Femtosecond laser micromachining of microstructures for sensing and detection

This thesis focuses on the fabrication and analysis of miniature functional devices that can be used for different sensing and detection applications. As the first step, the surface characteristics and morphology of the micro/nano structures on substrates (fused silica and silicon) fabricated in water and in air by femtosecond laser were investigated. Based on this study, three micro devices have been designed and fabricated by femtosecond laser, and they were analyzed for optical and chemical sensing and detections. The three micro devices are: 1) an optical Fabry-Perot interferometer in optical fiber (made from fused silica) with smooth cavity surfaces. The Fabry-Perot interferometer was used to measure accurately the refractive index of solution and, hence, is capable of identifying different solutions; 2) a fused silica glass substrate for surface enhanced Raman spectroscopy (SERS). The optimized morphology (in nano/micron scales) on the fused silica surface with post chemical silver planting which can achieve a cross-section enhancement factor (EF) of 2.5 x 10⁶, evaluated by 10⁻⁷ M Rhodamine 6G solutions; 3) a nanostructured silicon substrate for the surface-enhanced Raman scattering application. A simple and efficient method was developed to fabricate a substrate that was pre-coated with silver nitrite film to generate silver nanoparticles over a large-area. This study demonstrates that an EF greater than 5 x10⁵, measured by 10⁻⁶ M Rhodamine 6G solutions can be achieved. The proposed fabrication techniques and miniature devices can be used to integrate the SERS capability into a microchip (lab-on-a-chip) for biomedical and chemical analysis --Abstract, page iv

Stylistic scene enhancement GAN: Mixed stylistic enhancement generation for 3D indoor scenes

In this paper, we present stylistic scene enhancement GAN, SSE-GAN, a conditional Wasserstein GAN-based approach to automatic generation of mixed stylistic enhancements for 3D indoor scenes. An enhancement indicates factors that can influence the style of an indoor scene such as furniture colors and occurrence of small objects. To facilitate network training, we propose a novel enhancement feature encoding method, which represents an enhancement by a multi-one-hot vector, and effectively accommodates different enhancement factors. A Gumbel-Softmax module is introduced in the generator network to enable the generation of high fidelity enhancement features that can better confuse the discriminator. Experiments show that our approach is superior to the other baseline methods and successfully models the relationship between the style distribution and scene enhancements. Thus, although only trained with a dataset of room images in single styles, the trained generator can generate mixed stylistic enhancements by specifying multiple styles as the condition. Our approach is the first to apply a Gumbel-Softmax module in conditional Wasserstein GANs, as well as the first to explore the application of GAN-based models in the scene enhancement field

CFD: a collaborative feature difference method for spontaneous micro-expression spotting

Micro-expression (ME) is a special type of human expression which can reveal the real emotion that people want to conceal. Spontaneous ME (SME) spotting is to identify the subsequences containing SMEs from a long facial video. The study of SME spotting has a significant importance, but is also very challenging due to the fact that in real-world scenarios, SMEs may occur along with normal facial expressions and other prominent motions such as head movements. In this paper, we improve a state-of-the-art SME spotting method called feature difference analysis (FD) in the following two aspects. First, FD relies on a partitioning of facial area into uniform regions of interest (ROIs) and computing features of a selected sequence. We propose a novel evaluation method by utilizing the Fisher linear discriminant to assign a weight for each ROI, leading to more semantically meaningful ROIs. Second, FD only considers two features (LBP and HOOF) independently. We introduce a state-of-the-art MDMO feature into FD and propose a simple yet efficient collaborative strategy to work with two complementary features, i.e., LBP characterizing texture information and MDMO characterizing motion information. We call our improved FD method collaborative feature difference (CFD). Experimental results on two well-established SME datasets SMIC-E and CASME II show that CFD significantly improves the performance of the original FD

Institutional investors, real earnings management and cost of equity: Evidence from listed high-tech firms in China

This paper investigates the association between real earnings management and the cost of equity from the perspective of the heterogeneity of institutional investors. Based on a sample of publicly listed high-tech firms in China from 2008 to 2017, our empirical results suggest that there is a significant negative correlation between earnings management and the cost of equity capital. This finding is contrary to previous conclusion, indicating that, in China, real earnings management cannot be effectively identified by external investors, and the company could easily obtain financing from the capital market and reduce its cost of equity due to its masked excellent performance by manipulating the real earnings management. Furthermore, we find that compared with transient institutional investors, stable institutional investors with the intention of holding the stock for the long-term can effectively reduce the cost of equity. Our results also show that real earnings management under the supervision of stable institutional investors could be more easily identified by shareholders and stable institutional investors could diminish the impact of earnings management on the cost of equity

Active arrangement of small objects in 3D indoor scenes

Small object arrangement is very important for creating detailed and realistic 3D indoor scenes. In this article, we present an interactive framework based on active learning to help users create customized arrangements for small objects according to their preferences. To achieve this with minimal user effort, we first learn the prior knowledge about small object arrangement from a 3D indoor scene dataset through a probability mining method, which forms the initial guidance for arranging small objects. Then, users are able to express their preferences on a few small object categories, which are automatically propagated to all the other categories via a novel active learning approach. In the propagation process, we introduce a novel metric to obtain the propagation weights, which measures the degree of interchangeability between two small object categories, and is calculated based on a spatial embedding model learned from the small object neighborhood information extracted from the 3D indoor scene dataset. Experiments show that our framework is able to help users effectively create customized small object arrangements with little effort

