Does corporate governance impact on the sustainable return of IPOs? A comparative analysis of China and New Zealand IPOs listed between 1999 and 2004

This thesis examines the impact of corporate governance practices on the short and longer-term sustainable returns of initial public offerings (IPOs) in China and New Zealand. In particular, the analysis focuses on the Shanghai, Shenzhen and New Zealand exchanges, providing a comparison between them. The closer trade and economic relations between China and New Zealand and the increasing immigration to New Zealand of relatively well-off Chinese makes the study topical. Different political and cultural settings may give rise to differing relationships between IPO success and governance in these three markets and this will promote some interesting implications. Corporate governance involves regulatory and market mechanisms, and the roles and relationships between a company’s management, its board, its shareholders and other stakeholders. IPOs are very important for investors, stock markets and economic growth. The relationship between corporate governance mechanisms and IPO performance has received attention in studied by the literature. The bulk of prior research focuses on larger and more developed economies. There is an increasing volume of literature relating to Chinese IPOs but none have focused on this key issue of IPO performance and the corporate governance structures of the particular companies. New Zealand by comparison to China is a small economy, small population and has few IPOs. However, China has become New Zealand’s second largest trading partner since 2008 and China became one of the major sources of immigrants to New Zealand from late 1990s. This thesis makes an important contribution to knowledge demonstrating that the short-term IPO performance is associated with different corporate governance attributes to those apparent for long-term performance of IPOs. The use of a wide range of governance variables for the analysis as compared with prior studies provides greater confidence in the findings. The longer time period, up to 11 years, used in this study is helpful in reviewing the longer-term sustainable IPO performance. China is the fastest growth economy in the world with rapidly developing stock markets. New Zealand, on the other hand, is a mature economy with a small stock market. The quality of data and analysis for two such very different economics provides an opportunity for careful diagnostic and specification testing. In this study a more robust empirical analysis, extending the commonly used OLS approach, through several panel data methods of generalised least square, and generalised method of moment models are explored. The study uses data from Shanghai, Shenzhen and New Zealand stock exchanges, covering the IPOs listed from 1999 to 2004. Those data are secondary data collected from the websites of the three stock exchanges, NZX Deep Archive, CSMAR database and individual companies’ annual reports. The study includes an extensive range of governance variables, including board size, board demographics, board leadership, board education, and board evaluation variables. There are three long-term dependent variables and three short-term dependent variables. There are a group of control variables. Various tests and suitable regression models are used to find that relationship between corporate governance mechanisms and the short- and long-term performance of IPOs on three exchanges. Several diagnostic tests including serial correlation test, over-identification test, and joint significance test are also used to check the validity of the regressions. The thesis finds that the panel data and cross sectional regressions explain the long and short-term IPO performance well on the Shanghai Stock Exchange. The results indicate that the panel data regressions explain the long-term IPO performance well, but the cross sectional regressions do not have acceptable explanatory power for the short-term IPO performance on the Shenzhen Stock Exchange. In New Zealand the panel data regressions explain the long-term IPO performance well, but the cross sectional regressions do not contribute a useful explanation for the short-term IPO performance. This thesis contributes a number of important implications. The commonality of corporate governance variables associated with successful performance has implications for companies preparing to list, exchanges arresting companies with listing, investors looking for successful listings and policy makers wanting an efficient capital market. The difference in variables similarly provides insight for companies, exchanges, investors, and policy analysts

Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Automated classification of Schistosoma mansoni granulomatous microscopic images of mice liver using Artificial Intelligence (AI) technologies is a key issue for accurate diagnosis and treatment. In this paper, three grey difference statistics-based features, namely three Gray-Level Co-occurrence Matrix (GLCM) based features and fifteen Gray Gradient Co-occurrence Matrix (GGCM) features were calculated by correlative analysis. Ten features were selected for three-level cellular granuloma classification using a Scaled Conjugate Gradient Back-Propagation Neural Network (SCG-BPNN) in the same performance. A cross-entropy is then calculated to evaluate the proposed Sigmoid input and the ten-hidden layer network. The results depicted that SCG-BPNN with texture features performs high recognition rate compared to using morphological features, such as shape, size, contour, thickness and other geometry-based features for the classification. The proposed method also has a high accuracy rate of 87.2% compared to the Back-Propagation Neural Network (BPNN), Back-Propagation Hopfield Neural Network (BPHNN) and Convolutional Neural Network (CNN)

