dataesr/scientific-paragraphs-categorization huggingface dataset:
The highest difference in allele frequency was observed between early farmers and hunter-gatherers from West Eurasia. However, as these two populations are highly diverged, it remains possible that this significant difference in allele frequency is due to genetic drift shaped by population history, rather than natural selection. To test this model, using the same dataset, we extracted 2500 control SNPs with a global minor allele frequency similar to rs1061325 (M1316V) (up to an error of 5%) and minimum global sequencing depth of 100X. We obtained statistical significance (p-value 0. 036) when testing the difference in derived allele frequency in farmers compared to hunter-gatherers (+26. 58%), while we found no statistical support (p-value 0. 080) when testing the absolute difference in allele frequency between these two populations.
In this chimpanzee data set, we observed considerable diversity, with a potential tendency towards multiple variants. However, amino acid 1316 was not covered in this data set, possibly due to poor data mapping quality associated with the high nucleotide diversity observed. In another data set of 20 individuals  (Teixeira et al. , 2015) , we found a frequency of 10% for the T1316 allotype in chimpanzees but not in bonobos. We further investigated CLTCL1 variation in polar bears (Ursus maritimus) and their closest related species, brown bears (Ursus arctos). These two species, which diverged 479-343 KYA  (Liu et al. , 2014) , have very different diets  (Liu et al. , 2014; Bojarska and Selva, 2012)  and are phylogenetically closer to each other than chimpanzees and humans. Polar bears subsist on a high fat, low carbohydrate diet, whereas brown bears consume a more varied diet of carbohydrate, protein and fat.
Transfectants were photobleached in the circular region indicated (B) and recovery of fluorescence (FRAP) was visualized over time (bars, 10 mm) and quantified within the bleached regions (C). For the data in (C), area under the curves (D) and mobile fractions Mf (E) were calculated  (Lippincott-Schwartz et al. , 2001). We performed a one-way analysis of variance (ANOVA) with Tukey's multiple comparison post-hoc test: * p-value<0. 05, ** p-value<0. 01. DOI: https://doi. org/10. 7554/eLife. 41517. 015 The following source data is available for figure  7 : Source data 1. FRAP experiment data set 1. DOI: https://doi. org/10. 7554/eLife. 41517. 016 Source data 2. FRAP experiment data set 2. DOI: https://doi. org/10. 7554/eLife. 41517. 017 Source data 3. FRAP experiment data set 3. DOI: https://doi.
The extent of GLUT4 translocation was assessed in each experimental group before and after insulin stimulation; Student t-test, * p-value<0. 05. Transfected cells treated with siRNA to deplete endogenous CHC22, but not treated with insulin, were gated into thirds expressing equivalently low (L), medium (M) and high (H) levels of CHC-GFP for each type of CHC, then total levels of HA-GLUT4-mCherry in each population were plotted (B). We performed a one-way analysis of variance (ANOVA) with Tukey's multiple comparison post-hoc test: * p-value<0. 05. DOI: https://doi. org/10. 7554/eLife. 41517. 019 The following source data is available for figure  8:  Source data 1. GLUT4 translocation experiment. DOI: https://doi. org/10. 7554/eLife. 41517. 020 Source data 2. Total GLUT4-mCherry levels. DOI: https://doi.
An e-value threshold of 0. 001 with additional constraints applied by default in InParanoid version 7  (Ostlund et al. , 2010)  were used (at least 50% of the sequences are covered by the alignment, and at least 25% of the residues are aligned). The polar bear (Ursus maritimus)  (Liu et al. , 2014) , brown bear (Ursus arctos)  (Benazzo et al. , 2017)  and black bear (Ursus americanus) CHC17 and CHC22 protein sequences were manually added. For CLTCL1 only the elephant and horse sequences (XP_023397213. 1 and XP_023502410. 1 respectively) were manually added. The sequences corresponding to the longest transcripts were aligned with MAFFT  (Katoh and Standley, 2013)  and phylogenetic trees generated with PhyML  (Guindon et al. , 2010). The last two steps were repeated after manually removing outlier sequences lying on long branches (CLTC/ CLTCL1: ENSTNIP00000007811.
