dataesr/scientific-paragraphs-categorization huggingface dataset:
Meteorology The emission rate of isoprene can be influenced by a variety of meteorological factors, including temperature, solar radiation and atmospheric humidity. To illustrate the impact of these factors on isoprene emission estimated by SURFEX-MEGAN, two simulations were conducted using two different meteorological datasets: IFS forecast dataset (operational real-time weather 340 forecast, forecast grid data) and MERRA. MERRA was undertaken by NASA's Global Modelling and Assimilation Office. The data were generated with version 5. 2. 0 of the Goddard Earth Observing System (GEOS) atmospheric model and data assimilation system (DAS) and covers the period from 1979 to present  (Rienecker et al. , 2011). The MERRA data are defined on an hourly basis on a grid of 0. 625°latitude and 0. 5°longitude resolution.
(2014). The MEGAN-MACC average annual isoprene emissions dropped by 14% when using the emission potential values ϵ P F T instead of the emission potential map ϵ map. The decrease concerns Australia (-47%) and South Africa (-28%) and the increase concerns South America (+10%) and Europe (+18%). The results of this sensitivity test explains partially the differences observed in section 3. CAMS-GLOB-BIOv3. 0 and ALBERI inventories used ϵ P F T data to estimate isoprene flux, resulting in a lower isoprene emissions compared to other datasets, as annual isoprene flux dropped by 29% and 21% respectively compared to reference simulation. Sindelarova et al. (2022)  reported a similar decrease rate in isoprene emissions estimated at 30% of CAMS-GLOB-BIOv3. 0, which uses PFT-specific emission potential data and PFT distribution, compared to CAMS-GLOB-BIOv3.
(2004a). The γ SM is defined as follows: γ SM = 1 θ > θ 1 γ SM = (θ -θ w ) ∆θ 1 θ w < θ < θ 1 γ SM = 0 θ < θ w ( 8 ) where θ is soil moisture (volumetric water content, m3 m-3), θ w (m3 m-3) is wilting point (the soil moisture level below which plants cannot absorb water from soil) and ∆θ 1 (=0. 04) is an empirical parameter and θ 1 = θ w + ∆θ 1  (Guenther et al. , 2012). The third sensitivity test (S3) was conducted to examine the effect of soil moisture on isoprene emissions. To estimate γ SM , MEGAN uses wilting point data calculated in SURFEX from the sand and clay covers given as input to the coupled model following  Clapp and Hornberger (1978)  and  Lepistö et al. (1988)  approaches. The sand and clay data are extracted from HWSD (The Harmonised World Soil Database), which is a global soil database developed by the FAO (Food and Agriculture Organisation of the United Nations) in collaboration with IIASA (International Institute for Applied Systems Analysis) in order to provide information on the physical and chemical properties of soils across the world.
Acknowledgements. We thank  Alex Guenther  and the  University of California, Irvine (UCI)  for providing the MEGANv2. 1 code and the necessary data. We thank the  SURFEX team at CNRM  for their invaluable assistance in facilitating the online coupling of SURFEX-MEGAN, as well as for the provided access to the SURFEXv8. 1 code and physiographic fields data. The ERA5 and IFS meteorological data were provided by the  European Centre for Medium-Range Weather Forecasts (ECMWF). The MERRA meteorological data was provided by the  NASA's Global Modelling and Assimilation Office (GMAO). The  MEGAN-MACC  and  CAMS-GLOB-BIO  isoprene emissions were extracted from the  Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD ) database. The ALBERI isoprene dataset was made available by the  Tropospheric Modelling  team of the  Royal Belgian Institute for Space Aeronomy (BIRA-IASB).
module has been created. The A-TC procedures are applied to layers which are assumed to be homogeneous. Both the layering determination and the subsequent classification procedures, which are described below, are called from either the large-scale aerosol (and thin cloud) extinction and backscatter (A-AER) algorithm, or the optimal estimation Extinction and Backscatter retrieval algorithm (A-EBD). Both the A-AER and A-EBD procedures are components of the ATLID profile processor (A-PRO) described in  Donovan et al. (2022b). The layering procedures are necessary in part due to the need for averaging in order to increase the signal-to-noise ratio of the lidar derived inputs. The procedures for determining layers is described within  Donovan et al. (2022b) ; here we describe the procedures used to classify the layers.
