dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the production sequencing group of  McDonnell Genome Institute at Washington University  for the library construction, sequencing, and data curation. Great thanks to the members of the Comparative Genomics workshop held at the  Biotechnology Research Institute -Kenya Agricultural and Livestock Research Organization, Kikuyu, Kenya, including Abry ,  Willis Adero ,  Erick Aroko ,  Joel Bargul ,  Tania Bishola ,  Lorna Jemosop Chebon ,  Appolinaire Djikeng ,  John Irungu ,  Everlyn Kamau ,  Christine Kamidi ,  Caleb Kibet ,  Esther Kimani ,  Kelvin Kimenyi ,  Mathuriin Koffi ,  Benard Kulohoma ,  Clarence Mangera ,  Abraham Mayoke ,  David Mburu ,  Grace Murilla ,  Mary Murithi ,  Ramadhan Mwakubambanya ,  Sarah Mwangi ,  Nelly Ndungu ,  Joyce Njuguna ,  Benson Nyambega ,  Faith Obange ,  Samuel Ochieng ,  Edwin Ogola, Owallah  ( Martin) Ogwang ,  Sylvance Okoth ,  Luicer Olubayo ,  Irene Onyango ,  Fred Osowo ,  David Price ,  Martin Rono ,  Sharon Towett ,  Kelvin Wachiuri ,  Kevin Wamae , and  Mark Wamalwa.
Availability of data and materials The genomes, transcriptomes, and predicted protein-coding sequences are available from VectorBase and are included within the references  [100] [101] [102] [103] [104] [105]. The raw RNA-seq datasets generated and/or analyzed during the current study are available from the NCBI SRA database repository at the following link https://www. ncbi. nlm. nih. gov/sra/SRP158014 and are listed within the reference list  [157] [158] [159] [160] [161] [162] [163] [164] [165] [166] [167] [168] [169] [170] [171] [172] [173]. All data generated during the analyses of these datasets are included in this published article and its supplementary information files.
2, and Na2GTP 0. 3, (in mM)). Cells were identified under gradient contrast illumination and examined for transduction by epifluorescence illumination. Blue light (473nm, Doric LED, 4mW) was pulsed onto slices from an optical fiber (400um diameter) placed above the LC. Whole cell voltage and current clamp recordings were obtained with a Multiclamp 700A amplifier (Axon instruments). All membrane potentials were corrected for a junction potential of 13 mV. The reversal potential for chloride ions using this combination of internal and external solutions was calculated as -68. 7mV. Data was acquired using a Power1401 A-D converter (CED) and displayed and analyzed with Spike2 software (CED, Cambridge Electronic Design). Chronic experiments. In all chronic experiments, epidural EEGs (filtered 0.
Auditory stimulation All experiments were conducted in a double-wall sound-attenuating acoustic chamber (Industrial Acoustics Company, Winchester, UK). All sounds were stereo signals programmed in Matlab, where one channel (containing a 1s 4 kHz tone pip) was routed to the mono speaker in the chamber and the other channel was routed to the electrophysiology acquisition system. The sounds were transduced into voltage signals by a high-sampling rate (192 kHz) sound card (LynxTWO, Lynx, USA), amplified (SA1, Tucker Davis Technologies (TDT)), and played free-field through a magnetic speaker (MF1, TDT) mounted 35cm above the animal. For optogenetic experiments, where precise synchronization between auditory and laser stimulation was required, sounds were digitally loaded from a PC and routed to the mono speaker via a TDT RZ2 system (Tucker Davis Technologies, USA).
Pupillometry Rats were anesthetized with 1. 5% isoflurane and placed in a stereotaxic frame. An optic fiber connected the implanted optic fiber ferrule to the 473nm laser. The eye was illuminated with an infrared light and monitored continuously with a vision color camera (VGAC; TDT) synchronized to a dedicated RV2 video processor (TDT) allowing frame-by-frame synchronization between video data (30 frames/sec) and laser stimulation. We examined (Fig. 4 ) the effects of different pulse durations (10-90ms) on pupil size. For LC activation, we applied unilateral laser stimulation to the LC while recording from the contralateral pupil. For LC silencing, we applied bilateral laser stimulation while recording from the left eye followed by the right eye and then averaged the pupil size change of the two eyes.
