dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009). One of the factors, not related to latitude yet influencing the diversity, is salinity. A higher diversity with higher average salinity, as observed in our dataset, is in line with the classical Remane pattern of larger species richness at marine conditions compared to brackish conditions  (Remane, 1934; Attrill, 2002). A slight increase of diversity near freshwater conditions was also found in our results. However, in contrast to the classical pattern, a depression in diversity was also found at salinities around 35 to 40 ppt, which is just above the regular sea water salinity. These higher salinities occurred in the South-Eastern Mediterranean and thus may help to explain the lower diversity at these latitudes. Other environmental factors relating to diversity and density, that could also be causative for deviations from the LDG, were organic matter and mud content of the sediment and wave height.
CONCLUSION Conclusions of this study are: -By employing harmonized tools and methodologies, the EMBOS system delivered an extensive comparable dataset on the diversity and densities of the soft sediment macrozoobenthic community over a large-scale gradient along the European coastline -Species diversity has no linear (negative) relationship with latitude, yet in deviation to general theory, a bell-shaped relation is found in the studied latitudinal range. -In general, the diversity or densities of soft-sediment macrozoobenthos were positively related with environmental factors such as temperature, salinity, mud and organic matter content in sediment, and wave height. For some relationships an optimum curve (e. g. temperature from 15 to 20 °C; mud content of sediment around 40 %) or bimodal curve (e.
FINANCIAL SUPPORT AND ACKNOWLEDGEMENTS This article is based upon work from  COST Action ES1003 Development and implementation  of a pan-European  Marine Biodiversity Observatory System (EMBOS) , supported by  COST (European Cooperation in Science and Technology). Jonne Kotta , and  Helen Orav-Kotta  were supported by Institutional research Geographic patterns of benthic diversity in Europe funding  IUT02-20  of the  Estonian Research Council  and the  BONUS  project  BIOC3 , the joint  Baltic Sea research and development programme (Art 185 ), funded jointly from the  European Union 's  Seventh Programme  for research, technological development and demonstration and from  Estonian Research Council. Pedro Ribeiro  and  Puri Veiga  were supported by the  Portuguese Science Foundation (FCT)  through a post-doctoral grant (ref.
, Koukouras A. , Lampadariou N. , Laxamana E. ,  López-Fé  de la Cuadra C. M. , Lotze H. K. ,  Martin D. ,  Mouillot D. ,  Oro D. , Raicevich S. , Rius-Barile J. ,  Saiz-Salinas  J. I. , San Vicente C. , Somot S. , Templado J. , Turon X. , Vafidis D. , Villanueva R. and Voultsiadou E. (2010) The biodiversity of the Mediterranean Sea: estimates, patterns, and threats. PLoS One, 5(8):e11842. Costello M. J. , Bouchet P. , Boxshall G. , Fauchald K. ,  Gordon D. , Hoeksema B. W. , Poore G. C. B. , van Soest R. W. M. , Stöhr S, Walter T. C. , Vanhoorne B. , Decock W. and Appeltans W. (2013 ) Global Coordination and Standardisation in Marine Biodiversity  through the  World Register of Marine Species (WoRMS)  and Related Databases. PLoS One, 8, e51629. doi: 10. 1371/journal. pone. 0051629. Couto T.
Present-day fault activity To achieve the first goal, we deployed a small seismic network of five borehole seismometers with three-component 2 Hz sensors (short period S31f-2. 0a of IESE) running in continuous mode during a 1-year time window with a sample rate of 100 Hz. The equipment was installed near the fault trace, covering an area of 20 km by 15 km (see Fig. 4 ). The preliminary estimate of the origin times and hypocentral coordinates was determined by means of the HYPOINVERSE program  (Klein, 1984) , considering the initial velocity model proposed by  Villegas (2012)  for this area. Then we made a recursive process for the best estimate of velocity structure and event location using the VELEST and HYPOINVERSE code  (Kissling et al. , 1995). The model with the lowest RMS error has 10 layers and assumes a varying V p / V s ratio.
