dataesr/scientific-paragraphs-categorization huggingface dataset:
ENCODING Given an appropriate model of the stimulus, e. g. one which can provide an approximately linear representation of BOLD activation, an encoding approach allows one to quantify for each voxel to what extent its variability is captured by the model. A popular evaluation method is the predictive r 2 score, which uses a prediction on left out data to quantify the decrease in residual norm brought about by fitting a regression function as opposed to fitting a constant. The remaining variance consists of potentially unmodelled, but reproducible signal and spurious noise. On the Miyawaki dataset, we can observe that mere black and white pixel values can explain a large part of the BOLD variance in many visual voxels. Sticking to the notation that X represesents BOLD signal and y the stimulus, we can write an encoding model using the ridge regression estimator: Note here that the Ridge can be replaced by a Lasso estimator, which can give better prediction performance at the cost of computation time.
RESULTS Figure  3  gives encoding and decoding results: the relationship between a given image pixel and four voxels of interest in the brain. In decoding settings, Figures  3a  and 3c  show the classifier's weights as brain maps for both methods. They both give roughly the same results and we can see that the weights are centered in the V1 and nearby retinotopic areas. Figures  3b  and 3d  show reconstruction accuracy score using Logistic Regression (LR) and SVM (variable mean scores in the code above). Both methods give almost identical results. As in the original work  (Miyawaki et al. , 2008) , reconstruction is more accurate in the fovea. This is explained by the higher density of neurons dedicated to foveal representation in the primary visual area. In encoding settings, figure  3e  shows classifiers weights for encoding, that we interpret as receptive fields.
RESTING-STATE AND FUNCTIONAL CONNECTIVITY ANALYSIS Even in the absence of external behavioral or clinical variable, studying the structure of brain signals can reveal interesting information. Indeed,  Biswal et al. (1995)  have shown that brain activation exhibits coherent spatial patterns during rest. These correlated voxel activations form functional networks that are consistent with known task-related networks  (Smith et al. , 2009). Biomarkers found via predictive modeling on resting-state fMRI would be particularly useful, as they could be applied to diminished subjects that cannot execute a specific task. Here we use a dataset containing control and ADHD (Attention Disorder Hyperactivity Disorder) patients resting state data (subjects are scanned without giving them any specific task to capture the cerebral background activity).
vstack(Xs) from sklearn. decomposition import FastICA ica = FastICA(n_components=10) components_masked = ica. fit_transform(data_masked. T). T 6. 1. 3 Results On fig. 4  we compare a simple concat ICA as implemented by the code above to more sophisticated multi-subject methods, namely Melodic's concat ICA and CanICA-also implemented using scikit-learn although we do not discuss the code here. We display here only the default mode network as it is a well-known resting-state network. It is hard to draw conclusions from a single map but, at first sight, it seems that both CanICA and Melodic approaches are less subject to noise and give similar results. Scikit-learn proposes several other matrix decomposition strategies listed in the module 'sklearn. decomposition'. A good alternative to ICA is the dictionary learning that applies a 1 regularization on the extracted components  (Varoquaux et al.
CONCLUSION In this paper we have illustrated with simple examples how machine learning techniques can be applied to fMRI data using the scikit-learn Python toolkit in order to tackle neuroscientific problems. Encoding and decoding can rely on supervised learning to link brain images with stimuli. Unsupervised learning can extract structure such as functional networks or brain regions from resting-state data. The accompanying Python code for the machine learning tasks is straightforward. Difficulties lie in applying proper preprocessing to the data, choosing the right model for the problem, and interpreting the results. Tackling these difficulties while providing the scientists with simple and readable code requires building a domainspecific library, dedicated to applying scikit-learn to neuroimaging data.
2011). For the autumn run, a third trigger pipeline was added: the Multi-Band Template Analysis (MBTA)  (Beauville et al. 2008; Abadie et al. 2012a) , which sought inspiral waveforms from coalescing compact objects. The autumn run also added a second instance of cWB, configured to target linearly polarized GW signals, as might be expected from supernovae. To compare triggers from different pipelines and identify the ones suitable for observation, follow-up software made event candidate selections based on the estimated false alarm rate (FAR) of each trigger. The rate of background false alarms was estimated by forming a distribution of artificial triggers from data with one or more data streams shifted by at least several seconds. Time-shifting data removes correlations of possible gravitational-wave signals between detectors, so this distribution was considered to be free from any putative signals and represented the rate of triggers not due to transient GWs  (Abadie et al.
