dataesr/scientific-paragraphs-categorization huggingface dataset:
senegalensis feae (Fig. 7 ). Otus bikegila sp. nov. https://zoobank. org/0731A37D-B363-43C9-A1AC-69F5E10F6810 Figs  2A, 8  Material. Holotype. MHNC-UP-AVE7000: São Tomé and Príncipe • ♀, adult, moulting; Príncipe Island, South Príncipe, ca. 500 m NW of Ribeira Porco river mouth (Fig. 1 ); 1°33. 03'N, 7°22. 29'E; ca. 100 m a. s. l. ; 29 May 2017; HP and Ceciliano do Bom Jesus leg. ; skin prepared by Vanya Rohwer, skeleton prepared by Vanya Rohwer and Daniele Cataldo; left wing removed for wing mounting, all bones with the exception of the left tarso-metatarsus (tarsus) removed for skeleton preparation; Audio-recorded by HP (Xeno-canto audio: XC619445, XC619447; GenBank: 12S OM978880, 16S OM978895, ATP6 OM913485, COI, OM937282; CYTB, OM937307; ND2, OM937351; ND3, ON016156; KIAA1239, OM937319; MYO2, OM937336; RAG1, ON016107; SACS, ON016118; TGFB2, ON016136 and TTN, ON016141; Gulf of Guinea database of MM: P7-04.
senegalensis senegalensis, although we did identify a diagnostic morphological character (bill length from tip to nares, Suppl. material 12) and three molecular diagnostic characters at the TGFB2 gene (C vs. T in site 285, T vs. G in site 344, C vs. G in site 392). In a dataset with a wider taxonomic sampling of O. senegalensis but fewer sequencing data, the Annobón sample nested within the mainland samples (unpublished data). A better sampling of O. senegalensis senegalensis from across its range will help to resolve this taxonomic issue. Our sampling increased considerably the taxon coverage for the centre of the diversity of the Otus genus, the Indo-Malayan region  (Marks et al. 1999 ), but has failed to solve most of the many taxonomic pending issues. The Ryukyu Scops-Owl O. elegans (Cassin, 1852), a species restricted to small oceanic islands from the northern Philippines to Japan, and included for the first time in a phylogenetic study, was recovered as the fourth Asian representative of the Indo-Malayan/Indian Ocean clade (Fig.
6 ; sensu  Pons et al. 2013) , albeit with no statistical support. At this stage, our results mainly highlight the need for using a phylogeographic-level sampling scheme across the region (i. e. , multiple samples per site covering all areas of occurrence) to enable a thorough systematic revision of the Indo-Malayan taxa, a crucial step towards reconstructing the diversification history of the genus Otus. Our molecular dataset confirmed the low levels of divergence (well within intraspecific variation) of three taxa pairs that are currently treated either as separate species or subspecies. These pairs are: i) O. senegalensis senegalensis (mainland Africa) and O. s. feae (Annobón Island), treated as separate species by del Hoyo (2020) and  Gill et al. (2021)  based on  Collar and Boesman (2020) ; ii) O.
List of scops-owls (Otus) samples and GenBank accession numbers for the gene fragments used in this study. Accession numbers in bold indicate sequences newly produced for this study. STP -São Tomé and Príncipe; EG -Equatorial Guinea; DRC -Democratic Republic of the Congo; CHIMERA -sequences for a given taxon obtained from different individuals. CYTB and ND2 sequences of O. cyprius, O. i. icterorhynchus, Wallace's Scops-Owl O. silvicola (Wallace, 1864), and four subspecies of the Mountain Scops-Owl (Blyth, 1846) [O. spilocephalus vandewateri (Robinson & Kloss, 1916), O. s. vulpes (Ogilvie-Grant, 1906), O. s. luciae (Sharpe, 1888), and O. s. hambroecki (Swinhoe,.
