dataesr/scientific-paragraphs-categorization huggingface dataset:
Missense3D reports that the substitution will not damage the structure while VarSite mentions that the substitution is important and that the residue is highly conserved in 45 protein sequences. VarSite also points to known protein-protein interactions in this region in this protein family. The disease propensity is high with a value of 1. 93. For FB Arg113Trp, basically all 3D in silico predictions and the interactive analysis are all in good agreement with a likely effect on the protein structure. As this region of the protein is cleaved, the phenotype is benign. The 3D tools essentially give a different view as compared to PolyPhen-2. In this case the PolyPhen-2 prediction is in good agreement with the experimental data but this also seems to be a lucky prediction. This substitution also highlights the complexity of the biological system.
| Factor VIII The C2 domain (or discoidin domain) of FVIII is known to interact with negatively charged membranes and with other proteins such as von Willebrand factor (VWF)  (Liu et al. , 2000; Pratt et al. , 1999). We investigated the potential molecular effect of two substitutions, Met2238Val (moderate) and Ala2201Pro (mild), located on this domain of FVIII, both associated with hemophilia A  (Liu et al. , 2000; Pratt et al. , 1999; Spiegel et al. , 2001; Villoutreix & Miteva, 2016). Table  6  shows the results of the in silico analysis (PDB ID: 2R7E, UniProt ID: P00451). Twelve ligand-binding pockets for the FVIII C2 domain were predicted using the FTMap server  (Brenke et al. , 2009; Kozakov et al. , 2015 Kozakov et al. , , 2011;; Ngan et al. , 2012)  (Table  S5 ) and Met2238 and Ala2201 are not directly located in a predicted ligand-binding pockets.
| Antithrombin For Antithrombin (AT) (PDB ID: 2BEH, UniProt ID: P01008) we investigated the possible molecular effects of four clinically important amino acid substitutions. AT is a major blood coagulation protease inhibitor and becomes an effective inhibitor upon binding to heparin, a negatively charged and highly sulfated polysaccharide that enhances the anticoagulant activity of AT toward its main targets, thrombin, factor IXa, and factor Xa  (Huntington, 2011). The crystal structure of antithrombin-heparin and thrombin has been reported several years ago  (Li, Johnson, Esmon, & Huntington, 2004). Mutations in the gene encoding AT can cause AT deficiency that predisposes affected individuals to venous and arterial thromboembolism  (Ding et al. , 2013). According to the phenotype, AT deficiency has been classified into two main categories, type I, characterized by a parallel reduction of the AT antigen and activity levels and type II, defined by a reduction of the AT activity and normal or almost normal antigen levels.
For the other amino acid substitutions, we observed that the PolyPhen-2 predictions tend to agree with experimental data about 60% of the time and in most cases it is not possible to make suggestion about the possible underlying molecular mechanisms. In silico approaches evaluating stability change (stabilizing or destabilizing), tend to agree most of the time with experimental observations, even though, the molecular mechanisms involved cannot always be explained (e. g. , this is for example the case when a substitution favors macromolecular interactions). Conflicting stability predictions among the different tools are known, this can be due to many reasons including scoring function parameters or definition of a threshold to label the substitution as stabilizing or destabilizing. Indeed, it has been shown that for such computations, only the absolute value of the change matters, not the sign  (Peng & Alexov, 2016; Petukh, Kucukkal, & Alexov, 2015).
Interactive structural analysis can here be used to investigate further the predicted ΔΔG values. When this step is carried out, it is most often possible to clarify the calculation output. When PolyPhen-2 and 3D stability predictions differ, the residue that is substituted is most often involved in a saltbridge in the wild-type protein structure. The stabilizing or destabilizing nature of a substitution is difficult to estimate with fast computational approaches, but still a stability change can point toward some modification in the dynamics of the system that, if associated with interactive 3D analysis, can provide some insights about the possible impact of the amino acid change. Of importance, when a substitution introduces F I G U R E 7 Docking of heparin to AT (PDB ID: 2BEH) using the ClusPro server.
In this case, the generated hypotheses can in general be rationally translated into assays. 3D approaches complement and even challenge sequence-based approaches. The combined use of 2D and 3D computational tools is therefore highly recommended, even though, in most clinical genomic centers, the 3D approaches are for the time being largely ignored. Online 3D mapping tools such as VarSite and Missense3D should definitively help biologists with no training in structural biology or structural bioinformatics to rationalize data in 3D. Yet we observe in our dataset that for several substitutions and essentially the ones involving salt-bridges or salt-bridge networks, the provided report cards do not report the ionic interactions. For these types of charged substitution, interactive investigations are definitively needed.
