dataesr/scientific-paragraphs-categorization huggingface dataset:
As prior distributions on the ancestral population size (h) and root age (s) can affect the posterior probabilities for models  (Yang and Rannala, 2010)  we tested four different combinations of priors:  2000) , s = G (1, 10). The locus rate parameter that allows variable mutation rates among loci was estimated with a Dirichlet prior (a = 2). Since our dataset was autosomal only, the heredity parameter that allows h to vary among loci was set as default. We ran each of the rjMCMC analysis twice to confirm consistency between runs, each run for 5x10 5 generations with 10% discarded as burn-in. We considered probability values ! 0. 95 as strong evidence for speciation. h = G (1, 10), s = G (1, 10); h = G (2, 2000), s = G (2, 2000); h = G (1, 10), s = G (2, 2000); h = G (2, Divergence times for Pseudotrapelus were estimated using a multilocus coalescence-based Bayesian species tree using *  BEAST (v.
1. 8. 4; Heled and Drummond, 2010). We used jModelTest v. 2. 1. 7  (Darriba et al. , 2012; Guindon and Gascuel, 2003)  to select the best model for each marker under the Bayesian information criterion (BIC): 16S (HKY + G), ND4 (TrN + I + G), c-mos (K80 + I), MC1R (TrN + I). In this analysis, using the Pseudotrapelus dataset only, we set the average sequence evolution rates estimated for the agamid genus Phrynocephalus according to  Pang et al. (2003). Tamar et al. (2016a)  also used these rates and compared them to an analysis conducted using different calibration points; both analyses resulted in almost identical dates (see Table  1  in  Tamar et al. , 2016a). The rates and priors were: 16S (0. 0073-0. 0132 substitutions/site/million years) and ND4 (0. 0113-0. 0204 substitutions/ site/million years), uncorrelated relaxed clock with the ucld.
Results Our dataset comprised 94 Pseudotrapelus specimens sampled from localities across the distribution range of the genus in Arabia and North Africa, including type localities, and six specimens of Acanthocercus from Saudi Arabia, Yemen and Oman. The nuclear dataset included 17 haplotypes of c-mos and 43 haplotypes of MC1R. The ML and BI phylogenetic analyses of the complete concatenated dataset resulted in identical topologies with high bootstrap support (ML) and posterior probabilities (BI) values (Fig. 3A ). In both analyses, Pseudotrapelus, and each of the recognised species within it, were recovered as monophyletic, with a general phylogenetic structure of two major clades. The two Riyadh samples form a clade with P. chlodnickii and P. sinaitus, though the phylogenetic relationships within this clade are not supported in either the ML or BI analyses.
The BPP analyses, based on the nuclear dataset assigned to the 11 mitochondrial GMYC lineages, supported seven distinct lineages within the genus, identical to the six recognised species and the lineage of the Riyadh specimens (numbered A-G). In all BPP analyses carried out, the Riyadh specimens were distinct from the other species. The topologies of the BPP species trees, however, changed with the shifting phylogenetic positions within the clades as described above for the phylogenetic trees based on the concatenated dataset. The time-calibrated species tree of Pseudotrapelus is presented in Fig. 4. The topology was generally similar to that of the complete During the Late Miocene ca. 6. 4 Ma (95% HPD: 4. 4-9) the clade comprising the two Riyadh samples with P. chlodnickii and P. sinaitus diverged.
Discussion In this study we aimed to investigate the phylogenetic affinity of two specimens of Pseudotrapelus from the vicinity of Riyadh in the interior of Saudi Arabia. We applied multilocus phylogenetic and coalescent based methods to all recognised Pseudotrapelus   S1  in  Tamar et al. (2016a)  and the samples from this study, the two individuals from Riyadh, Saudi Arabia (IBECN6252, IBECN13348) and Acanthocercus adramitanus from Yemen (JEM135). (B) Unrooted haplotype nuclear networks. Circle size is proportional to the number of alleles. species, and the two unknown samples from Riyadh, to generate concatenated and species trees and mitochondrial-and nuclearbased species delimitation analyses. The inferred topologies in our study, based on both the concatenated dataset and the species trees, were mostly congruent across analyses and generally support the current taxonomy  (Tamar et al.
