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Corneal endothelial cells (CECs) have limited proliferation ability leading to corneal endothelium (CE) dysfunction and eventually vision loss when cell number decreases below a critical level. Although transplantation is the main treatment method, donor shortage problem is a major bottleneck. The transplantation of in vitro developed endothelial cells with desirable density is a promising idea. Designing cell substrates that mimic the native CE microenvironment is a substantial step to achieve this goal. In the presented study, we prepared polyacrylamide (PA) cell substrates that have a microfabricated topography inspired by the dimensions of CECs. Hydrogel surfaces were prepared via two different designs with small and large patterns. Small patterned hydrogels have physiologically relevant hexagon densities (∼2000 hexagons/mm2), whereas large patterned hydrogels have sparsely populated hexagons (∼400 hexagons/mm2). These substrates have similar elastic modulus of native Descemet's membrane (DM; ∼50 kPa) and were modified with Collagen IV (Col IV) to have biochemical content similar to native DM. The behavior of bovine corneal endothelial cells on these substrates was investigated and results show that cell proliferation on small patterned substrates was significantly (p = 0.0004) higher than the large patterned substrates. Small patterned substrates enabled a more densely populated cell monolayer compared to other groups (p = 0.001 vs. flat and p < 0.0001 vs. large patterned substrates). These results suggest that generating bioinspired surface topographies augments the formation of CE monolayers with the desired cell density, addressing the in vitro development of CE layers.

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Internal surfaces of pressure vessels used in many industrial sectors are subjected to corrosive effects leading to cavities. In this study, corrosive cavities on investigated pressure vessels are classified according to their shapes and dimensions. Distribution of stress was experimentally investigated around regions of different types of cavities using three-dimensional photoelastic models. An empirical expression is proposed to determine where maximum stress occurs in type of ellipsoidal cavity in the case of uniaxial loading. The obtained results show quite high stress levels around the cavity regions in pressure vessels, which increase the risk of crack formation.

Human gut microbiota is a complex community of organisms including trillions of bacteria. While these microorganisms are considered as essential regulators of our immune system, some of them can cause several diseases. In recent years, next-generation sequencing technologies accelerated the discovery of human gut microbiota. In this respect, the use of machine learning techniques became popular to analyze disease-associated metagenomics datasets. Type 2 diabetes (T2D) is a chronic disease and affects millions of people around the world. Since the early diagnosis in T2D is important for effective treatment, there is an utmost need to develop a classification technique that can accelerate T2D diagnosis. In this study, using T2D-associated metagenomics data, we aim to develop a classification model to facilitate T2D diagnosis and to discover T2D-associated biomarkers. The sequencing data of T2D patients and healthy individuals were taken from a metagenome-wide association study and categorized into disease states. The sequencing reads were assigned to taxa, and the identified species are used to train and test our model. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization, Maximum Relevance and Minimum Redundancy, Correlation Based Feature Selection, and select K best approach. To test the performance of the classification based on the features that are selected by different methods, we used random forest classifier with 100-fold Monte Carlo cross-validation. In our experiments, we observed that 15 commonly selected features have a considerable effect in terms of minimizing the microbiota used for the diagnosis of T2D and thus reducing the time and cost. When we perform biological validation of these identified species, we found that some of them are known as related to T2D development mechanisms and we identified additional species as potential biomarkers. Additionally, we attempted to find the subgroups of T2D patients using k-means clustering. In summary, this study utilizes several supervised and unsupervised machine learning algorithms to increase the diagnostic accuracy of T2D, investigates potential biomarkers of T2D, and finds out which subset of microbiota is more informative than other taxa by applying state-of-the art feature selection methods.