AGGLOMERATION ECONOMIES, INVESTMENT IN EDUCATION, AND REGIONAL DEVELOPMENT

This dissertation consists of two essays that study the linkages among agglomeration economies, investment in education, and regional development. In the first essay, I study the impact of a federal educational investment on various aspects of local economies. In the second essay, I examine the spillover effects among workers with different skills, which are identified by their college majors. The first essay presents evidence of direct spillovers from universities and examines the short- and long-run effects of university activities on geographic clustering of economic activity, labor market composition and local productivity. I treat the designation of land-grant universities as a natural experiment after controlling for the confounding factors with a combination of synthetic control methods and event-study analyses. Three key results are obtained. First, the designation substantially increased local population density. Second, the share of manufacturing workers in the population, an indicator of labor market composition, was not affected by the designation. Third, the designation greatly enhanced local manufacturing productivity, as measured by local manufacturing output per worker, especially in the long run. This positive effect on the productivity in non-education sectors suggests the existence of spillovers from universities. Over an 80-year horizon, I estimate that most of the increase in manufacturing productivity was because of direct spillovers from universities instead of induced agglomeration economies that arise from the increase in population. The second essay studies the manner and extent to which worker skill type affects agglomeration economies that contribute to productivity in cities. I use college major to proxy for skill type among workers with a Bachelor\u27s degree. Workers with college training in information-oriented and technical fields (e.g. STEM areas such as Engineering, Physical Sciences, and Economics) are associated with economically important within-field agglomeration economies and also generate sizeable spillovers for workers in other fields. In contrast to related work by Florida (2002a, 2002b), within-field and across-field spillovers for workers with college training in the arts and humanities are much smaller and often non-existent. While previous research suggests proximity to college-educated workers enhances productivity, these findings suggest that not all college educated workers are alike. Instead, positive spillover effects appear to derive mostly from proximity to workers with training in information-oriented and technical fields

Up in STEM, Down in Business: Changing College Major Decisions with the Great Recession

We use the American Community Survey (ACS) to investigate the extent to which college major decisions were affected during and after the Great Recession with special attention to business and STEM fields, as well as the heterogeneity by gender, race/ethnicity and combinations of race/ethnicity and gender. Several conclusions are reached. First, we see an overall increase in the frequency of STEM majors but a decrease in the frequency of business majors during and after the Great Recession. Second, the increase for STEM fields is spread across several detailed STEM fields, while the decrease in business majors is especially concentrated among finance and management. Third, we find strong heterogeneous effects by gender and race/ethnicity. Males are pushed away from business majors, while both males and females are pushed toward STEM majors; certain racial groups, such as white and Asian, seem to be affected more than others

Eliciting medication preferences of patients with type 2 diabetes under different insurance coverages in China

ObjectiveTo understand the medication preference of type 2 diabetes mellitus (T2DM) patients with different insurance coverages, and to provide reference for improving the patient-centered clinical treatment decision.MethodsThis study used Discrete Choice Experiment (DCE) to elicit preferences of T2DM patients with different insurance coverages in China. A multistage stratified cluster-sampling procedure for data collection and a total of 1,409 valid respondent were conducted.ResultsSeven attributes have significant influence on the preference of T2DM patients with Urban Employee Basic Medical Insurance (UEBMI) and Urban and Rural Residents Basic Medical Insurance (URRBMI) (p < 0.05). T2DM patients with UEBMI pay the most attention to Gastrointestinal adverse events, while T2DM patients with URRBMI pay the most attention to the Treatment efficacy/reduction in HbA1c. Patients with different medical insurance have different willingness to pay for Cardiovascular benefits, Mode of administration and Weight change. When Gastrointestinal adverse events is changed from higher (40%) to none (0%), patients with UEBMI are willing to pay ¥523.49 more per month, while patients with URRBMI are only willing to pay ¥266.62; When the Treatment efficacy/reduction in HbA1c changes from poor (0.5%) to Highest (2.5%), patients with UEBMI are willing to pay ¥518.44 more per month, while patients with URRBMI are willing to pay ¥328.33 more per month. The Gastrointestinal adverse events and the Treatment efficacy/reduction in HbA1c are the primary factors for T2DM patients with UEBMI and URRBMI, followed by the Hypoglycemic risk.ConclusionPhysicians should consider patients’ medication preferences in clinical medication treatment of T2DM patients with different insurance coverages, make targeted treatment decisions, and improve patients’ medication compliance to achieve better treatment results

What is valued most by patients with type 2 diabetes mellitus when selecting second-line antihyperglycemic medications in China

