dataesr/scientific-paragraphs-categorization huggingface dataset:
In Situ Measurements and Image Processing We implemented in situ measurements to assess the accuracy of UAV-estimated variables. We collected approximately 30 upward photos and approximately 30 downward photos following the two-diagonal sampling pattern in each plot (Figure  3 ). A Nikon D3000 digital camera was held by hand and leveled at 1. 5 m above ground with the camera settings, including aperture priority mode, automatic exposure, automatic focus, ISO 100, and 18-mm focal length, to produce fine JPEG images  (11, 430 ,144 pixels per image). A surveyor walked along the two diagonals beneath the tree canopy and took a photo around every 3 m. The upward-and downward-looking images of each plot were imported into MATLAB 2018a for subsequent processing using the LAB2 method  [17]. The LAB2 method was originally designed for estimating foliage cover in forest understory  [17]  and hereafter was used to estimate canopy gap fraction from upward-looking RGB images  [28, 29].
Many independent studies reported that the LAB2 method performed well for proximal sensing images  [14, 25, 30]  with a root-mean-square error (RMSE) of less than 0. 05. It was confirmed that the LAB2 method has an ability to produce reference values of canopy cover and understory foliage cover. The OCC and UVC of the entire sampling plot were the averages of the covers extracted from upward-and downward-looking images, respectively. The OCC and UVC values of each plot were considered to be the reference values (Table  1 ). The upward-and downward-looking images of each plot were imported into MATLAB 2018a for subsequent processing using the LAB2 method  [17]. The LAB2 method was originally designed for estimating foliage cover in forest understory  [17]  and hereafter was used to estimate canopy gap fraction from upward-looking RGB images  [28, 29].
Calculation of OCC Prior to the superpixel segmentation and SfM point cloud generation, all UAV-based RGB images were manually trimmed to remove the take-off and landing photos in accordance with the camera timestamp and visual interpretation. A simple linear iterative clustering (SLIC) algorithm, on the basis of k-means clustering in spectral and spatial domains with each pixel linked to a feature vector, was used to obtain superpixels given its efficient computational performance  [32]. We implemented superpixel segmentation with the MATLAB 2018a built-in SLIC function to segment each UAV-based RGB image into numerous superpixels, where each superpixel consisted of approximately 4000 pixels. These superpixels were not categorized into any class, such as overstory or understory classes. In addition to the usage in superpixel segmentation, the UAV-based RGB images of each plot and the correspondingly tagged geographic information were used to generate a 3D forest point cloud by using the PhotoScan Pro v1.
4. 5 software (Agisoft LLC, St. Petersburg, Russia). PhotoScan offers a completely automated SfM workflow, including feature identification, tie-point matching, and bundle adjustment. PhotoScan was firstly used to perform image alignment to detect and match points. The alignment also estimated internal and external camera orientation parameters. A bundle adjustment algorithm was used to estimate and optimize the 3D location of features corresponding to the internal and external parameters of the camera. Then, PhotoScan was used to build dense point clouds. Given that the height information in the 3D point cloud can depict the overstory and understory classes well, we classified the overstory and understory points using a height threshold of 2 m. After the elimination of the understory point cloud, the overstory point cloud was then used to identify overstory superpixels in the subsequent step.
Hence, we implemented a morphological dilation to mask the overstory superpixels, thereby minimizing overstory pixels in the remaining pixels. A MATLAB 2018a built-in function was used to implement morphological dilation on the binary mask images (Figure  6a ) derived from the classification map of superpixels (Figure  6d ). A 15 × 15 circle-shaped structuring element was applied in dilation, thereby yielding a dilated mask image (Figure  6b ). The dilated overstory areas (light-green areas in Figure  6b ) covered all the superpixels of crowns and crown The 2D projected points within each superpixel were discrete, which induced difficulties in the annotation of overstory superpixels. Hence, we calculated the hull area of the α-shaped concave [33] of the projected points within each superpixel.
