dataesr/scientific-paragraphs-categorization huggingface dataset:
Significance Given the current species extinction rates, evidence-based policies to conserve at-risk species are urgently needed. Ultimately, the extinction of a species is determined by birth and death rates, which drive populations to increase or decline. Therefore, demographic data are essential to inform species conservation policies or to develop extinction risk assessments. Demographic information provides an indispensable bedrock for insights to tackle species sustainable management and deepens understanding of ecological and evolutionary processes. We develop a Demographic Species Knowledge Index that classifies the demographic information for 32,144 tetrapod species. We found comprehensive information on birth and survival for only 1. 3% (613) of the species, and show the major potential of zoos and aquariums to significantly increase our demographic knowledge.
for international bodies aiming to conserve biodiversity [i. e. , Aichi Target 19, Convention on Biological Diversity  (3) ]. Despite the rapid growth in biodiversity information and data repositories (4), we still do not have a species knowledge index that indicates the types of information available, such as demography, even for the most well-known taxa. Two decades ago, Carey and Judge (5) pioneered the first major database of demographic diversity across species: They compiled maximum life spans for more than 3,000 vertebrates. Since then, various databases with fertility and mortality information have been launched, including the 22 listed in Table  1. These databases have been used for comparative analyses  (6, 7). They can also be used for studies of species conservation. Thus, for both uses, it is important to standardize and integrate knowledge from various sources to get an overall view of available information.
Up until our analysis, however, a map was lacking of the landscape of knowledge across species to summarize which taxa have the least information and which have the most. Digitized demographic data are becoming increasingly available, including characteristics of species such as maximum recorded life span, age at maturity, and litter/clutch size. This is also true for population-level data, including life tables and matrix models, which provide information for populations of individuals about fertility and survival over the ages or stages of life. Although such data repositories have been used for comparative analyses, their combined potential could be improved if inconsistencies in data standards and terminology were resolved  (8) , thus permitting crosstaxa studies by drawing information from multiple databases.
We developed the Demographic Species Knowledge Index based on a metadatabase analysis of 22 available data repositories (Table  1 ) on life history traits and demographic data. For 97% of the described tetrapods  (9) , we were able to obtain some demographic data or determine that no data were available. The index summarizes the existing level of demographic information available for each species. Species with the highest values have information on both survival and fertility across ages or stages (i. e. , life tables, population matrices). Low values are obtained when only summary species-level demographic measures are available, such as age at first reproduction or maximum recorded life span. We use the index to map the distribution of survival and fertility knowledge, to highlight current gaps, and to point out directions for future research.
The most commonly available demographic measure across tetrapods is litter/clutch size, which we found for 11% of amphibians and 64% of birds, followed by maximum recorded life span, which is available for less than 4% of amphibians but for 46% of mammals (Table  3 ). Knowledge gaps are extensive, especially for amphibians, where 88% of species have no available information, followed by reptiles, with 65% lacking any demographic information (Fig. 1  and SI Appendix, Figs. S1-S4 ). This deficiency of data is of particular concern since the data are needed for species threat assessments and to establish harvesting quotas. Population reduction, often measured on the scale of generation length, is one of the most important criteria for listing species under different levels of threat by the International Union  The level of information on fertility and survival is coded using a 2D color scale, with blue shades representing information on fertility and red shades representing information on survival.
Likewise, to set up harvesting quotas, it is necessary to predict the impact of harvesting on the sustainability of a population. Therefore, population viability analyses are often required; these preferably use detailed measures of age-or stage-specific survival and fertility because these measures greatly improve estimation of population trends under different management scenarios and the prediction of extinction risk  (12). For example, CITES, the Convention on International Trade in Endangered Species of Flora and Fauna usually requires these types of analyses for the establishment of exporting quotas for particular species to ensure that the international trade does not threaten the sustainability of their populations. Detailed demographic data are essential not only for managing populations but also for understanding life histories and population dynamics.