Objective: To estimate patient preferences for second-line antihyperglycemic medications in China. Methods: A face to face survey with the best-worst scaling (BWS) choices was administered in patients with diagnosed type 2 diabetes mellitus (T2DM). Study participants were asked to indicate which attribute they valued most and which attribute they valued least in 11 choice sets, each of which consisted of five alternatives out of 11 antihyperglycemic medication-specific attributes (treatment efficacy, weight change, hypoglycemic events, gastrointestinal side effects, cardiovascular health, urinary tract infection and genital infection side effects, edema, mode of administration, bone fracture, dosing frequency and out-of-pocket cost). A counting approach, a conditional logit model, and K-means clustering were used to estimate the relative importance of items and preference heterogeneity. Results: A total of 362 participants were included with a mean age of 63.6 (standard deviation: 11.8) years. There were 56.4% of participants were women, and 56.3% being diagnosed with diabetes for at least 5 years. Efficacy, cardiovascular health and hypoglycemic events were valued most, while dosing frequency, mode of administration and bone fracture were valued least. The K-means clustering further showed preference heterogeneity in out-of-pocket cost across the participants. Conclusion: Our study suggests that treatment efficacy, cardiovascular health and hypoglycemic events are valued most by Chinese patients with T2DM when selecting second-line antihyperglycemic medications. The study improves the understanding of patients’ preferences for second-line antihyperglycemic medications in China

Job preferences of undergraduate nursing students in eastern China: a discrete choice experiment

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were madeBackground Shortage and mal-distribution of nursing human resources is an intractable problem in China. There is an urgent need to explore the job preferences of undergraduate nursing students. The main aim of this study is to investigate the stated preferences of nursing students when choosing a job. Methods A discrete choice experiment (DCE) was conducted to assess job preferences of the final year undergraduate nursing students from four medical universities/colleges in Shandong Province, China. Job attributes include location, monthly income, bianzhi (which refers to the established posts and can be loosely regarded as state administrative staffing), career development and training opportunity, work environment and working strength. Mixed logit models were used to analyze the DCE data. Results A total of 445 undergraduate nursing students were included in the main DCE analysis. They demonstrated higher preference for a job with higher monthly income, and the probability of choosing a rural job would increase to 92.8% if monthly income increased from RMB 2000 (US$296) to RMB 8000 (US$ 1183). They expressed higher stated preferences for a job which required light working strength and with excellent work environment over other non-economic attributes. Among all attributes, location was the least important attribute. Subgroup analysis showed that students who came from city or county and whose family income was more than RMB 50 000 (US$ 7396) were significantly willing to pay more monthly income for a job in city. Conclusions This study confirmed that economic and non-economic factors both affected the job choices of the students. These results may be more effective for policymakers to perfect the employment policies and design strategies to attract more nursing students taking jobs in rural areas

Experimental Study on Hygrothermal Deformation of External Thermal Insulation Cladding Systems with Glazed Hollow Bead

This research analyzes the thermal and strain behavior of external thermal insulation cladding systems (ETICS) with Glazed Hollow Beads (GHB) thermal insulation mortar under hygrothermal cycles weather test in order to measure its durability under extreme weather (i.e., sunlight and rain). Thermometers and strain gauges are placed into different wall layers to gather thermal and strain data and another instrument measures the crack dimensions after every 4 cycles. The results showed that the finishing coat shrank at early stage (elastic deformation) and then the finishing coat tends to expand and become damaged at later stage (plastic deformation). The deformation of insulation layer is similar to that of the finishing coat but its variation amplitude is smaller. Deformation of substrate expanded with heat and contracted with cold due to the small temperature variation. The length and width of cracks on the finishing coat grew as the experiment progressed but with a decreasing growth rate and the cracks stopped growing around 70 cycles

Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity

Lesion segmentation of ultrasound medical images based on deep learning techniques is a widely used method for diagnosing diseases. Although there is a large amount of ultrasound image data in medical centers and other places, labeled ultrasound datasets are a scarce resource, and it is likely that no datasets are available for new tissues/organs. Transfer learning provides the possibility to solve this problem, but there are too many features in natural images that are not related to the target domain. As a source domain, redundant features that are not conducive to the task will be extracted. Migration between ultrasound images can avoid this problem, but there are few types of public datasets, and it is difficult to find sufficiently similar source domains. Compared with natural images, ultrasound images have less information, and there are fewer transferable features between different ultrasound images, which may cause negative transfer. To this end, a multi-source adversarial transfer learning network for ultrasound image segmentation is proposed. Specifically, to address the lack of annotations, the idea of adversarial transfer learning is used to adaptively extract common features between a certain pair of source and target domains, which provides the possibility to utilize unlabeled ultrasound data. To alleviate the lack of knowledge in a single source domain, multi-source transfer learning is adopted to fuse knowledge from multiple source domains. In order to ensure the effectiveness of the fusion and maximize the use of precious data, a multi-source domain independent strategy is also proposed to improve the estimation of the target domain data distribution, which further increases the learning ability of the multi-source adversarial migration learning network in multiple domains.Comment: Submitted to Applied Soft Computing Journa

Integration of genomics, clinical characteristics and baseline biological profiles to predict the risk of liver injury induced by high-dose methotrexate

BackgroundHigh-dose methotrexate (HD-MTX) is commonly employed in the treatment of malignant tumors in children and young adults due to its distinctive therapeutic efficacy. Nonetheless, the systemic exposure to MTX often results in liver injury (drug induced liver injury, DILI), thereby imposing limitations on the sustained administration of HD-MTX. Additionally, individual variations including genetic underpinnings attributable to disparities in therapeutic effects and clinical toxicity remain to be elucidated.MethodsA total of 374 patients receiving initial HD-MTX treatment were selected for this study, which aimed to establish a predictive model using binary logistic regression and a visual nomogram for DILI risk assessment. Demographic and clinical characteristics were collected at baseline and post-HD-MTX to explore their correlations with the occurrence of DILI. Additionally, genotyping of 25 single nucleotide polymorphisms from drug transporters and enzymes in the folic acid cycle was performed.ResultG allele mutation in ABCB1 rs1128503, *1b/*1b and *1b/*15 haplotypic mutation in SLCO1B1, female gender, and MTX dosage were identified as independent factors for moderate/severe DILI. Patients with GA or AA genotype in ABCB1 rs1128503 showed significant higher 24h MTX concentration than GG, and those with *1b/*1b haplotype group in SLCO1B1 exhibited lower dose adjusted concentration (C/D) than *1a/*1a group. Besides, patient administrated with HD-MTX were more prevalent to have higher C/D levels when using intravenous plus triple intrathecal injection route than those who were using intravenous injection alone. The composite predictive model (ROC curve: AUC = 0.805), comprising above four factors and 24h MTX concentration, exhibited high accuracy.ConclusionFemale gender, recessive mutation in ABCB1 rs1128503, and a range of MTX concentration may be risk factors for increased susceptibility to DILI. Conversely, the *1b/*1b and *1b/*15 mutations in SLCO1B1 may have a protective effect against DILI. The proposed predictive model facilitates early individual risk assessment, enabling the implementation of proactive prevention strategies

Characterization of the emerging recombinant infectious bronchitis virus in China

Infectious bronchitis virus (IBV) can cause serious harm to poultry industry. It is belong to Coronaviridae which is highly variable. A kind of emerging recombinant IBV (ahysx-1) has been detected in chicken from China in 2016. To understand the epidemiology and characterization of the emerging recombinant IBV, 35,455 samples of chickens from the 15 provinces in China were collected and detected. One hundred and ninety-six out of the 537 flocks (positive rate, 36.49%), and 908 out of 35,455 samples (positive rate, 2.56%) were positive in the detection. The results showed that the emerging recombinant IBV was pandemic in China. Thirteen emerging recombinant IBV isolates were selected and continuous subcultured to the fourth generation and analyzed by Next-generation sequencing. Compared with the reported sequence of ahysx-1, the genomic analysis showed that multiple position insertions and deletions were in 1a gene, 3b gene, M gene and N gene. The identity of the S gene nucleotide sequence between all the 13 emerging recombinant IBV isolates and reference stain ahysx-1 were 98.1–99.1%, while the identity of amino acid sequence were 98.0–99.8%. To better understand the recombination mechanism of the emerging recombinant IBV, the genomic sequence of the 13 isolates were compared with turkey coronavirus or guinea fowl coronavirus. The results suggest that all the 13 emerging recombinant IBV isolates were likely to be the recombination of turkey coronavirus or guinea fowl coronavirus with IBV. Turkey coronavirus or guinea fowl coronavirus as minor parents are the donors of S gene. The major parents donors of the genome backone of these recombination events were lineages GI-19 or GVI-1 of IBV. One isolate (IBV/chicken/Henan/H1173/2021) was selected for pathogenicity analysis. The results showed that IBV/chicken/Henan/H1173/2021 was avirulent to SPF embryonated eggs, but could cause intestinal symptoms in of chicks. This study provides a foundation for understanding the epidemic situation and characterization of the emerging recombinant IBV. It is of great significance for the prevention and control of avian coronavirus infection