Synthetic investigations on testerone and its analogues—I *1: The total synthesis of 8-isotestosterone and its anthracene analogue

The total synthesis of 8-isotestosterone (II) and the corresponding anthracene analogue (III) following the benzohydrindane route is reported. Catalytic hydrogenation of trans-1β-acetoxy-8-methyl-4,5-(3′-methyl-4′-hydroxybenzo)-hydrindane (V) followed by oxidation has furnished two isomeric tricyclic keto acetates, viz. 1β,2α-(3′-acetoxycyclopentano)-2,5-dimethyl-6-keto-1α,2,3,4,4aα,-5α,6,7,8,8aα-decahydronaphthalene (VII) and 1β,2α-(3′-acetoxycyclopentano)-2,5-dimethyl-6-keto-1α,2,3,4,4aβ,5,6,7,8,8aβ-decahydronaphthalene (IX) which are cis-non-steroid and cis-steroid configurations of the same cyclopentano-cis-decalins. A difference in the direction of enolization of the keto acetate (VII) in alkylation reaction and enol acetylation towards the methine and the methylene carbon atoms respectively has been observed.

Synthetic investigations in the sesamin group - Part I. Synthesis of samin, a degradation product of sesamolin

Ethylα-bromovinylacetate (VII) was condensed with the sodio derivative of ethyl piperonoylacetate (VIII) to give diethylα-vinyl-α′-piperonoylsuccinate (IX). The latter on reduction with lithium aluminium hydride furnished the triol (X), which underwent smooth cyclisation with 1% ethanolic hydrogen chloride to 2-(3′, -methylenedioxyphenyl)-hydroxymethyl-4-vinyltetrahydrofuran (XIa). The structure of XIa was established by Oppenauer oxidation to an aldehyde. Ozonolysis of XIa afforded samin (I).

Synthetic investigation on the building of ring A of steroids

Methyl 7-keto-1,2,3,4,4a,5,6,7-octahydronaphthoate (Va) has been prepared by the reduction of 7-methoxy-1,2,3,4-tetrahydronaphthoic acid (III) with lithium and ammonia followed by hydrolysis of the enol ether, esterification and migration of the double bond. Alkylation of Va has led to the substitution at the expected 8-position. Methyl 4-keto-7-methoxy-1,2,3,4-tetrahydronaphthoate (X), an intermediate in the preparation of III, has been converted into methyl 3-methyl-3-cyano-4-keto-7-methoxy-1,2,3,4-tetrahydronaphthoate (XIII).

GAP-RBF based NR image quality measurement for JPEG coded images

In this paper, we present a growing and pruning radial basis function based no-reference (NR) image quality model for JPEG-coded images. The quality of the images are estimated without referring to their original images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity factors such as edge amplitude, edge length, background activity and background luminance. Image quality estimation involves computation of functional relationship between HVS features and subjective test scores. Here, the problem of quality estimation is transformed to a function approximation problem and solved using GAP-RBF network. GAP-RBF network uses sequential learning algorithm to approximate the functional relationship. The computational complexity and memory requirement are less in GAP-RBF algorithm compared to other batch learning algorithms. Also, the GAP-RBF algorithm finds a compact image quality model and does not require retraining when the new image samples are presented. Experimental results prove that the GAP-RBF image quality model does emulate the mean opinion score (MOS). The subjective test results of the proposed metric are compared with JPEG no-reference image quality index as well as full-reference structural similarity image quality index and it is observed to outperform both.

Text localization and extraction from complex gray images

We propose two texture-based approaches, one involving Gabor filters and the other employing log-polar wavelets, for separating text from non-text elements in a document image. Both the proposed algorithms compute local energy at some information-rich points, which are marked by Harris' corner detector. The advantage of this approach is that the algorithm calculates the local energy at selected points and not throughout the image, thus saving a lot of computational time. The algorithm has been tested on a large set of scanned text pages and the results have been seen to be better than the results from the existing algorithms. Among the proposed schemes, the Gabor filter based scheme marginally outperforms the wavelet based scheme.

Automatic text block separation in document images

Separation of printed text blocks from the non-text areas, containing signatures, handwritten text, logos and other such symbols, is a necessary first step for an OCR involving printed text recognition. In the present work, we compare the efficacy of some feature-classifier combinations to carry out this separation task. We have selected length-nomalized horizontal projection profile (HPP) as the starting point of such a separation task. This is with the assumption that the printed text blocks contain lines of text which generate HPP's with some regularity. Such an assumption is demonstrated to be valid. Our features are the HPP and its two transformed versions, namely, eigen and Fisher profiles. Four well known classifiers, namely, Nearest neighbor, Linear discriminant function, SVM's and artificial neural networks have been considered and efficiency of the combination of these classifiers with the above features is compared. A sequential floating feature selection technique has been adopted to enhance the efficiency of this separation task. The results give an average accuracy of about 96.

Binarization and localization of text images captured on a mobile phone camera

This paper proposes and compares four methods of binarzing text images captured using a camera mounted on a cell phone. The advantages and disadvantages(image clarity and computational complexity) of each method over the others are demonstrated through binarized results. The images are of VGA or lower resolution.

Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.

