dataesr/scientific-paragraphs-categorization huggingface dataset:
7 kW/cm 2 (fluorescence intensity) at 37 °C. Under these conditions, the intensity of the spots is stable throughout the duration of the acquisition. Approximately 5-10 cells were acquired per condition, per experiment. For FLAG-ST-CCR5 tracking, movies were acquired with a TIRF microscope (IX81F-3, Olympus) equipped with a X 100 numerical aperture 1. 45 Plan Apo TIRFM Objective (Olympus) and fully controlled by CellM (Olympus). Images were collected using an IxonEM camera (DU885, Andor). Image acquisition was done at 10 Hz with an illumination intensity of about 0. 1 kW/cm 2. All live-imaging movies were analyzed using the open-source software Icy (Institut Pasteur).
Track analysis protocol Tracking receptors in TIRF imaging with Icy software To automatically detect eGFP-CCR5 tracks at the plasma membrane upon time, we used the software Icy (http://icy. bioimageanalysis. org) and the plugin Spot tracking, which reports their xy displacement and intensities, as previously described in  Bertot et al. , 2018. Spot tracking was set to detect spots with approximately 3 pixels, and a threshold of 135. All other parameters were as default. Tracks were analyzed with the Track manager plugin. All data was exported to Excel for further analysis. Tracks containing more than 10% of virtual detections and more than three successive virtual detections were excluded from the track classification.
Simulating synthetic receptors' trajectories To evaluate the robustness and accuracy of tracklet classification, we first simulated in Matlab n=100 confined trajectories ( dXt = -λ ( Xt -µ ) dt + σdBt ) with length N+1, where N=5 or 10 is the length of used tracklets for classification. Diffusion coefficient σ was fixed to σ = √ 2 and we varied the confinement parameter λ from λ = 0 to λ = 6 (step =0. 2). We then measured the accuracy of classi- In a second time, to measure the robustness of classification to image noise and particle (receptor) density, we simulated a mixture of n=1,000 of Brownian and Confined trajectories (the percentage of simulated confined trajectories was fixed to 10, 50, or 100%) and generated the associated synthetic fluorescence time-lapse sequences using a mixed Poisson-Gaussian model as described in  Lagache et al.
, 2021. We implemented the simulator of synthetic tracklets of fluorescent spots in ICY (Plugin Dynamics Simulator). Using our simulator, we varied the signal-to-noise ratio (SNR) from 10 to 2 (we measured a mean SNR~10 in our experimental dataset). Concerning the density of receptors' spots, we varied the size of the simulated sequence from xy = 1600 × 1600 pixels to 800 × 800 pixels and 400 × 400 pixels, corresponding respectively to spots' density = 0. 039, 0. 16, and 0. 63 spots/µm 2 , the measured density being <0. 5 spots/µm 2 in most experiments.
Stoichiometry analysis Icy software was used to determine the intensity distribution of eGFP-spots. Spots were detected using the Spot detector wavelet-based algorithm  (Olivo-Marin, 2002) , and then converted to ROIs with 2 pixels radius. Data was exported to Excel. We observed a multimodal distribution of eGFP spots' intensities, and we decided to use the AIC criterion (Akaike information criterion;  Akaike, 1974)  to uncover the number of modes in intensity distribution. Each mode putatively corresponds to a number of molecules. Therefore, statistical characterization of the multimodal distribution of eGFP spots' intensity will help to classify each spot with respect to its mode and, therefore, to its estimated number of molecules. AIC analysis starts with the modeling of the empirical distribution e(x) of eGFP spots'intensities with a weighted sum of Gaussian laws, e ( x ) = p ∑ i=1 α i N ( µ i , σ i ) where p is the number of Gaussian laws in the mixture, α i the weight of each law and ( µ i , σ i ) the corresponding mean and variance.
Acknowledgements We are grateful to  Françoise Baleux  ( Institut Pasteur ),  Stefano Marullo  ( Institut Cochin ) and  Franck Perez  ( Institut Curie ) for the gifts of chemokines and plasmids. We acknowledge  Oliver Hartley  ( University of Genova ) and the  Programme EVA Centre for AIDS Reagents  for the chemokine derivative PSC-RANTES. We acknowledge  Stéphane Petres  from the  Production and Purification of Recombinant Proteins (PPRP) platform  ( C2RT ,  Institut Pasteur ) for sCD4 production. We thank  Audrey Salles  from the  Photonic BioImaging (PBI)  platform (Imagopole) of  Institut Pasteur  for microscope maintenance and technical help. We thank  Vannary Meas-Yedid Hardy  ( Institut Pasteur ),  Stéphane Dallongeville  ( BioImage Analysis Unit, Institut Pasteur ), the  Image Analysis Hub  ( C2RT ,  Institut Pasteur), Gael Moneron (Synapse and Circuit Dynamics), and VizionR (Paris)  for help with the image and data analysis.
