Muhammad Jahangir

Inhibition of α-glucosidase by oleanolic acid and its synthetic derivatives

Abstract Oleanolic acid (1) and five synthetic derivatives (2–6) were tested spectrophotometrically for inhibition of urease, β-lactamase, acetyl cholinesterase and α-glucosidase. All products showed a positive response only against α-glucosidase but not against the other enzymes; IC50 calculations showed that the dihydroxy-olide derivative (4) was the most potent among all tested samples.

Three p-cymene derivatives from Zataria multiflora

Two new p-cymene derivatives named zataroside-A and zataroside-B and a new triacetoxy derivative of the same skeleton have been detected from Zataria multiflora along with p-hydroxybenzoic acid. Their structures were elucidated with the aid of NMR spectroscopy. Zataroside-A was also confirmed by X-ray diffraction technique and the same compound was found to be a strong plant growth inhibiting agent.

A Bis-Bithiophene from Tridax Procumbens L. (Asteraceae)

The ethyl acetate soluble part of hexane extract of Tridax procumbens yielded a new bis-bithiophene named tridbisbithiophene along with four known terpenoids: taraxasteryl acetate, g -amyrenone, lupeol and oleanolic acid, which have never been reported so far from Tridax procumbens . The structures of all the isolated constituents were elucidated with the aid of 1 D-NMR spectroscopy whereas, the structure of new constituent tridbisbithiophene was confirmed via COSY and HMBC interactions.

Structural Distinction between Sabandins A and B from Artemisia Scoparia Waldst. (Asteraceae)

The three possible isomeric structures of the sabandin coumarin were published by Reyes and Gonzalez in 1970. We have isolated two of these isomers sabandins A and B from Artemisia scoparia (Asteraceae) and distinguished by NOE difference NMR measurements.

Gnapholide: A New Guaiac-Dimer from Pulicaria Gnaphalodes (Asteraceae)

The ethyl acetate soluble part of the chloroform extract of Pulicaria gnaphalodes belonging to the family Asteraceae afforded a new sesquiterpene-dimer of guaiane class named as gnapholide and anabsinthin of the same skeleton. The structures of both the compounds were elucidated with the aid of spectroscopic techniques including 2D NMR.

Eleganoside-A, B and C from Pseudocalymma elegans, a native of Brazil

The toxic methanol-soluble part of Pseudocalymma elegans (leaves), a native of Brazil, yielded three new iridoidglucosides (1a-3a) as their acetate-derivatives (1-3) named eleganoside-A (1a), B (2a) and C (3a) which have been characterized with the aid of spectroscopic techniques, including 2D NMR.

Spectral and Spatial Feature Extraction of Electroencephalographic (EEG) Data Using Independent Component Analysis (ICA)

Purpose of this research is to extract features associated with human brain signal related to electroencephalographic measurements and classification of extracted EEG signals to the relevant the brain region. EEG brain signals from 14 electrodes placed on the human scalp is recorded non-invasively using Emotiv EPOC / EPOC+: Scientific contextual EEG system with a sampling rate of 128 Hz. EEG data of human brain functions related to evoked motor imagery tasks consisting of two different classes of activities, namely imagination of right arm-movement i.e. arm down (termed here as PUSH) and arm up (termed here as PULL) for three healthy subjects is recorded. After pre-processing for noise and artifacts removal, the EEG signals associated with investigated evoked activities are extracted using Independent Component Analysis (ICA). The results obtained show good contrast plots for the extracted brain signals recorded on F7, FC5 and FC6 electrodes, decomposed on independent components, namely IC1, IC4, IC5, IC6. Classification of extracted features is mapped on to the motor imagery parts of human brain. The algorithm based on independent component analysis gives good results for feature extraction corresponding to evoked signals. Power spectra are also determined for the extracted independent components.

Feature Extraction Using Independent Component Analysis Method from Non-Invasive Recordings of Electroencephalography (EEG) Brain Signals

Electroencephalography (EEG) is a well known procedure in neuroscience, performed to extract brain signal activity associated with voluntary and involuntary tasks. Scientists and researchers working in neuroscience are involved in the research of brain computer interfacing (BCI) and in improving the existing BCI systems. In BCI, it is possible for a person to control the external devices remotely using brain signals without neurophysical intervention. In the proposed work the new algorithm is introduced to extract the feature from EEG based recorded brain signals. The features are extracted for a specific motoryaction that is raising the right hand. The proposed algorithm is also verified from EEGLAB routines also based on Independent Component Analysis (ICA) method written in MATLAB platform.