dataesr/scientific-paragraphs-categorization huggingface dataset:
We can observe from the profile lines in Figure  8  that there is good agreement at the large scale for the three datasets, but significantly more topographic features are shown on profiles of the resultant LROC NAC MADNet DTM mosaic. 8 ), showing the superiority of significant topographic details and reduced artefacts over different areas. In general, we can observe from the colourised DTMs, an excellent agreement for large-scale topographic features between the PDS DTM and MADNet DTM, even though the MADNet DTM was referenced to the much lower-resolution CE-2 MADNet DTM. Such large-scale features can be referred to as the larger craters (e. g. , craters with ~500 m diameter and ~25 m depth in Figure  9  and ~750 m width and ~85 m depth in Figure  11 ), local hills, and the flat areas. Meanwhile, we can also observe a much larger number of fine-scale topographic features in the MADNet DTM compared to the PDS DTM, such as the small craters with less than 20 m diameter and less than 5 m depth.
Discussion The original MADNet work  [25]  was developed and demonstrated with different Mars orbital imaging datasets for the large-area high-resolution topographic mapping of the Martian surface  [39, 52]. In this paper, we retrain the same network with 392 pairs of the publicly available LROC NAC PDS DTMs and ORIs and demonstrate the same DTM production system can be robustly applied to a large number (total 370) of LROC NAC images in order to create a large-area (covering 260 × 209 km 2 ) high-resolution (at 1 m/pixel) DTM mosaic. The main challenge is the long shadows that are present on some of the LROC NAC images. This is not only a major issue for LROC NAC to CE-2 image co-registration but also for the MADNet image-to-height inference process. Since the finest scale inference is based on tiled small image patches (512 × 512 pixels), patches that are fully covered by shadow generally do not contain enough information for height inference.
Theoretically, with sufficient staffing and GPU resources, the same method could be extended to create a semi-global 1 m/pixel topographic of the Moon using the LROC NAC images as inputs and the CE-2 DTM and SLDEM as reference datasets. However, the same issue with shadowing still exists. Pre-filtering the input images with a strict threshold of incidence angle would increase success rates of the co-registration and DTM inference process but would result in much sparser coverage of the area, i. e. , more gaps or slivers. A future solution to this may be through the use of the new ShadowCam instrument that was developed by NASA and KARI (Korean Aerospace Research Institute)  [53]  and/or through applying fully trained de-shading networks to improve the LROC NAC images prior to the image-to-height inference process.
Acknowledgments: The research leading to these results received initial funding from the  UKSA Aurora program  ( 2018-2021 ) under grant  ST/S001891/1. The lunar processing was supported by  JPL  contract no. 1668434. The follow-up work is supported by the  German Space Agency (DLR Bonn) , grant  50 OO 2204  (Koregistrierung), on behalf of the  German Federal Ministry for Economic Affairs and Climate Action. We thank  Emily Law  and the following team members of the  NASA Solar System Treks :  Bach Bui ,  Richard Kim ,  Heather Lethcoe  and  Catherine Suh. CE-2 dataset acquisition was kindly supported by  Kaichang Di  of the  State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences.
During the mapping process, the input LROC NAC images have solar incidence angles between 45. 11° and 74. 95° and azimuth angles between 4. 49° and 357. 95°. At the initial training and tuning stage, we left out 1000 training pairs to form the test dataset. For testing purposes, root mean squared errors (RMSEs) and mean SSIMs [48] are.
RELATED WORK We discuss existing work that is most relevant to few-shot novel view synthesis in this section. Early deep learning based approaches used 2D convolutional encoder-decoder architectures mapping the sparse inputs to the target images  [9] [10] [11]. These methods were outperformed by 3D aware convolutional approaches  [12] [13] [14]. Although many of these could learn to generate 360-degree views from very sparse inputs especially for synthetic central object data, most of them could not scale to high resolution images, complex scenes, and real data such as MVS datasets (DTU  [8] ). Implicit neural radiance fields (NeRF)  [2]  emerged later on as a powerful representation for novel view synthesis. It presented initially however a few limitations such as compu-Fig. 2 : Overview: Given an input image, a 3D feature volume is built with a ConvNet (first black cube) and re-sampled into a volume representing the target view frustum (red cube).
