dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantitative Real-Time PCR reverse transcription analysis Briefly, RNA was extracted using Rneasy (Qiagen, Courtaboeuf, France), purified RNAs were checked in integrity and quantified using LabChip (Perkin Elmer, Villebon-sur-Yvette, France). Reverse transcription of mRNA into cDNA was performed using a QuantiTect Reverse Transcription kit (Qiagen, Courtaboeuf, France). Real-time PCR was performed using Sybr green I mastermix and the LightCycler 480 instrument (Roche, Boulogne-Billancourt, France) J o u r n a l P r e -p r o o f according to the manufacturer's optimized procedure. The expression of each target gene (Table  S1 ) was normalized with the average expression of RPL13 as housekeeping gene. Data analysis was completed using the Biosystems 7500 real-time PCR software V 3. 1 and relative quantification was determined by the 2-ΔΔCt method.
Statistical analysis Analysis of variance was performed with GraphPad Prism 8. 0. 0 software. Ordinary One-Way or Two-Way Anova with Dunnett's multiple comparison test versus DMSO UVA1 applied to determine p. value. Data with p<0. 05 were considered statistically significant: p< 0. 05 (*) p<0. 01 (**) or p<0. 001 (***).
Objectives The main goal of the EEO software is to answer all rapid-mapping challenges to the best of its ability. First of all, the chains have to support all the data provided during RM activations, which means handling a wide variety of sensor types, resolutions and data, which are disseminated differently from one satellite product to another. Regarding Sentinel-1/2 data, the download is included in the pipeline, regardless of the size of the area or the time series. There is a special focus on managing thematic products that can be automatized. This can differ from one sensor to another. For example, it is easy to delineate fire areas when SWIR bands are available (Sentinel-2), but becomes trickier when only a classic RGBNIR stack is provided (SPOT6/7). Some themes are also harder to map than others.
• the algorithm (and its developer) can focus on its core tasks (such as extraction), without needing to account for the sensor characteristics (how to load a band, which band corresponds to which band number, etc. ), • the addition of a new sensor is effortless and requires no algorithm modification, • maintenance is simplified and the code quality is significantly improved, • testing is simplified, as the sensor-related parts are tested in the EOReader library. The python open source library EOReader  [15]  has been designed according to these previous principles, and to answer to CEMS-RM activation challenges, which are the multiplicity of sensors and CEMS products that need to be rapidly delivered. The EOReader handles both optical and SAR sensors, as well as loads and stacks spectral and radar bands, cloud bands, Digital Elevation Models (DEMs), and spectral indices in a sensor-agnostic manner.
Of course, the EOReader follows software engineering best practices, such as code documentation, code reviews, python coding standards, and unit testing. The python open source library EOReader  [15]  has been designed according to these previous principles, and to answer to CEMS-RM activation challenges, which are the multiplicity of sensors and CEMS products that need to be rapidly delivered. The EOReader handles both optical and SAR sensors, as well as loads and stacks spectral and radar bands, cloud bands, Digital Elevation Models (DEMs), and spectral indices in a sensoragnostic manner. Of course, the EOReader follows software engineering best practices, such as code documentation, code reviews, python coding standards, and unit testing.
ExtractEO Generic Workflow EEO is a software implementing fully automated, end-to-end satellite data processing chains. Its plugin-oriented design enables easy maintenance (module-by-module) and easy evolution (removing and adding modules shouldn't affect others). Every module should implement core functions that are run in a predefined order by the EEO runner. EEO first splits the work into areas (AOI, Sentinel-2 tiles, etc. ) which are themselves split into the individual constituent satellite products. This workflow is summarized in Figure  2. The design paves the way for parallelized and distributed computing. The chains are configured through a configuration file, enabling the operator to use predefined chains or create/customize chains, in order to fine-tune module configuration according to the monitored area.
