dataesr/scientific-paragraphs-categorization huggingface dataset:
4E-4) colored with red/ brown. GO cellular component-cytoskeleton (FDR = 8. 9E-8) colored with light blue. A total of 39 genes (38%) related to the cytoskeleton OMIM-identified mutated genes. b Venn graph (http://www. intera ctivenn. net/) identifying genes that are shared with Autism: https:// gene. sfari. org/database/human-gene/ and ID: http://www. ccgenomics. cn/IDGenetics/gene. php?dataset=IDGD_gene_detail and http:// gfuncpathdb. ucdenver. edu/iddrc/iddrc/data/IDgenelist_gsym. html. c Shared genes described in (b) are named in table (red, AD-only mutations). Precise description of the genes can be found in Table  S10  which occur presumably somatically in selected cells, with ADNP tightly linked to cytoskeletal regulation and dendritic spine plasticity  [14, 23, 25, 29]. Regarding the cytoskeletal mutations and disease propagation, injection of human soluble Tau into the mouse dentate gyrus resulted in markedly reduced synapse numbers in the hippocampal molecular layer.
In conclusion, we revealed somatic aging/AD-linked mutations converging on tauopathy  [70] , including NAP/ ADNP  [25]. We further showed a significant correlation between the frequency of the ADNP c. 2187_2188insA mutation and aging, suggesting accumulation with aging and increasing Braak stages, implicating either a parallel or a causal relation, possibly linked to Tau-like prion-like propagation  [60] [61] [62]. Together, our results represent a paradigm-shifting concept in the perception of AD, whereby accumulating somatic gene mutations promote brain pathology and cognitive loss, and open new horizons for research and development. showing comparisons of mutated genes, including AD specific+ ADcontrol shared mutations, from four postmortem brain areas: olfactory bulb, Hippocampus (GSE67333), dorsolateral prefrontal cortex (DLPFC) (GSE53697) and fusiform gyrus (GSE95587) shared with a cytoskeletal genes (GO:020954 and GO:020801), b Autism and ID genes.
Note, the numbers of mutated genes are lower than the numbers depicted in Fig. 1d  as there are shared genes between ADspecific and AD+ controls shared mutations, however, the mutations/ gene are unique in the two different groups. c Mutation analysis of RNA-seq variant calling of postmortem superior frontal gyrus from different cell types: Endothelial (CD31+), Astrocyte (GFAP+) Myeloid (CD11B+) and Neuron (NeuN+) (GSE125050). The mutations were devided into three different groups: Controls (Cont. ) only, AD only and both in AD+ controls (AD+Cont. ). The Box plots represent number of mutations per subject and frequencies of mutations per subject for each group in all cell types. AD-associated mutations including AD only and AD+Cont. mutations: mutation numbers and mutation frequencies in AD subjects were compered with controls.
f Percentage of ADNP c. 2187_2188insA, p. Arg730Thrfs*4 mutation positive AD (black) and mutation positive controls (blue) in the respective tested populations were calculated showing significantly higher mutation frequency in AD samples (Braak stage IV-VI) compared to agematched controls (Braak stage I-III) (Pearson Chi-Square, P = 0. 007). Positive cases are subjects who were found to carry the c. 2187_2188insA, p. Arg730Thrfs*4, in the GEO database GSE95587 (postmortem fusiform gyrus tissue sections, see Fig. S8 ). g c. 2187_2188insA, p. Arg730Thrfs*4 mutation frequency to the intact ADNP sequence within the cDNA reads, shows a significant differences between controls (blue) and AD (black) samples, one tailed Mann-Whitney Test, P = 0. 006. h ADNP c. 2187_2188insA, p. Arg730Thrfs*4 mutation positive showing mutation frequency to intact ADNP (as in g) for all the different Braak stages (controlblue, AD-black) with increased mutation frequency in the control correlated with increased Braak stage, Spearman correlation, R = 0.
