dataesr/scientific-paragraphs-categorization huggingface dataset:
is allowed detecting the different phases of the mixture, i. e. , sandy grains, clay matrix, and macroporosity, separately. Each sample was placed into a thin kapton tube with an inner diameter of 4 mm to ensure sample stability during scanning. After the reconstruction of raw images, the grey level (8 bit) volumes were filtered by a 3D median filter in ImageJ software (2 × 2 × 2 voxels), followed by a nonlocal mean filter  [73, 78]. e 3D segmentation was set on volumes of 852 × 823 × 1000 voxels for each. e volume data from reconstructed images were then segmented in macroporosity, sand and clay matrix components by ImageJ to quantitatively investigate the differences in the three preparations. e segmentation threshold of macroporosity, FSand NE34, and K Clay was defined by using the histogram of grey levels with well distinct peaks over the stack of images  [58, 64, 79].
Data Availability e quantitative results from MIP and X-ray tomography are available online on the Mendeley data set at https://data. mendeley. com/datasets/6vbyfyhd7y/1 (DOI: https://doi. org/ 10. 17632/6vbyfyhd7y. 1). e porosimetry raw data (pore diameter vs. pore pressure and cumulative intrusion curves vs. pore diameter) and the raw sets of 8 bit-images of S1, S2, and S3 samples (0-100 images per sample) are shared.
Observations at GoAmazon 2014/5 site The T3 site was the main site of GoAmazon 2014/5, and it is located at Manacapuru (03 • 12 36 S, 60 • 36 00 W; 50 m), 70 km downwind of Manaus, in the central part of the state of Amazonas, Brazil. The T3 was a pasture site of 2. 5 km by 2 km surrounded by forest, with a forest canopy height of approximately 35 m  (Martin et al. , 2017). During the GoAmazon 2014/5 campaign, observational data were collected by the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF-1;  Mather and Voyles, 2013). Meteorological variables at near-surface level were measured by five different instruments. The averages of surface air temperature, specific humidity, and wind speed are from the surface meteorological system at a 30 min time average. Precipitation is used from the present weather detector at the conditional rain rate (e.
g. , mean rain rates are determined only for the periods when rainfall is equal to or greater than 1 mm h -1 ) to select the days with precipitation. The three wind components and surface sensible and latent heat fluxes are obtained from the eddy correlation flux (ECOR) (ARM, 2014b) measurement system 3 m above the ground in 30 min averages. Soil moisture (centered at 2. 5 cm of depth) and the average soil surface heat flux are from the ARM Surface En-ergy Balance System (ARM, 2014d) at 30 min time resolution. The surface net longwave and shortwave radiative fluxes were extracted from the ARM RADFLUX (ARM, 2014c) value-added product in 30 min averages. The BL height was retrieved by a laser ceilometer (ARM, 2014a) operating at the 905 nm wavelength. The ceilometer retrieves three BL height candidates provided by Vaisala BL-view software at 16 s resolution.
In order to determine the cloud base, the lowest estimates from the ceilometer were used. In addition, the onset of turbulence from the nocturnal BL to morning phase was determined by the vertical wind standard deviation data from the sonic detection and ranging (SODAR) wind profiler (ARM, 2014e) at 30 min time resolution that measures the wind profile ranging from 30 to 400 m. The environmental condition profile was measured with radiosondes (ARM, 2014f) launched over the T3 site at regular intervals around 01:30, 07:30, 10:30, 13:30, and 19:30 LST during the second Intensive Operating Period (IOP2). Lifting condensation level (LCL), convective available potential energy (CAPE), and convective inhibition (CIN) are computed using sounding data assuming an air parcel in which the level of the maximum virtual temperature is in the first 1000 m.
Due to the vertical resolution that varies with each launch and the atmospheric conditions, data were linearly interpolated with a resolution of 20 m. The cloud type profiles were extracted from the cloud type product by the ARM Active Remote Sensing of Clouds (AR-SCL). The RWP-WACR-ARSCL cloud mask product  (Feng and Giangrande, 2018)  estimates the cloud type profile by combining data from the W-band ARM cloud radar (WACR), radar wind profiler, micropulse lidar, and ceilometer  (Giangrande et al. , 2017; Feng and Giangrande, 2018). Reflectivity data from the S-band radar, located in Manaus which is 67. 8 km northeast of T3, was used to verify the presence of deep convective clouds in an area within 60 km of T3. A. Henkes et al. : Morning boundary layer conditions for shallow to deep convective cloud evolution.
