Synthesis and in vitro cytotoxic evaluation of novel diazaspiro bicyclo hydantoin derivatives in human leukemia cells: A SAR study

To study the structure activity relationship (SAR) on the cytotoxic activity and probe the structural requirement for the potent antitumor activity, a series of novel diazaspiro bicyclo hydantoin derivatives were designed and synthesized. Their structures were confirmed by H-1 NMR, LCMS and IR analyses. The antiproliferative effect of these compounds were determined against human leukemia, K562 (chronic myelogenous leukemia) and CEM (T-cell leukemia) cells using trypan blue and MTT assay, and the SAR associated with the position of N-terminal substituents in diazaspiro bicyclo hydantoin have also been discussed. It has been observed that these compounds displayed strong, moderate and weak cytotoxic activities. Interestingly, compounds having electron withdrawing groups at third and fourth position of the phenyl ring displayed selectively cytotoxic activities to both the cell lines tested with IC50 value lower than 50 mu M. In addition, the cytotoxic activities of the compounds 7(a-o) bearing the substituents at N-3 position of diazaspiro bicyclo hydantoin increases in the order alkene > ester > ether and plays an important role in determining their antitumor activities. The position and number of substituents in benzyl group attached to N-8 of diazaspiro bicyclo hydantoin nucleus interacted selectively with specific targets leading to the difference of biochemical and pharmacological effects.

Solution structure of O-glycosylated C-terminal leucine zipper domain of human salivary mucin (MUC7)

Solution structures of a 23 residue glycopeptide II (KIS* RFLLYMKNLLNRIIDDMVEQ, where * denotes the glycan Gal-beta-(1-3)-alpha-GalNAc) and its deglycosylated counterpart I derived from the C-terminal leucine zipper domain of low molecular weight human salivary mucin (MUC7) were studied using CD, NMR spectroscopy and molecular modeling. The peptide I was synthesized using the Fmoc chemistry following the conventional procedure and the glycopeptide II was synthesized incorporating the O-glycosylated building block (N alpha-Fmoc-Ser-[Ac-4,-beta-D-Gal-(1,3)-Ac(2)alpha-D-GalN(3)]-OPfp) at the appropriate position in stepwise assembly of peptide chain. Solution structures of these glycosylated and nonglycosylated peptides were studied in water and in the presence of 50% of an organic cosolvent, trifluoroethanol (TFE) using circular dichroism (CD), and in 50% TFE using two-dimensional proton nuclear magnetic resonance (2D H-1 NMR) spectroscopy. CD spectra in aqueous medium indicate that the apopeptide I adapts, mostly, a beta-sheet conformation whereas the glycopeptide II assumes helical structure. This transition in the secondary structure, upon glycosylation, demonstrates that the carbohydrate moiety exerts significant effect on the peptide backbone conformation. However, in 50% TFE both the peptides show pronounced helical structure. Sequential and medium range NOEs, C alpha H chemical shift perturbations, (3)J(NH:C alpha H) couplings and deuterium exchange rates of the amide proton resonances in water containing 50% TFE indicate that the peptide I adapts alpha-helical structure from Ile2-Val21 and the glycopeptide II adapts alpha-helical structure from Ser3-Glu22. The observation of continuous stretch of helix in both the peptides as observed by both NMR and CD spectroscopy strongly suggests that the C-terminal domain of MUC7 with heptad repeats of leucines or methionine residues may be stabilized by dimeric leucine zipper motif. The results reported herein may be invaluable in understanding the aggregation (or dimerization) of MUC7 glycoprotein which would eventually have implications in determining its structure-function relationship.

Subcontracting Relationship of Indian SMEs with a Global TNC: Do SMEs Gain and How?

TNCs having their production bases in developing countries provide increasing opportunity for local SMEs to have subcontracting relationships with these TNCs.Even though some theoretical and a few empirical studies throw light on the nature of assistance provided by TNCs to local SMEs through subcontracting relationships,none of the studies so far analysed the diversity of assistance that subcontracting SMEs of India would be able to obtain from a TNC using quantitative measurement.This paper probes the extent of linkages and diversity of assistance that Indian subcontracting SMEs would be able to obtain from a TNC customer based on primary data from SME subcontractors of a major TNC automobile manufacturer. Statistical analysis of direct assistance revealed that SMEs receive more of product and purchase process assistance. The extent of assistance for their process related,marketing, human resource and financial requirements is low whereas the assistance for their organisational know-how requirements is moderate. The major indirect benefits these SMEs could achieve are knowledge transfer, business volume, superior work culture, reputation and quality improvement.

Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.