Symmetry preference in shapes, faces, flowers and landscapes
Most people like symmetry, and symmetry has been extensively used in visual art and architecture. In this study, we compared preference for images of abstract and familiar objects in the original format or when containing perfect bilateral symmetry. We created pairs of images for different categories: male faces, female faces, polygons, smoothed version of the polygons, flowers, and landscapes. This design allows us to compare symmetry preference in different domains. Each observer saw all categories randomly interleaved but saw only one of the two images in a pair. After recording preference, we recorded a rating of how salient the symmetry was for each image, and measured how quickly observers could decide which of the two images in a pair was symmetrical. Results reveal a general preference for symmetry in the case of shapes and faces. For landscapes, natural (no perfect symmetry) images were preferred. Correlations with judgments of saliency were present but generally low, and for landscapes the salience of symmetry was negatively related to preference. However, even within the category where symmetry was not liked (landscapes), the separate analysis of original and modified stimuli showed an interesting pattern: Salience of symmetry was correlated positively (artificial) or negatively (original) with preference, suggesting different effects of symmetry within the same class of stimuli based on context and categorization.
Bertamini et al. (2019) Symmetry preference in shapes, faces, flowers and landscapes. PeerJ 7:e7078 https://doi.org/10.7717/peerj.7078
Identification and characterization of Gypsophila paniculata color morphs in Sleeping Bear Dunes National Lakeshore, MI, USA
Background Gypsophila paniculata (baby’s breath) is an invasive species found throughout much of the northwest United States and western Canada. Recently, plants exhibiting a different color morphology were identified within the coastal dunes along eastern Lake Michigan. The common baby’s breath (G. paniculata) typically produces stems that are purple in color (purple morph), while the atypical morph has stems that are green-yellow (green-yellow morph). The purpose of this study was to characterize these newly identified morphs and determine if they are genetically distinct species from the common baby’s breath in order to assess whether alternative management strategies should be employed to control these populations. Methods We sequenced two chloroplast regions, ribulose-bisphosphate carboxylase gene (rbcL), and maturase K (matK), and one nuclear region, internal transcribed spacer 2 (ITS2), from the purple morphs and green-yellow morphs collected from Sleeping Bear Dunes National Lakeshore, MI, USA (SBDNL). Sequences were aligned to reference sequences from other Gypsophila species obtained from the Barcode of Life Database and GenBank databases. We also collected seeds from wild purple morph and wild green-yellow morph plants in SBDNL. We grew the seeds in a common garden setting and characterized the proportion of green-yellow individuals produced from the two color morphs after 5-months of growth. Results Phylogenetic analyses based upon rbcL, matK, and ITS2 regions suggest that the two color morphs are not distinct species and they both belong to G. paniculata. Seeds collected from wild green-yellow morphs produced a significantly higher proportion of green-yellow individuals compared to the number produced by seeds collected from wild purple morphs. However, seeds collected from both color morphs produced more purple morphs than green-yellow morphs. Discussion Based upon these results, we propose that the two color morphs are variants of G. paniculata. Given the significant difference in the number of green-yellow morphs produced from the seeds of each morph type, we also suggest that this color difference has some genetic basis. We propose that current management continue to treat the two color morphs in a similar manner in terms of removal to prevent the further spread of this species.
Yang et al. (2019) Identification and characterization of Gypsophila paniculata color morphs in Sleeping Bear Dunes National Lakeshore, MI, USA. PeerJ 7:e7100 https://doi.org/10.7717/peerj.7100
iTRAQ-based quantitative proteome analysis reveals metabolic changes between a cleistogamous wheat mutant and its wild-type wheat counterpart
Background Wheat is one of the most important staple crops worldwide. Fusarium head blight (FHB) severely affects wheat yield and quality. A novel bread wheat mutant, ZK001, characterized as cleistogamic was isolated from a non-cleistogamous variety Yumai 18 (YM18) through static magnetic field mutagenesis. Cleistogamy is a promising strategy for controlling FHB. However, little is known about the mechanism of cleistogamy in wheat. Methods We performed a FHB resistance test to identify the FHB infection rate of ZK001. We also measured the agronomic traits of ZK001 and the starch and total soluble sugar contents of lodicules in YM18 and ZK001. Finally, we performed comparative studies at the proteome level between YM18 and ZK001 based on the proteomic technique of isobaric tags for relative and absolute quantification. Results The infection rate of ZK001 was lower than that of its wild-type and Aikang 58. The abnormal lodicules of ZK001 lost the ability to push the lemma and palea apart during the flowering stage. Proteome analysis showed that the main differentially abundant proteins (DAPs) were related to carbohydrate metabolism, protein transport, and calcium ion binding. These DAPs may work together to regulate cellular homeostasis, osmotic pressure and the development of lodicules. This hypothesis is supported by the analysis of starch, soluble sugar content in the lodicules as well as the results of Quantitative reverse transcription polymerase chain reaction. Conclusions Proteomic analysis has provided comprehensive information that should be useful for further research on the lodicule development mechanism in wheat. The ZK001 mutant is optimal for studying flower development in wheat and could be very important for FHB resistant projects via conventional crossing.
