dataesr/scientific-paragraphs-categorization huggingface dataset:
cerevisiae strains, a complete Zn2C6 domain is located upstream of the MHD-coding genomic region. This highlights an unexpectedly high rate of gene prediction errors in such closely related genomes. This high error rate was confirmed for the MHD-only TFs present in the expert curated Swiss-Prot database, since a DBD could be identified for all the proteins whose corresponding genomic sequence was available. Finally, concerning the TrEMBL proteins, our error-tracking protocol showed that 89% of the MHD-only TFs exhibit upstream genomic sequence regions coding for a DBD. These analyses, showing that MHD-only TF sequences result predominantly from prediction inaccuracies, are in line with the error rate of 66% observed in the manual analysis of MHD-only TF in Aspergillus flavus  [28]. Similarly, a recent high throughput RNA-sequencing experiment in fungal species identified a large proportion of prediction errors in TF sequences  [21].
Most importantly, the true number of MHD-only genes remains to be determined, if MHD can indeed act independently  [5, 7]. The notion of errors in public protein databases is a recurrent problem  [30] [31] [32]  and substantial efforts have been invested to identify and correct genome annotation errors  [33] [34] [35]. Some important causes of erroneous protein sequences have been identified, including the genome sequence quality and gene structure complexity  [36] , as well as redundant or conflicting information in different resources or in the literature  [32, 37]. Consequently, it has been estimated that 40 to 60% of the protein sequences in public databases are erroneous  [38] [39] [40]. Typical errors include missing exons, non-coding sequence retention in exons, wrong exon and gene boundaries, fragmenting genes, and merging neighboring genes.
Comparison to other global inventories such as the Hemispheric Transport of Air Pollution (HTAP) inventory  (Janssens-Maenhout et al. , 2015)  or the Community Emissions Data System (CEDS) inventory  (Hoesly et al. , 2018)  are not included in this analysis. HTAP was not included because it does not provide NMVOC speciation information  (Janssens-Maenhout et al. , 2015). CEDS was not included for reasons of scope, because of strong similarities between EDGAR and CEDS with respect to NMVOC distribution and speciation. For example, the data from the EDGAR inventory were used in CEDS for default non-combustion data sources where national inventory data were not available, such as for solvent use in the case of NMVOCs; the proxy data for gridding were also primarily from EDGAR  (Hoesly et al. , 2018).
Methods We utilize the VOC to acetylene ratios of NMVOCs in urban areas to evaluate emission inventories with both global and regional coverage and assembled a set of speciated NMVOC measurement data from urban areas around the globe. A conceptual overview of the process is provided in Figure  S1  in Supporting Information S1 and described in detail in the text of this section. We used the VOC/acetylene ratio rather than VOC/CO ratio as our metric for comparison with EIs due to the former being more widely available than the latter. We also compiled a list of NMVOCs that were routinely measured across cities and over time. The NMVOCs considered include ethane, propane, n-butane, i-butane, n-pentane, i-pentane, ethene, propene, ethyne, benzene, toluene, o-xylene, and m + p-xylene. The cross-listing of "species" in the EDGAR inventory with ambient measurements is provided in Table  1.
NMVOC Data Figure  1  shows the urban locations of the NMVOC measurement data included in this study. Table  S1  in Supporting Information S1 contains a more detailed list including the location latitude and longitude, dates, and sources (citations) of the 80+ locations for the measurement data. We tried to rely on the peer-reviewed literature as much as possible. Data were identified through a literature search (prior to 2019) and via the GEIA network. The data include measurements from short-term field campaigns as well as longer-term routine monitoring. Measurement techniques include both online and offline measurements, such as proton transfer reaction-mass spectrometry (PTRMS), whole air canister samples analyzed by gas chromatography, and online gas chromatography with flame ionization detection.
