dataesr/scientific-paragraphs-categorization huggingface dataset:
Human cancers display a restricted set of expression profiles, despite diverse mutational drivers. This has led to the hypothesis that select sets of transcription factors act on similar target genes as an integrated network, buffering a tumor's transcriptional state. Noninvasive papillary urothelial carcinoma (NIPUC) with higher cell cycle activity has higher risk of recurrence and progression. In this paper, we describe a transcriptional network of cell cycle dysregulation in NIPUC, which was delineated using the ARACNe algorithm applied to expression data from a new cohort (n = 81, RNA sequencing), and two previously published cohorts. The transcriptional network comprised 121 transcription factors, including the pluripotency factors SOX2 and SALL4, the sex hormone binding receptors ESR1 and PGR, and multiple homeobox factors.
However, this treatment profoundly impacts urinary function. Management of A transcriptional network of cell cycle dysregulation in noninvasive papillary urothelial carcinoma. Many studies have shown that NIPUC with greater cell cycle dysregulation has higher risk of recurrence and progression to advanced-stage disease (reviewed in  10  ). We thus sought to identify a network of TFs active in cell cycle dysregulation of NIPUC, to better understand the transcriptional changes that promote recurrence and prime tumors to invade. We first generated a transcriptional regulatory network from NIPUC expression data using the ARACNe algorithm, a method that takes a list of curated TFs and identifies target genes likely regulated by each of them, using mutual information between expression of a given TF and potential target genes  [14] [15] [16] [17].
Dual TF regulon analysis identifies two groups of TFs that oppose one another. Transcriptional network analysis is valuable in part because it identifies TFs that act on similar targets, providing insight into redundancy of their activity, a feature relevant to development of drugs and diagnostic tests. For example, in our analysis there was overlap in the targets of FOXM1, SALL4, and E2F7, including targets with a known role in the cell cycle, such as MKI67 and CCNDBP1 (Fig. 3 ). To further investigate similarity in targets genes among TF regulons, we utilized the RTNduals extension of the RTN package  15, 16, 24  to perform dual regulon analysis. This procedure identifies pairs of TF regulons that potentially co-activate or co-repress shared target genes, as well as pairs of TFs that potentially compete, influencing targets in opposite directions  24.
Materials and methods Methods details. Our study was retrospective and utilized three cohorts of NIPUC. These were used to develop the transcriptional network and test its relationship with biological features, such as pathway activity. Cohort 1 consisted of NIPUC on which whole transcriptome RNA sequencing, whole exome DNA sequencing, and immunohistochemistry were newly performed, specifically for this study (n = 81, RNA sequencing; DNA sequencing and immunohistochemistry for EZH2 on 36 and 21 of these, respectively). This cohort was studied with approval from the Penn State College of Medicine Human Subjects Protection Office (Institutional Review Board). Cohort 2 consisted of cases of NIPUC from the UROMOL study, for which RNA sequencing-based expression data are publicly available (n = 397)  12.
De-multiplexed and adapter-trimmed sequencing reads were generated using Illumina bcl2fastq (released version 2. 18. 0. 12) allowing no mismatches in the index read. BBDuk (https:// jgi. doe. gov) was used to trim/filter low quality sequences using "qtrim = lr trimq = 10 maq = 10" option. Next, alignment of the filtered reads to the human reference genome (GRCh38) was done using HISAT2 (version 2. 1. 0) (https:// ccb. jhu. edu/ softw are/ hisat2/ manual. shtml) applying -no-mixed and -no-discordant options. Read counts were calculated using HTSeq 66 by supplementing Ensembl gene annotation (GRCh38. 78). Only cases with alignment rate > 85% were included in downstream analysis. Expression was estimated from read counts using FPKM, via the edgeR package  67  , then log2 transformed. RNA sequencing on Cohort 2 is previously described  11.
The expression matrices for Cohort 1 and Cohort 2 were restricted to genes annotated in both data sets. Expression analysis of the Leeds cohort. The Leeds Cohort comprised 102 stage lamina propria-invasive (stage T1) and 107 NIPUC (stage Ta) tumors from the Leeds Multidisciplinary Research Tissue Bank (REC reference: 10/H1306/7). Cohort 3 comprised the NIPUC from this cohort. Total RNA was isolated from frozen tissue sections containing ≥ 70% tumor cells using a RNeasy Plus Micro Kit and amplified using the Affymetrix GeneChip WT PLUS Reagent Kit. The resulting cDNA was quantified using OD, normalized and hybridized onto Affymetirx Human Transcriptome 2. 0 microarrays. Microarrays were washed and stained using Affymetrix GeneChip Hyridisation, Wash and Stain Kit. Quality control checks, gene level normalization (using SST-RMA) and signal summarization was conducted using Affymetrix Expression console software.
