Navigating the dynamic leadership labyrinth: Exploring narratives of women academics in Chinese higher education

This thesis explores women academics’ leadership experiences in Chinese higher education. It is situated within the literature on gender, organisations, and women’s leadership. It takes a social constructionist perspective and brings together the ‘leadership labyrinth’ and the ‘leadership web’ to form an analytical framework to examine how women academics make sense of their leadership trajectories. A semi-structured interview method was adopted to gain insight into women’s leadership experiences, and a narrative approach was used to analyse and present the findings. Three narratives of women academics’ leadership journeys were identified, namely: navigating the winding path towards leadership, learning while in the centre of the labyrinth, and leaving the centre of the labyrinth. Through combining the leadership labyrinth with the leadership web to conceptualise the findings of the research, it was found that women academics navigated the leadership labyrinth with the support of their leadership web. Specifically, they encountered challenges and opportunities that are characteristic of gendered organisations, but they were able to use their individual agency to use resources and power to help them to achieve their leadership goals. It was also found that, while navigating the leadership labyrinth, women academics’ attitudes and behaviours were shaped by gendered organisations, while their actions, in turn, influenced organisational culture and structures. The thesis makes theoretical and empirical contributions to knowledge of women’s leadership. Firstly, it shows that the leadership labyrinth is dynamic as its walls move and ‘disappear’, and that the labyrinth is co-constructed by both individuals and organisations. Secondly, the thesis demonstrates that individuals and organisations are undergoing changes in contemporary China, which will provide both new challenges and opportunities, for women on their paths towards leadership

The determinants of corporate hedging decision and the impact of hedging strategies on firms' risk: evidence from Hong Kong and Chinese non-financial firms

An increasing amount of corporations are using corporate risk management programs to control the risk exposure. Derivatives hedging activities are most common hedging instruments. The majority of prior researches focus on US and UK market. This paper employs a sample of 501 HK and Chinese non-financial firms over 2008 to 2016 to investigate on the determinants of corporate hedging decision and the purpose of HK and Chinese firms to use the financial derivative products. The hedging data of the sampled firms is manually collected from annual reports. Chinese firms show relatively lower portion in undertaking hedging products, since the state-ownership characteristic provides certain guarantee towards risk exposure. A combined approach of univariate and multivariate logistic regressions is employed to investigate on the determinants of hedging decision. The empirical results show that foreign risk exposure and firm size exhibit most significant positive relationship with both hedging and FX hedging decision. All the regression tests are iterated four times on groups involving a more comprehensive definition of hedging, in order to prevent biased non-hedgers and non FX-hedgers samples. The results support that the alternative definition of hedging is necessary to obtain more reliable and accurate conclusion. Finally, pooled OLS regression is used to distinguish the purpose of hedging activities. The test results imply that HK and Chinese non-financial firms use derivatives for hedging rather than speculation purpose

Anodic stripping voltammetry with graphite felt electrodes for the trace analysis of silver

Graphite felt (GF) is a mass produced porous carbon electrode material commonly used in redox flow batteries. Previous studies have suggested GF may have valuable applications in electroanalysis as a low cost disposable carbon electrode material, although most GF sensors have used flow cell arrangements. In this work, an elegant wetting technique is employed that allows GF electrodes to be used in quiescent solution to detect trace levels of silver in water via anodic stripping voltammetry. GF electrodes display good repeatability and a limit of detection of 25 nM of Ag+ in 0.1 M HNO3, with a linear range spanning two orders of magnitude. This compares to a value of around 140 nM when using conventional carbon electrodes. Combined with their low cost and disposable nature, the results suggest GF electrodes can make a valuable contribution to electroanalysis

More income, less depression? Revisiting the nonlinear and heterogeneous relationship between income and mental health

This paper uses a large-scale nationally representative dataset to examine the nonlinear effect of income on mental health. To investigate their causal relationship, the exogenous impact of automation on income is utilized as the instrument variable (IV). In addition, to explore their nonlinear relationship, both income and its quadratic term are included in regressions. It is found that the impact of income on mental health is U-shaped rather than linear. The turning point (7.698) of this nonlinear relation is near the midpoint of the income interval ([0, 16.113]). This suggests that depression declines as income increases at the lower-income level. However, beyond middle income, further increases in income take pronounced mental health costs, leading to a positive relationship between the two factors. We further exclude the possibility of more complex nonlinear relationships by testing higher order terms of income. In addition, robustness checks, using other instrument variables and mental health indicators, different IV models and placebo analysis, all support above conclusions. Heterogeneity analysis demonstrates that males, older workers, ethnic minorities and those with lower health and socioeconomic status experience higher levels of depression. Highly educated and urban residents suffer from greater mental disorders after the turning point. Religious believers and Communist Party of China members are mentally healthier at lower income levels, meaning that religious and political beliefs moderate the relationship between income and mental health

Endoplasmic Reticulum Aminopeptidase 1 Is Involved in Anti-viral Immune Response of Hepatitis B Virus by Trimming Hepatitis B Core Antigen to Generate 9-Mers Peptides

