dataesr/scientific-paragraphs-categorization huggingface dataset:
Imaging acquisition and analysis Images were obtained with the Leica TCS SP5 and the Zeiss LSM880 confocal microscopes. The same imaging acquisition parameters were used for all the comparative analyses. Images were processed with the Imaris Software (Oxford Instruments), Fiji or Photoshop CS4 (Adobe). For DNA quantification and nuclear size of SGs, DNA staining intensity in the SG cells was obtained from z stacked images every 0. 25 μm of DAPI stained L3 larvae. Image analysis was performed using Fiji. The volume of the wing pouch region was measured in Imaris software (Oxford Instruments). Adult flies, nymphal parts and adult cockroach images were acquired using AxioImager. Z1 (ApoTome 213 System, Zeiss) microscope, and images were subsequently processed using Photoshop CS4 (Adobe).
Statistical analysis Statistical analysis and graphical representations were performed in GraphPad Prism 9 software. All experiments were performed with at least three biological replicates. Two-tailed Student's test and Welch's ANOVA followed by Dunnett's T3 post hoc tests were used to determine significant differences. also benefited from FEDER funds. S. C. is a recipient of a Juan de la Cierva contract from the MINECO.
Acknowledgements The  ICTS "NANOBIOSIS ", and particularly the  Custom Antibody Service (CAbS ,  IQAC-CSIC ,  CIBER-BBN) , is acknowledged for the assistance and support related to the E93 antibody used in this work. This project is supported by the  Spanish MINECO  (grant  PGC2018-098427-B-I00  and  PID2021-125661NB-100  to D. M. , and X. F-M. and grant  PGC2018-094254-B-100  to J. C) and by the  Catalan Government  ( 2017-SGR 1030  to D. M. and X. F-M and trhought BIST to J. C. ). The research has.
Disrupting FSHR signaling in the pituitary increases corticosterone levels and induces hepatic steatosis To uncover the site of action for FSH in mediating these effects, we performed RT-PCR for the Fshr on major tissues prepared from female mice. We detected Fshr in only two tissues, the ovary and the pituitary (Supplementary Fig. 15 ), which we further verified via RNA-scope (Fig. 5a ) and RT-qPCR (Fig. 5b ), but not in the liver. In addition, a reanalysis of bulk RNA-seq data confirmed that FSHR is also not expressed in the human liver (Supplementary Fig. 16 )  29. To understand the role of FSH signaling within the pituitary, we established an in vitro whole pituitary assay in which we blocked the action of FSH by incubating pituitaries taken from diestrus (post-ovulation) females with a monoclonal antibody targeting FSH prior to stimulation with GnRH to release the gonadotropins (Fig.
μCT analysis Femora were dissected from mice, stored at -80 °C, and then scanned by high-resolution μCT (Skyscan 1176, Bruker MicroCT, Kontisch, Belgium). The scanner was set at a voltage of 50 kV, a current of 200 μA, and an isotropic resolution of 9 μm by using a 0. 5 mm aluminum filter. We used image reconstruction software (NRecon (version 1. 6. 10. 6)), orientation software (DataViewer (1. 5. 1. 2)), data analysis software (CTAn (version)1. 16. 4. 1+), and three-dimensional model visualization software (CTVox (version 3. 2. 0r1294)) in order to analyze the parameters of the distal femoral metaphyseal trabecular bone. Trabecular bone was analyzed over 200 slices, starting with 50 slices distal from the growth plate. The trabecular bone volume fraction (BV/TV) and trabecular number (Tb.
Oil red O (ORO) staining Liver tissue sections at -80 °C were first allowed to reach room temperature over 30 min. Then the slides were incubated with 60% (v/v) isopropanol for five minutes, followed by 10 min incubation with fresh ORO (Oil Red O, O0625, Sigma) working solution (7:5 dilution of ORO stock solution with water; ORO stock solution: 300 mg ORO in 100 ml 100% isopropanol). Then two washing steps for three minutes in 60% isopropanol with 100 rpm shaking were performed. After three washing steps of one minute each in water, slides were mounted with Fluormount G onto Superfrost Plus microscopy slides. For visualization, slides were imaged using a ZEISS Axio Scan Z1 with ZenBlue software (supported by the DFG INST 256/434-1 FUGG) with 20× magnification. We noticed ORO signals were not evenly distributed in sections.