, 2016a). The Riyadh individuals are phylogenetically close relatives of two recognised species, P. sinaitus and P. chlodnickii, forming together a wellsupported clade. Although relationships within this clade are not supported and change between the different analyses (those of the concatenated dataset and the species trees), their phylogenetic affinity is strong. Additionally, the genetic distances of the two Riyadh specimens from the other known species is among the highest in the genus (16S, 6. 7-9%; ND4, 15. 2-19. 4%; Table  1 ). Moreover, the lack of shared haplotypes between the two Riyadh individuals and the remaining six recognised species of the genus in the two nuclear markers analysed further supports the notion that the Riyadh lineage has been genetically isolated for a long time without gene flow.
Acknowledgements We wish to thank  Benoit Chague  for his help in the field. This work was supported by the  Ministerio de Economía y Competitividad, Spain  (co-funded by  FEDER)  under grant numbers  CGL2015-70390-P  and  PGC2018-098290-B-I00. We are thankful to the Deanship of academic research at  Taif University  for support on the first field trip to  Saudi Arabia  under Grant no. 1-433-2108.
Data analysis The normality of data was checked by means of Shapiro-Wilk's W-test, and the homogeneity of variances by means of Levene's test. To obtain normal distribution of features, Box-Cox transformation was used. Differences between four groups of sampling sites in terms of the concentration of elements in water and plants as well as BF values were evaluated by ANOVA  (Zar 1999). The least significant difference (LSD test) was calculated to compare the concentrations of elements  (Sokal and Rohlf 2003). A value of p < 0. 05 was considered statistically significant. The bioaccumulation factor (BF) was used to evaluate the potential ability of the plant to accumulate elements from water. The BF was computed as follows: BF ¼ Cp Cw Cp represents element content in the plant (on a dry weight basis) and Cw is element concentration in water  (Ahmad et al.
2016). BF was calculated only for metals detected both in plant tissues and water in all study sites. The element stock in plants (g m -2 ) was calculated by multiplying the dry biomass yield and element content in the plant  (Brezinová and Vymazal 2015; Vymazal 2016). The calculations were done with CSS Statistica 13. 0 software (StatSoft, Inc. , STATISTICA data analysis software system).
During competitive training, the cluster centres are assessed to a radial layer by iteratively submitting training patterns to the SOFM and adjusting the winning unit and its neighbours to the training design. If two inputs are similar, the most active processing elements responding to inputs are located near each other in the map and the weight vectors of the processing elements are arranged in ascending or descending order  (Nisbet et al. 2009; Torma 1994). The SOFM overcomes the difficulty of human visualisation of high-dimensional data and accomplishes the reduction of dimensions while at the same time displaying similarities  (Kosior et al. 2015). The structure of the SOFM consisted of two layers of neurons connected by weights (i. e. connection intensities). The input layer consisted of 5 (trace metals that were detectable both in water and plant samples: Cu, Fe, Mn, Ni and Zn) neurons connected to 30 study sites.
, 2014) , yet the targets downstream of FAK are varied and may be tumor or stroma context-dependent  (Sulzmaier et al. , 2014; Haemmerle et al. , 2016). Interestingly, phenotypes associated with FAK knockout can be distinct from FAK inhibition, since kinase-inactive FAK retains important scaffolding roles  (Lim et al. , 2008). Several ATP-competitive FAK inhibitors have been developed. Acceptable Phase I safety profiles in patients with advanced solid tumors  (Jones et al. , 2015; Soria et al. , 2016; Hirt et al. , 2018)  have enabled current Phase II combinatorial clinical trials with FAK inhibitors in pancreatic, mesothelioma, and non-small cell lung carcinoma (NCT02758587 and NCT02546531). In ovarian and prostate carcinoma preclinical models, FAK inhibition (VS-6063, defactinib) enhanced taxane-mediated tumor apoptosis  (Kang et al.
Using an FPKM cutoff greater than one, 1040 mRNA transcripts were increased two-fold or more by FAK compared to FAK KO cells and significant after multiple testing correction (Figure  10 source data 1 ). By filtering out transcripts that were elevated in FAK-R454 versus FAK KO cells (FAK activity-independent targets), 591 genes were identified as FAK activity-dependent and showed KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment for MAPK Signaling, Cell Cycle, Axon Guidance, and DNA Replication. After unbiased filtering of the 591 FAK activity-dependent transcripts with those elevated by bcatenin DGSK, 241 shared transcripts were identified as co-regulated by FAK activity and b-catenin (Figure  10A ). KEGG enrichments were MAPK Signaling, Cell Cycle, Hippo Signaling, and Pluripotency.