Conclusions This study presents an observational analysis of Amazonian atmospheric BL process from the nighttime stable stage until the cloudy developed stage during the dry season. The data used here were collected during the Green Ocean Amazon 2014/5 (GoAmazon) field campaign. These data are used to study the BL evolution for ShCu and ShDeep days for case studies and statistical analysis. The detailed analysis of the case studies allowed us to develop a conceptual model to describe the BL evolution from clear to cloudy BL and set up the most important parameters characteristic of each stage and the differences among the convective day types. The main findings are summarized in the conceptual model shown in Fig. 10. The environmental conditions (e. g. , in the lower troposphere) in which the BL grows during ShDeep days are relatively moister and colder than in the environmental conditions in which ShCu days are observed.
Acknowledgements. We acknowledge the data from the  Atmospheric Radiation Measurement (ARM) Program  sponsored by the  U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Climate and Environmental Sciences Division. The authors  Gilberto Fisch  (process  307048/2018-7 ) and  Luiz A. T. Machado  (process 305301/2017-9) also thank  CNPq for their Bolsa de Produtividade em Pesquisa. This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES)  -finance code 001. We also acknowledge  FAPESP   CHUVA  project grant  2009/15235-8. We also thank  Thiago Biscaro  for his help in providing data from the S-band radar,  Rayonil Carneiro  for the discussion about ceilometer data,  Daiane Brondani  for her help with ECOR data, and  Yang Tian  for sharing the deep convection dates for our ShDeep days' comparison.
Further, the authors would like to acknowledge the two anonymous reviewers for their valuable comments. Financial support. This work has received funding from the  Brazilian National Council for Scientific and Technological Development (CNPq  grant agreement number  140347/2019-4 ) and from the  European Commission, H2020 Research Infrastructures (VESTEC  (grant no. 800904 )). Review statement. This paper was edited by  Zhanqing Li  and reviewed by three anonymous referees.
Data availability. The data used in this study are available in the ARM Climate Research Facility database for the GoAmazon2014/ 5 experiment. In addition, all data including ARSCL  (Feng and Giangrande, 2018) , SONDE (ARM, 2014f), and other value-added product (VAP) streams used in this study can be downloaded at http://www. arm. gov (last access: 20 November 2020). Author contributions. AH performed the analysis, GF and LATM participated in the GoAmazon2014/5 field campaign and proposed the idea investigated, and JPC participated in the discussion of the results. All the authors contribute to the writing of the manuscript.
Experimental design and ethical approval One-hundred and nine individuals undertook preliminary screening, from which 24 males aged 20-35 years (n = 12; Young) and 60-85 years (n = 12; Old) years participated based on predetermined eligibility criteria. Participants were recruited by local advertisement. The protocol was reviewed and approved by the South West, Bristol Central NHS Research Ethics Committee (REC reference: 16/SW/0003), was registered on ClinicalTrials. gov (NCT02777138) and was conducted in accordance with the Declaration of Helsinki. All participants provided their written informed consent. Participant recruitment was conducted between August 2016 and March 2018. Participant data was assigned identifying codes within respective groups, without blinding from group allocation.
1999; Travers et al. 2015). Waist-hip ratio was calculated according to guidelines from the World Health Organization  (WHO, 2011). Participants were fitted with a multisensor physical activity monitor (SenseWear; Bodymedia, Pittsburgh, PA, USA) for seven consecutive days to determine habitual physical activity levels. Data were uploaded onto SenseWear Professional, version 8. 0. 0 (Bodymedia) for analysis. PAL was quantified using SenseWear Professional, version 8. 0. 0, and categorized according to  Brooks et al. (2004)  and FAO/WHO/UNU  (2005)  guidelines. Individuals recording a PAL between 1. 4-1. 9 (low-to-moderately active)  (Brooks et al. 2004 ; FAO/WHO/UNU, 2005) were eligible for participation. Total energy expenditure (TEE) was then calculated as a product of recorded estimates of activity energy expenditure + RMR + diet induced thermogenesis (estimated at 10 % of TEE)  (Westerterp, 2004).
Dietary analysis Three day (two weekdays and one weekend day) weighed food and fluid records were collected during the 7 day activity monitoring period to assess habitual dietary intake. Participants recorded food mass (g) and fluids consumed (mL) pre-and post-meal on digital scales accurate to 1 g (Salter, Tonbridge, UK). Food and fluid records were analysed using Nutritics software (Nutritics, Dublin, Ireland).
