dataesr/scientific-paragraphs-categorization huggingface dataset:
Climate data was retrieved from the National Oceanic and Atmospheric Administration (NOAA)'s National Centers for Environmental Information (NCEI). The database from which the data was retrieved was the "Global Historical Climatology Network -Daily (GHCN-Daily), Version 3" (https://www1. ncdc. noaa. gov/pub/data/ghcn/daily/by_station/)  25, 26. Table  1  showed the search codes and names of five stations in the website. The climate data of region 1 and region 5 were for the periods of 1911-2011 and 1938-2018, respectively.
Then, the alcohol (percent by volume), extract (grams per 100 cc), tannin (grams per 100 cc), and fixed acid (grams per 100 cc) of wine were measured. The must °Brix was measured with a Brix hydrometer floating in a cylinder, must total acid was determined by titration with sodium hydroxide to a phenolphthalein end point, and must pH was measured with a quinhydrone electrode or a Beckman pH meter. In addition, wine alcohol was measured by the hydrometer and reported as percentage by volume, the extract and tannin of wine were measured by means of a special 0° to 8° Balling hydrometer and the Association of Official Agricultural Chemists method  24. Note that the fixed acid of wine are equal to total acid minus volatile Fig. 4  The suspicious data records (highlighted with red rectangle) in the report of Amerine and Winkler  24.
Data records This dataset was entered into three Excel spreadsheets and stored in the Figshare Digital Repository  29  , including daily temperature conditions for different climatic regions, GHDs, quality of musts and wines, and wine tasting notes for different cultivars under diversity environment conditions (Fig. 2 ). They were digitized and curated from four resources, including climate data  25, 26  , the seminal report of Amerine and Winkler  24  , Napa Valley vintage reports  30  , and the crush reports of California  31. The first and core subset was created by digitizing and curating the seminal report of Amerine and Winkler  24  , which provided grape harvest dates (GHDs), the quality of musts and wines, and wine tasting notes for 148 cultivars from 1935-1941 across five contrasting climatic regions of California (Fig.
3 ). This study not only explored the interrelations of environments and cultivars  24  but also provided the foundation to establish the long-lasting and widely used viticultural zoning index, the Winkler index (WI)  27. This index represents temperature characteristics over grapevine growing seasons for a given region and has been well recognized as one of the most important and reliable bioclimatic indices in viticulture. It has been cited at least 537 times when 'Winkler index' is searched in the Web of Science database. To explore whether the climate and grape performance of those regions studied in Amerine and Winkler  24  have been altered over time, we collected GHDs and must °Brix from 1991 through 2018 for four overlapping cultivars (Cabernet Sauvignon, Chardonnay, Merlot and Sauvignon Blanc) in one of the five studied regions (Napa) to create the second and complementary subset of data.
The choice of Napa and the four overlapping cultivars was based on data availability and complementarity with those reported in Amerine and Winkler  24  (Fig. 2 ). After full exploration of available data about climate and grape traits in the previously studied regions and cultivars, we found only Napa had complete records of phenology and °Brix for four wine grapes (Cabernet Sauvignon, Chardonnay, Merlot and Sauvignon Blanc) during 1991-2021. These datasets were obtained from two sources. GHDs were obtained from the Napa Valley vintage reports  30  and °Brix was derived from the crush reports of California in the website of United States Department of Agriculture (USDA)'s National Agricultural Statistics Service (NASS)  31. In detail, there were four cultivars (Cabernet Sauvignon, Chardonnay, Merlot and Sauvignon Blanc) from 1991 to the present in the Napa Valley vintage reports, which described the key phenological periods of grapes, including budbreak, flowering, veraison, and GHDs.
We extracted GHDs and °Brix for four cultivars (Cabernet Sauvignon, Chardonnay, Merlot and Sauvignon Blanc) during the period of 1991-2018 in Napa, meanwhile, the period of 1991-2018 was divided into four periods (1991-1997, 1998-2004, 2005-2011, and 2012-2018) to compare with the past 7-year period (1935-1941). The third subset is climate data in the Excel spreadsheet containing five sheets. Each sheet represents different climatic regions from region 1 to region 5. The source of raw data from which the data was retrieved was the "Global Historical Climatology Network -Daily (GHCN-Daily), Version 3" from the NOAA's National Centers for Environmental Information (NCEI) (https://www1. ncdc. noaa. gov/pub/data/ghcn/daily/by_station/)  25, 26. The CSV files of climate data for each station can be searched based on the station_code in Table  1.