So even if the chains are fully automated, the operator can still bring expertise to bear that ensures better results, thanks to fine-tuning. The python open source library EOReader  [15]  has been designed according to these previous principles, and to answer to CEMS-RM activation challenges, which are the multiplicity of sensors and CEMS products that need to be rapidly delivered. The EOReader handles both optical and SAR sensors, as well as loads and stacks spectral and radar bands, cloud bands, Digital Elevation Models (DEMs), and spectral indices in a sensoragnostic manner. Of course, the EOReader follows software engineering best practices, such as code documentation, code reviews, python coding standards, and unit testing.
ExtractEO SaaS EEO has also been deployed as a Software as a Service (SaaS), in order to make it available through a web browser that simplifies the user experience and allows people outside of SERTIT to use it. The home web page is illustrated in the Figure  3. Remote Sens. 2022, 14, x FOR PEER REVIEW 6 of 15 EEO is managed through Gitlab, and was released through Docker to ensure up-todate software that works the same way on every computer.
ExtractEO SaaS EEO has also been deployed as a Software as a Service (SaaS), in order to make it available through a web browser that simplifies the user experience and allows people outside of SERTIT to use it. The home web page is illustrated in the Figure  3. The parameters and the number of sensors managed are reduced. Indeed, the SaaS focuses solely on Sentinel-1 and Sentinel-2 automatic download and processing. The SaaS component of EEO was built to be shared with rapid-mapping partners and, eventually, to be sold to customers through a service subscription. Three chains are available: SAR water, optical water, and fire, based on optical imagery The user can track the processing status, and can download the event delineation masks output directly from the website. The SaaS is a cloud-native solution hosted by the University of Strasbourg's Data Center, where distributed computing has been enabled using open source, state-of-the-art technologies like Docker, Celery, RabbitMQ and PostgreSQL to orchestrate distributed jobs.
Generation of Scenarios Initially, literature search was performed with a selection of documents, including randomized studies, systematic reviews of the literature, meta-analyses and guidelines on perioperative prophylaxis for the prevention of SSI during plastic surgery. The literature search was carried out on the PubMed database, with a choice of articles in English published from 2000 until 2020. The key search terms were: "antimicrobial prophylaxis" OR "antibiotic prophylaxis" AND "neurosurgery" OR "craniotomy" OR "trans-nasal-transphenoidal surgery" OR "head fracture" OR "spinal surgery OR "shunt surgery" OR "neuroendovascular procedures" AND "neonate" OR "newborn" OR "paediatric" OR "pediatric" OR "children" OR "adolescent". Subsequently, using the Patient/Problem/Population-Intervention-Comparison/Control/Comparator-Outcome (PICO) model, a questionnaire was created on perioperative prophylaxis during neurosurgical procedures in neonatal and pediatric patients, which were divided into 7 clinical scenarios.
Two-Round Consensus Process Based on the scenarios, the questionnaire was submitted to experts on the online platform REDCap. REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing the following: (1) an intuitive interface for validated data capture; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; (4) procedures for data integration and interoperability with external sources. Each question included the clinical scenario and possible answers were whether or not SAP was recommended for the scenario and, in case of its recommendation, a list with all the antibiotics available on the EU market so that the expert could select the antibiotics that he/she considered as first choice.
