dataesr/scientific-paragraphs-categorization huggingface dataset:
71 The measurements yield Π R = 6. 22 ± 0. 88% along ψ R = 1. °9 ± 0. °3, consistent with the contemporaneous POLAMI observation. Optical polarization coverage was provided by the Calar Alto (Calar Alto Faint Object Spectrograph, CAFOS; R band), Higashi-Hiroshima Observatory (Kanata telescope) with the Hiroshima Optical and Near-InfraRed camera (HONIR; R and J band), NOT with the Alhambra Faint Object Spectrograph and Camera (ALFOSC; in BVRI), the 1. 8 m Perkins Telescope (BVRI), and the Sierra Nevada Observatory (SNO; R band). A detailed description of the observing strategy and data reduction of the aforementioned telescopes can be found in  Kawabata et al. (1999) ,  Akitaya et al. (2014 ), Hovatta et al. (2016) ,  Marscher & Jorstad (2021) ,  Liodakis et al. (2022), and Middei et al. (2023) , respectively, and references therein.
under a joint agreement with the  University of Copenhagen  and NOT. E. L. was supported by  Academy of Finland  projects  317636  and  320045. We acknowledge funding to support our NOT observations from the  Finnish Centre for Astronomy with ESO (FINCA) ,  University of Turku, Finland  ( Academy of Finland  grant nr 306531). The research at  Boston University  was supported in part by  National Science Foundation  grant  AST-2108622 ,  NASA   Fermi Guest Investigator  grants  80NSSC21K1917  and  80NSSC22K1571 , and  NASA   Swift Guest Investigator  grant  80NSSC22K0537. This study used observations conducted with the 1. 8 m  Perkins Telescope Observatory (PTO) in Arizona (USA) , which is owned and operated by  Boston University. The above study is based in part on observations obtained with  XMM-Newton , an  ESA  science mission with instruments and contributions directly funded by  ESA Member States  and  NASA.
We are grateful to the  NuSTAR team  for approving our DDT request. This work was supported under  NASA  contract No. NNG08FD60C , and made use of data from the NuSTAR mission, a project led by the  California Institute of Technology , managed by the  Jet Propulsion Laboratory , and funded by the  NASA. This research has made use of the NuSTAR Data Analysis Software (NuSTARDAS) jointly developed by the  ASI Science Data Center (ASDC, Italy)  and the  California Institute of Technology (USA). This work was supported by  JST , the establishment of university fellowships toward the creation of science technology innovation, grant No. JPMJFS2129. This work was supported by  Japan Society for the Promotion of Science (JSPS) KAKENHI  grant No. JP21H01137. This work was also partially supported by  Optical and Near-Infrared Astronomy Inter-University Cooperation Program  from the  Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan.
We are grateful to the observation and operating members of Kanata Telescope. M. N. acknowledges the support by  NASA  under award number  80GSFC21M0002. C. C. acknowledges support by the  ERC  under the  Horizon ERC Grants 2021 program  under grant agreement no. 101040021. S. K. , S. -S. L. , W. Y. C. , S. -H. K. , and H. -W. J. were supported by the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government  (MIST;  2020R1A2C2009003 ). The KVN is a facility operated by the  Korea Astronomy and Space Science Institute. The KVN operations are supported by  KREONET  ( Korea Research Environment Open NETwork) , which is managed and operated by  KISTI  ( Korea Institute of Science and Technology Information ). Partly based on observations with the 100 m telescope of the  MPIfR (Max-Planck-Institut für Radioastronomie) at Effelsberg.
Observations with the 100 m radio telescope at Effelsberg have received funding from the  European Unionʼs   Horizon 2020 research and innovation program  under grant agreement No  101004719  (ORP). A. L. P. acknowledges support from  NASA FINESST  grant  80NSSC19K1407  and the  Stanford Data Science Scholars program. Facilities: Calar Alto, Effelsberg-100 m, IRAM-30 m, IXPE, Kanata, KVN,  Nordic Optical Telescope, NuSTAR, Perkins, SMA, XMM-Newton , and Swift.