There were 18 variables in the downloaded CSV file, while two temperature-related variables were used in this paper, Fig. 6  The word clouds of wine tasting notes for recommended cultivars in five climatic regions of California. including daily maximum and minimum temperature. The daily average temperature was then calculated as the arithmetic mean between daily maximum and minimum temperature. It is worth noting that the original data of daily maximum and minimum temperature are in tenths of degrees C, as indicated in the 'GHCN-Daily README file' (https://www1. ncdc. noaa. gov/pub/data/ghcn/daily/readme. txt)  33. Furthermore, there were a few missing data for daily maximum and minimum temperature in the original data. The Python software was used to complement the data through calculating multi-year average value.
Then, the complete data was applied to calculating three bioclimatic indices important for grape, including growing season temperature (GST), Winkler index (WI) and Huglin index (HI) for grape-growing seasons. The data of three bioclimatic indices calculated in each region were entered into corresponding sheets of Excel spreadsheet. All data can be found in the data set file stored in the Figshare Digital Repository  29. The metadata for each data subset was shown in a specific sheet named 'metadata' in each subset xls file. Finally, we ensured that we had the right to release the final dataset under an open data license with the citation information indicated.
technical Validation We carefully checked the validity of climate data, GHDs, quality of musts and wines, and wine tasting notes recorded. We carried out manual validation checks to search suspicious data records before submitting data to the database. Shown in Fig. 4 , we found that total acid and tannin of wine for Cabernet Sauvignon, Croetto Moretto, and Mission obviously deviated from most records being one or two magnitudes higher in comparison with other values in the original Table  10  and 21. And these discrepancies were most likely due to a miss typing of the decimal. Thus, we modified the tannin values of 24, 11, and 15 into 0. 24, 0. 11, and 0. 15 with the unit of g/100 cc. Similarly, the total acid value of 60 in Table  21  (number in the original report) was changed into 0. 60 with the unit of g/100 cc.
Images with qualitative results of this method are shown in Figure  5. The fully automated segmentation was performed on a modern computer with an NVIDIA GPU T4 to automatically detect PCLs in abdominal CT scans. The programming language used was Python and the framework for the model development was PyTorch. The sensitivity for all cases was 93. 1% (SD 0. 1%), and the specificity was 81. 8% (SD 0. 1%). Additionally, due to the small amount of some subtypes of pancreatic cysts in the training database (Figure  6 ), we considered it reasonable to divide the whole cohort of patients into 2 big groups: on the one hand, the most dangerous cyst types, bearing malignant potential (IPMN and MCN), and on the other hand, the ones with malignant potential close to 0 (PCYST and SCA). If we consider this classification, the global specificity and sensitivity for the detection of the most dangerous group were 81.
Principal Findings In this study, we applied and validated an AGNet deep neural network to detect PCLs. The aim was to assist imaging specialists for a better diagnosis, and therefore, achieve better determining of treatment plans. First, a pancreatic CT image database with different types of cyst present was created based on the diagnosis of anatomical pathology or an imaging specialist. From this database, we established an AI system for the automatic detection of pancreatic cysts (with further classification) and then validated it in a test experiment. In our study, the sensitivity for the detection of PCL was 93. 1% (SD 0. 1%), and the specificity was 81. 8% (SD 0. 1%), demonstrating that PCLs can be automatically detected by AI with a diagnostic performance comparable to radiologists.
Limitations PCL detection algorithm was trained and tested on data from a single hospital, which limited the available amount of data and hindered the possibility to perform an external validation. As previously mentioned, the data in the training database were divided into 2 big groups (IPMN and MCN vs pseudocysts and SCA) due to the lack of data for each specific subtype of pancreatic cysts. For further validation, not only detection but also classification, more data are needed for the training database for each of the cyst subtypes that we are willing to differentiate. Next steps will be to obtain images from other hardware manufacturers and improve our database. This will need to be studied thoroughly to make the images from different hospitals compatible to each other. Another approach to improve the data set is to widen the samples of each type of cyst to make it more heterogeneous.
