dataesr/scientific-paragraphs-categorization huggingface dataset:
PCA was performed in RStudio v. 1. 1. 447 using in-house scripts. The Pearson correlation between bacterial taxa and betaines, Kruskal-Wallis one-way ANOVA, Mann-Whitney U test, and the fold change analyses between the diet groups were performed in SPSS 25 (IBM) and plotted with an inhouse R script utilizing ggplot2 package. The microbial abundance heatmap was produced in Multiple Experiment Viewer v4. 9. 0. For this purpose, the relative abundances were first normalized based on the following formula: x = (xx row )/SD row.
Availability of data and materials The datasets analyzed in the current study are available from the B2SHARE data repository maintained by the European research data infrastructure and service unit (EUDAT); DOI: 10. 23728/b2share. 648f1de094d84c40a3b17016b8a870c8. The scripts used in the processing of 16S rRNA sequencing data are available at http://github. com/JavierRamiroGarcia/NG-Tax.
Development of a shadow camera based nowcasting system Using the initial shadow camera system and the adaptions described in section 2, a forecasting capability is implemented into the system. In Figure  4 , the work flow of this shadow camera based nowcasting system is shown: Required inputs are the images taken by the shadow cameras and additional measurement data from a meteorological station. Corrections and normalizations might be applied on the jpg images (see section 2. 2. 1). Afterwards, shadows are segmented in the current orthoimage (see section 2. 2. 2). With methods described in  Kuhn et al. (2017a) , irradiance maps are generated. In this publication, we will focus on DNI maps. The system can also produce GHI maps (shown in  Kuhn et al. , 2017a). In order to predict future irradiance maps, cloud shadows are tracked and the derived shadow motion vectors are used for extrapolating future shadow positions.
This is caused by (1) the global tracking having more data for tracking, which makes deviations less likely. As the predictions are based on determined cloud shadow motion vectors, small deviations in the measured velocities and directions lead to deviations which increase with lead times. (2) Secondly, multi-layer cloud situations with overlapping shadows on the ground are found to be especially challenging for local tracking methods. Global tracking approaches, although only deriving one general motion vectors, yield less deviations in such complex situations. This is attributed to the larger database of global cloud shadow motion vectors in comparison to shadow individual motion vectors, which enables Kalman-filtering of outliers. Similar challenges are present for all-sky imager based nowcasting systems.
Acknowledgments The research presented in this publication has received funding from the  European Union 's  Horizon 2020 program  for the initial development of the shadow camera system (PreFlexMS, Grant Agreement no. 654984 ). With founding from the  German Federal Ministry for Economic Affairs and Energy  within the  WobaS  project, all-sky imager systems and the shadow camera system were further developed. The  European Union 's  FP7 program  under Grant Agreement no. 608623  ( DNICast  project) financed operations of the all-sky imagers and other ground measurements. Thanks to the colleagues from the  Solar Concentrating Systems Unit of CIEMAT  for the support provided in the installation and maintenance of the shadow cameras. These instruments are installed on CIEMAT's  CESA-I tower of the Plataforma Solar de Almería.
These results are robust to the inclusion of a set of additional factors that do not appear explicitly in our theoretical model. Finally, a simple simulation exercise indicates that the growth of the banking financialization index and private credit have slowed the industrialized countries' average annual growth rate in the post 2008 period by nearly 30%. The implications of these results from an international financial regulation perspective are discussed in the last section of the paper. The remainder of the paper is structured as follows. Section 2 presents our theoretical model. Section 3 presents the dataset, the econometric methodology, and discusses our empirical  Valdecantos (2015) ,  Giraud et al. (2016) ,  Keen (1995) ,  Caiani et al. (2016) ,  Seppecher (2014) ,  Reyes and Mazier (2014) ,  Mouakil (2014).
Dataset Our empirical analysis is based on a strongly-balanced panel of 29 high income countries 21 for the period 1998-2014. Our data is taken from a variety of sources listed in Appendix 2. Our macroeconomic variables include the annual growth rates of constant GDP and gross capital formation, as well as the growth rate of the wage share in GDP 22. In line with recent studies looking at the finance-growth nexus  (Cecchetti and Kharroubi (2012) ), our analysis also includes a set of variables which do not appear in our theoretical framework, but which are known to influence economic growth: trade openness, government spending to GDP ratio, working population growth, and CPI inflation. In particular, the inclusion of the inflation rate provides a robustness check for the mechanism presented in the model, which assumes constant prices.
