dataesr/scientific-paragraphs-categorization huggingface dataset:
piscoweb. org). In addition to scientific datasets, citizen science programmes across the globe (e. g. LiMPETS, Shorekeepers, MARINe, Sea Watchers, Reef Life Survey, Capturing Our Coast) provide broad-scale, interannual data on the distribution of intertidal species. These datasets could also be incorporated into networks studying climate change impacts on coastal ecosystems. The implementation of large-scale, global biological observing networks poses considerable challenges, including defining key variables and methodological standardization. To address these challenges, the Biology and Ecosystem Panel within the Global Ocean Observing System is working on the identification and definition of ecological Essential Ocean Variables (EOVs) that integrate biological observing in coordinated monitoring networks, emphasizing simplicity and societal relevance, and providing a unifying global standard observational framework  (Miloslavich et al.
2018 ). In addition, the implementation of a truly multinational observing network of marine biodiversity will require sustained support to facilitate integration of existing programmes and the development of new ones to promote high spatiotemporal resolution observations. Developing new technologies for automated acquisition of biological data will be key to operationalize an integrated, global observing biodiversity network. Freely available platforms such as the Ocean Biogeographic Information System are beginning to address data integration challenges, promoting stan-dardization of methodologies and providing open access to datasets from around the world. With an informed understanding of the quality checks and caveats, these platforms provide an invaluable means to explore macro-scale patterns of biodiversity and temporal changes in species distributions, although further development and integration is still required.
Hybrid datasets combining observational data with distributed experiments have been proposed to leverage scope and attribute causality, combining the large scope of observations with the strong causal inferential strengths of manipulative experiments  (Bene detti-Cecchi et al. 2018). A further step towards the integration of empirical data and models is offered by macro-scale modelling, involving mathematical representation of individual, population and/or ecosystem processes to assess changes in system properties and test responses to scenarios of change or management  (Queirós et al. 2016). By using information derived from survey and experimental work within modelling frameworks, climate change responses and effects of novel future stressor combinations can be assessed beyond the information contained in historical datasets, providing anticipatory information to marine adaptation and mitigation policies at policy-relevant spatiotemporal scales.
CONCLUSIONS A multi-faceted approach, combining experimental work on individual organisms, high-resolution field observations of populations across large spatiotemporal scales and mechanistic species distribution models based on life-history traits and species interactions is required to address the fundamental question of how coastal marine species will respond to climate change. The analysis of hybrid datasets with emerging techniques in time-series analysis and macroecology is a powerful approach combining the experimental ability to establish causality with the broad scope allowed by observational studies. Such toolkits are necessary to develop biologically realistic models to forecast the impacts of future climate change, and facilitate adaptive management of coastal ecosystems.
fast advancement of machine learning methods and constant growth of the areas of application open up new horizons for large data management and processing. Among the various types of data available for analysis, the Fourier Transform InfraRed (FTIR) spectroscopy spectra are very challenging datasets to consider. In this study, machine learning is used to analyze and predict a rheological parameter: firmness. Various statistics have been gathered including both chemistry (such as ethylene, titrable acidity or sugars) and spectra values to visualize and analyze a dataset of 731 biological samples. Two-dimensional (2D) and three-dimensional (3D) principal component analyses (PCA) are used to evaluate their ability to discriminate for one parameter: firmness. Partial least squared regression (PLSR) modeling has been carried out to predict the rheological parameter using either sixteen physicochemical parameters or only the infrared spectra.
Fast advancement of machine learning methods and constant growth of the areas of application open up new horizons for large data management and processing  1. Machine learning provides better results in many fields, where the problem become hard for classical approaches, such as medicine, with the speeding up of drug development and the design of chronic disease predictive models  2, 3  ; green chemistry and renewable energy by providing optimized materials for CO 2 capture  4, 5  or in the field of agri-food to contribute to smart farming and assess food quality  [6] [7] [8] [9] [10]. Among the various types of data available for analysis, the Fourier Transform InfraRed spectroscopy (FTIR) spectra are very interesting to consider. In fact, FTIR is a rapid technique to analyze and provide high quality spectra, that presents a wide range of applications  11.