4 NA objective. Quantification of extent of co-localization was carried out by Imaris software (Bitplane, Zurich, Switzerland). For histology of rats used in acute silencing experiments, images were acquired by Zeiss Axisoskop2 with pE2 LED excitation system (CoolLED, UK) and analyzed using a Zeiss axiocam and Axiovision software. For tissue from acute optogenetic silencing experiments, images were acquired using a Leica DMI6000 inverted epifluorescence microscope equipped with Leica DFC365FX monochrome digital camera and Leica LAS-X acquisition software.
Data Analysis Sleep staging and awakenings. Wakefulness was defined by low amplitude, high frequency EEG activity, co-occurring with high tonic EMG activity with occasional phasic EMG bursts. In chronic LC recording experiments, we divided the wakefulness epochs between active wake (grooming, eating, exploration) and quiet wake, based on video recording. NREM sleep was defined by high amplitude, low frequency EEG, co-occurring with reduced EMG tone. REM sleep was defined by low amplitude, high frequency EEG dominated by posterior theta activity co-occurring with flat EMG, as in  98. In arousal threshold experiments, each experimental trial was visually inspected and those occurring during sleep were categorized as either eliciting behavioral awakening or maintained sleep. Behavioral awakening was declared if wake-like EEG flattening (without dominant theta) was present within 3s from sound onset and lasted for at least 3s.
We then counted the number of spindle peaks that occurred in the 1. 5s before sound onset and compared "laser on" trials to "laser off" trials via paired t-test. Locomotor activity analysis. Rat position and movement were analyzed with a custom video software written in Matlab that determines the animal's center of mass and movement based on subtraction of consecutive images. After initial input from the user (who marks the initial animal location via a dedicated GUI) the software subtracts consecutive frames and, based on the number of pixels that were changed, estimates the degree of movement. Position and movements were compared between saline and yohimbine experiments via paired t-tests (Supplementary Fig. 1d ). We then calculated and compared the increase in locomotion time and distance travelled.
Pupillometry. To extract pupil area, video images were first cropped around the eye. A mask based on the median values was applied and the best centrally fitted circle was selected using the "regionprops" function in Matlab. For each trial separately, pupil area was normalized by the average baseline pupil area in the 1s preceding trial onset, and percent change dynamics were calculated for the [-5 25]s interval around laser stimulation. Trials were then averaged for each animal and condition separately (~15 trials per condition) to generate time-courses as seen in Fig. 4b. Statistically significant changes in pupil area in relation to baseline and between parameters conditions (e. g. 70ms vs 90ms pulse duration) were evaluated via Wilcoxon signed-ranked test. The peak (trough) in pupil size was found by searching for the maximal (or minimal) value in the pupil-size vector (excitation or silencing experiments respectively) between 1s before laser offset and 4s after laser offset.
Unit identification and spike sorting. Neuronal units were identified using the 'wave_clus' software package  105  as described previously. For acute activation recordings: extracellular recordings were high-pass filtered above 300 Hz and then a threshold at 5 s. d. above the median noise level was computed (for chronic recordings, a similar threshold was set manually). Then detected events were clustered using superparamagnetic clustering and were categorized as noise or single-or multiunit-clusters. For acute inhibition recordings, units were identified using the KiloSort clustering package  106. The Python based clustering package Phy [https://github. com/kwikteam/phy-contrib)] was then used to manually curate Kilosort output in order to select only well isolated single units with clear refractory periods, and to remove artefacts.
Acknowledgements We thank  Inna Slutsky  and members of  Nir lab  for discussions and suggestions,  Chiara Cirelli  and  Giulio Tononi  for comments on earlier draft,  Yaniv Sela  for help with spindle detection,  Menahem Segal  and  Susan Sara  for helpful discussions on LC experiments. We thank the  Sackler Cellular & Molecular Imaging Center (SCMIC)  and  Dr. Leonid Mittelman  for dedicated assistance. This work was supported by the  Israel Science Foundation (ISF)  grants  1326/15  and  51/11  (I-CORE cognitive sciences) and the  Adelis Foundation (YN). EJK is an INSERM fellow. OY is supported by the  European Research Council  ( ERC-2013-StG OptoNeuromod 337637 ) and the  Adelis Foundation. CAV-2 vector production was supported by  CNRS BioCampus (Montpellier). AS is a  Wellcome Trust  funded PhD student on the  Neural Dynamics program.
