Hiding in plain sight: DNA barcoding suggests cryptic species in all ‘well-known’ Australian flower beetles (Scarabaeidae: Cetoniinae)
DNA barcode data is presented for Australian cetoniine flower beetles to aid with species discovery and guide revisionary taxonomy. Sequences of the COI gene’s DNA barcode region were acquired from 284 cetoniine specimens, covering 68 described species and 33 genera. This equates to 48% of the known species and 83% of the genera which occur in Australia. Results suggest up to 27 putative undescribed species in our sample, only 11 of which were suspected to be undescribed before this study, leaving 16 unexpected (“cryptic”) species. The Australian cetoniine fauna may hence be increased by up to 19%. An unanticipated result of the work is that each of the five most visible and commonly collected Australian cetoniine species, Eupoecila australasiae (Donovan, 1805), Neorrhina punctatum (Donovan, 1805), Glycyphana (Glycyphaniola) stolata (Fabricius, 1781), Chondropyga dorsalis (Donovan, 1805) and Bisallardiana gymnopleura (Fischer, 1823), have unexpectedly high diversity in DNA barcode sequences and were consequently split into multiple clusters, possibly indicating the presence of cryptic species.
Mitchell et al. (2020) Hiding in plain sight: DNA barcoding suggests cryptic species in all ‘well-known’ Australian flower beetles (Scarabaeidae: Cetoniinae). PeerJ 8:e9348 https://doi.org/10.7717/peerj.9348
A retrospective study on the prognostic value of preoperative C-reactive protein to albumin ratio in patients with oral cavity squamous cell carcinoma
Background Although the C-reactive protein-to-albumin ratio (CAR) can predict poor outcomes in assorted cancers, its prognostic value in oral cavity squamous cell carcinoma (OSCC) remains unclear. We explored the value of preoperative CAR in predicting clinical outcomes in OSCC patients treated with radical surgery. Methods All the recommended cutoff values were defined analyzing receiver operating characteristic curves or overall survival (OS). Dichotomization was performed on the basis of optimal CAR cutoff, and we compared the clinicopathological features between groups. Kaplan–Meier analysis was also performed to compare OS curves between the two groups. Univariate and multivariate analyses using the Cox proportional hazards model were conducted to find the clinical characteristics that were most closely correlated with disease free survival (DFS) and overall survival (OS). A nomogram incorporated CAR and several clinicopathological factors was established to predict prognosis and its accuracy was evaluated using concordance index (c-index). Results In this retrospective study, a total of 326 patients with newly diagnosis of OSCC and received primary surgery between 2008 and 2017 were enrolled. Through the executed ROC curve analyses, the optimal CAR cutoff derived was 0.195 (area under the curve = 0.718, p < 0.001), with this cutoff exhibiting a discrimination ability superior to that of other inflammation-based prognostic scores after comparing the area under curves. Multivariate analysis demonstrated that CAR (≥0.195/<0.195) was associated with OS (hazard ratio 3.614; 95% CI [1.629–8.018]; p = 0.002) and DFS (hazard ratio 1.917; 95% CI [1.051–3.863]; p = 0.029). Kaplan–Meier analysis and log rank test revealed a significant difference in DFS and OS curves between patients with low CAR (<0.195) and those with high CAR (≥0.195; both p < 0.001). The c-index of the nomogram based on TNM system alone was 0.684 and could be increased to 0.801 if CAR and other clinicopathological factors were included. Conclusions Preoperative CAR could constitute an independent prognostic indicator for OS and DFS prediction in OSCC patients treated with curative surgery. The established nomogram that incorporated CAR and prognostic factors might increase the accuracy of prognostic prediction for patients with OSCC.
