dataesr/scientific-paragraphs-categorization huggingface dataset:
(Supplemental Table  3 ). We then quantified relative intratumoral abundances for each population within the 3 treatment groups. We found that the prevalence of neutrophils, DCs, mast cells, NK cells, and B cells declined in the tumors of mice treated with either PBT+αPD1 (by 43. 2%, 52. 9%, 93. 8%, 41. 4%, and 35. 9%, respectively) or NBTXR3+PBT+αPD1 (by 35. 9%, 42. 4%, 92. 8%, 58. 2%, and 63. 0%, respectively) relative to those of untreated control mice. In contrast, the percentages of innate lymphoid cells (ILCs), CTLs, γδ T cells, and noncytotoxic CD8 + T cells increased after treatment with PBT+αPD1 (by 32. 8%, 29. 9%, 123. 1%, and 14. 3% respectively) or NBTXR3+PBT+αPD1 (by 97. 2%, 148. 8%, 189. 2%, and 37. 7%, respectively) (Figure  4A ). We also found that, as suggested by gene expression data from the secondary tumors (Figure  3C ), NKT cells were present and plentiful, although their abundance did not change meaningfully in response to treatment.
When directly comparing the tumors of mice treated with Changes in gene expression in adaptive and innate pathways. Secondary tumors were harvested from mice (n = 4-5) treated with different combinations of NBTXR3, PBT, and αPD1 10 days after irradiation. Total RNAs were extracted from the tumors, followed by an analysis of immune-related genes with an nCounter PanCancer Immune Profiling Panel. The gene expression data were then analyzed with the PanCancer Immune Profiling Advanced Analysis Module. Activity scores of immune pathways and immune cell abundance were analyzed using either ordinary 1-way ANOVA or the Kruskal-Wallis test. Data are expressed as mean ± SEM. P < 0. 05 was considered statistically significant. *P < 0. 05; **P < 0. 01. NS, not significant.
Acknowledgments This work was supported by  Cancer Center Support (Core)  Grant  CA016672  to The  University of Texas MD Anderson Cancer Center  and  NIH   1S10OD024977-01  award to the  Advanced Technology Genomics Core at The University of Texas MD Anderson Cancer Center ; the  Goodwin  family research fund; the family of  M. Adnan Hamed  and the  Orr Family Foundation to MD Anderson Cancer Center 's  Thoracic Radiation Oncology program ; an  MD Anderson Knowledge Gap  award; and  Nanobiotix. We thank all those who supported the study. Address correspondence to:  James Welsh , 6565  MD Anderson Blvd. , Houston, Texas 77030, USA. Phone: 832. 829. 1520;Email: Jwelsh@mdanderson. org.
Thermal images are widely used for various applications such as safety, surveillance, and Advanced Driver Assistance Systems (ADAS). However, these images typically have low contrast, blurred aspect, and low resolution, making it difficult to detect distant and small-sized objects. To address these issues, this paper explores various preprocessing algorithms to improve the performance of already trained object detection networks. Specifically, mathematical morphology is used to favor the detection of small bright objects, while deblurring and super-resolution techniques are employed to enhance the image quality. The Logarithmic Image Processing (LIP) framework is chosen to perform mathematical morphology, as it is consistent with the Human Visual System. The efficacy of the proposed algorithms is evaluated on the FLIR dataset, with a sub-base focused on images containing distant objects.
Various combinations of these approaches have been tested to select the most efficient ones. Concerning the experiments, we know that multiple databases exist for visible images, but only a few exist for thermal images. CNN are known to perform better with large databases. In our case, available thermal images are in much lesser quantities than visible images. To compare our results to existing ones obtained with CNN object detectors like YOLOv4 or Ef-ficientDet, we have chosen to work with the classical dataset FLIR. More precisely, we have created a subset of FLIR, constituted of images containing small sized objects, namely pedestrians and cars.