This paper focuses on a new approach to describe coastal morphodynamics, based on optimization theory, and more specifically on the assumption that a sandy beach profile evolves in order to minimize a waverelated function, the choice of which depends on what is considered the driving force behind the coastal morphodynamic processes considered. The numerical model derived from this theory uses a gradient descent method and allows us to account for physical constraints such as sand conservation in wave flume experiments. Hence, the model automatically adapts to either wave flume or open sea settings and only involves two hyperparameters: a sand mobility and a critical angle of repose. The ability of OptiMorph to model cross-shore beach morphodynamics is illustrated on a flume configuration. Comparison of the beach profile changes computed with OptiMorph with experimental data as well as the results from the coastal morphodynamic software XBeach demonstrates the potential of a model by wave energy minimization.
Xbeach model XBeach is an open-source process-based model developed by Deltares, UNESCO-IHE, and Delft University of Technology to simulate the hydro-morphodynamic processes in coastal areas  (Roelvink et al. , 2009; Zimmermann et al. , 2012; Bugajny et al. , 2013; Williams et al. , 2015). In brief, XBeach uses four interconnected modules to model near-shore processes  (Daly, 2009; Roelvink et al. , 2010). The two hydrodynamic modules consist of the short wave module and the flow module. The first is based on wave action equations  (Holthuijsen et al. , 1989) , and incorporates breaking, dissipation  (Roelvink, 1993) , and wave current interactions, while the latter is governed by shallow water equations  (Andrews and Mcintyre, 1978; Walstra et al. , 2000). One of the two morphodynamic modules is the sediment transport module based on the equilibrium sediment concentration equation  (Soulsby, 1997)  and a depth-averaged advection-diffusion equation  (Galappatti and Vreugdenhil, 1985).
Hydrodynamic validation This section is devoted to the comparison of the two numerical hydrodynamic models to the experimental wave data obtained in the experimental flume of Section 3. 1. Mean wave height profiles were calculated over the short-term storm simulation, for both OptiMorph and XBeach, and compared to the mean wave height of the experimental model. The latter was calculated using the measures taken by the gauges of the flume. Fig. 2  shows that the hydrodynamic module of both OptiMorph (red) and XBeach (blue) are both comparable with respect to the experimental measurements (green) excluding, as is often the case, the second point at 𝑥 = 6 m. XBeach demonstrates a close qualitative fit over the 10-22 m section of the flume, whereas OptiMorph excels at the coast (21-27 m), with a near-perfect fit with the experimental data.
Numerical results of the morphodynamic simulations The OptiMorph model was applied to the configuration of the COPTER experiment of Section 3. 1, and the resulting beach profile Fig. 3. A. Results of the numerical simulation calculated over the initial seabed (gray) using the XBeach morphodynamic module (blue) and the OptiMorph model (red). These are compared with the experimental data acquired during the COPTER project (green). The mean water level is denoted MWL and is set at 0. 56 m. B. Zoomed in view of the sandbar, located between 6 m and 16 m. C. Zoomed in view of the plateau, located between 16 m and 24 m. D. Zoomed in view at the shoreline, located between 24 m and 32 m. E. Robustness analysis of the mobility parameter 𝛶. The reference profile is depicted in black. The orange (resp.
3. A, we observe that the red beach profile provided by the Opti-Morph model shows a general quantitative agreement when compared to the experimental data, as does the XBeach morphological module. In fact, both models produce profiles close to the experimental data over the plateau located at 15-25 m from the wave-maker (Fig. 3. C). At the shore, OptiMorph matches the experimental data whereas XBeach shows a vertically difference of up to 3 cm at 𝑥 = 27 m (Fig. 3. D). Discrepancies on the part of both models occur in the area surrounding the tip of the sandbar, as both OptiMorph and XBeach fail to predict the shoreward shift of the sandbar (Fig. 3. B); the experimental data show that the height of the sandbar remains unchanged with regards to the initial profile. Both sandbars have a height of 0.
In our situation, it correspond to the off-shore position (subscript 𝑖 = 0). Typical values in our simulation are: 𝑢 0 = 10 m s -1 , 𝛥𝑥 = 1 m, ℎ = 0. 55 m and 𝑔 = 9. 81 m s -2. This gives us 𝛥𝑡 = 0. 04 s, which is about two orders of magnitude smaller than our reference time step of 𝛥𝑡 = 3 s. In addition, the costs of one iteration of the Saint-Venant and OptiMorph models are comparable.