A B L E 2 In silico analysis of CYP1A2 (PDB ID: 2HI4, UniProt: P05177; GenBank: NM_000761. 5) PopMusic solvent accessibility (SA) values are shown (in percent). b For the program MaestroWeb, the confidence estimation Cpred is shown (0. 0-not reliable and 1. 0-highly reliable). c MSA-Multiple sequence alignment. d Flexibility class was determined by the program PredyFlexy (rigid-0, intermediate-1, flexible-2). Wild-type residue (bold) and amino acid substitution (Underlined).
Part of CYP2B6 (PDB ID: 3IBD, cartoon representation) is presented with a zoom on residue Arg140 (blue). The protein is in gray and the heme (part of the catalytic site) in red-orange. Arg140 is partially solvent exposed, forming a salt-bridge (~3. 2 Å) with Glu148 (red) 3. 2. 4 | Ile391Asn (allele CYP2B6*15).
In silico analysis of CYP2C9 (PDB ID: 1OG2, UniProt: P11712; GenBank: KF248057. 1) PopMusic solvent accessibility (SA) values are shown (in percent). b For the program MaestroWeb the confidence estimation Cpred is shown (0. 0-not reliable and 1. 0-highly reliable). c MSA-Multiple sequence alignment. d Flexibility class was determined by the program PredyFlexy (rigid-0, intermediate-1, flexible-2). Wild-type residue (bold) and amino acid substitution (Underlined).
T A B L E 2 In silico analysis of CYP1A2 (PDB ID: 2HI4, UniProt: P05177; GenBank: NM_000761. 5) PopMusic solvent accessibility (SA) values are shown (in percent). b For the program MaestroWeb, the confidence estimation Cpred is shown (0. 0-not reliable and 1. 0-highly reliable). c MSA-Multiple sequence alignment. d Flexibility class was determined by the program PredyFlexy (rigid-0, intermediate-1, flexible-2). Wild-type residue (bold) and amino acid substitution (Underlined).
F Part of CYP2B6 (PDB ID: 3IBD, cartoon representation) is presented with a zoom on residue Arg140 (blue). The protein is in gray and the heme (part of the catalytic site) in red-orange. Arg140 is partially solvent exposed, forming a salt-bridge (~3. 2 Å) with Glu148 (red) 3. 2. 4 | Ile391Asn (allele CYP2B6*15).
Background: Recently, commercial software tools have become available for dosimetry evaluations in clinical settings. The aim of this study was to compare a commercial dosimetry workstation (PLANETâDose) and the dosimetry approach (GE Dosimetry Toolkit® and OLINDA/EXM® V1. 0) currently used in our department for quanti cation of the absorbed dose in organs at risk after peptide receptor radionuclide therapy with 177 Lu-DOTATATE. Methods: First, an evaluation on phantom, with data acquisition at 5 time points, was performed to determine the SPECT calibration factor variations over time and to compare the Time Integrated Activity Coe cients (TIACs) and absorbed doses obtained with the two tools. Then, the two tools were used for dosimetry evaluation in 21 patients with neuroendocrine tumours after the rst and second injection of 7.
2 ± 0. 2 GBq of 177 Lu-DOTATATE (40 dosimetry analyses with each software). SPECT/CT images were acquired at 4h, 24h, 72h and 192h after 177 Lu-DOTATATE injection and were reconstructed using the Xeleris software (General Electric). The liver, spleen and kidney masses, TIACs and absorbed doses were calculated using i) GE Dosimetry Toolkit® (DTK) and OLINDA/EXM® V1. 0 and ii) the Local Deposition Method (LDM) or Dose voxel-Kernel convolution (DK) on PLANETâDose. Results: With the phantom, the 3D calibration factors showed a slight variation (0. 8% and 3. 3%) over time and TIACs of 225. 19h and 217. 52h were obtained with DTK and PLANETâDose, respectively. In patients, the mean of the relative standard deviations was -1% for the organ masses, 5. 6%, for the TIACs, and 4. 4% for the absorbed doses.