Catalog-Based Search for Pi of the Sky Pi of the Sky has an unusually wide field-of-view of 20×20 degrees, with a typical limiting magnitude of 11. 5 for a 10 second exposure. This allowed the telescope to image a large part of the sky in response to one LIGO/Virgo trigger, over 40 × 40 degrees on most nights. We used the standard Pi of the Sky pipeline to analyze the images taken by the telescope. A detailed description may be found in  Malek et al. (2009)  and  Sokolowski (2008). The full analysis was carried out in two steps. First, in each image taken by the telescope, the Guide Star Catalog  (Jenkner et al. 1990 ) was used to identify previously unknown sources. Second, Pi of the Sky's nova recognition algorithm was applied to the list of unknown sources. To separate optical transients from contaminating sources, the algorithm utilized several types of vetoes, including checks on background saturation, nearby bright objects, satellite databases, and the GSC catalog.
SkyMapper Search SkyMapper obtained two epochs of an eight image mosaic covering a total of ∼ 42 square degrees in response to the September 16, 2010 trigger. An image subtraction technique was applied to identify possible transients. The SkyMapper images were reduced via the normal bias subtraction, overscan correction and flat fielding using a custom made Pythonbased pipeline. Thereafter, frames from the two epochs were aligned with the WCSREMAP 151 routine and subtracted with HOTPANTS 152 to create residuals images. SExtractor was used to identify sources with SNR greater than three. Then, a series of cuts was applied to the SExtractor output parameters to identify noise and bad subtractions. These included using the ellipticity parameter, photometry from different size apertures, and catalog matching of variable stars.
The analysis made use of several freely available software packages, and was split into several sections written in Python. First, we combined the images from 2012 to create our reference images. This was done by aligning the images using the WCSRemap 153 package and combining them using the SWarp 154 package. We also combined sets of 5 SkyCamZ images on each night to improve image quality and provide a similar cadence to the RATCam images. We removed 1 RAT-Cam image and 2 SkyCamZ images due to quality issues. Second, as the SkyCamZ images used a non-standard filter 155 , they were calibrated using the USNO-B catalog of stars to determine the zero point offset required to calculate correct magnitudes, in the same way ROTSE and TAROT images were calibrated (See section 4. 1). This was done by comparing the USNO-B R-band magnitude of stars in the combined SkyCamZ fields with those same stars found using SExtractor.
G23004 The ROTSE-IIIb, c and d telescopes responded to G23004 at T+6h25m and collected data up to T+29d. These images contained one galaxy (NGC 1518) at 11. 5 Mpc within the FOV. Around 75% of the data was of poor quality; many of the images were out of focus and cloud cover was also a factor. This resulted in the analysis of 30 images with an average limiting magnitude of 16. 7. The ROTSE subtraction pipeline found 124 potential candidates of which none survived the candidate validation tests. The Liverpool Telescope observed a single field centered on the location of the galaxy NGC 1507, with one hour of observations taken at T+9h and a further one hour at T+30d. The limiting magnitude of the RATCam images was r ′ ≈ 20. 5, averaged over all images, with the calibrated limiting magnitude of the SkyCamZ images averaging R ≈ 17.
5. We found 406 unique objects in the RATCam images and 163 unique objects in the SkyCamZ images. After applying cuts described in Section 4 we found no candidates in either the RATCam or SkyCamZ images that met our criteria. The Pi of the Sky telescope responded at T+6h56m after the alert. On the first night the telescope used ten different pointing locations to cover an area containing 40% of the G23004 probability map. Each location was imaged twice. The limiting magnitudes for the first night's observations spanned 10. 5 -11. 0 mag. On the first night there were over 700 cases that were recognized by the pipeline as possible optical transients, but all of them were either already included in the database of weak stars or were noise due to ice crystals on the camera. There were no real optical transients found.