3D corrective nose reconstruction from a single image

There is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an important expressive part of the human face, the nose has received less attention than other expressive regions in the face reconstruction literature. When applying existing reconstruction methods to facial images, the reconstructed nose models are often inconsistent with the desired shape and expression. In this paper, we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image, where 3D and 2D nose curve correspondences are adaptively updated and refined. We first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences, and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result. A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve constraints. Experimental results show the advantages of our method for 3D nose reconstruction over existing methods

Transcriptome analysis provides insight into the regulatory mechanisms underlying pollen germination recovery at normal high ambient temperature in wild banana (Musa itinerans)

IntroductionCultivated banana are polyploid, with low pollen fertility, and most cultivars are male sterile, which leads to difficulties in banana breeding research. The selection of male parent with excellent resistance and pollen fertility is therefore essential for banana breeding. Wild banana (Musa itinerans) have developed many good characteristics during natural selection and constitute an excellent gene pool for breeding. Therefore, research on wild banana breeding is very important for banana breeding.ResultsIn the current analysis, we examined the changes in viability of wild banana pollens at different temperatures by in vitro germination, and found that the germination ability of wild banana pollens cultured at 28°C for 2 days was higher than that of pollens cultured at 23°C (pollens that could not germinate normally under low temperature stress), 24°C (cultured at a constant temperature for 2 days) and 32°C (cultured at a constant temperature for 2 days). To elucidate the molecular mechanisms underlying the germination restoration process in wild banana pollens, we selected the wild banana pollens that had lost its germination ability under low temperature stress (23°C) as the control group (CK) and the wild banana pollens that had recovered its germination ability under constant temperature incubation of 28°C for 2 days as the treatment group (T) for transcriptome sequencing. A total of 921 differentially expressed genes (DEGs) were detected in CK vs T, of which 265 were up-regulated and 656 were down-regulated. The combined analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that the activation, metabolism of various substances (lipids, sugars, amino acids) play a major role in restoring pollen germination capacity. TCA cycle and the sesquiterpenoid and triterpenoid biosynthetic pathways were also significantly enriched in the KEGG pathway. And we found that some DEGs may be associated with pollen wall formation, DNA methylation and DNA repair. The cysteine content, free fatty acid (FFA) content, H2O2 content, fructose content, and sucrose content of pollen were increased at treatment of 28°C, while D-Golactose content was decreased. Finally, the GO pathway was enriched for a total of 24 DEGs related to pollen germination, of which 16 DEGs received targeted regulation by 14 MYBs.DiscussionsOur study suggests that the balance between various metabolic processes, pollen wall remodelling, DNA methylation, DNA repairs and regulation of MYBs are essential for germination of wild banana pollens

Elucidating the anti-hypertensive mechanisms of Uncaria rhynchophylla-Alisma plantago-aquatica L: an integrated network pharmacology, cluster analysis, and molecular docking approach

Background: With the increasing global prevalence of hypertension, a condition that can severely affect multiple organs, there is a growing need for effective treatment options. Uncaria rhynchophylla-Alisma plantago-aquatica L. (UR-AP) is a traditional drug pair used for treating hypertension based on the liver-kidney synergy concept. However, the detailed molecular mechanisms underlying its efficacy remain unclear.Methods: This study utilized an integrative approach combining network pharmacology, cluster analysis, and molecular docking to uncover the bioactive components and targets of UR-AP in the treatment of hypertension. Initially, we extracted data from public databases to identify these components and targets. A Protein-Protein Interaction (PPI) network was constructed, followed by enrichment analysis to pinpoint the bioactive components, core targets, and pivotal pathways. Cluster analysis helped in identifying key sub-networks and hypothesizing primary targets. Furthermore, molecular docking was conducted to validate the interaction between the core targets and major bioactive components, thus confirming their potential efficacy in hypertension treatment.Results: Network pharmacological analysis identified 58 bioactive compounds in UR-AP, notably quercetin, kaempferol, beta-sitosterol (from Uncaria rhynchophylla), and Alisol B, alisol B 23-acetate (from Alisma plantago-aquatica L.), as pivotal bioactives. We pinpointed 143 targets common to both UR-AP and hypertension, highlighting MAPK1, IL6, AKT1, VEGFA, EGFR, and TP53 as central targets involved in key pathways like diastolic and endothelial function, anti-atherosclerosis, AGE-RAGE signaling, and calcium signaling. Cluster analysis emphasized IL6, TNF, AKT1, and VEGFA’s roles in atherosclerosis and inflammation. Molecular docking confirmed strong interactions between these targets and UR-AP’s main bioactives, underscoring their therapeutic potential.Conclusion: This research delineates UR-AP’s pharmacological profile in hypertension treatment, linking traditional medicine with modern pharmacology. It highlights key bioactive components and their interactions with principal targets, suggesting UR-AP’s potential as a novel therapeutic option for hypertension. The evidence from molecular docking studies supports these interactions, indicating the relevance of these components in affecting hypertension pathways. However, the study acknowledges its limitations, including the reliance on in silico analyses and the need for in vivo validation. These findings pave the way for future clinical research, aiming to integrate traditional medicine insights with contemporary scientific approaches for developing innovative hypertension therapies