dataesr/scientific-paragraphs-categorization huggingface dataset:
Surface temperature In addition to changes in SAT, the simulations of sea surface temperature (SST) using iCESM1. 2 also show a significant high-latitude sea surface warming (Figure  3 , top panel). This is consistent with the results of PlioMIP2 MME (Figure  3 , bottom panel). Compared to the PlioMIP2 MME, the iCESM1. 2 simulation in high latitude warming during the MPWP is more in line with the proxy data  (Dowsett et al. 2013a ). The decomposition results show that the simulated MPWP surface warming is mainly controlled by changes in downward clear-sky long wave radiation fluxes (∆𝑇 rlds_clearsky ) in both iCESM1. 2 and CESM1. 2 (Figure  4 ). iCESM1. 2 simulated a lager contribution of ∆𝑇 rlds_clearsky to high latitude surface warming than did the CESM1. 2 simulation (Figure  4 ). This could explain why MPWP surface warming at high latitudes simulated by iCESM1.
Data availability All PlioMIP2 simulation data are available from the PlioMIP2 data server located in the School of Earth and Environment at the University of Leeds; request access can be obtained by emailing Alan Haywood (a. m. haywood@leeds. ac. uk). (a. m. haywood@leeds. ac. uk). The proxy data in Figure  3  used for comparison with model simulation can be retrieved from https://doi. org/10. 1594/PANGAEA. 911847.
(1995) ,  Shevchenko et al. (1996 ), and Pilcher (2010 , 2011 , 2012)  and data available on the CdR database 2. These archival data were previously used for shape modelling of Cybele by  Franco & Pilcher (2015)  and  Viikinkoski et al. (2017). Information about the photometric data are provided in Appendix A.
Acknowledgements. Based on observations collected at the European Organisation for Astronomical Research in the  Southern Hemisphere under ESO program   107. 22QN. 001 (PI: Marsset). This research has made use of IMCCE's SsOD-Net VO tool (https://ssp. imcce. fr/webservices/ssodnet/). This work has been supported by the  Czech Science Foundation  through grants  20-08218S  ( J. Hanuš ) and 21-11058S (M. Brož), as well as by the  National Science Foundation  under Grant No. 1743015  ( F. Marchis ). T. Santana-Ros  acknowledges funding from the  NEO- MAPP  project ( H2020-EU-2-1-6/870377 ). In addition, this work was partially funded by the  Spanish   MICIN/AEI/10. 13039/501100011033and  by " ERDF A way of making Europe " by the  "European Union " through grant  RTI2018-095076-B-C21 , and the  Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia 'María de Maeztu ') through grant  CEX2019-000918-M.
This research has made use of the Asteroid Families Portal maintained at the  Department of Astronomy, University of Belgrade. TRAPPIST is a project funded by the  Belgian Fonds (National) de la Recherche Scientifique (F. R. S. -FNRS)  under grant  PDR T. 0120. 21. TRAPPIST-North is a project funded by the  University of Liège , in collaboration with the  Cadi Ayyad University of Marrakech (Morocco). E. Jehin  is F. R. S. - FNRS Senior Research Associate. A52, page 10 of 18 M. Marsset et al. : Did (65) Cybele preserve its primordial shape?.
We recently published an RCT (NCT04650516) where we highlighted that a VR treatment comprised of some sensory stimuli mentioned above (e. g. , BB, BAS) effectively diminished the mean pain intensity in 45 patients diagnosed with moderateto-severe endometriosis-related chronic pelvic pain, up to 4 h post-treatment, as well as reducing the mean perceived pain, compared to a 2D digital control  (84). These results encourage us to conclude that VR with added sensory stimuli can be a good addition to an arsenal for alleviating pain. However, since the control was with the same 2D environment without the additional stimuli, we can only conclude the potential effects of the combination of these stimuli. Future studies are needed to better characterize the potentializing effect of adding BB, BAS, EMDR, or different sound frequencies on the hypoalgesic effect of VR.
