Understanding bacterial biofilms: From definition to treatment strategies

Bacterial biofilms are complex microbial communities encased in extracellular polymeric substances. Their formation is a multi-step process. Biofilms are a significant problem in treating bacterial infections and are one of the main reasons for the persistence of infections. They can exhibit increased resistance to classical antibiotics and cause disease through device-related and non-device (tissue) -associated infections, posing a severe threat to global health issues. Therefore, early detection and search for new and alternative treatments are essential for treating and suppressing biofilm-associated infections. In this paper, we systematically reviewed the formation of bacterial biofilms, associated infections, detection methods, and potential treatment strategies, aiming to provide researchers with the latest progress in the detection and treatment of bacterial biofilms

HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation

Controllable human image generation (HIG) has numerous real-life applications. State-of-the-art solutions, such as ControlNet and T2I-Adapter, introduce an additional learnable branch on top of the frozen pre-trained stable diffusion (SD) model, which can enforce various conditions, including skeleton guidance of HIG. While such a plug-and-play approach is appealing, the inevitable and uncertain conflicts between the original images produced from the frozen SD branch and the given condition incur significant challenges for the learnable branch, which essentially conducts image feature editing for condition enforcement. In this work, we propose a native skeleton-guided diffusion model for controllable HIG called HumanSD. Instead of performing image editing with dual-branch diffusion, we fine-tune the original SD model using a novel heatmap-guided denoising loss. This strategy effectively and efficiently strengthens the given skeleton condition during model training while mitigating the catastrophic forgetting effects. HumanSD is fine-tuned on the assembly of three large-scale human-centric datasets with text-image-pose information, two of which are established in this work. As shown in Figure 1, HumanSD outperforms ControlNet in terms of accurate pose control and image quality, particularly when the given skeleton guidance is sophisticated

Association of psychological symptoms with job burnout and occupational stress among coal miners in Xinjiang, China: A cross-sectional study

ObjectiveThe study aimed to investigate the influencing factors of psychological symptoms in relation to job burnout and occupational stress among coal miners in Xinjiang, so as to provide data support for enterprises in an effort to help them identify internal psychological risk factors and improve the mental health of coal miners.MethodsA cross-sectional study was carried out. A total of 12 coal mines were selected using the stratified cluster random sampling method and 4,109 coal miners were investigated by means of online electronic questionnaires. The Symptoms Check List-90 (SCL-90), Chinese Maslach Burnout Inventory (CMBI), and Job Demand-Control (JDC) model were respectively used to measure the status of psychological symptoms, job burnout, and occupational stress among coal miners. The mediation analysis was performed through structural equation modeling (SEM) by using Analysis of Moment Structure (AMOS).ResultsThe prevalence of psychological symptoms was higher in the occupational stress group than in the non-occupational stress group, and increased with job burnout (P < 0.05). The multivariate logistic regression analysis results showed that mild (OR = 1.401, 95% CL: 1.165, 1.685), moderate (OR = 2.190, 95% CL: 1.795, 2.672), or severe levels of burnout (OR = 6.102, 95% CL: 3.481, 10.694) and occupational stress (OR = 1.462, 95% CL: 1.272, 1.679) were risk factors for psychological symptoms in coal miners. The results of structural equation modeling indicated that occupational stress (β = 0.11, P = 0.002) and job burnout (β = 0.46, P = 0.002) had significant positive direct effects on psychological symptoms, and job burnout was an intermediate variable between occupational stress and psychological symptoms.ConclusionHigh levels of job burnout and occupational stress were risk factors for psychological symptoms. Both occupational stress and job burnout had direct effects on psychological symptoms, and occupational stress could also have an indirect effect on coal miners' psychological symptoms through the intermediate variable of job burnout

Numerical investigation on flow-induced structural vibration and noise in centrifugal pump

A full scale structural vibration and noise induced by flow was simulated by a hybrid numerical method. An interior flow field was solved by large eddy simulation firstly. The sliding mesh technique was applied to take into account the impeller-volute interaction. A sensitivity analysis on effects of near-wall grid size and sampling time on amplitude of pressure pulsations was performed to impose appropriate vibration exciting source. Computed modal of pump components was validated by experimental results, before the volute vibration and sound field were simulated using a coupled vibro-acoustic model. The numerical results indicated that the amplitude of pressure fluctuation, especially on those points located at near the volute tongue, strongly depended on near-wall grid size. The dominated frequency of the vibration velocity of volute was also blade-passing frequency (BPF), which was in according with frequency spectral characteristics of unsteady pressure fluctuation. Directivity distribution of radiation acoustic field excited by volute vibration was typical dipoles. This study shows that it is feasible to use the hybrid numerical method to evaluate the flow-induced vibration and noise generated in centrifugal pump