One important prerequisite for our empirical investigation is to capture both the quantitative and qualitative aspects of the financialization process. To measure the 21 Australia, Austria, Belgium, Canada, Croatia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States. Country classification is the World Bank's Global Finance and Development database  (2015). 22 The use of the wage share appears relevant as it is equivalent to deflating real wages by labor productivity The wage share (αL) is computed as the ratio of nominal labor income (W * L) to nominal income (P * Y), or equivalently, as the ratio of real wages (W / P) to labor productivity (Y / L): αL = (W * L) / (P * Y) = (W / P) / (Y / L).
We then tested the model's main predictions by conducting an empirical analysis based on an international panel dataset of 29 high income countries, observed over the 1998-2014 period. In line with our theoretical findings, the inspection of impulse response functions and forecast error variance decomposition obtained using a Bayesian structural VAR model highlighted that increases in private credit and in a new banking financialization index positively affected each other and exerted a negative and significant impact on the wage share, the growth of gross capital formation, and the growth of GDP. These results were confirmed by a set of fixed-effect panel regressions, which revealed a sizable, yet credible, negative impact of financialization on both GDP per capita and GDP per worker growth.
At first glance, such values may not seem to be representative of the interest rates prevailing today in developed countries. However, according to data from the World Bank, in 2017 the value of this indicator for Australia was 5. 2%, Iceland's is 7. 3% and Singapore's 5. 3%. If we add emerging market economies to the list, Costa Rica, Indonesia and South Africa have lending rates slightly above 10% in 2017.
This is a GWAS of dementia and its clinical endophenotypes defined based on AD's clinical certainty and the burden of vascular comorbidity. The GR@ACE is a unique genomic resource comprising the largest number of dementia cases diagnosed in a single memory clinic to date. First, we determined whether we could identify categories of known LOAD genes linked to clinical subgroups of AD cases. Next, we explored whether these categories suggested different biological routes. Finally, to assess the impact of these clinical subgroups of AD cases in GWAS findings, we metaanalyzed the GR@ACE data with independent GWAS series.
01. Imputation was carried out using the Haplotype Reference Consortium panel in the Michigan Imputation Server (https://imputationserver. sph. umich. edu). Only common markers (MAF. 0. 01) with a high imputation quality (R 2. 0. 30) were selected for downstream analyses. The GWAS was performed for GR@ACE as a whole and for each endophenotype, ([N AD111 ). The GWAS was conducted for genotype dosages using an additive genetic model with PLINK 1. 9. A model including the top four PCs as covariates was used for the discovery stage because it exhibited the lowest inflation and optimal power compared with alternative tested models (Supplementary Fig. 2 ). Further description is provided in Supplementary Methods. Power analysis was performed using QUANTO software v1. 2. 4  [23]  to model the impact on statistical power at different MAFs and effect sizes in available case-control cohorts.
Results were depicted using the ggplot2 package in R. Analyses were performed for a GWAS experiment that would meet criteria for genome-wide significance (P , 5 ! 10 28 ) and for a replication experiment that would meet P ,. 05 (Supplementary Fig. 3 ). Calculations were performed considering a disease prevalence of 1. 73%, according to registers in the Spanish population. GR@ACE GWAS data have been deposited into the European Genome-phenome Archive (https://ega-archive. org), which is hosted by the European Bioinformatics Institute and the Center for Genomic Regulation, under the accession number EGAS00001003424.
AD 111 ). According to the global effect change and direction of the enrichment, i. e. , from VaD 11 to AD 111 or from AD 111 to VaD 11 , we classified LOAD genetic variants into three categories. Thus, category A includes variants with an increase in the association effect from VaD 11 to AD 111 endophenotypes and a global effect change. 0. 05, and category B includes variants with an increase in the association effect from AD 111 to VaD 11 and a global effect change. 0. 05. Category C comprises variants not fulfilling criteria for categories A or B (Supplementary Table  1 ). Finally, we assessed the biological pathways underlying each category. We incorporated data from gene coexpression for each specific gene category using the GeneFriends tool (http://genefriends. org/) and performed pathway analysis of top coexpressed genes using the overrepresentation enrichment method in WebGestalt (http:// www.
