dataesr/scientific-paragraphs-categorization huggingface dataset:
Operating mode: introduction to Pyfiber configuration file and operational modules The configuration file. [in. yaml format (https:// en. wikip edia. org/ wiki/ YAML)] describes the different phases and events of a behavioral session. If an event of interest is not already implemented in the configuration file (for example, a lever press), it can be defined and added by inserting the Imetronic nomenclature for the event. If non-Imetronic data files are being utilized, events are identified by specifying their names (corresponding to the header in the behavioral data file containing the timestamps) in the ' csv_events' section within the behavioral section. It also describes the structure of the data from the fiber photometry system, as well as all the events of interest used in the analyses.
It also contains the default analysis settings. Therefore, changing the configuration file will alter the default Pyfiber processes. Video 3 shows how to modify the configuration file if needed. For a more detailed description of the configuration file, see the supplementary information. The three operational modules. The analyze module: The Analyze module analyzes the combination of behavioral data and Doric Lenses fiber photometry data. It extracts identical behavioral responses or events occurring during fiber recordings and performs fiber perievent analysis on them. However, it is not used independently-it is always called by Pyfiber when either the MultiSession or Session modules are called. Session module: The session module imports a single session [one fiber data file and one behavioral file] for analysis of a single timestamp.
This permits simultaneous analysis of multiple recordings and events quickly and easily. The results can be exported to be graphed or for statistical analysis. This is the most complex function of Pyfiber and the most commonly used in our lab. For example, suppose the goal of the analysis is to see transients in response to a light presentation signaling cocaine delivery. In that case, MultiSession will be able to extract the time of every light presentation from the behavioral files and refer to each associated fiber photometry recording to perform perievent analysis. The fiber module: The fiber module is used by the Analyze modules (both MultiSession and Session). This module is responsible for processing the fiber photometry data in preparation for perievent analysis. However, suppose there is a specific interest in observing the fiber photometry data independently.
In that case, the Fiber module can be useful and can be used to analyze a single fiber photometry recording file. The behavior module: The behavior module is also used by the Analyze module. It either extracts events and their timestamps from the raw Imetronic data file or loads the events and their timestamps from specifically formatted behavioral data files from other systems. This is done in preparation for perievent analysis by the Analyze module(s). Like the Fiber module, the Behavior module can be used independently if there is a specific desire to look solely at behavioral events. It can be used in the two different following modes: Behavior module: The behavior module is used to look at a single data file. This is used in both the MultiSession and Session modules to extract timestamps for the associated perievent analysis.
It can be used independently to extract behavioral responses or events from a single operant session. MultiBehavior module: The MultiBehavior module is used to compare behaviors between subjects, groups or sessions using multiple data files. The MultiSession module can be used to visualize how the behavioral activities differ between the sessions that will be analyzed for perievent fiber photometry analysis. Pyfiber requires operant behavior file(s) and fiber photometry data file(s). From these files, Pyfiber can extract specific behavioral events and process the raw fiber photometry data. The main practicality of Pyfiber is the peri-event analysis of the fiber photometry signal surrounding events of interest that were extracted from the raw behavioral data. Created with BioRender. com.
How to run analyses with Pyfiber As stated above, the MultiSession module is the prototypical use of Pyfiber when running fiber photometry recordings during an operant task over several sessions and/or on several individuals. It analyzes multiple behavioral and fiber data files in parallel. It uses the Analyze, the Behavior and the Fiber modules. For the sake of space, only tutorials on the MultiSession Analyze module are described below. For tutorials on Fiber and Behavior (MultiBehavior, Behavior) modules, and the Session Analyze Module, please see the Jupyter Notebooks associated with those modules, and the SI. Tables in the SI contain a selection of the most pertinent features and the commands associated with the different analyze modules. An example is given on 3 recordings run during 3 independent behavioral sessions (all run on the same rat).
