Search

Pseudomonas aeruginosa is a common opportunistic pathogen that causes acute nosocomial necrotizing pneumonia and is the predominant source of chronic lung infections in patients with the genetic disorder cystic fibrosis. Early diagnosis in infected patients and monitoring P. aeruginosa contamination is therefore of great importance in controlling disease spread and development with timely drugs intervention treatment and cut off infection source. Traditional culture-biochemical methods are time consuming and highly dependent on technicians and expensive instruments. To address these challenges, the present study aimed to develop a rapid, sensitive, and specific, on-site detection method for P. aeruginosa based on recombinase polymerase amplification (RPA) combined with lateral flow strip (LFS) technology. The experimental process included screening and modification of primer and probe sets targeting the unique virulence gene elastase B (lasB); specificity detection in 29 strains of P. aeruginosa and 23 closely-related pathogenic bacteria; sensitivity measurements with gradient-diluted P. aeruginosa genomic DNA and probit regression analysis; and clinical application evaluation using 574 patients samples and calculating coincidence rate and kappa index value in comparison with the culture-biochemical method. The P. aeruginosa RPA-LFS assay could complete the amplification process at 37°C constant temperature within 30 min and results could be visualized by the naked eye within 10 min on LFS. The assay displayed high sensitivity with a limit of detection of 3.05 CFU/reaction. It also demonstrated high specificity by showing no cross reaction with other pathogenic bacteria, and rapidness by being completed in less than an hour. Furthermore, when used with clinical samples, the assay had a coincidence rate of 98.26% with the culture-biochemical method and a kappa index value of 0.9433. These data indicate that the RPA-LFS assay represents a major improvement for P. aeruginosa detection, especially in resource-limited areas.

Aeromonas sobria, a common conditional pathogenic bacteria, is widely distributed in the environment and causes gastroenteritis in humans or septicemia in fish. Of all Aeromonas species, A. sobria is the most frequently isolated from human infections especially in immunocompromised subjects. Innate immunity is the first protection system of organism to resist non-specific pathogens invasion; however, the immune response process of hosts against A. sobria infection re\mains unexplored. The present study established an A. sobria infection model using primary mouse peritoneal macrophages (PMφs). The adherence and cytotoxicity of A. sobria on PMφs were determined by May-Grünwald Giemsa staining and LDH release measurement. Pro-inflammatory cytokine expression levels were measured using qPCR, western blotting, and ELISA methods. We also investigated the levels of ASC oligomerization and determined the roles of active caspase-1 in IL-1β secretion through inhibition assays and explored the activated pattern recognition receptors through immunofluorescence. We further elucidated the roles of activated inflammasome in regulating the host’s inflammatory response through inhibition combined with ELISA assays. Our results showed that A. sobria induced lytic cell death and LDH release, whereas it had no adhesive properties on PMφs. A. sobria triggered various proinflammatory cytokine transcription level upregulation, and IL-1β occupied the highest levels. The pro-IL-1β protein expression levels increased in a dose-dependent manner with MOI ranging from 1 to 100. This process was regulated by ASC-dependent inflammasome, which cleavage pro-IL-1β into active IL-1β p17 with activated caspase-1 p20. Meanwhile, the expression levels of NLRP3 receptor significantly increased, location analysis revealed puncta-like surrounding nuclear, and inhibition of NLRP3 inflammasome downregulated caspase-1 activation and IL-1β secretion. Blocking of NLRP3 inflammasome activation through K+ efflux and cathepsin B or caspase approaches downregulated A. sobria–induced proinflammatory cytokine production. Overall, these data indicated that A. sobria induced proinflammatory cytokine production in PMφs through activating NLRP3 inflammasome signaling pathways.

