Structural Breakpoints Research Articles

Prognostic value of a modified pathological staging system for gastric cancer based on the number of retrieved lymph nodes and metastatic lymph node ratio.

The prognosis for gastric cancer (GC) remains grim, underscoring the importance of accurate staging and treatment. Given the potential benefits of using lymph node ratio (LNR) for improved prognostication and treatment planning, it is critical to incorporate examined lymph nodes (ELN) count in an integrated GC staging system. Patients data from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 was utilized as training set. The Mantel-Cox survival test was used to calculate chi-square values for 40 LNR segments with a 0.025 interval, defining a novel LNR-based N (rN) classification based on the cutoff points. A revised AJCC (rAJCC) staging system was established by replacing the 8th AJCC N staging with a rN classification. The relationship between the ELN count and prognosis or positive lymph node detection was conducted by using multivariable models. The series of the odds ratios and hazard ratios were fitted with a locally weighted scatterplot smoothing (LOWESS) smoother, and the structural break points were determined by Chow test to clarify an optimal minimum ELN count. The integrated GC staging system incorporated both rAJCC system and the ideal ELN count. Discriminatory ability and prognostic homogeneity of the rAJCC and integrated staging system was compared with AJCC staging system in the SEER validation set (2016-2017), the Cancer Genome Atlas Program (TCGA) database, and the Third Affiliated Hospital of Sun Yat-sen University database. The current study found that LNR and ELN count are both significantly associated with the prognosis of GC patients (HR = 0.98, p < 0.001 and HR = 2.51, p < 0.001). Four peaks of the chi-square value were identified as LNR cut-off points at 0.025, 0.175, 0.45 and 0.6 to define a novel rN stage. In comparison to the 8th AJCC staging system, the rAJCC staging system demonstrated significant prognostic advantages and discriminatory ability in the training set (5-Y OS AUC: 71.7 vs. 73.0; AIC: 57,290.7 vs. 57,054.9). The superiority of the rAJCC staging system was confirmed in all validation sets. Using a LOWESS smoother and Chow test, a threshold ELN count of 30 was determined to maximum improvement in the prognosis of node-negative patients without downgrading due to potential metastasis, while also maximizing the detection efficiency of at least one involved lymph node. The integrated staging system, combining the refined rAJCC classification with an optimized ELN count threshold, has demonstrated superior discriminatory performance compared to the standalone rAJCC or the traditional AJCC system. The development of a novel GC staging system, which integrated the LNR-based N classification and the minimum ELN count, has exhibited superior prognostic accuracy, holding promise as a valuable asset in the clinical management of GC. However, it is crucial to recognize the limitations from the retrospective database, which should be addressed in subsequent analyses.

Modelling Mixed-Frequency Time Series with Structural Change

AbstractPredictive ability of time series models is easily compromised in the presence of structural breaks, common among financial and economic variables amidst market shocks and policy regime shifts. We address this problem by estimating a semiparametric mixed-frequency model, that incorporate high frequency data either in the conditional mean or the conditional variance equation. The inclusion of high frequency data through non-parametric smoothing functions complements the low frequency data to capture possible non-linear relationships triggered by the structural change. Simulation studies indicate that in the presence of structural change, the varying frequency in the mean model provides improved in-sample fit and superior out-of-sample predictive ability relative to low frequency time series models. These hold across a broad range of simulation settings, such as varying time series lengths, nature of structural break points, and temporal dependencies. We illustrate the relative advantage of the method in predicting stock returns and foreign exchange rates in the case of the Philippines.

Delayed treatment in breast cancer patients during the COVID-19 pandemic: a population health information research infrastructure (PHIRI) case study.

