-
Notifications
You must be signed in to change notification settings - Fork 1
/
db_biii_perso.sql
16990 lines (16846 loc) · 801 KB
/
db_biii_perso.sql
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
-- phpMyAdmin SQL Dump
-- version 4.6.4
-- https://www.phpmyadmin.net/
--
-- Client : 127.0.0.1
-- Généré le : Mar 14 Février 2017 à 14:30
-- Version du serveur : 5.7.14
-- Version de PHP : 5.6.25
SET SQL_MODE = "NO_AUTO_VALUE_ON_ZERO";
SET time_zone = "+00:00";
/*!40101 SET @OLD_CHARACTER_SET_CLIENT=@@CHARACTER_SET_CLIENT */;
/*!40101 SET @OLD_CHARACTER_SET_RESULTS=@@CHARACTER_SET_RESULTS */;
/*!40101 SET @OLD_COLLATION_CONNECTION=@@COLLATION_CONNECTION */;
/*!40101 SET NAMES utf8 */;
--
-- Base de données : `db_biii_perso`
--
-- --------------------------------------------------------
--
-- Structure de la table `academicpaper`
--
CREATE TABLE `academicpaper` (
`id_paper` decimal(10,0) NOT NULL DEFAULT '0',
`id_type` decimal(10,0) DEFAULT NULL,
`Title` varchar(255) DEFAULT NULL,
`Journal` varchar(255) DEFAULT NULL,
`Volume` varchar(255) DEFAULT NULL,
`Number` varchar(255) DEFAULT NULL,
`Pages` varchar(255) DEFAULT NULL,
`Year` decimal(10,0) DEFAULT NULL,
`ISSN` varchar(255) DEFAULT NULL,
`doi` varchar(255) DEFAULT NULL,
`Abstract` longtext,
`Date` varchar(255) DEFAULT NULL,
`URL` varchar(255) DEFAULT NULL,
`ISBN` varchar(255) DEFAULT NULL,
`Issue` varchar(255) DEFAULT NULL,
`Month` decimal(10,0) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;
--
-- Contenu de la table `academicpaper`
--
INSERT INTO `academicpaper` (`id_paper`, `id_type`, `Title`, `Journal`, `Volume`, `Number`, `Pages`, `Year`, `ISSN`, `doi`, `Abstract`, `Date`, `URL`, `ISBN`, `Issue`, `Month`) VALUES
('2737', '102', 'Experimenters guide to colocalization studies finding a way thro', 'Methods Cell Biol', '123', NULL, '395-408', '2014', '0091-679X', '10.1016/B978-0-12-420138-5.00021-5', '<p>Multicolor fluorescence microscopy helps to define the local interplay of subcellular components in cell biological experiments. The analysis of spatial coincidence of two or more markers is a first step in investigating the potential interactions of molecular actors. Colocalization studies rely on image preprocessing and further analysis; however, they are limited by optical resolution. Once those limitations are taken into account, characterization might be performed. In this review, we discuss two types of parameters that are aimed at evaluating colocalization, which are indicators and quantifiers. Indicators evaluate signal coincidence over a predefined scale, while quantifiers provide an absolute measurement. As the image is both a collection of intensities and a collection of objects, both approaches are applicable. Most of the available image processing software include various colocalization options; however, guidance for the choice of the appropriate method is rarely proposed. In this review, we provide the reader with a basic description of the available colocalization approaches, proposing a guideline for their use, either alone or in combination.</p>', '2014', '', '', '', NULL),
('2760', '102', 'Autofocusing in computer microscopy selecting the optimal focus ', 'Microsc Res Tech', '65', NULL, '139-49', '2004', '1059-910X', '10.1002/jemt.20118', '<p>Autofocusing is a fundamental technology for automated biological and biomedical analyses and is indispensable for routine use of microscopes on a large scale. This article presents a comprehensive comparison study of 18 focus algorithms in which a total of 139,000 microscope images were analyzed. Six samples were used with three observation methods (brightfield, phase contrast, and differential interference contrast (DIC)) under two magnifications (100x and 400x). A ranking methodology is proposed, based on which the 18 focus algorithms are ranked. Image preprocessing was also conducted to extensively reveal the performance and robustness of the focus algorithms. The presented guidelines allow for the selection of the optimal focus algorithm for different microscopy applications.</p>', '2004 Oct', '', '', '3', NULL),
('2775', '102', 'Beyond colocalization inferring spatial interactions between sub', 'BMC Bioinformatics', '11', NULL, '372', '2010', '1471-2105', '10.1186/1471-2105-11-372', '<p><b>BACKGROUND: </b>Sub-cellular structures interact in numerous direct and indirect ways in order to fulfill cellular functions. While direct molecular interactions crucially depend on spatial proximity, other interactions typically result in spatial correlations between the interacting structures. Such correlations are the target of microscopy-based co-localization analysis, which can provide hints of potential interactions. Two complementary approaches to co-localization analysis can be distinguished: intensity correlation methods capitalize on pattern discovery, whereas object-based methods emphasize detection power.</p><p><b>RESULTS: </b>We first reinvestigate the classical co-localization measure in the context of spatial point pattern analysis. This allows us to unravel the set of implicit assumptions inherent to this measure and to identify potential confounding factors commonly ignored. We generalize object-based co-localization analysis to a statistical framework involving spatial point processes. In this framework, interactions are understood as position co-dependencies in the observed localization patterns. The framework is based on a model of effective pairwise interaction potentials and the specification of a null hypothesis for the expected pattern in the absence of interaction. Inferred interaction potentials thus reflect all significant effects that are not explained by the null hypothesis. Our model enables the use of a wealth of well-known statistical methods for analyzing experimental data, as demonstrated on synthetic data and in a case study considering virus entry into live cells. We show that the classical co-localization measure typically under-exploits the information contained in our data.</p><p><b>CONCLUSIONS: </b>We establish a connection between co-localization and spatial interaction of sub-cellular structures by formulating the object-based interaction analysis problem in a spatial statistics framework based on nearest-neighbor distance distributions. We provide generic procedures for inferring interaction strengths and quantifying their relative statistical significance from sets of discrete objects as provided by image analysis methods. Within our framework, an interaction potential can either refer to a phenomenological or a mechanistic model of a physico-chemical interaction process. This increased flexibility in designing and testing different hypothetical interaction models can be used to quantify the parameters of a specific interaction model or may catalyze the discovery of functional relations.</p>', '2010', '', '', '', NULL),
('2777', '102', 'Using CellX to quantify intracellular events', 'Curr Protoc Mol Biol', 'Chapter 14', NULL, 'Unit 14.22.', '2013', '1934-3647', '10.1002/0471142727.mb1422s101', '<p>Methods to quantify features of individual cells using light microscopy have become widely used in biology. A multitude of computational tools has been developed for image analysis; however, they are often only for specific cell types and microscopy techniques. This unit describes CellX, an open-source software package for cell segmentation, intensity quantification, and cell tracking on a variety of microscopy images. CellX can perform cell segmentation largely independently of cell shapes, and can also cope with images that are crowded with cells. The basic protocol describes how to use CellX for cell segmentation and quantification. This protocol remains the same whether there is a collection of images to be analyzed or whether cell tracking on a sequence of images is to be performed. The CellX output comprises control images for visual validation, text files for post-processing statistics, and MATLAB objects for advanced subsequent analysis.</p>', '2013', '', '', '', NULL),
('2778', '102', 'Avoiding twisted pixels ethical guidelines for the appropriate u', 'Sci Eng Ethics', '16', NULL, '639-67', '2010', '1471-5546', '10.1007/s11948-010-9201-y', '<p>Digital imaging has provided scientists with new opportunities to acquire and manipulate data using techniques that were difficult or impossible to employ in the past. Because digital images are easier to manipulate than film images, new problems have emerged. One growing concern in the scientific community is that digital images are not being handled with sufficient care. The problem is twofold: (1) the very small, yet troubling, number of intentional falsifications that have been identified, and (2) the more common unintentional, inappropriate manipulation of images for publication. Journals and professional societies have begun to address the issue with specific digital imaging guidelines. Unfortunately, the guidelines provided often do not come with instructions to explain their importance. Thus they deal with what should or should not be done, but not the associated \'why\' that is required for understanding the rules. This article proposes 12 guidelines for scientific digital image manipulation and discusses the technical reasons behind these guidelines. These guidelines can be incorporated into lab meetings and graduate student training in order to provoke discussion and begin to bring an end to the culture of "data beautification".</p>', '2010 Dec', '', '', '4', NULL),
('2782', '102', 'Momentpreserving thresolding A new approach', 'Computer Vision, Graphics, and Image Processing', '29', NULL, '377 - 393', '1985', '0734-189X', 'http://dx.doi.org/10.1016/0734-189X(85)90133-1', 'A new approach to automatic threshold selection using the moment-preserving principle is proposed. The threshold values are computed deterministically in such a way that the moments of an input picture is preserved in the output picture. Experimental results show that the approach can be employed to threshold a given picture into meaningful gray-level classes. The approach is described for global thresholding, but it is applicable to local thresholding as well.', '', 'http://www.sciencedirect.com/science/article/pii/0734189X85901331', '', '', NULL),
('2784', '102', 'Huygens Remote Manager A Web Interface for HighVolume Batch Deco', 'Imaging & Microscopy', '9', NULL, '57–58', '2007', '14394243, 18637809', '10.1002/imic.200790154', '', '', 'http://doi.wiley.com/10.1002/imic.200790154', '', '', NULL),
('2787', '102', 'Quantification of histochemical staining by color deconvolution', 'Anal Quant Cytol Histol', '23', NULL, '291-9', '2001', '0884-6812', '', '<p><b>OBJECTIVE: </b>To develop a flexible method of separation and quantification of immunohistochemical staining by means of color image analysis.</p><p><b>STUDY DESIGN: </b>An algorithm was developed to deconvolve the color information acquired with red-green-blue (RGB) cameras and to calculate the contribution of each of the applied stains based on stain-specific RGB absorption. The algorithm was tested using different combinations of diaminobenzidine, hematoxylin and eosin at different staining levels.</p><p><b>RESULTS: </b>Quantification of the different stains was not significantly influenced by the combination of multiple stains in a single sample. The color deconvolution algorithm resulted in comparable quantification independent of the stain combinations as long as the histochemical procedures did not influence the amount of stain in the sample due to bleaching because of stain solubility and saturation of staining was prevented.</p><p><b>CONCLUSION: </b>This image analysis algorithm provides a robust and flexible method for objective immunohistochemical analysis of samples stained with up to three different stains using a laboratory microscope, standard RGB camera setup and the public domain program NIH Image.</p>', '2001 Aug', '', '', '4', NULL),
('2789', '102', 'ImmunoRatio a publicly available web application for quantitativ', 'Breast Cancer Res', '12', NULL, 'R56', '2010', '1465-542X', '10.1186/bcr2615', '<p><b>INTRODUCTION: </b>Accurate assessment of estrogen receptor (ER), progesterone receptor (PR), and Ki-67 is essential in the histopathologic diagnostics of breast cancer. Commercially available image analysis systems are usually bundled with dedicated analysis hardware and, to our knowledge, no easily installable, free software for immunostained slide scoring has been described. In this study, we describe a free, Internet-based web application for quantitative image analysis of ER, PR, and Ki-67 immunohistochemistry in breast cancer tissue sections.</p><p><b>METHODS: </b>The application, named ImmunoRatio, calculates the percentage of positively stained nuclear area (labeling index) by using a color deconvolution algorithm for separating the staining components (diaminobenzidine and hematoxylin) and adaptive thresholding for nuclear area segmentation. ImmunoRatio was calibrated using cell counts defined visually as the gold standard (training set, n = 50). Validation was done using a separate set of 50 ER, PR, and Ki-67 stained slides (test set, n = 50). In addition, Ki-67 labeling indexes determined by ImmunoRatio were studied for their prognostic value in a retrospective cohort of 123 breast cancer patients.</p><p><b>RESULTS: </b>The labeling indexes by calibrated ImmunoRatio analyses correlated well with those defined visually in the test set (correlation coefficient r = 0.98). Using the median Ki-67 labeling index (20%) as a cutoff, a hazard ratio of 2.2 was obtained in the survival analysis (n = 123, P = 0.01). ImmunoRatio was shown to adapt to various staining protocols, microscope setups, digital camera models, and image acquisition settings. The application can be used directly with web browsers running on modern operating systems (e.g., Microsoft Windows, Linux distributions, and Mac OS). No software downloads or installations are required. ImmunoRatio is open source software, and the web application is publicly accessible on our website.</p><p><b>CONCLUSIONS: </b>We anticipate that free web applications, such as ImmunoRatio, will make the quantitative image analysis of ER, PR, and Ki-67 easy and straightforward in the diagnostic assessment of breast cancer specimens.</p>', '2010', '', '', '4', NULL),
