dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS. We thank  A. Buckling ,  A. Russell ,  B. Longdon , the handling editor, and 2 anonymous referees for helpful discussion and/or constructive comments on the manuscript. We thank  M. Staley  for growing and shipping the pathogen isolates,  M. Cooke  for assisting with bird captures, and  A. K. Ziegler  for assisting with the experiment. This research was supported by  Natural Environment Research Council   Standard Grant   NE/M00256X  (to C. B. ).
The DS-MLTL model comprises two VGG-style branches and a multitask loss including a classification loss and a Siamese loss. The Siamese loss leverages the intrinsic structure (similarities) of different images and contains a contrastive constraint and a similar constraint. In our framework, visual features and the multitask loss are learned jointly, and they can benefit from each other. To train the DS-MLTL model effectively, the model transfers knowledge from the large-scale ImageNet dataset to the PF classification task. For model evaluation, an SWE dataset of plantar fascia, which contains 282 images of a PF pattern and 60 images of a healthy pattern, is collected. Experimental results show that the DS-MLTL method achieves favorable accuracy of 85. 09 ± 6. 67% and performs better than human-crafted features extracted from B-mode ultrasound and SWE.
Many image processing methods have been proposed  [17] -  [21]  for radiomics. Most of these methods rely on traditional hand-crafted image features, which may not be robust enough to capture the discriminative features for disease diagnoses. In addition, these methods only learn a linear or shallow classification function, which is not comprehensive enough to represent the mapping from image features to classification results. Compared with these shallow models using hand-crafted features, the deep learning (DL) models can automatically extract robust features in an ''end-to-end'' way and learn effective nonlinear functions to classify disease. Many researchers have shown that DL models are valuable for CT, MR and ultrasound images  [22]. For example, Shi et al. proposed a novel stacked deep polynomial network for tumor classification, which achieved favorable performance with a small ultrasound image dataset  [23].
As shown in Figure  1 , in the training phase, a pair of 2D-SWE images is taken as input to the Siamese network with two identical branches. This network then outputs both classification results and similarity scores of the two images. In the test phase, only the output classification results are used. For training data, compared to datasets in regular computer vision tasks that have millions of training samples, medical image datasets are generally small (e. g. , hundreds/thousands of samples). Therefore, transfer learning provides an alternative for radiomics with small datasets. A simple and effective transfer learning methodology uses a pretrained network (typically on large-scale natural images) for model fine-tuning on new tasks. Here, the model uses the convolutional layers of VGG-16 net  [26]  as a feature extractor and fine-tunes the last 3 fully connected layers with the multitask loss.
To evaluate the performance of the DS-MLTL for PF classification, a doctor-labeled high-quality 2D-SWE image dataset, which contains 282 images of a PF pattern and 60 images of a healthy pattern, is collected. The main contributions of this paper are as follows: • To the best of our knowledge, this is the first prospective study that aims to utilize a DL model for the intelligent diagnosis of PF using 2D-SWE. It is helpful for conducting an effective diagnosis of PF, reducing the rate of misdiagnosis and missed diagnosis, and decreasing the burden of doctors. • Compared with a conventional single classification task with a single diagnostic variable as output (e. g. , disease present or not), the DS-MLTL model adopts a multitask learning strategy to simultaneously perform a classification task and similarity learning task.
B. DATA COLLECTION AND PREPROCESSING In this work, an SWE dataset is obtained by a Mindray R7 scanner with an L14-6 MHz transducer. During an ultrasound session, a B-mode ultrasound scan is first performed, and then 2D-SWE is implemented near the heal of the participants. The thickness of the plantar fascia and corresponding B-mode ultrasound images are obtained, and then the 2D-SWE value and corresponding 2D-SWE images are obtained from each patient. Two doctors with 10 years of ultrasound operating experience are employed as quality controllers for reviewing all 2D-SWE images and excluding unqualified images. Finally, a total of 342 2D-SWE images of plantar-fascia, including 282 PF and 60 healthy images, is collected. However, the 2D-SWE dataset is still too small to achieve good performance by using DL models.
To avoid overfitting, data augmentation is conducted for the training set and test set by a cutoff of 5 or 10 pixels from four directions of the raw images (See Figure  2A ). Next, the images are normalized to a fixed size of 313 * 526, which is the largest height and width in the dataset. These images have three channels (RGB). Twenty healthy individuals and 20 patients with PF were selected randomly as the test set while maintaining the others as the training set. It is noteworthy that In the fully connected layers, the dropout operator is carried out after FC1 and FC2 with a dropout rate of 0. 5, which is not shown in Figure  1. The rectified linear units (RELU)  [27] , as the activation function, is used after FC1 and FC2. The sigmoid activation function is used after FC3 for classification.
