dataesr/scientific-paragraphs-categorization huggingface dataset:
The raw detected signal was acquired at 12 kHz and then demodulated in real time to reconstitute the excited isosbestic (405 nm) and calcium-dependent GCaMP (465 nm) signals. Contact between the tongue and the lick port during spontaneous licking bouts were registered via an SEN-1204 capacitance sensor (Sparkfun) connected to the Arduino Uno R3 microcontroller board (Arduino) and acquired at 12 kHz via the Doric fiber photometry console. Automated markless pose estimation. Spontaneous and light-evoked licking sequences were filmed at portrait (Fig. 4a ) and profile angles (Fig. 4d ) with a CMOS camera (Jai GO-2400-C-USB) synchronized by a 5 V TTL pulse. The acquired frames (800 × 800 pixels, 120 fps,) were streamed to a hard disk using 2ndlook software (IO Industries) and compressed using a MPEG-4 codec.
Data analysis Fiber photometry. We analyzed behavioral and fiber photometry data using customwritten Python scripts (Python version 3. 7, Python Software Foundation). Fiber photometry and photostimulation data were resampled to 120 Hz to match the acquisition rate of video recordings. Fiber photometry and photostimulation data were resampled to 120 Hz to match the acquisition rate of video recordings. Photometry data were first processed by applying a low-pass filter (Butterworth) to the calcium-dependent 465 nm and isosbestic 405 nm signals with a 20 Hz cut-off. The 465 nm signal was then normalized using the function ΔF/F = (F-F0)/F0, in which F is the 465 nm signal, and F0 is the least-squared mean fit of the 405 nm signal. For each recording session in one animal, correlations between lick port contact and calcium signals were computed for all possible shifts at 120 Hz spanning from -10 to +10 s, producing one curve per session (Fig.
For fiber photometry experiments, the jaw position was normalized to its average location during quiescent periods between 1-3 s long. As the tongue was only present during stimulation of IRt Phox2b or spontaneous lapping, we normalized the tongue distance empirically by observing the first detected instance of tongue protrusion that succeeded jaw-opening events. All positional estimates of the tongue that had a probability <5%  71  were then set to the empirically determined baseline to filter out aberrant estimates of the tongue position during periods of the recording where it was not visible. Licking frequency. For data collected during optogenetic experiments, we first obtained the onsets of each lick during the 1000 ms stimulation window. These onsets were identified as the peaks of the first derivate of each lick within a lick bout.
Lick frequency was then calculated as the number of lick events divided by length of time from the last lick to the first lick within a lick bout. For data collected during fiber photometry, lick frequency was determined by the number of contact events of the capacitance sensor divided by the length of time from the last to the first lick within a lick bout. Statistical analysis. All data are reported as mean ± s. e. m (shaded area). P values for independent samples comparison were performed using a two-tailed Student's t-test.
Acknowledgements We thank the animal facility of IBENS, the imaging facility of IBENS (supported by grants from  Fédération pour la Recherche sur le Cerveau ,  Région Ile-de-France DIM NeRF  (2009 and 2011), and  France-BioImaging ),  Ofer Yitzar  for the AAV1-EF1a-DIO-stCoChR-P2A-mScarlet vector. The mouse Rosa FTLG mutant line was established at the  Institut Clinique de la Souris (Phenomin-ICS ) in the Genetic Engineering and Model Validation Department. Funding is from  CNRS ,  École Normale Supérieure, INSERM, Association Nationale pour la Recherche   ANR -15-CE16-0013  (to J. -F. B. ),  Association Nationale pour la Recherche   ANR-17-CE16-0006  (to J. -F. B. ), and  ANR-19-CE16-0029  (to G. F. ),  Fondation pour la Recherche Médicale   DEQ2000326472  (to J. -F. B. ), " Investissements d'Avenir" program   ANR-10-LABX-54   MEMO LIFE  and  ANR-11-IDEX-0001-02 PSL Research University ), and  Région Ile-de-France  (to S.
The electroactive GLAD-TiO 2 surface was delimited by depositing nail vanish to define a rectangular geometric electrode area S of 0. 56 cm 2. For Nafion TM coating, a Nafion TM D-520 dispersion, 5% w/w in water and 1-propanol, was diluted with a 1:1 mixture of water and ethanol in a 1:3 volume ratio. 20 µL of this mixture was drop-casted on the working electrode surface and then left to dry for 30 min at room temperature and under ambient air. Structural characterization. X-ray powder diffraction (XRD) measurements were carried out with a Bruker model D8/Discover X-ray diffractometer using a filtered Cu Kα radiation source (λ = 1. 54178 Å). The 2θ range for each sample was from 20° to 50° with a step size of 0. 02°. The collected data was carefully analyzed using EVA tm software. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to investigate the morphology of the GLAD electrodes and the size of TiO 2 crystallites.
