dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. Part of this work was supported by funds of the  Département de Physique Théorique, Université de Genève. Part of this work was supported by a postdoctoral grant from  Centre National d'Études Spatiales (CNES). Part of this work was performed in the context of a visitor grant from the  Swiss National Science Foundation , with award number  IZSEZ0 207059. I thank the referee for discussion and detailed suggestions that improved this article. Some of the computations made use of the CLASS code (Blas et al. 2011).
s. <63 μm and 63 < g. s. < 125 μm). The analysis was conducted using a Bruker D8 Advance X-ray system equipped with Lynxeye XE-T silicon-strip detector at the Department of Earth Sciences, Sapienza University of Rome (Italy). The instrument was operated at 40 kV and 30 mA using CuKα radiation (λ = 1. 5406 Å). Samples were run Downloaded from https://academic. oup. com/gji/article/233/1/211/6840213 by guest on 08 December 2022 between 2 • and 70 • 2θ with step sizes of 0. 02 • 2θ while spinning the sample. Data were collected with variable slit mode to keep the irradiated area on the sample surface constant and converted to fixed slit mode for semiquantitative analysis. Semiquantitative estimation of mineral phases was performed by calculating peak areas and using mineral intensity factors as calibration constants  (Moore & Reynolds 1997).
We conducted the  whole experiment with the humidifier inside the vessel, closed on both sides by plexiglass panels to ensure 100 per cent humidity. All experiments were run at room temperature (∼25 • C). The experimental dataset was designed also to study the influence of the grain size on frictional properties. For this reason, for each experimental condition (of normal stress and relative humidity) we carried out two experiments, one with an initial grain size <63 μm and the other with grain size between 63 and 125 μm (for more details see Table  S1 ). The deformation apparatus BRAVA uses two servo-controlled hydraulic rams to apply the horizontal and vertical loads  (Collettini et al. 2014). The applied load (both horizontal and vertical) was measured via strain gauge load cells (accuracy ±0.
Belle II is a new-generation B-factory experiment operating at the luminosity frontier, SuperKEKB collider, and started data-taking in April 2018. Belle II uses a synchronous data acquisition (DAQ) system based on a pipelined trigger flow control. It is designed to handle 30 kHz trigger rate, under the assumption of a raw event size 1 MB. Because a larger event size and rate are foreseen depending on the future background conditions, and the difficult maintainability of the current readout system during the Belle II entire operation period is expected, we decided to upgrade the Belle II DAQ readout system with state-of-art technology. A PCI Express based new-generation of readout board (PCIe40), which was originally developed for the upgrade of LHCb and ALICE experiments, has been used for the upgrade of Belle II DAQ system.
Belle II DAQ and readout system The Belle II DAQ  [3]  system is designed to process the data from the front-end electronics (FEE) to the storage system through several steps based on a highly unified system. As shown in Fig. 1 , the highly unified timing and trigger distribution (TTD) system  [4]   Common Pipelined Platform for Electronics readout (COPPER)  [5] , as shown in right pad of Fig. 1. A total of 203 COPPERs are used in the original Belle II DAQ system before upgrade. To unify the readout system, a high-speed data transmission based on the custom serial link protocol, called Belle2link  [6] , is adopted. Xilinx GTP or GTX RocketIO up to 2. 54 Gb/s, driven by the 127 MHz system clock, is used as the transceiver. As shown in right pad of Fig. 1 , each COPPER module hosts four custom High Speed Link Boards (HSLB).
PCIe40 based new Belle II readout system The current readout system, mainly the COPPER module and its readout PC, is obstructing high-speed data transfer and DAQ maintenability. The number of disconnected parts are increasing, such as the chipset of PrPMC. In addition, the smallest bandwidth is in the Gigabit Ethernet between the COPPER module and the corresponding readout PC. PCIe40  [8]  is a new generation of readout modules based on the PCI Express Gen3 solution with a high data throughput of 100 Gb/s  [9]. The module will also be used for the DAQ upgrade of the LHCb and ALICE experiments at the Large Hadron Collider (LHC). PCIe40 fulfills the requirements of the Belle II DAQ upgrade and a large amount of data can be easily transferred from a readout board to a PC server via PCI Express bus.
