dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the training program induced at the same time a slower muscle phenotype for strong and fast movements. With this in mind, further studies are required that use more advanced exercise programs such as eccentric, concentric, and combined modalities to develop a relevant training protocol of appropriate content and intensity. Thus, the aim of this study was to assess the effects of 6 weeks of eccentric training, plyometric training, or a combination of these two modalities, on measures of dynamic stability (Y-BAL), psychological readiness to return to sport (RTS) [LKS and return to sport index (RSI)], and leg symmetry index (LSI) for the single leg hop test in the post-ACL-surgical rehabilitation period of elite female athletes. Based on a relevant literature data  (Lindstrom et al. , 2013; Kinikli et al.
LSI for the Single Leg Hop Tests Data from unilateral lower limb hop tests were collected and performed as described by  Noyes et al. (1991) , including one jump with and without hands, triple jump, triple cross jumps for distance, and 6 m for time  (Gustavsson et al. , 2006). For the single one leg hop test for distance, the aim is to jump as far as possible on a single leg, without losing balance and landing firmly. The participant stands on the leg to be tested, jumps horizontally at maximal effort, and lands on the same limb, without touching the ground with hands and without tripping. The distance jumped was measured at the level of the big toe and recorded to the nearest centimeter from a standard tape measure that was permanently attached to the floor. With the triple jump test, the patient stood on one leg, performed three maximal and consecutive horizontal jumps, and landed on the same foot with the same procedure as performed during the single jump.
Statistical Analyses Data are presented as means and standard deviations (SD). Data normality was assessed and confirmed using the Shapiro-Wilk test. The data were then analyzed using a 4 (all groups) by 2 (time: pre, post) analysis of variance (ANOVA) for repeated measures. Where the assumption of sphericity was violated, Greenhouse-Geisser correction was used to interpret the results. If group × time interactions turned out to be significant, a simple contrast analysis was used. Additionally, effect sizes (ES) were determined from ANOVA output by converting partial etasquared to Cohen's d. Moreover, within-group ES were computed using the following equation: ES = (mean post-mean pre)/SD. In accordance with  Hopkins (2002) , ES were considered to be either "trivial" (<0. 2), "small" (>0. 2-0.
CMR Analysis All CMR images were analyzed by two experienced practitioners using Syngo. via software. The ventricular volume and function were measured for the left ventricle (LV) using standard techniques. Short and long axis cine-sequences images were used for the evaluation of LV function, LV volume and LV mass: the endocardial and epicardial borders were manually drawn on the end-diastolic and end-systolic short-axis. LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), ejection fraction (EF) and LV mass were determined using these outlines. In our study, the distribution and location of LGE in the myocardium were used to diagnose myocarditis (typical non-ischemic LGE affecting the subepicardial and mid areas regions). The extent of myocardial involvement was quantified using a simplified visual quantitative score (SQS) of LGE  [10]  with the 17-segment LV division of the American Heart Association, which has already been employed in several studies about myocarditis.
Statistical Analysis Statistical analyses were performed using SPSS version 12. 0. 1 (Statistical Package for the Social Sciences, IBM Inc, Armonk, NY, USA). Dichotomous variables were expressed as n (%) and continuous variables as median [interquartile range]. A Kolmogorov- The extent of myocardial involvement was quantified using a simplified visual quantitative score (SQS) of LGE  [10]  with the 17-segment LV division of the American Heart Association, which has already been employed in several studies about myocarditis. Segments with LGE were scored according to the number of quartiles involved (1-25%, 26-50%, 51-75% and 76 to 100%). The CMR results were finally expressed as the percentage of LV myocardium involved calculated by adding the 17 segment scores (LGE extent = n% VG = (addition of 17 segment scores/68) × 100).