) with low spring constants of 5. 0 N/m and 1 nm radius were used. The area fractions of gaps between posts were determined by image analysis using ImageJ software. To examine internal structural changes within the GLAD-TiO 2 electrodes after galvanostatic cycling, a focussed ion beam (FIB) process was perfomed using a Zeiss NVision 40. This tool is a dual-beam instrument combining a Schottky field emission SEM for imaging with a focused beam of gallium ions to remove sections of the film. To protect the film surfaces and avoid charging issues, the samples were pre-coated with a 200 nm thick layer of Au-Pd using a Gatan precision sputtering system. Ion milling was performed with a 30 kV gallium ion beam operating with a beam current of 80 pA. Electrochemical characterization. Cyclic voltammetry and galvanostatic cycling experiments were performed in a standard three electrode cell configuration using a BioLogic VSP potentiostat controlled by the EC-Lab software.
The model detailed in the SI is similar to the one we had previously reported,  56  except for the boundary conditions at the TiO 2 /electrolyte interface (at z = 0) where the standard Butler-Volmer equation was here combined with a Frumkintype intercalation isotherm to account for the average interactions between the insertion sites in TiO 2 , thus leading to eq. S7. In addition to the capacitive and faradaic current contributions, we have also included in the model the irreversible current response resulting from the HER (see SI for details). The model was solved numerically by a finite difference method implemented in the software Comsol Multiphysics (see SI), allowing us to simulate and predict the CV plots for any parameter conditions. The simulated CVs shown in Figure  3A  were obtained using the set of parameters listed in Table  S1.
We did not consider snow cover in the model, which was adequate for most of the measurement data in the analyzed time period from 2016 to 2020. An exception is displayed in Fig. 3 , showing the damped signal of RW01 due to snow cover. Besides spring 2017, RW01 was influenced by snow over a period of 2 to 3 months in spring 2019 and 2020. In the other measurement sites, snow cover was only briefly observed in May 2019 (RW05) and in May 2020 (RW05, RW06, RW08). Further evaluations on the possible influence of snow cover are given in the Supplement, where we present model runs considering snow cover in the rock walls following the model approach of  Magnin et al. (2017).
org/10. 5194/tc-15-2491-2021 The Cryosphere, 15, 2491-2509, 2021  perature at the rock walls subject to their distance to the open water body of the fjord. As sea ice coverage enlarges the distance to the open water body, we added an additional mean distance (Table  3 ), estimated by analysis of the web camera time series from the mountain Zeppelinfjellet  (Pedersen, 2013). We used the water temperature of Kongsfjorden recorded by the AWIPEV underwater observatory at a 12 m depth to estimate the longwave heat emission of the water body. The data provide a time series of water temperatures for the entire period from October 2015 to August 2020 with a resolution of 1 h  (Fischer et al. , 2018a (Fischer et al. , -c, 2019 (Fischer et al. , , 2021a, b), b). We simulated long-term climate impacts of three different representative concentration pathways (RCPs) RCP2.
Model validation We compared monthly average values of measured rock surface temperature RST to the model results of the near-coastal scenario (RW01, RW02, RW03), the open fjord scenario (RW04, RW05, RW06, RW07) and the frozen bay scenario (RW08). The measured RST was reproduced closely with the applied model setup, especially for temperatures near the freezing point (Fig. 5 ). Besides the visually good agreement of Fig. 5 , a root-mean-square error (RMSE) below 1. 2 • C, the bias (-0. 5 to 0. 4 • C), the coefficient of determination R 2 (above 0. 97) and the Nash-Sutcliffe efficiency NSE (above 0. 96) for all locations confirmed a good reproduction of the measured data (Table  6 ).
Acknowledgements. We acknowledge funding by  EU Horizon 2020  and the  Research Council of Norway. We are grateful to  Marion Maturilli  for providing us with data of the BSRN station and to  Philipp Fischer  for the support with datasets of the  AWIPEV Underwater Observatory. Financial support. This research has been supported by  EU Horizon 2020  (Nunataryuk, grant no. 773421 ) and the  Research Council of Norway (FrostCliff , grant no. 317378 ). Review statement. This paper was edited by  Jürg Schweizer  and reviewed by  Alessandro Cicoira  and one anonymous referee.
