dataesr/scientific-paragraphs-categorization huggingface dataset:
Measuring cell lengths Images of fixed cells were acquired using a Leica DMLA2 microscope and the length of bacteria were measured using the ObjectJ plugin of ImageJ (Rasband, W. S. , ImageJ, http:// imagej. nih. gov/ij/; Vischer, N. O. E, ObjectJ, http://simon. bio. uva. nl/objectj). The plugin was set to automatically measure all cells in each field. Each treated photograph was examined visually, and incomplete cells and contaminating materials were manually discarded. Cells that were too long for ObjectJ were manually measured. To create the histogram, data were transferred to Excel program and the frequency distribution was plotted as relative frequency. To ensure statistical significance, more than 1500 cell lengths were used for each condition. For cells carrying the pASK-CeCav:mcherry plasmid (CC), dual-color images were obtained after fixation followed by 1.
Acknowledgments-We thank  David Tareste  for critical reading of the article and helpful discussion. We finally thank all members of the  Thierry Galli laboratory  for their assistance and discussions. We acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod, and NeurImag, core facility  of the IPNP, members of the  France BioImaging  ( ANR-10-INBS-04 ) for their services. We acknowledge the B&B technological core facilities of IPNP for technical support. Author contributions-C. V. and T. G. conceptualization; S. F. , T. B. , R. G. P. , J. -M. V. , C. V. , and T. G. methodology; S. F. , N. P. S. , R. L. , and P. B. investigation; C. V. and T. G. supervision; S. F. , C. V. , and T. G. writing-original draft; S. F. , N. P. S. , T. B. , R. G. P. , J. -M. V. , C. V. , and T.
G. writing-review and editing. Funding and additional information-T. G. was supported by  Association Française contre les Myopathies  ( Research Grant   16612 ),  French National Research Agency  (NeuroImmunoSynapse  ANR-13-BSV2-0018-02 ; MetDePaDi  ANR-16-CE16-0012 ),  Institut National du Cancer  ( PLBIO 2018-149 ),  Fondation pour la Recherche Médicale (FRM) ,  Who am I? Labex  (Idex  ANR-11-IDEX-0005-01 ),  Idex USPC  ( ANR-11-IDEX-0005 ,  Conventional And Unconventional Secretion In Neurite Growth ), award of the  Association Robert Debré pour la Recherche Médicale , award of the  Fondation Bettencourt-Schueller. R. G. P. was supported by  National Health and Medical Research Council of Australia  (grants  APP1140064  and  APP1150083  and fellowship  APP1156489 ). N. P. S. is recipient of a fellowship of the  Université de Paris  - CD4Impact international program  ( ANR-18-IDEX-0001 ).
Over 70,000 excess deaths occurred in Europe during the summer of 2003. The resulting societal awareness led to the design and implementation of adaptation strategies to protect at-risk populations. We aimed to quantify heat-related mortality burden during the summer of 2022, the hottest season on record in Europe. We analyzed the Eurostat mortality database, which includes 45,184,044 counts of death from 823 contiguous regions in 35 European countries, representing the whole population of over 543 million people. We estimated 61,672 (95% confidence interval (CI) = 37,643-86,807) heat-related deaths in Europe between 30 May and 4 September 2022. Italy (18,010 deaths; 95% CI = 13,793-22,225), Spain (11,324; 95% CI = 7,908-14,880) and Germany (8,173; 95% CI = 5,374-11,018) had the highest summer heat-related mortality numbers, while Italy (295 deaths per million, 95% CI = 226-364), Greece (280, 95% CI = 201-355), Spain (237, 95% CI = 166-312) and Portugal (211, 95% CI = 162-255) had the highest heat-related mortality rates.
Temperatures and heat-related mortality numbers Observed European mean temperatures uninterruptedly exceeded the baseline climatological values of the 1991-2020 period in all of the weeks during summer 2022 (Fig. 2a ). European weekly temperature anomalies in 2022 ranged between +0. 78 and +2. 33 °C in June (weeks 22-26), between +0. 18 and +3. 56 °C in July (weeks 26-30), and between +0. 91 and +2. 67 °C in August (weeks 31-35). Although several subcontinental warmer-than-average weeks were recorded during the summer (Extended Data Figs. 2 3 4 5 6 ), the most intense, pan-European heat wave was observed during week 29 (between 18 and 24 July); this week alone was estimated to be associated with 11,637 (95% confidence interval (CI) = 7,639-15,970) heat-related deaths (Fig. 2b ), particularly in Central and Southern Europe (Extended Data Fig.
