Phenotypic Modulation of Smooth Muscle Cells on Biodegradable Elastomeric Substrates

Cardiovascular disease is the number one killer in the U.S. Cardiovascular tissue engineering holds enormous potential by providing synthetic materials as vessel replacements. This dissertation focused on the development of novel biodegradable and photo-crosslinkable polymers with controlled surface chemistry, stiffness, and topographical features in regulating smooth muscle cell (SMC) adhesion, proliferation and phenotypic conversion for cardiovascular tissue engineering applications. Chapter II presents a facile synthesis route to obtain a series of photocrosslinkable poly(epsilon-caprolactone) triacrylates (PCLTA) with varied mechanical properties and further demonstrated tunable cell responses using these polymer system. Chapter III demonstrates a model polymer network from PCLTA that can gradually stiffen in 24 h through impeded crystallization at body temperature (37 ºC) and distinct SMC attachment, proliferation and spreading are found. Chapter IV presents the fabrication of a series of PCLTA networks with defined gradients in stiffness for regulation of SMCs behaviors. Chapter V fabricates cylindrical pillars with three different heights of 3.4, 7.4, and 15.1 micrometers by photo-crosslinking PCLTA in silicon molds with predesigned micropatterns. Chapter VI prepared photo-crosslinked PCLTA nanowire arrays with diameters of 20, 100 and 200 nanometers using inorganic nanoporous aluminum oxide (AAO) templates. Chapter VII reports a series of novel poly(L-lactic acid) triacrylates (PLLATAs) networks with same chemical composition but different crystallinity and surface roughness achieved by increasing the annealing time from 0 to 5, 7, 10, and 20 h at 70 ºC. Chapter VIII presents a method for tuning surface chemistry by grafting hydrophilic photocrosslinkable mPEGA chains into the hydrophobic PCLTA at various compositions and reports the smooth muscle cell responses. Chapter IX incorporates poly(L-lysine) (PLL) dangling chains into PCLTA networks at different PLL compositions of 0.5%, 1.0%, 1.5%, and 3%. The surface morphology, hydrophilicity and serum protein adsorption of all these polymer networks were characterized. Primary rat SMCs were cultured on these polymer networks and their attachment, spreading, proliferation, focal adhesions, expression of four contractile gene markers (SM-MHC, smoothlin, transgelin, and calponin) and contractile proteins were characterized systematically. Chapter X makes a summary of these separate investigations and draws general conclusions from the results obtained in these studies

Multimodal Sentiment Analysis: A Survey

Multimodal sentiment analysis has become an important research area in the field of artificial intelligence. With the latest advances in deep learning, this technology has reached new heights. It has great potential for both application and research, making it a popular research topic. This review provides an overview of the definition, background, and development of multimodal sentiment analysis. It also covers recent datasets and advanced models, emphasizing the challenges and future prospects of this technology. Finally, it looks ahead to future research directions. It should be noted that this review provides constructive suggestions for promising research directions and building better performing multimodal sentiment analysis models, which can help researchers in this field.Comment: It needs to be returned for major modification

Mining behavior graphs for ”backtrace” of noncrashing bugs

Analyzing the executions of a buggy software program is essentially a data mining process. Although many interesting methods have been developed to trace crashing bugs (such as memory violation and core dumps), it is still difficult to analyze noncrashing bugs (such as logical errors). In this paper, we develop a novel method to classify the structured traces of program executions using software behavior graphs. By analyzing the correct and incorrect executions, we have made good progress at the isolation of program regions that may lead to the faulty executions. The classification framework is built on an integration of closed graph mining and SVM classification. More interestingly, suspicious regions are identified through the capture of the classification accuracy change, which is measured incrementally during program execution. Our performance study and case-based experiments show that our approach is both effective and efficient