Road Level Localization (RLL) The ubiquity of positioning devices on the vehicles allows the drivers to know the vehicle's position in the world. However, this estimation is very noisy due to the inherent inaccuracy of these positioning devices. To address this problem, a correcting procedure is required, that matches the vehicle position with a road network coming from a map. This technique is called Map-Matching. As stated by Quddus et al. [6] , Map-Matching not only enables the physical location of the vehicle to be identified, but also improves the positioning accuracy if good spatial road network data are available. This means that Road Level Localization knowledge is determined by Map-Matching algorithm. The RLL is also a prerequisite component of several applications. An exhaustive list of applications is available in  [7].
The third method is an upgrade of the second one, taking into consideration the topology of the road network. The last one is the curve-to-curve method as presented before. The experiment was conducted on four routes that were traveled in the town of Mercer County in New Jersey. None of these routes involved highways or arterials. The results have been summarized and reported in Table  1. The matching accuracy correspond to the proportion of correctly matched samples over the whole dataset. As expected, methods taking into account more context, e. g. , the heading or the route contiguity, lead to better results. [10]. The accuracy represents the percentage of correctly matched samples. Each interval depicts the worst-to-best performance range.
Using the same paradigm, Yang et al. [31]  characterize a CRF model for Map-Matching. To verify the effectiveness of the model, the authors performed the Map-Matching on a dataset from Shanghai taxis. Even if the overall accuracy reached was significant, the CRF used considered only first-order dependencies between states. Therefore, the CRF share the same inability as the HMMs to take into account contextual information. In addition, a learning procedure is required for the CRF to model the interactions between these states, which makes the CRF easy to utilize but heavy to structure.
Conclusion of Road Level Localization In the light of the investigated literature, we discuss what are the main requirements that need to be necessarily addressed by a Map-Matching algorithm. Despite more than twenty years of development on Map-Matching, there is not yet a solution that deals with all scenarios. To the best of our knowledge, there exists no consensus on how Map-Matching methods should be rated. Indeed, the majority of the authors utilize their own dataset. Nevertheless, according to the literature reviews  [8, 9] , the majority of the Map-Matching algorithm reaches an accuracy of around 95%. Accordingly, the accuracy of the Map-Matching will not be discussed in details. Indeed, the major bottleneck in Map-Matching algorithms development is the absence of a unanimous agreement on one publicly accepted dataset.
The network parameters can be learned via a training dataset or using a learning transfer technique from trained networks. These approaches reach significant accuracy in several computer science domains. However, most networks are trained and validated on one dataset, which makes the network less generalized to other datasets. Moreover, as claimed by Janai et al. [48]  neural network-based approaches are often hard to interpret as they present themselves as "black boxes" to the user which does not reveal why a certain error has occurred. The survey presented by Hillel et al. [49]  focused on the lane marking detection. The authors presented a review of ongoing research on road and lane detection, and covers a large part of the lane marking techniques used at that time. In addition, the authors presented the techniques without differentiating on the sensor used.
The algorithm was validated on several data collected on daytime and nighttime, proving that the classical machine learning methods can be useful for lane detection marking. Going back in time, the Convolutional Neural Network (CNN) gained notorious popularity in 2012 when Krizhevsky et al. [94]  proposed AlexNet and won the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). Following the success of CNN methods in computer science, researchers considered its uses to tackle lane detection. In that regard, Huval et al. [95]  were the first to use deep learning technique to identify pixel locations of the single lane on highways. Their model is based on OverFeat, a CNN developed by the team of Sermanet et al. [96]. They trained their CNN on a private collected dataset on a highway in San Francisco (USA).
Unlike most of the works, the network detected lanes based on images coming from two laterally-mounted cameras looking downward onto the lane markers. Despite the good results, the network was not widely adopted due to the fact that the network was trained on a private database. Reproducibility was the watchword on the lane detection challenge which was held in CVPR'17 and in which the Tusimple database was released. The winner of the challenge was Pan et al. [99]  with the Spatial Convolutional Neural Network, a deep learning technique designed for lane marking detection in traffic scene. The structure of this network allows it to exploit the spatial information in the image. On the same topic, Li et al. [100]  proposed a network called Line-CNN. The network had slightly better results than the Spatial Convolutional Neural Network.