Acknowledgments: We would like to thank the other members of the  Peri-Operative Prophylaxis in Neonatal and Paediatric Age (POP-NeoPed) Study Group  who participated in the Consensus:  Alberto Argentiero ,  Cosimo Neglia ,  Matteo Puntoni ,  Isabella Cremonini ,  Mara Caramia  ( University Hospital of Parma, Parma, Italy ),  Elena Carrara ,  Agnese Corbelli ,  Marcella Gaffuri ,  Elena Gusson ,  Francesca Opri ,  Rosa Longo ,  Michele Piazza ,  Tessari Denis ,  Marcella Sibani ,  Evelina Tacconelli  ( Azienda OspedalieraUniversitaria di Verona, Verona, Italy ),  Luigia Scudeller ,  Federico Pea  ( IRCCS Azienda Ospedaliera-Universitaria di Bologna, Bologna, Italy ),  Guido Castelli Gattinara ,  Andrzej Krizystofiak ,  Andrea Dotta ,  Sergio Picardo ,  Alessandro Inserra ,  Massimiliano Raponi ,  Paolo Rossi ,  Alberto Villani  ( IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy ),  Leonardo Bussolin ,  Carlotta Montagnani  ( Meyer Hospital, University of Florence, Florence, Italy ),  Alessandro Mugelli ,  Andrea Novelli  ( University of Florence, Florence, Italy ),  Carlo Pietrasanta  ( Università degli Studi di Milano, Milan, Italy ),  Claudia Colomba ,  Giovanni Corsello ,  Marcello Cimador  ( University of Palermo, Italy ),  Elisabetta Bignamini  ( Regina Margherita Hospital, Torino, Italy ),  Alfonso Papparella  ( Università Vanvitelli, Napoli, Italy ),  Giangiacomo Nicolini  ( Treviso Hospital, Treviso, Italy ),  Rossella Garra  ( Catholic University, Roma, Italy ),  Laura Marchesini  ( Perugia Hospital, Perugia, Italy ),  Stefania Stefani  ( University of Catania, Catania, Italy ),  Valeria Caldarelli  ( AUSL Reggio Emilia, Reggio Emilia, Italy ),  Gianni Sava , and  Gabriele Stocco  ( University of Trieste, Trieste, Italy ).
Cyclorotor based WECs This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 851885. Andrei Ermakov and John Ringwood are with the Centre for Ocean Energy Research, Maynooth University, Ireland (e-mail: andrei. ermakov@mu. ie, john. ringwood@mu. ie). Alice Marie is with Ecole Centrale de Nantes, France (email: alice. marie@eleves. ec-nantes. fr) have a number of appealing characteristics, including the potential for unidirectional rotation, simplified power takeoff, and reduction of wave loads by adjusting pitch angles, increasing survivability. However, as it has been shown in  [3] -  [6] , the performance of the cyclorotor requires real-time control (especially in panchromatic seas). The reviews of cyclorotor-based WECs  [6] -  [8]  have identified the variation of the rotational rate and hydrofoil pitch angle as the most advanced actuators.
The proposed technique was evaluated on a publicly available medical dataset. The results demonstrate the feasibility and the technical soundness of our method. Overall, our key contributions are summarized as follows: • A new approach to generate vocabulary for text generation and encoding was constructed from medical concepts and image captions. The rest of this paper is organized as follows. First, we introduce the context of our work and briefly summarize state-of-the-art models on medical image captioning in Section 2. Section 3 outlines our approach, which used datasets and evaluation metrics. Then, we discuss the experimental results of our approach in Section 4. Finally, we discuss the outcomes of our approach, conclude the paper, and provide future directions for medical image captioning in Section 5.
The generated captions are often small, grammatically correct, and hard-coded, constraining the variety and flexibility of the outputs  [3]. For instance, Onita et al. [11]  used kernel ridge regression combined with classification to map a given image to words, describing the content from a dictionary; the authors investigated how text and the feature extraction model could influence the captioning performance. They evaluated their proposal on the PadChest dataset (https://bimcv. cipf. es/bimcv-projects/ padchest/, accessed on 2 October 2022).
• Retrieval-based techniques: Are based on the retrieval of images visually similar to the input image from a large dataset, and use their captions to construct a new caption for the input image  [10]. Retrieved captions are either combined to create a completely new caption or the most similar caption is employed as a substitute for the new caption. For instance, Xuwen Wang  [12]  proposed using the Lucene Image Retrieval (LIRE) system to extract similar images of given medical images based on their underlying concepts, which were detected using a multi-label classifier and a topic modeling method. Then, semantic annotated concepts were combined with the body parts of images to cluster them into different groups. The proposed technique could be extended to retrieve the most relevant captions of the inputted image from captions of images of its class.
