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A good purification strategy for obtaining high-quality and low-cost perovskite QDs ink requires a complete removal of the impurities but with a minimal phase transition of QDs from the perovskite phases to the nonperovskite δ-phase. This pioneering work reports the electrochemical quantification on the phase transition level of CsPbI3 QDs in purification. Cyclic voltammetry of the purified QDs evidenced the formation of a new product in the purification process, which was demonstrated to be the undesired nonperovskite δ-phase by independent structural analysis. The developed electrochemical methodology further enabled the quantification of the extent of the phase transition of the QDs purified using different strategies by simply analyzing the charge associated with the relevant peaks and allowing optimization of the purification. The latter is of vital importance for commercialization and is an essential step for boosting their device performance.

The ligand/quantum dots (QDs) ratio is crucial for the liquid state ligand exchange process to ensure a high-quality surface passivation and stable QDs ink. Herein we report an electrochemical method to investigate the ligand exchanged PbS-PbI2 QDs. It is found that the shell and core Pb(II) are distinguished by their reduction peak position in the cyclic voltammogram and the peak charge ratio gives the shell/core composition of the QDs. Combined with XPS analysis and UV-vis spectroscopy, it is further indicated that the shell/core ratio of PbS-PbI2 QDs varies as the ligand PbI2 concentration changes. Specifically, below a certain concentration, more PbI2 binds to the QD surface, leading to better passivation when the PbI2 concentration increases; however, beyond that concentration, decomposition of QDs likely occurs via an anion exchange process. The presented electrochemical method provides a new and powerful tool to investigate and optimize QD surface chemistry for boosting the scale up applications of QD devices.

Mounting studies have shown that long noncoding RNAs (lncRNAs) play important roles in the development and occurrence of several human diseases. However, the role of LINC00461 in osteoarthritis (OA) remains obscure. A CCK-8 assay was performed to detect cell viability, and qRT-PCR analysis was used to measure mRNA expression. The targeting by miR-30a-5p of the LINC00461 3'UTR was detected using a luciferase reporter assay. Our data indicated that the inflammatory mediators IL-6 and TNF-α induced LINC00461 expression in chondrocytes and that the expression of LINC00461 was upregulated in OA tissues. Furthermore, we showed that TNF-α and IL-6 suppressed the expression of miR-30a-5p and that miR-30a-5p expression was lower in OA tissues than in normal samples. The expression level of miR-30a-5p in OA tissues was negatively related to LINC00461 expression. In addition, we showed that LINC00461 directly interacted with miR-30a-5p in chondrocytes. Elevated expression of LINC00461 induced chondrocyte proliferation, cell cycle progression, inflammation, and extracellular matrix (ECM) degradation. However, we demonstrated that ectopic expression of miR-30a-5p suppressed cell growth, cell cycle progression, inflammation and ECM degradation. Finally, we found that overexpression of LINC00461 enhanced chondrocyte proliferation, cell cycle progression, inflammation, and ECM degradation by downregulating miR-30a-5p. These data demonstrated that LINC00461 may modulate the development of OA by suppressing miR-30a-5p expression in chondrocytes. We propose that LINC00461 and miR-30a-5p may be potential therapeutic and diagnostic targets for OA.

Osteoarthritis (OA) is the most common degenerative joint disease. microRNAs (miRNAs) have been showen to act critical roles in several diseases including OA. However, the involvement and underlying mechanism of miR-137 in development of OA remains unkown. In our study, we firstly showed that IL-1β decreased the expression of miR-137 in the chondrocytes and we demonstrated that the miR-37 expression level was lower in the OA cases than in the control patients. Dual-luciferase reporter analysis was performed to confirm that ADAMTS-5 was a direct target gene of miR-137. Furthermore, we indicated that elevated expression of miR-137 decreased the protein expression of ADAMTS-5 in the chondrocytes. In additional, we showed that IL-1β induces the ADAMTS-5 expression in the chondrocytes. The ADAMTS-5 expression level was higher in the OA cases than in the control patients. We showed that the expression of ADAMTS-5 was negatively correlated with the miR-137 expression level in OA tissues. Overexpression of miR-137 suppressed cell growth, extracellular matrix (ECM) degradation and inflammation in chondrocytes. These preliminary data elucidated that miR-137 suppressed OA progression via inhibiting cell growth, inflammation and ECM degradation.