Fault geometry, dip and depth of rupture To determine the geometrical characteristics of the fault, we consider the data available in the literature  (Armijo et al. , 2010; Vargas et al. , 2014) , the spatial distribution of seismic events, and a 2-D resistivity image of a well-maintained scarp. The electrical imaging was obtained by carrying out a high-resolution TEM (transient electro magnetic) experiment that provides a good constraint for the first 150-200 m fault section. TEM technique is a geophysical method for obtaining an electrical resistivity image of the subsurface (for details of TEM theory and data processing see  Telford et al. , 1990 , for instance). In the field, we used the FastSnap TEM System, completing 24 TEM stations 25 m apart. To observe the location of the TEM profile see for 100 pulses at different sampling rates (frequencies).
e. ,  Telford et al. , 1990). We did 24 gravity profiles across the SRF, with lengths of 2-3 km. We used a sampling distance of 100 m (in the fault core) and 200 m (at the flanks) and a distance between profiles of 3 and 8 km (see profiles location in Fig. 6 ). We use a Scintrex CG-5 Autograv gravity meter and a Trimble R4 DGPS to measure the gravity and the precise position, respectively. To avoid measurement errors, we eliminated the data with a vertical position error over 30 cm. The mean of the elevation error is 9 cm with a standard deviation of 7 cm (0. 027 ± 0. 022 mGal error in free air gravity correction). We used the software ModelVision for the 2. 5D gravity forward modeling. This modeling effort involves the estimation of a second-order regional field to account for the basement heterogeneities and the modeling of the mass-deficiency residual gravity field (for details of the gravity methods and data reduction and modeling see  Telford et al.
Acknowledgements. We want to thank the important field support provided by  A. Mella ,  A. Bosh ,  N. Moraga ,  G. Sielfeld ,  I. Santibañez ,  B. Perez ,  S. Pérez ,  R. Figueroa , and  M. Lizama. T. García  kindly provided field support as well as her expertise in geophysical software. We thank  G. Cassasa  and  A. Yañez  for providing their homes to install a seismic station for over a year, and  ENERGÍA ANDINA  for providing their seismometers. CG5 gravimeter was provided by  CEGA, FONDAP-CONICYT  project no. 15090013. TEM experiment was partially supported by  Fondecyt  project no. 1141139. DICTUC S. A and CIGIDEN (FONDAP-CONICYT  project no. 15110017 ) provided economic support for the development of this work. We also thank  R. Rauld  and  G. Vargas  for improving the quality of the paper with their discussion and precise comments.
0. 8) NIS AR2. 30 Imaging Software (Nikon France, Champigny-sur-Marne, France) or Improvision Volocity LE (version 5. 0) (PerkinElmer, Waltham, MA, USA) and ImageJ software (https://imagej. nih. gov/ij/) [20]. Lentivirus-mediated gene knockdown A Pw1 targeting sequence (5′-GAGTCGCAGTCAATCGATT-3′), loop sequence (5′-TTCAAGAGA-3′) and its reverse complement were annealed and cloned into the BglII and HindIII sites of the pSuper. basic vector. Insert-containing clones were selected by PCR and verified by sequencing. Next, an EcoRI-SalI fragment containing the H1 promoter and the cloned oligo duplex was subcloned into the BamHI-ClaI sites of the pTripGFP vector and co-transfected with the pMD2g and pCMVΔ8. 4 plasmids into 293 T cells, as described previously  [21]. Viral vectors were obtained from P.
Ravassard (ICM -Institut du Cerveau et de la Moelle épinière, Hôpital Pitié-Salpêtrière, Paris, France) and 293 T cells from ATCC (LGC Standards, Molsheim Cedex, France). Viruses were collected from the culture medium, filtered and concentrated by ultracentrifugation. RNA analysis Total RNA isolation, amplification and normalisation of data were done as previously described  [17]. Primer and probe sequences for quantitative PCR are presented in ESM Table  2. Statistical analysis Data are expressed as the means ± SEM of at least three independent experiments and analysed by unpaired two-tailed Student's t test or two-way and one-way ANOVA followed by Tukey's honest significant difference post hoc testing. A p value of <0. 05 was considered statistically significant. Sample sizes are shown in the figure legends.
Acknowledgements We thank  A. Demarré ,  V. Laurysens ,  J. De Jonge ,  E. Quartier  and  G. Stangé  ( Diabetes Research Center, Vrije Universiteit Brussel ) for their technical assistance. Funding statement This work was supported by a  European Molecular Biology Organization  long-term fellowship (no. ALTF 826-2010 ), a  European Foundation for the Study of Diabetes/JDRF Young Investigator Award , a  University College London Excellence Fellowship , and grants from the  European Research Council  (no. ERC-Stg-2014-639429 ) and the  Rosetrees Trust  (no. M362 ) to PB; and grants from the  Vrije Universiteit Brussel Research Council, Stichting Diabetes Onderzoek Nederland , the  Fund for Scientific Research Flanders  and the  Interuniversity Attraction Pole  networks to HH. DS, GM and  KT  were.
