dataesr/scientific-paragraphs-categorization huggingface dataset:
16, 17 After data quality control, data from 339 cancer registries met the criteria and were included in this study, of which 129 located at urban areas and 210 at rural areas. 16, 17 These qualified cancer registries covered 288 million persons (146 males and 142 females), accounting for about 21. 07% of the total national population. 16, 17 ge-specific population size was estimated using the data released by the National Bureau of Statistics of China. 18 First, the changes in age-specific death probabilities between 2000 and 2010 were calculated by referring the data from the fifth and sixth National Population Census. 19 Second, we estimated age-specific death probabilities in 2011-2014 with the assumption of a linear trend from 2000 to 2014. 20 Third, the estimated death probabilities from 2011 to 2014 were used to calculated the age structure of the population in 2014.
This is a potential key target for HPV-attributable cancer control. Males contributed 3. 2 times head and neck cancers than that of females (2542 vs 798), and the number of cancer cases were much higher than that of previous assessment (950 vs 270). 2  Similarly, a global study reported more HPV attributable head and neck cancers in males than that in females worldwide. 2 We estimated more anal cancers in males than females (2258 vs 1678) in China, according to previous reported data (5900 vs 3600) 2 derived from a limited number of registry sites from China. 24 The male-tofemale ratio was opposite to that seen globally 2 and in specific reports from settings such as the US,  23  Europe  25  and Germany,  26  showing that anal cancer was more common in females. However, in our study, anal and mis-classified rectal adenocarcinomas that not linked to HPV infection were included because the pathological type is not reported in the Chinese cancer registry.
Moreover, effective control of HPV attributable cancers is a key towards global cancer prevention, and our estimation will be a nice illustration to the low-and middle-income countries. Our study analyzed sex-, cancer site-, age-, and geographical area-specific cancer burden attributable to HPV in China. Using the latest and most representative data of cancer incidence and mortality based on the published Chinese cancer registry annual report,  16  we assessed HPV-attributable cancer cases and deaths in all 31 provinces in mainland China, the registry data cover approximately 21% of the Chinese population. It is currently the first accurate and comprehensive estimation of HPV-attributable cancer burden in China. Our study provides a national profile of current cancer burden attributable to HPV infection at a population level, supplies fundamental data and scientific evidences for policy-making on HPV-attributable cancer control and prevention.
ACKNOWLEDGMENTS This study was funded by the  National Natural Science Foundation of China  (Grant number:  8161101254 ) and the  Chinese Academy of Medical Sciences Initiative for Innovative Medicine  (Grant number:  2016-I2M-1-019 ). Where authors are identified as personnel of the  International Agency for Research on Cancer/World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the  International Agency for Research on Cancer/World Health Organization. All datasets generated for this study are included in the manuscript or supplementary material.
Background: Human immunodeficiency virus type-1 (HIV) infection can be controlled with combination antiretroviral therapy (cART), but neurocognitive impairment remains common even in chronic and treated HIVinfected (HIV+) cohorts. Identifying the neuroanatomical pathways associated with infection has the potential to delineate novel neuropathological processes underlying persisting deficits, yet individual neuroimaging studies have yielded inconsistent findings. The ENIGMA-HIV Working Group was established to harmonize data from diverse studies to identify the common effects of HIV-infection on brain structure. Methods: Data were pooled from 12 independent neuroHIV studies from Africa, Asia, Australia, Europe, and North America. Volume estimates for eight subcortical brain regions were extracted from T1-weighted MRI from 1,044 HIV+ adults (aged 22-81 years; 72.
Added value of the study The aim of the current study was to investigate structural brain associations with two biomarkers universally used to monitor immune function and treatment response, namely plasma RNA viral load and CD4+ T-cell counts. Prior analyses have been performed in smaller, heterogeneous cohorts, but by combining data across cohorts, we can identify consistent associations between brain volume and indicators of HIV infection across cohorts. The ENIGMA-HIV Working Group was established to identify common neurobiological signatures of the HIVinfected brain by harmonizing data analysis from HIV neuroimaging studies worldwide. The value of this dataset is that it is well-powered and representative of many HIV+ people living in the cART era.