Microscopy image analysis Cells in the bright-field channel of pre-MALDI microscopy were segmented using a custom Cellpose model  34  trained on manually annotated fragments of images. Since the primary cell culture contains cells of varying size, segmentation for the largest cells had to be manually corrected. Ablation marks were detected in the post-MALDI microscopy by manually fitting a grid of circular shapes. Registration of the pre-and post-MALDI images was done using the SpaceM software tool as previously published  31.
Constructing single-cell isotopologue profiles MALDI images for unlabeled samples were annotated with the cloud software METASPACE (http://metaspace2020. eu) at 5% FDR to determine the most abundant detected fatty acids. For each fatty acid, intensities corresponding to theoretical isotopologue peak masses were extracted. Raw intensities were normalized for natural isotope abundance using IsoCor  35  , after which every isotopologue distribution was normalized by its sum. Ablation marks with a total raw intensity of less than 200 for a given fatty acid were removed from the analysis. Ablation marks which had at least 30% overlap with the cell mask were considered intracellular. After normalizing spectra in each pixel, the median of values in the intracellular ablation marks was assigned as a singlecell readout for each isotopologue peak.
Aim: Drug discovery is a long process, often taking decades of research endeavors. It is still an active area of research in both academic and industrial sectors with efforts on reducing time and cost. Computational simulations like molecular docking enable fast exploration of large databases of compounds and extract the most promising molecule candidates for further in vitro and in vivo tests. Structure-based molecular docking is a complex process mixing both surface exploration and energy estimation to find the minimal free energy of binding corresponding to the best interaction location. Methods: Hereafter, heterogeneous graph score (HGScore), a new scoring function is proposed and is developed in the context of a protein-small compound-complex. Each complex is represented by a heterogeneous graph allowing to separate edges according to their class (inter-or intra-molecular).
Dataset To compare the performance of the proposed method and other works, the Protein Data Bank-bind (PDBbind) 2020 dataset  [12]  is used. It contains 19,443 ligand-protein complexes, each input of which is composed of a Protein Data Bank (PDB) file representing the protein, a PDB file for the protein binding pocket, and a MOL2 file for the ligand. In PDBbind, a binding pocket contains all residues whose at least one heavy atom is closer than 10 Å� to one of the ligand atoms. Each ligand-protein complex has its associated binding affinity, express by -log(K i ) or -log(K d ) as a function of the complexes. Hereafter, only the protein binding pocket is used to describe the protein part. PDBbind authors have proposed splitting their database into several sets, as shown in Figure  1. The first is the general set containing all database inputs, while the second is the refined set created by selecting only the best-quality complexes from that first set.
Both sets are updated each year. Finally, the third set consists in a core set that contains very few inputs but with high-quality  [13]. In this work, the versions 2013 and 2016 of the core set are used as test sets (195 and 285 items, respectively), and 1,000 inputs from the refined set are randomly extracted to build the validation set and the remaining input as the training set (18,070 items). It is ensured that there are no duplicates between sets, except for the core sets which naturally have overlapping entries (elements from 2016 already present in the 2013 dataset). Also, in the pre-processing step, the protein files are cleaned by keeping only protein atoms, so that water and other hetero atoms are removed.
X: applicable; -: not applicable Figure  3. A protein-ligand complex and its associated heterogeneous graph. On the left, the 29G11 protein (in red) complexed with the ligand HEP in green (PDB: 1a0q). On the right, the heterogeneous graph represents the same complex, red nodes represent the protein binding site atoms, and the green nodes are the ligand atoms, both without hydrogen. Red and green edges represent intramolecular bonds, red edges belong to the protein while green edges belong to the ligand. Finally, the cyan edges are the inter-molecular links representing the protein-ligand interaction.
Results The method's implementation is based on PyTorch, Lightning, and PyTorch Geometric  [16]  to build the graph data, the datasets, and the model, and perform training and evaluation. Optuna was used to perform hyper-optimization to find the best architecture and perform the training with the same hyperparameter set several times over because the model is sensitive to weight initialization  [23]. As a result, the model is composed of 3 atomic embedding layers with mean Aggr whose sizes are {48, 57, 68} for the protein part and {48, 76, 121} for the ligand part, followed by 4 molecular embedding layers. All atomic parts have 2 heads for their respective GATv2Convs. The interaction fingerprint is a vector of size 189 fed to an MLP with channels {189, 94, 1}. During training, a learning rate of 10 -3 associated with the Adam optimizer and a weight decay equal to 10 -4 are used.
