dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We greatly appreciate the efforts of the  CRISM ,  HiRISE , and  HRSC  teams for collecting and archiving the images used in this study. We are grateful to support from the  NASA Astrobiology Institute  (grant # NNX15BB01 ) to JLB,  NASA 's  Mars Data Analysis Program  (grant # NNX19K1230 ) to JLB, CMW, and KW, and  NASA 's  Planetary Data Archiving, Restoration, and Tools Program  (grant # NNX16AG48G ) to MP, YI, and  AMS  for this work. We are also thankful for suggestions from  L. Roach ,  S. Murchie , and  E. Rampe  that improved this paper.
The Atmospheric Chemistry Suite (ACS) [1], one of the four science experiments on board ESA-Roscosmos ExoMars 2016 Trace Gas Orbiter (TGO) mission has started science operations in March 2018. ACS consists of 3 infrared spectrometers targeting the unambiguous detection of trace gases of potential geophysical or biological interest. The dataset reported here concerns the methane detection attempts conducted during the first complete Martian year (almost two Earth years) of observations using ultra-sensitive occultation observing mode in orbit around Mars.
Part of the attempts at that time was impaired by the presence of abundant amounts of dust particles that prevent observing the lower troposphere of Mars (<30 km) where methane, if detectable, presumably concentrates. The ACS dataset analyzed here covers a period of more than 25 months, which is five times more data (Figure  2 ) than previously analyzed. This gives us a chance to perform a deeper exploration into the potential presence of methane and the consequences it may have for our understanding of active geophysical and physicochemical processes prevailing at Mars.
Acknowledgements ExoMars is the space mission of  ESA and Roscosmos. The ACS experiment is led by  IKI Space Research Institute in Moscow. The project acknowledges funding by  Roscosmos  and  CNES. Science operations of ACS are funded by  Roscosmos  and  ESA. Science support in IKI is funded by  Federal agency of science organizations (FANO). M. V. acknowledges support from the  DIM ACAV  labelled by the  Ile-de-France  region in support for the research (Domaine d' Intérêt Majeur ,  Astrophysique et Conditions d'Apparition de la Vie )".
The revolution brought about by AlphaFold2 and the performance of AlphaFold2-Multimer open promising perspectives to unravel the complexity of protein-protein interaction networks. Nevertheless, the analysis of interaction networks obtained from proteomics experiments does not systematically provide the delimitations of the interaction regions. This is of particular concern in the case of interactions mediated by intrinsically disordered regions, in which the interaction site is generally small. Using a dataset of protein-peptide complexes involving intrinsically disordered protein regions that are non-redundant with the structures used in AlphaFold2 training, we show that when using the full sequences of the proteins involved in the interaction networks, AlphaFold2-Multimer only achieves 40% success rate in identifying the correct site and structure of the interface.
Finally, AlphaFold-Multimer performs better than AlphaFold2 at protein-peptide complex prediction, and sampling a larger part of the conformational space by enforcing dropout at inference time in AlphaFold-Multimer further increased the quality of protein-peptide complex models  40. In the present study, we investigate how best to use AlphaFold2 to make the leap from interaction networks to interfaces when dealing with binding partners containing intrinsically disordered regions (Figure  1a ). We carefully develop an unbiased benchmark of 42 protein-peptide complexes sharing no similarity with any complex from the AlphaFold-Multimer training dataset and assess the performance of AlphaFold-Multimer on this dataset using different MSA schemes. We show that performance is limited when full-length protein sequences are used as input and considering delimited fragments increases the success rate.
We set the fragment size at 200 amino acids in order to scan potential interacting regions within full-length sequences such as those derived from large-scale interactome data. Our study also raises the issue of prediction specificity, which may require the enumeration and ranking of potential anchoring sites, and assesses the usefulness of AlphaFold confidence scores in discriminating between possible binding regions. Finally, we show a synergistic effect of combining different MSA schemes and scores, allowing to reach 90% success rate on our benchmark dataset. 1.
