dataesr/scientific-paragraphs-categorization huggingface dataset:
In this example, we calculate residue centrality scores for each residue and compare them between the conformations. We wrote a script in R and an equivalent one in Python that, starting from a Cytoscape session containing one RIN for each conformer, perform RCA on each RIN. The scripts then gather the Z-scores for each residue in each RIN in a single recap table, allowing for easy comparison. The app also creates a style for each RIN that permits a visual comparison (Figure  1 ).
We wrote a script that, starting from a Cytoscape session containing these RINs, calculates residue centralities and predicts flexibilities for each structure. As in the use case 1, the results of centralities are gathered in a common table. In addition, the S² flexibility scores are also gathered in a recap table and in a single plot to permit easy comparison (Figure  2 ). These tables and plot allow for comparison between the centralities, to see which of the mutations have an impact on which centralities compared to the wild type, and to see the impact of each mutation on the backbone flexibility of the TPR domain.
The RINspector 1. 1. 0 API can be used in conjunction with other apps, especially with the structureViz2 app, which connects Chimera to Cytoscape, because it permits to open a structure in Chimera and work with it. This connection allows for the easy generation of RINs and the possibility of synchronization of colors between network and structure, which is a welcome feature for the generation of images. In this respect we would also like to point out the RINalyzer app, which enables the user to layout the RIN in function of coordinates of residues displayed in Chimera. In the use case 2, DynaMine results were exploited to build a chart for comparison of flexibilities between structures. For such analyses the proposed script is particularly interesting, as computation resources are not a limiting factor.
In most cases, however, only a few mutations are really of interest and the result panel designed for DynaMine in RINspector may be more convenient to visualize the effect of these mutations directly and select one or several for protein design. The user should also be aware that the sequence sent to the DynaMine server is built from the RIN/structure, which means that missing residues will simply be skipped in the DynaMine flexibility graph. Other use case examples might be the effect of one or several ligands in a structure by generating RINs with or without these ligands, the comparison of centralities of different structures of the same complexes or the comparison of the flexibility of structures of several orthologs. Our scripts constitute starting points to perform such analyses with recap tables and charts as output.
Summary The RINspector 1. 1. 0 API permits scripting of centrality analyses and flexibility predictions. The automation provided bridges between Cytoscape and other languages, such as R or Python, to prepare data, run batch analyses and treat output data. It enlarges the possibilities of treatments that were initially given by the app in particular to compare centralities and flexibilities between multiple structures. We provide R and Python scripts that illustrate two use cases and that are to be seen as starting scripts for more elaborate analyses. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. -There is a tutorial provided for the initial version of RINspector. A similar tutorial should also be prepared for the use cases presented in this paper.
Turkan Haliloglu -Relevant links to the software, the use case folders and scripts should be cited within the text. In the current version, it is hard to link the source code files with the options/conditions stated in the paper. Overall, RINspector app stands a useful resource that would corroborate structural and functional studies in the community. We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. -There is a tutorial provided for the initial version of RINspector. A similar tutorial should also be prepared for the use cases presented in this paper. In this way, the users can exactly to a short install manual for Cytoscape, cyREST and RINspector, plus any other necessary steps that must be performed before the example scripts can be run.
the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Partly Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Partly Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Modeling and simulation of macromolecules. Protein dynamics and allostery.
Analysis of cancer gene microarray datasets reveals consistent upregulation of DNAJC12, FABP5, and BOP1 in PCa tissues After confirming that transcript and protein expression of selected genes reflected the upregulation observed in the RNA-seq analysis, we sought to examine the expression of these genes in human PCa tissues. Transcript expression of the selected genes in PCa tissues, compared to normal prostate tissue, was analyzed using 16 PCa gene expression microarray datasets from the Oncomine database. All 16 datasets had data for FABP5, whereas data for DPP4 and TSPAN8 were available in 15 datasets, and data for BOP1, DNAJC12 and NES were available in 14, 10 and 8 datasets, respectively. Of the selected genes, DNAJC12, FABP5, and BOP1 were the most consistently upregulated in prostate tumors compared to normal prostate tissues in the dataset collection (Figure  5A -5C), with significant upregulation of DNAJC12 in 6 of the 14 datasets (Figure  5A ), FABP5 in 14 of the 16 data sets (Figure  5B ), and BOP1 in 7 of the 14 datasets (Figure  5C ).
