dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: Organic petrography has been performed in  ALBA (Academic Laboratory of Basin Analysis ) of  Department of Science of "Roma Tre" University. The authors are grateful to  Philippe Robion , responsible of the " Integrated microscopic Platform " in  CY Cergy Paris Université  where micro-Raman spectroscopy on dispersed organic matter has been performed  M. N. Zaghloul  from  "Abdelmalek Essaâdi" University  is warmly acknowledged for exciting discussions on Rif geology and geodynamic evolution and great days in the field in the early phase of the research.
Introduction This mechanism was patented by William P Brown and developed year by year for improving its performances  [1]. There are many researches on this mechanism. Manoj R. Patil1 and S. D. Kachave studied, using Pro-E software, the construction of this mechanism intended to reduce weight of scissor jack and, at the same time, maintaining its strength and service life. The study was made using 3D modelling  [2]. This mechanism is also used in the construction of the high scissor lift platform and some research were done using 3D Pro/E software  [3]. Other researches about this mechanism were done by researchers from different countries, each of them trying to improve it. [4] [5] [6] [7] [8] [9] [10] [11]. Agricultural machines have the most complex mechanisms. Therefore, it is good for the designers to know the methods for determining kinematic and kinetostatic parameters, with the help of calculation procedures on modular groups.
Absorbance was measured at 490 nm. Analyses of plasma lipophilic antioxidants (retinol, α-tocopherol, coenzyme Q10) were performed using a high performance liquid chromatograph coupled to an MS detector equipped with a triple quadrupole (Shimadzu LCMS/MS-8040). Ionization was conducted using the atmospheric pressure chemical ionization (APCI) mode. Data acquisition and processing were performed using Shimadzu LabSolutions LCMS software. The compounds were separated with a Kinetex XB-C18 100A analytical column (50 mm × 3. 0 mm, 1. 7 µm). The mobile phase consisted of an isocratic solvent A (methanol) for 0. 01-2 min and then isocratic solvent B (methanol-n-hexan, 72:28, v/v) for 2. 5-6 min. Flow rate was 0. 4 mL/min and temperature of the analytical column was 400 • C. Injection volumes of standard and sample solutions were 10 µL.
Statistical Analysis Statistical analysis was performed using parametric tests for normally distributed variables (Student's t-test and Pearson's correlation method). The data were processed using Statistica 12. 0 (StatSoft, Cracow, Poland) and GraphPad Prism 7 (GraphPad Software, La Jolla, CA, USA) and expressed as mean ± SD and median. Statistical significance was defined as p ≤ 0. 05.
Multistep genomic evaluation A study by  VanRaden et al. (2009)  using field data sets established a mature multistep methodology for genomic selection in dairy cattle. The steps included running pedigreebased BLUP with the national database, creating pseudoobservations for genotyped animals (bulls) such as daughter yield deviation (DYD). These pseudo-observations are fit into a model estimating on SNP effects assuming normal (linear) or non-normal (nonlinear) distributions. Finally, genomic predictions for a genotyped animal or candidate to selection are obtained combining pedigree and genomic-based predictions into an index: where GEBV is the genomic estimated breeding value, PA is parent average, DGV is direct genomic value, and PI is parental index created based on pedigree relationships for genotyped animals.
Different single-step formulations Several alternative single-step formulas were proposed. These included equations where G is not inverted  (Legarra and Ducrocq, 2012) , where SNP effects are estimated for the genotyped animals and a polygenic effect is fit for non-genotyped animals  (Legarra and Ducrocq, 2012; Liu et al. , 2014) , and where SNP effects were fit for all animals using imputed genotypes  (Fernando et al. , 2014; Taskinen et al. , 2017). The purpose of these formulas was to reduce computations with many genotyped animals. As opposed to a regular ssGBLUP, which can be applied to an existing BLUP software just by replacing the relationship matrix, SNP-based models require new programming. Meuwissen et al. (2014)  proposed an alternative single-step approach by combining identical by descent and identical by state approaches.
, 2014). The model was reformulated for economy of memory by  Fernando et al. (2016)  who called it "hybrid model," although the same model had already been proposed by  Legarra and Ducrocq (2012). As the imputation was expensive, and the model is conceived to use Gibbs sampling methods, the implementation of ssBR in the BOLT software used graphical processing units, that is, GPU  (Garrick et al. , 2018). Compared with ssGBLUP, ssBR allows the user to estimate SNP effects directly but the implementation of complex models (e. g. , correlated maternal effects with multiple traits) is quite complex. The method of ssBR was used for a multi-breed evaluation done by the Simmental association for more than 10 breeds  (Golden et al. , 2018)  but they decreased the number of SNPs from 50k to about 2.