('2792', '102', 'Biological imaging software tools', 'Nat Methods', '9', NULL, '697-710', '2012', '1548-7105', '10.1038/nmeth.2084', '<p>Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options.</p>', '2012 Jul', '', '', '7', NULL),
('2793', '102', 'Ncadherin and ?1integrins cooperate during the development of th', 'Dev Biol', '364', NULL, '178-91', '2012', '1095-564X', '10.1016/j.ydbio.2012.02.001', '<p>Cell adhesion controls various embryonic morphogenetic processes, including the development of the enteric nervous system (ENS). Ablation of ?1-integrin (?1-/-) expression in enteric neural crest cells (ENCC) in mice leads to major alterations in the ENS structure caused by reduced migration and increased aggregation properties of ENCC during gut colonization, which gives rise to a Hirschsprung\'s disease-like phenotype. In the present study, we examined the role of N-cadherin in ENS development and the interplay with ?1 integrins during this process. The Ht-PA-Cre mouse model was used to target gene disruption of N-cadherin and ?1 integrin in migratory NCC and to produce single- and double-conditional mutants for these two types of adhesion receptors. Double mutation of N-cadherin and ?1 integrin led to embryonic lethality with severe defects in ENS development. N-cadherin-null (Ncad-/-) ENCC exhibited a delayed colonization in the developing gut at E12.5, although this was to a lesser extent than in ?1-/- mutants. This delay of Ncad-/- ENCC migration was recovered at later stages of development. The double Ncad-/-; ?1-/- mutant ENCC failed to colonize the distal part of the gut and there was more severe aganglionosis in the proximal hindgut than in the single mutants for N-cadherin or ?1-integrin. This was due to an altered speed of locomotion and directionality in the gut wall. The abnormal aggregation defect of ENCC and the disorganized ganglia network in the ?1-/- mutant was not observed in the double mutant. This indicates that N-cadherin enhances the effect of the ?1-integrin mutation and demonstrates cooperation between these two adhesion receptors during ENS ontogenesis. In conclusion, our data reveal that N-cadherin is not essential for ENS development but it does modulate the modes of ENCC migration and acts in concert with ?1-integrin to control the proper development of the ENS.</p>', '2012 Apr 15', '', '', '2', NULL),
('2796', '102', 'Computational imaging in cell biology', 'J Cell Biol', '161', NULL, '477-81', '2003', '0021-9525', '10.1083/jcb.200302097', '<p>Microscopy of cells has changed dramatically since its early days in the mid-seventeenth century. Image analysis has concurrently evolved from measurements of hand drawings and still photographs to computational methods that (semi-) automatically quantify objects, distances, concentrations, and velocities of cells and subcellular structures. Today\'s imaging technologies generate a wealth of data that requires visualization and multi-dimensional and quantitative image analysis as prerequisites to turning qualitative data into quantitative values. Such quantitative data provide the basis for mathematical modeling of protein kinetics and biochemical signaling networks that, in turn, open the way toward a quantitative view of cell biology. Here, we will review technologies for analyzing and reconstructing dynamic structures and processes in the living cell. We will present live-cell studies that would have been impossible without computational imaging. These applications illustrate the potential of computational imaging to enhance our knowledge of the dynamics of cellular structures and processes.</p>', '2003 May 12', '', '', '3', NULL),
('2798', '102', 'SparkMaster automated calcium spark analysis with ImageJ', 'Am J Physiol Cell Physiol', '293', NULL, 'C1073-81', '2007', '0363-6143', '10.1152/ajpcell.00586.2006', '<p>Ca sparks are elementary Ca-release events from intracellular Ca stores that are observed in virtually all types of muscle. Typically, Ca sparks are measured in the line-scan mode with confocal laser-scanning microscopes, yielding two-dimensional images (distance vs. time). The manual analysis of these images is time consuming and prone to errors as well as investigator bias. Therefore, we developed SparkMaster, an automated analysis program that allows rapid and reliable spark analysis. The underlying analysis algorithm is adapted from the threshold-based standard method of spark analysis developed by Cheng et al. (Biophys J 76: 606-617, 1999) and is implemented here in the freely available image-processing software ImageJ. SparkMaster offers a graphical user interface through which all analysis parameters and output options are selected. The analysis includes general image parameters (number of detected sparks, spark frequency) and individual spark parameters (amplitude, full width at half-maximum amplitude, full duration at half-maximum amplitude, full width, full duration, time to peak, maximum steepness of spark upstroke, time constant of spark decay). We validated the algorithm using images with synthetic sparks embedded into backgrounds with different signal-to-noise ratios to determine an analysis criteria at which a high sensitivity is combined with a low frequency of false-positive detections. Finally, we applied SparkMaster to analyze experimental data of sparks measured in intact and permeabilized ventricular cardiomyocytes, permeabilized mammalian skeletal muscle, and intact smooth muscle cells. We found that SparkMaster provides a reliable, easy to use, and fast way of analyzing Ca sparks in a wide variety of experimental conditions.</p>', '2007 Sep', '', '', '3', NULL),
('2800', '102', 'Amplitude distribution of calcium sparks in confocal images theo', 'Biophys J', '76', NULL, '606-17', '1999', '0006-3495', '10.1016/S0006-3495(99)77229-2', '<p>Determination of the calcium spark amplitude distribution is of critical importance for understanding the nature of elementary calcium release events in striated muscle. In the present study we show, on general theoretical grounds, that calcium sparks, as observed in confocal line scan images, should have a nonmodal, monotonic decreasing amplitude distribution, regardless of whether the underlying events are stereotyped. To test this prediction we developed, implemented, and verified an automated computer algorithm for objective detection and measurement of calcium sparks in raw image data. When the sensitivity and reliability of the algorithm were set appropriately, we observed highly left-skewed or monotonic decreasing amplitude distributions in skeletal muscle cells and cardiomyocytes, confirming the theoretical predictions. The previously reported modal or Gaussian distributions of sparks detected by eye must therefore be the result of subjective detection bias against small amplitude events. In addition, we discuss possible situations when a modal distribution might be observed.</p>', '1999 Feb', '', '', '2', NULL),
('2804', '102', 'Why join groups Lessons from parasitemanipulated Artemia', 'Ecol Lett', '16', NULL, '493-501', '2013', '1461-0248', '10.1111/ele.12074', '<p>Grouping behaviours (e.g. schooling, shoaling and swarming) are commonly explicated through adaptive hypotheses such as protection against predation, access to mates or improved foraging. However, the hypothesis that aggregation can result from manipulation by parasites to increase their transmission has never been demonstrated. We investigated this hypothesis using natural populations of two crustacean hosts (Artemia franciscana and Artemia parthenogenetica) infected with one cestode and two microsporidian parasites. We found that swarming propensity increased in cestode-infected hosts and that red colour intensity was higher in swarming compared with non-swarming infected hosts. These effects likely result in increased cestode transmission to its final avian host. Furthermore, we found that microsporidian-infected hosts had both increased swarming propensity and surfacing behaviour. Finally, we demonstrated using experimental infections that these concurrent manipulations result in increased spore transmission to new hosts. Hence, this study suggests that parasites can play a prominent role in host grouping behaviours.</p>', '2013 Apr', '', '', '4', NULL),
('2811', '102', 'Simple system using natural mineral water for highthroughput phe', 'International Journal of High Throughput Screening', '', NULL, '1', '2013', '1179-1381', '10.2147/IJHTS.S40565', '', '', 'http://www.dovepress.com/simple-system-using-natural-mineral-water-for-high-throughput-phenotyp-peer-reviewed-article-IJHTS', '', '', NULL),
('2812', '102', 'Quickandclean article figures with FigureJ', 'J Microsc', '252', NULL, '89-91', '2013', '1365-2818', '10.1111/jmi.12069', '<p>We created FigureJ a new ImageJ plugin dedicated to scientific article figures preparation. Building a convincing figure is a demanding task that covers different steps ranging from content acquisition to figure assembly in editing software. Notions of image processing are required when it comes to even simple tasks such as cropping or resizing images and assembling them in a single figure. Scientific images are typically well handled in dedicated software but poorly supported in software used for laying out the final version of a figure for submission to review process.</p>', '2013 Oct', '', '', '1', NULL),
('2817', '103', 'ImageJ Macro Tool Sets for Biological Image Analysis', 'ImageJ User and Developer Conference 2012', 'None', NULL, 'None', '2012', 'None', '', '', '', '', '2-919941-18-6', 'None', NULL),
('2818', '102', 'ImmunoMembrane a publicly available web application for digital ', 'Histopathology', '60', NULL, '758-67', '2012', '1365-2559', '10.1111/j.1365-2559.2011.04142.x', '<p><b>AIMS: </b>Assessment of the human epidermal growth factor receptor 2 (HER2) with immunohistochemistry (IHC) is routine practice in clinical pathology laboratories. Visual classification of the staining reaction (usually into 0/1+, 2+ or 3+) is subjective and prone to significant inter- and intra-observer variation. In this study, we describe ImmunoMembrane, an easy-to-use HER2 IHC analysis software, which is freely available as a web application, requiring no download or installation.</p><p><b>METHODS AND RESULTS: </b>ImmunoMembrane uses colour deconvolution for stain separation and a customized algorithm for cell membrane segmentation. A quantitative score (IM-score, 0-20 points) is generated according to the membrane staining intensity and completeness. Specimens are classified into 0/1+, 2+ or 3+ based on IM-score cut-offs defined using a training set. The classification and membrane segmentation are presented as a pseudo-coloured overlay image. With a validation set (144 HercepTest(®) -stained whole tissue sections), ImmunoMembrane matched well with the pathologist\'s visual classification (weighted kappa ?(w) =0.80), as well as fluorescence in-situ hybridization (FISH) (IHC disagreement 3.5%, n=144) and chromogenic in-situ hybridization (CISH) (IHC disagreement 2.8%, n=144).</p><p><b>CONCLUSIONS: </b>We anticipate that publicly available web applications, such as ImmunoMembrane, will accelerate the adoption of automated image analysis in clinical diagnostics of HER2 IHC. ImmunoMembrane is freely accessible at: http://jvsmicroscope.uta.fi/immunomembrane/.</p>', '2012 Apr', '', '', '5', NULL),
('2830', '102', 'Robust singleparticle tracking in livecell timelapse sequences', 'Nat Methods', '5', NULL, '695-702', '2008', '1548-7105', '10.1038/nmeth.1237', '<p>Single-particle tracking (SPT) is often the rate-limiting step in live-cell imaging studies of subcellular dynamics. Here we present a tracking algorithm that addresses the principal challenges of SPT, namely high particle density, particle motion heterogeneity, temporary particle disappearance, and particle merging and splitting. The algorithm first links particles between consecutive frames and then links the resulting track segments into complete trajectories. Both steps are formulated as global combinatorial optimization problems whose solution identifies the overall most likely set of particle trajectories throughout a movie. Using this approach, we show that the GTPase dynamin differentially affects the kinetics of long- and short-lived endocytic structures and that the motion of CD36 receptors along cytoskeleton-mediated linear tracks increases their aggregation probability. Both applications indicate the requirement for robust and complete tracking of dense particle fields to dissect the mechanisms of receptor organization at the level of the plasma membrane.</p>', '2008 Aug', '', '', '8', NULL),
('2831', '102', 'Automated cell tracking and analysis in phasecontrast videos iTr', 'PLoS One', '8', NULL, 'e81266', '2013', '1932-6203', '10.1371/journal.pone.0081266', '<p>Cell migration is a key biological process with a role in both physiological and pathological conditions. Locomotion of cells during embryonic development is essential for their correct positioning in the organism; immune cells have to migrate and circulate in response to injury. Failure of cells to migrate or an inappropriate acquisition of migratory capacities can result in severe defects such as altered pigmentation, skull and limb abnormalities during development, and defective wound repair, immunosuppression or tumor dissemination. The ability to accurately analyze and quantify cell migration is important for our understanding of development, homeostasis and disease. In vitro cell tracking experiments, using primary or established cell cultures, are often used to study migration as cells can quickly and easily be genetically or chemically manipulated. Images of the cells are acquired at regular time intervals over several hours using microscopes equipped with CCD camera. The locations (x,y,t) of each cell on the recorded sequence of frames then need to be tracked. Manual computer-assisted tracking is the traditional method for analyzing the migratory behavior of cells. However, this processing is extremely tedious and time-consuming. Most existing tracking algorithms require experience in programming languages that are unfamiliar to most biologists. We therefore developed an automated cell tracking program, written in Java, which uses a mean-shift algorithm and ImageJ as a library. iTrack4U is a user-friendly software. Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U. Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences. Finally, iTrack4U is adapted for phase contrast and fluorescent cells.</p>', '2013', '', '', '11', NULL),
('2839', '103', 'method for normalizing histology slides for quantitative analysi', 'Biomedical Imaging: From Nano to Macro, 2009. ISBI \'09. IEEE International Symposium on', 'None', NULL, 'None', '2009', 'None', '10.1109/ISBI.2009.5193250', 'Inconsistencies in the preparation of histology slides make it difficult to perform quantitative analysis on their results. In this paper we provide two mechanisms for overcoming many of the known inconsistencies in the staining process, thereby bringing slides that were processed or stored under very different conditions into a common, normalized space to enable improved quantitative analysis.', 'June', '', '', 'None', NULL),
('2850', '102', 'PAR1dependent orientation gradient of dynamic microtubules direc', 'J Cell Biol', '194', NULL, '121-35', '2011', '1540-8140', '10.1083/jcb.201103160', '<p>Cytoskeletal organization is central to establishing cell polarity in various cellular contexts, including during messenger ribonucleic acid sorting in Drosophila melanogaster oocytes by microtubule (MT)-dependent molecular motors. However, MT organization and dynamics remain controversial in the oocyte. In this paper, we use rapid multichannel live-cell imaging with novel image analysis, tracking, and visualization tools to characterize MT polarity and dynamics while imaging posterior cargo transport. We found that all MTs in the oocyte were highly dynamic and were organized with a biased random polarity that increased toward the posterior. This organization originated through MT nucleation at the oocyte nucleus and cortex, except at the posterior end of the oocyte, where PAR-1 suppressed nucleation. Our findings explain the biased random posterior cargo movements in the oocyte that establish the germline and posterior.</p>', '2011 Jul 11', '', '', '1', NULL),
('2871', '102', 'PALMsiever a tool to turn raw data into results for singlemolecu', 'Bioinformatics', '', NULL, '', '2014', '1367-4811', '10.1093/bioinformatics/btu720', '<p>During the past decade, localization microscopy (LM) has transformed into an accessible, commercially available technique for life sciences. However, data processing can be challenging to the non-specialist and care is still needed to produce meaningful results. PALMsiever has been developed to provide a user-friendly means of visualizing, filtering and analyzing LM data. It includes drift correction, clustering, intelligent line profiles, many rendering algorithms and 3D data visualization. It incorporates the main analysis and data processing modalities used by experts in the field, as well as several new features we developed, and makes them broadly accessible. It can easily be extended via plugins and is provided as free of charge open-source software.</p><p><b>CONTACT: </b>[email protected].</p>', '2014 Oct 31', '', '', '', NULL),
('2882', '102', 'Arabidopsis plants acclimate to water deficit at low cost throug', 'Plant Physiology', '154', NULL, '357–372', '2010', '1532-2548', '10.1104/pp.110.157008', 'Growth and carbon (C) fluxes are severely altered in plants exposed to soil water deficit. Correspondingly, it has been suggested that plants under water deficit suffer from C shortage. In this study, we test this hypothesis in Arabidopsis (Arabidopsis thaliana) by providing an overview of the responses of growth, C balance, metabolites, enzymes of the central metabolism, and a set of sugar-responsive genes to a sustained soil water deficit. The results show that under drought, rosette relative expansion rate is decreased more than photosynthesis, leading to a more positive C balance, while root growth is promoted. Several soluble metabolites accumulate in response to soil water deficit, with K(+) and organic acids as the main contributors to osmotic adjustment. Osmotic adjustment costs only a small percentage of the daily photosynthetic C fixation. All C metabolites measured (not only starch and sugars but also organic acids and amino acids) show a diurnal turnover that often increased under water deficit, suggesting that these metabolites are readily available for being metabolized in situ or exported to roots. On the basis of 30 enzyme activities, no in-depth reprogramming of C metabolism was observed. Water deficit induces a shift of the expression level of a set of sugar-responsive genes that is indicative of increased, rather than decreased, C availability. These results converge to show that the differential impact of soil water deficit on photosynthesis and rosette expansion results in an increased availability of C for the roots, an increased turnover of C metabolites, and a low-cost C-based osmotic adjustment, and these responses are performed without major reformatting of the primary metabolism machinery.', '', 'http://www.ncbi.nlm.nih.gov/pubmed/20631317', '', '', NULL),
('2886', '102', 'Towards realtime image deconvolution application to confocal and', 'Sci Rep', '3', NULL, '2523', '2013', '2045-2322', '10.1038/srep02523', '<p>Although deconvolution can improve the quality of any type of microscope, the high computational time required has so far limited its massive spreading. Here we demonstrate the ability of the scaled-gradient-projection (SGP) method to provide accelerated versions of the most used algorithms in microscopy. To achieve further increases in efficiency, we also consider implementations on graphic processing units (GPUs). We test the proposed algorithms both on synthetic and real data of confocal and STED microscopy. Combining the SGP method with the GPU implementation we achieve a speed-up factor from about a factor 25 to 690 (with respect the conventional algorithm). The excellent results obtained on STED microscopy images demonstrate the synergy between super-resolution techniques and image-deconvolution. Further, the real-time processing allows conserving one of the most important property of STED microscopy, i.e the ability to provide fast sub-diffraction resolution recordings.</p>', '2013', '', '', '', NULL),
('2897', '102', 'Segmentation and quantification of subcellular structures in flu', 'Nat Protoc', '9', NULL, '586-96', '2014', '1750-2799', '10.1038/nprot.2014.037', '<p>Detection and quantification of fluorescently labeled molecules in subcellular compartments is a key step in the analysis of many cell biological processes. Pixel-wise colocalization analyses, however, are not always suitable, because they do not provide object-specific information, and they are vulnerable to noise and background fluorescence. Here we present a versatile protocol for a method named \'Squassh\' (segmentation and quantification of subcellular shapes), which is used for detecting, delineating and quantifying subcellular structures in fluorescence microscopy images. The workflow is implemented in freely available, user-friendly software. It works on both 2D and 3D images, accounts for the microscope optics and for uneven image background, computes cell masks and provides subpixel accuracy. The Squassh software enables both colocalization and shape analyses. The protocol can be applied in batch, on desktop computers or computer clusters, and it usually requires <1 min and <5 min for 2D and 3D images, respectively. Basic computer-user skills and some experience with fluorescence microscopy are recommended to successfully use the protocol.</p>', '2014 Mar', '', '', '3', NULL),
('2901', '102', 'Automated detection and measurement of isolated retinal arteriol', 'PLoS One', '9', NULL, 'e91791', '2014', '1932-6203', '10.1371/journal.pone.0091791', '<p>Pressure myography studies have played a crucial role in our understanding of vascular physiology and pathophysiology. Such studies depend upon the reliable measurement of changes in the diameter of isolated vessel segments over time. Although several software packages are available to carry out such measurements on small arteries and veins, no such software exists to study smaller vessels (<50 µm in diameter). We provide here a new, freely available open-source algorithm, MyoTracker, to measure and track changes in the diameter of small isolated retinal arterioles. The program has been developed as an ImageJ plug-in and uses a combination of cost analysis and edge enhancement to detect the vessel walls. In tests performed on a dataset of 102 images, automatic measurements were found to be comparable to those of manual ones. The program was also able to track both fast and slow constrictions and dilations during intraluminal pressure changes and following application of several drugs. Variability in automated measurements during analysis of videos and processing times were also investigated and are reported. MyoTracker is a new software to assist during pressure myography experiments on small isolated retinal arterioles. It provides fast and accurate measurements with low levels of noise and works with both individual images and videos. Although the program was developed to work with small arterioles, it is also capable of tracking the walls of other types of microvessels, including venules and capillaries. It also works well with larger arteries, and therefore may provide an alternative to other packages developed for larger vessels when its features are considered advantageous.</p>', '2014', '', '', '3', NULL),
('2906', '102', 'FISHquant automatic counting of transcripts in 3D FISH images', 'Nat Methods', '10', NULL, '277-8', '2013', '1548-7105', '10.1038/nmeth.2406', '', '2013 Apr', '', '', '4', NULL),
('2915', '102', 'rapidSTORM accurate fast opensource software for localization mi', 'Nat Methods', '9', NULL, '1040-1', '2012', '1548-7105', '10.1038/nmeth.2224', '', '2012 Nov', '', '', '11', NULL),
('2918', '102', 'PixFRET an ImageJ plugin for FRET calculation that can accommoda', 'Microsc Res Tech', '68', NULL, '51-8', '2005', '1059-910X', '10.1002/jemt.20215', '<p>Fluorescence resonance energy transfer (FRET) allows the user to investigate interactions between fluorescent partners. One crucial issue when calculating sensitized emission FRET is the correction for spectral bleed-throughs (SBTs), which requires to calculate the ratios between the intensities in the FRET and in the donor or acceptor settings, when only the donor or acceptor are present. Theoretically, SBT ratios should be constant. However, experimentally, these ratios can vary as a function of fluorophore intensity, and assuming constant values may hinder precise FRET calculation. One possible cause for such a variation is the use of a microscope set-up with different photomultipliers for the donor and FRET channels, a set-up allowing higher speed acquisitions on very dynamic fluorescent molecules in living cells. Herein, we show that the bias introduced by the differential response of the two PMTs can be circumvented by a simple modeling of the SBT ratios as a function of fluorophore intensity. Another important issue when performing FRET is the localization of FRET within the cell or a population of cells. We hence developed a freely available ImageJ plug-in, called PixFRET, that allows a simple and rapid determination of SBT parameters and the display of normalized FRET images. The usefulness of this modeling and of the plug-in are exemplified by the study of FRET in a system where two interacting nuclear receptors labeled with ECFP and EYFP are coexpressed in living cells.</p>', '2005 Sep', '', '', '1', NULL),
('2933', '102', 'TANGO a generic tool for highthroughput 3D image analysis for st', 'Bioinformatics', '29', NULL, '1840-1', '2013', '1367-4811', '10.1093/bioinformatics/btt276', '<p><b>MOTIVATION: </b>The cell nucleus is a highly organized cellular organelle that contains the genetic material. The study of nuclear architecture has become an important field of cellular biology. Extracting quantitative data from 3D fluorescence imaging helps understand the functions of different nuclear compartments. However, such approaches are limited by the requirement for processing and analyzing large sets of images.</p><p><b>RESULTS: </b>Here, we describe Tools for Analysis of Nuclear Genome Organization (TANGO), an image analysis tool dedicated to the study of nuclear architecture. TANGO is a coherent framework allowing biologists to perform the complete analysis process of 3D fluorescence images by combining two environments: ImageJ (http://imagej.nih.gov/ij/) for image processing and quantitative analysis and R (http://cran.r-project.org) for statistical processing of measurement results. It includes an intuitive user interface providing the means to precisely build a segmentation procedure and set-up analyses, without possessing programming skills. TANGO is a versatile tool able to process large sets of images, allowing quantitative study of nuclear organization.</p><p><b>AVAILABILITY: </b>TANGO is composed of two programs: (i) an ImageJ plug-in and (ii) a package (rtango) for R. They are both free and open source, available (http://biophysique.mnhn.fr/tango) for Linux, Microsoft Windows and Macintosh OSX. Distribution is under the GPL v.2 licence.</p><p><b>CONTACT: </b>[email protected]</p><p><b>SUPPLEMENTARY INFORMATION: </b>Supplementary data are available at Bioinformatics online.</p>', '2013 Jul 15', '', '', '14', NULL),
('2956', '102', 'Automated whole animal bioimaging assay for human cancer dissemi', 'PLoS One', '7', NULL, 'e31281', '2012', '1932-6203', '10.1371/journal.pone.0031281', '<p>A quantitative bio-imaging platform is developed for analysis of human cancer dissemination in a short-term vertebrate xenotransplantation assay. Six days after implantation of cancer cells in zebrafish embryos, automated imaging in 96 well plates coupled to image analysis algorithms quantifies spreading throughout the host. Findings in this model correlate with behavior in long-term rodent xenograft models for panels of poorly- versus highly malignant cell lines derived from breast, colorectal, and prostate cancer. In addition, cancer cells with scattered mesenchymal characteristics show higher dissemination capacity than cell types with epithelial appearance. Moreover, RNA interference establishes the metastasis-suppressor role for E-cadherin in this model. This automated quantitative whole animal bio-imaging assay can serve as a first-line in vivo screening step in the anti-cancer drug target discovery pipeline.</p>', '2012', '', '', '2', NULL),
