dataesr/scientific-paragraphs-categorization huggingface dataset:
Negative staining electron microscopy We diluted the purified material from size chromatography down to 0. 05 mg/ml concentration to analyze it by conventional Electron Microscopy (EM) using the negative staining method. We obtained EM micrographs showing homogeneous and well-dispersed particles, suggesting that a protein having a single conformation was present in our gel filtration samples (Figure  5A ). Analysis of these micrographs revealed a ring-shaped protein structure with a diameter of about 8 nm that resembles that previously shown for human sortilin (Figure  5B ). However, further attempts to crystallize Tg-NSORT or to analyze it by cryo-electron microscopy failed. Therefore, we used the recent AlphaFold2 program  (Jumper et al. , 2021)  to calculate a model of Tg-NSORT.
However, we were not able to obtain crystals of this parasite protein domain. Alternatively, we used the AlphaFold program for predicting the structure of Tg-NSORT. We observed that the resulting model exhibits a very large and poorly structured insertion when compared to human sortilin (insert 2; see Figure  6C ). We concluded that the presence of this parasite's specific loop hinders crystallization of Tg-NSORT. Our analysis also revealed the possible molecular bases of the interactions between TgSORT and ROP proteins. It indicated that the pro-peptide domain of ROP could bind to the tunnel of TgSORT in a manner similar to neurotensin with human sortilin. The specific motifs of ROP proteins are located in some intrinsically disordered regions (IDR) with all IDR tested binding to the same site of TgSORT.
Funding Financial support for this work was provided by the  Agence Nationale de la Recherche  grants  N°ANR-19-CE44-0006. AH was supported by the  International Campus France  and  African UEMOA  fellowships. This work benefited from the CryoEM platform of I2BC, supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)  [ ANR-10-INSB-05-05 ].
Introduction Studying plant roots is one of the keys to achieving the second Green Revolution  [1]. The study of plant roots requires, among other things, effective characterization and development of comparative methods for root development, architecture, and spatial distribution, including early in the root system's development. Current methods, such as the widely used WinRHIZO TM and WinRHIZO TM Tron software suite (Regent Instruments Inc. , Quebec, Canada) use 2D images of roots to measure individual parameters related to morphology and topology. These standard software packages have also been developed to further make accessible the analysis of individual root traits  [2, 3]. These tools capture information relevant to characterize root spatial distribution (RSD). However, standard software packages typically provide parameters that are only capturing one or a few specific aspects of a root system such as total root length, surface area, branching angle and root order.
Greenhouse setup and data collection Data presented and used in this work is coming from a previous study that has been published  [26]. A summary of the greenhouse setup and data collection of the materials and methods are presented here. Sesame was grown in 64 custom-made rhizoboxes. Each rhizobox was filled with 1550g of inert calcine clay (Turface Athletics, Buffalo Grove, IL, USA), henceforth referred to as soil. A single seed from one of four non-dehiscent sesame cultivars, all provided by Sesaco Inc. (Sesaco32, Sesaco35, Sesaco38 and Sesaco40, referred to as S32, S35, S38 and S40 thereafter), was planted per box. Four soil water content treatments were implemented: 60, 80, 100 and 120% of the soil water holding capacity, corresponding to 688, 918, 1147 and 1376 mL of water per rhizobox, respectively.
Algorithm 1 STARSEED Overall Process Partition each image into B regions 3: Compute feature and centroid location, X features ← f θ (X processed ) for each region to get a signature, P ∈ R B×3 , for each image 4: Compute EMD score and flow matrix between each image signature using Equation 2 to generate pairwise distance matrix, D ∈ R N ×N 5: Project D using MDS  [27]  into d-dimensional feature space to compute embedding, E ∈ R N ×d 6: Compute Calinski-Harabasz (CH) score using E and y 7: return EMD Scores, CH Index, Flow Matrices Fig. 1  Overview of the proposed STARSEED method step by step Image Preprocessing-Step 1 The initial step of the proposed approach was to perform preprocessing of the images to isolate the root pixels from the background. Raw images were manually traced using WinRHIZO TM Tron software to generate the labels for root pixels in each image.
