A statistical normalization method and differential expression analysis for RNA-seq data between different species

Background: High-throughput techniques bring novel tools but also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, the normalization procedure serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects. Results: In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors. Conclusions: Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named "SCBN" and the software is available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html

Effects of Personality on Trading Performance in Social Trading Platforms

Social trading platforms offer opportunities for amateur investors to copy professional traders’ behavior. However, past studies on behavioral finance have largely neglected the role of personality in shaping traders’ behavior. To this end, we aim to scrutinize the effects of leader traders’ personality on their trading behaviors and subsequent performance on social trading platforms. Particularly, we employ the Myers–Briggs Type Indicator (MBTI) personality classification scheme to delineate leader traders’ personality into the four dimensions of Extraversion-Introversion (E-I), Sensing-Intuition (S-N), Thinking-Feeling (T-F), and Judging-Perceiving (J-P). Next, we draw on machine learning techniques to advance a novel text-based approach for extracting the personality dimensions of leader traders automatically. Analytical results attest to the impact of personality dimensions on trading behavior and that of trading behavior on performance. Findings from this study yield insights for both social trading platforms and followers by identifying profitable leader traders based on their personality

The Innovation Waltz: Unpacking Developers’ Response to Market Feedback and Its Effects on App Performance

To remain competitive in the intensely competitive mobile app market, developers often rely on user feedback to fuel the innovation process. Past studies, however, have rarely examined the impact of developers’ incremental innovation strategies by treating app innovation as a continuous process. This knowledge gap prompted us to advance a framework of developers’ incremental innovation strategies comprising four coping strategies: sailing, optimizing, supplementing, and patching. Employing a multi-state Markov model to capture the probability of a developer employing an incremental innovation strategy in response to distinct types of market feedback during the app innovation process, we analyze data sourced from the Android app store that consists of 4,583 apps, 29,307 updates, and 231,817 reviews. We discovered that market feedback affects the adoption of the four incremental innovation strategies differently. Additionally, we found that sailing, supplementing, and optimizing strategies boost app downloads, while supplementing, optimizing, and patching strategies improve app ratings

Effects of Personality on Social Performance in Social Trading

On social trading platforms, the income of leader traders is largely dictated by the number of copy trades conducted by their followers. Consequently, it is imperative for leader traders to exhibit appealing personalities to entice their followers to conduct copy trades. Drawing on social capital theory, we endeavor to scrutinize the effects of traders’ personalities on the accumulation of social capital, which in turn bolsters social performance as measured by the number of copy trades. Data was extracted from a leading social trading platform. The Myers–Briggs Type Indicator personality classification system was then employed to depict leader traders’ personalities based on a novel text-based, machine learning approach. Preliminary analytical results reveal significant relationships among personality traits, social capital dimensions, and social performance. Findings from this study generate insights for social trading platforms and leader traders on exhibiting desirable personalities conducive for accumulating social capital that entice followers to conduct copy trades

Identification and Functional Evaluation of miR-4633-5p as a Biomarker and Tumor Suppressor in Metastatic Melanoma

Background/Aims: Sinonasal mucosal melanoma (SMM) is a rare but extremely aggressive disease. Interestingly, however, as lethal as SMM, a few patients could survive for over 5 years without metastasis. However, biomarkers for metastatic SMM are lacking. Methods: Laser-capture microdissection combined with microRNA microarray and RT-qPCR was performed in formalin-fixed paraffin-embedded tissue samples from SMM patients whose follow-up studies were carried out in parallel. In vitro cell proliferation and invasion assays, gelatin zymography, western blot analysis and RT-qPCR were performed in melanoma cell lines. Results: In the discovery stage, miR-4633-5p expressed differentially in sinonasal mucosal melanoma patients with short and long disease-specific survival. Subsequent large-sample validation revealed that expression of miR-4633-5p was lower in metastatic SMM than in non-metastatic patients (P< 0.001). Moreover, miR-4633-5plow was able to identify metastatic SMM with specificity of 100% (5/5) and sensitivity of 87.5% (21/24). Multivariate analysis further pinpointed miR-4633-5p as an independent marker for metastasis (relative risk: 54.22, P< 0.001). In vitro, overexpression of miR-4633-5p suppressed the growth and invasiveness of melanoma cells through inhibiting activation of Akt pathway and secretion of MMP2, while knockdown of miR-4633-5p reversed the inhibitory effects. Conclusion: Our findings underpin miR-4633-5p as a predictive biomarker in metastatic SMM and a pivotal tumor suppressor that negatively regulates the invasive growth of melanoma cells. Quantitative detection of miR-4633-5p can diagnostically predict the risk of metastasis in SMM patients, which, in turn, may lead to more personalized treatment with better prognosis

