dataesr/scientific-paragraphs-categorization huggingface dataset:
S2. ABSOLUTE DECODING ACCURACY OF VARIOUS METHODS Tables  S1  and S2  report absolute decoding accuracies for Experiment 1 and Experiment 2, to bring a di↵erent view of results presented in Figures  5  and 7 , as relative improvements brought over anatomical registration by various alignment methods. This "per dataset view" highlight that gains brought by best methods are substantial improvement over baseline, especially when compared to chance.
S6. IMPACT OF THE DATA REPRESENTATION AND RESOLUTION Oosterhof et al. 2011 argued that functional alignment benefits from working with a representation of the fMRI signal on the cortical surface  (Coalson et al. 2018). Relatedly, we would also expect that the resolution of the data representation-whether in the surface or the volumewill impact the quality of the alignment learnt. To assess the dependence of our 3mm volumetric results presented in the main text on sampling parameters, we replicated our inter-subject decoding framework with the IBC RSVP language task data on a high-resolution cortical surface representation(fsaverage7 )(obtained through freesurfer surfacic projection of full-resolution raw images in their respective subject space, later on mapped to the common surfacic template).
This surface mesh includes 168k cortical nodes per hemisphere, which we divided into 350 parcels per hemisphere using Schaefer atlas at scale 700. We provide results for the inter-subject decoding accuracy gains seen with the reference functional alignment method of piecewise Procrustes over standard, anatomical-only alignment. We had to limit to this setting because (i) replicating this analysis on every dataset would represent an important amount of processing work, and (ii) working on other methods than piecewise Procrustes on this very large data is computationally prohibitive. The results displayed in Figure  S4  show that although decoding gains are a little higher using high-resolution surface-based representation, they remain in the same range as the volume-based representation. This shows that a 10-fold higher resolution can help match more precisely topographies across subjects (and reduce the decoding variance as a consequence), but no important marginal gains can be expected from it.
Replication and Prediction Study (NARPS; Botvinik-Nezer et al. 2020),the Healthy Brain Network Serial Scanning Initiative (HBN-SSI; O'Connor et al. 2017), the interTVA dataset (Aglieri et al. 2019, available as Openneuro ds001771 ) and the The Dual Mechanisms of Cognitive Control Project (DMCC, Braver et al. 2020).
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 945539  ( Human Brain  Project  SGA3 ) and the  Digiteo French program. This work was also partially funded by the  National Institutes of Health (NIH)   NIH-NIBIB P41 EB019936 (ReproNim) NIH-NIMH R01 MH083320  (CANDIShare) and  NIH   RF1 MH120021  (NIDM), the  National Institute Of Mental Health  under Award Number  R01MH096906  (Neurosynth), as well as the  Canada First Research Excellence Fund , awarded to  McGill University for the Healthy Brains for Healthy Lives initiative  and the  Brain Canada Foundation  with support from  Health Canada. We wish to thank all researchers that made this study possible by making their datasets publicly available, especially  J.
Etzel ,  S. Takerkart , and  P. Bellec  for kindly taking the time to provide us preprocessed version of their datasets and thorough explanations of their experimental designs. Data from the  Courtois  project on neural modelling was made possible by a generous donation from the Courtois foundation, administered by the  Fondation Institut Gériatrie Montréal at CIUSSS du Centre-Sud-de-l'île-de-Montréal  and  University of Montreal. The Courtois NeuroMod team is based at  Centre de Recherce de l'Institut Universitaire de Gériatrie de Montréal , with several other institutions involved. See the cneuromod documentation for an up-to-date list of contributors (https://docs. cneuromod. ca).
8 ± 0. 1 37. 2 ± 0. 2 37. 0 ± 0. 4 TABLE S1. Fullbrain benchmark absolute decoding accuracy (%) Methods/Dataset IBC RSVP IBC Sounds Forrest BOLD5000 Neuromod Chance 16. 7 16. 7 20 25. 5 25 Anatomical Intra-subject Searchlight Procrustes Piecewise Procrustes 22. 2 ± 0. 1 22. 0 ± 0. 0 27. 8 ± 0. 1 24. 0 ± 0. 0 26. 8 ± 0. 5 30. 8 ± 0. 4 33. 8 ± 0. 1 25. 6 ± 0. 1 29. 2 ± 0. 4 34. 2 ± 0. 1 65. 5 ± 1. 9 64. 9 ± 0. 5 25. 9 ± 0. 2 29. 8 ± 0. 5 34. 1 ± 0. 1 57. 2 ± 5. 0 24. 2 ± 0. 7 28. 5 ± 0. 2 39. 6 ± 0. 1 56. 7 ± 4. 9.