The vast majority of edits were A>G conversions (Figure  5E ). The rate of A>T and A>C conversions was ~3 and ~4 orders of magnitude lower, respectively. NGS of the target area confirmed the A>G conversion rates detected by Sanger sequencing and determined that indels (mostly deletions) were < 1. 5% of all reads (Figure  5F  and Supplemental Figure  5 ). HSCs contain 2 HBG promoters (HBG1 and HBG2) on each allele. To assess editing of the 4 target sites on a single-cell level, we isolated individual progenitor colonies and sequenced their DNA (n = 20 colonies). The data show 100% biallelic editing of both promoters (Figure  5G ). γ-Globin protein was detected by flow cytometry in 80%-90% of peripheral RBCs starting after week 8 (Figure  6A ), in accordance with the HSC engraftment/expansion kinetics (Figure  5B ) and previous studies with CRISPR/Cas9  (37).
In vivo HSC genome editing. One of our hypotheses was that, after in vivo HSC transduction, HDAd genomes and, consequently, ABE8e and mgmt P140K expression would be gradually lost due to cell division and vector genome degradation (Supplemental Figure  7 ). We, therefore, designed a new in vivo HSC transduction/selection regimen shown in Figure  7A. After mobilization of CD46/β-YAC transgenic mice and i. v. injection of a single HDAd-EF1α. ABE8e vector, O 6 -BG/BCNU treatment was started at day 2 and repeated at days 12 and 26 to capitalize on mgmt P140K expression from episomal HDAd-EF1α. ABE8e genomes and to dilute out the vector by stimulating cell division. The latter was demonstrated by qPCR that measured vector copy numbers (VCN) in BM MNCs. In in vivo transduced primary mice, during the 16-week experiment, the VCN declined by 2-4 orders of magnitude (Figure  7B ), resulting in undetectable ABE8e and mgmt P140K mRNA levels at week 16 after transduction (data not shown).
The mice were euthanized at week 16-P. The data from primary in vivo transduced mice are sown in this figure. Lin -cells were isolated from BM and i. v. injected into lethally irradiated C57BL/6J mice. The secondary transplanted mice were followed for another 16 weeks (week 16-S; see Supplemental Figure  10 ). (B) Loss of vector genomes in PBMCs and BM MNCs. Vector copy number (VCN) was measured by qPCR with human mgmt P140K primers. ND, not detectable. n = 5 animals for d3 and wk16; n = 3 animals for wk4 and wk6. Notably, in patients with β-thalassemia and SCD, γ-globin-expressing erythroid cells would have a survival advantage created by the disease background. In this context, we expect that rates of γ-globin reactivation achieved with our approach could be even higher in patients with hemoglobinopathies and the in vivo selection with O 6 -BG/BCNU on demand.
Therefore, in upcoming NHP studies with an improved HDAd-EF1α. ABE8e vector, great attention will be given to genomic analyses and potential long-term effects of in vivo base editing. Our current attempts are focused on further improvement of the safety and efficacy of the approach, including new HDAd vector platforms  (54) , alternative mobilization regimens  (55) , and in vivo selection strategies. The latter includes an in vivo selection strategy that would not involve low-dose BCNU  (56). We believe that our study is an additional step toward the vision to treat hemoglobinopathies by a simple i. v. injection of a nonintegrating vector. (A) Analysis based on in silico OTS prediction using Cas-OFFinder software for the mouse genome. Seventy-four sites were nominated, from which the top 5 were subjected to NGS.
Amplicons were prepared using genomic DNA template from a naive mouse and an in vivo-transduced mouse with the highest on-targeting editing rate. The middle and lower panels show the frequency of A>G conversions and indels, respectively. (B) Analysis based on in silico OTS prediction using Cas-OFFinder software for the human genome. Eighty-nine sites were nominated, from which the top 5 were subjected to NGS. Amplicons were prepared using genomic DNA from erythroid cells derived from CD34 + cells of a thalassemia patient that were in vitro transduced with HDAd-EF1α. ABE8e.