ACKNOWLEDGMENTS The authors thank  G. Divitini , and other staff of the  Wolfson Electron Microscopy Suite  for their technical support. We also appreciate insightful discussions with  D. D. Sheka ,  R. P. Cowburn ,  P. Fischer  and  R. Streubel. AFP thanks the  Universities of Cambridge and Glasgow , where part of this research was conducted. All data was measured at  CIRCE  ( BL-24 ) beamline at ALBA synchrotron in collaboration with ALBA staff, proposal no. 2020024091. Preliminary measurements were performed at HERMES beamline at SOLEIL synchrotron in collaboration with SOLEIL staff, proposal nos. 20200879, 20190565 and 20170904. This work was supported by.
Stellar Occultation Observations Stellar occultation data used in this work are obtained using the Cassini/UVIS FUV spectrograph  (Esposito et al. 2004 ). It has a spectral range of 1115-1912 Å with a resolution of ∼1. 5 Å, which covers the spectral extinction features of a number of small hydrocarbon and nitrile species, as well as haze particles (Figure  1 ). The initial data calibration and reduction are described in Chapters 9 and 10 in the Cassini/ UVIS Users Guide (NASA-PDS 2017). The observed photon count spectra are first flat-fielded to remove the different detector sensitivities and then integrated by every 10 observations to improve the statistical accuracy. Using the navigation data from the SPICE (NASA NAIF; Acton 1996), geometry information is derived, including the impact parameter (IP) and its location, as well as the pointing motion.
Seasonal Variation of Chemical Species 18 occultation observations with decent data quality are considered in this analysis (Figure  2  and Table  1 ). They cover a time range from 2006 (T13, L S = 318°) to 2014 (T105, L S = 60°). There is a gap immediately after the equinox between 2010 and 2014 when UVIS stellar occultations were not scheduled during Titan flybys of Cassini. According to the latitude distribution of flybys (Figure  2 ), from 42. 8°S (T56) to 55. 5°N (T47e), observations are grouped for the investigation of seasonal variations: Group 1 in the equatorial region between 20°S and 20°N, including T13, T23, T41i, T47i, T48, and T58; Group 2 south of 20°S, including T21, T35, T41e, T56, T101e, T103, and T105; Group 3 north of 20°N, including T40, T47e, T52, T53, and T101i. LOS abundances are used for the comparison instead of local number densities, as the former ones are the quantities retrieved directly from observations, which therefore have smaller uncertainties and larger well-constrained ranges.
The profiles of the eight chemical species are shown in Figures  5  and 6 , and the values are presented as Data behind the Figure (see Figure  5  and on Zenodo: doi:10. 5281/ zenodo. 6466566). Seasonal variations of the chemical species indicate the changes of physical and chemical processes. The abundance of CH 4 , the second most abundant chemical component in Titanʼs atmosphere with a mixing ratio of a few percent  (Shemansky et al. 2005; Koskinen et al. 2011; Kammer et al. 2013) , is an indicator of the physical properties and dynamics of Titanʼs upper atmosphere when measurements of the most abundant species N 2 are not available. Yung et al. (1984)  proposed the major schemes of the productions of hydrocarbons and nitriles, which are reevaluated and updated using Cassini observations  (Ågren et al.
Vertical Local Density Profiles In Section 3. 2, seasonal variations in Titanʼs upper atmosphere are investigated using LOS abundances of chemical species as a result of their better constraints than local densities. However, vertical profiles of these local densities (Figures 9 and 10) are necessary for future comparison with photochemical models and/or general circulation models (GCMs). The data of these profiles are presented as Data behind the Figure (see Figure  9  and on Zenodo: doi:10. 5281/zenodo. 6466566). As the local density at a given altitude depends on all LOS abundances at IPs that are equal and above this altitude, they usually have larger relative uncertainties and smaller well-constrained regions than corresponding LOS abundances. Although these profiles are relatively poorly constrained, similar seasonal variations can still be identified for most of the species (CH 4 , C 2 H 2 , C 2 H 4 , C 4 H 2 , HC 3 N), while for the others with large LOS abundance uncertainties (C 2 H 6 , C 6 H 6 , HCN), the vertical profile conversion does not result in good constraints.
Comparison with Previous Retrievals Results obtained during two flybys, which are also considered in previous works using UVIS observations, are selected for the comparison of retrieval processes: T41i    West et al. 2011). The time of the spring equinox is denoted as the black vertical dashed line. Vertical uncertainties of UVIS results are defined as half of the vertical resolution of the corresponding flyby (Table  1 ). The LOS profiles are available as Data behind the Figure (see Figure  5  and on Zenodo: doi:10. 5281/zenodo. 6466566). Fan et al. (2019). LOS abundances of four species, CH 4 , C 2 H 2 , C 2 H 4 and HCN, are included in the comparison (Figure  11 ). Comparison of T41i shows good agreement between the two retrieval methods (Figures  11(a)-(d )) within the well-constrained regions, except for HCN at >800 km, where its LOS abundance is relatively poorly constrained.
