Helping Beginning Vloggers to Overcome Cold Start: the Perspective of Identity Construction

Beginning vloggers’ low enthusiasm for Vlog creation has garnered little consideration, even though a social media platform can highly improve user stickiness and user activity by engaging users to generate content. This paper investigates the effects of extrinsic and intrinsic motivations on Vlog creative behavior mediated by cognition and emotion based on social cognitive theory and self-discrepancy theory. The analysis of 342 questionnaire surveys shows that intrinsic motivation (social interaction and social cues presentation) positively affects identity construction and positive emotions. In contrast, extrinsic motivation (community incentives and social norms) only positively affects identity construction and does not significantly influence positive emotions. Identity construction and positive emotions further significantly affect the creative behavior of beginning vloggers. The results reveal the process of Vlog creative behavior and have important practical implications for enhancing the platform performance

Optimal allocation of distributed renewable generations in low-carbon distribution system considering impact of natural disasters

With global climate change, increasingly frequent natural disasters have brought great challenges to the safe and reliable power supply and low-carbon transition of power distribution systems. Most of the existing researches on the distribution system under the impact of natural disasters only focus on the improvement of power supply reliability, but have not consider the impact of disaster severity and disaster response measures on carbon emissions. In order to juggle the load restoration and carbon emission mitigation of distribution system under natural disasters, this paper proposes an optimal allocation method of distributed renewable generations (DRGs) considering carbon emission for multi-scenario natural disasters based on the framework of cyber-physical-social system in energy (CPSSE), and establishes a three-stage optimization model of pre-disaster prevention-disaster attack-post-disaster restoration. For the purpose of ensuring the practicability and robustness of the allocation results, the disaster scenario is modeled and the selection method of the worst fault scenario under disaster is proposed. The progressive hedging algorithm (PHA) is adopted to solve the proposed multi-scenario optimization problem. Finally, the simulation results indicate that the proposed method can restore more lost load at a lower cost of carbon emissions

Gut microbiota alterations are associated with functional outcomes in patients of acute ischemic stroke with non-alcoholic fatty liver disease

IntroductionPatients with acute ischemic stroke (AIS) with non-alcoholic fatty liver disease (NAFLD) frequently have poor prognosis. Many evidences suggested that the changes in gut microbiota may play an important role in the occurrence and development of AIS patients with NAFLD. The purpose of this study was to explore microbial characteristics in patients of AIS with NAFLD, and the correlation between gut microbiota and functional outcomes.MethodsThe patients of AIS were recruited and divided into NAFLD group and non-NAFLD group. The stool samples and clinical information were collected. 16 s rRNA sequencing was used to analyze the characteristics of gut microbiota. The patients of AIS with NAFLD were followed-up to evaluate the functional outcomes of disease. The adverse outcomes were determined by modified Rankin scale (mRS) scores at 3 months after stroke. The diagnostic performance of microbial marker in predicting adverse outcomes was assessed by recipient operating characteristic (ROC) curves.ResultsOur results showed that the composition of gut microbiota between non-NAFLD group and NAFLD group were different. The characteristic bacteria in the patients of AIS with NAFLD was that the relative abundance of Dorea, Dialister, Intestinibacter and Flavonifractor were decreased, while the relative abundance of Enorma was increased. Moreover, the characteristic microbiota was correlated with many clinical parameters, such as mRS scores, mean arterial pressure and fasting blood glucose level. In addition, ROC models based on the characteristic microbiota or the combination of characteristic microbiota with independent risk factors could distinguish functional dependence patients and functional independence patients in AIS with NAFLD (area under curve is 0.765 and 0.882 respectively).ConclusionThese findings revealed the microbial characteristics in patients of AIS with NAFLD, and further demonstrated the predictive capability of characteristic microbiota for adverse outcomes in patients of AIS with NAFLD

Overexpressed transient receptor potential vanilloid 1 (TRPV1) in lung adenocarcinoma harbours a new opportunity for therapeutic targeting