Selecting a test dataset of complexes not redundant with the training set of AF2-Multimer To assess the performance of AlphaFold2 (AF2) in predicting the mode of association between a protein (hereafter called the receptor) and a small binding motif within a structurally disordered partner (the ligand), it is important to study cases of complexes that do not have homologs in the database on which AlphaFold2 has been trained. An example of how AF2 models may be biased by existing structures in the PDB is illustrated in Supp. Figure  1a. AF2-Multimer was trained on structures released until 30 Apr 2018. An analysis of the structures released after that date revealed that nearly 2,500 structures of complexes involving small protein motifs had been deposited in the PDB (Figure  1b ). A large number of these structures have significant similarities in sequence or structure with structures released in the PDB before May 2018.
Following a strict treatment of this sequence and structure redundancy (Figure  1c , see Methods), we isolated a set of 42 complexes involving a receptor and a small peptide ligand that could provide an unbiased estimate of AF2 performance in different conditions. Among the 42 complexes, we observed a diversity of subunit lengths (Figure  1d ) and a representative occurrence of peptides with sizes ranging from 6 to 39 amino acids (Supp. Figure  1b ) that are binding their receptors as helices, strands, coils or combination of those (Supp. Figure  1c ). AF2 relies on multiple sequence alignments whose evolutionary depth on the ligand peptide region may be limited due to the difficulty of identifying homologs from a short IDR sequence. Hence, for each of the proteins in this dataset, we used the full-length sequences of the protein partners to construct MSAs and subsequently delineate the interacting domains (Supp.
Therefore, in the cases where prediction performance varies between alignment content and delineation protocols, a common explanation is that the binding motif may be trapped or masked in a conformational state that prevents prediction of its correct binding mode. Five cases out of 42 failed, regardless of the alignment protocol. In two of these cases (PDB: 6J08 and 7CZM), the receptor itself was not quite well folded, which may have made it difficult to sample a correct binding mode. For one case (PDB: 6A30) where none of the tested protocols converged to a correct model, we tested whether reducing the size of the receptor itself would help. We reran this case with the same alignments, testing if reduction in the size of the receptor could have an impact. Splitting the receptor as two inputs of similar size led to the generation of a correct model with a high AF2 confidence score with one of those inputs, reaching 0.
Discussion AF2 has shown remarkable performance for predicting the structure of multi-subunit machineries only known so far through PPI maps  26, 27, 30. In this study, we explored the potential of AlphaFold2 to further exploit the wealth of data contained in proteomics experiments and to enable a more comprehensive characterization of protein-protein interaction networks. We focused on interactions mediated by unstructured regions that are a cornerstone of the functional and dynamic organization of most cellular processes. The capacity of AF2 to perform well with small disordered regions binding a structured domain was established on different datasets  18, 37, 40  built from the structures available in the PDB  45. However, in most proteomics experiments, the boundaries of the interacting regions are not precisely known.
In addition, physical interactions can be indirect and it is crucial to disentangle the regions involved in direct interactions. To further assess the use of AF2 for this purpose, we built a dataset of complexes consisting of a folded receptor bound to a short protein fragment and evaluated several protocols representative of challenges faced following proteomics analyses. Because AlphaFold2-multimer was trained on complexes whose structures were published before May 2018, we carefully removed any homologs of the complex to ensure that our conclusions could not be biased by similarities in sequence or 3D geometry with the training dataset. For the 42 test cases selected in the benchmark, we first evaluated the ability of AF2 to discriminate the binding site when proteins are provided in their full length as in the output of a proteomic experiment.
Building the dataset of protein-peptide complexes non-redundant with the AF2 training structural dataset. An initial list of protein-peptide complexes was retrieved from the PDB server  45  on April 1, 2022 with the following request: 1) Release date after May 1st, 2018 to exclude complexes present in the AlphaFold2 training set; 2) The longest protein (called the receptor) must contain at least 60 amino acids and the smallest chain (called the peptide) must contain at most 40 amino acids. 3) The 'Number of Polymer Instances (Chains) per Assembly' has to be between 2 and 4 and should contain heteromeric assemblies. 4) The assemblies should not contain RNA or DNA chains. The initial request led to 2484 potential candidates. Using a sequence identity threshold of 30 %, we discarded all candidates for which a homolog of the receptor protein was released before May 1st, 2018 and bound to a ligand partner in the same region.