Replication of genome-wide significant findings We then explored genome-wide significant (GWS) signals in an independent cohort of Spanish ancestry (N 5 4959). We extracted variants of interest from GWAS data, which were genotyped and processed applying similar methods to those explained for the GR@ACE study (Section 2. 3). Finally, meta-analysis including the discovery stage, named stage I, and the replica data set, stage II, was performed as previously described. Results were interpreted according to the American Statistical Association guidelines  [25, 26].
Biological interpretation of meta-GWAS signals Gene expression quantitative trait locus (eQTL) analysis was conducted to link meta-GWAS top signals to genes. Markers with moderate-to-high LD (r 2 0. 6) with the novel lead markers were identified using LDlink  [27]  for European population and were included in this analysis. We used brain (n 5 11) and whole-blood (n 5 1) tissues from the GTEx v7 repository (https://www. gtexportal. org/home) for mapping cis-eQTLs (Supplementary Table  3 ). As an extension of GTEx tissue eQTL mapping, we explored brain eQTLs for GWS genomic regions using additional databases available via Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA)  [28]. We also performed functional annotation for GWS markers, chromatin interaction, and gene-based analysis using similar criteria to those previously described by Jansen et al.
Meta-analysis of GR@ACE study with other data sets To assess the impact of sample composition in AD GWAS, and look for new AD loci, we first combined the GR@ACE data set with nine additional genomic databases that had genotypic level data available. Subtle genomic inflation was detected, mainly explained by polygenicity (l GC 5 1. 10; LD score intercept 5 1. 04). Five regions were associated with LOAD (Fig. 3 ); of these, four (APOE-rs429358, PICALM-rs10792832, MS4A2-rs983392, and BIN1-rs6733839) have been previously linked to AD (Supplementary Table  8 ), and one is a new GWAS finding (ANKDR31-rs4704171; OR 5 1. 19 [1. 12-1. 27]; P 5 2. 78 ! 10 28 ) (Table  2 ). Then, we conducted a genome-wide meta-analysis combining the GR@ACE study with IGAP stage I. We identified 12 LOAD genomic regions reaching GWS.
CD33-rs3865444, which did not reach GWS in the IGAP meta-analysis, was significantly associated with LOAD (OR 5 0. 92 [0. 89-0. 95]; P 5 3. 61 ! 10 28 ) (Supplementary Fig. 7 ). Among the top suggestive signals, we detected HBEGF-rs4150233 (OR 5 0. 92 [0. 90-0. 95]; P 5 5. 10 ! 10 28 ) previously identified by a transethnic GWAS  [29]. Next, meta-analysis of the whole GR@ACE data set with IGAP I and II enabled the identification, for the first time, of NDUFAF6-rs10098778 as a GWS signal (OR 5 0. 94 [0. 91-0. 96]; P 5 2. 54 ! 10 28 ). When we combined GR@ACE AD 111 endophenotype with IGAP I and II, we also detected SCIMP-rs7225151 (OR 5 1. 11 [1. 07-1. 15]; P 5 1. 12 ! 10 28 ) (Table  2 ) (Supplementary Fig. 8 ). It was previously reported as a genome-wide suggestive signal by IGAP  [24]. Recently, SCIMP-rs113260531, which is in complete LD with our lead marker (r 2 5 1), was associated with AD  [17].
In this study, we explored how clinical heterogeneity might impact GWAS findings by integrating distinct GWAS data sets with either the GR@ACE cohort as a whole or its endophenotypes. We found several new GWS signals that seem strongly dependent on the sample composition. For example, after combining IGAP stages I and II with the entire GR@ACE data set, we identified genetic signals in the NDUFAF6 genomic region but not in the SCIMP region. When this exercise was conducted using GR@ACE endophenotypes, the SCIMP signal was detected using the clinically "pure" AD GR@ACE endophenotype. It should be noted that the power to detect SCIMP signal in the meta-analysis with GRA@ACE dementia and GR@ACE AD 111 was 75 and 70%, respectively. We tried to replicate this finding in a purer AD data set without clinical mixed dementia cases, but the available number of clinical AD 111 cases might be compromising the statistical power to replicate (N cases 5 584; power 5 19%).
