dataesr/scientific-paragraphs-categorization huggingface dataset:
A series of OCT section images (or B-scans) are acquired in order to create this cube of data. An initial review of these data is usually based on images that are generated from slabs of the cube. Slabs are sections of three-dimensional volumetric data. In the case of OCT-A slabs, the section is delimited by anterior and posterior retinal and choroidal boundaries. The OCT-A signal between these boundaries is displayed as a two-dimensional en face image, showing perfused vasculature. It is referred to as an en face image due to the transversal slab orientation; the resulting image gives the impression of looking onto the retina. With the PLEX Elite 9000® instrument from Zeiss (Carl Zeiss Meditec Inc. , Dublin, OH, USA), a 6 × 6-mm (~21° × 21°) scan pattern provides a relatively large overview of the retinal and choroidal circulation, ideal for detection of vascular abnormalities that may not be present at the avascular central macula.
Methods Clinic and demographic data were collected at the inclusion of each participant in the study using a single medical interview common to each of the studies mentioned above. All information was verified by an investigating operator from the patients' hospital medical chart, if available. Each participant underwent an SS OCT-A examination of one or both eyes using the PLEX Elite 9000®. Examinations were acquired by three different trained operators and were performed under standard dark conditions. Pupillary dilatation was systematically performed with one eye drop of tropicamide 0. 5% in both eyes if the B-scan signal strength was lower than 8/10. A 6 × 6-mm PLEX Elite 9000® angiography examination was performed for each of the included eyes. Only acquisitions with a signal strength greater than or equal to 8/10 were processed.
Conclusion Emerging modern imaging techniques such as SS OCT-A have created an unprecedented opportunity to comprehensively characterize the microscopic ophthalmic features associated with CVD, also known as the oculomics  [38, 39]. This oculomics revolution has opened up new avenues, including the use of the retina to obtain insights beyond the eye. Detecting microvascular changes before clinical manifestations can have predictive value, and ophthalmoscopic changes in the retinal microvasculature structure with SS OCT-A might represent a unique opportunity to fulfill this task. Here, we introduce the first existing dataset of SS OCT-A images combined with cardiovascular data. Our dataset called RASTA contains volumetric acquisitions from 499 patients and 2005 segmented en face images with corresponding quantitative microvascular features and clinical cardiovascular data.
2017 ). For M. musculus, we used a panel of seventeen microsatellite loci (D1Mit291, D2Mit456, D3Mit246, D4Mit17, D4Mit241, D6Mit373, D7Mit176, D8Mit13, D9Mit51, D10Mit186, D11Mit236, D14Mit66, D16Mit8, D17Mit101, D18Mit8, D19Mit30 and D5Mit30) (available from the MMDBJ database: http:// www. shigen. nig. ac. jp/ mouse/ mmdbj/ top. jsp;  Lippens et al. 2017). Polymerase Chain Reactions (PCR) and genotyping were conducted according to previously described procedures for R. rattus  (Berthier et al. 2016) , M. musculus  (Lippens et al. 2017)  and R. norvegicus  (Desvars-Larrive et al. 2017). Genotyping was performed with an ABI 3500xl sequencer (Applied Biosystems) with 2 μl of diluted PCR product to which a 15 μl mix of formamide and the GS 500 LIZ size marker (ABI 3100 model) were added. Microsatellite profiles were read independently by two persons using Gen-eMapper v.
Spatial patterns of population genetic structure Analyses were performed for each species separately. They were done first on sessions pooled for each site. Unless specified, only samples from sites with at least 11 individuals for a given species were used. Deviation from Hardy-Weinberg equilibrium (HWE) for each site and each locus as well as linkage disequilibrium (LD) between each pair of loci at each site were tested using GENEPOP v. 4. 6  (Rousset 2008). Corrections for multiple tests were performed using the false discovery rate (FDR) approach (Benjamini and Hochberg 1995) implemented in the Bioconductor qvalue R package  (Storey 2002). FreeNa  (Chapuis and Estoup 2007)  was used to estimate the presence of null alleles in each dataset in order to test whether they may explain heterozygote deficiencies (see below).
Mantel tests (10,000 permutations) were performed using the pairwise genetic differentiation estimator ê  (Watts et al. 2007 ) calculated between all pairs of individuals genotyped and the logarithm of their Euclidean geographical distances. The minimum distance between sites (d = 100 m) was used as a threshold to exclude pairs of individuals from the same site. Population genetic structure was then explored using the clustering approach implemented in STRU CTU RE V. 2. 3. 4  (Pritchard et al. 2000)  using all individuals genotyped in order to determine the number of homogeneous genetic clusters (K) present in each species-specific dataset. The analyses were performed with a model including admixture and correlated allele frequencies, for K ranging from 1 to 10. Each run included a burn-in phase of 200,000 iterations followed by 600,000 iterations.