Acknowledgements The authors are grateful to  Dr. Eliezer Giladi  for the help with cloning methods. The authors also thank  Professor Yoav Benjamini  and  Dr. Ran Elkon  for the help with statistical analyses. V. K. is supported by the  CAS  (RVO:  86652036 ) and  LQ1604   NPU II , project  BIOCEV  ( CZ. 1. 05/1. 1. 00/02. 0109 )from the  ERDF , and  MEYS CR. Professor Illana Gozes  is supported by the following grants,  ISF   1424/14  and  2340/15  (together with  Associate Professor David Gurwitz) ERA-NET neuron AUTISYN and ADNPinMED  ( Professors Gozes  and  Kooy ), AMN Foundation as well as  Drs. Ronith  and  Armand Stemmer ,  Mr Arthur Gerbi  ( French Friends of Tel Aviv University ), and  Spanish Friends of Tel Aviv University, Alicia Koplowitz Foundation. This study is in partial fulfillment of the Ph.
Data availability The datasets generated and data analyzed during the current study have been in the NCBI Gene Expression Omnibus and are accessible through GEO Series accession number GSE113524. Author contributions Y. I. P. , A. H. , I. Grigg. , O. K. , G. K. and V. K. performed the work. M. G. provided the RNA-Seq services, D. G. helped with the RNA-Seq planning and analysis. C. L. S. introduced us to the in vivo imaging technology. F. K. provided ADNP mutated cells for DNA preparation, J. A. performed the neuropathological assessments and provided postmortem samples, and I. Gozes. orchestrated the study and wrote the paper.
Clinical samples From January 2015 to June 2018, 200 patients with genetically heterogeneous disorders were included in our study. They were referred from different Lebanese areas, by private sector and academic physicians. All patients underwent a detailed review of their clinical history and a laboratory evaluation. The most common features of the 200 patients were global developmental delay/intellectual disability, encephalopathy, muscular weakness, failure to thrive and microcephaly. Approval to conduct the study was obtained from the Ethics Committee of Saint Joseph University, Beirut, Lebanon. All patients, parents or legal guardians signed an informed consent for participation, sample collection and data publication. Peripheral blood was then collected from each individual enrolled in this study and DNA was extracted using the salting out method  [8].
WES analysis Exon capture and sequencing: The exome was captured using the SureSelect Human All Exons, reagents (Agilent Inc. ® Santa Clara, CA) according to the manufacturer's standard protocol. The concentration of each library was determined using Agilent's QPCR NGS Library Quantification Kit (G4880A). Samples were pooled prior to sequencing with a final concentration of each sample equal to 10 nM. Sequencing was performed on the Illumina HiSeq2000 platform using TruSeq v3 chemistry. Mapping and alignment: Reads files (FASTQ) were generated from the sequencing platform via the manufacturer's proprietary software. Reads were aligned to the hg19/b37 reference genome using the Burrows-Wheeler Aligner (BWA) package v0. 6. 1  [9]. Local realignment of the mapped reads around potential insertion/deletion (Indel) sites was carried out with the Genome Analysis Tool Kit (GATK) v1.
6  [10]. Duplicate reads were marked using Picard v1. 62. Additional BAM file manipulations were performed with Samtools 0. 1. 18 [11]. Base quality (Phred scale) scores were recalibrated using GATK's covariance recalibration. SNP and Indel variants called using the GATK Unified Genotyper for each sample  [12]. Variants were called using high stringency settings and annotated with VarAFT software 1. 61  [13]  containing information from dbSNP147 and ExAC (http://exac. broadinstitute. org/). For all patients who were left undiagnosed, reanalysis of vcf files was carried out using VarAFT2. 131 and polymorphisms were further filtered using our in-house local database containing more than 300 exomes (Fig. 1 ). In terms of functional annotation, we included only protein-altering variants, including truncating variants (stop gain/loss, start loss, or frameshift), missense variants, canonical splice-site variants, inframe indels affecting protein-coding regions, and variants within the intron-exon boundary (ten bases flanking the exonic boundaries).
We focused on genotypes absent in our local control data sets. We systematically considered four different genetic models, using stratified European and African Americans in the Exon Variant Server (EVS) for minor allele frequency estimations: (a) germ-line de novo mutations, also absent in the local control populations; (b) recessive homozygous genotypes, which were heterozygous in both parents, never homozygous in controls, with a control allele frequency < 1%; (c) hemizygous X chromosome variants inherited from an unaffected heterozygous mother, with a control allele frequency < 1% and never observed in male controls or homozygous in female controls; and (d) compound heterozygous genotypes in the patient (one variant inherited from each heterozygous parent, with the two variants occurring at different genomic positions within the same gene), for which neither variant was ever homozygous in controls, and each had a control allele frequency < 1%.