Aiming at the vibration signal characteristics of multi-channel rolling bearing complex faults containing various shock components, a rolling bearing complex fault diagnosis model based on spatiotemporal intrinsic mode decomposition (STIMD) method and fast spectral kurtosis method was proposed. The spatiotemporal intrinsic mode decomposition method combines the signal atomic decomposition method with the idea of signal blind source separation. Through the fast independent component analysis and the nonlinear matching pursuit method of the established overcomplete dictionary base, various fault mode components are separated. The initial phase function selected based on the high kurtosis fault frequency band obtained by the fast spectral kurtosis method can better fit the bearing fault frequency domain characteristics, so that the spatiotemporal intrinsic mode decomposition method can more accurately separate various impact components in the vibration signal. The simulation model of bearing compound fault was established and the data collected from fault diagnosis experiment platform were used to verify that the STIMD method was effective in solving the problem of rolling bearing compound fault diagnosis. By analyzing the kurtosis changes under different signal noise ratio (SNR) conditions and comparing the simulation results with the fast independent component analysis method, it shows that the kurtosis index decomposed by the proposed method is more able to prove the existence of faults under the condition of low SNR, that is, the impact is completely covered by noise. Therefore, a spatiotemporal intrinsic mode decomposition method with fast spectral kurtosis optimization can solve the problem of blind source separation in the field of composite faults of multi-channel rolling bearings and realize composite fault diagnosis. [ABSTRACT FROM AUTHOR]

Abstract Background Yaks living in the high-altitude hypoxic environment of Tibetan plateau (3600 m) have special gut microbes. However, it is still little research on yak probiotics until now. Therefore, the purpose of our study was to evaluate the growth promoting effect, antioxidant capability, immune effect, and anti-inflammatory ability of Bacillus subtilis and Bacillus velezensis isolated from Tibetan yaks in mice model. Results The results showed that the isolated strains supplementation not only improve the growth performance but also increased the length of villus in the small intestine and intestinal digestive enzyme activity. Importantly, we observed that the T-AOC, SOD, and GSH-PX levels were increased and the MDA content was reduced in probiotic-treated mice, which implied that probiotics supplementation can ameliorate the antioxidative activity of mice. The levels of AST and ALT correlated with the hepatic injury were reduced and the levels of AKP, TP, GLB, ALB, Ca, and P were markedly higher than those in the control group. Additionally, mice treated with probiotics exhibited higher serum IgG, IgM and IgA, which can reflect the immune status to some extent. At the same time, the major pro-inflammatory factor TNF-α, IL-6, and IL-8 were down-regulated and the anti-inflammatory factor IL-10 was up-regulated compared with the control groups. Conclusions In conclusion, these results demonstrated that Bacillus subtilis and Bacillus velezensis supplementation can increase overall growth performance and ameliorate the blood parameters related to inflammation and immunity of mice.

Drowning is a major public health problem and a leading cause of death in children living in developing countries. We seek better machine learning (ML) algorithms to provide a novel risk-assessment insight on non-fatal drowning prediction. The data on non-fatal drowning were collected in Qingyuan city, Guangdong Province, China. We developed four ML models to predict the non-fatal drowning risk, including a logistic regression model (LR), random forest model (RF), support vector machine model (SVM), and stacking-based model, on three primary learners (LR, RF, SVM). The area under the curve (AUC), F1 value, accuracy, sensitivity, and specificity were calculated to evaluate the predictive ability of the different learning algorithms. This study included a total of 8390 children. Of those, 12.07% (1013) had experienced non-fatal drowning. We found the following risk factors are closely associated with the risk of non-fatal drowning: the frequency of swimming in open water, distance between the school and the surrounding open waters, swimming skills, personality (introvert) and relationality with family members. Compared to the other three base models, the stacking generalization model achieved a superior performance in the non-fatal drowning dataset (AUC = 0.741, sensitivity = 0.625, F1 value = 0.359, accuracy = 0.739 and specificity = 0.754). This study indicates that applying stacking ensemble algorithms in the non-fatal drowning dataset may outperform other ML models.

The urban street has evolved into an important indicator reflecting citizens’ living standard today, and pedestrian walking activity in the streets has been proved to be a major facilitator of public health. Uncertainties, however, exist in the factors affecting pedestrian walking behavior and walking experience in streets. Especially, the factors affecting pedestrian walking behavior and walking experience in the historical and cultural streets. For the study of their main influencing factors, Hefang Street business block and Gongchen Bridge life block in Hangzhou are selected here as the study objects. Both non-participatory and participatory research methods are adopted to collect pedestrian information and observe pedestrians’ ambiguous behavior, specific behavior, and stopping behavior. According to the study result, walking preference, walking time, environmental characteristics, and land-use mix (LUM) significantly impact pedestrian walking motivation. The type differences between Gongchen Bridge life block and Hefang Street business block leads to the difference in pedestrians’ behaviors and their stopping time in business. Meanwhile, gender differences bring pedestrians’ significant differences in walking motivation. Pedestrian walking preference and walking time are positively correlated with walking motivation in both streets. Environmental characteristics and LUM have also been proved to be important influencing factors of pedestrians’ walking motivation. In this article, design and planning strategies are proposed for streets of different types in an attempt to provide reference for the revitalization and utilization of cultural heritage streets.