The specific biological function of transient receptor potential vanilloid 1 (TRPV1) in pathogenesis of lung adenocarcinoma (LUAD) remains unclear. In this study, TRPV1 expression in tumor tissues, primary cells and cell lines of LUAD, as well as the mechanism mediating its hyperexpression were systematically studied. Multiple models and techniques were adopted to elucidate the relationship between TRPV1 hyperexpression and tumor recurrence and metastasis. Results showed that TRPV1 expression was increased in tumor tissues and primary tumor cells of LUAD patients. The increased expression was associated with worse overall survival outcome and raised HIF1α levels. TRPV1 expression in A549 and NCI-H292 cells was increased after pretreatment with cigarette smoke extract or spermine NONOate. Moreover, A549 cells with TRPV1 overexpression has enhanced tumor growth rates in subcutaneous grafted tumor models, and increased intrapulmonary metastasis after tail vein infusion in nude BALB/c nude mice. Mechanistically, TRPV1 overexpression in A549 cells promoted HIF1α expression and nuclear translocation by promoting CREB phosphorylation and activation of NOS1-NO pathway, ultimately leading to accelerated cell proliferation and stronger invasiveness. In addition, based on photothermal effects, CuS-TRPV1 mAb effectively targeted and induced apoptosis of TRPV1-A549 cells both in vivo and in vitro, thereby mitigating tumor growth and metastasis induced by xenotransplantation of TRPV1-A549 cells. In conclusion, TRPV1 hyperexpression in LUAD is a risk factor for tumor progression and is involved in proliferation and migration of tumor cells through activation of HIF1α. Our study also attempted a new strategy inhibiting the recurrence and metastasis of LUAD: by CuS-TRPV1 mAb precisely kill TRPV1 hyperexpression cells through photothermal effects

Systematic, comprehensive, evidence-based approach to identify neuroprotective interventions for motor neuron disease: using systematic reviews to inform expert consensus

Objectives: Motor neuron disease (MND) is an incurable progressive neurodegenerative disease with limited treatment options. There is a pressing need for innovation in identifying therapies to take to clinical trial. Here, we detail a systematic and structured evidence-based approach to inform consensus decision making to select the first two drugs for evaluation in Motor Neuron Disease-Systematic Multi-arm Adaptive Randomised Trial (MND-SMART: NCT04302870), an adaptive platform trial. We aim to identify and prioritise candidate drugs which have the best available evidence for efficacy, acceptable safety profiles and are feasible for evaluation within the trial protocol. Methods: We conducted a two-stage systematic review to identify potential neuroprotective interventions. First, we reviewed clinical studies in MND, Alzheimer’s disease, Huntington’s disease, Parkinson’s disease and multiple sclerosis, identifying drugs described in at least one MND publication or publications in two or more other diseases. We scored and ranked drugs using a metric evaluating safety, efficacy, study size and study quality. In stage two, we reviewed efficacy of drugs in MND animal models, multicellular eukaryotic models and human induced pluripotent stem cell (iPSC) studies. An expert panel reviewed candidate drugs over two shortlisting rounds and a final selection round, considering the systematic review findings, late breaking evidence, mechanistic plausibility, safety, tolerability and feasibility of evaluation in MND-SMART. Results: From the clinical review, we identified 595 interventions. 66 drugs met our drug/disease logic. Of these, 22 drugs with supportive clinical and preclinical evidence were shortlisted at round 1. Seven drugs proceeded to round 2. The panel reached a consensus to evaluate memantine and trazodone as the first two arms of MND-SMART. Discussion: For future drug selection, we will incorporate automation tools, text-mining and machine learning techniques to the systematic reviews and consider data generated from other domains, including high-throughput phenotypic screening of human iPSCs

Cassava genome from a wild ancestor to cultivated varieties

Cassava is a major tropical food crop in the Euphorbiaceae family that has high carbohydrate production potential and adaptability to diverse environments. Here we present the draft genome sequences of a wild ancestor and a domesticated variety of cassava and comparative analyses with a partial inbred line. We identify 1,584 and 1,678 gene models specific to the wild and domesticated varieties, respectively, and discover high heterozygosity and millions of single-nucleotide variations. Our analyses reveal that genes involved in photosynthesis, starch accumulation and abiotic stresses have been positively selected, whereas those involved in cell wall biosynthesis and secondary metabolism, including cyanogenic glucoside formation, have been negatively selected in the cultivated varieties, reflecting the result of natural selection and domestication. Differences in microRNA genes and retrotransposon regulation could partly explain an increased carbon flux towards starch accumulation and reduced cyanogenic glucoside accumulation in domesticated cassava. These results may contribute to genetic improvement of cassava through better understanding of its biology

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages