Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques.

OBJECTIVE: Motorcycle crashes often result in severe injuries on roads that affect people's lives physically, financially, and psychologically. These injuries could be notably harmful to drivers of all age groups. The main objective of this study is to investigate the risk factors contributing to the severity of crash injuries in different age groups. METHODS: This Objective is achieved by developing accurate machine learning (ML) based prediction models. This research examines the relationship between potential risk factors of motorcycle-associated crashes using (ML) and Shapley Additive explanations (SHAP) technique. The SHAP technique further helped interpreting ML methods for traffic injury severity prediction. It indicates the significant non-linear interactions between dependent and independent variables. The data for this study was collected from the Provincial Emergency Response Service RESCUE 1122 for the Rawalpindi region (Pakistan) over three years (from 2017 to 2020). The Synthetic Minority Oversampling Technique (SMOTE) is employed to balance injury severity classes in the pre-processing phase. RESULTS: The results demonstrate that age, gender, posted speed limit, the number of lanes, and month of the year are positively associated with severe and fatal injuries. This research also assesses how the modeling framework varies between the ML and classical statistical methods. The predictive performance of proposed ML models was assessed using several evaluation metrics, and it is found that Catboost outperformed the XGBoost, Random Forest (RF) and Multinomial Logit (MNL) model. CONCLUSION: The findings of this study will assist road users, road safety authorities, stakeholders, policymakers, and decision-makers in obtaining substantial and essential guidance for reducing the severity of crash injuries in Pakistan and other countries with prevailing conditions.

Analysis of clinical outcomes and prognosis of patients with early bronchogenic lung cancer after treatment of rigid bronchoscopy combining fiberoptic bronchoscopy: a single-center retrospective study.

OBJECTIVES: To investigate the clinical value of rigid bronchoscopy combined with fiberoptic bronchoscopy in patients with early bronchogenic lung cancer who underwent sleeve lobectomy. METHODS: A retrospective study was performed on 76 patients with early bronchogenic lung cancer admitted to our center from March 2016 to March 2017. Patients in the control group received conventional sleeve lobectomy (n = 38), while patients in the observation group underwent sleeve lobectomy by using rigid bronchoscopy combining fiberoptic bronchoscopy (n = 38). We compared perioperative period indicators and the recovery of pulmonary function indexes one month after the operation were compared in two groups. The prognosis of the patients were also analyzed. RESULTS: Compared with the control group, the intraoperative blood loss, operation duration and airway reconstruction duration in the observation group were significantly reduced. The total incidence of perioperative complications was markedly lower in the observation group than in the control group. The percentage of DLCO% was significantly improved in the observation group. The relapse-free survival (RFS) in the observation group was remarkably longer than in the control group. CONCLUSION: Rigid bronchoscopy combined with fiberoptic bronchoscopy is beneficial to improve the clinical outcome and prognosis of patients with early bronchogenic lung cancer more effectively.

Evaluation of driver demand for in-vehicle information: An integrated method combining clustering and multivariate ordered probit model.

INTRODUCTION: The proper execution of driving tasks requires information support. While new technologies have increased the convenience of information access, they have also increased the risk of driver distraction and information overload. Meeting drivers' demands and providing them with adequate information are crucial to driving safety. METHODS: Based on a sample of 1,060 questionnaires, research on driving information demands is conducted from the perspective of drivers. A principal component analysis and the entropy method are integrated to quantify the driving information demands and preferences of drivers. The K-means classification algorithm is selected to classify the different types of driving information demands, including dynamic traffic information demands (DTIDs), static traffic information demands (STIDs), automotive driving status information demands (ATIDs), and total driving information demands (TDIDs). Fisher's least significant difference (LSD) is used to compare the differences in the numbers of self-reported crashes among different driving information demand levels. A multivariate ordered probit model is established to explore the potential factors that influence the different types of driving information demand levels. RESULTS: The DTID is the driver's most in-demand information type, and accordingly, gender, driving experience, average driving mileage, driving skills, and driving style significantly affect the driving information demand levels. Moreover, the number of self-reported crashes decreased as the DTID, ATID, and TDID levels decreased. CONCLUSION: Driving information demands are affected by a variety of factors. This study also provides evidence that drivers who have higher driving information demands are more likely to drive more carefully and safely than their counterparts who do not exhibit high driving information demands. PRACTICAL IMPLICATIONS: The results are indicative of the driver-oriented design of in-vehicle information systems and the development of dynamic information services as a way to avoid negative impacts on driving.

