The Relationship Between Instructed Non-Immersion Additional Language (LX) Learning and Student Outcomes of Diversity-Equity-Inclusion (DEI)-Focused Education for U.S. High School Students

Research has found that teaching about diversity, equity, and inclusion (DEI) can raise U.S. high school students’ awareness about DEI-related topics and develop their critical thinking skills to analyze DEI-related issues. But the achievements of DEI-focused education are limited. Thus, DEI-focused education needs complementary efforts to enhance student outcomes. Research has also found that learning an additional language (LX) may heighten students’ interest in learning about other cultural communities, nurture students’ cross-cultural empathy, and elicit culturally responsive behaviors. The juxtaposition of these findings signifies the potential for LX learning to complement DEI-focused education to improve learning outcomes. However, the value of LX learning for DEI goals has not received much attention in the literature. This mixed methods phenomenological study aims to explore the relationship between instructed non-immersion LX learning and student outcomes of DEI-focused education in cognitive, affective, and behavioral aspects among U.S. high school students. In total, 76 U.S. high school students participated in this study. The research questions addressed by this study include: (1) What are the students’ perceptions of the achievements and limitations of DEI-focused education? (2) To what degree are the length of the students’ LX learning in an instructed non-immersion setting, the frequency of guided reflection on cross-cultural differences in instructed non-immersion LX classes, and the percentage of course time spent on such reflection associated with their intercultural competence? (3) Based on the students’ perceptions, what are the possible influences of instructed non-immersion LX learning on DEI-focused education in cognitive, affective, and behavioral aspects? This study has found that students acknowledge the achievements and limitations of DEI-focused education and see instructed non-immersion LX learning as a complementary experience to DEI-focused education. None of the LX learning factors examined by the second research question can reliably predict students’ appreciation of DEI. To actualize the complementary function, LX teachers need to explicitly draw connections with DEI-related topics and purposefully address certain factors in their LX curriculum design and teaching practice. Besides implications for teaching, these findings may also deepen people’s appreciation of the developmental benefits of LX learning and encourage policies to support LX programs. Keywords: diversity, equity, inclusion, DEI, education, LX learning, additional language learning, non-immersion, high school, intercultural competenc

Environmental Impacts of Effluent Containing EDTA from Dairy Processing Plants

Ethylenediaminetetraacetic acid (EDTA) is a well-known chelating agent, and has numerous applications in industries, for example in dairy industry to improve the cleaning efficiency of plant and equipment. As EDTA is water-soluble and not volatile, it is eventually released into the environment with wastewater effluent. In general, EDTA has a low toxic impact for both humans and natural environments. There are some concerns, however, about its poor biodegradation in conventional wastewater treatment plants and natural environments, and its effect in mobilizing heavy metals from solid phases to pose a risk to groundwater. In the late 1980's the environmental impact of EDTA was scrutinized in Europe. Since then, treatment and discharge of wastewater containing EDTA is increasingly required as environmental regulations become more stringent. This is the first investigation into the effects of EDTA in New Zealand. In the New Zealand dairy industry, EDTA has been used as an additive alongside caustic agents to improve cleaning efficiency within dairy processing plants and to minimize dairy wastewater discharge into the environment. There are two main disposal methods of dairy wastes; direct discharge into the local stream after treatment, and spray irrigation onto pasture land. The primary aim of this research is to identify whether EDTA is detectable in the environment after the release of dairy wastes containing EDTA into that environment. For the first time in New Zealand, an analytical method using reversed-phase ion-pair liquid chromatography, was established to determine EDTA present in dairy wastewater, and then applied to surface water, soils and groundwater with appropriate modifications. Method detection limits were 5 g/L for dairy wastewater, 1 g/L for surface water, 0.15 mg/kg (dry weight) for soils, and 2 g/L for groundwater. Significant concentrations of EDTA, as high as 83 mg/L, were observed in wastewater from dairy processing plants, when EDTA had been used alongside alkaline cleaning agents. The analyses have shown that approximate 93 % of EDTA was removed in the existing biological treatment process, which is an extended aeration activated sludge process, operated under alkaline pH 8.0-8.2 with a 3-week sludge retention time. For surface water receiving the dairy effluent, 1 - 2.7 g/L of EDTA were found, and no particular concerns were suggested about the associated heavy metals. A quasi one-dimension vertical mixing model and a two-dimension (depth-averaged) 3DD hydrodynamic model were applied to simulate EDTA dispersion in the river. The modelling results for 'a worst case scenario' of high EDTA release combined with a low river flow, suggest that the dairy effluent discharge at the Fonterra Waitoa dairy site will not lead to a significant effect on the Waitoa River in terms of EDTA concentration. Investigation of EDTA and heavy metal concentrations in pastoral topsoil and groundwater following the land application of dairy biomass concludes that there are no specific concerns. In contrast, the analyses suggest that heavy metals may be built up over long periods of irrigation with dairy effluent in soils, and then transported to the groundwater in the presence of EDTA. However, more research would be required to clarify this matter

