Effects of maximum residue limit of triflumezopyrim exposure on fitness of the red imported fire ant Solenopsis invicta
The impact of exposure to free feeding concentrations of triflumezopyrim to the red imported fire ant, Solenopsis invicta, in maximum residue tolerances for 56 days was investigated to understand whether triflumezopyrim, a novel neonicotinoid, poses unacceptable risks to the environment. Our results demonstrated that neither 0.5 μg/ml nor 0.2 μg/ml triflumezopyrim have a significant impact on the growth of the S. invicta colony and their food consumption (sugar water and locusts) during the length of treatment. While both 0.5 μg/ml and 0.2 μg/ml triflumezopyrim improved the grasping ability of S. invicta, and 0.5 μg/ml not 0.2 μg/ml triflumezopyrim increased their rate of locomotion. In addition, although 0.5 μg/ml and 0.2 μg/ml triflumezopyrim increased their individual aggressiveness index, the probability of the survival of S. invicta was not impacted by triflumezopyrim treatments in aggressive group encounters. This study suggests that triflumezopyrim did not have a negative impact on the fitness of S. invicta at 0.5 μg/ml and 0.2 μg/ml exposures.
Li et al. (2019) Effects of maximum residue limit of triflumezopyrim exposure on fitness of the red imported fire ant Solenopsis invicta. PeerJ 7:e8241 https://doi.org/10.7717/peerj.8241
Synthesis of silver nanoparticles using a Mentha spicata extract and evaluation of its anticancer and cytotoxic activity
In this study, silver nanoparticles (NP) were synthesized by two methods: using an aqueous extract of Mentha spicata leaves and using citrate ions as stabilizing agent, and the cytotoxicity and anticancer activity of both NP were evaluated in vitro. The particles synthesized with the aqueous extract were spherical with a size ranging from 15 to 45 nm. These NP decreased cell viability in all of the cells studied; however, the IC50 could only be estimated in the Chang liver cells (IC50 = 21.37 µg/mL). These particles also decreased the generation of reactive oxygen species in Chang and SiHa cells. Additionally, the dispersions decreased the activity of caspase-3. There was no significant difference between the biological activities of the NP obtained with the aqueous extract and the NP synthesized using citrate ions. This study showed that an aqueous extract of M. spicata is an excellent alternative for the synthesis of silver NP. These NP showed cytotoxicity and anticancer activity in vitro. Although more experiments are required, the cell death occurs probably through a mechanism different from apoptosis.
Torres-Martínez et al. (2019) Synthesis of silver nanoparticles using a Mentha spicata extract and evaluation of its anticancer and cytotoxic activity. PeerJ 7:e8142 https://doi.org/10.7717/peerj.8142
Expression and prognostic analyses of early growth response proteins (EGRs) in human breast carcinoma based on database analysis
Background Early growth response proteins (EGRs), as a transcriptional regulatory family, are involved in the process of cell growth, differentiation, apoptosis, and even carcinogenesis. However, the role of EGRs in tumors, their expression levels, and their prognostic value remain unclear. Methods Using the Oncomine database, Kaplan–Meier Plotter, bcGenExMiner v4.2, cBioPortal, and other tools, the association between the survival data of breast carcinoma (BC) patients and transcriptional levels of four EGRs was investigated. Results According to the Oncomine database, in comparison to normal tissues, the expression level of EGR2/3 mRNA in BC tissues was decreased, but there was no difference in the expression level of EGR4 mRNA. On the basis of the Scarff-Bloom-Richardson (SBR) grading system, the downregulated expression level of EGR1/2/3 and upregulated expression level of EGR4 were correlated with an increased histological differentiation level, with significant differences (p < 0.05). Kaplan–Meier curves suggest that a reduction in EGR2/3 mRNA expression is related to recurrence-free survival (RFS) in BC patients. In addition, the mRNA expression level of EGR1/2/3 was related to metastatic relapse-free survival (MRFS) in BC patients with metastatic recurrence (p < 0.05). Conclusion EGR1/2/3 can be utilized as an important factor for evaluating prognosis and may be relevant to diagnosis. EGR4 may play a role in the occurrence and development of BC. The specific function and mechanism of EGRs in BC deserve further study.
