Authorschen xueyun E-mail xueyun.chen@nlpr.ia.ac.cn Principal Component Analysis Based on Nonparametric Max... In this paper, we propose an improved principal component analysis based on maximum entropy (MaxEnt) preservation, called MaxEnt-PCA, which is derived from a Parzen window estimation of Renyi’s quadratic entropy. Instead of minimizing the reconstruction ... AuthorsRan He E-mail rhe@nlpr.ia.ac.cn Metropolis–Hastings algorithm Metropolis-Hastings alogrithm is a Markov chain Monte Carlo method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. Thi sequence can be used to approximate the distribution. AuthorsGong Xing E-mail xgong@nlpr.ia.ac.cn Tagssampling, distribution Maximum Correntropy Criterion for Robust Face Recogniti... This code is developed based on Uriel Roque's active set algorithm for the linear least squares problem with nonnegative variables in: Portugal, L.; Judice, J.; and Vicente, L. 1994. A comparison of block pivoting and interior-point algorithms for linear ... AuthorsRan HE E-mail rhe@nlpr.ia.ac.cn Tagspattern recognition Naive Bayes EM Algorithm OpenPR-NBEM is an C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. Op ... AuthorsRui XIA E-mail rxia@nlpr.ia.ac.cn Tagspattern recognition, natural language processing, text classification Local Binary Pattern This is a class to calculate histogram of LBP (local binary patterns) from an input image, histograms of LBP-TOP (local binary patterns on three orthogonal planes) from an image sequence, histogram of the rotation invariant VLBP (volume local binary patte ... AuthorsJia WU E-mail jwu@nlpr.ia.ac.cn Tagscomputer vision, image processing, pattern recognition Two-stage Sparse Representation This program implements a novel robust sparse representation method, called the two-stage sparse representation (TSR), for robust recognition on a large-scale database. Based on the divide and conquer strategy, TSR divides the procedure of robust recogni ... AuthorsRan HE E-mail rhe@dlut.edu.cn Tagspattern recognition CMatrix Class It's a C++ program for symmetric matrix diagonalization, inversion and principal component anlaysis(PCA). The matrix diagonalization function can also be applied to the computation of singular value decomposition (SVD), Fisher linear discriminant analysis ... AuthorsChenglin LIU E-mail liucl@nlpr.ia.ac.cn Tagspattern recognition P3P(Perspective 3-Points) Solver This is a implementation of the classic P3P(Perspective 3-Points) algorithm problem solution in the Ransac paper "M. A. Fischler, R. C. Bolles. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartogr ... AuthorsZhaopeng GU E-mail zpgu@nlpr.ia.ac.cn TagsComputer Vision, PNP, Extrinsic Calibration Linear Discriminant Function Classifier This program is a C++ implementation of Linear Discriminant Function Classifier. Discriminant functions such as perceptron criterion, cross entropy (CE) criterion, and least mean square (LMS) criterion (all for multi-class classification problems) are sup ... AuthorsRui Xia E-mail rxia@nlpr.ia.ac.cn Tagslinear classifier, discriminant function Naive Bayes Classifier This program is a C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. Th ... AuthorsRui Xia E-mail rxia@nlpr.ia.ac.cn Tagspattern recognition, natural language processing, text classification OpenCV Based Extended Kalman Filter Frame A simple and clear OpenCV based extended Kalman filter(EKF) abstract class implementation,absolutely following standard EKF equations. Special thanks to the open source project of KFilter1.3. It is easy to inherit it to implement a variable state and me ... AuthorsZhaopeng GU E-mail zpgu@nlpr.ia.ac.cn TagsComputer Vision, EKF, INS Supervised Latent Semantic Indexing Supervised Latent Semantic Indexing(SLSI) is an supervised feature transformation method. The algorithms in this package are based on the iterative algorithm of Latent Semantic Indexing. AuthorsMingbo Wang E-mail mb.wang@nlpr.ia.ac.cn SIFT Extractor This program is used to extract SIFT points from an image. AuthorsZhenhui Xu E-mail zhxu@nlpr.ia.ac.cn Tagscomputer vision OpenPR-0.0.2 Scilab Pattern Recognition Toolbox is a toolbox developed for Scilab software, and is used in pattern recognition, machine learning and the related field. It is