dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular Docking Molecular docking studies were performed to explore the interaction of CBH with HSA  [39]. Because of its heart-shaped structure, HSA has a central channel that allows the entry and binding of small molecules with its subdomains  [40]. The main ligand binding regions of HSA are situated in hydrophobic cavities of subdomains IIA and IIIA, and these subdomains are consistent with Sudlow sites I and II, respectively  [41, 42]. In this study, an autodock program was used to investigate possible binding conformations of CBH with HSA and the best conformation was selected based on the binding free energy. Table  5  and Figure  9  summarize the binding of CBH within the binding site of HSA, revealing that CBH was accommodated within the cavity of subdomain IIA of HSA. The binding of CBH with HSA was dominated by hydrophobic interactions, and just one hydrogen bond was involved in the binding of CBH to HSA.
Fluorescence Quenching and Data Analysis Fluorescence quenching experiments of HSA in the presence and absence of CBH were performed on a Jasco spectrofluorometer (FP-8300) with an attached water bath. The fluorescence quenching experiments were performed at three different temperatures: 25, 30 and 37 • C. The excitation wavelength was set to 295 nm to avoid Tyr excitation during the quenching experiment  [44]. The excitation and emission slit widths were 5 and 2. 5 nm, respectively. The emission spectra were recorded at 300-400 nm. To eliminate the inner filter effects of CBH absorbance, excitation and emission wavelengths (295 and 340 nm) were used and corrected using the equation: F corrected = F observerd × e (Aex+Aem) 2 (5) In this equation, F corrected and F observed are the corrected and observed intensities of fluorescence, respectively, while A em and A ex represent the emission and excitation wavelengths of the ligand-protein at 295 and 340 nm, respectively.
Thermodynamic Analysis of the Binding Process The binding constant (K b ) was calculated at different temperatures and further analyzed to understand the nature of CBH binding to HSA. All the thermodynamic data were extracted from the fluorescence quenching assay. During the experimental analysis, it was assumed that there was no significant change in the enthalpy (∆H • ) in the studied temperature range. The van't Hoff equation was used to calculate the thermodynamics parameters involved in the binding, i. e. , entropy change (T∆S • ) and enthalpy change (∆H • ). The equation used for the calculation was: ln K = - ∆H • RT + ∆S • R (7) In the above equation, "K" is the binding constant at the given temperature, while "R" is the ideal gas constant (1. 987 cal K-1 mol-1). "T" is the absolute temperature in Kelvin.
Molecular Docking A molecular docking study was performed to gain better insight into the binding of CBH within the active site of HSA. AutoDock 4. 2  [48]  was used to perform molecular docking experiments. First, the known three-dimensional structure of HSA was retrieved from the RCSB Protein Data Bank  [49]  (PDB id: 1AO6), while the 3-D structure of CBH was retrieved from the PubChem Database (pubchem id: 22576). The structure of HSA was prepared for molecular docking by removing all the water molecules and HETATM. The molecular docking of CBH against HSA was then performed for 20 runs using the Lamarckian genetic algorithm with default parameters. Out of 20 different conformers, the best one was selected based on the binding free energy.
Assessments Each participant attended the research laboratory on one occasion to complete questionnaires, a clinical interview, and neuropsychological testing. All procedures were conducted in a quiet environment. The following demographic data were collected: age, gender, number of times alcohol consumed per week on average, and education level. Quality of life was assessed using the Quality of Life Inventory (QOLI)  (Frisch et al. , 2005) , which comprehensively measures overall life satisfaction and well-being, has excellent psychometric properties, and is sensitive to effects of disease on quality of life; and beneficial effects of treatments  (Frisch et al. , 2005). Structured clinical interviews were conducted using the previously validated Mini International Neuropsychiatric Inventory (MINI)  (Sheehan et al.
Data analysis To identify demographic, clinical, and cognitive measures associated with statistical variation in quality of life, we utilized the statistical technique of partial least squares (PLS)  (Abdi and Williams, 2013; Cox and Gaudard, 2013; Garthwaite, 1994; Höskuldsson, 1988). This powerful statistical technique constructs one or more latent variables (known as PLS components) that optimally explain the relationship between a set of X variables (explanatory variables) and one of more Y variables (outcome variables). Here, the Y variable was quality of life, and the X variables were: age, gender, education level, number of times alcohol consumed per week, presence (or not) of each mental disorder identifiable by the MINI and MIDI, total gambling disorder symptoms endorsed (SCI-GD), Barratt impulsivity (motor, attentional, and planning), obsessive-compulsive traits (Padua total score), and the cognitive outcome measures for response inhibition, decision-making, and extra-dimensional set-shifting.