Genetic parameters under genomic selection Plant breeders estimate variance components at each genetic evaluation, partly because they have several random effects (e. g. , blocks) and partly because their data sets are small. In contrast, animal breeders tend to use either once-in-a-while estimates or to use pedigree-based estimates for genomic evaluation purposes, for example, as in  VanRaden (2008). Genetic parameters can be estimated with genomic information using ssGBLUP and normal tools such as REML or Monte Carlo Markov Chain via Gibbs sampling. The use of the genomic information increases the costs of computations because the inverse of G is usually dense, whereas non-genomic mixed model equations are sparse. Masuda et al. (2014)  developed a sparse matrix package that recognizes and processes dense blocks rapidly.
A four-trait single-step AIREML model with 15k genotyped animals took less than 1 h with the new package  (Masuda et al. , 2015) ; the computations increase cubically with the number of genotyped animals and of traits. Comparing genomic and pedigree-based estimates of variance components relies on the compatibility of genomic and pedigree information  (Legarra, 2016). Without selection and with a complete pedigree, the estimates of variance components ignoring or using the genomic information are usually similar, although with the genomic information they have lower standard errors  (Forni et al. , 2011). Under strong selection, the estimates ignoring the genomic information are biased  (Gao et al. , 2019; Hidalgo et al. , 2020). The computed bias due to preselection depends on the accuracy of modeling and intensity of selection.
When genotyped animals have only records from progeny or other relatives, this method is only applicable in a multiple-step manner, that is, creating pseudo-phenotypes such as DRP or DYD as it was typically the case in dairy cattle  (Boichard et al. , 2003) , but this is difficult to generalize to other species, where progeny sizes are smaller and many genotyped have phenotypes (e. g. , weights) but not genotypes. However, Gualdrón-  Duarte et al. (2014) and Bernal Rubio et al. (2016)  showed the equivalence of P-values in GBLUPbased models with P-values in single-marker fixed regressions with a polygenic effect. Lu et al. (2018)  extended the theory to ssGBLUP, and  Aguilar et al. (2019)  added this concept to the BLUPF90 package  (Misztal et al. , 2014b) , with a successful implementation using 1 million birth weight phenotypes for American Angus, almost 2 million animals in the pedigrees, and 1,424 genotyped sires.
Acknowledgments We gratefully acknowledge the very helpful comments by the two anonymous reviewers. This research was primarily supported by grants from  American Angus Association ,  Cobb-Vantress ,  Genus PIC, Holstein Association USA ,  Smithfield Premium Genetics, Zoetis , and  U. S. Department of Agriculture's National Institute of Food and Agriculture (Agriculture and Food Research Initiative  competitive grant number  2015-67015-22936 ). This paper is presented at the  ASAS 2019 Symposium.
PC7 cytosolic tail is critical for the shedding of hTfR1 ever, finely converge in the ensemble. Furthermore, it was found that the peptide does not have a regular secondary structural element (␣-helix, ␤-sheet, or turns), supporting the role of this segment as part of an accessible loop, because nonregular secondary structures are found in half of all PPIs as deduced from a survey of protein data banks  (26). The data support the notion that the three key residues Glu-719,721 and Leu-725 lie on the same side of the molecule (Fig. 5A ) and hence are wellpoised to possibly interact with a partner/adaptor protein, as identified below. It was shown that removal of the side chains from these triad residues (i. e. Ala mutations of Glu-719,721 and Leu-725) resulted in a substantial reduction in the activity of PC7 on hTfR1, which is likely due to eliminated interactions of these residues with one or more cytosolic adaptor protein(s).
4  (46). Structures were calculated using the ARIA 2. 2 and CNS programs on the CCPN grid server  (47, 48). After removing the violated NOE, the structures were recalculated on the CCPN grid server (https://www. ccpn. ac. uk/v2-software/software/web-apps-general). This server also refined the structures in water. The refined structures were further validated with the program Procheck (Fig. 3 )  (49). For the Ala-mutated peptide 3D structure, calculations were achieved with AMBER 14. 0 package (http://ambermd. org). The starting structure of the peptide has been generated with LEaP program embedded in AMBER 14. 0. For the solvated system, a truncated octahedral box of water around the peptide structure has been added (2245 TIP3P water residues). The side chains of one lysine and one aspartic acid were defined as positively and negatively charged, respectively, leading to a total peptide charge of 0.