Discussion This European-wide study quantified the mortality associated with record-breaking temperatures during the summer of 2022, currently the hottest season on record in Europe, and analyzed it within the broader context of the summer of 2003 and the accelerated warming observed in the continent during the last decade (2013-2022). Epidemiological models were applied to a mortality database representing the whole  In relative terms, the largest summer heat-related mortality rates were found in countries near the Mediterranean Sea, which included Italy, Greece, Spain and Portugal. The results showed that there was a large increase in heat-related mortality during June-August 2022, approaching the record-breaking excess mortality of June-September 2003. However, the comparison with 2003 needs to be interpreted with caution, given the large methodological differences in our study compared to previous estimates  12.
Article https://doi. org/10. 1038/s41591-023-02419-z expected to include mortality cases in which heat was not a contributor. Furthermore, we used weekly temperature and mortality data in our epidemiological models, which is expected to underestimate the day-to-day variability of the time series and possibly their lagged short-term associations. This is especially the case for heat, given that the risk of death is acute and generally does not last for more than 5 days  7, 28, 29. As a reference, a previous study  30  applied similar epidemiological models to daily temperature and mortality data for Spain only, and found that the summer heat-related mortality burden was 10% higher (that is, 12,504 deaths) than the one reported here for the same country (that is, 11,324 deaths). Finally, the excess mortality estimates during the summer of 2003 from Robine et al.
This study has some limitations worth acknowledging. First, we used weekly temperature and mortality data in the epidemiological models, which is expected to underestimate the heat-related mortality of the summer of 2022. The scientific question of characterizing the biases of epidemiological models applied to weekly temperature and mortality data was beyond the scope of the present work and it will be addressed in a follow-up study. Second, data according to sex and age groups were not available in a reduced number of countries, that is, the United Kingdom, Ireland and Germany, which emphasized the urgent need to coordinate national agencies for statistics to create protocols integrating and homogenizing health data sources and improving open-access data for research, translation and policymaking.
In our opinion, this should be a priority in the agenda of European governments, both from inside and outside the European Union, given that health data are managed at the country level and only integrated as open-access data in exceptional cases. Finally, the study analyzed records of all-cause mortality because cause-specific data were not available. This limitation is important because some of the differences in heat-related mortality risks and numbers between sex and age groups could be better interpreted within the framework of an in-depth analysis of the causes of death. Due to global warming, temperatures in Europe are rising at a faster rate than the global average  6. Taking into account the magnitude of heat-related mortality on the continent, our results call for a reevaluation and strengthening of heat surveillance platforms, prevention plans and long-term adaptation strategies.
Online content Any methods, additional references, Nature Portfolio 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/s41591-023-02419-z. Extended Data Fig. 7  | Sensitivity to the period of calibration of the epidemiological associations. Panels (a,b) depict the cumulative relative risk of death (unitless) in Europe (a) and the weekly heat-related mortality (weekly deaths) aggregated over Europe during the summer of 2022 (b), together with their 95% confidence intervals (shadings). Panels (c-f) display the regional relative risk of death (unitless) at temperature 95 th percentile (c,d) and the regional heat-related mortality rate (summer deaths per million) aggregated over the summer of 2022 (e,f).
Acknowledgements J. B. , M. Q. -Z. , R. F. M. T. and F. P. gratefully acknowledge funding from the  European Union 's  Horizon 2020 and Horizon Europe research and innovation programs  under grant nos. 865564  ( European Research Council  Consolidator Grant EARLY-ADAPT; https://www. early-adapt. eu/), 101069213 (European Research Council Proof-of- Concept HHS-EWS ) and  101123382  (European Research Council Proof-of- Concept FORECAST -AIR). J. B. , C. T. and J. M. A. acknowledge funding from the  European Union 's  Horizon Europe research and innovation program  under grant no. 101057131  ( Horizon Europe  project CATALYSE; https://catalysehorizon. eu/). J. B. and X. B. acknowledge funding from the  Ministry of Research  and https://doi. org /10. 1038/s41591-023-02419-z  Universities of the  Government of Catalonia  (no.
