The development of fluorophores for the second near‐infrared window (NIR‐II, 1000–1700 nm) represents an emerging, significant, and vibrant field in analytic chemistry, chemical biology, and ...biomedical engineering. The wavelength, brightness, and stability are three crucial factors that determine the performance of an NIR‐II fluorophore. Up to now, significant progress has been made in the development of NIR‐II fluorescence molecular probes, including the synthesis of D‐A‐D and D‐π‐A fluorophores with improved NIR‐II fluorescence imaging performance and the construction of off–on probes and ratiometric probes via energy transfer or molecular structure modification. In this review, we summarize the most recent advances in molecular engineering design strategies of NIR‐II fluorophores and probes, then highlight a selection of bioimaging and biosensing applications. We also provide perspectives on potential challenges and opportunities in this emerging field
Fluorescence imaging in the NIR‐II window has become a powerful method for biomedical research owing the combined merits of diminished background fluorescence and deep tissue penetration. NIR‐II fluorophores occupy a central position in this technology. This review discusses the strategies applied in the design of NIR‐II fluorophores and NIR‐II fluorescent probes.
This paper used the panel data of various regions in China from 2007 to 2018 and constructs a green economic efficiency measurement index system. Following this, the super-efficient DEA model has ...been employed to measure the efficiency of China's green economy. Then, the Tobit model is used to verify the environmental regulation influence on efficiency of China's regional green economy extent and direction. The results show that: (1) in 2007–2018, the green economy efficiency level of China's eastern, central and western regions is on the rise, accompanied by more obvious spatial differences. The green economy efficiency basically shows the spatial differentiation characteristics of the highest in the eastern region and the lowest in the western region. (2) From the national perspective, environmental regulation influence on efficiency of green economy presents a “U” shaped curve that promotes and then suppresses. At the eastern and national levels, environmental regulation has the same characteristics for green economy efficiency, and both exhibit U-shaped curve characteristics. Whereas, in the central and western regions shows negative correlation. (3) From the perspective of control variables, there are significant differences in variables at the national and regional levels. Finally, the study concludes with some policy suggestion for future green development and the formulation of environmental regulations in China.
•Measuring the Efficiency of China's Green Economy by Using Super Efficiency DEA Model.•Environmental regulations impact on green economy efficiency by the Tobit model.•Environmental regulation impact on green economy efficiency presents “U” process.•Environmental regulation impact on green economy efficiency difference in China.
Illustrations on the problems of current BBR losses. Each row shows the optimization results in different iterations with certain loss function. The Black denotes the anchor box. The Blue denotes the ...target box. The fist row denotes GIOU. The second row denotes CIOU. The third row denotes the proposed EIOU. EIOU attains more quick convergence speed and more accurate regression results. Display omitted
•We reveal the flaws of ℓn-norm and IOU-based losses for object detection.•We design a regression version of focal loss to emphasize the most promising anchors.•We conduct extensive experiments to validate the superiority of the proposed methods.
In object detection, bounding box regression (BBR) is a crucial step that determines the object localization performance. However, we find that most previous loss functions for BBR have two main drawbacks: (i) Both ℓn-norm and IOU-based loss functions are inefficient to depict the objective of BBR, which leads to slow convergence and inaccurate regression results. (ii) Most of the loss functions ignore the imbalance problem in BBR that the large number of anchor boxes which have small overlaps with the target boxes contribute most to the optimization of BBR. To mitigate the adverse effects caused thereby, we perform thorough studies to exploit the potential of BBR losses in this paper. Firstly, an Efficient Intersection over Union (EIOU) loss is proposed, which explicitly measures the discrepancies of three geometric factors in BBR, i.e., the overlap area, the central point and the side length. After that, we state the Effective Example Mining (EEM) problem and propose a regression version of focal loss to make the regression process focus on high-quality anchor boxes. Finally, the above two parts are combined to obtain a new loss function, namely Focal-EIOU loss. Extensive experiments on both synthetic and real datasets are performed. Notable superiorities on both the convergence speed and the localization accuracy can be achieved over other BBR losses.
In this paper, we propose a spectral-spatial unified network (SSUN) with an end-to-end architecture for the hyperspectral image (HSI) classification. Different from traditional spectral-spatial ...classification frameworks where the spectral feature extraction (FE), spatial FE, and classifier training are separated, these processes are integrated into a unified network in our model. In this way, both FE and classifier training will share a uniform objective function and all the parameters in the network can be optimized at the same time. In the implementation of the SSUN, we propose a band grouping-based long short-term memory model and a multiscale convolutional neural network as the spectral and spatial feature extractors, respectively. In the experiments, three benchmark HSIs are utilized to evaluate the performance of the proposed method. The experimental results demonstrate that the SSUN can yield a competitive performance compared with existing methods.