Memory-based reasoning approach for pattern recognition of binary images

Template matching is concerned with measuring the similarity between patterns of two objects. This paper proposes a memory-based reasoning approach for pattern recognition of binary images with a large template set. It seems that memory-based reasoning intrinsically requires a large database. Moreover, some binary image recognition problems inherently need large template sets, such as the recognition of Chinese characters which needs thousands of templates. The proposed algorithm is based on the Connection Machine, which is the most massively parallel machine to date, using a multiresolution method to search for the matching template. The approach uses the pyramid data structure for the multiresolution representation of templates and the input image pattern. For a given binary image it scans the template pyramid searching the match. A binary image of N × N pixels can be matched in O(log N) time complexity by our algorithm and is independent of the number of templates. Implementation of the proposed scheme is described in detail.

An unusual C-H center dot center dot center dot O hydrogen bond mediated reversal of polypeptide chain direction in a synthetic peptide helix

An unusual C-terminal conformation has been detected in a synthetic decapeptide designed to analyze the stereochemistry of helix termination in polypeptides. The crystal structure of the decapeptide Boc-Leu-Aib-Val-Ala-Leu-Aib-Val-(D)Ala-(D)Leu-Aib-OMe reveals a helical segment spanning residues 1-7 and helix termination by formation of a Schellman motif, generated by (D)Ala(8) adopting the left-handed helical (alpha(L)) conformation. The extended conformation at (D)Leu(9) results in a compact folded structure, stabilized by a potentially strong C-H ... O hydrogen bond between Ala(4) (CH)-H-alpha and (D)Leu(9)CO. The parameters for C-H ... O interaction are Ala(4) (CH)-H-alpha .. O=C (D)Leu(9) distance 3.27 Angstrom C-alpha-H .. O angle 176 degrees, and O .. H-alpha distance 2.29 Angstrom. This structure suggests that insertion of contiguous D-residues may provide a handle for the generation of designed structures containing more than one helical segment folded in a compact manner. (C) 2000 Academic Press.

A Novel Synthetic Route For The Preparation Of Ammonium And Alkali-Metal Tetrafluoroborates And Alkyl Substituted Ammonium Tetrafluoroborates Using Pyridinium Tetrafluoroborate As The Precursor

Ammonium and alkali metal tetrafluoroborates have been prepared by the cation exchange reaction of pyridinium tetrafluoroborate with the corresponding hydroxides/halides. The reaction of pyridinium tetrafluoroborate with primary, secondary and tertiary alkyl amines at room temperature gives rise to mono-, di- and tri-alkylammonium tetrafluoroborates, respectively. The yields are good and the samples are of high purity. The products have been characterised by elemental analysis, IR and PMR spectroscopy. The spectral data for most of the compounds are reported for the first time.

A Novel Synthetic Route for the Preparation of Silver Salts of Hexafluorophosphate, Tetrafluoroborate and Hexafluorosilicate

Silver salts of hexafluorophosphates, tetrafluoro-borates and hexafluorosilicates have been prepared by a metathetic reaction between the respective ammonium salts and silver nitrate in acetonitrile medium. This one step procedure at room temperature offers salts of high purity in good yields. The salts (AgpF6, AgBF4 and Ag2SiF6) have been characterised by IR spectral data analysis and chemical analysis.

Interaction of synthetic analogs of distamycin and netropsin with nucleic acids. Does curvature of ligand play a role in distamycin-DNA interactions?

Distamycin and netropsin, a class of minor groove binding nonintercalating agents, are characterized by their B-DNA and A-T basespecific interactions. To understand the CQI I ~OIT~ ~ I ~ ~aOnMd ~c hemical basis of the above specificities, the DNA-binding characteristics of a novel synthetic analogue of distamycin have been studied. The analogue, mPD derivative, has the requisite charged end groups and a number of potential hydrogen-bonding loci equal to those of distamycin. The difference in the backbone curvatures of the ligands, distamycin, the mPD derivative, and NSC 101327 (another structurally analogous compound),is a major difference between these ligands. UV and CD spectrosoopic studies reported here show the following salient features: The mPD derivative recognizes only B-DNA, to which it binds via the minor groove. On the other hand, unlike distamycin, it binds with comparable affinities to A-T and G-C base pairs in a natural DNA. These DNA-binding properties are compared with those reported earlier for distamycin and NSC 101327 [Zimmer, Ch., & Wahnert, U. (1986) Prog. Biophys. Mol. Biol. 47, 31-1121. The backbone structures of these three ligands were compared to show the progressive decrease in curvatures in the order distamycin, mPD derivative, and NSC 101327. The plausible significance of the backbone curvature vis-&vis the characteristic B-DNA and AT-specific binding of distamycin is discussed. To our knowledge, this is the first attempt (with a model synthetic analogue) to probe the possible influence of backbone curvature upon the specificity of interactions of the distamycin class of groove-binding ligands with DNA.

On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations

It is important to identify the ``correct'' number of topics in mechanisms like Latent Dirichlet Allocation(LDA) as they determine the quality of features that are presented as features for classifiers like SVM. In this work we propose a measure to identify the correct number of topics and offer empirical evidence in its favor in terms of classification accuracy and the number of topics that are naturally present in the corpus. We show the merit of the measure by applying it on real-world as well as synthetic data sets(both text and images). In proposing this measure, we view LDA as a matrix factorization mechanism, wherein a given corpus C is split into two matrix factors M-1 and M-2 as given by C-d*w = M1(d*t) x Q(t*w).Where d is the number of documents present in the corpus anti w is the size of the vocabulary. The quality of the split depends on ``t'', the right number of topics chosen. The measure is computed in terms of symmetric KL-Divergence of salient distributions that are derived from these matrix factors. We observe that the divergence values are higher for non-optimal number of topics - this is shown by a `dip' at the right value for `t'.