We performed 10 independent analyses for each K value. The number of genetic groups was inferred by the delta-K method applied to the log probabilities of data  (Evanno et al. 2005). For R. rattus, fifteen individuals from four districts (Agla, N = 4; Ladji, N = 4, Saint Jean, N = 5 and Zongo, N = 2) of the city of Cotonou were also included in our analyses  (Dossou et al. 2022). Population genetic structure was also investigated through Discriminant Principal Component Analysis (DAPC) which can handle the absence of HW equilibrium  (Jombart et al. 2010). Analyses were conducted using all individuals under the R software using the adegenet and devtools packages, for K = 1 to 10. The most likely number of genetic groups (K) was determined using the Bayesian Information Criterion (BIC;  Lebarbier and Mary-Huard 2006) , and the a priori criterion of a delta-BIC less than six  (Kass and Raftery 1995).
Changes in population genetic structure through time Genotype data for each species were then separated into three temporal datasets, regrouping sessions "before" (sessions 1 and 2), "during" (sessions 3 and 4) and "after" rodent control campaigns (sessions 5 and 6), respectively. Using STRU CTU RE, we investigated whether genetic clustering in "before" and "after" datasets was similar to that observed in the whole dataset for each species. We then used NeEstimator v2  (Do et al. 2014)  to estimate effective population size (N E ) for each site or group of sites corresponding to a genetic cluster in the industrial seaport before, during (when sufficient sampling) and after control campaigns. These analyses were conducted in both deratised and non-deratised sites, using the linkage disequilibrium (LD) method with a threshold minimum allele frequency of 0.
Detection of potentially newly introduced individuals For each species, individuals from three other countries (one individual/country) were genotyped and added to the APC dataset. For Rattus rattus, additional individuals were from Dakar (Senegal), Franceville (Gabon) and Niamey (Niger). The fifteen individuals from Cotonou core city were also included in the analyses. For Rattus norvegicus, the additional individuals were from Lyon (France), Bamako (Mali) and Ratchaburi (Thailand). For Mus musculus, extra individuals were from Dakar (Senegal), Lyon (France) and Niamey (Niger). Factorial correspondence analyses were performed using Genetix v 4. 05  (Belkhir et al. 1996 (Belkhir et al. -2004) )  in order to evaluate if the additional individuals could be clearly identified as different from those trapped within the APC.
Population genetic structure A total of 366 R. rattus, 188 R. norvegicus and 244 M. musculus were successfully genotyped. Considering only sites with at least 11 individuals for a given species, our datasets included 353 individuals from six sites (APC1, APC2, APC3, APC6, APC8 and APC9) in R. rattus, 179 individuals from five sites (APC4, APC5, APC6, APC7 and APC8) in R. norvegicus and 244 individuals from five sites (APC4, APC5, APC6, APC7 and APC8) in M. musculus. For R. norvegicus, linkage disequilibrium was significant for 9 out of the 600 tests performed (1. 5%), and 5 of the significant values implicated the allele pair D1VKORC1-C and D1VKORC1-A, which were thus considered as linked. After having removed D1VKORC1-C locus, only 3 out of the 525 pairs of loci remained significantly linked (0.
Funding  Sylvestre Badou  was granted a  Doctoral Research Fellowship (ARTS)  by  IRD  ( 2019IRD ( -2022 )). Data used in this work were produced through the genotyping and sequencing facilities of  Labex CeMEB mutualized GenSeq platform (Mediterranean Center for Environment and Biodiversity, Genotyping-Sequencing Platform ). This work is part of the PASPort/ PPSE  project funded by  ACP ,  IRD  and ENABEL.
SPARTICUS Observations The data obtained during the SPARTICUS field campaign are used in this study. The campaign took place from January to June in 2010 over mountains in the Central United States, and its flight tracks are shown in Figure  1. Kollias, 2014). The absolute accuracy of vertical velocity measurements is 0. 75 m s -1. Although the absolute accuracy of the instrument is too large to measure the vertical velocity, σ w can be used to represent the perturbation as a reasonably accurate measure  (Lenschow, 1972; Muhlbauer, Kalesse, & Kollias, 2014). In order to investigate the OGW effects in ice nucleation, this study focuses on the high σ w in cirrus clouds. Previous studies indicated that 0. 1 m s -1 is a reasonable indicator to categorize the high σ w in cirrus clouds  (Krämer et al.