Three target classification algorithms and L2 data products have been introduced: A-TC for ATLID, C-TC for CPR, and the synergistic product AC-TC. AC-TC will provide continuity with DARDAR-MASK while taking advantage of EarthCARE's novel capabilities: an improve detection of rimed snow from CPR's Doppler velocity, and a probabilistic aerosol discrimination framework using ATLID's measurements of depolarization and lidar ratio.
The inherently additive nature of the L-PBF process allows for the fabrication of complex lattice structures and components with intricate internal geometries  [9]. Due to the unique characteristics, new design approaches are required to overcome the barriers in the traditional design process. In structural engineering, lightweight design methods for AM emerge to make use of the benefits of AM  [8]  , including topology optimization (TO)  [10]  , generative design  [11]  , bio-inspired design  [ 6 , 12 ] , and lattice structure filling  [13]. However, these methods usually focus on three-dimensional (3D) model construction and optimization taking into account AM manufacturing constraints, which belong to the 3D CAD model construction stage in the whole AM preprocessing chain. After that, the CAD model obtained is converted into an STL file that can be recognized by AM preparation software.
Then, the STL model needs to be broken down into a 2D slicing model for toolpath filling. These model conversion stages lead to the loss of geometrical accuracy and the consumption of computational time and space, especially for printing complex porous lattice structures. In addition, Boolean operation of porous lattice structures would cause a large computing time and data storing space. To solve this problem, this paper proposes a novel toolpath constructive design method to achieve toolpath construction with optimized sequence planning to minimize the laser jumping route. At first, controllable lattice design domains are generated using some algorithms. Different kinds of parametric lattice patterns stored in a knowledge database are used to infill within lattice design domains with qualified manufacturability.
The sequence planning between lattice cells is optimized using a Particle Swarm Optimization (PSO)-based TSP solver. The sequence planning in lattice cells (zigzag toolpath) is determined by changing the sequence of zigzag to minimize the laser jumping routes. Then, all toolpaths are resorted and reconstructed using the sequence planning determined. At last, all toolpaths will be exported as a G-code file for AM machine. The remaining contents of this paper are organized as follows: the second section reviews the related works about porous structure design; the third section presents the proposed method in detail; the fourth section shows two lattice examples for demonstration; In Section 5 , the printing results are analyzed and discussed, and the last ends with conclusions and perspectives.
Global workflow In traditional lattice design methods, a CAD model is usually constructed using traditional CAD software, such as CATIA, SolidWorks, et al. Then, based on design requirements of surface quality and accuracy, the CAD model will be converted into an STL file. Next, the STL model converted is exported into AM pre-processing software for slicing model and toolpath configuration. In addition, it also involves building orientation determination and support structure generation before printing. In this paper, we propose a new toolpath-based constructive design method for porous structure printing to avoid model conversion. Fig. 1  shows the traditional and proposed design flows with the main model conversion stages. These model conversion processes usually cause accuracy loss and much computation consumption, especially for printing complex porous structures.
Compared with the traditional design method, the proposed method can significantly simplify key model conversion stages by using toolpaths to define porous structures directly. To further explain the difference between the two design flows, Fig. 2  shows these model conversion processes via cubic printing. In the traditional design flow, a 3D cubic model is usually obtained from 0D, 1D, 2D to 3D, which can be seen as the process of point, curve, surface to volume. Then, the original or modified 3D model will be converted into toolpaths in the AM pre-processing software. The model will be sliced into a set of 2D slicing models, which can be further infilled with 1D toolpaths guiding the printing process of AM machines. The proposed toolpath constructive design method aims to directly generate toolpaths based on a 1D model.