Variants confirmation and segregation studies by sanger sequencing Primers were designed using Primer 3 (http://frodo. wi. mit. edu) and OLIGOS v. 9. 3, and checked for specificity using BLAST (https://blast. ncbi. nlm. nih. gov/Blast. cgi). DNA sequences were obtained from UCSC and Genbank databases. Standard PCR reactions were performed using Taq DNA polymerase (Invitrogen Life Technologies, Carlsbad, CA, USA) and both strands of the resultant products were sequenced using the BigDye® Terminator v1. 1 Cycle Sequencing Kit (ThermoFisher Scientific, Waltham, MA, USA) under standard conditions. The labeled products were subjected to electrophoresis on an ABI 3500 Genetic Analyzer sequencing system (ThermoFisher Scientific, Waltham, MA, USA). Electropherograms were analyzed using Sequence Analysis Software version 5.
5%. In other words, genetic diagnosis was established in 99 patients, after the first analysis (Table  2 ). In order to improve the diagnosis yield, reanalysis of WES data, generated during the first 2 years of this study, was carried out. Reanalysis was performed using new bioinformatics algorithms and was based on the newly established local database, new genes discoveries and additional clinical information for each of the undiagnosed patients. The second analysis allowed the identification of the pathogenic mutations in 13 additional cases (Table  3 ) corresponding to 6. 5% increase in the genetic diagnosis rate, thus leading to an overall success rate of 56%. The 112 patients with established genetic diagnosis included 54 patients with autosomal dominant diseases, 49 with autosomal recessive diseases, and 9 with X-linked diseases.
In order to improve the diagnostic yield, WES raw data that was generated over the first 2 years were reanalyzed. Repeated WES analysis enabled us to establish the genetic diagnosis, for 13 additional patients who were left undiagnosed, corresponding to around 7% increase in the diagnosis rate. This positive outcome is due to many factors, among which: the development of new annotation and filtering tools (including new bioinformatics variant effect prediction logarithms), the continuous revelation of new genes in addition to the implementation at our research unit of a Lebanese WES database that allowed us to filter out all "private" Lebanese polymorphisms. Apart from these factors, the 2 years of experience in WES analysis were an asset that helped us improve our performance in data interpretation.
E1204G) in SHROOM4, inherited from his mother who shares with him milder clinical signs. Pathogenic mutations with a high frequency in a population exist! Filtering out common variants can be risky in the case of genetic disorders that are frequent in a population: In patient P87 presenting with G6PD deficiency, a disease frequently seen in the Lebanese population, WES reanalysis led to the identification of a pathogenic mutation in G6PD (p. S188F) that is present in high frequency (greater than 1%) in several arab populations  [17]. WES has technical limitations: Large deletions encompassing candidate genes were detected by CGH arrays in two patients (not included in the study), who were left undiagnosed by WES. WES coverage data might, in some cases, enable the detection of gene deletion: NPHP1 gene coverage was for instance equal to zero in patient P67 who presented with Joubert syndrome.
On the other hand, failure of genetic diagnostics in the remaining patients can be due to several factors: i) the clinical and genetic heterogeneity of some entities: More than 1000 genes are for instance linked to ASD, thus making genetic diagnostics very challenging (https://gen e. sfari. org/database/human-gene/); ii) the detection in isolated cases of private mutations whose pathogenicity is hard to be demonstrated in addition to iii) purely technical limitations associated with WES: Some bioinformatics pipelines are for example unable to detect CNVs (Copy Number Variation), rearrangements or triplet expansions. Homopolymers can for instance be miscalled, thus generating false positive variants. Furthermore, lack of coverage of coding regions (inefficient capture of regions rich in GC or bad read depth) and misalignment of reads to the reference sequence are all factors that can occur and lead to false negatives  [19, 20].
Quantitative All pregnant women, both newly and previously registered, who attended the antenatal clinic and underwent screening for TB between February and April 2018 were included. The sample size was calculated using OpenEpi software version 3. 01. Assuming an estimated TB prevalence of 0. 23%, a precision of 0. 12%, a 95% confidence level and a finite population correction factor for an assumed finite population of 10,000, a sample size of 3798 was obtained  [2].