Numerous studies in the literature have demonstrated the usefulness of spectroscopy techniques (Mid-infrared: MIR, Near Infrared: NIR) to predict and/or classify the rheological parameters of cheese (texture, flavor and structure)  18  , wheat  19, 20  or sludge (viscosity, elastic and viscous moduli)  21  , and a nondestructive approach has been tested using NIR spectroscopy for apples  22. In this work, the objectives are to: • visualize dataset samples (731 samples) according to the physicochemical data and the spectra; PCA will be used for visualization; • examine the results through 2D and 3D visualization and evaluate if they could be discriminated; • predict the rheological parameter "Firmness"; • see to what extent chemical parameters could be replaced by Mid-FTIR spectra to predict the firmness.
Dataset. The dataset on which this work relies has been produced by Bureau et al. 17. All data including spectra, chemical and rheological properties were acquired in 2005 on a large collection of apricot fruits representative of variability in terms of color, taste, texture and ability to be stored (In total, 731 fruits were individually characterized from eight varieties at different maturity stages). Some properties were determined on intact fruits, the day of harvest, such as firmness, color and ethylene production. After these measurements, fruits were cut in pieces and frozen at -20 °C. A few days later, the spectral and biochemical measurements were performed on homogeneous fruit samples after thawing and grinding. All data were acquired on each apricot at the same time to have the best relationship between spectra and the 23 measured properties.
The whole spectral collection included 731 ATR-FTIR spectra, each corresponding to a fruit. The dataset has been randomly split into two sets, the Training set representing 80% (584 spectra) and the Test set representing 20% (147 spectra). A stratified separation was made according to the targeted parameter, as predictions were only made for firmness. For each spectrum, a total of 23 chemical and rheological properties have been characterized. Eight apricot cultivars or hybrids, named 'Moniqui' , 'Goldrich' , 'Bergeron' , 'Iranien' , 'Badami' , 'Ravicille' , 'Ravilong' and ' A4034' were chosen for their contrasted fruit quality traits such as colour, taste, physiological behaviour. Fruits came from two INRA experimental orchards (Amarine (Gard) and Gotheron (Drôme), South of France) for 'Moniqui' , 'Goldrich' , 'Iranien' , 'Badami' , 'Bergeron' and ' A4034' , and from a traditional private orchard (Donzère, Drôme, South of France) for 'Ravicille' and 'Ravilong'.
(  1 ) and (  2 ) respectively: = - ∑ - ∑ - = = ˆR y y y y y y ( , ) 1 ( ) ( ) (1) i n i i i n i 2 1 2 1 2 ∑ = - =  RMSE y y n ( ) (2) i n i i 1 2 where, y i is the measured activity of the i th sequence, ŷ i is the predicted firmness of the i th sequence, ȳ is the average of the measured activities and n the number of sequences in the test set. Modeling and prediction schema. Physicochemical traits can be considered as fitness traits including firmness, skin colour, ethylene production, soluble solids content and titratable acidity… We use the FTIR spectra and the fitness (23 traits are used in this work) as inputs for the learning process of our model. Then, the aim of this learning is to set up a statistical model that links the fitness to the FITR spectra. In fact, the model uses the initial dataset (training set: spectra and fitness traits) to model the fitness of FTIR spectra.
Using the training set, a PLS regression is performed. To perform the PLS regression, linear combinations of the original variables (i. e. input datasets) are calculated to constitute the latent components, which explain the maximum variance observed in the spectra. Also, the number of latent components to be considered for the PLS regression is based on the number of components that yield the least cvRMSE and thus present the best predictive power. Then, the statistical model, obtained by the PLS regression method on the training dataset, is used to predict the firmness of the test dataset. Two methods of cross-validation are performed on the dataset: Leave-One-Out Cross-Validation (LOOCV) and 80-20 partitioning (80% training set and 20% validation set). And the efficiency of the predictions (test set) is evaluated using the previously discussed statistical parameters R 2 and RMSE.
