dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis Chromatin RNA-seq and data analysis. Chromatin RNA was extracted from one confluent 15 cm dish of pre-plated, feeder free mESCs as described in detail by  (Nesterova et al. , 2019) , quantified and 1µg of RNA used for library preparation using the Illumina TruSeq stranded total RNA kit (RS-122-2301) and KAPA Library Quantification DNA standards (Kapa biosystems, KK4903). Two biological replicas of each experiment were carried out and 2X 81 paired end sequencing was performed using Illumina NextSeq500  (FC-404-2002). Chromatin RNAseq data mapping and scripts for analysis were detailed previously  (Nesterova et al. , 2019) , with silencing quantified using the difference in allelic ratios between uninduced and induced samples, such that ( ) Dox NoDox Xi Xi Gene silencing z Xi Xa Xi Xa     = -     + +     The silencing degree was compared to our previously published dataset  (Nesterova et al.
Data availability Underlying data High-throughput sequencing data (chrRNA-seq and m6Aseq) on Gene Expression Omnibus (GEO), Accession number GSE142271: https://identifiers. org/geo:GSE142271 Mass spectrometry proteomics data on PRIDE, Accession number PXD017348: https://identifiers. org/pride. project:PXD017348 Mendeley Data: The role of the Xist 5' m6A region and RBM15 in X chromosome inactivation. http://doi. org/10. 17632/ 56f2m8nd9h. 3  (Coker et al. , 2020)  This project contains the following underlying data: -Figure  2 : Raw RNA-FISH images and scoring data -Figure  4 : Raw western blot and gel data.
Acknowledgements I thank  Željko Bošković ,  Marjo van Koppen ,  Jessica Rett , and  Yael Sharvit  for their valuable comments and feedback that helped to improve this paper. Earlier versions of this work were presented at CUSP 7 (UCLA), and in seminars at  UCLA and UConn. I thank the audiences for their input and questions. I am grateful to the editor and three anonymous reviewers from Glossa for their very useful and detailed feedback and suggestions. All remaining errors are mine.
Sampling effort curves To calibrate the completeness of the inventory of D. kuriphilus parasitoid communities, an evaluation of the sampling of the 15 studied areas was performed with sampling effort curves (SECs). SECs can estimate the degree of sampling of the different D. kuriphilus communities by sampling area and locality, to observe any possible bias that exists in the description of the community according to the data obtained, and to indicate the strength of the analyses. To perform this evaluation, the software EstimateS  (Colwell, 2018)  was used to calculate the Chao2 estimator of species incidences and CurveExpert  (Hyams, 2007)  to obtain the Clench adjustment curve  (Jiménez-Valverde and Hortal, 2003). The parameters of the curve slope (slope), the percent of registered species (%rs) and the percent of realized effort (%re) were calculated using the a and b parameters of the Clench curve.
Analyses of communities To describe biodiversity patterns in D. kuriphilus communities, three different parameters were analysed: (1) species richness (sS), (2) parasitoid emergence dates, and (3) species nestedness and structure. To calculate sS (1), the identified individuals from each locality were counted by species, as shown in table 2. The numbers of the Hill series or true diversity  (Jost, 2006)  of predicted sS (q 0 ), effective sS (q 1 ), dominant sS (q 2 ) and accessory sS (q 0 -q 1 ) were calculated using R Studio (RStudio Team, 2019) with the Spade R library  (Chao et al. , 2016). The ChaoJost Hill series of sS indicates a predicted sS according to the data for each biological community, showing the theoretical number of possible species that could constitute each community (table  5 ).
These analyses were performed with the Nestedness Temperature Calculator v. 2. 0 software  (Ulrich, 2006; Ulrich et al. , 2009)  using 10,000 random runs. Additionally, system temperatures were compared with temperatures obtained at random, while Monte Carlo-derived probabilities were generated to confirm the nestedness of communities, and the ordering of the species in the locality richness matrix was obtained together with the state occupancy percent (fig. 3 ). Differences between the 2017-2018 and 2018-2019 were not calculated due to the difference in sampling numbers between these periods. The probability of state occupancy probability for each species was also calculated by the program. Nested distributions imply that the most ubiquitous species will virtually always appear on the most hospitable locality, whereas the most marginal species will appear on the least hospitable locality; the other species vary in a specifiable manner between these two extremes, permitting us to calculate the probability of their occupancy in the other localities  (Atmar and Patterson, 1993).