2021-SGR-01563 ). J. B. also acknowledges funding from  FORMAS , the  Swedish Research Council for Sustainable Development  ( Cross-Cutting Climate Adaptation-Challenges and Measures Grant ADATES ), and from the  Spanish Ministry of Science and Innovation  under grant no. RYC2018-025446-I  (program  Ramón y Cajal ). H. A. acknowledges funding from the  European Union 's  Horizon Europe research and innovation program  under grant no. 101065876  ( Marie Skłodowska-Curie Actions Postdoctoral Fellowship TEMP-MOMO ). ISGlobal authors acknowledge support from grant no. CEX2018-000806-S  funded by  MCIN/AEI/10. 13039/501100011033  and support from the  Generalitat de Catalunya  through the  CERCA Program. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Data availability This study is based on publicly available datasets: mortality counts from Eurostat (https://ec. europa. eu/eurostat/statistics-explained/index. php?title=Weekly_death_statistics&stable); temperature values from the European Centre for Medium-Range Weather Forecasts (https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-land?tab= overview); and population numbers from Eurostat (https://ec. europa. eu/eurostat/cache/metadata/en/demo_r_gind3_esms. htm).
Data sources We obtained weekly counts of all-cause mortality according to sex and age groups from Eurostat  46. Missing data were complemented by contacting the corresponding national agencies for statistics. The final dataset included 45,184,044 counts of death (22,000,519 for women and 21,913,050 for men) between January 2015 and November 2022 from 823 contiguous regions representing over 543 million Europeans in 35 countries, namely Albania (12 regions), Austria (35), Belgium (44), Bulgaria (28), Croatia (1), Cyprus (1), Czechia (14), Denmark (11), Estonia (5), Finland (19), France (96), Germany (16), Greece (52), Hungary (20), Iceland (2), Ireland (1), Italy (103), Latvia (6), Liechtenstein (1), Lithuania (10), Luxembourg (1), Malta (1), Montenegro (1), the Netherlands (40), Norway (11), Poland (73), Portugal (25), Romania (42), Serbia (25), Slovakia (8), Slovenia (1), Spain (59), Sweden (21), Switzerland (26), and the United Kingdom (12).
Ab initio molecular dynamics DFT-based MD simulations were performed using the Quickstep module 41 of the CP2K software package, 42 using parameters already fine-tuned for ZIF-4 in previous works. 23, 25, 43 We used the hybrid Gaussian and plane wave method GPW, 41 the exchange-correlation energy was evaluated in the PBE approximation,  44  and the dispersion interactions were treated at the DFT-D3 level. 45 The multigrid system was set up with four different grids, a plane-wave cutoff for the electronic density of 600 Ry, and a relative cutoff of 40 Ry. The simulations were performed in the constant-volume (N, V, T ) ensemble with a fixed size and shape of the unit cell. A timestep of 0. 5 fs was used in the MD runs; the temperature was controlled by velocity rescaling  47  with a time constant of 1000 fs.
Classical force field We employed MOF-FF for ZIFs  52, 53  and molsys, the python package behind MOF-FF for the parametrization of systems. 54 No adaptation was required for the ZIF-4 crystal, and two FF adaptations of MOF-FF for ZIFs were employed for the glasses: Zn3tetra and Zn3trig. In Zn3tetra, every three-coordinated Zn atom (denoted zn3 n3 in molsys atypes format) was treated as a four-coordinated Zn (denoted zn4 n4). In Zn3trig, zn3 n3 were treated as zn4 n4 in all their interactions with other atoms, except for the N-Zn-N angle which was changed from the tetrahedral angle (arccos(-1/3) ≈ 109. 47 • ) to 120 • , using the same spring constant. As the identification of the building units (Zn atoms and imidazolates) of the glass models could not be performed by molsys, due to large fluctuations in the bond angles and lengths as well as the presence of defects, it was carried out with our python library aMOF , 40 interfaced with molsys.
