dataesr/scientific-paragraphs-categorization huggingface dataset:
The fit of the experimental data to the amplitudes calculated from Dock2 DHR2 -Rac1 gave a relatively low χ 2 value of 3. 5, indicating that the shapes of the DOCK5 DHR2 -Rac1 and Dock2 DHR2 -Rac1 complexes are similar. These observations suggest that, unlike unbound DOCK5 DHR2 , the DOCK5 DHR2 -Rac1 complex is well represented by a single major conformation. Compared to unbound DOCK5 DHR2 , the DOCK5 DHR2 /Rac1 envelope obtained with DAMMIF displays symmetrical bulges and these extra volumes are readily aligned with the bound GTPase by fitting the crystallographic Dock2 DHR2 -Rac1 into this envelope (Figs  4C  and S3C ). Altogether, the SAXS analysis of DOCK5 DHR2 suggests that unbound DOCK5 DHR2 has conformational dynamics in solution that is restrained by binding of the Rac1 GTPase. To get further insight into the allosteric inhibition of DOCK5 by C21, we attempted to characterize the solution structure of the DOCK5 DHR2 -C21 complex by SEC-SAXS analysis.
However, using a small volume analytic SEC column, we observed a small amount of a larger molecular weight species, which was not detected from the UV elution profiles but appeared in the I (0) analysis and could not be resolved by a computational method. This peak could be resolved by using a small-scale preparative SEC column, although at a large expense on the intensity of the SAXS signal that prevented further analysis (not shown). Although the data measured using both columns have a poorer fit to the DOCK5 model compared to unbound DOCK5, which could suggest that C21 affects the structure of DOCK5 DHR2 , further analysis was not carried out due to these technical limitations. Alternatively, we reasoned that the non-competitive mode of inhibition of C21 indicates that it targets a pre-catalytic Rac1-GDP/ Dock DHR2 complex that is not yet competent for nucleotide dissociation and must therefore differ from the known crystal structures of small GTPase/Dock DHR2 complexes  4, 5, 7.
5 lower concentration of the sample at the elution peak. switch 1 that has not been seen in other Rac1 structures (Fig. 5B ). When superposed to the Rac1/Dock2 DHR2 complex, the switch 1 of Rac1-GDP lacks contacts with α6-β3 in the B lobe while residues  35  TVF  37  in the switch 1 conflict with the catalytic nucleotide sensor in the C lobe (Fig. 5C ). Both the α6-β3 region in the B lobe and the nucleotide sensor in the C lobe are highly conserved between DOCK5 and Dock2 5. These data suggest that flexibility of the switch 1 supports the formation of a pre-catalytic Rac1-GDP/DOCK5 DHR2 complex that differs from the catalytically competent intermediate in that the switch1/B lobe interface is not established. The crystal structure of the Dock2 DHR2 dimer was superposed onto the DOCK5 DHR2 envelope using SUPCOMB.
These data suggest that C21 intercepts a site that forms transiently as the Rac1-GDP/DOCK5 DHR2 complex visits its conformational landscape. Although our data are not sufficient to map the binding site of C21 or identify structural rearrangements of the C21-DOCK5 DHR2 complex, they can be aggregated in a simple model (depicted in Fig. 6 ). In this model, the C lobe of DOCK5 DHR2 features conformational dynamics that allows Rac1-GDP to bind in successive steps: first, Rac-GDP docks onto the C lobe by its structurally invariant switch 2 region; then, the dynamics of the C lobe brings the switch 1 in contact with the B lobe; finally, this interaction remodels the switch 1 to stabilize the complex, making way for the nucleotide sensor to reach into the nucleotide-binding site and promote the dissociation of GDP.
0, 500 mM NaCl, 2 mM β-mercaptoethanol, 2 mM MgCl 2 , 10% glycerol, 0. 5% tween-20, anti-protease cocktail) and frozen in Figure  5. Crystallographic analysis of Rac1-GDP. (A) Crystal structure of Rac1-GDP. The switch 2 region is well ordered in Rac1-GDP and its conformation is identical to the switch 2 in Rac1-GDP-RhoGDI1 (PDB entry: 1HH4), Rac1-GDP-Arfaptin (PDB entry: 1I4D), Rac1-Dock2 DHR2 (PDB entry: 2YIN) and Rac1-GPPNP (PDB entry: 1MH1), overlaid in purple shades. The invariant position of Arg68 is shown for reference. The switch 1 region is in yellow, with poorly ordered residues 30 GEY 32 shown in red. (B) Close-up view of the overlay of the switch 1 regions in Rac1-GDP, Rac1-GDP-RhoGDI1, Rac1-GDP-Arfaptin, Rac1-Dock2 DHR2 and Rac1-GPPNP. (C) Model of the precatalytic Rac1-GDP-Dock2 DHR2 -bound complex1.