RNAseq analysis Data treatment was previously performed on  Noronha et al. (2021). Briefly, genes assigned as differentially expressed (P value < 0. 05 and |log2FC|> 1. 0) by DESeq  (Anders and Huber 2010)  were categorized according to the MapMan ontology (× 4. 1) with Mercator tool  (Lohse et al. 2014 ) and MapMan standalone software (v3. 5. 1) was used to explore the data.
Metabolomic analysis UPLC-MS After lyophilization of ground samples, 50 mg were extracted in 1 mL ethanol/water solution (60:40; v/v), shaken for 30 min at 1400 rpm at 83 °C and centrifuged at 18 000 g for 5 min. The extracts were diluted 1:5 with 0. 1% formic acid and stored at -20 °C until analyses. UPLC-MS was performed using an ACQUITY™ Ultra Performance Liquid Chromatography system coupled to a photodiode array detector (PDA) and a Xevo TQD mass spectrometer (Waters, Milford, MA, USA) equipped with an electrospray ionization (ESI) source controlled by Masslynx 4. 1 software (Waters). Analyte separation was achieved by using a Waters Acquity HSS T3 C18 column (150 mm × 2. 1 mm, 1. 8 µm) with a flow rate of 0. 4 mL min -1 at 55 °C. The injection volume was 5 µL. The mobile phase consisted of solvent A (0.
Analysis by HPLC-DAD Methanolic extracts were prepared as indicated above. Samples were run at 0. 8 mL/min, using 0. 1% formic acid in water (eluent A) and 0. 1% formic acid in methanol (eluent B) as mobile phase, through a Purospher Star RP-18 (25 × 0. 4 mm, particle size of 5 µm, Merck) column connected to an HPLC (Hitachi-LaChrom Elite) equipped with a DAD (Diode Array Detector). The elution gradient start with 5% eluent B at 0 min to 95% B after 45 min. Detection was acquired in the range of 230-650 nm for obtention of compounds UV-Vis spectra. Principal Component Analysis Biplot (PCA) was performed in R Studio software version 4. 1. 0 using the FactoMineR package v1. 34  (Lê et al. 2008).
To purify the samples, the GRS Total RNA Kit-Plant (GriSP, Lda. , Porto, Portugal) was used following the manufacturer's instructions. RNA concentration and purity were quantified spectrophotometrically in a NanoDrop ND-1000 (Termo Fisher Scientifc Inc. ) and integrity checked in a 1% (w/v) agarose gel. First-strand cDNA synthesis was performed using the Xpert cDNA Synthesis Mastermix protocol (GriSP, Lda. ). Quantitative real-time PCRs were performed with Xpert Fast SYBR Blue (GriSP, Lda. ) along with the conditions previously optimized in a CBX96 Real-Time Detection System (Bio-Rad). The amplification protocol included an initial denaturation step at 95 °C for 3 min, followed by an additional 40 cycles of denaturation for 3 s at 95 °C and 30 s at 60 °C. Experiments were done in biological replicates and then interpreted with the Bio-Rad CFX Manager (Bio-Rad) software, while VviGAPDH (glyceraldehyde-3-phosphate dehydrogenase) and VviACT1 (actin) were used as internal control.
Transcriptomic analysis of phenylpropanoid and shikimate pathways in grapevine woody tissues following bud burst The transcriptome profile was analyzed in woody tissues before (E-L 1) and after bud burst (E-L 4) using previously generated RNAseq data  (Noronha et al. 2021 ). The visualization of the phenylpropanoid pathway using MapMan showed a general stimulation of differentially expressed transcripts at E-L 4 (Fig. 2 ). Particularly, genes encoding three PAL (bin 16. 2. 1. 1), the first enzyme of the phenylpropanoid pathway, and those coding for 13 stilbene synthases and 3 chalcone synthases (bin 16. 8. 2. 1) were upregulated by up to fivefold (log2 fold expression). In grapevine, 48 putative VviSTS gene sequences were identified, and at least 31 of them encode full-length proteins  (Ciaffi et al.
