dataesr/scientific-paragraphs-categorization huggingface dataset:
62) and did not add explanatory power to nitrification rates. Respiration rates were well-explained by the environment, with an average adjusted R 2 of 0. 66 (Figure  3 ). Similar to the full dataset, microbial-only respiration models showed an average adjusted R 2 of 0. 29 and only 23% of respiration models were improved with the addition of 16S rRNA gene diversity or PLFA data (no functional gene abundance datasets existed), by an average increase in 0. 06 in adjusted R 2 across all respiration datasets. Interestingly, microbial biomass, when measured, improved 35% of models of respiration rates (n = 17, 0. 09 average increase in adjusted R 2 ), while 53% of models were improved with data on biomass and community structure in combination (0. 15 average increase in adjusted R 2 ). Microbial biomass was correlated to environmental variables with an average adjusted R 2 of 0.
56 but not to microbial community structure (average adjusted R 2 0. 11). Although most of our datasets were from natural soil studies (n = 47), we had sufficient replication of sediment (n = 12) and agricultural soil (n = 12) datasets to examine these independently; other environments (n < 12) were excluded from this analysis. While statistically significant differences were not found, possibly due to the large variation in our datasets, we observed some notable trends across environments. Datasets from sediment samples were less well-explained by environmental or microbial predictor sets than other datasets (Figure  4 ). Relationships between community diversity and environmental conditions were also weak in sediment systems (n = 8, average adjusted R 2 0. 17), and community diversity metrics provided more added value (0.
DISCUSSION Despite the vast amount of variation in our datasets, our results indicate that data on microbial community structure may strengthen predictions of C and N cycling rates. Within our full dataset, environmental variables alone had greater explanatory power than either metrics of microbial community structure or microbial biomass but left 44% of variation in process rates unexplained on average (Figure  1 ). While we acknowledge that factors such as unmeasured variables, macroinvertebrates  (Ferris et al. , 2004) , fungi  (Talbot et al. , 2008) , plant communities  (Hanson et al. , 2000) , and non-linearity could reduce the explanatory power of our models, a number of ecological dynamics can disassociate environmental conditions from microbial communities mediating C and N cycle processes and alter the relative value of environmental and microbial data for explaining rates of ecosystem processes  (Knelman and Nemergut, 2014; Nemergut et al.
, 2014). When such factors strongly impact microbial communities, data on microbial community structure should enhance our predictions of ecosystem processes beyond those based solely on environmental data. We observed stronger trends in the explanatory power of microbial predictors on ecosystem process rates when we reduced noise by examining subsets of data containing a single process or environment type. However, the lack of consistent trends within the full dataset is not surprising due to a variety of ecological and methodological factors. For instance, microorganisms can enter a state of lowered activity or dormancy in response to unfavorable environmental conditions  (Jones and Lennon, 2010; Lennon and Jones, 2011) , and research suggests that the percent of soil microbial communities catalyzing chemical reactions in soils at a given point in time can vary widely with resource history and disturbances  (Blagodatskaya and Kuzyakov, 2013).
These factors also induce spatially and temporally variable relationships between microbial communities and ecosystem processes, and the same microbial community may display different relationships to ecosystem function when sampled through time. Moreover, the extent to which functional traits are phylogenetically conserved varies with the trait of interest  (Martiny et al. , 2013) , and thus, some processes may exhibit inherently stronger correlations with taxonomic metrics than others. Although, rapidly improving transcriptomic and proteomic sequencing technology may enhance predictions of ecosystem processes by identifying active segments of microbial communities  (Abram, 2015) , too few datasets presently exist to conduct a robust analysis with these data. Studies investigating the extent to which these dynamics are masked within observations of microbial community structure and function are imperative to deciphering and improving the current ability of microbial data to predict ecosystem process rates.