Mid-term simulations This section is devoted to a medium term behavior of OptiMorph, the main question being, is this numerical model capable of creating an equilibrium state after being subjected to a great number of repeated events. Five forcing scenarios, lasting either 2 or 6 days, were applied to the same initial seabed in the same parametric configuration. The current OptiMorph code is in Python. Typically, using time-steps of 1 s simulating a day of forcing requires about 1. 5 h on a 2 GHz PC computer. Each time iteration gathering the steps presented in this paper requires therefore about 63 ms. Regarding Section 4. 1, we could use 3 s time-step and divide the simulation time by 3. An analysis of the resulting beach profiles is performed as well as their behavior throughout the simulation.
Acknowledgments This work was conducted as part as  M. Cook 's PhD studies which is funded by  BRLi and M. Dupont 's PhD studies which is funded by the  CNRS. We are grateful to  BRLi  for their assistance throughout the development of preliminary versions of the morphodynamic model by optimization. We thank also the  GLADYS network  (www. gladyslittoral. org) for its continuous logistical and financial support of academic research and applications on coastal hydrodynamics in the South of France. The  COPTER experiment  was conducted in the framework of project  ANR Blanche 2005-006. The development of OptiMorph has been made possible thanks to european funds in the framework of  OPTIBEACH  project and  CNRS, France  funds in the framework of the french  MITI program.
Statistical analyses Categorical variables were described as frequency rates and percentages, while continuous variables were expressed as mean, median and inter-quantile range (IQR). Normality was assessed with the Shapiro-Wilk test. Repeated-measures analyses were performed with Friedman's test or a repeated-measure analysis of variance (rANOVA). The Wilcoxon-Mann-Whitney test was used for comparing vaccine immune response at several time points. Correlations between IFN-g Ag1/Ag2 and serological results were measured according to Spearman's rank correlation coefficients. A p-value ≤0. 05 was considered statistically significant (a=0. 05). Statistical analyses were performed using R Statistical Software (version 4. 0. 1); and EasyMedStat (v3. 19 ).
Participants' characteristics Between May 2021 and June 2021, 20 patients were included and were followed over a six-month period. One patient was excluded from the final dataset due to positive serology for SARS-CoV-2 at baseline. One patient did not present for the M3 appointment. Among the 19 remaining patients, 14 (74%) were men and 5 were females (26%). Their median age was 51 years [range  [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56] [57] [58]. Patients had a median 12. 4 years' [range 5. 9-23. 9] HIV follow-up, and a CD4 nadir of 399 c/mm 3 [range 277-471]. All patients had an HIV-RNA viral load below 50 copies/ml at inclusion. Complete characteristics of patients are described in Table  1. None of the participants had symptoms of SARS-CoV-2 infection within the six months following vaccination.
Cell Lines and Viruses Viral production, as well as the study of growth kinetics, was achieved by using Vero cells. Growth kinetics were also assessed on CCL141 cells. Vero cells (ATCC CCL-81) were maintained at 37 • C, 5% CO 2 in Dulbecco modified Eagle's medium (DMEM, Thermo Fisher Scientific) supplemented with 5% fetal bovine serum (FBS, Lonza), 1 mM sodium pyruvate, and penicillin (1 U/mL)/streptomycin (1 µg/mL) (Thermo Fisher Scientific). The CCL141 cell line (provided by Nolwenn Jouvenet, Institut Pasteur de Paris) was maintained at 37 • C, 5% CO 2 in DMEM supplemented with 10% FBS, Lonza, and antibiotics as described above. The infectious cDNA clone derived from IS98, a highly virulent strain isolated from a white stork, Ciconia ciconia (IC-WNV-IS98; Genbank accession number: KR107956.
1), was constructed as described by Bahuon et al. , and cleared virus supernatants obtained after a single passage in Vero cells were used in in vitro and in vivo infections  [49]. The parental WT-WNV-Italy 2008 15803 strain, WNV-IT08 (Genbank accession number: FJ483549. 1), was isolated from a magpie, Pica pica, in 2008 at IZSLER and passaged once in Vero cells. This first-passage virus was used in the following experiments.
