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Amid the escalating conflicts between urban demography, resource availability, and environmental constraints, there is an accelerating demand for emergency medical services prompted by a spectrum of factors including diseases, natural calamities, and unforeseen events. This growth is further accentuated by an imbalance in the allocation of emergency facilities, amplifying public anxiety and underscoring the urgent necessity for scientifically informed optimization of pre-hospital emergency facilities. To this end, the present study articulates specific optimization goals for facility deployment and introduces a nuanced set coverage optimization model that integrates spatiotemporal determinants of emergency service requirements. The model is fortified with a constellation of constraints, including spatial constraint, temporal constraint, and coverage constraint. For spatial constraints, we use the Monte Carlo simulation to predict the spatial distribution of emergency demands. Temporal constraints involve determining the actual travel time matrix from predicted demand points to candidate sites. Coverage constraints specify the effective demand coverage rate. This study uses Nanjing as a case example, utilizing multisource big data, including ambulance GPS logs from June 2016 to May 2017, Amap traffic congestion indicators, and survey data on existing emergency facilities in Nanjing City. By preprocessing and analyzing the existing data, this study thoroughly investigates the spatiotemporal distribution of emergency demands and the impact of traffic congestion on emergency service effectiveness. Consequently, the model incorporates constraints that ensure, under the specified planning and actual traffic conditions, 95 % of the simulated emergency demands can be met within an 8-minute on-route time. Locations for pre-hospital emergency stations are optimized using a genetic algorithm, achieving the best solution at the 120th iteration. Verification confirms 134 optimal sites; after excluding 52 existing sites, 82 potential sites are identified. The new layout plan has reduced Nanjing’s average emergency response time from 18.6 min to 12 min. Additionally, under peak and average traffic conditions, emergency demand coverage rates improved from 76.92 % and 83.18 % at 15 min to 95.61 % and 98.10 % at 12 min, respectively. These results demonstrate that the new layout significantly enhances practical application effectiveness. The approach presented in this paper addresses the previously overlooked randomness of emergency incidents and traffic conditions, offering innovative strategies for improving the efficiency and effectiveness of emergency station planning and site selection models.

The complexity and fragmentation of people's activity space are challenging to planners. However, the relevant studies are mostly concerned on the relationship between the social attributes and the activity space of residents in a single or several communities, or the spatiotemporal laws of activity space on a macro scale. The research on the spatial characteristics of residents' activity space still needs to be strengthened. The present study analyses the spatial patterns of residents' activity space based on mobile phone signaling data to fill the gap of previous studies that assessed residents' activity space across small geographic areas. First, according to the spatial scope and direction of an activity space and residents' activity coverage rate, spatial patterns can be divided into three types: compact, extended, and directional extension patterns. The CatBoost method is then used to statistically analyze the influencing variables of spatial patterns, and the order of importance of the following influencing factors is determined: the built environment is more influential than social and economic situations. This study aims to strengthen the understanding of residents' activity space at the spatial level and provide a basis for the optimization of communities with different spatial patterns.

BackgroundAlzheimer’s disease is the most common neurodegenerative disease worldwide. Metabolic syndrome is the most common metabolic and endocrine disease in the elderly. Some studies have suggested a possible association between MetS and AD, but few studied genes that have a co-diagnostic role in both diseases.MethodsThe microarray data of AD (GSE63060 and GSE63061 were merged after the batch effect was removed) and MetS (GSE98895) in the GEO database were downloaded. The WGCNA was used to identify the co-expression modules related to AD and MetS. RF and LASSO were used to identify the candidate genes. Machine learning XGBoost improves the diagnostic effect of hub gene in AD and MetS. The CIBERSORT algorithm was performed to assess immune cell infiltration MetS and AD samples and to investigate the relationship between biomarkers and infiltrating immune cells. The peripheral blood mononuclear cells (PBMCs) single-cell RNA (scRNA) sequencing data from patients with AD and normal individuals were visualized with the Seurat standard flow dimension reduction clustering the metabolic pathway activity changes each cell with ssGSEA.ResultsThe brown module was identified as the significant module with AD and MetS. GO analysis of shared genes showed that intracellular transport and establishment of localization in cell and organelle organization were enriched in the pathophysiology of AD and MetS. By using RF and Lasso learning methods, we finally obtained eight diagnostic genes, namely ARHGAP4, SNRPG, UQCRB, PSMA3, DPM1, MED6, RPL36AL and RPS27A. Their AUC were all greater than 0.7. Higher immune cell infiltrations expressions were found in the two diseases and were positively linked to the characteristic genes. The scRNA-seq datasets finally obtained seven cell clusters. Seven major cell types including CD8 T cell, monocytes, T cells, NK cell, B cells, dendritic cells and macrophages were clustered according to immune cell markers. The ssGSEA revealed that immune-related gene (SNRPG) was significantly regulated in the glycolysis-metabolic pathway.ConclusionWe identified genes with common diagnostic effects on both MetS and AD, and found genes involved in multiple metabolic pathways associated with various immune cells.

