dataesr/scientific-paragraphs-categorization huggingface dataset:
However, vitamin K (VK) deficiency is not studied extensively, and thus, the genome-wide transcriptome profiles are not known. There are, indeed, associated VK phenotypes such as thrombosis, thrombocytopenia, myocardial infarction, renal failure and prostate cancer, which are used to ascertain the differentially expressed gene (DEG) profiles  [8]. Furthermore, calling the somatic or germline variants after the DEG profiles are checked could be a holistic measure for screening and characterizing biomarkers. Our study attempts to fill this gap wherein we have used myocardial, prostate cancer (both from the Sequence Read Archive) and renal (in-house) datasets and checked for the DEG profiles and candidate mutations associated with VK deficiency. We discuss the impending effects of the role of DEG profiles in the context of VK and associated deficiencies.
Datasets The myocardial data comprise the strand-specific RNA-Seq dataset for both coding and noncoding RNA profiling from 28 hypertrophic cardiomyopathy (HCM) patients and 9 healthy donors  [9]. For our analysis, we considered three control and three treatment samples from this study, obtained through the Sequence Read Archive (SRA: ncbi. nlm. nih. gov/sra, accessed on 9 June 2022). The following myocardial samples were taken: SRR8586402; SRR8586407; SRR8586429 (control) and SRR8586409; SRR8586423; SRR8586431 (treated). In addition, we used data from the complete transcriptome landscape of prostate cancer (PCa) using RNA-Seq from another study  [10] : ERR031017; ERR031029; ERR031031 (control) and ERR031018; ERR031030; ERR031032 (treated), which we also compared with our own PCa datasets from our lab (PRJNA616165).
Finally, the renal datasets were divided into three groups: rejection time point, well-functioning rejection matched, and post-therapy (PRJNA854340). There were four patients in the test group (R2, R4, R5 and R6), all of whom received post-therapy (R2_pt, R4_pt, R5_pt and R6_pt), and three patients in the control group who were in good health; without renal failure is abbreviated as WF (WF2, WF4 and WF6). All samples were analyzed using paired-end datasets, supplementing three of each pair of datasets (Supplementary Table  S1 ).
As the DEG analysis primarily relies on paired samples, we checked paired-end reads, i. e. , control vs. treated in case of the myocardial, prostate and renal datasets. The resulting tables were filtered (after ensuring the Cufflinks pipeline was used with the -g option to check for novel isoforms) by pand q-values of ≤0. 05 and ≥2 log2-fold-change ≤2, respectively. The output BAM files were subjected to the consensus mapping of SNPs with different tools such as SAMtools  [17, 18] , VarScan  [19] , FreeBayes  [20] , Vt  [21]  and GATK  [22] , using the default parameters such as the number of criteria, including coverage, read counts, p-value, variant allele frequency, base quality and the number of strands on which the variant was observed  [23]. The filtered variants were then compared with the ClinVar database.
in a minimal set of fragments, and this determines the transcript abundances using a statistical model  [16]  (Figure  1 ). RNA-Seq reads were trimmed, but there was no significant reduction in size; further aligned reads were processed to generate SAM, BAM and sorted BAM files through a cohort of tools. As the DEG analysis primarily relies on paired samples, we checked paired-end reads, i. e. , control vs. treated in case of the myocardial, prostate and renal datasets. The resulting tables were filtered (after ensuring the Cufflinks pipeline was used with the -g option to check for novel isoforms) by p-and q-values of <=0. 05 and >=2 log2fold-change <=2, respectively. The output BAM files were subjected to the consensus mapping of SNPs with different tools such as SAMtools  [17, 18] , VarScan  [19] , FreeBayes  [20] , Vt  [21]  and GATK  [22] , using the default parameters such as the number of criteria, including coverage, read counts, p-value, variant allele frequency, base quality and the number of strands on which the variant was observed  [23].
Clustering Coefficient Network Analysis Using cytoHubba The proteins showing significant changes and interacting between all three different datasets were used to build a gene interaction network using STRING-db, and later were visualized using String [24] and Cytoscape v 3. 9. 1  [25]. The network was checked for the top-ranking genes built using the expression correlation plugin with a 0. 95 correlation as the cut-off value. The cluster IDs for proteins showing similar abundance values as defined in the hierarchical clustering were used to annotate the network to reveal relationships between the different protein groups. Finally, the network analyzer, cytoHubba, was used to define the network measures  [26]. A lower contrast (faded yellow/orange) means the rank is lower and a bigger contrast (red/maroon) indicates the rank is greater.