The presence of viral RNA was confirmed in blood collected at 3 days pi (dpi) and in brain tissue (data not shown), recovered shortly after their death, by RT-qPCR. Total RNA was extracted using the MagVet Universal Isolation kit (Thermo Fisher Scientific) according to the manufacturer's instructions, from 100 µL of blood or 100 mg of brain tissue. To assess the induction of WNV-antibody in surviving animals at 21 dpi, blood was collected, and serum was separated after centrifugation for 5 min at 2044 RCF. ID Screen West Nile competition ELISA kit (ID Vet, Evreux, France) was performed on mice sera according to the manufacturer's instructions and demonstrated the induction of WNV antibodies in all surviving mice infected with doses ≥ 1 PFU (data not shown).
Comparison of Genome Sequences of Parental WNV-IS98 and IT08 Strains The complete genome sequence of the WNV-IS98 strain (GenBank accession number AF481864. 1) exhibits 446 nucleotide (nt) differences (4. 0%) compared with that of the WNV-IT08 strain (GenBank accession number FJ483549. 1). Nucleotide substitutions are distributed throughout the genome (5 UTR: 0 substitutions (0. 0%), C protein gene: 6 substitutions (1. 6%), prM-M protein gene: 20 substitutions (4. 0%), E protein gene: 58 substitutions (3. 9%), NS1 protein gene: 40 substitutions (3. 8%), NS2A protein gene: 34 substitutions (4. 9%), NS2B protein gene: 20 substitutions (5. 1%), NS3 protein gene: 85 substitutions (4. 6%), NS4A protein gene: 19 substitutions (4. 3%), NS4B protein gene: 35 substitutions (4. 6%), NS5 protein gene: 104 substitutions (3.
Replication of Parental and Chimeric Viruses in SPF Chickens Birds are the principal amplifying hosts for WNV, and young chicks have been shown to be susceptible to WNV infection and to allow pathotyping of WNV strains. One-day-old SPF chickens were inoculated intracranially (i. c. ) and subcutaneously (s. c) (data not shown), at a dose of 10 3 TCID 50 and monitored for 14 days pi. As previously shown, WNV strain virulence could be efficiently discriminated after i. c. inoculation in chicks  [36]. After i. c. inoculation, peak viremia determined at 3 dpi was similar irrespective of which virus was considered (Figure  7a ). Peripheral spread was estimated by RT-qPCR on crushed feather follicles obtained at 3 dpi. Viral RNA load was similar in groups infected with WNV-IS98 and the IS98-NS5/3 UTR chimera, while 1-log lower RNA quantities were reported from feathers sampled in chicks infected with WNV-IT08 and the IS98-NS4A/NS4B/5 NS5 chimera (not significant) (Figure  7b ).
At 3, 7, and 14 dpi, oral swabs were taken to estimate virus excretion levels, and viral RNA measured by RT-qPCR. Viral RNA was no longer detectable in oral swabs collected at 14 dpi (data not shown). While no significant differ-ences were observed at 3 dpi, the parental IT08 strain and the IS98-NS4A/NS4B/5 NS5 chimera displayed significantly less viral RNA at 7 dpi than the WNV-IS98 parental strain (Figure  7c , p < 0. 05).
To calculate AED from RL, we removed the urban component (Methods) of RL for each grid cell  44, 45  , given large fires don't occur in urban areas (Methods). Remaining RL was doubled to account for miscellaneous vegetation breaks and because the original road dataset exhibits a significant low bias (Methods, Table  1 ))  [78] [79] [80]. AED then defines the relationships between land surface variables and fire phenomena (Fig. 1b , Methods, Table  1 ). As fragmentation increases and AED decreases: (1) Individual fire size is restricted by patch size unless threshold conditions for crown fire spread and fuel bulk density limitation in forests and grasslands  81  , respectively, are surpassed. This was implemented because recent statistical evidence suggests that road density (m km -2 ) is the strongest predictor for decreases in annual BA at global scale 71.
Regional scale and population fragmentation impacts General patterns in regional DBAFrag are discernible in Fig. 2a, b , but precision evaluation requires an observational dataset that removes and adds fragmentation while holding population density, vegetation and climate constant -which is implausible. However, we can compare observed and modelled fire activity in areas over which large-scale increases in fragmentation have occurred during the period for which global satellite observations of fire are available (post-2000). This coincides with the 'boom' years of globalisation  91, 92  , in which lowered regulatory power and multinational corporate demand  93  incentivised large-scale supply of cheap commodities for global markets  94. Large swathes of the largelytropical global South 1 , most notably in Indonesia and Brazil  95, 96  , were given over to clearing, selective logging and plantation/mining establishment over the last two decades  97, 98  that were associated with systematic increases in fire activity  47, [99] [100] [101]  , and provides a 'before-after' comparison of fire behaviour with fragmentation.
