dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The gene expression profiles generated for this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE89342. The CAFs dataset has been published by Costa et al. 19  , and is accessible through the accession number EGAS00001002508. The single cell dataset of immune cells from lupus nephritis patients has been published by Arazi et al. 20  , and is accessible through the ImmPort repository (accession code SDY997).
2001; Huang et al. 2002). Antigenic proteins were also identified for other fungal EVs, such as the 70-kDa glycoprotein (Gp70) in S. brasiliensis  (Ikeda et al. 2018) , which was reported as an important virulence factor in Sporothrix (de  Almeida et al. 2015). Using an orthology approach, a comparative analysis of EV protein content from multiple fungal species has been recently conducted  (Parreira et al. 2021). The data were compiled in a novel free web repository created for fungal EV datasets, named ExVe (http://exve. icc. fiocruz. br). Interestingly, the OG6 100083 (Hsp70 Pfam domain) was the only ortholog group found in all considered species, suggesting that heat-shock related proteins might play a universal role in fungal EVs  (Parreira et al. 2021).
CONCLUDING REMARKS AND PERSPECTIVES ON FUNGAL EV RESEARCH The field of fungal EVs is only in the early stage, and the knowledge basis regarding their structure, composition and function needs to be improved. Although a general picture of the fungal EV starts to be revealed, it remains unclear due to several challenging points. First, the diversity of fungal species studied for EVs, and the expected diversity of fungal EVs render a general comparison complicated. In this context, the creation of other public databases, similar to the ExVe (Parreira et al. 2021), designed for fungal EV-proteins that could list and compare fungal EV components among different species, would be instrumental. Second, as it has been done for mammalian EVs, the efforts for standardization of purification and characterization protocols of fungal EVs need to be undertaken aiming to identify specific markers to validate the purity of the EV extracts.
ACKNOWLEDGMENTS This work was supported by the  CAPES-COFECUB Franco-Brazilian Research Exchange Program  ( 88887. 357947/2019-00 )(GJ). JR is supported by  Pasteur-Roux-Cantarini Fellowship  ( Institut Pasteur, Paris, France ). AT is funded by a PhD Fellowship from  Ecole Doctorale BioSPC ,  Universit é de Paris, 75006 Paris, France.
These data, which are encrypted, are then conveyed to the heart of the sensor network for analysis and exploitation. In our work, we used the performance of microcontroller boards, arduino and raspberry in order not only to make the system for controlling and balancing energy flows more efficient but also in case of addition of new components in the network and implementation of algorithms in arduino and python programming language for a dialogue between the platform and the cloud. The micro-grid, subject of our study pools four energy sources. The priorities come from solar panels and wind turbines. The energy generated by these two sources is each stored in a specific battery. The two batteries, combined constitute a battery bank which is the third source of energy. This source is used in the event of a shortfall in solar and wind power generation.
Proposed system and strategy The proposed system interconnects with the existing micro-grid through specific nodes that we named S. A. D (for Sensor / Actuator Device). It is nothing more than a set of sensors and actuators linked to a microcontroller. The S. A. D are placed in specific places depending on what we want to monitor and control. The S. A. D. are linked to a control and management center thus forming a network of microcontrollers. This control center is connected to the cloud to which it sends data for monitoring needs and which is also stored in a database which is analyzed for future uses. The Sensor / Actuator association for each S. A. D depends on the source or the load to be controlled; therefore varying the number of elements to be connected to the microcontroller as shown in Figure  3.
Conclusion In our work, we designed a microcontroller architecture interconnected to the renewable energy micro-grid. A strategy is then implemented for the control and balancing of energy flows in the micro-grid. The set of microcontroller-sensor-actuators (S. A. D) are deployed at strategic points in the micro-grid providing constantly data from power generated and consumed, equipment health and status. The arduino and raspberry boards offer performance that collects data from the various equipment to which the sensors are connected. This data then travels to the cloud for analysis. In the outlook, the power data will be analyzed and compared to the values collected over a given period, so as to detect the state of health of the equipment in order to take decisions in real time.