A. TRAINING AND TEST SETS The proposed DS-MLTL algorithm is evaluated on the collected 2D-SWE dataset, which contains 282 images of PF pattern and 60 images of healthy pattern. After data augmentation, 720 images (360 images with healthy labels and 360 images with PF labels) are selected as training images and 360 images are selected as test images (180 images with healthy label and 180 images with PF label). See Figure  2  for details. Each training image is paired with other training images with either the same category label or a different category label.
B. IMPLEMENTATION DETAILS The DS-MLTL model is implemented using the public deep learning library Keras. The parameter τ in the Siamese loss is set to 1. 0. The weight decay λ 1 is set to 0. 001. The weights of the three FC layers are initialized with Gaussian filter with a standard deviation of 0. 005 and constant bias of 0. In the training phase, we use 10% of the training data as a validation set. Three-fold cross-validation is employed to select appropriate hyperparameters. The learning rate of the model is set to 1E -5. The early stopping function is used to monitor the training process. The training model stops training when it has not improved after five epochs. In the test phase, we randomly split the dataset three times for model evaluation. The following experiments were run using a PC with an Intel I7-4790 3.
90 in the test phase (Figure  3B ). This verifies the advantages of transfer learning in the small medical dataset. It also shows the effectiveness of the joint learning of visual features and the multitask loss. In fact, our model has better ablility to avoid overfitting. In all test datasets in Table  1 , the augmented images only come from the translation of the original images. To evaluate the performance of these models more comprehensively, we reconstruct a test dataset without data augmentation for model evaluation, which only includes 40 raw images (20 healthy images vs 20 PF images). Table  2  shows the classification results on the test dataset without data augmentation. The DS-MLTL model achieves the best sensitivity, specificity and accuracy of 78. 33±5. 77%, 91. 67 ± 5. 78%, and 85.
2) PERFORMANCE COMPARISON WITH DIFFERENT QUANTITIES OF TRAINING DATA To evaluate the DS-MLTL model, the model is also trained on different size datasets (540 training images vs 540 test images, 900 training images vs 180 test images, 990 training images vs 90 test images). The deep Siamese-based method achieves consistently high performance on these datasets with different sizes, which again demonstrates the effectiveness of the DS-MLTL model (Table  1 ).
More effective information can be obtained through a larger area of the ROI. (2) DL models can automatically extract image features in an ''endto-end'' way, and more effective high-dimensional features can be obtained. (3) As convolutional neural networks have an apparent advantage and wide application in computer vision, it is a promising method for promoting the intelligent diagnosis of medical images. Although traditional deep learning methods can perform ultrasound image classification tasks, many studies have improved the traditional model to achieve better performance. For example, Shi et al. proposed a novel stacked deep polynomial network for tumor classification that achieves favorable performance with a small ultrasound image dataset  [23]. To improve our model performance, we proposed a deep Siamese-based network for PF classification.
Moreover, transfer learning is widely used in medical image processing because medical image datasets are usually small  [29]. Here, the DL model based on TL can effectively improve the performance of feature extraction and classification. It can be observed that the DS-MLTL model achieves better performance than the DS-ML model (Table  1 , Table  2 ). Because there are large-scale weights to be fitted in the DL model, deep learning is highly dependent on the quantity and quality of data. We believe that the performance of the deep Siamese-based model will be further improved as more diverse and high-quality 2D-SWE images of plantar fascia (healthy and PF) are collected in the future.
V. CONCLUSION This paper presents a deep Siamese-based architecture for plantar fascia classification based on 2D-SWE images. This architecture can effectively improve the discriminative ability of the model by adding comparisons of image pairs. The TL method avoids overfitting the training phase in the case of a small dataset. Compared with various features of sono and elastograms (thickness, hypoechogenicity, elasticity value, etc. ), the DS-MLTL model achieves the highest accuracy of 85. 09 ± 6. 67%. The model is also superior to the traditional CNN model. It is valuable and practical for the accurate diagnosis of PF. Motivated by the favorable performance of our framework, we intend to apply this approach to other medical image analysis tasks, such as breast cancer and thyroid nodules.
ACKNOWLEDGMENTS The authors thank all patients and their families for participating in the study. We are very grateful to  Pr Guy Leverger  ( Department of pediatric hematology/oncology, Trousseau Hospital, Paris ) and to  Pr Norbert Vey  ( Department of Hematology-Oncology, Paoli-Calmettes Institute, Marseille ) who had a very active role in the interim analysis as independent experts, to  Lilian Laborde , from the management and analysis center  IPC-PACA, Marseille. This work was supported by the  French national institute of cancer (INCA) , the  French  society of hematopoietic stem cell transplantation (SFGM-TC) and the  French society of childhood cancer (SFCE). We thank all the investigators from different centers who included patients:  Rohrlich PS. , Larosa F. (Pediatric Hematology, Saint Jacques Hospital, Besançon ); Legrand F.
