The hierarchical martensitic features in ultra-high strength stainless steel (UHSSS), including the prior austenite grains, martensite packets, blocks and laths with the descending size, were refined ...to various extents by employing different thermomechanical processes and then carefully characterized. Their relation to yield strength and impact toughness was analyzed. We conclude that the refinement of martensitic structures could lead to the significant increase of yield strength, which follows the Hall-Petch relation with the effect grain size defined by high angle boundaries (HABs). Impact toughness of UHSSS depends on the frequency and capability for retained austenite (RA) grains at both HABs and martensite lath boundaries to trap the propagating cracks via strain-induced transformation, in which the film-like RA grains at lath boundaries appear to make the greater contribution.
Metal-organic framework (MOF), a novel hybrid porous material which is composited by metal ions and organic linkers, has drawn increasing attention and became a promising material in the biomedical ...field owing to their unique properties including large pore volume, high surface area, tunable pore size, versatile functionality and high drug loading efficiency. However, the MOF families and members, and the drug release mechanisms in MOF-based stimuli-responsive drug delivery systems (DDSs) are rarely summarized. Here, we systematically classified the families of MOF and introduced some representative members in MOF families. Moreover, the underlying drug release mechanisms were interpreted according to endogenous stimuli (include pH, glutathione (GSH), adenosine-triphosphate (ATP), ion, glucose, enzyme, H2S, and etc.) and the exogenous stimuli (include light, temperature, pressure, and etc.). Furthermore, the remaining challenges and future directions of DDSs based on MOF are discussed and proposed. This review revealed the relationship between the structure and properties of MOF. A better understanding of these release mechanisms under different stimuli would benefit the designing of sophisticated DDSs based on the promising material of MOF.
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Nucleic acid aptamers are single-stranded DNA or RNA of 20-100 nucleotides in length that have attracted substantial scientific interest due to their ability to specifically bind to target molecules ...via the formation of three-dimensional structures. Compared to traditional protein antibodies, aptamers have several advantages, such as their small size, high binding affinity, specificity, flexible structure, being chemical synthesizable and modifiable, good biocompatibility, high stability and low immunogenicity, which all contribute to their widely applications in the biomedical field. To date, much progress has been made in the study and applications of aptamers, however, detailed information on how aptamers bind to their targets is still scarce. Over the past few decades, many methods have been introduced to investigate the aptamer-target binding process, such as measuring the main kinetic or thermodynamic parameters, detecting the structural changes of the binding complexes, etc. Apart from traditional physicochemical methods, various types of molecular docking programs have been applied to simulate the aptamer-target interactions, while these simulations also have limitations. To facilitate the further research on the interactions, herein, we provide a brief review to illustrate the recent advances in the study of aptamer-target interactions. We summarize the binding targets of aptamers, such as small molecules, macromolecules, and even cells. Their binding constants (KD) are also summarized. Methods to probe the aptamer-target binding process, such as surface plasmon resonance (SPR), circular dichroism spectroscopy (CD), isothermal titration calorimetry (ITC), footprinting assay, truncation and mutation assay, nuclear magnetic resonance spectroscopy (NMR), X-ray crystallography and molecular docking simulation are indicated. The binding forces mediating the aptamer-target interactions, such as hydrogen bonding, electrostatic interaction, the hydrophobic effect, π-π stacking and van der Waals forces are summarized. The challenges and future perspectives are also discussed.
In the past few years, graphene oxide (GO) has emerged as a unique platform for developing DNA-based biosensors, given the DNA adsorption and fluorescence-quenching properties of GO. Adsorbed DNA ...probes can be desorbed from the GO surface in the presence of target analytes, producing a fluorescence signal. In addition to this initial design, many other strategies have been reported, including the use of aptamers, molecular beacons, and DNAzymes as probes, label-free detection, utilization of the intrinsic fluorescence of GO, and the application of covalently linked DNA probes. The potential applications of DNA-functionalized GO range from environmental monitoring and cell imaging to biomedical diagnosis. In this review, we first summarize the fundamental surface interactions between DNA and GO and the related fluorescence-quenching mechanism. Following that, the various sensor design strategies are critically compared. Problems that must be overcome before this technology can reach its full potential are described, and a few future directions are also discussed.
Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing ...images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elementsoriented land-use classification method based on "partlets," which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed "sparselets," from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an L0-norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR landuse data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper.
Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis. In recent deep learning models with end-to-end training, pooling is a ...widely adopted procedure for view aggregation. However, pooling merely retains the max or mean value over all views, which disregards the content information of almost all views and also the spatial information among the views. To resolve these issues, we propose Sequential Views To Sequential Labels (SeqViews2SeqLabels) as a novel deep learning model with an encoder-decoder structure based on recurrent neural networks (RNNs) with attention. SeqViews2SeqLabels consists of two connected parts, an encoder-RNN followed by a decoder-RNN, that aim to learn the global features by aggregating sequential views and then performing shape classification from the learned global features, respectively. Specifically, the encoder-RNN learns the global features by simultaneously encoding the spatial and content information of sequential views, which captures the semantics of the view sequence. With the proposed prediction of sequential labels, the decoder-RNN performs more accurate classification using the learned global features by predicting sequential labels step by step. Learning to predict sequential labels provides more and finer discriminative information among shape classes to learn, which alleviates the overfitting problem inherent in training using a limited number of 3D shapes. Moreover, we introduce an attention mechanism to further improve the discriminative ability of SeqViews2SeqLabels. This mechanism increases the weight of views that are distinctive to each shape class, and it dramatically reduces the effect of selecting the first view position. Shape classification and retrieval results under three large-scale benchmarks verify that SeqViews2SeqLabels learns more discriminative global features by more effectively aggregating sequential views than state-of-the-art methods.