Summary and Conclusions In this study, we introduce the sub-grid scale σ w , derived from the McFarlane generated OGWs, to investigate its impact on cirrus ice nucleation in CAM6. We compare the simulation results with aircraft and satellite data to evaluate the modeled microphysical properties of cirrus clouds. Climatological simulations are also conducted to investigate the effects of OGW-induced σ w on the cloud properties and radiative forcing. Observational data from the SPARTICUS campaign are used to evaluate the simulated microphysics of cirrus clouds. OGWs in the McFarlane experiment generate larger sub-grid scale σ w over mountainous regions and show a better agreement with observations. In the CTL experiment, too small σ w induced only by TKE from CLUBB compared to the observation leads to the low bias of ice crystal number concentrations in orographic cirrus via heterogeneous nucleation.
Acknowledgments Xiaohong Liu  and  Kai Lyu  acknowledge funding support from  US National Science Foundation  (Grant  AGS-1642289/2001903 ). The authors would like to acknowledge the use of computational resources (https:// doi. org/10. 5065/D6RX99HX) at the  NCAR-Wyoming Supercomputing Center  provided by the  National Science Foundation  and the  State of Wyoming  and supported by  NCAR's Computational and Information Systems Laboratory. We acknowledge  Patnaude Ryan ,  Minghui Diao ,  Suqian Chu ,  Xiangjun Shi , and  Chenglai Wu  for their help in dealing with observational data. We also thank  Mingxuan Wu  and  Meng Zhang  for their help in dealing with simulation data.
Data Availability Statement The Community Earth System Model version 2. 1. 0 (CESM2. 1. 0) is available at Downloading CESM2 (CESM2. 1) -CESM CESM2. 1 documentation (escomp. github. io). The model datasets are archived at https:// doi. org/10. 5281/zenodo. 7734751. Data used for this work was obtained during the SPartICus field campaign. These data are freely available to interested researchers through the DOE ARM program. Data from the 2D-S and other cloud probes can be accessed (http://campaign. arm. gov/sparticus/measurements). The DARDAR-Nice data is provided by the AERIS/ICARE data center (https://www. icare. univ-lille. fr/dardar/data-access/).
Through these calculations, we indeed confirm that the coherent vibration of both oscillations with A 1g symmetry is causing the giant red-shift in the PL spectrum whilst retaining the delocalised character of the excited state due to conservation of the crystal symmetry. We used density functional theory (DFT  42, 43  ) to calculate the ground state structural properties of BiOI, and performed said calculations with the electronic structure code QUANTUM ESPRESSO, in this case without the inclusion of spin-orbit coupling (SOC). We obtained static-lattice parameters of a = b = 3. 97 Å and c = 9. 17 Å, which are in good agreement with the experimentally-determined low-temperature lattice parameters (Supplementary Fig. 8a, b ). From the finite-displacement method in conjunction with nondiagonal supercells (see details in Methods) we constructed the phonondispersion curve, which is illustrated in Fig.
The static and high frequency dielectric constants computed were 43. 33 and 8. 68, respectively (refer to Supplementary note 13). We found that both Raman modes discussed in the manuscript belong to the same A 1g irreducible representation, which is in contrast to prior assumptions in the previous literature, that instead assigned the second Raman mode (156 cm -1 ) to the E g irreducible representation, but are in-line with published first-principles calculations  61. In order to determine the quasiparticle GW 39 band structure we first calculated the single-particle Kohn-Sham trial wavefunctions from QUANTUM ESPRESSO, followed by a single-shot GW calculation as it is implemented in the YAMBO code  62, 63. The DFT calculations were performed with the same exchange correlation functional as described earlier (i.
Acknowledgements We would like to thank  Prof. Richard Phillips  ( University of Cambridge ) for useful feedback on the manuscript and help with optical measurements. The authors also thank  Zhuotong (Thomas) Sun  ( University of Cambridge ) for assistance on the powder X-ray diffraction measurements, and  Prof. James Marrow  and  Marcus Williamson  ( University of Oxford ) for assistance in taking radiographs. R. A. J. acknowledges funding from an  EPSRC   Department Training Partnership studentship  (no. EP/N509620/1 ), as well as  Bill Welland  and the  Winton Programme for the Physics of Sustainability. L. E. and T. V. D. G. acknowledge support from the  EPSRC Cambridge NanoDTC  (no. EP/L015978/1 ). L. E. acknowledges funding by the  DFG  (project no. 387651688 ). T. V. D. G. also acknowledges financial support from the  Schiff Foundation.
