dataesr/scientific-paragraphs-categorization huggingface dataset:
Nucleic Acids Research, 2023, Vol. 51, Web Server issue W63 To address this issue, the Critical Assessment of Protein Intrinsic Disorder (CAID)  ( 2 )  was introduced to benchmark ID and binding predictors on a communitycura ted da taset of novel proteins obtained from the Dis-Prot database  ( 5 ). In CAID, participants submit their implemented pr ediction softwar e to the organizers, who generate predictions by executing the software on selected protein targets whose disorder annotations were not previously availab le. Gi v en a new protein sequence, the task of an IDR predictor is to assign a score to each residue for the tendency to be intrinsically disordered at any stage of the protein life. In CAID, both the accuracy of prediction methods and technical aspects related to software implementation are e valuated.
Howe v er, accessing the prediction power of the tools is not al ways possib le. Often, the software is not pub licly availab le, e xists solely as a stand-alone e xecutab le, or is available as a w e b server with limitations. Moreover, pub licly availab le methods are not standardized and r equir e informed use, often entailing careful reading of the corresponding publication and interpreting predictors' output. To address these issues, we present the CAID Prediction Portal, a w e b server that executes all CAID methods with a single click on a user-defined input sequence. The server generates a standardized output and facilitates comparing methods, and it produces a consensus prediction that highlights high-confidence disordered regions. Disordered (or binding) residues are identified by selecting a threshold on the prediction score.
Standardization We used Singularity ( https://sylabs. io ) containers to containerize all the predictor software in order to standardize the input and output data, and ensur e r eproducible r esults. By containerizing the software, we can ensure that the software runs consistently across different machines, and most importantly it is not needed to install it manually in each machine. Furthermore, containerizing the predictors enables us to package all the necessary software and dependencies together, making it easier to deploy and update the predictors. With the creation of the container we also included scripts that are executed before and after the predictor, in order to standardize the input and output of the container, creating an interface with the predictor software. The input of the predictor is a FASTA file containing multiple sequences, and the predictor is executed on each sequence, producing one output per sequence (please note that this should not be confused with the input of the CAID server, which is restricted to a single sequence).
The execution time of the predictor for each sequence is also recorded. If the predictor generates multiple outputs, each output will be stored in a distinct directory corresponding to the different variations, or 'flavors,' of the predictor. Some softwar e pr esent in the CAID Pr ediction Portal r equires additional inputs, such as the results of PSI-BLAST, HHblits, or SPIDER2, to make their predictions. These additional inputs can be created inside the software's container itself, but they can also be provided in most of the cases as an additional parameter. This ensures that the computation of common inputs is not duplicated, leading to faster and more efficient predictions. We used Singularity containers o ver Dock er ( https:// www. docker. com ) containers because Singularity is designed specifically for high-performance computing environments and has se v eral advantages in the context of computing clusters.
Firstly, Singularity does not r equir e root access, making it easier to deploy and manage in a shared computing environment. Secondly, Singularity is optimized for running scientific workloads, with features such as support for MPI (Message Passing Interface) and GPUs (Graphical Processing Units). Thirdly, Singularity images can be easily hosted on a variety of storage systems, such as local filesystems, netw ork ed file systems, and cloud storage. To make the container size smaller, some large datasets such as UniRef90 ( 10 ), Uniclust30  ( 11 )  or large machine learning models are mounted inside the container at runtime. This approach allows the container to access these datasets only when needed, rather than including them in the container itself. Howe v er, it is important to note that if these mounts are not created, the script that runs the predictor inside the container will fail with an error, since it will not be able to access the r equir ed data.
