The efficacy of mobile phone-based text message interventions (‘Happy Quit’) for smoking cessation in China

Background Considering the extreme shortage of smoking cessation services in China, and the acceptability, feasibility and efficacy of mobile phone-based text message interventions for quitting smoking in other countries, here we propose a study of “the efficacy of mobile phone-based text message interventions (‘Happy Quit’) for smoking cessation in China”. The primary objective of this proposed project is to assess whether a program of widely accessed mobile phone-based text message interventions (‘Happy Quit’) will be effective at helping people in China who smoke, to quit. Based on the efficacy of previous studies in smoking cessation, we hypothesize that ‘Happy Quit’ will be an effective, feasible and affordable smoking cessation program in China. Methods/Design In this single-blind, randomized trial, undertaken in China, about 2000 smokers willing to make a quit attempt will be randomly allocated, using an independent telephone randomization system that includes a minimization algorithm balancing for sex (male, female), age (19–34 or \u3e34 years), educational level (≤ or \u3e12 years), and Fagerstrom score for nicotine addiction (≤5, \u3e5), to ‘Happy Quit’, comprising motivational messages and behavioral-change support, or to a control group that receives text messages unrelated to quitting. Messages will be developed to be suitable for Chinese. A pilot study will be conducted before the intervention to modify the library of messages and interventions. The primary outcome will be self-reported continuous smoking abstinence. A secondary outcome will be point prevalence of abstinence. Abstinence will be assessed at six time points (4, 8, 12, 16, 20 and 24 weeks post-intervention). A third outcome will be reductions in number of cigarettes smoked per day. Discussion/Implications The results will provide valuable insights into bridging the gap between need and services received for smoking cessation interventions and tobacco use prevention in China. It will also serve as mHealth model for extending the public health significance of other interventions, such as mental health interventions

NaCl Pretreatment Enhances the Low Temperature Tolerance of Tomato Through Photosynthetic Acclimation

Plants often need to withstand multiple types of environmental stresses (e.g., salt and low temperature stress) because of their sessile nature. Although the physiological responses of plants to single stressor have been well-characterized, few studies have evaluated the extent to which pretreatment with non-lethal stressors can maintain the photosynthetic performance of plants in adverse environments (i.e., acclimation-induced cross-tolerance). Here, we studied the effects of sodium chloride (NaCl) pretreatment on the photosynthetic performance of tomato plants exposed to low temperature stress by measuring photosynthetic and chlorophyll fluorescence parameters, stomatal aperture, chloroplast quality, and the expression of stress signaling pathway-related genes. NaCl pretreatment significantly reduced the carbon dioxide assimilation rate, transpiration rate, and stomatal aperture of tomato leaves, but these physiological acclimations could mitigate the adverse effects of subsequent low temperatures compared with non-pretreated tomato plants. The content of photosynthetic pigments decreased and the ultra-microstructure of chloroplasts was damaged under low temperature stress, and the magnitude of these adverse effects was alleviated by NaCl pretreatment. The quantum yield of photosystem I (PSI) and photosystem II (PSII), the quantum yield of regulatory energy dissipation, and non-photochemical energy dissipation owing to donor-side limitation decreased following NaCl treatment; however, the opposite patterns were observed when NaCl-pretreated plants were exposed to low temperature stress. Similar results were obtained for the electron transfer rate of PSI, the electron transfer rate of PSII, and the estimated cyclic electron flow value (CEF). The production of reactive oxygen species induced by low temperature stress was also significantly alleviated by NaCl pretreatment. The expression of ion channel and tubulin-related genes affecting stomatal aperture, chlorophyll synthesis genes, antioxidant enzyme-related genes, and abscisic acid (ABA) and low temperature signaling-related genes was up-regulated in NaCl-pretreated plants under low temperature stress. Our findings indicated that CEF-mediated photoprotection, stomatal movement, the maintenance of chloroplast quality, and ABA and low temperature signaling pathways all play key roles in maintaining the photosynthetic capacity of NaCl-treated tomato plants under low temperature stress

Identification of microturbine model for long-term dynamic analysis of distribution networks

As one of the most successfully commercialized distributed energy resources, the long-term effects of microturbines (MTs) on the distribution network has not been fully investigated due to the complex thermo-fluid-mechanical energy conversion processes. This is further complicated by the fact that the parameter and internal data of MTs are not always available to the electric utility, due to different ownerships and confidentiality concerns. To address this issue, a general modeling approach for MTs is proposed in this paper, which allows for the long-term simulation of the distribution network with multiple MTs. First, the feasibility of deriving a simplified MT model for long-term dynamic analysis of the distribution network is discussed, based on the physical understanding of dynamic processes that occurred within MTs. Then a three-stage identification method is developed in order to obtain a piecewise MT model and predict electro-mechanical system behaviors with saturation. Next, assisted with the electric power flow calculation tool, a fast simulation methodology is proposed to evaluate the long-term impact of multiple MTs on the distribution network. Finally, the model is verified by using Capstone C30 microturbine experiments, and further applied to the dynamic simulation of a modified IEEE 37-node test feeder with promising results

Upregulation of Barrel GABAergic Neurons Is Associated with Cross-Modal Plasticity in Olfactory Deficit