The indirect impact of the coronavirus disease 2019 pandemic on healthcare services was studied by assessing changes in the trend of the time to first treatment for women 18 or older who were diagnosed and treated for breast cancer between 2017 and 2021. An observational retrospective longitudinal study based on aggregated data from four European Union (EU) countries/regions investigating the time it took to receive breast cancer treatment. We compiled outputs from a federated analysis to detect structural breakpoints, confirming the empirical breakpoints by differences between the trends observed and forecasted after March 2020. Finally, we built several segmented regressions to explore the association of contextual factors with the observed changes in treatment delays. We observed empirical structural breakpoints on the monthly median time to surgery trend in Aragon (ranging from 9.20 to 17.38 days), Marche (from 37.17 to 42.04 days) and Wales (from 28.67 to 35.08 days). On the contrary, no empirical structural breakpoints were observed in Belgium (ranging from 21.25 to 23.95 days) after the pandemic's beginning. Furthermore, we confirmed statistically significant differences between the observed trend and the forecasts for Aragon and Wales. Finally, we found the interaction between the region and the pandemic's start (before/after March 2020) significantly associated with the trend of delayed breast cancer treatment at the population level. Although they were not clinically relevant, only Aragon and Wales showed significant differences with expected delays after March 2020. However, experiences differed between countries/regions, pointing to structural factors other than the pandemic.

Topological Approach for Detection of Structural Breakpoints in Philippine Stock Price Data Surrounding the COVID-19 Pandemic

Topological data analysis (TDA) is a novel but rigorous framework used to identify the underlying shape and structure of a huge data set. In this study, we demonstrated how TDA can be useful in analyzing and predicting stock market movement. The study collected time-series data from the Philippine Stock Exchange of companies that were heavily affected by lockdown measures implemented during the COVID-19 pandemic and those that thrived. The study found that during market crashes, persistence diagrams revealed a presence of persistence weakening phenomenon. Moreover, by analyzing the stock price movement given an interval of time, the persistence landscape graphs produced high points or bumps during a market drop, which corresponds to structural breakpoints in the time series. Indeed, persistent homology can be used to predict early warning signs of probable market crashes.

Exploring the influencing factors of environmental deterioration: evidence from China employing ARDL–VECM method with structural breaks

PurposeThe environmental deterioration has become one of the most economically consequential and charged topics. Numerous scholars have examined the driving factors failing to consider the structural breaks. This study aims to explore sustainability using the per capita ecological footprints (EF) as an indicator of environmental adversities and controlling the resources rent [(natural resources (NR)], labor capital (LC), urbanization (UR) and per capita economic growth [gross domestic product (GDP)] of China.Design/methodology/approachThrough the analysis of the long- and short-run effects with an autoregressive distributed lag model (ARDL), structural break based on BP test and Granger causality test based on vector error correction model (VECM), empirical evidence is provided for the policies formulation of sustainable development.FindingsThe long-run equilibrium between the EF and GDP, NR, UR and LC is proved. In the long run, an environmental Kuznets curve (EKC) relationship existed, but China is still in the rising stage of the curve; there is a positive relationship between the EF and NR, indicating a resource curse; the UR is also unsustainable. The LC is the most favorable factor for sustainable development. In the short term, only the lagged GDP has an inhibitory effect on the EF. Besides, all explanatory variables are Granger causes of the EF.Originality/valueA novel attempt is made to examine the long-term equilibrium and short-term dynamics under the prerequisites that the structural break points with its time and frequencies were examined by BP test and ARDL and VECM framework and the validity of the EKC hypothesis is tested.

Potential Asymmetries in the International trade and Economic Growth in China: The Role of Emission Intensity

&lt;p class="MsoNormal" style="margin-top: 10pt; line-height: 12pt; text-align: justify;"&gt;&lt;span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;"&gt;This study delves into the complex relationship between China's international trade and economic growth, specifically focusing on emission intensity and its environmental ramifications within the context of energy efficiency. The primary objective is to provide a comprehensive understanding of how China's international trade dynamics influence economic growth, emphasizing emission intensity and employing a model that considers the nonlinear impact of trade on output. Through this model, structural breakpoints are identified, revealing distinct outcomes for China amidst positive and negative shocks from 1972 to 2021. The study uncovers the dual nature of the energy sector, acting both as a source of environmental degradation and a potential avenue for positive change through increased efficiency. Complex interplays among economic activities, inflation, and investment on carbon intensity are revealed. Additionally, the varying effects of export volume on carbon emissions intensity across lag thresholds underscore the significance of global trade dynamics for environmental sustainability. The study concludes by emphasizing the importance of balancing economic prosperity with environmental responsibility, advocating for carefully balanced policies that foster sustainable growth. Recommendations include considering the dual nature of the energy sector and implementing policies promoting increased efficiency. Policymakers are urged to adopt growth models prioritizing environmental responsibility, balancing economic activities, inflation, and investment to mitigate carbon intensity while acknowledging the diverse effects of global trade dynamics on environmental sustainability.&lt;/span&gt;&lt;/p&gt;

