dataesr/scientific-paragraphs-categorization huggingface dataset:
Before injection, samples were diluted in 1% isopropanol up to 7. 5 mL and 0. 22 μm filter-sterilized. Samples were analyzed using a Metrohm (Herisau Switzerland) 925 ECO ion chromatograph equipped with a conductivity detector, Metrohm Suppressor Module (MSM), 250 μL injection loop with peristaltic pump, Metrohm high pressure pump with purge valve and 863 compact autosampler. The detection of SO 4 2-anions was investigated, at room temperature, in suppressor mode using the separation column "Metrosep A Supp 4" (250 mm × 4. 0 mm) with a Metrohm IC precolumn cartridge PRP-2 (Metrosep RP2 Guard/3. 5) and eluent 1. 7 mM NaHCO 3 /1. 8 mM Na 2 CO 3 , 20% acetone. The eluent flow-rate was 1 mL/min. The calibration of the instrument was done by injecting standard solutions of sulfate. The instrument was operated with the MagIC Net Basic software, that controls and monitors the instrument, evaluates the recorded data and manages it in a database.
Relative quantitative proteomic data analysis The protein-containing SDS-PAGE stacking bands containing 60 μg of extracted proteins from cells of S. fructosivorans WT and SM4/ pBGhnd6 strains cultured in PS2 and ES20 conditions and collected at stationary phase of growth, were treated in biological triplicates for proteomic analysis as previously described in  Gérard et al. (2022)  with slight modifications as follows. Eight hundred nanograms of peptides were injected for LC-MSMS analysis on nano liquid chromatography Ultimate 3,000 (Thermo Scientific) coupled to a Q-Exactive Plus mass spectrometer (Thermo Scientific). The spectra were processed by Proteome Discoverer software (ThermoFisher, version: 2. 4. 1. 15) using the Sequest HT algorithm with the search following settings: Solidesulfovibrio_fructosivorans database (TxID 596151) extracted from Uniprot (4,161 sequence entries); trypsin enzyme (maximum 2 missed cleavages); fixed modification: carbamidomethyl (Cys); variable modification: oxidation (Met); mass values specific for monoisotopic; precursor mass tolerance: ± 10 ppm; fragment mass tolerance: ± 0.
Acknowledgments The authors thank  Marianne Guiral  ( BIP, CNRS ) for fruitful discussions and help with ionic chromatography experiments and  Yann Denis  ( IMM, CNRS ) for RT-q-PCR experiments. The authors also thank  I. Meynial-Salles  ( INSA, Toulouse ) for kindly providing the plasmid pthl-Fd-LL-C-Tag.
Funding This work was supported by the  CNRS  and  Aix Marseille Université  and the  Région Sud  ( CR PACA -AAP Recherche 2020  volet général, projet No- O2 ). This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under the  Marie Skłodowska-Curie  (grant agreement no. 713750 ). Also it has been carried out with the financial support of the  Regional Council of Provence-Alpes-Côtes d' Azur  and with the financial support of the A*MIDEX (no. ANR-11-IDEX-0001-02 ), funded by the  Investissements d'   Avenir  project funded by the  French Government , managed by the  French National Research Agency (ANR).
Dionex Chromeleon® chromatography software was used for instrumentation control and data acquisition. External 5-point calibration curves were used for quantitative determination of each ion, standard solutions for calibrations were prepared daily diluting 1000 mg/L Merck standard solutions (Darmstadt, Germany) with MilliQ ultrapure water (Resistivity > 18 MΩ•cm). Detection limits (d. l. ) and reproducibility are reported in Table  1. Table  1. Detection limit and reproducibility (%) for each ion. Detection limits are obtained as the concentration corresponding to a signal of three times the standard deviation of 10 repetitions of a 10 g/L standard solution.