Comparison on DTU dataset We demonstrate the capability of our method to generate novel views from sparse input views using the DTU benchmark  [8]. Following the PixelNeRF  [7]  experimental settings, the data is split into 88 training scenes and 16 testing scenes. Each scene contains 49 views, including 4 views for testing as suggested by MVSNeRF  [5]  and GeoNeRF  [22]. Our training does not require mask supervision, thus all evaluation are performed on full resolution image(400 × 300) rather than only foreground. For quantitative comparison, we report the peak signal-tonoise ratio (PSNR), structural similarity (SSIM) and learned perceptual image patch similarity (LPIPS) reconstruction metrics in Table  1  for 3 and 6 view inputs averaged across all testing scenes. We report numbers of PixelNeRF(PN) and MVSNeRF(MN) from RegNeRF  [23].
The design of accurate virtual screening tools is an open challenge in drug discovery. Several structurebased methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high e ciency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring.
However, the origin of the bene t(s) remains to be understood and what docking programs or scoring functions should be used for the consensus is not yet known. In addition, a wide range of docking programs and rescoring methodologies exist  [9] ,  [16] , each of them requiring knowledge and expertise, which makes consensus docking not straightforward for non-expert users. To ease the implementation of consensus scoring for virtual screening, we developed Chem ow_py, a Python interface for docking and rescoring. Chem ow_py provides straightforward access to ve popular docking programs, eight scoring functions, seven consensus methods, and free energy rescoring powered by Amber or Gromacs  [17]. The code is based on customizable Python modules, so that other docking programs can be easily integrated.
In the following, we present a benchmark of Chem ow_py to analyze the performance of docking with consensus ranking on four different protein targets extracted from Directory of Useful Decoys Enhanced (DUD-E)  [18]. Chem ow_py is a development of ChemFlow, our 'in house' software, which was designed to bridge the gap between 2D chemical libraries and protein-ligand binding free energies calculations  [19]. In this study, the focus is on docking and no free-energy rescoring was considered.
Datasets Four biological targets and corresponding ligands were retrieved from the Directory of Useful Decoys Enhanced  [18]. We chose proteins belonging to different protein classes: the β1 adrenergic receptor (ADRB1, PDB: 2vt4), the cyclin-dependent kinase 2 (CDK2, PDB: 1h00), the human immunode ciency virus type 1 protease (HIVPR, PDB: 1xl2), and HMG-CoA reductase (HMDH, PDB: 3ccw). DUD-E provides the PDB structure of the receptor along with MOL2 structures for experimentally active compounds, decoys and the crystal structure of (at least) one ligand co-crystallized with the protein  [18]. For each receptor, we randomly selected 100 known actives and 3000 decoys.
Docking Programs Five non-commercial docking programs are currently supported in ChemFlow_py: Autodock4  [20] , Autodock Vina  [21] , PLANTS  [22] , Smina  [23]  and QVina2. 1  [24]. In this study, we analyzed the performances of Autodock4, one of the rst open-source docking programs that uses a Lamarckian Genetic Algorithm and an empirical free-energy scoring function as defaults; Autodock Vina that relies on an Iterated Local Search global optimizer and uses a simpler scoring function for a more e cient conformational search  [21] ; PLANTS, the rst docking program based on the ant colony optimization algorithm that supports three empirical scoring functions, i. e. chemplp, plp and plp95  [25] ; and Smina, a fork of Autodock Vina designed to support custom scoring functions and improved energy and exibility features  [23].
Consensus In consensus docking, a new score is assigned to each ligand by combining scores or rankings from different docking programs. Many consensus protocols have been developed to improve docking performances. In this work, we used seven consensus ranking methods as described in Ref. [9]. These methods can be classi ed depending on whether the consensus is based on the score or the rank. This approach elicits ligands with high ranks in all docking programs. ERC assigns to each ligand an exponential score based on the ligand ranking and a normalizing parameter sigma (σ). As a rule of thumb, sigma is the number of molecules to analyze. Finally, the exponential scores are summed up and normalized over sigma. In our work, we set both the RBV threshold and sigma value to 5% of the dataset (155 molecules).