Hence, we implemented a morphological dilation to mask the overstory superpixels, thereby minimizing overstory pixels in the remaining pixels. A MATLAB 2018a built-in function was used to implement morphological dilation on the binary mask images (Figure  6a ) derived from the classification map of superpixels (Figure  6d ). A 15 × 15 circle-shaped structuring element was applied in dilation, thereby yielding a dilated mask image (Figure  6b ). The dilated overstory areas (light-green areas in Figure  6b ) covered all the superpixels of crowns and crown borders although they were also mixed with some understory pixels. Correspondingly, the complementary areas of the masked areas constituted the subsets of the entire forest floor areas (Figure  6b ). We masked the dilated overstory pixels and extracted the forest floor pixels in Figure  6c.
OCC Estimation Using CHM CHM is a raster dataset that maps tree height as a continuous surface. Previous studies verified the capability of CHM in retrieving tree-level information, such as tree height, tree density, biomass, and projected crown area  [24, 35]. The plot-and stand-level descriptors (e. g. , OCC) can also be obtained by using the CHM data. The SfM point cloud of each plot was filtered by a cloth-simulation ground filter to separate the ground surface point cloud  [36]. The entire and ground surface point clouds were used to derive digital surface models (DSM) and digital terrain models (DTM), respectively. The DSM was normalized to the height above ground by subtracting DTM elevation from the Z-axis of each pixel projected on the ground. The rasterized CHM of each plot was produced from the normalized DSM with a 0.
Funding: This work was partially funded by the  General Program and Key R&D Program of Natural Science Foundation of China  (grant no. 41871230 ), partially supported by the program (grant no. 04-Y30B01-9001-18/20-3-1 ), and partially supported by the  Open Fund of State Key Laboratory of Remote Sensing Science  (grant no. OFSLRSS201920 ). The  China Scholarship Council  also offered funding for this study (grant no. 201706040156 ).
Structural and Biochemical Features of Connective Tissue All the methods used herein were previously described in detail  [16, 17]. For structural characterization, stained cross-sections were analyzed by image analysis via two programs developed using Visilog 6. 7 Professional Software (Noesis, Gif-Sur-Yvette, France). The binary images obtained with the programs were used to determine the area of the perimysium and endomysium (both expressed as percentage of the total image area). The skeletonized images obtained via the programs were used to determine the total length of the perimysium (expressed in mm × mm -2 of the image). For the muscle fiber study, the labelled images obtained with the programs were used to determine the fiber number per square millimeter. For biochemical characterization, total and insoluble collagen contents were determined according to the method of Woessner  [18]  updated by Listrat et al.
Multispectral Imaging of Beef Muscles MSI data were recorded from meat samples using a VideometerLab2 ® (Videometer A/S, Herlev, Denmark). The device was equipped with a multispectral camera (Scorpion SCOR-20SOM, 1200 × 1200 pixels, Point Gray Research, Inc. , Richmond, British Columbia, Canada) that enabled the recording of 19 images per sample in the VIS NIR region from 405 to 1050 nm  [3, 21]. Calibration of the camera was performed using three plates: a white one for reflectance correction, a dark one for background correction, and a dotted one for pixel position calibration. For each muscle and before image acquisition, the samples were defrosted at 4 • C in their plastic bag, equilibrated at 20 • C in a water bath, and then cut gently to 6 × 5 × 1. 5 cm 3 and wiped with paper towels in order to reduce the effect of reflection due to surface moisture.
The samples were then placed in the dark by lowering the hollow sphere of the MSI system onto the sample support plate. Each muscle was illuminated successively by strobing the LEDs. Finally, a data cube for each muscle was obtained with 700 pixels in the X axis, 575 pixels in the Y axis (together called the image in the rest of this document), and 19 wavelengths in the Z axis. Two data cubes were recorded per muscle by performing an acquisition on both sides of the muscle, representing after the analysis 240 data cubes or 4560 images (i. e. , 240 × 19).