For example, age-specific mortality and fertility data are crucial for studies of the biology of aging in humans and nonhuman species  (6, 7). Moreover, the patchy nature of the landscape of demographic knowledge is especially worrisome for threatened species for which data on closely related species are also lacking, as clearly illustrated by amphibians. After surviving four mass extinctions, amphibians now suffer the highest disappearance rate of all tetrapod classes  (13). It is important that data gaps like these are filled by collection of field data, when possible; otherwise, data from captive populations can provide important information or estimates can be derived from closely related species. Imputation methods are often used to fill information gaps when data on related species are available.
These methods estimate missing data by using suites of trait correlations among species  (14). For example, if detailed demographic measures are not available, simple life history traits, such as body size, have been used to make crude predictions of extinction risk. For highly data-deficient groups, a potential source of information lies in the availability of demographic and related measures from natural history museum collections, such as number of embryos in the uterus from preserved specimens, age estimates based on the characteristics of skulls or teeth, skeletal indicators of health, and size and weight of individuals at the time of capture. The incorporation of existing demographic data from unpublished studies, reports, and journals in languages other than English, as well as data from captive populations, will also play a key role in filling knowledge gaps.
To inform animal management decisions, zoos and aquariums collect detailed information on individuals under their care. For 45 y, Species360 has been gathering standardized information from institutions worldwide; currently, information is available for over 10 million individuals from 22,000 species  (15). We found that the use of Species360 members' data could significantly increase knowledge, such as age at first reproduction from 4,199 species to 7,273 species, a 73% increase. More dramatically, the availability of life tables or population matrices could be increased from 613 species to 4,699 species, an almost eightfold gain. Caution must be taken when using data from captive populations to model wild populations. Zoo and aquarium populations are intensively managed, and hence likely to differ from free-living populations, notably in survival (  16 ) and reproduction metrics.
Furthermore, we found that origin of the information for more than half of the species (66%) is unknown or not reported (Fig. 3  and SI Appendix, Table  S3 ). Therefore, whether demographic measures were estimated from imputation analyses or from wild or captive populations is unclear (Fig. 3  and SI Appendix, Fig. S5 ). We found that between 75% and 85% of the species have an unknown or not reported origin of information for interlitter or interbirth interval, age at first reproduction, and litter or clutch size (Table  4 ). Likewise, 57% of the species have an unknown origin for maximum life span. This is worrisome because these data are widely used for conservation and comparative studies. Thus, gaining a better understanding of biases of data from unknown origin, imputation analyses, or populations under captive management should be a priority.
In addition, it will be important to explore the uncertainty introduced by mixing data from wild and captive populations. In this sense, zoos, aquariums, and botanical gardens could become key allies in providing data that can help fill data gaps to understand species biology. To address current biodiversity crises, key questions must be answered. Which species should be selected for long-term population   monitoring programs? How much effort should be devoted to digitization of existing records? How reliably can data from captive populations or imputation analyses fill demographic knowledge gaps? To use available resources more efficiently, prescription decision analyses will be necessary to prioritize data needs  (4, 17). To achieve this goal, knowledge gaps in geographical, temporal, and taxonomic information must be addressed from field or zoo records or imputation analyses and, eventually, also from metrics of available genetic information.
Although research and decision making now rely on large databases, financial support for data digitization, field data collection, and the integration of databases remains scarce. Data, if grouped together, are greater than the sum of their parts. Imputation of fertility and mortality patterns becomes much more reliable if arrays of information are available for a species and for closely related species. Conservation action plans can be much more effectively targeted if based on multifaceted data. Initiatives such as the Darwin Core group by the Biodiversity Information Standards (TDWG)  (18)  are developing global data standards and uniform vocabularies on taxonomy, occurrence, and sampling events: This will facilitate the integration of biodiversity databases. Data on species interactions, physiology, genetics, and diseases remain among the most sought-after data types in biological research, conservation policy, and management practice.
