dataesr/scientific-paragraphs-categorization huggingface dataset:
The difference image analysis By definition, optical transients are new sources that suddenly imprint their patterns in the images with their fluxes strongly evolving with time. Performing the subtraction of the science images with some reference images allows to reveal the presence of these new sources as significant excesses in the residual images. While being a powerful tool to identify transient and flux-varying sources, it implies to perform several important steps, which require fine tuning, prior to the subtraction. The reference images have to be carefully chosen in order to not contain the transient sources. Depending on the timescale of the OT flux evolution, the reference images are usually taken days to months prior to the science images. A reference image can originate either from the same telescope which acquired the science image or from all-sky surveys providing public image databases such as PanSTARRS  (Chambers et al.
Purpose of O'TRAIN Artificial intelligence models are built to lighten the workload of the astronomers on duty and to help them with the decision making. They take on annotated data and try to understand the logic behind them by creating connections between their properties. Our work involves cutouts centered at the position of the OT candidates that we want to classify into two folders: real transients and bogus. The most dominant model in this sort of computer vision tasks is the convolutional neural network since they use every information in the input image (the pixels) without being computationally expensive. By applying filters, The CNN highlights regions of the image according to their relevance when classifying the image, hence the name convolutional  (Yamashita et al. 2018 ). These regions are commonly called "features".
Building the training data set To build a training data set for our O'TRAIN CNN, one has to start by creating two folders True and False in which we store the small cutouts of the subtraction residuals centered at the positions of the real transient sources and the bogus, respectively. Each folder must contain sources that will be as close as possible to the ones the imaging instruments and the detection pipelines will output in real conditions. In addition, we require some nonstandard keywords in the fits header of the True and False cutouts that will be used to quickly identify and characterise the candidates afterwards. We list them below: -CANDID: unique ID or name of the candidate -MAG: magnitude of the source (can be optional) -MAGERR: error on the magnitude of the source (can be optional) -FILTER: filter used for the observation -FWHM: estimation of the Full-Width at Half Maximum of the source (can be optional) -EDGE: True if the source is close an image edge (depends on the detection pipeline setup).
False otherwise. The label of all the candidates stored in the True folder is set to "1" while it is set to "0" for the candidates in the False folder. For a supervised ML algorithm as O'TRAIN, it is required to provide a large amount of labeled candidates for the training, typically several thousands. Depending on the telescope discovery potential, OT sources are sometimes too rare in the science images to sufficiently populate the True folder. To overcome this issue, point-like sources can be simulated in the original science images in order to artificially increase the number of real OT candidates (data augmentation techniques, see  (Gieseke et al. 2017) ). Artefacts can also be simulated if needed using softwares like SkyMaker  (Bertin 2009). Note that in all our analysis, we did not make use of such a technique for simulating artefacts as we had enough of them in the science and residual images after performing the image subtractions.
The training data cube Once the True and False folders are adequately filled by enough candidate cutouts, we process all of them to build a final data cube that will be given as a single input to train our CNN model. The final data cube contains several python numpy arrays (. npy format) zipped in a dictionary-like archive as a. npz format. The different objects of the. npz dictionary store the following arrays: 1. CUBE: a numpy array storing the cutouts (numpy array format) 2. CANDIDS: a numpy array storing the unique IDs or names of the candidates 3. MAGS: a numpy array storing the value of the MAG keywords 4. MAGERRS: a numpy array storing the value of the MAGERR keywords 5. FILTERS: a numpy array storing the value of the FILTER keywords 6. FWHMS: a numpy array storing the value of the FWHM keywords 7.
LABELS: a numpy array storing the value of the labels ("1" or "0") Note that if some candidates are flagged as "edge" sources, they are automatically discarded from the final data cube. Note also that the content of this datacube is not set in stone as additional relevant parameters could be added if needed. In the datacubes we have simulated, the ratio True/False is kept balanced (50%/50%) but we have implemented the possibility to produced unbalanced True/False data cube if needed.