Gliomas are the most common and aggressive malignancies of the central nervous system. Histone deacetylases (HDACs) are important targets in cancer treatment. They regulate complex cellular mechanisms that influence tumor biology and immunogenicity. However, little is known about the function of HDACs in glioma. The Oncomine, Human Protein Atlas, Gene Expression Profiling Interactive Analysis, Broad Institute Cancer Cell Line Encyclopedia, Chinese Glioma Genome Atlas, OmicShare, cBioPortal, GeneMANIA, STRING, and TIMER databases were utilized to analyze the differential expression, prognostic value, and genetic alteration of HDAC and immune cell infiltration in patients with glioma. HDAC1/2 were considerable upregulated whereas HDAC11 was significantly downregulated in cancer tissues. HDAC1/2/3/4/5/7/8/11 were significantly correlated with the clinical glioma stage. HDAC1/2/3/10 were strongly upregulated in 11 glioma cell lines. High HDCA1/3/7 and low HDAC4/5/11 mRNA levels were significantly associated with overall survival and disease-free survival in glioma. HDAC1/2/3/4/5/7/9/10/11 are potential useful biomarkers for predicting the survival of patients with glioma. The functions of HDACs and 50 neighboring genes were primarily related to transcriptional dysregulation in cancers and the Notch, cGMP-PKG, and thyroid hormone signaling pathways. HDAC expression was significantly correlated with the infiltration of B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells in glioma. Our study indicated that HDACs are putative precision therapy targets and prognostic biomarkers of survival in glioma patients.

With the increasing trend of residents and tourists sharing urban spaces, the boundary between leisure spaces and tourism spaces is gradually being blurred. However, few studies have quantified the spatiotemporal correlation patterns of residents’ leisure activities and tourists’ activities. To fill this gap, this paper takes Nanjing as an example to study the temporal and spatial correlation between residents’ leisure activities and tourists’ activities based on mobile phone signal data. First, through kernel density analysis, it is found that there is a spatial overlap between residents’ leisure activities and tourists’ activities. Then, the spatial and temporal correlation patterns of residents’ leisure activities and tourists’ activities are analyzed through the colocation quotient. According to our findings, (1) residents’ leisure activities and tourists’ activities are not spatially correlated, indicating that they are relatively independent in space both during the week and on weekends. (2) On weekday afternoons, the spatial correlation between residents’ and tourists’ leisure activities is strongest. On weekends, the night is when residents’ leisure activities and tourists’ activities are most closely related. (3) The correlation area is mainly distributed in areas near famous scenic spots, as well as public spaces such as parks and squares. Based on the above analysis, this paper aims to study the resident-tourist interaction in the spatial context to provide directions for improving the attractiveness of cities, urban transportation, services, and marketing strategies.