05 considered significant). NIPUC where likewise compared to lamina propria-invasive cancers in the UROMOL Cohort using the Wilcoxon rank sum test (Bonferroni adjusted p < 0. 05 considered significant). Gene expression data from NIPUC from the URO-MOL Cohort were normalized to expression data from the TCGA cohort, as detailed below, using an established approach. The validity of this normalization approach was considered affirmed by consistency with findings in the cohorts that did not require normalization. To compare gene expression data between NIPUC in the UROMOL Cohort to MIBC in the TCGA Cohort, the TCGAbiolinks R package  [73] [74] [75] [76] [77]  was used to download HT-Seq gene-level read counts for the TCGA BLCA cohort. The resulting data set was then restricted to protein coding genes and primary tumor samples.
Digitally scanned slides from the TCGA bladder cancer cases were reviewed by a urologic pathologist (JIW) using the digital archive, and cases were identified that demonstrate only NIPUC (Table  S9 ). These cases were excluded from this portion of the analysis (and were used for copy number analysis, below). After restricting to a common set of 19,012 genes, the HT-Seq read counts from the TCGA cohort were merged with the Salmon read counts from NIPUC from the UROMOL Cohort. The EDASeq R package  29  was used to create a SeqExpressionSet based on the combined read counts, then upper quantile normalization was performed. The RUVSeq R package  30  was then applied using k = 1 and a set of eleven housekeep genes described by Eisenberg and Levanon  78  to identify a covariate corresponding to factors of unwanted variation.
This covariate was then used to produce normalized counts that were corrected for the factors of unwanted variation. Exploratory analyses (PCA plots, unsupervised clustering, etc. ) performed on the normalized counts strongly suggested that there was no underlying batch effect in the merged data set. The normalized counts were then converted to transcript per million (TPM) values. Activity of each of the 121 TF regulons was quantified in each tumor using single sample GSEA, via the tni. replace. samples function in RTN. Activity of NIPUC from the UROMOL Cohort were then compared with MIBC from the TCGA cohort, using the Wilcoxon rank sum test (Bonferroni adjusted p < 0. 05 considered significant). DNA Sequencing and copy number analysis. DNA sequencing was performed from formalin fixed paraffin embedded (FFPE) tissue on 36 patients from Cohort 1.
Data availability Data are available for all datasets in this paper: Cohort 1 (log2 (x + 1) normalized expression data in Table  S8 ), Cohort 2 (EGAS00001004693), and Cohort 3 (GSE163209). The TCGA cohort is available through the GDC data portal (https:// portal. gdc. cancer. gov) and TCGAbiolinks  75.
Digital data must be compressed and encrypted to maintain confidentiality and efficient bandwidth usage. These two parameters are essential for information processing in most communication systems. Image compression and encryption may result in reduced restoration quality and degraded performance. This study attempts to provide a compression and encryption scheme for digital data named as Secure-JPEG. This scheme is built on the JPEG compression format, the most widely used lossy compression scheme. It extends the standard JPEG compression algorithm to encrypt data during compression. Secure-JPEG scheme provides encryption along with the process of compression, and it could be altered easily to provide lossless compression. On the other hand, the lossless compression provides less compression ratio and is suitable only in specific scenarios.
Results and Discussions The proposed scheme is an extension of our conference paper  [10]  which has provided compression and encryption simultaneously. The compression performance is demonstrated in the paper however the encryption is done using PRNG (Mersenee Twister) which is default PRNG of MATLAB. It has large period so can be used in many scenarios, but it lacks the cryptographic security which is necessary for an encryption scheme. To overcome this problem, the proposed modification uses a Generalized Bernoulli Shift Map to generate chaotic orbits which are subsequently used for permutation and diffusion. Chaos based systems has demonstrated cryptographic characteristics and the used scheme has demonstrated its results in  [12]. The chaotic nature of system is demonstrated by Fig. 5  and Fig.