Gel filtration experiments. All experiments were carried out in elution buffer (20 mM Tris pH 8. 0, 150 mM NaCl, 0. 5 mM EDTA) containing either 25 μM C21 diluted from a DMSO stock or the equivalent amount of DMSO. This is the maximum concentration at which the inhibitor is soluble. DOCK5 DHR2 was used at 25 μM in all experiments. DOCK5 DHR2 was pre-incubated with DMSO or 25 μM C21 for 30 min, before addition of truncated Rac1 (50 μM) and 10 mM EDTA to favor the formation of the nucleotide-free complex. Proteins samples were injected onto a Superdex 200 gel filtration column (GE-Healthcare). The concentration of DOCK5 DHR2 at the elution peak is estimated to ≈ 1 μM, ensuring that the inhibitor is in ≈ 25-fold excess. Chromatography-coupled SAXS data acquisition. All SAXS data were obtained from synchrotron SEC-SAXS experiments.
SAXS Data Analysis and Structural Modeling. All the data analyses were performed with programs from the ATSAS package  36  unless specified otherwise and are summarized in Table  1. Radii of gyration (R g ) were evaluated by Guinier Wizard using the data within the range of Guinier approximation sR g < 1. 3 and by Distance Distribution Wizard, both of which are modules of the PRIMUS program. The maximum distance D max was estimated with PRIMUS and refined by trial and error with GNOM. The distance distribution functions P I were calculated with GNOM. For DOCK5 DHR2 , we determined a range of D max values that accounted well for the experimental SAXS data. We then calculated DAMMIF models (see below) using D max values within this range, and selected the smallest D max value that accounted for the known 2-fold symmetry of DOCK5 DHR2.
The dimensionless Kratky plot was calculated by plotting (qR g ) 2 I (q) /I (0) against qR g  37. The molecular weights of the proteins were estimated by the ScÅtter program (q = 0. 25)  38. The fit between scattering amplitudes calculated for crystal structures and the SAXS profiles were carried out with CRYSOl. ab initio shape determinations and structural envelopes calculations were done by DAMMIF, using the SAXS data within a q range of 0. 01-0. 2 Å -1. Initial calculations were carried out without imposing P2 symmetry and this resulted in models that were visually symmetrical. Given that all Dock DHR2 proteins with known crystal structures are symmetrical dimers and DOCK5 DHR2 is itself a dimer, subsequent models were constrained by a P2 symmetry to improve the quality of the models. 20 independent runs were carried out for each dataset.
The resulting models were further compared and clustered by SUPCOMB. The Normalized Spatial Discrepancy (NSD) values were close to 1, which is in the high end for acceptable similarity between models but likely reflects the anisotropy of the structure. The model with the best fit to the experimental data was chosen for comparison with the crystal structure of Dock2 DHR2. The PDB coordinates of the crystal structures were superimposed onto the SAXS-constructed envelopes of the proteins by SUPCOMB. SAXS data sets will be deposited with the small angle scattering biological data bank SASBDB (https://www. sasbdb. org/). Crystallographic analysis. Crystallization screens were performed using the sitting-drop vapor diffusion method at 18 °C with a Mosquito robot (TTP LabTech) in 96-well crystallization plates by mixing 100 nL of Rac1 1-177 -GDP at 5 mg/mL solution with 100 nL of precipitant solution.
Crystals were obtained in 0. 1 M MES pH 6. 5 and 20% PEG 3350. Crystals were cryo-protected using the reservoir solution supplemented with 10% glycerol prior to flash freezing. A complete diffraction dataset was collected on PROXIMA-2A beamline (SOLEIL synchrotron, France) and was integrated with the program XDSme (https://code. google. com/archive/p/xdsme/). The structure was solved by molecular replacement with Phaser 39 using Rac1-GPPNHP 28 as a model. Refinement was carried out with the program BUSTER  40  using TLS parameters, in alternation with graphical building using Coot  41. The quality of the structure was assessed using MolProbity  42. Data collection and refinement statistics are reported in Table  2. Coordinates and structure factors have been deposited in the Protein Data Bank with entry code 5N6O.