Experiments and results Our dataset consisted of 220 images across four different runs. To select the best bin size of for the experiments, we investigated the impact of coarser (i. e. , small number of spatial bins-large grid) and finer (i. e. , large number of spatial bins-small grid) scales. The number of spatial bins per image varied from 100 to 2000 in steps of 100 for a total of 20 values. Then, we performed quantitative and qualitative analysis for local and global RSD supported by the STARSEED method. For our local analysis, we calculated the CH index using different features to evaluate the effectiveness of these extracted features at capturing RSD differences between different growing conditions and cultivars. For our global analysis, we aggregated each centered root image to create a representative for each treatment to elucidate the RSD spatial differences between these representative images.
Design and sample This is a cross-sectional and cross-cultural study, named InTrack project (https://www. intrackproject. com/). The sample will comprise runners, of both sexes, from different countries. The expected sample size is estimated to be at least 80 participants, in each country (sample size estimated based on the Gpower 3. 1 software, considering: effect size: 0. 35; α err prob: 0. 05; Power: 0. 95; number of predictors: 10). The study was approved by the ethics committee in the in Brazil (Federal University of Sergipe, protocol n˚5. 286. 914), Kenya (National Commission for Science, Technology and Innovation, NACOSTI/P/22/18904), and Portugal (Faculty of Sports, University of Porto-CEFADE 12/2022), and complies with the recommendations for the 7 th Revision of the Declaration of Helsinki.
Data collection procedures The research will occur in a virtual environment, from the participant recruitment to the data collection phases. The dissemination of the research will be carried out through contact with athletics Federations of the countries, higher education institutions, social media, personal social networks of the research team, and through prior contact with sports clubs during the years 2022/2023. After showing interest in participating in the research, a link with the questionnaire will be sent and the consent form. Only after agreeing to participate in the research, by signing the consent form, the participant will have access to the questionnaire to answer. The participant is allowed to abort the process of answering the questionnaire or abstain from answering the questions.
Macro-level information Demographic, economic, and sports financial support data will be obtained from free access web pages and official documents with open and unrestricted access from each country (National Institute of Statistics of the countries; National datasets). The following information will be gathered: a) population size and density; b) HDI; c) gross domestic product; d) per capita income; e) annual sports investment; f) the number of high-performance athletes in the sport's ranking at international (world) level; g) cultural aspects. Cultural information refers to the six dimensions for Hofstede  [43] -power distance, individualism, masculinity, uncertainty avoidance, long-term orientation, and indulgence.
Expected statistical procedures Descriptive information will be presented in the five-number summary in statistics (minimum value, first and third quartile, the median, and maximum values), mean (standard deviation), and frequencies (%). Given the nested structure of the data, for the main question of the InTrack project, the multilevel analysis will be computed. Multilevel analysis, also named hierarchical linear models, linear mixed-effect models, mixed models, or nested data models  [44] , considers the data organization at different levels. The first important information includes the intraclass correlation coefficient, which measures the total variance that can be attributed to group differences  [45]. If we confirm this assumption, two levels will be estimated-level one (runners) and level two (countries) (Fig  2 ).
Overview and function ADAS support the driver in operating a vehicle. Depending on the type, they provide more driving comfort, improve safety, reduce energy consumption or enable a more efficient traffic flow. The systems use sensors to record the driving situation, process the collected information with powerful computers, and provide the driver with visual, acoustic, or haptic feedback. In some cases, they intervene automatically, semi-autonomously, or autonomously in the control and operation of the vehicle as operating the accelerator, brake, signaling, or steering. These can extend all the way to fully autonomous driving. Essential requirements for ADAS are fast processing of data in near realtime and high system reliability  [16, 28].