Data variables, sources of data and data collection Quantitative data collection A structured questionnaire was used to collect data. The variables included baseline characteristics (UID, age in years), obstetric characteristics (gestational age in weeks), clinical characteristics [haemoglobin in g/ dl, HIV status, gestational diabetes mellitus (GDM), pregnancy-induced hypertension (PIH), syphilis and any other high-risk conditions], previous history of TB, current history of contact with a known case of TB, and results of symptom screening for pulmonary and extrapulmonary TB  [8]. Information on the baseline and epidemiological characteristics was collected directly from the study participants and clinical characteristics were extracted from the hospital case sheets. The results of the investigations were extracted from the laboratory registers maintained at the Department of Microbiology using UIDs and communicated to the pregnant women by telephone.
Qualitative data collection Seven key informant interviews were conducted and saturation was used to guide the sample size  [18]. The participants were one senior administrator, two senior nursing officers, one chest physician, two resident doctors from OG and one resident doctor from PMD. The mean duration of interviews was 31 minutes (range 23-42 minutes). The interview was conducted by the PI (a female medical doctor, trained in qualitative research). She had good knowledge of the setting, but was not involved in the provision of care for pregnant women. Interviews were conducted at a date, time and place convenient to the participants after establishing rapport. The PI held face-to-face interviews using an interview guide with broad open-ended questions and probes. Audio recording was done and verbatim notes were taken during the interview.
Quantitative Quantitative data were double-entered, validated and analysed using EpiData (version 3. 1 for entry and version 2. 2. 2. 183 for analysis; EpiData Association, Odense, Denmark). Key analytical outputs were the number and proportion of presumptive TB patients, and the number and proportion of active TB disease. Yield was calculated by dividing the number of TB patients by the total number of women screened. Multivariable analysis could not be performed because of insufficient cases of active TB.
Acknowledgments This research was conducted through the  Structured Operational Research and Training IniTiative (SORT IT ), a global partnership led by the  Special Programme for Research and Training in Tropical Diseases at the World Health Organization (WHO/TDR ). The model is based on a course developed jointly by the  International Union Against Tuberculosis and Lung Disease  ( The Union ) and Medécins sans Frontières (MSF/Doctors Without Borders). The specific SORT IT programme which resulted in this publication was jointly developed and implemented by: The  Union South-East Asia Office, New Delhi, India ; the  Centre for Operational Research, The Union, Paris, France ; the  Operational Research Unit (LUXOR) ,  MSF Brussels Operational Center, Luxembourg ;  Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India; Department of Community Medicine, GMERS Medical College, Vadodara, India; Department of Community Medicine, ESIC Medical College and PGIMSR, Bengaluru, India; Department of Community Medicine, Sri Manakula Vinayagar Medical College and Hospital, Puducherry, India; Department of Community Medicine, Velammal Medical College Hospital and Research Institute, Madurai, Tamil Nadu, India; and National Institute for Research in Tuberculosis, Chennai, India.
Background: Displaced intracapsular hip fractures are typically treated with hemiarthroplasty (HA) or total hip arthroplasty (THA). A number of professional bodies recommend considering THA for patients that were independently mobile and cognitively intact before injury. The aim of this study was to compare the outcomes between HA and THA for independently mobile older adults with hip fractures. Methods: A systematic review and meta-analysis of RCTs was undertaken alongside analysis of a propensity score matched national cohort of older adults (aged > 60) with hip fractures. Participants were identified for the propensity score matched cohort from the National Hip Fracture Database (NHFD), which was linked to Hospital Episode Statistics (HES) and civil death registration data. The primary outcomes were 12-month dislocation, revision, and mortality.
The secondary outcomes were length of stay, discharge home, unplanned re-admission, functional outcomes, and health-related quality of life. Results: Five RCTs reported higher THA dislocation but this was not statistically significant (THA risk ratio [RR] 2. 77, 95% CI 0. 81 to 9. 48). However, THA dislocation was significantly higher in the national observational dataset (sub-distribution hazard ratio [SHR] 1. 73, 95% CI 1. 24 to 2. 41). Meta-analysis of data from four RCTs did not identify a significant difference in terms of revision (RR 1. 52, 95% CI 0. 56 to 4. 14). However, THA revision was significantly lower in the national dataset (SHR 0. 66, 95% CI 0. 48 to 0. 90). Meta-analysis of data from 5 RCTs suggested higher mortality amongst patients undergoing HA (RR 0. 63, 95% CI 0. 38 to 1.
