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Research has shown that genetics and epigenetics regulate mating behavior across multiple species. Previous studies have generally focused on the signaling pathways involved and spatial distribution of the associated receptors. However a thorough quantitative characterization of the receptors involved may offer deeper insight into mating behavioral patterns. Here oxytocin, arginine-vasopressin 1a, dopamine 1, and dopamine 2 receptors were investigated across 76 vertebrate species. The receptor sequences were characterized by polarity-based randomness, amino acid frequency-based Shannon entropy and Shannon sequence variability, intrinsic protein disorder, binding affinity, stability and pathogenicity of homology-based SNPs, structural and physicochemical features. Hierarchical clustering of species was derived based on structural and physicochemical features of the four receptor sequences separately, which eventually led to proximal relationships among 29 species. Humans were found to be significantly distant phylogenetically from the prairie voles, a representative of monogamous species based on coital behavior. Furthermore, the mouse (polygamous), the prairie deer mouse (polygamous), and the prairie vole (monogamous) although being proximally related (based on quantitative genomics of receptors), differed in their coital behavioral pattern, mostly, due to behavioral epigenetic regulations. This study adds a perspective that receptor genomics does not directly translate to behavioral patterns.

Taste is one of the essential senses in providing the organism a faithful representation of the external world. Taste perception is responsible for basic food and drink appraisal and bestows the organism with valuable discriminatory power. Umami and sweet are “good” tastes that promote consumption of nutritive food, whereas bitter and sour are “bad” tastes that alert the organism to toxins and low pH, promoting rejection of foods containing harmful substances. Not every animal has the same sense of taste as humans. Variation in the taste receptor genes contributes to inter and intra organism differences of taste (sweet/bitter) sensation and preferences. Therefore a deeper understanding was needed to comprehend taste perception by various vertebrates and accordingly elucidate a possible proximity among them. In this study, a total 20 Type-1 (sweet) and 189 Type-2 (bitter) taste receptor complete-amino acid sequences were taken from the 20 vertebrate organisms (18 mammalian, 1 aves, and 1 amphibian). Among 10 primates, 8 including humans were very close based on genomics of taste receptors and rodent organisms viz. the rat and mouse were away from them. This investigation throws light on the similitude and dissimilitude of perception of sweet and bitter taste among 20 different organisms, steered by quantitative analysis of their genomic data. Furthermore, it enlightened that ligand binding affinity of sweet/bitter taste molecules in the taste receptors of any proximal pair of organisms would be similar.

Taste is one of the essential senses in providing the organism a faithful representation of the external world. Taste perception is responsible for basic food and drink appraisal and bestows the organism with valuable discriminatory power. Umami and sweet are "good" tastes that promote consumption of nutritive food, whereas bitter and sour are "bad" tastes that alert the organism to toxins and low pH, promoting rejection of foods containing harmful substances. Not every animal has the same sense of taste as humans. Variation in the taste receptor genes contributes to inter and intra organism differences of taste (sweet/bitter) sensation and preferences. Therefore a deeper understanding was needed to comprehend taste perception by various vertebrates and accordingly elucidate a possible proximity among them. In this study, a total 20 Type-1 (sweet) and 189 Type-2 (bitter) taste receptor complete-amino acid sequences were taken from the 20 vertebrate organisms (18 mammalian, 1 Aves, and 1 amphibian). Among 10 primates, 8 including humans were very close based on genomics of taste receptors and rodent organisms viz. the rat and mouse were away from them. This investigation throws light on the similitude and dissimilitude of perception of sweet and bitter taste among 20 different organisms, steered by quantitative analysis of their genomic data. Furthermore, it enlightened that ligand binding affinity of sweet/bitter taste molecules in the taste receptors of any proximal pair of organisms would be similar.

Saliva has emerged as an efficient screening sample for early stage detection of oral cancer (OC) owing to non-invasiveness coupled with high sensitivity and specificity. Although spectroscopic characterization of saliva in oral potentially malignant disorders OPMDs) and OC is extensively studied, its potential as imaging biomarker is sparsely explored. Further, the literature on crystalline pattern of saliva for other diseases or different physiological conditions is mostly qualitative. This paper proposed multifractal based methodology to quantitatively study alteration of the salivary fern pattern in different OPMDs and OC in relation to normal counterpart. The fern pattern of dried saliva is captured by stereo-zoom microscope in reflective mode and an image dataset is developed. We resort to two dimensional multi-fractal detrended fluctuation analysis (2d MFDFA) to elucidate the complexity and heterogeneity of these micro-structured patterns. Existence of multifractal nature embedded in salivary fern has been validated for the first time. Long range spatial correlation is found to be the origin of multifractality. Variation in multi-scale self-similarity of irregular pattern in different study groups is demonstrated by four features extracted from MFDFA. Statistical analysis shows discriminating nature of these features for combinations of pairwise interclass classification. This study sheds light on acceptability of microscopic images of arborized saliva in fast and cost effective screening of different oral lesions.