THE IMPACT OF RENEWABLE AND NON-RENEWABLE ENERGY CONSUMPTION ON ECONOMIC GROWTH IN AZERBAIJAN: FB-ARDL AND A-ARDL COINTEGRATION APPROACHES

The objective of this study is to examine the impact of renewable and non-renewable energy consumption on economic growth in Azerbaijan. It is believed that this study will contribute to the literature by using Fourier ADF, Fourier Bootstrap ARDL and Augmented ARDL methods for the first time in this context. The stationarity of the variables was first tested using ADF, Fractional Fourier ADF, Flexible Fractional Fourier ADF and single structural breakpoint ZA stationarity tests, which revealed different degrees of stationarity of the variables. The cointegration relationship between the variables was first examined using the Fourier Bootstrap ARDL bound test, which found no evidence of cointegration. Subsequently, the Augmented ARDL bounds test was used to examine the cointegration relationship between the variables, which revealed a long-run equilibrium relationship. FMOLS, DOLS and CCR long-run estimation methods were used to examine the impact of renewable and non-renewable energy consumption on economic growth and according to the results of all three methods, both renewable and non-renewable energy consumption contribute to the growth of the Azerbaijani economy. Holding other variables constant, a 1% increase in non-renewable energy consumption leads to a 3.28% increase in economic growth. Holding other variables constant, a 1% increase in renewable energy consumption leads to a 1.29% increase in economic growth. The study concludes with policy recommendations for the Azerbaijani economy based on the econometric results.

Evaluation of Laparoscopic Colorectal Resection Among Elderly Individuals With Colorectal Malignancy: A Single-center Retrospective Analysis.

The purpose of the current study was to statistically clarify the precise risk age in elderly patients undergoing colorectal surgery and to evaluate the safety and efficacy of laparoscopic colorectal resection in these patients. Patients' clinical variables were extracted from the database of the Gastrointestinal Surgery Centre, Third Affiliated Hospital of Sun Yat-sen University, from 2015 to 2019. Logistic regression was conducted to identify independent risk factors of postoperative complications and ORs for each age. Curves of odds ratios (ORs) and CIs for each age were fitted by using a locally weighted scatterplot smoother, and a structural breakpoint was determined by the Chow test to identify a precise cutoff risk age for elderly patients. Comparison and subgroup analysis were conducted between surgical approach groups using the Student t test and χ 2 analysis. Locally weighted scatterplot smoother OR analysis manifested that patients aged 69 years old or older suffered a higher possibility of postoperative complications and should be defined as high-risk age. Comparison according to the high-risk age revealed laparoscopic colorectal surgery is better than laparotomic surgery for elderly individuals in terms of hospital stay (9.46 ± 5.96 vs 15.01 ± 6.34, P < 0.05), the incidence of intensive care unit transfer (4 vs 20, P < 0.05), and incidence of surgical site infection (15 vs 20, P < 0.05). Patients who underwent laparotomic surgery had a greater prevalence of Clavien-Dindo II/III complications ( P < 0.05). These findings remained stable even after propensity matching. Furthermore, such superiority was proved especially significant for patients who underwent left-side colorectal resection. In addition, overall survival was improved in the laparoscopic surgery group, whereas no differences were observed in disease-free survival. In our study population, age 69 or older was a cutoff point age suggests a higher possibility of postoperative morbidity after colorectal surgery. Laparoscopic colorectal resection should be regarded as a superior therapeutic choice for these elderly individuals, especially for left-side colorectal surgeries.