DPP4 and TSPAN8 transcripts were significantly upregulated only in 4 of the 14 datasets (Figure  5D-5E ). Interestingly, significant upregulation of NES transcript was detected only in 1 of the 8 datasets (Figure  5F ). The magnitude of the foldincrease observed for the individual genes was modest, with only FABP5 showing over 2-fold increase in multiple datasets. However, the P values were <0. 01 for most of the DNAJC12, FABP5, and BOP1 datasets, indicating that upregulation of these transcripts is highly significant in PCa tissues compared to normal tissues. On the other hand, NES transcripts were significantly downregulated in 4 of the 8 datasets, whereas DPP4 and TSPAN8 transcripts displayed significant downregulation in 1 and 2 out of 15 datasets, respectively.
g. , PC3-bone metastasis vs. DU145-brain metastasis). Differentially expressed genes within this pool of overlap genes could potentially be exploited as therapeutic targets in heterogeneous metastatic prostate tumors that have acquired taxane resistance. GSEA of our RNA-seq data revealed several top ranked genes from the overlap dataset that an exhaustive PubMed literature review determined as being associated with tumor aggressiveness, chemoresistance, or CSC phenotype. Of note, GSEA yielded only one significant pathway enriched in the DTXresistant cell lines compared to sensitive cells that yielded 8 genes positive for core enrichment. Of these, 4 genes (ENPP1, CYC1, NDUFAB1, and MYC) are associated with stem cell maintenance, acquisition or reprogramming  [24] [25] [26] [27] [28] [29] , suggesting that in PC3 and DU145, DTX-resistance may be driven and maintained by the acquisition of CSClike characteristics.
NES, a cytoskeletal intermediate filament protein, has been associated with increased migration in PCa cells  [70] , and increased NES expression correlated with high tumor grade, invasive phenotype, and predictor of poor response to therapy  [71]. Consistent with our observation that NES is robustly upregulated in DTX-resistant mCRPC cells with CSC-like characteristics, NES expression was previously reported in PCa tumorspheres that showed increased chemoresistance to paclitaxel  [72] , and was associated with a mesenchymal phenotype  [73]. Oncomine data analysis comparing transcript expression of DPP4, TSPAN8, and NES between prostate tumor tissues and normal tissues revealed inconsistent upregulation of these genes in the different datasets. An explanation for this could be that the prostate tumors used to generate most of these gene expression datasets were not derived from advanced or chemoresistant disease.
Alternatively, gene expression changes found in DTXresistant cells occur in only a small subset of cells, most likely those with stemness properties. Since tumors contain varying proportions of cells with and without stemness properties, it will be difficult to consistently detect global gene expression changes in CSCs present in PCa tissues since they comprise a minority of the population. A limitation of the Oncomine database is the assessment of gene expression in normal vs. PCa tissues without extensive clinical data (type of treatment, tumor stage, etc. ) for several of the datasets. Therefore, to further validate the expression of selected genes of interest in clinically relevant tissues, it will be important in future studies to obtain mCRPC biospecimens with annotated clinical data from patients with and without taxane treatment, and that responded to or failed the treatment.
Short tandem repeat (STR) profiling is a recommended and validated method for authentication of human cell lines and tissues  [75]. The importance of cell line authentication is highlighted by the NIH initiative for rigor and reproducibility in scientific research  [76] , and is particularly important for scientific studies such as the present one that use established cancer cell lines for preclinical mechanistic studies. We utilized the STR service provided by ATCC (Cat# ATCC 135-XV) to authenticate the PC3 and DU145 cell lines used in this study. Both cell lines matched their respective database profiles. The DTXresistant PC3-DR and DU145-DR cell lines were derived from these validated PC3 and DU145 parental cell lines.
