dataesr/scientific-paragraphs-categorization huggingface dataset:
Benchmark of InDeep on conventional target binding sites To our knowledge, there is no tool dedicated to predicting iPPI binding sites. There are however several tools that aim to predict small molecule binding sites  [19, 23, 50] , among which Kalasanty  [25]  is a reference is the field. The other major difference between our tool and Kalasanty, is that Kalasanty was trained on VolSite  [51]  predicted cavities in ligand locations, whereas our model has been trained on ligand position directly. In the absence of other iPPI dedicated tool for ligandability, we benchmarked InDeep against Kalasanty. For fairness, we compare the ability of our tool to predict VolSite predicted cavities as well as the ligand pose with Kalasanty. We use the same dataset as the authors of Kalasanty did for validation: a distinct dataset, made by Chen et al  [52].
The original test set is composed of 111 protein-ligand holo structures and 104 corresponding apo structures. We have filtered out a few systems that were too similar to our training set according to the TM-score metric  [53] , as shown in Figure  2  and end up with 187 and 196 pockets evaluation for apo and holo structures respectively. Ligands coordinates were extracted from the holo structures of the Chen benchmark and VolSite  [51]  was used to describe cavities for each ligand. Following Kalasanty, the number of retained predicted pockets is the number of small molecules present in the deposited PDB system. We then used the metrics used by Kalasanty on their dataset: DCC and DVO. The DCC metric computes the distance in Angstrom between the center of mass of the prediction and the one of the ground truth.
In this benchmark dataset, InDeep outperforms Kalasanty on all settings except in the very low DCC range for cavities. At a 6 Å threshold, we have an average relative performance boost of 27%. We see that this difference is most important in the ligand setting that resembles our training procedure, but that the performance remains stable on the cavities setting. We also see that this boost of performance is observed across holo and apo predictions. We detect approximately 40% of the binding sites in the best ranked predictions. We then compute the DVO values for the different methods among the successful predictions at 6 Å. We obtain comparable values of DVO overall. Because Volsite cavities tend to be deeper into the pocket, Kalasanty predictions tend to be at the bottom of binding sites.
Benchmark of InDeep on PPI data sets As for PL, several tools exist that predict which region of a protein interact with another. Once again we choose to compare against the state of the art and reproduce the results of PInet. We use two benchmarking data sets they propose : DBD5  [54]  and EpiPred  [55]. DBD5 is a protein docking benchmark that offers several pairs of structures of interacting proteins. EpiPred is a dataset centered around interactions between antigens and antibodies. We then had to slightly adapt our validation pipeline. Indeed, we have found no study trying to predict the actual location of the partner in the vicinity of the protein surface. The tools we compare against always project the predicted interactability onto the surface residues of the protein or the sequence.
Therefore to compare against other tools, we needed to implement a sequence supervision and to project our 3D prediction onto the sequence. To do so, we use a convolution with Gaussian kernel between our 3D prediction and the coordinates of the atoms of the protein. We optimized the parameters of the projection, such as the kernel radius, using a grid search on the DBD5 train set. We then use those parameters on the DBD5 test set as well as the EpiPred data set. For fairness, we used the same method to annotate the data as in PInet and we have rerun their method. Finally, we have used their tool in the partner-specific setting (giving the partner as input) and in a blind setting (PInet blind) that is closer to our use case. The results are presented in Table  1  We see that on the DBD5 test set, InDeep is outperformed by PInet.
However, PInet was trained on the same data set, and we turn to EpiPred, that was not used for training by either methods. On this data set, our result falls close to the performance of the partner-specific setting. We achieve a state of the art performance in our blind setting. Moreover, it should be noted that InDeep performance suffers from the extra step of sequence projection. Overall this shows that our tool is able to accurately predict the interaction sites of a protein.
Epitope binding site prediction: atom typed channel validation We now turn to the channel evaluation. We have used five channels to encode our protein environment and our prediction : α-carbon ('CA'), donor and receptor of h-bonds ('HAD'), positively and negatively charged atoms ('POS' and 'NEG') and hydrophobic/aromatic atoms ('COB'). This means that beyond prediction of the presence or absence of a protein partner, the model also predicts which protein atom type should be at a given voxel. This is close to the idea developed by LigVoxel  [50]  but for the interactability model. However, finding a quantitative metric to describe the quality of these channels is not easy. Indeed, the target now contains several little volumes (each atom type environment) that can be split across the protein partner interface.
