dataesr/scientific-paragraphs-categorization huggingface dataset:
(3) The collapsed turbulence modulation data for various particle-laden flows show that turbulence attenuation occurs for 10 3 < Pa St < 10 5 , whereas turbulence augmentation was lower for Pa St < 10 3 and higher for Pa St > 10 5. A weakness in the experimental databases is noted, since there are no turbulence modulation experiments at higher Reynolds number than 3×10 4. Moreover, it can be mentioned that only the Stokes drag force is considered for the turbulence modulation, which is not able to describe the effects of the vortex shedding  [29]. Using the relation between the turbulent length scale and the Kolmogorov length scale l t /η ≈ Re 3/4 , Losche  [30]  suggested that the particle momentum number can be written as P a St ≈ S t Re 1/4. (4) Another rescaling of the particle momentum number is proposed by Luo et al.
The aim of this study was to investigate the management and outcome in the post-laser twin anemia polycythemia sequence (TAPS). Data of the international TAPS Registry, collected between 2014 and 2019, were used for this study. The primary outcomes were perinatal mortality and severe neonatal morbidity. Secondary outcomes included a risk factor analysis for perinatal mortality and severe neonatal morbidity. A total of 164 post-laser TAPS pregnancies were included, of which 92% (151/164) were diagnosed antenatally and 8% (13/164) postnatally. The median number of days between laser for TTTS and detection of TAPS was 14 (IQR: 7-28, range: 1-119). Antenatal management included expectant management in 43% (62/151), intrauterine transfusion with or without partial exchange transfusion in 29% (44/151), repeated laser surgery in 15% (24/151), selective feticide in 7% (11/151), delivery in 6% (9/151), and termination of pregnancy in 1% (1/151).
Due to the rarity of the complication, perinatal outcome is insufficiently investigated, and available information is based on small cohort studies. To expand our knowledge on TAPS, we set up the TAPS Registry, an international collaboration aimed at collecting data on diagnosis, treatment, and outcome in TAPS twins. In the current study, the data from this TAPS Registry were used to (1) characterize diagnosis, treatment modalities, and outcome in post-laser TAPS twins,  (2)  to compare perinatal outcome between TAPS donors and recipients, and (3) to investigate potential risk factors for adverse perinatal outcome.
Experimental Section The TAPS Registry, established in 2014, was a web-based registry for anonymous data collection (www. tapsregistry. org). Fetal therapy centers across the world were invited to participate. Participating centers were provided with personal credentials to enter the data of their TAPS cases into the online registry. Between 2014 and 2019, seventeen specialized fetal therapy centers contributed to data collection (see Appendix A). All monochorionic twin pregnancies diagnosed with post-laser TAPS were considered eligible for this study. Pregnancies with spontaneous TAPS were excluded and were described in a separate study  [8]. Antenatal diagnosis of TAPS was based on discordant middle cerebral artery peak systolic velocity (MCA-PSV) measures, with an increased MCA-PSV (>1.
The severity of antenatal TAPS was determined according to the previously published staging system by Slaghekke et al. [13]. For post-laser TAPS specifically, the following data regarding preceding TTTS were collected: Quintero stage, GA at laser, laser technique, operator's opinion on completeness of the laser procedure, and TTTS-donor-recipient role. In addition, the antenatal management for TAPS was recorded, including expectant management, delivery (defined as a delivery within 7 days after diagnosis), IUT (± PET), fetoscopic laser surgery, selective feticide, and termination of pregnancy (TOP). Furthermore, information on placental color dye injection was collected, including classification of the type (arterio-venous (AV), veno-arterial (VA), arterio-arterial (AA), veno-venous (VV)), and the number and size of anastomoses.