Code availability. The source code is available at https://doi. org/10. 5281/zenodo. 4277514 (Schmidt, 2020). Data availability. Data from field observations are available in the NIRD Research Data Archive: https://doi. org/10. 11582/2021. 00032 (Schmidt, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-15-2491-2021-supplement.
Comparison of the control simulations with observational estimates We evaluate the simulated IAV of NBP, soil moisture, temperature and VPD against available observations in Supplementary Figs. 6, 9-11. For NBP IAV (Supplementary Fig. 6 ), we note that although observational estimates of NBP variability exist, they do not agree well with each other, reflecting our limited knowledge of net carbon fluxes globally  52, 53  (Supplementary Fig. 6g , 'obs vs obs'). To focus on time periods in which these observational datasets are more reliable globally, we use the period 1980-2010 for the FLUXCOM RS+METEO dataset and the period 2000-2018 for the CAMS atmospheric CO 2 inversion. We show that models correlate with these observational estimates as much as the observations themselves correlate with each other (Supplementary Fig.
1, 3 ). We evaluate spatial patterns of IAV for soil moisture, temperature and VPD against available observational datasets in Supplementary Figs. 9-11. The simulated soil moisture IAV patterns agree reasonably well with the total soil moisture from the ERA5-Land reanalysis  54  and with satellite observations of shallow soil moisture (5-10 cm depth) from the ESA CCI Combined product v4. 5  55  (Supplementary Fig. 9 ). Regarding temperature and VPD IAV, we find that models and observational sources  56, 57  are in reasonable agreement (Supplementary Figs. 10, 11). Finally, we also evaluate spatial patterns of global long-term mean GPP, which is arguably better constrained by observations than long-term mean NBP. We find that the models agree very well with the observational data  52, 58  in terms of spatial patterns (Supplementary Fig.
1, 2 ) and local (Supplementary Fig. 3 ) scales, although the overall NBP variability is generally underestimated because of the reasons mentioned above. We also apply this statistical approach to two fully independent observational estimates of NBP fluxes. We use the FLUXCOM RS+METEO dataset (GSWP3 version) over the period 1981-2010  52  , which is a machine learning-based upscaling of flux tower measurements, and the CAMS v18r3 dataset  53  , which is an atmospheric CO 2 inversion, over the period 2000-2018. We find that the overall partitioning of global NBP IAV between the different drivers is similar to what models are suggesting (Extended Data Fig. 8 ). The ability of the regression to reproduce these observational estimates is shown in Supplementary Fig. 13. For FLUXCOM, the sensitivity analysis is able to capture the variability almost perfectly.
Sensitivity analysis compared to observational estimates. a, Contribution of different meteorological drivers to global NBP IAV as estimated from the control simulations and from two independent observational products. Here, NBP T&VPD is not separated into a LAC and non-LAC contribution as in Fig. 3b (because this cannot be done with the observational datasets). b, Same as Fig. 3b, but based on FLUXCOM. c, Same as Fig. 3b, but based on CAMS. Extended Data Fig. 9 | Contribution of LAC hotspots to global NBP IAV. Global NBP IAV from the control experiment (CTL) calculated over all land grid cells versus only over the LAC hotspots identified in Fig. 4. Extended Data Fig. 10 | Tropical temperature in CTL versus experiment A. a, IAV in tropical (24° N to 24° S) mean land temperature in model experiments with and without variability in soil moisture (similar to Fig.
Acknowledgements This research was funded by a  Postdoc. Mobility fellowship of the Swiss National Science Foundation  ( P400P2_180784 ). C. F. acknowledges funding through  NASA   IDS grant   80NSSC17K0687. P. G. acknowledges funding from  NASA   80NSSC18K0998  and  European Research Council  synergy grant  USMILE ERC CU18-3746. P. C. acknowledges funding from the  ANR  CLAND convergence institute. S. I. S. acknowledges partial support from the  European Union 's  Horizon 2020 Research and Innovation Program  (grant agreement  821003 (4C) ). We thank all modelling groups who participated in the GLACE-CMIP5 experiments and conducted the model runs, in particular  F. Cheruy ,  S. Hagemann  and  D. Lawrence. We also thank  G. Bonan ,  J. K. Green ,  M. Hirschi ,  D. Lawrence ,  D. Miralles ,  U.