Sensors for advanced driver assistance systems A growing number of environmental sensors such as radar, camera, ultrasonic, and lidar sensors are enabling the use of ADAS in modern vehicles and the associated functions for autonomous driving. At the same time, each sensor is very limited in its respective scope of application and cannot provide all the information on the vehicle environment required for safety functions. Only combining the data from different sensors (sensor fusion) results in a complete environment model-a fundamental requirement for the reliability and safety of driver assistance systems and autonomous driving  [6]. In the first step, the individual environment sensors are described below, followed by a more detailed discussion of sensor data fusion.
Basic modules of visual simultaneous localization and mapping The basic modules of vSLAM are Initialization, Tracking, and Mapping and are shortly presented in the following: • Initialization: For using vSLAM, the fundamental step is to define a specific coordinate system for camera position estimation and 3D reconstruction in an unknown environment. Therefore, the global coordinate system should be defined first during initialization. A part of the environment is thus reconstructed as an initial map in the global coordinate system  [26]. • Tracking: After the initialization process, tracking and mapping are performed. Tracking involves following the reconstructed map in the image to continuously estimate the camera position of the image to the map. For this purpose, distinctive matches between the captured image and the created map are first determined by feature matching or feature tracking in the image  [26].
Approach of using augmented reality in advanced driver assistance systems Focusing on the camera-based ADAS sensors, the environment of the test area shall be recorded as shown in Fig. 3. The path between the sensor fusion module and the eventual Advanced-Driver-Assistance-Systems-Electronic-Control-Unit (ADAS-ECU), which causes the vehicle to intervene, is to be interrupted and followed by a new path via the Augmented-Reality-Electronic-Control-Unit (AR-ECU). Within the AR-ECU, the recorded data of the environment is augmented by virtual objects such as traffic signs, cyclists, pedestrians, or road markings. The aim here is to achieve a realistic and consistent behavior of the ADAS-ECU compared to test approaches with physical objects. For the final augmentation of the virtual objects into the real sensor image, a detailed 3D model of the test environment must be created first.
Data Management Team (DM) The data management team (DM) is a new agent specifically introduced to support the data-driven PdAM process. DM is responsible for handling the aircraft condition data and generating diagnostics and RUL prognostics. DM first collects the condition monitoring data from aircraft condition monitoring systems (ACMS), the sensors installed on board of the aircraft. Here, DM may also integrate external databases such as weather data, airport data, and/or data shared by other airlines or maintenance organizations  [4]. Data processing and validation are also part of the role of DM. With such data, DM generates diagnostics and RUL prognostics for aircraft systems and structures. In this step, various data-driven algorithms are utilized to generate diagnostics and prognostics depending on the characteristics of the target system, the inspection/monitoring intervals, and the redundancy of the system  [21] [22] [23].
Unidentified Damage due to Incomprehensible Data Presentation In 2016, a helicopter lost its yaw control during landing  [35]. The helicopter has in place the Health and Usage Monitoring System (HUMS), which monitors the condition parameters such as engine vibration, rotor track balance, engine shaft balance, etc (HUMS performs the role of ACMS for aircraft in Figure  1 ). One day before the incident (Day -1), during flight, HUMS recorded vibration data, including a series of exceedences related to the tail rotor pitch change shaft (TRPCS) bearing. In the routine maintenance following this flight, the HUMS data were downloaded and analyzed. During the analysis, an abnormality for the tail rotor gear box bearing was detected, but the exceedence was not identified. During the first flight of the day of the incident (Day 0), the HUMS recorded further exceedence.
However, it was planned to download and analyze the data only after the helicopter returns to the base. During the lift-off of the second flight on Day 0, the helicopter went through an uncommanded yaw. However, this was regarded as the influence of the wind on the helicopter. During landing of the same flight, the helicopter totally lost yaw control and landed expeditiously and heavily. The root cause of the lost yaw control was identified as the damage on the TRPCS caused by the failed bearing. The following two contributing factors were discussed in the investigation report  [35] : Impending failure of the TRPCS bearing was detected by HUMS but was not identified during routine maintenance due to human performance limitations and the design of the HUMS Ground Station Human Machine Interface.