For proper model validation we withheld a test set. This test set has never been used during the model training. In other words, the test set was put an inner sanctum and nobody knew what was the spectra inside and/or the values of the parameter associated with the spectra and/or with the chemical properties. After model training, the final model was used to predict the latent variables of this test set. This approach is the best machine learning practice to evaluate the capacity of generalization of the trained model. Performances metrics, Statistical measures of correlation and Modeling and prediction schema have been fully described in our preceding paper  24. All the scripts have been written in Python.
Data preprocessing. As observed in the correlation analysis (Fig. 1 ), and given further insight on the different properties gathered, the parameter "Ethylene" was replaced by Log (Ethylene). However, these chemicals are only used for visualization purposes, as the whole point is to be able to obtain proper prediction of such variables given IR spectra collected using any device. In Fig. 6 , black transparent lines represent the different spectra, red lines represent the means of the spectra, and the blue lines represent the standard deviation of the spectra. The left-hand side plot represents the spectra from the dataset before any further modification, the right-hand plot represents the spectra after individual SNV. SNV spectra (right) are used for the next steps. Visualization. PCA was performed with either the Fitness traits or the spectra.
2D-PCA. A PCA was run on the dataset so as to have a global visualization of the spectra. Figure  7  (left) shows the PCA using the chemical and rheological parameters. The gradient of colour indicates the firmness. We can see a discrimination of the samples according to firmness. The principal component 1 gathers 30. 12% of the information, the second 19. 82%. Figure  7  (right) presents an image with more distinct representation but with the spectra. PC1 has a percentage of inertia of 46. 16%, PC2 25. 08%. In this case, fewer axes gather more of the information embedded in the samples. When the data points are plotted according to firmness, a gradient is revealed. This suggests that spectra could be a good descriptor for predicting firmness. These 2D plots could hide overlaps and could be misleading.
Acknowledgements O. L. T. is supported by a PhD grant from the  Region Reunion and European Union (FEDER)  under European Operational Program FEDER REUNION -2014/2020 File Number 20171389, tiers 216275. Peaccel gratefully acknowledge support froma research program co-funded by the  European Union (UE)  and  Region Reunion (FEDER). The funding agencies had no influence on the conduct of this research.
The emergence of longline fishing around the world has been concomitant with an increase in depredation-interactions by odontocete whales (removal of fish caught on hooks), resulting in substantial socio-economic and ecological impacts. The extent, trends and underlying mechanisms driving these interactions remain poorly known. Using long-term (2003-2017) datasets from seven major Patagonian toothfish (Dissostichus eleginoides) longline fisheries, this study assessed the levels and inter-annual trends of sperm whale (Physeter macrocephalus) and/or killer whale (Orcinus orca) interactions as proportions of fishing time (days) and fishing area (spatial cells). The role of fishing patterns in explaining between-fisheries variations of probabilities of odontocete interactions was investigated.
Methods Data collection and standardisation. Fishing and whale interaction data from the seven study fisheries were collected by fishery observers and/or crews over periods ranging from 3 to 14 years. These fisheries are all fully controlled by local and/or international (Commission for the Conservation of Antarctic Marine Living Resources -"CCAMLR") management authorities and all fishing operations are monitored. Data from Chile and the Falklands, regions which are not part of the CCAMLR Convention Area, were collected by fishery observers following protocols based on those used by CCAMLR observers in the other fisheries of the study. Data from all vessels legally operating in these fisheries and all fishing trips of these vessels were therefore accessed for the study. In all fisheries, the base unit was the longline set i.
e. a mainline bearing series of hooks (autoline) or clusters of hooks (trotline) with, at each end, one anchor at the bottom connected to a buoy at the surface by a downline. For each longline set, fishery observers and/or crews collected the same data on the date and time, as well as GPS coordinates, at setting (i. e. deployed at sea) and at hauling (i. e. retrieved and landed on-board), in the same way in all seven fisheries. The occurrence of whale depredation interactions with longline sets was recorded during hauling operations by visual cues. An interaction was confirmed when one of these two species, or the two species co-occurring with a typical depredation behaviour were sighted within a 500 m range from the vessel. During depredation, individuals made repeated dives towards the line being hauled and throughout the hauling process, they were usually surrounded by birds when surfacing after long dives, and slicks of fish oil were visible at the surface.