The Lin's concordance correlation coe cient was 0. 99 and the Bland-Altman plot analysis estimated that the difference of absorbed dose values between methods ranged from -0. 58 Gy to 0. 84 Gy for approximately 95% of the 40 dosimetry analyses. A difference of 2. 2% was obtained between the absorbed doses to organs at risk calculated with LDM and DK (PLANETâDose). Conclusions: The absorbed doses to organs at risk obtained with the new workstation are concordant with those calculated with the currently used software and in agreement with the literature. These results validate the use of PLANETâDose in clinical routine for patient dosimetry after targeted radiotherapy with 177 Lu-DOTATATE.
The TIA represents the total number of nuclear decays occurring in the source organ over a dose-integration period. It is calculated by integrating the time-activity curve supplied by 2D or 3D quantitative imaging at different time points. The TIA Coe cient (TIAC) is also used and is described as the TIA per unit of administered activity. The S value represents the mean absorbed dose in the target organ per unit of cumulative radioactivity in the source. It is speci c to the energy spectrum of all radionuclide emissions and is dependent on the anatomical phantom chosen to represent the patient. S values have been computed for several radionuclides (18,  19) , and software packages, such as OLINDA/EXM  (20) , offer the possibility of performing organ-level absorbed dose calculations thanks to a complete speci c absorbed fraction database.
It is also possible to personalize the calculation by adjusting the S values to the organ mass  (21). The International Commission on Radiological Protection (ICRP) has published a set of speci c absorbed fractions for the ICRP-110 voxel-based models  (22) , further implemented in the IDAC-Dose program  (23). More recently, the OpenDose collaboration  (24)  presented an open access database of S values based on the same models. Alternatively, other software packages propose to implement speci c dosimetry at the voxel level  (25)  based on SPECT/CT data, with different absorbed dose calculation algorithms, such as Local Deposition Method (LDM) (26) where the kinetic energy from each beta particle is deposited locally, within the voxel where the decay occurred. The absorbed dose in each voxel is obtained by multiplying the activity concentration by the energy deposition factor.
Recently, commercial software tools have become available. However, for their implementation in clinical routine, their performance in the different steps of dosimetry (i. e. calibration procedure, image acquisition, Loading [MathJax]/jax/output/CommonHTML/jax. js reconstruction, registration, segmentation tools, time integrated activity tting and absorbed dose calculation) needs to be evaluated. In our department, dosimetry is integrated in the clinical routine for the management of patients with neuroendocrine tumours treated by peptide receptor radionuclide therapy with 177 Lu-DOTATATE. The rst dosimetry analyses of OARs were performed using the combination "Dosimetry Toolkit® and OLINDA/EXM® V1. 0" for TIA assessment and organ-level dose calculation, respectively  (31). Recently, our department acquired PLANET→Dose, a new CE marked commercial dosimetry workstation.
Dosimetry software platforms The characteristics of the dosimetry software platforms used in this study are presented in Table  1. Dosimetry Toolkit® (DTK) is an application of the Xeleris® software (GE Healthcare)  (32, 33). To determine the calibration factor (CF; in counts. s - 1. MBq - 1 ), a procedure based on planar acquisition is recommended by GE Healthcare. For clinical dosimetry, different scenarios are available: whole-body, hybrid, or multi-SPECT/CT acquisitions. It includes two steps. The rst, "Preparation for Dosimetry Toolkit", is used for the reconstruction of SPECT/CT raw data and registration of the CT or planar whole body scans. The second, "Dosimetry Toolkit" (DTK), is used to segment the different organs, create the time activity curves tted by a mono-exponential function, and calculate the TIAC for each of them.
These TIACs are then uploaded in OLINDA/EXM® V1. 0 to calculate the organ absorbed doses (organ massadjusted) based on the MIRD approach (Fig. 1a ). PLANET→Dose is a treatment planning system from DOSIsoft SA (Cachan, France). The calibration procedure is left to the user's discretion and a CF (in Bq. count - 1 ) is required. This dosimetry platform does not reconstruct SPECT/CT data, but accepts reconstructed data in DICOM format from all devices. It provides multi-time point registration (rigid and elastic), organ segmentation (manual and automatic), and TIAC calculation with a wide choice of interpolation methods of the time-activity curve (linear, trapezoidal, mono-, X-, bi-, tri-exponential …). This software uses three approaches for the absorbed dose estimation: organ-based approach, DK using a convolution algorithm, and LDM, with and without density correction (26, 30, 34) (Fig.