By reducing methodological variability in image processing, boosting sample size, and assessing a diverse set of HIV+ cohorts, a generalizable pattern of HIV-related brain effects may be identified. The HIV Working Group was established within the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) consortium to pool data from neuroimaging studies using harmonized data analysis pipelines. The working group is a growing international collaboration open to all researchers investigating the neurological consequences of HIV infection. This current study included active involvement from investigators from 12 neuroHIV studies from six different countries: the United States, France, Serbia, Australia, Thailand, and South Africa (Table  1 ). We aimed to investigate structural brain volume associations with the most commonly collected clinical assessments of HIV burden; we surveyed CD4+ T-cell counts and the detectability of viral RNA, in plasma, and determined their relationships with MRI-derived subcortical brain volumes in HIV+ individuals.
Participants and Clinical Assessments T1-weighted magnetic resonance imaging (MRI) brain scans and clinical data from 1,044 HIV+ adult participants (aged 22-81 years; 70. 3% male) were collected at 18 sites from 12 independent studies. The participating studies include the multisite HIVNC Consortium (N=229 scanned across 7 sites), Brown University (N=79), University of Hawaii (N=53), University of California San Francisco (N=50) and two groups from the University of California Los Angeles (Hinkin N=12; Thames N=54), all in the United States; Nice University Hospital in France (N=155); the University of Cape Town in South Africa (N=181); two groups from the University of South Wales in Australia (Brew N=41; Cysique N=68); a group from Serbia (N=61), and the SEARCH-011 study from Thailand (N=61).
Inclusion and exclusion criteria for each study are summarized in Supplementary Table  1. Clinical assessments at the time of scan included current CD4+ T-cell counts (cells/mm 3 ) and HIV plasma RNA VL (copies/mL). Participant demographic and clinical characteristics for each of the 12 studies are reported in Table  1. Two transgender participants were excluded. Each study obtained approval from their local ethics committee or institutional review board; participants signed an informed consent form at each participating site. Volume extraction and quality control were completed using publically available protocols (http://enigma. usc. edu/protocols/). The average of the left and right volumes for each ROI was evaluated. Histograms were also created from each site's data to investigate normality of the data distribution for each ROI, and statistical outliers were identified.
Stratification by cART status and by sex: Post-hoc analyses tested for associations between brain volumes and HIV plasma markers when the data were stratified by 1) cART status at the time of scan (cART+ or cART-), and 2) sex. The same analytic framework was used, removing sex and sex-by-age interactions from statistical models when stratifying by sex. Group differences in demographic and clinical factors were evaluated with chi-square tests for dichotomous factors, and two-tailed ttests for continuous measures.
Discussion In one of the largest coordinated brain imaging studies of HIV+ individuals, we harmonized and pooled data from 1,044 individuals across six countries, and found associations between brain volumes and plasma markers used to monitor HIV infection. In the full group, brain volume associations specifically implicated the limbic system: lower CD4+ counts were associated with smaller hippocampal and thalamic volumes; a dVL was associated with smaller hippocampal and amygdala volumes. The limbic effects were largely driven by cART+ participants, the majority of whom were male. In the subset of cART-participants, however, smaller putamen volumes were associated with lower CD4+. Plasma CD4+ T-cell count and VL are routinely assessed in HIV clinics around the world. These peripheral markers have been correlated with neuropsychological performance in HIV+ individuals,  1, 13  as well as post mortem brain tissue VL and pathology.
Acknowledgements Funding for the ENIGMA Center for Worldwide Medicine Imaging and Genomics was provided by the  NIH Big Data  to  Knowledge Program  ( BD2K ;  U54 EB020403 ). Additional funding for this work was provided by  NIH  grants  R01AG059874  (NJ),  R01MH117601  (NJ), T32AG058507 (TMN and CRKC),  5T32MH073526  (CRKC), MH083553 (CHH), MH19535 (CHH),  R01HL095135  (KJK), MH095661 (TK),  UL1RR033176  (TK),  UL1TR000124  (TK), MH19535 (TK),  NS080655  (BAN),  K01AA025306  (ECP),  R01HL095135  (CMS),  U54MD007584  (CMS), K23MH095661 (ADT), K23AG032872 (VV),  R01NS061696  (VV), K24MH098759 (VV), K01AG050707 (AJW), P01AA019072 (RAC),  R01MH074368  (RAC),  P30 AI042853  (RAC), and by  R01MH085604  (RP). This study was also supported by the  SA Medical Research Council (DJS) ,  NHMRC   APP568746  and  APP1045400  (LAC), and the  European Research Council  Advanced Grant MedYMA 2011-291080 (XP).
