Lysophosphatidic acid receptor 6 (LPAR6) is a G protein–coupled receptor that plays critical roles in cellular morphology and hair growth. Although LPAR6 overexpression is also critical for cancer cell proliferation, its role in liver cancer tumorigenesis and the underlying mechanism are poorly understood. Here, using liver cancer and matched paracancerous tissues, as well as functional assays including cell proliferation, quantitative real-time PCR, RNA-Seq, and ChIP assays, we report that LPAR6 expression is controlled by a mechanism whereby hepatocyte growth factor (HGF) suppresses liver cancer growth. We show that high LPAR6 expression promotes cell proliferation in liver cancer. More importantly, we find that LPAR6 is transcriptionally down-regulated by HGF treatment and that its transcriptional suppression depends on nuclear receptor coactivator 3 (NCOA3). We note that enrichment of NCOA3, which has histone acetyltransferase activity, is associated with histone 3 Lys-27 acetylation (H3K27ac) at the LPAR6 locus in response to HGF treatment, indicating that NCOA3 transcriptionally regulates LPAR6 through the HGF signaling cascade. Moreover, depletion of either LPAR6 or NCOA3 significantly inhibited tumor cell growth in vitro and in vivo (in mouse tumor xenograft assays), similar to the effect of the HGF treatment. Collectively, our findings indicate an epigenetic link between LPAR6 and HGF signaling in liver cancer cells, and suggest that LPAR6 can serve as a biomarker and new strategy for therapeutic interventions for managing liver cancer.

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Plateau pikas (Ochotona curzoniae) are regarded as one of the main causes of the degradation of alpine meadows in the Qinghai-Tibet Plateau (QTP). The population density of plateau pikas is directly related to the degree of grassland damage. In this study, field observation was conducted for one week in the southeastern QTP in August 2019. A random encounter model (REM) was used to estimate the population density of plateau pikas from photographs and videos, and the frequencies of different behaviors were calculated. In addition, the effects of water-source distance and terrain on the distribution of plateau pikas and the frequencies of different pika behaviors under different population densities were explored. The observations and knowledge derived from this study provide a reference for the population control of plateau pikas.

Abundant data sets produced from long-term series of high-resolution remote sensing data have made it possible to explore urban issues across different spatiotemporal scales. Based on a 40-year impervious area data set released by Tsinghua University, a method was developed to map the speed and acceleration of urban built-up areas. With the mapping results of the two indices, we characterised the spatiotemporal dynamics of built-up area expansion and captured different types of expansion. Combined with socioeconomic data, we examined the temporal changes and spatial heterogeneity of driving forces with an ordinary least square (OLS) model and a panel data model, as well as exploring the environmental effects of the expansion. Our results reveal that China has experienced drastic urban expansion over the last four decades. Among all cities, megacities and large cities in eastern China, as well as megacities in central and northeast China have experienced the most dramatic urban expansion. A growing number of cities are categorised as thriving, which means that they have both high expansion speed and acceleration. The overall driving force of urban expansion has significantly increased. More specifically, it was associated with population increase in the early stages; however, since 2000, it has been substantially associated with increases in GDP and fixed asset investments. The major driving factors also differ between regions and urban sizes. Urban expansion is identified as being closely associated with environmental deterioration; thus, speed and acceleration should be included as key indicators in exploring the environmental effects of urban expansion. In summary, the results of the presented case study, based on a data set of China, indicate that speed and acceleration are useful in analysing the driving forces of urban expansion and its environmental effects, and may generate more interest in related research.

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Sound waves have proven to be effective in promoting the interaction and aggregation of droplets. It is necessary to theoretically study the motion of particles in sound field to develop new acoustic technology for precipitation enhancement. In this paper, the motion of cloud droplets due to a traveling sound wave field emitted from the ground to the air is simulated using the motion equation of point particles. The force condition of the particles in the oscillating flow field is analysed. Meanwhile, the effects of droplet size, sound frequency, and Sound Pressure Level (SPL) on the velocity and displacement of the droplets are also investigated. The results show that Stokes force and gravity play a dominant role in the falling process of cloud droplets, and the effect of sound wave is mainly reflected in the fluctuation of velocity and displacement, which also promotes the displacement of cloud droplets to a certain extent. The maximum displacement increments of cloud droplets of 10 µm can reach 9,200 µm due to the action of sound waves of 50 Hz and 143.4 dB. The SPL required for a noticeable velocity fluctuation for droplets of 10 µm with frequency of 50 Hz is 88.2 dB. When SPL < 100 dB and frequency > 500 Hz, the effect is negligible. The cloud droplet size plays a significant role in the motion, and the sound action is weaker for larger particles. For a smaller sound frequency and higher SPL, the effect of the sound wave is more prominent.

The balance between exploitation and exploration essentially determines the performance of a population-based optimization algorithm, which is also a big challenge in algorithm design. Particle swarm optimization (PSO) has strong ability in exploitation, but is relatively weak in exploration, while crow search algorithm (CSA) is characterized by simplicity and more randomness. This study proposes a new crow swarm optimization algorithm coupling PSO and CSA, which provides the individuals the possibility of exploring the unknown regions under the guidance of another random individual. The proposed CSO algorithm is tested on several benchmark functions, including both unimodal and multimodal problems with different variable dimensions. The performance of the proposed CSO is evaluated by the optimization efficiency, the global search ability, and the robustness to parameter settings, all of which are improved to a great extent compared with either PSO and CSA, as the proposed CSO combines the advantages of PSO in exploitation and that of CSA in exploration, especially for complex high-dimensional problems.