dataesr/scientific-paragraphs-categorization huggingface dataset:
Similar to the classic ternary complex, the B site in MolA of this structure was coordinated by the catalytic carboxylates Asp411 and Asp623 and by the backbone carbonyl of Leu412; in turn, it binds the triphosphate moiety of incoming dNTP with α,β,γ-tridentate coordination (Figure  2B ). Previously, a single Mg 2+ ion bound in the closed ternary complex at the B site was reported, which forms a β,γ-bidentate coordination (Figure  2C ) (PDB ID 3si6)  (Xia et al. , 2011a). Comparison of these two structures suggests that a conversion of the bidentate to tridentate coordination is likely to be a necessary step because Mg 2+ ions can typically neutralize only two negative charges of two phosphate groups by forming only the bidentate coordination of the triphosphate moiety of a free dNTP in solution, but not with three phosphates simultaneously (Figure  2D ).
This conversion requires complete dehydration of the B metal ion so that it can be fully buried inside the complex. When the non-exchangeable Rh 3+ /dNTP complex was used in kinetical studies, this step would be kinetically invisible because these studies revealed only the kinetic properties of the binding of the A metal ion  (Bakhtina et al. , 2005; Lee et al. , 2009; Wang et al. , 2009). Another conformation of β,γ,-bidentate Ca 2+ coordination was observed previously in an RB69pol ternary complex (Figure  2E ) (PDB ID 3uiq)  (Xia et al. , 2012b). However, that complex had two Ca 2+ ions bound, one at each of the B and A metal ions (Figures  2E, F ). Therefore, the order of the bidentate-to-tridentate conversion and the binding of metal ion A (the second site) may be stochastically independent processes, providing a mechanism of allostery and regulation of binding affinities on the kinetic pathway of dNMP incorporation.
Both processes should be essential for catalysis because with β,γ,-bidentate coordination to metal ion B (the first site), the α-phosphate of the triphosphate moiety of the incoming dNTP would be free to move in an arc, and because without the binding of metal ion A, the 3′-hydroxyl of the terminal nucleotide of the primer strand (ptO3′) could not be brought close enough to the freely moving α-phosphate of the dNTP. In the catalytically competent complex of RB69pol  (Xia et al. , 2011a) , metal ion A binds both the α-phosphate of dNTP and the ptO3', bringing them closer together for chemistry (Figure  2G ) (PDB ID 3spy)  (Xia et al. , 2011a). Without considering the stochastic kinetic schemes discussed above, kinetic studies may not be sufficient to explain the kinetic behaviors of nucleotide selectivity for dNMP incorporation by DNA pols.
, 2010). It also differed from the conformation of the apo structure (PDB ID 1ih7)  (Franklin et al. , 2001). In this binary complex, there was only one ordered water molecule placed between the Asn564 and the Tyr567 backbone in the kinked FIGURE 2 | metal coordination is in green or yellow in tridentate or bidentate coordination complexes, respectively. (E) Stereodiagram of bidentate Ca 2+ coordination in the two-metal ion replication complex (PDB ID 3uiq) with pentagonal bipyramidal geometries for sites A (CaA) and B (CaB), and CaA does not bind to pt O 3'. Ca 2+ ions and water molecules are in gray and red, respectively. (F) Superimposed representation of Ca 2+ coordination complexes of the one-metal ion with tridentate coordination (MolA, green) and two-metal ion with bidentate coordination (gray).
The Ca 2+ coordination is in green or gray in one-metal ion or two-metal ion replication complexes, respectively. (G) Stereodiagram of tridentate Mg 2+ coordination in the two-metal ion replication complex (PDB ID 3spy) with octahedral geometries for sites A (MgA) and B (MgB), and the MgA interacts with pt O 3'. Mg 2+ ions and water molecules are in magenta and red, respectively. (H) Superimposed representation of metal coordination complexes of a Ca 2+ -bound one-metal ion with tridentate coordination (MolA, green) and Mg 2+ bound two-metal ion with bidentate coordination (magenta). The metal coordination is in green or magenta in Ca 2+ -bound one-metal ion or Mg 2+ -bound two-metal ion replication complexes, respectively. helix p, whereas there were two ordered water molecules observed in the open conformation of the apo form.