Considering sample composition in the design of genetic studies might lead to the identification of genetic profiles, which can help clinicians distinguish subsets of patients within the disease spectrum and promote novel therapy targets for AD. Clinical leads responsible for data collection are Iwona K1oszewska (Lodz), Simon Lovestone (London), Patrizia Mecocci (Perugia), Hilkka Soininen (Kuopio), Magda Tsolaki (Thessaloniki), and Bruno Vellas (Toulouse). Imaging leads are Andy Simmons (London), Lars-Olad Wahlund (Stockholm), and Christian Spenger (Zurich). Bioinformatics leads are Richard Dobson (London) and Stephen Newhouse (London). Funding support for the Alzheimer's Disease Genetics Consortium (ADGC) was provided through the NIA Division of Neuroscience (U01-AG032984). The genotypic and associated phenotypic data used in the study "Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer's Disease (GenADA)" were provided by GlaxoSmithKline, R&D Limited.
The data sets used for the analyses described in this manuscript were obtained from dbGaP at http://www. ncbi. nlm. nih. gov/gap through dbGaP accession number phs000219. The Mayo Clinic Alzheimer's Disease Genetic Studies, led by Dr. Nil€ ufer Ertekin-Taner and Dr. Steven G. Younkin at the Mayo Clinic in Jacksonville, FL, used samples from the Mayo Clinic Study of Aging, the Mayo Clinic Alzheimer's Disease Research Center, and the Mayo Clinic Brain Bank. Data collection was supported through funding by NIA grants P50 AG016574, R01 AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01 AG017216, and R01 AG003949, NINDS grant R01 NS080820, the CurePSP Foundation, and support from the Mayo Foundation. The Neocodex-Murcia study was funded by the Fundaci on Alzheimur (Murcia), the Ministerio de Educaci on y Ciencia (Gobierno de España), Corporaci on Tecnol ogica de Andaluc ıa, Agencia IDEA (Consejer ıa de Innovaci on, Junta de Andaluc ıa), the Diabetes Research Laboratory, and the Biomedical Research Foundation.
RESEARCH IN CONTEXT 1. Systematic review: Recent literature search, conducted using PubMed database, revealed that the presence of Alzheimer's disease (AD) with vascular brain pathology is the most common form of dementia. Regardless, it is unclear whether subgroups of AD patients present differential genetic profiles or develop dementia through differential biological routes. 2. Interpretation: Our findings showed three gene categories operating differently across subgroups of patients with AD and highlighted the role of vascular pathways in pure forms of AD. We identified novel genome-wide significant signals, which seem strongly dependent on the AD subset used for metaanalysis. 3. Future directions: Our findings suggest the importance of investigating the role of cerebral amyloid angiopathy, a unique identified AD hallmark that connects both the vascular and amyloid hypotheses for AD.
Co. , Inc. ; Meso Scale Diagnostics, LLC. ; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc. ; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www. fnih. org). The grantee organization is the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of Southern California. The AddNeuroMed data are from a public-private partnership supported by EFPIA companies and SMEs as part of In-noMed (Innovative Medicines in Europe), an integrated project funded by the European Union of the Sixth Framework program priority FP6-2004-LIFESCIHEALTH-5.
Acknowledgments The authors would like to thank patients and controls who participated in this project. The  Genome Research @ Fundaci o ACE  project ( GR@ACE ) is supported by  Fundaci on bancaria "La Caixa", Grifols SA, Fundaci o ACE , and  ISCIII (Ministry of Health, Spain). They also want to thank the private sponsors who support the basic and clinical projects of our institution (Piramal AG,  Laboratorios Echevarne ,  Araclon Biotech S. A. , and Fundaci o ACE). They are indebted to the Trinitat Port-Carb o legacy and her family for their support of Fundaci o ACE research programs. Fundaci o ACE is a participating center in the  Dementia Genetics Spanish Consortium (DEGESCO). A. R. and M. B. receive support from the  European Union/ EFPIA Innovative Medicines Initiative  Joint undertaking ADAPTED and MOPEAD projects (grant numbers  115975  and  115985 , respectively).
M. B. and A. R. are also supported by national grants  PI13/02434 ,  PI16/01861 , and  PI17/01474. Acci on Estrat egica en Salud is integrated into the Spanish National R 1 D 1 I Plan and funded by  ISCIII (Instituto de Salud Carlos III)-Subdirecci on General de Evaluaci on  and the  Fondo Europeo de Desarrollo Regional (FEDER-"Una manera de Hacer Europa"). L. M. R. is supported by  Consejer ıa de Salud de la Junta de Andaluc ıa  (grant  PI-0001/2017 ). Control samples and data from patients included in this study were provided in part by the  National DNA Bank Carlos III  (www. bancoadn. org, University of Salamanca, Spain ) and  Hospital Universitario Virgen de Valme (Sevilla, Spain) ; they were processed after standard operating procedures with the appropriate approval of the Ethical and Scientific Committee.