The publication of data through organizations such as the Global Biodiversity Information Facility can be used to facilitate integration of databases in the future. Creating linkages with research infrastructures like the Distributed System of Scientific Collections  (19)  will enable the integration of data to serve a broader audience of researchers and will enable new research. The Demographic Species Knowledge Index developed here serves as a first step toward a complete assessment of biodiversity knowledge across different disciplines for every species. We envision that our assessment of demographic knowledge for tetrapods lays the foundation for the development of a species knowledge index of digital information that identifies and classifies the amount and types of digital data available in knowledge areas such as genetics, primary biodiversity data, and species legislation, such as compiled by Legal Atlas  (20)  for all of our planet's described species.
Methods Data Sources. To estimate the availability of demographic data for each of the 32,144 tetrapod species (97% of the extant described species), we developed a metadatabase using information contained in 22 published sources of demographic information (Table  1 ). We selected databases that contained machine-readable records and references to the original works. Also, we used databases for which data were freely available, although, in some cases, a memorandum of understanding was required before access was granted [e. g. , for the Primate Life History Database  (21) ]. We excluded those records that were derived from imputation analysis when reported as such. We omitted databases for which data sources (i. e. , references) could not be traced. Because of the low number of amphibians and reptiles represented in most databases, we conducted an online literature search for which we included all literature that had information on demographic data for at least 18 species.
Taxonomic and Terminology Standardization. We used TraitBank  (22)  as the reference for standardization of the terminology of demographic variables and rates across the 22 selected databases. However, for most, we could not find established standards; therefore, during an expert workshop with coauthors of this article, we developed an ontology that described eight demographic measures, as described below: five for fertility and three for survival. We standardized species taxonomy across all of the databases using the Catalogue of Life's (9) currently accepted nomenclature. To retrieve the The relative number of species per taxonomic class for which that measure exists is indicated in parentheses. Further information about measures of knowledge for the Demographic Species Knowledge Index is provided in Methods.
accepted names and the IUCN Red List status  (23) , we used the taxize  (24)  package in R version 3. 5. 1  (25)  and manually searched for species names that could not be retrieved. For 3% of the species, we were not able to resolve their taxonomy, so they were not included in the analyses. This process resulted in a metadatabase of 32,144 species, with 14,529 species with demographic data and 115,356 demographic records. We standardized each record's origin from populations reported as wild, captive, or unknown across all of the databases. When the origin was not provided in the database, we assigned it as "unknown" (Table  4 ); however, we still included those records because all of the databases included here have a reference to a publication. Developing the Demographic Species Knowledge Index.
To summarize the availability of demographic data for each tetrapod species we developed the Demographic Species Knowledge Index. This index provides scores that summarize the number of a total of eight measures available for fertility and survival for any species. These measures are as follows: Measures of fertility knowledge: (i) age at first reproduction; (ii) interlitter/ interbirth interval; (iii) litter/clutch size; (iv) proportion of adult females that are reproductive; and (v) birth or recruitment rate, with recruitment denoting the average number of individuals that reach a specific age or stage (e. g. , maturity, leaving the nest) per reproductive female. Measures of survival knowledge: (i) maximum recorded life span, (ii) mean age of the (adult) population, and (iii) crude mortality.
Information about mortality (or survival) includes the juvenile crude death rate, the adult crude death rate (or adult life expectancy, approximately the inverse of the adult crude death rate), and the crude death rate for juveniles and adults combined (or life expectancy at birth, which is approximately its inverse). The crude death rate is given by the number of deaths in some time interval over average population size in the interval. The probability of death equals the number of deaths in some time interval divided by population size at the beginning of the interval. Biologists sometimes refer to one minus either of these measures as the survival rate. Combined age or stage survival-fertility knowledge: The index is also based on the availability of population-level data in the form of population matrices or life tables.
These include both age-or stage-specific death or survival probabilities and age-or stage-specific fertility rates; life tables often contain only mortality data. Knowledge about survival is classified into four categories: • High: A life table or population matrix is available. • Fair: Such data are not available, but at least two variables are measured, such as maximum life span and adult mortality. • Low: Only one variable is available. • None: No information is available. Knowledge about fertility is also classified into four categories. • High: Fertility rates are available by age or stage. • Fair: Such data are not available, but at least two variables are measured, such as age at maturity and average litter/clutch size. • Low: Only one variable is available. • None: No information is available.
