dataesr/scientific-paragraphs-categorization huggingface dataset:
To our knowledge, there has only been one study conducted on seven adolescent (16-17 years old) novice divers  [16]. During this study, capillary glycemia (CG) was measured prior to (60, 30, and 10 min) and after diving for a total of 42 dives. It was noted that hypoglycemia was relatively rare over the course of the study, although male adolescents showed a marked pre-to post-dive CG decline. Conversely, a high occurrence of hyperglycemia was recorded during the study. The study concluded that the utility and impact of a deliberate increase of glycemia prior to the dive remained to be established. The strong need to extend the database for young T1D divers was also underlined  [16]. Here, we present a new study on T1D adolescent divers with a larger number of adolescents  (16)  and dives (74).
Acknowledgements The completion of this study would not have been possible without the full support of the " Diabète et Plongée " non-profit association, which is sincerely thanked for organizing and coordinating the level 1 diver certification stay and transport in Marseille for adolescent divers in France, as well as for the organization, financial support, and  logistical coordination of the study in Mayotte. "Diabète et Plongée " extends its thanks to its sponsors:  Abbott ,  Dinno Santé ,  Sanofi ,  Accenture , the  Lions Club de la Baule , and the FFESSM. We thank  Abbott  for providing the sensors and monitors for the FreeStyle Libre ® continuous glucose monitoring systems as well as the  Optium Medisens ® and Optium FreeStyle ® capillary glycemia  and ketonemia measuring systems used in this study.
However, there is no scientific study or publication tackling this question (see  Jeanmougin et al. 2016  for a bibliographic exploration quantitatively corroborating this idea). According to the database (ETC-BD 2015) constructed as part of the European-wide evaluation of the conservation status of HCI (European Union 2015), this habitat is present in no less than five countries in Europe (France, Greece, Italy, Spain and Portugal). Management schemes are hence devised and implemented all year long in the whole European Mediterranean region to manage this HCI, but there is no scientific guideline to decide how to solve the contradiction between the objectives to mitigate biological invasion and to promote the conservation status of habitat 3280. Like most complex problems at the science-policy interface, this specific problem certainly has multifarious origins, having to do with the complex challenges in (1) translating ecological theory into practice  (Knight et al.
Tissue injury, inflammation, and repair play a role in RIF development and progression. The first strategies to reduce or treat cutaneous and subcutaneous RIF were based on top-down approaches, such as the use of superoxide dismutase (SOD)  (10)  or of pentoxifylline combined with α-tocopherol (vitamin E)  (11, 12). A better understanding of RIF molecular mechanisms allowed defining potential therapeutic targets, such as transforming growth factor beta (TGF-β) that is activated by RT and is a fibrosis driver  (13). Galunisertib, a TGF-β receptor type I kinase inhibitor, combined with a platelet-derived growth factor receptor inhibitor significantly decreased lung RIF in mice models  (14). The safety of an anti-TGF-β antibody (GC1008) is currently assessed in early clinical trials in patients with idiopathic pulmonary fibrosis (NCT00125385).
At baseline, all patients underwent head and neck computed tomography (CT) to confirm HNSCC remission and high-frequency US (HF-US; at least 16-MHz linear transducer) to assess RIF thickness (in mm) compared with neighboring normal skin (upper part of the chest wall). The radiologists who performed the HF-US assessments (image collection and interpretation) were HF-US experts. As RIF is a dynamic process due to permanent extracellular matrix remodeling, RIF thickness was measured at its thickest point at baseline and at all the study time points. Before inclusion, a written informed consent was obtained from all patients. Then, investigators from the two centers sent by fax the data required for the patient's registration to the xxx. After eligibility check and registration, a unique identification number was assigned to each patient and provided by fax to the investigator and to the pharmacist.