ACKNOWLEDGMENTS This work was partially supported by the  "GIST Research Institute (GRI) IIBR " grant funded by  GIST  in 2021 and the  National Research Foundation (NRF) of the Korean government  ( NRF-2021R1A2C1006267 ). The X-ray diffraction data and coordinates for the two-pol complex structure are available from the PDB (PDB ID 7f4y). The authors thank the late  Professor T. A. Steitz  for advice and suggestions during the course of this study and  Professor W. H. Konigsberg  for plasmid gifts and comments on this manuscript. The authors also thank the staff at beamlines NW-12A of the  Photon Factory (Tsukuba, Japan)  for their kind help with data collection.
2 m in aperture at the National Observatory of Athens observe the flashes at the R-and I-bands almost simultaneously. Derivations of temperatures assuming blackbody radiation are possible. As the first scientific result from the project,  Bonanos et al. (2018)  obtained temperatures between 1600 and 3100 K for ten flashes. Avdellidou and Vaubaillon (2019)  then analyzed 55 flashes in the NELIOTA database. They found the temperature ranging between approximately 1300 and 5800 K with the typical value of about 2500-2600 K. Liakos et al. (2020)  summarized results of the first 30 months of the NELIOTA project and showed that the temperatures distribute between 1700 and 5700 K and two-thirds of them were 2000-3500 K. These groups also reported a decrease in temperature with time. The problem of synchronization of the cameras remains though they describe that the synchronization is better than 6 ms  (Bonanos et al.
The intervals of the stamped times should be constant and the same as the exposure time if the stamped times are exact and there is no frame drop. The frame drops occur when image data transferred through the USB3. 0 cable and stored into the disk exceeds the capability of the systems. We examined the intervals of the stamped times in successive frames over plus-minus 1 min around the times of the flashes. They fluctuate around 16 ms for System1, and 25 ms for System2. However, a longer interval is followed by a shorter one. Therefore, the average of the intervals over some frames is constant. We decided that the recording speed into the drive is fast enough and there is no frame drop. The application seems to stamp time when it stores a frame in a drive. It is slightly different from the time of the start or the end of the exposure of the camera.
We examined the positions of 17,754 satellites or space debris listed in the two-line element orbital datasets downloaded from Space-Track 1 on both 15th and December 17, 2018 using an application "StellaNavigator 10" by the Astro Arts company. We found no human-made object around the moon that moved slowly enough to be misinterpreted as a lunar flash at the times of Flash A. A geostationary satellite, Gorizont 23, was found at 0. 5 from the lunar disk center in the celestial south-east direction at the time of Flash F. It was close to the south-eastern edge of the disk 1 min before 1 Space-track: https://www. space-track. org/, last access on 4th March. 2020. the flash. We do not know the accuracy of the orbital data and the calculations in the application; however, the sudden brightening and an afterglow of this event support it to be a lunar impact flash.
Spectral analyses The spectral flux densities F flash ðλÞ at wavelength λ of a flash is calculated as F flash ðλÞ ¼ N flash ðλÞ N star ðλÞ Á k Á πB T star ðλÞ (1) where N flash ðλÞ and N star ðλÞ are count numbers as shown in Fig. 2b  after the corrections described below. Their subscripts represent a flash and a comparison star. B T star ðλÞ represents the Planck function at temperature T_star, and k is a non-dimensional value related to the brightness of the star. T_star is the effective temperature of the star derived from its color index, B À V, and Table  3  in  Flower (1996) , where B and V denote respectively the B-and V-magnitudes. We obtained B and V from the SIMBAD 3  database. It should be noted that F flash ðλÞ is a temporal average of the flux F flash ðλ; tÞ over an exposure time of the cameras Δt as F flash ðλÞ ¼ 1 Δt Z tþΔt t F flash ðλ; tÞ Á dt (2) Therefore, when the duration of the peak at the beginning of a flash is shorter than the exposure time, F flash ðλÞ underestimates its real flux.
Acknowledgments We are indebted to the students of the  Univ. Electro-Comm. , Nihon Univ. , and  National Dong Hwa Univ. for their efforts in the observations. MY, SA, ST, TK, and RY were supported by the  Bilateral Joint Research Program, Japan Society for the Promotion of Science, Japan. SA was supported by the  Nihon University College of Science and Technology Leading Research Grant. We are greatly indebted to  Prof. J. M. Madiedo  and another referee for their very useful comments. MY, SA, and RF appreciate the support and hospitality of the committee members of Meteoroids (2019) held in Bratislava, Slovakia.