In Table  1 , we summarize some important properties of the images produced by these four telescopes. In the following sections, we briefly describe the transient detection pipelines we used to produce the inputs for O'TRAIN and then, we detail the training data set we built for each telescope. (Bertin 2013) , finally the astrometric calibration is computed with SCAMP using the GAIA catalog  (Bertin 2006b ). The image is then subtracted using the HOT-PANTS code  (Becker 2015). Following the procedure described in section 4. 1. 1, we simulated N point-like sources for which we stored in an ascii file their positions in the different images. We then ran the gmadet transient detection pipeline on those images. By cross matching the positions of the transient candidates output by gmadet with those of the initial list of simulated pointlike sources, we retrieved a vast majority of them.
The STDPipe pipeline Science-ready images are passed through a custom STDPipe pipeline. The steps implemented in the pipeline includes extraction of objects in the image using SExtractor, cross-matching them with the PanSTARRS DR1 catalogue, determining the photometric solution for each frame using PanSTARRS r magnitude and the gr color for color correction, and image subtraction using PanSTARRS r band images acquired through the HiPS2FITS service  (Boch et al. 2020 ) as a template with the HOTPANTS code  (Becker 2015). Then the difference images are weighted with the image noise model, and transient detection is performed on these weighted images, taking into account the masks from both original images and templates to identify possible difference image artefacts. We simulated a set of artificial sources and injected them into the images before the image subtraction using the position-varying PSF model obtained with the PSFEx code  (Bertin 2013).
For the spatially coincident (within 1") transient candidate output by STDPipe with our simulated stars, we draw some cutouts (63 × 63 pixels) centered at the transient candidate position and stored them in a True folder. The rest of the transients non spatially coincident with the simulated sources are then pushed into a False folder. They mostly consist of either a variety of CCD defects or cases where the image alignment was not good enough to properly eliminate the transients (e. g. due to not precisely exact HOTPANTS kernel). The STDPipe pipeline is publicly available in the git repository https://github. com/karpov-sv/stdpipe.
-Precision (Prec): Calculates the number of real point-like sources well classified by the model amongst the candidates classified as real by the model. A good precision score (near 1) shows that the model is usually right in its predictions of the positive class: Real sources. -Recall : calculates how many real transients were well classified in the true transient dataset, so a good recall score indicates that the model was able to detect many positive candidates. -F1-score: calculates the harmonic mean of recall and precision: 2 × precision × recall precision + recall (2) The F1-score ranges from 0 to 1, the better the performance is, the higher the value of this score.
-Matthews correlation coefficient (MCC, Matthews 1975): Takes into account the four parts of the confusion matrix, with the following formula: TP × TN -FP × FN √ (T P + FP)(T P + FN)(T N + FP)(T N + FN) (3) The MCC score is in essence a measure of correlation between the observed and the predicted binary classification. They would be considered positively associated if most data falls along the diagonal cells in the confusion matrix. Since it's a typical correlation measure, a good MCC score is usually greater than 0. 7 as it shows a good agreement between prediction and observation. -ROC curve: displays the True Positive Rate (TPR) vs the False Positive Rate (FPR), an ideal model would have a vertical line on x=FPR=0 and horizontal line for y=TPR=1. This metric enables us to visualize the global performance; -Recall-Precision curve: like the F1-score it displays the model performance on the positive class (real sources) The evaluation of the confusion matrix displayed by the ROC and the Recall-Precision curves, although clear and easily interpretable, might not be realistic.
While the Recall-Precision curve helps us to compare the model with an always-positive classifier, it fails to include the evaluation on the negative class. On the other hand, the ROC curve leverages the four values in the confusion matrix, but its analysis could be misleading for unbalanced datasets. Even if our global datasets are balanced, we lose this property when, for example, we split the candidates into ranges of magnitude and uncertainty in magnitude. A more sophisticated solution was proposed by  Yamashita et al. (2018)  to assess the performance of the model over a varying threshold, based on the MCC and the F1-score. -F1-MCC: displays the MCC score versus the F1-score for a varying threshold. This allows us to determine the confidence score of the model for every telescope, by looking at the threshold that optimizes both scores.