To address this need, we present a standalone program named Cosmolian, built for running and interpreting an integrated luminescence-cosmogenic nuclide accumulation model. We demonstrate the applicability and capabilities of the program by modelling published data from the Australian continental interior as a case study. The numerical model encompassed in Cosmolian relies on the method presented by  Vainer et al. (2018)  that provides a statistically based scheme to reconstruct the chronological development of aeolian dunefields. This approach is set to reproduce both 26 Al and 10 Be concentrations measured in sand by simulating the accumulation of cosmogenic nuclides during aeolian transport, where displacement rates are based on luminescencederived chronologies. We incorporated the model into a graphical user-friendly interface for intuitive use that does not require programming knowledge (Figure  1 ).
| MODEL FRAMEWORK AND PARAMETRIZATION The Cosmolian program is based on the modelling approach of  Vainer et al. (2018)  that includes a pre-aeolian step of cosmogenic nuclides build-up, followed by the main simulation stage of aeolian sand migration. To determine realizations of simulations, the program requires the concentrations of in-situ 26 Al and 10 Be measured in aeolian sand as input. The production rates of these radioactive nuclides by cosmic-ray bombardment entering the quartz lattice in sand grains depend mostly on the elevation of the study site, the depth and density of overburden (if the target material is buried), and the geomagnetic latitude field strength  (Lal, 1991). Due to the uncertainty in determining these variables (especially for migrating sediments), Cosmolian allows multiple input values for the different parameters to define a range of boundary conditions for simulating the development of the studied aeolian landscape (Figure  1 ; Table  1 ).
| Phases of aeolian activity and their possible triggers The most striking output of the simulations is the distribution of the simulated timespans required until the simulated 26 Al and 10 Be T A B L E 1 Model parameters used to simulate the aeolian landscape development of the Australian Simpson Desert. Geographical settings during the pre-aeolian and aeolian stages were calculated from a 9-s DEM as the averages for the source areas of the sand and the Simpson Desert, respectively. Names of samples indicate the palaeosol horizons described by  Fujioka et al. (2009)  and are ordered in decreasing stratigraphical order, defined by the palaeosols. Cosmogenic nuclides and OSL data are the average concentrations and the weighted errors of 12 and 25 samples, respectively, reported by  Fujioka et al.
| Integrating data and process-oriented modelling to quantitatively interpret long-term aeolian dynamics Pliocene events of aeolian transport that are revealed by Cosmolian were not previously identified by the modelling approach that relied on 26 Al/ 10 Be ratios of the same dataset of  Fujioka et al. (2009) , which were interpreted as burial ages. This testifies to the importance of using process-based modelling that simulates aeolian transport for constructing a detailed framework of aeolian processes and landforms  (Thomas & Bailey, 2017). Furthermore,  Fujioka et al. (2009)  suggested that the problematic contradiction between dune stratigraphy and apparent 26 Al/ 10 Be burial ages likely stems from the recycling of older aeolian structures. These structures are probably represented by the identified aeolian phases that are older than 1.
| Required assumptions and limitations when using Cosmolian The VDR dataset that is used to construct migration rates/retention periods is a fundamental parameter that controls the dynamics, and hence determines the chronological characteristics of the dunes modelled by Cosmolian during the aeolian phase (Figure  5 ). Its distribution should be therefore carefully constructed. The user is asked to provide a VDR dataset (or multiple datasets) where the distribution is based on the most probable palaeoenvironmental conditions during the considered timeframe. For the Australian dunes case study, for example, since aridity in Australia's interior persisted throughout the late Cenozoic  (Vasconcelos et al. , 2008) , one should probably refrain from using a global dataset that includes data from aeolian deposits sampled in more humid settings at higher latitudes.