RNA-seq data analysis For mRNA-seq data visualization and analysis, we utilized pipelines that integrated the QC (FastQC, ShortRead), trimming process (trimmomatic), alignment (Tophat2), reads quantification (cufflinks), and differentially expressed gene (DEG) analysis (cuffdiff) as described previously  [77]. Briefly, the RNA-seq raw fastq data were first trimmed using Trimmomatic (V0. 35). The trimmed reads were aligned to the human reference genome (NCBI GRCh38) with TopHat V2. 1. 1 with default parameter settings. The aligned bam files were then processed using Cufflinks V2. 2. 1 for gene quantification. Reads were then mapped to ERCC transcripts and quantified using TopHat V2. 1. 1 and Cufflinks V2. 1. 1 with default parameter settings. Genes with FPKM ≥ 1 in all samples were used for DEG analysis.
Differentially expressed genes (DEGs) were identified by Cuffdiff with FDR > 0. 05, and fold change (FC) > 2. Hierarchical clustering heat map and PCA of global genes for all cell lines were performed with "R" program (http://cran. r-project. org/) [78] and Partek Genomics Suite 6. 6, respectively. GSEA (v3. 0, Broad Institute)  [79, 80] , was performed to compare parental DTX-sensitive PC3 and DU145 with DTX-resistant DU145-DR and PC3-DR. Gene sets were obtained from published gene signatures in the Molecular Signatures Database v1. 0 (MSigDB). Analysis was run with 1,000 permutations and a classic statistic. Normalized enrichment score and p-values were measured to find enrichments with statistical significance (p<0. 05).
The mouse anti-TSPAN8 primary antibody was from Celine Greco and Claude Boucheix (1:2000)  [81]. Following several washes with TBS-T, membranes were incubated with the appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies (antimouse IgG and anti-rabbit IgG, Cell Signaling Cat # 7076 and 7074, respectively; goat anti rat, Santa Cruz Cat# sc-2032). HRP-β-actin was utilized as a loading control (Cell Signaling Cat # 5125). After 2-hour incubation with secondary antibodies, the membranes were washed several times with TBS-T, and protein bands were detected by enhanced chemiluminescence (Thermo Fisher Scientific, Cat# 34580). Bands were quantified using Image J software (National Institutes of Health) and normalized to β-actin control. Samples were analyzed in at least 3 independent experiments using at least 3 biological replicates.
Bioinformatics analysis of oncomine cancer gene microarray database For analysis of mRNA expression of genes of interest in PCa and normal prostate tissues, we selected 16 datasets from the Oncomine database (Compendia Biosciences; Ann Arbor, MI; www. oncomine. org). These datasets, derived from gene microarray analyses of PCa and normal prostate tissues, provide fold-change data for gene expression with P values calculated by Oncomine using Student's t-tests. The Grasso dataset included 35 castration-resistant metastatic PCa, 59 localized PCa, and 28 benign prostate tissue specimens while the Varambally dataset included 6 hormone-refractory metastatic PCa samples in addition to 7 localized PCa, and 6 normal prostate samples. This allowed us to compare the transcript expression between these 3 categories of tissues in our genes of interest.
Cells were washed and resuspended in annexin-V binding buffer (50 mM HEPES, 700 mM NaCl, 12. 5 mM CaCl 2 ; pH 7. 4) and analyzed immediately on a MACSQuant Analyzer 10 equipped with violet, blue, and red lasers (Miltenyi Biotec). Post-acquisition data analysis was performed using FlowJo version 10. 08. 1 (BD). ALDH activity was detected using Aldefluor assay kit purchased from Stem Cell Technologies (Cat# 01700) and performed according to manufacturer's instructions. Briefly, 2D and 3D cells were prepared and harvested as described above. 400,000 cells were resuspended in 200 μl of aldefluor buffer and 2 μl of aldefluor reagent to form the "test" sample. 200 μl of that text mix were then immediately transferred to another microcentrifuge tube containing 2 μl of DEAB reagent to inactivate the aldefluor reagent and become the "control" sample.
