Screening and cloning of differentially expressed genes in Dendrobium nobile induced by orchid mycorrhizal fungus promoting the growth

Appropriate mycorrhizal fungi could effectively promote plant growth and development. Our previous research results showed that the growth of Dendrobium nobile was obviously promoted under inoculating one orchid mycorrhizal fungi, Epulorhiza sp. AR-18. To understand the growth-promoting molecular mechanisms, differential displayed real time polymerase chain reaction (DDRT-PCR), reverse Northern blot and Southern blot were used to isolate and identify genes whose transcription were altered in cultured D. nobile plants that were treated with Epulorhiza sp. AR-18. Amplified by 8 primer combinations from one anchor primers and 8 random primers, a total of 14 complementary DNA (cDNA) fragments including 12 differentially expressed cDNA bands were isolated. Reverse northern blot analysis showed that only 2 genes were differentially displayed cDNA bands. One band was an especially expressed fragment, expressed in the treated group but not in the control; while another was a differentially expressed fragment, weak in the treated and strength in the control. Southern blot analysis demonstrated that the two gene fragments were from the plant and not from the fungus. Sequence analysis and database searches revealed no significant homology to any known sequences. The results suggested that the usefulness of messenger RAN (mRNA) differential display technique for the detection of differentially expressed genes in D. nobile whose growth could be promoted by mycorrhizal fungi.Keywords: Dendrobium nobile, differential displayed real time polymerase chain reaction (DDRT-PCR), orchid mycorrhizal fungus, Epulorhiza sp., reverse northern blo

Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction

The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of future trajectories. However, the inference processes of most existing methods rely on Monte Carlo random sampling, which is insufficient to cover the realistic paths with finite samples, due to the long tail effect of the predicted distribution. To promote the sampling process of stochastic prediction, we propose a novel method, called BOsampler, to adaptively mine potential paths with Bayesian optimization in an unsupervised manner, as a sequential design strategy in which new prediction is dependent on the previously drawn samples. Specifically, we model the trajectory sampling as a Gaussian process and construct an acquisition function to measure the potential sampling value. This acquisition function applies the original distribution as prior and encourages exploring paths in the long-tail region. This sampling method can be integrated with existing stochastic predictive models without retraining. Experimental results on various baseline methods demonstrate the effectiveness of our method

Methodology of an exercise intervention program using social incentives and gamification for obese children

BACKGROUND: Traditional exercise [supervised exercise (SE)] intervention has been proved to be one of the most effective ways to improve metabolic health. However, most exercise interventions were on a high-cost and small scale, moreover lacking of the long-term effect due to low engagement. On the other hand, it was noteworthy that gamification and social incentives were promising strategies to increase engagement and sustain exercise interventions effects; as well as mobile technologies such as WeChat also can provide an appropriate platform to deploy interventions on a broader, low-cost scale. Thus, we aim to develop a novel exercise intervention (\u27SandG exercise intervention\u27) that combines SE intervention with gamification and social incentives design through WeChat, with the aim of improving metabolic health and poor behaviors among overweight and obesity children. METHODS: We propose a randomized controlled trial of a \u27SandG exercise intervention\u27 among 420 overweight and obese children who have at least one marker of metabolic syndrome. Children will be randomized to control or intervention group in a 1:1 ratio. The exercise intervention package includes intervention designs based on integrated social incentives and gamification theory, involving targeted essential volume and intensity of activity (skipping rope) as well as monitoring daily information and providing health advice by WeChat. Participants will undertake assessments at baseline, at end of intervention period, in the follow-up time at months 3,6,12. The primary outcome is outcome of metabolic health. Secondary outcomes include behavioral (e.g., diary physical activity, diet) and anthropometric measures (e.g., body fat rate and muscle mass). DISCUSSIONS: This will be the first study to design an exercise intervention model that combines traditional supervised exercise (SE) intervention with gamification and social incentives theory through WeChat. We believed that this study could explore a low-cost, easy-to-popularize, and effective exercise intervention model for improving metabolic health and promote healthy among obese children. Furthermore, it will also provide important evidence for guidelines to prevent and improve metabolic health and health behaviors. TRIAL REGISTRATION: 10-04-2019;Registration number: ChiCTR1900022396

Pt nanoparticles decorated heterostructured g-C3N4/Bi2MoO6 microplates with highly enhanced photocatalytic activities under visible light