Ongoing clinical trials are assessing different curative strategies: the Tocovid SupraBio-pentoxifylline combination in bowel radiation-induced disease after pelvic RT (NCT02230800); topical superoxide dismutase in skin RIF in patients after HNCC (NCT01771991). A phase I trial evaluates the efficacy of umbilical cord mesenchymal stem cells in established lung fibrosis (NCT02277145). Other clinical trials are assessing mitigation strategies, particularly for the prevention of lung fibrosis by administration of enalapril (NCT01754909), captopril (NCT00077064), or nicorandil (NCT02809456). To date, none of them has reported results comparable with those obtained here using a compound selected on the basis of its biological efficacy  (18, 20, 32). Finally, the results of our ancillary study are consistent with the clinical data, although performed in a limited number of patients.
2 mg) and with ethyl acetate (LMAE, 457. 5 mg), respectively, used for pharmacological, antioxidant, and phytochemical investigations. LMAE was used in the present study because it represented the most effective extract in inducing vascular relaxation, in terms of pD 2 in the mice aorta and pig coronary artery compared to the aqueous decoction extracts (data not shown). The dose of LMAE used in the present report was based on our preliminary studies (unpublished data). Invasive blood pressure measurements in rats showed that the 80% of effective dose of LMAE able to reduce hypertension was 0. 5 mg/kg. Because it is assumed that only 1% of total compounds could be absorbed in the digestive tract, the dose used was then 50 mg/kg/day. 2. 4. Animals. Four groups of 8 male Swiss mice (6 to 8 weeks old and weighing 32 to 36 g) were used: (i) group receiving infusion of saline by an osmotic pump for 2 weeks (NaCl), (ii) group receiving Ang II (0.
The same membrane was used to determine β-actin expression (loading) control using a polyclonal antimouse β-actin antibody (1 : 5000, Sigma Aldrich). The membrane was then incubated for 90 min at room temperature with the horseradish peroxidase-(HRP-) conjugated secondary antibody. Membranes were washed at least three times in Tris buffer solution containing 0. 05%. The bands were visualized using the enhanced chemiluminescence system and quantified by densitometry. Images analysis were performed using ImageJ software (National Institute of Mental Health, Bethesda, Maryland, USA). 2. 11. DPPH ⋅ Assay. The radical scavenging activity was performed using 2,2-diphényl-1-picrylhydrazyl (DPPH, Sigma-Aldrich) as previously described  [27, 28]. Briefly, the absorbance of 10 μL of samples, standard, and Trolox (Sigma-Aldrich) added to 200 μL of DPPH was measured at 490 nm after 30 min incubation in the dark at room temperature using a Bio-Rad spectrophotometer (Model 680, Japan).
2. 15. Statistical Analysis. Data were analyzed using GrapPad Prism 5. 02 software (GrapPad Software, San Diego, CA, USA). The results are expressed as mean ± SEM. For animal experiments, n represented the number of mice. Blood pressure, heart rate, and myography experiment values were compared using a two-way ANOVA with Bonferroni post hoc test. Data of western blot and electron paramagnetic resonance experiments as well as DPPH ⋅ and FRAP assays were compared using one-way ANOVA followed by a Bonferroni post hoc test. * p < 0 05 was considered to be significant.
Acknowledgments We gratefully acknowledge  Dr. L. Duluc  for the critical reading of the manuscript and English corrections. We are grateful to  Mireille Wertheimer  and the staff of " Service Commun d'Animalerie Hospitalo-Universitaire" of Angers  for taking care of the animals. All our gratitude to the  "Service de Cooperation et d'Action Culturelle (SCAC) de l'Ambassade de France au Burkina Faso " for the financial support.
5%; and share a significant degree of compatibility in proteolytic processing, replication, encapsidation, and genetic recombination. Approximately 107 serotypes/genotypes of NPEVs have been identified based on a threshold of 25% nucleotide divergence in the VP1 coding region  (Pallansch et al. , 2013). The serotype of an unknown isolate is inferred by comparison of the VP1 sequence with a database containing VP1 sequences for the prototype and variant strains of all human EV serotypes according to the following suggested guidelines: A partial or complete VP1 nucleotide sequence i. identity of 75% (>85% amino acid) identity between a clinical EV isolate and serotype prototype strain is used to establish the serotype of the isolate, on the provision that the second highest score is <70% A best-match nucleotide sequence identity of <70% ii.