developed for the purpose of education and research. AuthorsJia Wu E-mail jiawu83@gmail.com Tagspattern recognition Layer-Based Dependency Parser LDPar is an efficient data-driven dependency parser. You can train your own parsing model on treebank data and parse new data using the induced model. AuthorsPing Jian E-mail pjian@nlpr.ia.ac.cn Tagsnatural language processing Probabilistic Latent Semantic Indexing AuthorsMingbo Wang E-mail mbwang@nlpr.ia.ac.cn Calculate Normalized Information Measures The toolbox is to calculate normalized information measures from a given m by (m+1) confusion matrix for objective evaluations of an abstaining classifier. It includes total 24 normalized information measures based on three groups of definitions, that is, ... AuthorsBaogang Hu E-mail hubg@nlpr.ia.ac.cn Quasi-Dense Matching This program is used to find point matches between two images. The procedure can be divided into two parts: 1) use SIFT matching algorithm to find sparse point matches between two images. 2) use "quasi-dense propagation" algorithm to get "quasi-dense" p ... AuthorsZhenhui Xu E-mail zhxu@nlpr.ia.ac.cn Agglomerative Mean-Shift Clustering Mean-Shift (MS) is a powerful non-parametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets. For the purpose of algorithm speedup, an agglomerative MS clustering metho ... AuthorsXiao-Tong Yuan E-mail xtyuan@nlpr.ia.ac.cn Histograms of Oriented Gradients (HOG) Feature Extracti... This program is used to extract HOG(histograms of oriented gradients) features from images. The integral histogram is used for fast histogram extraction. Both APIs and binary utility are provided. AuthorsLiang-Liang He E-mail llhe@nlpr.ia.ac.cn 相关PPT下载详见 “视觉计算研究论坛”「SIGVC BBS」： http://www.sigvc.org/bbs/thread-272-1-1.html
不是给我的，但似乎是我正在经历的，潜伏在我脑子的一个东西，不小心被别人说出来了。 Dear Suleika: Noone can move into the space you currently occupy, although many of us have walked a similar path. Cancer is such a personal disease, but public too, among the treatment team we "go steady" with during all facets - from diagnosis to remission, and ultimately - survivorship. I have survived two separate cancers and value everything beyond words every day. Good, bad, or indifferent, everything in my life is precious and special. I have a deeper, stronger me that I take to the world, and I am gentler too, with myself and others. You are in my prayers and in my thoughts. Do not lose sight of your future, even on the most miserable day. Believe in miracles, and know you are loved by all of us. Big hugs. And keep believing and journaling - you lift us up. 没有人可以达到你目前所能达到的境界，虽然大多数人都会走一条相似的路。癌症是个体疾病，但也是公众的，在我们的治疗团队当中，我们和方方面面确立关系：从诊断到缓和，最终生存下来。 我得过两个癌后，生存下来，珍惜我生活中那些不能用言辞表达的一切，好的，坏的，无关的，我生活中的每一件事情都是特别的，珍贵的，我给世界一个深刻的，坚强的自己，而我也变得温和，不管是对自己还是对别人。 你是我为之祈祷的人。即使是在最悲惨的日子，都不要对你的未来失去信心。相信奇迹，要知道我们都爱着你。 抱抱，让我们紧紧地拥抱，请坚定信仰，并不断续写你的日志，你鼓舞我们奋发图强 哈，有点像诗，是博客后面的一段留言，别人信笔写来，我也信笔翻译了一下，不妥当之处，请大家指正。 原文来自： http://well.blogs.nytimes.com/2012/04/05/life-interrupted-countdown-to-day-zero/
Prenatal exposure to currently approved pesticides in greenhouse workers appears to be linked to earlier breast development in their daughters as measured 10 years later, according to a new report by Christine Wohlfahrt-Veje, MD, from the University Department of Growth and Reproduction, Rigshospitalet, Copenhagen, Denmark, and colleagues. The authors report their findings in an article March 9 in the . 根据哥本哈一所综合大学生长与发育系的研究人员报告，在温室花房的工作者中，其出生胎儿暴露到目前常用的经过批准的杀虫剂下，对她们的女儿们的乳房发育有影响。作者的研究结果报告已发表在２０１２年３月的《 International Journal of Andrology 》 published online 上。 欲知更多：请链接： http://www.medscape.com/viewarticle/760307
I currently meet some problems with the implementaion of the periodic boundary condition for transient Rayleigh-Benard convection problem. My code is based on FVM, SIMPLE algorithm and colocatted grid system (with Rhie-Chow interpolation) . i am prettly sure that my code works for adiabtic boundary condition for temperature (no-slip wall for velocities), as i compared my results with some beachmark solutions. To simulate a infinite long domain in horizontal direction (my solver is two - dimensional), i want to implment periodic boundary condition for temperature, velocity and pressure fields. Thus i did some modification of the code, for tempearture