Statistical methods in modeling disease surveillance data with misclassification

This thesis focuses on constructing appropriate statistical models to monitor the dynamics of disease transmission in animal disease surveillance system. One big challenge in analyzing such disease surveillance data is that the diagnostic tests are usually known to have imperfect sensitivity and specificity, thus the observations are usually misclassified, which introduces uncertainty in determination and modeling of the true disease status among animals. The thesis consists of three projects focusing on three different models and statistical inferences for different disease surveillance datasets. In the first project (Chapter 2), we propose a latent spatial piecewise exponential model for the misclassified disease surveillance data and apply the model to a data from the porcine reproductive and respiratory syndrome virus (PRRSV) disease. The misclassification of test outcomes are accounted for by using a two-level survival model. Spatial distance and time-varying covariates are incorporated to account for disease transmission. We show that our model is efficient in capturing the data features and easy to implement. In the second project (Chapter 3), we are motivated by parameter estimations in hidden Markov models (HMM) and mixed HMM (MHMM). The HMM can be applied to the animal disease surveillance data where the outcomes are with misclassification, and with a group level random effect added, the MHMM can model the correlation structure. However, the parameters estimation in these models are challenging because of the latent variables and random effect. We propose a pairwise fractional imputation using the idea of parametric fractional imputation as well as the Markov property. The proposed estimation method is shown to provide efficient parameter estimates and achieves computational efficiency. In the third project (Chapter 4), we further investigate into the piecewise exponential model and consider estimation of the hazard functions where a monotone restriction is put on the hazard. When observations are with misclassification, the estimation involves EM-algorithm and the principle of isotonic regression is used for constraint optimization of the model parameters. Details of the estimation algorithm is developed in this chapter and the bootstrap confidence interval is constructed for measuring the variability of the estimates. The proposed model is then applied to another PRRSV surveillance study in the swine population

Mineralization of 4-chlorophenol and analysis of bacterial community in microbial fuel cells

Abstract4-Chlorophenol (4-CP) was co-metabolically degraded and mineralized with the presence of glucose in microbial fuel cells (MFCs), achieving a degradation rate of 0.58 ± 0.036mg/L-h (7.2 ± 0.5mg/g VSS-h) with an electricity generation of 5.4 ± 0.4W/m3 at an initial 4-CP concentration of 25mg/L. Compared to the open circuit controls, current generation accelerated the removal of 4-CP. Coulombic efficiency decreased from 30.3 ± 2.9% at an initial 4- CP concentration of 5mg/L to 6.3 ± 0.9% at 40mg/L. 4-CP was degraded via the formation of phenol, which was further mineralized. Dominant bacteria most similar to both the exoelectrogenic and electrotrophic uncultured Desulfovibrio, the exoelectrogenic and recalcitrant degrader of uncultured Desulfobulbus, and the exoelectrogenic uncultured Microbacterium were identified in the biofilms. These results demonstrate that 4-CP mineralization using MFCs may be a promising process for remediation of water contaminated with 4-CP as well as for power generation

Prosumer-to-customer exchange in the sharing economy:Evidence from the P2P accommodation context

Numerous prosumers who share their spare resources have contributed significantly to sharing economy development in recent years. Existing research on the sharing economy has primarily focused on the service demand side of consumers, thus neglecting the service supply side of individual prosumers. Understanding of the service exchange between prosumers and customers in the peer-to-peer (P2P) sharing economy remains limited. Drawing on the motivation, opportunity, and ability (MOA) model and social exchange theory, we developed a conceptual framework to explore how prosumers' service attributes influence consumers in a P2P accommodation sharing context. Using 313 questionnaires and 112 paired objective data points from prosumers in one popular P2P accommodation platform (i.e., Xiaozhu.com), this research found that prosumers' economic motivation, service flexibility, and service knowledge level have distinct effects on consumers' transactional based and relational-based participation. We also found a moderating role of prosumers' shared property management on these effects

