The Effects of Weather on Passenger Flow of Urban Rail Transit

Predicting passenger flow on urban rail transit is important for the planning, design and decision-making of rail transit. Weather is an important factor that affects the passenger flow of rail transit by changing the travel mode choice of urban residents. This study aims to explore the influence of weather on urban rail transit ridership, taking four cities in China as examples, Beijing, Shanghai, Guangzhou and Chengdu. To determine the weather effect on daily ridership rate, the three models were proposed with different combinations of the factors of temperature and weather type, using linear regression method.   The large quantities of data were applied to validate the developed models.  The results show that in Guangzhou, the daily ridership rate of rail transit increases with increasing temperature. In Chengdu, the ridership rate increases in rainy days compared to sunny days. While, in Beijing and Shanghai, the ridership rate increases in light rainfall and heavy rainfall (except moderate rainfall) compared to sunny days. The research findings are important to understand the impact of weather on passenger flow of urban rail transit. The findings can provide effective strategies to rail transit operators to deal with the fluctuation in daily passenger flow

Integrating single-cell RNA-seq and spatial transcriptomics reveals MDK-NCL dependent immunosuppressive environment in endometrial carcinoma

ObjectivesThe tumor microenvironment (TME) play important roles in progression of endometrial carcinoma (EC). We aimed to assess the cell populations in TME of EC.MethodsWe downloaded datasets of single-cell RNA-seq (scRNA-seq) and spatial transcriptome (ST) for EC from GEO, and downloaded RNA-Seq (FPKM) and clinical data of TCGA-UCEC project from TCGA. The datasets were analyzed using R software.ResultsWe obtained 5 datasets of scRNA-seq, 1 of ST and 569 samples of RNA-seq. Totally, 0.2 billion transcripts and 33,408 genes were detected in 33,162 cells from scRNA-seq. The cells were classified into 9 clusters, and EC cells were originated from epithelial cells and ciliated cells. Gene set variation analysis (GSVA) indicated that the pathways enriched in the subclusters of epithelial cells and endothelial cells were significantly different, indicating great heterogeneity in EC. Cell-cell communication analyses showed that EC cells emitted the strongest signals, and endothelial cells received more signals than other cells. Further analysis found that subclusters of 1 and 2 of epithelial cells were showed a more malignant phenotype, which may confer malignant phenotype to subcluster of 0 of endothelial cells through MK pathway by MDL-NCL signal. We also analyzed communications between spatial neighbors with ST data and confirmed the findings on MDL-NCL in cell-cell communication. TCGA and GEO analyses indicated that the expression levels of NCL was inversely correlated with ImmuneScore.ConclusionOur study revealed EC cells can confer malignant phenotype to endothelial cells by MDK-NCL signal, and NCL is associated with suppressed immune activity. EC cells may shape TME by inhibiting immune cells and “educating” stromal cells via MDK-NCL signal

Liposomal Curcumin Targeting Endometrial Cancer Through the NF-κB Pathway

Background/Aims: Emerging evidence suggests that curcumin possesses chemopreventive properties against various cancers. However, its poor bioavailability limits its clinical application. In this study, we aimed to utilize encapsulation in liposomes (Lipo) as a strategy for the clinical administration of curcumin for endometrial carcinoma (EC). Methods: Curcumin was encapsulated in a liposomal delivery system to prepare a formulation of liposomal curcumin (LC). EC cell lines Ishikawa and HEC-1 were treated with the compound and cell proliferation was measured using MTT assay. Hoechst 33258 staining assay and flow cytometry were used to detect apoptosis of the cells. Wound healing and cell invasion assays were employed to monitor cell motility. Underlying target signaling, such as NF-κB, caspases, and MMPs, were further studied via qRT-PCR and western blot. Thereafter, a zebrafish model was used to assess the toxicity of LC. Finally, a zebrafish transplantation tumor model of EC was grown and treated with LC. Tumors were monitored and harvested to study the expression of NF-κB. Results: The formation of LC was successfully developed with excellent purity and physical properties. In vitro, LC resulted in dose-dependent inhibition of proliferation, induction of apoptosis, and suppression of Ishikawa and HEC-1 cell motility. LC treatment also suppressed the activation and/or expression of NF-κB, caspase-3, and MMP-9. No demonstrable toxicity was found in the zebrafish model and tumors were suppressed after treatment with LC. PCR analysis also showed down-regulated expression of NF-κB. Conclusions: LC was successfully prepared and played biological roles against EC probably through negative regulation of the NF-κB pathway in vitro and in vivo, which demonstrates its potential therapeutic effects in EC

Comprehensive Understanding of Aluminosilicate Phosphate Geopolymers: A Critical Review