Modern drone technology provides an efficient means to monitor the response of alpine glaciers to climate warming. Here we present a new topographic dataset based on images collected during 10 UAV surveys of the Gorner Glacier glacial system (Switzerland) carried out approximately every 2 weeks throughout the summer of 2017. The final products, available at https://doi. org/10. 5281/zenodo. 2630456 (Benoit et al. , 2018) , consist of a series of 10 cm resolution orthoimages, digital elevation models of the glacier surface, and maps of ice surface displacement. Used on its own, this dataset allows mapping of the glacier and monitoring surface velocities over the summer at a very high spatial resolution. Coupled with a classification or feature detection algorithm, it enables the extraction of structures such as surface drainage networks, debris, or snow cover.
Here we provide a homogenized and high-resolution remote-sensing dataset covering about 10 km 2 of the ablation zone of the Gorner Glacier glacial system (Valais, Switzerland, Fig. 1 ). The raw images have been acquired by UAV Published by Copernicus Publications. flights carried out approximately every 2 weeks during the summer of 2017 (from 29 May to 30 October). The dataset comprises 10 consecutive digital elevation models (DEMs) and associated orthomosaics of the area of interest at a 10 cm resolution. It is therefore one of the most exhaustive surveys of the short-term surface evolution of a temperate glacier currently available. Geometrical coherence of the dataset is ensured through the application of comprehensive photogrammetric processing (i. e. , images are ortho-rectified and properly scaled).
In addition, the orthomosaics are stackable thanks to a co-registration procedure. The dataset can therefore be seen as a high-resolution time lapse of the Gorner Glacier ablation zone, combining spectral (orthomosaics) and geometrical (DEMs) information on the glacier surface. In addition to orthomosaics and DEMs that are snapshots of the area of interest, we also provide a product that we call matching maps (MMs) to achieve temporal monitoring of the glacier. In practice, a MM associates with each pixel of an orthomosaic (respectively a DEM) the corresponding pixel in the next orthomosaic recorded 2 weeks later. MMs can then be used to track the flow of ice over time, and in turn to quantify ice surface displacements. Potential uses of this dataset are numerous. Single orthomosaics and DEMs can be used to map the surface of the glacier and to extract features of interest such as the surface drainage network  (Yang and Smith, 2012; Rippin et al.
, 2010). The Gorner Glacier system has been widely studied since the 1970s due to its significant size, its accessibility, and because a glacier-dammed lake threatened the downstream Matter valley with glacier outburst floods  (Sugiyama et al. , 2010; Werder et al. , 2009 Werder et al. , , 2013)). The long history of glaciological studies in this area has shown that the mass balance of the Gorner Glacier system was stable from the 1930s to the early 1980s, and significantly dropped since then  (Huss et al. , 2012). This can be associated with the rise of its equilibrium line altitude (ELA) due to an increase in the local average yearly temperature. The ELA is at around 3300 m today according to studies carried out at the neighboring Findel Glacier  (Sold et al. , 2016). In this context, the current dataset aims at complementing existing studies about the Gorner Glacier system by documenting the behavior of its ablation zone during an entire summer, at a time when this glacial system is thought to be out of equilibrium with a clear trend toward glacial retreat.
UAV surveys The primary data are RGB images acquired by repeated UAV surveys over an area of 10 km 2. A fully autonomous fixedwing UAV of type eBee from SenseFly, equipped with a 20megapixel SenseFly S. O. D. A camera, has been used for image acquisition  (Vallet et al. , 2011). The camera was static within the UAV body (no gimbal) and therefore all pictures are quasi-nadir (i. e. , images are taken with an angle ±10 • from the vertical line). For flight planning and UAV piloting, the eMotion3 software was used. Raw images were acquired with a ground resolution ranging from 7. 3 to 8. 8 cm for the glaciated parts of the area of interest. For the requirements of photogrammetric processing, flight plans have been designed to allow for an overlap between images ranging from 70 % to 85 % in the flight direction and from 60 % to 70 % in the cross-flight direction.