Quality assurance of the final build part in L-PBF is greatly influenced by the various process steps such as Powder handling, Powder bed spreading, and Laser-material interaction. Each process step is interlinked to each other and can affect the overall behavior of the succeeding steps. Therefore, it is vital to monitor each step individually, post-process, and establish a link among the data to develop an approach to quantify the defects via inline monitoring. This study focuses on using pre-and post-exposure powder bed image data and in-situ melt pool monitoring data to monitor the build's overall quality. Two Convolutional Neural Networks have been trained to treat the pre and post-exposure images with a trained accuracy of 93. 16 % and 96. 20 %, respectively. The supervised machine-learning algorithm called "Support Vector Machine" is used to classify and post-process the photodiodes data obtained from the melt pool monitoring.
Supervised ML requires a labeled dataset to train the specific model. In other words, each training datapoint has a label, e. g. , "good" and "bad," and users must know the ground truth of the training dataset before learning. In many applications such as AM, sometimes, it is a very costly and laborious task to understand the data points' labels. Whereas in the unsupervised ML approach, there is no prior requirement of the labels for the training dataset. The algorithm tries to find a pattern based on some relationships among the dataset. As the name suggests, the semi-supervised learning approach uses a combination of supervised and unsupervised approaches at the same time. In other words, it requires both labeled and unlabeled datasets. This approach can be helpful where obtaining the ground truth dataset is a very challenging task  [13] [14] [15] [16].
Yuan et al. developed a CNN model to monitor the single deposited tracks' quality using co-axial high-speed video camera data. The training dataset was prelabeled using the offline characterization techniques such as macroscopic analysis of the deposited tracks, which was further used to predict the deposited consistency using a trained CNN model  [25]. Imani et al. fabricated cylindrical samples with varying power, scanning speed, and hatch distance to induce varied samples' varied porosity. The obtained samples were quantified for the distribution of the porosity using computer tomography. In parallel, the parts' in-situ images were analyzed using the spectral graph approach, and extracted features were further used as input for machine learning algorithms  [26]  were captured with a combination of near-infrared range CMOS camera and photodiode.
The artificial neural networks (ANN) predominately works on a large set of the labeled training dataset, which is a very challenging and laborious task in the L-PBF process. It is very challenging to quantify and detect defects in the captured in-situ data and often requires expensive and time-consuming techniques such as computer tomography for quantification of the defects. Therefore, the inherent challenge to the L-PBF process is that the ML approaches developed tend to be a part, material, or geometry specific.
Part geometry and process parameters To prepare a training dataset for the CNN models (CNN 1: For pre-exposure images, CNN 2: For postexposure images) and SVM classifier (For melt pool monitoring), a balanced dataset comprising an equal number of each label is necessary. An artificial drift is introduced in the samples to obtain the balanced ground-truth dataset of each label. Therefore, the unique geometrical specimens were printed, as shown in Fig. 2. The process parameters tabulated in Table  2  were varied to obtain varied volumetric energy density in the range of 40-73 J/mm 3 for each shown geometry. The decision to vary the process parameters in the above-specified range was made based on the fact that drift is created in the final parts due to power, scanning speed, and hatch distance.
Melt Pool Monitoring System The co-axial melt pool monitoring (MPM) system installed on the commercial machine SLM 280 HL was used to collect thermal emissions from the melt pool formed due to laser-powder interaction. The melt pool systems are co-axial systems, which means it is in the laser path's alignment and collects the real- layer in a data file, accessed as 2D representation in MPM software provided by the SLM Solutions  [32]. The new file is created automatically for each layer after the complete exposure. For this work, no additional modifications are made to the installed hardware.
Image Preprocessing The captured raw images (1500*1460 px 2 ) possess difficulties such as in-homogeneity in light intensity. Therefore, they cannot be used directly for CNN operations. As the camera and environment conditions remain unchanged for all the captured images, the same homogeneity correction factor can be used for all images. For light intensity homogenization, we have used the in-built functions of MATLAB called "imcomplement," and "imreducehaze" [35  -37]. For our studies, we have used the maximum haze value of 1, which removes the maximum haze from the image. Firstly, the captured raw image was converted to a grayscale image to reduce the operation's complexity and our CNN model's requirement. The grayscale image was then complemented, which means it inverts the pixel values.