A single o none break point was assumed to be present in each profile and its coordinates were computed by means of linear estimation using the "segmented" package in R software  (Muggeo, 2003 , https://cran. r-project. org/web/packages/segmented/index. html). Therefore, the x value of break point coordinates (if found in the depth range of the snow-pit) represents the depth at which end the active layer (active for re-emission processes) and start the archive layer. The y coordinates the archived concentration at the site. In all the snow-pits a decreasing trend of nssCl -concentration as depth increases is observed, but only at AGO5 the constant concentrations are positive. Therefore, only at this site the deposited HCl partially stays stably fixed in the deep layer. Considering an average value of 100 g/L of HCl deposited in the superficial layer at AGO5, only the 24% is preserved at depth higher than 1m.
Founding This study was founded by the  ANR EAIIST  (grant  ANR-16-CE01-0011 ), of the  French Agence Nationale de la Recherche , the  BNP-Paribas foundation  and its  Climate Initiative Program , the  Institut Polaire Français IPEV , the  MIUR (Ministry of Education, University and Research)  through the  PNRA ( National Antarctic Research Program) EAIIST project  (grant  PNRA16_00049-B ) and  PRIN2017  project " Innovative Analytical Methods to study biogenic and anthropogenic proxies in Ice COres" AMICO  (grant  PRIN_2017EZNJWN_006 ).
It is based mainly on field interviews and observations, and secondarily on statistical and bibliographical resources. The fieldwork was organized in three steps presented in the Figure  2:  (1) landscape analysis, (2) historical investigation (3) characterisation of currents systems. Data were collected during a 4-month field study, between May and August 2020, by a single investigator. Phase 1, the landscape analysis, lasted three weeks. The main variables considered were plot size, slope, exposure, soil depth, soil chemical and physical properties, hydromorphy, drainage and irrigation systems, climate, eco-landscape mosaic, livestock, crops and equipment, both through direct observations and bibliographic resources, mainly maps  [39, 40, 45]. This first step served to formulate hypotheses about the constraints the environment exerts on agricultural activities, and about how the ecosystem is exploited.
In Phase 3, 41 active farmers were interviewed in order to characterise current production systems. Farms were selected using the information collected during phase 2, in order to cover the diversity of environments, socio-economic conditions and agricultural productions existing in the area. The work carried out was similar to that of phase 2, but for the recent period (1990-2020). The basic principle of the agrarian diagnosis method is the reduction of the diversity of farms reconstructed at different times into a limited number of ideotypic production modes, called production systems. Each production system is a model of the technical and Data were collected during a 4-month field study, between May and August 2020, by a single investigator. Phase 1, the landscape analysis, lasted three weeks.
Literature Search and Data Collection We systematically searched for all peer-reviewed journal articles that studied cultured aquatic animal mortalities due to viral infections, using the Web of Science, up to 1 March 2019. Grey literature (i. e. , student thesis and technical reports) was not included in the literature review to prevent bias (i. e. , only English grey literature would have been identified in our search). As articles studying viral infections in aquatic animals often use highly specific terminology (i. e. , virus or species names, in the title, keywords and topic) we used a two-step literature search. First, we conducted a general search to identify viral infections in aquatic animals, for which we could obtain at least five eligible articles. For this first search, we used the following generic keywords: (fish* OR shrimp* OR oyster* OR mollusc*) AND (mortality OR outbreak OR infection) AND virus AND temperature.
We reviewed each article to determine whether it met the following criteria: cumulative mortality and stable specific temperature (+/-2 • C) were reported for infected aquatic cultured animals. We identified only three viral pathogens (OsHV-1, CyHV-3 and Betanodaviruses), for which we could obtain sufficient data (threshold of 5 independent articles). We then conducted specific searches on these viral infections (up to 27 March 2023) to identify other relevant studies that did not appear in our first general search, using the following keywords: (carp* OR oyster* OR fish* OR shrimp* OR mollusc*) AND (mortality OR outbreak OR infection) AND (KHV OR CyHV-3 OR OsHV-1 OR betanodavirus*). This search yielded 736 articles, which were examined for eligibility. We only retained articles that reported cumulative mortality at a specific temperature.