K. G. and S. D. S. acknowledge the  EPSRC  (no. EP/R023980/1 ) for funding. S. D. S. acknowledges the  Royal Society and Tata Group  (no. UF150033 ) and  EPSRC  (no. EP/W004445/1 ) for funding. The work has received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (HYPERIONgrant agreement no. 756962 ;  PEROVSCI -957513 ). The work was supported by a  Royal Society International Exchanges Cost Share  award (no. IEC\R2\170108 ) and the  Alliance Hubert Curien Programme of the British Council  (no. 608412749 ). K. R. D. thanks the  Department of Chemistry at the University of Oxford  for a studentship. P. P. appreciates support from  National Science Centre Poland  within the  OPUS program  (no. 2019/33/ B/ST3/01915 ). This work was partially supported by  OPEP  project, which received funding from the  ANR-10-LABX-0037-NEXT.
B. M. also acknowledges support from a  UKRI   Future Leaders Fellowship  (no. MR/V023926/1 ) and from the  Gianna Angelopoulos Programme for Science, Innovation and Technology. The calculations are conducted using resources provided by the Cambridge Tier-2 system, operated by the  University of Cambridge Research Computing Service  (www. hpc. cam. ac. uk) and funded by  EPSRC  Tier-2 capital grant (no. EP/P020259/1 ).
Data availability The experimental data that support the plots within this paper and other findings of this study have been deposited in the Research Data Repository at Imperial College London under accession code 12435. The solved crystal structure for BiOI is available from the Cambridge Crystallographic Data Centre (CCDC) under the accession number 2201891, and can be found in: https://www. ccdc. cam. ac. uk/ structures/Search?Ccdcid=2201891&DatabaseToSearch=Published.
We found that identical stimuli (i. e. critical frames) were sampled differently, depending on the information participants acquired before viewing them. Specifically, we analyzed seven metrics characterizing oculomotor behavior and found differences related to the presence of relevant prior knowledge in six of them. This result demonstrates that prior knowledge about the recent past contributes to the control of ongoing oculomotor behavior. Open practices statement. This study was preregistered. The preregistration protocol is available from the Open Science Framework repository (https://osf. io/et7mr/?view_only=6f86dc8211d845c7b2c09ef 6f45baf64), together with the data from all experiments and a script for downloading our stimuli.
Apart from the entropy, that requires aggregating data from all participants viewing the same frame to calculate a single value, all metrics had unique values per participant per frame. The details of data pre-processing (including fixation-extraction procedure) and metrics calculation are provided in Supplemental Materials. Data analysis. We used functions from the R (R Core Team, 2020) package lme4  (Bates, Mächler, Bolker, & Walker, 2015)  to analyze how our metrics differed between the Continuous and Discontinuous conditions. Specifically, we fitted (generalized) linear mixed-effects models (GLMMs and LMMs) to the data. All our models included an intercept and a fixed effect of experimental condition. We used LMMs (fitted using function lmer) for all metrics but the probability of blinking, for which we used a GLMM (logistic regression model fitted using function glmer).
Method Participants and sample size. We collected data from 50 participants (two more than planned; 29 women, mean age: 20. 22 years). 44 were undergraduate students of Northeastern University. The remaining 6 belonged to the participant pool from which the participants for Experiment 1 were recruited. All participants declared having normal or corrected to normal vision. For the participants recruited at Northeastern University, this was confirmed using standard tests of visual acuity (ETDRS,  Ferris & Bailey, 1996) , color vision (H-R-R,  Hardy, Rand, & Rittler, 1954) , and stereo acuity (Titmus,  Ohlsson et al. , 2001). All excluded participants (4 cases) were replaced; see Excluding participants section in Supplemental Materials for details. Stimuli. We used the same 80 frame-sequences as in Experiment 1 and a novel set of attention-check questions.
A high genetic variability was found all over the 2 sequenced BLG I alleles. In particular, 16 polymorphic sites were found in introns, one in the 5′ flanking region, 3 SNPs in the 5′ untranslated region and one SNP in the coding region (g. 1871G > A) located at the 40th nucleotide of exon 2 and responsible for the AA substitutions p. Asp28 > Asn in the mature protein. Two SNPs (g. 920-922CAC > TGT and g. 1871G/A) were genotyped in 93 donkeys of 2 Italian breeds (60 Ragusana and 33 Amiatina, respectively) and the overall frequencies of g. 920-922CAC and g. 1871A were 0. 3065 and 0. 043, respectively. Only the rare allele g. 1871A was observed to be associated with the slower migrating β-LG I. Considering this genetic diversity and those found in the database, it was possible to deduce at least 5 different alleles (BLG I A, B, B1, C, D) responsible for 4 potential β-LG I translations.