Results Significant DEGs associated with blood disorders were commonly identified across the myocardial, renal and prostate datasets. Between the control and treatment myocardial datasets, apolipoprotein D (APOD ENSG00000189058) was found to be upregulated in the first set (Sc5-LV and HCM515) and downregulated in the other (ND2 and HCM273). It encodes a component of a high-density lipoprotein and shares many similarities with the plasma retinol-binding protein  [27]. This glycoprotein has associations with the lipoprotein acyltransferase enzyme lecithin:cholesterol acyltransferase. Breast cysts and androgen insensitivity syndrome (AIS) are two diseases linked to APOD due to cholesterol and sphingolipids being transported/recycled to plasma membranes in the lung (normal and CF), and the transport of glucose and other sugars, bile salts and organic acids; metal ions; and amine compounds are the other associated pathways.
CD163 (ENSG00000177575) is downregulated in both myocardial datasets, and is a member of the scavenger receptor cysteine-rich (SRCR) superfamily of scavenger receptors known to be associated with monocytes and macrophages  [28]. The gene serves as an acute phase-regulated receptor that helps macrophages remove hemoglobin/haptoglobin complexes and endocytose them, potentially protecting tissues from free hemoglobin-mediated oxidative damage. The protein-encoding gene was shown to act as a bacterial innate immune sensor and a local inflammatory inducer  [28]. Furthermore, it is associated with multisystem inflammatory syndrome and histiocytic sarcoma in children. The binding and uptake of ligands by scavenger receptors, hematopoietic stem cells (HSCs) and lineage-specific markers are two mechanisms linked with it.
Interestingly, we found a processed pseudogene (ENSG00000274295) that is associated with polymerase (DNA Directed), epsilon 2 and the accessory subunit (POLE2) in these datasets (Figure  2 ; Supplementary Tables  S1-S4 ). Two genes are common between the Sc5-LV/HCM515 and ND2/HCM273 of the myocardial datasets: COL1A1, which codes for the collagen type I α-1 chain (ENSG00000108821), is downregulated in the first dataset and upregulated in the third set; the pro-alpha1 chains of type I collagen, which have two α-1 chains and one α-2 chain, are encoded by this gene. Type I collagen forms fibrils and is found in most connective tissues, including bone, cornea, dermis, and tendon. Osteogenesis imperfecta types I-IV, Ehlers-Danlos syndrome, classical type VIIA, Caffey disease, and idiopathic osteoporosis are all linked to mutations in this gene.
Although it lacks the N-terminal signal sequence seen in the majority of FGF family members, it does include clusters of basic residues that have been shown to behave as a nuclear localization signal. This protein accumulated in the nucleus but was not secreted when transfected into mammalian cells. What remains interesting is that CPNE5 (ENSG00000124772), which encodes a calcium-dependent protein, is downregulated in all three datasets of myocardial function. It harbors an integrin A domain-like sequence in the C-terminus and may regulate molecular events at the interface of the cell membrane and cytoplasm, and is shown to have several alternatively spliced transcript variants encoding isoforms (see Supplementary Table  S3 ). Two genes are common between the Sc5-LV/HCM515 and ND2/HCM273 of the myocardial datasets: COL1A1, which codes for the collagen type I α-1 chain (ENSG00000108821), is downregulated in the first dataset and upregulated in the third set; the pro-alpha1 chains of type I collagen, which have two α-1 chains and one α-2 chain, are encoded by this gene.
Although it lacks the N-terminal signal sequence seen in the majority of FGF family members, it does include clusters of basic residues that have been shown to behave as a nuclear localization signal. This protein accumulated in the nucleus but was not secreted when transfected into mammalian cells. What remains interesting is that CPNE5 Three genes, AMACR (ENSG00000242110), PCAT14 ((ENSG00000280623) prostate cancer-associated transcript 14), and LTF (lactotransferrin) are frequently compared in studies of prostate data. AMACR (α-methylacyl-CoA racemase) is upregulated in the first two CA prostate datasets only when the latter dataset did not yield any significant DEGs. In addition, various transcript variants with alternative splicing have been identified, such as C1QTNF3 (C1q and tumor necrosis factor-related protein 3), which is known to cause a bile acid synthesis defect; congenital; 4 (Supplementary Table  S3 ).