Background: Loss of a sensory function is often followed by the hypersensitivity of other modalities in mammals, which secures them well-awareness to environmental changes. Cellular and molecular mechanisms underlying cross-modal sensory plasticity remain to be documented. Methodology/Principal Findings: Multidisciplinary approaches, such as electrophysiology, behavioral task and immunohistochemistry, were used to examine the involvement of specific types of neurons in cross-modal plasticity. We have established a mouse model that olfactory deficit leads to a whisking upregulation, and studied how GABAergic neurons are involved in this cross-modal plasticity. In the meantime of inducing whisker tactile hypersensitivity, the olfactory injury recruits more GABAergic neurons and their fine processes in the barrel cortex, as well as upregulates their capacity of encoding action potentials. The hyperpolarization driven by inhibitory inputs strengthens the encoding ability of their target cells. Conclusion/Significance: The upregulation of GABAergic neurons and the functional enhancement of neuronal networks may play an important role in cross-modal sensory plasticity. This finding provides the clues for developing therapeuti

The dynamic-process characterization and prediction of synthetic gene circuits by dynamic delay model

Summary: Differential equation models are widely used to describe genetic regulations, predict multicomponent regulatory circuits, and provide quantitative insights. However, it is still challenging to quantitatively link the dynamic behaviors with measured parameters in synthetic circuits. Here, we propose a dynamic delay model (DDM) which includes two simple parts: the dynamic determining part and the doses-related steady-state-determining part. The dynamic determining part is usually supposed as the delay time but without a clear formula. For the first time, we give the detail formula of the dynamic determining function and provide a method for measuring all parameters of synthetic elements (include 8 activators and 5 repressors) by microfluidic system. Three synthetic circuits were built to show that the DDM can notably improve the prediction accuracy and can be used in various synthetic biology applications

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This paper proposes a simplified microturbine (MT) model which allows for dynamic heat and power output prediction. Considering the time-scale difference of various dynamic processes occuring within MTs, the electromechanical subsystem is treated as a fast quasi-linear system while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A subspace model identification method is utilized to capture dominant dynamics and predict outputs of the electromechanical subsystem. For the thermo-mechanical process, a fast recursive algorithm assisted radial basis function model is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 MT show that the proposed modeling method can well capture the system dynamics and produce a good model to predict the interrelated heat and electricity outputs

Environmental Regulation and Green Technology Innovation under the Carbon Neutrality Goal: Dual Regulation of Human Capital and Industrial Structure

Green technology innovation is increasingly becoming an important driver of modern development. Facing the pressure of the carbon neutrality target, China has been strengthening environmental regulations in recent years, and the green technology innovation ability of market players has been affected. Moreover, the impact of environmental regulations on green technology innovation and the strategies to develop green technology innovation still need to be further explored. Here, we used 285 cities from 2010–2020 as research subjects, constructed indicators such as environmental regulation and green technology innovation capability, and used panel regression, threshold effect and mediating effect methods. The results reveal that: (1) environmental regulation has a disincentive effect on green technology innovation, as increased environmental regulation increases production costs, external costs and opportunity costs for firms, causing a mismatch of resources and creating a “crowding-out effect” that inhibits innovation development; (2) further study finds that when the human capital level reaches a certain threshold value, the impact of environmental regulation on green technology innovation shifts significantly, i.e., there is a threshold effect; (3) industrial structure can further be upgraded and optimized by environmental regulation, which will have an impact on green technology innovation, thus industrial structure optimization plays a mediating role. We conclude with recommendations for improving green innovation techniques for market players, and provide some lessons for accelerating the goal of carbon neutrality and promoting quality economic development

Systematic investigation of the pharmacological mechanism for renal protection by the leaves of Eucommia ulmoides Oliver using UPLC-Q-TOF/MS combined with network pharmacology analysis

Bark is the traditional medicinal component of Eucommia ulmoides Oliver (E. ulmoides). However, the demand for E. ulmoides medicinal materials seriously limits their sustainability. To alleviate resource constraints, the bioactivity of E. ulmoides leaves and its pharmacodynamic basis were investigated. In the present study, extracts of E. ulmoides leaves were found to display potential renal protective properties in rat glomerular mesangial (HBZY-1) cells treated with high levels of glucose, suggesting that they possess potential factors capable of treating diabetic nephropathy. Ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was used to comprehensively characterize the chemical components of E. ulmoides leaves. A total of 83 possible chemical components, including 12 iridoids, 13 flavonoids, 14 lignans, 20 phenylpropanoids, 14 phenolic acids, and 10 additional components, were identified in E. ulmoides leaves. Network pharmacology was used for a preliminary exploration of the potential mechanism of action of renal protection afforded by E. ulmoides leaves towards diabetic nephropathy. The network pharmacology results were verified using a series of biological experiments. The present study provided the basis for the comprehensive development and utilization of E. ulmoides leaves and the discovery of potential drugs

Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance

Accurate sleep stage classification is vital to assess sleep quality and diagnose sleep disorders. Numerous deep learning based models have been designed for accomplishing this labor automatically. However, the class imbalance problem existing in polysomnography (PSG) datasets has been barely investigated in previous studies, which is one of the most challenging obstacles for the real-world sleep staging application. To address this issue, this paper proposes novel methods with signal-driven and image-driven ways of noise addition to balance the imbalanced relationship in the training dataset samples. We evaluate the effectiveness of the proposed methods which are integrated into a convolutional neural network (CNN) based model. Experimental results evaluated on Sleep-EDF-V1, Sleep-EDF and CCSHS databases demonstrate that the proposed balancing approaches with specific tensity Gaussian white noise could enhance the overall or stage N1 recognition to some degree, especially the combination of two types of Data augmentation (DA) strategies shows the superiority of overall accuracy improvement.peerReviewe