Advance of Metal Compound Cathodes in Lithium Sulfur Batteries

With the rapid development of technology, people’s demand for energy is increasing day by day. Many energy devices electric vehicles and other technological products require high-energy lithium secondary batteries to operate. Higher electrochemical energy per unit volume and lower manufacturing cost than lithium batteries are the characteristic advantages of lithium sulfur (LiS) batteries. At the same time, it is environmentally friendly. The global reserves of elemental sulfur are also very abundant, and the cost is low. Therefore, LiS batteries have become one of the most promising secondary batteries in the future. However, LiS batteries also suffer from issues such as poor conductivity of the active substance sulfur, shuttle effect, volume expansion, and lithium dendrites. Research has found that the application of composite materials of metal compounds and sulfur in the cathode of LiS batteries can effectively limit the shuttle effect and poor conductivity of LiS batteries. It can effectively adsorb polysulfides generated in the reaction, optimize the electric performance of cathode sulfur, strengthen the rate performance and cycle stability of lithium ion battery, as well as reduce capacity degradation, significantly improving their electrochemical performance. This article reviews the research progress on the application of metal compounds, mainly metal oxides and metal sulfides, in the cathode of LiS batteries. It explores how this application can suppress shuttle effects and slow down capacity degradation and summarizes and looks forward to its development

Characterization of the fertilization independent endosperm (FIE) gene from soybean

Reproduction of angiosperm plants initiates from two fertilization events: an egg fusing with a sperm to form an embryo and a second sperm fusing with the central cell to generate an endosperm. The tryptophan-aspartate (WD) domain polycomb protein encoded by fertilization independent endosperm (FIE) gene, has been known as a repressor of hemeotic genes by interacting with other polycomb proteins, and suppresses endosperm development until fertilization. In this study, one Glycine max FIE (GmFIE) gene was cloned and its expression in different tissues, under cold and drought treatments, was analyzed using both bioinformatics and experimental methods. GmFIE showed high expression in reproductive tissues and was responsive to stress treatments, especially induced by cold. GmFIE overexpression lines of transgenic Arabidopsis were generated and analyzed. Delayed flowering was observed from most transgenic lines compared to that of wild type. Overexpression of GmFIE in Arabidopsis also leads to semi-fertile of the plants.Keywords: Polycomb proteins, fertilization independent endosperm (FIE), Glycine max, Arabidopsis thalian

ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12

As Computational Thinking (CT) continues to permeate younger age groups in K-12 education, established CT platforms such as Scratch face challenges in catering to these younger learners, particularly those in the elementary school (ages 6-12). Through formative investigation with Scratch experts, we uncover three key obstacles to children's autonomous Scratch learning: artist's block in project planning, bounded creativity in asset creation, and inadequate coding guidance during implementation. To address these barriers, we introduce ChatScratch, an AI-augmented system to facilitate autonomous programming learning for young children. ChatScratch employs structured interactive storyboards and visual cues to overcome artist's block, integrates digital drawing and advanced image generation technologies to elevate creativity, and leverages Scratch-specialized Large Language Models (LLMs) for professional coding guidance. Our study shows that, compared to Scratch, ChatScratch efficiently fosters autonomous programming learning, and contributes to the creation of high-quality, personally meaningful Scratch projects for children.Comment: 29 pages, 7 figures, accepted by CHI 202

VOXEL-FREE RADON TRANSFORM FOR IMPROVING SURFACE QUALITY IN COMPUTED AXIAL LITHOGRAPHY

Computed axial lithography (CAL) is an additive manufacturing process that projects light patterns onto a rotating vial of photosensitive resin to print the desired object through the superposition of light energy. Currently, these projection patterns are generated by voxelizing a target object from an STL or another 3D file format and applying the Radon transform through an iterative optimization process. In this work, a voxel-free method to generate projection sets is proposed. Here, the Radon transform is performed directly on a non-voxelized target object by calculating intersections between incident rays of light and triangles of the original STL surface mesh. Initial simulated results show a more consistent light dose at the surface of the object, indicating improved surface quality and smoothness. Additionally, simulations show a more uniform object interior using the proposed method as well as the potential for improved speed scaling with object size.Mechanical Engineerin

Enclave-Reinforced Inequality during the COVID-19 Pandemic: Evidence from University Campus Lockdowns in Wuhan, China