('2960', '102', 'Objective comparison of particle tracking methods', 'Nat Methods', '11', NULL, '281-9', '2014', '1548-7105', '10.1038/nmeth.2808', '<p>Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.</p>', '2014 Mar', '', '', '3', NULL),
('2962', '102', 'Fluorophore localization algorithms for superresolution microsco', 'Nat Methods', '11', NULL, '267-79', '2014', '1548-7105', '10.1038/nmeth.2844', '<p>Super-resolution localization microscopy methods provide powerful new capabilities for probing biology at the nanometer scale via fluorescence. These methods rely on two key innovations: switchable fluorophores (which blink on and off and can be sequentially imaged) and powerful localization algorithms (which estimate the positions of the fluorophores in the images). These techniques have spurred a flurry of innovation in algorithm development over the last several years. In this Review, we survey the fundamental issues for single-fluorophore fitting routines, localization algorithms based on principles other than fitting, three-dimensional imaging, dipole imaging and techniques for estimating fluorophore positions from images of multiple activated fluorophores. We offer practical advice for users and adopters of algorithms, and we identify areas for further development.</p>', '2014 Mar', '', '', '3', NULL),
('2964', '102', 'Software opens the door to quantitative imaging', 'Nat Methods', '4', NULL, '120-1', '2007', '1548-7091', '10.1038/nmeth0207-120', '', '2007 Feb', '', '', '2', NULL),
('2973', '102', 'Fluorescence Lifetime Imaging Microscopy FLIM Data Analysis with', 'Journal of Statistical Software', '18', NULL, '1–20', '2007', '1548-7660', '', 'Fluorescence Lifetime Imaging Microscopy (FLIM) allows fluorescence lifetime images of biological objects to be collected at 250 nm spatial resolution and at (sub-)nanosecond temporal resolution. Often ncomp kinetic processes underlie the observed fluorescence at all locations, but the intensity of the fluorescence associated with each process varies per-location, i.e., per-pixel imaged. Then the statistical challenge is global analysis of the image: use of the fluorescence decay in time at all locations to estimate the ncomp lifetimes associated with the kinetic processes, as well as the amplitude of each kinetic process at each location. Given that typical FLIM images represent on the order of 102 timepoints and 103 locations, meeting this challenge is computationally intensive. Here the utility of the TIMP package for R to solve parameter estimation problems arising in FLIM image analysis is demonstrated. Case studies on simulated and real data evidence the applicability of the partitioned variable projection algorithm implemented in TIMP to the problem domain, and showcase options included in the package for the visual validation of models for FLIM data.', '1', 'http://www.jstatsoft.org/v18/i08/paper', '', '', NULL),
('2983', '102', 'Microtubule dynamics reconstituted in vitro and imaged by single', 'Methods Cell Biol', '95', NULL, '221-45', '2010', '0091-679X', '10.1016/S0091-679X(10)95013-9', '<p>In vitro assays that reconstitute the dynamic behavior of microtubules provide insight into the roles of microtubule-associated proteins (MAPs) in regulating the growth, shrinkage, and catastrophe of microtubules. The use of total internal reflection fluorescence microscopy with fluorescently labeled tubulin and MAPs has allowed us to study microtubule dynamics at the resolution of single molecules. In this chapter we present a practical overview of how these assays are performed in our laboratory: fluorescent labeling methods, strategies to prolong the time to photo-bleaching, preparation of stabilized microtubules, flow-cells, microtubule immobilization, and finally an overview of the workflow that we follow when performing the experiments. At all stages, we focus on practical tips and highlight potential stumbling blocks.</p>', '2010', '', '', '', NULL),
('3000', '102', 'Distinctive Image Features from ScaleInvariant Keypoints', 'International Journal of Computer Vision', '60', NULL, '91-110', '2004', '0920-5691', '10.1023/B:VISI.0000029664.99615.94', '', '', 'http://dx.doi.org/10.1023/B%3AVISI.0000029664.99615.94', '', '', NULL),
('3001', '103', 'Multiimage matching using multiscale oriented patches', 'Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition', 'None', NULL, 'None', '2005', 'None', '', 'This paper describes a novel multi-view matching framework based on a new type of invariant feature. Our features are located at Harris corners in discrete scale-space and oriented using a blurred local gradient. This defines a rotationally invariant frame in which we sample a feature descriptor, which consists of an 8 &times; 8 patch of bias/gain normalised intensity values. The density of features in the image is controlled using a novel adaptive non-maximal suppression algorithm, which gives a better spatial distribution of features than previous approaches. Matching is achieved using a fast nearest neighbour algorithm that indexes features based on their low frequency Haar wavelet coefficients. We also introduce a novel outlier rejection procedure that verifies a pairwise feature match based on a background distribution of incorrect feature matches. Feature matches are refined using RANSAC and used in an automatic 2D panorama stitcher that has been extensively tested on hundreds of sample inputs.', '', '', '', 'None', NULL);
INSERT INTO `academicpaper` (`id_paper`, `id_type`, `Title`, `Journal`, `Volume`, `Number`, `Pages`, `Year`, `ISSN`, `doi`, `Abstract`, `Date`, `URL`, `ISBN`, `Issue`, `Month`) VALUES
('3023', '102', 'Sickle cell anaemia among EtiTurks haematological clinical and g', 'Br J Haematol', '64', NULL, '45-55', '1986', '0007-1048', '', '<p>Haematological and genetic observations have been made on 71 SS Eti-Turk patients and their relatives from Cukurova (southern Turkey) and of immigrant families in The Netherlands. Similar data were collected for 25 Black patients and their relatives from Surinam, Netherlands Antilles, and Kenya. Haematological and clinical results were the same for both groups; the haemolytic anaemia in the Turkish patients was as severe as in the others. Haplotyping, involving nine restriction sites, identified haplotype 19 (Antonarakis et al, 1984) as the major type among the Eti-Turks; this chromosome has previously primarily been observed among SS patients from West Africa. The suggestion that the beta S-chromosome among Eti-Turks originates from that area is supported by a relatively high incidence of alpha-thalassaemia-2 (the 3.7 kb deletion), also frequently present in the Black population of West Africa, and by the absence of other major haplotypes, such as types 20 and 3, characteristic for the beta S-chromosome in the population of Central Africa and Kenya, and in Senegal, respectively. The Saudi Arabian type of beta S chromosome in association with the haplotype 19 beta S chromosome was present in only one Eti-Turk patient; this 30-year-old female was mildly affected and exhibited a high level of fetal haemoglobin.</p>', '1986 Sep', '', '', '1', NULL),
('3026', '102', 'Active mask segmentation of fluorescence microscope images', 'IEEE Trans Image Process', '18', NULL, '1817-29', '2009', '1057-7149', '10.1109/TIP.2009.2021081', '<p>We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the "contour" to that of "inside and outside," or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively.</p>', '2009 Aug', '', '', '8', NULL),
('3028', '103', 'SLTLoG A Vesicle Segmentation Method with Automatic Scale Select', '{ISBI - 2014 IEEE International Symposium on Biomedical Imaging}', 'None', NULL, 'None', '2014', 'None', '', '', '', 'https://hal.inria.fr/hal-00921793', '', 'None', NULL),
('3031', '102', 'Stepbystep quantitative analysis of focal adhesions', 'MethodsX', '1', NULL, '56–59', '2014', '22150161', '10.1016/j.mex.2014.06.004', '', '', 'http://www.researchgate.net/publication/263737052\\_Step-by-step\\_quantitative\\_analysis\\_of\\_focal\\_adhesions', '', '', NULL),
('3040', '102', 'Endosomal WASH and exocyst complexes control exocytosis of MT1MM', 'J Cell Biol', '203', NULL, '1063-79', '2013', '1540-8140', '10.1083/jcb.201306162', '<p>Remodeling of the extracellular matrix by carcinoma cells during metastatic dissemination requires formation of actin-based protrusions of the plasma membrane called invadopodia, where the trans-membrane type 1 matrix metalloproteinase (MT1-MMP) accumulates. Here, we describe an interaction between the exocyst complex and the endosomal Arp2/3 activator Wiskott-Aldrich syndrome protein and Scar homolog (WASH) on MT1-MMP–containing late endosomes in invasive breast carcinoma cells. We found that WASH and exocyst are required for matrix degradation by an exocytic mechanism that involves tubular connections between MT1-MMP–positive late endosomes and the plasma membrane in contact with the matrix. This ensures focal delivery of MT1-MMP and supports pericellular matrix degradation and tumor cell invasion into different pathologically relevant matrix environments. Our data suggest a general mechanism used by tumor cells to breach the basement membrane and for invasive migration through fibrous collagen-enriched tissues surrounding the tumor.</p>', '2013 Dec 23', '', '', '6', NULL),
('3048', '102', 'Advances in image correlation spectroscopy measuring number dens', 'Cell Biochem Biophys', '49', NULL, '141-64', '2007', '1085-9195', '10.1007/s12013-007-9000-5', '<p>A brief historical outline of fluorescence fluctuation correlation techniques is presented, followed by an in-depth review of the theory and development of image correlation techniques, including: image correlation spectroscopy (ICS), temporal ICS (TICS), image cross-correlation spectroscopy (ICCS), spatiotemporal ICS (STICS), k-space ICS (kICS), raster ICS (RICS), and particle ICS (PICS). These techniques can be applied to analyze image series acquired on commercially available laser scanning or total internal reflection fluorescence microscopes, and are used to determine the number density, aggregation state, diffusion coefficient, velocity, and interaction fraction of fluorescently labeled molecules or particles. A comprehensive review of the application of ICS techniques to a number of systems, including cell adhesion, membrane receptor aggregation and dynamics, virus particle fusion, and fluorophore photophysics, is presented.</p>', '2007', '', '', '3', NULL),
('3084', '102', 'Highthroughput subpixel precision analysis of bacterial morphoge', 'Mol Microbiol', '80', NULL, '612-27', '2011', '1365-2958', '10.1111/j.1365-2958.2011.07579.x', '<p>Bacteria display various shapes and rely on complex spatial organization of their intracellular components for many cellular processes. This organization changes in response to internal and external cues. Quantitative, unbiased study of these spatio-temporal dynamics requires automated image analysis of large microscopy datasets. We have therefore developed MicrobeTracker, a versatile and high-throughput image analysis program that outlines and segments cells with subpixel precision, even in crowded images and mini-colonies, enabling cell lineage tracking. MicrobeTracker comes with an integrated accessory tool, SpotFinder, which precisely tracks foci of fluorescently labelled molecules inside cells. Using MicrobeTracker, we discover that the dynamics of the extensively studied Escherichia coli Min oscillator depends on Min protein concentration, unveiling critical limitations in robustness within the oscillator. We also find that the fraction of MinD proteins oscillating increases with cell length, indicating that the oscillator has evolved to be most effective when cells attain an appropriate length. MicrobeTracker was also used to uncover novel aspects of morphogenesis and cell cycle regulation in Caulobacter crescentus. By tracking filamentous cells, we show that the chromosomal origin at the old-pole is responsible for most replication/separation events while the others remain largely silent despite contiguous cytoplasm. This surprising position-dependent silencing is regulated by division.</p>', '2011 May', '', '', '3', NULL),
('3092', '102', 'High Resolution Tracking of Cell Membrane Dynamics in Moving Cel', 'Mathematical Modelling of Natural Phenomena', '5', NULL, '34–55', '2010', '1760-6101', '10.1051/mmnp/20105102', '', '1', 'http://www.mmnp-journal.org/article_S0973534810051023', '', '', NULL),
('3097', '102', 'FindFoci A Focus Detection Algorithm with Automated Parameter Tr', 'PLoS One', '9', NULL, 'e114749', '2014', '1932-6203', '10.1371/journal.pone.0114749', '<p>Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ.</p>', '2014', '', '', '12', NULL),
('3154', '103', 'Curvelet analysis of kymograph for tracking bidirectional partic', 'Image Processing (ICIP), 2010 17th IEEE International Conference on', 'None', NULL, 'None', '2010', 'None', '10.1109/ICIP.2010.5652479', 'In this paper we present a new procedure for tracking bi-directional objects in kymographs. The proposed technique is based on a novel adaptive and directional band-pass filtering method which allows us to separate particles which move in opposite directions. The filtering method exploits the curvelet analysis of the kymograph image to automatically adapt to the objects trails characteristics and select oriented features. The separation of bi-directional objects in separated images allows us to reliably detect and track fluorescent particles in fluorescence image sequences, despite numerous crossroad points in the kymograph space. The new abilities provided by the proposed technique are highlighted by the analysis of biological images which were previously impossible to analyze reliably.', 'Sept', '', '', 'None', NULL),