Data availability GLACE-CMIP5 model outputs can be obtained from S. I. S. (sonia. seneviratne@ethz. ch). FluxCom data are available at http://www. fluxcom. org/CF-Download/. CAMS data are available from the Atmosphere Data Store at https://atmosphere. copernicus. eu/data. ERA5 and ERA5Land data are available from the Climate Data Store at https://cds. climate. copernicus. eu. VPM-GPP is available at https://doi. org/10. 6084/ m9. figshare. c. 3789814. ESA CCI Soil Moisture is available at https:// www. esa-soilmoisture-cci. org. CRU TS data are available at https:// crudata. uea. ac. uk/cru/data/hrg/. GSWP3 data are available at https:// doi. org/10. 20783/DIAS. 501. The corresponding author can also be contacted at vincent. humphrey@bluewin. ch. Source data are provided with this paper.
Code availability Code and documentation for CCSM4 is publicly available at https:// www. cesm. ucar. edu/models/ccsm4. 0/. Code and documentation for ECHAM6 (MPI-ESM) is available for scientific users at https://mpimet. mpg. de/en/science/modeling-with-icon/code-availability. Code and documentation for the GFDL model is publicly available at https://www. gfdl. noaa. gov/modeling-systems-group-public-releases/. Code and documentation for the IPSL model is publicly available at https://cmc. ipsl. fr/ipsl-climate-models/ipsl-cm5/. Model outputs were processed using the software Matlab 2019a.
Extended Data Fig. 8 | 8 Sensitivity analysis compared to observational estimates. a, Contribution of different meteorological drivers to global NBP IAV as estimated from the control simulations and from two independent observational products. Here, NBP T&VPD is not separated into a LAC and non-LAC contribution as in Fig. 3b (because this cannot be done with the observational datasets). b, Same as Fig. 3b, but based on FLUXCOM. c, Same as Fig. 3b, but based on CAMS. Extended Data Fig. 9 | Contribution of LAC hotspots to global NBP IAV. Global NBP IAV from the control experiment (CTL) calculated over all land grid cells versus only over the LAC hotspots identified in Fig. 4. Extended Data Fig. 10 | Tropical temperature in CTL versus experiment A. a, IAV in tropical (24° N to 24° S) mean land temperature in model experiments with and without variability in soil moisture (similar to Fig.
See Appendix for full survey questions, in addition to standardized questions on age/ gender and language preference. Full data set and supporting documentation is available for open use and download at the Harvard Dataverse: dataverse. harvard. edu/dataset. xhtml?persistentId=doipercent3A10. 7910percent2FDVNpercent2F8NVOYQ. We note that because of RIWI limitations, full Likert scale question formats were not used and thus only indirect comparisons with similar surveys  (Oetzel and Getz, 2012)  were possible.
Research and societal implications This data set is designed to be a resource for scholars to analyze either in a standalone format or in conjunction with other databases/surveys/sources. Full data set and supporting documentation is available at the Harvard Dataverse: dataverse. harvard. edu/dataset. xhtml? persistentId=doipercent3A10. 7910percent2FDVNpercent2F8NVOYQ. Regarding possible uses of data, we see at least three research possibilities. First, this data may offer context and conditionality to the "business as conflict profiteer/ peace profiteer" literature. This literature comes down on all sides of the spectrum if firms are "good guys" or "bad guys" in conflict, with prolific examples. But, what most of the literature assumes is that conflictunless you are dealing in a sector like weaponsis bad for business, which is in turn bad for society (e.
Six out of the 97 high-confidence non-Mendelian lipid genes (HP, STAT3, POR, PON1, FASN, CYP7A1) have over 1000 lipid-related publications. Another 22 high-confidence non-Mendelian genes have 100-999 lipid-related publications. The remaining 63 high-confidence genes have 1-99 lipid related publications. Six high-confidence non-Mendelian genes (ALS2CR12, BRI3, EML3, HIST1H2BF, BC1D13, and TMEM150A) had no lipid-related publications retrieved by the text-mining algorithm, but we did find a subtle implication of ALS2CR12 to lipid metabolism  42  , suggesting potential candidate genes, implicated by most of the approaches we used, for future experimental work (Table  S5 ). We performed a comprehensive look-up of all prioritized lipid genes in the Therapeutic Target Database 2020  43  and found 9,438 drugs targeting at least one of our prioritized lipid genes (Table  S6 ).
Polygenic scores for lipid phenotypes and phenome wide association scans We have previously reported that a polygenic score (PGS) for LDL-C was most informative when generated from the multi-ancestry GWAS, and that the multi-ancestry PGS performed equally well in European-ancestry Americans, African-ancestry Americans, and continental Africans  24. Using a similar approach, we generated PGS for the other four lipid traits (Methods). We next performed a phenome-wide association scan (PheWAS) for the multi-ancestry lipid PGS (LDL-C PGS previously reported  24  ) to identify pleiotropic effects of lipids with other traits in the European subsets of the UK Biobank and the Million Veteran Program (MVP) cohorts. We compared the effect sizes from the PheWAS analysis between UK Biobank and MVP per lipid PGS and observed a moderate correlation between the two datasets (Figure  S3 ).
