dataesr/scientific-paragraphs-categorization huggingface dataset:
, Palo Alto, CA, USA). Single crystal X-ray diffraction data were collected from a crystal of dimensions 0. 57 × 0. 51 × 0. 38 nm with an Oxford Diffraction KM4 CCD diffractometer (Oxford Diffraction Ltd. , Abingdon, Oxfordshire, UK) (Mo Kα about wavelength λ = 0. 71073 Å). The structure was solved by direct methods and it was refined with the full-matrix least squares on F 2 using the software package SHELX-97  [50]. All of the figures were prepared in DIAMOND  [51]  and ORTEP-3  [52]. CCDC: 1477237; contains the supplementary crystallographic data for [Ag 5 (O 2 CC 2 F 5 ) 5 (H 2 O) 3 ]. These data can be obtained free of charge via http: //www. ccdc. cam. ac. uk/conts/retrieving. html, or from the Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK; fax: (+44)1223-336-033; or e-mail: deposit@ccdc.
Measurement of the Contact Angle and Surface Free Energy of Biomaterials Determination of the wettability was carried out by the measuring of the contact angle. The contact angle was measured using a goniometer with drop shape analysis software (DSA 10 Krüss GmbH, Hamburg, Germany). The liquids that were selected for measuring the contact angle were distilled water (H 2 O) and diiodomethane (CH 2 I 2 ). In the case of distilled water, the volume of the drop in the contact angle measurement was 3 µL, and in the case of diiodomethane 4 µL. The measurement of the contact angle was carried out immediately after deposition of the drop. In order to determine the surface free energy, mathematical calculations were performed using the Owens-Wendt method. Each measurement was carried out three times.
4. 4. Topographies and Mechanical Properties of the Produced Nanocoatings on the Surface of 3D Printed Implants Surface topographies were examined by means of atomic force microscopy (AFM, NaniteAFM, Nanosurf AG, Liestal, Switzerland) using a contactless module with a force of 55 mN in the 50 × 50 µm area. Hardness tests and Young modulus measurements were carried out using a nanoindenter (NanoTest Vantage, Micro Materials Ltd. , Wrexham, UK) using a pyramidal, diamond, three-sided Berkovich indenter, with an apical angle of 124. 4 •. Hardness tests were performed for the loads of 10 mN. The time of load increase from the zero value to the maximum load 10 mN was 15 s. Indentation involving one cycle with 5 s dwell at maximum load. Hardness values (H), reduced Young's modulus (Er), and Young's modulus were determined using the Oliver-Pharr method using the NanoTest results analysis program.
In order to convert the reduced Young's modulus into the Young's modulus, a Poisson coefficient of 0. 25 was assumed for the coatings. Tests of coatings adhesion were made using nanoindenter (NanoTest Vantage, Micro Materials Ltd. , Wrexham, UK) and using the Berkovich indenter, as in the case of the nanoindentation tests. The parameters of scratch tests were as follows: scratch load-0 to 500 mN, loading rate-3. 3 mN/s, scan velocity-3 µm/s, and scan length-500 µm. Based on the dependence of the friction force (Ft) on the normal force (Fn) in the program for the analysis of NanoTest results, the values of critical friction force (Lf) and critical force (Lc), which caused the separation of the layer from the substrate, were determined.
Methods The three-round campaign took place between December 2015 and June 2016 using: (i) oral poliomyelitis vaccine (OPV); (ii) combined diphtheria, tetanus and pertussis (DTP) vaccine, Haemophilus influenza type B (Hib) and hepatitis B (DTP-Hib-hepatitis B) vaccine; (iii) pneumococcal conjugate vaccine (PCV); (iv) measles vaccine; and (v) yellow fever vaccine. Administrative data were collected on vaccines administered by age group and vaccination coverage surveys were carried out before and after the campaign. Findings Overall, 294 054 vaccine doses were administered. Vaccination coverage for children aged 6 weeks to 59 months increased to over 85% for the first doses of OPV, DTP-Hib-hepatitis B vaccine and PCV and, in children aged 9 weeks to 59 months, to over 70% for the first measles vaccine dose.
Data collection and analysis The principal outcome was vaccination coverage of children aged 6 weeks to 59 months. Administrative data were collected during the campaign as the number of vaccines administered by age group, data on routine EPI activities running in parallel were not included. Data were collected on tally sheets and android applications and analysed using Excel 2003 (Microsoft Corporation, Redmond, United States of America). In addition, two vaccination coverage surveys were carried out in the five subprefectures included in the vaccination campaign: the first took place before the campaign, in December 2015, and the second, after the campaign, in June and July 2016. Gender and wealth disparities were not assessed. The surveys used a two-stage, cluster random sample method, as outlined in WHO's Vaccination Coverage Cluster Surveys Reference Manual.