Image Segmentation and Morphological Object Features The image correction and segmentation were performed by using the image processing toolbox for Matlab software R2012b (The MathWorks, Natick, MA, USA). Before image segmentation, the contrast of each image (i. e. , recorded per wavelength) was highlighted individually by using the contrast-limited histogram equalization method  [22]. The image was then processed in order to perform the segmentation that is needed for the extraction of the objects considered as being IMCT. A specific algorithm sequence was developed based on Prewitt edge detection and mathematical morphology, to target objects, which belong to the IMCT. Different processing operations were used in the algorithm in order to obtain the best IMCT parameters extraction, like erosion, dilatation and thresholding (Figure  1a ).
After edge detection, the geometrical characteristics of the image objects considered for IMCT were extracted from the binary images. Eight parameters were calculated: area (1), major axis length (2), minor axis length (3), eccentricity (4), orientation (5), solidity  (6) , and extent  (7)  and perimeter (8). Then, a classification of the objects by size into 10 classes (100, 200, 300, 400, 500, 600, 700, 800, 900 and 1000) between the minimum and maximum of the different object parameters was carried out to produce the characteristic histogram frequency parameter of each sample image. After extraction of the histogram frequency of the objects from each image, 10 new data cubes were obtained, one per histogram frequency class. Each cube presented in one dimension the number of samples (i. e.
Artificial Neural Network Design and Architecture The ANN design has been developed using the neural network toolbox in Matlab. A multilayer feed-forward network was selected while the back-propagation-training algorithm was applied to train the network. Uploading the entire image to the network would require thousands of components of the vector and, consequently, the same number of input neurons in the generated ANN. The learning process of a network with a large number of input neurons (and similar numbers of neurons in the hidden layers) would require a large number of learning examples, which, in turn, would increase the requirements in terms of computer processing capacity  [23] [24] [25] [26]. For this reason, in the present study, instead of directly using the histograms of the extracted morphological objects as input variables in the ANN model, we transformed them into synthetic variables by partial least squares (PLS).
PLS has the advantage of taking into account the correlation between the X and Y matrices, while extracting the latent variables (LVs) from the X matrix. In this way, the LVs directly refer to the given component  [27] , which is unlikely in principal components analysis (PCA). Before performing PLS, the histograms were normalized (area under the curve made equal to 1) and mean-centered. Ten LVs, for each Y parameter to predict (i. e. , biochemical and geometrical features of the structure of the IMCT of beef muscles), were chosen because they gathered more than 90% of X data variability. A feed-forward neural network trained by back-propagation was calculated using the nftool in Matlab. The neurons were organized into four layers: one input layer, two hidden layers, and one output layer. There were as many input neurons as LVs (n = 10) and one output neuron.
Concerning the two hidden layers, different networks were tested by varying the number of neurons from 2 to 8 in each hidden layer and testing each possible combination (8 × 8 = 64). Moreover, different numbers of training epochs (from 0 to 1000) were tested. In the training sequence, the output of the network was compared to known values, and errors were back-propagated to the hidden and input layers to adjust the weights and minimize the root mean squared error step by step using the method of Levenberg Marquardt. The procedure was repeated until the errors between the output and known values were minimized. Before performing PLS-ANN, the initial data set was subdivided into three data tables: a training dataset containing 70% of the observations (n = 168), a validation data set containing 15% of.
Concerning the two hidden layers, different networks were tested by varying the number of neurons from 2 to 8 in each hidden layer and testing each possible combination (8 × 8 = 64). Moreover, different numbers of training epochs (from 0 to 1000) were tested. In the training sequence, the output of the network was compared to known values, and errors were back-propagated to the hidden and input layers to adjust the weights and minimize the root mean squared error step by step using the method of Levenberg Marquardt. The procedure was repeated until the errors between the output and known values were minimized. Before performing PLS-ANN, the initial data set was subdivided into three data tables: a training dataset containing 70% of the observations (n = 168), a validation data set containing 15% of the observations (n = 36), and a testing dataset containing the remaining data (n = 36).
