dataesr/scientific-paragraphs-categorization huggingface dataset:
Extracellular recordings. For extracellular recordings, C57BL/6 mice (10-18 weeks old) were used. Mice were anesthetized under isoflurane (2%-5%), implanted with a head plate and allowed to recover for 48 h. Then, animals were anesthetized under isoflurane (2%-5%) and a small craniotomy (1 × 1 mm) was drilled over the primary visual cortex. The craniotomy was sealed with silicon (kwik-cast) and the animals were allowed to recover for 2 h. The head was then positioned with a pitch angle of 30 degrees (nose down). The silicon probe (IMEC, Belgium) was first coated with DiI (Molecular Probes, Thermo Fisher Scientific, USA). The probe was then positioned perpendicular to the brain surface and the tip inserted to 1750 or 2000 µm from the pial surface. The silicon probe data was acquired using an FPGA card (KC705, Xilinx, USA) and SpikeGLX (https:// github.
com/ billk arsh/ Spike GLX) and filtered at 300 Hz. To determine the middle of layer 5 electrophysiologically, we used the depth profile of the high-frequency LFP power  11. Briefly, for each non-reference channel on the probe, the average power in the frequency range 500-1250 Hz was computed (MATLAB, The MathWorks Inc. , bandpower function). Reference channels were interpolated (MATLAB interp1), and the depth profile was smoothed across 10 channels (MATLAB smooth, which implements a moving average) to reduce variability across channels. The location, in the cortex, of the peak in this depth profile was chosen as the middle of layer 5 (mid-VISp5). The increase in LFP power when entering the hippocampal formation was used as an additional landmark to confirm the correct depth.
Visual stimulation. Visual stimuli for receptive field (RF) mapping were generated in MATLAB using the Psychophysics Toolbox  21  and presented on an LCD monitor (Samsung C24FG73FQU, 120 Hz refresh rate) positioned 20 cm from the right eye at approximately 45° to the long axis of the animal, such that it covered ∼105° (azimuth) × 75° (elevation) of visual space. Stimuli consisted of black and white squares on a grey background, presented one at a time (1 Hz) in a randomised order, at one of 120 positions (12 × 10 matrix) covering the full surface of the monitor. Each position was repeated 10 times. The backlight of the monitor was synchronized to one turn-around point of the resonant scanner (where data is not acquired), so that the monitor flickered at 8 kHz and light from the screen is only on in between the scanning of two subsequent lines.
A photodiode was positioned over the upper-left corner of the monitor and data was acquired in parallel with imaging to provide an accurate stimulus timestamp. Receptive field analysis. Pre-processing of all raw calcium movie data including image registration and automated cell detection was performed with suite2p  22. All subsequent analysis was performed with custom scripts written in MATLAB. Neuropil subtraction to correct for contamination of the ROI calcium traces by surrounding neuropil and calculation of ΔF/F was computed as described in Ref. 23. The response to each individual patch was taken as the mean dF/F during the 1 s stimulus presentation minus the mean dF/F during the previous 1 s (baseline). In the following, responses to black and white stimuli were analysed separately. To assess whether ROIs had a significant RF, responses to each patch position were grouped and a Kruskal-Wallis test was performed (p < 0.
Manual segmentation and region analysis. To manually segment structures within the brain, a graphical user interface was developed using Qt (www. qt. io/ via qtpy https:// github. com/ spyder-ide/ qtpy) and napari  3. This tool (brainreg-segment) allows 1, 2, and 3-dimensional structures to be segmented, analysed (e. g. , distribution within brain regions) and summarised (e. g. , centroids or tissue volumes). A full user-guide for brainreg-segment is available at https:// docs. brain globe. info/ brain reg-segme nt, but a summary of the analysis process is as follows. Upon starting the plugin, the user is presented with an option to load data previously registered with brainreg. The user can load the entire brainreg output directory, which loads the appropriate atlas data and image metadata.