Similarly, coastal dunes, that are characterized by retention times that differ significantly from inland dunes  (Lancaster et al. , 2016) , should be eliminated from the selected dataset. We suggest that screening empirically derived values probably better reflects the dynamical history than analytically calculated distributions, providing that the available datasets sufficiently reflect their underlying distributions. The duration of a simulation until success is defined as the simulated time required for the simulated concentrations of both 26 Al and 10 Be to match their measured concentrations within analytical uncertainty (Figure  3 ). If several convergence events occur within the same 100 Ka interval, they are considered as a single event. In this paper, we focus on the first time this happens for each simulation, of the number of successes.
Thus, it could be possible to detect several phases of aeolian activity using a single sample. However, it is important to keep in mind that the VDR datasets rely on data collected under specific settings, that are not necessarily valid over very long time scales. Although further investigation is required, we suspect that the assumption of a steady-state mechanistic approach to this modelling scheme may not be suited beyond the Pliocene because migration rates were constructed from ages that are based on time-restricted chronometers (i. e. , luminescence ages). This is because a plausible scenario of inactive displacement for prolonged durations of more than several hundred thousand years is not recorded by modern observations. This limitation may be overcome by incorporating stable cosmogenic nuclides such as 21 Ne into the model, in order to extend the reconstruction of sand behaviour into a multi-million-year timescale.
Cosmolian comprises two sections that are used for simulation and the analyses of the results, respectively (Figure  1 ), and can be operated on any computer without licence requirements or prior programming knowldge. The program is available upon request, and a simplified online graphic interface for its analysis is available online at https://yoav-ben-dor. shinyapps. io/Cosmolian_analyzer/. The model used by the program is based on analytically derived datasets and is built from a process-oriented perspective. Thus, it provides a robust statistical scheme for reconstructing a chronological framework of aeolian landscape evolution over the million-year scale.
| CONCLUSIONS In this paper, Cosmolian was applied with data reported from the Australian Simpson Desert and revealed three key phases of aeolian emergence of sand into central Australia at 3. 8-3. 4, 2. 9-2. 5 and 1. 5-1 Ma. These results fit the independently established chronological framework of landscape and climate evolution and coincide with prominent environmental changes that would have encouraged the onset and/or intensification of aeolian processes, thus reinforcing their interpretation as the residence time of the aeolian material. The results suggest that present-day dunes comprise a mixture of sand with different transportation histories that can be resolved at the regional dunefield scale by using the program for interpreting data from selected sites. Cosmolian was designed for intuitive use by users with no programming skills and is available upon request.
5, 12, 20 and 40 m were used, based on field observations made by Fujioka et al. (2009)  and Pell et al. (2000). The cosmogenic nuclides production rates are calculated within the model using basin-averaged coordinates and altitude, extracted using GIS software. During transport, after each 20 cm displacement, the grains reside for a randomly sampled duration, based on a predefined distribution of vertical displacement rates (VDRs). Empirical VDR datasets were constructed by converting luminescence ages of selected dunes from different environmental settings (globally distributed, Australian dunes and Simpson Desert dunes) published by Lancaster et al. (2016). This is done by dividing the luminescence age of the sample by the sample's depth. An additional set of adjusted VDR datasets was constructed by removing the fastest 10% from each empirical VDR dataset, accounting for the possible bias stemming from oversampling young and shallow dune deposits (Chase, 2009; Fitzsimmons et al.
redistributed from existing storage within the LEB, new sand could also have been derived from the eroding periphery, where accelerated uplift and deformation took place (Lubiniecki et al. , 2020;   McLaren F I G U R E 3 A single simulation with multiple successful events (vertical dashed lines) where simultaneous convergence into the analytically determined concentrations of both 10 Be and 26 Al occurred at $1 and $15 Ma. (a) A depth path generated by randomly sampling a displacement direction (up/down) and a residence time from a predefined VDR dataset. (b) Cosmogenic nuclides build-up over time. Solid lines depict the simulated concentrations, while the gaps between the dashed lines are the analytical uncertainties of the measured cosmogenic nuclide concentrations, blue for 26 Al and red for 10 Be.
