dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by the  Agence Nationale de la Recherche  ( ANR-21-CE11-0008-01 ). We are grateful to  Artemis Kosta  and  Hugo Le Guenno  ( Plateforme de Microscopie, Institut de Microbiologie de la Méditerranée, Marseille ) for help with transmission electron microscopy. We would like to thank  Sophie Helaine  ( Harvard Medical School ),  Emilia Mauriello  ( Laboratoire de Chimie Bactérienne ) and  Romé Voulhoux  ( Laboratoire de Chimie Bactérienne ) for critical reading of this manuscript. preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this this version posted September 19, 2023. ; https://doi. org/10. 1101/2023. 09. 19. 558370 doi: bioRxiv preprint.
We observe a clear improvement in the accuracy of most data sets. This highlights the importance of weighting differently the persistent edges and the freshly appearing edges. Additionally, we would like to emphasise that Algorithm 1 estimates the values of α and β using the network data. Only two data sets (workplace 2015 and primary school) show no improvements. Nonetheless, we demonstrate in Figure  3  that we can improve the accuracy of these two data sets by taking into account higher-order correlations. 4. http://www. sociopatterns. org/.
One direction for future research is online/adaptive tuning of hyperparameters such as estimates of the bias of the noisy semi-supervised oracle. 5. To implement this VEM algorithm, we used the R package dynsbm provided by the authors of  [8]  and at http://lbbe. univ-lyon1. fr/ dynsbm. Several approximations and relaxations were made in deriving a weighted similarity matrix used for spectral clustering and for tuning its weights. Analysing the accuracy of these approximations, and proving that the proposed algorithms terminate in a finite time remain topics for future research. , we find that log P(A 1 | σ, θ) = 1 2 i,j a δ(A 1 ij , a) δ(σ i , σ j )ρ θiθj a + log ν θiθj a = 1 2 i,j δ(σ i , σ j ) a δ(A 1 ij , a)ρ θiθj a + c 1 (A), where c 1 (A) = 1 2 i,j a δ(A 1 ij , a) log ν θiθj a does not depend on the community structure.
Low pH promotes stronger interactions between His122 and the lower part of the CAP The model of the membrane-inserted TWIK1 protein used for MD simulations was built from the 2012 structure identified by PDB accession code 3UKM  (24). We compared the structures generated by MD simulation with those obtained more recently by cryo-EM at pH7. 4 and 5 (8). In the cryo-EM structures, the orientation of His122 with respect to the main axis of the protein decreases significantly upon extracellular acidification  [8]. For each frame, the protein was oriented along its major axis and the angle formed by the major axis and a line joining the Cα atom and the side chain of residue 122 was calculated. On average, the orientation of His122 relative to the major axis of the protein decreases from ∼80 to ∼50 degrees upon acidification.
Another interesting result is that this unique property of TWIK1 can be transferred to TASK3 by introducing only a few residues typical of TWIK1. In TASK3, Y85F and G82S mutant show a response to pH similar to that of TWIK1. TASK3 is known to be involved in cell proliferation and carcinogenesis  [18, 19]. Interestingly, a TASK3. G82S variant has been described in the COSMIC database, as a somatic mutation found in adenocarcinoma (COSM3897140, COSV57284824, ICGC (STAD-US): Gastric Adenocarcinoma -TCGA, US). This mutation was found in gastric tissues in which the channel exposed to acidic pH should be permeable to K + and Na +. It would be certainly interesting to study how this selectivity change of TASK3 affects cancer development.
Currents were evoked from a -80-mV holding potential followed by a 300-ms ramp from -120 to +60 mV. All recordings were performed at 20 °C. Stimulation, data acquisition, and analysis were performed using pClamp software (Molecular Devices). The graphs, statistical analyses and figures related to the electrophysiology have been realized on the GraphPad Prism software. Statistical significance was determined by using unpaired Student's t test and indicated as follows: *P < 0. 05; **P < 0. 01; ***P < 0. 001. ****<0. 0001 Computational section. The inter-residue distance calculations reported in Table  1  and the 3D-structure representations S1 and S2 have been realised on PyMOL software from the crystal structure of TWIK1 (PDB ID: 3UKM, chains A and B). The 3D-structure representations reported in Figures  4  and 5  were realised with VMD  [20].