Bud burst stimulates the shikimate and phenylpropanoid pathways in grapevine woody tissues In this work, we explored the expression profiles of the phenylpropanoid and shikimate pathways and performed a metabolomic analysis using UPLC-MS in grapevine woody tissues at two developmental stages. Most genes of the shikimate pathway, which controls the synthesis of aromatic amino acids, are stimulated upon bud burst, particularly Fig. 3  MapMan pathway for shikimate pathway with DEGs. Each square represents a gene. In blue, genes more expressed at E-L 4 (bud burst); in red, genes more expressed at E-L 1 (dormancy). When MapMan bin not available, grapevine VCost. v3 accession numbers are shown. Bin numbers can be found in additional files with gene details. DAHPS 3-deoxy-d-arabinoheptulosonate 7-phosphate synthase, DHQS 3-dehydroquinate synthase, SDH shikimate dehydrogenase, SK shikimate kinase, EPSPS 5-enolpyruvylshikimate-3-phosphate synthase, CM chorismate synthase, CM chorismate mutase, AS anthranilate synthase, PAT prephenate aminotransferase, ADT arogenate dehydratase, ADH arogenate dehydrogenase ◂ Fig.
Funding Open access funding provided by  FCT|FCCN  (b-on). This work was supported by  Fundação para a Ciência e Tecnologia (FCT)  under the strategic program  UIDB/BIA/04050/2020. The work was also supported by  FCT ,  CCDR-N (Norte Portugal Regional Coordination and Development Commission)  and  European Funds  ( FEDER/ POCI/COMPETE2020 ) through the project  AgriFood XXI  ( NORTE-01-0145-FEDER-000041 ) and the research project  GrapeMicrobiota  ( PTDC/BAA-AGR/2691/2020 ). AS and HN were supported by  FCT  grants  SFRH/BD/135782/2018  and  SFRH/BPD/115518/2016 , respectively. This work benefited from the networking activities within the  CoLAB Vines & Wines.
Rutherford backscattering spectrometry (RBS) was used to determine the composition of the single layers  [24]. Simulations with the ion beam analysis software SIMNRA  [25] , using a uniform and stoichiometric layer model, allowed the measured cation ratios Ti=ðTi þ SiÞ to be estimated (see Table  I ). As-deposited layers contain 0. 5% Ar. Two HR multilayer stacks were also deposited, using SiO 2 as the low-n partner to the high-n TiO 2 ∶SiO 2 layersone with 63. 2% Ti concentration (target 50%), using 17. 5 layer pairs (starting and ending with the high-n material), and the other with a 69. 5% Ti concentration (target 60%), using 21. 5 layer pairs. Each layer was nominally λ=4n in thickness to give high reflectivity. The Ti cation concentrations were verified using elastic recoil detection time of flight measurements  [26]  probing the elemental composition of the top four layers of each stack.
From transmission spectra of the single layers, thickness t and n were obtained using the software tool SCOUT  [27]. Table  I  shows the results for the as-deposited single layers and for selected annealing temperatures, showing that n increases with the Ti cation concentration. For the 69. 5% layers, n was estimated from a linear extrapolation of the single layers indices. Table I also shows the calculated thickness t HR of an HR stack of reflectivity R ≥ 99. 9994% using TiO 2 ∶SiO 2 layers partnered with SiO 2. The lowest Ti concentration from the single layers would require a very thick HR coating due to the low n, as each TiO 2 ∶SiO 2 layer becomes thicker, and more pairs of layers are required to generate the desired reflectivity  [28, 29]. Mechanical loss. -The mechanical loss of the coatings was measured using the gentle nodal support (GeNS) technique  [30] , in which a disk is balanced on a spherical lens.