From the list of selected candidates, an additional filter was used to check the absence of redundant assembly modes. For each of the selected complexes, the receptor sequence was used as a query of the PPI3D server  50  , in single sequence mode, to recover all the PDB codes of complexes involving homologs of the receptor (date of PPI3D query August 2022, on the PDB updated July 20, 2022). In PPI3D, distant receptor homologs were retrieved using PSI-BLAST  51  with 2 iterations and an E-value cutoff of 0. 002. For every candidate complex, PPI3D provided a detailed list of PDB codes with the chain ids involving the receptor or its homolog. We used the full list of interactions provided by PPI3D, except when it exceeded 2500 interfaces in which case the clustered list was chosen (95% sequence similarity and 50% similarity for residues in the binding region).
Generation of the alignments for the 42 database cases. Sequences of all the chains in the dataset of 42 complexes were retrieved from the UniProt database  53  and were submitted to three iterations of MMseqs2 54 against the uniref30_2103 database 41. The resulting alignments were filtered using hhfilter  55  using parameters ('id'=100, 'qid'=25, 'cov'=50) and the taxonomy assigned to every sequence keeping only one sequence per species. Full-length sequences in the alignments were then retrieved and the sequences were realigned using MAFFT  56  with the default FFT-NS-2 protocol. To build the so-called mixed co-alignments, sequences in the alignment of individual partners were paired according to their assigned species and left unpaired in case no common species were found  41. Unpaired alignments were obtained by unpairing the mixed alignments and alignments with no evolutionary information for the ligand were obtained by leaving the ligand region as a single sequence.
Generation of input data for cross-partners evaluation To generate the dataset mixing receptors and their non-cognate ligands, a subset of complexes that could be clustered according to the similarity of the type and length of the secondary structure of their ligand (reported in Supp. Table  1 ) was defined (Supp. Table  2 ). We selected 23 complexes with a monomeric receptor and a ligand that could be clustered into one of the 7 groups distinguished in Supp Table  2. The MSA of each receptor was concatenated with each ligand in the same cluster without adding MSA information on the ligand side. These alignments were used as input to generate structural models by AlphaFold2 following the protocol described below.
Ranks are assigned depending on the following criteria: High (fnat in [0. 8 , 1. 0] and (L-RMSD ≤ 1. 0 Å or iRMSD ≤ 0. 5 Å)), Medium (fnat in [0. 5 , 0. 8] and (L-RMSD ≤ 2. 0 Å or iRMSD ≤ 1. 0 Å) or fnat in [0. 8 , 1. 0] and (L-RMSD>1. 0 Å and iRMSD>0. 5 Å)) and Acceptable (fnat in [0. 2 , 0. 5] and (L-RMSD ≤ 4. 0 Å or iRMSD ≤ 2. 0 Å) or fnat [0. 5 , 1. 0] and (L-RMSD>2. 0 Å AND iRMSD>1. 0 Å))  43. Additional analyses were performed following the standard metrics calculated by CAPRI assessors to rate the similarity between the models and their reference structure (such as the fraction of interface residues FRIR or the fraction of non-native contacts FRNNAT) and are also available in Supp. Table  3. 3D structures were visualized and represented using ChimeraX 57. (a) Disentangling the complexity of a protein interaction network (sketched on the left) by analyzing binary interactions between a central grey protein and its blue binding partners can be complicated in case they contain intrinsically disordered regions (b) General pipeline to select the PDB entries that can be used as test complexes from those released after May 2018.
Purpose and organization of this paper This paper was written to provide information useful to the HiRISE data user community, updating the information in  McEwen et al. (2010)  about the Primary Science Phase (PSP) of Mars Reconnaissance Orbiter (MRO). There are 12 major sections: 1. Introduction 2. Science Publications and Team 3. Operations History 4. Summary of HiRISE Archived Dataset 5. Special Data Products and Analysis Tools 6. Imaging for Lander and Rover Missions 7. Science Planning and Uplink Tools and Techniques 8. Instrument Issues and Anomalies 9. Calibrations 10. Citizen Science and Machine Learning 11. Education and Public Outreach 12. The Future of Very High-Resolution Orbital Imaging of Mars.
