Objective: To evaluate the effect of vitamin D 3 on blood pressure in people with vitamin D deficiency. Methods: Randomized controlled trials (RCTs) were electronically searched databases including CNKI, VIP, WanFang Data, the Cochrane Library, PubMed, and EMbase which were about oral vitamin D 3 among people with vitamin D deficiency from inception to December 2017. Two reviewers independently screened literature according to the inclusion and extracted data; meta-analysis was performed using RevMan5.3. Results: A total of 17 RCTs with 22 arms involving 1687 participants were included. The results of meta-analysis showed that, there were no significant differences between the vitamin D deficiency group and the control group on the level of change in systolic pressure (ΔSBP) [weighted mean difference (WMD) = −1.94, 95% confidence interval (CI) (−3.93,0.04) P = .06] and on the level of change in diastolic pressure (ΔDBP) [WMD = −0.50, 95% CI (−1.17, 0.17) P = .14]. The results of subgroups showed that, there were statistically significant differences in the age of >50 years subgroup on ΔSBP [WMD = −2.32, 95% CI (−4.39, −0.25) P = .03]; there were statistically significant differences in the hypertension subgroup on ΔSBP [WMD = −6.58, 95% CI (−8.72, −4.44) P <.00001]; there were statistically significant differences in the hypertension subgroup on ΔDBP [WMD = −3.07, 95% CI (−4.66, −1.48) P = .0002]; there were statistically significant differences in the body mass index (BMI) >30 subgroup on ΔSBP [WMD = −3.51, 95% CI (−5.96, −1.07) P = .005]. Conclusion: Oral vitamin D 3 has no significant effect on blood pressure in people with vitamin D deficiency. It reduces systolic blood pressure in people with vitamin D deficiency that was older than 50 years old or obese. It reduces systolic blood pressure and diastolic pressure in people with both vitamin D deficiency and hypertension.

Vivipary in plants refers to a specific seed development and reproductive strategy where seeds minimize the dormancy stage and germinate while still attached to their maternal plants. It is one of the most unique adaptive genetic features used by many mangrove species where elongated hypocotyls aid in quick root emergence to anchor the seedling in coastal intertidal wetlands. The genetic mechanisms behind mangrove vivipary, however, remain elusive. Using comparative genomic and transcriptomic technologies to investigate viviparous mangroves and their close inland relatives, we found that a full array of gene expression profiles were altered, including key plant hormone metabolic pathways, high expression of embryonic signature genes, and reduced production of proanthocyanidins and storage proteins. Along with these changes, a major gene regulating seed dormancy, Delay of Germination-1 (DOG1), is entirely missing or defunct within the entire linage of the four genera with true viviparous characteristics. These results suggest a systemic level change is required to warrant the genetic program of mangrove vivipary. Understanding of the molecular processes of vivipary could benefit the design of pregerminated propagules for forestation in harsh environments or prevent precocious germination of grain crops pre- and post-harvest.

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With the rapid enhancement of computer computing power, deep learning methods, e.g., convolution neural networks, recurrent neural networks, etc., have been applied in wireless network widely and achieved impressive performance. In recent years, in order to mine the topology information of graphstructured data in wireless network as well as contextual information, graph neural networks have been introduced and have achieved the state-of-the-art performance of a series of wireless network problems. In this review, we first simply introduce the progress of several classical paradigms, such as graph convolutional neural networks, graph attention networks, graph autoencoder, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks, of graph neural networks comprehensively. Then, several applications of graph neural networks in wireless networks such as power control, link scheduling, channel control, wireless traffic prediction, vehicular communication, point cloud, etc., are discussed in detail. Finally, some research trends about the applications of graph neural networks in wireless networks are discussed.

The city landscape is largely related to the design concept and aesthetics of planners. Influenced by globalization, planners and architects have borrowed from available designs, resulting in the “one city with a thousand faces” phenomenon. In order to create a unique urban landscape, they need to focus on local urban characteristics while learning new knowledge. Therefore, it is particularly important to explore the characteristics of cities’ landscapes. Previous researchers have studied them from different perspectives through social media data such as element types and feature maps. They only considered the content information of a image. However, social media images themselves have a “photographic cultural” character, which affects the city character. Therefore, we introduce this characteristic and propose a deep style learning for the city landscape method that can learn the global landscape features of cities from massive social media images encoded as vectors called city style features (CSFs). We find that CSFs can describe two landscape features: (1) intercity landscape features, which can quantitatively assess the similarity of intercity landscapes (we find that cities in close geographical proximity tend to have greater visual similarity to each other), and (2) intracity landscape features, which contain the inherent style characteristics of cities, and more fine-grained internal-city style characteristics can be obtained through cluster analysis. We validate the effectiveness of the above method on over four million Flickr social media images. The method proposed in this paper also provides a feasible approach for urban style analysis.