Conclusion This paper intends to improve the security of our conference paper which provides efficient permutation and diffusion in image pixels. The algorithm was focused to perform simultaneous encryption and compression. However, we have used Mersenee Twister (default random number generator of MATLAB) to generate random matrix and permutation sequences. This is not a cryptographically protected random number generator that cannot guarantee the security of the cryptosystem. We replaced it with a chaos-based generator with strong security characteristics and resilience to initial conditions. General-ized Bernoulli Shift Maps are used to produce a permutation sequence and random matrices that are used for subsequent analysis. The findings of the cryptosystem are illustrated by several experiments, such as correlation coefficient analysis, information entropy analysis, keyspace analysis, mathematical and differential analysis.
The current scheme is cryptographically secured and provides good compression. The principal application of this scheme includes areas where compression is a necessary feature otherwise the same scheme can be used in lossless compression format as well. The proposed strategy can be extended for encryption of video, audio, or other multimedia data where lossy compression is often employed. Wavelet domain processing to integrate it with JPEG2000 is still our future consideration and may be explored for better compression and security characteristics. Figures Figure 1 Secure-JPEG Figure 2 Encryption scheme according to Secure-JPEG. Figure 3 Scenario-I; Decoder Layout using standard JPEG. Figure 4 Scenario-II; Decoder Layout using Secure-JPEG. Figure 5 Random behavior of chaotic orbits and sensitivity to initial conditions Figure 6 Auto-and Cross-correlation characteristics of Generalized Bernoulli Shift Map.
Study compilation We conducted a systematic search for publications reporting both niche change and SDM transferability for invasive species following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  (Moher et al. , 2009). First, we searched Clarivate Analytics' Web of Science database using the following terms: ([Introduce* OR inva* OR alien OR non-native OR nonnative OR exotic OR naturaliz* OR nonindigenous OR non-indigenous] AND niche). Literature searches were first conducted in December 2018 and updated in July 2020. We did not include terms related to transferability ("transfer*") because some candidate publications described SDM transferability using synonyms (e. g. , cross-applicability, generality)  (Yates et al. , 2018)  and were not found if "transfer*" was included in the search query (e.
g. ,  Broennimann et al. , 2007; Parravicini et al. , 2015). The titles and abstracts of publications returned by the search were screened for relevance. Second, we screened the reference section of each relevant article to find additional publications. After scanning the titles and abstracts of 8227 publications, we identified 306 potentially relevant articles. Finally, we downloaded these articles and screened the full texts to only include studies that evaluated SDM transferability between species' native and introduced ranges, as well as assessed niche change as the categorical classification by testing the niche conservatism hypothesis or as numerical dynamics using the COUE scheme. The final data set included empirical findings on 217 species from 50 studies and is provided in  Liu et al.
Data compilation and transformation From all identified studies we collected the estimates of the spatial transferability of native-range SDM in each introduced range for each species, following the authors' definition of species' native and introduced range. Given SDM transferability may vary with different modeling techniques or evaluation metrics, we collected the data for each SDM technique and each metric. For example,  Strubbe et al. (2013)  developed SDMs for 28 invasive birds using four techniques (generalized boosted models, generalized linear models, random forests, maximum entropy) and evaluated each developed SDM using both the area under the curve (AUC)  (Fielding & Bell, 1997)  and Boyce index (Boyce)  (Boyce et al. , 2002) , resulting in more than 200 estimates of SDM transferability  (Liu et al.
, 2021). We collected the information related to the type and quality of the data used in each study, including (1) spatial resolution, (2) number of predictors, (3) number of presence points in the native range (hereafter, native presences), and (4) number of presence points in the introduced range (hereafter, introduced presences). We also collected information on the earliest year in which a species was introduced to each range from each study and further supplemented our data set using the Global Alien Species First Records Database  (Seebens et al. , 2018). We requested such data from the authors if they were not available in the study or online, as well as the results of niche dynamics and SDM transferability.
Bayesian analyses We performed all analyses within a Bayesian inference framework using the brms package  (Bürkner, 2017). Bayesian analyses have the advantage of estimating parameter probabilities by controlling for the influence of noisy data and insufficient samples  (Ellison, 2004). To address whether SDMs are more transferable for species with a conserved niche, we compared SDM transferability among species with three classifications (i. e. , conserved, shifted, and ambiguous) using a Bayesian regression model. To address how niche dynamics (based on the COUE scheme) affect SDM transferability, we assessed the strength of the association between each dynamic metric (E, S, and U) and SDM transferability, using a Bayesian multilevel model. Because these two analyses used data from different studies, we also performed the foregoing analyses using the data from a subset of studies that both tested the niche conservatism hypothesis and estimated niche dynamics using the COUE scheme.