IGAP was made possible by the generous participation of the control subjects, the patients, and their families. The i-Select chip was funded by the  French National Foundation on Alzheimer's disease and related disorders. European Alzheimer's Disease Initiative (EADI)  was supported by the  LABEX (laboratory of excellence program  investment for the future) DISTALZ grant,  Inserm ,  Institut Pasteur de Lille, Universit e de Lille 2 , and the  Lille University Hospital. GERAD was supported by the  Medical Research Council  (grant no. 503480 ),  Alzheimer's Research UK  (grant no. 503176 ), the  Wellcome Trust  (grant no. 082604/2/07/Z ), and  German Federal Ministry of Education and Research (BMBF) :  Competence Network Dementia (CND)  grant no. 01GI0102 ,  01GI0711 ,  01GI0420. CHARGE was partly supported by the  NIH/NIA  grant  R01 AG033193  and the  NIA   AG081220  and  AGES  contract  N01-AG-12100 , the  NHLBI  grant  R01 HL105756 , the  Icelandic Heart Association , and the  Erasmus Medical Center and Erasmus University.
BACKGROUND: Metastatic breast cancer (MBC) behaviour differs depending on hormone receptors (HR) and human epidermal growth factor receptor (HER2) statuses. METHODS: The kinetics of central nervous system (CNS) metastases (CNS metastasis-free survival, CNSM-FS) and subsequent patient's prognosis (overall survival, OS) according to the molecular subtype were retrospectively assessed in 16703 MBC patients of the ESME nationwide multicentre MBC database (Kaplan-Meier method). RESULTS: CNS metastases occurred in 4118 patients (24. 6%) (7. 2% at MBC diagnosis and 17. 5% later during follow-up). Tumours were HER2-/HR+ (45. 3%), HER2+/HR+ (14. 5%), HER2+/HR-(14. 9%) and triple negative (25. 4%). Median age at CNS metastasis diagnosis was 58. 1 years (range: 22. 8-92. 0). The median CNSM-FS was 10.
Study design We performed a retrospective analysis of the 16,703 MBC patients included in the French Epidemiological Strategy and Medical Economics (ESME) research programme, to which 18 French specialised cancer centres are participating. The ESME MBC database (NCT03275311) was established, and is managed by R&D UNICANCER. 29, 30 The database included all adult patients treated in first line for an MBC between January 2008 and December 2014, in one of the participating centres.
Selection criteria For this study, two populations were identified. The overall population included all ESME MBC database patients. Two patients were excluded from analysis due to inconsistencies within the data (population 1). CNSM-FS was evaluated in this population. To evaluate the prognostics of patients after the occurrence of CNS metastases, population 2 included patients from the ESME MBC database diagnosed with CNS metastases at MBC diagnosis or later during the course of MBC. Of note, as the ESME MBC database did not differentiate intraparenchymal BM from LM, both metastatic sites were merged for the analyses.
Data collected For all patients, data were extracted from the ESME MBC database. Oestrogen receptors (ER), progesterone receptors (PR) and HER2 statuses were described both at the time of the primary tumour diagnosis and at the time of MBC diagnosis, as some phenotypic changes can be observed between the primary tumour and metastatic recurrence. The HER2 and HR statuses used for statistical analyses were derived from existing results about metastatic tissue sampling, when available, or, if not available, from the last sampling of early breast cancer.
Population analysed A total of 16,703 MBC patients were included in the ESME MBC database (Fig. 1 ). After exclusion of two patients due to inconsistent data, 16,701 patients were included in the present analysis (population 1). Among them, 4800 had de novo metastatic breast cancer and 11,901 relapsed MBC. After a median follow-up of 42. 8 months (95% CI: 42. 0-43. 7), 4118 patients of population 1 (24. 6%) developed CNS metastases. In total, 4033 of them were included in population 2 (85 excluded since diagnosis after 15/01/2016). The characteristics of the two populations of the study are described in Table  1.