Both the control and test sample were incubated for 45 minutes at 37°C. Samples were then centrifuged and resuspended in aldefluor buffer to be analyzed immediately on the MACSQuant Analyzer. Post-acquisition data analysis was performed using FlowJo version 10. 08. 1 (BD) with gates being drawn on the control DEAB+ samples for each cell line 2D and 3D. Initial gates for intact cells using FSC-A/SSC-A light scatter and doublet discrimination using FSC-H/ FSC-A profiles. Single-stained samples were used to define compensation matrices. Following compensation, dead cells were excluded based on annexin-V positivity and only live cells were assessed for putative CSC marker expression. Gate placements were defined using Fluorescence-Minus-One (FMO) controls using SSC-A versus marker of interest (see Supplementary Figure  5  for gating strategy and Supplementary Table  4  for staining strategy for FMO detection).
ACKNOWLEDGMENTS We thank the  Loma Linda University Center for Genomics and Dr. Wang's group  for the RNA-seq library constructions, sequencing and bioinformatics support. We also thank the members of the  CHDMM and Casiano Laboratory  who contributed to this work by providing technical or conceptual advice. Disparities and Molecular Medicine. In addition, JU and EC were supported by Grant  681157-Collaboration  and  Translation from Loma Linda University School of Medicine. This work was also partially supported by grant  S10OD019960  (CW) from the  NIH Office of the Director , the  Ardmore Institute of Health  grant  2150141  (CW) and  Dr. Charles A. Sims ' gift.
The range expansion of M. leidyi was reconstructed based on a comprehensive occurrence database, with 12,400 records throughout western Eurasia covering the past 35 years. A dramatic range contraction and subsequent recolonization observed during the early 2010s allowed for disentanglement of the effect of winter temperature on the distribution of M. leidyi in Northern Europe. We hypothesize that cold winter conditions caused a range contraction and that two cold winters in a row exacerbated the impact. We used this natural experiment as a proof of concept for the role of ocean currents in secondary spread dynamics and applied population genetic analyses to verify source regions for recolonization events. The identification of M. leidyi source areas and their connectivity are important to (a) predict secondary spread through ocean currents, (b) identify potential barriers to its current spread, (c) identify high-risk areas for further invasions and (d) assess possible mitigation strategies for non-native marine species in general.
or 'ctenophore*' or 'comb*', filtered with 'invasive*' or 'non-native*' and applying forward and backward citation search. Additionally, the bibliographies of all co-authors were searched (Supporting Information Appendix S1: Reference list). Data were extracted from the publications using Web Plot Digitizer (https://automeris. io/WebPlotDigitizer/) or were supplied directly by the authors. A detailed methodology section for all individual data points is given in Supporting Information Appendix S2, Table  S1. All confirmed data from 1982 to 2016 are visualized with regions of presence and absence indicated by colour code (Figure  1 ). Consecutive confirmed presence observations are used as the border for occurrence regions (ArcGIS, v. 10. 1). For Northern Europe, we have presence/absence data for a series of stations/investigation areas that have been covered at the same time of the year using   S1  for reference comparable methodologies.
The vectorial mean value of individually observed current vectors and the arithmetic mean velocity are obtained by averaging the speeds. This estimate is a measure of the variability of the general current patterns; hence, it is a proxy for connectivity between regions. High stabilities (values close to one) indicate strong evidence for persistent circulation patterns and, consequently, strong connectivity between neighbouring areas; vice versa, low stability values (<. 3) indicate high variability of currents; hence, low connectivity between areas (Figure  2 ). For display purposes, current velocities and directions were averaged with 28 longitudinal and 18 latitudinal resolution for all regions except the highly dynamic Skagerrak (resolution: 18 longitudinal; 18 latitudinal). Additionally, we calculated temperature anomalies and average current velocities (in metres per second) for the coldest winter months in Northern Europe (January-March) based on data for the years 2007-2015.