To this end, a phase I/II clinical trial for recurrent platinum-resistant ovarian cancer termed ROCKIF (Re-sensitization of platinum-resistant Ovarian Cancer by Kinase Inhibition of FAK, NCT03287271) has been initiated. ROCKIF will investigate whether the small molecule FAK inhibitor defactinib, in combination with carboplatin and paclitaxel chemotherapy, can provide benefit for this difficult to treat patient population.
Patient tumor samples De-identified human ovarian cancer tissue specimens from consented patients were obtained from the Fox Chase Cancer Center (FCCC) Biosample Repository Facility (BRF) under Institutional Review Board (IRB) approved protocols. FCCC staff queried the BRF sample database to identify participants that received carboplatin and paclitaxel neoadjuvant chemotherapy. Biopsy specimens were obtained from FCCC Surgical Pathology, sectioned, H and E stained, and reviewed by a board-certified pathologist. FFPE blocks from the biopsy and the corresponding surgical resection blocks banked by the BRF were cut to obtain one H and E stained slide and six additional unstained sections. One section each from pre-treatment biopsy and post-neoadjuvant treatment surgical resection specimen was stained for Pax8 by the FCCC Histopathology Facility.
Cell cycle analysis Cells were collected as a single cell suspension by limited trypsin treatment, fixed in 70% ethanol and stored at À20˚C overnight. Cells were incubated in 100 ml of PBS containing DNAse-free RNAse (100 mg/mL, Qiagen). After 45 min, propidium iodide (10 mg/mL) was added prior to flow cytometry and analyzed using FlowJo (v9. 5. 1) and ModFit LT (Verity Software House) software.
Viability and cytotoxicity studies For annexin V and 7-AAD staining, cells were cultured in adherent or non-adherent conditions as described above for 24 hr or 5 days, respectively, with increasing cisplatin treatment (0 to 100 mM). Cells were dissociated using limited trypsin treatment, washed by centrifugation, suspended in annexin-V binding buffer (10 mM Hepes, 140 mM NaCl and 2. 5 mM CaCl 2 ) and incubated with allophycocyanin-conjugated Annexin V (eBioscience) and 7-aminoactinomycin D (7-AAD) for 10 min at RT prior to analysis on a FACSCalibur flow cytometer (BD Biosciences). Post-acquisition analyses were performed using CellQuest Pro (BD Biosciences) or FlowJo software. For AlamarBlue (Life Technologies) assays, cells were cultured in 96-well poly-HEMA-coated plates as above for 5 days.
Exome sequencing and CNV analysis Exome sequencing was performed by Novogene (Beijing, China), using genomic DNA (1 mg) isolated from ID8 or KMF cells. Genomic DNA was sheared into 180-280 bp fragments using a Covaris Sonicator (Covaris). Exome enrichment and sequencing libraries were generated using Agilent SureSelect Mouse All Exon kit (Agilent Technologies) following manufacturer's recommendations. Each exome was sequenced using a 150 bp paired-end protocol on the Illumina HiSeq platform, generating 47M reads for the ID8 sample and 61M reads for the KMF sample. (https://software. broadinstitute. org/ gatk/best-practices). Reads were aligned with BWA MEM 0. 7. 12  (Li and Durbin, 2009)  to mouse genome GRCm38_68. Variants were called with GATK 3. 4 according to the Broad Institute's best practices (https://software.
, 2013). Novogene analyses used ClusterProfiler software for enrichment analysis, including GO Enrichment, DO Enrichment, KEGG and Reactome database enrichment to analyze and visualize functional profiles of genomic coordinates, genes and gene clusters. Novogene performed differential expression analysis of two conditions/groups by using the DESeq2 R package. Clustering and grouping analyses used transcripts with FPKM values > 1 and an adjusted p value < 0. 05. Each dataset was subject to Gene Set Enrichment Analysis (GSEA) and The Molecular Signatures Database (MSigDB) analysis. The murine FAK activity and b-catenin targets were compared with the total list of all genes gained in HGSOC by Genomic Identification of Significant Targets in Cancer (GISTIC, ov_tcga).
