The clinical utilization of SNIP1 and its pathophysiological mechanisms in disease.

Smad intranuclear binding protein 1 (SNIP1), a highly conserved nuclear protein, functions as a transcriptional regulator and exerts a significant influence on disease progression. In addition, the N-terminal domain of SNIP1 facilitates its interaction with Smad4, a signaling protein associated with the TGF-ß family, and RelA/p65, a transcription factor connected to NF-κB. This interaction further enhances the transcriptional activation of c-Myc-dependent genes. Presently, the primary emphasis in research is directed towards targeting the catalytic domain of SNIP1, as it holds promise as a potential therapeutic target for various diseases. While the significance of SNIP1 in pathological mechanisms remains uncertain, this review aims to comprehensively examine the existing literature on the association between SNIP1 and proteins implicated in the regulation of diverse clinical conditions, including cancer, inflammation, and related diseases.

SNIP1 reduces extracellular matrix degradation and inflammation via inhibiting the NF-κB signaling pathway in osteoarthritis.

Osteoarthritis (OA), the most common joint disease, is characterized by inflammation and cartilage degradation. Previous studies illustrated that Smad nuclear-interacting protein 1 (SNIP1) is an inhibitor of the TGF-ß signal transduction pathway and SNIP1 has been reported as an anti-inflammatory factor. This study aimed to explore the role of SNIP1 in OA progression. In this study, the SNIP1 expression was evaluated in OA human and OA mice tissue and interleukin-1 beta (IL-1ß)-induced chondrocytes. The Safranin-O (SO) staining and osteoarthritis research society international (OARSI) scoring system was used to evaluate cartilage injury. The gain- and loss-of-function studies for SNIP1 were performed in chondrocytes. The SNIP1 overexpression adenovirus was injected into mice by intra-articular injection. The SNIP1 expression was decreased in OA patients, OA mice, and IL-1ß-stimulated chondrocytes. The cartilage injury of medial meniscus-induced OA (DMM-OA) mice at 8 weeks showed more severe than that at 4 weeks. The expression of SNIP1 was lower at 8 weeks than that at 4 weeks. In IL-1ß-stimulated chondrocytes, SNIP1 overexpression reduced the expression of TNF-α and IL-6, alleviated ECM degradation, reduced the phosphorylation levels of p65 and IκBα, and decreased the p65 level in nuclear. Moreover, overexpression of SNIP1 alleviated cartilage injury in DMM-OA mice. In brief, our study suggested that SNIP1 alleviated OA and repressed inflammation by inhibiting the activation of NF-κB. This study might provide a new insight into OA treatment.

Contribution of Subway Expansions to Air Quality Improvement and the Corresponding Health Implications in Nanjing, China.

With China's rapid economic development, particularly its accelerated urbanization, air pollution has been one of the serious environmental issues across China. Most major cities in China expand their subway systems to handle this problem. This study takes both long- and short-term effects of subway expansions on air quality and its corresponding health implications into account based on a network density-based time series analysis and a distance-based difference-in-differences analysis. The daily and hourly monitor-level air quality data on Nanjing from 13 May 2014 to 31 December 2018, combining with corresponding weather variables, are used to quantify the effect of subway expansions on local air pollution caused by eight new subway lines in Nanjing. The results reveal that subway expansions result in a statistically significant decrease in the air pollution level; specifically, the air pollution level experiences a 3.93% larger reduction in the areas close to subway lines. Heterogeneous analysis of different air pollutants indicates that the air pollution reduction effect of subway expansions is more significant in terms of Particulate Matter (PM2.5) and CO. A back-of-the-envelope analysis of the health benefits from this air improvement shows that the total number of yearly averted premature deaths is around 300,214 to 443,498. A set of alternative specifications confirm the robustness of our results. These results provide strong support for putting more emphasis on the environmental effect of subway expansions in the cost-benefit analysis of subway planning.

Evaluating the Carbon Emissions Efficiency of the Logistics Industry Based on a Super-SBM Model and the Malmquist Index from a Strong Transportation Strategy Perspective in China.

Carbon emissions from the logistics industry have been rising year after year. Correct handling of the relationship between economic development and environmental protection is of great significance to the implementation of green logistics, which is an important component of China's strategy for strong transportation. This paper focuses on the evaluation of the carbon emissions efficiency of logistics industry from a new strong transportation strategy perspective. A super-efficiency slack-based measurement (Super-SBM) model and Malmquist index are combined to evaluate the static and dynamic carbon emissions efficiency of the logistics industry. The results indicate that compared with the SBM model, the Super-SBM model can more effectively measure the carbon emissions efficiency of the logistics industry. Pilot regions for the strong transportation strategy were divided into two categories, namely regions with slow carbon emission growth rates but high efficiency, and regions with high carbon emission growth rates but low efficiency. Some policy recommendations from the strong transportation strategy perspective were proposed to improve the carbon emissions efficiency of the logistics industry, especially for the second category of pilot regions. This study is expected to provide a basis for decision-making for efficient emissions reduction measures and policies, and to encourage the pilot regions to take the lead in achieving the goal of China's strategy for transportation.