PLS is ideal in situations in which variables are correlated with each other; and when the number of variables is large in comparison to the number of cases. Analysis was conducted using JMP Pro software Version 13. 0 (SAS Institute Inc. , 2017). Any missing data points were imputed automatically by JMP using study means. The PLS model was fitted using leave-one-out cross-validation (non-linear iterative partial least squares, NIPALS algorithm), and the optimal number of latent factors was selected by minimizing the predictive residual sum of the squares (PRESS). Initial explanatory variables that did not pass the Variable Importance Threshold (VIP) of 0. 8 were not retained in the model (2017). Explanatory variables significantly contributing to the model (i. e. explaining significant variance in quality of life) were identified on the basis of 95% confidence intervals for bootstrap distribution of the standardized model coefficients not crossing zero (N = 1000 bootstraps).
Gambling symptoms (number of criteria endorsed) had one of the strongest associations with quality of life compared to other variables examined, ranking similarly highly as impulsive personality traits measured using the Barratt impulsivity scale. DSM diagnoses of intermittent explosive disorder, binge-eating disorder, and skin picking disorder were all linked with worse quality of life. Prior data are consistent with this finding. Binge-eating disorder is in fact the most common eating disorder globally  (Kornstein, 2017). The majority of people with binge-eating disorder experience functional impairment especially in the domain of social functioning but also, to a lesser degree, in home and work settings  (Kornstein, 2017). As well as the psychological impact, binge-eating disorder can lead to obesity, diabetes, and sleep disruption, which may feed into these quality of life associations.
Acknowledgement Dr. Grant has received research grants from  NIDA, National Center for Responsible Gaming, American Foundation for Suicide Prevention , and  Forest and Roche Pharmaceuticals. Dr. Grant receives yearly compensation from Springer Publishingfor acting as Editor-in-Chief of the Journal of Gambling Studies and has received royalties from  Oxford University Press, American Psychiatric Publishing, Inc. , Norton Press, Johns Hopkins University Press , and  McGraw Hill. Dr. Chamberlain   consults for Cambridge Cognition, Shire, Promentis, and Ieso Digital Healthcare. Dr. Chamberlain 's research was funded by a  Clinical Fellowship  from the  Wellcome Trust  (reference  110049/Z/15/Z ).
Test Set-Up and Instrumentation The drop hammer test procedure was employed for the dynamic testing of FRP bars. The drop hammer set-up, depicted in Fig. 1 , had a maximum impact velocity and maximum kinetic energy of 10 m/s and 2. 5 kJ, respectively. One end of the bar was braced using artificial clay to prevent its slippage. The testing machine is equipped with a 20 t dynamic load cell, that measures the axial load applied to the bar specimens and a laser displacement transducer, to measure the deflection of the bars. The load cell and displacement transducer were both connected to a data acquisition unit, monitored by a computer. The acquisition system recorded the applied load and displacement data in a coordinated manner, with the aid of photocells. The acquisition of the data begins simultaneously once the suspended hammer is released from the predefined height, up until the hammer hits the bar with a specific velocity.
1 (more than five genes) compared with the other Lecythophora/Coniochaeta genomes (Table  1 ). Moreover, genes for CAZy families CBM24, GH76, CE1, GH47, GH31, GH71, AA8, GH55, AA3, GH11, AA4, AA1_2, AA12, AA3_3, GH13_40, GH45, and GH5_5 were highly abundant in 2T2. 1 (more than five genes) compared with the other fungi outside of the Coniochaetaceae. Including all the duplicated content of 2T2. 1, the results showed that 122 CAZy families were differentially abundant (two standard deviations above or below) compared to the whole dataset (Coniochaetaceae-derived plus other fungal genomes). Complete counts of all genes belonged to each CAZy family across genomes used in this study   S4.
1 sequencing chemistry with 1 × 360 and 1 × 600 sequencing movie run times. Filtered sub-read data were then assembled together with Falcon version 1. 8. 8  [35]. Plate-based RNA sample preparation was performed using TruSeq Stranded mRNA HT Sample Prep Kit. Total RNA starting material was 1 µg per sample and 8 cycles of PCR was used for library amplification. The prepared library was then quantified using KAPA Biosystem's next-generation sequencing library qPCR kit and run on a Roche LightCycler 480 real-time PCR instrument. The quantified library was then multiplexed with other libraries, and the pool of libraries was then prepared for sequencing on the Illumina HiSeq sequencing platform utilizing a TruSeq paired-end cluster kit, v4, and Illumina's cBot instrument to generate a clustered flow cell for sequencing.