Data analysis and visualisation. To compare segmented injection variability, a consensus injection image was calculated from all nine (three repeats by three experts) manual segmentations. Voxels which were found in over half of the segmentations (i. e. at least five) made up the binary mask of the consensus image. Individual expert segmentations were then compared to this consensus by calculating the DICE 32 score. Plots were generated using MATLAB (The MathWorks Inc. ) and image visualisation was performed using napari and brainrender.
Acknowledgements We thank  Molly Strom  for assistance with virus development and sample preparation. We thank  Rob Campbell  for technical assistance with microscopy. This work was supported by Grants from the  Gatsby Charitable Foundation  ( GAT3361 , T. W. M. and T. B. ) and  Wellcome  ( 090843/F/09/Z , T. W. M. and T. B. ; 214352/Z/18/Z, T. B. ; 214333/Z/18/Z, T. W. M. ).
Data and software availability The software outlined in this manuscript is available as part of the BrainGlobe suite of computational neuroanatomy tools. The software is open source, written in Python 3 and runs on standard desktop computing hardware. Brainreg is fully supported on Linux and Windows, and brainreg-segment on Linux, Windows and macOS. Source code is available at https:// github. com/ brain globe/ brain reg, https:// github. com/ brain globe/ brain reg-napari and https:// github. com/ brain globe/ brain reg-segme nt. Documentation and tutorials are available at https:// docs. brain globe. info.
| Study population The data were collected from the highly philopatric breeding population of albatrosses on Possession Island, in the Crozet archipelago (46. 8°S, 51. 8°E), which has been monitored since 1966  (Weimerskirch & Jouventin, 1987). Age, sex (determined via sexual dimorphism), and complete partnership histories (i. e. , identifying new and established pairs) were known for all individuals. Adults breed biennially in socially monogamous pairs. Parents return to land in November, before laying a single egg in late December or early January (Fressanges du  Bost & Segonzac, 1976; Weimerskirch, 1995). An ~78 day incubation period follows before the egg hatches from mid-March and chick-brooding commences (duration = ~30 days)  (Weimerskirch, 1995). Parents alternate on the nest to incubate the egg or brood the chick until April, when the chick is left alone and fed regularly by the parents  (Weimerskirch et al.
| Quantifying parental care behavior Based on data from non-breeding birds, the maximum continuous flight time of this species is estimated at 12 h (H. Weimerskirch, unpublished data). Dry periods longer than this (mean = 85. 23 h ± 65. 02 SD) indicated that the bird was on land, allowing differentiation between foraging trips and nest attendance shifts (the duration of which is termed "shift duration"). A foraging trip begins with the first wet period after a >12-h dry period and ends at the start of the next >12-h dry period. The term "individual trip duration" is applied to the length of a foraging trip of a focal bird. During incubation and for a month after hatching, albatross parents never leave their offspring unattended  (Weimerskirch et al. , 2000; Weimerskirch & Lys, 2000). As one parent's presence on land indicates that their partner is at sea, the individual trip durations of the birds without loggers were estimated from the shift durations of their monitored mates.
| Separation of breeding stages In our dataset, mean incubation trip durations (5. 60 ± 4. 28 SD days) were more than twice as long as mean brooding trip durations (2. 72 ± 1. 36 SD days). The differences in breeding behavior between incubation and brooding lead to significant changes in trip duration (Appendix S2), so data from these breeding stages were analyzed in two separate models. Individual lay and hatch dates were not available; however, this species is known to display remarkable consistency in their phenology (Fressanges du  Bost & Segonzac, 1976; Jones et al. , 2017; Weimerskirch, 1992). Incubation was therefore assumed to begin on 16th December (the earliest possible lay date) (Fressanges du  Bost & Segonzac, 1976; Weimerskirch, 1995). Where possible, hatch date was determined for each pair separately by observing a sudden drop in trip duration (n = 66) (Appendix S2).