Qualified parametric lattice pattern filling with hybrid scanning strategies Considering the proposed constructive design method with the island scanning strategy, different lattice patterns are populated into the lattice design domains. Each lattice design domain is seen as an island for toolpath filling. To obtain a parametric porous structure configuration, different kinds of lattice patterns are selected to infill within the design domains. Any predefined manufactured pattern can be stored as qualified lattice patterns. For a lattice design domain in Fig. 5  (a), parametric contour scans ( Fig. 5 (b) ) are used to generate offset toolpaths for keeping shape. Other parametric lattice patterns, such as circles, rhombi, rectangles, or stars, are illustrated in Figs. 5 (c-h ). To ensure AM manufacturability, all parametric lattice patterns to be infilled should be qualified and stored in a knowledge database for designers' customized needs.
Conclusion & Perspectives In this paper, a new toolpath-driven constructive design method is proposed to generate parametric and controllable porous structures. Process-related and scan-related parameters are embedded into the method for printing qualified porous structures. Compared with traditional lattice design methods, it can significantly simplify the model conversion stages in the AM preprocessing chain. The proposed method does not involve any generation of 2D and 3D models, which can avoid accuracy loss and computing time consumption during model conversion stages. A hybrid PSO-based TSP solver is used to determine the sequence planning between lattice cells. Parametric lattice patterns are stored in a knowledge database for the generation of controllable lattice internal patterns.
I n the framework of open science, sharing imaging databases offers specific benefits in terms of analytical tools development and validation  1. According to the FAIR Data Principles, the development of accessible imaging databases will help increase the reproducibility between studies  2. In this respect, neuroimaging scientists examine their respective policies and practices  3. This is also valid in preclinical research where in vivo imaging of non-human primates (NHPs) holds great potential in comparative biology and biomedical research  4, 5. NHP neuroimaging databases enable the scaling of findings for cross-species comparative and translational neurosciences and a better understanding of brain regions' functions in health and disease. Besides, the establishment and sharing of preclinical neuroimaging databases complies with the 3R principles, especially reduction through the use of imaging datasets, and refinement considering that in vivo imaging is non-invasive and favors clinical translation.
In recent years, the NHP research community has moved forward to tackle challenges ahead, most notably the limited availability of data. The PRIMatE Data Exchange (PRIMatE-DE) initiative addresses this challenge by aggregating independently acquired NHP in vivo imaging datasets  10. Initially intended for magnetic resonance imaging (MRI) data, the community has worked to standardize data collection with minimal acquisition specifications, and data architecture allowing data sharing within the framework of open science  10. This collaborative work allowed us to gradually improve our neuroimaging studies to human standards. In this context, the Brain Imaging Data Structure (BIDS) became the gold standard for organizing and sharing neuroimaging datasets  1. Nuclear imaging specialists jumped on board and published guidelines to improve the accuracy and sharing of positron emission tomography (PET) data  [11] [12] [13].
Therefore, while PET neuroimaging databases exist, their counterparts in NHP are either missing or not easily available. Furthermore, considering the development of hybrid PET/magnetic resonance (MR) scanners and their translational potential in NHP neuroimaging studies, there is a growing need for NHP PET/MR hybrid imaging databases. In this Article, we developed a multi-modal database of Macaca fascicularis, acquired on a clinical PET/MR scanner, and constructed with MRI, [  15    ]PK11195 is a PET radiotracer specifically targeting the translocator protein 18 kDa (TSPO) and is widely used to investigate brain inflammation in relation to various neurological disorders and notably brain ischemia  15, 16     ]PK11195 radiotracers. We describe the pipeline method to assemble baseline data from various cohorts and qualitatively assess all the data using signal-to-noise and contrast-to-noise ratios as well as the median of intensity and the pseudo-noise-equivalent-count rate (dynamic and at maximum) for PET data.