As a result, the estimates of depredation should to be considered as minimum estimates. Differences in spatial and temporal frequencies of killer whale and sperm whale interactions were estimated using two indices, which were both calculated annually for each fishery per vessel (one value for each vessel that operated in a given fishery during a given year), and per fleet (one value for all data collected in a given fishery during a given year regardless of the vessel identity). Firstly, we calculated the proportion of fishing days (days of hauling operations only) with a minimum of one depredated longline set during the day out of all fishing days per year (Pr(days)). Secondly, we calculated a proportion of the fishing area for which depredation interactions occurred as the number of 0. 1° latitude × 0.
Acknowledgements The contribution of captains, crews and fishery observers who collected the data is gratefully acknowledged. We thank the  Museum National d'Histoire Naturelle (MNHN) de Paris (Charlotte Chazeau ,  Patrice Pruvost ,  Alexis Martin  -PECHEKER database), the  Centre for Environment, Fisheries and Aquaculture Science (CEFAS, Chris Darby) , the  Government of South Georgia , the  Falkland Islands Fisheries Department/Department of Natural Resources (Alex Blake) , the  Australian Antarctic Division  ( AAD, Tim Lamb ), the  Australian Fisheries Management Authority (AFMA) , the Magellan Industrial Toothfish Fishing operators Association (AOBAC in spanish: http://home. aobac. cl/)and the  Instituto de Fomento Pesquero  ( IFOP -Renzo Tascheri ) for managing, consolidating and sharing the data.
Materials and Methods A retrospective, comparative, case-control study, including 2 groups of 96 patients who delivered vaginally between March 2011 and October 2015, was conducted in the obstetrics and gynecology department of the Women and Children's University Hospital (Hôpital Femme Mère Enfant) in Lyon, France. This study was approved by the French ethics committee, registered in the clinical trials register (N° NCT03876756) and declared to the National Commission on Informatics and Liberty (CNIL, N° 17-020). All patients were informed about the use of their medical data for research purposes. Selection criteria for PUR were patients presenting no spontaneous voiding within 6 hours after delivery, associated with a PVRBV greater than 400 ml, as previously defined  [3, 5, 13]. The PVRBV was measured using a bladder scanner, operated by competent midwives.
According to Peduzzi  [12] , a minimum of 90 patients were needed to analyze 9 factors in multivariate analysis. Between March 2011 and October 2015, all patients (n=96) presenting with PUR were selected for analysis based on data extraction from the medical records registry (BO / WEB100T -Hospices Civils de Lyon) of the obstetrics and gynecology department. The control group was selected respecting 1:1 matching criteria, and consists of 96 patients without PUR that were paired according to the year of delivery and patient's age at delivery. Data concerning patient characteristics (maternal age, parity, primiparity, BMI before pregnancy, average weight gain), delivery characteristics (labor duration > 360 minutes, instrumental delivery, perineal tear), volume of iatrogenic fluid administered during labor, the period between delivery and the first voiding, characteristics of epidural anesthesia (volume of local anesthesia for epidural > 50ml, dose of local anesthesia for epidural > 50mg, duration of epidural anesthesia > 500 minutes) and perineal complications (hemorrhoids, vulvar edema, perineal hematoma) were collected.
Medians of the following data were calculated to obtain a threshold value for further multivariate analysis: BMI before pregnancy, baby's birth weight, labor duration, volume of iatrogenic fluid administered during labor, period between delivery and the first voiding, volume of local anesthesia for epidural, dose of local anesthesia for epidural, and duration of epidural anesthesia. Threshold for second-stage duration was determined according to French guidelines  [14]. The statistical analysis was performed using SAS software (SAS 9. 4, SAS Institute Inc. , Cary, NC, USA). After a Shapiro-Wilk's W test for normality of distribution of the data, continuous data were expressed as mean ± standard deviation (SD) for normally distributed data, or median [interquartile range, IQR] for nonnormally distributed data.
