dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA extraction. Total RNA was extracted from control trophozoites, trophozoites pre-incubated with LB or KB and then exposed (or not) to H 2 O 2 (in three biological replicates) using the TRI reagent kit, according to the manufacturer's instructions (Sigma-Aldrich USA). Libraries were built using a Truseq mRNA-Seq Library Preparation Kit (Illumina, USA), according to the manufacturer's recommendations. Quality control was performed on an Agilent Bioanalyzer. Sequencing was performed on a HiSeq. 2500 system (Illumina, USA) and produced 65-base single-end reads. RNA-Seq data analysis. Adapter sequences and low-quality sequences were removed from RNA-Seq by applying an in-house program (https://github. com/baj12/clean_ngs). We found that almost 95% of the annotated coding regions had at least one read in all tested conditions -indicating that our dataset was deep enough to analyse the majority of transcripts annotated in public-access databases.
Only sequences of at least 25 nucleotides were considered for further analysis. Tophat version 1. 4. 1  38  with default parameters was used for alignment against the reference genome (AmoebaDB-1. 2, retrieved from http://amoebadb. org/amoeba/). Genes were counted using HTSeq-count version 0. 6. 1  39  (parameters: -t CDS -i ID -m intersection-nonempty -s no). Count data were then analysed using R version 3. 2. 2 and the Bioconductor package DESeq2 version 1. 10. 1  40. Normalization and dispersion estimation were performed with DESeq2 (using the default parameters), although statistical tests for differential expression were performed without applying the independent filtering algorithm. A generalized linear model including (i) the effect of stress (+OS vs -OS), (ii) the effect of bacteria (LB, KB vs.
The 3D structure for several templates (according to the Protein Data Bank) is shown, including 4fs7. 1. A (BspA protein from Bacteroides ovatus), 3cig. 1. A or 2a0z. 1. A (Toll-like receptor 3); 5gmf. 1. A (Toll-like receptor 7) and 5ijd. 1. D (Toll-like receptor 4, variable lymphocyte receptor B chimera). The SWISS-MODEL template library was searched with Blast and HHBlits for evolutionarily-related structures that matched the target sequence, and the corresponding 3D structural models were identified (Supplemental Table8). (c) 3D amoebic protein models were built using the templates indicated in B. The ID of the E. histolytica protein is shown with the modelling template.
Acknowledgements This work is dedicated to  Professor David Mirelman  ( Weizmann Institute, Israel ) who introduced us to the field of amoebiasis through his interest in bacteria and E. histolytica. We wish to thank  Professor Jean-Christophe Olivo-Marin  and  Professor Artur Scherf  ( Institut Pasteur ) for their friendly and helpful support. The project has received funding from the  European ERA-NET Infect-ERA program AMOEBAC (Israel Ministry of Health  Grant  031L000  and the  French National Agency for Research (ANR)  grants  ANR-14-IFEC-0001-01  and  ANR-14-IFEC-0001-02 ) and the  French Parasitology consortium Labex ParaFrap  (through the grant  ANR-11-LABX0024 ). The Transcriptome and  Epigenome Platform  is a member of the  France Génomique consortium  ( ANR10-NBS-09-08 ).
g. , Quantum geographic information systems (QGIS), gvSIG, Google Earth, Whitebow Geospatial Analysis Tools) and more specific  [10, 11]  open-source platforms, including those not specific to agricultural applications. It is clear that such functionalities are of major importance to start handling spatial data. However, when it comes to making informed management decisions, these visualization functions are not sufficient. It is necessary to provide users with more advanced or high-level functions so that they can turn this raw spatial data into information and decision layers. Most commonly required procedures in the precision agriculture domain are functions such as: (i) filtering, to ensure the quality of the datasets  [12, 13] , (ii) interpolation, to provide a continuous mapping of the property of interest  [14] [15] [16] , (iii) zoning, to define within-field zones for site-specific management  [17, 18] , or (iv) aggregation so that multiple layers of information can be combined  [19, 20].