M. Lopes). All the data and tools in this work are publicly available. Cassini/UVIS data are available on NASA PDS (pds. nasa. gov). The Python package emcee is available online (dfm. io/ emcee/current). We thank Tommi T. Koskinen for sharing Cassini/UVIS results, Jun Cui for sharing Cassini/INMS results and comments, Sandrine Vinatier and Christophe Mathé for sharing Cassini/CIRS results, and Karen Willacy for sharing photochemical model results.
Measures supporting carbon-intensive industries within fiscal stimuli are neglected, as well as recovery investments dedicated to carbon-intensive sectors (40 billion USD for traditional energy infrastructure and 600 billion USD for traditional transportation infrastructures), but could increase the carbon intensity of GDP. Hence, focusing on low-carbon investments may lead to overestimating subsequent emissions reductions. Second, low-carbon investments in recovery packages cannot relate unambiguously to low-carbon investments in IAMs for two reasons: (i) investments are not categorized in the same way between the Oxford Recovery Project database and IAMs, and (ii) the allocation of investments across sectors and regions is different. Low-carbon investments modeled by IAMs are mainly supply-side investments (70% of low-carbon investments (IEA 2020)).
10 ). 4 ). The structure (PDB ID: 6ZUI [https://doi. org/10. 2210/pdb6ZUI/pdb]) was solved by molecular replacement, using E. coli NemR C106 (PDB ID: 4YZE [https://doi. org/10. 2210/pdb4YZE/pdb]) and the cpYFPbased calcium sensor (PDB ID: 3O77 [https://doi. org/10. 2210/ pdb3O77/pdb]) as search models. HypocratesCS consists of a NemR C106S -based sensor domain (green) and a cpYFP domain (yellow) that contains the p-hydroxybenzylidene-imidazolidinone chromophore (orange), designated as "CR2" in the PDB (Fig. 4a ). Superposition of the NemR C106 (PDB ID: 4YZE [https://doi. org/ 10. 2210/pdb4YZE/pdb]blue) and HypocratesCS (sensor domaingreen) shows a similar structure with a root-meansquare deviation (rmsd) of 0. 506 Å for 159 atoms (Fig. 4b ). As such, the insertion of cpYFP had only a minor structural effect on the overall structure of the NemR-sensor domain.
X-ray structure of Asn95 is the connecting residue between the NemR-sensor domain and cpYFP (PDB ID: 6ZUI [https://doi. org/10. 2210/ pdb6ZUI/pdb]). Asn95 is located on the connecting loop between the α4 helix of the NemR-sensor domain and the N-terminus of the cpYFP domain. Asn95 interacts with the backbone of Gln96 and Phe97 in the NemR-sensor domain, and with Ser166, a conserved serine of the ESPT pathway in fluorescent proteins (Supplementary Fig. 12a ). In the Ca 2+ sensor, Case16, the domain connecting residue is Ser24 (Supplementary Fig. 12c )  52. Further inspection of the structure showed that non-covalent interface interactions between cpYFP β-barrel and the NemRsensor domain are relatively limited. In the corridor between both domains, we noticed possible hydrophobic π-π interactions between the rings of Tyr67 and Tyr106, and between the rings of Phe184 and Phe429.
Also, hydrogen bond distance interactions between the side chains of Gln91 and Gln165, Gln19 and Asn131, and between the side chain of Arg24 and the backbone oxygen of Glu133 were observed. Whether these interactions play a role in signal coupling between the sensor domain and the chromophore embedded within the β-barrel is not known and could form a topic for a detailed mechanistical study. The ESPT pathways are different for cpYFPs, YFP, and GFP (Supplementary Fig. 12a-d )  [51] [52] [53]  and consist of a hydrogenbonding network surrounding the chromophore, the conserved serine and glutamate residues, and the conserved water molecules W1 and W2. In cpYFP (PDB ID: 6ZUI [https://doi. org/10. 2210/ pdb6ZUI/pdb]), the phenol oxygen of CR2 interacts with W1 and with Asp109, which might stabilize a negative charge on the phenol oxygen, similar to the phenol oxygen in GFP, where Thr203 takes over the role of Asp109 (Supplementary Fig.
