Distribution and associations for antimicrobial resistance and antibiotic resistance genes of Escherichia coli from musk deer (Moschus berezovskii) in Sichuan, China.

This study aimed to investigate the antimicrobial resistance (AMR), antibiotic resistance genes (ARGs) and integrons in 157 Escherichia coli (E. coli) strains isolated from feces of captive musk deer from 2 farms (Dujiang Yan and Barkam) in Sichuan province. Result showed that 91.72% (144/157) strains were resistant to at least one antimicrobial and 24.20% (38/157) strains were multi-drug resistant (MDR). The antibiotics that most E. coli strains were resistant to was sulfamethoxazole (85.99%), followed by ampicillin (26.11%) and tetracycline (24.84%). We further detected 13 ARGs in the 157 E. coli strains, of which blaTEM had the highest occurrence (91.72%), followed by aac(3')-Iid (60.51%) and blaCTX-M (16.56%). Doxycycline, chloramphenicol, and ceftriaxone resistance were strongly correlated with the presence of tetB, floR and blaCTX-M, respectively. The strongest positive association among AMR phenotypes was ampicillin/cefuroxime sodium (OR, 828.000). The strongest positive association among 16 pairs of ARGs was sul1/floR (OR, 21.667). Nine pairs positive associations were observed between AMR phenotypes and corresponding resistance genes and the strongest association was observed for CHL/floR (OR, 301.167). Investigation of integrons revealed intl1 and intl2 genes were detected in 10.19% (16/157) and 1.27% (2/157) E. coli strains, respectively. Only one type of gene cassettes (drA17-aadA5) was detected in class 1 integron positive strains. Our data implied musk deer is a reservoir of ARGs and positive associations were common observed among E. coli strains carrying AMRs and ARGs.

EEG-based analysis for pilots' at-risk cognitive competency identification using RF-CNN algorithm.

Cognitive competency is an essential complement to the existing ship pilot screening system that should be focused on. Situation awareness (SA), as the cognitive foundation of unsafe behaviors, is susceptible to influencing piloting performance. To address this issue, this paper develops an identification model based on random forest- convolutional neural network (RF-CNN) method for detecting at-risk cognitive competency (i.e., low SA level) using wearable EEG signal acquisition technology. In the poor visibility scene, the pilots' SA levels were correlated with EEG frequency metrics in frontal (F) and central (C) regions, including α/ß (p = 0.071 < 0.1 in F and p = 0.042 < 0.05 in C), θ/(α + θ) (p = 0.048 < 0.05 in F and p = 0.026 < 0.05 in C) and (α + θ)/ß (p = 0.046 < 0.05 in F and p = 0.012 < 0.05 in C), and then a total of 12 correlation features were obtained based on a 5 s sliding time window. Using the RF algorithm developed by principal component analysis (PCA) for further feature combination, these salient combinations are used as input sets to obtain the CNN algorithm with optimal parameters for identification. The comparative results of the proposed RF-CNN (accuracy is 84.8%) against individual RF (accuracy is 78.1%) and CNN (accuracy is 81.6%) methods demonstrate that the RF-CNN with feature optimization provides the best identification of at-risk cognitive competency (accuracy increases 6.7%). Overall, the results of this paper provide key technical support for the development of an adaptive evaluation system of pilots' cognitive competency based on intelligent technology, and lay the foundation and framework for monitoring the cognitive process and competency of ship piloting operation in China.

Antibiotic-Resistant Escherichia coli Strains Isolated from Captive Giant Pandas: A Reservoir of Antibiotic Resistance Genes and Virulence-Associated Genes.

Recent studies showed that Escherichia coli (E. coli) strains isolated from captive giant pandas have serious resistance to antibiotics and carry various antibiotic resistance genes (ARGs). ARGs or virulence-associated genes (VAGs) carried by antibiotic-resistant E. coli are considered as a potential health threat to giant pandas, humans, other animals and the environment. In this study, we screened ARGs and VAGs in 84 antibiotic-resistant E. coli strains isolated from clinically healthy captive giant pandas, identified the association between ARGs and VAGs and analyzed the phylogenetic clustering of E. coli isolates. Our results showed that the most prevalent ARG in E. coli strains isolated from giant pandas is blaTEM (100.00%, 84/84), while the most prevalent VAG is fimC (91.67%, 77/84). There was a significant positive association among 30 pairs of ARGs, of which the strongest was observed for sul1/tetC (OR, 133.33). A significant positive association was demonstrated among 14 pairs of VAGs, and the strongest was observed for fyuA/iroN (OR, 294.40). A positive association was also observed among 45 pairs of ARGs and VAGs, of which the strongest was sul1/eaeA (OR, 23.06). The association of ARGs and mobile gene elements (MGEs) was further analyzed, and the strongest was found for flor and intI1 (OR, 79.86). The result of phylogenetic clustering showed that the most prevalent group was group B2 (67.86%, 57/84), followed by group A (16.67%, 14/84), group D (9.52%, 8/84) and group B1 (5.95%, 5/84). This study implied that antibiotic-resistant E. coli isolated from captive giant pandas is a reservoir of ARGs and VAGs, and significant associations exist among ARGs, VAGs and MGEs. Monitoring ARGs, VAGs and MGEs carried by E. coli from giant pandas is beneficial for controlling the development of antimicrobial resistance.

Correlation Evaluation of Pilots' Situation Awareness in Bridge Simulations via Eye-Tracking Technology.