Metrics Area Under the Curve (AUC): The Receiver Operating Characteristic (ROC) curve is used to evaluate the performance of a classi cation method. In drug discovery, ROC curves are used to compare the performances of different models and evaluate how good they are in discriminating between true positives (active compounds) and false positives (decoys). The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various fractions of the dataset. The area under the curve (AUC) provides a measure of the classi cation performance: an AUC close to 1 indicates a perfect classi er, and an AUC close to 0. 5 indicates a random classi er  [30].
Enrichment Factor (EF) the enrichment factor provides another measure of the classi cation performance. The EF at a given fraction of the dataset, i. e. , at 1%, indicates if the fraction of active compounds in the top 1% of the ranked dataset is higher or lower than the fraction of hits over the full dataset. It is calculated as where and indicate the number of active compounds in the dataset and its x% topranked fraction. An EF of 10 indicates that the probability of nding hits in the top-ranked x% fraction is ten-fold increased. Viceversa, an EF of 1 or 0 indicates that the classi cation method does not improve the hit rate or that no active compound could be found in the top-ranked x%. Method Score (MS) The Method Score (MS) is a metric to compare the performances of rescoring or consensus scoring methods with the average docking performance.
For a given protein target, if the EF1% of a given rescoring method is higher than the average EF1% from docking, its MS value is increased by one (+ 1). Vice versa, if the consensus EF1% is lower, the MS is decreased by one (-1). The MS remains unchanged if the two EF1% are the same. Since the docking performances are typically target-dependent, the MS value indicates whether a given rescoring strategy outperforms or underperforms the average docking. Considering our benchmark made of four targets, an MS of + 4 indicates better performance for each dataset, 0 implies no average improvement, and - 4 indicates that the docking average systematically leads to better results.
Implementation ChemFlow_py is a software conceived to improve the outcome of vHTS work ow by running and postprocessing docking simulations with simple commands. It contains default settings for each docking program, which are ready for use by non-experts, but it also allows customizations for advanced users. The implementation of different docking programs often requires going through time-consuming ligand preparations and/or post processing. ChemFlow_py e ciently automatizes these steps, so that the user can focus on the work ow rather than its implementation.
EF x% = ⋅ H its (x%) N (x%) N (tot) H its (tot) N (tot) N (x%) Chem ow_py is an evolution of ChemFlow  [19]. ChemFlow_py maintains the original features of ChemFlow, i. e. , support for Vina, Smina, Plants and Qvina. In addition, it supports Autodock and Gromacs MD simulations. While ChemFlow is written in bash, ChemFlow_py was developed in Python. ChemFlow_py is based on Python modules, which allows one to integrate new docking and/or rescoring programs following a module template. Furthermore, it can be used from the command line or imported into a Python script, to build a completely customizable work ow. Another relevant feature in ChemFlow_py is multiprocessing on multiple compute nodes, e. g. , when running on a cluster. Last, ChemFlow_py supports seven consensus ranking methods (see Methods).
The average execution time per protocol is shown in Table  1 , using a CPU Intel(R) Core(TM) i7-8700K (3. 70GH) on a ligands dataset with an average molecular weight of 375. 58 g/mol. Docking is the step which requires more execution time, while all the consensus methodologies are extremely fast. Rescoring requires an intermediate amount of time due to a short optimization of the structure according to the new scoring function. Even though performing consensus or rescoring is fast, docking must be run rst, which remains the bottleneck of the pipeline.
Results We performed docking, rescoring and consensus ranking at four protein targets (see Methods). The docking results are given in Table  2. For each docking program, two metrics were calculated: the Area Under the ROC Curve (AUC) and the Enrichment factor at 1% of the dataset (EF1%). The former quanti es the ability to prioritize active compounds over decoys, the latter is related to the probability of nding active compounds in a fraction of the dataset. Because experimental testing is expensive and time consuming, EF is generally more meaningful than AUC for drug discovery, as it is more directly related to the probability of nding hits in the fraction of compounds that can be tested experimentally. In this work, we analyzed classi cation performances based on EF1%, i. e. the larger the EF1%, the better the classi er.
