Knowledge, attitudes, and practices associated with bioterrorism preparedness in healthcare workers: a systematic review

IntroductionBioterrorism is an important issue in the field of biosecurity, and effectively dealing with bioterrorism has become an urgent task worldwide. Healthcare workers are considered bioterrorism first responders, who shoulder essential responsibilities and must be equipped to deal with bioterrorism. This study aims to extract and summarize the main research components of the bioterrorism knowledge, attitude, and practice dimensions among healthcare workers.MethodThis study utilized a systematic review research design based on the PRISMA 2020 guidelines. A literature search was conducted in the PubMed, Web of Science, and Scopus databases for peer-reviewed literature, and the Mixed Methods Appraisal Tool (MMAT) version 2018 was used to assess the quality of the literature.ResultA total of 16 studies were included in the final selection. Through the analysis and summary of the included studies, three main aspects and 14 subaspects of the knowledge dimension, three main aspects and 10 subaspects of the attitude dimension, and two main aspects and six subaspects of the practice dimension were extracted.ConclusionThis study conducted a literature review on bioterrorism knowledge, attitudes, and practices for healthcare workers based on the PRISMA 2020 guidelines. The findings can guide improvements in health literacy and provide beneficial information to professional organizations that need to respond effectively to bioterrorism

Development and application of a 6000-meter double decelerating lander

The deep-sea lander is an important equipment for in-situ detection and monitoring. It is of great significance to understand the benthic boundary layer’s physical, chemical, and ecological environment. A 6000-meter double decelerating lander was created to meet the deployment requirements of underwater detection and monitoring, allowing for long-term in-situ monitoring of several benthic boundary layer components. Protection of the installed ocean bottom seismometer (OBS) is required due to the lander’s and OBS’s different impact resistances. The double decelerating unit enables the OBS to avoid colliding with the seabed when the lander lands and then collides with the seabed at a slow speed rather than the speed at which the lander falls, which is intended to safeguard OBS from damage. To ensure a safe deployment, the lander’s static analysis and simulation were performed using ANSYS, and the motion characteristics of the application process were derived. Numerous data have been obtained after the lander’s successful application in the South China Sea. The lander provides an investigation approach for marine science and geochemistry, complementing a technical approach to marine environmental investigations

Field measurement of the erosion threshold of silty seabed in the intertidal flat of the Yellow River Delta with a newly-developed annular flume

Accurately measuring the critical shear stress is crucial for numerous applications, such as sediment transport modeling, erosion prediction, and the design of sustainable coastal engineering structures. However, developing reliable and precise in-situ measurement devices faces significant challenges due to the harsh and dynamic nature of aquatic environments. Factors like turbulence and waves introduce complexities that must be considered when designing and calibrating these devices. The newly developed Openable Underwater Carousel In-situ Flume (OUC-IF) was used to determine the critical shear stress (τc) and quantify erosion rates. Acoustic Doppler Velocimeter (ADV) was employed to measure 3D near-bottom velocities, which were then used to estimate and pre-calibrate bed shear stress (τ) applied on the seabed in the annular flume. Three computation methods of shear stress were evaluated: turbulent kinetic energy (TKE), direct covariance (COV), and log profile (LP). In-situ erosion experiments were conducted for the first time at two sites in the tidal flat of the Yellow River Delta (site 1 with a water depth of 1.32 m and site 2 with a water depth of 0.75 m). The critical shear stress was found to be 0.10 Pa at site 1 and 0.19 Pa at site 2, and the erosion rates of the sediments were successfully measured. The effect of wave-seabed interactions on erosion resistance was explored by theoretically estimating the wave-induced pore pressure of the seabed based on the observed data. The max liquefaction degree of the seabed at site 1 and site 2 was 0.035 and 0.057, respectively, and the average erosion coefficient Me was 2.63E-05 kg m-2s-1 at site 1 and 3.48E-05 kg m-2s-1 at site 2

Transcriptomics Changes in the Peritoneum of Mice with Lipopolysaccharide-Induced Peritonitis