Structural properties Structural analyses were performed using the parameters used and further detailed in a previous study,  40  using the Python library aMOF. Unless explicitly stated in the caption, properties are averaged over an (N, V, T ) trajec-tory of 1 ns for classical MD simulations and 60 ps for ab initio simulations. For classical MD, frames are taken every 1 ps for the radial distribution function (RDF) and bond angle distribution, and every 50 ps for pore statistics. For ab initio MD, those intervals are respectively of 0. 5 fs and 0. 5 ps. The potential of mean force (PMF)  60  is computed from the RDF g(r) through the relation F (r) = -k B T (ln g(r) -ln g max ) , where g max is the maximum of g(r) over r. N-Zn-N angle distribution is computed by taking a cutoff radius of 2.
High-throughput sequencing (HTS), more specifically RNA sequencing of plant tissues, has become an indispensable tool for plant virologists to detect and identify plant viruses. During the data analysis step, plant virologists typically compare the obtained sequences to reference virus databases. In this way, they are neglecting sequences without homologies to viruses, which usually represent the majority of sequencing reads. We hypothesized that traces of other pathogens might be detected in this unused sequence data. In the present study, our goal was to investigate whether total RNA-seq data, as generated for plant virus detection, is also suitable for the detection of other plant pathogens and pests. As proof of concept, we first analyzed RNA-seq datasets of plant materials with confirmed infections by cellular pathogens in order to check whether these non-viral pathogens could be easily detected in the data.
The interest in metagenomics as a diagnostic tool has gained popularity in the last few years with the rise of long read sequencing techniques  [16, 17]. Alternatively, specific sequences or e-probes can be used to mine metagenomics and RNA-seq datasets for the presence of certain pathogens  [18, 19]. Finally, mitochondrial metagenomics, where samples are enriched in mitochondrial fragments prior to library preparation, also seems promising when sufficient data is present in reference databases  [20]. As explained above, RNA-seq has become an essential tool for the detection and identification of plant viruses but has received little attention for the diagnosis of other pathogens and pests. Consequently, plant virologists generate numerous RNA-seq datasets but typically only investigate these datasets for the presence of viruses, being unaware of the possible presence of other plant pathogens or pests.
In this study, we hypothesized that RNA-seq data generated by virologists could be used to detect non-viral pathogens and pests as well. To check this hypothesis, we first analyzed RNA-seq data of plant materials known to be infected by cellular pathogens to see if these pathogens could be detected and correctly identified from the sequencing data. This was performed using both publicly available datasets and newly generated data. Next, we organized an "RNA-seq community effort", in which existing RNA-seq datasets from different diagnostic laboratories generated for the detection of viruses were revisited and checked for the potential presence of nonviral plant pathogens and pests. Participants were actively involved in the re-analysis of their data, and were asked to give feedback on the results, including the experimental confirmation of the presence of the detected cellular pathogens by (q)PCR.
The goal of this effort was two-fold: (1) to provide a broad vision of the type of pathogens that are typically detected in these RNA-seq datasets as well as on the relative proportion of rRNA and mRNA derived from each organism group (host plant, viruses, bacteria, phytoplasmas, fungi, insects, mites) in a broad range of plant materials and (2) to raise awareness among plant virologists that their data might be useful for other plant pathologists as well, which hopefully will stimulate collaboration with bacteriologists, mycologists, and entomologists.
Various Non-Viral Pathogens Can Be Detected and Identified in Plant RNA-Seq Data As proof of principle, 22 total RNA-seq datasets derived from plant materials with known cellular pathogen infections were analyzed to see whether or not these pathogens could be detected in the RNA-seq data. These datasets can be regarded as reference samples (positive controls), since they were obtained from plants with confirmed infections. An overview of the datasets, of the detected pathogens, and of the observed number of reads per million (rpm) (defined as the number of pathogen reads (pairs, in the case of paired-end sequencing) per million of total reads) is provided in Table  1. The results show that, for 19 of the 22 datasets, we were able to detect the pathogen in the RNA-seq data with more than 10 rpm.