TAROT and Zadko Telescope For each set of images collected by TAROT and the Zadko telescopes, 100 simulated transients were added to the data for each counterpart model and distance. To model PSF variations in the wide-field images, reference model stars were identfied in each image, and the PSF of the reference star closest to the injection position was used for each simulated object. For the GRB afterglows, we used a range of magnitudes uniformly distributed between the brightest and faintest GRBs (see normalization in Table  1 ). The results are presented in Figure  8. Long GRB afterglows/short GRB afterglows/kilonovae were recovered with 50 % efficiency in TAROT observations to distances of 400 Mpc/18 Mpc/6. 5 Mpc respectively for trigger G19377 and 355 Mpc/16 Mpc/13 Mpc for trigger G20190.
ROTSE For each set of images collected by ROTSE, 140 simulated transients were added to the data for each counterpart model for 10 different distances. The PSFs for the injected transients were modeled on 'good' objects PSFs within each image, as described in  White et al. (2012). The GRB models used the brightest normalizations shown in Table  1 ; i. e. , assuming magnitude 16 (  23 ) at 1 day from z = 1 for LGRB (SGRB) afterglows. The results are presented in Figure  13. For each GW trigger, the efficiencies for the different counterpart models are very similar as functions of the injection magnitude. The efficiencies peak at ∼ 70-80% for triggers G19377 and G20190, and at ∼ 55% for G21852. Trigger G23004 (not shown) contained images of very poor quality and the injection efficiency only reached a maximum of ∼ 20%.
Liverpool Telescope The efficiency of the Liverpool Telescope pipeline was measured with the same methods used for ROTSE. A Python script was written to inject 100 transient objects per 10 Mpc bin per model, with light curves following the three models described in Table  1 , assuming the brightest normalization for the GRB models. These images were then analyzed using the pipeline, and a script used to find and flag injections found in the pipeline output. Figure  14  shows that we obtained efficiencies around 90% for injections brighter than the limiting magnitude, including saturated objects normally discarded in other image subtraction methods. For RATCam, any of the tested models would have been observable out to 100 Mpc or more -well beyond the initial LIGO/Virgo horizon distance for neutron star mergers.
Pi of the Sky The efficiency of the Pi of the Sky transient search was investigated by adding simulated stars to existing images and reprocessing them. The objects that were injected had different magnitudes and were chosen from real observed stars during the autumn science run. Unlike the other simulations described in this paper, objects added to Pi of the Sky data did not follow model light curves, but instead measured the ability of the pipeline to recover a transient of a given magnitude using data from a single night. Stars injected in one image were also injected in subsequent images of that field taken during the same night. Only injections that were made to the inner part of the CCD chip, at least 150 pixels from CCD borders, were considered to estimate transient detection efficiency.
The border part of the CCD was rejected by the offline optical transient recognition algorithm due to the possibility of CCD anomalies that might be mistaken as short optical transients. Also, only injections starting on a good quality image were considered in efficiency estimation. This means that the effective field of view for optical transient recognition cor- -Some representative success rates in recovering simulated short GRB afterglow light curves with the QUEST data for triggers G20190 (left) and G23004 (right). Each curve represents the results from individual on-source galaxy regions, and so does not include efficiency lost due to observing only a fraction of the possible source locations. Each simulated afterglow lightcurve was randomly scaled within the range of normalization conditions showed in Table  1.
The images associated with trigger G23004 were of poor quality, so the efficiencies with this data are not shown. proach is forward-looking. The LIGO and Virgo collaborations have recently made an open call for partners to search for EM counterparts to GW events discovered with the next generation of GW detectors 160. It is likely that partners will use a variety of facilities and instruments, and each apply their own data analysis techniques. Both the successes and lessons of this work should serve as useful guideposts to investigators pursuing similar searches with the up-coming "advanced" generation of gravitational wave detectors. Strategies are also being discussed in the literature  (Metzger & Berger 2012; Nissanke et al. 2013; Singer et al. 2013). Rapidly down-selecting candidates for follow-up observations, integrating results for astrophysical interpretation, and communicating findings will require a common framework 160 http://www.