Intratumor heterogeneity is a common characteristic across diverse cancer types and presents challenges to current standards of treatment. Advancements in high-throughput sequencing and imaging ...technologies provide opportunities to identify and characterize these aspects of heterogeneity. Notably, transcriptomic profiling at a single-cell resolution enables quantitative measurements of the molecular activity that underlies the phenotypic diversity of cells within a tumor. Such high-dimensional data require computational analysis to extract relevant biological insights about the cell types and states that drive cancer development, pathogenesis, and clinical outcomes. In this review, we highlight emerging themes in the computational analysis of single-cell transcriptomics data and their applications to cancer research. We focus on downstream analytical challenges relevant to cancer research, including how to computationally perform unified analysis across many patients and disease states, distinguish neoplastic from nonneoplastic cells, infer communication with the tumor microenvironment, and delineate tumoral and microenvironmental evolution with trajectory and RNA velocity analysis. We include discussions of challenges and opportunities for future computational methodological advancements necessary to realize the translational potential of single-cell transcriptomic profiling in cancer.
Since the success of any educational system is tied to the teachers’ professional commitment, discovering the determinants of this construct seems vital. In line with this, a huge number of inquiries ...have evaluated the effects of personal, contextual, and professional variables on teachers’ professional commitment. However, the impacts of job satisfaction and collective efficacy have remained unclear. Against this backdrop, the current review article seeks to theoretically explain the impacts of these constructs on EFL teachers’ professional commitment using the available documents. The review findings illuminated that EFL teachers’ professional commitment heavily relies on their job satisfaction and collective efficacy beliefs. The implications for educational principals and teacher educators are finally discussed.
Due to the recent advances in satellite sensors, a large amount of high-resolution remote sensing images is now being obtained each day. How to automatically recognize and analyze scenes from these ...satellite images effectively and efficiently has become a big challenge in the remote sensing field. Recently, a lot of work in scene classification has been proposed, focusing on deep neural networks, which learn hierarchical internal feature representations from image data sets and produce state-of-the-art performance. However, most methods, including the traditional shallow methods and deep neural networks, only concentrate on training a single model. Meanwhile, neural network ensembles have proved to be a powerful and practical tool for a number of different predictive tasks. Can we find a way to combine different deep neural networks effectively and efficiently for scene classification? In this paper, we propose a gradient boosting random convolutional network (GBRCN) framework for scene classification, which can effectively combine many deep neural networks. As far as we know, this is the first time that a deep ensemble framework has been proposed for scene classification. Moreover, in the experiments, the proposed method was applied to two challenging high-resolution data sets: 1) the UC Merced data set containing 21 different aerial scene categories with a submeter resolution and 2) a Sydney data set containing eight land-use categories with a 1.0-m spatial resolution. The proposed GBRCN framework outperformed the state-of-the-art methods with the UC Merced data set, including the traditional single convolutional network approach. For the Sydney data set, the proposed method again obtained the best accuracy, demonstrating that the proposed framework can provide more accurate classification results than the state-of-the-art methods.
Due to the rapid technological development of various different satellite sensors, a huge volume of high-resolution image data sets can now be acquired. How to efficiently represent and recognize the ...scenes from such high-resolution image data has become a critical task. In this paper, we propose an unsupervised feature learning framework for scene classification. By using the saliency detection algorithm, we extract a representative set of patches from the salient regions in the image data set. These unlabeled data patches are exploited by an unsupervised feature learning method to learn a set of feature extractors which are robust and efficient and do not need elaborately designed descriptors such as the scale-invariant-feature-transform-based algorithm. We show that the statistics generated from the learned feature extractors can characterize a complex scene very well and can produce excellent classification accuracy. In order to reduce overfitting in the feature learning step, we further employ a recently developed regularization method called "dropout," which has proved to be very effective in image classification. In the experiments, the proposed method was applied to two challenging high-resolution data sets: the UC Merced data set containing 21 different aerial scene categories with a submeter resolution and the Sydney data set containing seven land-use categories with a 60-cm spatial resolution. The proposed method obtained results that were equal to or even better than the previous best results with the UC Merced data set, and it also obtained the highest accuracy with the Sydney data set, demonstrating that the proposed unsupervised-feature-learning-based scene classification method provides more accurate classification results than the other latent-Dirichlet-allocation-based methods and the sparse coding method.
The deep convolutional neural network (CNN) is of great interest recently. It can provide excellent performance in hyperspectral image classification when the number of training samples is ...sufficiently large. In this paper, a novel pixel-pair method is proposed to significantly increase such a number, ensuring that the advantage of CNN can be actually offered. For a testing pixel, pixel-pairs, constructed by combining the center pixel and each of the surrounding pixels, are classified by the trained CNN, and the final label is then determined by a voting strategy. The proposed method utilizing deep CNN to learn pixel-pair features is expected to have more discriminative power. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than the conventional deep learning-based method.
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