The corresponding Jupyter Notebook can be found and has to be downloaded together with the. csv (fiber data) and. dat (behavior data) files at: https:// gitlab. com/ inserm-u1215/ pyfib er/-/ tree/ main/ noteb ooks/ Multi Sessi on. Two prototypical types of analyses are detailed step by step in Video 2. The first analyzes an experimenterscheduled event (shift from no drug to drug period signaled by the shift between two distinct light conditions (LED2 to HLED, see Fig. 1 ). In contrast, the second presents the analysis of non-scheduled events resulting from the animals' behavior (in the present case: nose-pokes in the active hole or NP1 during drug periods). The following steps shown are the only ones necessary for these examples but are excerpts of a Jupyter Notebook containing a variety of analyses.
analyze(events='np1',interval='LED2_ON', window=(1,1), norm='F') Step 4 (MultiSession Jupyter Notebook: #9): To plot the perievent results, call. plot from the perievent object and indicate what to plot to see the individual (colored) and average (black) traces of the perievent signal. We use here as examples the data shown in Fig. 3. Figure  3A (left) shows the averaged plot for a scheduled event (shift from No drug to Drug period, "led2_on"). In Fig. 3D  and E (left) are shown individual and averaged plots for non-scheduled events (Cue presentation ("led1_on") and the first lick ("lk1") that followed each sucrose delivery, respectively). Step 5 (MultiSession Jupyter Notebook: #10): to see a table containing the average values for the perievent signal: including pre/post area under the curve (AUC), dF/F, Z-Scores, Robust Z-Scores, peak frequency, peak amplitude, etc…, call.
data from the perievent object. For the complete list of things that are in this table, see. data in the MultiSession table found in the SI (Table  2  shown for the scheduled event). Example paper-ready figures that can be extracted and plotted from this data table are shown in Fig. 3. Figure  3A (left) and B,C show perievent analysis on the average peak amplitude and frequency of both scheduled/unscheduled events in cocaine self-administration, while Fig. 3D, E  (right) show perievent analysis on the area under the curve (AUC) of both scheduled/unscheduled events in sucrose self-administration. Step 6 (MultiSession Jupyter Notebook: #12): to see a table that contains the dataframes for the perievent analysis, call. full_data from the perievent object (Table  3  shown for the scheduled event).
Step 7 (MultiSession Jupyter Notebook: #15): Lastly, an example of the extraction of the dataframes containing the adjusted timestamps leading up to the event and the processed signal that corresponds to the timestamps (Table  4  shown for the scheduled event). These can be exported to a. csv file so the data can either be used in statistical programs or to create figures (see step 8). Since the tables can be quite large, some columns and rows might not be shown with the default display settings. The display settings can be changed using pandas, the data analysis module for the Python programming language. Pandas is included in Pyfiber and the method to change display settings using pandas is shown in Video 2. Step 8 How to extract arrays: All of the data can be exported in multiple file formats depending on the user's needs.
Discussion Many papers have now started to show the benefits of fiber photometry paired with certain aspects of operant behavior  [11] [12] [13]. The research pairing photometry with operant behavior is still in its infancy but has immense potential due to the diversity of events occurring during operant behavior-but this also serves as a barrier that complicates data analysis. However, with appropriate analytic efficiency, the combination of techniques can uncover neural signatures surrounding drug/reward administration, lever pressing/nose-pokes, and conditioned/ discriminative stimuli presentation/changes. However, this relies on proper identification and tagging of behavioral events, which can be complicated when using complex behavioral paradigms. This led us to create Pyfiber, a python library for joint behavioral and fiber photometry analysis with the idea of complex operant behavioral paradigms in mind (such as-but not limited to-intravenous self-administration).