Figure10. Off-target analysis after in vitro and in vivo base editing with HDAd-EF1α. ABE8e (based on in silico prediction). (A) Analysis based on in silico OTS prediction using Cas-OFFinder software for the mouse genome. Seventy-four sites were nominated, from which the top 5 were subjected to NGS. Amplicons were prepared using genomic DNA template from a naive mouse and an in vivo-transduced mouse with the highest on-targeting editing rate. The middle and lower panels show the frequency of A>G conversions and indels, respectively. (B) Analysis based on in silico OTS prediction using Cas-OFFinder software for the human genome. Eighty-nine sites were nominated, from which the top 5 were subjected to NGS. Amplicons were prepared using genomic DNA from erythroid cells derived from CD34 + cells of a thalassemia patient that were in vitro transduced with HDAd-EF1α.
Acknowledgments The study was supported by  NIH  grants  R01HL128288  (AL),  R01HL130040  (AL),  R01HL141781  (AL),  U01 AI142756  (DRL),  RM1 HG009490  (DRL), and  R35 GM118062  (DRL); by a grant from  Ensoma Bio  (AL, HPK); and by grants from the  Bill and Melinda Gates Foundation :  INV-017692  (AL) and  INV-005701  (DRL), and the  HHMI (DRL). GAN was supported by a  Helen Hay Whitney Postdoctoral Fellowship  and  K99  award  HL163805. Under the grant conditions of the BMGF, a Creative Commons Attribution 4. 0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission.
Measurements All the ewes were individually monitored for their body reserves (BR) through body condition score (BCS), body weight (BW), subcutaneous back fat (BF) and muscle (BM) depth, and their pedigree information was recorded. All the data were recorded in an INRAE experimental database for sheep and goats (GEEDOC). BCS measurements were performed according the original scale described by Russel et al. [9]  (i. e. , ranging from 1, emaciated, to 5, obese) and the scale was adapted with a subdivision of 0. 1 increments instead of 0. 25. The same two operators systematically recorded the BCS measurements over the 14-year period and underwent regular training sessions for calibration. Subcutaneous back fat and muscle depths were measured by a real-time ultrasound system on the 12th rib. The two technicians mentioned above were also involved in BF and BM measurements (performed over the last 4 years of the period) and underwent regular training.
Descriptive statistics Analyses of variance were carried out, taking the repeated measurements into account, using the MIXED procedure of the Statistical Analysis System (SAS version 9. 4; SAS Institute Inc. , Cary, NC, USA) to test relevant effects and interactions affecting phenotypes. The age at first lambing, the parity of the ewe, the litter size class and the year of measurement were identified as fixed effects and a random animal effect was included to consider repeated measurements on the same animal. The age at first lambing effect took account of ewes that lambed for the first time at 1 or 2 years of age (classes 1 and 2, respectively). The parity effect took account of first, second, third and more lambing (classes 1, 2 and 3, respectively). The effect of litter size class took account of the number of lambs born and suckled (class 0, empty ewes or lambing but without suckling lambs; class 1, ewes lambing and suckling singleton from L to W; class 2 ewes lambing more than singleton and suckling one lamb; class 3, ewes lambing and suckling twins; and class 4, ewes lambing and suckling more than twins).
Phenotypes First, raw phenotypes were adjusted for significant fixed effects consisting of age at first lambing, parity of the ewe, litter size class, and year of measurement fitting the following linear model: where y is the vector of observations for one of the BR traits; b is the vector of fixed effects; and e is the vector of random residuals with incidence matrix X. The residuals resulting for this model  (1)  were used as adjusted phenotypes to fit an animal model to estimate individual values using ASREML 3. 0 software  [39]  with the following linear mixed model: where y* is the vector of adjusted phenotypes; c is the vector of random animal effects; and e is the vector of random residuals with incidence matrix Z. c and e were assumed to be normally distributed with means equal to zero and (co) variances Iσ 2 c, Iσ 2 e, respectively.
e. , 49 genotyped sires out of a total of 60 sires). Dams and sires were genotyped with either MD or HD chips (374 parents and 64 parents, y = Xb + e, [Model 1] y * = Zc + e, [Model 2] respectively). Concerning ewes genotyped with LD chip, both parents were genotyped. Individuals with a call rate below 0. 95 and with Mendelian inconsistencies were discarded (i. e. , five genotyped and phenotyped ewes were removed). The SNPs were removed from further analyses if they were not in Hardy-Weinberg equilibrium, had a minor allele frequency below 1% or had a call rate below 0. 98. PLINK software was used to detect incompatible genotypes between sires, dams and offspring  [42]. When the total number of incompatible SNPs was more than 2% of all SNPs, ewes were kept in the analyses and the parents in error were replaced by missing values (14 ewes concerned).