The results for the scoring power are presented in Figures  8  and 9  and Table  3. The scoring power results show a good R p , especially for the CASF 2016, and both error metrics (MAE and RMSE) are pretty low, thus, the model is validated with respect to the scoring power. ↑ indicates that the larger the value, the better; ↓ indicates that the smaller the value, the better Ranking power results are presented in Table  4 , which shows that the model has good performance for the ranking power, especially on the CASF 2016 dataset. Docking power results are presented in Tables  5  and 6. As shown in Table  5 , the method ranks perfectly 1/3 of the native ligands (or a closer one) among the decoys. Moreover, the method ranks more than half of the native ligands (or a closer one) in the top 2 and 3.
Discussion In addition to the intrinsic characterization of the new scoring method, the HGScore results are compared to some other DL scoring methods in Tables  7  and 8  respectively for CASF 2013 and 2016. The other methods compared are Pafnucy  [4] , DeepBindRG  [5] , OnionNet  [7] , IGN  [10] , and ECIF6::LD-GBT  [11]. Some classical scoring function performances on CASF 2013  [24]  and CASF 2016  [13]  are presented to provide a no AI-centric comparison. The scoring function was used with the software a Gold and b Sybyl. c the number of tested complexes is too low to be compared with the other methods. ↑ indicates that the larger the value, the better; ↓ indicates that the smaller the value, the better. N is the number of tested complexes, some methods have been tested only on a subset of the benchmark.
-: not applicable HGScore is among the best results on the CASF datasets for the scoring power metrics (Table  8 ), and the ability of the method to split the learning following the bond type may be an important part of the improvement. However, compared to existing scoring methods, the proposed new score does not bring significant improvement in the case of the CASF 2013 dataset (Table  7 ); it may be because the model of the present study has been trained on PDBbind version 2020. Indeed, the CASF datasets are the most representative complexes of PDBbind respectively in versions 2013 and 2016. Although the IGN method has a R p of 0. 83, the CASF 2013 test set is reduced to only 85 items which is far from the whole set, thus it is difficult to conclude about this R p value. Concerning the ECIF6::LD-GBT method, the CASF 2016 is used for both the validation and test sets, whereas for HGScore the test set is unknown when the model is tested on it, which is the canonical way of proceeding.
Since the training set impacts the results of the scoring, it is of utmost importance to compare the performance of the HGScore with the performance of the score of other methods trained, validated, and tested identically. That is why the Pafnucy method  [4]  was also trained from scratch on the same HGScore dataset split. Tested on CASF 2016 (Table  8 ), the HGScore method still outperforms Pafnucy trained in a completely identical way (starred line in Table  8 ). Because of the difficulty to get the source codes and then to execute them easily, in a strictly identical way to HGScore, only the Pafnucy method has been retrained. In conclusion, the new proposed DL method allows scoring protein-ligand complexes. Based on the Attentive FP method, the presented model brings the originality to split the learning process regarding the interaction class, ligand to ligand, protein to protein, ligand to protein, and protein to ligand.
That general idea allows the model to learn more precisely the effect of the molecules themselves and the interaction between them. The method does not propose a new type of layer but rather a new way to represent ligand-protein complexes, using a heterogeneous graph. Trained on the PDBbind dataset, like almost all counterpart methods, HGScore reaches the top of the methods assessed on the CASF 2016 dataset. Moreover, the method largely outperforms the classical scoring methods presented in a previous article  [1]. Although HGScore does not bring a huge improvement on the main test metric (R p on CASF 2016), it outperforms all methods blindly tested on CASF. Moreover, the quality already achieved by the metrics implies that their improvement cannot be very large anymore. As mentioned previously the network is split according to the interaction category.