Generation of co-registered orthomosaics and DEMs Pictures have been processed separately for each acquisition date with the photogrammetric software pix4DMapper version 3. 1  (Vallet et al. , 2011, Fig. 2 ) using default parameters for nadir flights (see the processing reports for details about these parameters). The output resolution has been set to 10 cm per pixel in order to prescribe a constant resolution across all final products. During the photogrammetric processing, the raw pictures are first oriented by bundle adjustment, and then a DEM and an ortho-rectified image (orthomosaic) are generated for each day of interest. Since the only geolocation information included into the bundle adjustment procedure is the trajectory of the UAV derived from codeonly GPS data, the initial geo-referencing of the orthomosaics and DEMs is limited to a few meters.
4. 1 for details), the geolocation errors related to the absence of GCPs can impair comparisons with other datasets covering the same geographic area. To improve absolute georeferencing and to link our dataset with the Swiss national reference system, Table 2 provides the parameters of the affine transformation be- tween the local reference frame of this dataset and the Swiss CH1903 -LV 03 reference system. This transformation has been estimated from 81 manual tie points identified in (1) the orthomosaic derived from the master bundle adjustment and (2) a 50 cm resolution orthomosaic of 2009 processed and georeferenced by the Swiss mapping agency swisstopo.
Surface displacement tracking: generation of MMs Consecutive co-registered orthomosaics enable us to quantify horizontal displacements at the surface of the glacier. In the present dataset, this information about ice surface displacements is provided by the MMs (Fig. 3 ). In practice, a MM is an image that pairs the positions of similar ice patches at times t and t + dt (dt being the time span between consecutive acquisitions) (Fig. 2 ). The footprint of the MM is the overlap of the footprints of the orthomosaics at times t and t + dt. MMs inherit the spatial resolution of the original orthomosaics (i. e. , 10 cm) and can therefore be used to directly relate any pixel of a given orthomosaic to its counterpart in the following orthomosaic (and therefore easily navigate within the whole dataset).
This operation is repeated for all possible patches in the reference orthomosaic. The MMs have been calculated using an open-source utility called MatchingMapMaker developed as part of this project, and made available along with the dataset (see Sect. 5. 2 for code availability). The MatchingMapMaker tool has been designed to account for the specificities of UAV-based orthoimages, and in particular their very high resolution. To ensure the reliability of this utility, MMs have been benchmarked against horizontal displacement maps calculated using well-established image correlation algorithms, namely IMCORR  (Scherler et al. , 2008)  and CIAS  (Heid and Kääb, 2012). The results of this benchmark (see Sect. 5. 2) show a very good agreement between horizontal displacement maps derived from MatchingMap-Maker, IMCORR, and CIAS.
Bundle adjustment and co-registration A first validation of this dataset can be performed by checking the relative orientation of the cameras during the bundle adjustment, as well as the co-registration of orthomosaics and DEMs. Processing reports detailing the quality of the bundle adjustment for each session are available along with the dataset (see Sect. 5. 1). Table  2  displays three indices summarizing the quality of both bundle adjustment steps. First, the mean reprojection error (in pixels) quantifies the mismatch in the raw images between the observed and the modeled position of tie points used during the relative orientation step. The sub-pixel level of errors (Table  2 , column 2) ensures that the orientations of the camera are reliable. Next, the co-registration step is assessed by the mean root-mean-square error (RMSE) of manual tie point coordinates.
Note that late in the season (i. e. , for the last acquisition on 30 October) it became difficult to identify manual tie points due to strong shadows, hence the small number of manual tie points at that time. Another important validation consists of assessing possible internal deformations within the local reference frame of the dataset. Figure  4a  displays the residuals of the coregistration of the master bundle adjustment on a georeferenced orthoimage, which are a proxy for the internal deformations of the master bundle adjustment. The results show that the internal deformations have a meter-level amplitude (mean deformation = 1. 07 m; max deformation = 2. 83 m) and are smoothly spread over the area of interest due to the bundle adjustment procedure, which tends to distribute errors over space.
It follows that, considering the extent of the study area (a few square kilometers), the relative error induced by the internal deformations of the local reference frame is on the order of 1/1000. Thanks to the co-registration procedure, the internal deformations of all orthomosaics and DEMs are similar to the ones of the local reference frame defined by the master adjustment. When measuring changes at the surface of the glacier from the present dataset, the error related to internal deformations is therefore on the order of 1 ‰ of the measured distances. This results in relatively small absolute errors because the changes at the ice surface of the Gorner Glacier are of moderate amplitude (e. g. , ice ablation reaches a few centimeters per day, and ice flows at less than 1 m d -1 in the ablation zone).
