dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-seq of H295R cells expressing CADM1 variants Extracted RNA from H295R cells transduced with empty pLOC vector (EV), WT CADM1 and mutant CADM1 (both isoforms and both variants), treated with or without Ang II, was sequenced by the Barts and the London Genome Center, Blizard Institute in London. Quality control was performed using Agilent RNA 6000 nano reagent kit and run on an Agilent 2100 bioanalyzer (Agilent). Only samples with a RIN number >0. 8 were sequenced. Library preparation using the mRNA library prep method was performed using NEBNext Ultra II RNA Library Prep Kit from Illumina. Sequencing was performed on Illumina's NextSeq 500 system, using the high-output 150-cycle kit v2. 5 and bcl2fastq Conversion Software. Partek Flow (Partek) was used for RNAseq analysis. Sequences were aligned hg38 with STAR -2.
6. 1d and annotated genes to Ensembl Transcripts release 93 with Partek's own annotation tool. Partek's GSA tool was used to generate lists of differentially expressed genes. Genes with a fold change >1. 5 or <0. 7 and ANOVA P < 0. 001 between WT and variant transduced cells were selected for analysis using DAVID Bioinformatics Resources v. 6. 8 (refs. 92,93).
Acknowledgements We thank  C. E. Gomez-Sanchez  ( University of Mississippi Medical Center, Jackson, MS, USA ) and  D. Trono  ( Queen Mary University of London, London, UK ) for their kind gifts of custom-made CYP11B2 antibody, and pLVTH, pMD2 and psPAX vectors, respectively. We are grateful to  J. Hogenesch ,  L. A. Luong ,  J. Zhou ,  G. Argentesi  and  S. M. O'Toole  for their thoughtful discussions. This work was supported by the following UK funding bodies:  National Institute for Health Research (NIHR, Efficacy and Mechanisms Evaluation  project  14/145/09  to M. J. B. , W. M. D. , M. G. ), the  British Heart Foundation  (PhD studentship  FS/14/75/31134  to M. J. B. supporting S. G. ),  Barts Charity  (Project  MGU0360  to M. J. B. and W. M. D. ),  NIHR Barts Biomedical Research Center  ( BRC-1215-20022  supporting M.
J. B. ),  NIHR   Integrated Academic Training Clinical Lectureship  (to W. M. D. and M. J. B. , supporting X. W. ),  NIHR Cambridge Biomedical Research Center  (BRC-1215-20014 supporting M. G. ),  UKRI  ( MR/S007776/1  to  F. Buss ) and  Wellcome Trust  ( 104955/Z/14/Z  to A. D. ). E. A. B. A. was funded by the  UK-MY Joint Partnership on Noncommunicable Diseases 2019 program  ( NEWTON-MRC/2020/002 ) and the  Royal Society-Newton Advanced Research Fellowship  ( NA170257/FF-2018-033 ). C. P. C. was funded by the  NIHR  as part of the portfolio of translational research of the  NIHR Biomedical Research Center at Barts and The London School of Medicine and Dentistry. E. G. was supported by a  Medical Research Council (UK)  Fellowship, MR/S006869/1. This research used  Queen Mary's Apocrita HPC  facility (https://doi.
org/10. 5281/zenodo. 438045),supported by  QMUL Research-IT. Immunofluorescence images were acquired in the  Blizard Advanced Light Microscopy Suite. The work in Japan was supported by the  Japan Society for the Promotion of Science KAKENHI  grants ( 18K07414  to  Y. Takaoka ,  17K08680  to M. H. ,  21K08557  to K. O. , and  15K15113 ,  18K07049  to A. I. ); the  Ministry of Education, Culture, Sports, Science and Technology-Supported Program  for the Strategic Research Foundation at Private Universities 2015-19 (to A. I. ); grant from  Takeda Science Foundation  (to M. H. ); and grants from the  Ministry of Health, Labour, and Welfare, Japan  ( 20FC1020  to F. S. ). M. Murakami was supported by the  Japan Heart Foundation/Bayer Yakuhin   Research Grant Abroad  and a postdoctoral fellowship of the  Uehara Memorial Foundation.