Acknowledgements We thank  William Sheppard  and the scientific staff at the PROXIMA-2 X-ray diffraction beamline and to  Javier Perez  and the scientific staff at the  SWING SAXS beamline (Synchrotron SOLEIL, Gif-sur-Yvette, France)  for making the beamlines available to us and for their help and excellent advice. We are grateful to  Aurélien Thureau  ( Synchrotron SOLEIL ) and  Dominique Durand  ( Institut de Biologie Intégrative de la Cellule, CNRS.
Magnetic resonance imaging (MRI) of the brain and spinal cord is routinely used in the diagnostic work-up of ALS, to rule out various pathological conditions that may masquerade as motor neuron disease, but rarely gives specific clues to the positive diagnosis. Automated techniques for analysing MRI images have been developed notably with statistical parametric mapping (SPM) software to carry out voxel-by-voxel analysis of the whole brain (voxel-based morphometry (VBM)). Most of the studies, have reported extensive grey matter (GM) atrophy, not confined to motor areas  [8]. Positron emission tomography (PET) combined with 18 F-fluorodeoxyglucose (FDG), a specific radiotracer for glucose metabolism, indicates glucose uptake by astrocytes and neurons  [9]  and reveals local brain activity. Early studies found reduced regional cerebral glucose utilization in patients with ALS  [10] , mostly in the frontal cortex but also in other cortical territories such as the superior occipital cortex  [11].
PET data Each participant underwent a PET examination the day after MRI examination. FDG scans were acquired on a Discovery RX VCT 64 PET-CT device (GE Healthcare) with a resolution of 3. 76 × 3. 76 × 4. 9 mm 3 FWHM (axial field of view = 157 mm). Forty-seven planes were obtained with septa out (3D acquisition), with a voxel size of 2. 7 × 2. 7 × 3. 27 mm 3. A CT transmission scan was performed for attenuation correction before PET acquisition. Participants were fasted for at least 6 h before scanning. After a 30-min resting period in a quiet and dark environment, ~180 MBq of FDG were intravenously injected as a bolus. A 10-min PET acquisition scan began 50 min after injection. During data acquisition, head motion was continuously monitored with laser beams projected onto ink marks drawn on the forehead, which also served to make any necessary corrections.
Neuroimaging data handling and transformation Preprocessing Using the VBM5. 1 toolbox (http://dbm. neuro. unijena. de/vbm/vbm5-for-spm5/), implemented in SPM5 software (Wellcome Trust Centre for Neuroimaging, London, UK), the raw MRI data were spatially normalized to Montreal Neurological Institute (MNI) space (voxel size = 1 mm 3 , matrix = 156 × 189 × 157) and segmented into the GM, white matter (WM) and cerebrospinal fluid (CSF). The normalized GM images were modulated by the Jacobian determinants to correct for nonlinear warping only, to obtain maps of local GM volumes corrected for brain size. FDG-PET data were first corrected for CSF and WM PVEs, using the voxel-by-voxel modified Müller-Gartner method  [29, 30] , described in detail elsewhere  [31] , and already used in our laboratory  [18] [19] [20].
W score maps To obtain measurements of atrophy and hypometabolism expressed in the same units so that we could undertake direct comparisons of the different modalities, we computed W score maps for each patient and each imaging modality, using the healthy control group as a reference. W scores are analogous to z scores but are adjusted for specific covariates  [35] -age in the present case. The smoothed MRI and PET data were used to create W score maps [(raw value for each patient) -(value expected in the control group for the patient's age)/(SD for each patient and each modality); see  [18]  for more details]. As the two datasets had different original spatial resolutions, the MRI data were coregistered and resliced into PET space to obtain images with the same dimensions.
Behavioural results Regarding executive functions, compared with the control group, the patients exhibited significant impairment of executive functions on the Trail Making Test B-A (p < 0. 001), letter verbal fluency score (p < 0. 001) and letter verbal fluency index (p = 0. 017). The same was true for both episodic memory, measured with a classic word learning test (p < 0. 001), and cognitive theory of mind, assessed with a false-belief task (p = 0. 009), with a trend for affective theory of mind, assessed with the Face/Eyes test (p = 0. 062) (for details, see Table  1 ). When comparing ALS patients with bulbar onset to those with spinal onset, we did not find any difference in their cognitive status (data not shown).
Acknowledgements The authors are grateful to  B. Landeau  and  R. La Joie  for their contribution and the  Cyceron MRI-PET  staff members ( C. Lebouleux ,  M. H. Noel  and  M. C. Onfroy ) for their help with patients and imaging examination acquisition. The authors also wanted to thank  S. Segobin  for his valuable advices during the revision phase.