The prevalent methods for monitoring ships are based on automatic identification and radar systems. This applies mainly to large vessels. Additional sensors that are used include video cameras with different resolutions. Such systems feature cameras that capture images and software that analyze the selected video frames. The analysis involves the detection of a ship and the extraction of features to identify it. This article proposes a technique to detect and categorize ships through image processing methods that use convolutional neural networks. Tests to verify the proposed method were carried out on a database containing 200 images of four classes of ships. The advantages and disadvantages of implementing the proposed method are also discussed in light of the results. The system is designed to use multiple existing video streams to identify passing ships on inland waters, especially non-conventional vessels.
This research problem is a part of the SHREC project, the main purpose of which is to develop a prototype system for the automatic identification of vessels in limited areas  [7, 8]. At this stage, the authors propose a method to detect and classify vessels belonging to four classes based on an image database. The proposed method is a component of the system and forms part of the detection and recognition layer. The recognition process is one of the most popular tasks due to the number of problems. Feature extraction is particularly important, thanks to which it is possible to further assign these features to a given object. However, quite often, two-dimensional images contain various kinds of additional elements such as sky, trees, or houses. In the case when an image is taken with a good camera, further objects can be blurred, which is a certain distortion causing a better extraction of the features of the closer elements.
Detecting Features of Ships The proposed technique of detecting features of ships in images is based on the parallel processing of a given image using different filters. This processing allows the distortion of images in such a way that only important elements giving features are mapped. Images obtained from the parallel filtering process are used to find features that appear in both images with different filters. The Hessian matrix was used for comparison and detection. The proposed technique relies on classical image processing to reduce the area used for further classification. The features of vessels were detected in order to minimize the amount of data in images such as background or surroundings. We used the idea of key points to detect only significant elements. An additional advantage of the proposed detection method is extension of the number of samples in each class.
Model of Ship Classification System The idea of the proposed system is to create software that can process real-time video samples and categorize ships appearing in them. The camera recording the image can be placed in any position, such as a water buoy or a bridge. The video being recorded is composed of many frames, which may not necessarily feature a vessel. Moreover, it makes no sense to process each frame-one second of a recorded video usually contains 24 frames, it is not possible to process this amount of data in real time. Hence, image processing can be minimized to one frame every one or two seconds. This prevents the task queue from overloading and blocking. However, keeping the camera in one position leads it to record the same elements in space. Therefore, to avoid searching for ships in nearly identical images, a pattern was created to avoid the need for continuous processing.
Experiments on Test Data To check the operation of the proposed method, the authors used an image database divided into four classes, each of them composed of 200 samples. The classes represented four types of ships-a boat, a motor yacht, a navy ship, and a historic ship. The image processing method presented in Section 2 was tested on different values of parameters in the range {0, 15}. The adjustment of these values for the system led to the setting of an average number of key points. All coefficients were used in different combinations, and the authors selected one that returned approximately 15-20 key points (after processing). The selected configuration for image processing is presented in Table  1. This number is not accidental because the authors noticed that if the number of key points was small, the worse area was returned.
On the contrary, when the number of key points was large, the original image was returned. This is why the configuration was chosen in an empirical manner. In this way, the best configuration was selected and the files were processed. It is worth noting that two images were always generated from one sample, as presented in Figure  2. Before these samples were used for training, images among them that did not contain vessels were removed. The database of each class (composed of 200 samples) was thus extended to an average value of 320 images (first class, 343; second class, 316; third class, 324; and fourth class, 299). The obtained samples did not always contain the entire vessel, but only fragments or other objects in the image. This was important for the training process because it could not be guaranteed that an incoming vessel would be oriented in a particular manner.