The following additional outcomes were determined: inter-twin hemoglobin difference (highest hemoglobin value-lowest hemoglobin value), reticulocyte count ratio (highest reticulocyte value (‰)/lowest reticulocyte value (‰)), the presence of severe growth restriction (defined as a birth weight < 3rd centile according to Hoftiezer  [19] ), postnatal TAPS stage (according to Slaghekke  [7] ), and the configuration of anastomosis type per TAPS placenta. Statistical analyses were carried out using SPSS Version 25. 0 (IBM, Armonk, NY, USA). Data are presented as the mean ± standard deviation (SD) or as median and interquartile range (IQR) and/or range (minimum-maximum), as appropriate. A p-value < 0. 05 was considered statistically significant. Differences between donors and recipients were calculated using the paired t-test for normally distributed continuous outcomes.
| Spider mite populations and rearing Four populations were used in this study, two belonging to the "red" form (AlRo and AMP), and two belonging to the "green" form (DEF and TOM) of Tetranychus urticae  (Auger et al. , 2013). These populations have been collected in the Iberian Peninsula from 2010 to 2017, on different plant species. Upon collection from the field, the populations AMP and TOM were found to be naturally and fully infected by two different strains of Wolbachia. The population AMP is infected by the Wolbachia strain ST481 (isolate "Turt_B_wUrtAmp," id: 1858 in the PubMLST Wolbachia database; http:// www. pubml st. org/wolba chia/), which is very similar to strain ST219 belonging to supergroup B and found in China by  Zhang, Ding, Zhang, and Hong (2013) ; and the population TOM is infected by the Wolbachia strain ST280 (isolate "Turt_B_wUrt-Tom," id: 1857), which has also been previously found in China by  Zhang, Ding, et al.
| Statistical analysis Analyses were carried out using the R statistical package (version 3. 5. 3). The general procedure for building the statistical models was as follows. Spider-mite populations (AlRo, AMP, DEF, and TOM), antibiotic treatment (treated with rifampicin or not), and infection treatment (sprayed with BB: Beauveria bassiana, with MB: Metarhizium brunneum, or with Tween 20 only as control) were fitted in as fixed explanatory variables, whereas discs nested within population and block were fitted as random explanatory variables. When a significant three-way interaction between the three fixed variables was found, each population was analyzed separately with the same model structure, except that the variable population was removed from the model. Survival data were analyzed using Cox proportional hazards mixed-effect models (coxme, kinship package).
ACK N OWLED G M ENTS We thank  Catarina Pinto  and  João Alpedrinha  for their help in collecting some data, as well as  Marta Palma  for technical support. We also thank all members of the  SM lab  for useful discussions and suggestions. This work was funded by an  FCT-Tubitak agreement  ( FCT-TUBITAK/0001/2014  and  TUBITAK TOVAG 115O610 ) to IC and SM, by the  cE3c FCT Unit UID/BIA/00329/2020  to FZ and SM, and by  Adnan Menderes University Research Foundation  ( ZRF-17055 ) to IC. FZ was funded through an  FCT Post-Doc fellowship  ( SFRH/ BPD/125020/2016 ). Funding agencies did not participate in the design or analysis of experiments.
More precisely, we estimate how catching large fish from the ocean may have affected this carbon sequestration potential through the sinking of dead fish carcasses. On the basis of previous studies showing the deadfall of large pelagic fish into the deep sea  (15, 16) , we assume that, in the open ocean, carcasses of deceased fish sink to the bottom rather than being eaten in surface waters. We use global fisheries catches data since 1950 to estimate the spatial and temporal dynamics of the blue carbon extracted from the ocean and released into the atmosphere as a result of fishing, instead of being sequestered in the deep sea. We also estimate the extent to which fishing remote unprofitable areas in the high seas, with the support of subsidies, expands the blue carbon extirpation and contributes to CO 2 emissions into the atmosphere.
To adopt a conservative strategy in our estimations, we only consider dense and large-bodied (>30 cm in total length) fusiform fish species, including most tunas, mackerels, sharks, and billfishes, since their carcasses are most likely to sink rapidly. We use the Sea Around Us (SAU) database to assemble spatially explicit global fisheries catches since 1950 for 24 tuna, 20 mackerel, 15 billfish, and 85 shark species (table  S1 ), hereafter referred to as "large fish. " We assume that each individual fish contains, on average, 12. 5% (±2. 5%) of carbon relative to its whole-body wet weight  (7, 17). We exclude areas shallower than 200 m and coastal upwelling regions, because these latter ecosystems can act both as a sink (upwelled nutrients stimulate phytoplankton productivity) and a source (upwelled waters are rich in dissolved inorganic carbon) of carbon  (18).