Time allocation is closely related to life quality and is a potential indicator of urban space utilization and sociospatial differentiation. However, existing time allocation studies focus on how time is allocated to various activities but pay less attention to where individuals allocate their time. In the context of China’s transformation, this study examines the differences in time allocation in different urban spaces between low- and non-low-income groups based on two methods, descriptive statistics and social area analysis. The results show that low-income participants’ daily activities (especially work) are highly dependent on the central city area. However, they are at a disadvantage in accessing the central city area. Nevertheless, non-low-income individuals have diversified activity spaces and can better choose locations according to the purpose of activities and make fuller use of various types of urban areas. This study indicates that there are social differences in time allocation and urban space utilization among different income groups. The results obtained with regression models reveal that in addition to income, activity characteristics and built environment characteristics are significant factors affecting the differences. Social policies should support the equitable distribution of urban resources for different social groups, especially for vulnerable groups who live in affordable housing.

Economic expansion has caused increasingly serious land resource problems, and the decoupling of urban industrial land expansion from economic development has become a big topic for intensive development. The current research has mainly concerned industrial land efficiency, a single, static indicator, compared to a decoupling model, which takes into account two variables and gives a full expression of the spatio-temporal dynamic characteristics. However, little attention has been paid to the relationship between industrial land expansion and economic development in China from the perspective of decoupling. Based on a combination of Tapio‘s decoupling model and spatial analysis methods, this paper investigates the decoupling relationship between industrial land expansion and economic development in Chinese cities from 2010 to 2019. On that basis, we divided the study area into three policy zones and made differentiated policy recommendations. In addition, based on the decoupling model, we obtained the decoupling indices of the cities and grouped the cities into eight decoupling types. After the spatial autocorrelation analysis, we further verified the spillover effect of decoupling with the results of urban spatial differentiation. This paper draws the following conclusions: (1) Urban industrial land expansion and economic development exhibit marked and increasingly significant spatial heterogeneity and agglomeration. (2) Industry and economy are in weak decoupling in most cities, but there are a growing number of cities in negative decoupling. (3) Decoupled cities are shifting from the southeast coast to the middle and lower reaches of the Yellow River and Yangtze River, while negatively decoupled cities keep spreading from northeast and south China to their periphery, with clear signs of re-coupling. (4) It is necessary to develop urban industrial land supply and supervision policies according to local actuality and to implement differentiated control of industrial land for cities and industrial sectors with different decoupling types. To some extent, this paper reveals the evolution dynamics, performances, and strategies of industrial land, providing a decision basis for industrial land management policies and industrial planning in China and other countries at similar stages.

The rapid expansion of cities brings in new challenges for the urban firefighting security, while the increasing fire frequency poses serious threats to the life, property, and safety of individuals living in cities. Firefighting in cities is a challenging task, and the optimal spatial arrangement of fire stations is critical to firefighting security. However, existing researches lack any consideration of the negative effects of the spatial randomness of fire outbreaks and delayed response time due to traffic jams upon the site selection. Based on the set cover location model integrated with the spatiotemporal big data, this paper combines the fire outbreak point with the traffic situation. The presented site selection strategy manages to ensure the arrival of the firefighting task force at random simulated fire outbreak points within the required time, under the constraints of the actual city planning and traffic situation. Taking Nanjing city as an example, this paper collects multi-source big data for the comprehensive analysis, including the full data of the fire outbreak history from June 2014 to June 2018, the traffic jam data based on the Amap, and the investigation data of the firefighting facilities in Nanjing. The regularity behind fire outbreaks is analyzed, the factors related to fire risks are identified, and the risk score is calculated. The previous fire outbreak points are put through the clustering analysis, the spatial distribution probability at points in each cluster is calculated according to the clustering score, and the random fire outbreak points are generated via the Monte Carlo simulation. Meanwhile, the objective emergency response time is set as five minutes. The average vehicle speed for each road in the urban area is calculated, and the actual traffic network model is built to compute the travel time from massive randomly-distributed simulated fire points. The problem is solved by making the travel time for all simulated demand points below five minutes. At last, the site selection result based on our model is adjusted and validated, according to the planned land use. The presented method incorporates the view of the spatiotemporal big data and provides a new idea and technical method for the modification and efficiency improvement of the fire station site selection model, contributing to a service cover ratio increase from 58% to 90%.

