Copper (II) is one of the most of important cofactors for numerous enzymes and has captured broad attention due to its role as a neurotransmitters for physiological and pathological functions. In ...this article, we present a reaction-based fluorescent sensor for Cu2+ detection (NIR-Cu) with near-infrared excitation and emission, including probe design, structure characterization, optical property test and biological imaging application. NIR-Cu is equipped with a functional group, 2-picolinic ester, which hydrolyzes in the presence of Cu2+ with high selectivity over completed cations. With the experimental conditions optimized, NIR-Cu (5μM) exhibits linear response for Cu2+ range from 0.1 to 5μM, with a detection limit of 29nM. NIR-Cu also shows excellent water solubility and are highly responsive, both desirable properties for Cu2+ detection in water samples. In addition, due to its near-infrared excitation and emission properties, NIR-Cu demonstrates outstanding fluorescent imaging in living cells and tissues.
•A reaction-based sensor for Cu2+ detection with NIR excitation and emission.•The detection limit for Cu2+ is as low as 29nM in aqueous buffer.•The probe shows special selectivity for Cu2+ over other metal ions.
Object detection is a challenging task that requires a large amount of labeled data to train high-performance models. However, labeling huge amounts of data is expensive, making it difficult to train ...a good detector with limited labeled data. Existing approaches mitigate this issue via active learning or semi-supervised learning, but there is still room for improvement. In this paper, we propose a novel active learning method for deep object detection that fully exploits unlabeled data by combining the benefits of active learning and semi-supervised learning. Our method first trains an initial model using limited labeled data, then uses self-training and data augmentation strategies to train a semi-supervised model using labeled and unlabeled data. We then select query samples based on informativeness and representativeness from the unlabeled data to further improve the model through semi-supervised training. Experimental results on commonly used object detection datasets demonstrate the effectiveness of our approach, outperforming state-of-the-art methods.
Hyperuricemia, caused by an imbalance between the rates of production and excretion of uric acid (UA), may greatly increase the mortality rates in patients with cardiovascular and cerebrovascular ...diseases. Herein, for fast‐acting and long‐lasting hyperuricemia treatment, armored red blood cell (RBC) biohybrids, integrated RBCs with proximal, cascaded‐enzymes of urate oxidase (UOX) and catalase (CAT) encapsulated within ZIF‐8 framework‐based nanoparticles, have been fabricated based on a super‐assembly approach. Each component is crucial for hyperuricemia treatment: 1) RBCs significantly increase the circulation time of nanoparticles; 2) ZIF‐8 nanoparticles‐based superstructure greatly enhances RBCs resistance against external stressors while preserving native RBC properties (such as oxygen carrying capability); 3) the ZIF‐8 scaffold protects the encapsulated enzymes from enzymatic degradation; 4) no physical barrier exists for urate diffusion, and thus allow fast degradation of UA in blood and neutralizes the toxic by‐product H2O2. In vivo results demonstrate that the biohybrids can effectively normalize the UA level of an acute hyperuricemia mouse model within 2 h and possess a longer elimination half‐life (49.7 ± 4.9 h). They anticipate that their simple and general method that combines functional nanomaterials with living cell carriers will be a starting point for the development of innovative drug delivery systems.
Armored red blood cell (RBC) biohybrids, integrated RBCs with proximal, cascaded‐enzymes of urate oxidase and catalase encapsulated within ZIF‐8 framework‐based nanoparticles, have been fabricated for fast‐acting and long‐lasting urate‐lowering therapy. RBC biohybrids could normalize the UA level of acute hyperuricemia model mice within 2 h, and possessed an elimination half‐life of 49.7 ± 4.9 h, which is over thrice longer than that of free UOX, representing an innovative drug carrier for hyperuricemia treatment.
A new base on grid clustering method is presented in this paper. This new method first does unsupervised learning on the high dimensions data. This paper proposed a grid-based approach to clustering. ...It maps the data onto a multi-dimensional space and applies a linear transformation to the feature space instead of to the objects themselves and then approach a grid-clustering method. Unlike the conventional methods, it uses a multidimensional hyper-eclipse grid cell. Some case studies and ideas how to use the algorithms are described. The experimental results show that EGC can discover abnormity shapes of clusters.
It is well known that the performance of a kernel method is highly dependent on the choice of kernel parameter. However, existing kernel path algorithms are limited to plain support vector machines ...(SVMs), which has one equality constraint. It is still an open question to provide a kernel path algorithm to <inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula>-support vector classification (<inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula>-SVC) with more than one equality constraint. Compared with plain SVM, <inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula>-SVC has the advantage of using a regularization parameter <inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula> for controlling the number of support vectors and margin errors. To address this problem, in this article, we propose a kernel path algorithm (KP<inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula>SVC) to trace the solutions of <inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula>-SVC exactly with respect to the kernel parameter. Specifically, we first provide an equivalent formulation of <inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula>-SVC with two equality constraints, which can avoid possible conflicts during tracing the solutions of <inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula>-SVC. Based on this equivalent formulation of <inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula>-SVC, we propose the KP<inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula>SVC algorithm to trace the solutions with respect to the kernel parameter. However, KP<inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula>SVC traces nonlinear solutions of kernel method rather than the errors of loss function, and it is still a challenge to provide the algorithm that guarantees to find the global optimal model. To address this challenging problem, we extend the classical error path algorithm to the nonlinear kernel solution paths and propose a new kernel error path (KEP) algorithm that ensures to find the global optimal kernel parameter by minimizing the cross validation error. We also provide the finite convergence analysis and computational complexity analysis to KP<inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula>SVC and KEP. Extensive experimental results on a variety of benchmark datasets not only verify the effectiveness of KP<inline-formula> <tex-math notation="LaTeX">\nu </tex-math></inline-formula>SVC but also show the advantage of applying KEP to select the optimal kernel parameter.