In order to provide a comparison baseline, we also integrate the AlphaFold-disorder  ( 12 )  method that infers disorder and binding predictions by exploiting AlphaFold predicted structures available in public databases  ( 13 ). As the last step of our standardization process, we opted to create individualized tasks for each predictor that can be conv eniently e x ecuted through the CAID Pr ediction Portal. This implementation grants users a heightened le v el of fle xibility in their selection of methods, allowing them to make informed decisions that best suit their specific needs. Each pr edictor ex ecution is linked to an API call through the portal's front-end interface, while also remaining compatible with stand-alone usage for batch executions. The API is pub licly availab le and lets thir d party services request specific predictions on demand.
). Gi v en the repertoire of different flavors predicted by the various methods, in the CAID Prediction Portal, we divided them into two broad disorder and binding categories. Users interested in specific subcategories or flavors are invited to read the description of the methods as reported on the w e bsite. All methods generate predictions from the protein sequence. Some methods r equir e additional input which is generated by helper methods, e. g. BLAST or HHblits for sequence profiles. In those cases, the additional input is generated once and shared with all dependent methods. The AlphaFold-disorder ( 12 ) method, instead of using the sequence, takes as input the protein structure predicted by AlphaFold. In the CAID Prediction Portal the structure is retrie v ed directly from the AlphaFoldDB ( 13 ) database by searching the UniProtKB accession number.
The server tries to retrieve the accession number by querying the UniProtKB mapping service with the provided sequence encoded with the CRC64 algorithm, and selecting the first result. If the protein sequence is not present in the Nucleic Acids Research, 2023, Vol. 51, Web Server issue W65 UniprotKB, no structure can be downloaded and the predictor will fail to execute. Methods are listed in alphabetical order. (*) The same package can include multiple predictors, each generating multiple outputs. The Type column indicates the type of output and the values in parentheses indicate the predictor name suffixes which correspond to different flavors or different implementa tions. W hen available, the corresponding publication is provided along with the corresponding authors. For new methods, authors are those that submitted the method to CAID.
The predictions can be filtered based on their type (disorder or binding), the threshold for the binary score can be changed from the predictor's default to optimized thresholds as provided by CAID. Optimized thr esholds corr espond to a selection of metrics reported by the CAID challenge. The optimization strategy depends on the type of metric and validation dataset, those available in the CAID Prediction Portal are described in the w e bsite documentation, while we refer to the CAID paper ( 2 ) for a full description of all possible benchmarks. The methods can be sorted based on their performance in CAID, disorder (or binding) content, or alphabetically based on their names. In the feature viewer, a consensus is also computed with the prediction of the availab le predictors, di vided in the two categories, disordered and binding.
Last but not least, the CAID portal will help inform and improve the selection ID predictors available in the Mo-biDB database  ( 47 )  for large-scale annotation of ID in proteins. The latter is the main source of ID data for core data r esour ces such as InterPro  ( 48 )  and UniProtKB  ( 49 ). Any small improvement in ID prediction performance documented in the CAID Portal ther efor e has a large potential knock-on effect in improving ID annotations across the known protein uni v erse. In summary, the CAID Prediction Portal is a valuable r esour ce that can help r esear chers de v elop more accurate and effecti v e methods for predicting intrinsic protein disorder and their binding regions. By enabling continuous assessment and benchmarking of different prediction methods, the portal can help accelerate progress in this important field and benefit the scientific community at large.
ACKNOWLEDGEMENTS This publication is part of a project that has recei v ed funding from the  European Union 's  Horizon 2020 r esear ch and innovation programme  under grant agreements No  778247  and No  823886. This work was supported by  ELIXIR , the r  esear ch infrastructur e  for life-science data. The authors are grateful to members of the  BioComputing UP group  for insightful discussions.
FUNDING The European Union's  Horizon 2020 r esear ch and innovation programme   MSCA-RISE [778 247 ,  823 886 ,  952 334 ];  ELIXIR , the  r esear ch infrastructur e for lifescience  data;  COST Action ML4NGP  [ CA21160 ] is supported by  COST (European Cooperation in Science and Technology)  under the  EU Frame wor k Programme Horizon Europe ;  Italian Ministry of Uni v ersity and Research (MIUR)  - PRIN [2017483NH8 ];  NextGenera -tionEU,  PNRR - ' ELIXIR × NextGenerationIT: Consolidamento dell'Infr astruttur a Italiana per i Dati Omici e la Bioinformatica -ElixirxNextGenIT ' [ IR0000010 ]. Funding for open access charge:  Uni v ersity of Padova.