The data division was performed randomly by using the function divider provided in the nftool for Matlab. In order to assess the performance of the models calculated, the determination coefficients for calibration (R 2 C), validation (R 2 V), and prediction (R 2 P) and their corresponding root mean square errors for calibration (RMSEC), validation (RMSEV), and prediction (RMSEP) were calculated. In detail, R 2 indicates the proportion of the variance of the predicted variable (Y) that can be explained by the variance of the independent variable (X), and the values of RMSEC, RMSEV, and RMSEP evaluate the fitting degree of regression during calibration, validation, and prediction. Generally speaking, a robust model should have high values of R 2 C, R 2 V, and R 2 P close to 1, and low values of RMSEC, RMSEV, and RMSEP close to 0.
96. Moreover, the MSI features enabling us to reach these results are the orientations of objects when considering the 900 or 800 histogram frequency. Considering the prediction for IMF, the regression model giving the best ANN model exhibited an R 2 P value of 0. 99 and RMSEP of 0. 10 g × 100 g -1 DM (Table  1 ). Our results were more accurate when compared to those of Ballerini et al. [47] , who also used a segmentation algorithm of muscle from fat developed on the basis of their characteristics in three-dimensional color space. A correlation value of 0. 79 was obtained between the chemical analysis and fat percentage computed by image segmentation. The calibration model constructed by Abouelkaram et al. [48]  (R 2 CV = 0. 86) also presented lower accuracy. This difference can be related to the small number of excitation light sources used to record muscle images and to the low variability in muscle databases previously used.
Indeed, during their studies, the previous authors used only three excitation light sources (white Xenon lamp and UV lights at 320 and 380 nm) on BF muscles coming from six animals. Nonetheless, in another study, El Jabri et al. [10] , using equivalent excitation light sources on a larger sample database, also reported lower accuracy after cross-validation (R 2 CV = 0. 76). The database included only SM muscles. Moreover, the muscles were taken from two breeds, Salers (n = 12) and Holstein (n = 12) cull cows. Du et al. [49]  reported, after cross-validation, an R 2 value of 0. 69, when considering 50 steaks taken from one muscle (Longissimus dorsi) from four breeds, namely, Charolais cross-breed, pure Charolais, AA, and Belgian Blue. The steak images were captured using four fluorescent lamps with plastic light diffusers and a polarizer, and they were saved in three-dimensional RGB (red, green, and blue) color space.
Compared to our results, El Jabri et al. [10]  and Abouelkaram et al. [48]  reported lower accuracy for total collagen: R 2 = 0. 74 and 0. 86, respectively. For the insoluble collagen content, the best predictive model gave an R 2 P value of 0. 99 and an RMSEP of 0. 007 mg OH-pro × g -1 DM. Compared to that for total collagen, the ANN model for insoluble collagen was obtained with a lower number of neurons in the second layer (4 vs. 6) and a lower number of epochs  (6 vs. 44). The best predictive accuracy reported in the present study was for total collagen. When compared to the literature, this can be explained by the same factors aforementioned for the IMF: (i) high database variability by considering three types of animals (LI, BA, and AA) and three muscle types (LT, SM, and BF); (ii) differences in excitation lights (i.
Acknowledgments: The authors express their thanks to the staff of the  Experimental Unit, Herbipôle of INRAE (Theix)  for the animal management and slaughter, and the staff of the Biomarkers team from the  Herbivore Research Unit  for their skilled technical assistance, particularly  Jean-Luc Montel  and  Catherine Jurie  for the collagen content measurement,  Agnès Thomas  for the IMF determination and  Fabienne Picard  for the NIR spectroscopy analyses.
( 7 )Figure 1. Figure 1. 711 Figure 1. (a) Detailed schematic sequence for extraction of the shape parameters of meat marbling for one multispectral image and (b) sequence describing the organization of the histograms of the Figure 1. (a) Detailed schematic sequence for extraction of the shape parameters of meat marbling for one multispectral image and (b) sequence describing the organization of the histograms of the frequencies of the objects extracted from the dataset. ROI: region of interest; LED: Light-emitting diode.