Aluminosilicate phosphate (ASP) geopolymers are a new kind of green cementitious materials synthesized from aluminosilicate precursors and acidic activators (phosphoric acid or phosphate), which have received extensive attention from researchers because of their excellent and unique characteristics. The current investigation indicates that ASP geopolymers have the characteristics of a low-carbon synthesis process, high mechanical properties (e.g., the highest compressive strength can reach 146 MPa), a strong heat resistance (e.g., withstanding a high temperature of 1500 °C), and excellent dielectric properties. These excellent properties make them have broad application prospects in the fields of new building materials, coating materials, insulating materials, and heavy metal curing. Based on the research findings of approximately 85 relevant literatures on ASP geopolymers in past decades, this paper focuses on the latest research progress of ASP geopolymers from the perspectives of synthesis processes, performances, modifications, and application developments. In addition, this study summarizes the key problems existing in the current research of ASP geopolymers and suggests their possible applications in the future, which will help to provide directions for further research activities of relevant researchers

Hydration and Properties of Magnesium Potassium Phosphate Cement Modified by Granulated Blast-Furnace Slag: Influence of Fineness

Magnesium potassium phosphate cement (MKPC) is an excellent rapid repair material for concrete, and many mineral admixtures have been applied to promote its performance. This study focuses on the quantitative characterization of the physical and chemical contributions of granulated blast-furnace slag with various finenesses to the performance development of MKPC. It was found that the addition of slag could increase the setting time, which is mainly due to the dilution of cement. Fine slag tends to decrease the fluidity of MKPC mortar. The physical contributions of ordinary and ultrafine slag to the early performance of MKPC mortar are 23% and 30%, while the chemical contributions are only 6%~10%. At late ages, the physical contribution is less than 10% and the chemical contribution of slag is even slightly negative. The addition of slag is beneficial to the compact packing of MKPC, which is the main reason for the physical contribution. Slag could react in the MKPC system, and increasing the fineness significantly promotes the reaction kinetics

Restoration of cefixime-induced gut microbiota changes by a prebiotic blend in a mouse model

Recent studies have provided compelling evidence linking the composition of the gut microbiota, host diet, and host physiology. Prebiotics are substrates that are selectively utilized by host microorganisms, conferring health benefits. Prebiotics, such as prebiotic blends (PB), are commonly used worldwide in food processing. Here, microbiome-metabolomics was used to evaluate how PB affect gut microbes and metabolic functions in C57BL/6 J mice administered cefixime. We found favorable effects of PB on obesity outcomes. PB supplementation significantly increased the abundance of Bifidobacterium, Parabacteroides, Alloprevotella, Alistipes, and Dubosiella, and decreased that of Robinsoniella, Blautia, Lachnoclostridium, Coprobacillus, Hungatella, Erysipelatoclostridium, Helicobacter, Clostridium sensu stricto 1, Enterococcus, and Akkermansia compared to that in the cefixime administration (CEF) group. In particular, PB increased the abundance of Parabacteroides goldsteinii and suppressed that of Robinsoniella peoriensis and Akkermansia muciniphila. In addition, it regulated the levels of microbial metabolites such as unsaturated fatty acids and bile acids. Thus, PB can alleviate metabolic disorders induced by antibiotic intervention, indicating a potential dietary strategy for populations with antibiotic-associated diarrhea

The Emergence Characteristics of Driver’s Intentions Influenced by Different Emotions

Drivers’ behavioral intentions can affect traffic safety, vehicle energy use, and gas emission. Drivers’ emotions play an important role in intention generation and decision making. Determining the emergence characteristics of driver intentions influenced by different emotions is essential for driver intention recognition. This study focuses on developing a driver’s intention emergence model with the involvement of driving emotion on two-lane urban roads. Driver emotions were generated using various ways, including visual stimuli (video and picture), material incentives, and spiritual rewards. Real and virtual driving experiments were conducted to collect the multi-source dynamic data of human–vehicle–environment. The driver intention emergence model was constructed based on an artificial neural network, to identify the influences of drivers’ emotions on intention, as well as the evolution characteristics of drivers’ intentions in different emotions. The results show that the proposed model can make accurate predictions on driver intention emergence. The findings of this study can be used to improve drivers’ behavior, in order to create more efficient and safe driving. It can also provide a theoretical foundation for the development of an active safety system for vehicles and an intelligent driving command system

The Emergence Characteristics of Driver’s Intentions Influenced by Different Emotions

Drivers’ behavioral intentions can affect traffic safety, vehicle energy use, and gas emission. Drivers’ emotions play an important role in intention generation and decision making. Determining the emergence characteristics of driver intentions influenced by different emotions is essential for driver intention recognition. This study focuses on developing a driver’s intention emergence model with the involvement of driving emotion on two-lane urban roads. Driver emotions were generated using various ways, including visual stimuli (video and picture), material incentives, and spiritual rewards. Real and virtual driving experiments were conducted to collect the multi-source dynamic data of human–vehicle–environment. The driver intention emergence model was constructed based on an artificial neural network, to identify the influences of drivers’ emotions on intention, as well as the evolution characteristics of drivers’ intentions in different emotions. The results show that the proposed model can make accurate predictions on driver intention emergence. The findings of this study can be used to improve drivers’ behavior, in order to create more efficient and safe driving. It can also provide a theoretical foundation for the development of an active safety system for vehicles and an intelligent driving command system