Impacts of regional socioeconomic statuses and global events on solid waste research reflected in six waste-focused journals

The research pertaining to solid waste is undergoing extensive advancement, thereby necessitating a consolidation and analysis of its research trajectories. The existing biblio-studies on solid waste research (SWR) lack thorough analyses of the factors influencing its trends. This article presents an innovative categorization framework that categorizes publications from six SWR journals utilizing Source Latent Dirichlet Allocation. First analyse changes in publication numbers across main categories, subcategories, journals, and regions, providing a macro-level study of SWR. Temporal analysis of keywords supplements a micro-level study of SWR, which highlights that emerging technologies with low Technology Readiness Level receive significant attention, while studies on widespread technologies are diminishing. Additionally, this study demonstrates the substantial influence of socioeconomic factors and previous SWR publications on current and future SWR trends. Finally, the article confirms the impact of global events on SWR trends by examining the structural breakpoints of SWR and their correlation with global events.

Herding behaviour in the Indian stock market: An empirical study

The rapid growth of the Indian stock market in recent decades has prompted much study on investing, focusing on understanding and analysing investors' tactics for investment and trading. Over the years, researchers have cited and conducted in-depth investigations into various psychological biases. Herding bias is extensively studied to understand how investors participate in investment and trading in the Indian stock market. The current study on herding provides an investigation into the existence of herd activity in the share market of India. The study attempts to identify herd activity during positive, negative, extreme positive, and extreme negative market conditions, as well as during structural breakpoints and trade volumes that are low as well as high. The researchers have used the modified CSAD (Cross Sectional Absolute Deviation) model to enhance its validity. The findings reveal the rationality of investors during the different market states. However, herding behaviour was evidenced during certain periods at the structural breakpoint. This implies that during positive as well as negative market conditions, herding behaviour is absent, except during certain structural breakpoints and high trading volumes.

Price discovery and volatility spillovers in the interest rate derivatives market

The interest rate derivatives market is an important force in promoting the development of the bond market and is an effective tool to manage interest rate risk. The research on price discovery and volatility spillover of the market can help provide valuable reference information for investors. Based on treasury bond futures and interest rate swaps, the paper aims to discuss the price discovery function and spillover structure of the interest rate derivatives market. The paper establishes the information share model and spillover index model for empirical analysis. The results show that: First, the calculation results of the information share model show that the price discovery of treasury bond futures and interest rate swap markets is stronger than that of the spot market. Second, based on structural break analysis, treasury bond futures and interest rate swaps do not have breakpoints, while the treasury bond spot has three breakpoints. The paper divides the entire sample into four stages based on structural breakpoints and finds that the price discovery ability of the interest rate derivative market dynamically changed. Third, as a net spillover in the market, treasury bond futures have developed relatively stable. Both treasury bond futures and interest rate swaps have spillover effects on the spot market, indicating that China’s interest rate derivatives market can impact the treasury bond spot market.

A study on time-varying dependence between energy markets and linked assets based on the Russia-Ukraine conflict

The energy industry, acutely sensitive to geopolitical shifts due to the Russia-Ukraine conflict, experiences sustained disturbances in global energy markets, reshaping global energy supply dynamics and significantly influencing global trade patterns. Utilizing static and dynamic GARCH-Copula models, this study elucidates the dependency between energy markets and related assets. The Copula function, when compared with the multivariate GARCH model, demonstrates distinct advantages, notably in delineating joint asset distributions, capturing market dependence's nonlinear traits, and highlighting robust tail correlation structures. Beyond the average inter-market dependence, its tail correlation offers a vital perspective on market risk. This research delves into the temporal and structural variations in interdependence between energy markets and related assets. It probes potential structural breakpoints in dynamic interdependence and pinpoints their occurrences. By focusing on the Russia-Ukraine conflict, this study offers a holistic view of the changing interplay between the energy market and other asset categories, providing pivotal insights for investor portfolio optimization, regulatory oversight, and risk mitigation. Moreover, employing wavelet analysis, this study examines the frequency domain traits of the interdependency between energy markets and associated assets. As frequency wanes, market price fluctuations become less pronounced. The continuous wavelet power spectrum indicates that price variations are predominantly mid to high frequency. Cross-wavelet transform results suggest that correlations between energy markets and related assets are more influenced by short-term perturbations than enduring shifts.