ACKNOWLEDGMENTS Chunlong Liu was supported by the  PRIME program  of the  German Academic Exchange Service (DAAD)  with funds from the  German Federal Ministry of Education and Research (BMBF) , the  Second Tibetan Plateau Scientific Expedition and Research Program (STEP)  (No. 2019QZKK0501 ), and the  International IGB Fellowship Program in Freshwater Science. Jonathan M. Jeschke ,  Franck Courchamp , and  Núria Roura-Pascual  were supported by the 2017-2018  Belmont Forum and BiodivERsA joint call  for research proposals, under the  BiodivScen ERA-Net COFUND program , with the national funders  German Federal Ministry of Education and Research BMBF  (Grant  01LC1807B ),  French National Research Agency  ( ANR-18-EBI4-0001-06 ), and  FEDER/ Ministerio de Ciencia e Innovaci on -Agencia Estatal de Investigaci on  ( AEI PCI2018-092966 ).
Funding information Agence Nationale de la Recherche , Grant/ Award Number:  ANR-18-EBI4-0001-06 ;  Deutscher Akademischer Austauschdienst , Grant/Award Number: the  PRIME program ;  German Federal Ministry of Education and Research BMBF , Grant/ Award Number:  01LC1807B ;  Ministerio de Ciencia e Innovaci on , Grant/Award Number:  PCI2018-092966 ; the  Second Tibetan Plateau Scientific Expedition and Research Program (STEP) , Grant/Award Number: 2019QZKK0501.
Associating Conjugation Efficiency with Genetic Variants The genome sequences of the 113 E. coli strains used in this study were determined for the TransPred project and can be retrieved with the SRA accession numbers given in Table  S1   [38]. Genomes were assembled in SPAdes 3. 13, with the k-mer length set to  33, 55, 67, 77, 87, 97, 107, 115, 125 , and annotated in Prokka, specifying the input to the species Escherichia coli and using BW25113 (NCBI accession number: CP009273. 1) as reference  [39, 40]. The phylogroup, sequence type and serotype were determined by the Web tools ClermonTyper, MLST 2. 0 and SeroTypeFinder and listed in Table S1  [41] [42] [43]. Next, the following genetic variants were determined: single-nucleotide polymorphisms (SNPs), clusters of orthologous groups of proteins (COGs), k-mers and unitigs.
Based on these plots, normality was rejected. Therefore, the non-parametric Kruskal-Wallis test was used to demonstrate for both 24 h and 48 h that the log-transformed conjugation efficiency differs between two or more strains (RStudio 4. 1. 0). Then, the post hoc non-parametric Dunn's test was used to compare the mutant strains with the negative control BW25113 ∆lacI::KmR pKJK10 (GraphPad Prism 9. 2. 0). An additional analysis was performed with the non-parametric Conover test, which has greater statistical power compared to Dunn's test, using a Bonferroni correction for multiple comparisons (RStudio 4. 1. 0, package DescTools). Adjusted p-values below 0. 05 were considered significant.
ACKNOWLEDGMENTS These guidelines were produced under the egis of the  Collège National de Sage Femmes de France  ( French National College of Midwives ), in partnership with the  Association Française Pédiatrie Ambulatoire (French Society of Outpatient Pediatrics) , the  Collège de Médecine Générale (College of General Medicine) , and the  Collectif interassociatif autour de la naissance (CIANE: Interassociation Collective around Birth ). We thank  Thierry Cardoso  and  Nathalie Houzelle  ( Santé Publique France ) for their conversations, written and oral, and their attentive readings. The authors also thank the entire reading group that participated in the validation of the texts (reviewers listed in alphabetical order):  Dr. Sophie Alexander , gynecologist, Brussels;  Ms. Elodie Badin , midwife,  Rhône Alpes ;  Prof.
