dataesr/scientific-paragraphs-categorization huggingface dataset:
The case of Latin America has been much less studied, and we observe strong seasonality in IASI inv. The two regions in Latin America are characterized by croplands, intensive livestock farming, and biomass burning activities. Over central Brazil, IASI inv reveals an important peak in September (> 4 g N m -2 yr -1 ) that is only represented in the CAMEO + and FAN + time series by a smooth increase. Andela et al. (2017)  has shown a strong positive spatial correlation between burned area and cropland fractions in this ecoregion, probably suggesting significant agricultural waste burning. In addition, Castro Videla et al. (  2013 ) have also shown a maximal biomass burning activity measured by MODIS via the monthly mean number of fires (MODIS fire dataset) in the Brazilian Caatinga shrublands and in the northeastern part of the Cerrado region in September.
, 2013; Pereira et al. , 2022). IASI inv depicts an important peak in the NH 3 emissions (See Fig. S6 ) during this time of the year which can be attributed to fire events. We observe that natural and agricultural emissions have very similar patterns and are in the same range. However, the fire contribution in the CEDS + and CAMEO + datasets appears to be very low, supporting the limitation of using the GFEDs4 inventory in bottom-up NH 3 emission estimates for comparison with IASI inv. The degradation of the correlation between IASI inv and CAMEO + compared with the correlation with CEDS + in the Chaco region is explained by the fact that there is almost no temporal variability in the CEDS + at the annual scale. In India, there is an interesting pattern with a clear longitudinal delimitation -western negative and eastern positive correlations -in CAMEO + and CEDS +.
Conclusions In this study, we implement a new module dedicated to global NH 3 emissions from agricultural practices including livestock waste management and mineral fertilizer application within the ORCHIDEE land surface model. Our development allowed us to consider dynamical variables (such as surface temperature and humidity) through different physical soil processes for the calculation of the NH 3 fluxes. This aspect, which is often neglected in bottom-up approaches, is key for a realistic seasonal representation of the emissions. In contrast to other emission models, our module interacts with the ORCHIDEE model for vegetation variables such as the biomass productivity. It allows the calculation of a grazing index, a global indicator of the pressure exerted on the vegetation. We estimate global agricultural emissions of about 44 Tg N yr -1 , with soil volatilization from fertilizer and manure application accounting for 78 % of emissions and indoor emissions (from livestock housing, yard and storage) accounting for the remainder.
Acknowledgements. The authors acknowledge support from the  ESM2025  project. We also acknowledge support from the  GENCI supercomputer system (Joliot-Curie supercomputer). Nikolaos Evangeliou  received support from the  Research Council of Norway  (project  ID  no. 275407 ; " COMBAT -Quantification of Global Ammonia Sources constrained by a Bayesian Inversion Technique "). Martin Van Damme  was supported by the  AREN-BERG  (" Assessing the Reactive Nitrogen Budget and Emissions at Regional and Global Scales ") FED-tWIN project funded via the  Belgian Science Policy Office (BELSPO). Lieven Clarisse  is a research associate supported by the  Belgian F. R. S. -FNRS. Financial support. This research has been supported by the  H2020 European Institute of Innovation and Technology  (grant no. 101003536 ).
Code and data availability. The ORCHIDEE model is available at https://forge. ipsl. jussieu. fr (last access: 6 February 2023). The modified version of ORCHIDEE including the CAMEO module used in this paper is available at https://doi. org/10. 14768/db1cf5ce-6fd2-4b4c-a3d1-598e2283c19d (Beaudor et al. , 2022b). The NH 3 emissions simulated by CAMEO as well as the manure production data and the soil ammonium concentrations are available at https://doi. org/10. 5281/zenodo. 6818373 (Beaudor et al. , 2022a); other model outputs and the IASI-derived emissions are available upon reasonable request from the authors. The NH 3 emission inventories used in this study are available in McDuffie et al. (2020)   for the CEDS and at https://doi. org/10. 5281/zenodo. 3841723 (Vira et al. , 2020)  for the FANv2 data.