To calculate average similarity of 2T2. 1 to close phylogenetic relatives (Lecythophora sp. AK0013 and Coniochaeta sp. PMI546) and representative lineages of varying ploidy, we used nucmer with default parameters from the mummer version 4. 4. 0 software package  [38]  and coordinates for all syntenic regions > 2000 bp were extracted using show-coords parameters -l -o -d -c -r -L 2000 -T. For comparison to assemblies of varying ploidy, potentially repetitive sequences (same position mapping to multiple locations) were removed. Since synteny is sometimes interrupted by unique sequence in one of the two copies, neighboring syntenic regions were extended if interrupted by less than 5 kb of non-syntenic sequence. If extended, % identity was averaged across duplicated regions. % of all duplicated content above 95% identity, or between 88.
[74] , we aligned protein sequences using MUSCLE  [75] , converted to codon alignments using PAL2NAL  [76]  and then calculated pairwise d N /d S using the YN00 model  [77]  implemented in PAML v4. 8  [78]. d N /d S distributions were similarly calculated between single-copy genes in related pairs of species (Lecythophora sp. AK0013 and Coniochaeta sp. PMI546, Coniochaeta sp. PMI546 and C. lignaria CBS111746, Coniochaeta sp. PMI546 and C. lignaria NRRL30616). To quantify similarities between genome-wide d N /d S distribution patterns in homeologs of 2T2. 1 and orthologues across different species, QQ plot analysis was conducted using the EnvStats v2. 3. 1 package implemented in R version 3. 5. 1. The same approach was used when attempting to separate parents through comparing d S  [29, 39]  between 2T2.
Clustering of orthologous genes and phylogenomic comparisons To perform phylogenomic comparisons, we selected 14 fungal genomes (including four from the Lecythophora/Coniochaeta lineage; and eight other Ascomycota, and two Basidiomycota species) that have been deposited on JGI-MycoCosm genome portal (Additional file 1: Table  S1 ). The filtered protein models of each taxon were downloaded, and clusters of orthologous genes among the five Lecythophora/Coniochaeta genomes were detected using the software OrthoVenn  [79]. Unique clusters of proteins found in the genome of Coniochaeta sp. 2T2. 1 were then annotated using the dbCAN web server  [80]. A species tree of Coniochaeta was generated using 2522 orthologous genes identified using mcl  [40]  that were aligned with MAFFT  [81]. mcl clusters can be viewed interactively here: https ://mycoc osm.
CAZyme genome profile Annotation of CAZymes in all the genomes evaluated in this study was performed using a combination of BLAST and HMMER searches conducted against the CAZy database  [85]. To avoid an overestimation on the number of CAZymes detected in enriched/ depleted in the Coniochaetaceae, we removed secondary duplicated gene copies (see methods section: analysis of Coniochaeta sp. 2T2. 1 genome with respect to duplication) for each CAZy family. For list of secondary duplicates, see Additional file 5: Table  S4. Following family assignment, we identified CAZyme families that differed significantly (FDR corrected p ≤ 0. 05) in abundance in Lecythophora/Coniochaeta genomes (Coniochaeta sp. 2T2. 1, C. ligniaria CBS111746, C. ligniaria NRRL30616, Coniochaeta sp. PMI546 and Lecythophora sp.
Acknowledgements We are grateful to  Dr. Bernard Henrissat  and his team for performing annotation of carbohydrate-active enzymes in Coniochaeta sp. 2T2. 1. Thanks to  Dr. Joseph Spatafora ,  Dr. Gregory Bonito ,  Dr. Jana U'Ren ,  Dr. Francis Michel Martin ,  Dr. Scott Baker , and  Dr. Keith Seifert  for permission to use unpublished genomic data for the purposes of this study.
Availability of supporting data The data sets supporting the findings of this study are included as Additional files 1 to 8. This whole-genome shotgun project has been deposited at DDBJ/ ENA/GenBank under the accession VSMA00000000 (BioProject PRJNA250595). The version described in this paper is version VSMA01000000. The transcriptome data were deposited under the following SRA accessions numbers (SRP170777, SRP170791, SRP170792, SRP170785, SRP170784, SRP170782, SRP170783, and SRP170789).
