Et3 B/Et2 AlCl/O2 -Mediated Radical Coupling Reaction between α-Alkoxyacyl Tellurides and 2-Hydroxybenzaldehyde Derivatives.

A newly devised radical-based strategy enabled coupling between multiply oxygenated α-alkoxyacyl tellurides and 2-hydroxybenzaldehyde derivatives. A reagent combination of Et3 B, Et2 AlCl, and O2 promoted the formation of the α-alkoxy carbon radical from the α-alkoxyacyl telluride and the addition of the radical to the carbonyl group of 2-hydroxybenzaldehyde. The reaction chemo- and stereoselectively forged the hindered C-C bond between two oxygen-functionalized carbons at ambient temperature. The method was applied to the preparation of 12 coupling adducts with three to six contiguous stereocenters and to the concise synthesis of an antitumor compound, LLY-283.

A new synthetic route to 5,6,11,12-tetraarylethynyltetracenes.

A new synthetic route to 5,6,11,12-tetrakis(arylethynyl)tetracenes, π-extended rubrenes, was developed via [4 + 2] cycloadditions of dialkynylisobenzofuran and 1,4-naphthoquinone. Introduction of arylethynyl groups by double nucleophilic additions to tetracenequinone gave sterically congested (arylethynyl)tetracenes after reductive aromatization. The photophysical properties of the newly prepared π-conjugated molecules are also evaluated.

Three-dimensional inverse synthetic aperture lidar imaging for long-range spinning targets.

We present a three-dimensional (3D) imaging method for long-range spinning targets. This method acquires multi-angle two-dimensional (2D) images of spinning targets by the inverse synthetic aperture lidar (ISAL) imaging technique. The 3D distribution of the scattering coefficients of a target has a mapping relationship with the series of 2D images. This mapping is analyzed, and a 3D Hough transform is used to implement inverse mapping. The parameter space of the Hough transform is the estimation of the 3D distribution of the scattering coefficients. The 3D point spread function obtained by the method has narrow main lobe widths and sufficiently low side lobes to achieve high image quality, which is verified by computer simulations. In the simulations, the main lobe widths in the three dimensions are 0.29 cm, 0.29 cm, and 3.48 cm, respectively. In outdoor experiments, 3D images of targets at 1 km away from the lidar were obtained. The images clearly show the 3D shape of targets.

Inverse synthetic aperture LADAR demonstration: system structure, imaging processing, and experiment result.

A long-distance inverse synthetic aperture LADAR (ISAL) imaging experiment outdoors over 1 km for cooperative targets is demonstrated, which gets a two-dimensional high-resolution image with resolution exceeding 2.5 cm. The system utilizes an electro-optic in-phase and quadrature modulator to output a linear frequency-modulated continuous waveform (LFMCW) with a bandwidth of 6 GHz and pulse repetition frequency (PRF) of 16.7 KHz. For the problem of the coherence of the laser, the effects of the coherent processing interval (CPI) and time delay of the local oscillator (LO) on the coherence are discussed. The fiber delay line is set and the CPI is reduced to lower the requirement of the frequency stability of the laser source. The images are formed by two-dimensional Fourier transform and joint time-frequency transform methods, respectively. In this paper, we present the system structure, imaging processing, and the experiment result in detail. The experiment result validates the performance of our system for ISAL imaging.

Linear frequency-modulated continuous-wave ladar system for synthetic aperture imaging.

A novel and high-efficiency linear frequency-modulated continuous-wave (FMCW) ladar system for synthetic aperture imaging is proposed and experimentally demonstrated. This novel system generates wide-bandwidth linear FMCW ladar signals by employing an electro-optic LiNbO3- in-phase and quadrature modulator with an effective bias controller. The effectiveness of the proposed system is experimentally validated. Optical synthetic aperture images are obtained by using two 0.41 cm aperture diameter telescopes at the distance of 1 km. The resolution of these images can reach to 4 cm. A resolution improvement by about 10 times is achieved when compared with the conventional real aperture imaging system.

A general framework for biclustering gene expression data.