We did so because cold winter conditions have been shown to lead to population die-offs for southern invasive subpopulations  (Shiganova et al. , 2001) , and overwintering adults are responsible for establishing the next generation  (Costello, Sullivan, Gifford, Van Keuren, & Sullivan, 2006). Average winter temperatures for the upper 50 m were obtained from the CMEMS model (see above) for all but the Baltic Sea, for which BSIOM data were used (Supporting Information Appendix S3).
| Statistical analyses of winter temperatures To investigate the effect of winter temperature on the distribution of M. leidyi in northern Europe, datasets from 13 selected monitoring stations/regional sampling programmes covering the entire distributional range (English Channel, 49. 5 8N to Bergen, 60 8N and the central Baltic Sea 15. 8 8E) were compiled and included in all statistical analyses (Supporting Information Appendix 3, Figure  S1 ). These data consist of consecutive presence/absence observations during the M. leidyi highabundance season (summer/autumn) for the years 2007-2015 and represent all comprehensive datasets available in Northern Europe. For statistical analyses, this information has been condensed to one value per station per year, representing either the presence (one) or the absence (zero) of M.
2 | M ETH OD S 2. 1 | Geo-referenced distribution data To reconstruct the distributional range and timing of the first occurrence of the non-native comb jelly M. leidyi, we compiled a database using 12,400 geo-referenced unpublished (60%) and published (40%) observations from western Eurasia (see Supporting Information Appendices S1 and S2). Unpublished data were collected as presence/ absence records by the authors and their institutes as part of dedicated gelatinous zooplankton or ichthyoplankton surveys, zooplankton investigations, and diving observations by citizen science projects with photographic documentation or other confirmed sightings evaluated by experts using morphology and/or DNA analyses. Published data were based on a comprehensive literature review using the Web of Science and Google Scholar search engines for the search terms 'Mnemiopsis*'.
ACKNOWLEDGMENTS We thank the  Global Runoff Data Centre, 56068 Koblenz, Germany  for providing river polylines; the  Copernicus Marine Environment Monitoring Service  for ocean model data;  Dr C. Clemmesen-Bockelmann ,  J. -P. Herrmann ,  Dr A. Szuklarek-Pawełczyk  and  Dr Felix Mittermayer  for Mnemiopsis data from the Baltic;  Dr R. Vorberg  for published North Sea Mnemiopsis records;  Dr A. Nowaczyk  and  C. Verwimp  for dried Mnemiopsis ( Bay of Biscay, France; Belgium );  Dr H. -U. Riisgaard  for discussions;  Dr M. Lilley  and  Dr M. Haraldsson  for published Mnemiopsis data;  Adriaan Gmelig Meyling  and  Niels Schrieken  ( ANEMOON-Foundation ) for extracting data and volunteers for their records. This work received funding from the  Danish Council for Independent Research  and the  European Commission  ( Marie-Curie program ) with the DFF-MOBILEX mobility grant number:  DFF-1325-00102B  (C.
Material and methods Ten males and two females were collected at the outlet of a thermal spring into Lake Volvi, Greece, by dip net and transferred alive to the laboratory. The examined specimens are deposited as voucher samples in the collection of the Laboratory of Fish Genetics, IAPG, CAS, Liběchov, under Accession Code CoS/97. Valid Animal Use Protocol was in force during study in IAPG (No. CZ 02386). Standard procedures for chromosome preparation followed  Ráb and Roth (1988). Silver (Ag-) staining and Chromomycin A 3 (CMA 3 ) fluorescence banding, for detection of NORs, followed  Howell and Black (1980)  and  Sola et al. (1992) , respectively. The sequence of stainings followed protocol of  Rábová et al. (2016). Fluorescence in situ hybridization (FISH) with a mouse rDNA biotinylated probe (clone I-19, a 4.
Acknowledgements This study was supported by the project  EXCELLENCE CZ. 02. 1. 01/0. 0/0. 0/15_003/0000460  OP RDE and RVO: 67985904 (E. H. , P. R. , Z. M. ,),  Czech Science Foundation   17-09807S  (L. Ch. ) and (partly) by a  Research network of the French Ministère de l´Education Nationale  (C. O. -C. ). The authors thank  P. S. Economidis ,  T. T. Nalbant  for valuable help, advices and stimulating discussions about diversity of Bicanestrinia loaches and  M. Rábová ,  IAPG, Liběchov , for laboratory assistance. This study is the part of a series Cytogenetics of bisexual species and their asexual hybrid clones in European spined loaches, genus  Cobitis (Cobitidae, Cypriniformes ).