1(a) ). The channel is filled with the same solvents at density matching composition. At the beginning of the experiment, solid tetrabutylammonium bromide (Fluka) is introduced to the channel. Data acquisition starts within 30 s after salt introduction. Our procedure induces practically no solvent flow in the observation cell. We confirmed the presence of undissolved salt several days after mixing, indicating that the obser-vation cell was brought to saturation concentration. Given the diffusion constants of Bromide and alkyl cation (6 and 2 × 10 -10 m 2 s -1  [43] ), we estimate the characteristic diffusion time of salt from top to bottom of the order of 10 s. Therefore, we reach uniform final salt concentration into the observation cell within only a few Brownian times of the colloids. Indeed we measured a delay of about 1 min between the aggregation at the bottom and at the top of the cell.
We define the initiation time of the aggregation process when the maximum of the g(r) jumps from the lattice constant of the Wigner crystal to the hard-core diameter σ. We collect the data on a Leica SP5 confocal microscope, using 532 nm laser excitation. The temperature was controlled on both stage and objective lens, allowing a more precise density matching. The scanned volume is at least 82 × 82 × 85 µm 3. The particle coordinates are tracked in three dimensions (3D) with an accuracy of around 0. 03σ  [44].
ACKNOWLEDGMENTS This study was partly supported by  Grants-in-Aid for Scientific Research (S)  ( 21224011 ) and  Specially Promoted Research (25000002)  from the  Japan Society of the Promotion of Science (JSPS). Collaboration between M. L. and  H. Tanaka  has been funded by  CNRS  through Projet international de coopération scientifique No 7464. M. L. acknowledges support from  ANR  grant GelBreak  ANR-17-CE08-0026. J. R. acknowledges support from the  European Research Council  Grant  DLV-759187  and the  Royal Society University Research Fellowship.
Randomisation Patients were randomly allocated to one of the two treatment options (either P-PDT for area A and C-PDT for area B or C-PDT for area A and P-PDT for area B) in a 1:1 ratio. The randomization sequence with stratification by treatment centre in blocks of four was generated by an independent statistician using the PROC PLAN procedure of SAS (SAS Institute Inc. , Cary, North Carolina, USA) and transferred to a sequence of sealed, opaque, consecutively numbered envelopes. When a patient entered the study, randomization was performed by opening the next envelope in sequence. The study is unblinded, both investigators and patients are aware of the treatment allocation.
Statistical analysis of the secondary outcomes The lesion complete response rate at six months will be processed using the same statistical analysis as the lesion complete response data at three months (previous paragraph). The differences in pain scores at the end of treatment between P-PDT and C-PDT will be assessed using a linear mixed model with patients as random effects (the significance level will be set at a two-sided alpha level of 0. 05). The cosmetic outcomes at 3 and 6 months and the DLQI scores throughout the study will be compared between C-PDT and P-PDT using the Wilcoxon signed-rank test.
Acknowledgements: This work was supported by  RSCF  No  18-17-00237  " Mechanisms of hydrochemical runoff of the Ob river flood zone " (analyses, modeling); RFBR projects №  18-35-00563\18  and  19-55-15002 ; the  TSU competitiveness improvement programme  Project  No 8. 1. 04. 2018 , the  Ministry of Education and Science of the Russian Federation  No. 1. 8195. 2017/P220 ,the  CNRS Chantier Arctique , via the  PARCS  project, and the  H2020 ERA-PLANET (689443 ) iGOSP and iCUPE programmes.
This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ • ) in a data assimilation context. The impact of independent estimates of the surface soil moisture (SSM) and leaf area index (LAI) of diverse vegetation types on ASCAT σ • observations is simulated over southwestern France using the water cloud model (WCM). The LAI and SSM variables used by the WCM are derived from satellite observations and from the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model, respectively. They permit the calibration of the four parameters of the WCM describing static soil and vegetation characteristics. A seasonal analysis of the model scores shows that the WCM has shortcomings over karstic areas and wheat croplands.