Tang et al. (2019) iTRAQ-based quantitative proteome analysis reveals metabolic changes between a cleistogamous wheat mutant and its wild-type wheat counterpart. PeerJ 7:e7104 https://doi.org/10.7717/peerj.7104
Characterization of bidirectional gene pairs in The Cancer Genome Atlas (TCGA) dataset
The “bidirectional gene pair” indicates a particular head-to-head gene organization in which transcription start sites of two genes are located on opposite strands of genomic DNA within a region of one kb. Despite bidirectional gene pairs are well characterized, little is known about their expression profiles and regulation features in tumorigenesis. We used RNA-seq data from The Cancer Genome Atlas (TCGA) dataset for a systematic analysis of the expression profiles of bidirectional gene pairs in 13 cancer datasets. Gene pairs on the opposite strand with transcription end site distance within one kb or on the same strand with the distance of two genes between 1–10 kb and gene pairs comprising two randomly chosen genes were used as control gene pairs (CG1, CG2, and random). We identified and characterized up-/down-regulated genes by comparing the expression level between tumors and adjacent normal tissues in 13 TCGA datasets. There were no consistently significant difference in the percentage of up-/down-regulated genes between bidirectional and control/random genes in most of TCGA datasets. However, the percentage of bidirectional gene pairs comprising two up- or two down-regulated genes was significantly higher than gene pairs from CG1/2 in 12/11 analyzed TCGA datasets and the random gene pairs in all 13 TCGA datasets. Then we identified the methylation correlated bidirectional genes to explore the regulatory mechanism of bidirectional genes. Like the differentially expressed gene pairs, the bidirectional genes in a pair were significantly prone to be both hypo- or hyper-methylation correlated genes in 12/13 TCGA datasets when comparing to the CG2/random gene pairs despite no significant difference between the percentages of hypo-/hyper-methylation correlated genes in bidirectional and CG2/random genes in most of TCGA datasets. Finally, we explored the correlation between bidirectional genes and patient’s survival, identifying prognostic bidirectional genes and prognostic bidirectional gene pairs in each TCGA dataset. Remarkably, we found a group of prognostic bidirectional gene pairs in which the combination of two protein coding genes with different expression level correlated with different survival prognosis in survival analysis for OS. The percentage of these gene pairs in bidirectional gene pair were significantly higher than the gene pairs in controls in COAD datasets and lower in none of 13 TCGA datasets.
Tu et al. (2019) Characterization of bidirectional gene pairs in The Cancer Genome Atlas (TCGA) dataset. PeerJ 7:e7107 https://doi.org/10.7717/peerj.7107
Worldwide scientific productions with immunotherapy of sepsis: a bibliometric analysis
Background Sepsis represents a significant healthcare problem worldwide and causes a high number of deaths every year but remains to be fully understood. During and after sepsis, the host immune response is complex and involves an initial excessive host inflammatory response to infection that is closely related to tissue damage and leads to organ failure. Over the past three decades, immunotherapy for sepsis has vastly improved, but in this area, the most influential articles, journals, authors, and countries have not yet been completely summarized and analyzed. Objective Performed a bibliometric analysis on all the articles concerning immunotherapy for sepsis from 1962 to 2019 was our objective, and we also explored the potential correlations between publications of different countries and their gross domestic product (GDP). Methods All articles about immunotherapy for sepsis were extracted from the Scopus database and analyzed. We also retrieved GDP data from all the countries that have published information from the World Bank. Results In summary, we have retrieved 1,483 related articles from the Scopus database starting from the first publication on immunotherapy for sepsis in 1962 through March 16, 2019. Over the past decade, the number of the articles published has increased year by year to reach 866 in total, which accounts for about 58% of all publications, with 2017 being the most prolific year when 179 articles were published. The US published 604 articles (41%), followed by China (n = 163, 11%), and Germany (n = 158, 11%). In terms of publishing media, the journal that published the highest number of the articles was Journal of Critical Care Medicine with 65 articles (4%), followed by Shock with 55 articles (4%), and Critical Care with 35 articles (2%). There was a strong correlation between the GDP of the different countries and their publication numbers (r = 0.811, P < 0.001). Conclusions Our present study analyzed all types of articles concerning immunotherapy for sepsis over the past 57 years and countries with high GDP tends to make more contributions to the medical field of this field. In the meantime, these studies highlight the importance of immunotherapy in the treatment of sepsis patients. The recognition of the historical status and development trend of this field can promote inter-agency cooperation, guide future research, and ultimately provide the basis for clinical practice guidelines.