), generating a raw read count of >38. 5 million reads per sample. FastQ sequencing files were uploaded to the Galaxy web platform (usegalaxy. org) for quality control analysis. Raw sequencing files were splice-aligned to the GRCh38/hg38 reference genome using Hisat2  (Pertea et al. 2016) , with mapping distance <500 kb between reads. Ensembl (Cambridge, UK) was used to annotate sequencing files against the reference genome  (Cunningham et al. 2019). Expression levels (fragments per kilobase of transcript per million mapped reads) were estimated using Stringtie  (Pertea et al. 2015). Differential expression analysis was undertaken using Cuffdiff v. 7  (Trapnell et al. 2012 ) and Deseq2  (Love et al. 2014) , using Gencode v. 29 as the reference database (GRCh38. p12) (Frankish et al. 2018).
P values were J Physiol 600. 4 adjusted for transcriptome-wide false discovery rate (FDR), with an adjusted significance threshold of q < 0. 05. P values are reported after adjustment, unless otherwise stated. Functional annotation analysis was performed in the database for annotation, visualization and integrated discovery (DAVID) 6. 8 (2019 release)  (Huang da et al. 2009a (Huang da et al. , 2009b) )  and Genesis 1. 8. 1  (Sturn et al. 2002). Pathway analysis was performed using Kyoto Encyclopaedia of Genes and Genomes (KEGG) (https://www. genome. jp/kegg) and Gene Ontology (GO) (http://geneontology. org), using a modified Fisher's exact test [EASE (expression analysis systematic explorer)]  (Hosack et al. 2003 ) with a significance threshold of P < 0. 05. As a result of the loss of one sample and the exclusion of another sample which was identified as a statistical outlier by principal component and hierarchical clustering analysis, the final sample size for adipose tissue was n = 11 for both Young and Old.
Descriptive data are presented as the mean ± SD, with the exception of DESeq2 outputs, which are presented as the mean ± SE as standard according to the analysis output provided by usegalaxy. org. Statistical analysis was performed using Prism, version 9. 0. 1 (GraphPad Software Inc. , San Diego, CA, USA) and SPSS, version 22 (IBM Corp. , Armonk, NY, USA). P ≤ 0. 05 was considered statistically significant.
Acknowledgements We acknowledge  Dr Harriet Carroll  for useful and assistance with statistics throughout the project. We would also thank  Professor Jean van den Elsen  and  Dr Ayla Wahid  for supplying reagents for carboxymethyl lysine residue measurements and technical advice with these measures. We also thank  Dr Virginie Bourlier  for her assistance in setting up the skeletal muscle digestion protocols for this project. We thank the  Oxford Genomics Centre at the Wellcome Centre for Human Genetics  (funded by  Wellcome Trust  grant reference  203141/Z/16/Z ) for the generation and initial processing of the sequencing data. FK is supported by  MRC  grant  MR/P0002927/1. Financial support was provided by the  Biotechnology and Biological Sciences Research Council  ( BBSRC: BB/N004809/1 ).
METHODS AND DATA The dataset comprises 28 sedimentary logs (totaling 821 m of stratigraphy) collected along the Bakio-Guernica coastline. The logs were collected at a 1:25 scale, with 1:10 scale used locally to capture additional detail. Halokinesis during BFG deposition  (García-Mondéjar 1996; Poprawski et al. 2014 Poprawski et al. , 2016;; Roca et al. 2020 ) generated syndepositional basin-floor relief and led to the development of multiple discrete depocenters  (Vicente Bravo and Robles 1991a , 1991b , 1995; Agirrezabala 1996 ). As such, correlating stratal surfaces in and between depocenters is difficult and un-crewed-aerial-vehicle (UAV) photography was used to aid stratigraphic correlations  (Hodgetts 2013). Paleocurrent, bedding, and structural data were collected to determine the influence of syndepositional basin-floor relief and to quality-control the pre-existing geological map of  Poprawski et al.
(2014 Poprawski et al. ( , 2016)). Paleocurrent readings were taken where sedimentary structures were clear enough to permit unambiguous data collection. Sparse biostratigraphic data (Agirrezabala and López-Horgue 2017) hinders correlation across the structures; hence we refer to the Lower (LBF) and Upper (UBF) Black Flysch group only, to avoid further subdivisions based on geographic location (e. g. ,  Robles et al. 1988; Vincente-Bravo and Robles 1991a , 1991b , 1995; Poprawski et al. 2014).