Convolutional Neural Networks (CNN) have also been tested for increasing thermal images resolution  [9] , which could improve measure and detection precision. Another problem regarding thermal images concerns the small number of databases, unlike the case of visible images. This is the reason why some authors predict a thermal image from a visible image. Using a segmentation approach, Lile et al. [10]  develop a method predicting a thermal image from a visible one, then comparing it to a real thermal image to detect defects based on the differences found. This situation can be connected to  [11] , in which a conditional-Generative Adversarial Network (c-GAN) is developed to generate synthetic SEM images. First, a Convolutional Neural Network is pre-trained on real images. Then the transfer-learned CNN is trained on synthetic SEM images and validated on real ones.
This approach could be of interest to synthetize thermal images from visible images. We can also refer to  [12] , in which a pose transfer method is proposed to produce a new image of a target person in a novel pose. Such a result is valuable in several applications. In the paper, it is used for person reidentification. In our case, it could be used to expand the thermal images database. Thermal images are often blurred, and CNN can be used to remove the blur or at least reduce it. [13]  uses a pre-processing step to sharpen thermal images, assisting in the detection of defects in scenarios where temperature variation within the scene is minimal or extreme. [14]  reviews multiple techniques to increase edge information in CNN based segmentation algorithms. The use of a multi-level attention Module (MAM) composed of two sub-modules: Context Aggregation Module (CAM) and Correlation Matrix Correction Module (CMCM) permits to enhance the object edge information across the different layers of the neural network.
In  [4]  and  [5] , the White Top-Hat transform is used with a rather empirical modification whose goal is to enhance the contrast of hot objects: the image f is replaced by f + WTH t (f ), which means that the peaks height is doubled, with the risk of going out of the grey scale. For this reason, knowing that the LIP addition remains inside the grey scale, we propose to use a LIP addition instead of a classical one, and thus compute f ⨹ WTH t (f ). An example is presented in Fig. 2  for a thermal image of the FLIR dataset. As shown in Fig. 2 , the classical addition of WTH t (f ) to f clearly generates saturated regions while the LIP addition of WTH t (f ) to f solves this problem. Another significant benefit is that such an enhancement preserves the objects representation in comparison to the learned ones, which will favor their detection.
Super resolution In the case of an already trained network, modern CNN architecture permits to increase the input image resolution without having to retrain it. Increasing the network resolution has the advantage of detecting smaller targets, despite an increase of the processing time. Considering the same original dataset, in this approach, the input images require an upscale too. A bilinear or bicubic interpolation is usually used for this task. But in our situation, image Super Resolution (SR) appears particularly interesting. SR algorithms aim at increasing images resolution while keeping precise and sharpened details in compar- ison of classical interpolation methods generating blur and approximated information (e. g. bicubic interpolation (BC)). Generally, SR methods provide an increase in resolution by a factor × 2, × 3 or × 4.
High resolution images correspond to the original images of the training dataset (with the native resolution). Low resolution images are obtained by reducing the images size by factors × 2, × 3 and × 4. The network is trained to retrieve high resolution images from their low-resolution version. Since three resized factors have been used in the training phase, we can choose between these different factors when executing the network. Figure  6  proposes a comparison between resized images by bicubic interpolation and by our specialized SR network. The SR image looks slightly sharper than the interpolated one. A comparison between SR and bicubic upscaling is proposed in Table  1. On some images of the FLIR dataset, PSNR and SSIM metrics are evaluated between Ground Truth (GT) and upscaled images.
FLIR dataset and validation subset selection The FLIR ADAS dataset [37] is a dataset of thermal images proposed by the FLIR company, a thermal image sensor manufacturer. This set is constituted of 10228 images with a resolution of 640 × 512 pixels and includes 80000 annotations. Images have been acquired in the Santa Barbara region in California in various situations of daytime and weather. Thermal images are proposed in two formats, RAW 14 bits images and 8 bits preprocessed images. The last ones are issued of FLIR own enhancement and domain adaptation algorithms. We choose to use the RAW thermal images provided by FLIR, normalized into an 8 bits greyscale with a dynamic expansion centered on a fixed mean m = 0. 5 and standard deviation of σ = 0. 25. The motivation to use RAW images instead of the already transformed ones is to avoid artifacts generated by FLIR's own enhancement algorithm and to apply a custom enhancement algorithm.