Predictive short/long-term efficacy biomarkers and resistance mechanisms of CD19-directed CAR-T immunotherapy in relapsed/refractory B-cell lymphomas

Genetically modified T-cell immunotherapies are revolutionizing the therapeutic options for hematological malignancies, especially those of B-cell origin. Impressive efficacies of CD19-directed chimeric antigen receptor (CAR)-T therapy have been reported in refractory/relapsed (R/R) B-cell non-Hodgkin lymphoma (NHL) patients who were resistant to current standard therapies, with a complete remission (CR) rate of approximately 50%. At the same time, problems of resistance and relapse following CAR-T therapy have drawn growing attention. Recently, great efforts have been made to determine various factors that are connected to the responses and outcomes following CAR-T therapy, which may not only allow us to recognize those with a higher likelihood of responding and who could benefit most from the therapy but also identify those with a high risk of resistance and relapse and to whom further appropriate treatment should be administered following CAR-T therapy. Thus, we concentrate on the biomarkers that can predict responses and outcomes after CD19-directed CAR-T immunotherapy. Furthermore, the mechanisms that may lead to treatment failure are also discussed in this review

Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved

Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved

Sustained efficacy of chimeric antigen receptor T-cell therapy in central nervous system lymphoma: a systematic review and meta-analysis of individual data

Background: Central nervous system lymphoma (CNSL) is considered an aggressive lymphoma with a poor prognosis. Studies investigating CNSL have shown that chimeric antigen receptor (CAR) T-cell therapy has demonstrated an effective response in limited sample sizes. Therefore, we conducted this systematic review and meta-analysis to clarify the sustained efficacy and factors associated with the sustained efficacy of CAR T-cell therapy in the treatment of CNSL.Methods: We searched studies from PubMed, Embase, Medline, and the Cochrane Center Register of Controlled Trials up to July 2023. Studies that included individual data on the duration of response (DoR) after receiving CAR T-cell therapy were enrolled. Pooled response rates were calculated using fixed-effects or random-effects models. Subgroup analysis was performed to analyze the heterogeneity, and a Cox regression model was performed to identify the factors associated with sustained efficacy.Results: In total, 12 studies including 69 patients were identified and included in this meta-analysis. The pooled relapse rate was 45% [95% CI 35, 56]. Subgroup analyses of relapse rates revealed that CAR T-cells using the CD28/4-1BB domain (CD28/4-1BB vs. CD28 vs. 4-1BB, p = 0.0151), parenchymal or leptomeningeal involvement (parenchymal or leptomeningeal vs. both parenchymal and leptomeningeal, p < 0.0001), and combined treatment with CAR T-cell therapy [Autologous stem cell transplantation (ASCT) plus CAR T-cell therapy vs. CAR T cells with maintenance therapy vs. CAR T-cell therapy alone, p = 0.003] were associated with lower relapse rates in patients. Time-to-event endpoints were assessed using reconstructed individual patient survival data to explore key modulators of DoR. Partial response status at CAR-T infusion and the use of ASCT plus CAR T-cell therapy were associated with longer DoR at the multivariate level, with hazard ratios of 0.25 and 0.26, respectively.Conclusion: CAR T-cell therapy shows promising and sustained efficacy in CNSL patients. However, further prospective large-scale studies are needed to assess these effect modifiers to optimize patient selection and improve the sustained efficacy of CAR T-cell therapy in the treatment of CNSL.Systematic review registration:https://clinicaltrials.gov/, identifier PROSPERO CRD42023451856