Simulation and Analysis In the Matlab simulation environment, an intelligent vehicle path tracking control model is constructed. Based on this model, circular road working condition test are conducted to analyze the impact mechanism of different parameters on the lateral tracking accuracy and handling stability evaluation parameters of intelligent vehicles. Furthermore, the critical threshold values of each parameter are analyzed under the conditions of road driving boundaries and system instability. The evaluation parameters for path tracking accuracy are lateral displacement deviation and lateral orientation deviation, while the evaluation parameter for handling stability is lateral acceleration deviation. The simulation conditions include fixed preview distance and variable preview distance.
The calibration factor was estimated using ALS data and imaging spectroscopy acquired simultaneously over flat targets with contrasted spectral signatures. The second step aimed at comparing ALS waveforms acquired with the same sensor over a tropical forest plot to simulated waveforms obtained from DART simulations on the 3-D mockup of the forest plot. Section II describes the experimental sites and setup as well as the ALS sensor and data acquired for the study. Section III details the methods implemented for the calibration of the ALS sensor, its integration into DART, and the evaluation of the resulting simulations. The results are presented in Section IV and discussed in Section V drawing perspectives for future work. Finally, Section VI concludes this article.
The second study area, hereinafter referred to as "forest plot" is included in one of the monitored forest plots of Paracou and was used for the comparison between measured and simulated ALS waveforms. TLS acquired over the forest plot was used to build a 3-D mockup for radiative transfer simulations. The following sections detail the specifications of the ALS sensor under investigation, as well as the data acquired and used for the sensor calibration and for the simulations with DART on both study areas.
A. ALS Sensor Specifications Full-waveform ALS data were acquired with a RIEGL LMS-Q780: the high sampling frequency of the backscattered signal provides the complete temporal distribution of the returned light energy. Sensor specifications are listed in Table  I. These specifications were extracted from the datasheet supplied by the manufacturer  [22] , except for the emitted pulse duration at half maximum (FWHM) and the frequency of the digitization which were retrieved from the lidar data, i. e. , RIEGL SDF files. The FWHM was estimated assuming a Gaussian shaped pulse after verification of this assumption on the pulse records. The assessment of a Gaussian shaped pulse can be found in Appendix C. As the diameter of aperture of the sensor (or its field of view) was not available in the specifications, it was assumed to be equal to the size the beam aperture window given in the datasheet.
B. ALS Data The ALS data was available in RIEGL SDF files, containing raw full waveforms, and in LAS files containing the point clouds extracted from the SDF files with RIEGL's software suite. The LAS files were delivered with point data record format 1 including Extra Bytes  [23] , which gives for each echo, among other variables, the ALS shot time-stamp as well as the x, y, and z coordinates, the intensity and the width of the echo  [24]. The ALS shot pattern (sensor position and direction of each emitted pulse) was obtained from the sensor trajectory combined with the ALS point cloud. The sensor location was linearly interpolated from the trajectory data at the ALS shot time-stamps. The shot direction was computed from the line connecting the sensor location to the echoes of the shot. The point clouds were classified and a 0.
5 m resolution digital terrain model (DTM) was computed from the points classified as ground. For our experiments, the waveform samples needed to be georeferenced. The usual pipeline to convert the raw waveforms to a georeferenced point cloud is first to extract the echoes, second to compute the echoes coordinates in the sensor coordinate system, which are then successively converted to the geocentric coordinate system and projected in the local coordinate system taking into account the geoid. The same could be done directly with the waveform samples (instead of the echoes). However, the registration of the returned waveform blocks to the correct emitted pulse can be complex for multiple pulses in the air  [25] , which was the case for our dataset. Moreover, these steps are very sensitive to an imprecision on the location and orientation of the different instruments involved (GPS, IMU, and sensor).