In addition, using the same method of extraction yielded a similar sample. The proposed technique for processing and extracting features allowed for an average of 320 samples, whereas the ideal solution would return exactly 400 (double the amount from the entry database). However, the different conditions in which the images were taken (in terms of quality, distance, and exposure) and the background posed problems. For ships in high seas, the extraction was perfect. However, a large part of these samples was set against the background of forests or cities. Thus, the recorded average extraction efficiency of 80% is a good result. It might have been affected by incorrect filter configuration. An empirical approach to the evaluation of the obtained data indicated the best adjustment of these values.
Based on the results, the authors determined the accuracy, overlap, sensitivity, specificity, and the F-measure. The results are presented in Table  3. The resulting effectiveness was nearly 92%, which is high considering that a small number of samples were used for training. A higher value was obtained for sensitivity, which implies accurate prediction of true samples. The F1 score was also high, which indicates that the classifier was efficient. Considerably worse results were obtained for specificity, nearly 0. 541, implying that the classification of true negatives was substandard. The same result was obtained for the rate of false omission, indicating that a large portion (almost 30%) of the false samples had been rejected. As part of the conducted research, classification tests on a given database were performed with and without the proposed detection method and the results are presented in Figure  7.
Experiments on Real Data In the next step, we analyzed the proposed solution on real data. To test the proposed solution in real conditions, we manage to create a database contained 18,361 image files that stored six classes of boats-barge (1703 images), yacht (1489 images), marine service (2362 images), motorboat (6980 images), passenger (3629 images), and other kinds (2198 images). The data were obtained by the setting of cameras mounted on bridges and placed on the bank in Szczecin in Poland. The resulting video data were processed with detection to get video frames containing the passing ship on inland waters. Within one second, a maximum of one frame was collected. For the needs of the experiments, a maximum of 20 photos of one ship crossing the bridge were left in the database. The examples of this images are presented in Figure  8.
Images obtained at the stage of detection differ from each other in size and quality. As part of the research, the same classification architecture was used, which is presented in Table  2 , with one difference being that there were six neurons in the output layer because of the number of classes. For the purposes of conducting experiments, the images within each class were divided in the ratio 80:20 (training:verification), with a training coefficient\eta = 0. 001 and 100 iterations. After the training process, the database was enlarged by an additional 300 pictures that did not fit into any of the six classes. For each of the classes, plus the previously mentioned fake images, the efficiency of the trained classifier was checked. The results in the form of confusion matrices are presented in Figure  9  for the classifier and for each class in Figure  10.
Statistical tests for each class are presented in Table  4. In the case of average performance of the proposed classification, accuracy reached 63%. As in the case of test data, the higher value was obtained for sensitivity. In addition, the recall is on the same level. The F1 score was also high, which indicates that the classifier is efficient. Here, we also see that worse results were obtained for specificity and miss rate. Next, for the database thus created, the operation of the proposed method was verified with other classic architectures of the convolution network as shown in Figure  11. In our experiments, we used a strategy to train some layers and leave others frozen in existing models of CNN such as VGG16 and VGG19  [19]. Such a strategy was used to transfer knowledge from previously trained classifiers so as not to train classifiers from scratch.
While choosing to leave specific layers, we paid attention to possible overfitting. We have chosen a few examples of architectures to illustrate the proposed solution with existing ones. In the case of the first version (on the chart v1), the last five layers were frozen, and in the second case (on the charts v2), it was the last six layers. In both cases, two layers were added to the architecture as fully connected ones-in both cases, a first layer was composed of 512 neurons and a ReLu function, and the last one had six neurons. This architecture yielded less effectiveness than the proposed technique. Summarizing, the proposed solution can be used in the system being built. Although the classes are similar, the network was able to classify the images. However, the number of samples in the database should be increased.
The Hessian matrix was used for this. For feature extraction, the database was extended to include additional samples used to train the classifier. During classification, an image was assumed to be a tensor with its number of components as its depth (RGB model). The convolutional neural network was used for the recognition process. Its structure was inspired by the action and mechanisms of the cerebral cortex. Following tests and the reconfiguration of the network, the obtained efficiency for test data was nearly 90%, which is a very good result considering the small number of samples. In the case of a real database with six classes the average accuracy reached 63%. The proposed solution will be implemented in the identification system being built. However, the database should be increased.