('3156', '102', 'highlevel 3D visualization API for Java and ImageJ', 'BMC Bioinformatics', '11', NULL, '274', '2010', '1471-2105', '10.1186/1471-2105-11-274', '<p><b>BACKGROUND: </b>Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set.</p><p><b>RESULTS: </b>Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts.</p><p><b>CONCLUSIONS: </b>Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. We offer the source code and convenient binary packages along with extensive documentation at http://3dviewer.neurofly.de.</p>', '2010', '', '', '', NULL),
('3167', '102', 'Threedimensional multiscale line filter for segmentation and vis', 'Med Image Anal', '2', NULL, '143-68', '1998', '1361-8415', '', '<p>This paper describes a method for the enhancement of curvilinear structures such as vessels and bronchi in three-dimensional (3-D) medical images. A 3-D line enhancement filter is developed with the aim of discriminating line structures from other structures and recovering line structures of various widths. The 3-D line filter is based on a combination of the eigenvalues of the 3-D Hessian matrix. Multi-scale integration is formulated by taking the maximum among single-scale filter responses, and its characteristics are examined to derive criteria for the selection of parameters in the formulation. The resultant multi-scale line-filtered images provide significantly improved segmentation and visualization of curvilinear structures. The usefulness of the method is demonstrated by the segmentation and visualization of brain vessels from magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA), bronchi from a chest CT, and liver vessels (portal veins) from an abdominal CT.</p>', '1998 Jun', '', '', '2', NULL),
('3172', '102', 'Automated segmentation and tracking for largescale analysis of f', 'J Microsc', '241', NULL, '37-53', '2011', '1365-2818', '10.1111/j.1365-2818.2010.03404.x', '<p>Cell adhesion, a process mediated by the formation of discrete structures known as focal adhesions (FAs), is pivotal to many biological events including cell motility. Much is known about the molecular composition of FAs, although our knowledge of the spatio-temporal recruitment and the relative occupancy of the individual components present in the FAs is still incomplete. To fill this gap, an essential prerequisite is a highly reliable procedure for the recognition, segmentation and tracking of FAs. Although manual segmentation and tracking may provide some advantages when done by an expert, its performance is usually hampered by subjective judgement and the long time required in analysing large data sets. Here, we developed a model-based segmentation and tracking algorithm that overcomes these problems. In addition, we developed a dedicated computational approach to correct segmentation errors that may arise from the analysis of poorly defined FAs. Thus, by achieving accurate and consistent FA segmentation and tracking, our work establishes the basis for a comprehensive analysis of FA dynamics under various experimental regimes and the future development of mathematical models that simulate FA behaviour.</p>', '2011 Jan', '', '', '1', NULL),
('3183', '102', 'idTracker tracking individuals in a group by automatic identific', 'Nat Methods', '11', NULL, '743-8', '2014', '1548-7105', '10.1038/nmeth.2994', '<p>Animals in groups touch each other, move in paths that cross, and interact in complex ways. Current video tracking methods sometimes switch identities of unmarked individuals during these interactions. These errors propagate and result in random assignments after a few minutes unless manually corrected. We present idTracker, a multitracking algorithm that extracts a characteristic fingerprint from each animal in a video recording of a group. It then uses these fingerprints to identify every individual throughout the video. Tracking by identification prevents propagation of errors, and the correct identities can be maintained indefinitely. idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. We tested it on fish (Danio rerio and Oryzias latipes), flies (Drosophila melanogaster), ants (Messor structor) and mice (Mus musculus).</p>', '2014 Jul', '', '', '7', NULL),
('3208', '102', 'Whole slide quantification of stromal lymphatic vessel distribut', 'ISRN Obstet Gynecol', '2011', NULL, '354861', '2011', '2090-4444', '10.5402/2011/354861', '<p>Peritumoral Lymphatic Vessel Density (LVD) is considered to be a predictive marker for the presence of lymph node metastases in cervical cancer. However, when LVD quantification relies on conventional optical microscopy and the hot spot technique, interobserver variability is significant and yields inconsistent conclusions. In this work, we describe an original method that applies computed image analysis to whole slide scanned tissue sections following immunohistochemical lymphatic vessel staining. This procedure allows to determine an objective LVD quantification as well as the lymphatic vessel distribution and its heterogeneity within the stroma surrounding the invasive tumor bundles. The proposed technique can be useful to better characterize lymphatic vessel interactions with tumor cells and could potentially impact on prognosis and therapeutic decisions.</p>', '2011', '', '', '', NULL),
('3217', '102', 'NonLocal Means Denoising', 'Image Processing On Line', '1', NULL, '', '2011', '', '10.5201/ipol10.5201/ipol.201110.5201/ipol.2011.bcm_nlm', '', 'Jan-01-2011', 'http://www.ipol.im/?utm_source=doihttp://www.ipol.im/pub/art/2011/?utm_source=doihttp://www.ipol.im/pub/art/2011/bcm_nlm/?utm_source=doi', '', '', NULL),
('3218', '102', 'Auxin depletion from leaf primordia contributes to organ pattern', 'Proc Natl Acad Sci U S A', '111', NULL, '18769-74', '2014', '1091-6490', '10.1073/pnas.1421878112', '<p>Stem cells are responsible for organogenesis, but it is largely unknown whether and how information from stem cells acts to direct organ patterning after organ primordia are formed. It has long been proposed that the stem cells at the plant shoot apex produce a signal, which promotes leaf adaxial-abaxial (dorsoventral) patterning. Here we show the existence of a transient low auxin zone in the adaxial domain of early leaf primordia. We also demonstrate that this adaxial low auxin domain contributes to leaf adaxial-abaxial patterning. The auxin signal is mediated by the auxin-responsive transcription factor MONOPTEROS (MP), whose constitutive activation in the adaxial domain promotes abaxial cell fate. Furthermore, we show that auxin flow from emerging leaf primordia to the shoot apical meristem establishes the low auxin zone, and that this auxin flow contributes to leaf polarity. Our results provide an explanation for the hypothetical meristem-derived leaf polarity signal. Opposite to the original proposal, instead of a signal derived from the meristem, we show that a signaling molecule is departing from the primordium to the meristem to promote robustness in leaf patterning.</p>', '2014 Dec 30', '', '', '52', NULL),
('3220', '102', 'unbiased detector of curvilinear structures', 'IEEE Transactions on Pattern Analysis and Machine Intelligence', '20', NULL, '113 - 125', '1998', '01628828', '10.1109/34.659930', '', 'Jan-01-1970', 'http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=659930', '', '2', NULL),
('3223', '102', 'NIH Image to ImageJ 25 years of image analysis', 'Nature Methods', '9', NULL, '671 - 675', '2012', '1548-7091', '10.1038/nmeth.2089', '', 'Apr-06-2014', 'http://www.nature.com/doifinder/10.1038/nmeth.2089', '', '7', NULL),
('3239', '102', 'OpenComet an automated tool for comet assay image analysis', 'Redox Biol', '2', NULL, '457-65', '2014', '2213-2317', '10.1016/j.redox.2013.12.020', '<p>Reactive species such as free radicals are constantly generated in vivo and DNA is the most important target of oxidative stress. Oxidative DNA damage is used as a predictive biomarker to monitor the risk of development of many diseases. The comet assay is widely used for measuring oxidative DNA damage at a single cell level. The analysis of comet assay output images, however, poses considerable challenges. Commercial software is costly and restrictive, while free software generally requires laborious manual tagging of cells. This paper presents OpenComet, an open-source software tool providing automated analysis of comet assay images. It uses a novel and robust method for finding comets based on geometric shape attributes and segmenting the comet heads through image intensity profile analysis. Due to automation, OpenComet is more accurate, less prone to human bias, and faster than manual analysis. A live analysis functionality also allows users to analyze images captured directly from a microscope. We have validated OpenComet on both alkaline and neutral comet assay images as well as sample images from existing software packages. Our results show that OpenComet achieves high accuracy with significantly reduced analysis time.</p>', '2014', '', '', '', NULL),
('3257', '102', 'selforganized biomechanical network drives shape changes during ', 'Nature', '524', NULL, '351-5', '2015', '1476-4687', '10.1038/nature14603', '<p>Tissue morphogenesis is orchestrated by cell shape changes. Forces required to power these changes are generated by non-muscle myosin II (MyoII) motor proteins pulling filamentous actin (F-actin). Actomyosin networks undergo cycles of assembly and disassembly (pulses) to cause cell deformations alternating with steps of stabilization to result in irreversible shape changes. Although this ratchet-like behaviour operates in a variety of contexts, the underlying mechanisms remain unclear. Here we investigate the role of MyoII regulation through the conserved Rho1-Rok pathway during Drosophila melanogaster germband extension. This morphogenetic process is powered by cell intercalation, which involves the shrinkage of junctions in the dorsal-ventral axis (vertical junctions) followed by junction extension in the anterior-posterior axis. While polarized flows of medial-apical MyoII pulses deform vertical junctions, MyoII enrichment on these junctions (planar polarity) stabilizes them. We identify two critical properties of MyoII dynamics that underlie stability and pulsatility: exchange kinetics governed by phosphorylation-dephosphorylation cycles of the MyoII regulatory light chain; and advection due to contraction of the motors on F-actin networks. Spatial control over MyoII exchange kinetics establishes two stable regimes of high and low dissociation rates, resulting in MyoII planar polarity. Pulsatility emerges at intermediate dissociation rates, enabling convergent advection of MyoII and its upstream regulators Rho1 GTP, Rok and MyoII phosphatase. Notably, pulsatility is not an outcome of an upstream Rho1 pacemaker. Rather, it is a self-organized system that involves positive and negative biomechanical feedback between MyoII advection and dissociation rates.</p>', '2015 Aug 20', '', '', '7565', NULL),
('3258', '102', 'CellTrack an opensource software for cell tracking and motility ', 'Bioinformatics', '24', NULL, '1647 - 1649', '2008', '1367-4803', '10.1093/bioinformatics/btn247', '', 'Mar-07-2009', 'http://bioinformatics.oxfordjournals.org/cgi/doi/10.1093/bioinformatics/btn247', '', '14', NULL),
('3260', '102', 'Phase locking and multiple oscillating attractors for the couple', 'Proceedings of the National Academy of Sciences', '111', NULL, '9828 - 9833', '2014', '0027-8424', '10.1073/pnas.1320474111', '', 'Aug-07-2014', 'http://www.pnas.org/cgi/doi/10.1073/pnas.1320474111', '', '27', NULL),
('3261', '102', 'Extracting Fluorescent Reporter Time Courses of Cell Lineages fr', 'PLoS ONE', '6', NULL, 'e27886', '2011', '', '10.1371/journal.pone.0027886', '', 'Mar-12-2012', 'http://dx.plos.org/10.1371/journal.pone.0027886', '', '12', NULL),
('3276', '102', 'FibrilTool an ImageJ plugin to quantify fibrillar structures in ', 'Nat Protoc', '9', NULL, '457-63', '2014', '1750-2799', '10.1038/nprot.2014.024', '<p>Cell biology heavily relies on the behavior of fibrillar structures, such as the cytoskeleton, yet the analysis of their behavior in tissues often remains qualitative. Image analysis tools have been developed to quantify this behavior, but they often involve an image pre-processing stage that may bias the output and/or they require specific software. Here we describe FibrilTool, an ImageJ plug-in based on the concept of nematic tensor, which can provide a quantitative description of the anisotropy of fiber arrays and their average orientation in cells, directly from raw images obtained by any form of microscopy. FibrilTool has been validated on microtubules, actin and cellulose microfibrils, but it may also help analyze other fibrillar structures, such as collagen, or the texture of various materials. The tool is ImageJ-based, and it is therefore freely accessible to the scientific community and does not require specific computational setup. The tool provides the average orientation and anisotropy of fiber arrays in a given region of interest (ROI) in a few seconds.</p>', '2014 Feb', '', '', '2', NULL),
('3281', '102', 'Statistical analysis of 3D images detects regular spatial distri', 'PLoS Comput Biol', '6', NULL, 'e1000853', '2010', '1553-7358', '10.1371/journal.pcbi.1000853', '<p>In eukaryotes, the interphase nucleus is organized in morphologically and/or functionally distinct nuclear "compartments". Numerous studies highlight functional relationships between the spatial organization of the nucleus and gene regulation. This raises the question of whether nuclear organization principles exist and, if so, whether they are identical in the animal and plant kingdoms. We addressed this issue through the investigation of the three-dimensional distribution of the centromeres and chromocenters. We investigated five very diverse populations of interphase nuclei at different differentiation stages in their physiological environment, belonging to rabbit embryos at the 8-cell and blastocyst stages, differentiated rabbit mammary epithelial cells during lactation, and differentiated cells of Arabidopsis thaliana plantlets. We developed new tools based on the processing of confocal images and a new statistical approach based on G- and F- distance functions used in spatial statistics. Our original computational scheme takes into account both size and shape variability by comparing, for each nucleus, the observed distribution against a reference distribution estimated by Monte-Carlo sampling over the same nucleus. This implicit normalization allowed similar data processing and extraction of rules in the five differentiated nuclei populations of the three studied biological systems, despite differences in chromosome number, genome organization and heterochromatin content. We showed that centromeres/chromocenters form significantly more regularly spaced patterns than expected under a completely random situation, suggesting that repulsive constraints or spatial inhomogeneities underlay the spatial organization of heterochromatic compartments. The proposed technique should be useful for identifying further spatial features in a wide range of cell types.</p>', '2010', '', '', '7', NULL),