The work in Paris was supported through institutional funding from  Inserm , by the  Agence Nationale de la Recherche  ( ANR-18-CE93-0003-01 ) and the  Fondation pour la Recherche Médicale  ( EQU201903007864  to M. -C. Z). Other funding bodies include the  National Medical Research Council and Biomedical Research Council, Singapore  (to R. S. F. ); the  National Science Foundation  grant  MC-2011577  (to S. A. M. ); the large research infrastructure project  BBMRI. czLM2023033  (to A. R. );  Else Kröner-Fresenius-Stiftung  in support of the  German Conn's Registry-Else Kröner Hyperaldosteronism Registry  ( 2013_ A182 ,  2015_A171  and  2019_A104  to M. R. ), the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (grant agreement  694913 ; to M.
Data availability The RNA-seq data used to generate Fig. 6 , Supplementary Fig. 10 , and Supplementary Tables  5b  and 6 -9 are provided in Supplementary Data 1-3. The WES raw data are publicly available from the Sequence Read Archive (https://www. ncbi. nlm. nih. gov/sra/docs/) under accession numbers PRJNA732946 and PRJNA729738. Source data are provided with this paper.
EXPERIMENTS In this section, we provide some experiments to demonstrate the tracking ability of αFAPI. Its performance is evaluated in comparison with the state-of-the-art subspace trackers, including FAPI  [28] , GRASTA  [25] , ROBUSTA  [18] , RYAST  [19] , and TRPAST  [20]. Source codes are available online at https://github. com/thanhtbt/aFAPI/.
To measure the tracking ability of ST algorithms, we used the following Subspace Estimation Performance (SEP) metric SEP = tr {U(t) # (I n -A(t)A(t) # )U(t)} tr {U(t) # (A(t)A(t) # )U(t)} , where (. ) # denotes the pseudo-inverse operator and U(t) is the tracked subspace at time t. The smaller the value of SEP is, the better accuracy the tracker has. The experimental results were averaged over 10 independent runs. We set the experimental parameters as follows: data dimension n = 50, rank r = 5, the time varying factor ϵ = 10 -3 , the noise level σ n = 1, the contaminate rate δ = 0. 2, and µ = η = 1. The forgetting factor β was fixed at 0. 99 for all subspace trackers. Both TRPAST and αFAPI are dependent on the αdivergence parameter which was set to 0. 9. Fig. 1  illustrates the performance of αFAPI with different choices of α and p.
Acknowledgements G. -L. L. gratefully acknowledges The  Hong Kong Polytechnic University  ((a)  University Research Facility in Chemical and Environmental Analysis (UCEA) ; (b)  University Research Facility in Life Sciences (ULS))  and the  Hong Kong   Research Grants  ( 15300919 ,  15301120 ,  153021/18P ). K. -L. W. gratefully acknowledges the financial assistance from the  Hong Kong Research   Grants Council Grant  No. 12300021 ,  Dr. Mok Man Hung  Endowed Professorship in Chemistry,  HKBU -Durham Lanthanide Joint Research Center -Lanthanide Tools for Systems Medicine  ( SDF19-1011-P02 ) and the  Centre for Medical Engineering of Molecular and Biological Probes  ( AoE/M-401/20 ). Chen Xie  and  Yue Wu  contributed equally to this work. Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)).
2023. 106231 0305-750X/Ó 2023 The Author(s). Published by Elsevier Ltd. the transition and their capacity to adapt their productive structure, we analyse their vulnerability and riskiness in these different dimensions. Using a Hybrid World Input-Output table for 189 countries, we define potential global sunset industries by taking into account their direct, upstream and downstream emissions, as indirect emissions often constitute a significant part of overall Greenhouse Gas (GHG) footprint  (Downie and Stubbs, 2013). Once these sunset industries are defined, we estimate direct and indirect dependence of the economy on these industries in the aforementioned dimensions for all 189 economies in the EORA 26 database  (Lenzen et al. , 2012; 2013). Specifically, we calculate the share of net foreign exchange generated by sunset industries (via exports discounted by their import content), the share of government revenue generated by taxing sunset industries, and the share of wages and employment these sunset industries create.