The HUMS Ground Station software in use at the time had a previously-unidentified and undocumented anomaly in the way that data could be viewed by maintenance personnel. The method for viewing data recommended in the manufacturer's user guide was not always used by maintenance personnel. For this incident, we identify the hazards related to the unclear communication (see hazards H 09 , H 11 , H 12 , and H 13 ), and the delayed data/information sharing (see hazard H 18 ). The damage to TRPCS was properly detected by the HUMS before the incident, but this was not identified and resolved by the operator (The operator is performing the role of TG in Figure  1 ) (see hazards H 11 , H 12 , and H 13 ). The first contributing factor was the design of the HUMS Ground Station Human Machine Interface.
Introduction The use of augmented reality (AR) in the learning of scientific concepts is highly emphasized  (Bujak et al. , 2013; Fleck and Audran, 2016; Huang et al. , 2016; Arici and Yilmaz, 2023; Yilmaz, 2023)  especially when it allows access to the invisible, which is important in science learning  (Cuendet et al. , 2013; Di Serio et al. , 2013; Ruiz-Ariza et al. , 2018). Chen et al. (2017)  analyzed the database of social sciences citation index, and found that 55 studies about AR were published between 2011 and 2016, which shows the increasing interest in this field. Another metanalysis based on 68 studies pointed out advantages and challenges of using AR in education  (Akçayır and Akçayır, 2017). Indeed, despite the profusion of AR studies, the scientific results on the students' learning do not fully converge.
To investigate the results and compare the evolution of scores in each group (experimental or control), paired samples Wilcoxon test have been performed. Furthermore, Spearman correlation tests were performed to analyze how mental effort can be related to learning outcome performances (research question 3: How does mental effort during learning tasks may influence scientific learning). The significance level is considered at p = 0. 05 or lower. More specifically, significant results have been interpreted as follows: p < 0. 05, significant; p < 0. 01, strongly significant; p < 0. 001, very strongly significant; and p > 0. 05, not significant. All of the statistical analyses were computed through the use of the R software V. 4. 1. 3 (R Core Team, 2020) using the Rcmdr package V. 2. 8  (Fox and Bouchet-Valat, 2022).
Funding This work was supported by the  Excellence Initiative of Aix-Marseille University  -A*MIDEX, under Grant  IDEX N°AAP-2017-AE-57  and the  French Ministry of Higher Education, Research and Innovation  under the  Doctoral Grant. This work was carried out within the pilot  Centre Ampiric , which is funded by the  French State 's  Future Investment Program  ( PIA3/France Relance ) as part of the " Territories of Educational Innovation " action.
One challenge faced by scientists from the alternative RNA splicing field is to decode the cooperative or antagonistic effects of splicing factors to understand and eventually predict splicing outcomes on a genome-wide scale. In this manuscript, we introduce SplicingLore, an open access database and web resource that help to fill this gap in a straightforward manner. The database contains a collection of RNA-seq-derived lists of alternative exons regulated by a total of 75 different splicing factors. All datasets were processed in a standardized manner, ensuring valid comparisons and correlation analyses. The user can easily retrieve a factor-specific set of differentially included exons from the database, or provide a list of exons and search which splicing factor(s) control(s) their inclusion.
The identification and quantification of splicing changes controlled by a given factor can be monitored by RNA sequencing (RNA-seq) technologies  (9). However, it is more difficult to understand or to predict the cooperative or antagonistic effects of SFs on alternative splicing, especially on a genome-wide scale. To address this question, we present SplicingLore, a database and web resource that contains the lists of alternative exons that are differentially included upon knockdown of 75 different SFs in various human cell lines. This collection is integrated into a website from which the user can easily download a given dataset of SF-regulated exons or submit a single exon or a list of exons to predict their potential regulation by the SFs of the database. As a proof of concept of the utility of our resource, we predicted and experimentally validated a new functional cooperation between RNA helicases DDX17 and DDX5 and the HNRNPC protein.