To integrate the current Belle II DAQ system, we particularly focus on the design of the firmware and software based on the PCIe40 board. Because all functionalities of the current readout system are retained, no modification of the firmware and hardware of the sub-detector FEE is required. A single PCIe40 board can connect up to 48 bidirectional optical links, while only 4 bidirectional links can be handled by the COPPER module. Thus, the new readout system will become much more compact by replacing 203 COPPERs with 21 PCIe40 boards (SVD: 5, CDC: 8, TOP: 2, ARICH: 2, ECL: 2, KLM: 1, TRG:1). As illustrated in Fig. 2 , four pairs of onboard MiniPOD optical transceivers are used to receive or transmit data from or to the FEE through optical fibers. A high-density Intel Altera Arria 10 field-programmable gate array (FPGA) chip (1.
Performance of new readout system The performance of PCIe40 based readout system have been tested on the test bench, and the Belle II DAQ system. As shown on the left and middle pads of Fig. 4 , the optical fibers from front-end electronics are connected to the patch panels, and then bundled into MPO fibers and connected to PCIe40 module, which is installed in the readout PC shown in the middle pad of Fig. 4. PCI Express hard IP-based DMA architecture of the new readout hardware is capable of handling a 260-kHz trigger rate with 17 Gb/s data throughput without data loss, and the maximum data transfer rate can reach to 31 Gb/s  [10]. The current performance of the data readout system  already fulfils the Belle II DAQ upgrade requirements, while the throughput limit of the new readout system is 10 Gb/s, which is determined by the NIC of new readout PC.
A test of remote access to the registers in the front-end electronics through Belle2link was performed together with data acquisition under different trigger rates. The register access rates for the COPPER-HSLB system decreased from 170 to 0 accesses / sec, when the data acquisition trigger rate increased from 0 to 30 kHz. This indicates that there was a limitation of bandwidth of the COPPER-HSLB system for slow control and data acquisition. However, the corresponding accessing rates for the PCIe40 board retained a constant value even when the data acquisition trigger rate was increasing. There was no limitation of bandwidth observed for the PCIe40 board  [10]. Right pad of fig. 4  shows the integrated total (red) and daily (blue) luminosity from April 2019 to June 2022. TOP and KLM readout systems were firstly replaced by the PCIe40 module for physics data-taking from autumn run of 2021, then ARICH started using PCIe40 readout system from Spring run of 2022.
Commissioning of full PCIe40 based Belle II DAQ system Installation and development for the replacement of SVD, CDC, ECL and TRG systems have been performed. As shown on the left pad of Fig. 5 , stress DAQ test running with 30 kHz Poissondistributed random dummy trigger configuration has been performed to confirm the performance for the remaining systems. It has been confirmed that the system could continue running for about 8 hours without error. Meanwhile, cosmic-ray runs were taken with both COPPER and PCIe40 based readout systems. These cosmic-ray runs can be used to check the quality of data readout by the PCIe40 system. For instance, right top pad of Fig. 5  shows an estimator, the cluster charge of SVD, read out by COPPER system, and the right bottom pad shows the same estimator read out by PCIe40 system.
Acknowledgements This work was financially supported by the  Max Planck Society and Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's  Excellence Strategy-Exzellenzcluster 2186 "The Fuel Science Center " ID:  390919832. A. B. is grateful to the  Max-Buchner-Forschungsstiftung  for his funding under Ref. Nr. 3827. Furthermore, the authors would like to thank  Aaron Kretschmer  ( MPI-CEC ) for his help with catalyst synthesis,  Dr. Eckhard Bill  and  Derya Demirbas  ( MPI-Kohlenforschung ) for access and assistance with 57 Fe Mössbauer experiments,  Gudrun Klihm  ( MPI-CEC ) for assistance with XPS experiments,  Lukas Pielsticker  ( MPI-CEC ) for his help with the XPS analysis,  Thibault Ourlin ,  Simon Cayez , and  Pier Francesco Fazzini  ( LPCNO ) for help concerning electron microscopy and XRD characterization,  Annika Gurowski ,  Alina Jakubowski , and  Justus Werkmeister  ( MPI-CEC ) for their support with GC and  GC-MS measurements.