In the SHREC system, the camera stream is analyzed using image processing methods for detection, classification, and text recognition to properly identify a vessel. The method for ship classification presented in this article will form part of ongoing research in the SHREC project. Future work will focus on improving the proposed classification method. The authors intend to modify the parameters of the method to be able to classify more types of ships. For this purpose, a more extensive database of images of ships is being gathered using video cameras at varying resolutions.
Substrate specificity and kinetic parameters The human ALDH9A1 (GenBank ID: AF172093) is a protein of 494 amino acids (Uniprot ID: P49189). A new polymorphism site was discovered in cDNA used in this work at the position 330. It appears in the codon for Ile 110 (ATT is changed to ATC). However, this variation does not alter the amino acid composition. Kinetic measurements were performed at the known optimal pH of 7. 5, as described in previous studies  [3, 8, 11]. The enzyme is very sensitive and becomes nearly inactive after two re-freezing cycles. The effect of the buffer composition on protein stability was further investigated using nano-differential scanning fluorimetry (nanoDSF) (Figure  2A ) and the highest melting temperatures were observed in buffers at pH 7. 0 and 7. 5. Several aminoaldehydes were screened as potential substrates of ALDH9A1 using a fixed 1 mM concentration of the NAD + coenzyme.
The enzyme displays a wide range of substrate specificity (Figure  2B ). Indeed, although ALDH9A1 shows the highest rate activity for TMABAL and BAL, it can oxidize other aminoaldehydes such APAL, ABAL, γ-dimethylaminobutyraldehyde (DMABAL) or GBAL. In line with previous studies where specific activities of 14, 1. 8, and 2. 3 nmol. s -. mg -  using ABAL were reported for the enzyme isolated from liver and brain  [3, 6, 8] , V max /K m ratios are expressed in relative values referring to the best TMABAL substrate (V max /K m = 1). Saturation curves for aldehydes were measured in 150 mM sodium pyrophosphate, pH 7. 5, using 1 mM NAD + ; saturation curve for NAD + was measured using 150 μM TMABAL. Kinetic constants including their standard error values were determined using GraphPad Prism 5. 0 software including K i , which is the substrate inhibition constant.
Crystal structure of HsALDH9A1 Crystallization of the HsALDH9A1 apoform was not successful. Co-crystallization with 50 mM NAD + with or without 10 mM TMABA product resulted into different crystal forms. However, the three solved structures from P2 1 2 1 2, P2 1 , and C2 space groups at 2. 5, 2. 9, and 2. 3 Å, respectively (Table  2 ), reveal an ALDH9A1 apoform in the absence of a bound NAD + or TMABA. The structures were determined by molecular replacement using the structure of BADH from cod (Gadus morhua subsp. callarias) liver as a search model (PDB IDs: 1BPW and 1A4S; 71% sequence identity)  [19]. This enzyme is also annotated GmALDH9A2  [1]. The asymmetric units of the P2 1 2 1 2 and P2 1 structures contain two similar tetramers (dimer-of-dimers) and that of C2 only one (Figure  3A ).
The tetrameric form in solution with a molecular mass value of 214 + -12 kDa was also confirmed by gel permeation chromatography (monomer of 55. 4 kDa including the His-tag). All monomers are very similar to each other with an RMSD up to 0. 24 Å. Superposition with monomers of GmALDH9A2 gives an RMSD of 1. 3-1. 6 Å. Each monomer displays the classical ALDH fold consisting of a catalytic domain (residues 258-448) with the catalytic Cys 288 , a coenzyme binding domain (residues 1-127, 146-257, 470-478), and an oligomerization domain (residues 128-145 and 479-494), which wraps over the groove between the catalytic and coenzyme domains of the other monomer forming the dimer (Figure  3B ). 2 CC 1/2 stands for a percentage of correlation between intensities from a random half-dataset. 3 The 5% test set.