To maintain situation awareness (SA) when exposed to emergencies during pilotage, a pilot needs to selectively allocate attentional resources to perceive critical status information about ships and environments. Although it is important to continuously monitor a pilot's SA, its relationship with attention is still not fully understood in ship pilotage. This study performs bridge simulation experiments that include vessel departure, navigation in the fairway, encounters, poor visibility, and anchoring scenes with 13 pilots (mean = 11.3 and standard deviation = 1.4 of experience). Individuals were divided into two SA group levels based on the Situation Awareness Rating Technology (SART-2) score (mean = 20.13 and standard deviation = 5.83) after the experiments. The visual patterns using different SA groups were examined using heat maps and scan paths based on pilots' fixations and saccade data. The preliminary visual analyses of the heat maps and scan paths indicate that the pilots' attentional distribution is modulated by the SA level. That is, the most concerning areas of interest (AOIs) for pilots in the high and low SA groups are outside the window (AOI-2) and electronic charts (AOI-1), respectively. Subsequently, permutation simulations were utilized to identify statistical differences between the pilots' eye-tracking metrics and SA. The results of the statistical analyses show that the fixation and saccade metrics are affected by the SA level in different AOIs across the five scenes, which confirms the findings of previous studies. In encounter scenes, the pilots' SA level is correlated with the fixation and saccade metrics: fixation count (p = 0.034 < 0.05 in AOI-1 and p = 0.032 < 0.05 in AOI-2), fixation duration (p = 0.043 < 0.05 in AOI-1 and p = 0.014 < 0.05 in AOI-2), and saccade count (p = 0.086 < 0.1 in AOI-1 and p = 0.054 < 0.1 in AOI-2). This was determined by the fixation count (p = 0.024 < 0.05 in AOI-1 and p = 0.034 < 0.05 in AOI-2), fixation duration (p = 0.036 < 0.05 in AOI-1 and p = 0.047 < 0.05 in AOI-2), and saccade duration (p = 0.05 ≤ 0.05 in AOI-1 and p = 0.042 < 0.05 in AOI-2) in poor-visibility scenes. In the remaining scenes, the SA could not be measured using eye movements alone. This study lays a foundation for the cognitive mechanism recognition of pilots based on SA via eye-tracking technology, which provides a reference to establish cognitive competency standards in preliminary pilot screenings.

Prevalence and characterization of antibiotic resistance genes and integrons in Escherichia coli isolates from captive non-human primates of 13 zoos in China.

Antimicrobial resistance (AMR) has become a public health concern; but antibiotic resistance genes (ARGs) and integrons that link to AMR of Escherichia coli from non-human primates remain largely unknown. This study aimed to investigate antibiotic resistance, emerging environmental pollutants ARGs, and integrons factors (intI1, intI2 and intI3) in 995 E. coli isolates obtained from 50 species of captive non-human primates of 13 zoos in China. Our result showed 83.62% of the E. coli isolates were resistant to at least one antibiotic and 47.94% isolates showed multiple drug resistances (MDR). The E. coli isolates mainly showed resistance to tetracycline (tetracycline 62.71%, doxycycline 61.11%), ß-lactams (ampicillin 54.27%, amoxicillin 52.36%), and sulfonamide (trimethoprim-sulfamethoxazole 36.78%). A total of 423 antibiotic resistance patterns were observed, of which DOX/TET (49 isolates, 4.92%) was the most common pattern. Antibiotic resistance rates among 13 zoos had a significant difference (P < 0.01). We further detected 22 ARGs in the 995 E. coli isolates, of which tetA had the highest occurrence (70.55%). The presence of integrons class 1 and 2 were 24.22% and 1.71%, respectively, while no class 3 integron was found. Significant positive associations were observed among integrons and antibiotics, of which the strongest association was observed for integrons / Gentamicin (OR, 2.642) and integrons / Cefotaxime (OR, 2.512). In addition, cassette arrays were detected in 64 strains of class 1 integron-positive isolates (26.56%) and 10 strains of class 2 integron-positive isolates (58.82%). Eighteen cassette arrays were found within 64 class 1 integron isolates, while 3 cassette arrays were identified within 10 class 2 integron isolates. Our results indicate a high diversity of antibiotic resistance phenotypes in non-human primate E. coli isolates, which carry multiple ARGs and integrons. Corresponding preventive measures should be taken to prevent the spread of integron-mediated ARGs in non-human primates and their living environments in zoos.

Identifying Cognitive Mechanism Underlying Situation Awareness of Pilots' Unsafe Behaviors Using Quantitative Modeling.

Situation awareness (SA) of pilots' unsafe behavior can ensure safety onboard. Thus, the cognitive mechanism that controls the SA leading to unsafe behavior must be articulated. This study employs the SA model and theory of planned behavior (TPB) to articulate a quantitative model of ship safe piloting. Firstly, the hierarchical classification framework of unsafe behaviors was constructed as an analytical foundation for rational and unconscious behaviors in sight of cognitive processes, and then the measurement elements of the cognitive mechanisms for behaviors were identified. Subsequently, based on the structural model, a hypothetical model of the cognitive path for unsafe behaviors was proposed by using the extended TPB, where there are four independent variables (i.e., attitude (ATD), subjective norm (SN), and perceived behavioral control (PBC)), one mediating variables (i.e., SA) and two dependent variables (i.e., behavioral intention (BI) and unsafe behaviors (BE)). Finally, this hypothetical model was analyzed with the data resources from extended TPB questionnaire of 295 pilots. Analysis results show that relationships of causation and mediation in the cognitive mechanism are in line with the behavior pattern and SA have a pronounced mediating effect and a strong relevance to the causal chain of extended TPB framework. This study integrated the SA three-level model to understand the motivation-cognition-action-feedback (MCAF) mechanism of pilots' unsafe behaviors under cognitive mode of information processing through structural model. It would make a valuable contribution to the assessment and intervention of safety behaviors, and provide a basic framework for monitoring the situation awareness of pilot by man-machine interactive measurement technology in the future.