•We proposed a chunk incremental learning algorithm for CSHL-SVM (i.e., CICSHL-SVM) that can update a trained model without re-training from scratch when incorporating a chunk of new samples.•Our ...method is efficient because it can update the trained model not only for one sample at a time but also for multiple samples at a time.•The experimental results on a variety of datasets not only confirm the effectiveness of CSHL-SVM but also show that our method is more efficient than the batch algorithm of CSHL-SVM and the single incremental algorithm.
Cost-sensitive learning can be found in many real-world applications and represents an important learning paradigm in machine learning. The recently proposed cost-sensitive hinge loss support vector machine (CSHL-SVM) guarantees consistency with the cost-sensitive Bayes risk, and this technique provides better generalization accuracy compared to traditional cost-sensitive support vector machines. In practice, data typically appear in the form of sequential chunks, also called an on-line scenario. However, conventional batch learning algorithms waste a considerable amount of time under the on-line scenario due to re-training of a model from scratch. To make CSHL-SVM more practical for the on-line scenario, we propose a chunk incremental learning algorithm for CSHL-SVM, which can update a trained model without re-training from scratch when incorporating a chunk of new samples. Our method is efficient because it can update the trained model for not only one sample at a time but also multiple samples at a time. Our experimental results on a variety of datasets not only confirm the effectiveness of CSHL-SVM but also show that our method is more efficient than the batch algorithm of CSHL-SVM and the incremental learning method of CSHL-SVM only for a single sample.
•We study a general PQP problem that can be instantiated into many learning problems.•Based on the general PQP problem, we provide a unified and robust kernel path implementation (i.e. GKP) for an ...extensive number of PQP problems, many of which still do not have kernel path algorithms.•We analyze the iterative complexity and computational complexity of GKP.•We conduct experiments on various datasets, these results not only confirm the identity between GKP and several exiting specific kernel path algorithms (SKP), but also show that our GKP is superior to SKP in terms of generality and robustness.
It is well known that the performance of a kernel method highly depends on the choice of kernel parameter. A kernel path provides a compact representation of all optimal solutions, which can be used to choose the optimal value of kernel parameter along with cross validation (CV) method. However, none of these existing kernel path algorithms provides a unified implementation to various learning problems. To fill this gap, in this paper, we first study a general parametric quadratic programming (PQP) problem that can be instantiated to an extensive number of learning problems. Then we provide a generalized kernel path (GKP) for the general PQP problem. Furthermore, we analyze the iteration complexity and computational complexity of GKP. Extensive experimental results on various benchmark datasets not only confirm the identity of GKP with several existing kernel path algorithms, but also show that our GKP is superior to the existing kernel path algorithms in terms of generalization and robustness.
This study explored FL-H
S, a novel fluorescein-based H
S donor, as an anti-inflammatory agent. The results demonstrated the efficient release of H
S by FL-H
S, along with its biocompatibility, ...real-time intracellular H
S release and imaging capability.
experiments using a rat model confirmed the anti-inflammatory effects of FL-H
S, evidenced by reduced foot swelling. We also successfully elucidated the anti-inflammatory mechanism through ELISA and WB analysis.
Two fluorescent probes (Naph-1 and Naph-2), which can be prepared via a facile process, have been developed to detect hypochlorite acid (HOCl). The N,N-dimethyl thiocarbamate group quenches the ...fluorescence of the probes through intramolecular charge transfer (ICT). Upon reaction with HOCl in aqueous buffer, Naph-1 shows ultra-high sensitivity towards HOCl through a 4600-fold increase in fluorescence intensity, as well as a detection limit of 2.37 nM. The probes have been applied to confocal fluorescence imaging of exogenous and endogenous HOCl in living cells.
Sequential minimal optimization (SMO) is one of the most popular methods for solving a variety of support vector machines (SVMs). The shrinking and caching techniques are commonly used to accelerate ...SMO. An interesting phenomenon of SMO is that most of the computational time is wasted on the first half of iterations for building a good solution closing to the optimal. However, as we all know, the stochastic subgradient descent (SSGD) method is extremely fast for building a good solution. In this paper, we propose a generalized framework of accelerating SMO through SSGD for a variety of SVMs of binary classification, regression, ordinal regression, and so on. We also provide a deep insight about why SSGD can accelerate SMO. Experimental results on a variety of datasets and learning applications confirm that our method can effectively speed up SMO.
PDF