, 2020) Natural emissions Not taken into account in this dataset Waste and industrial sources CEDS (McDuffie et al. , 2020) Biomass burning GFEDs4 (van der Werf et al. , 2017) 2018; Uwizeye et al. , 2020). The calculated global annual tal biomass ingested by the livestock (88 Tg N yr -1 ) is lower crop production (expressed in N) is about 74 Tg N yr -1 and than the range found in the literature (122-167 Tg N yr -1 ; compares well with the 72 and 74 Tg N yr -1 estimated by Billen et al. (2014) and Zhang et al. (2021), respectively. The calculated annual grass production (28. 8 Tg N yr -1 ) is more than 3 times lower than the 80. 3 Tg N yr -1 reported by Billen et al. (2014) and estimated from the difference be- tween livestock ingestion and available feed resources. By doing so, uncertainties from several components (crop pro- duction, net import of vegetal proteins, and human consump- tion of vegetal proteins) are accumulated.
Colour and pattern quantification Quantifying the warning signal is challenging due to the multiple pattern elements and colours that need to be analysed simultaneously to capture the whole visual signal. Here we used a combination of methods to capture the different types of colour and pattern variation. First, frog pictures were standardized for colours using the X-rite colour standard and the X-rite 2019 ColorChecker Camera Calibration V1. 1. 3 software. We then divided the frog pictures into three body parts: head, dorsum and hind legs, as we generally observe a transition of colours and patterns between these parts. For each of the 3 body parts, we quantified both the colour and the shape of the non-black elements, resulting in a total of 6 phenotypic characteristics. To quantify the colour of the warning signal, we used the Hue, Saturation, Brightness (HSB) domain  [37]  using the software Photoshop.
Using the magic wand tool in Photoshop CC 2019, we selected the area of interest and extracted the mean HSB value of the body part. The brightness (B) value was then removed from the HSB value as this consists of the luminance present where the frogs were photographed, which fluctuates depending on the amount of light present. Furthermore, although poison frogs are attacked by a variety of predators (snakes, spiders, ants…)  [38] , birds which are able to recognize colour across Hue and Saturation under diverse conditions of brightness  [39]  are believed to be the major agent of selection on these frogs' aposematic signal  [40, 41]. To quantify the pattern, we used three different methods. We first used the R package Patternize  [42]  to characterize the head pattern. The head pattern was analysed using the pixel-pixel homology comparison of stacked images implemented in Patternize.
Acknowledgements. We would like to thank  Marco Léon  and  Henri Delgado  from the  Peruvian INIBICO NGO  for the constant support and logistical help provided during the realization of this project. This project would not have been possible without the knowledge and frog catching abilities of  Josh Allen ,  Cesar Lopez ,  Manuel Panaifo ,  Mark Pepper ,  Manuel Sanchez  and  Evan Twomey. We would like to thank  Vincent Debat ,  Marianne Elias ,  Violaine Llaurens ,  Melanie McClure  and  Alexandre Roland  for their constructive comments on the manuscript. This research was authorized by the  Peruvian Servicio Nacional Forestal Y De Fauna Silvestre -SERFOR  ( RDG N°D000442-2021-MIDAGRI-SERFOR-DGGSPFFS  and  232-2016-SERFOR/DGGSPFFS ).
Pending the results of randomized controlled trials (RCT) evaluating endovascular treatment (EVT) for distal occlusions, we aimed to investigate the safety and efficacy profiles of EVT for distal-M2 strokes in clinical practice. We report data from the multicentric prospective registry ETIS (Endovascular Treatment for Ischemic Stroke) (ClinicalTrials. gov Identifier: NCT03776877) including 21 stroke centers in France. Our study received approval from our Intstitutionl Review Board (ID RCB 2017-A03457-46). Oral informed consent from the patient and/or a trustworthy person was collected by local investigators. We included patients with the following criteria: age ≥18 years; acute ischemic stroke (AIS) due to primary distal-M2 occlusion; and time from symptom onset to puncture ≤6 hours. An M2 occlusion was considered distal if it was above the horizontal line delineating the mid-height of the insula.