Fang et al. (2020) A retrospective study on the prognostic value of preoperative C-reactive protein to albumin ratio in patients with oral cavity squamous cell carcinoma. PeerJ 8:e9361 https://doi.org/10.7717/peerj.9361
Diversity and spoilage potential of microbial communities associated with grape sour rot in eastern coastal areas of China
As a polymicrobial disease, sour rot decreases grape berry yield and wine quality. The diversity of microbial communities in sour rot-affected grapes depends on the cultivation site, but the microbes responsible for this disease in eastern coastal China, has not been reported. To identify the microbes that cause sour grape rot in this important grape-producing region, the diversity and abundance of bacteria and fungi were assessed by metagenomic analysis and cultivation-dependent techniques. A total of 15 bacteria and 10 fungi were isolated from sour rot-affected grapes. High-throughput sequencing of PCR-amplicons generated from diseased grapes revealed 1343 OTUs of bacteria and 1038 OTUs of fungi. Proteobacteria and Firmicutes were dominant phyla among the 19 bacterial phyla identified. Ascomycota was the dominant fungal phylum and the fungi Issatchenkia terricola, Colletotrichum viniferum, Hanseniaspora vineae, Saprochaete gigas, and Candida diversa represented the vast majority ofmicrobial species associated with sour rot-affected grapes. An in vitro spoilage assay confirmed that four of the isolated bacteria strains (two Cronobacter species, Serratia marcescens and Lysinibacillus fusiformis) and five of the isolated fungi strains (three Aspergillus species, Alternaria tenuissima, and Fusarium proliferatum) spoiled grapes. These microorganisms, which appear responsible for spoiling grapes in eastern China, appear closely related to microbes that cause this plant disease around the world.
Gao et al. (2020) Diversity and spoilage potential of microbial communities associated with grape sour rot in eastern coastal areas of China. PeerJ 8:e9376 https://doi.org/10.7717/peerj.9376
State-space modeling of the dynamics of temporal plant cover using visually determined class data
A lot of vegetation-related data have been collected as an ordered plant cover class that can be determined visually. However, they are difficult to analyze numerically as they are in an ordinal scale and have uncertainty in their classification. Here, I constructed a state-space model to estimate unobserved plant cover proportions (ranging from zero to one) from such cover class data. The model assumed that the data were measured longitudinally, so that the autocorrelations in the time-series could be utilized to estimate the unobserved cover proportion. The model also assumed that the quadrats where the data were collected were arranged sequentially, so that the spatial autocorrelations also could be utilized to estimate the proportion. Assuming a beta distribution as the probability distribution of the cover proportion, the model was implemented with a regularized incomplete beta function, which is the cumulative density function of the beta distribution. A simulated dataset and real datasets, with one-dimensional spatial structure and longitudinal survey, were fit to the model, and the parameters were estimated using the Markov chain Monte Carlo method. Then, the validity was examined using posterior predictive checks. As a result of the fitting, the Markov chain successfully converged to the stationary distribution, and the posterior predictive checks did not show large discrepancies. For the simulated dataset, the estimated values were close to the values used for the data generation. The estimated values for the real datasets also seemed to be reasonable. These results suggest that the proposed state-space model was able to successfully estimate the unobserved cover proportion. The present model is applicable to similar types of plant cover class data, and has the possibility to be expanded, for example, to incorporate a two-dimensional spatial structure and/or zero-inflation.
Itô (2020) State-space modeling of the dynamics of temporal plant cover using visually determined class data. PeerJ 8:e9383 https://doi.org/10.7717/peerj.9383
Smoking cessation interventions on health-care workers: a systematic review and meta-analysis
Objective The authors carried out a systematic review and a meta-analysis on smoking cessation interventions on health -care workers to clarify the state of the art interventions and to identify the best one. Materials and Methods This review was registered with PROSPERO: CRD42019130117. The databases PubMed, Scopus, Web of Science and CINAHL were searched until December 2018. Quality of all studies included in the systematic review was assessed according to the Newcastle-Ottawa Scale (NOS) on cohort or cross-sectional studies and to the Cochrane Risk of Bias Tool for Randomized Controlled Trials. Meta-analysis and meta-regression analyses were also carried out for cohort studies (quasi-experimental or a before-after studies design) and clinical trials. Results Twenty–four studies have been included in the analysis: four before-after, 13 cross-sectional, three quasi-experimental studies and four clinical trials. Articles were heterogeneous (P for homogeneity <0.01), but they have all shown positive results since they reached the goal of smoking cessation among health-care workers, even if with different proportions. Meta-analysis was performed on 10 studies (six cohort studies and four clinical trials), showing a 21% of success rate from the application of smoking cessation interventions, either pharmacological or behavioral ones. The resulted pooled RR (Risk Ratio) was 1.21 (95% CI [1.06–1.38]), being 24% of success rate from clinical trials (pooled RR 1.244; 95% CI [1.099–1.407]) and 19% of success rate from cohort studies (pooled RR 1.192; 0.996–1.426). However, two studies have confidence intervals which include unity and one study has a wide confidence interval; as a consequence, the meta-analysis for its results depends heavily on one single study. Meta-regression analysis revealed that results were influenced by the number of participants. Conclusion Both policy and pharmaceutical interventions can obtain positive results in quitting smoking among health-care workers. However, as shown by our review, combination approaches can produce better results in terms of cessation percentages and smoking abstinence.