Decompression with fusion versus decompression in the treatment of lumbar spinal stenosis: A systematic review and meta-analysis.

OBJECTIVE: The goal of this study was to review relevant studies in order to determine the efficacy of decompression with fusion versus decompression in the treatment of lumbar spinal stenosis. METHODS: Using appropriate keywords, we identified relevant studies using PubMed, the Cochrane library, and Embase. Key pertinent sources in the literature were also reviewed, and all articles published through October 2019 were considered for inclusion. For each study, we used odds ratios, mean difference (MD), and 95% confidence interval (95% CI) to assess and synthesize outcomes. RESULTS: We found 13 studies that were consistent with this meta-analysis with a total of 29066 patients. Compared with decompression, decompression with fusion significantly increased the incidence of complications (RR: 1.41, 95%CI: 1.26-1.57), the length of hospital stay (WMD: 1.868, 95%CI: 1.394-2.343), operative time (WMD: 80.399, 95%CI: 44.397-116.401), estimated blood loss (WMD: 309.356, 95%CI: 98.008-520.704) and Zurich claudication questionnaire in symptom severity (WMD: 0.200, 95%CI: 0.006-0.394). The reoperation rate was lower in the decompression with fusion group than the decompression group but without significant difference (RR: 0.91, 95%CI: 0.82-1.00). There was no significant difference between 2 groups in visual analog scale (leg pain and back pain), ODI, Short Form 36 Health Survey physical component summary, Short Form 36 Health Survey mental component summary, and Zurich claudication questionnaire physical function. CONCLUSION: Decompression with fusion has no significant clinical advantages in treatment of lumbar spinal stenosis when compared with decompression.

Research on the Design of and Preference for Collection Modes of Reusable Takeaway Containers to Promote Sustainable Consumption.

The development of the online to offline business has accelerated the growth of the online food ordering market in China. The widespread use of disposable takeaway containers has resulted in a large amount of waste, which seriously affects the ecological environment. This paper studied the collection modes of reusable takeaway containers and the preferences of consumers and merchants. First, after two rounds of discussion and revision, four takeaway container collection modes were designed. Second, based on the survey results of consumers and merchants, a binary logistic regression model was applied to analyze the preferences of consumers and merchants. The results showed that the consumers' delivery requirements and the current disposal of takeaway containers had a significant impact on consumers' preferences. Consumers were more concerned about the hygienic status of the containers, food health and safety, while the merchants were more concerned about the increased costs. The promotion of collection modes requires the special consideration of the locations of dishwashing facilities and increased costs. Finally, according to the preferences and concerns of consumers and merchants, several suggestions on promoting the collection mode, such as the use of different promotion strategies for different people, the short distance of dishwashing facilities, reward systems, and food safeguard measures were proposed. This research provides guidance for decision making regarding the sustainable consumption and the promotion of reusable takeaway containers, which will contribute to resource conservation, ecological environmental improvement and sustainability.

A red-light running prevention system based on artificial neural network and vehicle trajectory data.

The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

Analysis of vehicle-following heterogeneity using self-organizing feature maps.

A self-organizing feature map (SOM) was used to represent vehicle-following and to analyze the heterogeneities in vehicle-following behavior. The SOM was constructed in such a way that the prototype vectors represented vehicle-following stimuli (the follower's velocity, relative velocity, and gap) while the output signals represented the response (the follower's acceleration). Vehicle trajectories collected at a northbound segment of Interstate 80 Freeway at Emeryville, CA, were used to train the SOM. The trajectory information of two selected pairs of passenger cars was then fed into the trained SOM to identify similar stimuli experienced by the followers. The observed responses, when the stimuli were classified by the SOM into the same category, were compared to discover the interdriver heterogeneity. The acceleration profile of another passenger car was analyzed in the same fashion to observe the interdriver heterogeneity. The distribution of responses derived from data sets of car-following-car and car-following-truck, respectively, was compared to ascertain inter-vehicle-type heterogeneity.

New algorithms for computing the time-to-collision in freeway traffic simulation models.

Ways to estimate the time-to-collision are explored. In the context of traffic simulation models, classical lane-based notions of vehicle location are relaxed and new, fast, and efficient algorithms are examined. With trajectory conflicts being the main focus, computational procedures are explored which use a two-dimensional coordinate system to track the vehicle trajectories and assess conflicts. Vector-based kinematic variables are used to support the calculations. Algorithms based on boxes, circles, and ellipses are considered. Their performance is evaluated in the context of computational complexity and solution time. Results from these analyses suggest promise for effective and efficient analyses. A combined computation process is found to be very effective.