The entire system was subjected to dynamics under the constant pressure for 10 ns with time-averaged distance restraints and dihedral restraints derived from NMR spectroscopy. A total of 97 interproton distance restraints (64 intraresidual, 26 sequential, and 7 medium range) and 17 dihedral angles restraints were applied for structure solution. The calibration peaks' volume-to-distance constraints were performed with CALIBA program included in CYANA 2. 1 (50) software. The interproton distances and dihedral angles were introduced in the molecular dynamics simulations with the force constants f ϭ 50 kcal/(mol ϫ Å 2 ) and 10 kcal/(mol ϫ rad 2 ), respectively. The geometry of the peptide groups was kept fixed according to NMR data (all-trans) with the force constant f ϭ 50 kcal/(mol ϫ rad 2 ).
MD simulations were performed with a 2-fs time step and a 10-Å cutoff radius. Isotropic position scaling was used to maintain the pressure. The system was heated from 10 to 303 K during the first 20 ps of MD and then the temperature was maintained at 303 K. The coordinates were recorded at each 4 ps (2000th step), and 20 conformations obtained in the last steps of MD simulations were chosen for final structure analysis and validated with program Procheck.
Medical Data Monthly dengue data for 2009 and 2014 were obtained from the Bangkok Metropolitan Administration in the context of the DENFREE project  [39]  (Figure  2 ). The rates of incidence were calculated by dividing the number of dengue incidence by the total populations. Expected values were computed using the incidence rate (disease cases per 100,000 population) in each district from Equation (1) as follows: IR dengue = (Cases/Population) × 100,000 where IR dengue is the incidence rate in each district; Cases are the total number of dengue incidence; and Population is the total population in each district. The average annual dengue incidence rates during the study period were 135. 0, 195. 5, 186. 4, 184. 8, 264. 6, and 98. 0 per people in 2009-2014, respectively.
Image Preprocessing Landsat images provide space-based moderate-resolution remote sensing data. Landsat 5 Thematic Mapper (TM) and Landsat 8 Thermal Infrared sensor (TIRS) images were acquired from 2009 and 2014 and only clear-sky images were analyzed for the land surface temperature (LST) calculations in the three different zones (inner city, urban fringe, and suburban) (Figure  1 ). To avoid the impact of remote sensing data instability, the images in November were selected to explore the urban land surface temperature (LST). A digital map of Bangkok, 1:4000 scale during 2009-2014, was obtained from the Town Planning Office Bangkok and used to categorize buildings into two groups as <8 floors (1-7 floors) and >8 floors (8-100 floors). Time-series of LANDSAT 5 Thematic Mapper (TM) and LANDSAT 8 Thermal Infrared Sensor (TIRS) images (Path 129/Row 50, 51) acquired for 2009 and 2014 (LANDSAT 5 TM: 19 November 2009; LANDSAT 8 TIRS: 17 November 2014) were analyzed in this study.
For LANDSAT 8 TIRS thermal infrared bands (band 10: 10. 60-11. 19 and band 11: 11. 50-12. 51 µm), the radiance was calculated by using the following equation  [41, 43]. L λ = M L × QCAL + A L ( 3 ) where L λ is the top of atmosphere (TOA) spectral radiance in W/(m 2 srad µm), M L is the band-specific multiplicative rescaling factor from the metadata, A L is the band-specific additive rescaling factor from the metadata, and QCAL is the quantized and calibrated standard product pixel values. Next, the radiance obtained from Equations (  2 ) and (  3 ) was converted to brightness temperature by using Equation (  4 )  [40, 41]. T = K 2 /ln [(K 1 /L λ ) + 1] ( 4 ) where T is the at-satellite brightness temperature in Kelvin (K), K 2 is the calibration constant 2 = 1282. 71 K (in case of LANDSAT 5 TM) or band-specific thermal conversion constant from the metadata (K2_CONSTANT_BAND_x, where x is the band number, 10 or 11) (in case of LANDSAT 8 TIRS), K 1 is the Calibration constant 1 = 666.