Since these two species (T. notatus and T. sinensis) emerge from the galls at the same time and given their morphological similarity, much care is necessary to avoid confusing them. From the sampling sites in Spain (table 2), 23 individual T. notatus (10 males and 13 females) were identified from three sites in the Málaga region: El Juanar, Júzcar and Puerto Ojén. One female of T. notatus was also found in Riós in the Mediterranean Region of Galicia. In addition, one male and four females were reared from galls collected at one of our sampling sites in Catalonia (Sant Hilari). In all cases, T. sinensis was also reared from the same D. kuriphilus gall samples. Our data indicate similar establishment rates for the two species in at least one sampling site in the Málaga region (Puerto Ojén). At this site, we reared 18 T.
Acknowledgements. This study was financed by an  Encomienda de Gestión  from  MAPAMA  to  Agencia Estatal CSIC ,  16MNES003  and by research project from  MEC ,  AGL2016-76262-R (AEI/FEDER ,  UE)  awarded to JLNA. We thank  María J. Lombardero  and  Juan R. Boyero  for their help and collaboration with this project. Special thanks to  Oscar N. Gavira  and  M. Pilar Rodríguez-Rojo  for assistance in sampling campaigns and field work.
Purpose -The purpose of this study is to investigate through an empirical research the factors that would attract visitors to heritage and cultural sites as museums applied on the Grand Egyptian Museum (GEM). The paper aims to study the impact of the proposed attraction features learning/knowledge, museum facilities, fun/entertainment and socializing and accessibility/location on pre-visit destination image. Design/methodology/approach -The author started by a thorough literature review to arrive to the suggested conceptual model, which is tested by adopting a quantitative approach where data were collected using a self-administered questionnaire from a convenient sample of 300 respondents with 90% response rate and used partial least squaresstructure equation modelling using Smart PLS v. 3.
Data collection process. The questionnaire was pre-tested before distributing the final form to the respondents. To pretest the data collection tool the researcher conducted 24 questionnaires in Arabic and English languages and amended some statements that the test respondents found slightly difficult to understand. Pilot testing is a small-scale trial, where a few examinees take the test and comment on any problems with the test instructions, instances where items are not clear and formatting and other typographical errors and/or issues that exist  (Dikko, 2016). The final form of the questionnaire was distributed to the individuals who are aware of the GEM. The author collected data from two Governorates of Egypt, which are Giza and Cairo. The author approached places, which included clusters of people as clubs, malls and cafes near the old Egyptian museum or near the pyramids, which is the location of the GEM.
A B FIG. 1. dN/dS is associated with vulnerability. dN/dS was computed using either (A) mapnh or (B) PAML. Regression lines, P values, and R 2 of the phylogenetically generalized least squares regressions are shown on each panel, respectively, for all terminal branches of more than 10,000 synonymous substitutions. The shaded gray represents the 95% CIs. x and y axes have been transformed in order to account for the phylogenetic relatedness between variables (script available in supplementary material S6, Supplementary Material online). Vulnerability, Life History Traits and Mutations. doi:10. 1093/molbev/msaa067 MBE Supplementary Material online), body size (P > 0. 05, R 2 ¼ 4. 85 Â 10 À3 ), or longevity (P > 0. 05, R 2 ¼ 0. 01). We thus hypothesize that if latitude is associated to population size, it may be by other mechanisms than an increase of body size and longevity.
Molecular Data and Phylogeny We used the alignments and the phylogenetic tree provided by the authors of  Malmstrøm et al. (2017). The authors aligned 1938 exons, from 120 to 1764 bp (supplementary material S10, Supplementary Material online), in 76 fish species using a multi-step blast procedure with 33,737 annotated zebrafish genes (from the Ensembl release 78;  Cunningham et al. 2015). All alignments provided by  Malmstrøm et al. are  available in the zenodo data set repository (https://zenodo. org/record/3624895) and correspond to the less strictly filtered data set before removal of third codon position  (Malmstrøm et al. 2017 ). The phylogenetic tree was constructed with RAxML v. 8. 1. 17 (Stamatakis 2014) and BEAST v. 2. 2 (Bouckaert et al. 2014 ) using 567 exons from 111 genes, with a total alignment length of 71,418 bp and 17 fossil calibrations (see  Malmstrøm et al.
Data Body length, latitude, and vulnerability data were obtained for all 76 species in Fishbase (http://www. fishbase. org/, last accessed April 2018, Froese and Pauly 2018). Longevity was obtained for 33 species. The Fishbase vulnerability index is calculated from a method averaging several traits related to the rate of reproduction, such as body size and generation time  (Cheung et al. 2005).