Fei et al. (2019) Expression and prognostic analyses of early growth response proteins (EGRs) in human breast carcinoma based on database analysis. PeerJ 7:e8183 https://doi.org/10.7717/peerj.8183
Effects of salinity on photosynthetic traits, ion homeostasis and nitrogen metabolism in wild and cultivated soybean
Background Carbon and nitrogen metabolism need to be highly regulated to achieve cell acclimation to changing environmental conditions. The understanding of physio-biochemical responses of crops to salinity stress could help to stabilize their performance and yield. In this study we have analyzed the roles of photosynthesis, ion physiology and nitrate assimilation toward saline/alkaline stress acclimation in wild and cultivated soybean seedlings. Methods Growth and photosynthetic parameters, ion concentrations and the activity of enzymes involved in nitrogen assimilation were determined in seedlings of one wild and one cultivated soybean accession subjected to saline or alkaline stresses. Results Both saline and alkaline stresses had a negative impact on the growth and metabolism of both wild and cultivated soybean.The growth, photosynthesis, and gas exchange parameters showed a significant decrease in response to increasing salt concentration. Additionally, a significant increase in root Na+ and Cl– concentration was observed. However, photosynthetic performance and ion regulation were higher in wild than in cultivated soybean under saline and alkaline stresses. Nitrate reductase (NR) and the glutamine synthetase/glutamate synthase (GS/GOGAT) cycle showed a significant decrease in leaves of both genotypes. The reduction in the GS/GOGAT cycle was accompanied by high aminating glutamate dehydrogenase (NADH-glutamate dehydrogenase) activity, indicating the assimilation of high levels of NH4+. A significant increase in the activities of aminating and deaminating enzymes, including glutamate dehydrogenase (GDH), alanine aminotransferase (AlaAT) and aspartate aminotransferase (AspAT), was observed, probably due to the high glutamate demand and maintenance of the Krebs cycle to correct the C: N status. Conclusions Cultivated soybean was much more stress sensitive than was the wild soybean. The decrease in growth, photosynthesis, ion regulation and nitrogen assimilation enzymes was greater in cultivated soybean than in wild soybean. The impact of alkaline stress was more pronounced than that of saline stress. Wild soybean regulated the physiological mechanisms of photosynthesis and nitrate assimilation more effectively than did cultivated soybean. The present findings provide a theoretical basis with which to screen and utilize wild and cultivated soybean germplasm for breeding new stress-tolerant soybean.
Environmental Contamination and Remediation
Ullah et al. (2019) Effects of salinity on photosynthetic traits, ion homeostasis and nitrogen metabolism in wild and cultivated soybean. PeerJ 7:e8191 https://doi.org/10.7717/peerj.8191
Pelvic floor pressure distribution profile in urinary incontinence: a classification study with feature selection
Background Pelvic floor pressure distribution profiles, obtained by a novel instrumented non-deformable probe, were used as the input to a feature extraction, selection, and classification approach to test their potential for an automatic diagnostic system for objective female urinary incontinence assessment. We tested the performance of different feature selection approaches and different classifiers, as well as sought to establish the group of features that provides the greatest discrimination capability between continent and incontinent women. Methods The available data for evaluation consisted of intravaginal spatiotemporal pressure profiles acquired from 24 continent and 24 incontinent women while performing four pelvic floor maneuvers: the maximum contraction maneuver, Valsalva maneuver, endurance maneuver, and wave maneuver. Feature extraction was guided by previous studies on the characterization of pressure profiles in the vaginal canal, where the extracted features were tested concerning their repeatability. Feature selection was achieved through a combination of a ranking method and a complete non-exhaustive subset search algorithm: branch and bound and recursive feature elimination. Three classifiers were tested: k-nearest neighbors (k-NN), support vector machine, and logistic regression. Results Of the classifiers employed, there was not one that outperformed the others; however, k-NN presented statistical inferiority in one of the maneuvers. The best result was obtained through the application of recursive feature elimination on the features extracted from all the maneuvers, resulting in 77.1% test accuracy, 74.1% precision, and 83.3 recall, using SVM. Moreover, the best feature subset, obtained by observing the selection frequency of every single feature during the application of branch and bound, was directly employed on the classification, thus reaching 95.8% accuracy. Although not at the level required by an automatic system, the results show the potential use of pelvic floor pressure distribution profiles data and provide insights into the pelvic floor functioning aspects that contribute to urinary incontinence.