Articles from field reports or experimental trials, where temperature was not fixed (+/-2 • C), were not included. Only articles where experimental infections were conducted at a specified infective dose and only one pathogen was detected and clearly identified (i. e. , through PCR or similar) were included (i. e. , multi-infection experiments or articles where secondary infections were identified were excluded). The following data were extracted from each of the selected articles: taxonomy of the pathogen and host (family and species), stage of host development (spats, only for oysters, juvenile, adult), country, temperature of the infection, cumulative mortality and type of infection (injection or experimental by immersion). When a study included several experiments at different temperatures, we considered them as separate observations.
Data Analysis The three datasets (CyHV-3, OsHV-1 and Betanodaviruses) were analysed separately to investigate the effect of temperature on mortality associated with each disease. We also analysed all the results together, to investigate the general trends in the effect of temperature on virus mortality in aquaculture. Nested linear regression models were constructed to examine the relationship between mortality of virus-infected animals and temperature. The fixed effects included temperature, life stage, type of infection and infection dose (log-transformed). Random effects were only used in the analysis of the Betanodaviruses dataset and overall dataset (i. e. , containing CyHV-3, OsHV-1 and Betanodaviruses), and included the host and pathogen taxonomy. Akaike's Information Criterion for small sample sizes (AICc) was used to assess the explanatory value and parsimony of each model.
Relationship between Mortality of Virus-Infected Animals and Temperature We collected data from 53 experimental studies to investigate the influence of temperature on the mortality of farmed aquatic animals infected with CyHV-3, OsHV-1 and different variants of Betanodaviruses (NVVs). Linear regression models showed that increasing water temperature resulted in higher mortality rates in CyHV-3-infected carp (Cyprinus carpio), OsHV-1-infected oysters (Crassostrea gigas) and fishes infected by NVVs (Table  1 , Figure  1A-C ). None of the models' fixed effects (life stage, type of infection and log(dose)), except temperature, were important predictors for the mortality OsHV-1infected oysters' mortality (Supplementary Information S2). In the CyHV-3 model, the type of infection was an important predictor of mortality in carps, and was therefore included in the selected model (Supplementary Information S3).
The Betanodaviruses dataset was comprised of different fish species and different variants of Betanodaviruses; therefore, host and pathogen taxonomy (species and family) were added in the models. The model with the best fit included the pathogen and host species as random effects and life stage and type of infection as fixed effects (Supplementary Information S4). Table  1. Details from the selected linear models (LMs) and linear mixed models (LMMs) to test the relationship between aquatic animal mortality and temperature (T) under viral infections. The p-value is provided for LMs to indicate term significance. In LMMs, a term is considered significant when its 95% confidence interval does not include 0, and is indicated by *. x FOR PEER REVIEW 5 of 15 in the selected model (Supplementary Information S3).
The Betanodaviruses dataset was comprised of different fish species and different variants of Betanodaviruses; therefore, host and pathogen taxonomy (species and family) were added in the models. The model with the best fit included the pathogen and host species as random effects and life stage and type of infection as fixed effects (Supplementary Information S4). 1 ). The dotted lines represent the 95% confidence intervals of the prediction. 1 ). The dotted lines represent the 95% confidence intervals of the prediction.
Model Predictions for OsHV-1, CyHV-3 and NVVs The models predicted that an increase in water temperature of 1 • C would result in an increase in mortality of 1. 47-8. 33% (95% confidence interval, CI) in OsHV-1-infected-oysters, 2. 55-6. 98% (95% CI) in CyHV-3-infected carps and 2. 18-5. 37% (95% CI) in NVV-infected fishes (Table  1 , Figure  1 ). The linear model combining the three datasets (CyHV-3, OsHV-1, NVVs) showed that an overall increase in water temperature of 1 • C would result in 3. 07-5. 70% (95% CI) increase in the mortality of these virus-infected animals (Table  1 , Figure  2 ). The selected model for the overall virus dataset included host species as the random effect, and life stage and type of infection as the fixed effects (Supplementary Information S5). Microorganisms 2023, 11, x FOR PEER REVIEW 6 of 15 3, OsHV-1, NVVs) showed that an overall increase in water temperature of 1 °C would result in 3.