Acknowledgements We thank  An Bollen  (first in Principe Trust Foundation, later in Forever Principe) for the support for the expeditions from 2016 onwards that led to the discovery of the new species, and that subsequently allowed the collection of the data required to confirm its status and prepare its description; An also participated in a field expedition. Felipe Spina  ( Fauna & Flora International ,  Principe Trust Foundation ) provided invaluable fieldwork support and enthusiasm for the 2016 and 2017 expeditions (discovery of the owl and holotype collection, respectively). We thank  Augusto Faustino  for his technical advice on the use of isoflurane. We thank everyone at  Forever Principe  for the full logistic support of the 2018 and 2019 expeditions and in particular:  Rombout  and  Ella Swanborn ,  Myles Oates ,  Walter Jubber , and everyone at  Roça Belo Monte.
pt). The  Portuguese Foundation for Science and Technology (FCT)  supported the post-doc grant  FCT/MCTES -SFRH/BPD/100614/2014  to MM, the PhD grant ( 2020. 04569. BD) to BF, the  Investigador FCT  ( IF/00209/2014 ) and  2020. 00823. CEECIND/CP1601/CT0003  research contracts to AC, and provided structural funding to CIBIO ( UIDB/50027/2021 ) and cE3c ( UID/00329/2021 ). GS was supported by a postdoctoral grant from the  Swedish Research Council  (grant  2015-06455 ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement nº  854248.
2, CD4, CD8a, NK1. 1, TER-119, CD49b) low CD11b high Ly-6G low Ly-6C high F4/80 low. Macrophages were identified as CD45 high (CD19, CD90. 2, CD4, CD8a, NK1. 1, TER-119, CD49b) low CD11b high Ly-6G low Ly-6C low F4/80 high. Data were acquired using FACS Canto™ II, LSRFortessa™ and FACS Diva software (BD Pharmingen). Experimental data were analyzed using FlowJo software.
Statistics Statistical analyses were carried out using GraphPad Prism software version 8 (GraphPad Software, Inc. ). Results are displayed as mean ± standard error of mean (S. E. M. ). First, values were tested for Gaussian distribution (D'Agostino-Pearson omnibus normality test). For two-group comparisons, unpaired t-test was applied to parametric data, Mann-Whitney test to non-parametric data. For comparing more than two groups an ordinary one-way ANOVA test followed by a Sidak's test for multiple comparisons was applied to parametric data; the Kruskal-Wallis test was applied to non-parametric data. P values of < 0. 05 indicated statistical significance.
e. GPIab, GPV, GPVI and ITGB3, was not detectable (Fig. 1D ). Performing CFU assays only with isolated LSK cells, we confirmed larger colonies on ADP (2. 5-3 µM)stimulated plates compared to the PBS control (Fig. 1E ). Since most current RNA sequencing databases of the hematopoietic BM do not describe P2Y 12 receptor expression on LSK cells, we matched our findings with an existing RNAsequencing repository of enriched hematopoietic stem and progenitor cells  [33]. Here, we were able to confirm detectable P2Y 12 receptor expression on LSK cells in comparison to Lineage -sca-1 -c-kit + progenitor cells (Supp. Fig. S1 ). For further characterization and functional evidence of the P2Y 12 receptor on LSK cells, we analyzed the intracellular Akt signaling pathway, which is known to be activated downstream of P2Y 12 and to be involved in cell cycle progression.
However, the functional relevance of P2Y 12 expression beyond platelet aggregation is largely unknown  [14]. Remarkably, after careful exclusion of platelet-complexes in the cell isolation process, we detected P2Y 12 receptor expression, specifically on hematopoietic stem and progenitor cells (LSK) in the murine BM. This finding could be validated interrogating an external RNA sequencing dataset of isolated LSK cells  [33]. In line with previous studies, we were also able to detect P2Y 12 expression on differentiated leukocyte subsets at lower expression levels as compared to LSK cells. Therefore, direct P2Y 12 dependent effects on leukocyte subsets have to be acknowledged and remain to be investigated. The phosphoinositide 3-kinase (PI3K)/Akt intracellular signaling pathway promotes cell survival, proliferation and growth by phosphorylation and inhibition of key transcription factors and has been reported to forward Akt phosphorylation downstream of the P2Y 12 receptor in activated platelets and vascular smooth muscle cells  [32, 48, 59 ].