For Pre-Exposure Images Area considered for CNN operation represents the areas that are free from above-discussed anomalies. The Fig. 5. (d) is the "OK" image for the case where there is no printed area underneath. In contrast, Fig. 5. (e) represents the printed part covered with the powder layer. Post-exposure images can serve as the verification step and contain vital information about the part and powder's quality. In our study, we have considered the same cases as pre-exposure images for our labels. Instead of "Part hopping," we have labeled it as the areas likely to an undergone overheating phenomenon, which leads to hopping in the next recoating step due to internal residual stresses. The labels are summarized in Fig. 6. For training the CNN models, a dataset of 500 images for each label was prepared for pre-and post-exposure images.
Training SVM Classifier For this study, artificial drifts, i. e. , overheating drift due to overhang and lack of fusion in the parts with special geometrical features (see Fig. 6 ) and by the varying process, were created. The careful selection of the layers from the build parts (81 parts) and labeling it as 'Drift' and 'No-drift' was performed by analyzing the layers in the MPM software provided by the SLM solutions and statistical analysis. For our study, we have chosen the mean and median of each layer as the input features. As we know, the mean and median measures the data's central tendency and gives us information regarding the data's skewness. Median is an important feature when the data has extreme values. It is also important to mention that other features such as standard deviation and root mean square for each layer were also studied.
Still, SVM results were not satisfactory compared to mean and median. The drawback of choosing two points as the features per layer is that it compresses the whole layer data into two issues. Therefore, in the study, individual hotspots' exact location in a particular layer is not studied. A balanced labeled dataset of 170 data points, which comprises an equal number of "drift" and "no-drift" data points so that the biasing of the SVM model can be avoided, was prepared. The best-fitting parameters are to be selected for the SVM classifier to increase the classifier's success rate. A Bayesian optimization algorithm was used to find the best-suited hyperparameters for the model. A hyperparameter is an internal parameter of an algorithm that needs to be optimized. For example, in our case (SVM model), the box constraint, kernel-function, and kernel-scale are the hyperparameters.
Confusion Matrices The training dataset was divided into three subcategories, i. noticeable that the "Uneven powder spread" and "OK" labels are the most difficult labels to predict for the trained CNN 1 model and often confused among each other. Similarly, CNN 2, which predicts labels for post-exposure images, is often confused in predicting "Uneven powder spread" and "OK" labels. This inaccurate prediction of labels is due to the uneven spreading anomaly, which has a signature similar to a good powder bed.
Programming Environment All the programming has been implemented using MATLAB R2020a software, which included the relevant add-on packages such as Image Processing Toolbox, Statistics Toolbox, and Deep Learning Toolbox. The system configuration used for implementation is as follows: Operating system: Microsoft Windows 10, System RAM: 4GB, Processor type: Intel(R) Core(TM) i5-6200U CPU@ 2. 30 GHz 2. 40 GHz, Graphics Card: NVIDIA GEFORCE 940MX.
51, provided in the public domain by the National Institutes of Health, Bethesda, MD, USA; http://www. imagej. nih. gov, accessed on 20 March 2021, RRID:SCR_003070) was used to quantify the collagen fraction of the complete tissue section. Red, green, blue deconvolution was performed on the image, and a threshold was applied on the red image to segment the picrosirius stained areas. Cellularity was quantified after 4 , 6-diamidino-2-phenylindole (DAPI) staining (Pro-Long Gold Antifade reagent with DAPI, Thermo Fisher Scientific, Bremen, Germany). Using an electronic camera (Axiocam, Zeiss, Jena, Germany), snapshots at ×200 magnification were taken across the tumor. The nuclei were counted using a plugin written with the Image J software. The cell nuclei were extracted using the Otsu thresholding algorithm, then a watershed algorithm was applied to distinguish each nucleus  [47].