All explanatory variables were scaled (mean 0 ± 1 SD) prior to the separation of breeding stages, so that the model coefficients would be directly comparable between breeding stages. Goodness-of-fit measures for the global models (marginal and conditional R 2 ) were calculated using the MuMin package (Bartoń, 2020) and reported in accordance with Nakagawa and Schielzeth (  2013 ). Akaike's information criterion, adjusted to account for a small sample size (AIC C ), was applied during model selection. Following the construction of the global models, all possible models were generated and ranked by AIC c score. A group of best-fitting models with ΔAIC c < 2 was then extracted. Nested models (more complex versions of simpler models with a lower AIC c ) were excluded to improve inference  (Arnold, 2010; Harrison et al.
Coordination may therefore be repurposed to ensure regular chick provisioning  (Grissot et al. , 2019; Welcker et al. , 2009). Previous work which has investigated the importance of coordination patterns in determining reproductive outcomes in seabirds has yielded mixed results  (Grissot et al. , 2019; Kavelaars et al. , 2019; Wojczulanis-Jakubas et al. , 2018) ; however, no studies have specifically investigated wandering albatrosses. As our data included only a tiny number of failed breeding attempts (N = 4), we did not have enough variation in breeding success within our sample to establish if a link exists within this species. Therefore, our findings are only representative of successful pairs in this population; however, an investigation into the fitness consequences of coordination featuring a different sample of birds would be a logical path for future work.
Thermal and visual video observations of eruptive phenomena, and their correlation with data from deformation, acoustic and seismic networks, however, are often limited by technical constraints. Limitations include time synchronisation, data volumes and power consumption. Although these constraints can all be addressed (e. g. ,  [33, 34] ), there are still few observatories that use permanent stations for long-term studies. In this work, we present VIGIA, an instrument based on thermal and visible imagery intended to serve as a permanent observation station that follows an edge computing approach whereby information is processed on site. We use the Reventador volcano in Ecuador as a case study to prove the capabilities of the instrument, which decides when to acquire high-temporal resolution data so as to capture key explosive eruptions and sends periodic reports to the Instituto Geofísico of the Escuela Politécnica Nacional (IGEPN), the local observatory.
It was in 1967, that Zettwoog and Tazieff promoted the use of permanently installed instrumentation, deployed at Etna, to measure vent temperatures  [38]. In the same context, in his 1971 report for UNESCO  [39] , Robert M. Moxham presented the state-of-the-art possibilities for permanent deployments, which included portable direct-reading radiometers with optional recorders, and aerial radiometric systems. The main challenges at that time were the harsh environmental conditions and equipment bulk  [40]. Moxham et al. (1972)   [41]  conducted an experiment in which temperature data from fumaroles at Mount Rainier were received in Washington via a satellite link. In this experiment, an insulated plywood box contained the instruments including the main power supply battery which was replenished by a solar-cell panel.
Materials and Methods Our instrument, VIGIA (visual and infrared ground-based imagery analyser; named in honour of the Vigia network of local observers during the Tungurahua 1999-2017 eruption;  [55, 56] ), was developed to record and analyse thermal and visible images at a high sampling rate (32 images per second) during volcanic explosive eruptions. The main components of this instrument are grouped as depicted in the diagram of Figure  3a , into four groups: (1) the power supply system; (2) the computing unit, which includes the timing module, the external pressure-temperature-relative-humidity (P-T-RH) sensor, the Ethernet port and the storage drive; (3) the cameras, consisting of the thermal module and the visible module; and (4) the communications module. VIGIA was conceived to work permanently, thus the power supply and communications were designed to keep the instrument running continuously and to optimize the amount of data transmitted.
The computing unit, hereinafter referred to simply as the central computer, is the backbone of the instrument, having the function of controlling the cameras, retrieving the images and processing them. It is based on a Raspberry Pi 4 as the hardware core running Raspberry OS (previously Raspbian) which is an operating system based on the Debian Linux distribution. Although the instrument can operate autonomously, there is also the possibility to control it remotely. The complete set of configuration files and scripts can be found as Supplementary Material.