Acknowledgements We thank  Sarah Schmidt ,  Tayrin Hopp  as well as the  Light House Core Facility  for assistance with experiments. Timon Bühler ,  Diana Chiang-Jurado ,  Jasmin Rettkowski ,  Ingo Hilgendorf ,  Dennis Wolf ,  Alexander Maier ,  Daniel Dürschmied ,  Nina Cabezas-Wallscheid ,  Christoph Bode ,  Constantin von zur Mühlen  and  Timo Heidt  are members of  SFB1425 , funded by the  Deutsche Forschungs-gemeinschaft (DFG, German Research Foundation )-Project # 422681845. Timon Bühler  is supported by a research grant of the  German Cardiac Society (DGK).
Distortion corrections were then achieved using the FSL software package, and tracking of the full cervical spinal cord was performed using the DSI Studio software (quantitative anisotropy-based deterministic algorithm). A unique region of avoidance was used to exclude everything that is not of the nervous system. Fiber tracking parameters used adaptative fractional anisotropy from. to. , fiber length from to , mm, and angular threshold of •. In all participants, a full cervical cord tractography was performed from the medulla to the C spine level. On a ventral view, the junction between the medulla and spinal cord was identified with its pyramidal bulging, and by an invagination corresponding to the median ventral sulcus. On a dorsal view, the fourth ventricle-superior, middle, and inferior cerebellar peduncles-was seen, as well as its floor and the obex; and gracile and cuneate tracts were recognized on each side of the dorsal median sulcus.
Distortion corrections Correction for susceptibility artifacts was performed using the "top-up" tool from FSL  (Smith et al. , 2004 ) (Functional magnetic resonance imaging of the brain [FMRIB] Software Library) to reduce inhomogeneity distortions  (Andersson and Sotiropoulos, 2016). Motion artifacts and artifacts generated by eddy currents produced during diffusion-encoding gradient application were corrected using the FSL "eddy". The methodology used herein has been demonstrated to be an essential step to obtain a good spinal cord tractography rendering  (Dauleac et al. , 2021).
The software used herein-DSI Studio-was preferred for its simplicity and the combination of all tractography steps in one tool but there remains a learning curve. With advances in computer science, post-processing could be fully automated, including machine learning for tumor segmentation and region of avoidance design; stitching of acquisition boxes for cervical/thoracic/lumbar tractograms  (Wang et al. , 2012a) ; and multiband MR acquisition in reversed-phase encoding to double the spatial and angular resolution  (Idiyatullin et al. , 2015). Full cervical cord tractography currently appears as a condensation of tracts without differentiation from one another. The output of the described pipeline demonstrates streamlines coursing through all of the gray matter in a cranio-caudal direction, indistinguishable from the adjacent white matter tracts.
One of the next challenges is therefore the differentiation of spinal tracts within the spinal cord. In this way, the "Spinal Cord Toolbox"  (De Leener et al. , 2017)  appears to be an interesting tool. This software package proposes a segmentation of the white and gray matter, and a probabilistic atlas of the spinal tracts  (De Leener et al. , 2017). This atlas could be superimposed on an axial view of a full spinal cord tractography to depict gray central "butterfly" matter and peripheral white fibers. However, this atlas was built at the cervical level, with extrapolation to the entire spinal cord. Since the exact location of the tracts might change across levels and subjects (especially in patients with intramedullary lesion), a "tailored" atlas of each spinal cord level should be built.