In recent years, increasingly prominent energy and environmental problems have pushed for higher requirements for buildings’ energy saving. According to the conventional energy-saving design method, the cooperative operation between architects, structural and equipment engineers and other professionals cannot run smoothly, so the energy-saving and emission reduction efficiency of the whole building cannot be improved effectively. The integrated design process (IDP) is a systematic method, which is applied in the scheme design stage and according to which the multi-level design factors of cities and buildings are considered comprehensively. It provides a concrete path of multi-specialty collaborative operation for the building’s climate responsive design. In this article, the development, operation process, software platform, evaluation and decision-making methods of the IDP are reviewed in a comprehensive manner. Finally, the prospect of IDP applied to the climate responsive design of buildings is analyzed, and some suggestions for future development are put forward. The IDP framework proposed in the research can provide a reference method for architectural climate responsive design practice and help formulate the future policy of energy-saving design.

In order to study the performance evaluation of spatial governance of village and town business communities, this paper establishes a five-dimensional system: industrial upgrading (I), environmental renovation (E), life quality (L), humanistic characteristics (H), and governance of society (G) based on the PSR (pressure–state–response) model. In the study, twelve village and town business communities of different types in the Anji County of China are selected as empirical cases; field research and information interviews are conducted in each sample community; the constructed project libraries and constructed ledgers in the past three years are adopted. The GIS 10.2 software is used to draw a policy fund distribution map for spatial governance, compare and study the core characteristics of financial investment for spatial governance in each sample community, and reveal how important the application of the evaluation system is for its performance analysis. The “precise strategies” and “driving foundations” of the spatial governance of village and town business communities are summarized.

The early fault signal of rolling bearings is very weak, and when analyzed under strong background noise, the traditional signal processing method is not ideal. To extract fault characteristic information more clearly, the second-order UCPSR method is applied to the early fault diagnosis of rolling bearings. The continuous potential function itself is a continuous sinusoidal function. The particle transition is smooth and the output is better. Because of its three parameters, the potential structure is more comprehensive and has more abundant characteristics. When the periodic signal, noise and potential function are the best match, the system exhibits better denoise compared to that of other methods. This paper discusses the influence of potential parameters on the motion state of particles between potential wells in combination with the potential parameter variation diagrams discussed. Then, the formula of output signal-to-noise ratio is derived to further study the relationships among potential parameters, and then the ant colony algorithm is used to optimize potential parameters in order to obtain the optimal output signal-to-noise ratio. Finally, an early weak fault diagnosis method for bearings based on the underdamped continuous potential stochastic resonance model is proposed. Through simulation and experimental verification, the underdamped continuous potential stochastic resonance results are compared with those of the time-delayed feedback stochastic resonance method, which proves the validity of the underdamped continuous potential stochastic resonance method.

This paper first analyzes the climate characteristics of five typical cities in China, including Harbin, Beijing, Shanghai, Shenzhen and Kunming. Then, based on Grasshopper, Ladybug and Honeybee analysis software, according to the indoor layout of typical residential buildings, this research extracts design parameters such as the depth and width of different rooms and their window-to-wall ratios etc., to establish a climate responsive optimization design process with indoor lighting environment comfort, with heating and cooling demand as the objective functions. Meanwhile, based on Monte Carlo simulation data, ANN (Artificial Neural Network) is used to establish a prediction model to analyze the sensitivity of interior design parameters under different typical cities’ climatic conditions. The study results show that the recommended values for the total width and total depth of indoor units under the climatic conditions of each city are both approximately 14.97 m and 7.88 m. Among them, under the climatic conditions of Harbin and Shenzhen, the design parameters of residential interiors can take the recommended value of UDI optimal or nZEB optimal. While the recommended values of window-to-wall ratios for the north bedroom, master bedroom and living room in Shanghai residential interiors are 0.26, 0.32 and 0.33, respectively. The recommended value of the window-to-wall ratio of the master bedroom in Kunming residences is 0.36, and that of the remaining rooms is between 0.15 and 0.18. The recommended values of window-to-wall ratios for the master bedroom and living room in Beijing residences are 0.41 and 0.59, respectively, and that for the remaining rooms are 0.15. The multi-objective optimization process based on parametric performance simulation used in the study can effectively assist architects in making energy-saving design decisions in the preliminary stage, allowing architects to have a case to follow in the actual design operation process.