('3285', '102', '3D reconstruction of histological sections Application to mammar', 'Microscopy Research and Technique', '73', NULL, '1019 - 1029', '2010', '', '10.1002/jemt.v73:1110.1002/jemt.20829', '', 'Jan-10-2010', 'http://doi.wiley.com/10.1002/jemt.v73%3A11http://doi.wiley.com/10.1002/jemt.20829http://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fjemt.20829', '', '11', NULL),
('3343', '102', 'Measuring image resolution in optical nanoscopy', 'Nat Methods', '10', NULL, '557-62', '2013', '1548-7105', '10.1038/nmeth.2448', '<p>Resolution in optical nanoscopy (or super-resolution microscopy) depends on the localization uncertainty and density of single fluorescent labels and on the sample\'s spatial structure. Currently there is no integral, practical resolution measure that accounts for all factors. We introduce a measure based on Fourier ring correlation (FRC) that can be computed directly from an image. We demonstrate its validity and benefits on two-dimensional (2D) and 3D localization microscopy images of tubulin and actin filaments. Our FRC resolution method makes it possible to compare achieved resolutions in images taken with different nanoscopy methods, to optimize and rank different emitter localization and labeling strategies, to define a stopping criterion for data acquisition, to describe image anisotropy and heterogeneity, and even to estimate the average number of localizations per emitter. Our findings challenge the current focus on obtaining the best localization precision, showing instead how the best image resolution can be achieved as fast as possible.</p>', '2013 Jun', '', '', '6', NULL),
('3344', '102', 'Fourier shell correlation threshold criteria', 'J Struct Biol', '151', NULL, '250-62', '2005', '1047-8477', '10.1016/j.jsb.2005.05.009', '<p>The resolution value claimed for an electron microscopical three-dimensional reconstruction indicates the overall quality of the experiment. The Fourier shell correlation (FSC) criterion has now become the standard quality measure. However, what has continued to be controversial is the issue of the FSC threshold level at which one defines the reproducible resolution. Here, we discuss the theoretical behaviour of the FSC in conjunction with the various factors which influence it: the number of "voxels" in a given Fourier shell, the symmetry of the structure, and the size of the structure within the reconstruction volume. Both the theoretical considerations and our model experiments show that fixed-valued FSC threshold (like "0.5") may never be used in a reproducible criterion. Fixed threshold values are-as we show here-simply the result of incorrect assumptions in the basic statistics. Two families of FSC threshold curves are discussed: the sigma-factor curves and the new family of bit-based information threshold curves. Whereas sigma-factor curves indicate the resolution level at which one has collected information significantly above the noise level, the information curves indicate the resolution level at which enough information has been collected for interpretation.</p>', '2005 Sep', '', '', '3', NULL),
('3345', '102', 'MorphoLibJ integrated library and plugins for mathematical morph', 'Bioinformatics', '', NULL, '', '2016', '1367-4811', '10.1093/bioinformatics/btw413', '<p><b>MOTIVATION: </b>Mathematical morphology (MM) provides many powerful operators for processing 2D and 3D images. However, most MM plugins currently implemented for the popular ImageJ/Fiji platform are limited to the processing of 2D images.</p><p><b>RESULTS: </b>The MorphoLibJ library proposes a large collection of generic tools based on MM to process binary and grey-level 2D and 3D images, integrated into user-friendly plugins. We illustrate how MorphoLibJ can facilitate the exploitation of 3D images of plant tissues.</p><p><b>AVAILABILITY AND IMPLEMENTATION: </b>MorphoLibJ is freely available at http://imagej.net/MorphoLibJ CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.</p>', '2016 Jul 13', '', '', '', NULL),
('3349', '102', 'neuTube 10 A New Design for Efficient Neuron Reconstruction Soft', 'eNeuro', '2', NULL, '', '2015', '', '10.1523/ENEURO.0049-14.2014', '', '', '', '', '1', NULL),
('3357', '102', 'SRTesseler a method to segment and quantify localizationbased su', 'Nature Methods', '12', NULL, '', '2015', '', '10.1038/nmeth.3579', '', '', '', '', '', NULL),
('3358', '102', 'Opensource image reconstruction of superresolution structured il', 'Nat Commun', '7', NULL, '10980', '2016', '2041-1723', '10.1038/ncomms10980', '<p>Super-resolved structured illumination microscopy (SR-SIM) is an important tool for fluorescence microscopy. SR-SIM microscopes perform multiple image acquisitions with varying illumination patterns, and reconstruct them to a super-resolved image. In its most frequent, linear implementation, SR-SIM doubles the spatial resolution. The reconstruction is performed numerically on the acquired wide-field image data, and thus relies on a software implementation of specific SR-SIM image reconstruction algorithms. We present fairSIM, an easy-to-use plugin that provides SR-SIM reconstructions for a wide range of SR-SIM platforms directly within ImageJ. For research groups developing their own implementations of super-resolution structured illumination microscopy, fairSIM takes away the hurdle of generating yet another implementation of the reconstruction algorithm. For users of commercial microscopes, it offers an additional, in-depth analysis option for their data independent of specific operating systems. As a modular, open-source solution, fairSIM can easily be adapted, automated and extended as the field of SR-SIM progresses.</p>', '2016', '', '', '', NULL),
('3364', '102', 'SIMToolbox a MATLAB toolbox for structured illumination fluoresc', 'Bioinformatics', '32', NULL, '318-20', '2016', '1367-4811', '10.1093/bioinformatics/btv576', '<p><b>UNLABELLED: </b>SIMToolbox is an open-source, modular set of functions for MATLAB equipped with a user-friendly graphical interface and designed for processing two-dimensional and three-dimensional data acquired by structured illumination microscopy (SIM). Both optical sectioning and super-resolution applications are supported. The software is also capable of maximum a posteriori probability image estimation (MAP-SIM), an alternative method for reconstruction of structured illumination images. MAP-SIM can potentially reduce reconstruction artifacts, which commonly occur due to refractive index mismatch within the sample and to imperfections in the illumination.</p><p><b>AVAILABILITY AND IMPLEMENTATION: </b>SIMToolbox, example data and the online documentation are freely accessible at http://mmtg.fel.cvut.cz/SIMToolbox.</p><p><b>CONTACT: </b>[email protected]</p><p><b>SUPPLEMENTARY INFORMATION: </b>Supplementary data are available at Bioinformatics online.</p>', '2016 Jan 15', '', '', '2', NULL),
('3406', '102', 'Parallel DistributedMemory Particle Method Enables AcquisitionRa', 'PLOS One', '11', NULL, 'e0152528', '2016', '', '10.1371/journal.pone.0152528', '', '', '', '', '4', NULL),
('3411', '102', 'ClusDoC A combined cluster detection and colocalization analysis', 'Mol Biol Cell', '', NULL, '', '2016', '1939-4586', '10.1091/mbc.E16-07-0478', '<p>Advances in fluorescence microscopy are providing increasing evidence that the spatial organization of proteins in cell membranes may facilitate signal initiation and integration for appropriate cellular responses. Our understanding of how changes in spatial organization are linked to function has been hampered by the inability to directly measure signaling activity or protein association at the level of individual proteins in intact cells. Here, we have solved this measurement challenge by developing Clus-DoC, an analysis strategy that quantifies both the spatial distribution of a protein as well as its colocalization status. We applied this approach to the triggering of the T-cell receptor (TCR) during T-cell activation as well as to the functionality of focal adhesions in fibroblasts, thereby demonstrating an experimental and analytical workflow that may be used to quantify signaling activity and protein colocalization at the level of individual proteins.</p>', '2016 Aug 31', '', '', '', NULL),
('3413', '102', 'Realtime analysis and visualization for singlemolecule based sup', 'PLoS One', '8', NULL, 'e62918', '2013', '1932-6203', '10.1371/journal.pone.0062918', '<p>Accurate multidimensional localization of isolated fluorescent emitters is a time consuming process in single-molecule based super-resolution microscopy. We demonstrate a functional method for real-time reconstruction with automatic feedback control, without compromising the localization accuracy. Compatible with high frame rates of EM-CCD cameras, it relies on a wavelet segmentation algorithm, together with a mix of CPU/GPU implementation. A combination with Gaussian fitting allows direct access to 3D localization. Automatic feedback control ensures optimal molecule density throughout the acquisition process. With this method, we significantly improve the efficiency and feasibility of localization-based super-resolution microscopy.</p>', '2013', '', '', '4', NULL),
('3415', '102', 'software framework for the analysis of complex microscopy image ', 'IEEE Trans Inf Technol Biomed', '14', NULL, '1075-87', '2010', '1558-0032', '10.1109/TITB.2010.2049024', '<p>Technological advances in both hardware and software have made possible the realization of sophisticated biological imaging experiments using the optical microscope. As a result, modern microscopy experiments are capable of producing complex image datasets. For a given data analysis task, the images in a set are arranged, based on the requirements of the task, by attributes such as the time and focus levels at which they were acquired. Importantly, different tasks performed over the course of an analysis are often facilitated by the use of different arrangements of the images. We present a software framework that supports the use of different logical image arrangements to analyze a physical set of images. This framework, called the Microscopy Image Analysis Tool (MIATool), realizes the logical arrangements using arrays of pointers to the images, thereby removing the need to replicate and manipulate the actual images in their storage medium. In order that they may be tailored to the specific requirements of disparate analysis tasks, these logical arrangements may differ in size and dimensionality, with no restrictions placed on the number of dimensions and the meaning of each dimension. MIATool additionally supports processing flexibility, extensible image processing capabilities, and data storage management.</p>', '2010 Jul', '', '', '4', NULL),
('3419', '102', 'Fast Optimized Cluster Algorithm for Localizations FOCAL a spati', 'Bioinformatics', '32', NULL, '747-54', '2016', '1367-4811', '10.1093/bioinformatics/btv630', '<p><b>MOTIVATION: </b>Single-molecule localization microscopy (SMLM) microscopy provides images of cellular structure at a resolution an order of magnitude below what can be achieved by conventional diffraction limited techniques. The concomitantly larger data sets generated by SMLM require increasingly efficient image analysis software. Density based clustering algorithms, with the most ubiquitous being DBSCAN, are commonly used to quantitatively assess sub-cellular assemblies. DBSCAN, however, is slow, scaling with the number of localizations like O(n log (n)) at best, and it\'s performance is highly dependent upon a subjectively selected choice of parameters.</p><p><b>RESULTS: </b>We have developed a grid-based clustering algorithm FOCAL, which explicitly accounts for several dominant artifacts arising in SMLM image reconstructions. FOCAL is fast and efficient, scaling like O(n), and only has one set parameter. We assess DBSCAN and FOCAL on experimental dSTORM data of clusters of eukaryotic RNAP II and PALM data of the bacterial protein H-NS, then provide a detailed comparison via simulation. FOCAL performs comparable and often superior to DBSCAN while yielding a significantly faster analysis. Additionally, FOCAL provides a novel method for filtering out of focus clusters from complex SMLM images.</p><p><b>AVAILABILITY AND IMPLEMENTATION: </b>The data and code are available at: http://www.utm.utoronto.ca/milsteinlab/resources/Software/FOCAL/ CONTACT: [email protected]</p><p><b>SUPPLEMENTARY INFORMATION: </b>Supplementary data are available at Bioinformatics online.</p>', '2016 Mar 1', '', '', '5', NULL),
('3421', '102', 'guided tour into subcellular colocalization analysis in light mi', 'J Microsc', '224', NULL, '213-32', '2006', '0022-2720', '10.1111/j.1365-2818.2006.01706.x', '<p>It is generally accepted that the functional compartmentalization of eukaryotic cells is reflected by the differential occurrence of proteins in their compartments. The location and physiological function of a protein are closely related; local information of a protein is thus crucial to understanding its role in biological processes. The visualization of proteins residing on intracellular structures by fluorescence microscopy has become a routine approach in cell biology and is increasingly used to assess their colocalization with well-characterized markers. However, image-analysis methods for colocalization studies are a field of contention and enigma. We have therefore undertaken to review the most currently used colocalization analysis methods, introducing the basic optical concepts important for image acquisition and subsequent analysis. We provide a summary of practical tips for image acquisition and treatment that should precede proper colocalization analysis. Furthermore, we discuss the application and feasibility of colocalization tools for various biological colocalization situations and discuss their respective strengths and weaknesses. We have created a novel toolbox for subcellular colocalization analysis under ImageJ, named JACoP, that integrates current global statistic methods and a novel object-based approach.</p>', '2006 Dec', '', '', 'Pt 3', NULL),