g. , working width, lag time and application speed  [24, 25]. From a general perspective, there are only a few dedicated software programs available to explicitly process precision agriculture data and incorporate expert knowledge into the process. Moreover, very few of them are open-source. Some free-or share-ware tools have been developed and proposed by the precision agriculture community, but these generally focus on specific processing tasks or on a particular type of data. For example, the Vesper program  [26] , developed by the University of Sydney, provides users with a graphical interface to spatially interpolate their data. Despite the quite advanced functions that are available, e. g. , local punctual and block kriging, users only end up with a continuous map of their data without much more practical information.
The Yield Editor software from the United States Department of Agriculture  [13, 27]  deals effectively with the filtering of within-field yield datasets that are known to contain a lot of defective observations  [28] , but it does not perform interpolation or other high-level functions. Another interesting example is a QGIS plugin that was put into place to process spatial data of vine shoot diameter arising from the mounted sensor Physiocap ® (E. RE. C. A, Vaulx-en-Velin, France). This tool mainly incorporates functions to filter these highly noisy datasets. Other platforms have been proposed by agronomist to give farmers access to crop models but are very specific in terms of crop, data and use  [29]. An open source platform that takes raw data through to a decision point is not available to the precision agriculture community yet.
The aim of the paper is to present the GeoFIS software (https://www. geofis. org/) that was developed by a joined team from IRSTEA, INRA and Montpellier SupAgro in France  [30]. The goal of this platform is to provide users with up-to-date and reliable algorithms to process their precision agriculture data and incorporate expert knowledge from the fields. GeoFIS has been mainly developed for academic and research purposes, i. e. , investigators and students willing to process their data, but also to a lesser extent for agronomists and advisors with a sufficient background in spatial analysis. The objective of this interface-based platform is to support users that do not necessarily have programming skills and to show that high level functions can be introduced in a GIS and could be integrated within precision agriculture programs.
Aim of the GeoFIS Project GeoFIS has been designed to facilitate the movement from spatial data to spatial information, and to spatial decision-making. It is an open-source program that proposes a simple and easy to use interface to build decision support systems (DSS) from spatial data  [30]. While its development has been inspired by agri-environmental applications, the framework itself is open and accessible to applications in other domains. It is designed to be adaptable to different usages and for different end-users, mostly for academic and research applications, for student and teaching applications and, to a lesser extent, for GIS-skilled agronomists and advisors. GeoFIS deviates from other GIS software, e. g. , QGIS, in the sense that specific tools have been implemented to answer the main expectations of agricultural professionals when it comes to processing precision agriculture data.
These will be presented later on. It is acknowledged that multiple other open-sources spatial programs (e. g. , QGIS) or languages (e. g. , R and Python) are available to process spatial and temporal data. However, these open-source tools do not have specific functions dedicated to the processing of precision agriculture data (as listed in the introduction section) and usually require users to have skills in programming. This is a major limiting factor for the practical use of spatial modelling in agriculture. Another strength of GeoFIS is that attention has been paid to the incorporation of expert knowledge into the data analysis. This is not available in other related spatial processing tools. Agricultural professionals have a lot of local expert knowledge on their production system that needs to be taken into account.
Finally, the incorporation of expert knowledge is made possible with FisPro (https://www. fispro. org), a system that uses fuzzy sets for conceptual modeling  [30]. GeoFIS is available in four languages (French, English, Spanish and Portuguese). The interface is designed with a man-machine cooperation objective. The goal is to facilitate the relationships between data, learning algorithms and expert knowledge. Documentation, scientific papers, and video tutorials are available to better understand the implemented function and to facilitate the adoption of the GeoFIS software (https://www. geofis. org/). Notifications are made when a new version of the software is available. The GeoFIS architecture  [30]. CGAL, Computational Geometry Algorithms Library; DSS, Decision Support Systems; GIS, Geographic Information System; 1D, One dimension.
