255 research outputs found
Weber (Christoph). Der «Fall Spahn» (1901). Ein Beitrag zur Wissenschafts- und Kulturdiskussion in ausgehenden 19. Jahrhundert
No full textPirotte Jean. Weber (Christoph). Der «Fall Spahn» (1901). Ein Beitrag zur Wissenschafts- und Kulturdiskussion in ausgehenden 19. Jahrhundert. In: Revue belge de philologie et d'histoire, tome 63, fasc. 2, 1985. Histoire médiévale, moderne et contemporaine — Middeleeuwse, moderne en hedendaagse geschiedenis. p. 426
Multi‐Color, Bleaching‐Resistant Super‐Resolution Optical Fluctuation Imaging with Oligonucleotide‐Based Exchangeable Fluorophores
No full textSuper-resolution optical fluctuation imaging (SOFI) is a super-resolution microscopy technique that overcomes the diffraction limit by analyzing intensity fluctuations of statistically independent emitters in a time series of images. The final images are background-free and show confocality and enhanced spatial resolution (super-resolution). Fluorophore photobleaching, however, is a key limitation for recording long time series of images that will allow for the calculation of higher order SOFI results with correspondingly increased resolution. Here, we demonstrate that photobleaching can be circumvented by using fluorophore labels that reversibly and transiently bind to a target, and which are being replenished from a buffer which serves as a reservoir. Using fluorophore-labeled short DNA oligonucleotides, we labeled cellular structures with target-specific antibodies that contain complementary DNA sequences and record the fluctuation events caused by transient emitter binding. We show that this concept bypasses extensive photobleaching and facilitates two-color imaging of cellular structures with SOFI
Programm | Sektion 2 | Freitag, 8. März 2013 | Digitale geschichtsdidaktische Lehr-Lern-Projekte | Best Practice-Beispiele
No full text15:40 Uhr WebQuests, Storytelling, Weiterentwicklung in Web2.0 und Web3.0 Alexander König 16:00 Uhr Classroom4.eu - Schüler schreiben ein multimediales Online-Schulbuch zur Kulturgeschichte Europas Daniel Bernsen 16:20 Uhr Diskussion 17:10 Uhr Einsatz von Weblogs in der historisch-politischen Bildung. Birgit Marzinka 17:30 Uhr Historisches Lernen durch digitales Erzählen? Thomas Spahn 18:00 Uhr Diskussion Moderation Christoph Pallask
Nanoscopy of bacterial cells immobilized by holographic optical tweezers
No full textDiekmann R, Wolfson D, Spahn C, Heilemann M, Schüttpelz M, Huser T. Nanoscopy of bacterial cells immobilized by holographic optical tweezers. Nature Communications. 2016;7(1): 13711
DeepBacs – Escherichia coli nucleoid denoising dataset and CARE model
No full textTraining and test images of H-NS-mScarlet-I expressing E. coli cells for image denoising, as well as a trained CARE model.
Additional information can be found on our github wiki.
The example images show confocal images of labelled E. coli nucleoids at low and high SNR.
Training and test dataset
Data type: Paired microscopy images (fluorescence)
Microscopy data type: Confocal fluorescence images
Microscope: Leica SP8 confocal microscope with a 1.40 NA 63x oil immersion objective
Cell type: E. coli strain CS01 expressing H-NS-mScarlet-I fusion protein (H-NS-mScarlet-I) in NO34 parental strain (MreB-sfGFPsw, kindly provided by Zemer Gitai)
File format: .tif (16-bit)
Image size: 512 x 512 px2 (Pixel size: 45 nm)
CARE model:
The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 100 epochs (600 steps/epoch) on 1400 paired image patches (image dimensions: (512 x 512 px²), patch size: (64 x 64 px²), 50 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.2), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla T4 GPU and data was augmented by a factor of 4 using rotation and flipping.
The model weights can be used with the ZeroCostDL4Mic CARE 2D notebook and the CSBDeep Fiji plugin.
Author(s): Christoph Spahn1,2, Mike Heilemann1,3
Contact email: [email protected]
Affiliation(s):
1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany
2) ORCID: 0000-0001-9886-2263
3) ORCID: 0000-0002-9821-357
DeepBacs – Escherichia coli MreB denoising dataset and CARE model
No full textTraining and test images of MreB-sfGFPsw expressing E. coli cells for image denoising, as well as a trained CARE model.
Additional information can be found on our github wiki.
The example images show confocal images of labelled E. coli MreB filaments at low and high SNR.