6. 3. Are we Overfitting or Underfitting? Giving the model just enough data to reasonably train it is crucial. With a small dataset, the model does not have enough information to conclude the distinctions between a true transient and a bogus. The ideal case would be to have a large dataset of several tens of thousands of candidates with no contamination, and to have computational resources to be able to train a model on a dataset of this size. The model should be at least complex enough to create good non-linear connection between these characteristics, but not too complex so as not to create connections that are not relevant to the classification, and that are only present in the training data set. When the model is not trained enough, we call it an underfitting, the performance is bad on both training and validation data sets.
If the model on the other hand is learning too much, and getting unimportant information, we call it an overfitting, and in this case the performance in the validation data set is considerably worse than it is on the training data set. We can estimate such an over(under)fitting behavior of our CNN thanks to two metrics: the loss and the accuracy. We can track these two values throughout the training process (i. e. the epochs), to see if at some point we notice the beginning of a divergence between these values on the training and the validation data sets. If so, we should configure the model so that it stops the training at that moment.
O'TRAIN classification performance We applied the aforementioned metrics and curves to track the performance of the model, in both the True and the False classes and for a varying threshold and ranges of magnitude. This allows us to get a very general and global perspective of the performances. Following the method described in §6. 2, we selected the best threshold, p t , for each telescope training set. Then we calculated the accuracy and loss of the last epoch on the validation data set, the precision, recall, F1-score and MCC as well as the confusion matrix with percentages of each part among the validation set. The results are summarized in Table  4. We pay particular attention to the MCC score since it gathers all parts of the classification (or the confusion matrix) and is more robust than other metrics.
For larger data set sizes, the architecture of our O'TRAIN CNN is finally well adapted for being trained on such a binary classification task as it converges towards ∆Acc ∼ 0. Complementary, we also show in Figure  11  the evolution of the Accuracy, the F1-score and the MCC metrics as functions of the data set size for the JAST-gmadet datacube only. We also see that the metrics converge towards high scores once the dataset size used for the training is larger than 5000 cutouts. These results allow us to conclude that our CNN model will have reproducible robust classification results while being trained on a relatively small amount of labeled cutouts (typically 10k of cutouts).
Whilst both of them consists in finding regions in the image that contains the object (i. e OT) we are looking for, The FASTER-R CNN will output bounding boxes containing these objects whereas the MASK-R-CNN goes one step further and gives us the exact pixels of the said objects. [0-1] [0-1] [0-1] [0-1] [0-1] [0-1] [0- For the training, the FASTER-R-CNN as explained by  Ren et al. (2015) , takes on a dataset of images with information such as the coordinates of the bounding boxes containing sources of the image. It applies a CNN model to extract features, and passes them to another model called "Region Proposal Network" (RPN) which performs a binary classification (presence of an object) and a regression on the coordinates. Like all training processes, the model tries to align its predictions with the ground truth.
In this case, it places a sliding window (anchors) that could vary in size and proportions and then calculates the score of Intersection over the Union (IoU) between its predicted boxes and the ones in the dataset we had given it. The RPN will output several proposals, for all objects detected in the image. Since these regions will not have the same size, a pooling will be applied before passing it through a classifier, that will have as input the feature maps of these parts of the original image (i. e the regions of interest), instead of the whole image at once. The MASK-R CNN outperforms the FASTER-R-CNN as it adds to this structure a branch for a binary mask, showing if the pixel is part of the object or not, thus performing a pixellevel detection of objects. He et al. (2017)  noticed a misalignment between the ROIs after the pooling layer, and the regions in the original image, this could cause a drop in the model's performance, since the mask requires precision at pixel level.
Conclusions Real/bogus classifiers are now standard in the pipelines aiming at detecting a large number of transient phenomena at optical wavelengths. We have developed a robust and flexible convolutional neural network model called O'TRAIN. It aims at distinguishing real point-like sources from various types of bogus in optical images. We have shown that O'TRAIN reaches high classification performances for a wide range of telescope pixel scales and observational conditions. In addition, we have demonstrated the capabilities of our CNN model to robustly behave against various types of inputs (cutout and dataset sizes, residuals images from different subtraction methods, etc. ) provided by two publicly available transient detection pipelines (gmadet and STDPipe). Indeed, on the five training datacubes, we obtained very good classification performances with MCC scores ranging in the interval [0.