3). Steps 0 to 2 will be exemplified through medium spatial resolution manual measurements performed over a banana field to map the plant vigor. High resolution yield data across several wheat fields will be used to illustrate the value of Step 3. 2 to rank the fields from the most to the less suitable for site-specific management. Step 3. 1 and 3. 3 will be applied on a precision viticulture example aimed at defining zones for differential irrigation management. The overall objective is to demonstrate how GeoFIS has the ability to address the main issues of data processing in precision agriculture. As the three case studies are performed on different crops (banana, wheat and grapevines), each exhibiting unique characteristics, the applicability and genericity of this open-source software will also be demonstrated.
612, N: 9. 639) in its 15th flowering cycle. The pseudostem circumference measurements were only taken on plants where vegetative growth had ceased, i. e. , plants that were either flowering or at a later phenological stage. There were 551 measurements taken using a tape measure at 1-m height and georeferenced with a trail type hand-held GPS (Table  1 ). The proposed analysis in GeoFIS consisted of the following steps: (i) the dataset was imported within GeoFIS (Step 0); (ii) pseudostem circumference values were filtered to ensure the quality of the dataset (Step 1); (iii) variograms were fitted to the filtered datasets and interpolation was performed using kriging with a local neighborhood onto a 1 × 1 m grid.
Application in GeoFIS The global distribution of the data was filtered within GeoFIS (Figure  3 ). Users can select the attribute to be filtered at the top of the window. Below the histogram, two threshold values that represent the two tails of the distribution can be changed, by either typing specific values or moving a slide bar. Observations outside these thresholds are then removed from the dataset. Note that there were two low values in this data set that were considered outside the normal distribution by the user (Figure  3 ). The lower threshold allowed the user to eliminate these non-compliant values. The spatial structure of the data can then be evaluated by plotting an experimental variogram, here using the within-field pseudostem circumferences. The number of lags and the maximum lag distance can be set in the left-hand corner of the window to make sure that the variogram is relevant.
This was not a problem here but removing local outliers would be a useful function in the software program. When present, local outliers (inliers) will affect the quality of interpolation procedures. Secondly, GeoFIS does not yet allow the fitting of nested variogram models. This was a potential issue in this case study. In Figure  4 , it could be argued that there is a short-range spatial structure within the first 10 m and a second spatial structure from 10 to 30 m (with a longer range). Nested spatial structures are not common but do occur in agricultural data. Thirdly, regarding the continuous mapping of the data, GeoFIS only provides a kriged map of the property of interest. The mean estimates are given but the error (kriging variance) associated with these estimates is not provided. This is a potential limitation for assessing the mapping accuracy and for interpreting uncertainty in future analyses with the interpolated data.
It may have been that spatial data not only exhibit global but also local outliers. This was not a problem here but removing local outliers would be a useful function in the software program. When present, local outliers (inliers) will affect the quality of interpolation procedures. Secondly, GeoFIS does not yet allow the fitting of nested variogram models. This was a potential issue in this case study. In Figure  4 , it could be argued that there is a short-range spatial structure within the first 10 m and a second spatial structure from 10 to 30 m (with a longer range). Nested spatial structures are not common but do occur in agricultural data. Thirdly, regarding the continuous mapping of the data, GeoFIS only provides a kriged map of the property of interest. The mean estimates are given but the error (kriging variance) associated with these estimates is not provided.
These are most likely to have the largest returns on investment in agri-technology, which should minimize the risk of investment for the farmer. In this case study, GeoFIS was used to evaluate and compare the opportunity for adopting site-specific management across multiple fields using a defined opportunity index  [31]. Opportunity indices are a way of assessing if the amount and structure of variation in a field makes site-specific management a potentially feasible option  [2, 25]. Seven yield datasets arising from two different farms located near Evreux, in the north-western part of France (Farm 1-WGS84: E: 0. 779, N: 48. 955; Farm 2-WGS84: E: 1. 032, N: 48. 828) were used. Fields were cropped in wheat and harvested with various combines, primarily New Holland (Turin, Italy) and Claas (Harsewinkel, Germany) combines.