Publications HiRISE data have played a part in thousands of peer-reviewed papers since the beginning of operations in 2006. The MRO project maintains a database of team publications that has 294 peer-reviewed HiRISE papers in the 15-year period from 2007 to 2021 (Fig. 2 ; left). Assessing the number of publications enabled by HiRISE data from the wider community is more difficult as these papers have not been tracked systematically. As a proxy, a search of the NASA Astrophysics Data System (ADS) for peer-reviewed publications containing the words HiRISE and Mars yields 1817 results between 2006 and 2021 (Fig. 2 ; right), with over 2000 results as of June 2023. These results likely include some papers where HiRISE use is minimal, but also miss some publications that are not tracked by the ADS or do not have full text search available.
Science highlights Here we try to summarize the most significant results from HiRISE. Most of these results depend on other datasets in addition to HiRISE. • HiRISE full-resolution stereo images enabled identification of smallscale hazards aiding successful landings by the Phoenix, MSL, InSight, and Mars2020 missions  (Golombek et al. , 2012b, 2017, and others ). • Many current and candidate landing and traverse sites have been mapped in color and stereo, leading to regional geologic studies  (Sun and Stack, 2020; Quantin-Nataf et al. , 2021) , enabling complementary surface results and traverse planning for rovers (see Section 6 and citations). • New impact craters have constrained current impact rates and assisted the interpretation of seismic signals  (Daubar et al. , 2022; Posiolova et al.
Science and operations teams and roles The original HiRISE team had 12 Co-Investigators (Co-Is), which has expanded over the years to 20 Co-Is. New Co-Is approved by the MRO project and NASA were chosen to add a range of expertise, institutions, and demographics. Each Co-I typically has both a relevant science specialty plus a skill essential to the successful outcome of a flight instrument investigation. The high data volume represented by a single HiRISE image (typically gigapixel size) dictated from the outset that extensive spatial coverage would not be possible. Ideally, every site imaged by HiRISE should have a specific investigation or hypothesis in mind. A target database and catalog (HiCat) was built to be a repository of all the requests for HiRISE images. HiRISE images are broadly useful for Mars science, so HiRISE Co-Is and affiliates take responsibility for a particular science theme, becoming a Science Theme Lead (STL).
The current list of STLs is given in Table  2. When someone requests a HiRISE image they must also designate an appropriate science theme, and the STL for that theme then assigns the target an initial priority. This is the timeindependent part of the planning process. The STL is also responsible for interacting with peers in their theme area in the general Mars community to ensure that science targets are being identified and entered into the database. HiRISE science planning is accomplished via a collaboration between a Co-I of the Pay Period (CIPP) science team member and a HiRISE Targeting Specialist (HiTS) uplink operations engineer. The HiRISE science team members serve as CIPP for typically 1-3 planning cycles per year. The CIPP role is open to all science team members and not limited to official Co-Is, thus giving early-career scientists the opportunity to gain experience in the planning and operations aspects of the mission.
, 2011). Limited spectral and morphological characterization of dynamic, seasonal features on Mars, such as recurring slope lineae and topographic slumps, have been possible with coordinated HiRISE-CRISM observations and MARCI daily global images  (Ojha et al. , 2017). SHARAD observations are also important for joint analyses (e. g. ,  Morgan et al. , 2021)  but are typically acquired at night, so they are not simultaneous with optical datasets. MCS operates both day and night, and MCS plus HiRISE data have been used to understand seasonal processes and the evolution of polar volatiles (e. g. ,  Wilcoski and Hayne, 2020). In addition to the compositional and morphological characterization, coordinated HiRISE-CRISM observations enable the characterization of the dynamic behavior of active geological features (e.