Ovarian cancer PTK2 transcriptomic survival analysis Survival analysis was performed using a database of ovarian cancer samples (Penzvaltoet al. , 2014). The TCGA dataset was used to link copy number gains to gene expression  (Cancer Genome Atlas Research Network, 2011). Samples with copy number gains were designated into one cohort and all remaining samples were designated into a second cohort. Gene expression was compared between cohorts using a non-parametric Mann-Whitney test. Genes with a fold change over two and a p-value below 1E-04 were accepted as statistically significant. The mean expression of all significant genes was computed for each sample and was used in subsequent analyses for the selected gene. Cox proportional hazards regression was performed for relapse-free survival and for overall survival.
0 m/z and a dynamic exclusion time of 20 s was performed. The top 10 ions were fragmented by higher energy collisional dissociation (HCD) with a normalized collision energy of 27 and scanned over the m/z range of 200-2000 with a resolution of 17,500. After the MS2 scan for each of the top 10 ions had been obtained, a new full MS scan was acquired and the process repeated until the end of the 70 min run. Three repeated runs per sample were performed. Tandem mass spectra were searched using MaxQuant software (v1. 5. 2. 8) against reviewed (SwissProt) mouse sequences of UniProtKB release 2018_08. Peptide-spectrum-match-and protein-level false discovery rates were set at 0. 01. Carbamidomethyl (C), as a fixed modification, and oxidation (M, P, K) as dynamic modifications were included. A maximum of two missed cleavages was allowed.
The LC-MS profiles were aligned, and the identifications were transferred to non-sequenced or non-identified MS features in other LC-MS runs (matching between runs). The extracted ion intensities of all peptides matching to the same protein from the three technical replicates were summed. Proteins were determined as detected in sample identification was derived from at least two unique peptide identifications. Contaminant proteins (according to the contaminants listed in MaxQuant), reverse identifications, and identifications only by site were removed. Only the proteins containing 'cell membrane', 'plasma membrane', 'cell surface', 'extracellular matrix' or 'secreted' in the cellular component gene ontology or in the subcellular location definition in the UniProt database were included in the final list.
Slides were counterstained with hematoxylin. Colon or breast carcinoma tumor samples were used as controls for active b-catenin staining. Highresolution digital scans were acquired (Aperio CS2 scanner) using Image Scope software (Leica Biosystems). Images were also acquired using an upright microscope (Olympus BX43) with a color camera (Olympus SC100). A board-certified pathologist evaluated H and E, Pax8, pY397 FAK, or Ki67 stained images of patient tumor samples in a blinded manner. Quantification was performed using Aperio Image Analysis software (v12. 3. 0. 5056) using the positive pixel count (v9) algorithm. Pax8positive regions were identified and then these regions were manually-transposed onto images from FAK pY397-stained serial section slides. Average intensity (I-Avg) values were obtained and percent FAK pY397 was calculated.
Frozen tumors were thin sectioned (7 mm) using a cryostat (Leica), mounted onto glass slides, fixed with acetone (or with 4% paraformaldehyde) for 10 min, permeabilized (PBS with 0. 1% Triton) for 1 min, and blocked (PBS with 8% goat serum) for 2 hr at room temperature. Sections were incubated in anti-ALDH1A1 (1:100) or anti-pY397 FAK (1:100) in PBS with 2% goat serum overnight. Antibody binding was detected with goat anti-rabbit conjugated with Alexa Fluor-488. Cell nuclei were visualized using Hoechst 33342 stain (Thermo). Images were sequentially captured at 20X magnification (UPLFL objective, 1. 3 NA; Olympus) using a monochrome charge-coupled camera (ORCA ER; Hamamatsu), an inverted microscope (IX81; Olympus), and Slidebook software (Intelligent Imaging). Images were pseudo-colored, overlaid, merged using Photoshop (Adobe), and quantified using Image J.
Acknowledgements We thank our colleagues for helpful discussion and comments. We thank  R Winters  (FCCC BRF manager) and  D Flieder  ( FCCC Pathology ) for identifying, reviewing the cases, and for procuring patient tumor samples used in this study. L Bean ,  K Anderson ,  K Taylor , and  A Barrie  are fellows of the  UCSD Reproductive Medicine Gynecologic Oncology program. K Taylor  and  A Barrie  are  Gaines Gynecologic Oncology Fellows. D Connolly thanks The Roberta Dubrow Fund  and  The Main Line Chapter of the Board of Associates  for charitable support. D Schlaepfer ,  M McHale , and  D Stupack  thank  Joan Wyllie  and 9  Girls Ask  for charitable support.