In this paper, we propose a new approach for localization in wireless sensor networks based on semi-supervised Laplacian regularized least squares algorithm. We consider two kinds of localization data: signal strength and pair-wise distance between nodes. When nodes are close within their physical location space, their localization data vectors should be similar. We first propose a solution using the alignment criterion to learn an appropriate kernel function in terms of the similarities between anchors, and the kernel function is used to measure the similarity between pair-wise sensor nodes in the networks. We then propose a semi-supervised learning algorithm based upon manifold regularization to obtain the locations of the non-anchors. We evaluate our algorithm under various network topology, transmission range and signal noise, and analyze its performance. We also compare our approach with several existing approaches, and demonstrate the high efficiency of our proposed algorithm in terms of location estimation error.

Wireless sensor location estimation is an important area and attracts considerable research interests. In this paper, we present a novel graph embedding method for the localization problem by using signal strengths. We view the wireless sensor nodes as a group of distributed devices, and employ an appropriate kernel function to measure the similarity between sensors. The kernel function can be naturally defined according to the signal strength matrix. Then we formulate the localization problem as a graph embedding problem. Finally, we use the kernel locality preserving projection (KLPP) technique to estimate the relative locations of all sensor nodes. Given sufficient number of anchors, the relative locations can be transformed into physical locations. The main advantage of formulating the localization problem as graph embedding problem is that it allows us to construct a graph to preserve the topological structure of the sensor networks. We evaluate our method based on various network topologies, and analyze its performance. We also compare our method with several existing methods, and demonstrate the high efficiency of our proposed method.

This paper studies the problem of determining the sensors' locations in wireless sensor networks. To alleviate the influence of the noise and the inaccurate measurement in the complicated environment, rather than estimating the pairwise Euclidean distance between sensors, we use the geodesic distance to measure the dissimilarity between sensors, and employ the isomap algorithm to determine the relative locations of sensors. Given sufficient anchors, the relative locations can be aligned to absolute locations by using coordinate transformation. The coordinate transformation matrix can be obtained by minimizing the sum of squares of the errors between the true locations of the anchors and their transformed locations. Since isomap is parameter-sensitive, we also present an adaptive parameter selection procedure based on the locations of anchors. Simulation results show that the isomap algorithm achieves smaller average location error with little quantity of anchors.

Our solution builds on a kernel-based method called the support vector machine (SVM) for determining the locations of the nodes. The basic SVM algorithm contains two steps: (1) one-region classification using the SVM; and (2) multi-region localization which is a repeated application of one-region classification for a number of different regions. In this paper, we first analyze the error effects of the choice of regions in the multi-region localization, which influences the accuracy of the localization results significantly. The realization of a choice of regions is posed as a beacon node coverage problem, i.e., the spatial distribution of the beacon nodes is determined from the coverage point of view. Second, we develop a method to arrange the regions, which we call expanded coverage region distribution, in order to avoid the problem of border effects in existing solutions. We show that expanded cover region distribution can reduce the localization errors. Our results show that, by optimally choosing and arranging the regions based on our analysis, we can significantly enhance the performance of SVM based localization. Furthermore, the optimal choice of regions to avoid the border effects can be similarly applied in other kernel-based learning methods for localization.

A foundational problem in kernel-based semi-supervised learning is the design of suitable kernels which can properly reflect the underlying data manifold. One of the most well-known semi-supervised kernel learning approaches is the spectral kernel learning methodology which usually tunes the spectra of the graph Laplacian empirically or through optimizing some generalized performance measures. In this study, we proposed a novel approach to do spectral kernel learning based on maximum margin criterion, which is theoretically justified as a more essential semi-supervised kernel learning measure than others, such as kernel target alignment. We have conducted lots of experiments on public data sets, showing promising performance of our scheme.

Kernel-based one-class classification is a special type of classification problem, and is widely used as the outlier detection and novelty detection technique. One of the most commonly used method is the support vector dada description (SVDD). However, the performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. In this paper, we focus on the problem of choosing the optimal kernel from a kernel convex hull for the given one-class classification task, and propose a new approach. Kernel methods work by nonlinearly mapping the data into an embedding feature space, and then searching the relations among this space, however this mapping is implicitly performed by the kernel function. How to choose a suitable kernel is a difficult problem. In our method, we first transform the data points linearly so that we obtain a new set whose variances equal unity. Then we choose the minimum embedding ball as the criterion to learn the optimal kernel matrix over the kernel convex hull. It leads to the convex quadratically constrained quadratic programming (QCQP). Experiments results on a collection of benchmark data sets demonstrated the effectiveness of the proposed method.