Rotating machines have been widely used in industrial engineering. The fault diagnosis of rotating machines plays a vital important role to reduce the catastrophic failures and heavy economic loss. ...However, the measured vibration signal of rotating machinery often represents non-linear and non-stationary characteristics, resulting in difficulty in the fault feature extraction. As a statistical measure, entropy can quantify the complexity and detect dynamic change through taking into account the non-linear behavior of time series. Therefore, entropy can be served as a promising tool to extract the dynamic characteristics of rotating machines. Recently, many studies have applied entropy in fault diagnosis of rotating machinery. This paper aims to investigate the applications of entropy for the fault characteristics extraction of rotating machines. First, various entropy methods are briefly introduced. Its foundation, application, and some improvements are described and discussed. The review is divided into eight parts: Shannon entropy, Rényi entropy, approximate entropy, sample entropy, fuzzy entropy, permutation entropy, and other entropy methods. In each part, we will review the applications using the original entropy method and the improved entropy methods, respectively. In the end, a summary and some research prospects are given.
Learning 3D global features by aggregating multiple views is important. Pooling is widely used to aggregate views in deep learning models. However, pooling disregards a lot of content information ...within views and the spatial relationship among the views, which limits the discriminability of learned features. To resolve this issue, 3D to Sequential Views (3D2SeqViews) is proposed to more effectively aggregate the sequential views using convolutional neural networks with a novel hierarchical attention aggregation. Specifically, the content information within each view is first encoded. Then, the encoded view content information and the sequential spatiality among the views are simultaneously aggregated by the hierarchical attention aggregation, where view-level attention and class-level attention are proposed to hierarchically weight sequential views and shape classes. View-level attention is learned to indicate how much attention is paid to each view by each shape class, which subsequently weights sequential views through a novel recursive view integration. Recursive view integration learns the semantic meaning of view sequence, which is robust to the first view position. Furthermore, class-level attention is introduced to describe how much attention is paid to each shape class, which innovatively employs the discriminative ability of the fine-tuned network. 3D2SeqViews learns more discriminative features than the state-of-the-art, which leads to the outperforming results in shape classification and retrieval under three large-scale benchmarks.
Fluorescent aptamer probes physisorbed on graphene oxide (GO) have recently emerged as a useful sensing platform. A signal is generated by analyte-induced probe desorption. To address nonspecific ...probe displacement and the false positive signal, we herein report a covalently linked aptamer probe for adenosine triphosphate (ATP) detection. A fluorophore and amino dual modified aptamer was linked to the carboxyl group on GO with a coupling efficiency of ∼50%. The linearity, specificity, stability, and regeneration of the covalent sensor were systematically studied and compared to the physisorbed probe. Both sensors have similar sensitivity, but the covalent one is more resistant to nonspecific probe displacement by proteins. The covalent sensor has a dynamic range from 0.125 to 2 mM ATP in buffer at room temperature and is resistance to DNase I. Intracellular ATP imaging was demonstrated using the covalent sensor, which generated a higher fluorescence signal than the physisorbed sensor. After the cells were stimulated with 5 mM Ca2+ for ATP production, the intracellular signal enhanced by 31.8%. This work highlights the advantages of covalent aptamer sensors using GO as both a quencher and a delivery vehicle for intracellular metabolite detection.
Identification of biomarkers is essential for diagnosis, targeted therapy and prognosis evaluation of diseases, especially cancers. Currently, the number of ideal clinical biomarkers is still limited ...partially because of lacking efficient methods in biomarker discovery. Nucleic acid aptamers are artificial single-stranded DNA or RNA sequences that can selectively bind to various targets with high specificity and affinity. Moreover, aptamers possess desirable advantages, including easy synthesis, convenient modification, relative chemical stability and low immunogenicity. Recently, different aptamer-based strategies have been developed to facilitate the discovery of biomarkers. Based on cell-SELEX technology, the selected aptamers can be used to identify cell-surface protein biomarkers of different cancer cells. SOMAscan can analyze thousands of proteins of different biological samples, which becomes a multiplexed protein biomarker discovery platform. Additionally, secreted protein biomarkers can be discovered by aptamers screened through secretome SELEX. In order to facilitate the identification of target proteins, several covalent cross-linking strategies have been developed, such as aptamer-based affinity labeling (ABAL), DNA-templated aptamer and protein-aptamer template (PAT). In this review, we mainly highlight the emerging nucleic acid aptamer-based biomarker discovery strategies and demonstrate their unique technological advantages in discovering cancer biomarkers. The challenges and perspectives of aptamer-based methods are also discussed.
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•Cancer protein biomarker discovery utilizing aptamers is a highly interesting topic and its advantages are reviewed.•Strategies of cancer protein biomarker discovery based on aptamers and their corresponding protein biomarkers are listed.•Covalent cross-linking strategies that facilitate the binding between aptamers and their target proteins are summarized.•The challenges and future prospects of aptamer-based cancer protein biomarker discovery are discussed and proposed.
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