Yield datasets are considered particularly relevant for this case study because the yield is directly related to the field economic returns. Quantifying the amount and structure of yield variance should therefore be a valuable indicator of whether site-specific management is opportune. Structured spatial variation in yield would indicate a potential for structured spatial crop management, particularly fertilizer and agri-chemicals. This case study also demonstrates the use of GeoFIS with dense sensor-derived spatial observations in contrast to the spatial manual measurements presented in Case Study 1. Yield data are collected with on-board sensors at 1 Hz as the combine traverses the field. These observations are therefore irregularly-distributed in space because (i) the intra-row and inter-row distances are different and (ii) the acquisition conditions, such as the GNSS accuracy or variable combine speed, can impact the spatial distribution of the observations.
The yield information is very dense (thousands of points per hectare) and very noisy because of stochastic error in sensor operation, the intrinsic local variability in production and errors associated with the combine harvester passing through the field  [13, 28]. These seven fields were selected as they exhibit various degrees of yield autocorrelation within the same systems (farms) and, as such, should represent a different opportunity for variable-rate applications. Within this case-study, several functions of GeoFIS were used to arrive at a solution that ranks and compares the seven fields in terms of a technical opportunity for site-specific management. More specifically, (i) global outliers were filtered out (Step 1); (ii) variograms were fitted to the previously filtered yield datasets and ordinary kriging with a global variogram and local neighborhood was performed onto a 3 × 3 m grid (Step 2); and (iii) the TOI was computed (see Section 2.
Application in GeoFIS The filtering and interpolation procedures have already been detailed in Case Study 1 and will not be discussed here. The technical opportunity index (TOI) can be computed in the Opportunity Index toolbar of the GeoFIS software. Figure  7  displays the window that appears when this menu is selected. The window is composed of three main sections. In the top drop-down menu (Border), users are asked to select the attribute on which the metric should be computed, e. g. , yield, and to provide the field boundaries to make sure that the calculation of the TOI is restricted to the field of interest. Note that the boundary can be automatically derived with a convex hull, however this may not be a good option for fields with an irregular geometric shape. In the second drop-down menu (Machine Footprint) the technical and operational constraints of future site-specific management can be specified.
In other words, all the univariate risks are high because the median and maximum values for a univariate risk are necessarily higher than the minimum value of the univariate risk. Note that the real logical operation "AND" would be reproduced by using the following set of weights (1;0;0). By changing these weights, practitioners might also be able to reproduce the logical operation "OR" (0;0;1) for which the resulting risk is high as soon as the maximum value of a univariate risk is high. It would also be possible to perform a simple average of the different univariate risks by using the same weights for each layer. From a more general perspective, GeoFIS simplifies the processing of the three layers of information as the entire process was done within a single software platform. It can be compared to the data processing in  [35]  in which data where cleaned with Excel, interpolated with Vesper, analyzed with Matlab and represented with ArcGIS.
Conclusions The increasing flow of precision agriculture data requires the development of free and open source processing software to manage and make use of these data and promote precision agriculture adoption. As such, GeoFIS has been specifically designed to facilitate the movement from spatial data to spatial information and to spatial decision-making. The application of GeoFIS on some example case studies that agricultural professionals may face when dealing with spatial data has demonstrated the potential of this software. GeoFIS is a released product however it is important to state that all the functionality currently introduced and implemented in GeoFIS are still areas of active investigation by the scientific community. GeoFIS will be updated when, and if, improved methodologies become available.