-Debrites can be sourced from either collapse on top of the diapir or its flanks, or from failures of the shelf-edge and/or slope (Doughty-  Jones et al. 2019; Rodriguez et al. in press; Wu et al. 2020 ). However, the source is difficult to decipher without a dataset covering the coeval shelf and slope succession. The presence of more debrites in the basal active part of the Sollube basin fill (Fig. 8 , 9, 10, 13) compared to the upper passive part of the basin fill (Fig. 10 ) suggests that mass failures are more common during initial development of salt-confined depocenters  (Wu et al. 2020). The Sollube basin is more confined than the Jata basin, and therefore can be expected to receive a larger proportion of material derived from diapir growth. In fact, the Jata basin received more and thicker debrites.
Recognition of Halokinetically Influenced Stratigraphy in the Field Before this study, most understanding of halokinetically-influenced deep-water systems came from subsurface datasets (e. g. ,  Booth et al. 2003; Madof et al. 2009; Carruthers et al. 2013; Doughty-Jones et al. 2017). Features that are common across several depositional settings where halokinetic movements are observed include multi-scalar thinning and onlap, growth faulting, pebble conglomerates, mixed siliciclastic-carbonate lithologies, MTDs, variable paleocurrents, angular unconformities, and abrupt facies variability  (Dalgarno and Johnson 1968; Dyson 1999; Kernen et al. 2012 Kernen et al. , 2018;; Carruthers et al. 2013; Counts and Amos 2016; Counts et al. 2019). Deposition of thick-bedded sandstones along the axis of the Sollube basin, and thinner beds and mudstones on the flanks of the Sollube and Jata basins, is comparable to fluvial facies distribution  (Banham and Mountney 2013a , 2013b , 2014; Ribes et al.
ACKNOWLEDGMENTS This paper contains work conducted during ZCs' Ph. D. study undertaken as part of the  Natural Environment Research Council (NERC) Centre for Doctoral Training (CDT) in Oil & Gas  (grant number  NEM00578X/1 ). ZC is a recipient of an  American Association of Petroleum Geologists (AAPG) Foundation   Grant-in-Aid ''Gustavus E. Archie Memorial Grant '' which supported this fieldwork. Stereonet 10 is acknowledged for data plotting. Dave Lee  is acknowledged for building digital outcrop models. Ross Grant  is thanked for his comments, figure beautifying, and proofreading. Gemma Doughty-Jones  and  Yvonne Spychala  are thanked for clear and constructive reviews which greatly improved the manuscript, and for providing the foundations for which much of this work is based. Associate Editor  Steve Hubbard  and  Editors Gary Hampson  and  John Southard  are thanked for their handling of the manuscript.
Patients with germline neurofibromatosis type 1 (NF1) microdeletions frequently exhibit hereditary syndromes such as cardiovascular anomalies and have an increased risk of malignant peripheral nerve sheath tumors (MPNSTs). This study aimed to identify the genes codeleted with SUZ12 that are related to MPNST. We used differential gene expression and enrichment analyses to analyze the SUZ12-mutant and SUZ12-wild-type gene expression profiles in the GSE118186 and GSE66743 datasets in Gene Expression Omnibus (GEO). PPI network analysis combined with MPNST patient survival analysis was used to identify ADCY1, which catalyzes the conversion of ATP to cAMP, as a key gene. Moreover, chromatin immunoprecipitation sequencing (ChIP-Seq) showed that the distribution of H3K27me3 in the ADCY1 promoter region and gene body was significantly reduced in SUZ12-mutant cells.
Sequencing Data Gene Expression Omnibus (GEO, http://www. ncbi. nlm. nih. gov/ geo) is a public functional genomics data repository of highthroughput gene expression, gene chip and microarray data. Two gene expression datasets (GSE118186 and GSE66743) were downloaded from GEO  (18). GSE118186 contains SUZ12wild-type (SUZ12-WT, 2 cases) and SUZ12-mutant (SUZ12-Mut, 2 cases) RNA-Seq data and H3K27me3-related chromatin immunoprecipitation sequencing (ChIP-Seq) data for ipNF05. 5 cells. GSE66743 is a gene chip dataset containing data for 30 MPNST patients, including the survival times of the patients.
KEGG Pathway Enrichment Analysis, GO Enrichment Analysis and GSEA of DEGs To understand the functions of the DEGs, we used R software to perform Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and gene set enrichment analysis (GSEA). Set H (hallmark gene sets; 50 gene sets), a predefined GSEA dataset, was used in this study. The GO enrichment analysis results show the top 10 pathways in descending order, the KEGG enrichment analysis results show the top 20 pathways in descending order, and the GSEA results show activated and inhibited pathways.