The authors adapted the strategy of the regional proposal network, which exists in Yolo  [101]  for instance, to the lane marking detection. They also trained their network with an additional dataset which was not publicly released. A succession of neural networks has been released after that. However, as pointed out by authors in  [102] , many of the published works on deep learning for lane marking detection do not share their code, thus hindering the comparison. The detection of lane markings using deep learning is an ongoing research topic, with multiple networks released every year dealing with the subject. The majority of these algorithms are benchmarked. Currently, the Tusimple benchmark, of which an excerpt is shown in Table  4 , is saturated with high values in accuracy and F1 score.
This can be explained by the fact that the dataset is not complex and the metrics are permissive  [102]. In that regard, a database called CULane was released. The objective of this database is to offer a more complex and larger public dataset for lane detection. Considering the abundance of literature on the subject, tracking every deep learning algorithm proposed for lane detection is beyond the scope of this survey. However, a database named paperswithcode https://paperswithcode. com/ (accessed on 1 November 2021) reviews all the lane marking detection methods that has been benchmarked on either Tusimple or CULane. In Tables  4  and 5 , we provide an overview of the five best performing methods with available code on each benchmark, at the time of writing of this manuscript. Compared to the available tables on paperswithcode, we added whether extra training data were required in the CULane benchmark, and added the reference  [103].
Regardless of the method used, a model is required to fit the detected features to a predefined road model. As a consequence, the generalization of these methods is complicated and challenging for highly complex road scenarios. As claimed by Hillel et al. [49] , the model-based methods does perform well in the majority of road scenarios. On the other side, the monolithic learning approach based on Neural Networks reaches a better accuracy for the detection of lane marking as the majority of benchmarks' leaders are deep learning methods, as shown in Tables  4  and 5. Besides, they perform well when a model cannot be formalized or is not available. The learning approach requires a learning phase that is performed on an annotated dataset. The procedure is performed offline, preliminary to the deployment of the network.
However, in real-world applications, data is limited in quantity and is usually gathered for a specific task and for a specific configuration (e. g. , same city, same camera). Therefore, a change between the dataset and the validation test leads to a decline in the accuracy obtained. In addition, the learning procedure is time-consuming, and the output can not be predicted in advance. The major drawback of the methods based on this paradigm is that the network-based algorithm is hard to interpret as it is represented as "black boxes" to the user, which does not reveal why a certain error has occurred  [48]. In the next section, we will discuss the last component of the algorithm localization, which is the Lane-Level Localization (LLL). A review of the literature is given, presenting the works and the techniques used to this end.
The method relies on the detection of lane markings in the image frame. The detected lane marking was matched using the GPS trajectory with the map database that contains the center-line of each lane. The map matching method was based on an Iterative Closest Point (ICP) based rigid map. The results showed an improvement in the accuracy in terms of localization. Indeed, the average error obtained by the state-of-the-art devices and GPS was 2. 340 m, and this error was reduced to 0. 475 m. In the same manner, Ghallabi et al. [121]  presented an approach in which a lanelevel localization is performed using mesoscale map that is composed of many links (lane markings) represented by polylines. The objective of their work is to match a polyline (i. e. , lane marking) detected with an onboard sensor to the corresponding lane markings stored in the map.
Eventually, the experimental results showed promising results in identifying the ego-lane. In the same manner, Ballardini et al. [129]  proposed a LLL method based on a Hidden Markov Model (HMM) to filter the outcome of a marking-lane detector based on stereo images. To do so, the HMM is modeled based on a lane detector, for which some score functions are introduced depending on the reliability of the detector. Results on real datasets show very good results. Nevertheless, lane-changing situations have not been addressed. In addition, the probabilistic HMM calculation and formalization were not explicitly defined, leading to a non-intuitive definition for the emission and transition probabilities. Kasmi et al. [130]  proposed an improvement of the latter work by defining a formalization of the HMM that avoids empirical definitions and thus leads to a better understanding of the system.