La Torre et al. (2020) Smoking cessation interventions on health-care workers: a systematic review and meta-analysis. PeerJ 8:e9396 https://doi.org/10.7717/peerj.9396
Prevalence and correlates of depressive symptoms among Chinese patients with cataracts treated in tertiary general hospitals
Background Previous studies recruited unrepresentative samples of Chinese patients with cataract and reported a wide range of prevalence of depressive symptoms in this patient population (18.0–89.7%). The present study determined the prevalence and correlates of depressive symptoms among a consecutive sample of Chinese patients with cataract treated in tertiary general hospitals. Methods A total of 339 patients with cataract were consecutively selected from ophthalmology departments of two large general hospitals in Wuhan, China. Depressive symptoms were assessed with the Chinese Hospital Anxiety and Depression Scale. Logistic regression was used to identify factors that were associated with depression. Results The prevalence of depressive symptoms was 23.9% (95% CI [19.4–28.4]%) among patients with cataract. Correlates for depressive symptoms include an education level of primary school and below (OR = 1.93, P = 0.038), marital status of “others” (OR =3.15, P < 0.001), poor family economic status (OR = 2.26, P = 0.010), nuclear cataract (OR =4.32, P < 0.001), and mixed cataract (OR = 2.76, P = 0.017). Conclusions Depressive symptoms are common among Chinese patients with cataract treated in large general hospitals. Patients who are poorly educated, have a marital status other than “married”, have poor family economic status, and suffer from nuclear and mixed cataracts are at greater risk for depressive symptoms.
Liu et al. (2020) Prevalence and correlates of depressive symptoms among Chinese patients with cataracts treated in tertiary general hospitals. PeerJ 8:e9397 https://doi.org/10.7717/peerj.9397
Comprehensive assessment of the coupling coordination degree between urbanization and ecological environment in the Siberian and Far East Federal Districts, Russia from 2005 to 2017
The urbanization growth in the 20th and 21st centuries has led to a series of unprecedented problems in the ecological environment. Based on constructing an integrated urbanization-ecological environment index system, this article conducts a comprehensive evaluation of the coupling coordination degree between urbanization and the ecological environment and uncovers its spatiotemporal variation characteristics in the Siberian and Far East Federal Districts, Russia from 2005 to 2017. The coupling coordination of urbanization and the ecological environment in the Siberian and Far East Federal Districts improve from slightly unbalanced development stage to barely balanced development stage from 2005 to 2017. In 2017, more than half regions achieved the barely balanced development of urbanization and the ecological environment. However, the most desirable development stage, the superior balanced development stage, is never achieved in the Siberian and Far East Federal Districts during the study period. The spatial pattern of the coupling coordination degree of urbanization and the ecological environment in the Siberian and Far East Federal District gradually changes from “dumbbell” to “high-north low-south”. The south part of the Siberian and Far East Federal Districts should be paid more attention in the future urban development process. This research will provide support in the future coordination of urban development in the Siberian and Far East Federal Districts.