Endoplasmic reticulum aminopeptidase 1 (ERAP1) is a processing enzyme of antigenic peptides presented to major histocompatibility complex (MHC) class I molecules. ERAP1-dependent trimming of epitope repertoire determines an efficacy of adoptive CD8+ T-cell responses in several viral diseases; however, its role in hepatitis B virus (HBV) infection remains unknown. Here, we show that the serum level of ERAP1 in patients with chronic hepatitis B (CHB) (n = 128) was significantly higher than that of healthy controls (n = 44) (8.78 ± 1.82 vs. 3.52 ± 1.61, p < 0.001). Furthermore, peripheral ERAP1 level is moderately correlated with HBV DNA level in patients with CHB (r = 0.731, p < 0.001). HBV-transfected HepG2.2.15 cells had substantially increased ERAP1 expression and secretion than the germline HepG2 cells (p < 0.001). The co-culture of ERAP1-specific inhibitor ERAP1-IN-1 pretreated HepG2.2.15 cells or ERAP1 knockdown HepG2.2.15 cells with CD8+ T cells led to 14–24% inhibition of the proliferation of CD8+ T cells. Finally, liquid chromatography tandem mass spectrometry (LC-MS/MS) test demonstrated that ERAP1-IN-1 blocks completely the production of a 9-mers peptide (30–38, LLDTASALY) derived from Hepatitis B core antigen (HBcAg). The predictive analysis by NetMHCpan-4.1 server showed that human leukocyte antigen (HLA)-C*04:01 is a strong binder for the 9-mers peptide in HepG2.2.15 cells. Taken together, our results demonstrated that ERAP1 trims HBcAg to produce 9-mers LLDTASALY peptides for binding onto HLA-C*04:01 in HepG2.2.15 cells, facilitating the potential activation of CD8+ T cells

Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places

BackgroundPeople usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations.MethodsIn this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models.ResultsThe final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables.ConclusionIn this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction

CD64 plays a key role in diabetic wound healing

IntroductionWound healing poses a clinical challenge in diabetes mellitus (DM) due to compromised host immunity. CD64, an IgG-binding Fcgr1 receptor, acts as a pro-inflammatory mediator. While its presence has been identified in various inflammatory diseases, its specific role in wound healing, especially in DM, remains unclear.ObjectivesWe aimed to investigate the involvement of CD64 in diabetic wound healing using a DM animal model with CD64 KO mice.MethodsFirst, we compared CD64 expression in chronic skin ulcers from human DM and non-DM skin. Then, we monitored wound healing in a DM mouse model over 10 days, with or without CD64 KO, using macroscopic and microscopic observations, as well as immunohistochemistry.ResultsCD64 expression was significantly upregulated (1.25-fold) in chronic ulcerative skin from DM patients compared to non-DM individuals. Clinical observations were consistent with animal model findings, showing a significant delay in wound healing, particularly by day 7, in CD64 KO mice compared to WT mice. Additionally, infiltrating CD163+ M2 macrophages in the wounds of DM mice decreased significantly compared to non-DM mice over time. Delayed wound healing in DM CD64 KO mice correlated with the presence of inflammatory mediators.ConclusionCD64 seems to play a crucial role in wound healing, especially in DM conditions, where it is associated with CD163+ M2 macrophage infiltration. These data suggest that CD64 relies on host immunity during the wound healing process. Such data may provide useful information for both basic scientists and clinicians to deal with diabetic chronic wound healing

Regulatory Network and Prognostic Effect Investigation of PIP4K2A in Leukemia and Solid Cancers

Germline variants of PIP4K2A impact susceptibility of acute lymphoblastic leukemia (ALL) through inducing its overexpression. Although limited reports suggested the oncogenic role of PIP4K2A in cancers, regulatory network and prognostic effect of this gene remains poorly understood in tumorigenesis and leukemogenesis. In this study, we conducted genome-wide gene expression association analyses in pediatric B-ALL cohorts to discover expression associated genes and pathways, which is followed by the bioinformatics analyses to investigate the prognostic role of PIP4K2A and its related genes in multiple cancer types. 214 candidates were identified to be significantly associated with PIP4K2A expression in ALL patients, with known cancer-related genes rankings the top (e.g., RAC2, RBL2, and TFDP1). These candidates do not only tend to be clustered in the same types of leukemia, but can also separate the patients into novel molecular subtypes. PIP4K2A is noticed to be frequently overexpressed in multiple other types of leukemia and solid cancers from cancer cohorts including TCGA, and associated with its candidates in subtype-specific and cancer-specific manners. Interestingly, the association status varied in tumors compared to their matched normal tissues. Moreover, PIP4K2A and its related candidates exhibit stage-independent prognostic effects in multiple cancers, mostly with its lower expression significantly associated with longer overall survival (p < 0.05). Our findings reveal the transcriptional regulatory network of PIP4K2A in leukemia, and suggest its potentially important role on molecular subtypes of multiple cancers and subsequent treatment outcomes

The determinants of corporate hedging decision and the impact of hedging strategies on firms' risk: evidence from Hong Kong and Chinese non-financial firms

An increasing amount of corporations are using corporate risk management programs to control the risk exposure. Derivatives hedging activities are most common hedging instruments. The majority of prior researches focus on US and UK market. This paper employs a sample of 501 HK and Chinese non-financial firms over 2008 to 2016 to investigate on the determinants of corporate hedging decision and the purpose of HK and Chinese firms to use the financial derivative products. The hedging data of the sampled firms is manually collected from annual reports. Chinese firms show relatively lower portion in undertaking hedging products, since the state-ownership characteristic provides certain guarantee towards risk exposure. A combined approach of univariate and multivariate logistic regressions is employed to investigate on the determinants of hedging decision. The empirical results show that foreign risk exposure and firm size exhibit most significant positive relationship with both hedging and FX hedging decision. All the regression tests are iterated four times on groups involving a more comprehensive definition of hedging, in order to prevent biased non-hedgers and non FX-hedgers samples. The results support that the alternative definition of hedging is necessary to obtain more reliable and accurate conclusion. Finally, pooled OLS regression is used to distinguish the purpose of hedging activities. The test results imply that HK and Chinese non-financial firms use derivatives for hedging rather than speculation purpose