Metrics computed on the CASF 2016The scoring function was used with the software a Gold and b Sybyl. c a problem for comparison, either the dataset is not fully tested, or the test set is also used for training or validation. ↑ indicates that the larger the value, the better; ↓ indicates that the smaller the value, the better. N is the number of tested complexes, some methods have been tested only on a subset of the benchmark, methods with N of 290 have been tested on a former CASF 2016 version.
The k3s-server embeds all the Kubernetes Control Plane components, replaces etcd with a more lightweight in-process SQLite database, and adds networking, monitoring, and load-balacing components. The k3s-agent packages in a single binary all the Kubernetes Compute Plane components. It is worth noticing that etcd is the most resource demanding component of Kubernetes. 3) KubeEdge: it is an extension to Kubernetes for Edge hosts. The goal of KubeEdge is to enable the deployment of applications designed for Kubernetes at the Edge. KubeEdge components are organized in Cloud components and Edge components. KubeEdge Cloud components are the Control Plane components, they communicate with an external Kubernetes cluster northbound and with KubeEdge nodes southbound. KubeEdge control plane is expected to be deployed in a centralized location.
Deep learning models for cell instance segmentation have recently reached the quality of manual annotations  (2) (3) (4)  , especially thanks to the emergence of models like U-Net  (5). Cellpose  (4)  is a notable U-Net-based approach, which uses a multi-modal training dataset spanning several cell types and cell lines imaged under a variety of different imaging methods. It also benefits from being associated with a large community which incrementally increases the size and diversity of the dataset, in turn improving the performance of the model. Cellpose approaches the problem of multiple instance segmentation by predicting spatial gradient maps from the images, from which individual cell segmentations can be inferred. Other recent deep learning segmentation methods for cell biology are StarDist  (2)  and Nucle-AIzer  (3)  which make use of the U-Net architecture as well but with different representation for their images, and Mask-RCNN  (6)  which directly segments regions of interests (ROIs) in images with deep learning methods.
We select Cellpose as a foundation for our approach as it is a widely used and welldesigned framework which offers to be the most generalist with its community-driven, ever-expanding training dataset. However, a limitation of Cellpose is its heavy reliance on structural FLs for nucleic and cytoplasmic segmentation. Cellpose was trained on several datasets, most of which relied on active staining of organelles' structural proteins (e. g. , cytoskeleton for cytoplasm)  (7). Indeed, only 15% of the Cellpose training dataset contains other fluorescent FLs. While such structural FLs are generally integrated in assay designs, being able to segment cells without using them allows to maximize the number of nonstructural FLs. Since the total number of available microscopy channels is subject to numerous limitations-such as the bleedthrough effect  (8)  -and is typically limited to 4, the two extra channels made available can lead to assays delivering richer information about cellular processes.
Our framework requires few annotations to finetune a pre-trained generalist deep learning segmentation base model-here Cellpose-on each FL with a small set of annotated images. We show, on multiple datasets that we provide, that by combining the predictions from multiple nonstructural FLs with various structural characteristics, we are able to reach segmentation performance comparable to the state of the art without relying on structural FLs. Our contribution has a significant impact on (a) cost, as we reduce the number of assays needed to extract the same biological information, by removing the dependency on one or two experimental structural FLs for accurate cell segmentation and (b) possible discoveries in downstream analysis, as we can monitor additional functionally relevant FLs for the exact same cells, thus obtaining a richer description of each cell's phenotypic response to a perturbation.
Image data We acquired data on five multi-color reporter cell lines derived from commonly used U2OS  (10)  and A375  (11)  cancer cell lines, that we designated CL1, …, CL5. In this article, we work with live-cell imaging data where fluorescent labeling is done by tagging proteins of interest with fluorophores, the combination of which we refer to as fluorescent reporter proteins (FRPs). Each reporter cell line expresses three or four spectrally distinct FRPs. Unless otherwise stated, we acquired all images with a Nikon A1R confocal microscope as a live video microscopy imaging sequence. For each experimental condition, we acquired images on three xy positions every 3 hr for 72 hr, at various optical zooms. The dataset was acquired in the context of perturbation screens, in which cells are treated with multiple compounds, in a high-content screening (HCS) setup.