NONHSAT106693 among Significantly Enriched Genes One of the favorite candidate DEGs that is often sought is lncRNAs, and what remains compelling is the list of lncRNAs that were upregulated and downregulated across the three datasets. Among them, ENSG00000260604 (lncRNA) is upregulated in both datasets, whereas ENSG00000276980 (intronic complement component 3-C3) and ENSG00000287891 are downregulated. ENSG00000260604 is 1357 nucleotides long (GeneCards, Ensembl, LNCipedia, and Ensembl/GENCODE) and is a well-annotated candidate, with the sense-intronic C3 sequence forming a product of the genes ENSG00000276980. 1, ENSG00000276980 and lnc-GPR108-3  [31]. On the other hand, C3 (ENSG00000125730) and LINC02208 (ENSG00000250891) are downregulated in the latter two datasets, as this helps activate the complement system, and the encoded preproprotein is proteolytically processed  [32].
Mutations related to this gene are linked to atypical hemolytic uremic syndrome and age-related macular degeneration. The deficiency leads to autosomal recessive and hemolytic uremic syndrome and is widely associated with immune responses, in addition to the lectin-induced complement pathway and peptide ligand-binding receptors. Furthermore, LINC02208 is expressed in tissue samples of the heart  [33] , even as ENSG00000287891, which is also identified as a novel lncRNA, is downregulated in the latter two myocardial datasets. Interestingly, a novel lncRNA (ENSG00000285534) is downregulated in both sets of R2-R2_pt and R4-R4_pt, whereas CXCL8 (C-X-C motif chemokine ligand 8 (ENSG00000169429)), which is associated with melanoma and bronchiolitis is downregulated in R5-R5_pt and upregulated in R6-R6_pt.
This chemokine also has angiogenic properties. Higher levels of IL-8 are positively connected with the increased severity of numerous illness outcomes, and IL-8 binding to one of its receptors (IL-8RB/CXCR2) enhances blood vessel permeability (e. g. , sepsis). On the other hand, LEF1 (ENSG00000138795) is upregulated in both datasets (R5 and R6) and three other genes, viz. , G0S2 (ENSG00000123689), HSD11B1 (ENSG00000227591) and PTGS2 (ENSG00000073756), were commonly downregulated in the R4 and R5 sets. G0S2 (G0/G1 Switch 2) is a protein-coding gene located in the mitochondria involved in the extrinsic apoptotic signaling pathway, which plays a role in the positive regulation of the extrinsic apoptotic signaling pathway regulating Van der Woude syndrome. HSD11B1 (ENSG00000227591), or more specifically, HSD11B1-AS1 (HSD11B1 antisense RNA 1), is a lncRNA and is associated with the cortisone reductase deficiency.
Heatmaps Showed Distinct Gene Expression Profiles with a Variance in Principal Components When dealing with multivariate data, using a matrix in high-throughput experiments for ascertaining gene expression patterns is of considerable interest. From the varied datasets, we have attempted to identify the transcriptome expression by comparing the control and treated datasets to check their variance (Supplementary Figures  S1  and S2 ). Overall, we obtained 392 DEGs, of which 172 genes were upregulated and 220 genes were downregulated (Supplementary Table  S3 ). In myocardial datasets, 10 genes were upregulated when comparing the first datasets (Sc5-LV and HCM515), whereas 6 genes were upregulated and 28 genes were downregulated in the second datasets and 28 genes were upregulated and 24 genes were downregulated in the third datasets (N102_LV and HCM506).
Among the prostate data, 14 genes were upregulated in the first datasets (10N and 10T), 34 genes were upregulated in the second datasets (2N and 2T) and there were no significant DEGs found in the third datasets (3N and 3T). In comparing renal datasets, the test group and post-therapy group were analyzed, of which 9 genes were upregulated and 31 genes were downregulated in the first datasets (R2 and R2_pt); 7 genes were upregulated and 30 genes were downregulated in the second datasets (R4 and R4_pt); 13 genes were upregulated and 21 were downregulated in the third datasets (R5 and R5_pt); and 19 genes were upregulated and no genes were downregulated in the fourth datasets (R6 and R6_pt). The results of the control group include 9 genes that were upregulated in the first datasets (WF2 and WF4); 4 genes that were upregulated and 11 genes that were downregulated in the second datasets (WF4 and WF6); and 19 genes that were upregulated and 1 gene that was downregulated in the third datasets (WF6 and WF2).