Current open-source tools for joint behavioral/fiber photometry analysis While open-source tools exist that aim to analyze fiber photometry recordings surrounding behavioral events, there were significant limitations that prevented us from using them. The pMAT program, despite having a very user-friendly interface, was quite rigid regarding the types of analysis performed  7. For example, signal processing was limited to ΔF/F, without the possibility of other techniques. A main limitation of pMAT is that despite the. exe being executable without Matlab, modification of the pMAT program are not truly open-source due to the requirement to have a Matlab license. itself, drug seeking/taking behaviors, and more depending on the paradigm, a quick way to categorize events for subsequent analysis is highly beneficial.
Identification of identical behavioral events can also be incredibly beneficial in the scope of other behavioral tests. For example, through the use of the Behavior module, quick identification of regressive and perseverative errors in operant cognitive flexibility tasks can be produced by quick modification and customization of the configuration file. By extension, the tool can be used to analyze data from 5-choice serial reaction time tasks (5-CSRTTs) to tag the behavioral events associated with impulsive action, attention, and reaction times. Regardless of the behavioral paradigm, ensuring that the timestamps used for subsequent neurobiological analysis are identical is necessary to uncover neurobiological correlates of complex behaviors. The Behavior module within Pyfiber helps do that.
Limitations of Pyfiber There are some limitations that must be acknowledged and addressed in future updates. One primary limitation is that the Behavior module can only directly process raw data from Imetronic software. Data from other behavioral systems need to be reformatted before being used in Pyfiber. Since many researchers utilize behavioral apparatus from other companies (e. g. Med Associates), it would be beneficial to expand Pyfiber's capability to directly handle raw data from other behavioral systems. Likewise, currently Pyfiber only supports fiber photometry files from Doric systems. As fiber photometry data files are relatively similar across most systems, the conversion from other systems to the Doric layout is quite simple. However, in a future version of Pyfiber, there will be the possibility to upload data types from other photometry systems.
Lastly, another limitation is that currently, Pyfiber is only able to analyze single-site recordings. Given that researchers are now using multi-site recordings, it will be interesting to update Pyfiber to enable multi-site analysis. In conclusion, Pyfiber can be a major asset to fluorescent imaging studies during operant behavior and favors their implementation, which remains limited. This open-source analytical python library helps eliminate analytical difficulties that behavioral labs face when implementing bulk fluorescent imaging techniques. Above all, Pyfiber permits a selective and exhaustive collection and analysis of the various events occurring during FP recordings to optimize data analysis. By bridging the analytical gap between behavioral events occurring in an operant behavior paradigm with fiber photometry signals, the use of Pyfiber and its adaptation to other set-ups open the door to an exhaustive analysis of the neuronal signature of the events occurring while freely behaving animals are tested in complex protocols.
Acknowledgements This work was supported by  l' Agence Nationale de la Recherche (ANR ; grant  ANR-10-EQX-008-1 ,  EquipEx OptoPath  to VD-G and CH), by  INCa/IReSP   16130 , by  l'Institut National de la Santé et de la Recherche Médicale (INSERM)  and the  University of Bordeaux (UB). DC benefitted from the help of the  Bordeaux Neurocampus Graduate Program , managed by the  French National Research Agency  reference  ANR-17-EURE-0028. MC is a recipient of the  2021-2022 Année Recherche Program  ( DG-ARS ,  CHU Bordeaux). We thank  S. Laumond ,  J.
Data availability Pyfiber is developed for Python 3. 10 and is compatible with Python 3. 11. The library is available from the Python pip package manager by executing the following command in a terminal: 'pip install pyfiber'. The source code, documentation which is automatically built with Sphinx, and issue tracker are also available from the following Github repository: https:// gitlab. com/ inserm-u1215/ pyfib er. Pyfiber can be integrated into a homemade command-line application and interfaced in a notebook. For this, we recommend the Anaconda environment. Conditions of use and access are described step by step in the results section.