Computer Unit The computer unit is responsible for data acquisition, processing and communication. It counts with a GPS module (Figure  3e-7 ) to ensure that all data recorded by the VIGIA is UTC time stamped to microsecond accuracy. The GPS module used is based on the L80-39 chip, which uses a serial port for communication and needs a GPIO pin to send the pulse-per-second signal. The system synchronises its own clock with the GPS time using standard Linux utilities (gpsd, pps-tools and ntp). An external sensor (P-T-RH) (Figure  3f-8 ) measures ambient pressure, ambient temperature and relative humidity to check the possible influence of these parameters on the thermal data (script included within the Supplementary Material). The transmission of the data processing results takes place mainly via the Ethernet connection (Figure  3b,d-9 ).
Thermal Module We used an 8-14µm Optris PI 640 thermal camera (Figure  3b ,g-12) to record static 640 × 480 pixel images and 32 frames-per-second (fps) video. Evocortex provides a Software Development Kit (SDK) called IRImagerDirect SDK  [57]  which includes the PI imager library to control the camera and the documentation. The library can be integrated into C++ programs and also in Python scripts to connect to the camera and retrieve images including metadata. VIGIA has different modes for recording thermal data, depending on the status of the volcano. If the volcano is clouded, the instrument remains in a waiting state, just checking periodically whether the volcano becomes clear or not (see Volcano Recognition section below). Once the volcano becomes unclouded, the crater is identifiable, then the system goes into an acquisition state in which it acquires periodic static thermal pictures and, by obtaining the maximum temperature within a region of interest (ROI) located just above the crater rim, it generates a thermal timeseries with the maximum temperature in the ROI at 32 samples per second (sps).
The central computer applies a triggering system on the thermal timeseries to identify explosive eruptions. Every time an explosion is detected, the system records a thermal video. For computers, a thermal image is an array of floating-point numbers (32 or 64 bits) representing the temperature of each pixel, plus an array of integers containing the metadata (time stamp, camera identifier, among other information). Optris, the thermal camera manufacturer, uses lossless compression algorithms to change the representation to 16-bit integers. We used the Python module Numpy to manage and store data with its conventional. npz extension. Each thermal file has an identifier in the name telling the type of data contained. Examples of thermal filenames are VIGIA_IR_<date>_<time>. npz for still thermal infrared picture, VIGIA_THVID_<date>_<time>.
Visible Module The visible module of the VIGIA includes a 5-megapixel visible camera (Figure  3b ,g-13) rendering images through the MIPI Camera Serial Interface (MIPI CSI) connection provided by the Raspberry Pi, and an 18-megapixel DSLR Canon camera (Figure  3b -11) connected using the USB port. A python script, written by the OPGC technical team, retrieves videos from the 5-megapixel camera when an explosion is triggered. The video recorded includes a five-second pre-trigger period and its length is set to 1. 5 min. At night, all images and videos from the 5-megapixel camera are dark, the DSLR camera is used then in bulb mode to capture long exposure pictures rendering the volcano visible. During the daytime, the DSLR camera captures a series of 10 individual shots. Both visual cameras are triggered by the thermal module.
Communication Module The communication module of the VIGIA instrument works on the physical communication components of the central computer, i. e. , on the Ethernet interface. It is through this interface that we have access to the different components and data generated by the instrument. Several communication protocols allow us to remotely control the central system, such as SSH and VNC connections, and remote desktop applications such as AnyDesk  [59]  or DWService  [60]. Access to the graphical environment of the host computer is necessary specifically to observe the images from the cameras in real time in the process of framing and focusing during field installation. The logic part of the communications module is in charge of delivering the results of the measurements and processing.
We created a series of scripts that allow VIGIA to deliver periodic reports about the volcano (see Supplementary Material). Each time a thermal image is acquired, the volcano recognition script appends to a daily log file the date and time, the normalised cross-correlation coefficient, the image quality, the position of the volcano summit pattern, and a "Clear" or "Clouded" label. At the end of the day, a script is dedicated to obtaining statistical data from the log file; more specifically the portion of the day observed, the portion of that period when the volcano was unclouded, and the time when the volcano was last seen to be clear. These data are included in a report together with the thermal image with the best quality factor of the day and VIGIA distributes it using a telegram bot.