As well, discrepancies in methodology may also confound the relationship we observed in the full dataset between microbial communities and biogeochemical processes  (Sinsabaugh et al. , 2015). Microbial DNA is typically extracted from a fraction of gram of soil at a single time point and methods detect both dormant and active organisms, while corresponding process data are measured in several grams of soil over longer timeframes. Further, process rate measures are either collected as net measurements that aggregate multiple chemical reactions, potential rates that represent microbial response to substrate addition, or gross rates that trace individual chemical transformations  (Bier et al. , 2015). These approaches inevitably introduce data aggregation issues  (Clark et al. , 2011)  and rely on assumptions that microbial and biogeochemical data are spatially and temporally representative.
Recently, studies have attempted to reduce uncertainty associated with scalar differences by pairing spatially and temporally explicit field studies with ecological modeling approaches that interpolate data across FIGURE 4 | We analyzed natural soil (n = 47), sediment (n = 12), and agricultural soil (n = 12) environments separately. Bar height represents the average explanatory power of each predictor set, with error bars denoting standard errors. While predictor sets in natural soils nearly matched those within the full dataset, environmental and microbial predictor sets had lower explanatory power within sediments and higher explanatory power within agricultural soils. Microbial community diversity was less tightly coupled to the environment and provided more model improvement in sediments, with the opposite relationship in agricultural soils.
scales  (Lira-Noriega et al. , 2013; Walker and Wardle, 2014). Leveraging these approaches in conjunction requires substantial resource investment but is increasingly feasible and presents a promising avenue for maximizing the value of microbial data. Despite inherent limitations in microbial data, our data analysis suggests that microbial data types differ in their ability to explain processes that are phylogenetically broad vs. narrow and that are products of facultative vs. obligate metabolisms (Figure  2 ). Nitrification is the product of an obligate chemolithotrophic metabolism, and functional markers for this process are narrowly constrained within ammoniaoxidizing bacteria and archaea that catalyze the rate-limiting step in nitrification, putatively representing a likely dataset for detecting relationships between microbial data and process rates  (Schimel, 1995; Schimel and Gulledge, 1998).
While both functional gene abundance and community diversity metrics had similar average explanatory power within the full dataset (an effect that also did not vary among microbial technologies, Supplementary Figure  S2A ), models based on functional gene abundances or functional diversity explained more variation in process rates than community diversity within datasets measuring rates of nitrification (Figure  2C ). Although these metrics were more strongly correlated with environmental factors than community diversity, they also provided greater explanatory power in combination with environmental variables than community diversity metrics, which include variation in many microbial guilds external to nitrification. Notably, models based on environmental variables alone also had higher explanatory power for nitrification than for denitrification or N mineralization, suggesting that the environment may also be more strongly coupled to nitrification rates than other N cycle processes.
Taken together, these results suggest that despite a relatively tight connection between the environment, microbial community structure, and process data, the microbial guilds driving nitrification rates are partially decoupled at a functional level from environmental drivers of nitrification. Importantly, although nitrification was the only narrow process in our dataset with sufficient replication to examine independently, the high explanatory power of functional genes, both alone and in combination with environmental variables, for describing nitrification rates relative to other processes also conveys the potential for the inclusion of functional metrics in improving predictions of ecosystem processes that are mediated by phylogenetically narrow functional guilds. By contrast, denitrification is a broad process, catalyzed by diverse facultative anaerobic microorganisms, and community diversity metrics provided more added value to denitrification rates than functional gene diversity or abundances (Figure  2B ).
Thus, it is plausible that denitrifying communities are decoupled from environmental variables when measured at a broad taxonomic level and that such taxonomic metrics encompass more variation in within community interactions affecting denitrification rates than functional approaches. Although the sample size of datasets measuring denitrification was small, our analysis suggests that measurements of community diversity may be more likely than functional metrics to improve predictions of ecosystem processes that are catalyzed by facultative metabolisms and/or phylogenetically broad suites of organisms. Similarly, the ability to respire carbon is widely distributed among microorganisms, and community diversity metrics (no datasets measured functional markers) described rates of respiration better than all other processes except denitrification (Figure  3 ).