Serial echocardiography Echocardiograms were obtained at 1, 2 and 3 months after MI. Sham control mice were investigated at similar time points. The animals were sedated by intraperitoneal injection of ketamine (40 mg/kg). Echocardiograms were performed with GE Healthcare Echocardiography VIVID 7 system equipped with i13L-14 MHz probe. EF and FS data were obtained using M mode and 2D mode echocardiography following the vendor's User Manual as previously described  [17, 18]. From LV parasternal 2D long-axis view, LV enddiastolic and LV end-systolic volumes were derived from the area-length method using a prolate-ellipsoid formula (volume=8*[A 2 /(L*3*π)], A=LV area, L=LV length, Simpson's method). LV Stroke Volume (SV) was calculated as the difference between the two LV volumes and LVEF was calculated (LVEF=[(LVSV)/LV end-diastolic volume]*100)  [19].
MI by direct imaging Hearts were harvested after the final hemodynamic measurement. The hearts were perfused with 10 mL cold PBS and 1% heparin. The LV was cut from the root of the pulmonary artery to the ventricular apex and photographed. MI size (% area) was the average of the infarct area of the whole heart, left half of the LV, and right half of the LV and quantified using Image-Pro software (Media Cybernetics, Bethesda, MD, USA)  [21].
Statistics Variables are expressed as mean ± Standard Error of the Mean (SEM). One-way ANOVA was used for time course of heart function and Student's t-test was used for all other statistical analyses to compare experimental groups using GraphPad Prism software. Pvalues <0. 05 were considered statistically significant. Cohen's effect size based on the difference between two means was calculated by dividing the difference between means by the standard deviation (0. 2-0. 3 is considered a small effect and 0. 8 or higher is considered a large effect).
Variation in hemodynamics and digital photo of MI PV loop data showed significant differences in ± dP/dt, Tau, ventricle volume, stroke volume, and LV weight in the MI group compared to controls (Table  2 ). Figure  6  shows PV loop examples of steady-state, saline-treated, transient occlusion (OCC) of the thoracic vena cava, and end-systolic PV relationships (ESPVR) in control and MI mice. The average EF was significantly decreased in MI compared to control mice (P<0. 01), but scatter plot analysis of this data revealed that 41% of EF values overlapped between the MI and control groups (Figures  7A  and 7B ). EF negatively correlated with MI size measured by direct imaging, even though the EF varied by 30-40% for most MI size points. Positive correlation was found in MI size measured by echocardiography and direct imaging.
Discussion In this study, we examined EF, FS, and MI size in a large cohort of mice post-MI by echocardiography and hemodynamics in order to determine the range of values typically observed in these methods. In addition to reporting the mean, standard error, and P-value, we analyzed the data by multiple statistical methods in order to gage a truthful comparison between functional measurements. The inherent variability in induction of MI in small rodents results in high variability in functional parameters. The data from the 70 mice presented in this study can be used as a reference index for subsequent heart failure studies in small rodents, as our large sample size and robust statistical analysis could provide a frame of reference for a typical data set. In the examination and assessment of heart failure models in mice, several parameters are obtained, such as EF, FS, end-diastolic volume, and end-systolic volume  [22, 23].
We could not be sure that the change in EF is due to experiment injury or medical treatment (Figure  2 ). In the present study, we examine the variability that occurs in assessment of MI by echocardiography and PV loop. Cardiac function data in mice post-MI are variable between laboratories  [24, 25] ; in some instances, the EF can be as low as 21% with a standard error of less than 1% even in a group of 5-7 mice  [8, 26]. Our data show that EF values measured by M mode are higher than 2D mode echocardiography, but there is a higher chance of obtaining P values <0. 01 due to low deviation and small scatter range. FS values measured in M mode have lower overall values but a higher overlap rate and SD/mean ratio. Manual analysis of LV volume can be adapted to different pathophysiology, but is usually accompanied by a higher deviation (Figures  2C  and 4B ).
Acknowledgments This work was supported in part by grants from the  National Institutes of Health :  R01HL078731 ,  R01HL083156 ,  R01HL093183 ,  R01HL088434  and  P20HL100396  (RJH) and the  Transatlantic Leducq Foundation (RJH). DKC is supported by a fellowship from the  American Heart Association  ( 15POST25090116 ).