All other genome-wide significant variants within this window were discarded from the list of sentinel variants, and similar windows were defined for the remaining genome-wide significant variants. We ran an eQTL colocalization using GTEx v8 eQTL summary statistics within each window for all lipid traits. For each of the 49 GTEx tissues, we first identified all genes within 1Mb of the sentinel variant, and then restricted analysis to those genes with eQTLs ('eGenes') in that tissue (FDR < 0. 05). We used the R package 'coloc' (run on R version 3. 4. 3, coloc version 3. 2. 1)  88 with default parameters to run co-localization between the GWAS signal and the eQTL signal for each of these cis-eGenes, using as input those variants in the defined window, i. e. all variants present in both datasets.
Transcriptome-wide association studies (TWAS) For our transcriptome-wide association analysis (TWAS), we integrated the results of our GWAS with eQTL summary statistics from GTEx v8. The SPrediXcan software  90  allows us to integrate these two datasets using only summary statistics from GWAS without needing individual-level genotype data. We used the multi-ancestry lipid GWAS summary statistics  24  and harmonized them with the GTEx summary statistics. Then we performed the TWAS using the eQTL models estimated on GTEx v8 expression data. For each of the 49 GTEx tissues, we identified 'significant genes' those genes with P-values more significant than an FDR threshold of 0. 05.
Mouse knock-out lipid phenotypes silver set genes Human gene symbols (9,557 unique genes) were mapped to gene identifiers (HGNC) and their corresponding mouse ortholog genes were obtained using Ensembl (www. ensembl. org). Phenotype data for single-gene knock-out mouse models were obtained from the International Mouse Phenotyping Consortium (IMPC) (www. mousephenotype. org) latest data release 12. 0 (www. mousephenotype. org/data/release). The knock-out mouse models were primarily produced by IMPC but also include some models that have been reported from the relevant literature and were curated by Mouse Genome Informatics (MGI) data release 6. 16 (www. informatics. jax. org). For each mouse model, reported phenotypes were grouped using the mammalian phenotype ontology hierarchy into broad categories relevant to Lipids: growth and body weight (MP:0001259), lipid homeostasis (MP:0002118), cholesterol homeostasis (MP:0005278) and lipid metabolism (MP:0013245).
CAD/T2D colocalization analysis with lipid traits We used R package coloc v3. 2. 1  88  to perform summary statistics-based colocalization via a Bayesian approach and test whether the 5 lipid traits share common genetic causal variants with CAD or T2D. We first defined a window of +/-100KB around each index variant  24. Then for each window of the 10 pairs of traits, we ran colocalization with default parameters using those SNPs present in both datasets. A colocalization posterior probability of PP4 > 0. 5 was used to define those loci that show significant colocalization.
For each trait, the single best score was selected based on the adjusted r 2 calculated in UK Biobank of the linear model for the lipid trait with the risk score and age, sex, batch, and PC1-4 as covariates. This corresponded to PRS-CS for HDL-C and nonHDL-C and pruning and thresholding for LDL-C (r 2 =0. 1, p-value=5x10 -4 , 500kb), TG (r 2 =0. 1, p-value=5x10 -3 , 500kb), and TC (r 2 =0. We constructed a weighted PGS for each of the lipid traits, based on the corresponding genome-wide significant multi-ancestry index variants. We used the PheWAS package in R  97  to map ICD-10 codes from hospital records into clinically relevant phenotypes (phecodes) and to implement these association analysis, while adjusting for sex, age, 10 genetic principal components, genotyping array (for UK Biobank only) in each cohort.
Meta-analysis of MVP and UK Biobank PheWAS results We combined, via meta-analysis, PheWAS lipid-specific PGS results for all intersecting phecodes and biomarkers between UK Biobank and MVP (Europeans only) per lipid trait. We used ICD10-based phecodes and manually-matched biomarkers to identify intersecting phenotypes between the two datasets. We restricted our meta-analysis to phenotypes that had at least 100 samples (total number for continuous traits or number of cases for binary traits) in each cohort. After the meta-analysis, we excluded phenotypes that had less than 500 combined samples (total number for continuous traits or number of cases for binary traits), to avoid reporting spurious results  98. That resulted in a total of 773 phenotypes (739 phecodes and 34 biomarkers/measurements).