(c) Simulated OSL age based on reported dose rates (Table 1). The simulation was carried out for sample S-P1 with the following parameters: dune height of 12 m, source erosion rate of 3. 3 m Ma À1 , empirical vertical displacement rate dataset of the Simpson Desert dunes [Color figure can be viewed at wileyonlinelibrary. com] F I G U R E 4 Probability density functions of the simulated durations until the measured concentrations of 26 Al and 10 Be were simultaneously reached for the first time. Full description of the analysed data is shown in Table 1. Results from simulations that were carried out using the most extreme parameters of global vertical distribution rates dataset, erosion rate at the source of 0. 7 m Ma À1 and dune depth of 5 m were excluded from this analysis [Color figure can be viewed at wileyonlinelibrary.
I G U R E Probability density functions of simulated durations until the measured concentrations of 26 Al and 10 Be were simultaneously reached for the first time for sample P3-BR, using the empirical vertical distribution rates datasets. Outliers in the results that are marked with grey rectangles and dashed lines are excluded from the ages shown in Figure4[Color figure can be viewed at wileyonlinelibrary. com] bedrock, further implies that an aeolian process-based model provides a practical way for deciphering the long-term development of an entire dunefield by sampling a fairly small number of sites (Vainer et al. , 2018). Furthermore, examining how different environmental factors control aeolian transport is feasible by exploring the way that various combinations of input parameters, representing distinct environmental settings, affect aeolian migration patterns.
Cosmolian outputssuch as sand exhumation events and burial frequency (number of OSL cycles in TablesS1, S2, S3, S4 and S5)-change in accordance with other output parameters, for instance the depth at which grains are commonly buried (mean dune depth in TablesS1, S2, S3, S4 and S5). The output parameters describe the response of aeolian structures to the predefined boundary conditions (input parameters) such as the maximum dune height and the VDR dataset, which together represent varied climatic and geological settings. Therefore, inspecting the relationships between these parameters could be used to mark and quantify triggers and patterns of aeolian transport. Finally, studying the sensitivity and the dynamics of dunes in the past could shed light on the way in which dunefields will respond to forthcoming climate changes that can widely affect vast areas in southern Africa and.
F I G U R E Probability density functions of simulated durations until the measured concentrations of 26 Al and 10 Be were simultaneously reached for the first time for sample P3-BR, using the empirical vertical distribution rates datasets. Outliers in the results that are marked with grey rectangles and dashed lines are excluded from the ages shown in Figure4[Color figure can be viewed at wileyonlinelibrary. com] bedrock, further implies that an aeolian process-based model provides a practical way for deciphering the long-term development of an entire dunefield by sampling a fairly small number of sites (Vainer et al. , 2018). Furthermore, examining how different environmental factors control aeolian transport is feasible by exploring the way that various combinations of input parameters, representing distinct environmental settings, affect aeolian migration patterns.
However, with the development of the Illumina MiSeq (up to 2x300 bp), the V4 region is now preferred, in particular because it is longer, more variable, and better covered in reference databases  (Pawlowski et al. 2012). Other eukaryotic genes, and in particular the mitochondrial cytochrome oxidase 1 gene (COI or cox1), have been used for Metazoa  (Valentini et al. 2009 ) but their use is debated in particular because of the lack of universal primers  (Andújar et al. 2018; Deagle et al. 2014)  and the absence of this gene in lineages that have lost the mitochondrial genome (e. g. Yahalomi et al. 2020). For protists, the 18S pr2-primers database -p. 4 rRNA gene appears to be most appropriate as a general marker  (Pawlowski et al. 2012) , although other genes such as rbcL (large unit of the RUBISCO) have been used for targeting photosynthetic organisms (e.
A large diversity of primer and primer sets targeting the 18S rRNA gene have been developed over the years, although a few dominate in protist metabarcoding studies. Few resources are available that list eukaryotic 18S primers and primer pairs, provide information on their taxonomic specificity and allow testing of new primer pairs. Most existing primer databases do not focus on protists. For example, the primer database linked to the Barcode of Life Data System website Bold Systems (https://boldsystems. org/index. php/Public_Primer_PrimerSearch) focuses on metazoans, and Probebase (http://probebase. csb. univie. ac. at/node/8, Greuter et al. 2016 ) focuses on bacteria. A few programming tools have been developed to test primer set specificity, for example EcoPCR  (Ficetola et al. 2010 ), a Python program, or R libraries such as PrimerMiner  (Elbrecht and Leese 2017).