It employed visual features and retrieved similar textual features to generate the final captions. encoderdecoder model  [1]  medical images diagnosis report IU X-ray + own created dataset Deep-learning-based method: encoderdecoder model with CNN-based multi-label classifier and RNN as decoder  [14]  chest X-ray images medical report IU X-ray dataset Deep-learning-based method: encoderdecoder with attention, show, attend and tell model  [16]  natural images captions Flickr8k, Flickr30k and MS COCO Deep-learning-based method: encoderdecoder with attention, decoder with GRU  [15]  medical images descriptions ImageCLEF 2021 Deep-learning-based method: merge model with a variational auto-encoder  [17]  chest X-ray images medical report IU X-ray dataset Deep-learning-based method: encoderdecoder with SSA-based hyperparameter optimizer  [18]  natural images captions MSCOCO and Flick8K Hybrid method: topic-guided attention mechanism  [19]  lateral and frontal chest X-ray images description IU X-ray dataset Hybrid method: abnormality graph learning with retrieval technique  [20]  lateral and frontal chest X-ray images medical report IU X-ray dataset Hybrid method: method based on a Retrieval-Generation Reinforced Agent  [6]  lateral and frontal chest X-ray images medical report IU X-ray + ChexPert dataset Hybrid method: retrieval-based method combined with generation-based method  [21]  natural images captions MSCOCO.
Visual Feature Encoding The first step of our proposal is visual feature extraction; we employed a pre-trained CNN model. We used the VGG-16 model since it is small, has been trained on the large ImageNet dataset, and performs very well on several other classification tasks. The model is composed of 16 layers, to which we input the medical images and generated a feature vector of size 4096 after removing the last classification layer, as illustrated in Figure  3. The features extracted from this layer were learned by the model while trying to predict the image class and distinguish the visual content of images. Images were pre-processed before fitting them to the VGG-16 model. They were normalized and resized to fit in the encoder and augmented with some traditional image augmentation techniques.
Text Pre-Processing We pre-processed the captions to clean the text and keep only significant words. Specifically, we tokenized each caption, converted all characters to lowercase, removed stop-words, filtered out the punctuation, and calculated the stems of the identified words. Two other words were added to each caption to identify the beginning (<start>) and the end (<end>) of the sentence. This pre-processing process was performed using the NLTK package (https://www. nltk. org, accessed on 2 October 2022). The maximum length of one caption for the whole training set was 50 words, which was used to pad the remaining captions at the end. Further, embeddings were calculated from these captions to capture the semantic meaning of each sentence. Finally, each image was represented with a vector of size 50, encoding the corresponding caption.
Dataset To evaluate our proposed method, we employed the ImageCLEFmed 2021 dataset  [22, 23] , composed of three subsets for training, validation, and testing. It is composed of 2756 medical images where each image is associated with a set of medical concept unique identifiers (CUIs) and a caption consisting of one or several sentences. CSV Files (mapping images to their corresponding CUIs and captions) are provided with the raw images. Multiple image modalities and body parts are considered in this dataset, making it very challenging and more general.
Results are shown in Table  3 , and are also compared to other state-of-the-art techniques on the same dataset. We can see that the model proposed by  [24] , a winner of the ImageCLEF challenge in 2021, gave the best BLEU result with a value of 51. 00%. In Castro et al. [24] , the authors introduced a multi-label classification-based method, where each word in the caption was considered to be a class of the image. The authors then trained their model to predict high-ranking words (i. e. , classes of the image) based on a statistical method and then used them to construct the caption. Our results outperformed those of  [25] , which employed a pattern-based captioning method by combining the medical concepts identified for the image and achieved a BLEU score of 25. 70%. Captions were then created based on the characteristics of captions in the training and validation sets and depended marginally on the outcomes of the model, which used concept identification.
80%, which was not the best one compared to some state-of-the-art methods. However, this value was obtained because we considered 10 medical concepts for each image, which was the maximum number of concepts for images of the used dataset. As can be seen in Figure  11 , regarding the distribution of CUIs per image, most of the medical images only had two CUIs. In contrast, very few images had either one or more than five CUIs. The results, in terms of F_measure, are compared in Table  4. Our results are comparable to the state-of-the-art results on the ImageCLEF 2021 dataset. Again, the best results (50. 50%) were obtained by the same team Schuit et al. [27]  for caption prediction, where a VGG network was used to encode medical concepts. A K_nearest neighbor algorithm was used with perceptual similarity to select concepts that are most likely closest to the given image.