Asymmetric and Nonlinear Foreign Debt–Inflation Nexus in Brazil: Evidence from NARDL and Markov Regime Switching Approaches

This paper augments the sparse literature on the inflationary impact of foreign debt in Brazil while addressing methodological caveats in previous studies. We depart from the linearity assumption and employ two nonlinear techniques: the nonlinear autoregressive distributed lag (NARDL) model and a Markov Switching Regression (MSR) to investigate the connection between foreign debt and inflation within a multivariate framework. The analyses consider the presence of structural breaks via assessing variable stationarity using the Zivot and Andrew unit root test and incorporating a residual-based cointegration test proposed by Gregory and Hansen. Additionally, we apply a multiple structural breakpoints test by Bai and Perron to determine the presence of structural breaks in the impact of foreign debt on inflation. Our findings robustly indicate that the domestic money supply has a statistically significant positive effect, while the nominal effective exchange rate has a negative effect on inflation in both the short and long run. The NARDL model reveals that only positive changes in foreign debt have a statistically significant negative effect on inflation in the short run, whereas both positive and negative foreign debt changes significantly affect inflation in the long run. The results from the MSR model are generally consistent with those of the NARDL model.

Abstract B092: Intrinsic genomic plasticity of extrachromosomal DNA (ecDNA) enables oncogene amplified tumor cells to develop rapid acquired resistance to targeted therapy

Abstract Background: Focal high copy number oncogene amplification frequently occurs on extrachromosomal DNA (ecDNA), acentric circular DNA derived from chromosomes during neoplastic transformation and tumor progression. The unique properties of ecDNA facilitate a high degree of genomic plasticity and tumor heterogeneity, which enables rapid acquired resistance to targeted therapies and results in poor patient outcomes. Reintegrated ecDNA, also referred to as homogeneously staining regions (HSR), can exist in dynamic equilibrium with their circular form, although their potential adaptive and resistance properties have not been well characterized. Elucidating the genomic plasticity and mechanisms afforded to cancer cells harboring HSR may provide insights into new therapeutic approaches necessary to address this ecDNA-derived focal amplification. Methods: A novel model was developed that recapitulates the genomic plasticity of focal oncogene amplifications involving FGFR2 and EGFR in response to the FGFR inhibitor, infigratinib. Results: KATOIII gastric cancer cells harbor focal amplification of the FGFR2 oncogene (absolute copy number (CN) = 136 based on next-generation sequencing (NGS) that resides on an HSR as determined by FISH. Short- and long-read NGS revealed prior DNA breakpoints consistent with circular structures containing FGFR2, suggestive of reintegrated ecDNA as HSR. Despite initial sensitivity to FGFR2 inhibition (EC50 = 20 nM), cells acquired resistance following infigratinib dose escalation within 12 weeks. Infigratinib-resistant KATOIII cells retained the FGFR2 HSR while further amplifying FGFR2 on ecDNA, which correlated with increased FGFR2 CN, mRNA, and protein expression. Structural breakpoint analysis of the FGFR2 amplicon revealed conservation between baseline and ecDNA-amplified infigratinib resistant cells, suggesting that the HSR gave rise to ecDNA. Removal of infigratinib resulted in near-complete disappearance of FGFR2 ecDNA and return to near-baseline FGFR2 CN, protein expression, and signaling within 3 weeks. Strikingly, at high infigratinib concentrations that effectively inhibit FGFR2 signaling, cells rapidly switched to EGFR amplified on ecDNA, with concomitant reduction of FGFR2 ecDNA (CN = 553 to 238) and signaling and increased EGFR (CN = 3 to 30), mRNA, and EGFR signaling. Following ecDNA switching from FGFR2 to EGFR, KATOIII cells remained insensitive to infigratinib while acquiring sensitivity to the EGFR inhibitor erlotinib. Mutational analysis revealed no new oncogenic mutations including FGFR2 and EGFR, indicating the EGFR overexpression afforded by ecDNA was sufficient to drive acquired resistance to high infigratinib concentrations. Conclusions: These findings indicate that tumor cells with ecDNA-derived chromosomally reintegrated HSR retain the capacity to amplify ecDNA, thus enabling rapid adaptation to therapeutic pressure. Consequently, new treatment strategies that disable adaptive genomic plasticity are needed to address oncogene amplified cancers that leverage ecDNA biology. Citation Format: Kristen Turner, Homa Hemmati, Cory DuPai, John Bibay, Claire Klebs, Joan Chen, Debbie Liao, Christian Hassig, Shailaja Kasibhatla. Intrinsic genomic plasticity of extrachromosomal DNA (ecDNA) enables oncogene amplified tumor cells to develop rapid acquired resistance to targeted therapy [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B092.