Dosimetry work ow The dosimetry work ow for the two platforms is presented in Fig. 2. SPECT/CT images were acquired at 4 h, 24 h, 72 h and 192 h after infusion. Dosimetry was performed after the rst two infusions. Reference dosimetry method (Fig. 1a ). For infusion 1 and 2, after the last SPECT/CT image acquisition at 192 h, all SPECT/CT data were loaded on the "Preparation for Dosimetry Toolkit" application. Imaging data were reconstructed and an automatic rigid registration between CT scans was performed. The Loading [MathJax]/jax/output/CommonHTML/jax. js results were loaded on the "Dosimetry Toolkit" application. The OARs (liver, kidneys and spleen) were manually segmented using the CT images collected at 4 h post-injection, and then rigidly propagated to the 24 h, 72 h, and 192 h images.
For each time point, the segmented volume was maintained, but sometimes it was adjusted by translation or rotation. Information about the administered activity, the date and time of administration, the radionuclide and the CF (in counts. s - 1. MBq - 1 ) were entered. To obtain the TIAC, the time-activity curves were tted using a mono-exponential function, the only tting model available in the "Dosimetry Toolkit" application. Then, the TIAC values were exported to OLINDA/EXM® V1. 0 to calculate the OAR absorbed doses. The organ masses included in this software were determined from the organ volume de ned on the CT images using "Dosimetry Toolkit" and the biological tissue density (1. 06 g. cm - 3 for liver and spleen; 1. 05 g. cm - 3 for kidney). PLANET→Dose (Fig. 1b ). The transversal slices reconstructed using the "Preparation for Dosimetry Toolkit" application and the corresponding CT images were uploaded on PLANET Dose.
Patients In the 21 patients with neuroendocrine tumours, OAR dosimetry (liver, kidneys and spleen) was performed after the rst and second infusion (7. 2 ± 0. 2 GBq of 177 Lu-DOTATATE/infusion) using the two dosimetry platforms. However, dosimetry data after one infusion were not evaluable in one patient and another patient died before the second infusion. Therefore, in total, 40 dosimetry analyses were performed with each dosimetry platform. In nine patients, due to health problems, technical issues or calendar reasons, SPECT/CT images were acquired at only three time points after injection: 4 h, 24 h, 192 h (n = 7), and 4 h, 24 h, 72 h (n = 2).
However, it accepts reconstructed data supplied by others workstations, unlike the "Dosimetry Toolkit" application. The used reconstruction parameters were determined in a previous study  (31). In a clinical dosimetry study, the preliminary step of calibration is crucial to obtain accurate activity quanti cation  (40, 41). According to the manufacturer's recommendations, a 2D CF is preconized for DTK, whereas the calibration procedure is left to the physicist's discretion in PLANET®Dose. In the phantom-based study, the same 3D methodology was followed to obtain the CF for each software package. As described by Gustafsson et al. (42) , SPECT segmentation is essential to determine the activity concentration. Different segmentation methods are proposed: manual, automatic or semiautomatic (from the technically very easy, such as xed threshold, to the most complicated, such as the Fourier surface method).
This observation is explained by the use of the application "Preparation for Dosimetry Toolkit". The crucial recommendation at this step is that the conditions used in clinical studies in terms of acquisition and reconstruction parameters have to be similar to those used for calibration. On the other hand, we showed a negligible variation of CF values over time, from T = 0 to 216 h. This means that the same CF can be used for each time point. This observation is particularly interesting for DTK in which a single CF must be entered. With the body phantom, the mono-exponential function provided by each software package to t time activity curves showed a slight difference especially at the Y-intercept. Indeed, in PLANET®Dose, the mean CF used for each time point was 67 Bq. count - 1 and 63. 4 Bq.
Due to the limitations of the mean organ-level absorbed dose approach, especially the impossibility of accounting for the heterogeneous activity distributions in organs, the choice between voxel-based dosimetry and the mean absorbed dose approach is currently debated (47). Grimes et al. (25)  showed that the mean absorbed dose to an organ is in good agreement with the voxelized absorbed dose calculated using the Monte Carlo method. They also demonstrated that the voxel S value method gives 3D absorbed dose distributions nearly equivalent to those obtained with Monte Carlo simulations. PLANET→Dose calculates the absorbed dose at the voxel level and then rescales them at the organ level to provide the mean absorbed dose to the segmented organ. The software is not validated currently for clinical use to produce a fully 3D dosimetry, allowing the edition of absorbed dose-volume histograms and isodose curves (16, 48).