Modern implementations of widefield fluorescence microscopy often rely on sCMOS cameras, but this camera architecture inherently features pixel-to-pixel variations. Such variations lead to image artifacts and render quantitative image interpretation difficult. Although a variety of algorithmic corrections exists, they require a thorough characterization of the camera, which typically is not easy to access or perform. Here, we developed a fully automated pipeline for camera characterization based solely on thermally generated signal, and implemented it in the popular open-source software Micro-Manager and ImageJ/Fiji. Besides supplying the conventional camera maps of noise, offset and gain, our pipeline also gives access to dark current and thermal noise as functions of the exposure time. This allowed us to avoid structural bias in single-molecule localization microscopy (SMLM), which without correction is substantial even for scientific-grade, cooled cameras.
We demonstrate that we can accurately characterize diverse (s)CMOS cameras and use the calibrations to avoid bias in 2D and 3D SMLM and in diffraction-limited imaging. Our camera characterization algorithm is implemented for the popular software packages Micro-Manager  16  as well as ImageJ/Fiji 17 and enables (s)CMOS specific corrections for the broad imaging community. Camera characteristics are conventionally determined by evaluating mean and variance of the signal in each pixel over many images at several light levels  1. The mean and variance of the signal with no light reaching the camera correspond to offset and read noise squared, respectively. Due to the stochastic and discrete nature of photon detection, the gain can be calculated as the ratio of the variance and mean signal at different light levels.
Both NCS (Figure  3o ) and ACsN (Figure  3p ) removed noise and bias of such bad pixels (Figure  3q ) and considerably increased the image quality. Besides the conventional (s)CMOS corrections for pixel specific noise, gain and offset, our results indicate the benefit of characterizing and correcting for the effects of dark current and associated thermal noise in high-and super-resolution microscopy, both for uncooled and cooled cameras. To make our approach easily accessible for the imaging community, we implemented the automated camera characterization via electron noise tool (ACCeNT) in the popular open-source software Micro-Manager  16  and ImageJ/Fiji 17 (Supplementary Note 2). All relevant camera properties, including thermal effects, can be determined without user intervention and there is no need for additional hardware implementations.
l, Gallery showing lateral and axial views on individual nuclear pore complexes indicated in k. m, The axial view on the region indicated in k shows two parallel lines from the nucleo-and cytoplasmic rings 57 nm apart. n, First frame of experimental raw data from time-lapse TIRF imaging of AP-2 tagged with eGFP in live U373 cells recorded at 1000 ms single frame exposure time. o, NCS corrected and, p, ACsN corrected frame. The entire time-lapse for np is shown in Supplementary Video 2. q, The noise of a pixel of high dark current is strongly reduced via both approaches after appropriate characterization of the camera including thermal effects. The signal has been offset-corrected by the pixel value of the first frame from the time-lapse.
Camera calibration For the photon-free calibration, all light to the camera chip was blocked by screwing a lid to the camera mount. Before starting the measurement, the camera was pre-run to give the detector time to thermally equilibrate either to the targeted cooling temperature (-10 °C as this was the manufacturer's calibration setpoint) or warming up to the operating temperature in case of uncooled cameras. We acquired around 8,000 to 20,000 sets of typically 5 to 10 different exposure times. To maintain a constant average detector temperature, recording was performed in a nested manner, i. e. we changed the exposure time after each camera frame and then repeated acquisition of all exposure times. Initially, we used custom-written scripts in Micro-Manager, Fiji and MATLAB for data acquisition and analysis, but later implemented the entire workflow into independent Micro-Manager and Fiji ACCeNT plugins (see next paragraph).
After recording of raw data as described in the former paragraph, Fiji was used to process the TIFF stacks. For each exposure time and pixel, the mean value and standard deviation were calculated and saved as TIFF files: One after another, the stack corresponding to one exposure time was imported to Fiji and the "z-project" function was called with projection type "Average Intensity" for the mean value or projection type "Standard Deviation" for the standard deviation. The resulting TIFF files were imported into MATLAB for further processing. For each pixel linear functions were fitted using the polyfit function to (i) the mean value as a function of the exposure time to determine the baseline from the y-intersect and the dark current per time from the slope, (ii) the variance (i. e. the standard deviation squared) as a function of the exposure time to determine the read noise squared from the y-intersect and the thermal noise squared per time from the slope, and (iii) the variance as a function of the mean value to determine the gain (i.