CytoHubba Yielded Top Niche Ranks with Variant Analysis Showing No Mutations Attributed to DEGs We sought to ask whether the common DEGs form top niche ranks in all three datasets. To check this, we imported the network of DEGs (Figure  3A ) into Cytoscape and visualized all other networks, such as closeness, betweenness, stress and clustering genes, in cyto-Hubba. The network with the top 10 genes yielded a rank list indicating the top niche genes associated through the clustering coefficient. Although the color indicates the score of the genes interacting in the network, we found that among the top-ranking genes, four DEGs are known to be associated with VK deficiency (Figure  3B ). COL1A2 (α-2 type I collagen) is one of the profibrotic genes that expresses osteocalcin in the liver  [35]  and is also used as a marker of cardiac fibrosis  [36].
000318/6 (ALFA)/C = 0. /0 (TWINSUK)/C = 0. 000223/1 (Estonian)/C = 0. 000404/107 (TOPMED)/C = 0. 000407/57 (GnomAD)/C = 0. 000519/2 (ALSPAC)/C = 0. 001002/1 (GoNL)). This was further considered as a candidate for the classification of disease prevalence and penetrance estimates and was, therefore, classified as a variant of unknown significance (VOUS). Taken together, none of the DEGs seem to be commonly enriched from our prostate RNA-Seq datasets screened in-house (data unpublished). This VOUS was common, particularly in the control sets (Sc5-LV; ND2) and the myocardial treated set (HCM506); the control set of 2T and the treated set of 3T in prostate samples; the test group of R4, R5 and R6 and the post-therapy group of R4_pt and R5_pt; and the control group of WF2 in Renal datasets. This indicates that it is found across different phenotypes.
Conclusions Vitamin K (VK) plays an important role in human metabolism. In this work, we investigated whether any common DEGs were significantly enriched among various datasets and, if so, whether or not the variants in them are noteworthy for VK disease phenotypes, viz. , myocardial, renal and prostate cancer. Although we found a large number of lncRNAs among the DEGs, NONHSAT106693 was found to be a significantly enriched lncRNA across the renal and myocardial datasets, implying that it plays an important role in atypical hemolytic uremic syndrome and age-related macular degeneration. Our work also emphasizes the role of variants of unknown significance (VOUS) in these phenotypes, especially the common variant, viz. , rs563109158, seen in GGCX, which is associated with VK-dependent clotting factors.
Genes 2022 , 11 Figure 2. 2022112 Figure 2. Differentially expressed genes between control and treated in all three datasets, viz. , (A) (control and treated) myocardial, (B) (control and treated) prostate, and (C) test (treated) group of renal data; (D) control group of renal data, (E) control and test group of renal data, and (F) common in all three, myocardial, renal and prostate data.
Real-world applications We show the application of the NBurr distribution in the analysis of large-scale complex network data sets from various disciplines. The examples of such large-scale real-world complex networks include Twitter, Facebook, Orkut, Youtube, Amazon, LinkedIn, Wiki networks, etc. where the number of nodes is of the order of thousands or millions. There has been significant interest and attention devoted toward modeling aspects of such large-scale complex networks. Recent research  [23, 28, 44]  involved in the analysis of various important structural characteristics of network such as degree distribution, average nearest neighbor, clustering coefficient, community discovery, motif distribution, etc. Most of the interest has been focused on the analysis of the node degree distribution corresponding to these real-world networks  [2, 6, 7].
Sexuality and apomixis are interconnected plant reproductive routes possibly behaving as polyphenic traits under the influence of the environment. In the subtropical grass Paspalum notatum, one of the controllers of apospory, a main component of gametophytic apomixis reproduction, is TRIMETHYLGUANOSINE SYNTHASE 1 (TGS1), a multifunctional gene previously associated with RNA cleavage regulation (including mRNA splicing as well as rRNA and miRNA processing), transcriptional modulation and the establishment of heterochromatin. In particular, the downregulation of TGS1 induces a sexuality decline and the emergence of aposporous-like embryo sacs. The present work was aimed at identifying TGS1 target RNAs expressed during reproductive development of Paspalum notatum. First, we mined available RNA databases originated from spikelets of sexual and apomictic plants, which naturally display a contrasting TGS1 representation, to identify differentially expressed mRNA splice variants and miRNAs.
