Real-world crash configurations and traffic violations among newly licensed young drivers with different route familiarity levels

Young drivers aged 24 and below are at heightened risks of being influenced by their route familiarity levels. This study aims to compare prevalences of crash culpability, crash configurations and risky driver behaviors among newly licensed young drivers when they are driving on roads with different route familiarity levels. Based on the road traffic crash and violation data in Yunnan Province of China from January 2017 through December 2019, we classified drivers’ different route familiarity levels by utilizing spatial distance away from residence-based method, including driving on high route familiarity (HRF) and low route familiarity (LRF) roads. Prevalence ratios were estimated using generalized estimating equation log-binomial regression models. We identified 12016 newly licensed young drivers driving on HRF roads and 2189 drivers on LRF roads. Within 48 months of licensure, young drivers on LRF roads were more likely to be at fault for their motor vehicle crashes than those on HRF roads. Young drivers on LRF roads were more likely to be with failure to obey traffic control device, with failure to yield right of way, wrong way driving, backing unsafely and improper parking compared with those on HRF roads. Drivers on LRF roads were less likely to be inattentive and driving with unsafe speed and following too closely compared with those on HRF roads. Several basic aspects of targeted countermeasures can be put forward. Visual impacts such as rectangular rapid-flashing beacon (RRFB) can be used in order to prevent wrong way driving on the tourist roadways. Arranging safety talks and programs in colleges and universities and technical interventions like Advanced Driver Assistance Systems (ADAS) can be used to reduce young drivers’ driving distraction and overconfidence. It is recommended that the driving schools can use these research findings to include in licensure program to make young drivers more aware of the various factors that expose them to crash risks so that more defensive driving may be needed under different situations, and this can also help build the graduated driving licensure (GDL) programme in China.</p

Reversibly Switching Wormlike Micelles Formed by a Selenium-Containing Surfactant and Benzyl Tertiary Amine Using CO₂/N₂ and Redox Reaction

Multiresponsive wormlike micelles (WLMs) remain a significant challenge in the construction of smart soft materials based on surfactants. Herein, we report the preparation of a viscoelastic wormlike micellar solution based on a new redox-responsive surfactant, sodium dodecylselanylpropyl sulfate (SDSePS), and commercially available benzyl tertiary amine (BTA) in the presence of CO₂. In this system, SDSePS can be reversibly switched on (selenide) and off (selenoxide) by a redox reaction, akin to that previously reported for benzylselanyl or phenylselanyl surfactants. By alternately adding H₂O₂ and N₂H₄·H₂O, WLMs can be reversibly broken and formed because of the transformation of the hydrophilic headgroup of SDSePS, originating from the reversible formation of selenoxide. Moreover, WLMs can also be switched on and off by cyclically bubbling CO₂ and N₂ because of the variation of the binding interaction between SDSePS and BTA, resulting from the reversible protonation of BTA. This interesting and unique multiresponsive behavior makes the current WLMs a potential candidate for smart control of the “sol–gel” transition or substantial thickening of solutions

Additional file 1: of QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model

Proof: w ̂ t , i , ρ − z t , i , ρ $$\left({\widehat{w}}_{t,i,\rho }-{z}_{t,i,\rho}\right)$$ is an unbiased estimator for υ t , i , ρ . (PDF 383 kb

Sequence motifs of the DmM sites in mDrGenes.

<p>The motifs were identified using MEME-ChIP webserver. The shown motifs are the most enriched motifs in each dataset.</p

Counts of hyper/hypo mDrGene.

<p>As is expected, there are more hyper mDrGenes in KD-FTO and more hypo mDrGenes in KD-METTL3, KD-METTL14 and KD-WTAP.</p

Distribution of DmM sites in mDrGenes.

<p>All the sites are consistently enriched in the 3'UTR and CDS in the 4 datasets. The plots was generated using the Guitar R/Bioconductor package [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005287#pcbi.1005287.ref047" target="_blank">47</a>].</p

Biological processes regulated by FTO.