How does the digital transformation of corporates affect green technology innovation? An empirical study from the perspective of asymmetric effects and structural breakpoints

With the continuous advancement of the digital economy and green development strategies, exploring the impact of digital transformation on corporate green technology innovation becomes an importantly urgent issue. Based on the perspectives of asymmetric effects and structural breakpoints, this paper empirically analyses the effect of digital transformation on green technology innovation, as well as its heterogeneity and non-linear characteristics, using fixed effect models and panel threshold model with a sample of Chinese A-share-listed corporates from 2011 to 2021. The findings suggest that digital transformation of corporates can significantly promotes green technology innovation, which remains valid after a series of endogenous and robustness tests. Heterogeneous regression results shows that digital transformation exhibits an asymmetric impact on green technology innovation, with greater incentive effect for state-owned corporates, manufacturing corporates, and corporates located in areas with stricter environmental regulations. Additionally, threshold effect analyses prove that a positive non-linear relationship between digital transformation and green technology innovation, when corporate human capital, industry competition and regional institutional quality exceeded their respective structural breakpoints, the marginal contribution of digital transformation is further strengthened. This research provides clearer policy insights for corporates to promote cleaner production and low carbon development through digitization.

Government land control approach, industrial agglomeration, and urban air quality: Analysis based on spatial spillovers and threshold effects

Local government intervention in industrial land concessions has contributed significantly to the country's miraculous growth during the past four decades, but there are concerns about its environmental impact. Assessing the model's impact on the Air Quality Index (AQI) is critical for China as it focuses, now, on sustainable development. We examine the spatiotemporal effects and pathways of land use regulation by local governments in China on air quality index. The data used are real-time air quality data and land transaction micro data of 282 cities from 2015 to 2020. The results demonstrate that local government use controls have a favorable impact on the AQI in the city and its surrounding areas. Land intervention attracted capital from low-tech and high-polluting industries to their more aggressive locations, triggering “bottom-up competition” in the industrial structure through the industrial aggregation effect. The study also found structural breakpoints in the model's industrial agglomeration effect. There are two structural breakpoints in the national region; the model's impact on AQI increases significantly after exceeding each structural breakpoint. At the sub-regional level, there are two structural breakpoints in the industrial agglomeration effect of the eastern and western cities; both weaken after exceeding the second structural breakpoint. There is one structural breakpoint in the central cities; after exceeding it, the model's impact on AQI increases.

The nexus between COVID-19 and finfish export from Sri Lanka to its main trading partners; the United States and the European Union

The Sri Lankan seafood sector has been seriously disturbed during the Covid 19 pandemic due to a bleak demand outlook as well as an array of supply challenges. Identification of the effects of this new challenge to the global fish trade is critical to take actions to sustain the market and manage resilience in such uncertain periods. Study aims to assess the impact of the Covid-19 outbreak on Sri Lanka's finfish exports to its key trading partners, the United States of America (USA) and the European Union (EU). The time series data were gathered from the United Nations commodity trade database between January 2017 and December 2022. Study used structural break point analysis of time series data, followed by time series regression with the Covid-19 impact as a dummy variable using EVIEWS statistical software. Compared to 2019, total finfish exports to the United States dropped by 56.5% in 2020 and 72.5% in 2021 while finfish exports to European market increased by 7% and 28% in 2020 and 2021, respectively. The Covid-19 dummy was statistically significant in the time series regression, with a coefficient value of -71.93 in USA trade flow which implies that Covid-19 has a negative impact on finfish trade to the USA. In contrast, statistically significant impact was not found in finfish trade to the EU during the pandemic and this mainly because European seafood market impacted due to Covid-19 yet imports from developing countries fell slightly and overall demand for seafood has remained largely stable throughout 2020. However, study suggested that future research should focus on the country-specific product sensitivity at the individual product level.