PILOT allows for the selection of images to be sent for further processing (i. e. , map projection) via the USGS Map Projection on the Web (POW) service  (Hare et al. , 2013)  and provides tools for identifying unplanned stereo acquisitions. Perspective views from projected orthoimages and DTMs can also be explored at Areo-Browser (https://areobrowser. com/). The online GIS application, MarsSI, is a searchable global map of Mars data that includes services to process HiRISE image data and DTMs  (Quantin-Nataf et al. , 2018). HiRISE data (including DTMs) are available within the Java Missionplanning and Analysis for Remote Sensing (JMARS) application  (Dickenshied et al. , 2019). The program is installed locally, but data are served from ASU via the internet. The interface functions as a geospatial information system (GIS) in that it is map-based and can display various datasets in the same geographic coordinate system.
ISIS updates HiRISE image processing pipelines utilize individual programs from the Integrated Software for Imagers and Spectrometers (ISIS;  Becker et al. , 2007). ISIS is frequently updated with new capabilities  (Backer et al. , 2018). Few such updates have been made specifically for HiRISE in recent years, but some of the updates do change the HiRISE data products in subtle ways, so products are made in different ways over time. In addition, the HiRISE team has made some changes to how images are processed, i. e. , using different ISIS input parameters. As part of the PDS-4 conversion effort, we plan to reprocess all images to ensure consistency.
One of the major challenges with the technique is that most martian datasets are not orthorectified, or are orthorectified to low-resolution topographic data such as MOLA that does not remove small-scale (<1 km) geometric effects on viewing geometry. As such, to pan-sharpen HiRISE with other datasets, a widespread and dense network of 10s to 100 s of Ground Control Points (GCPs) must be defined between the two to warp (i. e. , "rubber-sheet") the lower resolution color dataset to overlay precisely with HiRISE prior to pan-sharpening, with more GCPs being needed in rugged terrain. Fig. 12  shows 25 cm/pixel pan-sharpened example based on a 4 m/ pixel CaSSIS 4-filter color infrared observation, which provides the equivalent to HiRISE IRB colors, of Moni crater situated in the larger Kaiser crater.
HiRISE image sharpening There are several techniques used to sharpen digital images, including deconvolution of the point-spread function (PSF) or use of multiple images to derive a sharper image. PSF deconvolution was used on landing site images  (Kirk et al. , 2008; Golombek et al. , 2012a)  and on the MSL parachute image (https://www. uahirise. org/releases/msl/). The MSL parachute image was acquired via a special sequence for a longer exposure time and higher SNR than is possible for almost all other HiRISE images of Mars. Tao and Muller (2016) describe stacking and matching HiRISE images of the same area taken from different angles on different orbits for what they call super-resolution restoration. However, the HiRISE dataset is far from ideal for this image restoration, which ideally requires: 1.
Tianwen-1 lander and Zhurong rover imaging On 15 May 2021, the lander/rover portion of the Tianwen-1 mission from the Chinese National Space Administration (CNSA) successfully touched down on Mars, and on 22 May, the Zhurong rover drove onto the martian surface  (Sun and Rao, 2022). CNSA requested a list of NASA remote sensing datasets covering a region in southern Utopia Planitia a few months before landing, indicating that this would be their preferred landing. HiRISE was able to plan and acquire sample images throughout this region, covering a few percent of the total area; all surfaces were similarly free of small-scale hazards. After the Tianwen-1 landing, with known coordinates, HiRISE acquired a stereo pair over the landing site (ESP_069665_2055 and ESP_069731_2055) and created a DTM.
HiPhoP From 2006 to 2019, the HiRISE Photometry Program (HiPhoP) used a Hapke photometric function  (Hapke, 1981) , requiring manually-input values for surface and atmospheric parameters for each observation  (McEwen et al. , 2007). This procedure was one of the most timeconsuming steps in uplink planning. A more automated approach was needed to predict scene brightness (radiance) and set image parameters for CaSSIS  (Almeida et al. , 2023) , based initially on HiRISE data (before sufficient CaSSIS data was acquired). Subsequently, we modified this automated method for HiRISE. With a database of >60,000 HiRISE images, we determined that scene brightness could be predicted with 10% accuracy (sufficient for setting image parameters) for >95% of the images, by using a simple model: P1 [DN/ms] = (m 1 x cosi + b 1 ) x (m 2 x TES + b 2 ) x 1.