07-5. 70% (95% CI) increase in the mortality of these virus-infected animals (Table 1, Figure  2 ). The selected model for the overall virus dataset included host species as the random effect, and life stage and type of infection as the fixed effects (Supplementary Information S5). ). Solid lines represent the predicted mortalities according to selected linear and linear mixed models (Table  1 ), with dotted lines representing the 95% confidence interval for each of the models.
Impact of Global Warming on Viral Epidemics in Aquaculture Systems While our results highlight the paucity of empirical data available to date on the effects of global water temperature increase and its impact on viral virulence (host mortality rate) in farmed aquatic animals, we did find a positive trend between increased water temperature and increased virulence for CyHV-3, OsHV-1 and NVVs infections. Interestingly, even small increases in water temperature, such as a 1 °C rise, could result in a 3. 07-5. 70% (95% CI) increase in mortality of animals infected with CyHV-3, OsHV-1 and NVVs. This is 1. 09-1. 83 times higher than the increases in bacterial mortality expected in warm waters, but slightly lower (1. 05-1. 26 times lower) than the bacterial-related mortality expected in temperate animals (Figure  2 )  [6].
Impact of Global Warming on Viral Epidemics in Aquaculture Systems While our results highlight the paucity of empirical data available to date on the effects of global water temperature increase and its impact on viral virulence (host mortality rate) in farmed aquatic animals, we did find a positive trend between increased water temperature and increased virulence for CyHV-3, OsHV-1 and NVVs infections. Interestingly, even small increases in water temperature, such as a 1 • C rise, could result in a 3. 07-5. 70% (95% CI) increase in mortality of animals infected with CyHV-3, OsHV-1 and NVVs. This is 1. 09-1. 83 times higher than the increases in bacterial mortality expected in warm waters, but slightly lower (1. 05-1. 26 times lower) than the bacterial-related mortality expected in temperate animals (Figure  2 )  [6].
e. , cell-to-cell) and amongst hosts, leading to higher viral loads  [43, 44]. These studies suggest that the relationship between temperature and viral replication and propagation remains complex  [45] , often species-specific, and needs to be better characterized. However, evidence suggests that increases in temperature outside the normal range cause cellular stress and weaken the immune system of most aquatic ectotherm species, making them more vulnerable to infection and perhaps mortality  [46, 47]. Here, we sought to conduct a comprehensive meta-analysis to clarify the role that increased temperature might play in mortality caused by viral infections in cultured aquatic species. Although our meta-analysis focused only on the effects caused by three types of viral pathogens in aquaculture (CyHV-3, OsHV-1 and NVVs), due to the lack of comparable experimental and field data between species and pathogens, thus preventing he inclusion of other pathogens in the analyses, we showed that for these two globally important diseases, a higher temperature leads to increased host mortalities.
The datasets used here for KHVD and OsHVD include studies in which viral inoculums were experimentally injected into healthy animal hosts, and the presence of bacteraemia following viral infections was not reported. Although unlikely, the presence of secondary bacterial infections cannot be completely excluded, and a potential undetected co-infection could be related to the higher mortality increases (4. 06-9. 17% increased mortality/ • C) observed in OsHVD-infected oysters. Finally, Betanodaviruses are the agents responsible for the highly infectious disease VNN, which results in the necrosis and vacuolation of the brain, retina and spinal cord of infected fish and invertebrates  [32, 52]. Furthermore, VNN is considered one of the most important threats to fish farming, as it can affect 44 fish species from 24 families worldwide  [52].
parallel")  71  and principal components analysis (function "principal")  71  with varimax rotation to extract composite variables (principal components). Next, we conducted statistical analysis (Generalized Additive Model for Location, Scale and Shape, function 'gamlss')  72  to test if the acoustic properties (the principal components) of the speci c types of syllables (syllable type "L" of the Oriental tit and syllable type "f" of the Varied tit) are different among predators or between the two species. The goodness of t of the models was assessed using a worm plot (function 'wp' in package 'gamlss')  72  , which generated residual quantiles that aligned with a uniform distribution, indicating a reasonable t of the model to the data. The "L" syllable of the Oriental Tit in response to a plastic snake had n = 322 phrases from 10 nests, in response to a jay had n = 229 phrases from 8 nests, and in response to a cat had n = 196 phrases from 8 nests.