1, ENSTGUP00000014952. 1, XP_023397213. 1, XP_023502410. 1; CLTA/CLTB: ENSPSIP00000012669. 1) and, in the case of genomes not retrieved from Ensembl (therefore lacking the gene-to-transcript mapping), sequences most likely corresponding to alternative transcripts (XP_015350877. 1, XP_007899998. 1, XP_007899997. 1, XP_007904368. 1, XP_007904367. 1, XP_020375861. 1, XP_020375865. 1, XP_020375862. 1, XP_020392037. 1, XP_020375864. 1, XP_020375859. 1). Trees were manually reconciled based on the Ensembl species tree extended by elephant shark, whale shark, brown bear, black bear, porpoise and marmot with TreeGraph (Sto ¨ver and Mu ¨ller, 2010). Branch lengths were estimated based on the multiple sequence alignment (MSA) with PhyML fixing the manually reconciled topology, with options '-u' and '-constraint_file'.
Population genetics Phased genotypes were obtained by querying Variant Call Format (VCF) files  (Danecek et al. , 2011)  from the 1000 Genomes Project database Phase 3 (1000  Auton et al. , 2015)  for all available 2504 samples. Only high-quality variants were retained using vcflib (https://github. com/vcflib/vcflib) with options 'VT = SNP and QUAL > 1 and AC >1 and DP >5000'. Missing genotypes were assigned to homozygotes for the reference alleles. Finally, only sites with a recorded annotated function of being missense, nonsense, stop-loss or frame-shift for tested genes according to the UCSC Table Browser were retained  (Speir et al. , 2016)  (tables snp150 and snp150CodingDbSnp). For each retained position, the reference sequence for chimpanzee from the UCSC Table Browser  (Speir et al.
, 2016)  (table  snp150OrthoPt5Pa2Rm8 ) was initially used to infer the putative ancestral state. For ambiguous or multiallelic states in the chimpanzee sequence, the human reference base was used as an initial proxy for the ancestral state. The predicted functional impact of amino acid replacements was obtained by using Polyphen  (Adzhubei et al. , 2010)  and SIFT  (Kumar et al. , 2009). Additional frequency information for a single mutation of interest in more than 50 human populations was retrieved from the HGDP CEPH Panel  (Cann et al. , 2002)  from http://hgdp. uchicago. edu/cgi-bin/ gbrowse/HGDP/. Genotype data for farmer and hunter-gatherer individuals were collected from the Simons Genome Diversity Project Dataset  (Mallick et al. , 2016). Populations were merged based on their assigned geographical region with the following classification for hunter-gatherers: Africa (Biaka, Ju|'hoan North, Khomani San, Mbuti), Central Asia and Siberia (Aluet, Chukchi, Eskimo Chaplin, Eskimo Naukan, Eskimo Sireniki, Even, Itelman, Tlingit, Tubalar, Ulchi, Atayal), East and South Asia (Atayal, Kusunda).
, 2009)  as previously described  (Mathieson and Mathieson, 2018). These are genetic groups and not directly based on differences in material culture or subsistence, but importantly in the case of HG and EF, these genetic classifications correspond closely to hunter-gatherer and agricultural subsistence strategies  (Haak et al. , 2015; Skoglund et al. , 2014; Skoglund et al. , 2012). We then restricted analysis to samples dated between 10,000 and 5,000 years before present that were classified as either HG or EF, leading to a dataset of 119 HG and 316 EF of which 85 and 188 respectively had coverage at rs1061325. Frequencies for South-East Asians and ancient Eurasians were down-sampled to ensure numerical stability. The HeLa genomic data were accessed through the NIH database of Genotypes and Phenotypes (dbGaP at http://www.
ncbi. nlm. nih. gov/sites/entrez?db=gap) through dbGaP accession number phs000640. High-coverage VCF files for 79 individuals from six species of great apes were retrieved  (Prado-Martinez et al. , 2013). Data was filtered using vcflib on the combined data set with the options 'QUAL > 32 and DP >50 and DP <7000 and FS <27 and MQ > 25 and AC >1', similarly to the original manuscript describing this data set  (Prado-Martinez et al. , 2013). To retrieve nonsynonymous changes, only variants where the translated proteins for each allele differ were retained. We finally phased the data and assigned individual haplotypes using shapeit v2. r837 with the options '-burn 50 -prune 20 -main 100 -window 0. 5 -effective-size 20000'. Additional 110 genomes of chimpanzees and bonobos were analyzed  (Teixeira et al.