To do so, the methods take advantage of features extraction from the sensors data and from the adjacent vehicles. Furthermore, they have the benefit of not using maps that are costly to produce. In our opinion, several research directions are worth exploring. First, the use HD maps is appealing because of the inherent resulting precision of localization. However, these maps are not easy to build or maintain. Indeed, they require high computational power and considerable amounts of storage, while their quality heavily depends on their update frequency. Thus, researches about collaborative construction and update of such maps can lead to a decreased cost as well as a better coverage around the world  [132]. Second, deep learning techniques also prove themselves useful, despite a lack of guaranties and their appetite for expansive databases.
Funding: This work was sponsored by a public grant overseen by the  French National Research Agency as part of the "Investissements d'Avenir"  through the  IMobS3 Laboratory of Excellence  ( ANR-10-LABX-0016 ), the  IDEX-ISITE initiative CAP 20-25  ( ANR-16-IDEX-0001 ) and the  RobotEx Equipment of Excellence  ( ANR-10-EQPX-0044 ).
, 2012). Contrary to females, spermatogenesis in male gammarids is not related to the molting cycle, and morphological parameters are not available to depict accurately the organisms at different spermatogenesis stages. In this study, all the male organisms sampled were in amplexus to ensure they were at similar spermatogenesis stage (mature). Female gammarids were collected at the beginning of intermolt stage (C1) and at premolt stage (D1) to investigate lipid alterations related to oocyte maturation. By shotgun lipidomics, more than 200 molecular lipid species were quantified, corresponding to 11 major lipid classes in wild male and female adult gammarids -triacylglycerols (TAGs), diacylglycerols, phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), ether lipids (PE-O and PC-O), phosphatidylinositols (PIs), lysophosphatidylcholines (LPCs), lysophosphatidylethanolamines (LPEs), sphingomyelins (SMs), and cholesterol (Figure  1A  and Data S1).
For ions generated in negative ion mode, FA 18:1 shared similar distribution as the PC head group. The ion at m/z 297. 2 was found in both the lumen and epithelium of the HP with higher abundance in the lumen. The summed ion image of the ions at m/z 588. 5-618. 5 and its overlay with FA 18:1 and m/z 297. 2 confirm that these ion species are localized principally in the epithelium of the HP and intestine (Figure  5B ). To characterize these unknown ion species, HP tissues from 10 male gammarids were pooled and then extracted according to a modified Folch lipid extraction procedure. Full scan mode and MS/MS on a   S1 ). Based on these accurate MS and MS/MS data, various databases including METLIN  (Smith, et al. , 2005)  and LIPID MAPS  (Fahy, et al. , 2007)  were interrogated without getting any possible matches.
Shotgun lipidomics experiment Shotgun lipidomics analyses were performed on a Q Exactive instrument (Thermo Fischer Scientific, Bremen, Germany) equipped with a robotic nanoflow ion source TriVersa NanoMate (Advion BioSciences, Ithaca, NY, USA) using nanoelectrospray chips with a diameter of 4. 1 µm. The ion source was controlled by the Chipsoft 8. 3. 1 software (Advion BioSciences). Ionization voltage was + 0. 96 kV in positive and -0. 96 kV in negative mode. Backpressure was set at 1. 25 psi in both modes by polarity switching according to Schuhmann, et al. (Schuhmann, et al. , 2012). The temperature of the ion transfer capillary was set to 200°C and S-lens RF level 50%. FTMS spectra were acquired within the range of m/z 400-1000 from 0 min to 1. 5 min in positive and then within the range of m/z 350-1000 from 4.
2 min to 5. 7 min in negative mode at a mass resolution of 140,000 (at m/z 200), automated gain control (AGC) of 3 × 10 6 and with a maximal injection time of 3000 ms. Free cholesterol was quantified by parallel reaction monitoring FT MS/MS within runtime 1. 51 to 4. 0 min in positive ion mode. For FT MS/MS (PRM method), the micro scans were set to 1, isolation window to 1 Da, stepped normalized collision energy to 15, 25, 35, AGC to 2 × 10 4 and maximum injection time to 650 ms. All acquired data was filtered by PeakStrainer that can be found in gitlab https://git. mpicbg. de/labShevchenko/PeakStrainer/wikis/About (Schuhmann, et al. , 2017). Lipids were identified by LipidXplorer 1. 2. 7 software  (Herzog, et al. , 2012).