Data availability The exome sequencing FASTA files have been deposited to the NCBI Sequence Read Archive under accession number SRP194638. The RNA-Sequencing FASTQ files have been deposited to the NCBI Gene Expression Omnibus under the accession number GSE129099. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Perez-Riverol et al. , 2019)  partner repository with the dataset identifier PXD013062. The following datasets were generated:.
The global behavior of the smoke is investigated through the analysis of mean experimental temperature fields, smoke dynamics by large scale PIV as well as numerical simulations. For numerical simulations, the Fire Dynamics Simulator (FDS) software is used. The experimental measurements are used to evaluate the validity of FDS in under-ventilated conditions. It is observed that FDS code is able to reproduce both temperature and velocity data of enclosure fires with accuracy depending on the power to volume ratio. A criterion capable to discriminate the ventilation status of confined fire is observed and discussed.
Moreover only one point data is evaluated. For this reason, laser techniques can be used. The smoke velocities can be studied using the PIV technique  [13, 14, 15]. This technique has already been used in the study of fires  [16, 17, 18]  for well-ventilated and underventilated conditions. A natural extension of this technique is to use the PIV to highlight exchanges between hot exhaust fumes and fresh incoming air. These large-scale measurements highlight areas of interest for a better understanding of fires. In order to gain further insights into smoke dynamics in a confined environment, we propose a dual approach based on experimental and numerical data. The scalar quantity (temperature) and the vector quantity (velocity) are evaluated and compared considering both experimental and numerical data.
Conclusion In this study, the flow topology of a large scale confined fire is evaluated. The test cell is representative of a standard room and the power of the burner went up to 1 MW. A dual approach is proposed. Experimental data for the scalar quantity temperature and 2D velocity fields are recorded and analyzed. Simulations using FDS are performed. The results are listed below. First, the analysis of temperature evolution against time shows very good agreement between experimental data and simulation results since wellventilated conditions are respected. For under-ventilated conditions, i. e. power of 800kW, discrepancies exist between the experimental and simulation results. FDS is shown to be inadequate for predicting under-ventilated conditions. Second, large scale PIV data are recorded below the outlet.
Fifth, the velocity profiles of the vertical component along the x-axis are evaluated for both well-ventilated and under-ventilated conditions. For ventilated conditions, it is shown that a well-established structure where fresh air and hot fumes enter and exit the cell exists. However, for under-ventilated conditions there is no significant fresh air inlet and only the air provided by the injection system facilitates combustion. In conclusion, the clear existence of well-ventilated and under-ventilated conditions from temperatures and velocity fields is demonstrated. The experimental data is in agreement with the numerical data under well-ventilated conditions. Moreover, we pointed out the criteria based on flow dynamics that might make it easier the determination of ventilation status considering large scale confined fires.
Statistical analysis Cronbach's alpha (α) was used to measure the internal consistency of the skills scaled with values > 0. 8 considered good internal consistency. Mean skill scale and item scores were calculated and compared across groups (faculty, students) using independent samples t tests. A 5% level of significance was used for all tests. Cohen's d was used to measure effect size with 0. 2 considered small, 0. 5 medium and 0. 8 large. Linear regression was used to predict skill scale scores using demographic data (age group, gender, country of origin) as predictors for both faculty and students. R squared was used a measure of goodness of fit of the models. Data was analysed using SPSS Statistics version 22 for Windows.
A smaller σ ψ value corresponds to higher discrimination sensitivity in the task. The PSE is given by the projection at 50% of the response. The two indices, PSE and σ ψ, characterize the participant's performance, and λ accounts for stimulus-independent errors (e. g. , due to participant lapses) and was restricted to small values (0 < λ < 0. 06,  Wichmann and Hill, 2001). This parameter is not informative about the perceptual decision, thus we disregarded it for the subsequent analyses. Psignifit toolbox, implemented in MATLAB software (The Mathworks, Natick, MA, USA) was used to fit the psychometric curves. Differences in the PSE and uncertainty range between the groups, before cosmetic treatment, were evaluated with an unpaired Student's t-test. The impact of cosmetic application upon tactile sensitivity was estimated within each group with a paired Student's t-test.