For ewes genotyped with the HD chip, only SNPs present on the MD chip and LD chip were kept. The ewes genotyped with the LD chip were imputed to the MD chip using Fimpute software, as were LD chip SNPs absent in the MD chip  [43]. This resulted in a data set, used for QTL analyses, containing 1034 ewes with BR phenotypes genotyped for 48,593 autosomal SNPs.
LEPR structure and effect of LEPR genotype Multiple LEPR protein sequence alignment of several mammals was performed using Weblogo software  [23, 48]  Analyses of variance were performed to test the effect of the LEPR genotype (i. e. , SNP oar3_ OAR1_40,857,869) on BR phenotypes using the MIXED procedure of SAS (version 9. 4; SAS Institute Inc. , Cary, NC, USA). The dependent variables were phenotypes used in GWAS for BR and BRD at each physiological stage, as described above. The three possible LEPR genotypes were fitted as a fixed explanatory variable and a significance threshold of p < 0. 05 was selected. The Varcomp procedure of SAS was used to fit the genotype effect as random and to estimate the proportion of variance explained by the genotype. The animal was included as a random effect in both the Mixed and Varcomp models.
For instance, TopNetTree  [36]  applies convolutional neural networks (CNN) and gradient-boosting trees on a set of pre-computed features representing geometric, topological and contact patterns within the neighbourhood of the mutation site. GraphPPI  [23]  further obviates the need for feature engineering by using a graph neural network (GNN) that automatically extracts graphical features from an input 3D structure. The GNN is pre-trained on a large body of protein complex structures through self-supervised learning. Selfsupervised representation learning has proven successful for predicting various protein structural Preprint. Under review. and functional properties in the context of protein language models  [30, 12, 3] , for fixed-backbone protein design  [1, 17, 8] , and for protein stability predictions  [5, 39].
Protein-protein interface representation We represent a protein-protein interface as a set of locally oriented cubic volumetric maps centered around each interfacial residue (Fig. 1A ) (See Supplementary Information for details on building the cubic volumetric map). We define interfacial residues as those displaying a change in solvent accessibility between the free (isolated) protein and the complex  [21]. We used NACCESS  [18]  with a probe radius of 1. 4Å to compute residue solvent accessibility. Masking procedure. The common practice when applying self-supervised learning to protein sequences is to reconstruct some masked or the next amino acid(s), given their sequence context. This task proved successful for natural language processing  [11]  before being transferred to proteins. We employed a similar strategy here, by training DLA to recognize which amino acid would fit in a given local 3D environment extracted from a protein-protein interface.
Databases Experimental values for ∆∆G bind. We used SKEMPI v2. 0  [19] , the most complete source for experimentally measured binding affinities of wild-type and mutated protein complexes. It reports measurements for over 7 000 point mutations coming from 345 protein complexes, including antibodyantigen (AB/AG) and protease-inhibitor (Pr/PI) assemblies, and assemblies formed between major histocompatibility complex proteins and T-cell receptors (pMHC-TCR). We selected a subset of 2 003 single-point mutations associated with 142 complexes. We call this subset S2003. Protein-protein complex 3D structures. We created two databases of protein complex structures, namely PDBInter and S2003-3D, for training and validation purposes. PDBInter contains 5 055 complex experimental structures curated from the PDB  [4].
They do not share any family level similarity between them nor with the 142 complexes from S2003, according to the SCOPe hierarchy  [14, 7]. S2003-3D contains 3D models of the wild-type and mutated complexes from S2003. They were generated using the "backrub" protocol implemented in Rosetta  [34]  See Supplementary Information for more details about the mutation selection process and the generation of the structural databases.