Peritonitis caused by LPS is a severe clinical challenge, which causes organ damage and death. However, the mechanism of LPS-induced peritonitis has not been fully revealed yet. Here, we investigated the transcriptome profile of the peritoneal tissue of LPS-induced peritonitis in mice. A model of LPS-induced peritonitis in mice was established (LPS 10 mg/kg, i.p.), and the influence of TAK 242 (TLR4 inhibitor) on the level of inflammatory cytokines in mouse peritoneal lavage fluid was investigated by using an ELISA test. Next, the peritoneal tissues of the three groups of mice (Control, LPS, and LPS+TAK 242) (n = 6) were isolated and subjected to RNA-seq, followed by a series of bioinformatics analyses, including differentially expressed genes (DEGs), enrichment pathway, protein-protein interaction, and transcription factor pathway. Then, qPCR verified-hub genes that may interact with TAK 242 were obtained. Subsequently, the three-dimensional structure of hub proteins was obtained by using homology modeling and molecular dynamics optimization (300 ns). Finally, the virtual docking between TAK 242 and hub proteins was analyzed. Our results showed that TAK 242 significantly inhibited the production of inflammatory cytokines in the peritoneal lavage fluid of mice with peritonitis, including IL-6, IFN-γ, IL-1β, NO, and TNF-α. Compared with the Control group, LPS treatment induced 4201 DEGs (2442 down-regulated DEGs and 1759 up-regulated DEGs). Compared with the LPS group, 30 DEGs were affected by TAK 242 (8 down-regulated DEGs and 22 up-regulated DEGs). A total of 10 TAK 242-triggered hub genes were obtained, and the possible docking modes between TAK 242 and hub proteins were acquired. Overall, our data demonstrated that a large number of DEGs were affected in LPS-triggered peritonitis mice. Moreover, the TLR4 inhibitor TAK 242 is capable of suppressing the inflammatory response of LPS-induced peritonitis. Our work provides clues for understanding the pathogenesis of LPS-induced peritonitis in mice

Newly Designed and Experimental Test of the Sediment Trap for Horizontal Transport Flux

The transport processes of marine suspended sediments are important to the material cycle and the shaping of seafloor topography. Existing sediment monitoring methods are limited in their use under high concentration conditions, and are not effective in monitoring and capturing sediment in 3D directions, and there is an inability to accurately explain sediment transport processes. To infer the transport process of suspended sediments, this study proposed a time-series vector in situ observation device. An accompanying time-series analytic method was developed for sediment transport fluxes. The correlation between the internal and external flow velocities of the capture tube was established through indoor tests, and then the applicability of the device was verified by the correlation between the theoretical capture quality and the actual capture quality, and the analytic formula of the flux was refined. The proposed observation technique can be used for in situ long-term observation and sampling of marine suspended sediments under conventional and even extreme sea conditions, achieving accurate time-series suspended sediment capture and high-resolution transport flux analysis. The technique thus provides a more effective means for scientific research into the dynamics of seafloor sedimentation, the mechanisms of ocean carbon sinks, and the processes of the carbon cycle

Prediction of shear stress induced by shoaling internal solitary waves based on machine learning method

Recently, the interactions between internal solitary waves (ISWs) and the seabed have directed increasing attention to ocean engineering and offshore energy. In particular, ISWs induce bottom currents and pressure fluctuations in deep water. In this paper, we propose a method for predicting the shear stress induced by shoaling ISWs based on machine learning, and the developed approach can be used to quickly determine the safety and stability of ocean engineering. First, we provided a basic dataset for model training. Four machine learning models were selected to predict the shear stress induced by shoaling ISWs under different trim conditions. The results indicated that the performance of the convolutional neural network-long short-term memory (CNN-LSTM) forest prediction model was significantly better than the three other tested models, including long short-term memory (LSTM), support vector regression (SVR) and deep neural network (DNN) models. Therefore, the CNN-LSTM forest prediction model was the optimal model for predicting the shear stress induced by shoaling ISWs. Specifically, each metric of the CNN-LSTM model was smaller than that of the other three, and the root mean squared error to the standard deviation ratio was closest to 0.7. In addition, the CNN-LSTM model significantly outperformed the SVR and DNN models in terms of the length of prediction time. The predicted values by the CNN-LSTM model were consistent with the experimental values. The method for predicting shear stress based on machine learning in this paper can be used to predict the shear stress induced by shoaling ISWs, guide future field experiment designs, reduce damage to the seabed caused by ISWs, and promote the development of ocean engineering in deep water

A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic

China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as − 25.88 in Wuhan and − 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities

A Novel Deep Learning Model for Mining Nonlinear Dynamics in Lake Surface Water Temperature Prediction

As one of the critical indicators of the lake ecosystem, the lake surface water temperature is an important indicator for measuring lake ecological environment. However, there is a complex nonlinear relationship between lake surface water temperature and climate variables, making it difficult to accurately predict. Fortunately, satellite remote sensing provides a wealth of data to support further improvements in prediction accuracy. In this paper, we construct a new deep learning model for mining the nonlinear dynamics from climate variables to obtain more accurate prediction of lake surface water temperature. The proposed model consists of the variable correlation information module and the temporal correlation information module. The variable correlation information module based on the Self-Attention mechanism extracts key variable features that affect lake surface water temperature. Then, the features are input into the temporal correlation information module based on the Gated Recurrent Unit (GRU) model to learn the temporal variation patterns. The proposed model, called Attention-GRU, is then applied to lake surface water temperature prediction in Qinghai Lake, the largest inland lake located in the Tibetan Plateau region in China. Compared with the seven baseline models, the Attention-GRU model achieved the most accurate prediction results; notably, it significantly outperformed the Air2water model which is the classic model for lake surface water temperature prediction based on the volume-integrated heat balance equation. Finally, we analyzed the factors influencing the surface water temperature of Qinghai Lake. There are different degrees of direct and indirect effects of climatic variables, among which air temperature is the dominant factor