85) as observed for the literature data (green and black points). The gray, dashed lines show exponential least-square fits through all three datasets, indicating their trends. This may suggest that the loss of our SiO 2 is high compared to other SiO 2 films. Discrepancies of CTN calculated from single layer and HR losses have been observed previously. In the literature no strong evidence of significant interface losses between layers has been found  [35, 36]. While stress relaxation may cause excess loss particularly on thin substrates  [36] , it seems unlikely to be the dominant source in our case of relatively thick samples, with consistent loss for different thicknesses. Discrepancies in the literature predominantly arose from differences in the frequency dependence of single-layer and multilayer losses  [12, 16] , which we do not observe.
We are grateful for financial support from  STFC  ( ST/ V005634/1 ,  ST/V001736/1 ), the  University of Glasgow , the  Royal Society  ( RG110331 ),  ETpathfinder (Interreg Vlaanderen-Nederland) ,  E-TEST  ( Interreg Euregio Meuse-Rhine) , the  Province of Limburg  and  National Science Foundation  Awards No. PHY-2207998  and No. PHY-2219109. Work done at  U. Montréal  is funded by  NSERC ,  CFI , and  FRQNT  through the RQMP. We thank our colleagues within the  LIGO Scientific Collaboration and Virgo Collaboration  and within  SUPA  for their interest in this work. This Letter has LIGO Document No. P2300203. LIGO was constructed by the  California Institute of Technology and Massachusetts Institute of Technology  with funding from the  National Science Foundation , and operates under cooperative agreement  PHY-1764464.
Methods: The estimated numbers of NPC cases and deaths were retrieved from the GLOBOCAN 2020 data set. Age-standardized incidence rates (ASIRs) and age-standardized mortality rates (ASMRs) were calculated using the world standard. The future number of NPC cases and deaths by 2040 were estimated based on global demographic projections. Results: Globally, approximately 133,354 cases and 80,008 deaths from NPC were estimated in 2020 corresponding to ASIRs and ASMRs of 1. 5 and 0. 9 per 100,000 person-years, respectively. The largest numbers of both global cases and deaths from NPC occurred in Eastern Asia (65,866/133,354, 49. 39% and 36,453/80,008, 45. 56%, respectively), in which China contributed most to this burden (62,444/133,354, 46. 82% and 34,810/80,008, 43. 50%, respectively). The ASIRs and ASMRs in men were approximately 3-fold higher than those in women.
Data Sources The numbers of new cases of and deaths from NPC (International Classification of Diseases, tenth revision C11) were extracted from the GLOBOCAN 2020 database for 185 countries or territories by sex and by 5-year age groups (0-4, 5-9, …, 80-84, 85 years and older)  [16] [17] [18]. Corresponding population data for 2020 were retrieved from the United Nations website  [19]. The population projections used in this study are based on the future fertility rates  [19]. The data sources and methods used in compiling the global cancer estimates for 2020 have been described in detail elsewhere  [17]. Briefly, the GLOBOCAN national estimates are dependent on the availability of recorded high-quality national and subnational incidence (from population-based cancer registries) and national mortality data (from vital registration systems)  [17].
We did not use scenarios of rates changing by SD 5% or more because such changes would be unlikely to occur in real life  [4, 5]. Predictions were calculated by applying the age-specific rates for the year 2020 (and each of the increasing or decreasing scenarios described) to the corresponding projected population data as estimated by the United Nations Development Program. The results are presented by country and aggregated across 20 United Nations-defined world regions  [19]  and according to the HDI group in 2020. Data management and analyses were performed in R software (version 4. 0. 2; R Foundation for Statistical Computing)  [27]. Figures were plotted using SigmaPlot software (version 12. 5; Systat Software Inc)  [28]. Global maps of NPC incidence and mortality rates by country are shown using R software (version 4.
Comparison With Prior Work To the best of our knowledge, this study is the first to provide a comprehensive picture of the most up-to-date epidemiological profile of NPC incidence and mortality on a global scale based on the GLOBOCAN 2020 data set, which is highly relevant for cancer control and clinical practice. The numbers and rates of NPC presented in this study are estimates based on the best available data (reviewed for their completeness, coverage, and accuracy) from population-based cancer registries.