and velocity field, i exchange the values on boundaries, like (a N by N grid ) phi(1, j) = phi(N-1,j) phi(N,j) = phi(2,j) then treat the boundary type like Dirichlet condition; for pressure field, at the beginning i think we may not need any modification as the problem i considered is not like the other type periodic boundary condition , like there is a incomming flow and we need to guarantee the constant pressure drop. then i realized that the pressure correction genearlly implicitly incoperates with Neumann boundaries for all boundaries, then i did some modification like temperature field. However, i failed, as the solver directly divergence in the first two or three time steps and i do not know the reason. I wish someone who has similar experience can help me. If someone can provide some usful materials, it would be great. in each time step, i firstly compute U, V then compute P finally compute T in the subroutine for computing pressure correction, i first assemble AE, AW, AN, AS, AP and right hand side (Su) then i set the value on boundaries according to the periodic boundary conditions, take the west side as example, i set pp(1,j) = pp(N-1,j) , pp stands for pressure correction; since i have the values on boundaries, i treat it like Dirichlet boundary condition then i call the linear sysmeter solver ...
GBrowse and GFF The purpose of this document is to explore how the tables in the mysql database used by gbrowse relate to the GFF from which they are populated. The conclusions summarize how I currently think it all works, and are most likely to be of interest to others. Methods Results Table Structure Fate of typical GFF fields Population of fattribute and fattribute_to_feature tables Alignments Conclusions Methods I will be using a system set up as described in my previous document . In addition to the work described on that page, I have additionally loaded in wormbase release 130. It is that dataset that I will be exploring in the following examples. Results Table Structure The table structure of GBrowse is as follows: Fate of typical GFF fields First, I will explore where exactly the information for a particular line of gff ends up. Here is an example line from the ws130 gff file: I Genefinder CDS 252119 253587 . + . CDS "Y48G1BM.gc6" or, split into traditional GFF fields by tabs, example line from ws130 GFF reference sequence I source Genefinder type CDS start position 252119 end position 253587 score . strand + phase . group CDS "Y48G1BM.gc6" I will now go through the seven tables in GBrowse depicted above to determine the fate of this information. fdata Table fdata (1 row) fref fstart fstop fbin ftypeid fscore fstrand fphase gid ftarget_start ftarget_stop I 252119 253587 10000.000025 6 null + null 121 null null !-- fdata (1 row) fref I fstart 252119 fstop 253587 fbin 10000.000025 ftypeid 6 fscore null fstrand + fphase null gid 121 ftarget_start null ftarget_stop null -- ftype Table ftype (1 row) ftypeid fmethod fsource 6 CDS Genefinder fgroup Table fgroup (1 row) gid gclass gname 121 CDS Y48G1BM.gc6 fdna Table fdna contained sequence as referenced by the fref field of fdata . fmeta There were no relevant rows in fmeta fattribute_to_feature There were no relevant rows in fattribute_to_feature fattribute There were no relevant rows in fattribute Comparison with the Gbrowse Tutorial reveals that the ftype table is used to determine which track to display this GFF span in, and the fgroup table is used when the user searches for a feature. This all makes intuitive sense. The fields that are null in fdata may pose a problem. Two of the field that are null, fscore and fphase , are analogous to the similarly named features in the GFF file. There are at least two unresolved issues: How can fattribute get populated? How can ftarget_start and ftarget_end get populated? Population of fattribute and fattribute_to_feature tables Consider the fate of the following line of GFF, I Coding_transcript intron 11690 14950 . + . Transcript "Y74C9A.2.4" ; Confirmed_EST yk1139h01.3 I Coding_transcript intron 11690 14950 . + . Transcript "Y74C9A.2.3" ; Confirmed_EST yk1139h01.3 I Coding_transcript intron 11690 14950 . + . Transcript "Y74C9A.2.2" ; Confirmed_EST yk1139h01.3 I curated intron 11690 14950 . + . CDS "Y74C9A.2" ; Confirmed_EST yk1139h01.3 I Coding_transcript intron 11690 14950 . + . Transcript "Y74C9A.2.1" ; Confirmed_EST yk1139h01.3 I Genefinder intron 11690 14950 . + . CDS "Y74C9A.gc2" ; Confirmed_EST yk1139h01.3 or fattribute example line from ws130 GFF reference sequence I source Coding_transcript type intron start position 11690 end position 14950 score . strand + phase . group Transcript "Y74C9A.2.4" ; Confirmed_EST yk1139h01.3 Yields the following table contents: fdata Table fdata (6 rows) fid fref fstart fstop fbin ftypeid fscore fstrand fphase gid ftarget_start ftarget_stop 9406655 I 11690 14950 10000.000001 20 null + null 8 null null 9406656 I 11690 14950 10000.000001 20 null + null 6 null null 9406657 I 11690 14950 10000.000001 20 null + null 9 null null 9406658 I 11690 14950 10000.000001 21 null + null 10 null null 9406659 I 11690 14950 10000.000001 20 null + null 7 null null 9406660 I 11690 14950 10000.000001 22 null + null 11 null null ftype Table ftype (3 rows) ftypeid fmethod fsource 20 intron Coding_transcript 21 intron curated 22 intron Genefinder fgroup Table fgroup (6 rows) gid gclass gname 6 Transcript Y74C9A.2.3 7 Transcript Y74C9A.2.1 8 Transcript Y74C9A.2.4 9 Transcript Y74C9A.2.2 10 CDS Y74C9A.2 11 CDS Y74C9A.gc2 fdna Table fdna contained sequence as referenced by the fref field of fdata . fmeta There were no relevant rows in fmeta fattribute_to_feature Table fattribute_to_feature (6 rows) fid fattribute_id fattribute_value 9406655 2 yk1139h01.3 9406656 2 yk1139h01.3 9406657 2 yk1139h01.3 9406658 2 yk1139h01.3 9406659 2 yk1139h01.3 9406660 2 yk1139h01.3 fattribute Table fattribute (1 row) fattribute_id fattribute_name 2 Confirmed_EST It should be mentioned that in searching for yk1139h01.3 in fattribute_to_feature, I found the following: fid fattribute_id fattribute_value 12236 2 yk1139h01.3 12237 2 yk1139h01.3 12238 2 yk1139h01.3 12239 2 yk1139h01.3 12240 2 yk1139h01.3 12241 2 yk1139h01.3 However, there are no enties in fdata that correspond to those fid's, and thus they are in principle useless. I am not entirely sure where these rows are coming from, but I note that when "grepping" the ws130 GFF files for yk1139h01.3, all the entires appeared to be duplicated. It is possible that here is a ton of redundancy in my ws130 database because of the change in names from CHROMOSOME_I to I that occured recently. These naming issues appear to be resolved by the bp_process_wormbase.pl, but the duplicate lines are not collapsed. Perhaps it would be worth running a second-pass script after bp_process_wormbase.pl that removed redundant lines; this is a non-trivial propspect because of the enormous size of the complete GFF for wormbase. Alignments The following lines of GFF likely give rise to alignment entries in the GBrowse tables. I BLAT_EST_BEST EST_match 11539 11561 99.7 - . Target "Sequence:yk1139h01.3" 658 636 I BLAT_EST_BEST EST_match 11618 11632 99.7 - . Target "Sequence:yk1139h01.3" 635 621 I BLAT_EST_BEST EST_match 11633 11689 99.7 - . Target "Sequence:yk1139h01.3" 619 563 I BLAT_EST_BEST EST_match 14951 15160 99.7 - . Target "Sequence:yk1139h01.3" 562 353 I BLAT_EST_BEST EST_match 16473 16781 99.7 - . Target "Sequence:yk1139h01.3" 352 44 I BLAT_EST_BEST EST_match 16783 16800 99.7 - . Target "Sequence:yk1139h01.3" 43 26 I BLAT_EST_BEST EST_match 16802 16817 99.7 - . Target "Sequence:yk1139h01.3" 25 10 I BLAT_EST_BEST EST_match 16820 16827 99.7 - . Target "Sequence:yk1139h01.3" 8 1 Searching fdata for just the start and stop of the first one, I get fdata fid fref fstart fstop fbin ftypeid fscore fstrand fphase gid ftarget_start ftarget_stop 9627963 I 11539 11561 1000.000011 55 99.7 - 12476 636 658 fgroup gid gclass gname 12476 Sequence yk1139h01.3 ftype ftypeid fmethod fsource 55 EST_match BLAT_EST_BEST fattribute_to_feature There were no rows in fattribute_to_feature with fid 9627963 This doesn't make a whole lot of sense... I don't understand where those other entries in fattribute_to_feature are coming from or what role they serve. Conclusions Fate of GFF lines The typical line of GFF results in entries into the fdata , ftype and fgroup tables. The fdata table holds most of the information from the GFF file, except for the contents of the source , type and group fields. The source and type fields form a unique pair in the ftype table, and are referenced by the ftypeid in the fdata table. The first semicolon-separated pair of terms in the group table is placed as a unique pair in the fgroup table, and referenced by the fgroupid in the fdata table. Special case : Alignments If the line of GFF represents an alignment, the group field will have a special structure similar to Target "Sequence:yk1139h01.3" 658 636 The Target group class is recognized by bp_load_gff.pl as signifying an alignment, and the next token is split on a colon to generate the real group class and name. The two tokens after that are taken as the start and stop of the alginment on the target sequence. I am not sure if the class of the target must always be sequence, but it would make sense. Special case : Additional attributes If there are one or more semicolon in the group field, such as Transcript "Y74C9A.2.4" ; Confirmed_EST yk1139h01.3 it is split and the first pair of terms is used as the group class and name, and the later pairs of terms form attribute name and values. Perhaps for performance reasons, instead of storing both the name and value in the fattribute_to_feature table, the attribute name is stored in the fattribute table and referenced by the fattribute_id . Enduring Mysteries The only thing I haven't been able to figure out about the GBrowse tables is where the seemingly useless entries in the fattribute_to_feature table come from, and for what they could concievably be used. My best explanation is that they are an artifact cause by the repetition of the GFF in the bp_process_wormbase script output. This web page was written by Alok Saldanha ( alok at caltech dot edu ).
The President, currently on a state visit to China, held a summit talk with President Hu Jintao on January 9 at the Great Hall of the People in Beijing, where they exchanged in-depth views covering an intensive agenda, discussing development of bilateral relations, trade, co-prosperity, and peace in the region. The two leaders gave a positive evaluation of the steady progress of bilateral relations since the establishment of diplomatic ties in 1992. The talks between the two heads of state broached the means of deepening the scope of dialogue and effective collaboration between the two countries in various spheres. With regards to the recent accident in the Yellow Sea -- which prompted the death of one Korean coast guard who was on standby to stop illegal fishing on South Korea’s west coast -- the two heads of the state agreed to work closely together to prevent any further tragedies and set up a cooperative system. As the talk revolved around North Korean affairs following the death of Kim Jong-il, the two presidents sought common objectives towards peace on the Korean Peninsula, reaching the consensus that further development of the strategic partnership between the two nations would contribute to stability and prosperity in East Asia. Both leaders also gave high marks to the rapid progress in the realms of trade and economic cooperation and concurred on taking steps forward to a Korea-China free trade pact. Bilateral trade reached the USD 200 billion breakthrough in 2011, more than a 30-fold increase compared to the USD 6.3 billion mark from the first year of the bilateral diplomatic relationship. Marking the 20th anniversary of the establishment of bilateral diplomatic relations, the two parties perceived that strengthening ties among the peoples will prove crucial in the further development of diplomatic relationship. They agree to boost cultural and human resource exchanges -- including youths -- in celebration of the Year of Korea-China Friendship. Following the summit talk, Korea and China announced an annual plan comprising a series of events commemorating the 20th anniversary of diplomatic ties During the summit, The President extended an invitation to the Seoul Nuclear Security Summit slated for March this year, to which his Chinese counterpart accepted gratefully. The president also pledged the active participation and cooperation from China for the successful hosting of the 2012 Yeosu Expo. The presidential visit was arranged to mark the 20th anniversary of the establishment of diplomatic relations between Korea and China. This is The President’s sixth visit to China and his second state visit following the itinerary in May 2008. By Hwang Dana Korea.net Staff Writer 原文见 http://english.president.go.kr/pre_activity/summit/diplomacy_view.php?board_no=E05uno=6192
