RNA-Seq Analyses of the Role of miR-21 in Acute Pancreatitis

Background/Aims: Our previous study demonstrated that a deficiency of microRNA 21 (miR-21) protects mice from acute pancreatitis, yet the underlying molecular networks associated with miR-21 in pancreatitis and pancreatitis-associated lung injury remain unexplored. Methods: We used next generation sequencing to analyze gene expression profiles of pancreatic tissues from wild-type (WT) and miR-21 knockout (KO) mice treated with caerulein by using a 1-day treatment protocol. The Database for Annotation, Visualization, and Integrated Discovery gene annotation tool and Ingenuity Pathway Analysis were used to analyze the molecular pathways, while quantitative real-time PCR, western blotting, and immunohistochemistry were used to explore the molecular mechanisms. Results: We identified 152 differentially expressed genes (DEGs) in pancreata between WT and KO mice treated with caerulein. Cellular biogenesis and metabolism were the major pathways affected between WT and KO mice, whereas cell death and inflammatory response discriminated between WT and KO mice under acute pancreatitis. We validated 16 DEGs, consisting of 6 upregulated genes and 10 downregulated genes, involved in pancreatic injury. In particular, the upregulation of Pias3 and downregulation of Hmgb1 in KO pancreata coincided with a reduced severity of pancreatitis. In addition, we found Hmgb1 stimulation resulted in the overexpression of miR-21 in peripheral blood mononuclear cells, and deletion of miR-21 led to a reduction of caerulein-induced acute pancreatitis-associated lung injury by repressing Hmgb1 expression. Conclusion: Our data support the hypothesis that miR-21 modulates the inflammatory response during acute pancreatitis through the upregulation of Pias3 and downregulation of Hmgb1. Our findings further underscore a role for miR-21 in the promotion of acute pancreatitis

Quantifying the social impacts of the London Night Tube with a double/debiased machine learning based difference-in-differences approach

There is a worldwide trend toward a growing number of people involved in various night-time activities. The night-time public transport service is of central importance for the urban night-time mobility. In London, the Night Tube service was launched in 2016 to meet the constantly growing night-time travel demand and support London’s night-time economy. Yet limited empirical evidence on the ex-post impacts of the London Night Tube has been provided. In this study, we conduct a causal analysis on such impacts using a double/debiased machine learning based difference-in-differences approach. Specifically, we quantify the impacts of the Night Tube on London’s night-time economy, house prices, road crashes and related casualties, and crimes. We further investigate the spatial variations in such impacts. Our results indicate a rise in house prices associated with the announcement and the implementation of the service. The number of night-time workplaces showed a limited response. Regarding the safety dimension, we find that the Night Tube service led to a small reduction in the frequency of road crashes but a substantial reduction in crash-related casualties. However, the crime rate in areas served by the Night Tube was increased, especially for the following two categories, robbery of personal property and violence against the person. Moreover, the impact on the crime rate is found to be larger in the inner London area. These findings provide practical implications for urban planners and policy makers, and reveal the need for monitoring the social impacts of the Night Tube service from a long-term perspective

The Impact of Vehicle Ownership on Carbon Emissions in the Transportation Sector

As one of the important sources of carbon emissions, the transportation industry should be given attention. This study investigates the relationship between vehicle ownership, economic growth, and environmental pressure on the Chongqing transportation industry (CQTI) based on CQTI data, then constructs a comprehensive regression model and couples the EKC curve and Tapio model for integrated analysis, and finally constructs a LEAP-Chongqing model to forecast CQTI from multiple perspectives. The innovations are that the multi-model examines the effects of different variables and has a better classification of transportation modes in scenario simulation. The results show that: (1) there is an inverse N-shaped relationship between car ownership, economic growth, and environmental pressure of CQTI; (2) every 1% of transportation output, urbanization rate, or car ownership will cause 0.769%, 0.111%, and 0.096% of carbon emission change, respectively; (3) gasoline, diesel and aviation kerosene consumption account for 80–90%, private cars cause 41–52% of carbon emissions, and the energy structure and transportation structure of CQTI are unreasonable; (4) the results of a multi-scenario simulation show that the energy saving and emission reduction effect of a single policy is not satisfactory, and the integration of energy saving and emission reduction measures should be strengthened