Carafini et al. (2019) Pelvic floor pressure distribution profile in urinary incontinence: a classification study with feature selection. PeerJ 7:e8207 https://doi.org/10.7717/peerj.8207
Systematic analysis and prediction model construction of alternative splicing events in hepatocellular carcinoma: a study on the basis of large-scale spliceseq data from The Cancer Genome Atlas
Growing evidence showed that alternative splicing (AS) event is significantly related to tumor occurrence and progress. This study was performed to make a systematic analysis of AS events and constructed a robust prediction model of hepatocellular carcinoma (HCC). The clinical information and the genes expression profile data of 335 HCC patients were collected from The Cancer Genome Atlas (TCGA). Information of seven types AS events were collected from the TCGA SpliceSeq database. Overall survival (OS) related AS events and splicing factors (SFs) were identified using univariate Cox regression analysis. The corresponding genes of OS-related AS events were sent for gene network analysis and functional enrichment analysis. Optimal OS-related AS events were selected by LASSO regression to construct prediction model using multivariate Cox regression analysis. Prognostic value of the prediction models were assessed by receiver operating characteristic (ROC) curve and KaplanMeir survival analysis. The relationship between the Percent Spliced In (PSI) value of OS-related AS events and SFs expression were analyzed using Spearman correlation analysis. And the regulation network was generated by Cytoscape. A total of 34,163 AS events were identified, which consist of 3,482 OS-related AS events. UBB, UBE2D3, SF3A1 were the hub genes in the gene network of the top 800 OS-related AS events. The area under the curve (AUC) of the final prediction model based on seven types OS-related AS events was 0.878, 0.843, 0.821 in 1, 3, 5 years, respectively. Upon multivariate analysis, risk score (All) served as the risk factor to independently predict OS for HCC patients. SFs HNRNPH3 and HNRNPL were overexpressed in tumor samples and were signifcantly associated with the OS of HCC patients. The regulation network showed prominent correlation between the expression of SFs and OS-related AS events in HCC patients. The final prediction model performs well in predicting the prognosis of HCC patients. And the findings in this study improve our understanding of the association between AS events and HCC.
Yang et al. (2019) Systematic analysis and prediction model construction of alternative splicing events in hepatocellular carcinoma: a study on the basis of large-scale spliceseq data from The Cancer Genome Atlas. PeerJ 7:e8245 https://doi.org/10.7717/peerj.8245
Assessing the effect of fish size on species distribution model performance in southern Chilean rivers
Despite its theoretical relationship, the effect of body size on the performance of species distribution models (SDM) has only been assessed in a few studies, and to date, the evidence shows unclear results. In this context, Chilean fishes provide an ideal case to evaluate this relationship due to their short size (fishes between 5 cm and 40 cm) and conservation status, providing evidence for species at the lower end of the worldwide fish size distribution and representing a relevant management tool for species conservation. We assessed the effect of body size on the performance of SDM in nine Chilean river fishes, considering the number of records, performance metrics, and predictor importance. The study was developed in the Bueno and Valdivia basins of southern Chile. We used a neural network modeling algorithm, training models with a cross-validation scheme. The effect of fish size on selected metrics was assessed using linear models and beta regressions. While no relationship between fish size and the number of presences was found, our results indicate that the model specificity increases with fish size. Additionally, the predictive importance of Riparian Vegetation and Within-Channel Structures variables decreases for larger species. Our results suggest that the relationship between the grain of the dataset and the home range of the species could bias SDM, leading in our case, to overprediction of absences. We also suggest that evolutionary adaptation to low slopes among Chilean fishes increases the relevance of riparian vegetation in the SDMs of smaller species. This study provides evidence on how species size may bias SDM, which could potentially be corrected by adjusting the model grain.
Zamorano et al. (2019) Assessing the effect of fish size on species distribution model performance in southern Chilean rivers. PeerJ 7:e7771 https://doi.org/10.7717/peerj.7771
Genome-wide identification and expression analysis of the 14-3-3 gene family in soybean (Glycine max)
In eukaryotes, proteins encoded by the 14-3-3 genes are ubiquitously involved in the plant growth and development. The 14-3-3 gene family has been identified in several plants. In the present study, we identified 22 GmGF14 genes in the soybean genomic data. On the basis of the evolutionary analysis, they were clustered into ε and non-ε groups. The GmGF14s of two groups were highly conserved in motifs and gene structures. RNA-seq analysis suggested that GmGF14 genes were the major regulator of soybean morphogenesis. Moreover, the expression level of most GmGF14s changed obviously in multiple stress responses (drought, salt and cold), suggesting that they have the abilities of responding to multiple stresses. Taken together, this study shows that soybean 14-3-3s participate in plant growth and can response to various environmental stresses. These results provide important information for further understanding of the functions of 14-3-3 genes in soybean.