An additional systematic review with meta-analysis has been performed on distal-M2 thrombectomy and is detailed in Supplementary Material. To identify outcome predictors in the present series, multiple regression models were fitted using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). 2 Missing data were excluded. All analyses were completed using Stata 17 (StataCorp, College Station, TX, USA), RStudio version 1. 4. 1106 (https://posit. co/), and the R General Package for Meta-Analysis (version 6. 0-0; R Foundation for Statistical Computing, Vienna, Austria). During the study period, 157 patients underwent EVT for an AIS due to a distal-M2 occlusion. Demographic and procedural data are shown in Table  1. Median NIHSS at baseline was 12.
3  (9. 4%) and suggests that EVT for distal-M2 occlusions does not carry an additional risk of sICH. Sixty-one patients (46. 2%) achieved a favorable outcome. In   2. The strengths of our study are: (1) its prospective and multicentric design; (2) the large number of patients included compared to the previous series published on distal-M2 EVT; and (3) the systematic reporting of outcome data adopted by the participating centers of the ETIS registry. Nevertheless, the data drawn from the ETIS registry have some limitations: (1) the decision of thrombectomy was left at the discretion of the operator with In conclusion, EVT for distal-M2 occlusions resulted in a high rate of successful reperfusion and favored contact aspiration over the use of a stent retriever only in our study. RCTs are needed to demonstrate a potential benefit of EVT in addition to intravenous thrombolysis and identify the safest and most efficient first-line thrombectomy strategy in regard to this indication.
Supplementary Material. Systematic review and meta-analysis on endovascular treatment for distal-M2 occlusions 5] [6]  Distinguishing between proximal-M2 occlusions and distal-M2 occlusions seems reasonable and clinically relevant, as we could consider this segmentation to be the threshold between LVOs and DMVOs. In addition to the ETIS (Endovascular Treatment for Ischemic Stroke) study, we performed a comprehensive systematic search of PubMed, MEDLINE, Embase, and Cochrane databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  7  on endovascular treatment (EVT) for distal-M2 strokes. No written review protocol was prepared, and the review was not registered before its realization. We used the keywords: "thrombectomy," "endovascular treatment," "stroke," "occlusion," "M2," and "distal" combined with Boolean operators to increase search sensitivity and specificity.
Initial screening and study selection were performed using Covidence (Veritas Health Innovation, Melbourne, Australia), a web application for systematic reviews, by two independent reviewers. We included articles published in the English language between the inception of each database and March 2022, reporting EVT of acute ischemic stroke due to primary distal-M2 occlusion. Articles reporting fewer than five cases and cadaveric, animal, or in vitro studies were excluded. Articles reporting patients with distal-M2 occlusion among other occlusion locations were assessed in full text to determine whether data specific to distal-M2 could be separately extracted. If not possible, these studies were excluded. Centers and periods of each study were assessed, and those with overlapping populations were excluded while maintaining the article with the most recent and complete dataset to avoid the risk of multiplicity.
Information extracted from each study included sample size, age, treatment modality, percentage of patients receiving intravenous thrombolysis, National Institutes of Health Stroke Scale (NIHSS) at admission, time from symptoms onset to puncture, rate of successful recanalization, rate of symptomatic intracranial hemorrhage (sICH), clinical outcomes, and mortality at 90 days. A favorable outcome was defined as achieving a modified Rankin Scale (mRS) ≤2. Successful recanalization and sICH were considered according to the definition provided in each study. Muszynski et al. 8  reported data on 67 distal-M2 occlusions from the ETIS registry; therefore, we pooled this article with the most recent data from the ETIS registry and labeled it as "current series" in the meta-analysis. An assessment of the risk of bias for each study was made independently by the two reviewers.