Thus, biomass and structure may jointly contribute to explaining rates of respiration as biomass may serve as a proxy for unmeasured environmental variables that regulate activity, such as soil structure  (Gupta and Germida, 1988) , while community structure may relate to the genetic capability of a community to respire carbon. Regardless, our results suggest that these data are at least partially independent and that measurements of microbial community structure could reduce uncertainty within Earth Systems models. Lastly, the relationship between microbial community structure and ecosystem processes in our dataset varied by environment type (Figure  4 ). Sediment systems had weaker explanatory power of process rates by both environmental variables and microbial community structure than natural or agricultural soils.
Sediment processes are influenced by both sediment and porewater chemistry  (Middelburg and Levin, 2009) , and these datasets may be more likely to have unmeasured variables or errors due to spatial discrepancies than other systems. In particular, dissolved oxygen concentration is a strong determinant of redox potential and ecosystem processes in sediments  (Abell et al. , 2011)  but was only measured in three of 12 datasets. Despite the low explanatory power of sediment processes by all predictor sets, microbial community diversity added more value to explaining process rates and was more decoupled from environmental variables in sediments than in natural soil or agricultural systems, indicating that measurements of microbial community structure may improve predictions of sediment processes.
Here, we present the first extensive investigation into the relationship between environmental conditions, microbial community structure, and ecosystem function by re-analyzing 82 datasets collected from an international team of collaborators, and we demonstrate that nuanced relationships between the environment and microbial communities can influence the importance of microbial community structure for explaining ecosystem-level processes. Our analysis provides an empirical basis for future hypothesis-testing on the roles of assembly, dormancy, redundancy and phenotypic plasticity in microbial community structure and function, and despite complexity in our datasets, the trends we observed represent an encouraging step for linking microbial community data to ecosystem function. As a whole, our results indicate that a greater understanding of microbial community structure informed by ecological principles may further our ability to accurately predict rates of ecosystem processes beyond environmental variables and bulk physiological characterizations of microbial communities.
The phenotypic and molecular results reported confirmed that cryostorage was an efficient and reliable technique to conserve P. vulgaris seeds and regenerate true-to-type plants. This paper aims at providing more data about the impact of cryostorage on mineral nutrition of young common bean plantlets. We hypothesized that seeds subjected to low temperature stress produce seedlings with changed mineral nutrition. The following elements were measured on leaves of 10-day-old plantlets from noncryopreserved and cryopreserved seeds: Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, S, Se, Sr and Zn. To our knowledge, such information has not been published to date on seeds. In the literature, many publications describe cryopreservation techniques  [5] -  [11]. By contrast, only few studies have been carried out on the understanding of the biochemical and physiological changes occurring after liquid nitrogen exposure of seeds  [3]  [4] [12]-  [16].
The samples were analyzed by inductively coupled plasma optical emission spectrometry (ICP-OES) (iCAP 6000 ICP Spectrometer, Thermo Fisher Scientific Corporation). Precautions were used to avoid inadvertent contamination of plant materials with micronutrients-i. e. plastic forceps were used. The growth chamber was isolated from other soil samples. The SPSS software (Version 8. 0 for Windows, SPSS Inc. ) was used to perform t-tests and compare results of the two treatments studied: non-cryopreserved and cryopreserved seeds (p ≤ 0. 05). The overall coefficients of variation (OCV) were calculated as follows: (standard deviation/average)*100. In this formula, we considered the average values of non-cryopreserved and cryopreserved seeds to calculate the standard deviation and average of the two treatments.