Structure and availability of the dataset All the data presented in this dataset are available in the following repository (Rep): https: //zenodo. org/record/2630456, with the following DOI: https://doi. org/10. 5281/zenodo. 2630456 (Benoit et al. , 2018). The results of the photogrammetric processing, i. e. , the orthomosaics and the DEMs, are available in the compressed folder Rep\Photogrammetric_Products. zip. Within this folder, the products are grouped in sub-folders by acquisition date using the following standard: 2017_mm_dd with "mm" the month and "dd" the day of acquisition. Finally, these sub-folders contain the following files: -2017_mm_dd_orthomosaic. tiff: contains the orthomosaic. -2017_mm_dd_dem. tiff: contains the DEM. -2017_mm_dd_report. pdf: contains the processing report (generated by Pix4D Mapper) that summarizes the quality of the photogrammetric processing for the date of interest.
Code availability The photogrammetric processing has been carried out using the proprietary software Pix4D Mapper, commercially available at https://pix4d. com/ (last access: 16 November 2018). The MM have been computed using MATLAB routines written by Mathieu Gravey. The related utilities are freely available on the following repository: https://github. com/GAIA-UNIL/MatchingMapMaker  (Gravey, 2018). The sub-repository Benchmarking_tests contains the results of benchmarking tests aiming at comparing the displacement maps computed by the MatchingMapMaker utility (i. e. , MMs) with displacement maps computed by wellestablished glacier surface tracking algorithms, namely IM-CORR (https://nsidc. org/data/velmap/imcorr. html, last access: 29 April 2019) and CIAS (https://www. mn. uio. no/geo/ english/research/projects/icemass/cias/, last access: 29 April 2019).
(Insert table 1) All patients were right-handed and screened negative for history of psychiatric or neurological diseases. They were tested in the Department of Neurosurgery on average [M = 159. 33, (SD = 37. 6)] days after the surgery. Then, using the mricro software (www. mricro. com; Rorden & Brett, 2000)  lesions were reported on slices of a T1-Weighted template MRI scan from the Montreal Neuroradiological Institute, orientated to approximately matched Talairach space  (Talairach & Tournoux, 1988).
(Insert figure 1 and table 2) Lesions were then superimposed onto the Automated Anatomical Labeling (AAL) template provided by Mricro  (Tzourio-Mazoyer et al. , 2002). The software allows us to calculate the total lesion volume (in cm 3 ) and to identify the cerebral regions affected by the lesion with reference to the list of anatomical ROIs (regions of interest) provided by  Tzourio-Mazoyer et al. (2002). The details of the lesion volume (cm 3 ) and the damaged anatomical structures for each patient are summarized in Table  2.
Introduction This file contains supplementary text and figures. The scenarios and model configuration are described in the main manuscript. The analysis code is available via https://github. com/grandey/draft2017a-region-rfp, archived at https://doi. org/10. 5281/zenodo. 1987185. Readers interested in regional analysis of the net radiative effect are referred to presentation slides archived at https://doi. org/10. 6084/m9. figshare. 7380191.
Acknowledgments This research was supported by the  National Research Foundation of Singapore  under its  Campus for Research Excellence and Technological Enterprise programme. The Center for Environmental Sensing and Modeling is an interdisciplinary research group of the  Singapore-MIT Alliance for Research and Technology. This research was also supported by the U. S. National Science Foundation  ( AGS-1339264 ) and the  U. S. Department of Energy, Office of Science  ( DE-FG02-94ER61937 ). The  CESM  project is supported by the  National Science Foundation  and the  Office of Science (BER)  of the  U. S. Department of Energy. The computational work for this research was performed on resources of the  National Supercomputing Centre, Singapore  (https://www. nscc. sg).
Code and Data Availability CESM 1. 2. 2 is available via http://www. cesm. ucar. edu/models/cesm1. 2/. The scripts used to generate input data, configure the simulations, and produce the figures are available via https://github. com/grandey/draft2017a-region-rfp/, archived at https://doi. org/10. 5281/zenodo. 1987185. The simulation input data and the simulation output data analysed in this manuscript are archived at https://doi. org/10. 6084/m9. figshare. 6972827.