Fibrosis and necrosis were assessed on hematoxylin/eosin stained sections. Collagen I and III were further quantified after picrosirius red staining. The whole tissue sections were digitized with an Aperio Scanscope (Aperio Technologies, Vista, CA, USA) at ×20 magnification. A homemade semi-automated algorithm developed with the Image J software (version 1. 51, provided in the public domain by the National Institutes of Health, Bethesda, MD, USA; http://www. imagej. nih. gov, RRID:SCR_003070) was used to quantify the collagen fraction of the complete tissue section. Red, green, blue deconvolution was performed on the image, and a threshold was applied on the red image to segment the picrosirius stained areas. Cellularity was quantified after 4′, 6-diamidino-2-phenylindole (DAPI) staining (Pro-Long Gold Antifade reagent with DAPI, Thermo Fisher Scientific, Bremen, Germany).
A cutoff of 1. 7%, corresponding to the value that best discriminated between hepatocellular carcinoma and cholangiocarcinoma collagen content in our study, was used to define substantial collagen content. Statistical analysis was performed with the Medcalc version 19. 5. 3 software (Medcalc, Ostend, Belgium, RRID:SCR_015044), and statistical significance was set at p < 0. 05.
Preliminary Experiment We conducted a preliminary experiment using a many-to-one multilingual model trained on a TED talk dataset  (Qi et al. , 2018) , covering top-20 source languages with the most data. We observed that patterns of attention heads (measured with the "confidence" metric) for both encoder self-attention and encoder-decoder attention were very similar among the language pairs. For the main experiment, we decided to use a larger and stronger multilingual model for the following reasons: (1) the TED dataset is quite small and the model trained on it achieves lower BLEU scores and may not be regularised very well; (2) the network capacity of the TED model could be too limited for the language-pair-specific patterns to emerge (if any). According to a study on BERT's multilinguality  (Dufter and Schütze, 2020) , the increased network capacity (i.
Experimental Settings For the sake of reproducibility, all experiments were conducted using a strong publicly available many-to-one multilingual NMT model released by  Bérard et al. (2020). The model can translate French, German, Italian, Spanish, and Korean sentences into English. It is trained with standard open-accessible datasets, including biomedical corpora where available. The model uses a variant of the Transformer-Big architecture  (Vaswani et al. , 2017)  with a shallower decoder: 16 attention heads, 6 encoder layers, and 3 decoder layers. The model produces SOTA-or near-SOTA-level results for news, IWSLT, and biomedical translation tasks. As the model is many-to-one, we could set up a controlled experiment where the BLEU scores were directly compared among the language pairs. We employed the development and test sets from the TED talk dataset, and utilized only the multilingual sentence pairs where both source and reference sentences were present for all five language pairs.
A Experimental Details All the experiments were conducted using PyTorch  (Paszke et al. , 2019)  and Fairseq  (Ott et al. , 2019)  toolkit. The multilingual NMT model used in the experiments can be downloaded online. 9 Please refer to  Bérard et al. (2020)  for more details on the model. When running an experiment for each language pair, a single V100 GPU was used. We note that computing the SBS rankings for encoder self-attention was the most computationally intensive part, where almost 96 2 translations of the development set were conducted. When computing the BLEU curves for the rand-ranking, we ran the procedure with a randomly created ranking five times, and averaged the resulting BLEU scores.
Acknowledgments This work was done as part of the  Multidisciplinary Institute in Artificial Intelligence MIAI@Grenoble-Alpes  ( ANR-19-P3IA-0003 ). Authors would like to thank  Jaesong Lee  and  Hyun Chang Cho  from  NAVER Corp. ,  Kwang Hee Lee  from  KAIST  for insightful comments on the experiments. We would also like to extend our gratitude to the anonymous reviewers for their valuable feedback.
Aurora kinase, a group of enzymes that belongs to a serine-threonine family and plays a critical role in cellular division. Aurora Kinase A is overexpressed and distributed beyond the nucleus and is involved in tumorigenesis. So, Aurora Kinase A is used as a drug target for cancer. Flavones are a class of avonoids that are present in plants that show anticancer activity. Hence, the study will be on molecular binding modes between the derivative compounds of avone-containing uoro groups and the Aurora kinase A. Similar compounds of 2'Fluoro avones are retrieved from the PubChem database. Then drug-like lters viz. REOS and PAINS were applied to remove toxic compounds using Canvas software. 3882 compounds are ltered, and 2449 compounds were obtained and are used as ligands with Aurora Kinase protein (PDB ID: 2J47) as a target, docking was done using Glide (Schrödinger) software.