Wan et al. (2019) Worldwide scientific productions with immunotherapy of sepsis: a bibliometric analysis. PeerJ 7:e7116 https://doi.org/10.7717/peerj.7116
A tropomyosin receptor kinase family protein, NTRK2 is a potential predictive biomarker for lung adenocarcinoma
Neurotrophic receptor tyrosine kinase 2 (NTRK2) is a member of the tropomyosin receptor kinase family associated with the tumor development. However, the detailed function of NTRK2 in lung cancer, especially in lung adenocarcinoma (LUAD), is still not fully understood. Here, we investigated the effects of NTRK2 on LUAD biology. Through analyzing bioinformatics data derived from several databases, such as Oncomine, Gene Expression Profiling Interactive Analysis and UALCAN, we found that NTRK2 expression was significantly decreased in LUAD tissues. Clinical data acquired from Wanderer database, which is linked to The Cancer Genome Atlas database, demonstrated that the expression and methylation site of NTRK2 were significantly related to the clinical characteristics and prognosis of LUAD. Furthermore, NTRK2 expression was increased remarkably after treatment with the protein kinase B (AKT) inhibitor MK2206 and the anticancer agent actinomycin D. Functional enrichment analysis of NTRK2-associated coexpression genes was further conducted. Together, our results suggested that downregulated NTRK2 might be used in the diagnostic and prognostic evaluation of LUAD patients, or as a potential therapeutic target for the treatment of LUAD.
Wang et al. (2019) A tropomyosin receptor kinase family protein, NTRK2 is a potential predictive biomarker for lung adenocarcinoma. PeerJ 7:e7125 https://doi.org/10.7717/peerj.7125
An integration of deep learning with feature embedding for protein–protein interaction prediction
Protein–protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein–protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein–protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In this work, a new residue representation method named Res2vec is designed for protein sequence representation. Residue representations obtained by Res2vec describe more precisely residue-residue interactions from raw sequence and supply more effective inputs for the downstream deep learning model. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline to infer protein–protein interactions, even when protein structure knowledge is entirely unknown. The proposed method DeepFE-PPI is evaluated on the S. Cerevisiae and human datasets. The experimental results show that DeepFE-PPI achieves 94.78% (accuracy), 92.99% (recall), 96.45% (precision), 89.62% (Matthew’s correlation coefficient, MCC) and 98.71% (accuracy), 98.54% (recall), 98.77% (precision), 97.43% (MCC), respectively. In addition, we also evaluate the performance of DeepFE-PPI on five independent species datasets and all the results are superior to the existing methods. The comparisons show that DeepFE-PPI is capable of predicting protein–protein interactions by a novel residue representation method and a deep learning classification framework in an acceptable level of accuracy. The codes along with instructions to reproduce this work are available from https://github.com/xal2019/DeepFE-PPI.
Yao et al. (2019) An integration of deep learning with feature embedding for protein–protein interaction prediction. PeerJ 7:e7126 https://doi.org/10.7717/peerj.7126
PhD7Faster 2.0: predicting clones propagating faster from the Ph.D.-7 phage display library by coupling PseAAC and tripeptide composition
Selection from phage display libraries empowers isolation of high-affinity ligands for various targets. However, this method also identifies propagation-related target-unrelated peptides (PrTUPs). These false positive hits appear because of their amplification advantages. In this report, we present PhD7Faster 2.0 for predicting fast-propagating clones from the Ph.D.-7 phage display library, which was developed based on the support vector machine. Feature selection was performed against PseAAC and tripeptide composition using the incremental feature selection method. Ten-fold cross-validation results show that PhD7Faster 2.0 succeeds a decent performance with the accuracy of 81.84%, the Matthews correlation coefficient of 0.64 and the area under the ROC curve of 0.90. The permutation test with 1,000 shuffles resulted in p < 0.001. We implemented PhD7Faster 2.0 into a publicly accessible web tool (http://i.uestc.edu.cn/sarotup3/cgi-bin/PhD7Faster.pl) and constructed standalone graphical user interface and command-line versions for different systems. The standalone PhD7Faster 2.0 is able to detect PrTUPs within small datasets as well as large-scale datasets. This makes PhD7Faster 2.0 an enhanced and powerful tool for scanning and reporting faster-growing clones from the Ph.D.-7 phage display library.