Database and web interface SplicingLore database was implemented on Linux Ubuntu 22. 04, using a MySQL database (www. mysql. com) and organized in 9 tables. The web interface was developed using the programming languages PHP, CSS and JavaScript. Tools performing statistical tests were driven in R (v4. 2. 0) using R packages Tidyverse (doi. org/10. 21105/joss. 01686) or in Python (v3. 10), using Plotly  (10)  for graphics.
Data collection SplicingLore is a database of 160 public datasets retrieved from GEO omnibus  (11)  and ENCODE consortium  (12) , linked to a user-friendly web interface (Table  S1 ). All datasets were derived from Illumina RNA-seq experiments performed in 21 different human cell lines (293T, 786-O, A498, A673, Endoc-BH1, GM19238, HeLa, HepG2, SH-SY5Y, HMLE, Huh-7, K562, MCF7, MDA-MB-231, MG63, RD, RH30, RH41, RKO, LNCaP, LM2) in which the expression of 75 splicing factors was inhibited by siRNA or shRNA treatment.
Differential exon inclusion analysis For each dataset, we performed a qualitative and quantitative analysis of splicing variations using the FaRLine tool (13) (hg19 genome), comparing the condition in which the expression of the splicing factor was inhibited to the control condition. The following parameters were used to define exons that were differentially included: |ΔPSI| ≥ 10%, adjusted p-Val-≤ 0. 05. The ΔPSI corresponds to the change in exon inclusion (PSI: percent spliced-in) between two conditions. We then retrieved and fed the SplicingLore database with the lists of differentially included cassette exons corresponding to each dataset.
Data collection and analysis To feed the SplingLore database, we searched the GEO omnibus  (10)  and ENCODE  (11)  databases for datasets generated from Illumina RNA seq experiments that all consisted in knocking down a given splicing factor (SF) in a human cell line, by means of a treament with siRNA or shRNA (Figure  1A ). We filtered out the datasets for which the sequencing quality or depth was too low to reach our objective, which was to obtain robust and reliable lists of alternative exons regulated by the tested SF. We eventually obtained a curated list of 160 datasets, corresponding to 75 SFs tested in 21 different cell lines (Figure  1A , Table  S1 ). Three cell lines (HepG2, K562 and 293T) represented more than 75% of the datasets (Figure  1B ), and some SFs were tested in up to 5 cell lines.
The selected datasets were all processed in the same way using FaRLine, a splicing-dedicated pipeline developed earlier in our lab  (13). We thus obtained uniformly formatted lists of alternative splicing events for each dataset. Although FaRLine detects and quantifies variations in alternative 5' and 3' splice sites, as well as multiply spliced exons, we decided to focus only on single cassette exons, which represent the vast majority of regulated splicing events and which are easier to handle when comparing multiple datasets. Note that the annotation of all exons is based on the FasterDB database (https://fasterdb. ens-lyon. fr/faster/home. pl)  (16).
Web interface and graphic visualisation SplicingLore can be queried in two different ways (Figures  1A  and 2A ). The first method allows the user to analyse a list of exons associated with parameters of splicing regulation. The default format requested for this analysis includes the gene symbol and exon number, chromosomal coordinates, as well as values of differential exon inclusion (ΔPSI) and p-value (Method 1, Figure  2A , left window). Gene symbols and exon numbers correspond to gene annotations of the FasterDB database (  16 ), but the critical details to provide here are the chromosomal coordinates. Incorrect gene symbol and/or exon number will not impede the search but instead will issue a warning message and a proposition for correcting the input, based on coordinates (see below).
The query list of exons can be automatically retrieved from one of the projects referenced in the database, upon selection of the dataset of interest from the drop-down menu (Figure  2A , top right window). Alternatively, the user can upload a list of exons of interest to query SplicingLore for their potential regulation by the SFs of the database. As each exon is associated to a ΔPSI value, this first method will allow to look for positive and negative correlations between the input regulation and the regulation by SFs from the database. The second query method is less restrictive as it does not require any parameter of exon inclusion (ΔPSI and p-value), if these values are not available (Method 2, Figure  2A ). In this mode, the user can upload a list of exons that are only identified by their chromosomal coordinates (Figure  2A , bottom right window).