The profitability of blue carbon extraction Reducing fisheries subsidies is being pursued as a policy to improve the ecological  (23)  and socioeconomic  (14, 24)  status of fisheries in the high seas  (25). To evaluate whether subsidies reduction policies also contribute to carbon mitigation, we analyze catches of largebodied fish from the high seas in 2014 and combine it with data on fisheries subsidies. Specifically, on the basis of published data on the profitability of high-seas fisheries  (14) , we separate areas that are either estimated to be profitable or unprofitable to fish without subsidies. We estimate the proportion of blue carbon extracted from each of these areas relative to the total catches from the high seas. Globally, 43. 5% of the extracted blue carbon in the high seas comes from areas that would be unprofitable in the absence of subsidies (Fig.
Catch data since 1950 Catch data from 1950 to 2014 were extracted from the SAU database. These data are reconstructed catch data. The SAU uses the S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 6 of 8 United Nations' Food and Agriculture Organization's reported catch data as baseline, combined with a catch reconstruction methodology, to provide a better estimate of world catches  (49, 50). This methodology provides catch data from all fishing sectors (industrial, artisanal, subsistence, and recreational) not only by year, flag state (country), and catch type (landings versus discards) but also by functional and commercial group  (50). Catch data are spatialized onto a grid of 0. 5° × 0. 5° latitude and longitude cells. Catch data from the SAU database are in metric tons of fresh biomass.
Using Eqs. 4 and 5, we obtain the fished biomass that would have sunk in the deep ocean if not fished with fishing at MSY, for each species i for which the required data for the calculations are available C MSY ─ F = B t 1 (1 -e -Z t •( t 2 -t 1 ) ) ─ Z and SB MSY ─ N i = B t 1 (1 -e -Z t •( t 2 -t 1 ) ) ─ Z SB MSY,i = C MSY,i • N i ─ M i where M = F at MSY (6) S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 7 of 8 is the mortality rate from senescence and disease, and M is the natural mortality rate (equal to the fishing mortality rate, F, with fishing at MSY). Several values of N and M at the species level were obtained from outputs of Ecopath with Ecosim models (http://ecobase. ecopath. org/#docs) and FishBase, respectively. As we do not have a value of N and M for each species, we grouped the estimates of SB by major taxonomic group (i.
Impact of government subsidies on blue carbon extraction Using data from the study of Sala et al. (14) , we assessed the extent to which fishing unprofitable areas, with the support of government subsidies, contribute to the blue carbon extirpation in the high seas. By computing the cost of fishing in the high seas (that takes into account fuel, labor, repair/maintenance transhipment, and fuel replenishment costs) and the incomes of high-seas fisheries (based on global catch and their landed value), they estimated the global profits of high-seas fisheries with and without government subsidies. We combined these data with the catch data of large fish from the SAU database in 2014 that occurred only in the high seas. We estimated the amount of blue carbon extracted in profitable and unprofitable areas with and without subsidies by mapping the profitability of fishing each 0.
There is little economic analysis relating to wave power systems currently in operation near the coast. In  [14] , two wave power systems are compared: Pelamis and Wavestar. The costs of produced kWh are very important (LCoE between $3 and $6 kWh). The levelized cost of energy (LCoE) or energy cost of pilot wave energy production farms in the South Pacific Islands, mentioned by Babarit  [15] , amounts to 0. 5 euro/kWh. The same author quotes a cost of 0. 42 euro/kWh for the Searev wave power system. In addition, the AIER  [16]  describes LCoEs of 0. 407 to 0. 52 euro/kWh for the first wave power installations, followed by LCoEs of 0. 32 to 0. 37 euro/kWh in a second wave. Here again, the estimates relate only to offshore. This paper aims to fill the lack of knowledge and data on the energy of onshore waves by illustrating the problem through the case of the Channel on the Atlantic coast of France.
