Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence ...residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord Formula: see text method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method's ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Spatial transcriptomics (ST) data have emerged as a pivotal approach to comprehending the function and interplay of cells within intricate tissues. Nonetheless, analyses of ST data are ...restricted by the low spatial resolution and limited number of ribonucleic acid transcripts that can be detected with several popular ST techniques. In this study, we propose that both of the above issues can be significantly improved by introducing a deep graph co-embedding framework. First, we establish a self-supervised, co-graph convolution network–based deep learning model termed SpatialcoGCN, which leverages single-cell data to deconvolve the cell mixtures in spatial data. Evaluations of SpatialcoGCN on a series of simulated ST data and real ST datasets from human ductal carcinoma in situ, developing human heart and mouse brain suggest that SpatialcoGCN could outperform other state-of-the-art cell type deconvolution methods in estimating per-spot cell composition. Moreover, with competitive accuracy, SpatialcoGCN could also recover the spatial distribution of transcripts that are not detected by raw ST data. With a similar co-embedding framework, we further established a spatial information–aware ST data simulation method, SpatialcoGCN-Sim. SpatialcoGCN-Sim could generate simulated ST data with high similarity to real datasets. Together, our approaches provide efficient tools for studying the spatial organization of heterogeneous cells within complex tissues.
•We examine the effects of democracy and financial openness on environmental pollution.•Panel properties and quantile regression techniques are exploited to control for two kinds of ...heterogeneity.•The effect of democracy on carbon dioxide emissions is higher heterogeneity across conditional distribution of pollution.•The level of financial openness does not influence carbon dioxide emissions.•Our results for the pollution determinants not only support some findings in the literature, but also provide new conclusions.
The determinants of CO2 emissions have attracted many researchers over the past few decades. Most of studies, however, ignore the possibility that effect of independent variables on CO2 emissions could vary throughout the CO2 emission distribution. We address this issue by applying quantile regression methods. We examine whether greater democracy and more financial openness consistently reduce emissions among the most and least emission nations. Our results show that the effect of democracy on CO2 emissions is heterogeneous across quantiles. Among the most emissions nations, greater democracy appears to reduce emissions, but more financial openness does not appear to reduce it.
The integration of visual data obtained from unmanned aerial vehicles (UAVs) has ushered in an era of computer vision, greatly expanding the possibilities for object tracking applications. ...Nevertheless, existing UAV datasets predominantly focus on large-scale objects characterized by distinct contours, overlooking single tiny objects encountered in real-world flight scenarios. Extracting appearance information from these diminutive objects poses a considerable challenge for object tracking. To rectify this imbalance in data distribution, we proposed a UAV dataset called Overhead Look Of Drones (OLOD), encompassing 70 sequences meticulously designed to address tiny object tracking. It contains over 55k frames and provides supplementary information about altitude and flight attitude. Additionally, we incorporated 11 challenging attributes to enhance the complexity of the scenes, thereby establishing a comprehensive benchmark for single object tracking. OLOD serves as a valuable tool for evaluating the tracking capabilities of various algorithms when it comes to tiny objects. Subsequently, through experimental results, we shed light on the limitations of existing methods for tracking tiny objects on this benchmark, underscoring the necessity for further research in this field. Our dataset and evaluation code will be released at
https://github.com/yuymf/OLOD
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Red blood cell distribution width (RDW) was frequently assessed in COVID‐19 infection and reported to be associated with adverse outcomes. However, there was no consensus regarding the optimal cutoff ...value for RDW. Records of 98 patients with COVID‐19 from the First People's Hospital of Jingzhou were reviewed. They were divided into two groups according to the cutoff value for RDW on admission by receiver operator characteristic curve analysis: ≤11.5% (n = 50) and >11.5% (n = 48). The association of RDW with the severity and outcomes of COVID‐19 was analyzed. The receiver operating characteristic curve indicated that the RDW was a good discrimination factor for identifying COVID‐19 severity (area under the curve = 0.728, 95% CI: 0.626–0.830, p < 0.001). Patients with RDW > 11.5% more frequently suffered from critical COVID‐19 than those with RDW ≤ 11.5% (62.5% vs. 26.0%, p < 0.001). Multivariate logistic regression analysis showed RDW to be an independent predictor for critical illness due to COVID‐19 (OR = 2.40, 95% CI: 1.27−4.55, p = 0.007). A similar result was obtained when we included RDW > 11.5% into another model instead of RDW as a continuous variable (OR = 5.41, 95% CI: 1.53−19.10, p = 0.009). RDW, as an inexpensive and routinely measured parameter, showed promise as a predictor for critical illness in patients with COVID‐19 infection. RDW > 11.5% could be the optimal cutoff to discriminate critical COVID‐19 infection.
Highlights
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RDW is an inexpensive and routinely measured parameter that was independently associated with the disease severity of COVID‐19 infection.
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RDW > 11.5% could be the optimal cutoff to discriminate critical COVID‐19 infection and might be helpful in clinical practice to identify critical cases at an early stage.
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Future studies should focus on elucidating the underlying mechanism of the association between RDW and the severity of illness in COVID 19 infection.