He et al. (2019) PhD7Faster 2.0: predicting clones propagating faster from the Ph.D.-7 phage display library by coupling PseAAC and tripeptide composition. PeerJ 7:e7131 https://doi.org/10.7717/peerj.7131
Comprehensive analysis of an lncRNA-miRNA-mRNA competing endogenous RNA network in pulpitis
Background Pulpitis is a common inflammatory disease that affects dental pulp. It is important to understand the molecular signals of inflammation and repair associated with this process. Increasing evidence has revealed that long noncoding RNAs (lncRNAs), via competitively sponging microRNAs (miRNAs), can act as competing endogenous RNAs (ceRNAs) to regulate inflammation and reparative responses. The aim of this study was to elucidate the potential roles of lncRNA, miRNA and messenger RNA (mRNA) ceRNA networks in pulpitis tissues compared to normal control tissues. Methods The oligo and limma packages were used to identify differentially expressed lncRNAs and mRNAs (DElncRNAs and DEmRNAs, respectively) based on expression profiles in two datasets, GSE92681 and GSE77459, from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were further analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein–protein interaction (PPI) networks and modules were established to screen hub genes using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and the Molecular Complex Detection (MCODE) plugin for Cytoscape, respectively. Furthermore, an lncRNA-miRNA-mRNA-hub genes regulatory network was constructed to investigate mechanisms related to the progression and prognosis of pulpitis. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was applied to verify critical lncRNAs that may significantly affect the pathogenesis in inflamed and normal human dental pulp. Results A total of 644 upregulated and 264 downregulated differentially expressed genes (DEGs) in pulpitis samples were identified from the GSE77459 dataset, while 8 up- and 19 downregulated probes associated with lncRNA were identified from the GSE92681 dataset. Protein–protein interaction (PPI) based on STRING analysis revealed a network of DEGs containing 4,929 edges and 623 nodes. Upon combined analysis of the constructed PPI network and the MCODE results, 10 hub genes, including IL6, IL8, PTPRC, IL1B, TLR2, ITGAM, CCL2, PIK3CG, ICAM1, and PIK3CD, were detected in the network. Next, a ceRNA regulatory relationship consisting of one lncRNA (PVT1), one miRNA (hsa-miR-455-5p) and two mRNAs (SOCS3 and PLXNC1) was established. Then, we constructed the network in which the regulatory relationship between ceRNA and hub genes was summarized. Finally, our qRT-PCR results confirmed significantly higher levels of PVT1 transcript in inflamed pulp than in normal pulp tissues (p = 0.03). Conclusion Our study identified a novel lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of pulpitis.
Lei et al. (2019) Comprehensive analysis of an lncRNA-miRNA-mRNA competing endogenous RNA network in pulpitis. PeerJ 7:e7135 https://doi.org/10.7717/peerj.7135
Coleoptera genome and transcriptome sequences reveal numerous differences in neuropeptide signaling between species
Background Insect neuropeptides are interesting for the potential their receptors hold as plausible targets for a novel generation of pesticides. Neuropeptide genes have been identified in a number of different species belonging to a variety of insects. Results suggest significant neuropeptide variation between different orders, but much less is known of neuropeptidome variability within an insect order. I therefore compared the neuropeptidomes of a number of Coleoptera. Methodology Publicly available genome sequences, transcriptomes and the original sequence data in the form of short sequence read archives were analyzed for the presence or absence of genes coding neuropeptides as well as some neuropeptide receptors in seventeen beetle species. Results Significant differences exist between the Coleoptera analyzed here, while many neuropeptides that were previously characterized from Tribolium castaneum appear very similar in all species, some are not and others are lacking in one or more species. On the other hand, leucokinin, which was presumed to be universally absent from Coleoptera, is still present in non-Polyphaga beetles. Conclusion The variability in neuropeptidome composition between species from the same insect order may be as large as the one that exists between species from different orders.
Veenstra (2019) Coleoptera genome and transcriptome sequences reveal numerous differences in neuropeptide signaling between species. PeerJ 7:e7144 https://doi.org/10.7717/peerj.7144