e. the conversion factor from electrons to ADU counts) from the slope. For each pixel, the exposure time dependent offset was calculated as the baseline plus the dark current per time multiplied by the single frame exposure time. For each pixel, the exposure time dependent noise squared was calculated as the read noise squared plus the thermal noise squared per time multiplied by the single frame exposure time. For each pixel, the photon response was calculated as the median of the gain map for all pixels multiplied by the pixel-wise value of the flatfield map. To find the flatfield map, we exposed the camera to a homogeneous illumination via ambient light and applied the algorithm presented by Lin et al. 5. We implemented the photon-free calibration workflow including the automated nested data acquisition, fitting of individual pixel properties and calculation of exposure time dependent camera maps as the ACCeNT plugin for Micro-Manager 2.
Additionally, we implemented the fitting of individual pixel properties and calculation of exposure time dependent camera maps as an ACCeNT plugin for Fiji. The latter is intended to be used for processing of data acquired with software different from Micro-Manager 2, e. g. if the microscope is run using Micro-Manager 1. 4 or the manufacturer's software. For Micro-Manager 1. 4 users, we provide a script for the automated nested data acquisition. We checked the consistency of all implementations against each other.
Microscope All data was acquired on a custom-built microscope as described in the following. Laser light was emitted from the single mode fiber output of a laser box (iChrome MLE, Toptica) (640 nm for excitation of Alexa Fluor 647 and Atto 655 and TetraSpeck beads, 561 nm for excitation of mEos and TetraSpeck beads, 488 nm for excitation of GFP and 405 nm for active photoswitching in STORM and PALM experiments) and collimated using an achromatic lens (either f = 50 mm for DNA-PAINT imaging using the industry-grade CMOS camera or f = 30 mm for live-cell experiments and all STORM and PALM experiments, all lenses from Thorlabs) or emitted from a fiber laser (F-04306-107, MPB Communications) (642 nm for excitation of Atto 655), filtered through an AOTF (AA Opto Electronic) and expanded using telescope of achromatic lenses (f = 50 mm and f = 100 mm, both Thorlabs).
Data acquisition PALM imaging was performed using an uncooled, industry-grade CMOS camera (µeye UI-3060CP-M-GL R, IDS). Fixed U2OS cells were imaged in buffer containing 95 % D2O and 50 mM Tris/HCl pH9. Raw data was acquired in HILO illumination at 561 nm and laser output powers of 20 mW to 50 mW. The single frame exposure time was set to 50 ms. STORM imaging was performed using either an uncooled, industry-grade CMOS camera (µeye UI-3060CP-M-GL R, IDS) or a different uncooled, industry-grade CMOS camera (Chameleon3 CM3-U3-50S5, FLIR). Fixed U2OS cells were imaged in blinking buffer containing 50 mM Tris/HCl pH8, 10 mM NaCl, 10% (w/v) D-glucose, 500 µg/ml glucose oxidase, 40 µg/ml catalase, 143 mM BME and 2 mM COT. Raw data was acquired in HILO illumination at 640 nm and at a laser output power 70 mW.
The single frame exposure time was set to 50 ms. DNA-PAINT imaging of tubulin in U2OS cells was performed using an uncooled, industry-grade CMOS camera (µeye UI-3060CP-M-GL R, IDS). Fixed U2OS cells were imaged in buffer containing 500 mM NaCl, 1x PBS, 40 mM Tris/HCl pH8 and imager strands (I1 650, i. e. Atto655, Ultivue) at a concentration of about 500 pM. Raw data was aquired in HILO illumination at 640 nm and a laser output power of 70 mW. The single frame exposure time was set to 500 ms. DNA-PAINT imaging of Nup96 in U2OS cells was performed using a cooled, scientific-grade CMOS (sCMOS) camera (Edge 4. 2bi, PCO). Fixed U2OS cells were imaged in buffer containing 500 mM NaCl, 40 mM Tris/HCl pH8 and imager strands (I1 650, i. e. Atto 655, Ultivue) at a concentration of about 500 pM. Raw data was acquired in HILO illumination at 642 nm and a laser output power of 4.
5 mW. The single frame exposure time was set to 500 ms. Diffraction limited TIRF imaging of AP-2 in U373 cells was performed using an uncooled, industrygrade CMOS camera (µeye UI-3060CP-M-GL R, IDS). Live U373 cells were imaged at room temperature in growth medium. Raw data was acquired in shallow TIRF illumination at 488 nm and a laser output power of 0. 1 mW. The single frame exposure time was set to 1000 ms.