Ser36 > Glu, p. Thr97 > Ser and p. Ile150 > Val. As a result, the M r (molecular mass) of the variant A is 18,528 Da, whereas the M r of the variant B is of 18,514 Da  (Godovac-Zimmermann et al. , 1988, UniProtKB: P13613; Herrouin et al. , 2000). Recently, a new β-LG I variant, with a significantly higher mass (20,428. 5 Da) than known variants, has been detected by tandem MS analysis. The new variant showed an N-terminal extension likely related to the signal peptide sequence and apparently also characterized by the AA substitution p. Asp28 > Asn  (Auzino et al. , 2022). Despite the interest in this species and the new attention surrounding it, thus far, this important gene has not been adequately investigated in donkeys. Thanks to the recently updated donkey genome assembly (BioProject: PRJNA688408), the complete β-LG I encoding gene (BLG I) sequence has been published and annotated on chromosome 10.
PCR Amplification, Sequencing, and Bioinformatics Donkey genome sequences (GenBank acc. no. NC_052186. 1, region: 8316254 to 8322698 and acc. no. PSZQ01002145. 1, region: 1252372 to 1258209) were used to design primers for PCR amplification and sequencing. The BLG I gene spanning from the 5′-to the 3′-flanking regions of 2 Amiatina donkeys were amplified by iCycler (BioRad). One donkey produced milk characterized by a β-LG I SDS-PAGE band with a normal electrophoretic mobility (18,514. 25 Da), whereas the milk of the other was characterized by the presence of a unique β-LG I band with a higher electrophoretic mobility (20,428. 5 Da), as determined by  Auzino et al. (2022). A typical 25-μL PCR reaction mix included 100 ng of genomic DNA, 50 mM KCl, 10 mM Tris-HCl (pH 9. 0), 0. 1% Triton X-100, 3 mM MgCl 2 , 200 nmol of each primer, dNTPs each at 400 μM, 0.
Homology searches, comparisons among nucleotide and AA sequences, and multiple alignments for polymorphism discovery were accomplished using Dnasis Pro (Hitachi Software Engineering Co. ). The regulatory regions were analyzed for potential transcription factors (TF) by AliBaba v2. 1 program (http: / / www. gene -regulation. com/ pub/ programs/ alibaba2/ index. html). The haplotype structure was defined according to  Gabriel et al. (2002)  using Haploview software version 4. 2 (http: / / www. broadinstitute. org/ haploview/ haploview). 95% confidence bounds on D′ are generated and each comparison is called strong LD, inconclusive, or strong recombination. A block is created if 95% of informative (i. e. , noninconclusive) comparisons are strong LD. This method by default ignores markers with MAF <0.
Genotyping and Data Analysis Genotyping methods based on allele specific PCR (AS-PCR) were developed to screen the mutations g. 1871G > A and g. 920-922CAC > TGT in the population. Primer sequences are reported in Supplemental Table  S1. Data are deposited into Harvard Dataverse (https: / / dataverse. harvard. edu/ dataset. xhtml ?persistentId = doi: 10. 7910/ DVN/ DLFGKD;  Cosenza, 2023). Reaction mix and thermal conditions were performed as reported above except for the annealing temperatures of AS-PCR protocols that were specifically defined according to the mutations targeted. The entire panel of 93 donkey DNA samples was genotyped. Allelic frequencies and Hardy-Weinberg equilibrium (chi-squared test) were calculated (https: / / gene -calc. pl/ hardy -weinberg -page).
BLG I Gene Structure in Donkeys By using genomic DNA as template, we sequenced the whole gene encoding the β-LG I (BLG I) of 2 Amiatina donkeys, homozygotes for the electrophoretic bands of 18,514. 25 Da (β-LG I wild type) and 20,428. 50 Da (β-LG I new variant), respectively (GenBank accession no. ON886232 and ON886233). The sequenced DNA region including the BLG I gene is about 5,900 bp long, and it includes 1,407 bp of exonic regions, about 4,000 bp of intronic regions, 289 nucleotides at the 5′ flanking region and 216 bp at the 3′ flanking region. The level of sequence similarity with the 2 investigated alleles is about 99. 5%. The BLG I gene is characterized by a relatively simple structure. Taking as reference the gene coding for the nonmutated form, it contains 7 exons ranging in size from 45 bp (exon 6) to 738 bp (exon 1), with exons 1 to 6 that contribute to the open reading frame.