The SPME was then desorbed in the injection port for 1 min at 250°C. The initial column temperature was held at 30°C for 2 min, increased to 140°C for 5 min, then increased to 270°C over a period of 3 mins and held at that temperature for the duration of the analysis. The GC-MS interface was maintained at 200°C. No signal was acquired for the first minute following sample injection and the system was run in split-less mode. The mass spectrometer was operated in the electron ionisation mode at 70eV and the mass signals were recorded in total ion count (TIC) mode. Data acquisition began 1 min after injection and continued for 45 mins utilising a mass range of 45-450 m/z.
Non-targeted GC-MS headspace analysis As the C. maritima hexane extract had the most potent anti-proliferative activity against both the CaCo2 and HeLa cell lines (as determined by IC 50 ;   3 ). Putative empirical formulas and identifications were achieved for 97 of these compounds (approximately 71 %) by comparison with the database. The major components by area were: Peak 1: 2-hydroxy-1, 8-cineole (12. 7 min; 1. 2 % of the total area under all chromatographic peaks; Figure  5a ). Peak 2: decane (13. 2 min; 5. 1 % of the total area under all chromatographic peaks). Peak 3: limonene (14. 1 min; 2. 0 % of the total area under all chromatographic peaks; Figure  5b ). Peak 4: citronellal (14. 6 min; 0. 4 % of the total area under all chromatographic peaks; Figure  5c ). Peak 5: 3, 3-dimethylhexane (15.
Statistical analysis Statistical analyses were performed using Graphpad Prism (version 6, GraphPad Software Incorporated, La Jolla, CA, 268 USA). Outliers were identified using the ROUT method based on the False Discovery Rate (with a Q set at 1%). Normality and homogeneity of variance were respectively checked using D'Agostino-Pearson test and Bartlett test. For nka a1a, cftr, clc-3, rhcg1, nkcc1, nhe3, ncc2a and vha-a, data were square root transformed to fit homogeneity of the variance assumption. Two-way factorial analysis of variance with temperature and salinity as the main factors was performed. Critical differences between groups were appraised using the Fisher's least-square difference test. Linear correlations were determined using the Spearman correlation (Two-tailed). Data are presented as box and whisker plots showing median, minimum and maximum values.
The agamid Pseudotrapelus lizards inhabit the mountainous areas of the Arabian Peninsula and eastern North Africa. Currently six Pseudotrapelus species are recognised, though diagnostic morphological characters are still lacking, creating great difficulty in describing new species. Recently, two specimens of Pseudotrapelus were collected from the vicinity of Riyadh in central Saudi Arabia, an area that was not sampled in previous phylogenetic studies. In here we used both mitochondrial and nuclear data to investigate the phylogenetic position of the new samples, and assess their phylogenetic relationships with the other recognised species of Pseudotrapelus from across the distribution range of the genus. We used a multilocus approach of haplotype networks, concatenated datasets and species trees, performed mitochondrial and nuclear species delimitation analyses, and estimated divergence times.
Materials and methods The two Saudi Arabian Pseudotrapelus individuals were collected from the vicinity of Riyadh (see Fig. 1 ). The vouchers and their tissue samples were deposited in Salvador Carranza's collection at the Institute of Evolutionary Biology (IBE), Barcelona, Spain. Specimen with IBE collection code IBECN6252 (field code 9642X; Fig. 2A ), an adult male, was collected on March 25th 2016, from a hill in a wadi, north-west to Thumamah, 25. 592N 46. 401E, 668 m elevation (Fig. 2B  and C ). Specimen with IBE collection code IBECN13348 (field code 9689X; Fig. 2D ), adult female, was collected on April 27th 2018, from the foothill of Jebel Baloum, 23. 699N 46. 173E, 803 m elevation (Fig. 2E  and F ). GenBank accession numbers of the two specimens are MK176908-MK176914. The monophyly of Pseudotrapelus and the close phylogenetic relations with Acanthocercus have been shown in  Tamar et al.
(2016a). Therefore, our dataset included a total of 100 specimens: the two Riyadh individuals, 92 specimens of the six recognised species of Pseudotrapelus (ten of P. aqabensis, six of P. chlodnickii, 22 of P. dhofarensis, 31 of P. jensvindumi, three of P. neumanni, 20 of P. sinaitus), and six outgroup specimens of Arabian Acanthocercus (one specimen of Acanthocercus adramitanus from Yemen, code JEM135, was additionally sequenced in this study; GenBank accession numbers for this sample are MK182721-MK182723). The remaining sequences of Pseudotrapelus and Acanthocercus were retrieved from GenBank from the study of  Tamar et al. (2016a). DNA of alcohol-preserved tissue samples was extracted using the SpeedTools Tissue DNA Extraction kit (Biotools, Madrid, Spain). The final dataset comprised four gene fragments that were previously used in  Tamar et al.