A B C Fig. 4  Changes related to carbohydrate metabolism in winter bears. Maximum enzymatic activities of pyruvate kinase (PK) and lactate dehydrogenase (LDH) were measured in vastus lateralis muscle samples from brown bears in summer (white bars) and winter (black bars) at 33 °C and 37 °C (panel A; N = 7 per season). Gene expression levels of monocarboxylate transporter 1 (MCT1 or SLC16A1), monocarboxylate transporter 4 (MCT4 or SLC16A3), and aquaporin 7 (AQP7) were measured using RT-qPCR in bear vastus lateralis muscles (panel B and C; N = 8 per season). Data are expressed as means ± sem. Statistical significance is shown for paired student t-tests (** P < 0. 01) and post-hoc Tukey tests that followed type III ANOVA (values that do not share the same superscript letter are significantly different; P < 0.
4 s, and MS/MS acquisition time to a range from 0. 05 s (intensity > 250,000) to 1. 25 s (intensity < 5000), and ions were excluded after acquisition of one MS/MS spectrum with release of exclusion after 0. 2 min. Up to 10 most intense multiply charged precursors per MS scan were isolated, using an isolation window adapted to the isolated m/z (2-5 m/z), then fragmented using energy collisional dissociation. The sample pool (quality control) was repeatedly analyzed throughout the whole duration of these experiments to enable verification of the stability of the nanoLC-MS/MS system and reproducibility of the quantitative measurements. MS raw data were processed using MaxQuant (v1. 5. 3. 30) [67]. Peak lists were searched using the decoy mode (revert) of the Andromeda search engine implemented in MaxQuant against a protein database created using the MSDA software suite  [68].
The database contained bear protein sequences (UniprotKb; Taxonomy ID: 9632; downloaded in September 2017) to which sequences of common contaminants (e. g. trypsin and keratins) were automatically added via MSDA and Max-Quant. Regarding search parameters, MS tolerance was set to 0. 07 Da for the first search and 0. 006 Da for the main search. MS/MS tolerance was set to 40 ppm. A maximum number of two missed cleavages was accepted, and carbamidomethylation of cysteine residues was set as fixed modification, while acetylation of protein N-termini and oxidation of methionine residues were set as variable modifications. False discovery rates (FDR) were set to 1% for both peptide spectrum matches (minimum length of 7 amino acids) and proteins. The final list of identified proteins did not consider common contaminants, which were removed.
Gel images were analysed using the Progenesis Samespots software (v4. 5; Nonlinear Dynamics, Newcastle upon Tyne, UK). After automatic alignment, the quality of spot matching was checked and, when necessary, improved by application of minor manual corrections. After background subtraction and normalization of Cy3 and Cy5 spot volumes to that of corresponding Cy2 spots, the spots highlighted as differential by statistical analysis were excised. Three to four matched spots were put together, and proteins were in-gel reduced and alkylated using a Massprep Station (Waters, MicroMass, Manchester, UK) as previously reported  [69]. For in-gel digestion of proteins, 25 μL of a 12. 5 ng/L trypsin (Promega) solution in 25 mM NH 4 HCO 3 were added to gel pieces before incubation for 12 h at 37 °C. The resulting peptides were extracted using 30 μL of a 60% acetonitrile (Carlo Erba, Val de Reuil, France) solution containing formic acid (0.
1%). Acetonitrile was removed by vacuum drying using a Speedvac. Tryptic peptides were analyzed on a UPLC-system (nanoAcquity, Waters) coupled to a quadrupole-TOF hybrid mass spectrometer (maXis 4G; Bruker Daltonik GmbH, Bremen, Germany), exactly as reported above for label-free (XIC) MS-based analyses, except from the elution gradient (t = 0 min 1% B, t = 9 min 35% B, t = 10 min, 90% B) and the fact that isolation and fragmentation was performed here for the 6 most intense multiply charged ions, with exclusion being set to 1 min. MS/MS data were analysed using the Mascot™ search engine (v2. 5. 1, Matrix Science, London, UK) installed on a local server. Spectra were searched against a target-decoy version of the same bear protein database that was used for label-free (XIC) MS1-based analyses (see above), to which sequences of common contaminants (e.