5 cm radius) for 15 min. Behavior was video recorded (Ikegami ®) from above using EthoVision® XT 9 software (Noldus Bnovel prey^experiment was conducted after the exploration test for a subsample (n = 20 per treatment). This test aimed to measure the ability of individuals to feed on unknown prey. Individuals were placed in separate glass aquaria (28 × 19 × 17 cm) in darkness, using water depending on their origin, and fasted for 24 h. Afterwards, fish were provided with 10 live white worms (Enchytraeus albidus) and given 60 min to feed. Success to feed (food consumed or not) and amount of prey consumed per fish were recorded. The white worms came from a local aquarium store and were unfamiliar to the fish. We choose this worm species because it has been previously recorded in the stomach contents of wild juveniles  (Acolas et al.
Growth For growth measurements, larvae were chosen randomly from all treatments and replicas data were pooled together because no differences between them were found. Initial weight and length were not measured because larvae were too small to be accurately measured alive. On this basis, we assume no initial difference due to random sampling in the same hatching tanks for all treatments. The multinomial logistic regression model pointed out Brearing^and Bcrossing^as the best growth predictors explaining 82 % of data variation (AIC = 1950. 6, Cox-Snell = 0. 82, Table  5 ), tank and temperature effects were not considered to be significant explanatory variables. Differences in growth between rearing environments favored enriched rearing from 14 dph onwards (H 2 = 59. 7, p < 0. 01). Both crossings had similar body weight (Mann-Whitey; enriched, U = 298.
Acknowledgments This study was funded by the  Office National de l'Eau et des Milieux Aquatiques (ONEMA)  and the  National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA). We wish to thank the personnel of  Saint Seurin experimental station (IRSTEA)  for their contribution in the fish rearing,  Maud Pierre  ( IRSTEA ) for her advices in statistical analysis and  Alexandre Gentil  for his help on video recording analysis.
Data collection After enrolment into the study, patients are assigned a unique subject number associated with their initials. Data are collected by investigators with the help of clinical research assistants and recorded on paper case report forms (CRF). A CRF must be completed for each participant. The data entered into each CRF will be assessed by sponsor representatives, according to good clinical practices. In addition to the patient's medical file, the investigator agrees to complete the documents related to treatment management provided by the sponsor and the CRF at each step of the protocol (Fig. 1 ). All corrections and alterations of data on the CRF must be made by the investigator him/herself and highlighted according to predefined instructions. The data management will be carried out by the Clinical Research Unit of Hôpital Européen Georges Pompidou.
A list of variables of interest will be developed with an Operational Data Model format by the study primary investigator and the study statistician. This list will allow the implementation of the case report form and of the database. A datamanagement plan, developed jointly by the study coordinator, the data-manager, and the statistician will be implemented. After correcting for potential errors detected by this plan, a data review meeting will be held to validate the entire database with the study coordinator. The database will then be frozen prior to statistical analysis. The data will be analysed using SAS software (SAS Institute Inc. , Cary, NC).
Number of subjects required The number of subjects required is 340 patients (170 per group). This sample size has been obtained using the Nquery Advisor statistical software according to the following assumptions: (1)Incidence of the composite endpoint in the control group 65 % (proportion estimated from our personal data); (2)Incidence of the composite endpoint in the levosimendan group 50 %. According to Levin et al. [15] , postoperative administration of levosimendan in patients having a LCOS has reduced by 28 % the use of an additional inotropic drug and by 12 % the use of a mechanical assist device, compared to dobutamine. (3)Alpha risk of 5 % and power of 80 %.
Experimental The synthesis of the ternary sulfides Ba2Co1-xZnxS3 (x = 0. 1, 0. 2, 0. 25, 0. 4, 0. 5, 0. 6, 0. 75, 0. 8) was achieved via a solid-gas reaction. Stoichiometric mixtures of barium carbonate (99%) cobalt (99. 8%) and zinc oxide (99. 8%) were reacted with carbon disulfide (99%) for 24 hours at 1000°C. Time-of-flight (TOF) neutron powder diffraction (NPD) data were collected on selected samples on the Polaris beamline at the ISIS neutron source at the Rutherford Appleton Laboratory. Approximately 5 g of sample was loaded into vanadium sample cans. Data for Ba2Co0. 5Zn0. 5S3 was collected at ~ 4 K with all other samples run at room temperature. Co K-edge and Zn K-edge XAS measurements were performed at station 9. 3 of the Daresbury Laboratory SRS operating at 2 GeV with 100-250 mA circulating currents.