Note that the real logical operation "AND" would be reproduced by using the following set of weights (1;0;0). By changing these weights, practitioners might also be able to reproduce the logical operation "OR" (0;0;1) for which the resulting risk is high as soon as the maximum value of a univariate risk is high. It would also be possible to perform a simple average of the different univariate risks by using the same weights for each layer. From a more general perspective, GeoFIS simplifies the processing of the three layers of information as the entire process was done within a single software platform. It can be compared to the data processing in  [35]  in which data where cleaned with Excel, interpolated with Vesper, analyzed with Matlab and represented with ArcGIS.
Conclusions The increasing flow of precision agriculture data requires the development of free and open source processing software to manage and make use of these and promote precision agriculture adoption. As such, GeoFIS has been specifically designed to facilitate the movement from spatial data to spatial information and to spatial decision-making. The application of GeoFIS on some example case studies that agricultural professionals may face when dealing with spatial data has demonstrated the potential of this software. GeoFIS is a released product however it is important to state that all the functionality currently introduced and implemented in GeoFIS are still areas of active investigation by the scientific community. GeoFIS will be updated when, and if, improved methodologies become available.
It is one of the strengths of the GeoFIS platform that it is able to integrate the latest research developments to make sure that users are provided with the most up-to-date, reliable and powerful processing algorithms. As it is, GeoFIS is an excellent tool to promote teaching in precision agriculture. Indeed, GeoFIS has already been used within many higher education institutions in France to teach researchers and professionals how to process spatial data. The user-friendly interface effectively facilitates the understanding of some major precision agriculture concepts. The analysis of the three case studies has been an opportunity to also evaluate the limits of the current algorithms and to propose areas for future developments within the software. For instance, the data filtering procedure focuses solely on global outliers while spatial datasets may contain outliers more deeply rooted within the data and sometimes referred to as spatial outliers.
A second example is that the variography analysis is limited to single data layers while cross-variography studies might be relevant to evaluate the spatial relationships between multiple layers of information. To foster the adoption of GeoFIS, the authors are more than open to collaboration and are ready to integrate relevant algorithms for processing precision agriculture data. Another possibility to promote the processing of precision agriculture data could be to create links between GeoFIS and existing GIS programs, such as QGIS that is an open-source GIS already widely used by many communities working on spatial data. There is a possibility to integrate all the algorithms of GeoFIS directly within this open-source GIS software to benefit from the display and processing algorithms already implemented in QGIS.
Electrophysiological recordings & spike sorting Signals from all electrodes were recorded using an Intan Technologies amplifier chip (RHD2216, sampling rate 20 KHz). Local field potentials were sampled and stored at 1,250 Hz. Analyses were performed with custom made Matlab programs, based on generic code that can be downloaded at www. battaglia. nl/computing/ and http://fmatoolbox. sourceforge. net/. Local field potentials were recorded using tungsten wires with PFA isolation (0. 002" bare) and single units using home-made tetrodes formed by twisting insulated Nichrome Wire (0. 001" bare). Tetrodes were plated to reduce impedances to around 100kW using gold solution (Neuralynx). For spike sorting, extracted waveforms were sorted via a semi-automated cluster cutting procedure using KlustaKwik and Klusters (http://neurosuite.
Optogenetic stimulation Fear conditioning experiment: On day 1, during the habituation session, mice received 13Hz blue light stimulations (45s). The stimulation intensity was chosen to see clear LFP oscillations in the OB and the PiCx, an output structure. No stimulation was delivered on day 2, during the habitation to sounds and the conditioning phase. During the test session on day 3, mice first received 3 shorts bouts of light stimulation (10s, inter-stim interval 10s) before the onset of the first CS. No stimulation was delivered during the CS-and the first pair of CS+. 13Hz blue light stimulations were delivered during subsequent CS+ (45s, locked at the CS+ onset, i. e. extending 15s after the end of each sound). Sleep stimulation experiment: After an initial light-phase day of baseline recording in their homecage to habituate to sleeping with electrophysiological and optic cables, mice were tracked using homemade Matlab software to identify periods of prolonged immobility corresponding to sleep.