For each summary statistic, the empirical percentile rank for the value observed in CLTCL1 compared to the whole distribution of control genes was calculated. Low or high values are suggestive of CLTCL1 being an outlier in the empirical distribution. For plotting purposes, summary statistics and populations were clustered according to a dendrogram inferred from their respective distances based on the calculated matrix of empirical percentile ranks. That is, populations clustering together exhibit similar patterns of percentile ranks, and thus of summary statistics. The underlying dendrograms are not reported. The heatmap plot was generated using the function heatmap. 2 in R with the package gplots. Cells with an empirical percentile rank lower than 0. 10 or greater than 0. 90 were filled with the exact rank value.
Data was acquired with Diva acquisition software by LSRII flow cytometer (Becton Dickinson). Typically, 10,000 to 30,000 events were recorded and Mean Fluorescence Intensity (MFI) values for surface GLUT4 (Brilliant Violet 421) and total GLUT4 (mCherry) were recorded using 450/50 and 530/30 filters, respectively. The ratio of surface to total MFI was calculated to quantify the extent of GLUT4 translocation. MFI values for total GLUT4 (mCherry) were plotted for GLUT4 stability assays. Histograms and postacquisition analysis were performed using FlowJo software (Treestar). Total GLUT4 and surface GLUT4 values are reported separately for cells expressing CHC22 variants with equalized GFP signals at the top third (high), middle third (medium) and bottom third (low) levels of expression.
Immunoblotting HeLa cells protein extracts were quantified by BCA (Pierce), separated by SDS-PAGE (10% acrylamide), transferred to nitrocellulose membrane (0. 2 mm, Biorad), labeled with primary antibodies (1-5 mg/mL), washed and labeled with species-specific horseradish peroxidase-conjugated secondary antibodies (ThermoFisher). Peroxidase activity was detected using Western Lightning Chemiluminescence Reagent (GE Healthcare). The molecular migration position of transferred proteins was compared to the PageRuler Prestain Protein Ladder 10 to 170 kDa (Thermo Fisher Scientific). Signals were detected using the Chemidoc XRS + imaging system (Biorad) and quantifications were performed using Image J software (NIH).
Acknowledgements This work was supported by grants from the  National Institutes of Health (USA)   DK095663  (FMB and MF) and  AI090905  (PP) and  Medical Research Council  grant  MR/S008144/1  and  Wellcome Trust   Investigator Award   107858/Z/15/Z  to FMB. We are grateful to  Libby Guethlein  for helpful discussions on genomic data retrieval and processing. Genomic sequence data and cell culture studies reported here utilize the HeLa cell line. Henrietta Lacks , and the HeLa cell line that was established from her tumor cells, have made significant contributions to scientific progress and advances in human health.
g. during tonometer examination. Without any doubts, currently, the leading equipment allowing the assessment of corneal biomechanics are tonometers: ORA (Ocular Response Analyzer, Reichert Ophthalmic Instruments, Depew, NY) and introduced 5 years later (in 2010) tonometer Corvis ST (OCULUS Optikgeräte GmbH, Wetzlar, Germany). According to PubMed publication database from 2014 262 publications were published (most in 2016) in the subject of the ORA tonometer (search for the password: Ocular Response Analyzer). The search for the slogan: Corvis ended with the result of 159 publications, mostly published in 2017. Non-contact tonometer ORA was the first to emphasise the importance of biomechanics in measuring intraocular pressure. The main difference compared to traditional air-puff tonometers is the registration of the corneal deformation process by an electro-optical infrared radiation (IR) sensor  [16, 25].
Both ORA and Corvis ST in their software contain algorithms allowing to determine the so-called corrected IOP value. The principle of the mentioned correction is different in each of the devices. In the ORA tonometer, it is a corneal compensated IOP (IOPcc), in which the IOP value from CCT as the main factor responsible for stiffness of the cornea and based on the pressure values at the moments of the first and second applanation  [16]. For the Corvis ST tonometer, a biomechanically corrected IOP (bIOP) was developed. Its value is determined on the grounds of an algorithm based on numerical simulations of dynamic corneal deformation, also taking into account the effect of variable corneal thickness  [50, 51]. In this work, we will present the latest and already known biomechanical parameters possible to be obtained from tonometers using air puff.