Results The collection of participants' information enabled a detailed des crip tion of the cohort's age (6 ± 0. 65 years) and weight (7. 37 ± 1. 11 kg). A summary of the age and weight distribution is provided in Table  1. After converting the data to BIDS standards, the sequences' availability was assessed using an automated counter. The data availability is provided in Fig. 1. This analysis highlighted the presence of a test-retest for two subjects in the dataset and a few missing acquisitions. The lack of data is explained by issues with radiosynthesis, contrasting agents or movement during acquisition for PET, contrast imaging and MRI sequences, respectively. This figure also indicates the study year for each subject, ranging from 2016 to 2019. This parameter is relevant to explain variability in data quality due to software updates on the PET-MRI system.
The quality of the acquisitions in the shared BIDS database is expressed with signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and median of intensity. The quality assessment highlighted the consistency of the acquisition quality (averaged and over time) as displayed in Fig. 2. While acquisition quality was highly reproducible, an outlier in T 2 -weighted and T 2 * -weighted sequences was identified. This outlier is due to a variation in acquisition parameters during session 2 of subject 4 as shown in Table  2. The dynamic contrast-enhanced (DCE)-perfusion acquisitions also uncovered an outlier due to the movement of subject 15 during acquisition. Two groups can be discriminated in the diffusion-weighted imaging (DWI) metrics evaluation. This distinction is due to a software update in 2019 after which the sequence's parameters were strongly modified for this acquisition.
As a result of the software update, the median intensity is considerably reduced. A variation in SNR and CNR can also be observed, but with a milder difference between software versions. A variability in the PET signal intensity can be observed for both tracers. The maximum of pseudo-noise-equivalent count rate (PNECR) metric shows a difference between 2016 acquisitions and later campaigns. These early data show a lower maximum of PNECR with [ 15 O]H 2 O tracer and a wider variability across subjects with [ 11 C]PK11195 tracer. Figure  3  shows the evolution of PNECR over time for both tracers expressed on a logarithmic scale. This representation highlights the variability in tracer injection time for acquisitions before 2017. While we uncovered acquisition parameters variations throughout acquisition years, the dataset protocol used did not vary.
Discussion In line with human neuroimaging advances, the NHP scientific community is evolving toward more reproducibility and transparency in biomedical sciences, notably through the means of data sharing. To the best of our knowledge, this NHP imaging database is the first open-source collection including simultaneous dynamic PETacquisition and MRI sequences. We provide a wide range of NHP MRI acquisitions for download, including structural, diffusion and perfusion imaging. BIDS standards were originally designed to guide best practices for the storage and sharing of fMRI datasets 1. PET imaging modalities came afterwards as extensions to the original BIDS specifications  12. In this context, Drs. Gitte Knudsen and Robert Innis initiated a collaborative working program to address specific PET imaging challenges through the definition of standards for organizing and sharing  11.
We followed the current recommendations regarding the description of data acquisition and reconstruction methods as well as the molecular description of the radiotracers. The purpose of the present brain NHP database is to provide the molecular imaging community with a full dataset with detailed quality descriptions. We included quality control measures of PET images using the median intensity metric. Although the methods we propose here are derived from MRI applications, we believe that they can be used to quantify the variability of the different PET data acquisitions. This variability is due to the injected radioactivity,   DSC contrast agent: 4 ml Dotarem (Guerbet) followed by intravenous injection of 10 ml saline (injection rate, 3 ml/s) using a power injector (MEDRAD, Bayer). DCE contrast agent: 0.
Therefore, baseline acquisitions (before stroke induction) were acquired in the same conditions as further occlusion-reperfusion acquisitions  18, 19  , which precludes the use of a stereotaxic frame for a repeatable animal position in the scanner. Moreover, due to the specificity of our model, acquisitions had to be shortened to fit experimental conditions in stroke phases. While we observed variability in our data quality that might alter the automation of pipelines, the diversity of our database can represent a source of interest for a wide range of applications from noise reduction to anatomical studies. Furthermore, simultaneous acquisitions enable modality comparison; for instance our database provides perfusion imaging with MRI (dynamic susceptibility contrast, DSC) and PET ([  15  O]H 2 O) for potential cross-comparative studies of the resulting parameters.