7) where x 1 ,. , x N and λ 1 ,. , λ N are complex parameters (with |λ k | < 1 for evey k). These parameters are related to the nodes z 1 ,. , z N and the weights c 1 ,. , c N through the identities: z k = N j=1 x j λ k 1 -λ k λ j and c k = N j=1 x j x k (1 -λ k λ j ) 2 for all k = 1,. , N. ( To solve this system we use the pre-implemented Matlab function fsolve, which is based on an iterative method. Since the nodes and weights are supposed to be the harmonic moments of the domain ω N , it follows that N k=1 c k = |ω N | is real and positive. We initialize the method with the parameters: λ 0 k = 0, x 0 k = r k |ω N | N j=1 |c j | , for all k = 1,. , N, (3. 9) where r k := |c k |/π. With these values for the parameters, the image of the unit disk by the conformal mapping φ N is a disk of area |ω N | i.
In order to identify the main performance skill of the satellite-derived dust data, in this section we perform a quality check based on comparisons with ground-based AERONET observations. AERONET data have already been used as a "gold standard" for validating most satellite AOD products. Although MIDAS, MIRS and AEROIASI have been evaluated using dust-related AERONET retrievals in independent analysis  (Gkikas et al. , 2021; Kahn and Gaitley, 2015; Kalashnikova and Kahn, 2006; Cuesta et al. , 2015 Cuesta et al. , , 2020)) , here we seek to assess the performance of the different satellite-based dust products in a common framework (spatial and temporal) for the later comparison with the dust reanalysis. The comparison between satellite-based dust products and AERONET is performed for each station individually using collocated satellite and ground-based measurements.
In addition, field hazards such as dust, heat and physical obstacles, also make things more difficult (risk of damaging the sensor, lack of mobility of the vehicle in the field, weather conditions, sensor overheating, etc. ). Sometimes, unexpected and nondetectable issues may even make a dataset unusable (faulty GNSS system, camera lens obscured by dust). However, it can be difficult to get into the details of these problems, considering the wide range of optical sensors, crop types and vectors that can be used. The agricultural and scientific community actors have thus specialized on very specific research domains, in order to gain field experience and design the best-suited acquisition systems. Scientific validation and promotion of these works is typically done through research articles covering the methodology and the results, most of the time with a description of the acquisition system.
Data publishing is also more and more encouraged, leading to more occurrences of articles in which the original data are freely made available for the community to use  [18]. More details about the availability of the individual datasets presented in this study can be found in the Data Availability Statement section. The choices made during the acquisition system design step tend to be less discussed in the literature, even though they are critical to the success of the application. A discussion about the choice of the most appropriate vehicle for proximal sensing in agriculture can be found in  [19]. Still, that study remains general and does not focus on the specificity of the grapevine crop. Depending on the context and the application, other grapevine studies may discuss matters related to vehicle choice, lighting and image framing.
Downy mildew detection is mostly related to spraying management, which is a costly operation with high environmental impact. Finally, esca is a major cause of long-term wilting in grapevine with no curative treatment, and early detection of symptoms may allow viticulturists to quickly replace diseased plants and limit losses. Table  1  presents a synthetic view of the four projects with complementary references. To our knowledge, no other work on in-field downy mildew detection using optical proximal sensing exists. The paper is organized as follows: In the first part , the acquisition system used for all the previously mentioned projects is presented, both from a hardware and a software perspective. The integration and mounting of the system on different vehicles are discussed, as well as the organization and the processing of the obtained datasets.
This meant the system could be easily mounted on and connected to agricultural tractors. If needed, an optional ultrasonic rangefinder (Maxbotix HR-USB-EZ1) could be added to the sensor to measure the distance from the vines and detect the missing ones. The distance data were used to determine the area covered by the photo. These data could also be combined in real time with the GNSS positions for a better control of the acquisition process. For instance, acquisition could be stopped when too many missing plants were detected by the sensor or when the system left the acquisition zone, reducing power consumption, the number of irrelevant images and preserving the flash. As a whole, one key advantage of the system was the ability to easily plug different devices (camera, flash unit, GNSS system, rangefinder) on a standardized central unit (Raspberry Pi 4+).