In Fig. 7 , a FLIR enhanced image is compared to our normalization combined with Unsharp Masking: this last approach preserves the details of the persons while the FLIR transformation saturates the information. The FLIR ADAS dataset provides annotations for four classes: persons, cars, bicycles, and dogs. Table  2  presents the classes distribution. In this paper, due to the large unbalance between the classes, we decided to focus only on the two following classes: persons and cars. The FLIR ADAS dataset is not perfect. The annotations of small objects are not present. It can be explained by the difficulties to annotate them due to the poor quality of thermal images. To obtain accurate scores of detection performance, 693 images from the validation dataset were chosen and reannotated. One hundred of these images were chosen because of their interesting scene, with vehicles relatively far, and pedestrians crossing the street in the background.
Fine tuned YOLOv4 YOLOv4 is an object detector proposed by Bochkovskiy et al. [39]  leveraging multiple features to obtain the best trade-off between inference time and detection accuracy. The authors aim to design a detector suitable for systems production and optimizable for parallel computation. This detector is built with CSPDarknet53  [40]  as backbone due to its state-of-the-art classification results on MSCOCO  [41]. CSPDarknet53 is associated with a Spatial Pyramid pooling module, increasing the separation of significant features without reducing the processing speed. The path-net aggregation neck of the detector is PANet  [42] , shortening the information path between lower layers and topmost feature. At last, the detector head is the same as YOLOv3  [43] , anchor based. YOLOv4 also uses state-of-the-art training strategies and data augmentation methods, increasing detection  performance and overall robustness.
The network architecture is presented in Fig. 9  and a performance comparison chart given in  [39]  is shown in Fig. 10. In our experiments, we use a fine-tuned YOLOv4 on the FLIR ADAS dataset  [28]. The fine-tuning was made with the following parameters: -Resolution: 640 × 512 pixels -Optimizer: Stochastic Gradient Descent -Learning Rate: 0. 001 The optimizer and learning rate were selected in accordance with the original publication of the network. The train and validation split provided by FLIR was kept. The mean Average-Precision, mAP, score was computed on the validation set at each epoch for a minimum of 60 epochs, the model obtained is the one with the best score along the training. This model has the state-of-the-art detection performance on the FLIR ADAS Dataset.
Fine-tuned EfficientDet EfficientDet  [44]  is a detector using the Efficient-Net  [45]  architecture as backbone. EfficientNet was designed to improve efficiency and accuracy of CNN's by using a compound scaling method to scale the network architecture and feature resolution. This allows to balance the trade-off between accuracy and efficiency. Ef-ficientDet uses a weighted Bi-Directional Feature Pyramid Network, BiFPN, to fuse features from different scales and a Single Shot Detector, SSD, as its head, to predict the bounding boxes and class probabilities of the objects in a scene. As shown in the performance comparison chart depicted in Fig. 10 , the deepened architecture of EfficientDet, like EfficientDet-D3 and EfficientDet-D4 outperforms YoloV4 on the MS COCO dataset, albeit with a significantly decreased inference speed.
We have fine-tuned the EfficientDet-D3 model, pretrained on the MS COCO dataset. We used the following parameters: -Resolution: 640 × 640 pixels -Optimizer: AdamW  [46]  -Learning Rate: 0. 0002 As with the YoloV4 fine-tuning, we maintained the optimizer and learning rate used by the original authors, as well as the train validation split of the FLIR dataset. Unlike the previous architecture, our network required training with a square image size. To accommodate this requirement, we employed a resolution of 640 × 640 pixels. Starting from the original resolution of the FLIR images, we applied the letterbox algorithm, which encloses the original image within a square frame, preserving its aspect ratio. The model was trained on a minimum of 60 epochs, we retained the model with the best mAP score on the validation set.
Results and discussion In our study, we present the results in Tables  4 5 6 7 , which provide comparisons of Average Precision (AP) and mean Average Precision (mAP). The computation method for AP is defined in the MS COCO Dataset  [41]. Specifically, we used the AP 50 score, which corresponds to the AP computed at an IoU (Intersection over Union) threshold value of 0. 5. To further analyze the performance, we also considered the classification of AP 50 scores into Small, Medium, and Large categories as defined in  [41]. These categories are used to assess the performance of the detection model across different object sizes. The baseline score is obtained with the fine-tuned model presented in  [28]  on our validation subset. The four presented methods are: MTH (Multiscale Top-Hat), MTH LIP, UM (Unsharp Masking deblurring), SR (Super Resolution).