Ratios We calculated ratios between individual NMVOCs to acetylene for both measurements and emission inventory data to facilitate comparison, following previous work  (Borbon et al. , 2013; Warneke et al. , 2007). While ratios to CO have also been used in the past  (Hassler et al. , 2016; von Schneidemesser et al. , 2010) , the datasets available for use are much more limited, owing to the requirement that CO is measured in parallel to the NMVOC measurements. CO and acetylene have been used as they are tracers of combustion sources, specifically vehicle exhaust in urban areas, and are relatively inert  (Warneke et al. , 2007) , but may have substantial secondary sources in some regions  (Simon et al. , 2018). For comparison with the EI data, the year and month of the measurement data were matched to the year and month of the EI data, as EDGAR provides monthly data from 1970 to 2012 (EDGARv4.
3. 2) and 1970-2018 (EDGARv6. 1). The temporal matching is done to capture seasonal differences, as well as the evolution of emission sources over time. A number of assumptions were made for the sake of simplification in the first step, including an assumption that urban measurements represent relatively fresh emissions. For this reason, we used ratios that were estimated using the photochemical age method, as well as simple ratios using linear regression fits from data in urban areas, where a photochemical age estimate was not available, as well as ratios that we calculated based on reported (hourly) VOC data. Possible nighttime ratios are used because under these conditions photochemistry is limited. The data therefore include ratios that were reported as ratios in the published literature, as well as ratios that we calculated based on published mixing ratio data.
, 2010) , as well as daily (24 h) and nighttime ratios  (Dominutti et al. , 2020)  (see also Figure  S3  in Supporting Information S1 for a comparison from one site included in this work). The ratios from the EI reflect the ratios present in the emission inventory grid cells above the urban areas, matching the coordinates to where the measurements were made. Different numbers of grid cells were extracted from EDGAR v4. 3. 2, including 1 cell that overlapped the measurement site as well as 9 and 25 cells that overlap and surround the measurement site. Each grid cell is 0. 1° × 0. 1° spatial resolution. While all were evaluated, no substantial differences were observed for the number of cells extracted (data not shown) and therefore the nine-cell extraction is presented in the rest of the paper.
While observational ratios were included for London through 2012 for the comparison with EDGAR, the comparison to the NAEI only went through 2009 owing to the availability of the speciation information for the inventory. The sectoral distribution at national level for total NMVOCs was retrieved from https://naei. beis. gov. uk/data/data-selector-results?q=155503 (accessed date 3. 3. 22) for 2012 (UKNAEI, 2012). The year 2012 was chosen because both monitoring and campaign data exist for London during that time. US: The anthropogenic VOC emissions for the US are from recent studies for Los Angeles  (McDonald et al. , 2018)  and New York City  (Coggon et al. , 2021). Briefly, mobile source emissions are constructed using a fuel-based approach, and volatile chemical product (VCP) emissions from a mass balance of the petrochemical industry are described in detail in  McDonald et al.
(2018). Speciation profiles are based on literature values for road transportation  (Gentner et al. , 2012 (Gentner et al. , , 2013))   et al. , 2014, 2019). The total VOC emissions were calculated based on a technology-based methodology, as detailed described in a series of papers  (Li et al. , 2014 (Li et al. , , 2019;; Zheng et al. , 2018). Specifically, an accurate speciation framework of VOC to individual species for each source category were developed by  Li et al. (2014) , which reduced the uncertainties of inaccurate mapping processes. A composite profile database was established by integrating the local measured profiles and supplementing with SPECIATE v4. 5 (available at https://www. epa. gov/ air-emissions-modeling/ speciate-version-45-through-40, last access: 21 April 2018) database for absent sources  (Li et al.