Results and Discussion At 10 days after sowing, both treatments showed 100% seed germination without any visual phenotypic difference. Germination progress curves, i. e. germination rate, were the same (data not shown). However, contents in several elements in leaves were different (Table  1 ). According to the highest OCVs, the most important changes in the mineral uptake can be summarized as follows. Exposure of seed to liquid nitrogen decreased Cu, Cd and Na uptake and increased absorption of B and Al; although no symptom of deficiency or excess was recorded in our experiment. Cu is associated with enzymes involved in redox reactions being reversibly oxidized from Cu + to Cu 2+ , such as those enzymes related to electron transfer during the light reactions of photosynthesis  [20]. The initial symptom of Cu deficiency is the production of dark green leaves, which may contain necrotic spots.
Acknowledgements The authors wish to thank warmly all the colleagues and friends who supported them during the making of this article with literature, suggestions and advice. They are too many to list them and we are afraid of missing someone. The authors are also very deeply grateful to two very special reviewers,  Patrice Bouchard  ( Canadian National Collection of Insects, Ottawa ) and  Christopher H. C. Lyal  ( Natural History Museum, London ) who greatly improved these pages with their wide knowledge of Coleoptera.
2 numerical aperture [NA]) or Plan-Apochromat 63× (1. 40 NA) oil objectives (Carl Zeiss, Jena, Germany). Size and number of lipid droplets were quantified using MetaMorph software (Molecular Devices, Sunnyvale, CA). To follow the dynamics of lipid droplets, Huh7 cells were incubated in DMEM with 10% (vol/vol) fetal bovine serum plus 20 ng/ml Nile red at 37°C in a humidified chamber equilibrated at 5% CO 2. Time-lapse sequences were recorded at 15-min intervals on an Olympus FV1000 confocal microscope (Olympus, Tokyo, Japan) using a Plan-Apo 60× (1. 10 NA) oil immersion objective.
, 2013). The sgRNAs were designed using the CRISPR. mit. edu design software. Oligonucleotides (5′-caccg-GCCTCTCAAAGTCTTATGTG-3′ and 5′-aaacCACATAAGACTTTGA-GAGGCc-3′ for ACAT1; 5′-caccgGCAGTCCAGTCAATAGGGAT-3′ and 5′-aaacATCCCTATTGACTGGACTGCc-3′ for ACAT2) were annealed and cloned into the BbsI site of the pSpCas9(BB)-2A-puro vector (plasmid 48139; Addgene). The plasmids were introduced into Huh7 cells using Lipofectamine 3000 (Thermo Fisher Scientific, Waltham, MA). After 3 d, single cells were selected with puromycin, and individual clones were expanded to cell lines. Genomic DNA was extracted from the cell lines using GenElute Mammalian Genomic DNA Miniprep Kits (Sigma-Aldrich). PCR-amplified products were directly sequenced using the Big Dye Terminator Cycle Sequencing v3. 1 Kit (PE Applied Biosystems, Foster City, CA).
The high temporal frequency of acquisitions, associated with a spatial resolution of 20 m, allows investigation of the ability of such data to be used to monitor variations in seasonal floods at the local scale. Similar or higher temporal frequencies of acquisitions characterize the TerraSAR-X (11 days) and COSMO-SkyMed (4 days) sensors operating in the X band. However, the conflict which may occur with other planned acquisitions, the cost, and the smaller spatial coverage (~30 ˆ30 km associated to spatial resolution of 20 m) hamper their systematic use over large areas and long time periods. Furthermore, L band data are acquired by the PALSAR sensor onboard the ALOS-2 satellite with a revisit time of 14 days. Although the L band would be well suited for floods detection, thedata are not as easily available as Sentinel-1 data.
Therefore, this study focuses on evaluating the potential of Sentinel-1 data. The studied Sentinel-1A image time series are acquired in two polarizations (VV-HV). Since intensity data are only available for half of the available products, no polarimetric analysis based on the differential phase between VV and HV statistics is possible. Besides, as one of the main constraints of the study is the preservation of the spatial resolution, textural analysis is not performed due to the associated spatial resolution degradation. For these reasons, we focused on the intensity analysis of the Sentinel-1A time series in both HV and VV polarizations. The retained method is a hysteresis thresholding  [37]  in the spatio-temporal domain which is similar to the region-growing algorithms used only in the spatial domain in  [32].
