Time-dependent crashworthiness of polyurethane foam

Mechanics of Time-Dependent Materials - Time-dependent stress–strain relationship as well as crashworthiness of polyurethane foam was investigated under constant impact energy with different...     

Quantitative measurements of nanoparticle layer thicknesses near the contact line region after droplet drying-out

Journal of Mechanical Science and Technology - Confocal fringe patterns of evaporating sessile drops have provided initial evidence of the presence of a sub-micron thin liquid film emanating from...     

A Novel Approach for Bisphosphonates: Ionic Liquids and Organic Salts from Zoledronic Acid

Novel ionic liquids and organic salts based on mono- or dianionic zoledronate and protonated superbases, choline and n-alkylmethylimidazolium cations, were prepared and characterized by spectroscopic and thermal analyses. Most of the prepared salts display amorphous structures and very high solubility in water and saline solutions, especially the dianionic salts. Among the zoledronate-based ionic compounds, those containing choline [Ch] and methoxyethylmethylimidazolium [C₃OMIM] cations appear to have significant cytotoxicity against human osteosarcoma cells (MG63) and low toxicity toward healthy skin fibroblast cells. Because osteosarcoma is a bone pathology characterized by an increase in bone turnover rate, the results presented herein may be a promising starting point for the development of new ionic pharmaceutical drugs against osteosarcoma.     

Combining 1,3-Ditriazolylbenzene and Quinoline to Discover a New G-Quadruplex-Interactive Small Molecule Active against Cancer Stem-Like Cells

Quadruplex nucleic acids are promising targets for cancer therapy. In this study we used a fragment-based approach to create new flexible G-quadruplex (G4) DNA-interactive small molecules with good calculated oral drug-like properties, based on quinoline and triazole heterocycles. G4 melting temperature and polymerase chain reaction (PCR)-stop assays showed that two of these compounds are selective G4 ligands, as they were able to induce and stabilize G4s in a dose- and DNA sequence-dependent manner. Molecular docking studies have suggested plausible quadruplex binding to both the G-quartet and groove, with the quinoline module playing the major role. Compounds were screened for cytotoxicity against four cancer cell lines, where 4,4′-(4,4′-(1,3-phenylene)bis(1H-1,2,3-triazole-4,1-diyl))bis(1-methylquinolin-1-ium) ( 1 d ) showed the greater activity. Importantly, dose–response curves show that 1 d is cytotoxic in the human colon cancer HT-29 cell line enriched in cancer stem-like cells, a subpopulation of cells implicated in chemoresistance. Overall, this study identified a new small molecule as a promising lead for the development of drugs targeting G4 in cancer stem cells.     

Application of a phase-locked loop counterpulsation control algorithm to a pneumatic pulsatile VAD

Journal of Mechanical Science and Technology - This study applied a counterpulsation (CP) control method based on a phase-locked loop (PLL) algorithm to synchronise a pneumatic pulsatile...     

In situ Measurement of the Adhesion Strength and Effective Elastic Stiffness of Single Soft Micropillar

We report the deformation behavior and mechanical properties of a polymeric micropillar, which measures approximately 10 μm by 30 μm in size by measuring the loading/unloading response using an in situ force measurement system. When the single poly(dimethylsiloxane) (PDMS) micropillar was subjected to compression, we observed a periodic wrinkle and global (Euler) buckling at the sidewall. During unloading, we found the pull-off force (adhesion force) to increase for higher values of preloading and also for lower loading/unloading rates. From the slope of the load–displacement curves measured in situ, we calculated the effective elastic stiffness of the PDMS micropillar to be about 2.03 MPa. In addition to the current work, we report that this method can be used more broadly for in situ measurement of the intrinsic mechanical and adhesion properties of polymers and other relatively soft materials.     

Template Engineering of CuBi2O4 Single‐Crystal Thin Film Photocathodes

To develop strategies for efficient photo‐electrochemical water‐splitting, it is important to understand the fundamental properties of oxide photoelectrodes by synthesizing and investigating their single‐crystal thin films. However, it is challenging to synthesize high‐quality single‐crystal thin films from copper‐based oxide photoelectrodes due to the occurrence of significant defects such as copper or oxygen vacancies and grains. Here, the CuBi₂O₄ (CBO) single‐crystal thin film photocathode is achieved using a NiO template layer grown on single‐crystal SrTiO₃ (STO) (001) substrate via pulsed laser deposition. The NiO template layer plays a role as a buffer layer of large lattice mismatch between CBO and STO (001) substrate through domain‐matching epitaxy, and forms a type‐II band alignment with CBO, which prohibits the transfer of photogenerated electrons toward bottom electrode. The photocurrent densities of the CBO single‐crystal thin film photocathode demonstrate ?0.4 and ?0.7 mA cm^?2 at even 0 V_RHE with no severe dark current under illumination in a 0.1 m potassium phosphate buffer solution without and with H₂O₂ as an electron scavenger, respectively. The successful synthesis of high‐quality CBO single‐crystal thin film would be a cornerstone for the in‐depth understanding of the fundamental properties of CBO toward efficient photo‐electrochemical water‐splitting.     

A novel transfer learning approach for the classification of histological images of colorectal cancer

Colorectal cancer (CRC) is the second most diagnosed cancer in the United States. It is identified by histopathological evaluations of microscopic images of the cancerous region, relying on a subjective interpretation. The Colorectal Histology dataset used in this study contains 5000 images, made available by the University Medical Center Mannheim. This approach proposes the automatic identification of eight types of tissues found in CRC histopathological evaluation. We apply Transfer Learning from architectures of Convolutional Neural Networks (CNNs). We modify the structures of CNNs to extract features from the images and input them to well-known machine learning methods: Naive Bayes, Multilayer Perceptron, k-Nearest Neighbors, Random Forest, and Support Vector Machine (SVM). We evaluated 108 extractor–classifier combinations. The one that achieved the best results is DenseNet169 with SVM (RBF), reaching an Accuracy of 92.083% and F1-Score of 92.117%. Therefore, our approach is capable of distinguishing tissues found in CRC histopathological evaluation.