In this surrounding, His122, Glu45, Glu49 and Asp230 were protonated at low pH only, Glu235 was left negatively charged in all cases, whereas Lys246 and Lys131 were deprotonated in all cases. Molecular Dynamics simulations. We constructed models of the channel based on the crystal structure with PDB code 3UKM, which was prepared at a pH greater than 7. 4 and is believed to represent an open conformation. These models were protonated to simulate either pH6. 0 or pH7. 4 and were embedded in a double bilayer system, following a previously established method [9]. It's important to note that this system was different from the one used for the pKas calculations. In each extracellular-facing leaflet of the bilayer, there were 105 molecules of 1-palmitoyl-2-oleoyl-sn-glycerol-3-phosphocholine (POPC) and 10 of 1-Palmitoyl-2-oleoylphosphatidylethanolamine (POPE).
Acknowledgements This work was funded by the  Agence Nationale de la Recherche  (grants  ANR-11-LABX-0015-01  and  ANR ANR-22-CE14-0048-01 ), by the  Fondation pour la Recherche Médicale  (équipe labellisée FRM 2020), and the  Swiss National Supercomputing Center (CSCS)  under the project  IDs s968 ,  s1037 ,  s1099  and  s1223. We thank  Solène Gibaud  for excellent technical help, and  S. Bernèche  and  D. Minor  for useful comments on the manuscript.
, t Tp/δ = t + T p , with δ the sampling time in Section 2. In each arc [t i , t i+1 ], i ∈ {1,. , T p /δ -1}, the input is kept constant, while the final state at t i+1 is derived from (2) using the Runge Kutta 4th order method with step size δ = 0. 1 and the initial condition at t i constrained to be the final state of the previous interval [t i-1 , t i ], whereas the initial condition at t 0 is x 0. The ellipsoidal terminal sets are plotted with MPT3 toolbox  (Herceg et al. , 2013) , while their volumes are calculated using CORA  (Gaßmann & Althoff, 2022). The simulations are done on a lab computer with an AMD Ryzen 5 2600 6-core processor, 3. 4GHz, 12 CPUs, 16GB RAM, Python 3. 8. 5. The sampling time δ in Section 2 is chosen as 0. 1s. For the prediction horizon T p , we choose the minimum length such that the problem attains the feasibility property, i.
Acknowledgments The authors are grateful for the contributions of  Prof. Fernando Lobo Pereira  during several stages of this research. This work has been partially supported by the  PHC Alliance Hubert Curien Programme, France  ( PHC PESSOA , Projet no. 47913XD ),  La Région ,  Pack Ambition Recherche   2021 -PlanMAV  and by the  French National Research Agency, France  in the framework of the '' Investissements d'avenir" program  '' ANR-15-IDEX-02 " and the  LabEx PERSYVAL, France  '' ANR-11-LABX-0025-01 ". Huu Thien Nguyen  is supported by  FCT/MCTES (PIDDAC), Portugal  under grants  2020. 07959. BD ,  2021. 09306. CBM ,  2022. 02801. PTDC ,  LA/P/0112/2020 ,  UIDB/00147/2020 ,  UIDP/00147/2020. Ngoc Thinh Nguyen  is funded by the  German Ministry of Food and Agriculture (BMEL)  Project No.
(2018)  and  Braun et al. (2019) , respectively. In short, the raw data was cleaned off adapter sequences using the software TagDust  (Lassmann et al. , 2009). All RNAseq datasets were combined to create a reference library. Assembly was performed using the software Trinity  (Grabherr et al. , 2011). The reads of the individual libraries were hereafter mapped to the reference library and single nucleotide polymorphisms (SNPs) were identified using the software SNVMix  (Goya et al. , 2010). SNPs between the H. bulbosum and H. vulgare genomes were identified and sequences 100 bps up-and downstream of each SNP were determined. Annotation of the SNP-containing sequences was done by using the database Swiss-Prot  (Boeckmann et al. , 2003). Additional polymorphisms were retrieved from exome capture sequencing, performed according to  Wendler et al.
Modeling by AlphaFold. For each OG representative sequence we computed three AF models differing by the way of building multiple sequence alignment (MSA). The MMseqs2 MSA was obtained from the MMseqs2  [69, 70]  web server as implemented in the ColabFold version of AlphaFold 2. 0  [71]. We also used the version of AlphaFold 2. 2. 0 that builds MSAs by Jackhmmer on uniclust, mgnify and uniref90 databases. These two implementations use PDB templates. Finally, a Custom MSA was build from Muscle_v3. 8. 31  [72]  by inserting (-profile option) the query sequence on top of a previously computed MSA, termed ß1ß4_MSA. The ß1ß4_MSA was build from a Muscle alignment of the OG sequences (S1 Table ) by filtering out those having the two flanking cysteine residues in the ß1 and in the loop between ß4 and ß5 strands not correctly aligned to the 8 3D template sequences.