The Co and Zn Kedge energies were obtained using a Si(111) double crystal monochromator, and harmonic rejection used a Pd coated mirror. The samples were diluted in boron nitride and the data collected in transmission mode at ca. 80 K. The spectra were calibrated using the first maximum in the first derivative of Co (7709. 0 eV) or Zn (9659. 0 eV) foils. Averaging and calibration (using monochromator position) of the spectra was achieved within PAXAS,  16  and background subtraction employed PySpline. 17 The pre-edge peak positions were taken as the maximum of the peak/crossing point in the first derivative, whereas the edge position was taken as the maximum of the first derivative in the edge region. The EXAFS data were fitted using curved wave, single scattering theory within EXCURV98. 18 The quality of fit is indicated by an R-factor, but the statistical significance of including additional shells made use of the reduced  2 function within EXCURV98.
Biological data collection. At each site, three 20 m transect lines were placed parallel to the shore, 2. 5 m apart. Habitat and fish surveys were performed in March 2015. For habitat surveys, living coral-macro algae-pavement-sand or rubble substrate was recorded every metre using the line intercept transect method, defining the percentage of cover for the different substrates along the transect. Fish surveys were conducted along the same transects as the substrate surveys with a width of 1 m. Active (not hiding in coral) juvenile and adult fishes were recorded to species level. The same diver conducted fish surveys during two passes over each transect  6. On the first pass, the diver swam quickly to record mobile fishes that swam within transects but usually fled with the divers' approach.
On the second pass, the diver swam more slowly to record site-attached species. All surveys were carried out during the day-time (06:00 to 17:00), as fish could not be identified accurately at night because of poor visual conditions. Survey data were used to calculate the number of fish species, the number of fishes and the Shannon-Wiener index of diversity (H) for the 8 sites. Based on the list of species obtained, the number of "known-to-be" vocal species and number of vocal fishes were also calculated.
Acoustic data collection and processing. Two autonomous underwater digital spectrogram (DSG) long-term acoustic recorders (Loggerhead Instruments, Sarasota, FL, USA) were deployed. Each was connected to a HTI96-min hydrophone (sensitivity: -180 dB re: 1 V for a sound pressure of 1 μ Pa; High Tech Inc, Long Beach, MS, USA). The recorders were scheduled to record sounds for 5 min every hour for 48 hours at a sampling rate of 48 kHz (16-bit resolution). Recorders were attached to blocks of lead, positioned on the outer slope of the reef at 10 m depth by professional divers and remained in place during the course of each replicate. Simultaneous recordings were made at two sites, i. e. an nMPA and its nearby MPA. Sites were therefore coupled as follows: S1A/S1B, S2A/S2B, S3A/S3B and S4A/S4B. For each site, 3 replicates were obtained with a 3-week time interval between them, and were distributed on different lunar cycle stages.
The positions of the recorders were localized with a GPS for each replicate. Replicates made at one site were no more than 10 m apart. At the end of each replicate sampling period, data were retrieved. Recordings were downloaded at a sampling rate of 20 kHz, providing an analysis range of 1 Hz to 10 kHz. A 20 Hz high-pass filter was applied to all recordings to eliminate very low frequencies. The root mean square (RMS) of the Sound Pressure Level (SPL, in dB re: 1 μ Pa) was measured over a low frequency band (20 Hz to 2 kHz) (SPL RMS Low) and also over a high frequency band (2 kHz to 10 kHz) (SPL RMS High) for each sample using Avisoft SASLab Pro 5. 2. 07 software (Avisoft Bioacoustics, Glienicke, Germany). The acoustic spectrum was partitioned into these 2 non-overlapping frequency bands in order to focus on the dominant sound sources within each band.
The low frequency band (20 Hz to 2 kHz) corresponds to the range in which most fish species vocalize 47 and hear  48. This band may also include noise generated by the wind and waves. The high frequency band (2 to 10 kHz) encompassed the range that is typically dominated by snapping shrimp  14. Boat noise covers a large frequency band and may interfere with both bands. To examine the complexity of the soundscape, the Acoustic Complexity Index (ACI)  34  was calculated using R and the Seewave package  49. ACI calculates the differences in amplitude of adjacent time samples in each frequency bin within the entire signal, then sums the differences across all bins, to provide a single measure of the changing composition of the soundscape  34. Higher values are generated by greater variability in sound level resulting from multiple sound sources or different sound types, whereas sounds with a more constant sound level produce low values.