The challenge presented by false positives is likely to increase with the advent of Advanced LIGO/Virgo, when a larger horizon distance will require imaging to fainter magnitudes, and so increase the number of potential contaminants. The Monte Carlo studies we performed demonstrated that we typically recovered a range of light curve models to a depth consistent with the limiting flux of the observations, proving the validity of our selection criteria. During the observing periods, typical position averaged sensitive ranges for NS-NS mergers was 18 Mpc, or 35 Mpc for NS-BH mergers  (Abadie et al. 2012c). The efficiency curves shown in Figures 9 through 15 show that the data sets with better limiting magnitudes (QUEST, Liverpool Telescope) were succesful in recovering all the considered models at these distance scales.
The less sensitive data sets (ROTSE, TAROT, Zadko) would have missed a kilonova at these distances, but were potentially sensitive to GRB afterglows. Looking towards the fu-ture, the simulation results show that short exposures (∼ 1 minute) with small aperture telescopes, with observations to depths of less than 18th magnitude, failed to recover short GRB or kilonova light curves at distances comparable to the expected 200 Mpc range of advanced GW detectors to NS-NS mergers  (Abadie et al. 2010b; Aasi et al. 2013). This means that, while smaller telescopes may be valuable in searching for counterparts to galactic GW sources, they may require long total exposures, and/or a hierarchical observing strategy with larger telescopes, to be able to detect the expected optical signature of distant compact object mergers.
Lesions on electron micrographs Lesions were first counted in the corpus callosum when the mice were 20 months old (n = 4 TgPAC-Notch3 R169C mice and 4 control mice). Images were acquired randomly, with a magnification of x3,400 (6 fields per section, 1-2 sections per mouse). A 400 μm 2 square was randomly drawn on each image and the lesions located within the square (excluding 2 borders) were counted manually. Results are expressed as the number of lesions per 1000 μm 2. The lesions present in TgPAC-Notch3 R169C mice were characterized further. A mean of 200 lesions per section (from 7-12 fields, 1-2 sections per mouse, n = 4 mice) were manually and systematically identified with NIH ImageJ software and assessed to determine the lesion type, its subcellular location, its position relative to the myelin sheath, and the presence or absence of an axon.
Foci were distributed throughout all the WM tracts, including those of the corpus callosum (Figure  3B ), fimbria, internal capsule and anterior commissure (data not shown), but were essentially absent from the cortex (Additional file 7: Figure  S6 ). We quantitatively  assessed myelin degradation with a quantification algorithm based on the local maxima method, which we applied to the corpus callosum of control and mutant brains labeled with SMI94 (Figure  3C ). We found that there was 3. 9 times more myelin debris in TgPAC-Notch3 R169C mice than in control mice at 20 months of age (Figure  3E , p = 2. 6 × 10 -5 , n = 5 TgPAC-Notch3 R169C and 8 control mice). We assessed the progressive nature of myelin degradation in TgPAC-Notch3 R169C mice, by analyzing the cerebral WM of younger mice and comparing the results obtained with those for 20-month-old mice.
Electron micrographs showed no appreciable ultrastructural abnormalities of the oligodendrocytes in the corpus callosum (data not shown). We then evaluated oligodendrocyte density. Immunolabeling for the oligodendroglial lineage marker Olig2 showed that the density of oligodendrocytes was similar in the cerebral WM of TgPAC-Notch3 R169C and control mice (Additional file 8: Figure  S7 , p >0. 05, n = 4 TgPAC-Notch3 R169C and 4 control mice). Furthermore, no apoptotic cells were detected on cleaved-caspase 3 immunostaining of the WM tracts of TgPAC-Notch3 R169C mice (data not shown). Microglia plays an essential role in removing tissue debris. There is recent evidence to suggest that the phagocytic clearance of myelin debris is required to prevent a detrimental effect of these debris on the brain and to trigger repair  [22].