<p>We show here a binary map depicting the GO biological process (BP) categories enriched in m⁶A-driven genes identified in KD-FTO experiment. The enrichment analysis is conducted for the hyper and hypo m⁶A-driven genes respectively using DAVID. The hyper FTO targeted m⁶A-driven genes are closely link to synaptic transmission and cell-cell signaling, which is consistent with previous research. And we also find several other significant biological processes and genes regulated by m⁶A such as embryonic development and neuron differentiation. This result demonstrates that m⁶A-Driver can identify biological functionally significant m⁶A-driven genes.</p

Number of mDrGenes predicted by m⁶A-Driver and DmMGs identified by exomePeak in four datasets.

<p>We can see that exomePeak predicts more genes in the three methylase knockdown datasets but m⁶A-Driver can find genes missed by exomePeak due to biological variance.</p

Matrix Metalloproteinase-9 -1562C/T Promoter Polymorphism Confers Risk for COPD: A Meta-Analysis

<div><p>Background</p><p>The role of matrix metalloproteinase (MMP) gene polymorphisms in the development of chronic obstructive pulmonary disease (COPD) has been reported with inconsistent results. This meta-analysis was performed to assess the association of MMP-1 -1607G/GG and MMP-9 -1562C/T promoter polymorphisms with COPD susceptibility.</p> <p>Methods</p><p>Published case-control studies from Pubmed and China National Knowledge Infrastructure (CNKI) databases were retrieved. Data were extracted and pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated.</p> <p>Results</p><p>A total of fourteen case-control studies were included in this meta-analysis. Pooled effect size showed an association of MMP-9 -1562 C/T with the risk of COPD (dominant model: TT+CT vs CC; OR: 1.46; 95% CI: 1.02–2.08; p = 0.04). However, no correlation with COPD was revealed in MMP-1 -1607G/GG polymorphism. When stratified by ethnicity, results indicated MMP-1 -1607G/GG (recessive model: G/G vs G/GG+GG/GG; OR: 1.20; 95% CI: 1.01–1.44; p = 0.04) and MMP-9 -1562 C/T (dominant model; OR: 1.66; 95% CI: 1.01–2.71; p = 0.04) were correlated with COPD susceptibility among Caucasians and Asians respectively. According to source of controls, signifiant association of MMP-9 -1562 C/T (additive model: T vs C; OR:1.71, 95% CI: 1.42–2.07; p<0.00001, and dominant model; OR: 1.92; 95% CI: 1.34–2.76; p = 0.0004) with COPD susceptibility was revealed in the subgroup with smoker-based controls. However, in the aforementioned risk estimates, only the association of MMP-9 -1562 C/T (additive and dominant models) with the risk of COPD in the subgroup with smoker-based controls persisted significantly after Bonferroni correction for multiple testing. Moreover, after excluding the studies without Hardy–Weinberg equilibrium and/or with small sample size, the pooled results were robust and no publication bias was found in this study.</p> <p>Conclusion</p><p>This meta-analysis suggests, when using healthy smokers as controls, MMP-9 -1562 C/T, but not MMP-1 -1607 G/GG polymorphism is associated with the risk of COPD.</p> </div

Sub-mDrNet of KD-METTL3, KD-METTL14 and KD-WTAP.

<p>(a) Sub-mDrNet associated with Pathways in cancer. (b) Sub-mDrNet associated with Spliceosome. (c) Sub-mDrNet associated with chronic myeloid leukemia. The circle nodes are hypo mDrGenes and the triangle ones are hyper mDrGene. The orange nodes denote mDrGenes reappearing in at last two consensus networks. The yellow nodes denote mDrGenes specifically from KD-METTL3 mDrNet. The cyan nodes denote KD-METTL14 specific mDrGenes and the green ones denote WTAP specific mDrGenes. The node size and the edge width represent the frequency that they reappear in different replicates sets (RSs). Though enriched in the same pathways but the target mDrGenes are not all the same for the 3 methylation enzymes. Mean that, the 3 methylation enzymes may drive different genes to influence the same pathway.</p