Image data analysis SMLM data was fitted and analyzed as previously described 28 using our custom-written, open-source superresolution microscopy analysis platform SMAP  21  in MATLAB (Mathworks). The software is available at github. com/jries/SMAP. In case of (s)CMOS specific fitting, the predetermined camera maps were applied for the exposure time of the respective experiment. 3D SMLM data (STORM, PALM, DNA-PAINT) was fitted with an experimentally derived PSF model measured via z-stacks of 100 nm sized fluorescent beads as previously described  6. For STORM data, the localizations were filtered for a lateral localization precision better than 12. 7 nm, a relative log-likelihood value better than -2. 9, and the first 600 frames were filtered out. For PALM data, the localizations were not further filtered.
For DNA-PAINT data, the localizations were filtered for a localization precision from 0 to 12 nm and a z-coordinate of -200 nm to 100 nm. 2D DNA-PAINT data was fitted with a Gaussian PSF model and the localizations were filtered for a localization precision better than 30 nm and a PSF width of 100 to 175 nm. Diffraction-limited TIRF images were processed using the NCS software and ACsN software, respectively, as provided by the authors. As input, we use the camera maps determined via the photon-free approach described in this work, encoding the pixel-wise properties for gain, offset, and noise in case of NCS and gain and offset in case of ACsN. We parameterized the NCS MATLAB "single pixel with normalization"-algorithm by an alpha weight factor of 10, a pixel size of 0. 0977 µm, an emission wavelength of 0.
Supplementary • EITHER using the Micro-Manager 2 ACCeNT plugin for acquisition and simultaneous ACCeNT camera map computation ("Micro-Manager 2 calibration") followed by SMAP SMLM fitting ("Single-molecule localization using SMAP"); • OR using the Micro-Manager 1. 4 ACCeNT script for acquisition followed by using the Fiji plugin for ACCeNT camera map computation ("Micro-Manager 1. 4 and Fiji calibration") followed by using SMAP for SMLM fitting ("Single-molecule localization using SMAP"). As an alternative to the complete workflow, users can choose to perform only certain parts of the pipeline. For instance, the Micro-Manager 2 plugin allows to perform acquisition and computation independently, or the Fiji plugin can be used for computation of appropriately acquired raw camera data from other software.
To test our pipeline on provided example data, start at point 2. of the "Micro Manager 1. 4 and Fiji calibration" workflow below. This includes using Fiji for ACCeNT camera calibration and using SMAP for fitting of 3D SMLM data. The example data corresponds to the 3D STORM data of nuclear pore complexes shown in Fig. 3. The example data has been recorded using an uncooled, industry-grade CMOS camera (IDS UI-3060CP-M-GL Rev. 2, Sensor Sony IMX174) and can be found here: https://rieslab. de/#accent. We successfully tested ACCeNT with the following cameras: • UI-3060CP-M-GL Rev. 2, IDS • Edge 4. 2bi, PCO • DCC1545M, Thorlabs • Chameleon3 CM3-U3-50S5M, FLIR • Chameleon3 CM3-U3-31S4M-CS, FLIR • Prime BSI, Photometrics We did not manage to run ACCeNT with the Hamamatsu Orca Flash4. 0v2 because of an apparent dark current correction implemented by the manufacturer.
Micro-Manager 2 calibration 1. Installation a. Download the latest version of Micro-Manager 2 from: • https://micro-manager. org/wiki/Download_Micro-Manager_Latest_Release b. Download the latest release of the ACCeNT Micro-Manager 2 plugin: • https://github. com/ries-lab/Accent/releases c. Place the downloaded. jar in the "mmplugins" folder of your Micro-Manager 2 installation folder. 2. Start Micro-Manager 2 using a hardware configuration that includes your camera. Refer to the Micro-Manager wiki or the Image. sc forum for any trouble regarding hardware. a. ACCeNT can be started from the plugins menu, under "Acquisition Tools". The plugin consists of three steps: acquisition, processing and maps generation. All steps can be carried out together or separately.
Micro-Manager 1. 4 and Fiji calibration In this pipeline, we make use of Micro-Manager 1. 4 to generate the data. In principle the Fiji plugin can be used regardless of the way the raw data was generated. The only conditions are: • The images are tiff (stacks or individual images) • Each stack's name contains "XXXms", where XXX is the corresponding exposure time, and all stacks are in the same folder. Alternatively, each exposure (stack or individual images) can be in a folder with name containing "XXXms". All exposure folders must be grouped in one folder. d. Then load "accent-acquisition_script. bsh" from the Github repository: https://github. com/ries-lab/Accent/blob/master/accent-mm1/accent-acquisition_script. bsh e. In the script, modify the parameters "path", "exposures" and "numFrames".
