Facial expression recognition using lightweight deep learning modeling

Facial expression is a type of communication and is useful in many areas of computer vision, including intelligent visual surveillance, human-robot interaction and human behavior analysis. A deep learning approach is presented to classify happy, sad, angry, fearful, contemptuous, surprised and disgusted expressions. Accurate detection and classification of human facial expression is a critical task in image processing due to the inconsistencies amid the complexity, including change in illumination, occlusion, noise and the over-fitting problem. A stacked sparse auto-encoder for facial expression recognition (SSAE-FER) is used for unsupervised pre-training and supervised fine-tuning. SSAE-FER automatically extracts features from input images, and the softmax classifier is used to classify the expressions. Our method achieved an accuracy of 92.50% on the JAFFE dataset and 99.30% on the CK+ dataset. SSAE-FER performs well compared to the other comparative methods in the same domain

Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm

Recommender systems are intelligent data mining applications that deal with the issue of information overload significantly. The available literature discusses several methodologies to generate recommendations and proposes different techniques in accordance with users’ needs. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. The biggest challenge for a recommender system is to produce meaningful recommendations by using contextual user-item rating information. A context is a vast term that may consider various aspects; for example, a user’s social circle, time, mood, location, weather, company, day type, an item’s genre, location, and language. Typically, the rating behavior of users varies under different contexts. From this line of research, we have proposed a new algorithm, namely Kernel Context Recommender System, which is a flexible, fast, and accurate kernel mapping framework that recognizes the importance of context and incorporates the contextual information using kernel trick while making predictions. We have benchmarked our proposed algorithm with pre- and post-filtering approaches as they have been the favorite approaches in the literature to solve the context-aware recommendation problem. Our experiments reveal that considering the contextual information can increase the performance of a system and provide better, relevant, and meaningful results on various evaluation metrics

Apigenin inhibits pancreatic cancer cell proliferation through G2/M cell cycle arrest

BACKGROUND: Many chemotherapeutic agents have been used to treat pancreatic cancer without success. Apigenin, a naturally occurring flavonoid, has been shown to inhibit growth in some cancer cell lines but has not been studied in pancreatic cancer. We hypothesized that apigenin would inhibit pancreatic cancer cell growth in vitro. RESULTS: Apigenin caused both time- and concentration-dependent inhibition of DNA synthesis and cell proliferation in four pancreatic cancer cell lines. Apigenin induced G2/M phase cell cycle arrest. Apigenin reduced levels of cyclin A, cyclin B, phosphorylated forms of cdc2 and cdc25, which are all proteins required for G2/M transition. CONCLUSION: Apigenin inhibits growth of pancreatic cancer cells through suppression of cyclin B-associated cdc2 activity and G2/M arrest, and may be a valuable drug for the treatment or prevention of pancreatic cancer

Stanniocalcin-1 Regulates Re-Epithelialization in Human Keratinocytes

Stanniocalcin-1 (STC1), a glycoprotein hormone, is believed to be involved in various biological processes such as inflammation, oxidative responses and cell migration. Riding on these emerging evidences, we hypothesized that STC1 may participate in the re-epithelialization during wound healing. Re-epithelialization is a critical step that involves keratinocyte lamellipodia (e-lam) formation, followed by cell migration. In this study, staurosporine (STS) treatment induced human keratinocyte (HaCaT) e-lam formation on fibronectin matrix and migration via the activation of focal adhesion kinase (FAK), the surge of intracellular calcium level [Ca2+]i and the inactivation of Akt. In accompanied with these migratory features, a time- and dose-dependent increase in STC1 expression was detected. STC1 gene expression was found not the downstream target of FAK-signaling as illustrated by FAK inhibition using PF573228. The reduction of [Ca2+]i by BAPTA/AM blocked the STS-mediated keratinocyte migration and STC1 gene expression. Alternatively the increase of [Ca2+]i by ionomycin exerted promotional effect on STS-induced STC1 gene expression. The inhibition of Akt by SH6 and GSK3β by lithium chloride (LiCl) could respectively induce and inhibit the STS-mediated e-lam formation, cell migration and STC1 gene expression. The STS-mediated e-lam formation and cell migration were notably hindered or induced respectively by STC1 knockdown or overexpression. This notion was further supported by the scratched wound assay. Collectively the findings provide the first evidence that STC1 promotes re-epithelialization in wound healing