From the left-lower sub-figure, we can see that the image has some writing on the right and some other type of data on the bottom, which influence the feature extraction process. Finally, the right-lower sub-figure shows a foggy image and the features extracted would obviously depend on the visibility of the object in the image that we cannot distinguish easily from this image. We can see that the model was able to identify the objects in the figures but could not create a correct caption, such as in the two right sub-figures. The model succeeded in producing the words 'axial ct demonstrates a rounded mass in the right upper' for the upper sub-figure and 'delayed image, demonstrates tracer activity' for the lower sub-figure. Similarly, the reliability of the generated captions depends on the words constituting the original captions and their frequency in the dataset.
For instance, if the word reoccurs in the dataset, (i. e. , present in many captions from the dataset), the model could easily predict that word, but it would likely fail if the word is less frequent or present only in one caption. We illustrate in Figure  14  some samples of the generated captions where the original captions constitute rare words. Even though the system was able to predict that the left sub-figures are a radiograph of the chest and a knee, respectively, it was not able to predict efficient captions because of the words 'parahilar', 'perobronchial', 'tricompartment', 'patellofemoral', and 'cartilage', which are rare in the dataset. Similarly, the model failed in generating good captions for the right sub-figures because of rare words, such as 'cartilage', 'trochlear', 'groove', and 'hyperintensity', or unknown words, such as 'extramuscular' and 'forefoot', which do not exist in the training captions.
Roughly speaking, the model can predict words that are recurrent and thereby have a higher likelihood to construct the caption rather than a word that does not exist or rarely appears in the training dataset because our model is not well trained on the embeddings. We show in Figure  15  recurring and rare words from the training captions. Words, such as 'emission', 'tomography', and 'computed' are the most recurrent in the training dataset; the model could smoothly identify their embeddings and predict them because it was well trained on them. However, words, such as 'tarsal', 'bolus', and 'micronodule', were crossed by the model only once and, thereby, were not predicted accordingly. Furthermore, some captions include numerical information or punctuation that are semantically important. However, by removing the punctuation, stop words, and some of the characters during pre-processing, we may lose some important information, which in turn affects the meaning and the generation of the new caption.
Conclusions We presented an end-to-end network that integrates medical concepts alongside visual features extracted from original raw images for medical image captioning. The model is composed of different sub-models (a semantic feature encoder and visual feature encoder) trained simultaneously for a better generation of captions. The outputs of a visual encoder were merged with the outputs of a semantic encoder and inputted into the decoder to create captions. The visual encoder is a pre-trained network, the semantic encoder is a multi-label classifier, and the decoder is an LSTM network. A beam search with different indices was employed at the end to allow the LSTM to select better words for the newly created caption. Our model was evaluated on the ImageCLEF 2021 dataset by considering the images, their associated concepts, and their ground-truth captions as inputs of the model and generated captions as outputs.
shows two samples of image-text pairs from the ImageCLEFmed 2021 dataset. The UMLs are retrieved to identify each CUI of the image. They are related to the content of the image and could give more insight into its content. For example, in the left sub-figure, CUI 'C0003842' is substituted by 'Arteries' and the CUI 'C0040398' by 'Tomography, Emission-Computed'. Both concepts give more information on the content of the image and could be used to boost the understanding of the image.
Huge advances in peer-to-peer systems and attempts to develop the semantic web have revealed a critical issue in information systems across multiple domains: the absence of semantic interoperability. Today, businesses operating in a digital environment require increased supplychain automation, interoperability, and data governance. While research on the semantic web and interoperability has recently received much attention, a dearth of studies investigates the relationship between these two concepts in depth. To address this knowledge gap, the objective of this study is to conduct a review and bibliometric analysis of 3511 Scopus-registered papers on the semantic web and interoperability published over the past two decades. In addition, the publications were analyzed using a variety of bibliometric indicators, such as publication year, journal, authors, countries, and institutions.