Acknowledgments We gratefully acknowledge the contribution of all cancer registry staff in generating the cancer registry data used to create the cancer incidence and mortality estimates in the GLOBOCAN database. Where authors are identified as personnel of the  International Agency for Research on Cancer  and the  World Health Organization (WHO) , the authors alone are responsible for the views expressed in this paper, and they do not necessarily represent the decisions, policy, or views of the  International Agency for Research on Cancer  and WHO. YZ is supported by funds for  PhD researchers of Guangdong Medical University  (grant  4SG23187G ). ML is supported by  Science and Technology Projects in Guangzhou  (grant  202201011243 ). All funding parties did not have any role in the design of this study or in the explanation of the data.
Discriminative Classifiers in Generative HMMs One of the interests of HMMs and some CRFs with hidden discrete finite data lies in possibilities of analytic fast computation of Bayesian classifiers. As examples of classic Bayesian classifiers, let us consider MPM (Equation (  4 )) and MAP (Equation (  5 )). However, in some domains such as NLP, CRFs are preferred to HMMs for the following reasons. As HMM is a generative model, MPM and MAP used in HMM are also called "generative", and people consider that HMM-based MPM and MAP require the knowledge of p(y n |x n ). Then people consider it as improper to use the HMM-based MPM and MAP in situations where the distributions p(y n |x n ) are hard to handle. We show that this reason is not valid. More precisely, we show two points: (i) First, we notice that regardless of the distribution p(x 1:N , y 1:N ), all Bayesian classifiers are independent from p(y 1:N ), so that the distinction between « generative » and « discriminative » classifiers is misleading: they are all discriminative; (ii) Second, we show "discriminative" computation of MPM and MAP in HMMs is not intrinsic to HMMs but is due to its particular classic parameterization Equation  (1).
These compounds were dissolved in a mixture of acetic acid, acetonitrile, water, and methanol (Merck, Darmstadt, Germany) and were infused using a Chemyx Fusion 100 syringe pump (Thermo Fisher Scientific, Bremen, Germany). XCalibur 3. 0 software (Thermo Fisher Scientific, Bremen, Germany) and Trace Finder 3. 2 (Thermo Fisher Scientific, San José, CA, USA) were used for UHPLC control and data processing, respectively. Q Exactive 2. 0 SP 2 from Thermo Fisher Scientific was used to control the mass spectrometer. Solvent delivery was performed at a flow rate of 1 mL/min. Ultra-pure water with 1% formic acid (A) and acetonitrile with 1% formic acid (B) were used. The program started with 5% B at zero time. After that, the system maintained 5% B for 5 min, then transitioned to 30% B within 10 min.
The system then maintained 30% B for 15 min before moving to 70% B for 5 min. Afterward, the system maintained 70% B for 10 min. Finally, the system returned to 5% B for 10 min and maintained this condition for an additional 12 min to stabilize the column before the next injection of 20 µL. The HESI parameters were as follows: sheath gas flow rate, 75 units; auxiliary gas unit flow rate, 20; capillary temperature, 400 °C; auxiliary gas heater temperature, 500 °C; spray voltage, 2500 V (for ESI-); and S lens, RF level 30. Full scan data in positive and negative modes were acquired at a resolving power of 70,000 FWHM (full-width half maximum) at m/z 200. A scan range of m/z 100-1000 for the compounds of interest was chosen; the automatic gain control (AGC) was set at 3 × 106, and the injection time was set to 200 ms.
_________________________________________________________________________________________________________Ralph Makhoul, Abdelhakim Bourennane, Luong Viêt Phung, Frédéric Richardeau, Mihai Lazar, Nour Beydoun, Sergueï Kostcheev, Philippe Godignon, Dominique Planson, Hervé Morel, David Bourrier, LAAS-CNRS, Ampère, Laplace, L2n, CNM -France. IEEE 30 th International Conference Mixed Design of Integrated Circuits and Systems, June 29 -30, 2023, Kraków, Poland. N° ANR-21-CE05-0005. Postprint Accepted Author CC BY 4. 0 Manuscript : https://hal. science.