09 W/(m 2 sterµm) (in case of LANDSAT 5 TM) or band-specific thermal conversion constant from the metadata (K1_CONSTANT_BAND_x, where x is the band number, 10 or 11) (in case of LANDSAT 8 TIRS), L λ is the spectral radiance at the sensor's aperture in W/(m 2 ster µm) (in case of LANDSAT 5 TM) or top of atmosphere (TOA) spectral radiance in W/(m 2 srad µm). Then, the brightness temperature was converted from Kelvin to • C using the following equation. T S = T -273. 15 (5) where T S is the surface radiance temperature in • C, T is at-satellite brightness temperature of a black body in Kevin. Finally, the surface radiance temperature maps of BMA at various times were calculated. Land surface emissivity (LSE) is one of the key parameters to retrieve accurate LST from remotely sensed imagery. Landsat 5 (band 3, 4) and Landsat 8 (band 4, 5) were used to calculate the Normalized Difference Vegetation Index (NDVI).
Dengue Incidence Map Dengue incidence data were obtained from the Bangkok Metropolitan Administration. Figure  4a  shows the location of significant local hotspots of spatial association using Moran's I statistics. The Moran index reflects how neighboring values are associated with each other. Those locations were classified by type of association as the red and blue districts indicating spatial clusters (high surrounded by high (HH), and low surrounded by low (LL)), the orange and green indicating spatial outliers (high surrounded by low, and low surrounded by high). It was found that the cluster districts with high hotspots (HHHH) both in 2009 and 2014 were found in 15 districts (68%) in the inner zone and three districts (13%) in the urban fringe as shown in dark red, which are related to high-rise buildings distribution shown in Figure  3.
43 ** and 0. 37, respectively. Correlation is significant at the p-value < 0. 01  (2-tailed). The relationship between land surface temperatures and dengue incidences is shown in Figure  9a  2009 and Figure  9b  2014 with 85 and 75% of high dengue cases surrounded by high dengue cases (HH) in the inner city by Moran's I statistical analysis, respectively. Meanwhile, the relationship between LST and dengue incidences by HH in 2009 and 2014 in the urban fringe are 15 and 26%, respectively. Pearson correlation was used to calculate the correlation matrix (Figure  9  and Appendix A). In each zone, the LST data were extracted from the satellite imageries for each year using statistical analysis. The highest positive correlations in 2009 were the density of high-rise buildings in each zone with dengue incidence at 0.
| Counting of settled larvae For experiments 1 and 3, the total number of settled larvae on the tested substrates was counted using a stereomicroscope (Zeiss™ Stemi™ DV4) with a magnification of 32× (Figure  3 (2)). For experiment 2, photographs were taken with an ultra-high definition (4K) camera (Nikon™ Coolpix™ W300), with a positioned scale bar. Owing to the specific shape of 3D-ReefVival-Experimental-Reefs ® , 780 ± 4. 97 cm 2 of each replicate reef, corresponding to 40% of each horizontal settlement area (Figure  2 ), was counted. All data (Table  1 ). Larval losses during experiments 1 and 2 were determined by subtracting the total number of settled larvae from the initial number of seeded larvae.
| Statistical analysis For experiment 1, the differences between the total numbers of settled larvae on all of the tested substrates were determined within each category. Each substrate category was studied in a separate tank and statistical comparisons were only done within each substrate category. For each substrate category (e. g. c-shells, c-inorganics and c-sediments), count data were analysed using negative binomial generalized linear models  (Poirier et al. , 2019). Negative binomial generalized linear models (selected based on the lowest AIC values) were fitted for each substrate category. For all substrate categories, model structure included substrate type as a fixed effect, and the inclusion of the shell orientation (bottom-side or upper-surface) was added as an additional fixed effect for c-shells.
All statistical analyses were performed using R version 3. 5. 2 (R-Development-Core-Team, 2018) using the 'lme4' package and post-hoc tests were completed using the 'emmeans' package  (Bates, Mächler, Bolker, & Walker, 2015; Lenth et al. , 2018; Lüdecke, 2018). Post-hoc test results were fitted with the log scale. For experiment 3 (field study), independent data were tested for normality (Shapiro's test) and homogeneity of variances (Levene's test). One-way ANOVAs followed by Tukey's tests were then used to determine significant differences among the different substrate types. Effects of substrate orientation (upper-surface or bottom-side orientation) were tested using a one-way ANOVA (c-shells and cwoods) and a Student's t-test (c-inorganics). In c-shells, the effects of shell surface (inner or outer) were tested using a one-way ANOVA followed by Tukey's tests.