Value of IL-1ß and IL-23 in Predicting 28-Day Mortality Due to Sepsis: A Retrospective Study.

BACKGROUND This research aimed to explore the utility of Interleukin-1ß (IL-1ß) and IL-23 as potential biomarkers for the diagnosis and prognosis of sepsis. MATERIAL AND METHODS This study included 74 adult individuals with sepsis, 45 ICU controls, and 50 healthy individuals attending routine physical examinations. IL-1ß and IL-23 levels were assessed and analyzed on the admission day. Univariate Cox regression analyses were utilized to explore the association of IL-1ß and IL-23 with sepsis survival. Furthermore, receiver operating characteristic (ROC) analysis was employed to evaluate the value of IL-1ß and IL-23 to predict 28-day mortality due to sepsis. RESULTS Serum concentrations of IL-1ß and IL-23 were significantly higher in septic patients relative to healthy and ICU controls (P<0.001). IL-1ß and IL-23 levels in non-survivors were significantly higher than in survivors (P<0.001). IL-1ß (hazard ratio; HR=1.06, P<0.001) and IL-23 (HR=1.02, P=0.031) were independent risk variables for 28-day mortality in sepsis patients, which were strongly associated with the severity of sepsis. The area under the ROC curve for predicting 28-day fatality in sepsis was 0.66 for IL-1ß (P=0.024, 95% confidence interval; CI: 0.54-0.76) and 0.77 for IL-23 (P<0.001, 95% CI: 0.65-0.86). Furthermore, compared with low serum IL-1ß (<9.41 pg/mL) and IL-23 (<6.77 pg/mL) levels, septic patients with high serum IL-1ß (≥9.41 pg/mL) and IL-23 (≥6.77 pg/mL) levels had poorer survival. CONCLUSIONS Serum IL-1ß and IL-23 values were higher in patients with sepsis and are potential diagnostic and prognostic markers for sepsis, but this needs to be confirmed by prospective studies.

On the Impact of Prior Experiences in Car-Following Models: Model Development, Computational Efficiency, Comparative Analyses, and Extensive Applications.

A major shortcoming of the conventional car-following models is that these models only consider the current spacing and speeds of the target vehicle and its immediate leading vehicle, without taking into account prior driving actions, even for those from the same driver. In other words, the numerous prior experiences have no influence in predicting vehicular movements for the next time step. In this research, we propose a machine-learning-based data-driven methodology that is able to take advantage of the high-resolution historical traffic data in the current data-rich era, to predict vehicular movements in an accurate manner with high computational efficiency. The proposed car-following model has a simple model structure based on a fixed-radius near neighbors (FRNN) search algorithm and it can be applied to high-resolution, real-time vehicle movement prediction, modeling, and control. A comprehensive performance comparison is also conducted among the proposed car-following model, another similar data-driven model, and two conventional formula-based models. The results indicate that the FRNN algorithm-based car-following model is superior to all other three models in terms of prediction accuracy and is more computationally efficient compared to its data-driven-based counterpart. Some extensive applications of the proposed car-following model are also discussed at the end of this article.

Responses of JNK signaling pathway to the toxic dinoflagellate Prorocentrum lima in the mussel Perna viridis.