('3425', '102', 'Intravital placenta imaging reveals microcirculatory dynamics im', 'PLoS Pathog', '9', NULL, 'e1003154', '2013', '1553-7374', '10.1371/journal.ppat.1003154', '<p>Malaria in pregnancy is exquisitely aggressive, causing a range of adverse maternal and fetal outcomes prominently linked to Plasmodium-infected erythrocyte cytoadherence to fetal trophoblast. To elucidate the physiopathology of infected erythrocytes (IE) sequestration in the placenta we devised an experimental system for intravital placental examination of P. berghei-infected mice. BALB/c females were mated to C57Bl/6 CFP+ male mice and infected with GFP+ P. berghei IE, and at gestational day 18, placentas were exposed for time-lapse imaging acquisition under two-photon microscopy. Real-time images and quantitative measurements revealed that trophoblast conformational changes transiently restrain blood flow in the mouse placental labyrinth. The complex dynamics of placental microcirculation promotes IE accumulation in maternal blood spaces with low blood flow and allows the establishment of stable IE-trophoblast contacts. Further, we show that the fate of sequestered IE includes engulfment by both macrophagic and trophoblastic fetal-derived cells. These findings reinforce the current paradigm that IE interact with the trophoblast and provide definitive evidence on two novel pathogenesis mechanisms: (1) trophoblast layer controls placental microcirculation promoting IE sequestration; and (2) fetal-derived placental cells engulf sequestered IE.</p>', '2013 Jan', '', '', '1', NULL),
('3428', '102', 'SynPAnal software for rapid quantification of the density and in', 'PLoS One', '9', NULL, 'e115298', '2014', '1932-6203', '10.1371/journal.pone.0115298', '<p>Continuous modification of the protein composition at synapses is a driving force for the plastic changes of synaptic strength, and provides the fundamental molecular mechanism of synaptic plasticity and information storage in the brain. Studying synaptic protein turnover is not only important for understanding learning and memory, but also has direct implication for understanding pathological conditions like aging, neurodegenerative diseases, and psychiatric disorders. Proteins involved in synaptic transmission and synaptic plasticity are typically concentrated at synapses of neurons and thus appear as puncta (clusters) in immunofluorescence microscopy images. Quantitative measurement of the changes in puncta density, intensity, and sizes of specific proteins provide valuable information on their function in synaptic transmission, circuit development, synaptic plasticity, and synaptopathy. Unfortunately, puncta quantification is very labor intensive and time consuming. In this article, we describe a software tool designed for the rapid semi-automatic detection and quantification of synaptic protein puncta from 2D immunofluorescence images generated by confocal laser scanning microscopy. The software, dubbed as SynPAnal (for Synaptic Puncta Analysis), streamlines data quantification for puncta density and average intensity, thereby increases data analysis throughput compared to a manual method. SynPAnal is stand-alone software written using the JAVA programming language, and thus is portable and platform-free.</p>', '2014', '', '', '12', NULL),
('3430', '102', 'Automatic Bayesian single molecule identification for localizati', 'Sci Rep', '6', NULL, '33521', '2016', '2045-2322', '10.1038/srep33521', '<p>Single molecule localization microscopy (SMLM) is on its way to become a mainstream imaging technique in the life sciences. However, analysis of SMLM data is biased by user provided subjective parameters required by the analysis software. To remove this human bias we introduce here the Auto-Bayes method that executes the analysis of SMLM data automatically. We demonstrate the success of the method using the photoelectron count of an emitter as selection characteristic. Moreover, the principle can be used for any characteristic that is bimodally distributed with respect to false and true emitters. The method also allows generation of an emitter reliability map for estimating quality of SMLM-based structures. The potential of the Auto-Bayes method is shown by the fact that our first basic implementation was able to outperform all software packages that were compared in the ISBI online challenge in 2015, with respect to molecule detection (Jaccard index).</p>', '2016', '', '', '', NULL),
('3435', '102', 'ViBEZ a framework for 3D virtual colocalization analysis in zebr', 'Nature Methods', '9', NULL, '', '2012', '', '10.1038/nmeth.2076', '', '', 'http://www.nature.com/nmeth/journal/v9/n7/pdf/nmeth.2076.pdf', '', '7', NULL),
('3456', '102', 'Infection Counter Automated Quantification of in Vitro Virus Rep', 'Viruses', '8', NULL, '', '2016', '1999-4915', '10.3390/v8070201', '<p>The ability to accurately and reliably quantify viral infection is essential to basic and translational virology research. Here, we describe a simple and robust automated method for using fluorescence microscopy to estimate the proportion of virally infected cells in a monolayer. We provide details of the automated analysis workflow along with a freely available open-source ImageJ plugin, Infection Counter, for performing image quantification. Using hepatitis C virus (HCV) as an example, we have experimentally verified our method, demonstrating that it is equivalent, if not better, than the established focus-forming assay. Finally, we used Infection Counter to assess the anti-HCV activity of SMBz-CsA, a non-immunosuppressive cyclosporine analogue.</p>', '2016', '', '', '7', NULL),
('3475', '102', 'jicbioimage a tool for automated and reproducible bioimage analy', 'PeerJ', '4', NULL, 'e2674', '2016', '2167-8359', '10.7717/peerj.2674', '', '', 'https://peerj.com/articles/2674', '', '', NULL);
-- --------------------------------------------------------
--
-- Structure de la table `authorpaperrelation`
--
CREATE TABLE `authorpaperrelation` (
`id_author` decimal(10,0) NOT NULL DEFAULT '0',
`id_paper` decimal(10,0) NOT NULL DEFAULT '0',
`Position` varchar(255) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;
--
-- Contenu de la table `authorpaperrelation`
--
INSERT INTO `authorpaperrelation` (`id_author`, `id_paper`, `Position`) VALUES
('1', '2737', '0'),
('2', '2737', '1'),
('6', '2760', '0'),
('7', '2760', '1'),
('8', '2760', '2'),
('9', '2775', '0'),
('10', '2775', '1'),
('11', '2775', '2'),
('12', '2777', '0'),
('13', '2777', '1'),
('14', '2777', '2'),
('15', '2777', '3'),
('16', '2778', '0'),
('17', '2782', '0'),
('18', '2784', '0'),
('19', '2784', '1'),
('20', '2784', '2'),
('21', '2784', '3'),
('22', '2787', '0'),
('23', '2787', '1'),
('24', '2789', '0'),
('25', '2789', '1'),
('26', '2789', '2'),
('27', '2789', '3'),
('28', '2789', '4'),
('5', '2792', '0'),
('29', '2792', '1'),
('30', '2792', '2'),
('31', '2792', '3'),
('32', '2792', '4'),
('33', '2792', '5'),
('34', '2792', '6'),
('35', '2792', '7'),
('36', '2792', '8'),
('37', '2792', '9'),
('38', '2792', '10'),
('39', '2792', '11'),
('40', '2792', '12'),
('41', '2792', '13'),
('42', '2792', '14'),
('43', '2793', '0'),
('44', '2793', '1'),
('45', '2793', '2'),
('46', '2793', '3'),
('47', '2793', '4'),
('48', '2796', '0'),
('49', '2796', '1'),
('50', '2798', '0'),
('51', '2798', '1'),
('52', '2798', '2'),
('53', '2798', '3'),
('54', '2800', '0'),
('55', '2800', '1'),
('56', '2800', '2'),
('57', '2800', '3'),
('58', '2800', '4'),
('59', '2800', '5'),
('60', '2800', '6'),
('61', '2804', '0'),
('62', '2804', '1'),
('63', '2804', '2'),
('64', '2804', '3'),
('65', '2804', '4'),
('66', '2804', '5'),
('67', '2804', '6'),
('68', '2811', '0'),
('69', '2811', '1'),
('70', '2811', '2'),
('71', '2811', '3'),
('72', '2811', '4'),
('73', '2811', '5'),
('74', '2811', '6'),
('75', '2811', '7'),
('76', '2812', '0'),
('77', '2812', '1'),
('71', '2817', '0'),
('24', '2818', '0'),
('28', '2818', '2'),
('78', '2818', '1'),
('79', '2830', '0'),
('80', '2830', '1'),
('81', '2830', '2'),
('82', '2830', '3'),
('83', '2830', '4'),
('84', '2830', '5'),
('85', '2830', '6'),
('1', '2831', '0'),
('86', '2831', '1'),
('87', '2831', '2'),
('88', '2831', '3'),
('89', '2831', '4'),
('90', '2831', '5'),
('91', '2831', '6'),
('92', '2839', '0'),
('93', '2839', '1'),
('94', '2839', '2'),
('95', '2839', '3'),
('96', '2839', '4'),
('97', '2839', '5'),
('98', '2839', '6'),
('99', '2839', '7'),
('100', '2850', '0'),
('101', '2850', '1'),
('102', '2850', '2'),
('103', '2850', '3'),
('104', '2850', '4'),
('105', '2850', '5'),
('106', '2850', '6'),
('107', '2850', '7'),
('117', '2871', '0'),
('118', '2871', '1'),
('119', '2871', '2'),
('120', '2882', '0'),
('121', '2882', '1'),
('122', '2882', '2'),
('123', '2882', '3'),
('124', '2882', '4'),
('125', '2882', '5'),
('126', '2882', '6'),
('127', '2882', '7'),
('128', '2882', '8'),
('129', '2882', '9'),
('130', '2882', '10'),
('131', '2882', '11'),
('132', '2886', '0'),
('133', '2886', '1'),
('134', '2886', '2'),
('135', '2886', '3'),
('136', '2886', '4'),
('137', '2886', '5'),
('138', '2886', '6'),
('10', '2897', '1'),
('11', '2897', '8'),
('139', '2897', '0'),
('140', '2897', '2'),
('141', '2897', '3'),
('142', '2897', '4'),
('143', '2897', '5'),
('144', '2897', '6'),
('145', '2897', '7'),
('146', '2901', '0'),
('147', '2901', '1'),
('148', '2901', '2'),
('149', '2901', '3'),
('150', '2901', '4'),
('151', '2906', '0'),
('152', '2906', '1'),
('153', '2906', '2'),
('154', '2906', '3'),
('155', '2906', '4'),
('156', '2906', '5'),
('157', '2906', '6'),
('158', '2906', '7'),
('159', '2906', '8'),
('160', '2906', '9'),
('161', '2915', '0'),
('162', '2915', '1'),
('163', '2915', '2'),
('164', '2915', '3'),
('165', '2915', '4'),
('166', '2915', '5'),
('167', '2915', '6'),
('168', '2918', '0'),
('169', '2918', '1'),
('170', '2918', '2'),
('171', '2918', '3'),
('172', '2918', '4'),
('173', '2933', '0'),
('174', '2933', '1'),
('175', '2933', '2'),
('176', '2933', '3'),
('177', '2933', '4'),
('183', '2956', '0'),
('184', '2956', '1'),
('185', '2956', '2'),
('186', '2956', '3'),
('187', '2956', '4'),
('188', '2956', '5'),
('189', '2956', '6'),
('190', '2956', '7'),
('11', '2960', '4'),
('44', '2960', '21'),
('191', '2960', '0'),
('192', '2960', '1'),
('193', '2960', '2'),
('194', '2960', '3'),
('195', '2960', '5'),
('196', '2960', '6'),
('197', '2960', '7'),
('198', '2960', '8'),
('199', '2960', '9'),
('200', '2960', '10'),
('201', '2960', '11'),
('202', '2960', '12'),
('203', '2960', '13'),
('204', '2960', '14'),
('205', '2960', '15'),
('206', '2960', '16'),
('207', '2960', '17'),
('208', '2960', '18'),
('209', '2960', '19'),
('210', '2960', '20'),
('211', '2960', '22'),
('212', '2960', '23'),
('213', '2960', '24'),
('214', '2960', '25'),
('215', '2960', '26'),
('216', '2960', '27'),
('217', '2960', '28'),
('218', '2960', '29'),
('219', '2960', '30'),
('220', '2960', '31'),
('221', '2960', '32'),
('222', '2960', '33'),
('223', '2960', '34'),
('224', '2962', '0'),
('225', '2962', '1'),
('42', '2964', '0'),
('226', '2973', '0'),
('227', '2973', '1'),
('228', '2973', '2'),
('229', '2973', '3'),
('230', '2973', '4'),
('231', '2973', '5'),
('232', '2983', '0'),
('233', '2983', '1'),
('234', '2983', '2'),
('235', '2983', '3'),
('236', '2983', '4'),
('237', '2983', '5'),
('238', '2983', '6'),
('239', '2983', '7'),
('240', '2983', '8'),
('241', '2983', '9'),
('242', '2983', '10'),
('243', '2983', '11'),
('244', '2983', '12'),
('245', '2983', '13'),
('246', '2983', '14'),
('247', '2983', '15'),
('248', '2983', '16'),
('249', '3000', '0'),
('250', '3001', '0'),
('251', '3001', '1'),
('252', '3001', '2'),
('253', '3023', '0'),
('254', '3023', '1'),
('255', '3023', '2'),
('256', '3023', '3'),
('257', '3023', '4'),
('258', '3023', '5'),
('259', '3023', '6'),
('260', '3023', '7'),
('261', '3026', '0'),
('262', '3026', '1'),
('263', '3026', '2'),
('264', '3026', '3'),
('265', '3026', '4'),
('212', '3028', '3'),
('266', '3028', '0'),
('267', '3028', '1'),
('268', '3028', '2'),
('269', '3028', '4'),
('270', '3031', '0'),
('271', '3031', '1'),
('272', '3031', '2'),
('44', '3040', '5'),
('273', '3040', '0'),
('274', '3040', '1'),
('275', '3040', '2'),
('276', '3040', '3'),
('277', '3040', '4'),
('278', '3040', '6'),
('279', '3040', '7'),
('280', '3040', '8'),
('281', '3040', '9'),
('282', '3040', '10'),
('283', '3040', '11'),
('284', '3040', '12'),
('285', '3048', '0'),
('286', '3048', '1'),
('287', '3084', '0'),
('288', '3084', '1'),
('289', '3084', '2'),
('290', '3084', '3'),
('291', '3092', '0'),
('292', '3092', '1'),
('293', '3092', '2'),
('294', '3092', '3'),
('295', '3097', '0'),
('296', '3097', '1'),
('297', '3097', '2'),
('298', '3154', '0'),
('299', '3154', '1'),
('300', '3154', '2'),
('301', '3154', '3'),
('302', '3154', '4'),
('303', '3156', '0'),
('304', '3156', '1'),
('305', '3156', '2'),
('306', '3156', '3'),
('307', '3156', '4'),
('308', '3167', '0'),
('309', '3167', '1'),
('310', '3167', '2'),
('311', '3167', '3'),
('312', '3167', '4'),
('313', '3167', '5'),
('314', '3167', '6'),
('315', '3167', '7'),
('316', '3172', '0'),
('317', '3172', '1'),
('318', '3172', '2'),
('319', '3172', '3'),
('178', '3183', '0'),
('179', '3183', '1'),
('180', '3183', '2'),
('181', '3183', '3'),
('182', '3183', '4'),
('320', '3208', '0'),
('321', '3208', '1'),
('322', '3208', '2'),
('323', '3208', '3'),
('324', '3208', '4'),
('325', '3208', '5'),
('326', '3208', '6'),
('327', '3208', '7'),
('328', '3208', '8'),
('329', '3217', '0'),
('330', '3217', '1'),
('331', '3217', '2'),
('332', '3218', '0'),
('333', '3218', '1'),
('334', '3218', '2'),
('335', '3218', '3'),