Microarray Hybridization An aliquot (100 ng) of RNA from each sample was labeled with cyanine 3-CTP using Low Input Quick Amp Labeling kits (Agilent Technologies, Waldbronn, Germany). Labeled cRNAs were purified and hybridized onto Agilent G4851B SurePrint G3 Human Gene Expression 8 × 60 K v2 microarrays™ allowing a full coverage of the human transcriptome (Agilent Technologies, Waldbronn, Germany). Slides were washed and read by an Agilent G2505C™ microarray scanner with a 3 µm resolution and data were extracted using Agilent Feature Extraction software version 11. 0 as described previously by Chézeau et al. [17]. The experiments (N = 4) were performed according to MIAME standards  [18]. Microarray data have been uploaded to the NCBI Gene Expression Omnibus database  [19] , where they are accessible under GEO Series accession number GSE197987 (http://www.
Statistical and Functional Analyses of Microarray Data Data were quantile-normalized using Solo software (Institut de Génétique et de Biologie Moléculaire et Cellulaire, Strasbourg, http://www-microarrays. u-strasbg. fr/Solo/ index. html, accessed on 1 May 2022). Genes displaying differential expression between control and exposed groups were identified using a method based on Fold Change Rank Ordering Statistics (FCROS;  [20] ). Genes for which Fold-Changes (FC) for exposed groups versus matched controls was at least 1. 5 in either direction, and with f-values ≤ |0. 01|, were considered as significantly differentially expressed. Details and functional information on genes differentially expressed were obtained using GeneCards™ database (https://www. genecards. org, accessed on 1 May 2022).
All transcripts displaying a relative fluorescence average (N = 4) higher than 109,000 relative units were considered for analysis. Gene-gene interactions were investigated using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, https://string-db. org, accessed on 1 May 2022) database of physical and functional interactions v11. 5 as described previously by Chézeau et al. [21]. The basic settings were: (i) full string network, (ii) meaning of network edge by evidence, (iii) involvement of all active interaction sources, and (iv) high confidence (0. 7). Network nodes represent proteins and edges represent protein-protein associations  [22]. A K-means clustering (K = 3) was also performed using the STRING software tool.
52 and f-value = 0. 998). As the transcriptomes of THP-1 monocytes in presence and in the absence of potassium niobate have shown high similarities in gene-expression profiles, we retrieved transcripts displaying relative fluorescence above 109,000 relative units, namely 114 dots that corresponded to 87 genes after removing redundancy. Of them, 85 were coexpressed in both samples (Table  1 ). Sixty-eight among the 85 common series were recognized as human proteins and plotted (Figure  7 ) by the STRING database v11. 5. Seventeen transcripts were not recognized by Ensembl: (i) the A_XX series (10 transcripts) as they are specific to the used array, (ii) one X_LOC not recognized by Ensembl, (iii) FAM74A4, RNA28S5, SNAR-A3, SNAR-B2, SNAR-D and THC2526015 (Table  1 ).
, 2002; Morard et al. , 2011; Weck et al. , 2013)  and a DECTRIS Pilatus 3XM CdTe detector (version 300K-W), using an energy of 37. 077 keV for the incoming X-ray beam. Note. Pressure and temperature are indicated for each run. Position of Fe-O and Fe-Fe bonds are measured on total g(r) by Gaussian fit of the first and second oscillations (using Fityk software  [Wojdyr, 2010] ).
Numerical Simulations Ab initio calculations were performed using the ABINIT package  (Gonze et al. , 2020). We used a cubic simulation cell of 64 atoms to simulate the B1 structure (8 units) and the liquid, similar to the one used in a recent work on ferropericlase  (Holmström & Stixrude, 2015 , 2016). In coherence with previous simulations  (Dewaele et al. , 2006; Morard et al. , 2018) , PAW atomic data with a short cutoff radius (2. 0 a. u. ) and including 16 electrons in the valence were used. We employed a plane wave energy cutoff equal to 544 eV to span the wavefunctions, and a 2 × 2 × 2 k points mesh to sample the irreducible Brillouin zone. It is well known that in FeO the electronic correlations between the 3d electrons are strong and cannot be described using the local density approximation (LDA) or the generalized gradient approximation (GGA).