Cox Survival Analysis by Hub Gene Expression via Cox Proportional Hazards Regression A total of 28 MPNST patients in GSE66743 were included in the survival analysis, and 2 MPNST patients with unknown survival times were removed. The patients were divided into high-risk groups and low-risk groups based on the average core gene expression levels. To analyze the relationships between the hub genes and survival, Cox proportional hazards regression was used for survival analysis with R software. Genes with P<0. 05 were defined as key genes.
Distribution of H3K27me3 in the Promoter Regions of Key Genes and Across the Genome The gene bodies and promoter regions of key genes were searched in the NCBI database, and the BW file obtained by ChIP-Seq analysis was imported into Integrative Genomics Viewer (IGV). The H3K27me3 distribution in key gene promoter regions and across the genome were compared between SUZ12-WT and SUZ12-Mut cells.
RT-PCR Analysis Total RNA was extracted with TRIzol (Life Technology, USA), and reverse transcription was performed using a high capacity reverse transcription kit (Thermo Fisher Scientific, USA) according to the manufacturer's protocol. Real-time PCR (RT-PCR) was performed using Power SYBR Green Master Mix (Thermo Fisher Scientific, USA) according to the manufacturer's instructions. RT-PCR data were analyzed by the 2-DDCt method. The following primer sequences were used: SUZ12 (F: 5′-CAA ACT GAA GCA AGA GAT GAC C-3′; R: 5′-GCT ATG GCA GAG TTT AAG ATG C-3′).
PPI Network Construction and Hub Gene Selection The PPI network of the DEGs was constructed (Figure  2A ) and the most significant module identified using Cytoscape (Figure  2B ). The top 10 hub genes were ADCY5, ADCY1, BDKRB1, LPAR5, BDKRB2, CHRM2, S1PR5, S1PR3, ADRA2A, and ADRA2C; all of these were upregulated DEGs. TSSs are one of the most important regions in the genome, and the altered distribution of H3K27me3 in TSS regions regulates gene expression. The distribution of H3K27me3 near TSS regions was significantly higher in SUZ12-WT cells than in SUZ12-Mut cells (Figure  2C ). 4 of 10 hub genes were detected in the gene microarray dataset containing survival, and the other genes were not expressed. Subsequently, associations between overall survival and the expression levels of the 4 hub genes were analyzed using Cox proportional hazards regression for survival analysis (Supplementary Figure  S1 ).
However, to take full advantage of its potential, big NHP neuroimaging data need to be enriched by accompanying information on the subjects (or species) who have undergone brain imaging, i. e. metadata. Here we describe metadata that have been identified as particularly promising. This list is not exhaustive and is likely to be ex-haviour measured outside the scanner. Two types of behavioural data can be collected off-line: stimulus-induced behaviour and spontaneous home-cage behaviour. The standardization of stimulus-induced tests (e. g. cognitive tasks, temperament tests), combined with data sharing, offer the opportunity to collect both behavioural and MRI data in a large number of subjects (e. g. Oler et al. , 2010 ; Birn et al. , 2014 ; Fox et al. , 2015 ). The generalization of automatic testing within the home-cage where individuals interact willingly with a protected computing interface  ( Calapai et al.
Welfare information To understand environmental contributions to brain structure and function, researchers must consider the animals past and present welfare. One way to assess NHP welfare is to rely on physical and social characteristics of their past and present environment. Rearing history (mother/peer-rearing; rearing cage size; weaning age), social nature of current housing (single-, pair-or social housing), cage size, presence of foraging opportunity, and other types of physical enrichment should be easy to collect and share along each MRI dataset. These kinds of data are already beginning to provide insight, as rearing history, cage size, and number of cagemates have been shown to impact neuroimaging measures (e. g. Sallet et al. , 2011 ; Noonan et al. , 2014 ; Howell et al. , 2019 ).
However, these environmental characteristics only provide indirect evidence about NHP welfare and do not take into account the fact that different animals may react differently to a similar environment. Another way to assess NHP welfare is to take individual-based measures. Body weight and body condition can be measured in a standardised way but probably lack sensitivity to detect anything but the biggest welfare problems. In-depth welfare assessment requires incorporating regular behavioural observations of each individual. While such direct observations are usually made in every primate facility, there are usually not done in a standardised way. Such a standardization relies on using the same list of quantifiable behaviours, the same behavioural definitions and the same data collection protocols.