For comparison, AUC scores are given in SI. The docking results in Table  2  do not follow a clear trend. Autodock, for instance, is best for CDK2 but it is the worst for ADRB1 and HIVPR. Smina is best-performing for ADRB1 and HMDH, but is poor for HIVPR. Although limited to four proteins, this benchmark shows that the docking performances are highly system-dependent and that there is no docking method that is better than the others. Since large and diverse datasets of known active compounds for a protein target are rarely available before virtual screening, the performances of different rescoring or consensus scoring strategies (see Methods) were evaluated on the quest for more robust classi cation methods. To this aim, we took a statistical approach and evaluated the rescoring/consensus performances by comparing the EF1% per protein target with the average EF1% over the four docking methods.
The other consensus scoring strategies have MS equal zero or below. In the limit of the dataset explored, these results indicate that docking can be improved by consensus ranking. Although consensus comes at no cost per se (Table  1 ), being able to evaluate consensus requires collecting results from multiple docking programs, which introduces an additional cost that is approximately linear with the number of docking programs in use. In this respect, docking methods based on machine learning, such as DeepDocking  [31] , which decrease the computational time quite extensively, make consensus ranking more appealing. Table  2 : Docking performances evaluated by EF 1%. The results are highlighted in blue when below the docking average, and highlighted in red when above it. Table  3 : For each protein target, we calculate the average enrichment factor at 1% for docking and rescoring.
Acknowledgements This work was funded by the  French National Research Agency (ANR)  through the  Programme d'Investissement d'Avenir  under contract  17-EURE-0016  and received nancial support from the  Fondation pour la Recherche Medícale  (Grant  DBI20141231319 ). Computational resources and support at the high-performance computing center (Mesocentre) of the  University of Strasbourg  are gratefully acknowledged.
The rank-based methods implemented in ChemFlow_py are Rank by Rank (RbR), Rank by Vote (RbV) and Exponential Consensus Ranking (ECR). The rst method computes an average of the ranking positions per ligand. It is equivalent to Rank by Number with scores substituted by the rank order. Rank by Vote requires xing a threshold in the ranking (e. g. , top 5% of the dataset). If the ligand is positioned above this value, it gets a vote (+ 1), and the votes are summed up for all rankings.
A phylogenetic analysis was carried out and two approaches were followed to identify conserved genes and mutations related to host associations. The first approach targeted the genes compiled in the MEGARESv2, Resfinder, VFDB and SPI databases. Plasmid and phage contents were also investigated. The second approach refers to an in-house algorithm developed for this study that computes sensitivity, specificity, and accuracy of accessory genes and core variants according to predefined genomes groups. Results and discussion: All the analyzed strains belong to the multi-locus sequence type profile ST413, and the phylogenomic analysis revealed main clustering by host (bovine and poultry), emphasizing the circulation of 12 different major clones, of which seven circulate in poultry and five in the bovine sector in France and a likely food production chain adaptation of these clones.
, 2017; Sevellec et al. , 2018b). The aim of this study was to investigate the genomic diversity of the S. Mbandaka serovar using a large dataset (n = 304) representative of the bovine and poultry production sectors in the north-western production area of France, where this serovar is frequently isolated. The collection was investigated by multi-locus sequence type (MLST) and single nucleotide polymorphism (SNP) analyses. Because resistance of Salmonella to antimicrobial agents is a worldwide problem, identification of acquired antimicrobial resistance gene was investigated. We also explored the detection and characterization of SPIs, coding for virulence factors involved in adhesion and invasion of the host, and of the fimH gene, known to be a marker of host specificity within Salmonella.