ACKNOWLEDGMENT This national inter-lab research work received financial support from the  French National Research Agency (ANR). The project name is " MUS²-IC" for Monolithic Ultimate power Switching cell in SIlicon Carbide  (n°  ANR-21-CE05-0005 ), over the period  2022 -2025. This work was supported by  LAAS-CNRS  and NanoMat micro and nanotechnologies platforms, members of the  Renatech french national network.
Background High-level exposure to indoor air pollutants (IAPs) and their corresponding adverse health effects have become a public concern in China in the past 10 years. However, neither national nor provincial level burden of disease attributable to multiple IAPs has been reported for China. This is the first study to estimate and rank the annual burden of disease and the financial costs attributable to targeted residential IAPs at the national and provincial level in China from 2000 to 2017. Methods We first did a systematic review and meta-analysis of 117 articles from 37 231 articles identified in major databases, and obtained exposure-response relationships for the candidate IAPs. The exposure levels to these IAPs were then collected by another systematic review of 1864 articles selected from 52 351 articles.
Estimated exposure levels for targeted IAPs In this study, exposure levels to the 11 IAPs were estimated for residences only, as people spent the longest time per day in residences in China from 2000 to 2017, and concentration data in previous systematic reviews for other indoor environments, such as offices and schools, were insufficient to yield reliable estimates. γ RR = 1,C≤C cf 1 + α 1 -exp [-(C -C cf ) δ [ ,C>C cf RR = RR 0 C C0 ,C≤C max RR 0 C max C0 ,C>C max See Online for appendix 1 Indoor PM 2•5 , nitrogen dioxide, sulphur dioxide, ozone, and carbon monoxide originate both outdoors and indoors. The contribution of outdoor origin to indoor concentrations was estimated by the infiltration factor method, which used the outdoor concentration data and an infiltration factor (appendix 1 pp 22-33).
Estimating PAF and attributable burden of disease The PAF of health outcome i attributable to pollutant A (denoted as PAF A,i ) can be calculated as  2, 42  where RR A,i is the relative risk of outcome i for C exposure to pollutant A, and p A is the population fraction for C exposure to pollutant A. Based on the estimated PAF, the attributable burden of disease for pollutant A (denoted as DALY A ) can be calculated as  2, 13  where DALY total,i is the total DALYs for outcome i, as derived from the GBD database (appendix 1 p 34). 2,  43  Then the IAPs can be ranked according to their relative attributable burden of disease so as to prioritise the urgency of their control. Then, to evaluate the proportion accounted for by the health effects of IAPs among all risk factors, the total attributable burden of diseases for IAPs (denoted as DALY IAPs ) was estimated.
Results Median exposure levels of the ten targeted IAPs in all 31 provinces of mainland China in 2000, 2005, 2010, 2015, and 2017 were estimated (appendix 1 p 8). The exposure level of most IAPs decreased from 2000 to 2017 in China, except ozone and radon. Detailed analysis of the spatiotemporal distribution of IAP exposure levels is available (appendix 1 pp 37-38). National-level DALY numbers and rates attributable to IAPs in China are shown in table 1. Generally, there C in =C out F inf + C S PAF A,i = ∫ p A (RR A,i -1)dC C ∫ p A (RR A,i -1)dC +1 C = RR A,i -1 RR A,i DALY A =ΣPAF A,i × DALY total,i i DALY IAPs =ΣDALY i =Σ( 1 -∏ (1 -PAF A,i ) ) × DALY total,i i i A Costs=DALY IAPs × GDP per capita The GBD database shows that the leading three risk factors in China in 2017 were tobacco, high blood pressure, and dietary risks, and the corresponding DALY rates were 4370, 3750, and 3240 per 100 000, respectively.