本来打算用TM做点东西，因为ETM+早就坏了。 今天打开QQ，才晓得TM也坏了！ 啊呀，要抓紧啊！一个思路一年前都有了，到现在还没有一点起色。我笨啊，我懒啊！ Landsat 8, currently called the Landsat Data Continuity Mission, is now scheduled to be launched in January 2013. On March 1, 1984, NASA launched Landsat 5 , the agency’s last originally mandated Landsat satellite. http://landsat.gsfc.nasa.gov/news/news-archive/news_0405.html Landsat 5 Mission in Jeopardy Source: USGS Nov. 18, 2011 • The U.S. Geological Survey (USGS) has stopped acquiring images from the 27-year-old Landsat 5 Earth observation satellite due to a rapidly degrading electronic component. Landsat 5 was launched in 1984 and designed to last 3 years. The USGS assumed operation of Landsat 5 in 2001 and managed to bring the aging satellite back from the brink of total failure on several occasions following the malfunction of key subsystems. There is now an increasing likelihood that the Landsat 5 mission is nearing its end. “This anticipated decline of Landsat 5 provides confirmation of the importance of the timely launch of the next Landsat mission and the need for an operational and reliable National Land Imaging System,” stated Anne Castle, Assistant Secretary for Water and Science at the U.S. Department of the Interior. “The USGS is committed to maintaining the unique long term imaging database that the Landsat program provides.” For several months, the Landsat flight operations team has been closely tracking the fluctuating performance of an amplifier essential for transmitting land-surface images from the Landsat 5 satellite to ground receiving stations in the U.S. and around the world. Over the past 10 days, problems with the amplifier have led to drastically reduced image download capabilities, a sign of impending failure. Numerous engineering and technical adjustments have been made to Landsat 5 in the past several days to sustain at least a limited imaging capability, but performance has continued to decline. Instead of continuing to operate until the amplifier fails completely, which could bring the mission to an end, USGS engineers have suspended imaging activities for an initial period of 90 days in order to explore every possible option for restoring satellite-to-ground image transmissions. The USGS-operated Landsat 7 remains in orbit collecting global imagery. Since its launch in 1999 with a 5-year design life, Landsat 7 has experienced an instrument anomaly which reduces the amount of data collected per image. Landsat 8, currently called the Landsat Data Continuity Mission, is now scheduled to be launched in January 2013.
2011年8月25日更新的MODIS Conversion Toolkit ，支持143种现有的modis数据产品直接处理，同时提供批量数据处理的IDL开发函数接口。 简介： The MODIS Conversion Toolkit (MCTK) is a plugin for ENVI that can ingest, process, and georeference every known MODIS product (currently 143) through your choice of an easy-to-use interactive widget interface or a fully-accessible programmatic interface. Supported products include: - Level 1A Uncalibrated Radiance - Level 1B Calibrated Radiance - Level 2 Swath - Level 2G, Level 3, and Level 4 Grid The interface allows you to take a "cafeteria" approach to MODIS data by providing a list of all available datasets within a file, from which you can choose the ones to process. The ENVI equivalents of MRT and MRTSwath are built in as well, which means that you can input a file, have its contents converted to scientifically meaningful values, and then project those contents into the coordinate system of your choice--all within one interface. Bow tie correction is also available for all swath products. The user guide (included with the plugin) contains: Descriptions of how the plugin interacts with each major MODIS product categoryScreen captures to aid in using the interactive versionA complete explanation of the programming interface with fully functional sample programs for each major MODIS product category.A list of all supported MODIS product。
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