dataesr/scientific-paragraphs-categorization huggingface dataset:
Temperature data from each lake came from in situ temperature profiles  [60] [61] [62] [63] [64]  for lakes smaller than 169 km 2 and from a combination of in situ temperature profiles and remotely sensed surface water temperatures for 21 larger lakes. Remote sensing data were used in recognition that temperature and warming rates can vary substantially across latitude and longitude for large lakes  [19] [20] [21]. The mean length of the temperature time series was 36 years with a range from 15 to 101 years. All lakes had temperature data which started in the year 2000 or earlier and ended in 2000 or later. Lakes had on average 29 temperature profiles per year (inner quartile range: 7-26). In situ temperature data were measured using a wide variety of temperature sensors. Data collection methods included regularly collected discrete temperature profiles, high-resolution thermistor chains and other commonly accepted tools for measuring aquatic temperature.
C d at each depth for each lake was calculated using standard, three-parameter models for estimating lake cross-sectional area based on surface area, maximum depth and mean depth  67. For large lakes with temperature data at multiple locations across latitude and longitude, C d was divided by the number of latitude-longitude bins used for each lake. Temperature values from large lakes were sampled regardless of their associated latitude-longitude bins. As a result of the volume-weighting procedure, temperature measurements were sampled in proportion to the volume of water represented by each value, with temperatures representing larger volumes being sampled more often. As a consequence of this volume-weighting procedure, the resulting temperature distributions were robust to moderate changes in the depths used for the temperature interpolation (Supplementary Fig.
d. (4, 6, 8 °C). We simulated a range of additional temperature distributions by increasing and decreasing the mean and s. d. of the baseline temperature distributions and then calculated the corresponding values of TNO. The simulated change in both mean and s. d. varied from -3 to +3 °C. We found that TNO was sensitive to changes in mean and s. d. but was slightly more sensitive to reductions in s. d. compared with increases. TNO values also depended on the baseline s. d. , such that lower starting s. d. elevates values of non-overlap given an equivalent change in temperature (Extended Data Fig. 1 ). We also quantified null values of thermal non-overlap (TNO o ) by repeating the thermal non-overlap calculations but where parts a and b were defined by randomly dividing the individual years of data into two separate groups as opposed to sequentially dividing them along the time series.
Acknowledgements This work was initiated through the  Global Lake Ecological Observatory Network (GLEON)  and benefitted from continued participation and travel support from  GLEON. We thank  C. Munteanu ,  K. Kakouei ,  M. Thayne  and  U. Scharfenberger  for their constructive discussions about the manuscript. Funding and data in support of this work also came from the following sources:  Belarus Republican Foundation for Fundamental Research ;  Leibniz Institute for Freshwater Ecology and Inland Fisheries Long-Term Research (Germany) ;  2017-2018 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme ; and with the funding organizations  German Science Foundation  (grant no. AD 91/22-1 );  Universidad del Valle de Guatemala; Oklahoma Department of Wildlife Conservation  (through the  Sport Fish Restoration Program , Grant F-61-R; United States);  Archbold Biological Station ;  Oklahoma Water Resources Board ; Grand River Dam Authority;  United States Army Corps of Engineers; City of Tulsa; Ministry of Business, Innovation and Employment  (grant no.
Data availability The remote sensing lake temperature data used can be found at https://doi. org/10. 5067/GHAAO-4BC02 and through the Earthdata website (https:// earthdata. nasa. gov/). The in situ lake temperature data used are available through the Environmental Data Initiative (EDI) data portal (https:// portal. edirepository. org/nis/home. jsp#) at https://doi. org/10. 6073/pasta/ f03d7d682eae2f467642e4260686ea15. Geospatial and morphometric data for each lake are available from the HydroLAKES database at https://doi. org/10. 1038/ ncomms13603 and can be found at http://www. hydrosheds. org. The country vector data shown in Fig. 3  were sourced from Natural Earth Data (https:// www. naturalearthdata. com) under a public domain licence that allows full and unrestricted use. The lake-specific panels in Figs.