Repeatability of the dynamic corneal response parameters Biomechanics of the cornea significantly affect the values of intraocular pressure  [10, 16, 22, 25, [76] [77] [78]. This was also confirmed by the latest work analysing the correlation of new dynamic corneal response parameters (DCRs) with IOP values  [51]. Therefore, not only age and central corneal thickness are parameters requiring consideration during the tests  [47, 79]. In the new version of Corvis ST tonometer software in the tab containing so-called The Vinciguerra Screening Report (Fig. 2 ) there are two IOP values-IOPnct, i. e. uncorrected pressure (hereinafter referred to as IOP in the paper) and bIOP-i. e. biomechanically corrected IOP. bIOP was developed in its original version by the team of Joda et al. [50] , whereas in the Corvis ST tonometer the reported pressure value is calculated according to the modified algorithm  [51, 80].
The basic biomechanical parameters available to date in the Corneal Visualization Scheimpflug Technology tonometer software were limited to the quantitative evaluation of certain distinct corneal features. Unfortunately, the lack of standardisation of available indicators significantly limited their use in clinical practice. An example of work on the analysis of the corneal dynamic response after surgery is shown in Table  4. As can be seen from Table  4 , bIOP values showed a statistically significant index enabling to distinguish between patients after LASIK and SMILE procedures. Similarly, parameters obtained from the Goldman device allow to distinguish patients qualified for cataract surgery  [75, 83, 96]. The latest dynamic corneal response parameters analysed in this review, available in the updated version of the Corvis ST tonometer software are still being tested and their clinical application is being verified.
Every measurement is stored in the device and may become available to the physician later  [102, 103]. Triggerfish: This modern device consists of a contact lens sensor and a small antenna near the eye that transmits data via wireless technology. One of its major advantages is that it may be used at home. It also provides permanent measurement. The 24-h IOP patterns, for example, can be monitored continuously without waking up the patient [104]. 6. Very often the aspect of repeatability of measurements is omitted in the literature on biomechanical parameters measurement. It should be noted here, in particular, that at high individual variability, for different biomechanical parameters of the cornea, as well as its shape and arrangement, the measurements may differ significantly from each other.
Validation is a crucial aspect in the reliability assessment of models. Validation accuracy is measured with respect to experimental data. In this regard, there is not any experimental technique with sufficient spatial resolution capable of measuring the incident solar flux onto the receiver of solar power tower plants to model validation. Therefore, the individual optical efficiency of the field and the thermal performance of the receiver cannot be accurately obtained experimentally. To calculate these efficiencies, the development of numerical models is mandatory. Although, numerous receiver models can be found in the literature, the accuracy of most of them is not checked because of the scarcity of experimental data to compare with. In this study, the simulations of a model that includes the heliostat field and an external tubular receiver, taking into account all the receiver tubes, have been compared with available experimental data from Solar Two plant, in order to check its accuracy.
Recently, SolarPILOT integrates both ray tracing and convolution methods based on DESOL and SolTrace tools to analyse more complex geometries  [20]. In the present work, it is used FluxSPT software tool that is grounded on the projection to the central receiver panels of the flux distribution in the image plane obtained from an analytic function, UNIZAR in this instance  [21]. The quantity of models that solve the receiver thermal behaviour is also abundant, they can be classified in detailed and simplified models. The first category is solved using CFD tools  [22] [23] [24] [25] [26] [27] [28] [29] [30] ; while the second category comprises low-detail models between them it is worth highlighting: CAVITY, which solves the energy balance in tubular cavity receivers coupling the radiative exchange to conduction and convection  [31] ; DRAC and TOPAZ simulate onedimensional fluid flow in pipes with both steady and transient heat and mass transfer  [31] ; CGM is a simplified 2D model for external tubular receivers, that takes into account circumferential variations of the tube wall temperature, with a grid size equal to the panel width assuming the same behaviour for all the tubes in a panel  [32] ; FGM is an evolution of the previous model, where the grid size is reduced to the tube diameter and then all the tubes of the receiver are calculated individually  [33] ; ASTRID code, developed by DLR, comprises a lowdetail model that utilizes a number of simplifications for optimization purposes and a highdetail model based on FEM  [34].