14 In the first stage, clusters (i. e. villages) were selected with a probability proportional to size; in the second stage, households in each village were randomly selected according to the modified WHO-EPI method. 14 Data on each child's vaccination history were based on the caretaker's recall and the vaccination card, if available. Reasons for nonvaccination were also noted. For the baseline survey, data were transferred from paper to Excel 2003 and analysed in Epi Info (Centers for Disease Control and Prevention, Atlanta, USA). Data from the postcampaign survey were collected electronically and analysed using Stata v. 12. 0 (StataCorp LP. , College Station, USA). Data from the caretaker's recall and the vaccination cards took sampling weights into account and were reported with 95% confidence intervals (CIs).
Discussion In a chronic humanitarian emergency in the Central African Republic, our preventive, multi-antigen, vaccination campaign resulted in a substantial and rapid increase in vaccine coverage after three vaccination rounds, thereby potentially decreasing the burden of targeted diseases and the risk of epidemics. 11, 15, 16 In addition, most of the beneficiaries were aged 12 to 59 months and thus benefited from catch-up vaccination that would have otherwise been unavailable to them. It is possible that actual vaccination coverage might have been higher than we found from administrative data. Some doses recorded as first doses were probably, in reality, second or third doses because children presenting without a vaccination card may have been previously immunized. Hence, coverage of the first dose may have been lower than that indicated by administrative data and coverage of second or third doses may have been higher.
After treatments, NRVMs were fixed in 4% paraformaldehyde, permeabilized with 0. 2% triton X-100 and blocked with 0. 1% bovine serum albumin (BSA). Cells were incubated 1 h with the anti-α actinin primary antibody at room temperature and then 1 h with an anti-mouse secondary antibody coupled to Alexa fluor 594. Cells were visualized with an Axioskop 40 microscope and images were captured at ×200 magnification. Cell size (>70 cells per sample) was determined using Image J software. Evaluation of protein synthesis. Amino acid incorporation into proteins was evaluated by measuring the incorporation of [ 14 C]-phenylalanine into proteins of cultured NRVMs 63. [ 14 C]-phenylalanine (Perkin Elmer) (1µCi/ml) was added to NRVMs at the same time than other treatment. At the end of the treatment, NRVMs were washed twice with ice cold phosphate-buffered saline (PBS) and lysed as described above.
4). Isolation of ARVMs was obtained by adding 0. 2 mM Ca ++ , 1 mg/ml collagenase (Worthington), and 0. 4% (wt/vol) BSA to the perfusate for 30 min. The hearts were then removed from the perfusion system and cut into small fragments. Ca ++ was progressively reintroduced to reach a final concentration of 1 mM. The pellet containing ARVMs was filtered and resuspended in MEM medium (Sigma) containing 1. 2 mM Ca ++ , 2. 5% FBS, 1% penicillin-streptomycin, and 20 mM HEPES pH 7. 6. ARVMs were equally distributed in dishes coated with laminin (Sigma) and stored in a 95% O 2 , 5% CO 2 incubator at 37 °C for 2 h. Cells were incubated in serum-free medium for 24 h before stimulation with PE 100 µM to induce hypertrophy. Phase-contrast images of cultured ARVMs were taken after 48 h treatment and cell surface areas were analyzed using the Image J software as described previously  37.
In sham-operated mice, the ligature was not tightened. Chest was closed with a polypropylene suture. Animals were treated with 0. 1 mg/kg of buprenorphine after surgery. Doppler measurements of trans-stenotic gradients were systematically performed at day 1 and 3 weeks post-surgery. Only TAC mice with a velocity higher than 2. 5 m/s were kept into experiment. Blood pressure telemetry recording in vivo. Miniaturized implants (Data Sciences International) were surgically inserted in the aortic arch leading to measurement of blood pressure (BP) signals in conscious and unrestrained animals  67. After 10 days of recovery, BP signals were measured (Ponemah v5. 20) during 24 h as baseline data. Then, measurements were repeated after 2 weeks of AngII and metformin treatments in the same mice. Cardiac AMPK activation in vivo.