Wang et al. (2019) Genome-wide identification and expression analysis of the 14-3-3 gene family in soybean (Glycine max). PeerJ 7:e7950 https://doi.org/10.7717/peerj.7950
Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model
River inflow prediction plays an important role in water resources management and power-generating systems. But the noises and multi-scale nature of river inflow data adds an extra layer of complexity towards accurate predictive model. To overcome this issue, we proposed a hybrid model, Variational Mode Decomposition (VMD), based on a singular spectrum analysis (SSA) denoising technique. First, SSA his applied to denoise the river inflow data. Second, VMD, a signal processing technique, is employed to decompose the denoised river inflow data into multiple intrinsic mode functions (IMFs), each with a relative frequency scale. Third, Empirical Bayes Threshold (EBT) is applied on non-linear IMF to smooth out. Fourth, predicted models of denoised and decomposed IMFs are established by learning the feature values of the Support Vector Machine (SVM). Finally, the ensemble predicted results are formulated by adding the predicted IMFs. The proposed model is demonstrated using daily river inflow data from four river stations of the Indus River Basin (IRB) system, which is the largest water system in Pakistan. To fully illustrate the superiority of our proposed approach, the SSA-VMD-EBT-SVM hybrid model was compared with SSA-VMD-SVM, VMD-SVM, Empirical Mode Decomposition (EMD) based i.e., EMD-SVM, SSA-EMD-SVM, Ensemble EMD (EEMD) based i.e., EEMD-SVM and SSA-EEMD-SVM. We found that our proposed hybrid SSA-EBT-VMD-SVM model outperformed than others based on following performance measures: the Nash-Sutcliffe Efficiency (NSE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Therefore, SSA-VMD-EBT-SVM model can be used for water resources management and power-generating systems using non-linear time series data.
Nazir et al. (2019) Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model. PeerJ 7:e8043 https://doi.org/10.7717/peerj.8043
Dynamic changes in prescription opioids from 2006 to 2017 in Texas
Background The US is experiencing an epidemic of opioid overdoses which may be at least partially due to an over-reliance on opioid analgesics in the treatment of chronic non-cancer pain and subsequent escalation to heroin or illicit fentanyl. As Texas was reported to be among the lowest in the US for opioid use and misuse, further examination of this state is warranted. Materials and Methods This study was conducted to quantify prescription opioid use in Texas. Data was obtained from the publicly available US Drug Enforcement Administration’s Automation of Reports and Consolidated Orders System (ARCOS) which monitors controlled substances transactions from manufacture to commercial distribution. Data for 2006–2017 from Texas for ten prescription opioids including eight primarily used to relieve pain (codeine, fentanyl, hydrocodone, hydromorphone, meperidine, morphine, oxycodone, oxymorphone) and two (buprenorphine and methadone) for the treatment of an Opioid Use Disorder (OUD) were examined. Results The change in morphine mg equivalent (MME) of all opioids (+23.3%) was only slightly greater than the state’s population gains (21.1%). Opioids used to treat an OUD showed pronounced gains (+90.8%) which were four-fold faster than population growth. Analysis of individual agents revealed pronounced elevations in codeine (+387.5%), hydromorphone (+106.7%), and oxycodone (+43.6%) and a reduction in meperidine (−80.3%) in 2017 relative to 2006. Methadone in 2017 accounted for a greater portion (39.5%) of the total MME than hydrocodone, oxycodone, morphine, hydromorphone, oxymorphone, and meperidine, combined. There were differences between urban and rural areas in the changes in hydrocodone and buprenorphine. Conclusions Collectively, these findings indicate that continued vigilance is needed in Texas to appropriately treat pain and an OUD while minimizing the potential for prescription opioid diversion and misuse. Texas may lead the US in a return to pre-opioid epidemic prescription levels.
Ighodaro et al. (2019) Dynamic changes in prescription opioids from 2006 to 2017 in Texas. PeerJ 7:e8108 https://doi.org/10.7717/peerj.8108