Any long-term analysis would require dynamic modelling which, as we stressed above, is beyond the scope of this paper. The main data source for analysing countries' exposure is the EORA-26 Hybrid-MRIO  (Lenzen et al. , 2012; 2013). This database provides information for monetary input-output coefficients for 189 countries and 26 industries, as well as physical data on material use, waste and emissions. The level of sectoral disaggregation, however, is a relevant issue for our analysis. As sunset industries cannot be defined at the 26-industry level, exposure analysis is complemented by EXIOBASE 3xr, which updates EXIOBASE  (Peters, 2008). EXIOBASE 3xr standardized production for 200 products and 164 countries, which allows us to identify which sunset industries are effectively within the potential sunset industries.
Emission intensity can be defined as CO2 emissions by unit of production or as CO2 equivalent, which includes other Greenhouse Gases (GHG) emissions, per unit of production. Once all monetary relations in MRIO tables are in the same currency (USD), the unit of production adopted here is US dollars. Therefore, emission intensity is defined as the relation of CO2 or CO2 equivalent (CO2e) emissions per dollar. The EORA-26 database compiles direct emissions by industry and country from different sources. The emissions calculated by the Emissions Database for Global Atmospheric Research (EDGAR) was compiled in the EORA-26 structure, and hence CO2 emissions data for all the 189 countries are available. 1  By dividing CO2 emissions by total production by industry for a given country, we get the direct emission intensity (e d ) of an industry in a country (i), as follows: e d i ¼ Emission i x i ð1Þ where x i is the production of an industry in a country, and Emissions i is this industry emissions.
Taxation on products by unit of production (t P i ) can be obtained directly from the EORA-26 database by dividing the total taxation on products by the total output t P i ¼ Taxes i x i ð15Þ where Taxes i is the effective tax on products, which includes valueadded taxes and other taxes levied on the production and sale. Besides production taxes, some sectors may contribute more to countries' total revenue than others if profits and wages are taxed at different rates. For most of the countries under consideration, however, we do not have the sectoral taxes levied on profits and wages. Therefore, we consider that tax on profits is uniform across sectors within a country as well as tax on wages, which includes social contributions. Based on the Government Finance Statistics (GFS/IMF), we estimate the sectoral tax contributions on profits and wages as: t Y i ¼ ProfTaxes Profits Profits i x i þ WageTaxes Wages Wages i x i ð16Þ where ProfTaxes and WageTaxes are taxes on profits and wages, respectively (both from the GFS/IMF database), and Profits and Wages are total profits and total wages, respectively.
Profit taxes includes Taxes on income, profits, and capital gains payable by corporations and other enterprises, whilst Wage taxes includes Taxes on income, profits, and capital gains payable by individuals, Taxes on payroll & workforce and Social contributions. The summation of taxes on products and on income per monetary unit of production then gives the sectoral direct contribution to fiscal revenues: ti ¼ tPi þ tiY ð17Þ 3. 4. Socio-economic exposure EORA-26 has data on wages, allowing us to calculate direct and indirect wage contributions of sunset industries directly. Direct sectoral wage contribution by unit of production is given by w i;j ¼ Wages i;j x i ; j ð18Þ Based on this, we can calculate the direct and indirect share of wages in sunset industries (W s ) as W S ¼ 1 À P i w i;j x n i;j w i;j x i;j ð19Þ EORA-26 does not contain employment data but ILOSTAT provides employment data for 177 of the 189 countries available in EORA-26.
The sectoral aggregation is however different between the two data-sources, thus requiring conversion manipulation. We assume that sectoral employment per unit of production in ILOSTAT sectors is the same for all corresponding sectors in EORA-26 for a given country, as presented in Table  1. Therefore, direct employment per output (n) in sector i for country j is given by: n i;j ¼ Empl k;j x k;j ð20Þ where k is the sector according to ILOSTAT database. Once we have direct sectoral employment per output, we can calculate the direct and indirect share of employment in sunset industries (N s ): N S j ¼ 1 À P i n i;j x n i;j P i n i;j x i;j ð21Þ 4. Countries' exposure and vulnerabilities In order to determine each country's exposure, we first define potential sunset industries. We then combine direct and indirect dependencies, along the three dimensions already defined, of each country with its capacity to migrate to other industries or its capacity to absorb losses to evaluate that country's exposure to a low carbon transition.
