Effects of Virtual Humans’ Facial Emotional Displays on Persuasion

This dissertation explores the effect of virtual humans’ facial emotional displays in the context of persuasion. In a collaborative problem-solving game, participants received persuasive information from a virtual teammate. The first study demonstrated that a subservient virtual teammate’s facial emotional displays reduced his or her persuasive capacity. The second study revealed that the effect of a virtual human’s facial emotional displays was jointly determined by whether the observer was in power, and/or whether the observer considered it appropriate to express emotions. Emotional expressions undermined persuasion when the observer overpowered the virtual human, and/or when the observer perceived emotional expressions to be inappropriate. When both conditions were satisfied, emotional expressions reduced persuasion the most. The third study suggested that the amount of anger expressions predicted the outcome of persuasion. Photorealism, while on one hand enhanced the perception of anger expressions, did not moderate the effect of emotional expressions. Findings from this study inform theories of emotional expressions in persuasion, and guide the design of persuasive virtual humans

Whole exome sequencing and system biology analysis support the "two-hit" mechanism in the onset of Ameloblastoma

Ameloblastoma is the most frequent odontogenic tumor. Various evidence has highlighted the role of somatic mutations, including recurrent mutation BRAF V600E, in the tumorigenesis of Ameloblastoma, but the intact genetic pathology remains unknown. We sequenced the whole exome of both tumor tissue and healthy bone tissue from four mandibular ameloblastoma patients. The identified somatic mutations were integrated into Weighted Gene Co-expression Network Analysis on publicly available expression data of odontoblast, ameloblast, and Ameloblastoma. We identified a total of 70 rare and severe somatic mutations. We found BRAF V600E on all four patients, supporting previous discovery. HSAP4 was also hit by two missense mutations on two different patients. By applying Weighted Gene Co-expression Network Analysis on expression data of odontoblast, ameloblast, and Ameloblastoma, we found a proliferation-associated gene module that was significantly disrupted in tumor tissues. Each patient carried at least two rare, severe somatic mutations affecting genes within this module, including HSPA4, GNAS, CLTC, NES, and KMT2D. All these mutations had a ratio of variant-support reads lower than BRAF V600E, indicating that they occurred later than BRAF V600E. We suggest that a severe somatic mutation on the gene network of cell proliferation other than BRAF V600E, namely second hit, may contribute to the tumorigenesis of Ameloblastoma

Uncertainty and Explainable Analysis of Machine Learning Model for Reconstruction of Sonic Slowness Logs

Logs are valuable information for oil and gas fields as they help to determine the lithology of the formations surrounding the borehole and the location and reserves of subsurface oil and gas reservoirs. However, important logs are often missing in horizontal or old wells, which poses a challenge in field applications. In this paper, we utilize data from the 2020 machine learning competition of the SPWLA, which aims to predict the missing compressional wave slowness and shear wave slowness logs using other logs in the same borehole. We employ the NGBoost algorithm to construct an Ensemble Learning model that can predicate the results as well as their uncertainty. Furthermore, we combine the SHAP method to investigate the interpretability of the machine learning model. We compare the performance of the NGBosst model with four other commonly used Ensemble Learning methods, including Random Forest, GBDT, XGBoost, LightGBM. The results show that the NGBoost model performs well in the testing set and can provide a probability distribution for the prediction results. In addition, the variance of the probability distribution of the predicted log can be used to justify the quality of the constructed log. Using the SHAP explainable machine learning model, we calculate the importance of each input log to the predicted results as well as the coupling relationship among input logs. Our findings reveal that the NGBoost model tends to provide greater slowness prediction results when the neutron porosity and gamma ray are large, which is consistent with the cognition of petrophysical models. Furthermore, the machine learning model can capture the influence of the changing borehole caliper on slowness, where the influence of borehole caliper on slowness is complex and not easy to establish a direct relationship. These findings are in line with the physical principle of borehole acoustics