Molecular Evolution and dN/dS We estimated the number of synonymous and nonsynonymous substitutions in the terminal branches of the 76 teleost species from the alignment of 1938 concatenated genes (378,663 bp for the concatenated alignment) using the probabilistic substitution mapping implemented in the software mapnh  (Romiguier et al. 2012 , https://github. com/BioPP/ testnh, YN98 codon model) after a first run of the software bppml for estimating branch lengths and parameter values under an homogeneous YN98 codon model  (Nielsen and Yang 1998). Examples of the bppml and mapnh control files are available on the zenodo data set depository (https://zenodo. org/record/3624895). The mapnh analysis estimates a probabilistic count of synonymous and nonsynonymous substitutions. We used the ratio of the sum of nonsynonymous substitutions and the sum of synonymous substitutions then divided it by the ratio of total nonsynonymous sites and total synonymous sites (computed via the script pNpSdNdS.
Acknowledgments We thank  Konstantin Popadin  and two anonymous reviewers for insightful comments. We also thank  Camille Roux  and  Etienne Loire  for providing the code pNpSdNdS. pyand for useful discussions and advices, and finally  Tom Booker ,  Xuhua Xia ,  Matthew Hahn  for advices on the analyses. J. Rol. received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under the  Marie Skłodowska-Curie  (Grant No. 785910 ) and the  Banting Postdoctoral Fellowship  (Grant No. 151042 ).
……………………………………………………………………………………………………. Introduction:- Local Area Network (LAN) is built with the help of network switches which by default creates a single flat network with large broadcast domain. The increase in the number of devices on LAN becomes paramount as we populate the network with more switches and workstations. Since most workstations tend to be loaded with existing operating system, it results in unavoidable broadcasts being sent occasionally on the network. Unfortunately, each host on such network cannot escape from the effects generated by such uncontrollable broadcast which decreases network performance. Security is never guaranteed in the above network infrastructure since all users are able to see all devices on local area network. In the case of Ebonyi University network having critical file servers, organizational databases and other confidential information, this would mean that everyone would have network access to these resources and naturally, they are prone to different attacks.
VLAN: A VLAN is a logical grouping of network users and resources connected to administratively defined ports on a switch. When you create VLANs, you're given the ability to create smaller broadcast domains within a layer 2 switched internetwork by assigning different ports on the switch to service different sub-networks. A VLAN is treated as its own subnet or broadcast domain, meaning that any frames broadcast from Ebonyi State University Database Admin can only be switched between the ports logically grouped within the same Admin VLAN thereby restricting access from any other network groups within the University. By default, hosts in a specific VLAN can't communicate with hosts that are members of another VLAN. This concept of grouping hosts with a common set of requirements regardless of their physical location by VLAN can greatly simplify network design and increase performance.
Dynamic VLANs: Dynamic VLANs uses software for assigning the VLANs automatically, and it is based on the hardware address (MAC), protocols and applications. For example, In a centralized VLAN management application software, If a MAC address has entered into it, If you are attaching it to an unassigned switch port, The VLAN management database will be searching the hardware address regarding to the MAC address, and It will assign and configure that switch port to the correct VLAN. At initial level, it is very tough to setup a database.
, 2013; Musiani et al. , 2019; Ong et al. , 2004)  to reduce the false discovery rate (FDR) associated to our methylpeptide dataset. In particular, we compared our dataset with a repository of hmSILAC-labeled methyl sites that was generated using 127 raw files from multiple methyl-proteomics experiments from three distinct human cancer cell lines  (Massignani et al. , 2019). The majority (400/441) of peptides composing our dynamic methyl proteome were hmSILAC validated and then used for subsequent analysis (Figure  1B ). These 400 methyl peptides belonged to 136 proteins and contained 296 methyl sites in total, corresponding to 238 mono-methylation and 115 dimethylation events (Figure  1C ; Table  S1 ). The analysis of the six paired CDDP/untreated (UT) experiments allowed defining statistically significant changes in R-methylation induced by the drug (Figure  1D ), with 59 and 18 methyl peptides being significantly down-and upregulated, respectively (Figure  1E ).
