Chemotherapy for human solid tumors in clinical practice is far from satisfactory. Despite the discovery and synthesis of hundreds of thousands of anticancer compounds targeting various crucial units ...in cancer cell proliferation and metabolism, the fundamental problem is the lack of targeting delivery of these compounds selectively into solid tumor tissue to maintain an effective concentration level for a certain length of time for drug-tumor interaction to execute anticancer activities. The enhanced permeability and retention effect (EPR effect) describes a universal pathophysiological phenomenon and mechanism in which macromolecular compounds such as albumin and other polymer-conjugated drugs beyond certain sizes (above 40 kDa) can progressively accumulate in the tumor vascularized area and thus achieve targeting delivery and retention of anticancer compounds into solid tumor tissue. Targeting therapy via the EPR effect in clinical practice is not always successful since the strength of the EPR effect varies depending on the type and location of tumors, status of blood perfusion in tumors, and the physical-chemical properties of macromolecular anticancer agents. This review highlights the significance of the concept and mechanism of the EPR effect and discusses methods for better utilizing the EPR effect in developing smarter macromolecular nanomedicine to achieve a satisfactory outcome in clinical applications.
In this paper, we present a novel deep learning architecture for infrared and visible images fusion problems. In contrast to conventional convolutional networks, our encoding network is combined with ...convolutional layers, a fusion layer, and dense block in which the output of each layer is connected to every other layer. We attempt to use this architecture to get more useful features from source images in the encoding process, and two fusion layers (fusion strategies) are designed to fuse these features. Finally, the fused image is reconstructed by a decoder. Compared with existing fusion methods, the proposed fusion method achieves the state-of-the-art performance in objective and subjective assessment.
Global warming, environmental pollution, and an energy shortage in the current fossil fuel society may cause a severe ecological crisis. Storage and conversion of renewable, dispersive and ...non-perennial energy from the sun, wind, geothermal sources, water, or biomass could be a promising option to relieve this crisis. Carbon materials could be the most versatile platform materials applied in the field of modern energy storage and conversion. Conventional carbon materials produced from coal and petrochemical products are usually energy intensive or involve harsh synthetic conditions. It is highly desired to develop effective methods to produce carbon materials from renewable resources that have high performance and limited environmental impacts. In this regard, biochar, a bio-carbon with abundant surface functional groups and easily tuned porosity produced from biomass, may be a promising candidate as a sustainable carbon material. Recent studies have demonstrated that biochar-based materials show great application potential in energy storage and conversion because of their easily tuned surface chemistry and porosity. In this review, recent advances in the applications of biochar-based materials in various energy storage and conversion fields, including hydrogen storage and production, oxygen electrocatalysts, emerging fuel cell technology, supercapacitors, and lithium/sodium ion batteries, are summarized, highlighting the mechanisms and open questions in current energy applications. Finally, contemporary challenges and perspectives on how biochar-based materials will develop and, in particular, the fields in which the use of biochar-based materials could be expanded are discussed throughout the review. This review demonstrates significant potential for energy applications of biochar-based materials, and it is expected to inspire new discoveries to promote practical applications of biochar-based materials in more energy storage and conversion fields.
Biochar, a bio-carbon with abundant surface functional groups and easily tuned porosity produced from biomass, shows great application potential in energy storage and conversion. In this review, recent advances in the applications of biochar-based materials in various energy storage and conversion fields are summarized, highlighting the mechanisms and open questions in current energy applications.
A look at how biochar is formed in the biomass pyrolysis process is offered. Research points toward a biochear-based sustainable platform carbon material.
Embedding cubane M4(OH)4 (M=Ni, Co) clusters within the matrix of metal–organic frameworks (MOFs) is a strategy to develop materials with unprecedented synergistic properties. Herein, a new material ...type based on the pore‐space partition of the cubic primitive minimal‐surface net (MOF‐14‐type) has been realized. CTGU‐15 made from the Ni4(OH)4 cluster not only has very high BET surface area (3537 m2 g−1), but also exhibits bi‐microporous features with well‐defined micropores at 0.86 nm and 1.51 nm. Furthermore, CTGU‐15 is stable even under high pH (0.1 m KOH), making it well suited for methanol oxidation in basic medium. The optimal hybrid catalyst KB&CTGU‐15 (1:2) made from ketjen black (KB) and CTGU‐15 exhibits an outstanding performance with a high mass specific peak current of 527 mA mg−1 and excellent peak current density (29.8 mA cm−2) at low potential (0.6 V). The isostructural cobalt structure (CTGU‐16) has also been synthesized, further expanding the application potential of this material type.