Regression analysis of mixed sparse synchronous and asynchronous longitudinal covariates with varying-coefficient models

We consider varying-coefficient models for mixed synchronous and asynchronous longitudinal covariates, where asynchronicity refers to the misalignment of longitudinal measurement times within an individual. We propose three different methods of parameter estimation and inference. The first method is a one-step approach that estimates non-parametric regression functions for synchronous and asynchronous longitudinal covariates simultaneously. The second method is a two-step approach in which synchronous longitudinal covariates are regressed with the longitudinal response by centering the synchronous longitudinal covariates first and, in the second step, the residuals from the first step are regressed with asynchronous longitudinal covariates. The third method is the same as the second method except that in the first step, we omit the asynchronous longitudinal covariate and include a non-parametric intercept in the regression analysis of synchronous longitudinal covariates and the longitudinal response. We further construct simultaneous confidence bands for the non-parametric regression functions to quantify the overall magnitude of variation. Extensive simulation studies provide numerical support for the theoretical findings. The practical utility of the methods is illustrated on a dataset from the ADNI study.Comment: 57 pages, 5 figure

Metagenomics analysis of disease-related human gut microbiota

The human gut microbiota have been linked with various pathological disorders. Yet, our understanding of the underlying mechanisms is still limited by the inconsistent results of different publications and the inherent complexity. These separate studies and incomparable data sets missed the forest for the trees, thus encouraging us to carry out meta-analysis of human gut microbiome regarding different kinds of diseases and dip into the question about what kinds of human gut microbial community are healthy. 1. This dissertation underpins the consistent discipline behind disease-related dysbiosis by conducting a pan-microbiome analysis, which annotated and analyzed the microbiome contigs and genes identified from raw reads of whole genome sequencing (WGS) data of human gut. Consistent pattern shift was discovered in the microbial mutually dependent community, which revealed that the microbial members in diseases are more competitive while less cooperative than health, remarkably driven by the 20-times increase of competitive pairs between potential pathogens and 10-times decrease of cooperative pairs between non pathogens. Additionally, taking all the microbiota in the same community as a ‘super organism’, our mathematical model of gene-gene interaction network revealed the significance of cell motility, though it was not a dominant functional category. This part of work answered the question about how the ecological niches of gut modulate human health in a systematic matter. 2. This dissertation discovered some inflammation and cancer related genera increase in the advanced aging individuals while some beneficial genera are lost, and proved the existence of aging progression of human gut microbiota, by applying an unsupervised machine learning algorithm to recapitulate the underlying aging progression of microbial community from hosts in different age groups. Aging process captures many facets of biological variation of the human body, which leads to functional decline and increased incidence of infection in gut of elderly people. Different from diseases, the aging transformation is a continuous progress. We obtained raw 16S rRNA sequencing data of subjects ranging from newborns to centenarians from a previous study, and summarized the data into a relative abundance matrix of genera in all the samples. Without using the age information of samples, we applied multivariate unsupervised analysis, which revealed the existence of a continuous aging progression of human gut microbiota along with the host aging process. The identified genera associated to this aging process are meaningful for designing probiotics to maintain the gut microbiota to resemble a young age, which hopefully will lead to positive impact on human health, especially for individuals in advanced age groups. 3. This dissertation develops a machine learning model LightCUD for disease discrimination based on human gut microbiome, which was designed for discriminating UC and CD from non-IBD colitis. Using a set of WGS data from 349 human gut microbiota samples with two types of IBD and healthy controls, we assembled and aligned WGS short reads to obtain feature profiles. Owing to the well-designed feature selection and machine learning algorithms comparison, LightCUD outperforms other pilot studies. LightCUD was implemented in Python and packaged free for installation with customized databases. With WGS data or 16S rDNA sequencing data of gut microbiota samples as the input, LightCUD can discriminate IBD from healthy controls with high accuracy and further identify the specific type of IBD. The executable program LightCUD is released as open source at the webpage http://cqb.pku.edu.cn/zhulab/lightcud/. 4. This dissertation constructed a comprehensive database, named DREEM, of DiseaseRElatEd Marker genes in human gut microbiome, which retrieves a large scale WGS data released in GenBank and EMBL. Short reads with the size of 18.63T consisting of 1,729 samples are processed with unified procedure, involving the state-of-the-art bioinformatics tools and well-designed statistical analysis, and covering six types of pathological conditions, i.e., T2D, Crohn’s diseases, ulcerative colitis, liver cirrhosis, symptomatic atherosclerosis and obesity. Furthermore, the database annotates the disease-related marker genes functionally and taxonomically. DREEM contains 1,953,046 disease-related marker genes and 5100 core genes. The database is accessible at http://cqb.pku.edu.cn/ZhuLab/DREEM. This dissertation conducted a pan-microbiome analysis integrating multiple diseases, revealed the aging progression of human gut microbiota, released the tool LightCUD for discriminating diseases based on human gut microbiome and constructed a disease-related marker gene database within human gut microbiota.Ph.D