From MDPI via Jisc Publications RouterHistory: accepted 2021-11-23, pub-electronic 2021-11-26Publication status: PublishedFunder: China Prosperity Fund Programme; Grant(s): PF3051 CH-WS3HBUE YR1Funder: Ministry of Education; Grant(s): 20YJC630149The COVID-19 pandemic has impacted urban life and created spatial and social inequalities in cities. The impacts of lifting full lockdown restrictions once fast-spreading and community-acquired infection waves were under control are still not fully understood. This study aims to explore spatial inequality reinforced in the intervals between the waves of infection during the COVID-19 pandemic. Enclave-reinforced inequality resulting from enclave-based lockdown policies in Chinese cities was investigated through an analysis of the impacts of university campus enclave closures on the accessibility and crowdedness of urban green spaces. Using a modified two-step floating catchment area (2SFCA) and inversed 2SFCA (i2SFCA) method, accessibility and crowdedness were calculated and compared under two different scenarios. Additionally, the Lorenz curve, Gini coefficient, and Theil index were used to measure and compare intra-city global and local inequalities under each scenario. The results indicate that the lockdown of university campus enclaves decreased the supply of urban green spaces. Campus closures not only exacerbated the unequal distribution of urban green space, but also reduced the inequality of crowdedness in urban parks due to increased crowdedness in parks near the closed enclaves. Moreover, both accessibility and crowdedness worsened when the calculations were weighted for population size and the total supply of green space. Enclave-based lockdown in cities reinforced spatial inequality, and it is highly complex and has multidimensional impacts on urban inequalities and environmental injustice which should be considered by urban planners and decision-makers hoping to create healthy, inclusive, resilient, and sustainable cities in the “new normal” of the COVID-19 pandemic

A latent spatial piecewise exponential model for interval-censored disease surveillance data with time-varying covariates and misclassification

Understanding the dynamics of disease spread is critical to achieving effective animal disease surveillance. A major challenge in modeling disease spread is the fact that the true disease status cannot be known with certainty due to the imperfect diagnostic sensitivity and specificity of the tests used to generate the disease surveillance data. Other challenges in modeling such data include interval censoring, relating disease spread to distance between units, and incorporating time-varying covariates, which are the unobserved disease statuses. We propose a latent spatial piecewise exponential model (PEX) with misclassification of events to address the challenges in modeling such disease surveillance data. Specifically, a piecewise exponential model is used to describe the latent disease process, with spatial distance and timevarying covariates incorporated for disease spread. The observed surveillance data with imperfect diagnostic tests are then modeled using a binary misclassification process given the latent disease statuses from the PEX model. Model parameters are estimated through a Bayesian approach utilizing non-informative priors. A simulation study is performed to evaluate the model performance and the results are compared with a candidate model where no misclassification is considered. For further illustration, we discuss an application of this model to a porcine reproductive and respiratory syndrome virus (PRRSV) surveillance data collected from commercial swine farms

Preparation of N-, O-, and S-tri-doped biochar through one-pot pyrolysis of poplar and urea formaldehyde and its enhanced removal of tetracycline from wastewater

In this study, biochar was prepared via hybrid doping of N, O, and S by applying one-pot pyrolysis of poplar wood and S-containing urea formaldehyde at 900 °C. Different doping ratios were adopted, and the contents of O, N, and S were in the ranges of 2.78 – 5.56 %, 2.16 – 4.92 %, and 1.42 – 4.98 %, respectively. This hybrid doping significantly enhanced the efficiency of the removal of tetracycline (40 mg/L) from wastewater to 71.84 % in comparison with that attained by using normal poplar biochar (29.45 %). The adsorption kinetics and isotherms indicated that the adsorption process was favorable and was dominated by chemisorption instead of physisorption; the dominant adsorption process may be justified by the existence of abundant functional groups. The adsorption capacity was barely related to the surface area (R2 = 0.478), while it was closely related to the concentration of graphitic N (R2 = 0.985) because graphitic N enhanced the π–π interactions. The adsorption capacity was also highly related to the proportion of oxidized N and oxidized S owing to hydrogen bonding, which may have overlapped with the contribution of O-containing functional groups. This study presents a simple hybrid doping method for biochar modification and provides fundamental insights into the specific effects of O-, N- and S-containing functional groups on the performance of biochar for tetracycline removal