The "f" syllable of the Varied Tit in response to a plastic snake had n = 288 phrases from 11 nests. Additionally, we conducted variance component analysis on each principal component to explore the variability of acoustical properties by levels of explanatory or random variables (e. g. , predator, species, and nest)  73. All the statistical analyses were conducted in R  74  (see Supplementary Table  S10  for the basic information of acoustical dataset). As we used two types of microphones that may differ in their frequency response curves, comparisons involving frequency variables can only be conducted within the set of data obtained using the same microphone model. Therefore, we decided to conduct all statistical comparisons within the same microphone model. Hence, we conducted three separate statistical analyses (Table  1 ): comparison of the Oriental Tit's "L" syllables in snake calls with those in jay alarm calls (Sennheiser microphone) and with those in cat alarm calls (Primo EM172 microphone), as well as comparison of snake alarm call syllables ("L" for Oriental Tits and "f" for Varied Tits) between species (Primo EM172 microphone).
Microclimate -proximal climatic variation at scales of meters and minutes -can exacerbate or mitigate the impacts of climate change on biodiversity. However, most microclimate studies are temperature-centric, and do not consider meteorological factors such as sunshine, hail and snow. Meanwhile, remote cameras have become a primary tool to monitor wild plants and animals, even at micro-scales, with deep learning tools to rapidly convert images into ecological data. However, deep learning applications for wildlife imagery have focused exclusively on living subjects. Here, we identify an overlooked opportunity to extract latent, yet ecologically relevant, meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africa's Maloti-Drakensberg and the Swiss Alps.
Several studies have manually extracted the presence, cover or depth of snow in ecological imagery  (Lumbrazo et al. , 2022). Furthermore, some studies automate the quantification of snow in PhenoCam images  (Caparó Bellido & Rundquist, 2021; Julitta et al. , 2014) , or the classification of frost  (Noh et al. , 2021). However, the use of a single, efficient classifier to simultaneously detect sunshine and frozen precipitation in wildlife images has not been explored. Working with wildlife imagery from matching experimental sites in mountains in the northern (Switzerland; CH) and southern (South Africa; ZA) hemispheres, we train ensemble deep learning models to detect micrometeorological conditions of sunshine, hail, and snow. Our objectives are as follows: (1) Build a dataset and train deep learning classifiers to derive micrometeorological conditions in images; (2) evaluate different data-splitting and ensemble approaches to maximize model performance with out-of-sample and out-of-distribution test datasets; and (3) demonstrate the application of image-derived micrometeorological variables for ecological research.
Model validation We used model validation with early stopping to minimize overfitting to the training data. We stopped training if validation performance did not improve for 30 epochs and saved the model from the epoch with the best validation performance (concepts explained in Appendix S2). To account for variance related to data splitting for validation, we used cross-validation. Specifically, we carried out a 5-fold cross-validation in which each of five mutually exclusive data folds is iteratively treated as the validation dataset (e. g. Fig. S1 , inner validation loop). Additionally, we compared two data splitting methods: "Cis" and "Trans"  (Beery et al. , 2018 (Beery et al. , , 2020)). Cis validation involves random splitting of images from all cameras. Trans validation splits cameras rather than images, such that images from one camera are always constrained to a single fold (Fig.
Model testing and deployment We aimed for models that would transfer to (1) novel camera positions within our sites and (2) novel sites with a similar recording protocol. To test transferability to novel positions within our sites, each 5-fold validation loop was nested within an outer 6-fold test loop (e. g. Fig. S1 ). In other words, we used a nested cross-validation or "double-cross" to obtain a robust and unbiased measure of model generalization  (Cawley & Talbot, 2010). We randomly split 49 cameras into six folds that were stratified across the four sites  (Wieczorek et al. , 2022). During each test loop iteration, one of six folds was withheld as a test dataset. We compared model accuracy and macro-F1 (the mean of class F1 scores) following Eq. 1-4 where: TP = true positives; TN = true negatives; FP = false positives; FN = false negatives; Pr = class precision; and Re = class recall.