A large number of biclustering methods have been proposed to detect patterns in gene expression data. All these methods try to find some type of biclusters but no one can discover all the types of patterns in the data. Furthermore, researchers have to design new algorithms in order to find new types of biclusters/patterns that interest biologists. In this paper, we propose a novel approach for biclustering that, in general, can be used to discover all computable patterns in gene expression data. The method is based on the theory of Kolmogorov complexity. More precisely, we use Kolmogorov complexity to measure the randomness of submatrices as the merit of biclusters because randomness naturally consists in a lack of regularity, which is a common property of all types of patterns. On the basis of algorithmic probability measure, we develop a Markov Chain Monte Carlo algorithm to search for biclusters. Our method can also be easily extended to solve the problems of conventional clustering and checkerboard type biclustering. The preliminary experiments on simulated as well as real data show that our approach is very versatile and promising.

Efficient and robust feature extraction by maximum margin criterion.

In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the two most popular linear dimensionality reduction methods. However, PCA is not very effective for the extraction of the most discriminant features, and LDA is not stable due to the small sample size problem. In this paper, we propose some new (linear and nonlinear) feature extractors based on maximum margin criterion (MMC). Geometrically, feature extractors based on MMC maximize the (average) margin between classes after dimensionality reduction. It is shown that MMC can represent class separability better than PCA. As a connection to LDA, we may also derive LDA from MMC by incorporating some constraints. By using some other constraints, we establish a new linear feature extractor that does not suffer from the small sample size problem, which is known to cause serious stability problems for LDA. The kernelized (nonlinear) counterpart of this linear feature extractor is also established in the paper. Our extensive experiments demonstrate that the new feature extractors are effective, stable, and efficient.

Robust and accurate cancer classification with gene expression profiling.

Robust and accurate cancer classification is critical in cancer treatment. Gene expression profiling is expected to enable us to diagnose tumors precisely and systematically. However, the classification task in this context is very challenging because of the curse of dimensionality and the small sample size problem. In this paper, we propose a novel method to solve these two problems. Our method is able to map gene expression data into a very low dimensional space and thus meets the recommended samples to features per class ratio. As a result, it can be used to classify new samples robustly with low and trustable (estimated) error rates. The method is based on linear discriminant analysis (LDA). However, the conventional LDA requires that the within-class scatter matrix S(w) be nonsingular. Unfortunately, Sw is always singular in the case of cancer classification due to the small sample size problem. To overcome this problem, we develop a generalized linear discriminant analysis (GLDA) that is a general, direct, and complete solution to optimize Fisher's criterion. GLDA is mathematically well-founded and coincides with the conventional LDA when S(w) is nonsingular. Different from the conventional LDA, GLDA does not assume the nonsingularity of S(w), and thus naturally solves the small sample size problem. To accommodate the high dimensionality of scatter matrices, a fast algorithm of GLDA is also developed. Our extensive experiments on seven public cancer datasets show that the method performs well. Especially on some difficult instances that have very small samples to genes per class ratios, our method achieves much higher accuracies than widely used classification methods such as support vector machines, random forests, etc.

Minimum entropy clustering and applications to gene expression analysis.

Clustering is a common methodology for analyzing the gene expression data. In this paper, we present a new clustering algorithm from an information-theoretic point of view. First, we propose the minimum entropy (measured on a posteriori probabilities) criterion, which is the conditional entropy of clusters given the observations. Fano's inequality indicates that it could be a good criterion for clustering. We generalize the criterion by replacing Shannon's entropy with Havrda-Charvat's structural alpha-entropy. Interestingly, the minimum entropy criterion based on structural alpha-entropy is equal to the probability error of the nearest neighbor method when alpha = 2. This is another evidence that the proposed criterion is good for clustering. With a non-parametric approach for estimating a posteriori probabilities, an efficient iterative algorithm is then established to minimize the entropy. The experimental results show that the clustering algorithm performs significantly better than k-means/medians, hierarchical clustering, SOM, and EM in terms of adjusted Rand index. Particularly, our algorithm performs very well even when the correct number of clusters is unknown. In addition, most clustering algorithms produce poor partitions in presence of outliers while our method can correctly reveal the structure of data and effectively identify outliers simultaneously.