LV mass was computed using the following equation: LV mass = 1. 05[(IVS + LVID + LVPW) 3 -(LVID) 3 ] where IVS is the interventricular septal thickness, LVID, the LV internal diameter and LVPW, the LV posterior wall thickness. LV mass was normalized by the tibia length measured at sacrifice. Measurements were performed by the same operator, blinded to the experimental groups. In vivo cardiomyocyte cross sectional area measurement. Cardiomyocyte membranes were stained on heart sections deparaffinized and rehydrated. Slices were incubated 2 h with WGA/rhodamine (1/150) at room temperature before washing with PBS and mounting (vectashield H-1000, Vector Laboratories). Pictures were acquired with a Zeiss AxioImager microscope with a 40× objective. Cell size (>70 cells per sample) was determined using Axiovision software.
After blocking, the membrane was incubated 1 h at room temperature with RL2-HRP antibody. The data obtained were confirmed by a second method (data presented in Supplementary Figure  7 ). In this protocol, lysates were first precleared with protein G sepharose (PGS) in order to fully remove endogenous immunoglobulins. For this preclearing, lysates (140 µg of protein) were incubated with 5 µl of PGS for 1 h before centrifugation. Twenty microgram of proteins of the resulting supernatants were then used for immunoblotting with RL2 and goat anti-mouse secondary antibody conjugated to HRP. Band intensities were quantified by scanning and processing with program Image J. Band intensities obtained were normalized relative to a loading control (anti-eEF2 or anti-GAPDH, performed on the same gel).
Statistical analysis. The sample size was not pre-determined by statistical analyses and was chosen according to previous publications. The 'N' number represents the number of animals for in vivo experiments and the number of biological replicates for in vitro experiment. No particular inclusion/exclusion criteria were applied. No particular randomization method was applied in this study. Quantification of cardiomyocyte area and echocardiography data analyses were performed by blinded researchers, the others experiments were performed in a non-blinded manner. Results are expressed as mean ± s. e. m. unless otherwise stated. The statistical significance was calculated using unpaired Student's t-test or a One-way and Twoway analysis of variance using the Bonferroni post-hoc test. The significance threshold was set at p < 0.
Acknowledgements The authors thank  Louis Hue  for his constant encouragement, helpful advice and critical reading of the manuscript. They also thank  Cora Weigert  ( University of Tuebingen ) for her technical help for GFAT phospho immunoblot assay. The authors wish to thank  Servier Medical Art  (http://smart. servier. com)for freely providing some of the illustrations used in this paper. This work was supported by grants from  Fonds National de la Recherche Scientifique et Médicale (FNRS) ,  Belgium , by  Action de Recherche Concertée de la Communauté Wallonie-Bruxelles ,  Belgium  ( ARC11-16/035 ), by a  PHC Tournesol from Communauté Wallonie-Bruxelles, Belgium , and by unrestricted grants from  Astra Zeneca. F. M. and B. D. were supported by the  Fund for Scientific Research in Industry and Agriculture, Belgium.
The numbers of TUNEL-positive or BrdU-positive beta-cells were determined by blind counting with a fluorescent microscope. For each condition in each individual experiment over 1000 cells were analyzed. Statistical analyses. Data are expressed as means ± SEM, with the number of individual experiments presented in the figure legends. All data were tested for normality and analyzed with PRISM (GraphPad, San Diego, CA). Differences were evaluated using Student's t test or ANOVA with Bonferroni post hoc test for multiple comparison analysis. Significance was set as p < 0. 05. The differential gene expression analysis was performed with DESeq2 19 starting with gene read counts and using the GC mean content of the sample libraries as covariate. Three types of comparisons were performed: between myotubes, between biopsies and between the same type of myotubes and biopsies.
Acknowledgements Human islets were kindly provided by the  Islet for Basic Research program  through the  European Consortium for Islet Transplantation , supported by the  Juvenile Diabetes Research Foundation  (JDRF award  31-2008-413 ). We thank  Melanie Cornut  and  Katharina Rickenbach  for expert technical assistance. We thank  Pr Philippe Halban  for precious discussions and advices on the project. This work was supported by the  Swiss National Science Foundation  (Grant No. 31-135645  to K. B), the  Bo & Kerstin Hjelt Diabetes Foundation  and the  Young Independent Investigator Grant  from the  SSED.