Regime-Specific Quant Generative Adversarial Network: A Conditional Generative Adversarial Network for Regime-Specific Deepfakes of Financial Time Series

Simulating financial time series (FTS) data consistent with non-stationary, empirical market behaviour is difficult, but it has valuable applications for financial risk management. A better risk estimation can improve returns on capital and capital efficiency in investment decision making. Challenges to modelling financial risk in market crisis environments are anomalous asset price behaviour and a lack of historical data to learn from. This paper proposes a novel semi-supervised approach for generating regime-specific ‘deep fakes’ of FTS data using generative adversarial networks (GANs). The proposed architecture, a regime-specific Quant GAN (RSQGAN), is a conditional GAN (cGAN) that generates class-conditional synthetic asset return data. Conditional class labels correspond to distinct market regimes that have been detected using a structural breakpoint algorithm to segment FTS into regime classes for simulation. Our RSQGAN approach accurately simulated univariate time series behaviour consistent with specific empirical regimes, outperforming equivalently configured unconditional GANs trained only on crisis regime data. To evaluate the RSQGAN performance for simulating asset return behaviour during crisis environments, we also propose four test metrics that are sensitive to path-dependent behaviour and are also actionable during a crisis environment. Our RSQGAN model design borrows from innovation in the image GAN domain by enabling a user-controlled hyperparameter for adjusting the fit of synthetic data fidelity to real-world data; however, this is at the cost of synthetic data variety. These model features suggest that RSQGAN could be a useful new tool for understanding risk and making investment decisions during a time of market crisis.

Speculation on optimal numbers of examined lymph node for early-stage epithelial ovarian cancer from the perspective of stage migration.

In early-stage epithelial ovarian cancer (EOC), how to perform lymphadenectomy to avoid stage migration and achieve reliable targeted excision has not been explored in depth. This study comprehensively considered the stage migration and survival to determine appropriate numbers of examined lymph node (ELN) for early-stage EOC and high-grade serous ovarian cancer (HGSOC). From the Surveillance, Epidemiology, and End Results database, we obtained 10372 EOC cases with stage T1M0 and ELN ≥ 2, including 2849 HGSOC cases. Generalized linear models with multivariable adjustment were used to analyze associations between ELN numbers and lymph node stage migration, survival and positive lymph node (PLN). LOESS regression characterized dynamic trends of above associations followed by Chow test to determine structural breakpoints of ELN numbers. Survival curves were plotted using Kaplan-Meier method. More ELNs were associated with more node-positive diseases, more PLNs and better prognosis. ELN structural breakpoints were different in subgroups of early-stage EOC, which for node stage migration or PLN were more than those for improving outcomes. The meaning of ELN structural breakpoint varied with its location and the morphology of LOESS curve. To avoid stage migration, the optimal ELN for early-stage EOC was 29 and the minimal ELN for HGSOC was 24. For better survival, appropriate ELN number were 13 and 8 respectively. More ELNs explained better prognosis only at a certain range. Neither too many nor too few numbers of ELN were ideal for early-stage EOC and HGSOC. Excision with appropriate numbers of lymph node draining the affected ovary may be more reasonable than traditional sentinel lymph node resection and systematic lymphadenectomy.

Are climate risks helpful for understanding inflation in BRICS countries?

Scholars have studied the effect of climate risks on economies and finances, but they have yet to agree. We take the BRICS countries as an example to join this discussion and adopt a series of tests to detect the existence of this effect. We find that, on average, climate risk has an impact on inflation in China, India and Russia. The impact of climate risk on Russia has more structural breakpoints than that of other countries. Furthermore, climate risk appears to have a short-term impact on inflation in India and South Africa, implying that these two countries are more sensitive to climate change.