Liquidity and Stock Returns: Evidence from the UK Market

The existence of liquidity premium has been supported by much evidence from various empirical studies which use different liquidity measures, especially on the US stock market. However, few empirical studies focus on UK stock market. Therefore, the liquidity effect on UK stock returns needs more evidence to support. In this study, two common liquidity measures are used. One is turnover rate,capturing the trading quantity dimension. Another one used in robustness test is the bid-ask spread, which captures the transaction cost dimension. By employing these two liquidity proxies, the purpose of this study is to test whether the UK stock market shows a liquidity premium over the 10 years period from July 2002 to June 2012. In the time-series portfolio analysis, not only CAPM model but also the Fama-French three-factor model is used to control the size and book-to-market effects. Results from the time-series portfolio analysis show that although there are other factors would affect stock returns in the UK stock market, the liquidity factor still explain some time series variations in stock returns

Comparative transcriptome analysis and simple sequence repeat marker development for two closely related Isodon species used as ‘Xihuangcao’ herbs

Purpose: To facilitate the molecular identification of original plants, resolve taxonomic problems and identify standards for ‘Xihuangcao’-based products on the market.Methods: A transcriptomic analysis of two closely related species, i.e., Isodon serra (Maxim.) (IS) and I. lophanthoides (Buch.-Ham. ex D. Don) Hara, was conducted by using the Illumina HiSeq 2500 platform, and expressed sequence tag-derived simple sequence repeat (EST-SSR) markers were developed based on these transcriptomes.Results: In total, 149,650 and 103,221 contigs were obtained, with N50 values of 1,400 and 1,516, from the IS and I. lophanthoides RNA-Seq datasets, respectively. These contigs were clustered into 107,777 and 68,220 unigenes, which were functionally annotated to identify the genes involved in therapeutic components. In total, 14,138 and 11,756 EST-SSR motifs were identified, and of these motifs, 7,453 and 6,428 were used to design primers for IS and I. lophanthoides, respectively. After PCR verification and fluorescence-based genotyping, 24 SSR markers with bright bands, high polymorphism, and single amplification were obtained and used to identify closely related Isodon species/varieties.Conclusion: These data could help herbal scientists identify high-quality herbal plants and provide a reference for genetic improvement and population genetic and phylogenetic studies investigating ‘Xihuangcao’ herbs.Keywords: Xihuangcao, Transcriptome, EST-SSR, Molecular marker

Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning

Multimodal (e.g., RGB-Depth/RGB-Thermal) fusion has shown great potential for improving semantic segmentation in complex scenes (e.g., indoor/low-light conditions). Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion. To address this issue, we propose a surprisingly simple yet effective dual-prompt learning network (dubbed DPLNet) for training-efficient multimodal (e.g., RGB-D/T) semantic segmentation. The core of DPLNet is to directly adapt a frozen pre-trained RGB model to multimodal semantic segmentation, reducing parameter updates. For this purpose, we present two prompt learning modules, comprising multimodal prompt generator (MPG) and multimodal feature adapter (MFA). MPG works to fuse the features from different modalities in a compact manner and is inserted from shadow to deep stages to generate the multi-level multimodal prompts that are injected into the frozen backbone, while MPG adapts prompted multimodal features in the frozen backbone for better multimodal semantic segmentation. Since both the MPG and MFA are lightweight, only a few trainable parameters (3.88M, 4.4% of the pre-trained backbone parameters) are introduced for multimodal feature fusion and learning. Using a simple decoder (3.27M parameters), DPLNet achieves new state-of-the-art performance or is on a par with other complex approaches on four RGB-D/T semantic segmentation datasets while satisfying parameter efficiency. Moreover, we show that DPLNet is general and applicable to other multimodal tasks such as salient object detection and video semantic segmentation. Without special design, DPLNet outperforms many complicated models. Our code will be available at github.com/ShaohuaDong2021/DPLNet.Comment: 11 pages, 4 figures, 9 table

mHealth Intervention is Effective in Creating Smoke-Free Homes for Newborns: A Randomized Controlled Trial Study in China.

Mobile-phone-based smoking cessation intervention has been shown to increase quitting among smokers. However, such intervention has not yet been applied to secondhand smoke (SHS) reduction programs that target smoking parents of newborns. This randomized controlled trial, undertaken in Changchun, China, assessed whether interventions that incorporate traditional and mobile-phone-based education will help create smoke-free homes for infants and increase quitting among fathers. The results showed that the abstinence rates of the fathers at 6 months (adjusted OR: 3.60, 95% CI: 1.41-9.25; p = 0.008) and 12 months (adjusted OR: 2.93, 95% CI: 1.24-6.94; p = 0.014) were both significantly increased in the intervention group compared to the control. Mothers of the newborns in the intervention group also reported reduced exposure to SHS at 12 months (adjusted OR: 0.53, 95% CI: 0.29-0.99; p = 0.046). The findings suggest that adding mHealth interventions to traditional face-to-face health counseling may be an effective way to increase male smoking cessation and reduce mother and newborn SHS exposure in the home

The Role of Butyric Acid in Treatment Response in Drug-Naive First Episode Schizophrenia

Background: Butyric acid, a major short-chain fatty acid (SCFA), has an important role in the microbiota-gut-brain axis and brain function. This study investigated the role of butyric acid in treatment response in drug-naive first episode schizophrenia. Methods: The study recruited 56 Chinese Han schizophrenia inpatients with normal body weight and 35 healthy controls. Serum levels of butyric acid were measured using Gas Chromatography-Mass Spectrometer (GC-MS) analysis at baseline (for all participants) and 24 weeks after risperidone treatment (for patients). Clinical symptoms were measured using the Positive and Negative Syndrome Scale (PANSS) for patients at both time points. Results: At baseline, there was no significant difference in serum levels of butyric acid between patients and healthy controls (p = 0.206). However, there was a significant increase in serum levels of butyric acid in schizophrenia patients after 24-week risperidone treatment (p = 0.030). The PANSS total and subscale scores were decreased significantly after 24-week risperidone treatment (p\u27s \u3c 0.001). There were positive associations between baseline serum levels of butyric acid and the reduction ratio of the PANSS total and subscale scores after controlling for age, sex, education, and duration of illness (p\u27s \u3c 0.05). Further, there was a positive association between the increase in serum levels of butyric acid and the reduction of the PANSS positive symptoms subscale scores (r = 0.38, p = 0.019) after controlling for potential confounding factors. Conclusions: Increased serum levels of butyric acid might be associated with a favorable treatment response in drug-naive, first episode schizophrenia. The clinical implications of our findings were discussed