Our team is currently working on this (NCT05079945). This simplified spinal cord tractography protocol is aimed to enable its use in routine practice. As for brain tractography, which can be incorporated into a neuronavigation system, it could be helpful for neurosurgeons to see the spinal cord tractograms merged with anatomical images (T2-weighted imaging) into an image guidance platform. However, it would require translating images obtained with DSI Studio back into neuronavigation systems. Implementing spinal cord tractography in navigation systems could also promote neuroanatomy teaching and presurgical planning. In addition, incorporating 3Dtractography rendering into augmented reality and virtual reality devices could help young neurosurgeons to better understand the anatomy and anticipate the pitfalls of such intramedullary surgery.
We ensured our preparations were stable and performed basic XY motion correction using image registration against a template. We observed that JEDI-2P could report spikes with a À18. 2% ± 4. 8% change in fluorescence (DF/F 0 ); numbers reported as mean ± 95% CI here and henceforth in this section (Figures 5C, 5D, and S6A-S6C). To quantify our ability to infer spikes from the optical data, we used VolPy, an automated pipeline for spike detection from voltage imaging datasets  (Cai et al. , 2021). We compared the actual and predicted spike rates in 40-ms bins, the primary bin size used in a spike-inference competition using data from calcium indicators  (Berens et al. , 2018). The correlation (Pearson's r 2 ) between spikes predicted by VolPy and those identified by juxtacellular recordings was 0.
64 ± 0. 18 (range: 0. 43-0. 84 across individual neurons). Vol-Py's default parameters were used, and optimizing these parameters would likely improve correlations. For context, the spike-infer-ence competition using calcium imaging data produced correlations between 0. 25 and 0. 44, depending on the algorithm. However, we note that a direct comparison of spike inference using JEDI-2P and calcium dyes or indicators would require datasets where both calcium and voltage were monitored simultaneously. Given the faster temporal resolution of voltage imaging, we also evaluated spike inference under conditions where electrical and optical spikes were matched within narrower time spans. Because quantifying the correlation coefficient with narrow time bins can produce boundary artifacts (method details), we used the F 1 score to represent the accuracy of spike detection.
The reference orange/red FP cyOFP1 was imaged first. 50 frames were captured so that noise could be reduced by averaging. Electric field stimulation was performed during continuous imaging of the green channel for 4000 frames or $9 s. The stimulation protocol started with twenty monophasic square pulses with 1-ms width, 60-V amplitude, and an inter-pulse duration of 300-ms. This was followed by a 100-Hz train (10 monophasic square pulses with 2. 5-ms width, 30-V amplitude, and an inter-pulse duration of 10 ms), although responses to this train were not used as a performance metric in the experiments described here. Analysis of high-throughput screening data Image analyses were performed by custom routines in MATLAB (version r2019b, MathWorks). Time-lapse images recorded in nd2 format were imported to MATLAB using the Bio-Formats toolbox (version 6.
Whole-cell voltage clamp was achieved using a MultiClamp 700B amplifier (Molecular Devices). Patch clamp data was recorded with an Axon Digidata 1550B1 Low Noise system with HumSilencer (Molecular Devices). Command voltage waveforms were compensated for the liquid junction potential. Recordings were considered satisfactory and were included in the final analysis only if the patched cell had an access resistance (Ra) smaller than 7 MU and a membrane resistance (Rm) larger than 10 times of Ra both before and after the recording. Voltage clamp under one-photon illumination GEVI characterization under 1PM was performed with the same microscope as for GEVI screening (above). Cells were illuminated with 470/24-nm light (SpectraX, Lumencor) and conditioned using the 477-503-nm band of a multi-band dichroic mirror (89100bs, Chroma).