Alignments of sexual and apomictic isoforms and BLAST surveys on the NCBI green plants nucleotide databases revealed that 20 DETs represented possible splice variants (Supplementary Table  S1 ). Six of them were chosen for subsequent analysis, based on the following criteria: (1) putative differential processing occurring within internal regions of the transcript (to discard incomplete sequencing); (2) presumed alternative splicing sectors representing characterized introns in other species and (3) presence of the intron typical context GU-AG. A graphic displaying the structure of the six selected transcripts' putative splice variants from apomictic and sexual libraries are provided in Figure  1. To construct Figure  1 , the variants carrying introns were used as query, to reveal the absence of this part of the sequence in the rest of the variants.
While i10779 amplification levels did not significantly differ between apomictic and sexual samples (not shown), i23387 displayed higher relative expression levels in sexual genotypes in comparison with apomictic ones (Figure  2B , Supplementary Data S2). Since the i23387 processed (-intron) variant cannot not be specifically amplified in qPCR experiments, we decided to examine its expression in recently delivered Illumina RNAseq libraries originated from florets of sexual and apomictic genotypes at different developmental steps (premeiosis, meiosis, postmeiosis, anthesis)  [36]. Blast analysis revealed it corresponded to transcript TRpn_180064  [36]  (ID: 100%; E-value: 0. 0) and was overexpressed in apomictic libraries respect to sexual ones at anthesis (logFC: 17516; p-adjusted value: 0.
In agreement with the ontology analysis made by  Ortiz et al. (2017)   [34] , our NCBI BLASTX surveys confirmed significant homology (E-value: 3 × 10 (transcript TRpn_99393) (ID: 99. 85%; E-value: 0. 0), which was represented at low levels in the libraries and for which no significant differential expression could be detected, due to low count (Figure  3A ). In summary, our results indicate in flowers of apomictic P. notatum plants there is a drastic upregulation of the processed (-intron) form of i23387, while the non-processed (+intron) form remains at low levels in both genotypes but, at least according to the more sensitive qPCR results, it is overexpressed in sexual ones. To investigate a possible causal association between the differential TGS1 representation detected in apomictic and sexual plants  [37]  and the occurrence of particular splice variants of the i23387 homologs, we examined two independent tgs1 lines (tgs1-1: E2.
Discussion In the last few years, an unprecedented amount of data originated from genome and transcriptome sequencing projects have flooded the apomixis field. Hundreds of candidate genes showing differential expression in sexual and apomictic plants were identified at different reproductive developmental stages  [53]. However, little is known on the functional role of these candidates, the operative interactions among them or the identity of the molecules controlling the coexistence of a subtle balance between both reproductive types in natural populations. The identification of molecular markers co-segregating with apomixis, the generation of artificial sexual polyploids after colchicine duplication, the construction of transcriptome and genomic databases and the establishment of biolistic transformation platforms represent good perspectives for selecting traits of interest in natural apomictic species such as P.
Thus, the modulation exercised on QGJ could partially imply the activity of a non-cell autonomous mechanism, that, as we will discuss in the next paragraph, might be mediated by epigenetic mechanisms. In human fibroblasts, TGS1 was shown to control the processing of a group of miRNAs associated with the Exportin 1 function  [44]. Given that the low degree of conservation among plants and human miRNAs prevents direct identification of homologs via sequence similarity analysis, and considering that sexual P. notatum plants have upregulated TGS1 activity, we decided to examine the influence of TGS1 expression on miRNAs overexpressed in sexual plants. Using previously available sRNA databases, we detected a single miRNA significantly upregulated in spikelets of sexual plants: miR2275  [35]. Stem-loop qPCR analysis revealed that this molecule was downregulated in tgs1 lines at meiosis, reaching similar levels to those detected in apomictic plants.
Identification of Splice Variants in Floral Transcriptomes Splice variants were initially identified from a long-read 454/Roche (Branford, CT, USA) FLX+ (454 Life Sciences Corporation, Branford, Connecticut, USA) reference floral transcriptome of sexual and apomictic P. notatum, in which raw data originated from floral samples had been assembled de novo into sexual, apomictic and global (sexual + apomictic) transcriptomes (NCBI Bioproject: PRJNA330955)  [34]. DETs identified using the global transcriptome as reference were ordered according to the False Discovery Rate (FDR) value  [34]. The top 316 genes with the lowest FDRs were selected to investigate the presence of alternative splice variants in the apomictic (NCBI SRX1971037) and the sexual (NCBI SRX1971038) assemblies. The apomictic and sexual transcripts were aligned with Clustal Omega (http://www.