Adverse drug reactions were defined as: "a response to a medicine which is noxious and unintended, and which occurs at doses normally used in man. " Additional information was requested in case of ADR, death, active TB, or treatment discontinuation. In addition, during adherence counseling, expert clients asked about side-effects experienced by the patients, without further precision on the definition of side-effect. Data were entered on site weekly using Epi-Data 3. 0 software (The EpiData Association, Odense Denmark), initially by a data entry clerk and later by the study nurse. Data were analyzed in Stata 12. 1 software (College Station, TX). No imputation was done in the case of missing variables, apart from the date of death which was chosen randomly between date of last visit and date of information about deaths, in the absence of other information.
Acknowledgments The authors would like to thank the patients who participated in this study, the staff, and expert clients working in the clinics, and the entire MSF teams working in Swaziland. Special acknowledgment to  Mathieu Bastard  for statistical advice, to  Clothilde Rambaud-Althaus  for support in quality monitoring, and to  Béatrice Vasquez  and  Calorine Mekiedje  for help in data cleaning and project supervision.
Genomic DNA extraction was obtained by using the standard phenol chloroform method. Using Sanger sequencing technique, the DNA of the younger affected child was tested negative for the mostly involved mutations in Moroccan family. Subsequently, the patient DNA was analyzed by wholeexome sequencing (WES) performed as previously described  13  with minor modifications, using the SureSelect V5 capture kit (Agilent, Santa Clara, CA, USA) and HiSeq 2000 sequencer (Illumina, Santa Clara, CA, USA). Bioinformatics analysis of sequence data was based on the pipeline provided by Illumina (CASAVA 1. 8), CASAVA aligns reads to the human reference genome (hg19) and the algorithm ELANDv2e (Malony alignment and multiseed mismatch reducing artifact) was used for sequence alignment. Genetic variation was annotated with the IntegraGen (Evry, France) in-house pipeline.
We achieved an average percentage of 98% of covered consensus coding DNA sequence at 4 × , 96% at 10 × and 89% at 25 × , providing sufficient depth to analyze variants. Candidate pathogenic variants were defined as missense, nonsense, frameshift and splice site mutations with a minor allele frequency o 0. 01, using the 1000 Genomes Project database and dbSNP132. Then the mutation identified was confirmed by Sanger sequencing to validate the family segregation. To predict the effect of amino acid substitution on protein function and structure, we used Polyphen2, and other prediction programs like SIFT, PROVEN, MUTATION TASTER, CUPSAT and MAESTRO web. Furthermore, the molecular modeling was performed to predict the structural impact of the amino acid substitution. The tridimensional structure of PEX1p was predicted using the protein homology modeling server SWISS-MODEL.
FOLD-X software (Centre for Genomic Regulation, Barcelona, Spain) was used to generate mutated structure and for energy minimization. Protein structures were visualized using YASARA program. Referring to the data filtering approach, we detected a novel causative missense variant in homozygous state in PEX1 gene (Figure  1a ). The mutation corresponds to a single base change c. 3077T4C and causes a leucine to proline substitution at position 1026 of the protein. Sanger sequencing results confirmed segregation and revealed that the two parents are heterozygous, while the affected died brother carried the same homozygous variant (Figure  1b ). The p. Leu1026Pro is predicted to be probably damaging by Polyphen2 and SIFT programs. Using PROVEN and MUTATION TASTER, the variation was found respectively deleterious and disease causing.
The MAESTRO and CUPSAT tools predicted the amino acid change destabilizing this protein. A three-dimensional structure was constructed based on the crystal structure of the N-terminal domain of PEX1 AAA-ATPase (PDB ID: 1WLF) to evaluate the structural impact of the p. Leu1026Pro mutation (Figure  1c ). The substitution of the highly conserved amino acid (leucine) (Figure  2a ) may have led to loss of hydrogen bonds, thus altering the interactions between residues Leu1026 and Val1030. Moreover, it is likely to disrupt hydrophobic interactions between Leu1026 and its neighboring residue Leu1032 (Figure  2b, c ). The Pex1p contains two nucleotide-binding domains (D1 and D2) preceded by two N-terminal domains (N1 and N2). 3, 14 he D2 domain is highly conserved and contains an essential substrate-binding loop, and two arginine finger residues probably participate in ATP hydrolysis and support oligomerization.
Large background levels can be introduced if radioactive contamination is implanted around the β-detectors  [1]. In order to minimize the focusing effects during the deceleration process, the ion beam is passed through an array of 52 equidistant disks of 1. 5 mm thickness. Each disk has an outer diameter of 14 cm, and a concentric hole of 4cm diameter. The CAD design and the ion (atom) beam trajectories optimized with the ion-trajectory software SIMION are shown in figure  6 (b). The decelerator system is the same as used in past experiments  [1] , but an adjustable slope for the deceleration potential, and an einzel lens between the decelerator and the CEC were implemented to control the focus for both atom and ion beams  [32]. The focus of the atom beam can be controlled by changing the focus of the ion beam before the neutralization in the CEC.