From a qualitative point of view, PLANET→Dose is a user-friendly commercial solution that proposes a wide range of tools for segmentation, and several analytic t functions. The time necessary for a dosimetry analysis is signi cantly reduced. Therefore, considering the good agreement between our reference dosimetry method and PLANET Dose, the concordance of the dosimetry results with the literature, the added value of this software in terms of easy contouring, wide choice of time activity curve tting models, time saving, and the fact that the observed differences were explicable and clinically acceptable, we think that the PLANET®Dose software can replace our current dosimetry package without any correction for dosimetry analysis.
Conclusion This comparative analysis validated PLANET®Dose in clinical practice during peptide receptor radionuclide therapy with 177 Lu-DOTATATE. The potential of this software package can now be fully explored, particularly for the determination of the absorbed doses by tumours and for investigating absorbed dose-response correlations. Tables  Table 1:  General characteristics for each step of the dosimetry procedure of the two dosimetry software platforms: the currently used dosimetry combination "DTK+OLINDA" (the reference in this study), and the new PLANETâDose workstation.
Comparison of IPSL-CM5A and IPSL-CM5A2 with observations A historical experiment was performed starting from the last year of the 2800-year PREIND spin-up. This transient simulation was integrated for 155 years between 1850 and 2005. Boundary conditions are described in  Dufresne et al. (2013). Yearly evolution of long-lived greenhouse gases concentrations and total solar irradiance (TSI) correspond to the historical requirements of the CMIP5 project, with notably reduced TSI to consider the volcanic radiative forcing based on an updated version of  Sato et al. (1993). Tropospheric aerosols, tropospheric and stratospheric ozone were prepared as described in  Szopa et al. (2013)     and observations is made by using a mean seasonal cycle for the period of  1980-2005. The comparison between simulated and observed cloud radiative forcing is made using the Clouds and the Earth's Radiant Energy System (CERES) energy balanced and filled fluxes (EBAF) gridded dataset over the period of  2000 -2004 (Loeb et al.
, 2009)). 4. 1 Atmosphere mean state and identified biases 4. 1. 1 Cloud radiative forcing LMDZ5A (and hereby IPSL-CM5A) has been shown to underestimate low and mid-level cloud cover and overestimate high-level clouds at midlatitudes and high latitudes  (Hourdin et al. , 2013) , biases shared with many climate models  (Zhang et al. , 2005). LMDZ5A cloud representation has been since strongly improved with the new set of physical parameterizations and associated increase in vertical resolution  (Hourdin et al. , 2020)  that is currently being used for CMIP6 project. In IPSL-CM5A2, the biases of the standard parameterization translate into CRF, with too-reflective clouds and therefore too-negative shortwave CRF at midlatitudes and high latitudes (-15 to -25 W m -2 ), and in some convective regions of the tropics an underestimation of outgoing longwave trapping and therefore too-positive LW CRF (+5 to +15 W m -2 ), when compared to the CERES dataset.
They are made mostly of large-scale precipitation that is overestimated due to a toostrong cooling over the highest-altitude grid points. Figure  9c  shows Hovmöller diagrams for GPCP dataset compared to IPSL-CM5A2. Although at the first order, the seasonal cycle of precipitation is correctly represented over the "Maritime Continent", IPSL-CM5A2 depicts a strong humid bias over the region (up to +5 mm d -1 during the humid season). This bias has been described earlier with LMDZ preindustrial simulations run either in atmospheric-only or fully coupled mode  (Toh et al. , 2018; Sarr et al. , 2019)  and it was shown that LMDZ systematically overestimates rainfall over both land and sea surfaces of the Maritime Continent, to- gether with western Indian Ocean and Bay of Bengal. Given the complexity of the Indonesian region, with a myriad of islands that the model spatial resolution cannot capture, it is no surprise that rainfall is misrepresented in this model  (Qian, 2008).
Oceanic boundary conditions Bathymetry. The Turonian bathymetry (Fig. 18b ) is obtained from the merging of two datasets. First, the regular longitude-latitude Cenomanian/Turonian paleogeography from  Sewall et al. (2007)  is masked over continental areas and remapped onto the curvilinear PALEORCA grid. Given that the bathymetry of the Pacific and Indian oceans is mostly flat (without ridges) and deep (∼ 5000 m), in  Sewall et al. (2007) , we have decided to merge the more recent bathymetric dataset of  Müller et al. (2008)  with the land-sea mask and continental margins of  Sewall et al. (2007)  to create a more realistic deep bathymetry. Handmade corrections are then applied to the bathymetry to ensure that narrow straits (e. g. , west Africa) are at least two grid points wide. This avoids the implementation of specific hard-coded schemes in the model to represent water advection across those straits.