Annotated dataset We manually annotated the nuclei and cell boundary of 50 (resp. 28 and 22) images for each cell line/FL combination of cell lines CL1, CL4, and CL5 (resp. cell lines CL2 and CL3). We randomly selected these images from different experimental conditions and evenly distributed over time in order to capture the dynamic localization of certain proteins (e. g. , due to experimental treatments and levels of expression at different stages of cell cycle) as well as possible variations in population size caused by cell division or cell death. The number of cells per image ranges from 20 cells to more than 100 in some images. For each set of annotated images, 80% were used for training, 10% for validation, and the last 10% for evaluation. With an average of 50 cells per images, we have about 250 cells per validation/test set and approximately 2,000 individual cells in the training set, an appropriate number for training and evaluation.
The annotations were carried out and validated by multiple biologists using the different channels available in combination. Example images and manual annotations are displayed in Figure  1. Furthermore, to test our method for robustness to changes in assay and acquisition parameters, we also annotated five additional images of the CL1 cell line acquired with a different microscope (widefield) and higher temporal resolution, resulting in noisier images. We used this dataset for evaluation purposes only as presented in Section 4. 3. 2. All datasets are publicly available at doi. org/10. 6084/m9. figshare. 21702068.
Generalist segmentation model backbone In this article, we use Cellpose  (4)  , a state-of-the-art cell segmentation model, as a generalist segmentation model backbone. We note, however, that our approach is model-agnostic and could therefore use alternative backbones  (2, 3). Cellpose segments images using a three-part pipeline: Firstly, it resizes the images so that the average cell diameter of the dataset conform to the model original training cells' diameter. Secondly, for a cell object o ∈ nuclei, cyto f g , a Cellpose model M o maps a rescaled image Î intrinsic intensity space to a flow and probability space F X , F Y ,P ð Þ. The flow maps F X ,F Y are the derivatives (along the X and Yaxes) of a spatial diffusion representation of individual cell pixels from the cell's center of mass to its extremities.
Segmentation model finetuning parameters To finetune Cellpose, we train from the available generalist pre-trained model on our dataset using the approach detailed in Section 3. 2. We retain the model's hyper-parameters from the original Vanilla Cellpose training with the following two exceptions: (a) as detailed in Section 3. 4, we use nondeterministic augmentations on each of our training samples; (b) we stop the training using early stopping on the validation set  (12)  , with a patience of 50. Both additions are efficient regularization methods limiting over-fitting and contributing to the overall robustness of the segmentation methods with respect to changes in the imaging setting.
Data augmentation Augmentation methods are used during training of the Vanilla Cellpose model to both virtually increase the size of our dataset as well as offer better generalization. They are performed iteratively from scratch on each image batch. For methods involving random distributions, the parameters are uniformly sampled from a pre-defined parameter range. Each augmentation has an application probability p augment ¼ 0:5, adding more variability across epochs and samples. The augmentation methods are described in Table  2. N μ ¼ 0, σ ð Þ σ $ U 0,0:1 ð Þ Emulate variation in the expression of the lit-up fluorescent pixels and random background noise  (13)  Poisson Noise --Emulate the noise generated by the fluorescence\microscope imaging  (14, 15)  Salt and pepper I s , I p , and β sap ¼ 0:05 I s=p $ U 0,1 ð Þ Emulate variation in the location and expression of the FLs by simulating activation or inhibitions of fluorescent proteins  (16).
Segmentation evaluation 4. 3. 1. Results Figures  3  and 4  display the performance of our method using the F1-score metric. Those results were computed using fivefold cross-validation on annotated datasets. We evaluate the performance of Vanilla Cellpose against our method across every combination of training channels (channel-wise and multichannel) upon every combination of evaluation channel aggregated together using the FA fusion method. The Vanilla Cellpose scores are evaluated on several channels at once using this fusion method as well. Examples of our segmentation are displayed in Figure  5. Our results indicate that fine-tuning is an essential step when dealing with datasets which do not contain cytoplasmic and nucleic structural FLs, as indicated in Table With fine-tuning, we indeed obtain state-of-the-art level results on individual channels trained independently (CW), the fusion of channels evaluated through independently trained models (CW), and the fusion of channels trained together (MC).