When formatting a retrospective and/or preclinical database to BIDS standards, we frequently encounter missing data and missing Digital Imaging and Communications in Medicine (DICOM) tags. These inconsistencies are difficult to identify and compensate. Therefore, we found a need for a self-designed tool to format data following BIDS guidelines. Additionally, this tool enables the selection of specific setting/experimental phase to include in the converted database. We think this functionality could encourage scientists to share parts of their data while holding the remaining settings until results are published. The tool also supports a variety of raw data folder organi zation, as we know it varies between structures and institutes. A common issue faced in data formatting and sharing is the time and resources these initiatives require.
This issue highlights the need for data scientists dedicated to these tasks in research teams. We are hoping that the provided tool will facilitate such initiatives that are urgently needed to address challenges raised by the use of NHP in biomedical research. To conclude, we generated an original and diverse NHP hybrid PET/MR database available for the community through PRIME-DE platform and hope that the present work, describing the quality of the published data and metadata, will encourage the neuroimaging community to use it.
Methods Animal cohort description. This dataset, which includes 20 mature male cynomolgus macaques (M. fascicularis), was generated using baseline images from the primate stroke model described by Debatisse et    20, 21. The subjects underwent combined PET-MRI acquisitions following anesthesia induced by intramuscular injection of ketamine (4 mg/kg; KetamineVR 1000, Virbac) and midazolam (1. 3 mg/kg; MidazolamVR 5 mg/ml, Mylan). Sevoflurane (1%, variable depending on the animal's anesthetic depth; SevoFloVR, Abbott Laboratories) maintained anesthesia during acquisition. Animals were intubated and monitored through heart and respiratory rate, end-tidal CO 2 , systolic, diastolic and mean arterial pressure, oxygen saturation and body temperature. PET-MRI acquisitions. PET-MRI sequences were acquired on a fully integrated hybrid Biograph mMR PET-MRI 3T Siemens scanner (Siemens Healthcare).
Imaging acquisitions were conducted between 2016 and 2019 with software versions 'syngo MR B20P' and 'syngo MR E11'. MRI sequences are previously described in Results with corresponding parameters according to years of acquisition (Table  2 ). Images from PET radiotracers [  15  O]H 2 O (255 ± 15 MBq) and [ 11 C]PK11195 (140. 1 ± 21. 4 MBq) were acquired for 6 min and 70 min, respectively, after bolus injections. While molar activity could be measured for [ 11 C]PK11195 (48. 0 ± 24. 5 GBq/μmol) providing information on injected mass (3. 50 ± 1. 7 nmol), the half-life of [  15  O]H 2 O did not permit such precise radioactivity measurements. The data were reconstructed on a 256 × 256 × 127 matrix (voxel size 0. 7 × 0. 7 × 2. 0 mm 3 ), 26 cm axial field of view using a point-spread function and ordinary poisson ordered subset expectation maximization (OP-OSEM) iterative reconstruction method including normalization as well as correction for attenuation, scatter, random counts and dead time.
Before the PET-MRI session, a computed tomography scan (Siemens Biograph mCT64, Siemens Healthcare) was obtained for each animal and used for PET attenuation correction. Data processing. The data processing pipeline consisted in formatting datasets to BIDS  1. Existing formatting tools such as dcm2bids  22  have limited support for missing DICOM tags frequently encountered in preclinical and retrospective datasets. Therefore, an automated Python script compatible with MRI and PET acquisitions was developed. Raw images are loaded in DICOM format and converted to NIfTI format with associated metadata in JSON format in accordance with BIDS guidelines. An overview of the Python script tasks is provided in Supplementary Fig. 1. The script's ambition is to provide flexibility in data organization and selection when formatting raw data for sharing purposes.
Because raw-data folder organization varies between structures, additional parameters are provided enabling the definition of the raw folder structure. For instance, single-session and multiple-session studies are supported by the pipeline. It is also possible to select which acquisitions and sessions to format to BIDS. This option may be useful in multi-phase studies in which data exploration of key phases delays data sharing of baseline acquisitions. Additionally to the data conversion, the script provides templates for mandatory BIDS files such as dataset description and participants list.