The relative abundance of the pathogen varies greatly (in this case, from 1 to 51,974 rpm) depending on the pathogen and the host tissue. In some cases, the numbers are extremely high, for example Plasmopara viticola in grapevine leaves has 51,974 rpm assigned, corresponding to >5. 2% of the total number of reads. In other cases, for example, for Xylella fastidiosa in willow leaves, the observed numbers are very low  (35 rpm) ; thus, the sensitivity of detection can be limited. Table  1. Overview of samples with confirmed pathogen infection used for RNA-seq analysis and of datasets downloaded from public sequence databases. The number of reads per million (rpm) together with the most likely species assignment based on the Kraken2 analysis is given. The latter was based on the species that had the majority of the reads assigned to them.
After confirming that it is indeed possible to detect non-viral pathogens in total RNAseq datasets, we set up an "RNA-seq community effort", with the goal to re-analyze existing RNA-seq datasets available in the plant virology community for the presence of otherpossibly unnoticed-pathogens (Figure  1 ). This community effort consisted of three phases. In Phase I, the participants analyzed the data themselves by mapping the generated reads against the SILVA ribosomal RNA database to obtain a first evaluation of the fraction of non-plant reads that might be present in the data. SILVA provides comprehensive, quality checked, and regularly updated datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea, and Eukarya).
In Phase II, performed by a bioinformatics expert, some selected datasets (showing a substantial amount of non-plant rRNA reads) were selected for a more thorough taxonomic classification of all RNA reads (instead of only the rRNA reads) using Kraken2. Finally, in Phase III, the presence of pathogens and pests detected in Phase II was evaluated by participants and, in some cases, subjected to experimental (q)PCR confirmatory efforts. Finally, participants answered a survey designed to evaluate the usefulness of this approach to detect pathogens and pests. An overview of all datasets that were entered in the "RNA-seq community effort" can be found in File S1. In total, 15 participants from 10 countries participated. One participant worked exclusively in a diagnostic lab, while 13 were from labs doing both research and diagnostics, and 1 was from a research-only lab.
In total, 101 datasets were analyzed, with individual participants providing between 1 and 20 datasets. An overview of the datasets per participant is shown in Figure  S1. The datasets were derived from 51 different plant species belonging to 29 different families. Most species were food crops (74), but there were also some industrial  (5)  and ornamental crops  (11) , as well as some wild species  (11). The most frequent species was grapevine (10 datasets), followed by potato  (6)  and several fruits, such as apple  (5) , blackberry (4), and pear (4), as well as rose (4) and tomato (4). Four samples represented pools of different plant species. The vast majority of the datasets were derived from leaf material (69), branches (1), petioles  (10) , or combined leaves + petioles/branches (9), while only a minority of them were derived from other plant parts, such as tubers/rootstocks  (10)  or pollen  (2).
At least 9 different RNA extraction methods/kits were used (File S1). About half of the samples (45) were treated with DNase during RNA extraction, and the majority of the samples underwent an rRNA depletion step (70) prior to HTS. Interestingly, one participant entered the same sample twice, once processed with and once without an rRNA depletion step. Most samples were paired-end sequenced (70), with 2 × 150 bp being the most popular read length  (40) , while, for the single-end datasets, 1 × 75 bp occurred the most often  (25). The number of reads ranged from 1. 2 M to 142. 0 M per dataset, with an average of 22 M. Figure  1. Overview of the different phases in the "RNA-seq community effort", in which total RNAseq datasets previously analyzed for the presence of viruses were re-analyzed in order to find possible traces of non-viral pathogens and pests.
An overview of all datasets that were entered in the "RNA-seq community effort" can be found in File S1. In total, 15 participants from 10 countries participated. One participant worked exclusively in a diagnostic lab, while 13 were from labs doing both research and diagnostics, and 1 was from a research-only lab. In total, 101 datasets were analyzed, with individual participants providing between 1 and 20 datasets. An overview of the datasets per participant is shown in Figure  S1. The datasets were derived from 51 different plant species belonging to 29 different families. Most species were food crops (74), but there were also some industrial (5) and ornamental crops  (11) , as well as some wild species  (11). The most frequent species was grapevine (10 datasets), followed by potato (  6 ) and several fruits, such as apple  (5) , blackberry (4), and pear (4), as well as rose (4) and tomato (4).