The presence of uncleared myelin debris in the central nervous system has been associated with inefficient remyelination and impaired axon regeneration  [22, 23]. Our study has several methodological strengths. These include the use of a genetically defined model of SVD in which no surgery was required to obtain the WM phenotype. Bilateral carotid artery stenosis or ligation is commonly used to reproduce mild chronic hypoperfusion and associated ischemic injury to the WM. This model has the disadvantage of requiring a surgical procedure that may itself cause various degrees of transient brain ischemia potentially leading to brain lesions  [36, 40]. We assessed the focal degradation of myelin by a quantitative and semi-automatic approach that we developed, which proved to be highly sensitive and robust, with very low interobserver variability (data not shown).
Acknowledgements This work was supported by grants from the  French National Research Agency  (grant number  ANR Genopath 2009-RAE09011HSA  and  ANR Blanc 2010-RPV11011HHA ) and the  Fondation Leducq (Transatlantic Network of Excellence on the Pathogenesis of Small Vessel Disease of the Brain)  to AJ. EC is a recipient of fellowship from  Inserm. We thank  Aurélien Dauphin  and  Andrée Rouche  of the  Confocal Laser Scanning Microscope Core Facility at "Plate-forme d'Imagerie Cellulaire Pitié Salpêtrière"  for providing assistance with confocal microscopy as well  Carmen Diaz  and  Jean Paul Rio  of the electron microscopy  Core Facility at Institut du Fer-à-Moulin  for providing assistance with electron microscopy. Received: 22 April 2014 Accepted: 22 April 2014 Published: 30 April 2014.
The P2CS database (http://www. p2cs. org/) is a comprehensive resource for the analysis of Prokaryotic Two-Component Systems (TCSs). TCSs are comprised of a receptor histidine kinase (HK) and a partner response regulator (RR) and control important prokaryotic behaviors. The latest incarnation of P2CS includes 164 651 TCS proteins, from 2758 sequenced prokaryotic genomes. Several important new features have been added to P2CS since it was last described. Users can search P2CS via BLAST, adding hits to their cart, and homologous proteins can be aligned using MUSCLE and viewed using Jalview within P2CS. P2CS also provides phylogenetic trees based on the conserved signaling domains of the RRs and HKs from entire genomes. HK and RR trees are annotated with gene organization and domain architecture, providing insights into the evolutionary origin of the contemporary gene set.
INTRODUCTION Two-component signal transduction systems (TCSs) are found across all three domains of life and allow adaptive responses to changes in environmental conditions  (1). They are mainly found in prokaryotes, where they control diverse and important behaviors  (2). TCSs are composed of two proteins, a histidine kinase (HK) and a partner response regulator (RR), which interact physically with each other, and which are often encoded next to each other in genomes  (3). The P2CS database provides easy identification, characterization and analysis of the TCS proteins encoded in prokaryotic genomes. The latest version of P2CS contains 164 651 TCS proteins from 2758 prokaryotic genomes and can be accessed at http://www. p2cs. org/. TCS are a diverse group of signaling pathways, which makes their constituent proteins difficult to characterize by sequence analysis  (4, 5).
Consequently, there is a need for tools, which shed light on the gene, protein and domain partnerships of TCSs. P2CS is a resource for the TCS research community, providing consistent identification and annotation of TCS genes and their products. P2CS users are able to find TCS proteins of interest through a variety of routes, including domain architecture searches, and browsing through genomes. A full description of each TCS protein is provided, with links to analysis servers for downstream investigations. A 'gene cart' facility allows users to keep track of their proteins of interest and users can download their cart in FASTA,. xls or tab-delimited formats. The P2CS database was first made public in 2008 (9) and has since been accessed by users nearly 20 000 times. New features and functionalities are regularly added to P2CS, some of which were described in 2011  (10) , and it has spawned related websites, including the P2TF database of transcription factors  (11)  and the P2RP web server, which identifies and characterizes transcription factors and TCS proteins in user sequence queries  (12).
THE P2CS DATABASE IN 2014 P2CS has continued to grow with the increase in the number of publicly available completely sequenced prokaryotic genomes. It currently contains 164 651 TCS proteins from 2758 genomes, more than doubling its size over the last three years. In addition to updating with new genomes, new features have been introduced into P2CS. These features allow users to investigate the evolution and sequence relationships between selected TCS proteins, the results of which can potentially provide information regarding partnerships between TCS proteins.