The crystal structure of HypocratesCS is a cpFP-based redox biosensor that reveals its CR2 chromophore environment within its overall structure (Fig. 4a, c ). Overall structure comparison of the β-barrel of cpYFP in Hypocrates (PDB ID: 6ZUI [https:// doi. org/10. 2210/pdb6ZUI/pdb]) with the calcium biosensor, Case16 (PDB ID:3O77 [https://doi. org/10. 2210/pdb3O77/ pdb])  52  , showed that these β-barrels are very similar (rmsd of 0. 282 Å for 190 atoms). Further, both have a CR2 chromophore, while GFP is characterized by a CRO chromophore (Supplementary Fig. 12d ). As such, the cpFP in Case16 is actually a cpYFP, and not a cpGFP as mentioned in Leder et al. 52. The structure of HypocratesCS is not only important for understanding the functioning of this biosensor, but also for revealing the features of other cpYFP-based probes, which show subtle differences in their CR2 chromophore environment (Supplementary Fig.
The fluorescence quantum yields (QY) were calculated according to the following equation QY NemR-cpYFP = QY EYFP •(A EYFP •Em NemR- cpYFP /(A NemR-cpYFP •Em EYFP )), where A was the optical density at the studied absorption maximum, and Em was the emission intensity at the studied excitation maximum (λ ex = 425 nm or 500 nm for NemR-cpYFP variants, and 519 nm for EYFP). QY EYFP is a standard value of 0. 67 according to the literature (Fpbase ID: 8DNLG). The data were analyzed with OriginPro 9. 0 (OriginLab). The purified sensor samples might contain not fully folded and matured molecules, reducing the accuracy of the optical parameters' measurements. Therefore, the molar extinction coefficients and QYs of Hypocrates were investigated in more detail. To estimate the concentration of fully matured chromophores, the samples of Hypocrates and EYFP in PBS were mixed with 1 M NaOH at a volume ratio of 1:1 and incubated for 5 min.
The measurements were performed with the use of a Varian Cary Eclipse Fluorescence Spectrophotometer controlled by Cary Eclipse Scan 1. 1(132) Application. The data were analyzed with OriginPro 9. 0 (OriginLab). To investigate whether Hypocrates oxidation is reversible, purified protein samples (0. 5-2 μM) were treated with saturating oxidant concentrations (5-50 μM) and incubated for 5 min, after which the fluorescence excitation spectra were recorded. Next, DTT was added to the reaction mix to the final concentration of 1-5 mM, and the probes were incubated for 40-60 min prior to the spectra registration. In some cases, two additional control probes (intact and with the same oxidant concentration) were prepared and incubated for the same time to control for possible artifacts caused by prolonged atmosphere exposure.
00. 03). The data were analyzed with OriginPro 9. 0 (OriginLab). Biosensor secondary structural changes with circular dichroism. Changes in the overall secondary structure of Hypocrates between its reduced and oxidized (NaOCl or H 2 O 2 ) forms were evaluated with circular dichroism (CD) spectroscopy. The protein was reduced with 30 mM DTT for 30 min at room temperature. A Hi-Trap® desalting column (GE Healthcare), equilibrated with 20 mM sodium phosphate buffer pH 7. 4, was used to remove excess DTT. To prepare the oxidized samples, Hypocrates (25 µM) was incubated for 10 min at room temperature with different concentrations of NaOCl (1:1, 1:5, and 1:10 ratios) or H 2 O 2 (1:1, 1:3, and 1:6 ratios of protein to oxidant concentration), with a reaction buffer composed of 20 mM sodium phosphate, pH 7.
4, and 200 mM sodium fluoride. Micro Bio-Spin ® Chromatography Columns (BIO-RAD), equilibrated with the same buffer, were used to remove the oxidants. Following sample preparation, a Jasco J-715 spectropolarimeter controlled by J-700 1. 07. 00 software was used to analyze 4 µM of each sample at 25 °C in a quartz cuvette with a 1-mm path length. Far-UV CD spectra (190-260 nm) were measured, and the data were analyzed with GraphPad Prism8 and OriginPro 9. 0 (OriginLab). To determine whether the overall secondary structure could be restored, DTT was used. NaOCl-oxidized Hypocrates (1:10 protein/oxidant ratio) was incubated with 200 µM DTT for 5 min at room temperature. The background from the buffer and the presence of 200 µM DTT were subtracted. pKa determination of reduced and oxidized NemR-cpYFP versions.