Inspecting more specifically the Cα-and hydrophobic-atom-typed channels within the interactability patch (Right panel), one can observe respectively 1) a faithful depiction of the α-helix shape of the Bax epitope binding the BH3 groove (as a cylinder shape), and 2) a proper localization of the hydrophobic hotspots known for this system  [4]. It is important to note that all the InDeep predictions were performed only on Bcl-2, without any knowledge of protein partners or ligands binding information. If we now use the ligandability prediction of InDeep on the same structure of Bcl-2 as co-crystallized with Bax (pdb: 2xa0), one can see on Figure  8  that InDeep correctly highlights the known aforementioned hotspots as the most ligandable regions of the BH3 groove of Bcl-2. The same regions that were indeed targeted by Venetoclax analogs (ex with pdb: 4lvt).
Acknowledgements We thank  IBM  for their sponsorship for the optimization of the hyperparamaters. In particular, we thank  Jean Armand Broyelle ,  Maxime Deloche , and  Xavier Vasquez  for their expert support. We are grateful to the  Institut Pasteur and the CNRS  for their continued support for our research. V. M. is recipient of a doctoral fellowship from the the  INCEPTION  project  [PIA/ANR-16-CONV-0005 ] and benefits from support from the  CRI  through " Ecole Doctorale FIRE -Programme Bettencourt ". The authors also thank  Arnaud Blondel  for feedback and discussions. They declare no conflict of interest.
Cell-free DNA (cf-DNA) has been reported to represent a suitable material for liquid biopsy in the diagnosis and prognosis of various cancers. We performed a meta-analysis of published data to investigate the diagnostic value of cf-DNA for renal cancer (RCa). Systematic searches were conducted using Pubmed, Embase databases, Web of Science, Medline and Cochrane Library to identify relevant publications until the 31st March 2021. For all patients, we evaluated the true diagnostic value of cf-DNA by calculating the number of true positive, false positive, true negative, and false negative, diagnoses by extracting specificity and sensitivity data from the selected literature. In total, 8 studies, featuring 754 RCa patients, and 355 healthy controls, met our inclusion criteria. The overall diagnostic sensitivity and specificity for cf-DNA was 0.
Search Strategy We carried out systematic literature searches to identify relevant publications in the PubMed, embase databases, Web of Science, Medline and Cochrane Library up to the March 31, 2021, without language or date restrictions. The search strategy included the following terms: ("kidney neoplasms" OR "kidney cancer" OR "renal cancer") AND ("diagnosis" OR "biomarker") AND ("overall survival (OS)" OR "disease-free survival (DFS)" OR "progression-free survival (PFS)" OR "prognosis" OR "survival" AND "circulating tumor DNA" OR "cell-free nucleic acids" OR "ct-DNA" OR "cf-DNA". Three researchers (Yipeng Xu, Yingjun Jiang and Mingke Yu) independently assessed the eligibility of each potentially relevant study by screening the titles and abstracts. Disagreements between the two researchers were resolved by discussion with two additional researchers (An Zhao and Shaoxing Zhu).
Data Extraction All eligible studies were independently reviewed by two investigators (Y. J. Y. X. ). The following items were extracted from each article: first author's name, year of publication, number of patients, TNM stage, sample origin, methods of DNA detection, detection markers, and information relating the article's quality. A range of diagnostic data were also extracted, including specificity, sensitivity, true positive (TP) rate, false positive (FP) rate, true negative (TN) rate, and false negative (FN) rate. We also acquired a range of survival data, including OS, PFS, hazard ratio (HR), p value, Kaplan-Meier survival curves, and 95% confidence intervals (95% CIs). Engauge Digitizer 4. 1 was used to read the Kaplan-Meier curves in order to identify articles with accurate HRs.