From the label-free quantification of proteins in 3 coIP replicates, we identified 129 proteins significantly co-enriched with PRMT1, whose specificity was verified by repeating the coIP in the presence of a large excess of a soluble peptide competing with the bait for the binding to the antibody, followed by the assessment of the selective eviction of the bait and the relative interactors (Figure  3B ; Table  S3 ). By intersecting these two interactomic datasets, we identified 49 proteins that appear specifically co-enriched with PRMT1 and represent the nuclear PRMT1 interaction network (Figure  3C ; Table  S3 ). This is characterized by the overrepresentation of various RBPs previously known as direct PRMT1 targets, such as Sam68  (Côteet al. , 2003)  and hnRNPA1  (Wada et al. , 2002) , and whose methylation was indeed found to be dynamically regulated in the triple-SILAC methyl-proteomics experiment (Table  S3 ); these results are in line with previous evidence of the preferential affinity of PRMT1 toward its own enzymatic substrates  (Rust et al.
In light of these data and of published evidence of PRMT1 phosphorylation at seven potential serine/threonine (S/T) sites  (Hornbeck et al. , 2015) , we tested whether DNA-PK can phosphorylate PRMT1. We measured PRMT1 phosphorylation by WB with a pan-phospho-S/T antibody upon protein enrichment by IP, using as input the chromatin fraction from cells treated with CDDP, which were either pre-treated or not with NU7026. The phosphorylation of chromatin-bound PRMT1 in CDDPtreated cells was reduced when the pre-treatment with NU7026 was performed  (Figure 3G, arrow). Notably, treatment of the chromatin fraction from CDDP-stimulated cells with l phosphatase led to a further decrease of the phosphorylation signal of the IPed PRMT1, which confirmed that the signal detected with the pan-phospho-S/T antibody is a true phosphorylation (Figure  3G ).
Cells were pre-treated for 24 h with either MS094 or MS023 and then treated for 2 h with CDDP before drug washout. Cells were grown for 6 days and then stained with Crystal violet and quantified using ImageJ. Data represent mean ± SD from 4 biological replicates (n = 4). Statistical significances were determined applying the unpaired 2-tailed t test. *p < 0. 05. (F) Apoptosis assay after 72 h of CDDP (20 mM) exposure of control and PRMT1 knockdown SK-OV-3 cells (shPRMT1#1, #3) analyzed by flow cytometry. Data represent mean ± SD from 3 biological replicates (n = 3). Statistical significances were determined using ANOVA two-factor test; *p < 0. 05. chemotherapeutic agents to sensitize cancer cells and reduce chemotherapy resistance and eventually tumor relapse.
Data analysis of the PRMT1 interactome Protein interactors from the PRMT1 co-IP and PRMT1 X-ChroP were identified and quantified using MaxQuant software v. 1. 5. 2. 8. using the Andromeda search engine  (Cox and Mann, 2008; Cox et al. , 2011). In MaxQuant, the estimated false discovery rate (FDR) of all peptide identifications was set to a maximum of 1%. The main search was performed with a mass tolerance of 6 ppm. Enzyme specificity was set to Trypsin/P. A maximum of 3 missed cleavages was permitted, and the minimum peptide length was fixed at 7 amino acids. Carbamidomethylation of Cysteine was set as a fixed modification. The 2016 version of the Uniprot sequence was used for peptide identification. Proteins identified both in the PRMT1 co-IP / X-ChroP experiments and in the corresponding negative controls (competition by specific blocking peptide and IgG, respectively) were profiled by quantitative label-free analysis, activating the label-free software MaxLFQ  (Cox et al.
, 2014)  as well as the ''match between runs'' feature. The ''protein groups'' MaxQuant output file was analyzed using Perseus  (Tyanova et al. , 2016) , plotting the LFQ values in a volcano plot graph, where the proteins enriched in the PRMT1 immunoprecipitations were compared to the negative controls. p values were calculated by Perseus using a two-tailed t test. For each protein, the variance of the LFQ quantitation in the 3 PRMT1 IPs and in the 3 control IPs was comparable. One missing value out of three replicates in each experimental group was allowed. Threshold setting for differential protein expression were S0 = 0. 4, FDR = 0. 01 for the comparison between PRMT1 co-IP in presence and absence of the blocking peptide. The PRMT1 interaction network was generated with experimentally validated protein-protein interactions from the STRING database  (Snel et al.
, 2000)  and visualized using Cytoscape 3. 0  (Su et al. , 2014). Data analysis of arginine methyl peptides (SILAC and hmSILAC) Acquired raw data were analyzed using the integrated MaxQuant software v1. 6. 2. 10, using the Andromeda search engine  (Cox and Mann, 2008; Cox et al. , 2011). In MaxQuant, the estimated false discovery rate (FDR) of all peptide identifications was set to a maximum of 1%. The main search was performed with a mass tolerance of 6 ppm. Enzyme specificity was set to Trypsin/P. A maximum of 3 missed cleavages was permitted, and the minimum peptide length was fixed at 6 amino acids. Carbamidomethylation of Cysteine was set as a fixed modification. The September 2018 (2018_07) version of the Uniprot sequence database was used for peptide identification. To assign and quantify SILAC methyl peptides, N-terminal acetylation, Methionine oxidation, mono-methyl-K/R, di-methyl-K/R were set as variable modifications.