Diarrheic shellfish poisoning (DSP) toxins are widely distributed over the world, causing diarrhea, vomiting, and even tumor in human. However, bivalves, the main carrier of the DSP toxins, have some tolerant mechanisms to DSP toxins, though it remains unclear. In this study, we scrutinized the role of Jun N-terminal kinases (JNK) in tolerance of DSP toxins and the relationship between JNK, apoptosis and nuclear factor E2-related factor/antioxidant response element (Nrf2/ARE) pathways. We found that the phosphorylated level of JNK protein was significantly increased both in hemocytes (6 h) and gills (3 h) of the mussel Perna viridis after short-term exposure to DSP toxins-producing dinoflagellate Prorocentrum lima. Exposure of P. lima induced oxidative stress in mussels. Hemocytes and gills displayed different sensitivities to the cytotoxicity of DSP toxins. Exposure of P. lima activated caspase-3 and induced apoptosis in gills but did not induce caspase-3 and apoptosis in hemocytes. The short-term exposure of P. lima could activate Nrf2/ARE signaling pathway in hemocytes (6 h), while longer-term exposure could induce glutathione reductase (GR) expression in hemocytes (96 h) and glutathione-S-transferases (GST) in gills (96 h). Based on the phylogenetic tree of Nrf2, Nrf2 in P. viridis was closely related to that in other mussels, especially Mytilus coruscus, but far from that in Mus musculus. The most likely phosphorylated site of Nrf2 in the mussels P. viridis is threonine 504 for JNK, which is different from that in M. musculus. Taken all together, the tolerant mechanism of P. viridis to DSP toxins might be involved in JNK and Nrf2/ARE signaling pathways, and JNK play a key role in the mechanism. Our findings provide a new clue to further understand tolerant mechanisms of bivalves to DSP toxins.

Up-regulation of Nrf2-dependent antioxidant defenses in Perna viridis after exposed to Prorocentrum lima.

It is well documented that diarrhetic shellfish poisoning (DSP) toxins have strong genetic toxicity, cytotoxicity and oxidative damage to bivalve species. However, these toxic effects seem to decrease with the extension of exposure time and the increment of the toxin concentration, the mechanism involved remained unclear, though. In this paper, we found that expression of the genes related to cytoskeleton and Nrf2 signaling pathway displayed different changes over time in the gill of Perna viridis after exposure to DSP toxins-producing microalga Prorocentrum lima. During the short-term exposure (3 h and 6 h), KEAP1 gene expression was significantly up-regulated, coupled with up-regulation of MRP, ABCB1 and CAT transcriptions and down-regulation of GPx1 and NQO1 mRNA. After longer exposure to high density of P. lima, Nrf2 was significantly up-regulated, accompanied with up-regulation of Nrf2 pathway related genes such as NQO1, SOD, GST-ω and ABCB1, whereas KEAP1 was down-regulated. TUBA1C and TUBB1 transcripts were significantly down-regulated after short-term exposure of P. lima, but both of them were up-regulated at 96 h after exposure to high density of P. lima. Paraffin section demonstrated that P. lima had a strong damage on the gill of mussels during the short-term exposure. However, the negative effect to the gill decreased, and the gill restored after longer exposure (96 h). Taking together, we proposed that P. lima had a negative impact on cytoskeleton of mussel gill tissue, could cause oxidative damage to the gills. However, longer exposure of P. lima in high density could activate Nrf2 signaling pathway, thereby reducing the influence of toxin on mussel. Our study might provide a novel clue for the resistance mechanism of shellfish to DSP toxins.

Selfish routing equilibrium in stochastic traffic network: A probability-dominant description.

This paper suggests a probability-dominant user equilibrium (PdUE) model to describe the selfish routing equilibrium in a stochastic traffic network. At PdUE, travel demands are only assigned to the most dominant routes in the same origin-destination pair. A probability-dominant rerouting dynamic model is proposed to explain the behavioral mechanism of PdUE. To facilitate applications, the logit formula of PdUE is developed, of which a well-designed route set is not indispensable and the equivalent varitional inequality formation is simple. Two routing strategies, i.e., the probability-dominant strategy (PDS) and the dominant probability strategy (DPS), are discussed through a hypothetical experiment. It is found that, whether out of insurance or striving for perfection, PDS is a better choice than DPS. For more general cases, the conducted numerical tests lead to the same conclusion. These imply that PdUE (rather than the conventional stochastic user equilibrium) is a desirable selfish routing equilibrium for a stochastic network, given that the probability distributions of travel time are available to travelers.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

Inhibition of five natural products from Chinese herbs on the growth of Chattonella marina.