In the meta-analysis, estimated rates of reperfusion, good clinical outcome, sICH, and mortality at 90 days were weighted for each study sample size. Odds ratios (ORs) and 95% confidence intervals (CIs) for pooled data for good recanalization, sICH, favorable outcomes, and mortality were also calculated. The extent of heterogeneity among studies was assessed with an I 2 test. Pooled analyses were performed with fixed effect and Der-Simonian and Laird random-effects models. All analyses were completed using Stata 17 (StataCorp, College Station, TX, USA), RStudio version 1. 4. 1106 (https://posit. co/), and the R General Package for Meta-Analysis (version 6. 0-0; R Foundation for Statistical Computing, Vienna, Austria). 2 https://j-stroke. org Supplementary Table  3. Demographic, procedural and outcome data of the studies included in the meta-analysis Tomsick et al.
We present a computational framework to simultaneously perform image acquisition, reconstruction, and analysis in the context of open-source microscopy automation. The setup features multiple computer units intersecting software with hardware devices and achieves automation using python scripts. In practice, script files are executed in the acquisition computer and can perform any experiment by modifying the state of the hardware devices and accessing experimental data. The presented framework achieves concurrency by using multiple instances of ImSwitch and napari working simultaneously. ImSwitch is a flexible and modular open-source software package for microscope control, and napari is a multidimensional image viewer for scientific image analysis. The presented framework implements a system based on file watching, where multiple units monitor a filesystem that acts as the synchronization primitive.
Different general solutions have been proposed, such as Pycromanager  [11] , AutoScanJ  [12] , and MicroMator  [13]. They interface with lManager  [14] , a well-established open-source software package with extensive driver support, distributed as an ImageJ  [15]  plugin. However, lManager presents limitations as the complexity of the microscopy modalities increases. Some examples are controlling multiple cameras independently, synchronizing multiple hardware devices, performing simultaneous image acquisition and reconstruction, or controlling specialized devices such as spatial light modulators or point detectors. Python-based software alternatives have been developed  [16] [17] [18] [19] , making debugging and contributing more convenient in contrast to Java. Sample-adaptive automation methods for image acquisition have been recently proposed  [20, 21] , in which experiments and image analysis are combined to achieve fast, directed imaging by adapting to events in the sample of interest.
Image analysis of data in real-time is a promising direction, with solutions like ImJoy  [44] , BioImageIT  [45] , and cell profiler  [46]. They implement several algorithms and plugins, which can, in principle, be applied to data acquired directly from the microscope. However, combining these pipelines with the entire experimental acquisition scheme, for example, using their output as active feedback on the acquisition, reminds a challenge. Therefore, a general open-source framework that integrates image acquisition, reconstruction, visualization, and data analysis and flexibly adapts to different microscopy modalities is still missing. Here we present a framework to simultaneously perform image acquisition, reconstruction, and analysis using ImSwitch  [19]  and napari  [47]. On the one hand, ImSwitch is an open-source software solution for controlling microscopes, adaptable to different levels of complexity.
It includes image acquisition and reconstruction modules, where the experimental images can be visualized in real-time. On the other hand, napari is a community-driven image viewer with multiple plugins for image analysis and is widely used in the microscopy field. Our solution is based on the simple concept of file watching, where different units monitor a filesystem searching for new files to process and add the arriving items into a queue. The solution doesn't require implementing any client-server-based approach, which relieves the burden of using a centralized server in terms of complexity, security, and dependency on other software packages. We provide a scripting engine in python that enables microscopy automation of any experiment designed by the user. The experiments are orchestrated remotely by creating and editing the scripts and adding them to the acquisition unit queue.
For example, the user can develop their own python scripts for image reconstruction and add them to the workflow by including the file watcher model. The imaging data and metadata are fetched from ImSwitch and saved into either Zarr or HDF5 files, and the reconstructed images are then saved in both OME-Zarr and Tiff. OME-Zarr is an implementation of the Zarr format using the Open Microscopy Environment (OME) specifications  [48]. Zarr can store chunks of data in a directory tree, which is highly beneficial for access times in a shared filesystem. The chunk retrieval time was compared to HDF5 and Tiff, and proven to be less sensitive to data location. This is the main reason we used Zarr for our experiments when using different physical computers. We have implemented a napari plugin that orchestrates the ImSwitch experiments and displays the images as layers.