Training and evaluation protocols Training and validation of ssDLA. We divided the PDBInter database into train and validation sets at the level of complexes. We generated 247 662 interfacial cubes from the training set and 34 174 from the validation set. In both sets, we observed some differences in the frequencies of occurrence of the different amino acids. Leucine is the most frequent one, while cysteine is the rarest (Fig. SI 1). To compensate for such imbalance and with the aim of penalizing more the errors made for the less frequent amino acids, we assigned a weight to the loss of each amino acid type that is inversely proportional to its frequency of occurrence. We trained ssDLA for 50 epochs (Fig. SI 2). Training and validation of DLA-Mutation. We trained DLA-Mutation by fine-tuning the weights of ssDLA and integrating all the auxiliary features.
Machine learning approaches for predicting mutation-induced ∆∆G bind typically consider each sample independently when splitting the data between train and test sets  [15, 31, 32, 36, 40]. However, this assumption is not valid since several samples may correspond to different mutations taking place in the same complex or even the same position of a complex. Here, we assessed the two types of split, namely mutation-based, where all samples are treated independently, and complex-based, where we guaranteed that no complex was shared between the train and test sets. We performed 10-fold cross validation only with the mutationbased splitting procedure. For the complex-based one, we hold out 32 complexes displaying 391 mutations for the testing phase, and trained DLA-Mutation on the rest of the dataset.
Curation of PDBInter We downloaded all PDB biological assemblies (June 2020 release) from the FTP archive rsync. wwpdb. org::ftp/data/biounit. We discarded the entries with more than 100 chains or with a resolution lower than 5Å. We also removed the protein chains smaller than 20 residues or with more than 20% of unknown residues. We then redundancy-reduced the resulting dataset using annotations from the SCOPe database  [14, 7].
66 m s -1. A Doppler clutter filter (Siggia and Passarelli, 2004) was applied and a post-processing algorithm was used to correct spurious Doppler velocities caused by the dual-PRF velocity unfolding  (Holleman and Beekhuis, 2003 ; implementation from Altube, 2020, https: //github. com/meteocat/vcor_dual_prf, last access: 2 November 2022). The radar data were ingested and processed using the open-source package Py-ART  (Helmus and Collis, 2016). Other relevant radar specifications are summarised in Table  2.
Methods Although the Doppler lidar and X-band radar are co-located and observe along the same path, the measurements themselves have different radial and azimuthal resolutions. Therefore, to compare the Doppler velocity measurements, the measurements were interpolated onto a shared Cartesian grid with 250 m × 250 m pixel size. The grid was limited to 14. 5 km from the site (i. e. the maximum range of the lidar). The interpolation was performed with the nearest-neighbour interpolation implemented in the wradlib Python package  (Heistermann et al. , 2013). After interpolation, a median filter was applied in the radar measurements to remove any incorrectly unfolded dual-PRF velocities that were not corrected by the dual-PRF correction algorithm (see Sect. 2. 3). The pixel-wise agreement of the measurements from the closest-in-time lidar and X-band radar scans was then studied using an ordinary least squares regression model.
The root mean squared difference (RMSD) and mean error (ME) were also calculated, with the RMSD being defined as and ME as RMSD = 1 N N i=1 V X i -V L i ME = 1 N N i=1 V X i -V L i. (6) The regression model and the statistics were calculated with the statsmodels Python package  (Seabold and Perktold, 2010). Additionally, for each scan, the fraction of measurement bins with valid measurements compared to the total number of bins in the scan was calculated. The fraction was calculated taking into account the blocked beams separately for each instrument, so that a value of 1 indicates that every nonblocked bin had a valid measurement value. These fractions were then used to compare the measurement performance in different weather conditions. Finally, the performance of the instruments as a function of the measurement range was studied by calculating the fraction f (r) of valid measurements at range r, as follows: f (r) = 1 N scans N rays N scans i=1 N rays j =1 I (r, j, i), ( 7 ) where N scans is the number of scans, and N rays is the number of non-blocked rays for the instrument.
5, since the maximum range is about double this; this indeed corresponds to the value we observe for a cloud base height of 250 m. This is the case for both summer and autumn. When the cloud layers are at higher altitudes, and not intercepted by the lidar beam, summer months still show reasonable availability, which is not dependent on cloud base height. It is not certain whether the different response seen in cloud base height above 1 km in autumn months is due to a lack of clouds at these heights in our dataset. The X-band radar exhibits a more bimodal distribution with respect to cloud base height (Fig. 10b, d ). For all cloud base heights shown, the availability of radar measurements is either mostly low, which we attribute to non-precipitating clouds, or mostly high, which we attribute to precipitating clouds.