Statistical Methods The systematic review and meta-analysis were performed using RevMan Version 5. 3 and STATA 11. 0 (Stata Crop). Diagnostic variables, including positive likelihood ratios (PLR), negative likelihood ratios (NLR), and summary receiver operating characteristic curves (SROC), were analyzed by STATA 11. 0 (Stata Crop), and the diagnostic odds ratio (DOR) was analyzed by Meta-DiSc software, version 1. 4. Specificity was defined as the proportion of patients with no cf-DNA detection in the blood samples when compared with all negative control volunteers without RCa. Sensitivity was defined as the proportion of RCa patients containing cf-DNA in their blood samples. TP, FP, TN, and FN, were calculated by analyzing the specificity, sensitivity, and the number of people enrolled in each group (experimental group and control group).
Study Selection In total, we retrieved 6,855 articles. Of these, 6,777 articles were excluded because they did not specifically refer to cf-DNA and RCa. By reviewing each title and abstract, we identified 25 review articles, 16 comments, seven editorials, and 30 articles, that were outside of the scope of our meta-analysis. Twenty studies were recognized as potentially relevant publications, and a full-text review was performed to identify data relating to diagnoses and prognoses. As shown in Figure  1 , careful screening and verification identified eight studies that were eligible for metaanalysis. The main characteristics and details of these eligible studies Salinas-Sanchez et al. (  2021  are summarized in Table  1. These eight eligible studies featured a total of 754 patients, with a median sample size of 87 (range: 33-229, mean: 94).
, 2019) ; 9 million of which are unique protein-coding genes  (Yang et al. , 2009). Our microbial residents thus substantially expand our genetic repertoire and enable us to navigate our environment in ways that we would not be able to alone. The Human Microbiome Project (HMP) launched in 2007 by the National Institutes of Health, enabled the characterization and understanding of the importance of the microbiome in human health and disease  (Turnbaugh et al. , 2007). Furthermore, eukaryotic double-stranded DNA (dsDNA) viruses have been analyzed by looking at the metagenomic sequence data generated by the HMP. The study detected on average 5. 5 viral genera in each individual and reported high interpersonal diversity  (Wylie et al. , 2014). Studies on the human virome are relatively limited in number due to challenges such as; lack of common markers for viruses, the heterogeneity of the virome elements, low biomass samples, confounding by host DNA background, lack of standard computational tools for analysis of the virome, and an exponentially growing virome database.
, 2015). Future studies should aim to achieve a clearer understanding of the underlying mechanism(s) of action of specific probiotic strains, and strain mixes, responsible for the improvements seen in clinical endpoints  (Ozen et al. , 2015). A 2015 Cochrane review examined 12 probiotic RCTs for the prevention URTI, involving a total of 3,720 participants  (Hao et al. , 2015). The trials included data from all age groupschildren, adults, and older peoplefrom Europe, North America, South America and Asia. The authors concluded that probiotics were better than placebo when measuring the number of participants experiencing episodes of acute URTI (at least one episode: odds ratio (OR) 0. 53; 95% confidence interval (CI) 0. 37 to 0. 76, P value < 0. 001),; the mean duration of an episode of acute URTI [mean difference (MD) -1.
7 ppm, number of scans 2048, dummy scans 32, interscan delay 1. 5 s. Shaped pulse parameters were set via the getprosol command and the hard 90 • 1 H pulse. The mixing times were set to 5, 25, 50, 50, 100, 100, 150, 200, 300, 400, 400 and 500 ms for both duplexes. The NMR spectra were processed in TOPSPIN 4. 0. 9 using a line broadening factor of 10 Hz (lb value set to 10). The absolute peak intensities of the CLEANEX-PM experiments and the reference experiments were determined and used to obtain the relative intensities. The data was then exported to MATLAB and the build-up curves were fitted to the following equation: I I 0 = k (R 1 A + k -R 1,water ) × e -R (1,water) τ mi x ) -e (-R 1,A +k) τ mi x with I/I 0 relative peak intensity, k imino proton-bulk water exchange rate (s -1 ), R 1,water longitudinal water exchange rate, τ mix CLEANEX-PM mixing times (s) and R 1,A is a combination of the longitudinal and transverse relaxation rate of the imino proton resonance (floating parameter during fitting).
