Representation of Stereoisomers in ALWIN

Tlhe well-known Cahn-lngold-Prelog method of specifying the stereoisomers is introduced within the framework of ALWIN-Algorithmic Wiswesser Notation. Given the structural diagram, the structural ALWIN is first formed; the speclflcation symbols are then introduced at the appropriate places to describe the stereoisomers.

Conformation of the LL and LD hairpin bends with internal hydrogen bonds in proteins and peptides

The conformation of three linked peptide units having an internal 4 → 1 type of hydrogen bond has been studied in detail, and the low energy conformations are listed. These conformations all lead to the reversal of the chain direction, and may therefore be called as “hairpin bends” or “U-bends”. Since this bend can occur at the end of two chains hydrogen-bonded in the antiparallel β-conformation, it is also known as the “β-bend”. Two types of conformation are possible when the residues at the second and third Cα atoms are both of type L (the LL bend), while only one type is possible for the LD and the DL bend. The LL bend can also accommodate the sequences LG, GL, GG (G = glycine), while the LD bend can accommodate the sequences LG, GD and GG. The conformations for the sequences DD and DL are exact inverses (or mirror images) of those for the sequences LL and LD, respectively, and have dihedral angles (phi2, ψ2), (phi3, ψ3) of the same magnitudes, but of opposite signs as those for the former types, which are listed, along with the characteristics (length, angle and energy) of the hydrogen bonds. A comparison of the theoretical predictions with experimental data (from X-ray diffraction and NMR studies) on proteins and peptides, show reasonably good agreement. However, a systematic trend is observable in the experimental data, slightly deviating from theory, which indicates that some deformations occur in the shapes of the peptide units forming the bend, differing from that of the standard planar peptide unit.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

Multiple instance dictionary learning for activity representation

This paper presents an effective feature representation method in the context of activity recognition. Efficient and effective feature representation plays a crucial role not only in activity recognition, but also in a wide range of applications such as motion analysis, tracking, 3D scene understanding etc. In the context of activity recognition, local features are increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational requirements, their performance is still limited for real world applications due to a lack of contextual information and models not being tailored to specific activities. We propose a new activity representation framework to address the shortcomings of the popular, but simple bag-of-words approach. In our framework, first multiple instance SVM (mi-SVM) is used to identify positive features for each action category and the k-means algorithm is used to generate a codebook. Then locality-constrained linear coding is used to encode the features into the generated codebook, followed by spatio-temporal pyramid pooling to convey the spatio-temporal statistics. Finally, an SVM is used to classify the videos. Experiments carried out on two popular datasets with varying complexity demonstrate significant performance improvement over the base-line bag-of-feature method.

The effect of internal audit function quality and internal audit contribution to external audit on audit fees

Corporate governance mandates and listing rules identify internal audit functions (IAF) as a central internal control mechanism. External audits are expected to assess the quality of IAF before placing reliance on its work. We provide evidence on the effect of IAF quality and IAF contribution to external audit on audit fees. Using data from a matched survey of both external and internal audits, we extend prior research which is based mainly on internal audits' assessment and conducted predominantly in highly developed markets. We find a positive relationship between IAF quality and audit fees as well as a reduction in audit fees as a result of external auditors' reliance on IAF. The interaction between IAF quality and IAF contribution to external audit suggests that high quality IAF induces greater external auditor reliance on internal auditors' work and thus result in lower external audit fees.

New light on the optical equivalence theorem and a new type of discrete diagonal coherent state representation

In many instances we find it advantageous to display a quantum optical density matrix as a generalized statistical ensemble of coherent wave fields. The weight functions involved in these constructions turn out to belong to a family of distributions, not always smooth functions. In this paper we investigate this question anew and show how it is related to the problem of expanding an arbitrary state in terms of an overcomplete subfamily of the overcomplete set of coherent states. This provides a relatively transparent derivation of the optical equivalence theorem. An interesting by-product is the discovery of a new class of discrete diagonal representations.

Effect of viscosity on internal waves from a source in a wall

The solution for a line source of oscillatory strength kept at the origin in a wall bounding a semi-infinite viscous imcompressible stratified fluid is presented in an integral form. The behaviour of the flow at far field and near field is studied by an asymptotic expansion procedure. The streamlines for different parameters are drawn and discussed. The real characteristic straight lines present in the inviscid problem are modified by the viscosity and the solutions obtained are valid even at the resonance frequency.