Software Architecture The sensor was controlled by the embedded single-board computer. A piece of software handled the camera, the user interface, the GNSS module, the storage device and the image processing pipeline. As the embedded Raspberry Pi computer was not particularly powerful, the piece of software had to be highly optimized, mainly by parallelizing the different processes to run in parallel on separate CPU cores. As the sensor can be hard to reach during the image acquisition, the main operation mode was totally autonomous. In this case, the image acquisition was started and stopped using the positioning data. The active plots could be defined in any geographical information systems (GISs), including Google Earth, as polygons and saved as ". kml" files. As the sensor entered one of these plots, the acquisition process started and stayed active as long as the sensor was inside the plot.
The sensor could also be controlled manually using a remote Wi-Fi connection. An Android smartphone application was developed (Figure  4 ), which connected to the sensor. The user interface was exhaustive, showing the status of every component (Figure  4a ), the live captured image (Figure  4b ), free storage space and other information. It also allowed us to start and stop the acquisition manually and to set the camera parameters, such as exposure time and frequency, as well as managing the data (deleting, copying to or extracting on a USB device, updating the software tool, etc. ). The embedded piece of software analyzed the acquired images for over-and underexposure and other errors. If needed, it could also process the images in real time using deep neural networks and other GPU-based computer vision algorithms on the Nvidia embedded computers.
Data Storage The image files were stored in jpeg format (85% compression, around 1 megabyte). Each image was linked to a text file containing geolocalization, timestamp and distance information (when the rangefinder was plugged in). The files were temporarily stored on the sensor then transferred to a server equipped with a high-end GPU. Datasets were organized using a simple PostgreSQL spatial database, keeping track of the different pictures (combined with the GNSS position when possible) and their associated plots/sensors/vehicles.
In the case of flavescence dorée detection, a random forest algorithm was also trained on the leaves detection to predict whether or not the whole vine suffered from flavescence dorée. A summary of that information, as well as visual examples of end results, can be found in Table  2. More details about these projects, the databases and the quantitative evaluation of the results can be individually found in the associated published papers (Table  1 ). While the individual examination and evaluation of the used algorithms is beyond the scope of this paper, it is worth noting both traditional handmade features extracting algorithms and state-of-the-art deep learning algorithms were used, and in some cases compared  [28]. While traditional methods tend to lag behind in terms of raw performance, we believe handmade features may be more robust in some cases, and their potential shortcomings with different datasets may be easier to predict.
Results After examining the datasets and the results for the four projects in the study, the acquisition parameters with an effect on the obtained images (and a potential effect on the end result) were roughly separated into three categories: Problems related to GNSS data were omitted from that list. One major issue with geolocalization is that, even when access to subdecimetric RTK/PPK precision is possible, the camera position does not amount to the vine position. A geometric transformation is possible but the distance between the camera and the plant (as well as the view angle) may not be constant during the acquisition. This means converting a position dataset to a lattice vine position grid can be tricky. Even though they are critical for some applications, GNSS logs are auxiliary data that do not directly contribute to the integrity and the quality of the obtained pictures.
However, this implies using different cameras, adding variability in the image database and specific image treatments (distortion correction). When working with several projects, this is not a desirable behavior since algorithms will have to be designed accordingly. It is also worth noting border effects may occur, related to the casing or the camera optics. Trained algorithms may thus also fail to operate in these areas. Another important consequence of the framing variability is the objects' size variability in the resulting images. Even though image size is constant, objects appear bigger when the camera is close to the plant. That, however, does not necessarily mean they are easier to detect for the trained algorithms. If we add to this variability the natural seasonal variability (phenological stages BBCH71 through BBCH89 in the case of grapevine berries  [36] ) and cultivar differences, huge differences are to be expected.