Context: Withaferin A (WFA) exhibits diverse pharmaceutical applications on human diseases, including rheumatoid arthritis, cancers and microbial infection. Objective: We evaluated the neuroprotective role of WFA using a mouse model of spinal cord injury (SCI). Materials and methods: BALB/c mice were administrated 10 mg/kg of WFA. Gene expression was measured by real-time PCR, western blot and immunohistochemistry. Cell morphology and apoptosis were determined by H&E staining and TUNEL assay. Motor function was evaluated by the BBB functional scale for continuous 7 weeks. Results: WFA significantly improved neurobehavioural function and alleviated histological alteration of spinal cord tissues in traumatized mice. Brain-derived neurotrophic factor (BDNF) and glial cell line-derived neurotrophic factor (GDNF) significantly increased in WFA-treated mice. Meanwhile, the expression of Nogo-A and RhoA remarkably decreased in the presence of WFA. Furthermore, the apoptotic cell death was attenuated in mice treated with WFA (31.48 ± 2.50% vs. 50.08 ± 2.08%) accompanied by decreased bax and increased bcl-2. In addition, WFA decreased the expression of pro-inflammatory mediators such as IL-1β (11.20 ± 1.96 ng/mL vs. 17.59 ± 1.42 ng/mL) and TNF-α (57.38 ± 3.57 pg/mL vs. 95.06 ± 9.13 pg/mL). The anti-inflammatory cytokines including TGF-β1 (14.32 ± 1.04 pg/mL vs. 9.37 ± 1.17 pg/mL) and IL-10 (116.80 ± 6.91 pg/mL vs. 72.33 ± 9.35 pg/mL) were elevated after WFA administration. Discussion and conclusion: This study demonstrated that WFA has a neuroprotective role by inhibition of apoptosis and inflammation after SCI in mice.

Our previous studies demonstrated that tentacle extract (TE) from the jellyfish, Cyanea capillata, could cause a dose-dependent increase of systolic blood pressure, which seemed to be the result of direct constriction of vascular smooth muscle (VSM). The aim of this study is to investigate whether TE could induce vasoconstriction in vitro and to explore its potential mechanism. Using isolated aorta rings, a direct contractile response of TE was verified, which showed that TE could induce concentration-dependent contractile responses in both endothelium-intact and -denuded aortas. Interestingly, the amplitude of contraction in the endothelium-denuded aorta was much stronger than that in the endothelium-intact one, implying that TE might also bring a weak functional relaxation in addition to vasoconstriction. Further drug intervention experiments indicated that the functional vasodilation might be mediated by nitric oxide, and that TE-induced vasoconstriction could be attributed to calcium influx via voltage-operated calcium channels (VOCCs) from the extracellular space, as well as sarcoplasmic reticulum (SR) Ca2+ release via the inositol 1,4,5-trisphosphate receptor (IP3R), leading to an increase in [Ca2+]c, instead of activation of the PLC/DAG/PKC pathway or the sympathetic nerve system.

Thioredoxins (Trx proteins) are a family of small, highly-conserved and ubiquitous proteins that play significant roles in the resistance of oxidative damage. In this study, a homologue of Trx was identified from the cDNA library of tentacle of the jellyfish Cyanea capillata and named CcTrx1. The full-length cDNA of CcTrx1 was 479 bp with a 312 bp open reading frame encoding 104 amino acids. Bioinformatics analysis revealed that the putative CcTrx1 protein harbored the evolutionarily-conserved Trx active site 31CGPC34 and shared a high similarity with Trx1 proteins from other organisms analyzed, indicating that CcTrx1 is a new member of Trx1 sub-family. CcTrx1 mRNA was found to be constitutively expressed in tentacle, umbrella, oral arm and gonad, indicating a general role of CcTrx1 protein in various physiological processes. The recombinant CcTrx1 (rCcTrx1) protein was expressed in Escherichia coli BL21 (DE3), and then purified by affinity chromatography. The rCcTrx1 protein was demonstrated to possess the expected redox activity in enzymatic analysis and protection against oxidative damage of supercoiled DNA. These results indicate that CcTrx1 may function as an important antioxidant in C. capillata. To our knowledge, this is the first Trx protein characterized from jellyfish species.