Twenty Sites along French Coast Our first objective is to identify and characterize the wave energy potential of 20 sites along France s coastlines in order to select the most relevant sites which could be equipped with wave energy converters (WEC). This work was done from the statistical analysis of the numerical wave database Anemoc, built from 23 years hindcast, developed by Cetmef and EDF R&D LNHE  [34]  and in situ measurements from French buoys network Candhis (Cetmef). For each site, analytical calculations based on the theory of wave propagation were used to characterize wave climates and wave power levels. Particular attention should be paid to this approach: indeed, this work is about preliminary wave energy resource assessment of 20 identified sites (see Figure  5 ). Hence, a simple analytical method of wave propagation from offshore to onshore  [17]  was used as an indicator of the onshore wave energy potential.
in situ measurements from French buoys network Candhis (Cetmef). For each site, analytical calculations based on the theory of wave propagation were used to characterize wave climates and wave power levels. Particular attention should be paid to this approach: indeed, this work is about preliminary wave energy resource assessment of 20 identified sites (see Figure  5 ). Hence, a simple analytical method of wave propagation from offshore to onshore  [17]  was used as an indicator of the onshore wave energy potential. Offshore wave climate is assessed from the numerical wave database Anemoc. This database is built from hindcast simulations from 1979 to 2002 using the software Tomawac coupled with the wind database ERA-40 and validated with in situ buoys. For the purpose of this study, only deep-water wave data (depth over 50 m) from the Anemoc database were selected.
Data analysis calculations and graphic representations were made using the software Scilab  [35]. For each deep-water Anemoc point (200,000 sea states), parameters such as spectral mean significant wave height H m0 (m), mean energy period T e (s) and mean wave direction ( • ) were calculated. The wave power level (in kW/m) is known for each sea state by applying the deep water formula  (1). Seasonal differences led to study separately three offshore wave climates (see Table  1 ): a winter one (from October to March), a summer one (from April to September) and, for better comprehension and comparisons, an annual climate was kept. The water depth is indicated for the lowest level of astronomical tide (LAT). It represents the minimum water depth during a Saros cycle. In Cherbourg, the data are separated into two different wave systems associated with two mean wave directions.
Wave transformation processes from offshore to the coastal structures are taken into account using the time domain non-hydrostatic wave-flow Swash model, an open-source tool solving the nonlinear shallow-water equations, developed at Delft University of Technology  [37]. The Swash model allows discrete waves analysis and takes wave refraction, breaking, diffraction, reflection and spectral harmonic transfers into consideration. The two meshes are built using grids centered on the breakwaters. Their vast spatial domains are essential to understand physical phenomena influencing wave propagation, such as refraction, reflection and diffraction. A Jonswap spectrum is used as input to simulate the incoming wave. Moreover, spectrally-derived wave parameters (H m0 , T p , the direction of wave propagation, directional spreading) are introduced from the numerical database Homere developed by Ifremer  [38].
Results from Swash contribute to a better understanding of wave transformation processes on both spots. The hot spots of Esquibien and Saint-Guénolé, located on the west coast of Brittany (France), present a good resource of wave energy conversion with, respectively, an annual mean wave power of 6. 9 and 21. 1 kW/m established in Section 3. 1. 1. The length of useful structures, which could be equipped with WEC, is about 340 and 250 m, with a water depth of 2 m at the lowest tides. Nautical charts published by SHOM n • 7147 (port of Audierne) and n • 6645 (Penmarc h cap) were reproduced in the two numeric domains using a grid size of 1 m by interpolation with Blue Kenue, a software tool of Canadian National Research Council  [39]. The bathymetric grid is an important input grid and must be large enough so that it completely covers the computational grid of Swash.
The simulation of the Esquibien model takes about 4 h on 8 cores of Intel Xeon processor with MPI parallel computing. The model input parameters are the significant wave height, wave peak period and wave direction (H m0 , T p , θ) and the water levels. At this site, waves generally arrive from the south-southwest (200 • N) since the dominant west waves (270 • N) are refracted by the bathymetry. The three selected water levels represent 80% of the tidal regime from an 18-year tidal simulation (Saros cycle). The corresponding levels are 1. 45 m (low tide), 3. 07 m (mid-tide) and 4. 75 m (high tide). The choice of (H m0 , T p )-couples was based on the analysis of the wave parameters at Esquibien, point W458N4796 extracted from the  19-year (1994-2012)  Homere database, distributed through the French Previmer project  [38].