Training and test dataset:
Data type: Paired microscopy images (fluorescence)
Microscopy data type: Confocal fluorescence images
Microscope: Leica SP8 confocal microscope with a 1.40 NA 63x oil immersion objective
Cell type: E. coli strain NO34 expressing MreB-sfGFPsw fusion protein (kindly provided by Zemer Gitai)
File format: .tif (16-bit)
Image size: 512x512 (Pixel size: 45 nm)
The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 100 epochs (600 steps/epoch) on 1500 paired image patches (image dimensions: (512 x 512 px²), patch size: (64 x 64 px²), 50 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.3), numpy (v 1.21.5), cuda (v 11.1.105). The training was accelerated using a Tesla K80 GPU and data was augmented by a factor of 4 using rotation and flipping.
The model weights can be used with the ZeroCostDL4Mic CARE 2D notebook and the CSBDeep Fiji plugin.
Author(s): Christoph Spahn1,2, Mike Heilemann1,3
Contact email: [email protected]
Affiliation(s):
1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany
2) ORCID: 0000-0001-9886-2263
3) ORCID: 0000-0002-9821-357
DeepBacs – Escherichia coli growth stage object detection dataset and YOLOv2 model
No full textTraining and test images of E. coli cells for object detection and classification using YOLOv2, as well as a trained YOLOv2 model.
Additional information can be found on this github wiki.
The example shows a bright field image of live E. coli cells and the respective annotation for specific growth stages.
Training and test dataset
Data type: Paired microscopy images (bright field) and annotations in PASCAL VOC format
Microscopy data type: 2D bright field images recorded at 1 min interval
Microscope: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective
Cell type: E. coli MG1655 wild type strain (CGSC #6300).
File format: .png (8-bit)
Image size: 256 x 256 px² (158 nm / pixel), 100/15 individual frames (training/test dataset)
1024 x 1024 px² (79 nm / pixel), 9 regions of interest with 80 frames @ 1 min time interval (live-cell time series)
Image preprocessing: Raw images were recorded in 16-bit mode (image size 512x512 px² @ 158 nm/px). 256 x 256 px² patches were extracted from individual frames and converted into 8-bit .png images after adjusting brightness and contrast. Annotation was performed online using LabelImg (https://github.com/tzutalin/labelImg).
YOLOv2 model
The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 100 manually annotated images (image dimensions: (256, 256)) with a batch size of 8 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12.1). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla T4 GPU and data were augmented by a factor of 4 using flipping and rotation.
The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook.
Author(s): Christoph Spahn1,2, Mike Heilemann1,3
Contact email: [email protected]
Affiliation(s):
1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany
2) ORCID: 0000-0001-9886-2263
3) ORCID: 0000-0002-9821-357
Bionahrung online
No full textDie Homepage www.oekolandbau.de soll "Bio für alle" bieten und als Plattform für Information und Kommunikation von Biolandwirt, Verarbeiter, Handel und Verbraucher dienen. "Insgesamt haben über 100 Leute seit Mai diesen Jahres an der Erstellung dieser Homepage gearbeitet", berichtet Christoph Spahn von der Agentur Synergie, die den Link "Handel" entwickelt hat. Künast..
Programmänderung | Sektion 2 | Ulf Kerber: Historische Medienkompetenz durch »Digitale Narration«
No full textAufgrund der Verhinderung von Thomas Spahn trägt jetzt Ulf Kerber in Sektion 2 zum Thema: Historische Medienkompetenz durch "Digitale Narration" vor. Den aktualisierten Tagungsflyer können Sie hier herunterladen
Stop the Press: A Baseball Legend and Biography
No full textThis chapter explores Warren Spahn's lawsuit against a publishing house in Spahn v. Julian Messner, Inc. Spahn was remarkable baseball pitcher and a veteran of World War II. In 1964, Julian Messner Inc. published a child-targeted biography (called in the business at the time, a juvenile biography) of Spahn, who then sued to stop publication on the grounds that it violated all four of the tenants of privacy: invasion, false light, private facts, and appropriation. The Warren Spahn Story told the story of the perfect man: a good son, a good baseball player, a good husband, and a good soldier. The author of the book admitted that his research consisted of looking at a few magazine stories and clippings, and that he had made no effort to speak with Spahn himself, his family, his teammates, or any of his friends or acquaintances. Spahn won an injunction against future distribution of the book and $10,000 in damages. Ultimately, the U.S. Supreme Court ordered the case re-tried using the actual malice standard of the Butts case, and Spahn won again. The decisions concluded that Spahn had the right to demand that the basic facts of his life be told accurately, and it required authors of biographies to make a good faith effort to represent their subjects truthfully.</p
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