Acknowledgements We are grateful to  Liisa Holm  for feedback in implementing Dali. The authors are grateful to  J. Maidment  ( CBS, Montpellier ) for careful proof reading and language correction of our manuscript. We are grateful to  Matthew Bowler  and  Didier Nurizzo  at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France  for providing assistance in using beamline ID30A-1. The authors gratefully acknowledge the  ESRF  for provision of synchrotron radiation facilities via Block Allocation Group beamtime.
NMR samples consisted on approximately 0. 4 mM 15 N-labeled protein dissolved in 10 mM Na-Phosphate buffer (pH 6. 8) and 150 mM NaCl with 5% D 2 O for the lock. The structure coordinates have been deposited at the Protein Data Bank under the NMR Structure determination of MAX28, MAX47, MAX60 and MAX67. following accession codes: MAX28 (PDB_8C8A), MAX47 (PDB_7ZKD), MAX60 (PDB_7ZK0), MAX67 (PDB_7ZJY),). Uniformly labeled 15 N MAX28 was expressed in E. coli BL21 (DE3) cells (Invitrogen, All NMR spectra were processed with Topspin 3. 6 (Bruker) and analyzed with Cindy Thermo Fisher Scientific, Waltham, USA) from a homemade plasmid pDB-his-CCDB-2. 1 (Padilla, www. cbs. cnrs. fr). Assignments for MAX28, MAX47, MAX60 and MAX67 3C (courtesy of Frederic Allemand, CBS Montpellier, France). Protein expression have been deposited to and are available from the BMRB data bank under the was carried out in 15 NH 4 Cl (1 g/l) enriched M9 medium.
Whole organ volumetric sULM The construction pipeline of the 3D sULM mapping is shown in Fig. 1. In Fig. 1  a classical ultrasound Bmode volume reconstruction, b clutter filtering using a low-threshold Singular Value Decomposition (SVD) where the first six singular values were annulled, c microbubble localization using radial symmetry algorithm  9, 10  , and d tracking using the Hungarian algorithm  10. It is worth to mention that in this work, the localization and tracking were done on an unique dataset. Contrary to 2D sULM  6  , where the dataset was divided into two sup-groups: fast moving microbubbles and slow moving microbubbles. Thanks to the volumetric acquisition, we were able to precisely track the microbubble along its entire path within the blood vessels without any loss of visual continuity without the need of separating dataset.
Acknowledgment We thank the  "Live Imaging Platform" (Université de Paris-Cité) , and specifically  Lotfi Slimani  for the micro-CT acquisitions with the  Skyscan 1172 Bruker. This study was funded by the  European Research Council  under the  European Union   Horizon 586 H2020 program / ERC Consolidator  grant agreement No  772786-ResolveStroke. O. C. holds patents in the field of ultrasound localization microscopy and is a cofounders and shareholders of the Resolve-Stroke startup.
Environmental data Mean monthly temperature (°C) and precipitation (mm) climatologies (period 1970-2000) were retrieved from the 1-km spatial resolution WorldClim version 2 dataset (http://worldclim. org/version2)  40. Climatologies were obtained by performing the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package; more information on the numerical procedures was provided by Hijmans and colleagues  41  and Fick and Hijmans  40.
ACKNOWLEDGEMENTS This work was supported by the ' Centre National de la Recherche Scienti que' (CNRS) , the  Research Programme CPER CLIMIBIO (Feder, Nord-Pas de Calais) , the  regional programme   INDICOP (Pas-de-Calais)  and the  ANR  project  TROPHIK. The authors also thank the  French Ministère de l'Enseignement Supérieur et de la Recherche , the  Hauts de France Region  and the  European Funds for Regional Economical Development  for their nancial support to this project.
I. INTRODUCTION R OBOTS can assist humans and potentially alleviate their workload during collaborative tasks, while the human user typically contributes to the adaptation of trajectories and efforts. Effective human-robot coordination is crucial to ensure that the interaction is transparent to the user. A supervised teleoperation scheme can allow the robot to follow human commands  [1] -  [3]. Similarly, the robotic agent of  [4]  assists in trajectory training for novices based on task-dependent reinforcement learning. From a cognitive perspective, robots could be made more flexible and better interact with human users by learning from human movements humans using logical inference. Recent works have emphasized the importance of the robot to generate commands based on the prediction of This work was partly supported by the UK EPSRC program grant FAIR-SPACE EP/R026092/1 and by the European Commission grants H2020 PH-CODING  (FETOPEN 829186)  and NIMA  (FETOPEN 899626).