Strain dataset for France The panel of 304 Salmonella enterica subsp. enterica Mbandaka strains analyzed in this study with their metadata (ID, sample name, ID owner, country, collection year, and origin) is presented in Supplementary Table  1  ("Sample DATA" tab). These strains were isolated over a period of 6 years, from 2016 to 2021. Among the panel selected, 140 strains were isolated from the bovine sector and 164 from poultry. All the bovine strains were collected from north-western France, in the Normandie region where this serovar is prevalent for bovine sector. The poultry strains, to be compared with bovine strains, were collected mainly (70%) from north-western France to can compare the genomic poultry profiles with the bovine ones but also in the Pays de la Loire (n = 47), Bretagne (n = 34), and Normandie (n = 32) regions.
Sequencing and assembly All strains were sequenced using Illumina chemistry producing paired end reads as described by  Radomski et al. (2019). Genome assemblies were generated using an in-house workflow called ARTwork  (Vila Nova et al. , 2019). 2  The raw reads were normalized to 100× with Bbnorm using the S. Mbandaka SA20026234 reference complete genome (NCBI ID CP022489). Then, Trimmomatic  (Bolger et al. , 2014)  was used for the trimming step. The applied quality rules were: (1) length of read higher than or equal to 50 base pairs (bp) otherwise excluded, (2) phred score per base higher than or equal to 30×, and (3) filter away adapters based on an internal database with Illumina adapters. FastQC version 0. 11. 5 was used to check the read quality and ConFindr to identify intraand cross-species contamination  (Low et al.
Multi-locus sequence typing (MLST) All genomes were characterized by in silico MLST using the seven housekeeping gene sequences (aroC, dnaN, hemD, hisD, purE, sucA, and thrA) described in the PubMLST database  (Jolley and Maiden, 2010). The sequence type (ST) of each genome was obtained with the MLST tseeman tool. 3.
Variant calling (SNPs) The core genome SNPs and small InDels were detected based on the variant caller HaplotypeCaller implemented in the iVARCall2 workflow  (Felten et al. , 2017). The S. Mbandaka strain SA20026234 (accession number CP022489. 1) was used as reference complete genome referring to the studies previously published by  Cherchame et al. (2022b) , and following the best practices proposed by the Genome Analysis ToolKit (GATK)  (McKenna et al. , 2010). More precisely, secondary alignments around small InDels were performed, and duplications were excluded before variant calling analysis via local de novo assembly of haplotypes in active regions. The matrices of pairwise SNP differences and pseudogenomes were computed using the scripts called "VCFtoMATRIX" and "VCFtoPseudoGenome. " 4.
Phylogenomic inference and calculation of bootstrap support Phylogenomic inferences were performed by maximum likelihood based on SNP alignment produced by the iVARCall2 workflow and the transversional model TVM + F implemented in the IQ-TREE program  (Nguyen et al. , 2015). Node support was evaluated with 1,000 rapid bootstrap inferences  (Nguyen et al. , 2015). Phylogenetic trees were visualized and annotated using interactive Tree Of Life 5  (Letunic and Bork, 2016).
Identification of virulence factors and resistance genes All genomes from France were screened for the presence/absence of genes mediating resistance and virulence using ABRICATE. 6 ABRICATE was used against the VFDB database  (Chen et al. , 2016)  available at the Institute of Pathogen Biology (Beijing, China), MEGAResV2  (Doster et al. , 2020) , ResFinder  (Zankari et al. , 2012) , and SPIFinder  (Roer et al. , 2016) , available at the Center for Genomic Epidemiology (CGE) (Denmark). The ABRICATE outputs displayed only the genes detected in at least one genome of the analyzed panel. The threshold was set at 90% identity over at least 3/5 of the length of the gene or genomic region. Particular attention was paid to the FimH sequence. The fimH gene sequence was isolated from the PRJNA297164 project and searched in all 304 S.
Phage identification The presence of phage sequences in the assemblies of French S. Mbandaka strains was investigated using the PHASTER online application  (Arndt et al. , 2016). Only prophages identified as "intact" were considered. 4 https://github. com/afelten-Anses/VARtools/tree/master/iVARCall2 5 iTOL https://itol. embl. de/ 6 https://github. com/tseemann/abricate.