Bright-field microscopy (BFM) encrypts the optical transillumination profile of the transmitted light attenuated by the complex micro-structural tissue convolutions, manifested by the dense and compact regions of the specimen under examination. The connotations of idiosyncratic tissue interaction dynamics with the onset of pre-cancerous activity are encoded in the BFM acquired oral mucosa histopathological images (OMHI). In the present study, our analysis is focused on the sub-epithelium region of the oral mucosa, which has high clinical significance but sparsely explored in the literature from the textural domain. Histopathology being the gold-standard technique till date, we have used the light microscopic histopathology images for tissue characterization. The tissue-index transmission patches (TITP) from the sub-epithelium region are cropped under the guidance of oral onco-pathologists. After that, the TITPs are characterized for its multi-scale spatial-deformation dynamics, while keeping the intrinsic anisotropic geometry, and local contour connectivity within tolerable limits. With recent studies exhibiting multifractal's potency in diverse biological system analysis, here, we exploit the 2D multifractal detrended fluctuation analysis (2D-MFDFA) on TITPs for exploring a discriminative set of multifractal signatures for healthy, oral potentially malignant disorders and oral cancer tissue sample. The predictive model's competency is validated on an experimentally collected corpus of TITP samples and substantiated via confirmatory data statistics and analysis, showing its inter-class segregation efficacy. Moreover, the 2D-MFDFA analysis evinces the complex multifractal patterns in TITPs, which is due to the presence of composite long-range correlations in the oral mucosa tissue fabric.

Computational analysis on altered micro-nano-textural attributes of the oral mucosa may provide precise diagnostic information about oral potentially malignant disorders (OPMDs) instead of an existing handful of qualitative reports. This study evaluated micro-nano-textural features of oral epithelium from scanning electron microscopic (SEM) images and the sub-epithelial connective tissue from light microscopic (LM) and atomic force microscopic (AFM) images for normal and OPMD (namely oral sub-mucous fibrosis, i.e., OSF). Objective textural descriptors, namely discrete wavelet transform, gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP), were extracted and fed to standard classifiers. Best classification accuracy of 87.28 and 93.21%; sensitivity of 93 and 96%; specificity of 80 and 91% were achieved, respectively, for SEM and AFM. In the study groups, SEM analysis showed a significant (p < 0.01) variation for all the considered textural descriptors, while for AFM, a remarkable alteration (p < 0.01) was only found in GLCM and LBP. Interestingly, sub-epithelial collagen nanoscale and microscale textural information from AFM and LM images, respectively, were complementary, namely microlevel contrast was more in normal (0.251) than OSF (0.193), while nanolevel contrast was more in OSF (0.283) than normal (0.204). This work, thus, illustrated differential micro-nano-textural attributes for oral epithelium and sub-epithelium to distinguish OPMD precisely and may be contributory in early cancer diagnostics.

Oral sub-mucous fibrosis (OSF) is a pathophysiological state of oral cavity or oropharynx having a high chance of conversion to oral squamous cell carcinoma (OSCC). It involves fibrotic transformation of sub-epithelial matrix along with epithelial abnormalities. The present work aims to unveil the mechanistic domain regarding OSF to OSCC conversion exploring the scenario of hypoxia associated oxidative stress, epithelial-mesenchymal transition (EMT), metastasis and stemness acquisition. The study involves histopathological analysis of the diseased condition along with the exploration of oxidative stress status, assessment of mitochondrial condition, immunohistochemical analysis of HIF-1α, E-cadherin, vimentin, ERK, ALDH-1, CD133, Shh, Gli-1 and survivin expressions in the oral epithelial region together with the quantitative approach towards collagen deposition in the sub-epithelial matrix. Oxidative stress was found to be associated with type-II EMT in case of OSF attributing the development of sub-epithelial fibrosis and type-III EMT in case of OSCC favoring malignancy associated metastasis. Moreover, the acquisition of stemness during OSCC can also be correlated with EMT. Alteration of Shh and Gli-1 expression pattern revealed the mechanistic association of hypoxia with the phenotypic plasticity and disease manifestation in case of OSF as well as OSCC. Shh/ Gli-1 signaling can also be correlated with survivin mediated cytoprotective phenomenon under oxidative stress. Overall, the study established the correlative network of hypoxia associated oxidative stress, EMT and manifestation of oral pre-cancerous and cancerous condition in a holistic approach that may throw rays of hope in the therapeutic domain of the concerned diseases.