COI sequencing. The retrieved cytochrome oxidase subunit I (COI) partial sequences of the tilapiine species (Table  1 ) varied between 395-631 bp, and matched those in the Barcode of Life Data System (BOLD) and the NCBI GenBank Database. , although nuclear markers clearly indicated the differences between these two species. Double digest RAD library sequencing. In total, 54,643,883 paired-end raw reads were produced from the three ddRAD libraries. After removing low quality sequences, ambiguous barcodes and orphaned paired-end reads, 85. 6% of the raw reads were retained. In total, the Stacks analysis identified 71,806 unique RAD-tags (i. e. , the total number of loci across all species, with overlapping subsets of loci among species) in the de novo-based analysis (DBA) and 28,224 unique RAD-tags in the reference genome-based analysis (RBA) (Fig.
The analysis found species-specific markers from seven species, but none were found for O. andersonii, O. aureus or O. macrochir. However, when the number of SNPs per locus criterion was relaxed to a maximum of five, species-specific markers were identified for all ten species. The species-specific markers from loci having 1-2 SNPs could be genotyped using single SNP assays, while those containing more SNPs would require other methods of analysis, e. g. PCR and sequencing. It would be desirable to have a simple technique to distinguish between the species in the Oreochromis genus that are commonly used in aquaculture. Analysis of the ddRADseq dataset allowed the development of PCR-based assays for several SNP markers that served this purpose. Only twenty-four were screened in this way; larger numbers could be developed, particularly if the requirement was only to compare pairs of species.
One of the SNPs was located in LG1 (marker id_5516A), with the allele A in O. n. niloticus and G in O. n. cancellatus, in the region coding for testis-specific serine/threonine-protein kinase 1-like locus (99% identity: NC_022199). There was an insertion polymorphism (one bp) in the ddRAD locus compared to reference sequences in the NCBI gene bank. Double digest RAD-seq using SbfI and SphI restriction enzymes generated over 1000 SNP markers that were used to produce a phylogeny of the ten species analysed. Principal component analysis of this dataset also clearly separated these ten species. From this dataset, 57 species-specific SNP markers were identified across the set of 10 tilapia species when 1-2 SNPs were allowed per ddRAD locus: larger numbers of species-specific markers were found when more SNPs were allowed per locus, or when the dataset was reduced to lower numbers of species.
Species discrimination analysis of SNP data. Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC) were carried out on the SNP data using R v3. 3. 2  41  and an associated R/adegenet package v1. 4-1  42. PCA creates simplified models of the total variation within the dataset and DAPC identifies clusters of genetically related individuals  43. PCR-based SNP genotyping. SNP assays were designed and manufactured for 24 species-specific SNP markers using KASP (Kompetitive Allele-Specific PCR) genotyping technology by LGC Genomics Ltd. (Supplementary Table  S3 ). In most cases, these were derived from the set of species including O. niloticus, O. mossambicus, O. aureus and O. u. hornorum, but some markers were selected only to distinguish O. mossambicus from O.
Summary of diagnostic allele frequency for 24 putative diagnostic markers identified by ddRAD and tested by KASP genotyping assays in 91 individuals to distinguish among O. niloticus, O. mossambicus, O. aureus and O. u. hornorum. Refer to Supplementary Table S3 for full genotype data. *Frequencies of the putative diagnostic alleles in the target species. Analysis of fixed allele patterns in the ddRADseq dataset also identified three potential subspecies-specific SNP markers in O. niloticus. These markers distinguished the sub-species O. n. niloticus and O. n. cancellatus, based on Egyptian and West African O. n. niloticus, and O. n. cancellatus from three different sources in Ethiopia.
Acknowledgements We are grateful to  Dr Andrew M. Deines  for the O. andersonii and O. macrochir samples and to the  Temasek Life Sciences Laboratory ,  National University of Singapore  for O. mossambicus samples. We thank  Keith Ranson  of the  Tropical Aquarium Facility, Institute of Aquaculture, University of Stirling , for help in rearing fish. This publication is based largely on the PhD thesis of the first author.
The survey was first piloted in our own department. An email was sent to the 370 eligible residents in December 2013, followed by 2 reminders in February and in April 2014. We collected answers until June 2014, and we only included fully answered questionnaires in the study. A questionnaire was considered eligible only when all questions were answered. We could not find any validated questionnaire on the subject in the literature, so we designed our survey taking into account all the co-authors' input. We included binary and open-ended questions, as well as questions with the "other" option in answers in order to elicit alternate responses we could have missed. The final survey we sent (Additional file 1) had three sections. The first had 12 questions that covered demographic and institutional data and the second had 38 questions about the theoretical and practical knowledge of the residents concerning PPH.
limesurvey. com) where the survey was posted. The survey was completed online. Unique identifiers were assigned to each participant's computer, thus ensuring that they could complete the questionnaire only once. Data were collected via Web-link and email and downloaded to a spreadsheet. The primary endpoint of our study was the level of knowledge of the residents for each of the surgical techniques included in the survey. This was achieved by self-assessment and the participants had to reply whether they considered their level of expertise as complete, sufficient, insufficient, or absent, for each of the procedures (questions number 18, 24, 30,36, 42 and 48). Our secondary endpoint was the type of surgery the residents would use as a first, second and third line treatment for PPH (questions 49-51).
