Siamese transformer network-based similarity metric learning for cross-source remote sensing image retrieval

Neural Computing and Applications - As a fundamental technique for mining and analysis of remote sensing (RS) big data, content-based remote sensing image retrieval (CBRSIR) has received a lot of...     

Biomedical applications of mRNA nanomedicine

As an attractive alternative to plasmid DNA, messenger RNA (mRNA) has recently emerged as a promising class of nucleic acid therapeutics for biomedical applications. Advances in addressing the inherent shortcomings of mRNA and in the development of nanoparticle-based delivery systems have prompted the development and clinical translation of mRNA-based medicines. In this review, we discuss the chemical modification strategies of mRNA to improve its stability, minimize immune responses, and enhance translational efficacy. We also highlight recent progress in nanoparticle-based mRNA delivery. Considerable attention is given to the increasingly widespread applications of mRNA nanomedicine in the biomedical fields of vaccination, protein-replacement therapy, gene editing, and cellular reprogramming and engineering.     

Entrapping multifunctional dendritic nanoparticles into a hydrogel for local therapeutic delivery and synergetic immunochemotherapy

Nano Research - Developing multifunctional nanoparticles to support new therapy models is a promising and challenging task to address the current dilemma on antitumor treatment. Herein, we...     

Aggregation-induced emission luminogen-assisted stimulated emission depletion nanoscopy for super-resolution mitochondrial visualization in live cells

Nano Research - Aggregation-induced emission luminogens (AIEgens) are fluorescent agents that are ideal for bioimaging and have been widely used for organelle targeting, cellular mapping, and...     

Escherichia coli prlC gene encodes a trypsin-like proteinase regulating the cell cycle

BACKGROUND AND PURPOSE: Mood disorders are common after stroke and may impede physical, functional, and cognitive recovery, making early identification and treatment of potential importance. We aimed to compare the accuracy of the General Health Questionnaire (GHQ-30) and the Hospital Anxiety and Depression (HAD) Scale in detecting psychiatric morbidity after stroke and to determine the most suitable cutoff points for different purposes. METHODS: One hundred five hospital-referred stroke patients completed both the GHQ-30 and HAD Scale 6 months after onset before a blinded psychiatric assessment in which the Schedule for Affective Disorders and Schizophrenia with some supplementary questions was used to determine a DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition) diagnosis. Measures were compared in terms of sensitivity, specificity, and receiver operating characteristic curves. RESULTS: No significant differences were found between the GHQ-30 and the HAD Scale in identifying those patients with any DSM-IV diagnosis (P=0.95), grouped depression (P=0.56), or anxiety (P=0.25) disorders. The previously recommended cutoff points for identifying "cases" for the GHQ (4/5) and for the HAD Scale (8/9 and 11/12) were found to be suboptimal in this population. CONCLUSIONS: The GHQ-30 and HAD scale exhibited similar levels of sensitivity and specificity. Data are presented, taking into account the "cost" of false-positives and negatives, to allow a choice of cutoff points suitable for differing situations.     

Mechanical behaviour of metal–matrix composite deposits

The electroplating technique is used for producing thin sheets of copper- or nickel-based composites containing different volume fractions of -alumina dispersions. The microhardness and tensile behaviour of such composites, in both the as-deposited and the annealed state, are characterized. The strengthening mechanism of electroplated composites is found to be a combination of Orowan-type strengthening and the Hall–Petch effect.     

A robust motion estimation with center-biased diamond search and its parallel architecture for motion-compensated de-interlace

For motion compensated de-interlace, the accuracy and reliability of the motion vectors have a significant impact on the performance of the motion compensated interpolation. In order to improve the robustness of motion vector, a novel motion estimation algorithm with center-biased diamond search and its parallel VLSI architecture are proposed in this paper. Experiments show that it works better than conventional motion estimation algorithms in terms of motion compensation error and robustness, and its architecture overcomes the irregular data flow and achieves high efficiency. It also efficiently reuses data and reduces the control overhead. So, it is highly suitable for HDTV applications.     

Leader-follower consensus control of Lipschitz nonlinear systems by output feedback

This paper deals with the leader-follower consensus problem of Lipschitz nonlinear systems under fixed directed communication networks. Both state and output feedback control are proposed based on state and output measurements of neighbouring agents, respectively. Laplacian matrix features are explored for the stability analysis, and the sufficient conditions are derived to solve the consensus problem. Finally, simulation results are included to demonstrate the effectiveness of the output-based consensus controller.     

Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.