These distributions were normal. To assign hmSILAC peptide sequences, we defined new modifications in MaxQuant with the mass increment and residue specificities corresponding to heavy mono-methylation (mono-methyl4-K/R) and di-methylation (di-methyl4-K/R). Additionally, we defined new modifications for heavy Methionine (Met4) and oxidized heavy Methionine (OxMet4). To reduce the search complexity, raw data were analyzed twice with the following sets of variable modifications: (1) N-terminal acetylation, Met4, OxMet4, oxidation, mono-methyl-K/R, mono-methyl4-K/R; (2) N-terminal acetylation, Met4, OxMet4, oxidation, di-methyl-K/R, di-methyl4-K/R. Identification of high confidence methyl sites was carried out with the Perl-based pipeline hmSEEKER  (Massignani et al. , 2019). To increase the confidence of our findings, we used hmSEEKER to automatically remove from our data any peptide with Andromeda score less than 25, Andromeda delta score less than 12 and/or carrying a modification with a localization probability less than 0.
75. Heavy and light methyl-peptide pairs were considered true positive when the difference between calculated and expected mass shift was < 2 ppm and their retention time difference was < 30 s. Validation of the methylated peptide identified in the SILAC experiments through the hmSILAC identifications was achieved by using a second bioinformatics tool developed in house (hmLINKER), which compares the sequences of the peptides in the SILAC dataset to those in the hmSILAC dataset. If a match is not immediately found at the sequence level, the peptide is not discarded, but a second round of match-attempt is performed at the individual modification sites level, using a window sequence. Clustering and motif analysis of R-methyl peptides Unsupervised clustering of methyl peptides was based on the distinct patterns of response to CDDP and PRMT1 knockdown (i.
Each field contained at least 50 cells and the quantification was performed using an in-house script which automatically calculates the intensity of the staining in the cytoplasm and nucleus of each cell. p65 was considered nuclear in the cells that display a p65 nuclear mean intensity/cytoplasm mean intensity R 1. 1. RNA-seq analysis RNA-seq was generated from Illumina platform. Data was aligned to human reference genome (GRCh37) using HISAT2  (Kim et al. , 2015). Reference guided alignment was performed with GENCODE annotated transcripts. Differential analysis was performed on the basis of read counts using DESeq2  (Love et al. , 2014). Genes with fold change of 4 and with adjusted P-value less than 0. 05 are regarded as differentially regulated genes. Gene Ontology on differentially regulated genes was performed using DAVID  (Huang da et al.
RNA extraction, RT-PCR and qPCR Total RNA was isolated from cells using Quick-RNA Miniprep kit (Zymo research), according to manufacturer's instructions. One mg of RNA from each sample was reverse-transcribed using ImProm-II Reverse Transcriptase (Promega) and random primers (Thermo Fisher Scientific), according to the manufacturer's protocol. Real-time quantitative PCR reactions were prepared using Fast SYBR TM Green reaction mix (Thermo Fisher Scientific) and performed on an Applied Biosystems 7500 Fast Real-time PCR system. The relative expression level was calculated with the 2 [ÀDDCt] method and expressed as a ''fold change'': data were normalized to housekeeping gene (GAPDH) expression and compared to the untreated control. Primers used for profiling the mRNA expression levels of inflammatory related genes are as follows: IL6 Fwd:5-CCAGAGCTGTGCAGATGAGT-3; Rev:5-ATTTGTGGTTGGGTCA GGGG-3; IL1a Fwd:5-AGGTTCTGAAGAAGAGACGGT À3; Rev:5-GGTGCTGACCTAGGCTTGAT-3; IL1b Fwd: 5-CTCTTCAGCCA ATCTTCATTGC-3; Rev: 5-GAACAAGTCATCCTCATTGCCA-3; IL8 Fwd:5-CTTGGCAGCCTTCCTGATTTCT-3; Rev:5-GTTTTCCT TGGGGTCCAGACAG-3; TNFa Fwd:5-ACTTTGGAGTGATCGGCCC-3; Rev: 5-CATTGGCCAGGAGGGCATT-3.