Defining sunset industries Total CO2 emission-intensity is considered here as the key variable to determine which industries can be considered as sunset industries. However, as mentioned above, some industries may display high emission-intensity, but they should not be considered a sunset industry because they will replace industries with higher environmental impacts. This is the case with Recycling, which is among the most CO2 emission-intensive industries, but is excluded from the list of potential sunset industries, as it is an industry capable of substituting many other industries with much higher environmental impacts. Figs. 2 and 3 presents a box-plot for CO2 emission-intensity for the sectors of the EORA-26 database. From Fig. 2 , which accounts for direct and upstream indirect emissions, we can see that, besides Electricity, gas and water, which is the sector with the highest emission-intensity, three other sectors also present high levels: Petroleum, chemical and non-metallic mineral products; Metal products and Recycling.
Such strategies start at the bottom, where every country is a separate cluster, and then they recursively merge countries into larger clusters based on the smallest intergroup dissimilarity. We utilise Ward's minimum variance method, which minimises the within-cluster inertia, or error sum of squares. The result is a dendrogram, with each hierarchy level representing disjoint clusters of countries. From the resulting dendrogram structure, however, it is hard to pinpoint the optimal number of clusters. For this reason, we use a combination of statistical heuristics as well as economic intuition, in order to identify the potential clusters. 6  Fig. 8  presents the four clusters in the first two main components, after reducing the dimension of the dataset via Principal Component Analysis (PCA).
A. 3 Extensions The sectoral aggregation in the EORA-26 does not allow us to identify precisely what the sunset industries are in each country but only the broad sectors where these industries are located. With the aim of reducing this bias, we used other data sources to estimate the share of effective sunset industries within these sectors, but the technical coefficients do not account for differences within these sectors. One possibility of refining these results is using more disaggregated Input-output tables. Shapirp (2021) , for example, uses the EXIOBASE, which disaggregates 48 countries into 163 industries to measure CO2 intensity for each international and intra-national trade flow in the global economy. This dataset, as well as all other multiregional tables available cover only a few developing economies.
Acknowledgements We would like to thank the  Research Department at AFD , with special regards to  Brian Hartley ,  Paul Hadji-Lazaro ,  Marion Heman ,  Cecile Valadier ,  Emmanuelle Mansart Monat ,  Hélène Djoufelkit , the participants of the conferences where we presented later versions of this paper, and the three anonymous reviewers for their important contribution for the final version of this paper. The usual disclaimer applies.
| Data We extracted monthly pricing data from the World Bank on Brent and WTI crude oil, US natural gas (USGAS), and European natural gas (EURGAS). The GECON of  Baumeister et al. (2022)  can be extracted from Baumeister's website. The monthly dataset covers the period from January 1982 to July 2022 (1982M1-2022M7), for a total of 486 observations. Table  1  depicts the descriptive statistics, and Figure  1  shows the evolution of the first difference in the studied time series.
An accurate prediction would offer information about the course that the macro economy is expected to take in the future, depending on how uncertainty evolves, as oil and gas price volatility reflect economic uncertainty. This information can be incorporated to quantify economic activity. This would enable policymakers to devise appropriate to avert negative effects. Energy price projections may be used by governments to examine the anticipated effects of various policy alternatives as well as the effects of policies on energy production, consumption, and pricing. Policymakers may increase the effectiveness of energy programs and reduce unexpected effects by adopting more precise projections. Third, it is possible for international organizations, central banks, and financial institutions to collaborate in order to share data and best practices, as well as to develop more advanced forecasting models that F I G U R E 6 CumMSFE of EURGAS, USGAS, Brent, and WTI, respectively.
This increases the uncertainty but makes ECOSSE a universal tool that can be applied for large scale simulations even if the data availability is limited. To increase the accuracy in the site-specific version of the model, detailed information about soil properties, plant input, nutrient application and management can be added as available. During the decomposition process, material is exchanged between the SOM pools according to first order rate equations, characterized by a specific rate constant for each pool, and modified according to rate modifiers dependent on the temperature, moisture, crop cover and pH of the soil. The model includes five pools with one of them being inert. The N content of the soil follows the decomposition of the SOM, with a stable C:N ratio defined for each pool at a given pH, and N being either mineralized or immobilized to maintain that ratio.