Community detection can reveal unknown spatial structures embedded in spatial networks. Current spatial community detection methods are mostly modularity-based. However, due to the lack of ...appropriate spatial networks serving as a benchmark, the accuracy and effectiveness of these methods have not been tested sufficiently so far. This study first introduced a spatial autoregressive and gravity model united method (SARGM) to simulate benchmark spatial networks with known regional distributions. Then, a novel spectral clustering-based spatial community detection method (SCSCD) was proposed to identify spatial communities from eight kinds of benchmark spatial networks. Comparative experiments on SCSCD and three other methods showed that SCSCD performed the best in accuracy and effectiveness. Moreover, the scale parameter and the community number setting of the SCSCD were investigated experimentally. Finally, a case study was applied to the SCSCD to demonstrate its ability to extract the internal community structure of a high-speed train network in China.
Phospho-tau accumulation and adult hippocampal neurogenesis (AHN) impairment both contribute importantly to the cognitive decline in Alzheimer’s disease (AD), but whether and how tau dysregulates AHN ...in AD remain poorly understood. Here, we found a prominent accumulation of phosphorylated tau in GABAergic interneurons in the dentate gyrus (DG) of AD patients and mice. Specific overexpression of human tau (hTau) in mice DG interneurons induced AHN deficits but increased neural stem cell-derived astrogliosis, associating with a downregulation of GABA and hyperactivation of neighboring excitatory neurons. Chemogenetic inhibition of excitatory neurons or pharmacologically strengthening GABAergic tempos rescued the tau-induced AHN deficits and improved contextual cognition. These findings evidenced that intracellular accumulation of tau in GABAergic interneurons impairs AHN by suppressing GABAergic transmission and disinhibiting neural circuits within the neurogenic niche, suggesting a potential of GABAergic potentiators for pro-neurogenic or cell therapies of AD.
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•Phospho-tau is accumulated in DG GABAergic interneurons of AD patients and mice•Interneuron overexpressing human tau impairs adult hippocampal neurogenesis•Tau accumulation reduces GABA, disinhibits local circuits, and promotes astrogliosis•THIP, a δ-GABAAR agonist, improves neurogenesis and cognition in AD mice
Impaired adult hippocampal neurogenesis contributes to the cognitive decline in Alzheimer’s disease. Zheng et al. report that phospho-tau accumulation in dentate gyrus GABAergic interneurons disrupts adult hippocampal neurogenesis and increased astrogliosis. Importantly, strengthening GABAergic signaling can rescue neurogenesis and improve cognitive functions in mouse models of Alzheimer’s disease.
The growth and development of muscle stem cells (MuSCs) are significant events known to affect muscle plasticity, disease, meat production, and meat quality, which involves the types and functions of ...mRNA and non-coding RNA. Here, MuSCs were cultured from Guangxi fetal cattle. RNA sequencing was used to analyze the RNA expression of mRNA and non-coding RNAs during the cell proliferation and differentiation phases.
Two thousand one hundred forty-eight mRNAs and 888 non-coding RNAs were differentially expressed between cell proliferation and differentiation phases, including 113 miRNAs, 662 lncRNAs, and 113 circRNAs. RT-qPCR verified the differential expression levels of mRNAs and non-coding RNAs, and the differentially expressed circUBE2Q2 was subsequently characterized. Expression profile analysis revealed that circUBE2Q2 was abundant in muscle tissues and intramuscular fat. The expression of cricUBE2Q2 was also significantly upregulated during MuSCs myogenic differentiation and SVFs adipogenic differentiation and decreased with age in cattle muscle tissue. Finally, the molecular mechanism of circUBE2Q2 regulating MuSCs function that affects skeletal muscle development was investigated. The results showed that circUBE2Q2 could serve as a sponge for miR-133a, significantly promoting differentiation and apoptosis of cultured MuSCs, and inhibiting proliferation of MuSCs.
CircUBE2Q2 is associated with muscle growth and development and induces MuSCs myogenic differentiation through sponging miR-133a. This study will provide new clues for the mechanisms by which mRNAs and non-coding RNAs regulate skeletal muscle growth and development, affecting muscle quality and diseases.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This paper investigates the relationship between urbanization and CO2 emissions in a sample of 20 emerging countries over the period 1992–2008 using the semi-parametric panel data model with fixed ...effects, proposed by Baltagi and Li (2002). We find little evidence in support of an inverted-U curve, and thus the Kuznets hypothesis is not confirmed by our analysis. Our findings shed new light on the urbanization- CO2 emissions nexus.
► We examine the impact of urbanization level on CO2 emissions. ► We use panel data from 20 emerging countries over the period 1992–2008. ► A semi-parametric panel data model with fixed effect is employed. ► We find little evidence for the existence of an environmental Kuznets curve. ► Our findings shed new light on the urbanization–CO2 emissions nexus.
To effectively provide the handicapped with mobility aids, studies on the shared autonomy of robotic systems have been widely cultivated. This study proposes an adaptive shared control strategy to ...realize reliable and safe driving assistance on an intelligent electric wheelchair with protection against human errors. The theoretical framework of the system is analyzed by the linearized reference wheelchair model and stable characteristics of obstacle avoidance behavior can be subsequently derived according to the Lyapunov analysis and Liénard-Chipart criterion. Based on the convex analysis, the relationships between human input and robot control are investigated to determine shared control weights. As such, safety and reliability can be guaranteed. To verify the performances of the proposed approach, human errors including skill-based errors, decision errors, and violations are considered in the experiments. The experimental results based on a comprehensive study show that the proposed method is capable of enhancing driving safety and reducing operation burden in terms of the designed criteria with fluency, smoothness, and time efficiency while protecting the user from human manual errors.
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