A. Experiments This experiment was conducted at the Institute of Perception, Action, and Behaviour at Edinburgh University. The bimanual hand-foot coordination system is composed of two torque-controlled, 7-DoF Franka Emika robotic manipulators, which are both equipped with a flat 3D-printed contact plate mounted on the end-effector. The bimanual impedance controller  [37]  is implemented in velocity and offset modes to make full use of the robot workspace while ensuring safe interaction with the environment. Real-time module calculation is performed on the basis of the rigid-body dynamic library Pinocchio, where EiQuadProg++ is used to solve quadratic programs for inverse kinematics. The operator can provide 6-DoF (position and orientation) commands via the foot interface and Sigma-7 haptic device (Force Dimension Inc.
B. Results To examine the performance of the GP-IT2PFL approach, we evaluate the estimation performance by root-mean-square error: RMSE = 1 N N i=1 ∥y i -ŷi ∥ 2 , (24) where y i and ŷi represent the actual and the estimated endeffector position, respectively. We construct the neural network  [39]  and Gaussian process  [40]  for three tasks in bimanual hand-foot coordination for comparison, where the dataset is identical with the GP-IT2PFL approach, and the number of samples is 5000 (training: 4000, test: 1000) based on an empirical ratio in  [41]. In particular, the neural network (NN) is composed of 2 hidden layers with 40 and 20 neurons with hyperbolic tangent and rectified linear unit (ReLU) activation functions, the final output layer is with log-sigmoid activation function, where the parameters of the NN are initialized by  [42].
In our case, the human assistant is replaced by an automatic robotic partner to facilitate the surgeon to complete the task. The training data was collected from human-human collaboration and was tested in human-robot collaboration. We collect the trajectory data from the Omega-7 endeffector in human-human cooperation. As shown in Fig. 5a , the PSM gripper follows the commands from the end-effector of the local interface, where the left PSM gripper is controlled by the main operator and the right one by an assistant. The main operator navigates the left gripper onto the initial peg, grabs the orange block, lifts it, and moves it to the assistantcontrolled gripper on the right for the handover. Then, the right-hand gripper moves the block to the target peg and finally releases it (see video 1), as demonstrated in Fig.
The long isoform of chimpanzee and gorilla SLC22A10 was expressed on the plasma membrane whereas the short isoform was not. The organic anion transporters depicted in Fig. 1A  consist of 536 to 563 amino acid proteins. Predictions from reliable sources such as Uniprot, Ensembl, and NCBI Nucleotide databases confirmed that the human SLC22A10 gene produces a 541 amino acid isoform. Conversely, according to reports from Ensembl and UniProt, the orthologs of SLC22A10 found in great apes are predicted to have two isoforms: a short isoform comprising 540 amino acids and a longer isoform containing 552 amino acids. Because there was no detectable expression of the human SLC22A10 on the plasma membrane of cells recombinantly expressing the transporter (Fig. 1B ), we inquired whether the direct species orthologs in great apes exhibited a similar lack of plasma membrane expression when expressed recombinantly in cells.
2C ). We utilized our previously generated human and liver RNAseq data 12 alongside a large human RNA-seq databases (recount3  13  ) to validate the splicing event in human and chimpanzee SLC22A10. Our analysis confirmed that splicing mostly occurs at the exact orthologous genomic region in both species, utilizing the canonical splice sites in their corresponding genomes. Thus, we found no coordinated splicing alterations that compensate for the A nucleotide insertion. Consequently, the additional A nucleotide remains present in the final transcribed transcript in humans. This additional nucleotide provides evidence that human SLC22A10 protein contains 541 amino acids, while the chimpanzee SLC22A10 protein comprises 552 amino acids.
Analyses of SLC22A10 isoforms in chimpanzee, bonobo, orangutan and gibbon Upon examining various databases (UniProt, Ensembl, and NCBI nucleotide) that predict SLC22A10 isoforms, we observed that chimpanzee, bonobo, orangutan, and gibbon are predicted to have different isoforms, including (i) chimpanzee (XM_024347215. 1, 533 amino acid); (ii) bonobo (XM_034932815. 1, 538 amino acid); (iii) gibbon (XM_003274123. 1, 533 amino acid); and (iv) orangutan (XM_024255628. 1, 533 amino acid). The shorter isoforms are derived from alternative acceptor sites (chimpanzee and bonobo) and exon extensions (orangutan and gibbon) resulting in 533-amino acid proteins (see Supplementary Fig. 3 ). We conducted experiments using HEK293 Flp-In cells that were stably transfected with GFP-conjugated chimpanzee, bonobo, and gorilla SLC22A10 to examine the localization and function of the shorter isoforms.