Directional Dipole Dice Enabled by Anisotropic Chirality

Directional radiation and scattering play an essential role in light manipulation for various applications in integrated nanophotonics, antenna and metasurface designs, quantum optics, etc. The most elemental system with this property is the class of directional dipoles, including the circular dipole, Huygens dipole, and Janus dipole. A unified realization of all three dipole types and a mechanism to freely switch among them are previously unreported, yet highly desirable for developing compact and multifunctional directional sources. Here, we theoretically and experimentally demonstrate that the synergy of chirality and anisotropy can give rise to all three directional dipoles in one structure at the same frequency under linearly polarized plane wave excitations. This mechanism enables a simple helix particle to serve as a directional dipole dice (DDD), achieving selective manipulation of optical directionality via different "faces" of the particle. We employ three "faces" of the DDD to realize face-multiplexed routing of guided waves in three orthogonal directions with the directionality determined by spin, power flow, and reactive power, respectively. This construction of the complete directionality space can enable the unprecedented high-dimensional control of both near-field and far-field directionality with broad applications in photonic integrated circuits, quantum information processing, and subwavelength-resolution imaging.Comment: 17 pages, 16 figure

Chemopreventive Activities of Sulforaphane and Its Metabolites in Human Hepatoma HepG2 Cells

Sulforaphane (SFN) exhibits chemopreventive effects through various mechanisms. However, few studies have focused on the bioactivities of its metabolites. Here, three metabolites derived from SFN were studied, known as sulforaphane glutathione, sulforaphane cysteine and sulforaphane-N-acetylcysteine. Their effects on cell viability, DNA damage, tumorigenicity, cell migration and adhesion were measured in human hepatoma HepG2 cells, and their anti-angiogenetic effects were determined in a 3D co-culture model of human umbilical vein endothelial cells (HUVECs) and pericytes. Results indicated that these metabolites at high doses decreased cancer cell viability, induced DNA damage and inhibited motility, and impaired endothelial cell migration and tube formation. Additionally, pre-treatment with low doses of SFN metabolites protected against H₂O₂ challenge. The activation of the nuclear factor E2-related factor 2 (Nrf2)-antioxidant response element (ARE) pathway and the induction of intracellular glutathione (GSH) played an important role in the cytoprotective effects of SFN metabolites. In conclusion, SFN metabolites exhibited similar cytotoxic and cytoprotective effects to SFN, which proves the necessity to study the mechanisms of action of not only SFN but also of its metabolites. Based on the different tissue distribution profiles of these metabolites, the most relevant chemical forms can be selected for targeted chemoprevention

Prenatal diagnosis of fetuses with ultrasound anomalies by whole-exome sequencing in Luoyang city, China

Background: There is a great obstacle in prenatal diagnosis of fetal anomalies due to their considerable genetic and clinical heterogeneity. Whole-exome sequencing (WES) has been confirmed as a successful option for genetic diagnosis in pediatrics, but its clinical utility for prenatal diagnosis remains to be limited.Methods: A total of 60 fetuses with abnormal ultrasound findings underwent karyotyping or chromosomal microarray analysis (CMA), and those with negative results were further subjected to WES. The identified variants were classified as pathogenic or likely pathogenic (P/LP) and the variant of uncertain significance (VUS). Pregnancy outcomes were obtained through a telephone follow-up.Results: Twelve (20%, 12/60) fetuses were diagnosed to have chromosomal abnormalities using karyotyping or CMA. Of the remaining 48 cases that underwent WES, P/LP variants were identified in 14 cases (29.2%), giving an additional diagnostic yield of 23.3% (14/60). The most frequently affected organ referred for prenatal WES was the head or neck system (40%), followed by the skeletal system (39.1%). In terms of pathogenic genes, FGFR3 was the most common diagnostic gene in this cohort. For the first time, we discovered five P/LP variants involved in SEC24D, FIG4, CTNNA3, EPG5, and PKD2. In addition, we identified three VUSes that had been reported previously. Outcomes of pregnancy were available for 54 cases, of which 24 cases were terminated.Conclusion: The results confirmed that WES is a powerful tool in prenatal diagnosis, especially for fetuses with ultrasonographic anomalies that cannot be diagnosed using conventional prenatal methods. Additionally, newly identified variants will expand the phenotypic spectrum of monogenic disorders and greatly enrich the prenatal diagnostic database