8, show power variations, P ranging from 2. 4 at point 1 (located in front of the breakwater) to 33. 6 kW/h at point 7, placed just off the steep rocky shoal. There is not a strong impact of the water depth due to wave breaking on the power values at four points 3 to 6 (water depth from 5 to 10 m) because, despite the variations of water depth, the wave power remains around 9 kW/m. Even at the 2 m-deep point 2, the wave power is still 6 kW/m. Points 7 and 8 are the most energetic points-their locations, near the shoal, in the southwest of the breakwater, favor wave-power focusing. Two numerical simulations are run in the South of the Bay of Biscay in order to assess the wave power on the North breakwater of the port of Bayonne and on the Artha breakwater of Saint-Jeande-Luz. Simulations consist of forcing a numerical wave model  (Swan)  with two different hindcast databases.
• Saint-Jean-de-Luz Located along the Gulf of Biscay, at 20 km East from the Spanish border, the Bay of Saint-Jeande-Luz houses the cities of Saint-Jean-de-Luz, Ciboure and Urrugne. The bay is partially closed by three breakwaters from West to East: Socoa, Artha and Sainte-Barbe. Since the continental shelf is narrow in front of the bay, the waves are little dissipated, and the breakwaters frequently receive strong waves. The breakwaters were built between 1617 and 1895. Impacted by severe storms for  Two numerical simulations are run in the South of the Bay of Biscay in order to assess the wave power on the North breakwater of the port of Bayonne and on the Artha breakwater of Saint-Jean-de-Luz. Simulations consist of forcing a numerical wave model  (Swan)  with two different hindcast databases.
The areas in blue cyan represent the areas discovered at low tide. The remaining colors (green to red and purple) represent water depths up to 50 m. Water depths in front of breakwaters vary from 0 to 18 m. The wave energy flow is calculated using data from the Anemoc base over 23. 5 years. The wave propagation is simulated from the extraction points of the Anemoc database from the Basque coast to the coast of Saint-Jean-de-Luz. This provides information on the statistical wave characteristics near the site covering a long period, taking into account the tide and the bathymetry of the site. This simulation was carried out with the calculation code simulated wave nearshore (Swan)-Delft University of Technology  [46]. many years, they were frequently repaired and reinforced. Almost every year, the breakwaters Artha and Socoa are reinforced by concrete armor units that have made after centuries a wide armor layer.
The bathymetry is presented in Figure  11. The colors white, blue, to dark blue represent the areas that are constantly out of water. The areas in blue cyan represent the areas discovered at low tide. The remaining colors (green to red and purple) represent water depths up to 50 m. Water depths in front of breakwaters vary from 0 to 18 m. The wave energy flow is calculated using data from the Anemoc base over 23. 5 years. The wave propagation is simulated from the extraction points of the Anemoc database from the Basque coast to the coast of Saint-Jean-de-Luz. This provides information on the statistical wave characteristics near the site covering a long period, taking into account the tide and the bathymetry of the site. This simulation was carried out with the calculation code simulated wave nearshore (Swan)-Delft University of Technology  [46].
Swan is a third-generation spectral wave model based on the conservation of the density of the wave action. It simulates the spread of sea states (wind seas and ocean waves) in the coastal domain. The model takes into account the effects of refraction and shoaling related to variations of bathymetry, diffraction by obstacles, wave generation by wind action, wave dissipation by breaking, bathymetric and friction on the bottom. The wave propagation is forced at the northern borders of the domain by the characteristics of the three-point waves at the northern border of the domain, in which the Anemoc database is available. At these points, the significant height of the Hs waves, their Tp period, the propagation direction and the directional range at the peak are known with a time step of 1 h.
The time step of the model (one hour), fixed by the data of the Anemoc base, allows taking into account the tide. The change in water level over a year was calculated with the harmonic constituents of the tide using the FES2004 software  [47]. The significant wave heights of the Anemoc database are compared with those of the result of the simulation by the Swan model at the three points. The comparison is for a 20-day duration. Swan model correctly propagates the wave forced by Anemoc to the borders of the domain. The average quadratic error lies between 13 cm and 33 cm. The main reasons why the results are slightly lower quality at one point are that the models used are different (Tomawac and Swan) and that for our simulation, wind patterns are not used. Since the propagation zone is large, the spread of the waves can be altered by the wind.