1029/2022JD037906 9 of 16 emission factors. The speciation data applied may also be more representative, although the spatial resolution of the speciation information tends to be more coarse than total emissions information (e. g. , speciation at national level vs. total emissions by sector at 1 km × 1 km resolution). For example, the UK NAEI gets its speciation information from detailed work by Passant specific to the UK  (Passant, 2002). By comparison, the EDGAR inventory NMVOC speciation for Europe is based on the Institute of Energy Economics and Rational Energy Use database, as well as information from the EMEP/EEA guidebook for road transport  (Huang et al. , 2017 and references therein). In many cases, however, the speciation information applied is not based on region specific data (e.
g. ,  Chen et al. , 2010). For example, given the lack of data for Africa, the EDGAR inventory relies on data from the US SPECIATE database, which is a common practice for regions where such data is not available  (Huang et al. , 2017). The challenge of understanding source sector contributions and measuring sufficient speciated VOCs to address source sectors, such as solvents, exists in both regional and global inventories. Previous work in Europe from a decade or more ago compared measurements in urban areas with regional inventories in France, Germany, and Switzerland  (Gaimoz et al. , 2011; Lanz et al. , 2008; Niedojadlo et al. , 2007; Theloke & Friedrich, 2007). All study results suggested a mismatch between the emissions attributed to traffic and solvent sectors, with the regional inventories attributing much larger contributions to solvents than measurements would suggest.
The Role of Source Sectors Figure  4  shows the dominant source sectors in the inventories for the US, UK, and China. While yearly emissions information is provided in EDGAR and in the UK NAEI (2012 was selected because of the availability of measurement data), the years for the US and China reflect the time for which the regional emission inventories were reported. As noted in the methods section, the source sector contributions from the extracted cells over the urban centers generally showed a linear relationship to the source sector contributions at the national level (Section S3, Figures S4-S6 in Supporting Information S1) and the national level data are used for comparison. Previous work comparing VOC ratios in urban areas demonstrated substantial similarity across urban areas in different regions and attributed this in large part to the predominance of vehicle emissions for the compounds investigated  (Parrish et al.
, 2009; von Schneidemesser et al. , 2010). It is, however, not only a matter of speciation, but also total emissions. While solvent sector emissions are becoming more important over time  (McDonald et al. , 2018) , the data included here spans over a decade, with much of the data from the 2000s. In that sense, part of the poor agreement between the inventories and the observations may be owing to the specific VOCs included in the comparison. In EDGAR, the contribution of the source sectors shows significant differences across the regions, especially for solvents (21%-38%). Interestingly, the regional inventories show greater similarities in the relative contribution of the source sectors to overall emissions across the three countries, such as for mobile sources (13%-20%) and solvents (37%-42%).
In almost all cases, mobile sources have a smaller contribution than the solvent sector. Many of the VOCs that dominate solvent sector emissions are not those included in this study because observations of these species are lacking, nor does the data from recent years necessarily always reflect this change in importance. In order to further consider this issue, we did a comparison of the observational ratios to source sector ratios from EDGAR (rather than total NMVOC ratios). The comparison to the residential, mobile, and solvent sectors all showed a large amount of scatter and no discernible agreement (all r 2 ≤ 0. 05) (Figures S9-S11 in Supporting Information S1). This may be an indication that the observations reflect the aggregate of total emissions and cannot be used for such a sector specific comparison.
Trends in Emission Inventory and Measurement Ratios One hypothesis for some of the scatter may be the wide spread of time that the data covers  (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014). Figure  5  shows the changes over time in the global average of the ratios in the observations and inventory for all locations (Table  S1  in Supporting Information S1) included in the study. The data were binned to 4-year intervals (1998-2014), and a global mean value and associated 95% confidence interval were calculated and evaluated with a simple linear regression for a qualitative understanding of differences in long-term trends. All species exhibit a greater than 5% per year difference between the global EDGAR inventory and the observations, with the exception of pentanes.