Exploring an efficient and photostable heterostructured photocatalyst is a pivotal scientific topic for worldwide energy and environmental concerns. Herein, we reported that Pt decorated g-C3N4/Bi2MoO6 heterostructured composites with enhanced photocatalytic performance under visible light were simply synthesized by one-step hydrothermal method for methylene blue (MB) dye degradation. Results revealed that the synthetic Pt decorated g-C3N4/Bi2MoO6 composites with Bi2MoO6 contents of 20 wt.% (Pt@CN/20%BMO) presented the highest photocatalytic activity, exhibiting 7 and 18 times higher reactivity than the pure g-C3N4 and Bi2MoO6, respectively. Structural analyses showed that Bi2MoO6 microplates were anchored on the wrinkled flower-like g-C3N4 matrix with Pt decoration, leading to a large expansion of specific surface area from 10.79 m2/g for pure Bi2MoO6 to 46.09 m2/g for Pt@CN/20%BMO. In addition, the Pt@CN/20%BMO composites exhibited an improved absorption ability in the visible light region, presenting a promoted photocatalytic MB degradation. Quenching experiments were also conducted to provide solid evidences for the production of hydroxyl radicals (•OH), electrons (e−), holes (h+) and superoxide radicals (•O2−) during dye degradation. The findings in this critical work provide insights into the synthesis of heterostructured photocatalysts with the optimization of band gaps, light response and photocatalytic performance in wastewater remediation

What factors influence older people’s intention to enrol in nursing homes? A cross-sectional observational study in Shanghai, China

Objectives Given the increasing need of long-term care and the low occupancy rate of nursing homes in Shanghai, this study attempts to explore what factors influence older people’s intention to enrol in nursing homes. Design A cross-sectional observational study based on the theory of reasoned action was conducted. Survey data were collected from subjects during face-to-face interviews. Structural equation modelling was employed for data analysis. setting This study was conducted in six community health service centres in Shanghai, China. Two service centres were selected in urban, suburban and rural areas, respectively. Participants A total of 641 Shanghai residents aged over 60 were surveyed. results Structural equation modelling analysis showed that the research model fits the data well (χ2/df=2.948, Comparative Fit Index=0.972 and root mean squared error of approximation =0.055). Attitude (β=0.41, p<0.01), subjective norm (β=0.28, p<0.01) and value- added service (β=0.16, p<0.01) were directly associated with enrolment intention, explaining 32% of variance in intention. Attitude was significantly influenced by loneliness (β=−0.08, p<0.05), self-efficacy (β=0.32, p<0.01) and stigma (β=−0.24, p<0.01), while subjective norm was significantly influenced by life satisfaction (β=−0.15, p<0.01) and stigma (β=−0.43, p<0.01). Conclusions This study advances knowledge regarding the influencing factors of older people’s intention to enrol in nursing homes. It suggests that Chinese older persons’ perceived stigma has the strongest indirect effect on their intention to enrol in nursing homes. This is unique to the Chinese context and has practical implications for eldercare in China and other Asian countries with similar sociocultural contexts

Using Gamification and Social Incentives to Increase Physical Activity and Related Social Cognition among Undergraduate Students in Shanghai, China

Gamification and social incentives are promising strategies to increase the effectiveness of web-based physical activity (PA) interventions by improving engagement. In this study, we designed a PA intervention integrating gamification and social incentives based on the most popular social networking service in China, WeChat. A controlled trial involving 52 Chinese undergraduate students was implemented to evaluate the effectiveness of the intervention. Subjects in the intervention group received a 7-week intervention. PA behavior and related social cognitive variables according to the theory of planned behavior were measured at the baseline and after the intervention. Daily physical activity duration was measured during the intervention. The results showed that PA-related subjective norms, perceived behavior control, and intention, as well as self-reported vigorous physical activity and moderate physical activity in the intervention group, were increased after the intervention, compared with the control group (p <0.05). During the intervention, perceived daily physical activity duration in the intervention group was on the rise, while it declined in the control group (p <0.001). The findings indicate that WeChat-based intervention integrating gamification and social incentives could effectively increase subjectively measured PA and related social cognition among Chinese undergraduate students and that it is a promising way to ameliorate the problem of insufficient PA among youths

UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realizes global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation. Additionally, current methods are not robust and efficient for heavy-duty medical segmentation tasks such as predicting a large number of tissue classes or modeling globally inter-connected tissue structures. To address such challenges and inspired by the nested hierarchical structures in vision transformer, we proposed a novel 3D medical image segmentation method (UNesT), employing a simplified and faster-converging transformer encoder design that achieves local communication among spatially adjacent patch sequences by aggregating them hierarchically. We extensively validate our method on multiple challenging datasets, consisting of multiple modalities, anatomies, and a wide range of tissue classes, including 133 structures in the brain, 14 organs in the abdomen, 4 hierarchical components in the kidneys, inter-connected kidney tumors and brain tumors. We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency. Particularly, the model achieves whole brain segmentation task complete ROI with 133 tissue classes in a single network, outperforming prior state-of-the-art method SLANT27 ensembled with 27 networks.Comment: 19 pages, 17 figures. arXiv admin note: text overlap with arXiv:2203.0243