Errors in exchange rates were estimated from replicate experiments and obtained from 1000 Monte-Carlo runs. Determination of the pK a value of 3-deazaguanosine by NMR spectroscopy. For the pH dependent 1 H and 13 C NMR experiments, 22 mg of 3-deazaguanosine were dissolved in 600 l buffer (15 mM sodium phosphate, 25 mM NaCl, 0. 1 mM EDTA, pH 6. 9) and transferred into a 5 mm NMR tube. The chemical shifts of C2 and C6 were recorded on a Bruker 600 MHz Avance II+ equipped with a Prodigy TCI probe. The pH-value was determined using a Sigma-Aldrich micro pH combination glass electrode. At each pH-value, 1 H NMR spectrum and 13 C NMR spectra at natural abundance with power gated decoupling were measured. The pK a values were obtained by fitting the data to the following equation: δ obs = δ depr otonated x 10 ( pH-pk a ) + δ pr otonated 1 + 10 ( pH-pk a ) With δ obs observed 13 C chemical shift at the respective pH value, δ depr otonated 13 C chemical shift of deprotonated species and δ depr otonated 13 C chemical shift of the protonated species.
Data Acquisition and Reconstruction Images were acquired on a PET/CT Biograph mCT (Siemens) at the CERMEP-imaging platform. Before PET emission scan, a rapid CT acquisition was performed to compute a brain attenuation map. The PET emission scan was performed for 90 min in list mode immediately after intravenously injection of [ 18 F]F13640. A dynamic PET image was reconstructed in a series of 28 sequential frames (4 × 30 s; 4 × 60 s; 8 × 180 s and 12 × 300 s). PET images were reconstructed with a fully three-dimensional (3D) ordinary Poisson OSEM reconstruction (OP-OSEM). PET data were preprocessed using MINC Toolkit (McConnell Brain Imaging Centre, Montreal, QC, Canada) and modeled with programs of the Turku PET Center library. For each cat, a PET sum image was computed and used as target for the warping of a multi-subject MRI template  (Lancelot et al.
Voxel-Based Analysis of the Pharmacological Challenge Study Ratio images were generated by dividing the PET signal in each voxel by the mean signal in the centrum semiovale using the Turku PET Center software (Imgratio function) to generate one baseline image from 15 to 30 min and one global post injection image from 30 to 90 min. These ratio images were smoothed using a 1 mm × 1 mm × 1 mm isotropic Gaussian filter, spatially normalized in the template space, and statistically analyzed with SPM 12. Pooling the four subjects, a statistical map of the significant decreases of [ 18 F]F13640 binding after the pharmacological challenge was computed with two successive paired t-test, the first performed for each scan using the relative contrast (Baseline image -Post injection), the second to compare the saline and challenge conditions, corresponding to the final contrast [(Baseline image -Post injection) challenge -(Baseline image -Post injection) saline ] for each post-injection time interval.
g. ,  Canup et al. 2021 ) and the surface of the Moon, being devoid of traces of erosion apart from the effects of impacts is recording data pertinent to the longeroded record of the early history of our own planet. Other planetary satellites of particular interest to geophysics and covered in this article include Ganymede, the biggest of the Galilean satellites, covered with a thick ice mantle, with traces of lateral tectonic movements and a self-sustained magnetic field, and Enceladus, measuring only 500 km in diameter with geysers emanating from the surface near the south pole. Enceladus has been speculated to potentially harbor primitive life and together with Mars, Titan, and Europa belongs to the bodies in the solar system that are regarded as those with the greatest potential for detecting extraterrestrial life.
By comparing observed propagation properties with laboratory measurements at high pressure and high temperature conditions (Sect. 2. 1), seismological data can provide strong constraints on possible interior layering, composition and mineralogy. Seismic waves travel as compressional and shear waves and are reflected and refracted at discontinuities. They are subject to dispersion and attenuation and compressional waves may induce shear waves and vice versa. In addition to body waves traveling through the interior, seismic energy may propagate as surface waves. Because the velocities of these waves differ, with compressional waves being the fastest followed by shear and surface waves, differences in arrival times of phases can be used. On Earth, strong seismic events are monitored by seismic observatories all over the world while smaller events are recorded by local arrays of stations, sometimes arranged as antennas.
g. ,  Thurber and Ritsema 2015)  allowing exploration of the lateral variation of the velocities and the mapping of undulations of interfaces as well as of tectonic features such as subducting slabs. The Apollo 11 mission in July 1969 not only landed the first human being on the Moon but the Apollo astronauts succeeded in installing the first seismometer on the surface of an extraterrestrial body, followed by three more successful installations by Apollo missions (e. g. , Lognonne and Johnson 2015). Two seismometers were aboard flown on Viking 1 and 2 sent to Mars. The Viking seismometers were less successful than expected, largely because they could not be installed properly on the surface of the planet and protected against the Martian winds but remained mounted on the lander platform, whereas the Apollo seismometers yielded a wealth of data on the interior of the Moon.