04), which was also observed within the national registry dataset (hazard ratio 0. 45, 95% CI 0. 37 to 0. 54). Conclusions: National clinical registries can provide important context when interpreting RCT data, which may alone be inadequate for comparing the safety profile of surgical interventions. These data suggest that THA is at significantly higher risk of dislocation but lower risk of revision within 12 months. The finding from both RCT and clinical registry data that THA is associated with lower 12-month mortality amongst the fittest patients with hip fractures requires urgent further study to determine whether or not this can be replicated in other balanced populations.
Systematic review and meta-analysis A scoping review identified a number of previous systematic reviews that compared HA and THA for patients with displaced intracapsular hip fractures. We therefore employed a simplified search strategy using a modification of the method first proposed by Sampson et al. [14] , which has been shown to be highly sensitive (median sensitivity 100%) for identifying RCTs when applied to systematic reviews with clinically focussed research questions  [15]. We used a broad search strategy: (fracture* AND ("total hip" OR hemiarthroplasty) AND "systematic review") to search three databases (Medline 1966-, EMBASE 1947-, and CINAHL 1982-) on 1st August 2018 to identify previous systematic reviews comparing HA and THA. The reference lists of all reviews were searched and the forward citation facility in PubMed used to identify trials published after each systematic review.
Study characteristics and outcome data were extracted by one author (DM) and checked by a second (CZ). We planned to report all outcomes at 12-months for consistency. Two authors (DM and CZ) independently determined risk of bias using criteria recommended by the Cochrane Handbook  [16]  and resolved disagreements by consensus. These data were presented to guide judgements about the certainty of the evidence and not to determine eligibility for inclusion within meta-analyses. Data were pooled to estimate risk ratios (for categorical outcomes) and mean differences (for continuous outcomes) using the DerSimonian and Laird method for random-effects meta-analysis as high levels of between-study heterogeneity were anticipated when pooling trials from different patient populations and healthcare settings  [17].
Standardised mean differences were reported when studies reported the same outcome measured on difference scales. When studies did not provide standard deviations necessary to inform confidence intervals, these were calculated from absolute p-values  [16]. Meta-analyses were undertaken using RevMan v. 5. 0 (Cochrane Collaboration, Vienna, Austria). The systematic review was reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement  [18]  and the protocol registered prospectively in the PROSPERO database with reference CRD42018109415  [19]. Observational "real world" data An observational study was undertaken using a comprehensive national cohort of older adults with displaced intracapsular hip fractures to extend and contextualise the existing RCT literature.
Data sources The cohort was defined using the National Hip Fracture Database (NHFD) and patient records linked to administrative data (Hospital Episode Statistics) and civil death registrations. National hip Fracture Database The National Hip Fracture Database (NHFD) is the largest hip fracture registry in the world. It is commissioned by the Healthcare Quality Improvement Partnership (HQIP) and captures data on almost all (> 95%) adults that are aged > 60 years and admitted to hospital in England, Wales, or Northern Ireland with a proximal femoral fracture  [20]. There were 177 hospitals contributing data to the NHFD in 2016  [21]. Data are collected by specialist nurses in each hospital and submitted through an online platform. Submissions are linked to hospital payments through the Hip Fracture Best Practice Tariff and so completeness of core variables is high.
Hospital episode statistics The Hospital Episode Statistics Admitted Patient Care (HES APC) dataset includes data on all admissions to National Health Service (NHS) hospitals or to independent sector providers that are funded by the NHS  [22]. Approximately 99% of hospital activity in England is funded by the NHS  [23]  and so should be included within the HES APC. All activities are included that require a hospital bed (e. g. planned and emergency admissions) but outpatient and Emergency Department are excluded unless they lead to admission. The dataset includes approximately 20 million episodes of care annually from around 450 individual NHS organisations  [22]. Office for National Statistics The Office for National Statistics (ONS) captures data (including date and cause) on all registered deaths directly from civil registration records  [24].