Query dedicated mtDNA databases which are regularly updated (e. g. , Mitomap, HmtDB). • Consider the variant's frequency both within the general population and specific haplogroup. • Detection and interpretation of low heteroplasmy levels should be carefully evaluated. • Integrate additional information that might modulate the clinical penetrance (e. g. , mitochondrial haplogroup, synergistic or helper mtDNA variants, nuclear variants). • Evaluate inter-species and primates amino-acid or nucleotide conservation.
The development of next generation sequencing (NGS) has greatly enhanced the diagnosis of mitochondrial disorders, with a systematic analysis of the whole mitochondrial DNA (mtDNA) sequence and better detection sensitivity. However, the exponential growth of sequencing data renders complex the interpretation of the identified variants, thereby posing new challenges for the molecular diagnosis of mitochondrial diseases. Indeed, mtDNA sequencing by NGS requires specific bioinformatics tools and the adaptation of those developed for nuclear DNA, for the detection and quantification of mtDNA variants from sequence alignment to the calling steps, in order to manage the specific features of the mitochondrial genome including heteroplasmy, i. e. , coexistence of mutant and wildtype mtDNA copies. The prioritization of mtDNA variants remains difficult, relying on a limited number of specific resources: population and clinical databases, and in silico tools providing a prediction of the variant pathogenicity.
, 2004). Hundreds of pathogenic mtDNA variants implicated in a variety of human diseases  (Lott et al. , 2013)  have now been reported in the continuously updated Human Mitochondrial Genome Database-the Mitomap  (Ruiz-Pesini et al. , 2007; Lott et al. , 2013)  but as of today (July 2018) only 84 variants have a confirmed status, whereas a total of 595 other variants classified as reported, awaiting a final confirmation of pathogenicity (Figure  1 ). These mtDNA variants lead to a broad spectrum of maternally-inherited diseases, ranging from lethal neonatal syndromes to multisystemic disorders, with high variable clinical phenotypes and penetrance, mainly resulting from shifts and differences in the mutant load  (Wallace et al. , 2010). Indeed, due to stochastic segregation of mtDNA, the percentage of mutant and normal mtDNAs may drift during cellular divisions, and the percentage of the mutation load may vary drastically among the different tissues and organs, from 100% mutant load, defining homoplasmy, to the coexistence of mutant and wildtype copies, defining heteroplasmy.
, 2013) , the discovery of variants of unknown significance (van der  Walt et al. , 2012) , and mutations unrelated to the patient phenotype, generate difficulties in prioritizing the variants, and as a consequence different interpretations of mtDNA variants in the diagnostic process. This review provides an overview of the current strategies, databases and bioinformatics tools for an accurate annotation, prioritization and report of mtDNA variations coming from NGS, in order to carry out fast and accurate genetic counseling in patients with primary mitochondrial disease.
mtDNA VARIANT ANNOTATION Careful mtDNA annotation of mtDNA variants is a prerequisite for accurate prioritization, addressed by several pipelines and online tools (Table  1A ), like MSeqDR mvTool  (Shen et al. , 2018) , Mitomaster  (Lott et al. , 2013) , mtDNA-Server  (Weissensteiner et al. , 2016a) , MitoTool  (Fan and Yao, 2011) , or SG-ADVISER  (Rueda and Torkamani, 2017). Then, the general workflow for mtDNA variants prioritization could parallel that of the nuclear genome and rely on an accurate standardized annotation, based on consensus databases and in silico prediction tools.
Population and Clinical Databases Whereas mtDNA data are available from exome and genome sequencing data  (Griffin et al. , 2014; Patowary et al. , 2017) , the frequency of mtDNA variants in the general population is not reported in the databases such as GnomAD  (Lek et al. , 2016)  or MARRVEL  (Wang et al. , 2017). The number of dedicated databases focusing on mtDNA with an active curation is limited (Table  1B ), only three being available online: Mitomap  (Kogelnik et al. , 1996; Lott et al. , 2013) , HmtDB  (Clima et al. , 2017)  and FIGURE 1 | Graphical representation of human mitochondrial DNA variations. The outer circle depicts the mitochondrial genome with annotated tRNAs (gray), rRNAs (purple), protein-coding genes  (Bentley et al. , 2008) , and non-coding regions (white). In the inner circles each point represent an mtDNA variant reported in GenBank sequences collected from Mitomap according to the variant status reported in Mitomap (polymorphisms in green, reported pathogenic variants in orange, confirmed pathogenic variants in red) and variant frequency in GenBank (<0.