ACKNOWLEDGEMENTS We thank the  German Federal Agency for Nature Conservation  for funding and supporting this project in close cooperation. We thank the  Invertebrate Physiology Laboratory of Ifremer (France) , the  Centre for Aquaculture Research of AWI (Germany) ,  Novostrea Bretagne SAS (France) ,  Ferme Marine de l'île d'Arun EARL (France), Roland Krone/Reefauna (Germany), Boskalis Nederland BV (Netherlands)  and  Reef Design Lab (Australia)  for their support. We thank the captain and crew of  FS Heincke  for supporting the sampling and especially the following colleagues for their help throughout the supervision of this study, the experimentation, the analysis and the writing stages:  Karen Wiltshire ,  Henning von Nordheim ,  Thomas Rouault ,  Massoud Qayomi ,  Laurie Hofmann ,  Nick Mackay-Roberts ,  Kerstin von Twistern  and  Henrike Hohnholz.
Microarray gene expression analysis OSC from two different healthy individuals were collected 48 h after treatment with siRNAs. Genome-wide transcriptional profiling was performed by Human Exon 1. 0 ST arrays (Affymetrix) following manufacturer's instructions. Each condition was performed in duplicate. Microarray data analysis is detailed in Supplementary Information. The microarray data (GSE122434) can be accessed in the Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information.
Oxidative stress induction, ROS assay and flow cytometry analysis Twenty-four hours after seeding, cells were stressed for 2 or 4 h in the presence of H 2 O 2 (500 µM) before RNA extraction. Intracellular ROS was measured by staining with CellRox® Deep Red Flow Cytometry Assay kit (Life Technologies) using FacsCanto cytometer (Becton Dickinson). Data were analyzed using FACS DIVA software.
Statistical evaluation Statistical analyses were performed by using standard unpaired Student's t-tests or one-way analysis of variance, and were carried out using Prism software (GraphPad Software, La Jolla, CA, USA) or Excel software (Microsoft, Redmond, WA, USA). All data are presented as mean ± s. d. and represent a minimum of three independent experiments with at least three technical replicates. Estimate of variance was performed within each group of data being statistically compared. A value of P < 0. 05 was considered as statistically significant.
COSMOC, an antisense long noncoding RNA upstream of MOCOS As a first attempt to apprehend the transcriptional control of MOCOS expression, we knocked down several putative upstream regulators, as detected using MatInspector (Genomatix) and TFBind software, but none of them successfully deregulated MOCOS expression (Supplementary Fig. 1a-c ). We then used the data generated by advanced genomic tools, such as ENCODE and FANTOM5 and identified a divergent transcription in the MOCOS promoter region. This GC-rich region (GC-content: 65%) induces a transcription initiation on both sides with opposite orientations, generating an upstream antisense lncRNA near the 5'end of MOCOS, to which we coined the name COSMOC (Fig. 1a ). COSMOC is a 609 nucleotides long and polyadenylated RNA (Supplementary Fig.
2a ). Similarly to many other lncRNAs, it contains only two exons  [19] , separated by a long intron of 6845 bp and the noncoding anti-sense initiation of COS-MOC is located 73 bp upstream of the transcription starting site of MOCOS (Fig. 1a  and Supplementary Fig. 2b ). COSMOC has no evidence of coding potential as indicated by its absence in protein database, a low PhyloCSF score  [20]  and no open reading frame longer than 150 nucleotides. Consistent with a crosstalk between coding and noncoding transcripts arising from the same locus  [21] , COSMOC and MOCOS exhibit a broad and similar pattern of expression across different human cell lines and tissues (Fig. 1b  and Supplementary Fig. 2c ). Of note, cell fractioning followed by qPCR analysis revealed that COSMOC can be found in the nucleus but it is predominantly located in the cytosol of OSCs (Fig.
COSMOC is involved in lipid metabolism, DNA regulation and redox homeostasis To start identifying putative roles of COSMOC, we performed a transcriptomic study on OSCs from two healthy individuals in which we knocked down COSMOC. We found a pool of 639 mis-expressed genes, of which 150 were upregulated and 477 downregulated (Fig. 2a  and GEO database GSE122434). As expected, COSMOC-depleted OSCs cultures showed that MOCOS was downregulated (fold change: -1. 36 and -1. 86 in individual 1 and 2, respectively). Of higher interest, among the top misexpressed genes upon COSMOC depletion, eleven of them -ALDH9A1, DHCR7, ENPP2, FABP3, GLO1, GLUL, LIPA, MOCOS, PPT1, PTBP2, SEMA5A -have been previously associated with ASD (1-4; https://gene. sfari. org/). Furthermore, DAVID-based gene clustering analysis revealed two significantly distinct groups (Fig.