Zheng et al. (2020) Comprehensive assessment of the coupling coordination degree between urbanization and ecological environment in the Siberian and Far East Federal Districts, Russia from 2005 to 2017. PeerJ 8:e9125 https://doi.org/10.7717/peerj.9125
Physiological and anatomical differentiation of two sympatric weed populations
In the vineyards of Rhineland-Palatinate (Germany), two different types of Shepherd’s Purse (Capsella bursa-pastoris) coexist: (1) the common type called ‘wild type’, and (2) the decandric type called Capsella apetala or ‘Spe’ with four stamens in place of the four petals. In this study, we compare the anatomical and physiological characters of rosette leaves of the respective types. Progeny of individual plants was cultivated in growth chambers under low- and high-light conditions. Under low-light conditions, the stomata densities of the adaxial and abaxial epidermis did not differ between the two types. When grown under high-light conditions, wild type and Spe, both exhibited increased stomata densities compared to low-light conditions, but Spe to a lesser extent than the wild type. The maximal photosynthetic capacity of Spe was lower in both, low-light and high-light conditions compared to wild-type plants. Under all CO2 concentrations, Spe seemed to be less productive. The less effective CO2 assimilation of the Spe mutant C. apetala was accompanied by later flowering. This fact prolonged the vegetative phase of Spe by about two weeks and was sufficient for the maintenance of both populations stably over years.
Neuffer et al. (2020) Physiological and anatomical differentiation of two sympatric weed populations. PeerJ 8:e9226 https://doi.org/10.7717/peerj.9226
Optimizing a reliable ex vivo human blood model to analyze expression of Staphylococcus epidermidis genes
Human blood is often used as an ex vivo model to mimic the environment encountered by pathogens inside the host. A significant variety of experimental conditions has been reported. However, optimization strategies are often not described. This study aimed to evaluate key parameters that are expected to influence Staphylococcus epidermidis gene expression when using human blood ex vivo models. Our data confirmed that blood antimicrobial activity was dependent on initial bacterial concentration. Furthermore, blood degradation over time resulted in lower antimicrobial activity, with a 2% loss of leukocytes viability correlating with a 5-fold loss of antimicrobial activity against S. epidermidis. We further demonstrated that the volume of human blood could be reduced to as little as 0.18 mL without affecting the stability of gene expression of the tested genes. Overall, the data described herein highlight experimental parameters that should be considered when using a human blood ex vivo model for S. epidermidis gene expression analysis.
Brás et al. (2020) Optimizing a reliable ex vivo human blood model to analyze expression of Staphylococcus epidermidis genes. PeerJ 8:e9295 https://doi.org/10.7717/peerj.9295
Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis
Background Pancreatic cancer is one of the most common malignant cancers worldwide. Currently, the pathogenesis of pancreatic cancer remains unclear; thus, it is necessary to explore its precise molecular mechanisms. Methods To identify candidate genes involved in the tumorigenesis and proliferation of pancreatic cancer, the microarray datasets GSE32676, GSE15471 and GSE71989 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between Pancreatic ductal adenocarcinoma (PDAC) and nonmalignant samples were screened by GEO2R. The Database for Annotation Visualization and Integrated Discovery (DAVID) online tool was used to obtain a synthetic set of functional annotation information for the DEGs. A PPI network of the DEGs was established using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and a combination of more than 0.4 was considered statistically significant for the PPI. Subsequently, we visualized the PPI network using Cytoscape. Functional module analysis was then performed using Molecular Complex Detection (MCODE). Genes with a degree ≥10 were chosen as hub genes, and pathways of the hub genes were visualized using ClueGO and CluePedia. Additionally, GenCLiP 2.0 was used to explore interactions of hub genes. The Literature Mining Gene Networks module was applied to explore the cocitation of hub genes. The Cytoscape plugin iRegulon was employed to analyze transcription factors regulating the hub genes. Furthermore, the expression levels of the 13 hub genes in pancreatic cancer tissues and normal samples were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Moreover, overall survival and disease-free survival analyses according to the expression of hub genes were performed using Kaplan-Meier curve analysis in the cBioPortal online platform. The relationship between expression level and tumor grade was analyzed using the online database Oncomine. Lastly, the eight snap-frozen tumorous and adjacent noncancerous adjacent tissues of pancreatic cancer patients used to detect the CDK1 and CEP55 protein levels by western blot. Conclusions Altogether, the DEGs and hub genes identified in this work can help uncover the molecular mechanisms underlying the tumorigenesis of pancreatic cancer and provide potential targets for the diagnosis and treatment of this disease.
Jin et al. (2020) Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis. PeerJ 8:e9301 https://doi.org/10.7717/peerj.9301