The effects of five natural products from Chinese herbs including evodiamine, curcumin, 4-methoxysalicylaldehyde, esculin hydrate, and gramine on the growth of Chattonella marina, one of the most noxious red tide algae, were observed. Among them, gramine exhibited the highest inhibitory rate with LC50, 96h of 0.51 mg/l. After exposure to gramine, the activities of superoxide dismutase (SOD) and catalase (CAT), and content of malondialdehyde (MDA) increased in C. marina, suggesting that gramine could induce microalgae oxidative stress. In addition, chlorophyll a and the maximum quantum yield of photosynthesis (Fv/Fm) decreased following exposure to gramine, indicating the inhibition of photosynthesis activity in the microalgae. Combined with the fast inhibition against the algal cells and environmentally friendly character of gramine, we proposed that gramine might be a potential algaecide against marine harmful algae and that the oxidative damage and photosynthesis inhibition might be responsible for the toxicity of gramine on harmful algae.

[Effects of cholinergic anti-inflammatory pathway on ventilator-induced lung injury in rats].

OBJECTIVE: To investigate the effects of cholinergic anti-inflammatory pathway on ventilator-induced lung injury (VILI) in rats. METHODS: Thirty-six healthy Sprague-Dawley (SD) rats were randomly divided into three groups: control group, in which rats did not receive ventilation; high-tidal volume (HVT) ventilation group; nicotine treatment (HVT+nicotine) group, in which rats received intraperitoneal injection of nicotine (2 mg/kg) 10 minutes before HVT ventilation; equal amount of normal saline was given to rats in other two groups. A rat model of VILI was reproduced by volume-controlled mechanical ventilation with HVT. Hemodynamic parameters were measured throughout the study period. Arterial blood gases were measured every 1 hour. After maintaining ventilation for 2 hours, rats were sacrificed and lung tissue specimens were harvested. Lung wet-dry weight ratio (W/D) and myeloperoxidase (MPO) activity were measured. Interleukin-8 (IL-8) level in bronchoalveolar lavage fluid (BALF) and intercellular adhesion molecule-1 (ICAM-1) level in lung tissue homogenate were measured by enzyme-linked immunosorbent assay (ELISA), respectively. After hematoxylin and eosin (HE) staining, pathological examination of lung tissue was performed, and diffuse alveolar damage (DAD) score was estimated. RESULTS: Mean pH of arterial blood in HVT group and HVT+nicotine group tended to be higher than the baseline value during the ventilation. Mean partial pressure of oxygen in arterial blood (PaO2), mean partial pressure of carbon dioxide in arterial blood (PaCO2), and mean arterial pressure (MAP) in HVT group and HVT+nicotine group were lower than baseline value during the ventilation. Mean PaO2 (mm Hg, 1 mm Hg=0.133 kPa) in HVT+nicotine group was significantly higher than that in HVT group after 2 hours of ventilation (85+/-4 vs. 76+/-3, P<0.05). Mean W/D ratio and mean MPO activity in HVT group were significantly higher than those in control group [W/D ratio: 5.66+/-0.33 vs. 4.53+/-0.21, P<0.01; MPO (U/g): 1.73+/-0.50 vs. 0.89+/-0.17, P<0.05]. Mean W/D ratio (5.02+/-0.37) and mean MPO activity (1.11+/-0.33) in HVT+nicotine group were significantly lower than those in HVT group (both P<0.05). Compared with control group, DAD scores in HVT group and HVT+nicotine group (10.40+/-1.85, 7.90+/-1.67 vs. 1.60+/-1.20), IL-8 concentration (ng/L: 1 625.3+/-271.7, 965.5+/-310.5 vs. 428.5+/-120.6) and ICAM-1 concentration (microg/L: 589.4+/-87.5, 452.5+/-89.3 vs. 247.5+/-73.7) were significantly higher (all P<0.01). But DAD score, IL-8 concentration, ICAM-1 concentration in HVT+nicotine group were significantly lower than those in HVT group (P<0.05 or P<0.01). CONCLUSION: Activation of cholinergic anti-inflammatory pathway can protect the lung against VILI by suppressing IL-8 and ICAM-1 expression, inhibiting neutrophil aggregation and infiltration.