Results The response rate was 47. 6% (176/370). Twenty questionnaires were not fully completed and were excluded from the study. One hundred fifty-six questionnaires were thus included in the final analysis. The participants' demographic data are presented in Table  1. The median age of participants was 29 years (range: 28-30). They were equally distributed between PGY4 (77) and PGY5 (79) The mean amount of time spent training in surgical departments was 2. 4 semesters. The mean number of obstetrical calls was 5. 3 per month, and 94% of participants reported having a supervising obstetrician present on site during the calls. Only 14% (22/156) had already covered senior obstetrical calls. 68. 6% of residents (107/156) found surgical treatment of PPH to be "stressful" and 30. 8% (48/ 156) found it "extremely stressful", whereas only one participant considered it not stressful The primary learning tools reported by residents were textbooks (88%), lectures (53%), the internet (41%), specialized medical journals (31%), and multimedia tools (28%).
As a consequence, the omission of higher modes leads in general to more conservative upper limits on the IMBHB merger rate  [68]. In this work, we improve on past studies in two distinct ways. We use numerical relativity (NR) simulations with higher modes to model GW signals from IMBHBs for computing upper limit estimates. Additionally, our combined analysis now includes the matched-filter search Py-CBC  [69, 70]  in addition to GstLAL and cWB. Because of these novelties, we have, in addition to analyzing the O2 data set, reanalyzed the O1 data set and report here combined upper limits for the O1 and O2 observing runs. In this paper, we report upper limits on the merger rate density of 17 targeted (non-precessing) IMBHB sources. Our most stringent upper limit is 0. 20 Gpc -3 yr -1 for equal-mass binaries with component spins aligned with the orbital angular momentum of the system and dimensionless magnitudes χ 1,2 = 0.
8. The rest of this paper is organized as follows. In Sec. II we describe the data set, outline the individual search algorithms that make up the combined search, and report our search's null detection of IMBHBs. In Sec. III we describe the NR simulations that we use to compute upper limits on IMBHB merger rates report these for the case of 17 IMBHB sources. We draw final conclusions in Sec. IV.
IV. CONCLUSIONS We have conducted a search for IMBHBs in the data collected in the two observing runs of the Advanced LIGO and Virgo detectors. This search combined three analysis pipelines: two matched-filter algorithms Gst-LAL and PyCBC and the model-independent algorithm cWB. The PyCBC and cWB analyses use data from the Advanced LIGO detectors, and GstLAL uses data from the Advanced LIGO and Advanced Virgo detectors. No IMBHB detections were made in this search. The loudest candidate event was found with a marginal p-value P = 0. 36 in our combined search. A detailed detector characterization study of this event suggested that it is likely explained by the detector noise. Given the null detection, we place upper limits on the merger rate density for 17 IMBHB systems. For estimation of the rate upper limits, we use NR waveforms provided by the SXS, RIT, and Georgia Tech groups that include higher modes in the gravitational-wave emission.
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory and Advanced LIGO as well as the Science and Tech-nology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium.
Reverse Osmosis System Analysis (ROSA) software developed by Filmtec  (Dow Water and Process Solutions, 2011)  was applied in this study to simulate the proposed filtration design. The effect of membrane choice on power consumption in terms of specific energy consumption (kWh. m -3 ), capital, operation and maintenance (O&M) costs and scale potential was investigated. Interest of the use of NF instead of RO was analyzed by comparing both processes. A process analysis and a comparative economic analysis of capital and operating costs for both technologies were performed. Cost of ozonation was estimated based on experimental results concerning ozonation of real wastewater matrice concentrated by nanofiltration  (Azais et al. , 2016a). Thus, ozone doses are known for this effluent. Finally, impact of the treatment of concentrates by ozonation on the treatment costs was considered.
Ethics and dissemination The study and related consent forms were approved by an institutional review board (CPP SUD-EST 3) on 2 October 2018, under reference number 2018-033 B. The study has been registered in https:// clinicaltrials. gov registry on 23 January 2019, under the number NCT03812471. This study will enable an improved understanding and dissemination of the potential benefits of 3D automated measurements and is a prerequisite for the design of intention to treat randomised studies assessing their impact. trial registration number NCT03812471; Pre-results.