Genome-Wide Profile of Pleural Mesothelioma versus Parietal and Visceral Pleura: The Emerging Gene Portrait of the Mesothelioma Phenotype

Malignant pleural mesothelioma is considered an almost incurable tumour with increasing incidence worldwide. It usually develops in the parietal pleura, from mesothelial lining or submesothelial cells, subsequently invading the visceral pleura. Chromosomal and genomic aberrations of mesothelioma are diverse and heterogenous. Genome-wide profiling of mesothelioma versus parietal and visceral normal pleural tissue could thus reveal novel genes and pathways explaining its aggressive phenotype.Well-characterised tissue from five mesothelioma patients and normal parietal and visceral pleural samples from six non-cancer patients were profiled by Affymetrix oligoarray of 38 500 genes. The lists of differentially expressed genes tested for overrepresentation in KEGG PATHWAYS (Kyoto Encyclopedia of Genes and Genomes) and GO (gene ontology) terms revealed large differences of expression between visceral and parietal pleura, and both tissues differed from mesothelioma. Cell growth and intrinsic resistance in tumour versus parietal pleura was reflected in highly overexpressed cell cycle, mitosis, replication, DNA repair and anti-apoptosis genes. Several genes of the “salvage pathway” that recycle nucleobases were overexpressed, among them TYMS, encoding thymidylate synthase, the main target of the antifolate drug pemetrexed that is active in mesothelioma. Circadian rhythm genes were expressed in favour of tumour growth. The local invasive, non-metastatic phenotype of mesothelioma, could partly be due to overexpression of the known metastasis suppressors NME1 and NME2. Down-regulation of several tumour suppressor genes could contribute to mesothelioma progression. Genes involved in cell communication were down-regulated, indicating that mesothelioma may shield itself from the immune system. Similarly, in non-cancer parietal versus visceral pleura signal transduction, soluble transporter and adhesion genes were down-regulated. This could represent a genetical platform of the parietal pleura propensity to develop mesothelioma.Genome-wide microarray approach using complex human tissue samples revealed novel expression patterns, reflecting some important features of mesothelioma biology that should be further explored

Surfactants with colloids: Adsorption or absorption?

The interaction of Aerosol OT (AOT) surfactant with systems of model colloids in nonaqueous solvents (water-in-oil microemulsions, surfactant-stabilized silica organosols, and sterically-stabilized PMMA latexes) is expected to be system specific. Two limiting cases are expected: adsorption, with surfactant located at the particle surfaces, or absorption, with surfactant incorporated into the particle cores. Experiments: Two approaches have been used to determine how AOT is distributed in the colloidal systems. The stability of the colloids in different alkanes (heptane to hexadecane, including mixtures) has been studied to determine any effects on the colloid surfaces. Contrast-variation small-angle neutron scattering (SANS) measurements of the colloid cores and of AOT-colloid mixtures in colloid-matched solvent have also been performed. Normalization to account for the different scattering intensities and different particle radii have been used to enable a system-independent comparison. Findings: AOT in water-in-oil microemulsions and surfactant-stabilized silica organosols is determined to be adsorbed, whereas, surprisingly, AOT in sterically-stabilized PMMA latexes is found to be absorbed. Possible origins of these differences are discussed

NATURE INSPIRED ARCHITECTURE

The thesis explores the various ways in which Biomimicry can be used to develop sustainable solutions that are derived from the natural cycles of organisms. Some of these include the use of solar and wind energy, as well as waste management. Aside from these, the thesis also explores the various ways in which Biomimicry can be used to develop more sustainable solutions for interior and exterior designs. The thesis explores different ways to help in sustainable architecture for the future as we are heading into the future. By using computational programming, we are able to design innovative solutions to help offset the carbon footprint of the planet. The design goal is to create a façade system inspired from the principles of biomimicry which helps in offsetting the carbon footprint of building over its lifespan