Direct electrochemical detection of oligonucleotide hybridization on poly(5-hydroxy-1,4-naphthoquinone- co-5-hydroxy-3-thioacetic acid-1,4-naphthoquinone) film

We describe the construction of a new DNA-modified electrode based on an electroactive film. 5-Hydroxy-1,4-naphthoquinone is coelectrooxidized with 5-hydroxy-3-thioacetic acid-1,4-naphthoquinone to give a copolymer, presenting both electroactive and chemically reactive groups. The carboxylic function acts as a precursor for the covalent grafting of ODN probes while the quinone group acts as the transduction element of hybridization. Electrochemical detection was performed by differential pulse voltammetry in the electroactivity domain of the quinone group (i.e., at very low potentials, 0 to -0.8 V vs SCE). A very clear modification of the redox activity is observed between unmodified and probe-modified films and especially upon addition of target ODN.     

用USB启动Windows XP Embedded

Windows XP Embedded Service Pack 2 Feature Pack 2007（以下简称FP2007）推出了用于嵌入式应用的USB启动功能。现在，用户可以通过一个USB闪存驱动器（UFD）、a．k．a．、U盘和USB密钥等来启动并构建一个Windows XPe的映像。构建这种启动Windows XP Embedded的USB设备的过程中，只要直接使用Target Designer，并准备好需要启动的UFD，就像使用ufdprep．exe（FP2007中包含的一个工具）文件一样简单。     

Deflation-based power iteration clustering

Spectral clustering (SC) is currently one of the most popular clustering techniques because of its advantages over conventional approaches such as K-means and hierarchical clustering. However, SC requires the use of computing eigenvectors, making it time consuming. To overcome this limitation, Lin and Cohen proposed the power iteration clustering (PIC) technique (Lin and Cohen in Proceedings of the 27th International Conference on Machine Learning, pp. 655–662, 2010), which is a simple and fast version of SC. Instead of finding the eigenvectors, PIC finds only one pseudo-eigenvector, which is a linear combination of the eigenvectors in linear time. However, in certain critical situations, using only one pseudo-eigenvector is not enough for clustering because of the inter-class collision problem. In this paper, we propose a novel method based on the deflation technique to compute multiple orthogonal pseudo-eigenvectors (orthogonality is used to avoid redundancy). Our method is more accurate than PIC but has the same computational complexity. Experiments on synthetic and real datasets demonstrate the improvement of our approach.     

Urban growth and change analysis using remote sensing and spatial metrics from 1975 to 2003 for Hanoi,Vietnam

Since the beginning of the ‘Doi Moi’ policy in 1986, Hanoi has witnessed significant changes in its urban areas. Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes were used to identify built-up areas in Hanoi, and spatial metrics were used to characterize urban change patterns from 1975 to 2003. Firstly, a spatial metric called the ‘percentage of like adjacency’ was used to discern urban growth patterns, which were classified into three sub-patterns: expansion growth, infill growth and outlying growth. Secondly, the driving force underlying the urbanization of the city for the 1975–1984, 1984–1992, 1992–2001, 2001–2003 periods was investigated using a spatial metric analysis programme (FRAGSTATS). The expansion of urban areas along major transportation routes in the latter 1980s was identified as the main form of urbanization in Hanoi. This paper shows the potential application of spatial metrics as secondary sources of information for supporting remotely sensed data and their use to characterize urban growth patterns.     

New criteria for exponential stability of nonlinear difference systems with time-varying delay

General nonlinear time-varying difference systems with time-varying delay are considered. Some new explicit criteria for global exponential stability are given. Two examples are given to illustrate the obtained results.     

Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data

ABSTRACT

Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R² = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha^?1 (average = 87.67 Mg ha^?1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.     

Molecular recognition in the sphingosine 1-phosphate receptor family

Computational modeling and its application in ligand screening and ligand receptor interaction studies play important roles in structure-based drug design. A series of sphingosine 1-phosphate (S1P) receptor ligands with varying potencies and receptor selectivities were docked into homology models of the S1P(1-5) receptors. These studies provided molecular insights into pharmacological trends both across the receptor family as well as at single receptors. This study identifies ligand recognition features that generalize across the S1P receptor family, features unique to the S1P(4) and S1P(5) receptors, and suggests significant structural differences of the S1P(2) receptor. Docking results reveal a previously unknown sulfur-aromatic interaction between the S1P(4) C5.44 sulfur atom and the phenyl ring of benzimidazole as well as pi-pi interaction between F3.33 of S1P(1,4,5) and aromatic ligands. The findings not only confirm the importance of a cation-pi interaction between W4.64 and the ammonium of S1P at S1P(4) but also predict the same interaction at S1P(5). S1P receptor models are validated for pharmacophore development including database mining and new ligand discovery and serve as tools for ligand optimization to improve potency and selectivity.     

Proadrenomedullin N-terminal 20 peptide: minimal active region to regulate nicotinic receptors

Proadrenomedullin N-terminal 20 peptide (PAMP-[1-20]; ARLDVASEFRKKWNKWALSR-amide) is a potent hypotensive and catecholamine release-inhibitory peptide released from chromaffin cells. We studied the mechanism of PAMP action and how its function is linked to structure. We tested human PAMP-[1-20] on catecholamine secretion in PC12 pheochromocytoma cells and found it to be a potent, dose-dependent (IC50 approximately 350 nmol/L) secretory inhibitor. Inhibition was specific for nicotinic cholinergic stimulation since PAMP-[1-20] failed to inhibit release by agents that bypass the nicotinic receptor. Nicotinic cationic (22Na+,45Ca2+) signal transduction was disrupted by this peptide, and potencies for inhibition of 22Na+ uptake and catecholamine secretion were comparable. Even high-dose nicotine failed to overcome the inhibition, suggesting noncompetitive nicotinic antagonism. N- and C-terminal PAMP truncation peptides indicated a role for the C-terminal amide and refined the minimal active region to the C-terminal 8 amino acids (WNKWALSR-amide), a region likely to be alpha-helical. PAMP also blocked (EC50 approximately 270 nmol/L) nicotinic cholinergic agonist desensitization of catecholamine release, as well as desensitization of nicotinic signal transduction (22Na+ uptake). Thus, PAMP may exert both inhibitory and facilitatory effects on nicotinic signaling, depending on the prior state of nicotinic stimulation. PAMP may therefore contribute to a novel, autocrine, homeostatic (negative-feedback) mechanism controlling catecholamine release.     

Maximum likelihood estimation for continuous-time autoregressive models by relaxation on residual variances ratio parameters

In this paper we derive an explicit expression for the log likelihood function of a continuous-time autoregressive model. Then, using earlier results relating the autoregressive coefficients to the set of positive parameters called residual variances ratios, we develop an iterative algorithm for computing the maximum likelihood estimator of the model, similar to one in the discrete-time case. A simple noniterative estimation method, which can be used to produce an initial estimate for the algorithm, is also proposed.     

Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set (PFS) and Neutrosophic Set (NS). Our contribution is to propose a new optimization model with four essential components: clustering, outlier removal, safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data. The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods, standard Picture fuzzy clustering (FC-PFS) and Confidence-weighted safe semi-supervised clustering (CS3FCM) on benchmark UCI datasets. The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.