A diaphragm was used to reduce the diameter of the excitation spot so that during imaging only one cell at the center of the FOV was illuminated. To maximize photon collection, we used an objective with higher numerical aperture (NA) than above (403 NA-0. 95, CFI Plan Fluor oil immersion, Nikon Instruments). To capture fluorescence changes at higher temporal resolution than achievable with our camera, a multialkali photomultiplier tube (PMT, PMM02, Thorlabs) was installed on one of the side ports of the microscope. A LabVIEW (version NXG 5. 0, National Instruments) routine was used to control the PMT bias voltage and record the output voltage using the data acquisition and output boards. Data were collected at 80 kHz. The output voltage from the PMT was analyzed by a custom routine written in MATLAB to obtain fluorescence signal for each cell.
The pH of the saline equilibrated near 7. 3 when bubbled with 95% O 2 /5% CO 2. Two-photon imaging Neurons were imaged with a Leica TCS SP5 II two-photon microscope with a 203/1. 0-NA water immersion objective (Leica HCX APO) and a pre-compensated femtosecond laser (Chameleon Vision II, Coherent). The excitation wavelength was 920 nm and 5-20 mW of power was applied to the sample. Emitted photons were filtered by a 525/50-nm filter and collected with a Hybrid Detector (HyD, Leica). The data were acquired at a constant frame rate of 82. 4 Hz using a frame size of 200320 pixels, 153 digital zoom, a line scan rate of 1,400 Hz, and bidirectional scanning. L2 cells were imaged at their arbor in medulla layer M2. Total imaging time per fly never exceeded 1 h.
The radiance at 482 nm was approximately 78 mW$sr -1 $m -2. The imaging and the visual stimulus presentation were synchronized using triggering functions provided by the LAS AF Live Data Mode software (Leica) as well as the signal from the photodiode directly capturing projector output. A data acquisition device (NI DAQ USB-6211, National Instruments) connected to the computer was used to acquire the photodiode signal, generate a trigger signal at the beginning of stimulus presentation, and acquire the trigger produced by the LAS software at the start of each imaging frame. This allowed the imaging and the stimulus presentation to initialize in a coordinated manner and ensured that stimulus presentation details were saved together with imaging frame timings (in MATLAB. mat files) to be used in subsequent processing.
Data was acquired at 5 kHz. The visual stimuli used were: 300-ms search stimulus: alternating full contrast light and dark flashes, each 300 ms in duration, were presented at the center of the otherwise dark screen. The stimulus was such that from the perspective of the fly, the flashing region was 8 from each edge of the screen. In subsequent analysis, the responses to this stimulus were used to select regions of interest (ROIs) with receptive fields located at the center of the screen instead of at the edges. This stimulus was presented for 5,000 imaging frames (61 s) per field of view. 20-ms light and dark flashes from gray (Figures  4C  and 4D ): single 20-ms light and dark flashes, with 500-ms of gray between the flashes, were presented over the entire screen. The light and dark flashes were randomly chosen at each presentation.
Bias currents were zeroed once the pipette was placed in the bath. JEDI-2P-expressing cells were targeted for recording by approaching the cell under 2P guidance and establishing a juxtacellular seal that enabled visualization of neuronal spiking. After each recording, positive pressure was applied, which often broke open the cell membrane and enabled intracellular injection of the Alexa Fluor 594 dye that enabled us to confirm that we were recording from the cell that we had been imaging. Voltage imaging without in-vivo patching Voltage imaging experiments without in vivo patching were done in awake behaving head-fixed mice on a linear non-motorized treadmill under the two-photon microscope (Figure  5A ). Data were collected while mouse was presented with visual stimuli consisting of Gaussian noise with coherent orientation and motion.
Data analysis (general procedures) In all cases, neurons in the fluorescence traces were manually segmented from the mean image of the optical recordings. We performed basic motion correction using image registration against a template. Raw fluorescence traces were computed as the average of pixels inside neurons. To correct for background fluorescence, we subtracted from the raw traces the running average (10 s window) of the darkest pixels within the FOV. Changes in baseline fluorescence due to focus drift or photobleaching were corrected by using a Butterworth filter of order 3 and cutoff frequency at 0. 005 Hz. DF/F 0 was computed using the baseline corrected traces.