Temperature sensitivity and wider implications The overall sensitivity of rock weathering and total CO 2 emissions to temperature is provided by the average growth exponential value α (0. 070 ± 0. 007 °C-1 ) of the whole dataset (Fig. 3b ). This value can be used to calculate a Q 10 factor, by which the oxidative weathering processes responsible for the CO 2 flux change as a result of a 10 °C rise in temperature:  In the Laval catchment, total CO 2 release during rock weathering responds to temperature with a Q 10 factor of 2. 0 ± 0. 1 over the range 0-25 °C. This value is 'apparent' as it is the integrated response of many biogeochemical processes, constrained by field conditions  37, 38. Q 10 = exp(10α) (10) To explore this temperature sensitivity further, we partition the total fluxes into carbonate-derived (reaction (3) and reactions (7)  and (  8 )) and OC petro -derived (reaction (1)) CO 2 using δ 13 C and F 14 C values of the CO 2 sampled in the chambers (Methods).
0 ± 1. 1 for the 0-20 °C range from a global soil respiration database  38. It is also equivalent to the value of 2. 4 ± 0. 3 observed for a whole-soil warming experiment  37. Such high Q 10 values demonstrate that the OC petro in these rocks, which are type III kerogen with a moderate to low thermal maturity  25, 29  , is highly reactive. These Q 10 values could reflect first-order reaction kinetics and hence occur abiotically. For OC petro oxidation, apparent Ea is 45 ± 11 kJ mol -1 (R 2 = 0. 53, P < 0. 001, n = 18) for chamber H6. These values are lower than the lowest Ea of ~100 kJ mol -1 determined for abiotic thermal oxidation of sedimentary organic matter  42. The lower Ea for OC petro oxidation at our site supports that microbial activity can enhance the oxidation kinetics of OC petro  42, 43  as it does for plant-derived organic matter oxidation in soils  44.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/ s41561-021-00805-1. ), the minimum value, the 25th percentile, the median, the 75th percentile, the maximum value and outliers (crosses). The dash-dot line (-• -) is the elevation versus CO 2 flux median linear fit (r 2 = 0. 98; P < 0. 001). Symbols are the F 0 (Methods and Supplementary Table  2 ) obtained through the growth exponential fits on data for each chamber. Error bars show 1σ uncertainty on the amplitude parameter. The dashed line (-) is the elevation versus F 0 linear fit (r 2 = 0.
21. In summary, to measure the CO 2 flux, the chamber is connected to an infrared gas analyser (EGM 5 Portable CO 2 Gas Analyzer, PP Systems) using the molecular sieve sampling system (MS 3 ) described in ref. 52. This allows the operator to first bring the chamber CO 2 concentration to ~400 ppm of the local atmosphere (using soda lime or a zeolite material to remove CO 2 from the chamber) before then recording the CO 2 accumulation in the chamber over time. During each field visit, we typically recorded a sequence of n repeats of 5-min-long accumulations of CO 2 for each chamber. The number of repeats (n) was at least seven but usually eight or more. From one sequence, we calculated n rates (q i ) of CO 2 accumulation (µg min -1 C) fitting the data with the exponential model described in ref.
Background. Unpaid carers who look after another member of their household (home-carers) have poorer mental health than the general population. The first COVID-19 national lockdown led to an increasing reliance on home-carers and we investigate the short-and longer-term impacts of lockdown on their mental health. Methods. Data from 9737 adult participants (aged 16+) from the UK Household Longitudinal Study (Understanding Society) were used to explore changes in 12-item General Health Questionnaire (GHQ-12) score between (a) pre-pandemic (2019) and early lockdowns (April 2020) and (b) early and later (July 2020) lockdowns. Results. GHQ-12 scores among home-carers were higher pre-lockdown and increased more than for non-carers from 2019 to April 2020 with further increases for home-carers compared with non-carers between April and July.
Results April web-survey interviews were completed by 17 761 participants and July web-survey interviews by 13 754 participants, with 12 680 individuals taking part in both. Of these, 12 209 (96%) also took part in a survey in 2019. The current analyses include respondents with complete data available in 2019, April and July surveys. The weighted analytical sample is 9737. However, some participants had missing data on one or more covariate, and hence the weighted analytical sample for individual models varies between 9369 and 9614 depending on the covariates included. Table  1  describes the basic characteristics of the analytical sample. The mean age of participants was 50. 8 [standard deviation (S. D. ) 15. 8] and 5037 (52%) were female. The majority of respondents were white British (88%) and 42% held a degree or higher qualification.