And as we cannot know in advance which waveform is crossing the scene, the whole SDF file would have to be processed before extracting the waveforms belonging to the area of interest. Therefore, we chose another strategy considering that the point cloud had been produced with the same pipeline already implemented in RIEGL's software suite. The chosen alternative, hereinafter referred to as "SDF Based WaveForms" (SB-WF), consisted in extracting from the RIEGL SDF files the waveform sample blocks based on the time stamps of the echoes belonging to the area of interest. As the echo time stamp was corresponding to the time of emission, we were able to discriminate the waveform blocks belonging to multiple pulses in the air according to the light travel time between the pulse emission and the echo.
C. Experimental Data on the Calibration Plot In order to determine the calibration factor of the ALS sensor, 14 square tarps of various size (1 to 4 m width) with different reflective properties at the laser wavelength were laid out on the ground of the calibration plot. The targets were manually delineated by photointerpretation of the RGB orthophoto in order to georeference them. The reflectance at the laser wavelength of each target was extracted from imaging spectroscopy data acquired simultaneously to ALS data. Imaging spectroscopy was operated at a flying altitude of 450 m AGL leading to a spatial resolution of 0. 8 m. Targets 6 and 8 (1 m width) were excluded from the study as the spatial resolution of imaging spectroscopy could not guarantee pure pixels. The central pixel was selected from each remaining target in order to avoid mixed pixels at the border of the targets.
D. Experimental Data on the Forest Plot The forest plot corresponds to an 80 × 80 m square subset of Plot P9 of the INRAE plot network monitored in Paracou. This undisturbed control plot was used to compare ALS data simulated with DART to real ALS data for a complex natural forest. The acquisitions covering the forest plot were performed with a flying altitude of 900 m and a laser power set to 12% of the full power. We selected one ALS flightline with minimum scan angle (lidar shots close to nadir) over the forest plot in order to perform the comparison. Both the structure and the optical properties of the forest elements on the site were measured to build a 3-D mockup of the forest. TLS data of the forest plot were acquired with a RIEGL VZ400 system to derive finely resolved forest structure.
Scan positions were set in a regular 10 m grid. Capturing a complete sample of the scene at each location required two scans (upright and tilted by 90°), owing to the 100°vertical field of view of the scanner. Therefore, 162 scans were collected from 81 locations. The angular resolution between sequentially fired pulses was set to 0. 04°, resulting in approximately 22. 4 million emitted pulses per scan (i. e. , 3. 6 billion for the entire plot). Up to five targets could be resolved per pulse, with a nominal ranging accuracy of 5 mm. The laser is characterized by a beam divergence of less than 0. 3 mrad, and the diameter of the beam at emission is 7 mm (corresponding to a beam diameter of 22 mm at a range of 50 m). The pulse repetition rate was set to 300 kHz. All scans were co-registered using RiScanPro software.
The position of the corners of the plots was registered using a total station and these corners were used to transform the scans of the plot to the UTM 22N coordinate system used for the ALS data analysis. Leaf optical properties (reflectance and transmittance), as well as bark reflectance were measured with an ASD FieldSpec-3 spectroradiometer equipped with a LiCor 1800-12 integrating sphere. Leaves were collected by climbers at several heights in the crown and for several species.
A. Calibration Equation (  2 ) was converted into (3) in order to determine K as the solution of a linear regression problem between the normalized received energy E N and the reflectance ρ: E N = E DN • 4R 2 E T • cos (α) = K • ρ. ( 3 ) For an opaque target of reflectance ρ and for a footprint fully contained within the target limits, E DN is obtained from the following: σ = FWHM 2 2ln (2) (4) E DN = I DN • σ • √ 2π (5) with I DN being the peak value of a Gaussian shaped return characterized by a full width at half maximum FWHM. I DN and FWHM, correspond to the intensity and the pulsewidth values delivered by RIEGL software during point cloud extraction  [24]. The E DN values used in the linear regression were computed for each target and each flight line as the average values of all single lidar returns for which the footprint was fully included within the target limits.