Acknowledgments This work was supported by the  IAP -project  P7/12 , the  FWO-Vlaanderen , GOA grant  15/ 010  from  KU Leuven , the  BMBF  Contracts  05P15RDICA  and  05P15RDFN1 , the  Max-Planck Society , the  EU   FP7  via ENSAR No. 262010 , and the  ERC  Grant No. 648381  FNPMLS. We would like to thank the  ISOLDE technical group  for their support and assistance.
The diaphragm is then further reduced in thickness (up to 2-3 µm) by etching with hydrofluoric (HF) acid in order to ensure that the sensor's desired operational range and resolution is linear. The complete diaphragm polishing and etching is monitored online using a LabVIEW TM program developed by the authors by estimating the thickness theoretically, in order to ensure that the desired diaphragm thickness is achieved. The sensor diaphragm deflects when under pressure, and this deflection of the glass diaphragm can be accurately estimated using Youngs modulus (E) and the Poisson ratio (υ) of glass. A deflection of the diaphragm due to an external pressure changes (∆P) changes the cavity length (∆L), which in turn results in a wavelength shift of the optical spectrum returned from the sensor.
Lipolytic enzymes As mentioned when biomass was not treated with boiling water, important amounts of free fatty acids are detected in the total lipid extracts: this may suggest the presence of endogenous lipolytic enzymes. Moreover, the high proportion of PUFAs in I. galbana could also suggest attractive enzyme specificities. Very few data can be found in literature concerning this point. For these reasons, the exploration of lipolytic enzymes from I. galbana Parke (CCAP 927/1) was implemented. Starting from the total mRNA extract, complete cDNA of putative lipolytic enzymes were obtained after RACE and Nested PCR (Fig. 4 ). Specific primers used for RACE and Nested PCR were designed by consulting I. galbana Express Sequence Tag Database. This protocol allowed to obtain three coding sequences.
In silico analysis using BLASTP alignment led to assign the first sequence as being a putative esterase designated "Igest1" and registered under the accession number FR870450. The second sequence showed close relationships with fungal lipases and has been designated "Iglip" and recorded under the accession number FR870450. The third sequence was designated as "IgTeCe" standing for I. galbana thioesterasecarboxylesterase  (Godet et al. , 2010; 2012; Kerviel et al. , 2014). To go further, the heterologous expression of IgTeCe was then implemented. IgTeCe was ligated in pJET plasmid and cloned into E. coli strain TOP 10 (Fig. 4 ). Cloning products were verified by sequencing and the expression of IgTeCe was performed into E. coli strain Rosetta2 using the plasmid pET 52. It has to be noticed that pET 52 contains an induction system working with IPTG and allows for the production of a protein containing a deca-histidine C-terminal tag  (Kerviel et al.
METHODS Standard protocol approvals, registrations, and patient consents. DNA samples of muscle disease patients and healthy family members were obtained from clinicians in different countries. The study was approved by the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa. The samples were obtained according to the Helsinki declaration. Written informed consent was obtained from all patients. CNV assessment from NGS data. CNVs were analyzed in smaller batches from NGS data alignment files (. bam) obtained by analyzing DNA from 791 myopathy patients with MyoCap. 1  We used 4 CNV analysis programs: Copy Number Inference From Exome Reads (CoNIFER) v0. 2. 2, 6 eXome-Hidden Markov Model (XHMM) v1. 1,  7  ExomeDepth v1. 1. 10,  8 and COpy number Detection by EXome sequencing (CODEX) v1.
4. 0, 9 with the recommended default settings of each program. A minimum of 1-bp overlap was used to determine whether calls intersecting between different programs originated from the same CNV. In this article, we prioritize the specific cases with a very high clinical interest, focusing on 7 variants detected by multiple programs and verified by using independent tools. CNV validation. PCR was performed to confirm CNVs in patients I, IIIa, IIIb, IV, V, VI, and VII. Primers were designed using Primer3 v4. 0. 0 (primer3. ut. ee) (table e-1, http://links. lww. com/NXG/A12), and PCR was performed with DreamTaq DNA Polymerase (Thermo Fisher Scientific, Waltham, MA) (figure e-1). A custom aCGH (manuscript in preparation), investigating 187 of the genes included in MyoCap, was used to confirm CNV detected in patient IIa.