g. trypsin and keratins) were automatically added using the MSDA software suite  [68]. Mascot search parameters included a mass tolerance of 10 ppm in MS and 0. 05 Da for MS/MS modes, a maximum of one trypsin missed cleavage allowed, carbamidomethylation of cysteine residues set as fixed modification, and oxidation of methionine residues and acetylation of protein N-termini set as variable modifications. Stringent filtering criteria were applied using Proline software (v2. 5. 1; http://proline. profiproteomics. fr/) to obtain high-confidence identifications (FDR < 1% at both protein set and PSM level; and a minimal PSM score of 25). Single-peptide-based protein identifications, as well as the identification of common contaminants such as keratin and trypsin, were not considered. Among the different proteins that were identified in a given spot, only the major (more abundant) ones were considered to be responsible for variations of spot intensities.
The nuclear overhauser effect spectroscopy sequence (noesygppr1d with water presaturation and gradients) and the Carr-Purecell-Meiboom-Gill sequence (cpmgpr1d with water presaturation) were used with low power irradiation of the water resonance during the recycle delay of 4 s and the mixing time of 10 ms. For the cpmgpr1d sequence the echo time was fixed to 70 ms. For all the spectra, 512 scans were collected with a 90°i mpulsion time of 9. 75 μs, an acquisition time of 3. 3 s, a spectral window of 10,000 Hz and 64 K data points zero-filled to 128 K before Fourier transformation with 0. 3 Hz line broadening. Spectra were treated with Topspin version 3. 1. All NMR spectra were recorded at 300 K. For the identification of metabolites in the pooled sample, we used the NMR spectrometer at the TGIR NMR Facility (TGIR-RMN-THC Fr3050 CNRS-Gif/Yvette, France), a Bruker Avance III 950 MHz equipped with a 5 mm TCI ( 1 H/1 3 C/ 15 N/ 2 H) cryoprobe with z-gradient coil probe (Bruker Biospin).
For 1D 1 H-Spectra, a standard one dimensional spectroscopy sequence (noesygppr1d) was used with low power irradiation of the water resonance during the recycle delay of 4 s and a mixing time of 10 ms. For each spectrum, eight scans were collected with a 90°i mpulsion time of 7. 7 μs, an acquisition time of 3. 3 s, a spectral window of 10,000 Hz and 64 K data points zero-filled to 128 K before Fourier transformation with 0,3 Hz line broadening. All 2D homonuclear ( 1 H-1 H COSY, 1 H-1 H TOCSY, 1 H-1 H JRES) and heteronuclear ( 1 H- 13 C HSQC and HMBC) experiments were performed with quadrature phase detection in dimensions, using state-TPPI or QF detection mode in the indirect one. For each 512 increments in the indirect dimension, 2 K data points were collected and 32 or 64 transients were accumulated in the direct dimension.
A 13 C decoupling (GARP) was performed during acquisition time for heteronuclear experiments. A π/2 shifted square sine-bell function was applied in the both dimensions before Fourier transformation. Spectra were treated with Topspin version 3. 5pl5. All NMR spectra were recorded at 300 K. We divided 1D 1 H-NMR spectra into 10,000 regions (buckets) of 0. 001 ppm wide. After reduction over the chemical range of -0. 5 to 10 ppm and considering an exclusion area around the residual water signal (4. 6 to 5. 1 ppm), bucketing was performed using the AMIX software (Bruker GmbH). The signal intensity in each bucket was integrated using the sum intensities mode and spectra were scaled to total intensity. From Noesy and CPMG spectra, the buckets with a VIP score above 1, as revealed by PLS discriminant analysis (PLD-DA), were analyzed deeply (1d and 2d NMR) in order to identify the related metabolites.
Homoscedasticity was then tested using the Bartlett test (p-value > 0. 01). Changes in the abundance of proteins between hibernating and summer-active bears were assessed using Welch two sample t-tests for both proteomics approaches or paired student t-tests only for the 2D-DIGE approach, significance being set to p-values < 0. 05. Paired student t-tests were not applicable to XIC-derived data because of missing values. For quantitative RT-PCR and western-blot analyses, as well as metabolites measured using kits, and for amino acid and fatty acid levels, we compared winter and summer seasons using paired student t-tests, with significance being set to p-values < 0. 05. For glycogen levels, we used one-way ANOVA followed by post-hoc Tukey tests to compare values in free-living and captive bears.