Ion images were generated from areas of 500 µm × 500 µm divided by 256 × 256 pixels. The total ion dose applied on each area is ~ 5×10 11 ions/cm 2. Data processing was performed using SurfaceLab 7 software (IONTOF GmbH, Münster, Germany). Mass spectra were internally calibrated using small fragments commonly observed in SIMS spectra such as CH + , CH2 + , CH3 + , C2H3 + , C2H5 + in positive ion mode and CH -, CH2 -, C3 -, C4 -, C4H -in negative ion mode. Improvement of mass accuracy was obtained by adding characteristic ion peaks of Vitamin E (m/z 429. 373, C29H49O2 -and m/z 430. 381, C29H50O2 +• ) to the mass calibration list.
ACKNOWLEDGMENTS This work was supported by the French National Research Agency (ANR) (young investigator grant, ANR-18-CE34-0008 PLAN-TOX), the Chemistry Institute of Lyon (young investigator inter-laboratory cooperation grant,ICL-2017 DOSAGE), the ISA research funds grant, and also the French GDR ''Aquatic Ecotoxicology'' framework which aims at fostering stimulating discussions and collaborations for more integrative approaches. MALDI-TOF instrument at CNRS-ICSN was founded by the Region Ile-de-France (DIM Analytics). We would like to thank Elodie Chauvet, Yves de Puydt from Tescan Analytics (Fuveau, France) for the access to TOF-SIMS instrument and the help with the experiments. We thank Jean-Valery Guillaubez for his technical assistance on the lipid MS analysis of the hepatopancreas sample.
g. , time of day, season, or location). A collection or sequence of linked data elements that are inconsistent with the rest of the dataset is referred to as a collective or group anomaly. Individual data elements in collective anomalies may not be anomalous on their own, but when joined with additional elements, they constitute a group anomaly. Anomalous conditions in a farm environment were detected in  [4]  by the application of linear regression to sensor data (soil temperature, humidity, and electrical conductivity, light intensity, and air temperature and humidity). Anomalous data may indicate that the agricultural environment is not conducive for crop growth. Slopes of the regression line were used to determine the trends of each sensor datum over time. The outlier detection threshold was set using the interquartile range (IQR) technique, which was used to calculate the upper and lower bounds, such that any slopes exceeding the set thresholds were classified as anomalies.
The study forecast multiple degrees of hazard for tea tree frost catastrophes, so that producers may both detect frost incidence and nonoccurrence using the model, and respond to varying levels of hazard. In  [7] , two machine-learning models, autoregressive integrated moving average model (ARIMA) and long short-term memory (LSTM), were used to find anomalies in time-series data. The temporal linkages between digital farm sensor data were considered in the development of a temporal anomaly detection technique. With the goal of finding abnormal data readings, LSTM and ARIMA models were evaluated on actual data gathered from deployed agricultural sensors. Employing LSTM improved anomaly detection prediction while also necessitating additional training time. LSTM and ARIMA approaches consider important training datasets and time for each application; therefore, they are not always usable in the case of spatial multivariate data streams with limited allowed computational power.
Precision agriculture grapples with the automated detection of crop plots with aberrant vegetation growth. Detecting crop patches that have significantly different phenological patterns than those of the rest of the crop might help farmers and agricultural cooperatives improve their agricultural practices, disease detection, and fertiliser management. The isolation forest unsupervised outlier detection algorithm was used in  [8]  to detect the most abnormal wheat and rapeseed crop parcels within a growing season using synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites. Heterogeneity problems, growth anomalies, database inaccuracies, and nonagronomic outliers were the four major categories of studied anomalies. Experiments revealed that using both Sentinel-1 and Sentinel-2 characteristics in anomaly detection resulted in the discovery of more late-growth abnormalities and heterogeneous parcels.