In total 565 (6%) of respondents selfidentified as home-carers in July 2020. Compared to non-carers, home-carers were older (22% v. 16% aged 71+), more likely to be women (58% v. 52%), and less likely to hold a degree or higher qualification (36% v. 43%). The final column of Table  1  presents comparable data for the full UKHLS sample who took part in the most recent pre-COVID-19 wave for which all data are available  [wave 9 (2017-2019) , N = 25 696]. Respondents included in the analytic sample were somewhat older (reflecting the later data collection date), had higher educational qualifications, and were less likely to consider themselves to be home-carers than those in the full wave 9 survey. Mean GHQ-12 scores were higher among home-carers v. noncarers in  2019 (2. 45 v. 1. 89). By April 2020 GHQ-12 scores in both groups had risen before falling again in July 2020.
, 2020) , problems accessing non-COVID-19 health care (Office for National Statistics, 2020b), and difficulties accessing food and other essential supplies (Carers UK, 2020a; Reaching Families, 2020). It has been suggested that individuals with pre-existing mental health problems might have been particularly vulnerable to COVID-19 mitigation measures  (Brooks et al. , 2020; Gunnell et al. , 2020; Holmes et al. , 2020)  as well as disruptions to mental health services, particularly those based in the community  (World Health Organisation, 2020). Consistent with this, our results suggest that home-carers for those with mental health problems, a group already known to be under significant stress, had particularly poor mental health themselves by July. The current analyses are based on a large longitudinal dataset including detailed questions regarding respondents' caring experiences during lockdown and use of a validated mental health measure in repeated waves before and throughout the lockdown period.
However, this was measured in July, and so may not accurately reflect circumstances in April although the error is likely to be small and results for new and pre-existing home-carers were almost identical. Although detailed data were available on the caring experiences of respondents during the pandemic these were not exhaustive, for example we lacked information regarding the type of care provided. In addition, responses required the use of quite broad categories for some variables, for example hours of care provided, although these coincide with previous definitions of 'high intensity' caregiving  (Hirst, 2005; Schulz et al. , 2020; Smith et al. , 2014). In addition, we only consider respondents caring for another household member as comparable information was not available for those providing care for someone living elsewhere.
The restarting of relevant health and social care services for those requiring care should be a priority to reduce the burden on informal home-carers, with consideration given to how to support carers of those with conditions such as dementia, learning disabilities, and mental illness, whose mental health has been particularly badly affected. Assessment should be made of increased need due to COVID-19, and temporary care provided by family members during lockdown should not be regarded as a long-term solution or replacement for formal care that was withdrawn. Data should be collected to better understand service withdrawal and the impact this had on care-recipients and their carers. In addition, carers should be recognised as a vulnerable group, regardless of the diagnosis of the person they care for and, in the event of further restrictions, be given priority access to food and other essential services.
RESULTS Thirty-four LA-MRSA genomes of t899 isolates were analyzed, of which 20 belonged to ST398, 13 to ST9, and 1 to ST4034 (a single-locus variant [SLV] of ST398, differing by one substitution [A 294 T] in the arcC gene). Metadata associated with the selected isolates were recorded (Table  1 ). All t899 isolates harbored the mecA gene on a SCCmec IVa(2B) element, except for two which presented either the SCCmec V element or an undefined cassette. Both the spanning tree and the SNP-based phylogenetic tree (Fig. 1B  and 2 ) confirmed a strong clustering according to the ST, with the ST4034/t899 isolate differing from ST398/t899 by only 41 core alleles. Other characteristics, including matrix/sample origin, did not appear to cluster in this SNP analysis. The phylogenetic tree (Fig. 2 ) also showed major divergences in the antimicrobial resistance and virulence patterns depending on the ST.