Building energy waste has become one of the major challenges confronting the world today, so specifications and targets for building energy efficiency have been put forward in countries around the world in recent years. The schematic design stage matters a lot for building energy efficiency, while most architects nowadays are less likely to make energy efficiency design decisions in this stage due to the lack of necessary means and methods for analysis. An integrated multi-objective multivariate framework for optimization analysis is proposed for the schematic design stage in the paper. Here, the design parameters of the building morphology and the design parameters of the building envelope are integrated for analysis, and an integrated performance prediction model is established for low-rise and medium-rise residential buildings. Then, a comparison of the performance indicators of low-rise and medium-rise residential buildings under five typical urban climatic conditions is carried out, and the change patterns of the lighting environment, thermal environment, building energy demand, and life cycle cost of residential buildings in each city under different morphological parameters and design parameters of the building envelope are summarized. Specific analysis methods and practical tools are provided in the study for architectural design to ensure thermal comfort, lighting comfort, low energy consumption, and low life-cycle cost requirement, and this design method can inspire and guide the climate adaptation analysis and design process of low-rise and medium-rise residential buildings in China, improve architects’ perception of energy-saving design principles of low-rise and medium-rise residential buildings on the ontological level, as well as provide them with a method to follow and a case to follow in the actual design process.

To extract weak faults under strong noise, a method for feature extraction of weak faults with time-delayed feedback mixed potential stochastic resonance (TFMSR) is proposed. This method not only overcomes the saturation characteristics of classical bistable stochastic resonance (CBSR), but also verifies a new potential function model. Based on this model, considering the short memory characteristics of the CBSR method, a method is proposed that can add historical information to the negative feedback process of the stochastic resonance (SR). Through the combination of the above two methods, the weak fault extraction under strong background noise is realized. The article analyzes the effects of the delay term, feedback term, and system parameter on the effect of SR and uses the ant colony algorithm (ACA) to optimize the above parameters. Finally, through simulated and engineering experimental results, it is proved that the proposed method has more advantages than the CBSR method in weak fault feature extraction.

Summary: Background: Coronary heart disease is rapidly increasing in developing countries, but access to cardiac rehabilitation and secondary prevention remains low. In this study, we aimed to assess the effectiveness of a smartphone-based cardiac rehabilitation and secondary prevention programme delivered via the social media platform WeChat (SMART-CR/SP). Methods: In this parallel-group, single-blind, randomised controlled trial, we recruited patients aged 18 years or older with coronary heart disease who had received percutaneous coronary interventions from a large tertiary hospital in Shanghai, China. Participants were randomly assigned (1:1) by block randomisation to either a 2-month intensive programme followed by a 4-month step-down phase of SMART-CR/SP or to usual care. In the SMART-CR/SP group, participants received comprehensive cardiac rehabilitation and secondary prevention via WeChat. The usual care group received standard outpatient cardiology follow-up but without formal cardiac rehabilitation and secondary prevention. Assessments were done at baseline, 2 months, 6 months, and 12 months. The primary outcome was change in functional capacity from baseline, measured by 6-min walk distance, at 2 months and 6 months. Analysis was by intention to treat. Research personnel involved in assessments were blinded to group allocation. Adverse-event analysis was based on percentage of patients who discontinued the study owing to adverse events. SMART-CR/SP programme-related safety issues were also recorded. This study was registered with the Chinese Clinical Trial Registry, number ChiCTR-INR-16009598. Findings: Between Nov 17, 2016, and March 18, 2017, 312 patients (mean age 60·5 years [SD 9·2]), of whom 58 (19%) were female and 254 (81%) were male, were recruited and subsequently randomly assigned to SMART-CR/SP (n=156) or usual care (n=156). The improvement in 6-min walk distance at 2 months was significantly greater in the SMART-CR/SP group (from 489·2 m [99·4] at baseline to 539·1 m [68·0]) than in the control group (from 485·0 m [93·5] at baseline to 517·8 m [74.6]), with an adjusted mean difference of 20·64 m (95% CI 7·50–33·77; p=0·034). This improvement was maintained at 6 months (mean 6-min walk distance 543·4 m [67·5] in the SMART-CR/SP group vs 523·5 m [60·2] in the control group), with a mean between-group difference of 22·29 m (8·19–36·38; p=0·027). No adverse events or SMART-CR/SP programme-related safety issues were reported by participants during the study. Interpretation: SMART-CR/SP was found to be a cardiac rehabilitation and secondary prevention service model with high efficacy and accessibility and to be easy to use. These results justify the implementation of similar models of care on a broader scale. Funding: Curtin University.