EUROCOM Top-down estimates at regional scales (up to 0. 25°x0. 25° resolution) for the period 2009 -2018 are taken from three models used within EUROCOM  (Monteil et al. , 2020) : LUMIA, PYVAR, and CSR. The NAME model was excluded as visual inspection of monthly values identified it as a clear outlier. FLEXINVERT was excluded after visual inspection of annual values identified it as a clear outlier (Fig. A5 ). These inversions make use of more than 30 atmospheric observing stations within Europe, including flask data and continuous observations. The CarboScope-Regional (CSR) inversion system results were re-run for VERIFY using the extended period 2009-2020 using four different settings: three network configurations using 15, 40, or 46 sites, and one using all 46 sites but a factor two larger prior error correlation length scale (200 instead of 100 km).
Grass leaves were collected at midday on eight days in different seasons and over a 24-hour period in June for triple oxygen and hydrogen isotope analyses of bulk leaf water. Through a model-data approach, we re-examined the parameters determining the triple oxygen isotope compositions of bulk leaf water. In addition, grass leaves were harvested in spring, summer and autumn for phytolith extraction and triple oxygen isotope analysis to examine which RH average is recorded in 17 O-excessphyto of phytolith assemblages that are formed over growth periods of several months. Further, we investigate the relationship between the triple oxygen isotope composition of phytoliths and leaf water to assess the potential of fossil phytoliths for reconstructing past changes in leaf water.
, Utah, USA), and sky temperature (Tsky). Tplot is the temperature integrated over the field of view of the IR radiometer that covered ~ 90 % of the grass plot surface. Each parameter was extracted in hourly resolution from the COOPERATE database (COOPERATE database, 2022). On sampling days, stomatal conductance (gs) and transpiration, were monitored continuously over the day on a single grass leaf of 4-5 mm width using a Li-6400 XT gas exchange system (LI-COR Biosciences Inc. , Nebraska, USA). To assess the spatial variability of gs, this parameter was additionally measured hourly on the adaxial side of ten leaves of at least 3 mm width, randomly selected on the plot, using an AP4 porometer (Delta-T Devices LTD, Cambridge, UK). In addition, leaf temperature (Tleaf) was measured in situ on the adaxial side of ten grass leaves, randomly selected, in one-hour intervals using an Optris CT IR thermometer (Optris GmbH, Berlin, Germany).
Irrigation water, precipitation and atmospheric water vapor A Picarro L2140-i CRDS, operated in 17 O Dual Liquid/Vapor mode was installed on-site for the experiment. The isotope composition and mixing ratio of water vapor in the air at 0. 4 m above the grass plot was measured for 70 min every 140 min during the spring monitoring and every 280 min during the monitoring in summer and autumn. In between these measurements, the instrument was used for another experiment. The atmospheric water vapor data from the first 10 minutes of each measurement cycle were removed to account for memory effects and provide sufficient time to establish a stable baseline. The remaining 60 minutes were averaged. During the 24-hour monitoring, air sampling was performed continuously without interruption. Liquid water standard measurement runs were performed on a weekly basis.
The mean of four measurement runs of liquid water standards was used to normalize the atmospheric water vapor isotope data to VSMOW-SLAP scale. The calibration protocol is described in detail by  Voigt et al. (2022). In brief, three liquid water standards that covered the expected isotope range of atmospheric water vapor at the study site were analyzed at a water mixing ratio of 11000 ppmv using a Picarro autosampler system (A0325, Picarro Inc. , California, US) coupled to a high-precision vaporizer (A0211, Picarro Inc. , California, US). The liquid standards were injected in a dry air stream, produced by a lubricated mobile air compressor (MONTECARLO FC2, ABAC air compressors, Italy), further dried using two drierite columns combined with a dry ice trap  (Voigt et al. , 2022). Raw isotope compositions of the liquid standards of four consecutive measurement runs were averaged and then corrected to the water mixing ratio of the measured atmospheric water vapor, using the mean of three mixing ratio dependency functions that were determined on site for water mixing ratios between 3000 and 30000 ppmv in May 2021, October 2021 and January 2022 (Fig.
5 ‰, and 12 per meg (1 SD), respectively. The sample measurements were corrected using a quartz laboratory standard analyzed at the beginning of the day until a 17 O-excess plateau was reached and again at the end of the day. The isotope composition of the reference gas was determined against NBS28. For robust comparisons between silica and water isotope compositions, the phytolith data are normalized to VSMOW-SLAP scale  (Outrequin et al. , 2021).