In both cases: single method and combined ones, the MTH LIP appears essential. In our experiments, using FLIR images as training dataset, the scores obtained by EfficientDet-D3 are globally lower than those obtained with YoloV4 while these networks show more similar performances on the COCO dataset. These performance differences can be explained by the dataset sizes (328,000 images in the COCO dataset vs 10,228 in the FLIR dataset) and the ability of YoloV4 to perform new augmentation techniques  [39]. YoloV4 appears more adapted to reduced datasets. The execution times of fine-tuned models and preprocessing algorithms are presented in Tables  8  and 9 , respectively. Additionally, the frames per second (FPS) obtained with the full pipeline using YoloV4 or Effi-cientDet are also reported. These times were obtained using an Intel Core i9-10850k CPU and an NVIDIA RTX3090 GPU, with Python, PyTorch 1.
Conclusion and perspectives The objective of the present paper was to overcome the main drawbacks of thermal images and improve the detection of small objects. Three methods were studied, each addressing a specific problem: the Multiscale Top-Hat transform and its LIP version to detect objects far from the sensor and enhance their contrast with the neighboring background, the Unsharp Masking method to deblur thermal images, and the CARN network to enlarge the spatial resolution. The most efficient combinations of these techniques allowed for a significant gain compared to the baseline results. These techniques showed a real impact on performance without having to retrain a detection network, knowing that dataset acquisition and annotation are tedious tasks, especially in the case of thermal images.
Abstract The measurements of dissolved organic carbon (DOC), nitrogen (DON), and phosphorus (DOP) are used to characterize the dissolved organic matter (DOM) pool and are important components of biogeochemical cycling in the coastal ocean. Here, we present the first edition of a global database (CoastDOM v1; available at https://figshare. com/s/512289eb43c4f8e8eaef) compiling previously published and unpublished measurements of DOC, DON, and DOP collected in coastal waters. These data are complemented by hydrographic data such as temperature and salinity and, to the extent possible, other biogeochemical variables (e. g. , Chlorophyll-a, inorganic nutrients) and the inorganic carbon system (e. g. , dissolved inorganic carbon and total alkalinity). Overall, CoastDOM v1 includes observations from all continents however, most data were collected in the Northern Hemisphere, with a clear gap in coastal water DOM measurements from the Southern Hemisphere.
The data included were collected from 1978 to 2022 and consist of 62339 data points for DOC, 20360 for  DON and 13440  for DOP. The number of measurements decreases progressively in the sequence DOC > DON > DOP, reflecting both differences in the maturity of the analytical methods and the greater focus on carbon cycling by the aquatic science community. The global database shows that the average DOC concentration in coastal waters (average (standard deviation; SD): 182 (314) µmol C L -1 ; median: 103 µmol C L -1 ), is 13-fold greater than the average coastal DON concentrations (average (SD): 13. 6 (30. 4) µmol N L -1 ; median: 8. 0 µmol N L -1 ), which was itself 39-fold greater than the average coastal DOP concentrations (average (SD): 0. 34 ± 1. 11 µmol P L -1 ; median: 0. 18 µmol P L -1 ).
This dataset will be useful to identify global spatial and temporal patterns in DOM and to facilitate reuse of DOC, DON and DOP data in studies aimed at better characterising local biogeochemical processes, closing nutrient budgets, estimating carbon, nitrogen and https://doi. org/10. 5194/essd-2023-348 Preprint. Discussion started: 27 September 2023 c Author(s) 2023. CC BY 4. 0 License.
, 2021). Most data, however, are often unavailable or stored in an inaccessible manner, making it difficult to e. g. , analyse global spatial and temporal patterns effectively. A global open ocean DOM data compilation already exists, but it contains few coastal samples (< 200m)  (Hansell et al. , 2021). Hence, there is a clear need for a comprehensive global and integrated database of DOC, DON and DOP measurements for coastal waters. To address this need, we have prepared the first edition of a coastal DOM database (named CoastDOM v1), by compiling both previously reported as well as unpublished data. These data have been obtained from authors of the original studies or extracted directly from the original studies. In order to allow the DOM measurements to be interpreted across larger scales, and to better understand their relationship with local environmental conditions, we have included concurrently collected ancillary data (such as physical and/or chemical seawater properties) whenever available.