Primary structure of a Thomsen-Friedenreich-antigen-specific lectin, jacalin [Artocarpus integrifolia (jack fruit) agglutinin]. Evidence for the presence of an internal repeat

Jacalin [Artocarpus integrifolia (jack fruit) agglutinin] is made up of two types of chains, heavy and light, with M(r) values of 16,200 +/- 1200 and 2090 +/- 300 respectively (on the basis of gel-permeation chromatography under denaturing conditions). Its complete amino acid sequence was determined by manual degradation using a 4-dimethylaminoazobenzene 4'-isothiocyanate double-coupling method. Peptide fragments for sequence analysis were obtained by chemical cleavages of the heavy chain with CNBr, hydroxylamine hydrochloride and iodosobenzoic acid and enzymic cleavage with Staphylococcus aureus proteinase. The peptides were purified by a combination gel-permeation and reverse-phase chromatography. The light chains, being only 20 residues long, could be sequenced without fragmentation. Amino acid analyses and carboxypeptidase-Y-digestion C-terminal analyses of the subunits provided supportive evidence for their sequence. Computer-assisted alignment of the jacalin heavy-chain sequence failed to show sequence similarity to that of any lectin for which the complete sequence is known. Analyses of the sequence showed the presence of an internal repeat spanning residues 7-64 and 76-130. The internal repeat was found to be statistically significant.

An algorithm to find similar internal sequence repeats

In recent years, identification of sequence patterns has been given immense importance to understand better their significance with respect to genomic organization and evolutionary processes. To this end, an algorithm has been derived to identify all similar sequence repeats present in a protein sequence. The proposed algorithm is useful to correlate the three-dimensional structure of various similar sequence repeats available in the Protein Data Bank against the same sequence repeats present in other databases like SWISS-PROT, PIR and Genome databases.

Evaluating viewpoint entropy for ribbon representation of protein structure

While many measures of viewpoint goodness have been proposed in computer graphics, none have been evaluated for ribbon representations of protein secondary structure. To fill this gap, we conducted a user study on Amazon’s Mechanical Turk platform, collecting human viewpoint preferences from 65 participants for 4 representative su- perfamilies of protein domains. In particular, we evaluated viewpoint entropy, which was previously shown to be a good predictor for human viewpoint preference of other, mostly non-abstract objects. In a second study, we asked 7 molecular biology experts to find the best viewpoint of the same protein domains and compared their choices with viewpoint entropy. Our results show that viewpoint entropy overall is a significant predictor of human viewpoint preference for ribbon representations of protein secondary structure. However, the accuracy is highly dependent on the complexity of the structure: while most participants agree on good viewpoints for small, non-globular structures with few secondary structure elements, viewpoint preference varies considerably for complex structures. Finally, experts tend to choose viewpoints of both low and high viewpoint entropy to emphasize different aspects of the respective structure.

Geometric Representation of Graphs in Low Dimension Using Axis Parallel Boxes

An axis-parallel k-dimensional box is a Cartesian product R-1 x R-2 x...x R-k where R-i (for 1 <= i <= k) is a closed interval of the form [a(i), b(i)] on the real line. For a graph G, its boxicity box(G) is the minimum dimension k, such that G is representable as the intersection graph of (axis-parallel) boxes in k-dimensional space. The concept of boxicity finds applications in various areas such as ecology, operations research etc. A number of NP-hard problems are either polynomial time solvable or have much better approximation ratio on low boxicity graphs. For example, the max-clique problem is polynomial time solvable on bounded boxicity graphs and the maximum independent set problem for boxicity d graphs, given a box representation, has a left perpendicular1 + 1/c log n right perpendicular(d-1) approximation ratio for any constant c >= 1 when d >= 2. In most cases, the first step usually is computing a low dimensional box representation of the given graph. Deciding whether the boxicity of a graph is at most 2 itself is NP-hard. We give an efficient randomized algorithm to construct a box representation of any graph G on n vertices in left perpendicular(Delta + 2) ln nright perpendicular dimensions, where Delta is the maximum degree of G. This algorithm implies that box(G) <= left perpendicular(Delta + 2) ln nright perpendicular for any graph G. Our bound is tight up to a factor of ln n. We also show that our randomized algorithm can be derandomized to get a polynomial time deterministic algorithm. Though our general upper bound is in terms of maximum degree Delta, we show that for almost all graphs on n vertices, their boxicity is O(d(av) ln n) where d(av) is the average degree.