2%, light color; 0. 2-2. 0%, medium color; >2. 0%, dark color). HmtVar  (Preste et al. , 2018). Together these resources gathered for instance more than 45,000 whole mtDNA sequences and over 70,000 mtDNA control region sequences for the Mitomap database. However, the interpretation of the variant frequency in the general population is difficult given that databases include patient data and because of the peculiarities of mtDNA genetics (incomplete penetrance, heteroplasmy level, influence of mitochondrial haplogroup background). For example, the m. 3460G>A primary LHON mutation is reported 20 times in GenBank  (Bentley et al. , 2008)  as of July 10, 2018 (Mitomap database), the pathogenic and unquestionable variant being reported both in patients, and also from phylogenetic studies. There is currently no consensus threshold to consider, often mtDNA variant is frequent in the population, thresholds between 0.
2 and 0. 5% being arbitrarily chosen in several studies  (Wang et al. , 2012; Lieber, 2013). However, they do not consider the variant frequency within a peculiar haplogroup which can lead to misinterpretation as some haplogroups are underrepresented in databases. For instance, Asian and African lineages represent only 21 and 13% of the Mitomap GB dataset. To overcome this problem, the Mitomap database warns if a variant is identified at >1% in at least one of the macro-lineages or over 10% in the major haplogroups for tRNA variants  (Sonney et al. , 2017). For the first step of prioritization, forensic databases such as EMPOP  (Parson and Dur, 2007)  are useful. Clinical databases combining mtDNA variants and clinical files are available (Table  1C ). General databases such as CLINVAR  (Landrum et al.
, 2016) , CLINVAR Miner  (Henrie et al. , 2018) , or OMIM  (Amberger et al. , 2015)  carrying information on mtDNA in addition to nDNA variants, can be distinguished from databases specifically dedicated to mtDNA such as Mitomap, HmtVar or HmtDB which are more exhaustive. In the latter, more than 10,000 variants are gathered throughout the whole mtDNA (Figure  1 ). For example in MT-ND1 gene only 42 variations are reported in CLINVAR, whereas 693 variants are available in Mitomap  (Lott et al. , 2013) , and among them, 62 are reported associated with a clinical phenotype. MtDNAspecific information, such as heteroplasmy and haplogroup frequency are not systematically reported in each database, hence questioning the value of pipelines collecting information from multiple resources in order to provide a complete description of the variant.
Other specialized databases are also helpful for variant prioritization: Mamit-tRNA database which compiles information on mammalian mitochondrial tRNA genes  (Putz et al. , 2007)  or the Mitobreak database focusing on mtDNA rearrangements  (Damas et al. , 2014). Even if the prevalence of mitochondrial diseases is in the order of 1/4,300  (Gorman et al. , 2015) , it was shown that at least 1 in 200 newborn cord bloods carry one of the ten most common pathogenic mtDNA mutations, which is much more frequent than expected  (Elliott et al. , 2008). Many of these mutations will probably be lost through stochastic segregation, but there is still a chance that these mutations will be transmitted to the offspring and causing mtDNA diseases in next generations. As mtDNA variations can be obtained from WES  (Griffin et al.
bmc. lu. se/PON-mt-tRNA/ datasets. html/. MITOTIP, the most recent tool available through Mitomap combines conservation data, structural analogies with other tRNA variants and secondary structure information  (Sonney et al. , 2017) , giving the best prediction performances in terms of sensitivity and specificity. However, specific tools for the interpretation of VUS in MT-RNR1 and MT-RNR2 combining conservational information with functional and structural data are helpful to better interpretation of ribosomal variants  (Smith et al. , 2014; Elson et al. , 2015). Thus, many bioinformatics tools are useful for predicting the functional impact of mtDNA variants, nevertheless results should still be considered with caution, due to the high rate of false negative and false positive predictions as demonstrated in Figure  2.
, 2013). Indeed, it is commonly accepted that mtDNA mutations have clinical consequences only over a certain heteroplasmy level, also called threshold effect  (Rossignol et al. , 2003). It was recently shown that clinical phenotypes are associated with low heteroplasmic mtDNA pathogenic variants  (Ng et al. , 2018)  or deletions  (Leung et al. , 2018) , and additional low-level heteroplasmic variants can explain the phenotypic variability of mtDNA homoplasmic mutations  (Ballana et al. , 2008). Unfortunately, mtDNA databases do not specify the level of heteroplasmy leading to clinical phenotype in patient, with the exception of Mitomap  (Kogelnik et al. , 1996; Lott et al. , 2013) , which provides partial information mentioning the homoplasmic or heteroplasmic nature of pathogenic variants.