Results We deployed the instrument and installed it as a permanent station at Reventador volcano, Ecuador. All the code scripts are available at https://github. com/fvasconez/ VIGIA. git (accessed on 10 February 2022). Our instrument is located 7 km away from the active cone, within the property of the Hostería Reventador. It has been regularly recording data since August 2021. Figure  6  show examples of still images, in infrared and visible spectra, during the beginning (Figure  6a ) and the second pulse (Figure  6b ) of an explosion; it also shows a false event triggered by a simple-threshold triggering algorithm. In the first phase, we setup VIGIA to record still thermal images every five minutes. We used these images to define the threshold that allows the instrument to discriminate whether the volcano appears in the image or not.
Observation techniques such as seismicity or infrasound do not encounter this problem although they lack information about the external activity of the volcano (explosions, passive degassing, rock falls, pyroclastic currents, etc. ). Unlike VIGIA, most video-and seismic-based monitoring systems count with a module for data processing at the receiving end (observatory), so data processing performed on-site remains minimal. Table  1  shows a comparison between VIGIA and generic seismicity-and video-based monitoring systems. Infrared thermal images with an adequate time stamp hence complement seismic and acoustic observational data to provide an integral description of external manifestations of volcanic activity as shown in Figure  9. For those explosions detected by thermal imaging, we can retrieve more information, such as the plume height, ascent rate, the volume of ejected material, and the size and dispersion direction of the plume  [30, 31, 33, 61].
Moving to a shorter time scale, data recorded as thermal video allows us to analyse and characterise individual explosions (cf. [62] [63] [64] ). As an example, Figure  10  shows the comparison of the temperature of the material erupted in one of the explosions that occurred at Reventador volcano on 2022-01-16 with its corresponding seismic waveform recorded by the REVN seismic station, located ~4 km from the current active vent. ~4 GB (depends on the resolution and compression) One second of thermal video file contains, in the case of VIGIA, 32 images of 640 × 480 pixels, which use 18. 75 MB of disk space. This means that one hour of continuous thermal video takes up about 66 GB, and one day will use 1582 GB (1. 5 TB). Continuous acquisition of high-resolution thermal video therefore makes little practical sense, especially considering periods where the view of the volcano is impeded by clouds, which, in the case of our study, was about 60% of the time.
Conclusions We have presented VIGIA, a visible and thermal infrared observation system for active volcanoes that follows an edge computing approach whereby the station decides when to acquire high frequency data and transmits statistics and daily reports to the observatory while keeping energy consumption and data volume to a minimum. We have presented the results from the permanent deployment of such a station at the Reventador volcano showing that it successfully identifies adequate observational conditions and automatically triggers the recording of explosions at high resolution; explosions of which the nature and magnitude can be directly compared to seismic information. We have described the.
hardware and software components of the system extensively (all code scripts available at https://github. com/fvasconez/VIGIA. git (accessed on  10 February 2022 )) in the hope that the system can be reproduced, used, and in time, improved by other volcano observatories. An increased and more permanent monitoring of surficial activity at active volcanoes, possible with such systems, should enhance understanding of the volcanic processes at work, as well as forecasting capacities. Acknowledgments: We thank the  Institut de Recherche pour le Développement, France  ( IRD, Ecuador office ), especially  Jean-Luc Le Pennec  and  Pablo Samaniego  for their collaboration and support for the installation of the instruments. We would also like to express our gratitude for the openness and collaboration of  Joselito Amaguay  and  Rosa Alulema , who kindly opened the doors of the Hostería Reventador to us.
Two-thirds of human hormones and one-third of clinical drugs act on membrane receptors that couple to G proteins to achieve appropriate functional responses. While G protein transducers from literature are annotated in the Guide to Pharmacology database, two recent large-scale datasets now expand the receptor-G protein 'couplome'. However, these three datasets differ in scope and reported G protein couplings giving different coverage and conclusions on GPCR-G protein signaling. Here, we report a meta-analysis unifying GPCR-G protein coupling, by standardized normalization and consensus support, into a common coupling map. This unravels novel consensus couplings for receptors supported by two independent sources and insights on coupling selectivity of GPCRs and classification of co-coupling G proteins.