The main problem of most of the numerical models is that they handle individually either the solar field or the receiver. Nevertheless, Rodríguez-Sánchez et al. [35]  pointed out that both solar field and receiver subsystems have to be studied together to obtain representative results, since they are interrelated, as some aiming strategies pay attention to Sánchez-González et al. [36]. Some of the most important models that solve the global SPT are  [37] : SAM, which is a system-level software that predicts the performance and economics of different concentrated solar power technologies  [38] , in the case of SPT it is based in the code developed by  [39] ; SOLERGY simulates the operation and annual power output of SPTs utilizing weather data, including parasitic power requirements of the plant  [40] ; SOFIA consists of a simulation environment that approaches the analysis of concentrated solar technologies with different quality levels, in the design phase a medium quality model is used for the optical, thermal and electrical calculations, the validation of the optimal designs is carried out with a high quality model, which consists of several modules  [41] ; Fully-coupled multi-level analytical methodology, that using the models 0-1-2-3 enables both system level-performance prediction and component-level targeting insight  [42].
Pacheco  [1]  documented the experiments carried out in Solar Two SPT, a molten-salt power tower refurbishment of Solar One, at full and partial load. During the experiments the data collected were the mass flow rate, the inlet and outlet temperatures of the heat transfer fluid, the DNI and the wind speed, but there are no measurements regarding the wall temperature, the incident power on the receiver or the optical efficiency of the field. The main goal of this work is to validate the numerical FluxSPT-FGM developed by  [21, 33] , which englobes the optics of the solar field and the thermal behaviour of the receiver, by means of the experimental tests conducted in Solar Two plant. Firstly, Solar Two plant and the tests carried out on it have been detailed. Secondly, the numerical model, its main assumptions and its input data have been described, to finally validate it with the experimental data collected in Solar Two plant: mass flow rate and global SPT efficiency.
Experimental tests During 9 clear-sunny days four different tests were performed symmetrically about solar noon between 11:00 a. m. and 1:00 p. m. solar time. In the different periods of time, all the heliostats of the field (full power: cases A and C) or half of them scattered around the receiver (half power: cases B and D), were under operation, see Table  2  for further information. The experimental data collected in Pacheco  [1]  during each test were: field availability, mass flow rate at the receiver, inlet and outlet temperature of the salt, DNI, and wind direction and speed, see Table  3. The heliostat reflectivity, cleanliness, tracking error and field availability were also gathered. Finally, the gross electrical output in steady-state was measured, being 1 to 8 MWe at partial load and 9 to 12 MWe at full load.
, 2014; Ballantyne et al. , 2015). Here we collated destructively-measured BGB datasets from individual trees and shrubs sampled from a broad range of stands from differing climatic regions of Australia, including those in natural ecosystems or otherwise established through human intervention (i. e. planted). We then analysed this data set to assess whether D-based allometric models of BGB were improved: (i) when based on species rather than broader categories such as plant functional groups; and (ii) by the inclusion of stand characteristics (age and management) or climatic variables. Our objectives were firstly to recommend the most appropriate allometric model(s) for estimating BGB in ecosystems across the Australian continent, and secondly to quantify the accuracy of the recommended model(s) when tested against direct measurements of stand-level BGB obtained using whole-plot excavation across a range of contrasting sites.
Functional groups The data set included 128 species. Only seven species (Eucalyptus polybractea, E. loxophleba, E. kochii, E. globulus, E. occidentalis, Pinus pinaster and P. radiata) were sampled in sufficient numbers (N > 100 individuals) to have confidence in developing species-specific models that are likely to reflect the true population (i. e. targeted coefficient of variation in predicted BGB being ca 5%;  Roxburgh et al. , 2015). This relatively high sample size requirement was based on the assumption that, due to measurement errors being relatively high when extracting roots from the soil, species-specific BGB allometric models are likely to have a relatively high inter-sample variability, with residual standard deviations being in the order of about 0. 50. Because the sample size of most (95%) species was insufficient to assess the allometry of BGB at a species-specific level, we categorised all species in the data set into plant functional types of unique physiognomic growth form  (Gitay & Noble, 1997) , i.