MATERIALS AND METHODS Bacterial collection. Thirty-four t899 S. aureus isolates were found in the publicly available databases, and their corresponding characteristics (MLST, matrix [human, food, and animal origins], and geographical origin) were recorded. Raw reads were downloaded from NCBI, reads were quality checked with FastQC v. 0. 65, and low-quality reads were trimmed using Trimmomatic v. 0. 36. 4  (39). Subsequently, contigs were generated using the SPAdes ve. 3. 5. 0 algorithm  (40) , and those whose length exceeded 200 bp were retained in the assembly. In the literature, spa type t899 was also found to belong to 15 other SLVs and multilocus variants (MLVs) of ST9 and ST398 (see Table  S1  in the supplemental material). These isolates could unfortunately not be included in our analysis because of the absence of associated WGS data.
cgMLST analyses. Isolates were subjected to cgMLST analyses. Genome-wide gene-by-gene microbial typing was performed using Ridom SeqSphere1 S. aureus cgMLST analysis with default parameters  (41). The cgMLST data contain 1,861 coding loci representing the core genome  (41). Once an allelic profile was assigned to each genome, a minimum spanning tree was constructed from the concatenated core genome sequences and visualized using the online tool PHYLOViZ. cgMLST loci with no allele calls were ignored in the pairwise comparison during the tree construction. The minimum spanning tree constructed on the basis of cgMLST data illustrates clusters by ST, spa type, or matrix (Fig. 1 ). SNP analysis and phylogenetic tree. A phylogenetic tree was constructed based on single nucleotide polymorphism (SNP) analysis  (9, 10, 22).
SNPs were identified by mapping reads against the ST398 reference genome (strain S0385; GenBank accession no. AM990992). The maximum-likelihood phylogenetic tree was established in CSI Phylogeny using default settings  (42). The phylogenetic tree visualization was realized using iTOL (Interactive Tree of Life)  (43). Detection of resistance genes and selected virulence markers using WGS data. The online tools ResFinder v. 3. 2  (44)  and Virulence Finder v. 2. 0 (45) from the Center for Genomic Epidemiology web- based platform were used to detect genes encoding potential resistance to antimicrobials and virulence markers, respectively. For a hit to be reported by the two programs, it had to cover at least 60% of the length of the gene sequence in the database with sequence identities of 60% and 90%, respectively.
ACKNOWLEDGMENTS We thank the service  Transversal Activities in Applied Genomics from Sciensano  for the paired-end sequencing reactions and for the development and maintenance of the in-house instance of the Galaxy workflow management system and  Andrew D. Miller  for his help in proofreading the manuscript. We thank  Mirko Rossi  for his valuable input in the course of designing this study and  Beatriz Guerra  and  Antonio Rinaldi  for cgMLST analysis. This study was supported by the  Ministry of Education, Youth and Sports , project no. CZ. 1. 05. /2. 1. 00/19. 0385. Funding sources did not affect the design of this study, data collection, data analysis, decisions on publication, or preparation of the manuscript.
Compared to multicellular fungi and unicellular yeasts, unicellular fungi with free-living flagellated stages (zoospores) remain poorly known and their phylogenetic position is often unresolved. Recently, rRNA gene phylogenetic analyses of two atypical parasitic fungi with amoeboid zoospores and long kinetosomes, the sanchytrids Amoeboradix gromovi and Sanchytrium tribonematis, showed that they formed a monophyletic group without close affinity with known fungal clades. Here, we sequence single-cell genomes for both species to assess their phylogenetic position and evolution. Phylogenomic analyses using different protein datasets and a comprehensive taxon sampling result in an almost fully-resolved fungal tree, with Chytridiomycota as sister to all other fungi, and sanchytrids forming a well-supported, fast-evolving clade sister to Blastocladiomycota.
Results and discussion The new zoosporic fungal phylum Sanchytriomycota. We isolated individual sporangia of A. gromovi and S. tribonematis (Fig. 1 ) by micromanipulation and sequenced their genomes after whole-genome amplification (WGA). After thorough data curation (see 'Methods'), we assembled two high-coverage genome sequences (123. 9× and 45. 9×, respectively) of 10. 5 and 11. 2 Mbp, encoding 7220 and 9638 proteins, respectively (Table  1 ). Comparison with a fungal dataset of 290 near-universal single-copy orthologs  37  indicated very high completeness for the two genomes (92. 41% for A. gromovi; 91. 72% for S. tribonematis). Only half of the predicted sanchytrid proteins could be functionally annotated using the 'evolutionary genealogy of genes: nonsupervised orthologous groups' (EggNOG) database  38  (3838 for A.