Spike inference Patch recordings and imaging data were synchronized by copying the frame pulse signal generated at the start of each imaging frame to the patch clamp acquisition software. The patch clamp recordings were acquired at 10 kHz and filtered using a Butterworth filter of order 3 and cutoff at 0. 1Hz. The filtered signal was convolved with a Gaussian filter with standard deviation equal to 3 to remove small peaks. Ground truth spikes were determined by a manual threshold and imposing a minimal inter-spike interval of 3 ms. While MLSpike  (Deneux et al. , 2016)  distinguished apparent subthresholds and spikes in our ULoVE recordings, we obtained poor results with our resonant scan recordings, possibly due to their lower SNR. To extract optical spike times and maximize the SNR of traces, we instead used the VolPy algorithm  (Cai et al.
Homologs were identified by BLASTing the NCBI non-redundant protein sequences database (nr) with the sequence of the voltage-sensing domain from ASAP2s. Homologs were aligned using the BLOSUM62 scoring matrix. The shaded areas correspond to the four transmembrane helixes (S1-S4). Many residues with high evolutionary conservation scores were targeted for mutation (small blue stars). JEDI-2P incorporates mutations at the two positions labeled with large green stars. (D) Directed evolution path leading to JEDI-2P. (E) Candidates were evaluated based on single characteristics (first three columns) and compound measures (last two columns). The detectability index is defined as response amplitude times the square root of the relative brightness. The detectability budget is defined as the detectability index times the square root of the photostability.
ACKNOWLEDGMENTS We thank  Dr. B. Arenkiel ,  J. Ortiz-Guzman , and  Z. Chen  at the  TCH Neuroconnectivity Core  for AAV packaging; this facility is supported by  NIH  grant  P50HD103555  and the  Charif Souki Fund. We thank  J. M. Kirk ,  H. Johnson  and the  Imaging & Vital Microscopy Core  ( Baylor College of Medicine, BCM ) for training on the confocal microscope and the plate reader. We thank  F. A. Blanco ,  C. A. Cronkite , and  Dr. J. G. Duman  from  Dr. K. R. F. Tolias   lab (BCM)  for preparing neurons. We acknowledge  H. Yang  and  M. Xie  ( Clandinin lab ) for developing the stimulus presentation and analysis code used in the fly imaging pipeline. We thank  R. Kroeger  ( Reimer lab ) for helping with 2P imaging. We thank  B. Mathieu  and the  IBENS imaging facility (IMACHEM-IBiSA ), member of the  French National Research Infrastructure France-BioImaging  ( ANR-10-INBS-04 ), which received support from the  "Federation pour la Recherche sur le Cerveau-Rotary International France " (2011) and from the  program «Investissements d'Avenir»   ANR-10-LABX-54   MEMOLIFE.
, 2019). The additional components which are interactively coupled to HadGEM3-GC3. 1 in UKESM1. 0 are terrestrial carbon and nitrogen cycles, which include dynamic vegetation and representation of agricultural land use change  (Harper et al. , 2018) ; ocean biogeochemistry  (Yool et al. , 2013) ; and a unified troposphere-stratosphere chemistry model, tightly coupled to a multi-species modal aerosol scheme  (Archibald et al. , 2020). The UKESM1. 0 configuration of HadGEM3-GC3. 1 has a global resolution of N96 (∼ 135 km) and 85 vertical levels in the atmosphere and ORCA1 (1 • longitude) and 75 vertical levels in the ocean. Its component models are the Unified Model (UM) for the atmosphere  (Brown et al. , 2012) , Nucleus for European Modelling of the Ocean (NEMO) for the ocean  (Madec and the NEMO Team, 2016) , the Community Ice CodE (CICE) for the sea ice  (Hunke et al.