Radar Data Radar data were acquired by the Sentinel-1A satellite equipped with a C band (λ = 5. 6 cm) sensor. The study period extends from December 2014 to May 2015, corresponding to the flooding season. Data were downloaded from the Scientific ESA hub website  [13]. Sentinel-1A acquisitions are separated by 12 days. Of the 15 potential acquisitions during the study period, 14 are available (23 January is missing, Table  1 ). To obtain identical configurations, only data acquired during the same ascending pass (track 30) are selected. The incidence angle over the study site ranges from 36° to 42°. The data were acquired in IW (Interferometric Wide Swath) mode at dual-polarization (VV/HV) and are delivered in GRDH (Ground Range Detected High Resolution) products. They have a spatial resolution of about 20 × 22 m 2 in the ground range and azimuth direction, respectively, with an equivalent number of looks (ENL) of 4.
Radar Data Radar data were acquired by the Sentinel-1A satellite equipped with a C band (λ = 5. 6 cm) sensor. The study period extends from December 2014 to May 2015, corresponding to the flooding season. Data were downloaded from the Scientific ESA hub website  [13]. Sentinel-1A acquisitions are separated by 12 days. Of the 15 potential acquisitions during the study period, 14 are available (23 January is missing, Table  1 ). To obtain identical configurations, only data acquired during the same ascending pass (track 30) are selected. The incidence angle over the study site ranges from 36 ˝to 42 ˝. The data were acquired in IW (Interferometric Wide Swath) mode at dual-polarization (VV/HV) and are delivered in GRDH (Ground Range Detected High Resolution) products. They have a spatial resolution of about 20 ˆ22 m 2 in the ground range and azimuth direction, respectively, with an equivalent number of looks (ENL) of 4.
Piezometric Probe Data Eleven piezometric probes were installed in October 2014 in meadows across the Poitevin marsh (Figure  1 ). Water level elevations were recorded hourly with an accuracy of 0. 5 cm. Probe values are expressed relative to sea level in the French national geo-referencing system (NGF-93). All probes were horizontally georeferenced with a differential GPS (10 cm accuracy). The database of each probe was collected during a field visit in June 2015 and hourly water levels were extracted for each radar acquisition date. Probe data are used to detect floods at the intra-field scale.
Pre-Processing of Radar Data Pre-processing is performed with SNAP (Sentinel Application Platform) software  [41]. First, images are calibrated to derive radar backscattering coefficient σ values  [42]. Then, data are orthorectified using the Range Doppler Terrain Correction algorithm  [41]  and the SRTM (Shuttle Radar Topographic Mission) DTM. This procedure produces orthorectified σ 0 images with a relative localization error of about one pixel. The 4. 9 ENL data are still influenced by speckle noise, which has to be removed. Adaptive filters are generally used, since they reduce the noise over homogeneous areas while preserving sharp transitions. The Perona-Malik filter  [43] , which provides better visual results than classic radar filters  [44, 45] , are used in this study. The number of iterations that reaches idempotence and the gradient threshold K = 3 distinguish homogeneous regions from transitions, depending on the image content.
Assessing Accuracy Results are validated for two entities: ponds and flooded grasslands. Large permanent ponds are well suited to the spatial resolution of radar images. In contrast, flooded grasslands have smaller areas (1-15 ha), with features consisting mainly of small channel networks with high spatio-temporal variability. Hydrological dynamics at the intra-field scale are analyzed for these areas. For permanent ponds, similar to  [47] , floods are simulated in DTMs by adjusting the water level until it best matches (i. e. , best Kappa value) that of flooded areas detected from the radar data on each acquisition date. For the flooded grasslands, water levels measured by the piezometric probes (P01-P11) at 11 locations (Figure  1 ) are available for each acquisition date. From the DTM, within a 500 m buffer around each piezometric probe, a connected area is delineated that corresponds to the water height measurement.