The changes on the inventory side are likely owing to changes in source sector strengths as the speciation profiles used have not changed substantially. That the trends in inventory and observations are generally in contradiction suggests real-world speciation may have changed over time and is not represented in the time series of the global inventory. However, in compiling the update to speciation profiles, the profiles are, out of necessity, based largely on existing profiles from established For the specific grouping of source sectors into broad comparable categories, see Table  S3  in Supporting Information S1. databases and publications, which are geographically limited and not updated regularly owing to the amount of effort associated to such a task  (Huang et al. , 2017). Changes in speciation profiles are also compounded with changes in the relative strength of sources for the various NMVOCs, adding complexity to their representation in inventories over time.
Summary and Further Work This work aims to shed light on the issue of NMVOC speciation in EI and how this can lead to improvements and further research efforts in this area. Initial comparisons have shown large differences in the agreement/disagreement between the ratios in the global, and to a more limited extent regional, emission inventories compared to those in the measurements that vary depending on the specific NMVOC. The comparison shows that regional inventories have a significantly better correlation between inventory and observational species ratios. This suggests that their source sector contributions in combination with NMVOC profiles are more realistic. Global datasets are essential for global assessments and as a data resource for regions where no local information is available.
Therefore, further work is needed how this regional information can be used or integrated in global datasets to improve their quality and representation. There are a number of challenges associated with such comparisons: the spatial and temporal resolution of such inventories is often rather coarse (0. 1° and monthly for EDGAR v4. 3. 2) or for some other inventories, just select year(s) that may not match the time period of the measurements. The observational data come from ambient measurements in urban areas, which are not necessarily spatially representative, and therefore not a perfect match. Furthermore, spatial proxies used to distribute emissions, as well as atmospheric chemistry processes can add a confounding factor (as discussed in the context of the different types of ratios used for the observations).
Going forward, considering alternative options for the denominator in such an analysis may be needed as both acetylene and CO are tracers linked to combustion emissions from vehicles in urban areas, and a tracer related to volatile chemical products (solvents) may be better suited as this source increases in importance. Several recommendations can be made based on this work, beyond the recognition of the clear need for additional research on VOCs and their representation in emissions inventories. As is true for many atmospheric composition observation datasets, the coverage over the globe is unbalanced with too few observations in the tropics and southern hemisphere. Most of the data compiled here were available for North America and Asia (largely China). Even in Europe the datasets were limited.
Data Availability Statement Data used in this study are provided in Supporting Information S1 and is made available on Zenodo (https:// zenodo. org/record/7796264; https://doi. org/10. 5281/zenodo. 7796264). Specifically, the reference for each data location included is in Table  S1  in Supporting Information S1. Further associated data (not used in the analysis, but gathered, e. g. , further NMVOC species) can be provided upon request.
, and surveys of consumer products and architectural coatings by the California Air Resources Board (CARB, 2018 (CARB, , 2019)). Other non-mobile and non-VCP emission sources are taken from the US Environmental Protection Agency's National Emissions Inventory (NEI) and speciated utilizing the Speciate database (USEPA, 2014). The Los Angeles inventory is constructed for 2010 and New York inventory for 2018. China: The speciated VOC emissions for Beijing are estimated following the bottom-up framework of the Multi-resolution Emission Inventory for China (MEIC, available at http://www. meicmodel. org; Li.
3 mm steps, followed by 300 s relaxation per step for 2 consecutive steps. The magnitudes of cyclic force and displacement steps were chosen to be within the elastic region based on tests with the loading device prior to the experiment. SAXS acquisitions were conducted at intervals of 1. 2 s during ramp to failure and cyclic loading. During stress relaxation, 10 SAXS acquisitions were conducted at intervals of 1. 2 s during the initial loading phase and early relaxation, followed by 12 SAXS acquisitions conducted at intervals of 25 s during the remaining time of the relaxation phase (Fig. 1. B). SAXS measurements were conducted using a beam energy of 12. 4 keV (wavelength of »1 A ) and a sample-detector distance of 7. 146 m, enabling data acquisition in the q-range of approximately 0. 02-1.