Background: In the context of early diagnosis and prevention of oral cancer, precise assessment of malignant potentiality of the oral potentially malignant disorders, particularly oral submucous fibrosis (OSF) is crucial. Till date, the assessment of malignant potentiality suffers from predictive ambiguity due to the lack of precision in the gold standard techniques. This can be addressed by integrating heuristic domain knowledge with quantitative analysis. Aim: The aim of this study is to propose an index for enhancing accuracy in malignant potentiality evaluation. Materials and Methods: The present study analyzes important histomorphometric attributes (epithelial thickness, basal cell nuclear size, nuclear-to-cytoplasmic area ratio of basal cells, chromaticity of basal cell nucleus, thickness of basement membrane, ratio of vasculature in juxta-epithelial connective tissue [i.e., area covered by blood vessels/total area], collagen density in the lamina propria) of oral mucosa in dysplastic and nondysplastic OSF in association with relevant oncopathological appreciations (weightage of different features as suggested by oral pathologists) toward proposing a “Malignant Potentiality Index” (MPI). Results: Analysis of variance and notch box plot analysis depict statistically significant differences (P < 0.0001) in the histopathological features among different study groups (normal oral mucosa, OSF without dysplasia, OSF with dysplasia). Histopathological observation of one OSF patient with calculated MPI is shown. Conclusion: This newly proposed diagnostic cum prognostic decision-making parameter, the “MPI” may bring a value addition to the conventional diagnostic gold standard.

Oral cancer is a leading malignancy and a rising concern in India. Early detection of the disease is essential at reducing mortality. In this paper, we propose a computer assisted method for diagnosis of oral pre-cancer/cancer using oral exfoliative cytology. A combination of features were extracted from expert delineated cells and nuclei collected from cytology of patients suffering from oral sub-mucous fibrosis, oral leukoplakia or oral squamous cell carcinoma and subject with no lesion. These features were used to train predictive machine learning models like support vector machine, k nearest neighbor, random forest, etc. These models were verified using validation data set. The verification experiments showed promising results with the random forest classifier having a test accuracy of 90%.

Optical coherence tomography (OCT) is widely used in Ophthalmology for visualizing retinal layers and cornea width profiles which are indexed to various pathologies. Image processing algorithms have been deployed to automatically delineate various layers for autonomous width profiles computation. Classical OCT segmentation algorithms are anchored around gradient and its derivatives. Such pixel information can vary from subject to subject so prior approximations of different layers are imposed in the name of regularization. Regularization restrains the generalization capability of the model which is crucial for segmentation in pathological subjects. The presented approach aims to model signal interactions with a tissue type through back scattered signal characteristics. During prediction phase, given an unknown interaction, the model estimates the probability of signal being backscattered from a tissue type. Sensor's ballistic models are employed to estimate the uncompressed signal statistics. These models have been experimented and evaluated on 5000 AMD (age-related macular degeneration) and 5000 normal B-scans from Duke OCT dataset. With two percent training data the delineation results are comparable to graph based approaches. The anterior retina, retinal pigment epithelium (RPE) and posterior retina are identified with sensitivity of 0.87, 0.84 and 0.91 respectively.

Spectral domain optical coherence tomography (SD-OCT) is widely employed across ophthalmology practices for visual investigation of live tissues. The involuntary movements of subjects frequently infuse motion artifacts to SD-OCT images. Sub-sampling of signals is introduced in imaging protocol to avoid such artifacts which causes fall in spatial resolution and peak signal to noise ratio (PSNR). Sparse coding (SC) is opted for restoration and rectification of complete signals from sparse samples through constructing complete and sparse space dictionaries independently. Convolutional neural networks (CNN) can be casted as SC for jointly learning dictionaries resulting less number of CNN filters (equivalence of SC dictionaries) to be trained. The proposed approach extends the separable filters to CNN through architectural constrain. This results in a parallel architecture and reduced number of parameters without compromising on performance. The approach scaled down trainable parameters by 46% with a trade-off of 0.108 PSNR during training and 0.107 PSNR during testing in comparison to conventional CNN.