Neural Mixed Platoon Controller Design

Vehicle platooning can be formulated as an optimal control problem and many solving paradigms, such as Pontryagin's maximum principle-based and dynamical programming methods, have been recently developed. However, these methods usually rely on solving a group of necessary conditions or Hamilton-Jacobi-Bellman (HJB) partial differential equations, which is hard to calculate. Besides, due to the heterogeneous dynamics of different vehicles in a mixed and complex platoon which comprises of not only connected autonomous vehicles (CAVs), but also human-driven vehicles (HDVs), it is also challenging to coordinate the behaviors of different vehicles in an unified control framework. Here we provide a Neural Mixed Platoon Control (NMPC) framework, a novel control design for mixed vehicle platooning based on a neural ordinary differential equation (NODE). We first formulate an optimal control model that incorporates the heterogeneous dynamics of a leading CAV and several following HDVs. We use a neural network to parameterize a state-feedback controller and join the neural controller and the mixed platooning dynamics into the NODE solver to create a closed-loop and learnable controlled system. The resulting system can learn optimal control inputs driving the mixed platoon to evolve from a given beginning condition to the target state within a finite duration in an unsupervised manner. Finally, simulation results validate our suggested method's usefulness in terms of space headway and velocity tracking

Eugene: Towards deep intelligence as a service

National Research Foundation (NRF) Singapore under its International Research Centres in Singapore Funding Initiativ

STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks

Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human brain, thereby leading to significant advantages at perceptual tasks such as vision and speech recognition. IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain. This observation leads to a fundamental question: For IoT applications, can one develop a new brand of neural network structures that synthesize features inspired not only by the biology of human perception but also by the fundamental nature of physics? Hence, in this paper, instead of using conventional building blocks (e.g., convolutional and recurrent layers), we propose a new foundational neural network building block, the Short-Time Fourier Neural Network (STFNet). It integrates a widely-used time-frequency analysis method, the Short-Time Fourier Transform, into data processing to learn features directly in the frequency domain, where the physics of underlying phenomena leave better foot-prints. STFNets bring additional flexibility to time-frequency analysis by offering novel nonlinear learnable operations that are spectral-compatible. Moreover, STFNets show that transforming signals to a domain that is more connected to the underlying physics greatly simplifies the learning process. We demonstrate the effectiveness of STFNets with extensive experiments. STFNets significantly outperform the state-of-the-art deep learning models in all experiments. A STFNet, therefore, demonstrates superior capability as the fundamental building block of deep neural networks for IoT applications for various sensor inputs

Metabolomics and transcriptomics analyses for characterizing the alkaloid metabolism of Chinese jujube and sour jujube fruits

IntroductionJujube is an important economic forest tree whose fruit is rich in alkaloids. Chinese jujube (Ziziphus jujuba Mill.) and sour jujube (Ziziphus spinosa Hu.) are the two most important species of the jujube genus. However, the mechanisms underlying the synthesis and metabolism of alkaloids in jujube fruits remain poorly understood.MethodsIn this study, the fruits of Ziziphus jujuba ‘Hupingzao’ and Ziziphus spinosa ‘Taigusuanzao’ in different harvest stages were used as test materials, we first integrated widely targeted metabolomics and transcriptomics analyses to elucidate the metabolism of alkaloids of jujube fruits.ResultsIn the metabolomics analysis, 44 alkaloid metabolites were identified in 4 samples, 3 of which were unique to sour jujube fruit. The differential alkaloid metabolites (DAMs) were more accumulated in sour jujube than in Chinese jujube; further, they were more accumulated in the white ripening stage than in the red stage. DAMs were annotated to 12 metabolic pathways. Additionally, transcriptomics data revealed 259 differentially expressed genes (DEGs) involved in alkaloid synthesis and metabolism. By mapping the regulatory networks of DAMs and DEGs, we screened out important metabolites and 11 candidate genes.DiscussionThis study preliminarily elucidated the molecular mechanism of jujube alkaloid synthesis. The candidate genes regulated the synthesis of key alkaloid metabolites, but the specific regulation mechanism is unclear. Taken together, our results provide insights into the metabolic networks of alkaloid synthesis in Chinese jujube and sour jujube fruits at different harvest stages, thereby providing a theoretical reference for further research on the regulatory mechanism of jujube alkaloids and their development and utilization

Investment Case for a Comprehensive Package of Interventions Against Hepatitis B in China: Applied Modeling to Help National Strategy Planning.

BACKGROUND content: In 2016, the first global viral hepatitis elimination targets were endorsed. An estimated one-third of the world's population of individuals with chronic hepatitis B virus (HBV) infection live in China and liver cancer is the sixth leading cause of mortality, but coverage of first-line antiviral treatment was low. In 2015, China was one of the first countries to initiate a consultative process for a renewed approach to viral hepatitis. We present the investment case for the scale-up of a comprehensive package of HBV interventions. METHODS content: A dynamic simulation model of HBV was developed and used to simulate the Chinese HBV epidemic. We evaluated the impact, costs, and return on investment of a comprehensive package of prevention and treatment interventions from a societal perspective, incorporating costs of management of end-stage liver disease and lost productivity costs. RESULTS content: Despite the successes of historical vaccination scale-up since 1992, there will be a projected 60 million people still living with HBV in 2030 and 10 million HBV-related deaths, including 5.7 million HBV-related cancer deaths between 2015 and 2030. This could be reduced by 2.1 million by highly active case-finding and optimal antiviral treatment regimens. The package of interventions is likely to have a positive return on investment to society of US$1.57 per US dollar invested. CONCLUSIONS content: Increases in HBV-related deaths for the next few decades pose a major public health threat in China. Active case-finding and access to optimal antiviral treatment are required to mitigate this risk. This investment case approach provides a real-world example of how applied modeling can support national dialog and inform policy planning