Split pores: A new 3D microporous metal–organic framework containing cubane Ni4(OH)4 clusters can serve as an electrocatalyst for the methanol oxidation reaction (MOR). The optimal hybrid material shows impressive electrocatalytic performance including a high mass specific peak current of 527 mA mg−1 and excellent peak current density (29.8 mA cm−2) at a very low potential (0.6 V).
Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a better performance, ...especially in image fusion tasks. We propose a multi-level image decomposition method based on latent low-rank representation(LatLRR), which is called MDLatLRR. This decomposition method is applicable to many image processing fields. In this paper, we focus on the image fusion task. We build a novel image fusion framework based on MDLatLRR which is used to decompose source images into detail parts(salient features) and base parts. A nuclear-norm based fusion strategy is used to fuse the detail parts and the base parts are fused by an averaging strategy. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation.
In this article, we propose a novel method for infrared and visible image fusion where we develop nest connection-based network and spatial/channel attention models. The nest connection-based network ...can preserve significant amounts of information from input data in a multiscale perspective. The approach comprises three key elements: encoder, fusion strategy, and decoder, respectively. In our proposed fusion strategy, spatial attention models and channel attention models are developed that describe the importance of each spatial position and of each channel with deep features. First, the source images are fed into the encoder to extract multiscale deep features. The novel fusion strategy is then developed to fuse these features for each scale. Finally, the fused image is reconstructed by the nest connection-based decoder. Experiments are performed on publicly available data sets. These exhibit that our proposed approach has better fusion performance than other state-of-the-art methods. This claim is justified through both subjective and objective evaluations. The code of our fusion method is available at https://github.com/hli1221/imagefusion-nestfuse .
Microglia are innate immune cells of the central nervous system that sense extracellular cues. Brain injuries, inflammation, and pathology evoke dynamic structural responses in microglia, altering ...their morphology and motility. The dynamic motility of microglia is hypothesized to be a critical first step in sensing local alterations and engaging in pattern‐specific responses. Alongside their pathological responses, microglia also sense and regulate neuronal activity. In this review, we consider the extracellular molecules, receptors, and mechanisms that allow microglia to sense neuronal activity changes under both hypoactivity and hyperactivity. We also highlight emerging in vivo evidence that microglia regulate neuronal activity, ranging from physiological to pathophysiological conditions. In addition, we discuss the emerging role of calcium signaling in microglial responses to the extracellular environment. The dynamic function of microglia in monitoring and influencing neuronal activity may be critical for brain homeostasis and circuit modification in health and disease.
Main Points
Neuronal hyperactivity induces microglial process extension that dampens neuronal activity.
Neuronal hypoactivity disinhibits microglial dynamics that increases their surveillance.
Microglial calcium signaling is attuned to neuronal activity.
With efficient appearance learning models, discriminative correlation filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the ...existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filters. Consequently, the process of learning spatial filters can be approximated by the lasso regularization. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimization framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123, and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches.
Due to its storage and retrieval efficiency, cross-modal hashing (CMH) has been widely used for cross-modal similarity search in many multimedia applications. According to the training strategy, ...existing CMH methods can be mainly divided into two categories: relaxation-based continuous methods and discrete methods. In general, the training of relaxation-based continuous methods is faster than that of discrete methods, but the accuracy of relaxation-based continuous methods is not satisfactory. On the contrary, the accuracy of discrete methods is typically better than that of the relaxation-based continuous methods, but the training of discrete methods is very time-consuming. In this paper, we propose a novel CMH method, called Discrete Latent Factor model-based cross-modal Hashing (DLFH), for cross modal similarity search. DLFH is a discrete method which can directly learn the binary hash codes for CMH. At the same time, the training of DLFH is efficient. Experiments show that the DLFH can achieve significantly better accuracy than existing methods, and the training time of DLFH is comparable to that of the relaxation-based continuous methods which are much faster than the existing discrete methods.
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