In this case, using separate computers for each unit is beneficial to maximize the resources. Then, the acquisition will not be affected by delays in the reconstruction unit. If the resources of the computer allow, the framework can run on the same computer. This can be implemented either with the same unit for acquisition, reconstruction, and analysis using multithreading or by distributing the three tasks to different units. We chose the latter approach because it offers two benefits with respect to the first. Firstly, it is compatible with multiple software for data acquisition and image reconstruction. For example, the framework can use ImSwitch for acquisition and a Python script for reconstruction using the File Watcher python file provided (Design Files). Secondly, multiple microscopes (acquisition units) can delegate to the same unit for image reconstruction, which can be beneficial in microscope facilities and laboratories.
Each of the presented units serves a different task: acquisition, reconstruction, and orchestration. Firstly, the acquisition unit features an ImSwitch instance with the control module -imcontrol. This unit will perform experiments on the microscope by controlling hardware devices and storing the imaging data (raw data) and experimental metadata in disk or RAM. Each experiment is represented by a python script file and is executed in the scripting module of ImSwitch -imscripting. The scripts have access to the custom-designed and exported Application Programming Interface (API), which can be easily extended to include new functionality. In practice, scripts can automate the microscope by changing the state of any hardware device and accessing the resulting data. Secondly, the reconstruction unit features an ImSwitch instance with the reconstruction module -imreconstruct.
This module is designed for those microscopy applications that require signalprocessing algorithms to generate the final images from the raw data (e. g. , n frames are reconstructed into one image). Finally, the orchestrator instance features a napari instance with the napari-file-watcher plugin. The plugin provides basic script editing functionality and displays the new reconstructed images as napari layers for further analysis. The user performs experiments using the script editing module in the orchestrator graphical user interface (GUI) and visualizes the incoming data as image layers. The benefit of using napari for image analysis is that the reconstructed data can be post-processed by one of the available plugins in the napari hub (for example, image segmentation). Users can also develop plugins and contribute to the broader community.
The only requirement is a shared file system and a python software package for image reconstruction and file watching. The software pipeline is described in Fig. 2. The user interacts with the orchestrator unit, which is implemented as a napari plugin (napari-file-watcher) and contains two widgets (Fig. 2a ). The ImSwitch scripting widget implements an editor that the user can use to define experiments and adjust imaging parameters. Once the script is finalized, it is added to the shared filesystem (FS). The second widget, the File watcher, displays the experimental results as napari layers (already reconstructed). The user can post-process the images using existing napari plugins or develop other processing pipelines on top. Fig. 2. Software pipeline using multiple units. a. The orchestrator is an instance of napari with the napari-file-watcher plugin.
The user interacts with the orchestrator unit by defining experiments as python scripts, which are then added to the filesystem using the ImSwitch scripting widget. Once the reconstructed images are received, they are displayed as napari layers available for post-processing. b. The logic for each file watcher in Python. It periodically monitors a filesystem (FS), adds the incoming files to a queue and sends them using the sigNewFiles signal. c. The acquisition unit is an instance of ImSwitch. The file watcher widget displays and tracks the new files in each folder. Whenever new files are in the FS, it adds them to a queue and executes them sequentially. After every execution, the signal sigScriptExecutionFinished is called. d. The reconstruction unit is also an instance of ImSwitch, featuring the imreconstruct module.
The workflow is similar to the acquisition, with the difference that the files are raw images that are reconstructed with an image processing algorithm. The reconstruction function is not called directly but through the sigReconstruct signal instead. This choice is due to the architecture of the imreconstruct module. Solid lines represent direct connections, and dotted lines are calls using python signals. X. Casas Moreno, M. M. Silva, J. Roos et al. HardwareX 13 (2023) e00400 Each unit implements the file watcher thread (Fig. 2b ) to monitor a folder periodically (pollTime) until new files arrive. Then, it adds the files to a queue and sends them using the signal sigNewFiles. This signal is connected to the main engine in each unit so that files are processed. We provide the file for the file watcher thread so that it can be used by other python software in any of the units.