Because fault characteristics are often difficult to extract from a strong noise background, it is essential for mechanical fault diagnosis to extract a weak characteristic signal with a very small signal-to-noise ratio from a noisy interference. Therefore, this paper proposes a new method for diagnosing weak faults in asymmetric potential stochastic resonance. Compared with the existing methods, the asymmetric potential stochastic resonance method not only has characteristics common to the symmetric potential stochastic resonance, but can also change the inclination of the barrier and slope of the wall to obtain a better model structure. The proposed method solves the local adjustment problem of the existing method from the perspective of potential structure and optimizes the asymmetric system shape to better target frequency detection during much interference from noise. After simulation, a bearing failure test, and rolling mill gearbox bearing failure experiments, we concluded that the asymmetric potential stochastic resonance detection technology can effectively identify faults. Compared with the symmetric potential stochastic resonance method, the proposed method has better recognition.

This paper proposes an optimization process based on a parametric platform for building climate responsive design. Taking residential buildings in six typical American cities as examples, it proposes thermal environment comfort (Discomfort Hour, DH), building energy demand (BED) and building global cost (GC) as the objective functions for optimization. The design variables concern building orientation, envelope components, and window types, etc. The optimal solution is provided from two different perspectives of the public sector (energy saving optimal) and private households (cost-optimal) respectively. By comparing the optimization results with the performance indicators of the reference buildings in various cities, the outcome can give the precious indications to rebuild the U.S. residential buildings with a view to energy-efficiency and cost optimality depending on the location.

Pulses caused by rotating mechanical faults are weak and often submerged in strong background noise, which can affect the accuracy of fault detection. To solve this problem, we study the stochastic resonance phenomenon of a tristable potential system based on strong noise background and also investigate the influence of time-delayed feedback on this stochastic resonance model. The effects of time-delayed feedback strength on potential energy, steady-state probability density function, and signal-to-noise ratio (SNR) are discussed. The results show that stochastic resonance can be enhanced or suppressed by adjusting the delay time and feedback strength. Combined with bearing fault diagnosis simulation research and experimental verification evaluation, the proposed time-delayed feedback tristable stochastic resonance fault diagnosis method is more effective than the classical stochastic resonance method.

Autophagy is a highly conserved and regulated process in eukaryotic cells and remodels cytoplasm, recovers essential nutrients, and disposes of unwanted cytoplasmic components. Autophagy-related gene (ATG) 8, identified in Heortia vitessoides Moore, which is an oligophagous pest of Aquilaria sinensis (Lour.), was characterized (HvATG8). Multiple sequence alignment showed that HvATG8 possesses highly conserved domain structures. Stage- and tissue-specific expressions indicated that HvATG8 is highly expressed in prepupal, pupal, and adult stages and in the midgut of larvae and abdomen of adults. Lack of function of HvATG8 by RNA interference resulted in a significant decrease in survival rate and an increase in abnormal or nonviable phenotypes in H. vitessoides. Transition rate from larval to pupal stages was 33.0% and from pupal to adult stages was 15.0% after injection. Reduction of ATG8 expression reduced survival of H. vitessoides. Therefore, HvATG8 possibly plays a key role in normal growth stage of H. vitessoides. HvATG8 suppression downregulates HvATG3 expression, suggesting that the two genes are interconnected. Further, HvATG8 expression increased by 20-hydroxyecdysone treatment, starvation, and extreme temperature exposure. Starvation also altered expression of other ATGs in H. vitessoide. This study may be used to guide research on molecular mechanisms of autophagy in insects.

With the improvement of technologies, people’s demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energy-saving and comfort.