Telescope and image properties of the four telescopes used to test our CNN model The gmadet pipeline is publicly available in the following git repository: https://github. com/dcorre/gmadet. It takes science-ready images (dark, flat, bias calibrated) as inputs. First, several preprocessing steps are performed: cosmic rays are removed from this science-ready image using either lacosmic (van Dokkum 2001) or astroscrappy (McCully et al. 2018) python package, the background is estimated using photutils (Bradley et al. 2021b) routines, the Point Spread Function is estimated using PSFEx.
Acknowledgements. DT is funded by the  CNES   Postdoctoral Fellowship program  at the  CEA-Saclay/AIM/Irfu laboratory. SK acknowledges support from the  European Structural and Investment Fund  and the  Czech Ministry of Education, Youth and Sports (Project CoGraDS -CZ. 02. 1. 01/0. 0/0. 0/15_003/0000437 ). FRAM-CTA-N operation is supported by the  Czech Ministry of Education, Youth and Sports  (projects  LM2015046 ,  LM2018105 ,  LTT17006 ) and by  European Structural and Investment Fund  and the  Czech Ministry of Education, Youth and Sports  (projects  CZ. 02. 1. 01/0. 0/0. 0/16_013/0001403  and  CZ. 02. 1. 01/0. 0/0. 0/18_046/0016007 ). DAK acknowledges support from  Spanish National Research  Project  RTI2018-098104-J-I00  (GRBPhot). This research has made use of the VizieR catalogue access tool,  CDS, Strasbourg, France.
8b ). SLC1A5 was identified as a major P-LPK receptor. Beads conjugated with a streptavidin-biotin-P-LPK part were used to incubate with a protein extract from HCT116 lysates to perform a pull-down assay (Fig. 5a ). A total of 737 proteins were found in the P-LPK condition, of which 241 proteins were relatively specific compared to the P-CON condition (Fig. 5b , Supplementary Data file 1). The most possible binding partner for the P-LPK peptide was analyzed from which 4 proteins (ATP2A2, SLC1A5, ANXA3, CACNA2D1) were interested (Supplementary Data file 2). Further analyses revealed that CACNA2D1 is in endoplasmic reticulum membrane and ATP2A2 in sarcoplasmic or endoplasmic reticula membrane. SLC1A5 and ANNX3 are in the cell surface that might represent possible receptors of P-LPK (Fig. 5b ).
Molecular interactions between P-LPK and candidate receptors. The interaction between P-LPK peptide and SLC1A5 or ANXA3 was investigated via molecular docking method (Supplementary Fig. 9a ). First, the BLAST program was used to search a template protein for modelling the structure of the P-LPK peptide. The fairly homologous sequence of the P-LPK peptide was included in the complex structure of the tRNA methyltransferase Trm1 from Aquifex aeolicus (PDB code: 3AXS, with a resolution of 2. 16 Å). The P-LPK peptide showed about 67% sequence identity with the Trm1, which provided a solid template for modeling the 3D structure of the P-LPK peptide. The Discovery Studio 2019 program was employed to assemble the 3D models of the peptide using the structure of the Trm1 as a template. The FASTA program (http://www.
ebi. ac. uk/Tools/ fasta/index. html) was used to conduct sequence alignment. Residues in most favored regions in the Ramachandran plot were 77. 8% and residues in additional allowed regions were 22. 2%, suggesting that the 3D structure of the P-LPK peptide was accurate (Supplementary Fig. 9b ). Next, the X-Ray structures of SLC1A5 (pdb code: 6gct) and ANXA3 (pdb code:1AXN) were characterized and downloaded from PDB database. The disulfide bonds between the N-terminal and C-terminal in SLC1A5 and ANXA3 were created by Discovery Studio 2019 program. The docking program Hex v8. 0 21 was used for the preliminary protein-protein docking, and the structure-prediction-based RosettaDock with Monte Carlobased algorithm  22  was used to probe the possible minimumenergy conformations of the peptide binding sites of SLC1A5 and ANXA3.