Full line corresponds to thermal equation of state (EoS) parametrized in the present work, whereas dotted line corresponds to the thermal EoS proposed by  Fischer, Campbell, Shofner, et al. (2011). A single equation of state well describes the entire data set, and no clear discontinuity is visible around the metallic transition, occurring around 60 GPa at this temperature (see also Figure  1 ). performed on liquid Fe 2 O 3 under high temperature  (Misawa et al. , 2014)  our results show no indication of O-O dimer formation. Guided by results from AIMD calculations, experimental g(r) were further analyzed in order to quantitatively assess the structural evolution of liquid FeO with pressure. Peak positions of the different g(r) contributions were refined by performing a Gaussian fit using Fytik software  (Wojdyr, 2010)  (Table  1 ; Tables  S4  and S5  in Supporting Information S2), with the two first Gaussians assigned to Fe-O and Fe-Fe bonds respectively, and a sigmoid used to fit the rest of the g(r) (Example of fit is shown in Figure  S2  of Supporting Information S1).
Acknowledgments The authors would like to acknowledge technical and scientific support from  S. Bauchau  and  J. Jacobs  during high-pressure experiments on ID27 beamline at  ESRF , and V. Prakapenka during high-pressure experiments at the13-IDD beamline at  APS. We would like also to thank  S. Jacobsen  for supplying the FeO starting material. G. Morard  and  F. Miozzi  have received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020.
The implicit positive exponent defines the exponential growth. The four unknown parameters were solved jointly given the observed datasets consisting of (time, microbial concentration) couples for each TS × strain combination and each repetition. The solution targeted minimising the least-squares of observations vs. the predicted values with the following constraints: (1) The targeted adjusted coefficient of determination R 2 adjusted (4) was within [0. 9, 1] and (2) The predicted concentration at T24 was equal to the observed mean concentration at T24 among the six repetitions. n was the number of observations per combination (n = 4 measurements × 6 repetitions = 24 per combination), and k was the number of estimated explanatory parameters in the function using the n observations (k = 4, per combination).
R 2 adjusted = 1 -1 - ∑ n i=1 (y i -ŷi ) 2 ∑ n i=1 (y i -y) 2 n -1 n -(k + 1) (4) The R 2 adjusted measures the goodness-of-fit (GoF) of the model and thus the degree of prediction of observations. An R 2 adjusted equal to 1 indicates the perfect prediction of the observed concentrations, while an R 2 adjusted equal to 0 shows that the model does not predict any better than random guesses. Two required initial inputs (the partial concentrations at T0: C 1 and C 2 ) were defined such that C 1 e 0 + C 2 e 0 = C(0), given e 0 = 1. Data management and statistical analyses were performed with the xt module in Stata 17, StataCorps LLC, College Station, TX, USA. The fitting of the pharmacodynamic model to the data was performed in Microsoft Excel 2019 version 2205, Microsoft Corporation, Redmond, WA, USA, using the Solver function.
Statistical Analysis and Outcome Measures All statistical analyses were performed using the IBM SPSS Statistics Software Version 25 (Chicago, IL, USA). Adjusted Cox proportional hazards regressions were conducted to investigate the associations of qualitative VF components with the development of dementia and MCI at follow-up. All analyses were adjusted for the following scale variables: age at baseline (in years), years of education, and standardized MMSE scores (i. e. , MMSE scores of illiterate participants were converted to the literate MMSE scale of 30). All analyses were additionally adjusted for the following categorical parameters: sex, main occupation (manual or mental), and socioeconomic status (low or high)  [37]. Inclusion of the MMSE score aimed towards controlling for global cognitive function.
Data and processing We considered four blood pressure phenotypes (DBP, SBP, PP, MAP), and three lipids levels (TG, HDL, LDL). Two binary smoking exposures, current smoking and ever smoking, were considered and measured similarly across all smoking GWIS. The current smoking variable was coded as 1 if the subject smoked regularly in past year and as 0 otherwise. Ever smoking status was coded as 1 if the subject smoked at least 100 cigarettes during his/her lifetime and 0 otherwise. For alcohol consumption, two binary variables were considered, referred further as current drinking and drinking habit. All studies conducted a two-stage approach. In stage 1 (referred to as Discovery), a standard GWIS was performed using up to 18 million genetic variants. In stage 2 (referred to as Replication), only a subset of variants with a p value for either 1df or the 2df test below a certain threshold (P < 10 -6 or P < 10 -5 ) at stage 1 were further considered.