('336', '3218', '4'),
('337', '3218', '5'),
('338', '3218', '6'),
('339', '3218', '7'),
('340', '3220', '0'),
('3', '3223', '0'),
('4', '3223', '1'),
('5', '3223', '2'),
('341', '3239', '0'),
('342', '3239', '1'),
('343', '3239', '2'),
('344', '3239', '3'),
('345', '3239', '4'),
('346', '3257', '0'),
('347', '3257', '1'),
('348', '3257', '2'),
('349', '3257', '3'),
('350', '3258', '0'),
('351', '3258', '1'),
('352', '3258', '2'),
('353', '3260', '0'),
('354', '3260', '1'),
('355', '3260', '2'),
('356', '3260', '3'),
('357', '3260', '4'),
('358', '3260', '5'),
('359', '3260', '6'),
('360', '3260', '7'),
('361', '3260', '8'),
('362', '3260', '9'),
('363', '3260', '10'),
('364', '3260', '11'),
('365', '3260', '12'),
('366', '3261', '0'),
('367', '3261', '1'),
('368', '3261', '2'),
('369', '3261', '3'),
('370', '3261', '4'),
('371', '3261', '5'),
('372', '3261', '6'),
('373', '3261', '7'),
('374', '3276', '0'),
('375', '3276', '1'),
('376', '3276', '2'),
('377', '3276', '3'),
('378', '3276', '4'),
('379', '3276', '5'),
('380', '3276', '6'),
('381', '3281', '0'),
('382', '3281', '1'),
('383', '3281', '2'),
('384', '3281', '3'),
('385', '3281', '4'),
('386', '3281', '5'),
('387', '3281', '6'),
('388', '3281', '7'),
('389', '3281', '8'),
('390', '3281', '9'),
('391', '3281', '10'),
('392', '3281', '11'),
('393', '3281', '12'),
('394', '3281', '13'),
('395', '3281', '14'),
('396', '3281', '15'),
('397', '3285', '0'),
('398', '3285', '1'),
('399', '3285', '2'),
('400', '3285', '3'),
('401', '3343', '0'),
('402', '3343', '1'),
('403', '3343', '2'),
('404', '3343', '3'),
('405', '3343', '4'),
('406', '3343', '5'),
('407', '3343', '6'),
('408', '3344', '0'),
('409', '3344', '1'),
('381', '3345', '2'),
('397', '3345', '1'),
('410', '3345', '0'),
('411', '3349', '0'),
('412', '3349', '1'),
('413', '3349', '2'),
('414', '3357', '0'),
('415', '3357', '1'),
('416', '3357', '2'),
('417', '3357', '3'),
('418', '3357', '4'),
('419', '3357', '5'),
('420', '3357', '6'),
('421', '3358', '0'),
('422', '3358', '1'),
('423', '3358', '2'),
('424', '3358', '3'),
('425', '3358', '4'),
('426', '3364', '0'),
('427', '3364', '1'),
('428', '3364', '2'),
('429', '3364', '3'),
('430', '3364', '4'),
('431', '3406', '0'),
('432', '3406', '1'),
('433', '3411', '0'),
('434', '3411', '1'),
('435', '3411', '2'),
('436', '3411', '3'),
('437', '3411', '4'),
('438', '3413', '0'),
('439', '3413', '1'),
('440', '3413', '2'),
('441', '3413', '3'),
('442', '3413', '4'),
('443', '3415', '0'),
('444', '3415', '1'),
('445', '3415', '2'),
('446', '3419', '0'),
('447', '3419', '1'),
('448', '3421', '0'),
('449', '3421', '1'),
('450', '3425', '0'),
('451', '3425', '1'),
('452', '3425', '2'),
('453', '3425', '3'),
('454', '3425', '4'),
('455', '3428', '0'),
('456', '3428', '1'),
('457', '3430', '0'),
('458', '3430', '1'),
('459', '3430', '2'),
('460', '3430', '3'),
('461', '3430', '4'),
('479', '3435', '0'),
('480', '3435', '1'),
('481', '3435', '2'),
('482', '3435', '3'),
('483', '3435', '4'),
('484', '3435', '5'),
('485', '3435', '6'),
('486', '3435', '7'),
('487', '3435', '8'),
('488', '3435', '9'),
('489', '3435', '10'),
('490', '3435', '11'),
('491', '3435', '12'),
('492', '3456', '0'),
('493', '3456', '1'),
('494', '3456', '2'),
('495', '3456', '3'),
('496', '3456', '4'),
('497', '3475', '0'),
('498', '3475', '1');
-- --------------------------------------------------------
--
-- Structure de la table `authors`
--
CREATE TABLE `authors` (
`id_author` decimal(10,0) NOT NULL DEFAULT '0',
`Complete_name` varchar(255) DEFAULT NULL,
`Affiliation` longtext
) ENGINE=MyISAM DEFAULT CHARSET=utf8;
--
-- Contenu de la table `authors`
--
INSERT INTO `authors` (`id_author`, `Complete_name`, `Affiliation`) VALUES
('1', 'Cordelières, Fabrice P', ''),
('2', 'Bolte, Susanne', ''),
('3', 'Schneider, Caroline A', ''),
('4', 'Rasband, Wayne S', ''),
('5', 'Eliceiri, Kevin W', ''),
('6', 'Sun, Yu', ''),
('7', 'Duthaler, Stefan', ''),
('8', 'Nelson, Bradley J', ''),
('9', 'Helmuth, Jo A', ''),
('10', 'Paul, Grégory', ''),
('11', 'Sbalzarini, Ivo F', ''),
('12', 'Mayer, Christian', ''),
('13', 'Dimopoulos, Sotiris', ''),
('14', 'Rudolf, Fabian', ''),
('15', 'Stelling, Jörg', ''),
('16', 'Cromey, Douglas W', ''),
('17', 'Wen-Hsiang Tsai', ''),
('18', 'Ponti, Aaron', ''),
('19', 'Schwarb, Patrick', ''),
('20', 'Gulati, Asheesh', ''),
('21', 'Bäcker, Volker', ''),
('22', 'Ruifrok, A C', ''),
('23', 'Johnston, D A', ''),
('24', 'Tuominen, Vilppu J', ''),
('25', 'Ruotoistenmäki, Sanna', ''),
('26', 'Viitanen, Arttu', ''),
('27', 'Jumppanen, Mervi', ''),
('28', 'Isola, Jorma', ''),
('29', 'Berthold, Michael R', ''),
('30', 'Goldberg, Ilya G', ''),
('31', 'Ibáñez, Luis', ''),
('32', 'Manjunath, B S', ''),
('33', 'Martone, Maryann E', ''),
('34', 'Murphy, Robert F', ''),
('35', 'Peng, Hanchuan', ''),
('36', 'Plant, Anne L', ''),
('37', 'Roysam, Badrinath', ''),
('38', 'Stuurman, Nico', ''),
('39', 'Stuurmann, Nico', ''),
('40', 'Swedlow, Jason R', ''),
('41', 'Tomancak, Pavel', ''),
('42', 'Carpenter, Anne E', ''),
('43', 'Broders-Bondon, Florence', ''),
('44', 'Paul-Gilloteaux, Perrine', ''),
('45', 'Carlier, Camille', ''),
('46', 'Radice, Glenn L', ''),
('47', 'Dufour, Sylvie', ''),
('48', 'Eils, Roland', ''),
('49', 'Athale, Chaitanya', ''),
('50', 'Picht, Eckard', ''),
('51', 'Zima, Aleksey V', ''),
('52', 'Blatter, Lothar A', ''),
('53', 'Bers, Donald M', ''),
('54', 'Cheng, H', ''),
('55', 'Song, L S', ''),
('56', 'Shirokova, N', ''),
('57', 'González, A', ''),
('58', 'Lakatta, E G', ''),
('59', 'Ríos, E', ''),
('60', 'Stern, M D', ''),
('61', 'Rode, Nicolas O', ''),
('62', 'Lievens, Eva J P', ''),
('63', 'Flaven, Elodie', ''),
('64', 'Segard, Adeline', ''),
('65', 'Jabbour-Zahab, Roula', ''),
('66', 'Sanchez, Marta I', ''),
('67', 'Lenormand, Thomas', ''),
('68', 'Macherel, David', ''),
('69', 'Abdelilah BENAMAR', ''),
('70', 'Pierart, Antoine', ''),
('71', 'Baecker, Volker', ''),
('72', 'Avelange-Macherel, Marie-Hélène', ''),
('73', 'Rolland, Aurélia', ''),
('74', 'Gaudichon, Sabine', ''),
('75', 'Di Gioia, Lodovico', ''),
('76', 'Mutterer, J', ''),
('77', 'Zinck, E', ''),
('78', 'Tolonen, Teemu T', ''),
('79', 'Jaqaman, Khuloud', ''),
('80', 'Loerke, Dinah', ''),
('81', 'Mettlen, Marcel', ''),
('82', 'Kuwata, Hirotaka', ''),
('83', 'Grinstein, Sergio', ''),
('84', 'Schmid, Sandra L', ''),
('85', 'Danuser, Gaudenz', ''),
('86', 'Petit, Valérie', ''),
('87', 'Kumasaka, Mayuko', ''),
('88', 'Debeir, Olivier', ''),
('89', 'Letort, Véronique', ''),
('90', 'Gallagher, Stuart J', ''),
('91', 'Larue, Lionel', ''),
('92', 'Macenko, M.', ''),
('93', 'Niethammer, M.', ''),
('94', 'Marron, J.S.', ''),
('95', 'Borland, D.', ''),
('96', 'Woosley, J.T.', ''),
('97', 'Xiaojun Guan', ''),
('98', 'Schmitt, C.', ''),
('99', 'Thomas, N.E.', ''),
('100', 'Parton, Richard M', ''),
('101', 'Hamilton, Russell S', ''),
('102', 'Ball, Graeme', ''),
('103', 'Yang, Lei', ''),
('104', 'Cullen, C Fiona', ''),
('105', 'Lu, Weiping', ''),
('106', 'Ohkura, Hiroyuki', ''),
('107', 'Davis, Ilan', ''),
('108', 'Cédric Balsat', ''),
('109', 'Silvia Blacher', ''),
('110', 'Nicolas Signolle', ''),
('111', 'Aude Béliard', ''),
('112', 'Carine Munaut', ''),
('113', 'Frédéric Goffin', ''),
('114', 'Agnès Noël', ''),
('115', 'Jean-Michel Foidart', ''),
('116', 'Frédéric Kridelka', ''),
('117', 'Pengo, Thomas', ''),
('118', 'Holden, Seamus J', ''),
('119', 'Manley, Suliana', ''),
('120', 'Hummel, Irène', ''),
('121', 'Pantin, Florent', ''),
('122', 'Sulpice, Ronan', ''),
('123', 'Piques, Maria', ''),
('124', 'Rolland, Gaëlle', ''),
('125', 'Dauzat, Myriam', ''),
('126', 'Christophe, Angélique', ''),
('127', 'Pervent, Marjorie', ''),
('128', 'Bouteillé, Marie', ''),
('129', 'Stitt, Mark', ''),
('130', 'Gibon, Yves', ''),
('131', 'Muller, Bertrand', ''),
('132', 'Zanella, R', ''),
('133', 'Zanghirati, G', ''),
('134', 'Cavicchioli, R', ''),
('135', 'Zanni, L', ''),
('136', 'Boccacci, P', ''),
('137', 'Bertero, M', ''),
('138', 'Vicidomini, G', ''),
('139', 'Rizk, Aurélien', ''),
('140', 'Incardona, Pietro', ''),
('141', 'Bugarski, Milica', ''),
('142', 'Mansouri, Maysam', ''),
('143', 'Niemann, Axel', ''),
('144', 'Ziegler, Urs', ''),
('145', 'Berger, Philipp', ''),
('146', 'Fernández, José A', ''),
('147', 'Bankhead, Peter', ''),
('148', 'Zhou, Huiyu', ''),
('149', 'McGeown, J Graham', ''),
('150', 'Curtis, Tim M', ''),
('151', 'Mueller, Florian', ''),
('152', 'Senecal, Adrien', ''),
('153', 'Tantale, Katjana', ''),
('154', 'Marie-Nelly, Hervé', ''),
('155', 'Ly, Nathalie', ''),
('156', 'Collin, Olivier', ''),
('157', 'Basyuk, Eugenia', ''),
('158', 'Bertrand, Edouard', ''),
('159', 'Darzacq, Xavier', ''),
('160', 'Zimmer, Christophe', ''),
('161', 'Wolter, Steve', ''),
('162', 'Löschberger, Anna', ''),
('163', 'Holm, Thorge', ''),
('164', 'Aufmkolk, Sarah', ''),
('165', 'Dabauvalle, Marie-Christine', ''),
('166', 'van de Linde, Sebastian', ''),
('167', 'Sauer, Markus', ''),
('168', 'Feige, Jérôme N', ''),
('169', 'Sage, Daniel', ''),
('170', 'Wahli, Walter', ''),
('171', 'Desvergne, Béatrice', ''),
('172', 'Gelman, Laurent', ''),
('173', 'Ollion, Jean', ''),
('174', 'Cochennec, Julien', ''),
('175', 'Loll, François', ''),
('176', 'Escudé, Christophe', ''),
('177', 'Boudier, Thomas', ''),
('178', 'Pérez-Escudero, Alfonso', ''),
('179', 'Vicente-Page, Julián', ''),
('180', 'Hinz, Robert C', ''),
('181', 'Arganda, Sara', ''),
('182', 'de Polavieja, Gonzalo G', ''),
('183', 'Ghotra, Veerander P S', ''),
('184', 'He, Shuning', ''),
('185', 'de Bont, Hans', ''),
('186', 'van der Ent, Wietske', ''),
('187', 'Spaink, Herman P', ''),
('188', 'van de Water, Bob', ''),
('189', 'Snaar-Jagalska, B Ewa', ''),
('190', 'Danen, Erik H J', ''),
('191', 'Chenouard, Nicolas', ''),
('192', 'Smal, Ihor', ''),
('193', 'de Chaumont, Fabrice', ''),
('194', 'Maška, Martin', ''),
('195', 'Gong, Yuanhao', ''),
('196', 'Cardinale, Janick', ''),
('197', 'Carthel, Craig', ''),
('198', 'Coraluppi, Stefano', ''),
('199', 'Winter, Mark', ''),
('200', 'Cohen, Andrew R', ''),
('201', 'Godinez, William J', ''),
('202', 'Rohr, Karl', ''),
('203', 'Kalaidzidis, Yannis', ''),
('204', 'Liang, Liang', ''),
('205', 'Duncan, James', ''),
('206', 'Shen, Hongying', ''),
('207', 'Xu, Yingke', ''),
('208', 'Magnusson, Klas E G', ''),
('209', 'Jaldén, Joakim', ''),
('210', 'Blau, Helen M', ''),
('211', 'Roudot, Philippe', ''),
('212', 'Kervrann, Charles', ''),
('213', 'Waharte, François', ''),
('214', 'Tinevez, Jean-Yves', ''),
('215', 'Shorte, Spencer L', ''),
('216', 'Willemse, Joost', ''),
('217', 'Celler, Katherine', ''),
('218', 'van Wezel, Gilles P', ''),
('219', 'Dan, Han-Wei', ''),
('220', 'Tsai, Yuh-Show', ''),
('221', 'Ortiz de Solórzano, Carlos', ''),
('222', 'Olivo-Marin, Jean-Christophe', ''),
('223', 'Meijering, Erik', ''),
('224', 'Small, Alex', ''),
('225', 'Stahlheber, Shane', ''),
('226', 'Sergey Laptenok', ''),
('227', 'Katharine M. Mullen', ''),
('228', 'Jan Willem Borst', ''),
('229', 'Ivo H. M. van Stokkum', ''),
('230', 'Vladimir V. Apanasovich', ''),
('231', 'Antonie J. W. G. Visser', ''),
('232', 'Gell, Christopher', ''),
('233', 'Bormuth, Volker', ''),
('234', 'Brouhard, Gary J', ''),
('235', 'Cohen, Daniel N', ''),
('236', 'Diez, Stefan', ''),
('237', 'Friel, Claire T', ''),
('238', 'Helenius, Jonne', ''),
('239', 'Nitzsche, Bert', ''),
('240', 'Petzold, Heike', ''),
('241', 'Ribbe, Jan', ''),
('242', 'Schäffer, Erik', ''),
('243', 'Stear, Jeffrey H', ''),
('244', 'Trushko, Anastasiya', ''),
('245', 'Varga, Vladimir', ''),
('246', 'Widlund, Per O', ''),
('247', 'Zanic, Marija', ''),
('248', 'Howard, Jonathon', ''),
('249', 'Lowe, DavidG.', ''),
('250', 'Brown, Matthew', ''),
('251', 'Szeliski, Richard', ''),
('252', 'Winder, Simon', ''),
('253', 'Aluoch, J R', ''),
('254', 'Kilinç, Y', ''),
('255', 'Aksoy, M', ''),
('256', 'Yüregir, G T', ''),
('257', 'Bakioglu, I', ''),
('258', 'Kutlar, A', ''),
('259', 'Kutlar, F', ''),
('260', 'Huisman, T H', ''),
('261', 'Srinivasa, Gowri', ''),
('262', 'Fickus, Matthew C', ''),
('263', 'Guo, Yusong', ''),
('264', 'Linstedt, Adam D', ''),
('265', 'Kovacevi?, Jelena', ''),
('266', 'Basset, Antoine', ''),
('267', 'Boulanger, Jérôme', ''),
('268', 'Bouthemy, Patrick', ''),
('269', 'Salamero, Jean', ''),
('270', 'Horzum, Utku', ''),
('271', 'Ozdil, Berrin', ''),
('272', 'Pesen-Okvur, Devrim', ''),
('273', 'Monteiro, Pedro', ''),
('274', 'Rossé, Carine', ''),
('275', 'Castro-Castro, Antonio', ''),
('276', 'Irondelle, Marie', ''),
('277', 'Lagoutte, Emilie', ''),
('278', 'Desnos, Claire', ''),
('279', 'Formstecher, Etienne', ''),
('280', 'Darchen, François', ''),
('281', 'Perrais, David', ''),
('282', 'Gautreau, Alexis', ''),
('283', 'Hertzog, Maud', ''),
('284', 'Chavrier, Philippe', ''),
('285', 'Kolin, David L', ''),
('286', 'Wiseman, Paul W', ''),
('287', 'Sliusarenko, Oleksii', ''),
('288', 'Heinritz, Jennifer', ''),
('289', 'Emonet, Thierry', ''),
('290', 'Jacobs-Wagner, Christine', ''),
('291', 'Tyson,R.A.', ''),
('292', 'Epstein,D.B.A.', ''),
('293', 'Anderson,K.I.', ''),
('294', 'Bretschneider,T.', ''),
('295', 'Herbert, Alex D', ''),
('296', 'Carr, Antony M', ''),
('297', 'Hoffmann, Eva', ''),
('298', 'Chenouard, N.', ''),
('299', 'Buisson, J.', ''),
('300', 'Bloch, I.', ''),
('301', 'Bastin, P.', ''),
('302', 'Olivo-Marin, J.-C.', ''),
('303', 'Schmid, Benjamin', ''),
('304', 'Schindelin, Johannes', ''),
('305', 'Cardona, Albert', ''),
('306', 'Longair, Mark', ''),
('307', 'Heisenberg, Martin', ''),
('308', 'Sato, Y', ''),
('309', 'Nakajima, S', ''),
('310', 'Shiraga, N', ''),
('311', 'Atsumi, H', ''),
('312', 'Yoshida, S', ''),
('313', 'Koller, T', ''),
('314', 'Gerig, G', ''),
('315', 'Kikinis, R', ''),
('316', 'Würflinger, T', ''),
('317', 'Gamper, I', ''),
('318', 'Aach, T', ''),
('319', 'Sechi, A S', ''),
('320', 'Balsat, C', ''),
('321', 'Blacher, S', ''),
('322', 'Signolle, N', ''),
('323', 'Beliard, A', ''),
('324', 'Munaut, C', ''),
('325', 'Goffin, F', ''),
('326', 'Noel, A', ''),
('327', 'Foidart, J M', ''),
('328', 'Kridelka, F', ''),
('329', 'Buades, Antoni', ''),
('330', 'Coll, Bartomeu', ''),
('331', 'Morel, Jean-Michel', ''),
('332', 'Qi, Jiyan', ''),
('333', 'Wang, Ying', ''),
('334', 'Yu, Ting', ''),
('335', 'Cunha, Alexandre', ''),
('336', 'Wu, Binbin', ''),
('337', 'Vernoux, Teva', ''),
('338', 'Meyerowitz, Elliot', ''),
('339', 'Jiao, Yuling', ''),