7. 3  (Katoh and Standley, 2013). We calculated inter-and intraspecific uncorrected p-distance of the Pseudotrapelus species for 16S and ND4, with pairwise deletion, in MEGA v. 7. 0. 14  (Kumar et al. , 2016). We performed phylogenetic analyses using the complete concatenated dataset of the four markers. Partitions and substitution models were defined by PartitionFinder v. 2  (Lanfear et al. , 2016)  with the following parameters: linked branch length; BEAST models; BIC model selection; greedy schemes search algorithm; single data block for 16S, and by codons for the protein-coding genes. We treated alignment gaps as missing data, and the nuclear gene Identification and localities of the known species correlate to specimens in Table  S1  in  Tamar et al. (2016a). Colours correspond to those in Figs.
3 4. sequences were not phased. We analysed the complete concatenated dataset with the following partitions and substitution models: 16S + ND4_1 (HKY + G), ND4_2 (HKY + G), ND4_3 (GTR + G), c-mos_1 + c-mos_2 + MC1R_1 (JC + I), c-mos_3 + MC1R_2 (HKY + I), and MC1R_3 (HKY + G). Phylogenetic analyses were performed using maximum likelihood (ML) and Bayesian (BI) methods. We conducted the ML analysis in RAxML v. 8. 1. 2 as implemented in raxmlGUI v. 1. 5 (Silvestro and Michalak, 2012) , with the GTRGAMMA model, 100 random addition replicates, and 1000 bootstrap replicates. The BI analysis was conducted in BEAST v. 1. 8. 4  (Drummond et al. , 2012)  with the following priors (otherwise by default): partitions and models as detailed above; unlinked parameter values for clock and substitution models, linked trees; Coalescent tree model; random starting tree; base substitution parameter (0-100); alpha prior uniform (0-10); uncorrelated relaxed clock for the mitochondrial partitions and strict clock for the nuclear partitions (uniform distribution; mean 1, 0-1).
Three individual runs of 10 8 generations were carried out with sampling at intervals of every 10 4 generations. We assessed posterior trace plots and effective sample size values in Tracer v. 1. 6 (Rambaut et al. , 2014) , and LogCombiner and TreeAnnotator were used to infer the ultrametric tree after discarding 10% of the trees as burn-in. We inferred genealogical relationships among the haplotypes of Pseudotrapelus species using the phased nuclear sequences. To resolve heterozygous sites we first used the on-line web tool Seq-PHASE  (Flot, 2010)  to convert the input files, and used the software PHASE v. 2. 1. 1  (Stephens et al. , 2001; Stephens and Scheet, 2005)  to resolve phased haplotypes using default settings, apart from the phase probabilities of 0. 9 for c-mos and 0. 5 for MC1R.
We then used the TCS statistical parsimony network approach  (Clement et al. , 2000)  implemented in the software PopART  (Leigh and Bryant, 2015; Clement et al. , 2000)  to generate the networks. To evaluate the number of distinct mitochondrial and nuclear lineages within Pseudotrapelus we applied two methods of species delimitation using the ingroup dataset only. To evaluate mitochondrial divergence within Pseudotrapelus we carried out the GMYC analysis  (Pons et al. , 2006)  implemented in R (R development Core Team, 2018) using the 'splits' package  (Ezard et al. , 2009)  and applying a single threshold algorithm. For this analysis, we used the concatenated mitochondrial dataset and reconstructed a tree using BEAST with partitions and models defined by PartitionFinder (with parameters as previously detailed).
Partitions, models, and other priors were as described above. We carried out three individual runs of 5 Â 10 7 generations with sampling at intervals of every 5 Â 10 3 generations. To evaluate nuclear divergence within Pseudotrapelus we used the software BPP v. 3. 3  (Rannala and Yang, 2003; Yang and Rannala, 2010)  with the full-length phased nuclear loci only. We carried out two analyses with ''species" assignation based on the GMYC entities: (i) Conducting species delimitation analyses using a fixed guide tree (the topology of the BI tree using the complete concatenated dataset). (ii) Performing a joint analysis of species delimitation while estimating the species tree  (Yang, 2015). For these analyses, algorithms 0 and 1 were used assigning each species delimitation model equal prior probability.