Objective: To study the safety and efficacy of imiquimod in the treatment of infantile hemangioma (IH). Method: Systematic search was conducted in nine electronic databases for selecting relevant articles reporting the safety and efficacy of imiquimod as a therapeutic agent for treatment of IH. Meta-analysis was used to pool the results. Results: Of total 180 records screened, we included 9 studies for this systematic review and meta-analysis. About one-fifth of the patients (20. 9%) have showed clinical resolution with 95% confidence interval (CI) of 11. 8% to 34. 1%. Regarding IH type, superficial type showed the highest rates of both clinical resolution and excellent response rates with 31. 2% (95% CI= 16. 6% to 50. 8%) and 26. 5% (95% CI= 11. 6% to 49. 6%), respectively. A relatively high prevalence of any side effects with 63.
Search strategy and study selection: The study process was conducted following the accepted methodology recommendations of the PRISMA checklist for systematic review and meta-analysis where registration of the protocol is not mandated  [31]. We conducted a systematic electronic database search for suitable studies from inception till 10 th December 2019 in nine databases including Google Scholar, System for Information on Grey Literature in Europe (SIGLE), Scopus, Web of Science (ISI), PubMed, Virtual Health Library (VHL), Clinical trials. gov, metaRegister of Controlled Trials (mRCT) and The WHO International Clinical Trials Registry Platform (ICTRP) databases using the following search term: (imiquimod and infantile hemangioma). We conducted manual search of references in our included papers for collecting missed relevant studies  [32].
We included all relevant original publications reporting saftety and efficacy of treatment of infantile hemangioma with imiquimod. There were no restrictions on study design, country, language or publication date. Papers were excluded if there were one of the following exclusion criteria: i) in vitro or animal studies; ii) data duplication, overlapping or unreliably extracted or incomplete data; iii) abstract only articles, reviews, thesis, books, conference papers or articles without available full texts (conferences, editorials, author response, letters, and comments. Three independent reviewers screened title and abstract for selecting eligible papers. Further full text screening was performed to ensure the inclusion of relevant papers in our systematic review. Any disagreement was done by discussion and consulting the senior member when necessary.
Data extraction : The data extraction form was developed by two authors, using a Microsoft Excel file. Three reviewers independently extracted data from included studies using the excel sheet. Data checking was performed through a fourth reviewer. All the disagreements and discrepancies were resolved by discussion and consultation with a senior member when necessary. Quality assessment : Three independent reviewers evaluated the risk of bias in included studies. The National Institutes of Health (NIH) quality assessment tool was used to assess the quality of each included studies  [33]. Quality assessment of each study was obtained through a scoring system including 14 questions. The criterion was judged as following; a score of 13 to 14 was good, 9 to 12 was fair, and studies scoring below 9 are considered of poor quality for cohort studies  [34].
Experimental protocol The subjects had to walk from 10 different starting positions with 4 different orientations to a goal position in front of a table as represented in Fig. 1. They performed 3 trials on each starting position for each orientation. Thus, 120 trajectories were measured and at least one trial per each participant was analysed. The starting positions were chosen to be representative of common locomotion paths within a range of 0. 6 to 5. 5 m from the goal. Data recording Fifteen infrared cameras (VICON, Oxford, United-Kingdom) sampling at 200 Hz collected the positions of passive markers placed on the body of the subjects. The 4 markers fixed on the pelvis were used to approximate the BCOM position  (Gard et al. , 2004)  and to compute the orientation of the pelvis during gait.
Methods: A pragmatic, multimethod feasibility study was conducted with patients with heart failure (HF), hypertension (HTN), and/or diabetes. Patients were asked to take physiological readings at home via a smartphone-based TM app for 6 months. The recommended frequency of taking readings was dependent on the condition, and adherence data were obtained through the TM system database. Patient questionnaires were administered, and patient interviews were conducted at the end of the study. An inductive analysis was performed, and codes were then mapped to the normalization process theory and Implementation Outcomes constructs by Proctor.