Olfaction, the sense of smell detects and discriminate odors as well as social cues which
influence our innate responses. The olfactory system in human beings is found to be weak as compared
to other animals; however, it seems to be very precise. It can detect and discriminate millions
of chemical moieties (odorants) even in minuscule quantities. The process initiates with the binding
of odorants to specialized olfactory receptors, encoded by a large family of Olfactory Receptor
(OR) genes belonging to the G-protein-coupled receptor superfamily. Stimulation of ORs converts
the chemical information encoded in the odorants, into respective neuronal action-potentials which
causes depolarization of olfactory sensory neurons. The olfactory bulb relays this signal to different
parts of the brain for processing. Odors are encrypted using a combinatorial approach to detect a
variety of chemicals and encode their unique identity. The discovery of functional OR genes and
proteins provided an important information to decipher the genomic, structural and functional basis
of olfaction. ORs constitute 17 gene families, out of which 4 families were reported to contain more
than hundred members each. The olfactory machinery is not limited to GPCRs; a number of non-
GPCRs is also employed to detect chemosensory stimuli. The article provides detailed information
about such olfaction machinery, structures, transduction mechanism, theories of odor perception,
and challenges in the olfaction research. It covers the structural, functional and computational studies
carried out in the olfaction research in the recent past.
All fruits emit some specific volatile organic compounds (VOCs) during their life cycle. These VOCs have specific characteristics, by using these characteristics fruit ripening stage can be identified without destructing the fruit.
In this study, an application-specific electronic nose device was designed for monitoring fruit ripeness.The proposed electronic nose is cost-efficient and does not require any modern or costly laboratory instruments. Metal oxide semiconductor (MOS) sensors were used for designing the proposed electronic nose. These MOS sensors were integrated with a microcontroller board to detect and extract the meaningful features of VOCs, and an artificial neural network (ANN) algorithm was used for pattern recognition. Measurements were done with apples, bananas, oranges, grapes, and pomegranates. The designed electronic nose proved to be reliable in classifying fruit samples into three different fruit ripening stage (unripe, ripe, and over-ripe) with high precision and recall. The proposed electronic nose performed uniformly on all three fruit ripening stages with an average accuracy of ≥ 95%.
Breast cancer is the most common cancer in women both in the developed and less developed countries, and it imposes a considerable threat to human health. Therefore, in order to develop effective targeted therapies against Breast cancer, a deep understanding of its underlying molecular mechanisms is required. The application of deep transcriptional sequencing has been found to be reported to provide an efficient genomic assay to delve into the insights of the diseases and may prove to be useful in the study of Breast cancer. In this study, ChIP-Seq data for normal samples and Breast cancer were compared, and differential peaks identified, based upon fold enrichment (with P-values obtained via t-tests). The Protein–protein interaction (PPI) network analysis was carried out, following which the highly connected genes were screened and studied, and the most promising ones were selected. Biological pathway involved in the process were then identified. Our findings regarding potential Breast cancer-related genes enhances the understanding of the disease and provides prognostic information in addition to standard tumor prognostic factors for future research.
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