2), in which hydrogen bonds were shown in green dotted lines and hydrophobic interaction were shown in red edges. g The affinity of P-LPK for SLC1A5 was measured by surface plasmon resonance (SPR) and expressed as resonance units (RU). h Co-localization of SLC1A5 (Red) and P-LPK (Green) was observed by dual immunostaining. Bar,10 μm. i P-LPK was mutated (Sequence APATVSSDMSLA) and the fluorescence intensity of FITC labeled mutant peptide (FITC-P-MUT) bound to HCT116 cells. Bar,10 μm. cells was significantly reduced (Fig. 5i ). Furthermore, data from CRC TCGA and GEO database (Accession number is GSE41258) showed that SLC1A5 was remarkably unregulated in CRC compared with adjacent non-tumorous tissues (Supplementary Fig. 9e ). The SLC1A5 expression level was significantly higher in a panel of CRC cell lines (HCT116, LoVo, SW480, HT29 and Colo320HSR), but not in normal NCM460 (Fig.
The cells were fixed with 4% paraformaldehyde for 30 min at room temperature, and then 4,6-diamidino-2-phenylindole (DAPI) was used for nucleic acid stain. A laser scanning confocal microscope was used to visualize the cells. The fluorescence intensity was analyzed using the Image-Pro Plus 6. 0 software. For cell localization experiment, 20 μg/ml rhodamine-labeled peptides were incubated with NCM460 and HCT116 for 2 h, and then washed with PBS for three times. After that, the cell membrane was stained with a blue cell membrane probe (Thermo Fisher Scientific, Cat. No. W11263). The binding of Rhodamine-P-LPK to cells and their localization in cells was observed under a confocal microscope. Radiosynthesis of 68 Ga-peptide. To obtain 68 Ga, 0. 1 M HCl was used to elute a 68 Ge-68 Ga generator.
Acknowledgements We thank  Leilei Shi  for helpful scientific discussions and technical advice. This work was supported by  National Natural Science Foundation of China  (Grant No. 82003167 ,  81872419  and  32071377 ). Shanghai Sailing Program  ( 19YF1426900 ,  20YF1424400 ). Interdisciplinary Program  of  Shanghai Jiao Tong University  ( ZH2018QNA01 ),  ANR ITI InnoVec (CG, IG). Project of Biobank from Shanghai Ninth People's Hospital ,  Shanghai Jiao Tong University School of Medicine  (No. YBKB201918 ).
Aircraft emissions The global three-dimensional and time-varying aircraft emission inventories used in this study are essentially based on the previous European Union (EU) projects: Reducing Emissions from Aviation by Changing Trajectories for the benefit of Climate (REACT4C;  Matthes et al. , 2012)  and Quantifying the Climate Impact of Global and European Transport Systems (QUANTIFY;  Lee et al. , 2010). These inventories are based on the fuel-flow model PIANO (Project Interactive Analysis and Optimization) and the global emission model FAST (Future Aviation Scenario Tool) with air traffic movements coming from radar data for European and North American flights and the Official Airline Guide (OAG) database for the remaining global flight movements  (Lee et al. , 2009; Owen et al. , 2010).
Present-day baseline emissions The aircraft emission inventory used for present-day baseline emissions is based on the EU project REACT4C  (Matthes et al. , 2012 (Matthes et al. , , 2021;; Søvde et al. , 2014; Grewe et al. , 2014)  and is representative of the year 2006. This inventory will be referred to as REACT4C_2006 in this paper. The RE-ACT4C_2006 data include three-dimensional gridded distributions of kilometres travelled, fuel consumption, as well as CO 2 , NO 2 , and BC (soot) emissions. Flight data are derived from CAEP8 data using the "great circle distance" method corrected with the CAEP8 formula. This dataset is available on a latitude-longitude-altitude grid for 12 months with a horizontal resolution of 1 • × 1 • and a 610 m vertical resolution. In this inventory, the global mean NO x emission index (EI) is 12.