3) Forest Plot: a) Mockup construction: The conversion of TLS point clouds into a 3-D vegetation mockup of the forest plot was performed as follows  [36]. 1) Filter mixed points in original point cloud based on deviation value  [24]. 2) Classify leaf and wood points from the filtered TLS point cloud using a random forest model based on local geometric and reflectance indices (the balanced accuracy, evaluated for a manually segmented small scene of ∼10 million points, was found to be 0. 94). 3) Reintegrate mixed points and assign leaf or wood class label to mixed points based on the label of the nearest neighbor in the filtered point cloud. 4) Reconstruct the 3-D meshes of the wood elements (trunks and large branches) from the wood points with the Simpletree method implemented in the Computree software  [37].
8). 2) Trunk meshes were defined with a deciduous bark reflectance measured in the field which was 0. 42 at 1064 nm. 3) The ground optical property was chosen after evaluating the reflectance of single ALS ground returns on the forest plot, which showed an average reflectance of 0. 42 (and 0. 07 of standard deviation) at a wavelength of 1064 nm. b) Simulation/reality comparison: For complex environments, comparing real and simulated waveforms instead of echoes appears to be most relevant. First, discrepancies between real and simulated echoes could arise as the exact approach used by the data provider for echo extraction is undocumented. Second, the 3-D representation (1) and the optical properties (2) of the canopy of the forest plot cannot be considered to be exactly the same as those of the real forest 1) The nonwoody vegetation is represented by voxels of 0.
Indeed, Fig. 8  shows a linear regression between K and scan angle. Although the relationship between echoes peak intensity and scan angle was evidenced for the whole ALS dataset (not shown here), such a relationship was unexpected and we did not manage to formulate any solid physical justification for this influence except a possible issue with the sensor or the sensor set-up on the airplane (which could not be checked). In order to reduce the possible impact of the scan angle on the rest of the experiments, the scan used for the simulations over the forest plot was selected based on a criterion of minimum scan angle in that area. When accounting for the effect of scan angle, the coefficient of variation of K between flightlines was reduced to 7. 5%, i. e. , 40% of the original variation, although 20% deviation still remained for the farthest values from the regression line as shown in Fig.
At the sensor level, Appendices D and E show that work remains to be done in order to understand, disentangle, and model the different sources of signal imperfections in order increase simulation realism at small scale. We did not compare measured and simulated point clouds in this study because it raised additional questions on the processing chain of the ALS data, such as the methods implemented in RIEGL software for waveforms decomposition. Still, Fig. 9  highlights what can be achieved after a standard Gaussian decomposition of the forest plot simulation. It illustrates the level of consistency that could be expected with such a simulation as well as some of the differences noticed on the waveform comparison (e. g. , the higher density of echoes in the simulated point cloud, particularly visible at the ground level).
Indeed, simulations could be used to better analyze, understand and model impacts of acquisition conditions, e. g. , pulse density  [54] ,  [55] ,  [56]  or scan angles  [57]. They could also provide large datasets and facilitate the set-up of data augmentation strategies to implement deep learning approaches  [13]  in a field of application for which acquiring large data sets with synchronous field measurements and ALS acquisitions is challenging. The accuracy of the DART lidar model at 20 m scale is also interesting for spaceborne lidar as it corresponds to the ground footprint diameter of actual (and under development) sensors. Simulating large footprint waveforms for spatial missions (e. g. , GEDI-NASA  [58] , MOLI-JAXA  [59] , LEAF-CNES  [60] ,  [61] ) can be envisioned in several ways with the DART model.