Divergent Innovation: Directing the Wisdom of Crowd to Tackle Societal Challenges

Crowdsourcing is acknowledged as a promising avenue for addressing societal challenges by drawing on the wisdom of the crowd to offer diverse solutions to complex problems. Advancing a new conceptual framework of ‘divergent innovation’ which delineates between topic and quality divergence as focal metrics of performance when crowdsourcing for solutions to societal challenges, this study investigates the impacts of four ideation stimuli on divergent innovation. These four stimuli include task description concreteness, resource richness, topic entropy, and judging criteria comprehensiveness. Empirical analysis based on data sourced from an online crowd-ideation platform reveals that task description concreteness negatively affects topic divergence but positively influences quality divergence, whereas resource richness positively affects topic divergence but negatively influences quality divergence. Additionally, the relationship between topic entropy and topic divergence is U-shaped, with no significant impact on quality divergence. These findings contribute to extant literature on crowdsourcing and offer invaluable insights for practitioners

Two new genera of Apsilocephalidae from mid-Cretaceous Burmese amber

Apsilocephalidae is an enigmatic dipteran family erected by Nagatomi et al. (1991), including three extant genera and three additional extinct genera from the Eocene Baltic amber, Eocene Florissant, and mid-Cretaceous Burmese amber. We describe herein two new taxa, Myanmarpsilocephala grimaldii gen. et sp. nov. and Irwinimyia spinosa gen. et sp. nov., from mid-Cretaceous Burmese amber. The female genitalia of Myanmarpsilocephala gen. nov. and male genitalia of Irwinimyia gen. nov. are described and illustrated. The distribution of all Apsilocephalidae species and a key to all genera of Apsilocephalidae is provided. The described diversity of Apsilocephalidae in Burmese amber strongly suggests that apsilocephalid flies diversified at least by the mid-Cretaceous.This research was supported by the National Natural Science Foundation of China (41572010, 41622201, 41688103), the Chinese Academy of Sciences (XDPB05), and Youth Innovation Promotion Association of CAS (No. 2011224)

A META-ANALYSIS OF ABNORMAL GLUCOSE METABOLISM IN FIRST-EPISODE DRUG-NAIVE SCHIZOPHRENIA

Background: Patients with schizophrenia exhibit a higher mortality rate compared with the general population. This mortality has been attributed predominantly by the high risk of type 2 diabetes mellitus in the patients. We aimed to assess the inherent risk of glucose metabolism abnormalities in first-episode drug-naïve schizophrenia. Subjects and methods: We searched English database (PubMed, EMBASE, MEDLINE, Cochrane Library databases) and Chinese database (Wan Fang Data, CBM disc, VIP, and CNKI) from their inception until Jul 2018 for case-control studies examining glucose metabolism abnormalities. Measurements, such as fasting plasma glucose levels, fasting plasma insulin levels, insulin resistance and HbA1c levels in first episode antipsychotic-naive patients were used to test for prediabetes. Standardized/weighted mean differences and 95% confidence intervals were calculated and analyzed. Results: 19 studies (13 in English and 6 in Chinese) consisting of 1065 patients and 873 controls were included. Fasting plasma glucose levels (95% CI; 0.02 to 0.29; P=0.03), 2 h plasma glucose levels after an OGTT (95% CI; 0.63 to 1.2; P<0.00001), fasting plasma insulin levels (95% CI; 0.33 to 0.73; P<0.00001), insulin resistance (95% CI; 0.29 to 0.6; P<0.00001) in patients with firstepisode schizophrenia were significant elevated. There was no significant difference in HbA1c level (95% CI; -0.34 to 0.18; P=0.54) in patients with first-episode schizophrenia compared with controls. Conclusions: This meta-analysis showed that glucose metabolism was impaired in patients with first-episode schizophrenia. Higher quality studies with larger samples are warranted to confirm these findings

Reproductive Toxicity Assessment of Surface Water of the Tai Section of the Yangtze River, China by in vitro Bioassays Coupled With: Chemical Analysis. Environ. Pollut

a b s t r a c t Reproductive toxicity of organic extracts of the surface water from the Tai section of the Yangtze River was assessed by in vitro cytotoxity assays and selected persistent organic pollutants including PCBs, OCPs and PAHs were quantified by instrumental analysis. Eleven of the US EPA priority PAHs were detected. Individual PAHs were found to range from 0.7 to 20 ng/L. Concentrations of BaP did not exceed the national drinking water source quality standard of China. However, a 286-fold concentrated organic extract induced significant reproductive toxicity in adult male rats. The morphology of cells, MTT assay and LDH release assay were all affected by exposure to the organic extracts of water. The results of the reproductive toxicity indicated that PAHs posed the greatest risk of the chemicals studied. The compounds present in the water could be bioconcentrated and result in adverse effects