ACKNOWLEDGMENTS. We especially thank  Bengt Holst  for facilitating the expert workshop for standardization of the demographic vocabulary across the 22 data repositories used here. We thank  Jon Paul Rodríguez ,  Yvonne Buckley ,  Jim Guenter ,  Iain Stott ,  Simeon Q. Smeele ,  Stephan M. Funk , and the two reviewers for useful comments on the manuscript. We thank the commitment of zoo and aquarium staff for managing their animal records in Zoological Information Management System (ZIMS), providing vast amounts of data to create a wealth of demographic knowledge. We acknowledge the following institutions for their financial support:  Interdisciplinary Centre on Population Dynamics, Max Planck Institute for Demographic Research, Bir Ventures, University of Southern Denmark, Gerhard und Ellen Zeidler-Stiftung , and Sponsor-Partners of the  Species360 Conservation Science Alliance (the Copenhagen Zoo, World Association of Zoos and Aquariums, and Wildlife Reserves of Singapore ).
A GIF Quantum ER as Electron Energy Loss Spectrometer (EELS) was used for EELS measurements. Both low-and core-loss EELS spectra were acquired with the DualEELS capability through Gatan Digital Micrograph software, which allows the accurate energy calibration of EELS spectra, thanks to the simultaneous alignment of the zero-loss peak position for every single acquisition, which removes any artefact coming from energy shifts. The use of Fourier logarithmic deconvolution on a full spectrum obtained by splicing together low-and core-loss EELS allows removing thickness-related plural scattering. 3 All the STEM-EELS and STEM-EDS measurements were performed simultaneously by using the Gatan spectrum imaging (SI) tool. Gas Chromatography. GC analyses were performed on a Shimadzu GC-14A gas chromatograph (with polar column) equipped with flame ionization detector and a SPB-1 Supelco fused silica capillary column (30 m, 0.
X-ray Absorption Spectroscopy (XAS). To study in depth the nature of the interaction between the palladium atoms on the surface of the nanoparticles in contact with P atoms of bP sheets, X-ray Absorption Spectroscopy (XAS) experiments at the Pd-K edge (Eedge=24350 eV) have been carried out at the LISA beamline at the European Synchrotron Radiation Facility. 4 The monochromator was  Reaction conditions: solvent: methanol, [substrates] = 0. 242 M; room temperature; 5 bar H2; magnetic stirring, 1200 rpm. Sub/Cat: substrate/catalyst ratio (mol/mol). a) Data from GC analysis and GC-MS; b) Turn over frequency 6. Test for the heterogeneous nature of the catalyst, ruling out the presence of catalytically active species in solution. A catalytic run was started under the standard reaction conditions described above.
ORCID  M. Peruzzini :  0000-0002-2708-3964 The authors declare no conflict of interest. Thanks are expressed to EC for funding the project  PHOSFUN  " Phosphorene functionalization: a new platform for advanced multifunctional materials " ( ERC ADVANCED GRANT  to M. P. ). STEM-EELS analysis was performed at  Beyond-Nano CNR-IMM , which is supported by the  Italian Ministry of Education and Research (MIUR)  under project  Beyond-Nano  ( PON a3_00363 ). The authors gratefully acknowledge for  SEM  and  TEM images "Ce. M. E. -Centro Microscopie Elettroniche Laura Bonzi" in Sesto Fiorentino (Italy)  financed by " Ente Cassa di Risparmio di Firenze" and through the projects "EnergyLab", POR FESR 2014-2020 and FELIX (Fotonica ed Elettronica Integrate per l'Industria , project code n. 6455). Finally, MIUR is kindly acknowledged for financial support through Project  PRIN 2015  (grant number  20154X9ATP ).