The objective of this database is multifaceted. Firstly, we aimed to compile all available coastal DOM data into a single repository. Secondly, our intention was to make these data easily accessible to the research community and thirdly, we sought to achieve long-term consistency of the measurements, to enable data intercomparison, and establish a robust baseline for assessing, for example, the impacts of climate change and land use changes. The measurements included in CoastDOM v1 were obtained either directly from authors of previously published studies, online databases, or scientific papers. An extensive search of published reports, Ph. D. theses, and peer-reviewed literature was performed to identify studies dealing with DOM in coastal waters. First, a formal search was performed using Google Scholar in January 2022 using the search terms "dissolved organic carbon", "dissolved organic nitrogen", and "dissolved organic phosphorus" in connection with "marine" or "ocean", which yielded a total of 897 articles (after filtering the query by searching content in the title and abstract and excluding non-coastal articles).
Description of the dataset The data compiled in CoastDOM v1 were collected, analysed and processed by different laboratories, however, all data included have undergone quality control measures, either by using reference samples or internal quality assurance procedures. While many of the included DOC and TDN data have been systematically compared against consensus reference material (CRM) mainly provided by the University of Miami's CRM program  (Hansell, 2005)  measurements (e. g. ,  (Sharp et al. , 2002). Since additional quality control is not possible in retrospect, we assessed the quality of CoastDOM v1 based on its internal consistency. In CoastDOM v1, we defined "coastal water" as encompassing estuaries (salinity > 0. 1) to the continental shelf break (water depth < 200 m). However, some locations, such as deep fjords which are close to the coast cannot be classed as coastal due to bathymetry (deeper than > 200 m).
Therefore, we evaluated the inclusion of some datasets on a case-by-case basis. For inclusion in the database, each DOM measurement needed at a minimum to contain the following information (if reported in the original publication or otherwise available): -Country where samples were collected -TA -QA flag: Quality flag for TA measurement Quality control of large datasets is crucial to ensure their reliability and usefulness. Thus, we have not included data that were deemed compromised, such as records that had not gone through quality control by the data originators. We also accepted a certain degree of measurement error since multiple groups have been involved in the collection, analysis, and/or compilation of the information. Some of these errors were corrected (e. g. , when a value was placed in a wrong column, or clearly inaccurate locations were reallocated for consistency with the place of study), while others could not be rectified (e.
g. , values showing clear signs of contamination) and were consequently excluded from CoastDOM v1. It should also be noted that differences in analytical capabilities between laboratories and individual measurement campaigns likely caused additional uncertainty. Outliers, arising for example from contamination, were removed from the dataset. The data were moreover screened for zero values (i. e. , concentrations below the detection limit or absence of data). In cases where concentrations were below the detection limit, the zero values were replaced with half the value of the limit-of-detection. To ensure the inclusion of only high-quality data, we only accepted entries with specific World Ocean Circulation Experiment (WOCE) quality codes: "2-Acceptable measurement" and "6-Mean of replicate measurements".
Summary of dissolved organic nitrogen (DON) observations The DON measurements were collected between 1990 and 2021, with a total of 20357 data points (Table  1 ). Concentrations of DON ranged from < 0. 1 to 2095. 3 µmol N L -1 (average (SD): 13. 6 (30. 4) µmol N L -1 ; median: 8. 0 µmol N L -1 ; Table  1 ), with the most common range (42%) for DON concentrations between 4 to 8 µmol N L -1 (Fig. 1 ). Overall 75% of DON concentrations were above 5. 5 µmol N L -1 , while 25% were above 15. 8 µmol N L -1 (Table  1 ). The lowest DON concentrations were recorded in Young Sound, Greenland, which receives direct run-off from the Greenland Ice Sheet, whereas the highest concentrations were detected during a flood event in the Richmond River Estuary, Australia. Since 1995, there has been a large increase in the number of DON measurements conducted in coastal waters globally (Fig.