The Impact of Vehicle Ownership on Carbon Emissions in the Transportation Sector

As one of the important sources of carbon emissions, the transportation industry should be given attention. This study investigates the relationship between vehicle ownership, economic growth, and environmental pressure on the Chongqing transportation industry (CQTI) based on CQTI data, then constructs a comprehensive regression model and couples the EKC curve and Tapio model for integrated analysis, and finally constructs a LEAP-Chongqing model to forecast CQTI from multiple perspectives. The innovations are that the multi-model examines the effects of different variables and has a better classification of transportation modes in scenario simulation. The results show that: (1) there is an inverse N-shaped relationship between car ownership, economic growth, and environmental pressure of CQTI; (2) every 1% of transportation output, urbanization rate, or car ownership will cause 0.769%, 0.111%, and 0.096% of carbon emission change, respectively; (3) gasoline, diesel and aviation kerosene consumption account for 80–90%, private cars cause 41–52% of carbon emissions, and the energy structure and transportation structure of CQTI are unreasonable; (4) the results of a multi-scenario simulation show that the energy saving and emission reduction effect of a single policy is not satisfactory, and the integration of energy saving and emission reduction measures should be strengthened

Changing Structure and Sustainable Development for China’s Hog Sector

International audienceSupply shortages and competitive disadvantages are the main problems faced by China's hog sector. The non-essential import of pork products, triggered by competitive disadvantages, poses great challenges to hog farms. Structural changes are an important policy concern in China and elsewhere. Previous literature has ignored whether the ongoing structural changes from backyard to large farms can contribute to sustainable development. This study adopts the micro-level data of hog farms collected from Jiangsu Province, and uses a two-step metafrontier model and a primal system approach. The empirical results reveal that the ongoing structural changes are capable of boosting the growth in output in China's hog sector, since the stronger increase in comparable technical efficiency compensates for the inappropriate technology. Furthermore, the ongoing structural changes are also beneficial in the reduction of production costs and in improving competitiveness in China's hog sector. The decline in technical and allocative inefficiency costs, particularly for technical inefficiency costs, contributes to the cost advantage with the increasing farm size

Transcript Profiling Reveals Abscisic Acid, Salicylic Acid and Jasmonic-Isoleucine Pathways Involved in High Regenerative Capacities of Immature Embryos Compared with Mature Seeds in japonica Rice

Induced pluripotent cell mass plays a role in genetic transformation mediated by Agrobacterium. Mature seeds are more recalcitrant to the induction of suitable calli than immature embryos in rice, but the exact molecular mechanisms involved remain elusive. In this study, the morphological structure of calli induced from mature seeds and immature embryos were observed under a scanning electron microscope using a paraffin embedded technique. Meanwhile, a total of 2 173 up- and down-regulated genes were identified in calli induced from mature seeds and immature embryos by RNA-seq technique and furtherly confirmed by quantitative real-time PCR. The results revealed the remarkable morphological differences in calli induced from mature seeds and immature embryos, and plant hormone signal transduction and hormone biosynthesis pathways, such as abscisic acid, salicylic acid and jasmonic-isoleucine, were found to play roles in somatic embryogenesis. This study provided comprehensive gene expression sets for mature seeds and immature embryos that were served as an important platform resource for further functional studies in plant embryogenesis. Keywords: Callus, Immature embryo, Mature seed, Japonica rice, RNA sequence, Hormon

Time Series Contrastive Learning with Information-Aware Augmentations

Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been less explored for time series data. A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such that an encoder can be trained to learn robust and discriminative representations. Unlike image and language domains where "desired'' augmented samples can be generated with the rule of thumb guided by prefabricated human priors, the ad-hoc manual selection of time series augmentations is hindered by their diverse and human-unrecognizable temporal structures. How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question. In this work, we address the problem by encouraging both high fidelity and variety based on information theory. A theoretical analysis leads to the criteria for selecting feasible data augmentations. On top of that, we propose a new contrastive learning approach with information-aware augmentations, InfoTS, that adaptively selects optimal augmentations for time series representation learning. Experiments on various datasets show highly competitive performance with up to a 12.0% reduction in MSE on forecasting tasks and up to 3.7% relative improvement in accuracy on classification tasks over the leading baselines