The Earth's magnetic field data set is plentiful compared to other planetary magnetic data. In particular, its combination of groundbased and satellite observations at the same time and location as well as the long temporal record of observations has allowed for detailed study of the dynamics and structure of Earth's core. Data from spacecraft magnetometers typically consist of vector magnetic field measurements along the track of a spacecraft orbiting a planetary body. For the terrestrial bodies and satellites in the solar system (other than Earth), missions that provided orbital magnetic data from intrinsic magnetic fields include Mariner 10 and MESSENGER at Mercury  (Ness et al. 1976; Anderson et al. 2012) , Lunar Prospector at the Moon  (Richmond and Hood 2008) , Mars Global Surveyor and Maven at Mars  (Acuna et al.
The heat flow data base for Earth (https:// ihfc-iugg. org/ produ cts/ global-heat-flow-datab ase/ data) contains almost 75,000 data points covering most of the oceanic and continental areas. The heat flow map (e. g. , Lucazeau 2019) mirrors the elements of plate tectonics such as mid-ocean ridges with the highest heat flow values, subduction zones, volcanic arcs and continents. To measure the heat flow one usually measures the thermal conductivity and the temperature gradient, the product of the two giving the heat flow. The difficulty with the measurement comes from two sources: First, the temperature gradient and the conductivity are mostly small values of order 1 K/m and 10 -2 W/mK. But more severe is the disturbance of the near-surface temperature gradient by heat transferred from the surface to depth.
The evolution of the elastic thickness is considered an important quantity to reconstruct the thermal history of a planet (e. g. ,  Grott and Breuer 2008 ). The elastic thickness can be estimated by analyzing the response of the lithosphere to loading stresses such as volcanoes or by analyzing fault structures. The mechanical thickness of the lithosphere can then be inferred from the elastic thickness using a rheological model and the mechanical thickness can, in turn, be identified with an isotherm  (McNutt 1984). Assuming a crustal thermal conductivity, an estimate of the surface heat flow can be made. Most elastic thickness estimates have been derived from gravity and topography data (e. g. ,  McGovern et al. 2004 ), but some geological features allow more direct approaches such as studies of lithosphere deflection due to volcanic or polar cap loading.
In this short overview, three first order aspects of the lunar interior and its evolution are discussed: (1) Moon formation; (2) the lunar core and lunar magnetic field generation, and (3) the hypothesis of an initial global lunar magma ocean (LMO), that can tie together many of the geochemical and geophysical constraints on the interior of the Moon. The scientific goal of the Apollo missions was to figure out how the Moon formed. Although the giant impact model, in which the Moon formed from the debris of a giant collision between Earth and another planet, is almost universally accepted, many unresolved issues, some of them fundamental, remain (e. g. , review by  Canup et al. 2021). Even with the vast amount of geochemical and geophysical data obtained during and since these missions, the origin of the Moon therefore remains hotly debated.
Mars Mars is arguably the best explored planet in the solar system next to Earth. The Nasa Space Science Data Coordinated Archive NSDCC counts 50 missions up to Mars 2020 but not all of them have been successful. Nevertheless, Mars has been visited by an international fleet of orbiters, four rovers and eight landers, including the InSight geophysical observatory. Most recently, as of this writing, the landing of the Chinese Tianwen1 lander and rover on May 15th 2021 has been reported by the Chinese space administration CNSA  (Zou et al. 2021). Given that Mars' surface temperature varies through the day near the equator from 180 to 295 K, as recently measured by the HP 3 radiometer on InSight  (Piqueux et al. 2021; Müller et al. 2021) , Mars is marginally habitable, in particular at some depth were liquid brines may be present and temperatures more benign.