Significance was set to p-values < 0. 05 for both ANOVA and Tukey tests. Statistical analysis of NMR-derived data was performed using SIMCA 13. 0 software. Data matrices were filtrated using the Wilcoxon signed-rank test. A total of 96 buckets from NOESY spectra and 103 from CPMG spectra remained for further multivariate analyses. PCA did not reveal any outliers, and PLS-DA (according to the seasons; Pareto scaling) showed one principal component (Q 2 cum = 0. 797) for NOESY data and one (Q 2 cum = 0. 766) for CPMG data. Validations were made through "900 random permutations" and "observed vs. predicted". PLS-DA showed VIP scores (i. e. importance into the model) for all buckets. On NOESY data, 11 buckets had a VIP score above 1. 5 and 23 buckets above 1. On CPMG data, 11 buckets had a VIP score above 1.
Bioinformatics analysis of proteomics data Hierarchical clustering of proteomics data was performed using Cluster v3. 0 software  [80]. Parameters were set as follows: median centering and normalization of genes for adjusting data and centroid linkage clustering for both genes and arrays. Dendrograms were generated and viewed using the Treeview v1. 6. 6 program  [81]. From the list of identified bear proteins, and in order to benefit from the more complete annotation of the human proteome, Swissprot identifiers of human protein homologues were retrieved using BLAST searches (FASTA program v36; downloaded from http://fasta. bioch. virginia. edu/fasta_www2/fasta_down. shtml). Only the best hits were retained, with a minimal sequence similarity of 78% for differential proteins, and their adequacy was checked manually for all proteins.
Enrichment and functional annotation analysis of proteomics data was performed using the desktop version of DAVID (Ease v2. 1) and an updated version of Gene Ontology (GO) databases (October 2017). Enriched GO terms were filtered by only considering those with an Ease score lower than 0. 1, a Benjamini p-value lower than 0. 01, and a fold enrichment higher than 2. Enriched GO terms were grouped together into broad functional categories, which were then considered as enriched broad functions.
Acknowledgments The authors wish to thank the field capture team ( S Brunberg ,  D Ahlqvist ,  A Friebe ,  H Nordin ,  H Blomgren ,  S Persson ), the Orsa Predator park team and veterinarians  K Madslien ,  J Debenham  and  T Lien , as well as people involved in amino acid measurements ( N Hafnaoui ,  D Rémond ,  C Buffière ), lipidomics analyses ( P Daira ), electron microscopy (A Schmitt), bioinformatics analysis (P Guterl), and NMR-based analyses (PFEM team; MetaboHUB). This is scientific paper no. 272  from the SBBRP.
Funding This work was supported by the  French Space Agency (CNES) ,  CNRS and Strasbourg University  ( H2E  project;  MyoBears  project of the  PEPS ExoMod program ),  French Proteomic Infrastructure (ProFI ;  ANR-10-INSB-08-03 , and  MetaHUB (French infrastructure in metabolomics & fluxomics ;  ANR-11-INBS-0010 ). BC and AZi were supported by grants from the  CNES  and  CNRS , and SC by a grant from the  National Swiss Fund. KBS is funded by grants from  NSERC Canada. The long-term funding of  Scandinavian Brown Bear Research Project (SBBRP)  has come primarily from the  Swedish Environmental Protection Agency , the  Norwegian Environment Agency , the  Austrian Science Fund , and the  Swedish Association for Hunting and Wildlife Management. Financial support from the  TGIR-RMN-THC Fr3050 CNRS  is also gratefully acknowledged.
Participants We used data of 1794 healthy 14-year-old adolescents (mean age = 14. 4, SD = 0. 45 years; 54% males) who were recruited within the scope of the IMAGEN project, a European multi-centre genetic-neuroimaging study in adolescence  (Schumann et al. , 2010). The selection of the participants was based on the fact that structural imaging data at age of 14 years was present. At the time of analyses reported here (age 14 years), retest data at age 16-17 years was present for 1439 participants (mean age = 16. 6, SD = 0. 64 years; 55% males) and at age 19 years for 1284 participants (mean age = 19. 0, SD = 0. 77 years; 53% males). Written informed consent was obtained from all participants as well as from their legal guardians. The adolescents were recruited from secondary schools. The study was approved by all local ethics committees separately (in Germany this was accomplished by the medical ethics committee of the University of Heidelberg, reference number: 2007-024N-MA) and approved by the head teachers of the respective schools.