Flow cytometry and apoptosis Cells at 70%-80% confluency were washed in PBS, trypsinized and resuspended in staining solution SS5 (5% FBS, 2 mM EDTA, 0. 01% NaNH 3 in PBS) and centrifuged. Cells were fixed by resuspending the cell pellet in 70% EtOH in PBS and incubated on ice for 1 h. The fixed cells were then centrifuged and the pellet resuspended in PBS with RNase A 0. 1 mg/mL, 0. 05% Triton X100, TO-PRO-3 iodide (642/661), FITC anti-H2AX phospho (Ser139) antibody and incubated for 45 min at 37°C. Cells were washed in PBS and resuspended in 250 µL SS5. Ten-thousand to 50,000 cells were measured by FACS ona BD FACSCanto II (BD Biosciences), and data were analyzed using FlowJo software (7. 6. 1). Apoptosis was analyzed using CellEvent Caspase-3/7 Green flow cytometry assay kit (C10427 Thermofisher); ROS formation was analyzed using CellROX Green flow cytometry assay kit (C10492 Thermofisher) as per manufacturer's instructions on BS LSRFortessa cell analyzer (BD Biosciences).
8 M27 objective for fixed, with fully open pinhole on LSM710 confocal microscope (Carl Zeiss AG). Live-cell images were obtained every 10 sec from 30-sec preirradiation to 420 sec after irradiation. For UV laser irradiation, cells were irradiated using a Team Photonic UV laser SNV-04P-100 through an iLas 2 FRAP head (Cairn Research) connected to Nikon TE-2000 microscope with a Nikon Plan Fluor 20×/0. 45 objective (Nikon). Laserized pattern and image acquisition were controlled via the software Metamorph (Molecular Devices). Where indicated, cells were incubated with DMSO or PARP inhibitor (10 µM olaparib) for 3 h prior to and during irradiation. Images were analyzed using Fiji and Matlab software. Background to Stripe intensity ratio was calculated for each time point and expressed as percentage change in intensity compared with background.
DDA MS analysis MS data were stored, searched, and analyzed using the ProHits laboratory information management system (LIMS) platform  (Liu et al. 2016). Within ProHits, AB SCIEX WIFF files were first converted to an MGF format using WIFF2MGF converter and to an mzML format using ProteoWizard (version 3. 0. 4468) (Kessner et al. 2008 ) and the AB SCIEX MS Data Converter (version 1. 3 beta). The mzML and mzXML files were then searched using Mascot (version 2. 3. 02) and Comet (version 2012. 02 rev. 0). The spectra were searched with the RefSeq database (version 57, January 30, 2013) acquired from NCBI against a total of 72,482 human and adenovirus sequences supplemented with "common contaminants" from the Max Planck Institute (http://141. 61. 102. 106:8080/share. cgi?ssid=0f2gfuB) and the Global Proteome Machine (GPM; http://www.
thegpm. org/crap/index. html). The database parameters were set to search for tryptic cleavages, allowing up to two missed cleavage sites per peptide with a mass tolerance of 40 ppm for precursors with charges of 2+ to 4+ and a tolerance of ±0. 15 amu for fragment ions. Deamidated asparagine and glutamine and oxidized methionine were allowed as variable modifications. The results from each search engine were analyzed through TPP (the Trans-Proteomic Pipeline version 4. 6 OCCUPY revision 3)  (Deutsch et al. 2010 ) via the iProphet pipeline  (Shteynberg et al. 2011). SAINTexpress version 3. 3  (Teo et al. 2014 ) was used as a statistical tool to calculate the probability value of each potential protein-protein interaction from background contaminants using default parameters. The four controls samples were kept uncompressed for SAINTexpress analysis.
MS data visualization and archiving Dot plots and heat maps were generated using  ProHits-viz (prohits-viz. lunenfeld. ca (Knight et al. 2017) ). All MS files used in this study were deposited at MassIVE (http://massive. ucsd. edu; MSV000080598; ftp://massive. ucsd. edu/MSV000080598) and to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org; PXD006017).