Conversely, when the amino acid at position 220 in chimpanzee SLC22A10 was mutated to proline, the uptake of estradiol-17β-glucuronide was completely abolished (Fig. 3B ). Cordes F. et al. (2002) reported that distortions of transmembrane helices can be induced by the presence of proline  24. Using AlphaFold2 model of human SLC22A10, we observed that the proline 220 in humans introduces a kink in the alpha-helix, which in turn can affect the conformation of the 225-230 loop and, consequently increasing the accessibility of the lysine 230 for ubiquitination. In addition, based on the ΔΔG calculation the proline 220 is significantly less stable with a higher ΔΔG. Based on comprehensive genomics datasets from large-scale populations such as gnomAD  25  , GDBIG (http://www. bigcs. com. cn/), TopMed Freeze 8 26 , and GME Variome  27  , there have been no reported individuals with SLC22A10-Leu220.
Allele frequencies from non-human great apes were obtained from available whole-genome sequencing data including 59 chimpanzees, 10 bonobos, 49 gorillas and 16 orangutans  [48] [49] [50] [51]. All samples were mapped to Hg19. We extracted the genotyping information in position chr11:63078478 (Hg19 coordinates) to calculate the allele frequencies per population. We manually curated the genotypes by checking the raw reads overlapping this region in the BAM files. In chimpanzees we report a frequency of the insertion to be 3/108=2. 5%; in bonobos is 0/20=0%; in gorillas is 0/98=0%; and in orangutans is 0/32=0%. The global human allele frequencies were obtained from 1000 Genomes Project Phase 3  52  database in Ensembl for the rs562147200 SNP.
SLC22A10 variants in archaic hominins and ancient humans Archaic hominin genotypes for three Neanderthals and a Denisovan were retrieved from http://ftp. eva. mpg. de/neandertal/Vindija/VCF/ and http://ftp. eva. mpg. de/neandertal/Chagyrskaya/VCF/  [53] [54] [55] [56]. Table  1. The protein levels of SLC22A10 in HEK293 Flp-In cells that were transfected with expression vectors containing open reading frames of SLC22A10 orthologs. The protein levels are reported in fmol/µg protein and the values are the mean ± standard deviation from two independent experiments. The proteomic analysis was conducted using UniProt IDs to identify and characterize the detected SLC22A10 protein in the sample. Specifically, the SLC22A10 protein levels were searched against the UniProt database to find matches with the SLC22A10 protein sequence.
We used the LDproxy tool from LDlink  57  to identify variants in high LD with rs1790218 in all Thousand Genomes populations and intersected these variants with those assayed in ancient humans from the Allen Ancient DNA Resource (AADR)  58  , retrieved from https://reichdata. hms. harvard. edu/pub/datasets/amh_repo/curated_releases/V54/V54. 1. p1/SHARE/public. dir/v54. 1. p1_1240K_public. tar. We identified two such variants: rs1783634 (D' = 1, r2 = 0. 9839) and rs1201559 (D' = 0. 9975, r2 = 0. 9775). We filtered the AADR genotypes for these SNPs, excluding samples from archaic hominins and individuals that were not genotyped at both loci. We calculated allele frequency in 17 time periods, stratifying by sample location. We also retrieved the allele age estimate for rs1790218 using the Human Genome Dating tool 59 with all default settings.
Besides, based on the very similar interactomic properties of the other variants to WT BIN1, it is likely that these are benign variants. In support of this, the common variant T532M, that displays affinity interactome quasi identical to WT BIN1, is present 116 times at homozygous state in genomic databases with no connection to any clinical phenotype  (Karczewski et al. , 2020). In a previous study, point mutations were used to map the DNM2 binding interface on BIN1 and two of their mutations coincide with the natural variants displaying perturbed affinity interactomes  (Owen et al. , 1998). Similarly to our findings with Y531S and D537V, the authors found Y531F and D537A variants to cause a marked loss of DNM2 binding activity. Since the binding of DNM2 is highly synergistic between many weak PRMs, we also measured the binding of a larger section of the DNM2 PRR (residues 823-860) against all tested BIN1 variants (Figure  6A ).
Our systematic unbiased study suggests that this complex may play a role in the symptomatic manifestations of both conditions. Nevertheless, disruptive mutations in the SH3 domain of BIN1 affect all identified interactions, and even if they display weak affinities, the loss of these interactions collectively contributes to CNM. Rare variants of uncertain clinical significance are a major challenge in interpreting genetic results. Only considering the very short SH3 domain of BIN1, we could identify dozens of sequence variations in genomic databases out of which nearly none were studied with functional assays previously. To shed light on the potential importance of these rare variants, we systematically screened affinity interactomes of a panel of naturally occurring missense variants of uncertain clinical significance.