The urban heat island (UHI) effect over Hangzhou, east China during a long-lasting heat wave was simulated by a weather research and forecasting (WRF) model coupled with an urban canopy model (UCM) ...at a horizontal resolution of 1km. Based on satellite-measured nighttime light data and the normalized difference vegetation index, a human settlement index was used to represent the current urban land cover and define three urban land subcategories in the UCM. Three numerical simulations representing different urbanization scenarios and an idealized simulation with all the urban surface replaced with cropland were performed. Using up-to-date urban land use data, the coupled WRF/UCM model reasonably reproduced the majority of the observed spatial and temporal characteristics of the 2-m temperature field over the simulation period in Hangzhou. Strong UHI effects that can cause intensification and expansion of the areas experiencing extreme heat stress were observed in both actual measurements and simulations. In the simulation, an average temperature increase of 0.74°C in the city center was observed under high urbanization conditions. The UHI peak reached a maximum value of 1.6°C at 1900 LST around sunset. Analysis of the surface energy balance showed that the UHI is mainly caused by a greater heat storage in the urban fabric during the day and the release of this heat in the evening. Comparisons among the results of four sensitivity runs showed that urban land use, classification of three urban land subcategories, and consideration of anthropogenic heat release respectively contributed 56.8% (0.42°C), 13.5% (0.10°C), and 29.7% (0.22°C) to the simulated UHI effects.
•A detailed urban land use map was developed and used for urban canopy modeling.•The average temperature in Hangzhou city increased by 0.74°C due to urbanization.•Consideration of AHR contributed by 29.7% to the UHI effect simulation.•Reasonable treatment of urban land cover is a non-negligible factor in simulating the UHI effect.
Composite event detection is one of fundamental tasks for wireless sensor networks. In existing approaches, typically, a routing tree is used to enable information exchange among sensor nodes and ...collaborative detection of composite events. However, such a tree is not optimal in terms of energy efficiency, because the relations included in composite events have not been fully utilized. In this letter, we propose a new type of routing tree called event detection tree (EDT) to achieve energy-efficient composite event detection. EDT reduces the amount of data to be transmitted by aggregating data in to events, at the cost of an increased distance in the data transmission to achieve such aggregations. EDT achieves a tradeoff of them to minimize the overall energy consumption. Simulation results show that our approach outperforms existing approaches and yields energy savings of up to 20%.
A highly selective and sensitive OFF-ON fluorescent sensor 1, employing the PET mechanism, was designed and synthesized. It could be used to detect Cd2+ ion in aqueous solution and to image Cd2+ ion ...in living cells. The fluorescence intensity significantly enhanced about 195-fold and the quantum yield increased almost 100-fold. Moreover the fluorescence intensity of 1 increased linearly with high sensitivity (0−1 μM) toward Cd2+.
In the past decade, time series data have been generated from various fields at a rapid speed, which offers a huge opportunity for mining valuable knowledge. As a typical task of time series mining, ...Time Series Classification (TSC) has attracted lots of attention from both researchers and domain experts due to its broad applications ranging from human activity recognition to smart city governance. Specifically, there is an increasing requirement for performing classification tasks on diverse types of time series data in a timely manner without costly hand-crafting feature engineering. Therefore, in this paper, we propose a framework named Edge4TSC that allows time series to be processed in the edge environment, so that the classification results can be instantly returned to the end-users. Meanwhile, to get rid of the costly hand-crafting feature engineering process, deep learning techniques are applied for automatic feature extraction, which shows competitive or even superior performance compared to state-of-the-art TSC solutions. However, because time series presents complex patterns, even deep learning models are not capable of achieving satisfactory classification accuracy, which motivated us to explore new time series representation methods to help classifiers further improve the classification accuracy. In the proposed framework Edge4TSC, by building the binary distribution tree, a new time series representation method was designed for addressing the classification accuracy concern in TSC tasks. By conducting comprehensive experiments on six challenging time series datasets in the edge environment, the potential of the proposed framework for its generalization ability and classification accuracy improvement is firmly validated with a number of helpful insights.