The phylogenic program ARB offers a function to design and test probes and primers  (Ludwig 2004 ). Unfortunately, these tools need to be installed in a specific computing environment and require some background programming skills. Many existing online tools such as Probematch (https://rdp. cme. msu. edu/probematch/search. jsp) only allow testing primer sets against bacteria, archaea and fungi. Silva TestPrime (https://www. arb-silva. de/search/testprime) is the only tool that covers protists. It provides very detailed feedback on the taxonomy of amplified sequences, and the location of mismatches. Such detailed information comes at the expense of speed, with a typical test needing a few minutes to run. Moreover, the taxonomic annotation of the Silva database for protists is not optimal at this time, particularly for environmental sequences which are often only assigned at the class level or above (typically "Chrysophyceae;uncultured;eukaryotic picoplankton environmental sample").
To fill this gap and to provide protist researchers with a usable tool, we constructed a database pr2-primers database -p. 5 of primers and primer sets used for eukaryotic 18S rRNA metabarcoding. These primer sets were tested in silico against the PR 2 database  (Guillou et al. 2013 ) that contains more than 180,000 18S rRNA sequences with expert taxonomical annotation and a subset of the Silva database for archaea and bacteria. We developed an R-based web application that allows exploration of the database, to visualize pre-computed in silico amplification results according to taxonomy (% of amplification, size of amplicons and location of mismatches), and to test any user-defined primer set.
Material and Methods 18S rRNA gene primers (Table  S1 ) and primer sets (Table  S2 ) used in metabarcoding studies were collected from the literature. Primer sequences and primer sets (knowing that several primer sets may share at least one primer) were stored in a MySQL database. Primer sets were tested by performing in silico amplification of eukaryotic sequences stored in the PR 2 reference database  (Guillou et al. 2013 ) version 4. 12. 0 (https://github. com/pr2database/pr2database/releases/tag/v4. 12. 0). We also used a small subset of the Silva database version 132 provided by the mothur website (https://mothur. org/wiki/silva_reference_files) containing 8517 bacteria and 147 archaea sequences to test whether these two groups were amplified. Database sequences with ambiguities were discarded (any nucleotide that is not A, C, G or T).
mismatch=0, with. indels=FALSE, fixed=FALSE, algorithm="auto". We computed the position of mismatches using the mismatch function with parameter fixed=FALSE. A faster version of the script is also available that does not compute mismatch position using the vectorized form of the matchPattern function (vmatchPattern). The latter function is used in the Shiny application (see below) allowing users to test their own primer or primer sets. The data were tabulated using the dplyr package and plotted using the ggplot2 package  (Wickham 2016 ). An R shiny application to interact with the database was developed using the following R packages: shiny, shinyFeedback and shinycssloaders  (Sali and Attali pr2-primers database -p. 6 2020).
Database of primers and primer sets We were able to recover a total of 108 general eukaryotic primers and 177 primers specific to some taxonomic groups from the literature (Tables  1  and S1 , https://app. pr2-primers. org). Some of these primers were designed early on when researchers began to amplify and sequence the 18S rRNA gene (e. g. Medlin et al. 1988 ). More recently, researchers have been designing primers specific to some taxonomic groups, mostly targeting phylum level (e. g. S19F and S15rF for Foraminifera,  Morard et al. 2011)  or class level (e. g. primer PRYM03+3 for Prymnesiophyceae,  Egge et al. 2013). Some primers were also designed to block specific taxa (e. g. 18SV1V2Block against the coral Pocillopora damicornis,  Clerissi et al. 2018 ) to be used in combination with more general primers (18SV1V2F in this case) or to avoid amplification of some groups (e.