The spatial distinction between PFTs can either be forced through an input vegetation map, defining the fractions of each grid cell covered by each PFT, or through the dynamic global vegetation model in ORCHIDEE, which predicts PFT type and allocation according to the biophysical suitability of each PFT to primarily climatic input variables. Fixed C is then allocated to foliage, fruit, roots, above/below -ground sapwood, heartwood and C reserves, that upon death or senescence are shunted to two reactivity-differentiated litter pools. ORCHIDEE-MICT is hard-coded with an adaptation of the SPITFIRE fire module  63, 66, 121, 122  , which divides the aboveground vegetation components described above and apportions them to potential fuel type categories differentiated by their potential time to oxidation.
Fragmentation Representation The average edge distance (AED) per grid (𝐴𝐸𝐷 * ) given per-grid road length sum (𝑅𝐿 * ) was solved analytically and is given by the following: 𝐴𝐸𝐷 * = (2 * 𝐴𝑟𝑒𝑎 * * 𝑓(𝐶𝑜𝑛𝑡))/ 𝛴(𝑅𝐿 * )) (1) Where 𝐴𝑟𝑒𝑎 * is the grid area in m 2 , and 𝑓(𝐶𝑜𝑛𝑡) the fraction of each grid cell area taken up by the continental landmass. The gridded RL dataset in Meijer et al. (2018) gives a global road length estimate that is about 50% lower than that estimated by the World Road Statistics database (~30 million km), and about 300% lower than the estimate provided by the CIA World Factbook. Furthermore, a recent report  78  showed that the Global Roads Inventory Project (GRIP) database consistently and strongly under-predicted the existence of small roads, leading to large low biases against manually-observed road data in the report's two case study sites in the Congo and Canada.
The primary reason hypothesised for these mismatches, which are acknowledged in the  46  paper, is the under-representation of unofficial and unpaved roads in their source database. GRIP was shown to under-represent total manuallymeasured road length in a grid cell by a factor of over 8 in one area (Fig. 19  of 78 ). For this reason, in Eq. 1, which generates the AED map used as input to ORCHIDEE, we make the assumption that the gridded road length data underrepresent actual road length by a factor of 2, which implies a total global road length roughly in between the WRS and CIA estimates. Further, as river and stream length as well as large topographic discontinuities can reasonably be expected to act as fire breaks in most circumstances, and given that these are excluded from the input data, we take these to be potentially integrated into the factorial AED map.
We acknowledge that multiplying RL uniformly by a single factor masks the likely spatial distribution of bias inherent to the GRIP database, however given that the source bias has not been assessed or quantified, we retain this spatial uniformity assumption for simplicity in this study. In order to modulate the effects of fire by fragmentation, ORCHIDEE must first be fed a gridded input map containing the AED data. This is derived from  46  , which gives global gridded road length in m km - 2 at 5 arc-minute (~8km) resolution for a single time period (~2017), downloaded from (https://zenodo. org/record/6420961, accessed 20/11/2022), converted to netcdf format and regridded to this study's simulation resolution of 0. 5° (~50km) using the conservative interpolation function in the Climate Data Operator (CDO) package 123.
The raw data were provided in five classes of road type: highway, primary, secondary, tertiary and local. Although we can reasonably expect each of these road classes to represent different scales of fragmentation, each conferring differential effects in their relation with fire phenomena, the paucity of empirical data on what these might be, coupled with the range of impacts that may, as mentioned be contradictory, mean that for the moment we take all road classes to be equal in effect, and as such sum them to a single road length density (RL) variable. Equation 1 is then applied to the dataset to generate a global gridded map of the average circular patch radius associated with each grid cell (𝐴𝐸𝐷 * ). Next, we assume that spatially extensive fires do not occur on land that can be considered 'urban'.
This assumption is made on the basis that urban areas are characterised by very low fuel densities (compared to, say, a pine forest), large areas of concrete, asphalt and steel, which do not burn easily, and high population densities that strongly increase the probability of successful human fire suppression. Because road density in urban areas is very high, this assumption should also require that the urban proportion of road density in each grid cell is removed from the original RL data, and a corresponding AED map generated. To do so, we download the output data from [ref. 45  ] which gives the urban area fraction (UAF) of grid cells at global 0. 125° resolution, and projects this variable globally to 2100 under the Shared Socioeconomic Pathways (SSP) scenario suite: (https://dataverse.