Data analysis Analysis of the 2D scattering patterns was performed using in-house codes in Matlab Ò (R2019a, MatchWorks Inc, USA) as previously described by Turunen et al. (2017)  [36]. The beam stop was masked away from the scattering patterns, along with dead or over-exposed pixels and gaps between detector modules. To obtain the I(u) curves, the scattering patterns were angularly integrated over 0-360°in the q-regions of the 3 rd -10 th meridional reflections (indicated by non-shaded areas in Fig. 1. C). The I(u) peaks were fitted with Gaussian curves and the predominant orientation of the collagen fibrils was determined from the position of the fit (Fig. 6. C). The degree of anisotropy, i. e. the dispersion in fibril orientation, was determined as the full-width-at-tenth of maximum (FWTM) of this fit  [36, 37].
Acknowledgements This project has received funding from the  Knut and Alice Wallenberg Foundation  ( WAF2017 ) and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  101002516 ). We acknowledge the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  731019  ( EUSMI ), which provided beamtime at the cSAXS beamline,  Paul Scherrer Institut, Switzerland , as well as the  Paul Scherrer Institut  and the beamline staff at cSAXS for help before, during and after the beamtime.
Maps for 42 species are routinely produced online at subnational administrative unit resolution. On VectorNet maps, there are relatively few areas where surveillance has been recorded but there are no distribution data. Comparison with other continental databases, namely the Global Biodiversity Information Facility and VectorBase show that VectorNet has 5-10 times as many records overall, although three species are better represented in the other databases. In addition, VectorNet maps show where species are absent. VectorNet's impact as assessed by citations (ca 60 per year) and web statistics (58,000 views) is substantial and its maps are widely used as reference material by professionals and the public. Conclusion: VectorNet maps are the pre-eminent source of rigorously validated arthropod vector maps for Europe and its surrounding areas.
The network has been strengthened with VectorNet Entomological Network (VEN) national contact points who facilitate information transfer between national colleagues and project members. VectorNet now includes recording of surveillance activities but no longer commissions field sampling. While many studies have mapped vectors at subnational, national, continental or global level  [17] [18] [19] , there are few repositories that contain standardised data. Besides VectorNet, these include VectorBase (https:// vectorbase. org/vectorbase/app/), the ENHanCEd Infectious Diseases Database (EID2)  [20] , focussing on pathogens and vectors and the Global Biodiversity Information Facility (GBIF)  [21]. There are also a significant number of citizen science initiatives such as Mosquito Alert, Tekenradar and the United Kingdom (UK) Health Security Agency Tick Surveillance Scheme.
From the end of VectorNet phase 1, data collection expanded from polygon-based presence/absence data to recording point location occurrence, including abundance, seasonality and sampling effort. The metrics used to specify these variables are also set out in detail in Supplementary Table  S3. In the latest phase of the project, the data entry was repeated for approximately 1% of these records, compared with the original data and quality metrics calculated. The quality indicators used are given in Supplementary Table  S4. The database thus contains both polygon-based distribution status data and (more) recent point-based abundance data. The spatial units at which vector distribution status is determined are based on NUTS3  [22]  and Global Administrative Unit Layers Second-Level (GAUL 2)  [23] , where the NUTS system does not reach.
The mapped species distribution categories within each polygon are as follows: (i) 'present' or 'established' are confirmed or assumed by expert opinion to be established; (ii) 'introduced' is recorded as present but not considered by expert opinion to be established; (iii) 'anticipated absent' is inferred as absent by expert opinion or known environmental constraints; (iv) 'absent' or 'observed absent' are recorded as absent; (v) 'no data' confirms that no data are available; (vi) 'unknown' means there is no information on data availability, or data are unreliable. Expert opinion here means assessed by members of the VectorNet network and validated and reconciled for each vector group by a dedicated project member. In the 'introduced' and 'present' categories, the vectors may not be present everywhere within the polygon as they may contain unsuitable areas.