1 ), as commonly observed in Fungi  51  , and their size (24,749 and 27,055 bp, respectively) and G + C content (25. 86% and 30. 69%, respectively) were substantially smaller than those of most other Fungi. However, despite these signs of reductive evolution, most of the typical core mitochondrial genes were present, indicating that they have functional mitochondria endowed with complete electron transport chains. To resolve the previously reported unstable phylogenetic position of sanchytrids based on ribosomal RNA (rRNA) genes  33  , we carried out phylogenomic analyses on a manually curated dataset of 264 conserved proteins (93,743 amino acid positions)  13, 49, 52  using Bayesian inference (BI) and maximum likelihood (ML) with the CAT  53  and PMSF 54 models of sequence evolution, respectively.
Both mixture models are known to alleviate homoplasy and longbranch attraction (LBA) artefacts  22, 53. We used the same 59 species as above (dataset GBE59), including a wide representation of Holomycota plus two holozoa, two amoebae and one apusomonad as outgroups. BI and ML phylogenomic analyses yielded the same tree topology for major fungal groups with only minor changes in the position of terminal branches (Fig. 2a  and Supplementary Fig. 2a, b ). We recovered maximum statistical support for both the monophyly of sanchytrids (A. gromovi + S. tribonematis) and their sister position to Blastocladiomycota. Thus, sanchytrids form a new deep-branching zoosporic fungal clade. Given their divergence and marked genomic differences with their closest relatives (Blastocladiomycota), we propose to create the new phylum Sanchytriomycota to accommodate these fungal species (see description below).
An updated phylogeny of Fungi. In addition to the monophyly of Sanchytriomycota and Blastocladiomycota, our analysis retrieved chytrids as the sister group to all other fungi with full Bayesian posterior probability (PP = 1) and smaller ML bootstrap support (BS = 92%). However, this position of the root of fungi on the chytrid branch became fully supported (BS = 100%) after removing the fast-evolving sanchytrids in a new dataset of 57 species and 93,421 amino acid positions (dataset GBE57; Supplementary Fig. 2c ), suggesting an LBA artefact on the tree containing the sanchytrids. In fact, as observed in rRNA genebased phylogenies  33  , sanchytrids exhibited a very long branch (Fig. 2a ), indicating a fast evolutionary rate. Long branches associated with fast-evolving genomes, well known in Microsporidia  48  and other Holomycota, can induce LBA artefacts in phylogenetic analyses  [55] [56] [57].
To further study if LBA affected the position of the fast-evolving sanchytrids and other fungi in our tree, we carried out several tests. First, we introduced in our dataset additional long-branching taxa, metchnikovellids and core Microsporidia, for a total of 74 species and 86,313 conserved amino acid positions (dataset GBE74). Despite the inclusion of these LBA-prone fast-evolving taxa, we still recovered the monophyly of sanchytrids and Blastocladiomycota (Supplementary Fig. 2d ). However, they were dragged to the base of Fungi, although with low support (BS 78%). To confirm the possible impact of LBA on this topology, we removed the fast-evolving sanchytrids (dataset GBE72; 72 species and 84,949 amino acid positions), which led to chytrids recovering their position as the sister group of all other fungi with higher support (BS 91%; Supplementary Fig.
We then further tested the robustness of the position of chytrids using alternative topology (AU) tests. For the dataset GBE59, these tests did not reject alternative positions for the divergence of Chytridiomycota and Blastocladiomycota + Sanchytriomycota (p values > 0. 05; Supplementary Data 3), which likely reflected the LBA due to the long sanchytrid branch. In fact, the position of Blastocladiomycota at the base of fungi was significantly rejected (p values < 0. 05; Supplementary Data 3) after removing the fast-evolving sanchytrid clade (dataset GBE57). Finally, we compared the results based on the GBE dataset with those based on a different dataset. We decided to use the BMC dataset  59  (which includes 53 highly conserved proteins and 14,965 amino acid positions) because it was originally designed to study fast-evolving Holomycota (e.