It is commonplace for people to perform various kinds of activities in groups. The recognition of human groups is of importance in many applications including crowd evacuation, teamwork coordination, ...and advertising. Existing group recognition approaches require snapshots of human trajectories, which is often impossible in the reality due to different data collection start time and frequency, and the inherent time deviations of devices. This study proposes an approach to synchronize the data of people for group recognition. All people’s trajectory data are aligned by using data interpolating. The optimal interpolating points are computed based on our proposed error function. Moreover, the time deviations among devices are estimated and eliminated by message passing. A real-life data set is used to validate the effectiveness of the proposed approach. The results show that 97.7% accuracy of group recognition can be achieved. The approach proposed to deal with time deviations was also proven to lead to better performance compared to that of the existing approaches.
On the basis of tricabocyanine, two near-infrared fluorescent sensors CYP-1 and CYP-2 have been designed and synthesized. Both of them can selectively and sensitively recognize Cd2+ from other metal ...ions, especially the CYP-2, which can distinguish Cd2+ in neutral buffer solution.
Protein vicinal dithiols play fundamental roles in intracellular redox homeostasis due to their involvement in protein synthesis and function through the reversible vicinal dithiol oxidation to ...disulfide. To provide quantitative information about the global distribution and dynamic changes of protein vicinal dithiols in living cells, we have designed and synthesized a ratiometric fluorescent probe (VTAF) for trapping of vicinal dithiol-containing proteins (VDPs) in living cells. VTAF exhibits a ratiometric fluorescence signal upon single excitation, which enables self-calibration of the fluorescence signal and quantification of endogenous vicinal dithiols of VDPs. Its potential for in situ dynamic tracing of changes of protein vicinal dithiols under different cellular redox conditions was exemplified. VTAF facilitated the direct observation of subcellular distribution of endogenous VDPs via ratiometric fluorescence imaging and colocalization assay. And the results suggested that there are abundant VDPs in mitochondria. Moreover, some redox-sensitive VDPs are also present on cell surface which can respond to redox stimulus. This ratiometric fluorescence technique presents an important extension to previous fluorescence intensity-based probes for trapping and quantifying protein vicinal dithiols in living cells, as well as its visible dynamic tracing of VDPs.
A novel dual-emission fluorescence probe has been developed for specific and sensitive detection of hypochlorite (ClO(-)). Upon addition of ClO(-), significant changes in fluorescence emission ...intensity at two discrete wavelengths were observed. Meanwhile OONO(-) led to only a single-channel fluorescence enhancement. This feature makes it a clear advantage in distinguishing ClO(-), RNS from other ROS.
Background Investigating novel therapeutic strategies for colorectal cancer (CRC) is imperative. However, there is limited research on the use of drugs to target peripheral blood immune cells in this ...context. To address this gap, we performed a two-sample Mendelian randomization (MR) analysis to identify potential therapeutic targets for CRC. Methods We applied two-sample MR to identify the causal relationship between peripheral blood immune cells and CRC. GWAS data were obtained from the IEU OPEN GWAS project. Based on the implications from the MR results, we conducted a comprehensive database search and genetic analysis to explore potential underlying mechanisms. We predicted miRNAs for each gene and employed extensive research for potential therapeutic applications. Results We have identified causal associations between two peripheral immune cells and colorectal cancer. Activated & resting Treg %CD4 + cell was positively associated with the risks of CRC, while DN (CD4-CD8-) %leukocyte cell exhibited a protective role in tumor progression. NEK7 (NIMA related kinase 7) and LHX9 (LIM homeobox 9) expressed in Treg cells were positively associated with CRC risks and may play a vital role in carcinogenesis. Conclusions This study identified causal relationship between peripheral immune cell and CRC. Treg and DN T cells were implicated to own promoting and inhibiting effects on CRC progression respectively. NEK7 and LHX9 in Treg cells were identified as potential biotarget for antitumor therapies. Keywords: Colorectal cancer, Mendelian randomization, Peripheral blood immune cell, Therapeutic target
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