Identification of independent signals and associated loci We defined two levels of association when reporting genome-wide significant variants in the combined meta-analyses (P < 5 × 10 -8 ): independent signal (lead SNPs after clumping) and associated locus (genetic regions of 1 Mb with at least one independent signal). Independent signals represent independent SNPs associated at genome-wide significance level. Independent signals were defined using the clumping framework from the PLINK software  [13] , using a linkage disequilibrium (LD) threshold of 0. 2 and a maximum physical distance from the lead SNP (i. e. , the most associated variant) of ±500 kb. The LD was derived using 1000 Genomes Project  [14]  individuals as a reference panel while accounting for ancestry. We used the EUR, AFR, combined EAS-SAS, and AMR samples as proxies for the individuals from European ancestry (EA), African ancestry (AA), Asian ancestry (ASA), and Hispanic ancestry (HA), respectively.
Variance explained and heritability For each ancestry and each phenotype-exposure combination, we derived from the combined (stage 1 and stage 2) results the fraction of phenotypic variance explained by top SNPs was decomposed into main effects, interaction effects and those effects jointly using the R package VarExp  [22]. The significance of the variance explained by interaction effects was derived using an approximation of the joint test of all interaction effects. For EA samples, we further assessed potential differences in heritability across exposure-specific strata using stage 1 genome-wide association results using the LDSC approach  [23]. We used the pre-computed LDscore relative to EA samples provided with the software. When unavailable from the original studies, stratified results were derived from the interaction model using J2S  [24].
Differential pathways across exposures To explore further differences in genetic effect between exposure strata, we partitioned the genetic heritability estimated in individuals from European ancestry across different functional annotations  [25, 26]. We first considered baseline annotations provided with the LDSC package and the GenoSkyline  [33]  annotation set, a cell-type-specific annotation database derived mainly from the Roadmap Epigenomics  [34]  (Figs. S11-15 ). Because of the relatively modest sample size in some strata (N = 12,578 in the smallest strata, see Table  S1 ), we focused on the distribution of the estimated enrichment coefficient between exposed and unexposed. The majority of phenotype-exposure pairs exhibited a similar enrichment pattern (Fig. S16 ). For example, the enrichment estimates were significantly correlated for drinking habits exposure and lipids (correlations equal 0.
ACKNOWLEDGEMENTS We warmly thank all the past and present members of the  Gene Lifestyle Interaction Working Group. The  various Gene-Lifestyle Interaction  projects, including this summary project, are largely supported by a grant from the  U. S. National Heart, Lung, and Blood Institute (NHLBI) , the  National Institutes of Health ,  R01HL118305. This work was also supported by the  INCEPTION  project ( PIA/ANR-16-CONV-0005 ). This research was supported in part by the  Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health. PSV was supported by  American Heart Association  grant number  18CDA34110116. YJS was supported by the  K25HL121091  award from  NHLBI. JG was partly supported by the  P01CA196569  grant from the  National Institutes of Health.
This study generates a daily temperature and precipitation dataset over Vietnam at a high resolution of 0. 1°for the historical period 1980-2005 and the future period 2006-2100 under four representative concentration pathway (RCP) scenarios, namely RCP2. 6, RCP4. 5, RCP6. 0, and RCP8. 5. The bias correction (BC) and spatial disaggregation (SD) method is applied to the outputs of 31 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to create the new dataset called CMIP5-VN. To guide the BC and SD steps, gridded temperature and precipitation data interpolated from daily observations of 147 and 481 stations respectively are used. Results with the CMIP5-VN show that warming over Vietnam is projected to continue till the end of the 21st century under all four RCPs.
These relationships are assumed to be unchanged with time; thus, they can be used to project future conditions. The primary advantage of statistical downscaling is that it is computationally inexpensive and much faster than dynamical downscaling so that it can be easily applied to generate high-resolution simulations with multiple GCMs. Additionally, statistical downscaling can provide climate information at any specific resolution, so its results can be directly used in climate change impact assessments. Previous studies showed the skillful performance of different statistical methods  (Salathé 2003; Widmann et al. 2003; Maurer & Hidalgo 2008; Noël et al. 2021 ). Among those methods, the bias correction and spatial disaggregation (BCSD) approach is reliable and effective for downscaling temperature and precipitation data  (Wood 2002; Wood et al.