Data sources and in silico mock metagenomes construction. The eukaryotic and prokaryotic genomes used to benchmark bioinformatics tools were downloaded from NCBI Genbank  19  , RefSeq  20  , and JGI Genome Portal  21. A dataset was created including 33 eukaryotic genomes and 216 prokaryotic ones (Table  S1 ). These genomes were selected after determining their absence from training sets of the k-mer based tools tested in this study. To evaluate eukaryotic sequence identification tools, test contigs sets were created by extracting 100 randomly selected sequences of length 1, 3 and 5 kbp from contigs present in the downloaded genomes, similarly to previous studies  [22] [23] [24] [25]. Benchmark samples and assemblies for eukaryotic sequence binning were obtained using CAMISIM v1. 3  26. Specifically, fifteen mock metagenomes were simulated using the genomes from Table  S1 , followed by generation of three metagenomic assemblies (five mock metagenomes per assembly).
Benchmarking workflows for the identification of eukaryotic sequences using in silico mock metagenomes. EukRep v0. 6. 6 25 , Tiara v1. 0. 2 23 , Whokaryote v0. 0. 1  24  and DeepMicrobeFinder 22 were used to identify eukaryotic contigs in the generated test contigs sets using k-mer-based approaches. EukRep and Tiara were implemented using three different thresholds varying from lenient to stricter classifications. Furthermore, majority voting identification (ties excluded) was obtained by combining the results from EukRep, Tiara and Whokaryote, and, alternatively, Tiara, Whokaryote and DeepMicrobeFinder to test the complementarity of the different tools. Sequences were also classified using Kaiju v1. 8. 2 27 (nr_euk database version: 2021-02-24) and CAT v5. 2. 3  28  (database version: 20210107) which relies on Prodigal 29 and DIAMOND  30.
Finally, a hybrid strategy combining the results of reference and k-mer-based approaches was tested. This strategy identified eukaryotic contigs using reference-based tools and then used k-mer-based characterization for contigs with no available reference-based annotations. After the classification of each contig, the sequences were randomly subsampled to achieve a eukaryotic to prokaryotic bp ratio equal to 0. 05, considered as representative ratios of the two superkingdoms abundances in DWDS metagenomes  31  to obtain performance estimates representative of real-world situations  22. To properly account for both false positive and negatives in imbalanced datasets  32  , classification performances were evaluated based on the Matthews Correlation Coefficient (MCC), precision, and recall using yardstick v1.
Datasets used Metagenomes derived from DWDSs were downloaded from NCBI using the SRA Toolkit v2. 9. 6  39  or, in case deposited on MG-RAST 40 retrieved directly from corresponding researchers for a total of 181 distinct samples. Only sequences derived from finished water at drinking water treatment plant (DWTP) and samples from DWDS were considered, excluding the ones either collected in raw water, within drinking water treatment plants and of DWDSs biofilms. Table  S3  includes a list of the samples with the details regarding experimental procedures used for each sample  31, [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52].
Bioinformatic analyses Raw reads from metagenomes were quality filtered and trimmed using fastp v0. 20. 1/v0. 23. 2 53 , followed by vector contamination removal using the UniVec_Core database  19  and BWA-MEM v0. 7. 17 or BWA-MEM2 v2. 2. 1 54 , SAMtools v1. 9 55 and bedtools v2. 30. 0 56. Cleaned reads were merged in case obtained in multiple sequencing of the sample and used to estimate the metagenome sequencing coverage using Nonpareil v3. 4. 1  57  and screened to identify the number of read pairs properly mapping to the 16S or 18S rRNA genes contained in the SILVA database v138. 1 58 using PhyloFlash v3. 4  59  and SAMtools. Cleaned reads from samples collected within each distribution system were co-assembled using MetaSpades v3. 10. 1/v3. 15. 3 60 after filtering for contigs greater than 1 kbp using SeqKit v2.
Before the beta diversity estimation, assemblies with less than 250 contigs were removed, rarefying the remaining assemblies to an equal number of contigs. The existence of significant presence/absence co-occurrence patterns of 18S rRNA genes was evaluated using CoNet v1. 1. 1  63  and Cytoscape v3. 9. 1 64 using a hypergeometric distribution-based approach and a significance threshold of 0. 05. The obtained network was divided in modules maximizing modularity using the Leiden algorithm 65 implemented in leidenbase v0. 1. 12 66. The 18S rRNA gene sequence percentage identity of genes within in each network module was estimated using blastn as carried out by Wu and co-workers  67.