Additionally, change the roi on line 12. The parameters of the setRoi method are in order x0, y0, width and height; with x0 and y0 defining the top-left corner of the roi. Note that certain cameras do not support this function. In such case, comment out (using "//") line 12 and set the roi manually in Micro-Manager (refer to Micro-Manager user's guide) f. Run the script to acquire the images. This can take some time, watch the console for progress and wait for the acquisition to complete.
The map generation step generates the average and variance maps for the required exposures. • For the example data, type "999" into the field "Exposures (ms):" and click "Generate". The generated camera maps for offset and variance corresponding to 999 ms exposure time will be saved to the folder "ACCENT_raw". Familiarize yourself with SMAP by consulting the documentation, using example data downloaded at https://rieslab. de/#accent. Make sure to install Micro-Manager and select its path in the Preferences menu. 2. Copy the 'results. calb' file in the SMAP/settings/cameras directory. 3. As described in the user guide (SMAP_UserGuide. pdf (embl. de), page 6), add your camera to the camera manager. In the 'correctionfile' field, select the results. calb file. For the example data, the camera is already registered in the camera manager.
2 in a U2OS cell using the same camera and applying CMOS-specific fitting including thermal effects. j, Axial view of region indicated in i. k, Experimental 3D STORM data of Nup107-SNAP, labeled with AF 647, in a U2OS cell using the same camera and applying CMOS-specific fitting including thermal effects. l, Gallery showing lateral and axial views on individual nuclear pore complexes indicated in k. m, The axial view on the region indicated in k shows two parallel lines from the nucleo-and cytoplasmic rings 57 nm apart. n, First frame of experimental raw data from time-lapse TIRF imaging of AP-2 tagged with eGFP in live U373 cells recorded at 1000 ms single frame exposure time. o, NCS corrected and, p, ACsN corrected frame. The entire time-lapse for np is shown in Supplementary Video 2. q, The noise of a pixel of high dark current is strongly reduced via both approaches after appropriate characterization of the camera including thermal effects.
Start here to test ACCeNT on provided example data. The example data can be downloaded at https://rieslab. de/#accent. The zip-archive for the example data contains raw data from the camera calibration, 3D stacks of beads for the point spread function (PSF) characterization and raw STORM data corresponding to Figure 3k-m. 3. Install Fiji a. Download Fiji: https://imagej. net/Fiji/Downloads Please note that the ACCeNT plugin uses features that have been added recently to Fiji. If you encounter errors, make sure to use the latest version of Fiji. b. Download the latest release of the ACCeNT Fiji plugin: https://github. com/ries-lab/Accent/releases c. Place the downloaded accent-fiji-1. 0. jar in the "plugins" folder of your Fiji installation folder. 4. Start Fiji and the ACCeNT plugin from the plugins menu.
Acknowledgements We thank  Ingmar Schoen  for providing secondary antibodies for the DNA-PAINT experiments and  Steve Boulant  for providing the U373 cell line. We thank  Alejandro Colchero  for assistance in setting up the microscope,  Philipp Hoess  and  Jervis Thevathasan  for additional sample preparations,  Amir Rahmani  for assistance in data acquisition and  Takahiro Deguchi  and  Sheng Liu  for testing of the software. This work was supported by the  European Research Council  (grant no. ERC CoG-724489  to JR), the  National Institutes of Health Common Fund 4D Nucleome Program  (grant no. U01 EB021223  to JR), the  Human Frontier Science Program  ( RGY0065/2017  to JR), the  Engelhorn Foundation  ( Postdoctoral Fellowship  to RD) and the  European Molecular Biology Laboratory.
2. 2 Start here to test ACCeNT on provided example data. The example data can be downloaded at https://rieslab. de/#accent. The zip-archive for the example data contains raw data from the camera calibration, 3D stacks of beads for the point spread function (PSF) characterization and raw STORM data corresponding to Figure 3k-m. 3. Install Fiji a. Download Fiji: https://imagej. net/Fiji/Downloads Please note that the ACCeNT plugin uses features that have been added recently to Fiji. If you encounter errors, make sure to use the latest version of Fiji. b. Download the latest release of the ACCeNT Fiji plugin: https://github. com/ries-lab/Accent/releases c. Place the downloaded accent-fiji-1. 0. jar in the "plugins" folder of your Fiji installation folder. 4. Start Fiji and the ACCeNT plugin from the plugins menu.