Large Portfolio Research Articles

Distinct Short-Term Response of Intracellular Amino Acids in Saccharomyces cerevisiae and Pichia pastoris to Oxidative and Reductive Stress

Despite being frequently encountered, the effect of oxidative or reductive stress on the intracellular metabolism and the response of the intracellular metabolome of yeasts is severely understudied. Non-conventional yeasts are attracting increasing attention due to their large substrate portfolio of non-canonical pathways as well as their production and secretion of proteins. To understand the effects of both stresses on yeast, the conventional model yeast S. cerevisiae and the non-conventional model yeast P. pastoris were perturbed with 5 mM of hydrogen peroxide for oxidative stress and 20 mM of dithiothreitol for reductive stress in well-defined chemostat cultures at a steady state, and fermentation profiles, intracellular amino acid levels, and intracellular glutathione levels were measured. Although stable profiles of extracellular metabolites were observed, significant changes were measured in intracellular amino acid levels within the first five minutes. Collectively, the amino acids ranged from 0.5 to 400 µmol/gDW, with the most significant increase upon the induction of oxidative stress being seen in cysteine (up to 90%) for S. cerevisiae and in aspartate (up to 80%) for P. pastoris. Upon the induction of reductive stress, asparagine nearly halves in S. cerevisiae, while tryptophan decreases by 60% in P. pastoris. By inspecting the time traces of each amino acid, possible mechanisms of pathway kinetics are speculated. This work furthers our understanding of the response of metabolism to oxidative stress in two model yeasts.

Robust Prediction Intervals for Valuation of Large Portfolios of Variable Annuities: A Comparative Study of Five Models

Robust Prediction Intervals for Valuation of Large Portfolios of Variable Annuities: A Comparative Study of Five Models

Review of Asset Management Practice in Indonesian State-Owned Enterprise

This study examines Indonesian State-Owned Enterprises (SOEs) asset management methods, problems, and financial and operational performance effects. The report synthesizes case studies and academic research on how large organizations manage their huge and diverse asset portfolios and how governance, regulatory frameworks, and human resource practices affect their effectiveness. The research begins with case studies of talent management innovations from big Indonesian SOEs. These cases show how proactive human resource approaches can boost company commitment and reduce turnover, improving asset management efficiency. According to the research, governance and legal frameworks influence asset management techniques. Studies show that corporate governance quality affects SOE operational performance. The paper explores how reforms and legislation affect state asset management, highlighting the major changes in SOE governance and legal frameworks, particularly after economic and political reforms. Asset management difficulties for Indonesian SOEs include managing large and diverse asset portfolios, integrating modern management frameworks, and optimizing state asset revenue. According to the study, comprehensive asset management systems, governance transparency, and professional management can address these difficulties. The research examines how asset management strategies affect Indonesian SOE profitability, corporate governance, and performance measures. Strategic asset management boosts financial performance, especially profitability. SOE profitability is greatly affected by current asset and liability management. The study offers advice to Indonesian SOEs and policymakers. Enhancing financial and operational performance requires comprehensive asset management, governance changes, and strategic innovation. These efforts boost Indonesia's economy, demonstrating the importance of asset management in SOE performance.

What explains foreign portfolio investment inflows to BRICS countries?

Large and volatile foreign portfolio investment (FPI) inflows raise financial instability concerns and create several macroeconomic challenges for developing countries. Identifying the factors driving FPI inflows is therefore essential for formulating appropriate policies to manage these inflows efficiently. This study uses a GM-FMOLS panel cointegration approach to examine and identify such factors for BRICS countries, the leading recipients of FPI inflows among all developing countries. The study finds that the 10-year US Treasury bond yield exerts a significant negative influence on FPI inflows. These bonds provide risk-free long-term returns to portfolio investors; hence, a rise in the yield on such bonds prompts them to readjust their portfolios away from assets in developing countries to US Treasuries, leading to a decline in FPI inflows. The study further finds that a thriving domestic stock market, a higher interest rate differential, an appreciating domestic currency, an improvement in the recipient country’s creditworthiness, and a greater degree of trade openness are conducive to FPI inflows. However, a rising consumer price index, higher foreign output growth, and greater global financial volatility discourage these inflows.

АНАЛИЗ РОССИЙСКОГО РЫНКА ЛЕКАРСТВЕННЫХ ПРЕПАРАТОВ, ПРИМЕНЯЕМЫХ ДЛЯ ЛЕЧЕНИЯ ГАСТРОДУОДЕНИТОВ У ПОДРОСТКОВ

The Russian pharmaceutical market (RPhM) has a large assortment portfolio of medicines (MP) used to treat various diseases of the digestive system. But not all of these drugs are approved for use in children and adolescents. Due to their insufficient safety for this age group, it is necessary to be more careful in the selection of suitable medicines that will not be inferior in effectiveness to other drugs used in adults. So, in particular, some medicines used for the treatment of digestive organs are included in the List of vital and essential medicines, according to the decree of the Government of the Russian Federation dated 12.10.2019 No. 2406-r "On approval of the List of VED for 2023, as well as lists of medicines for medical use and the minimum range of medicines necessary for the provision of medical care." The purpose of this study was to analyze the range of drugs on the Russian pharmaceutical market used for the treatment of gastroduodenitis in adolescents. In the course of the study, various official information sources about medicines registered in the territory of the Russian Federation were reviewed and studied: the Register of Medicines of Russia "RADAR" (2023), Internet sources, collections and forms containing statistical data. Research methods: content analysis, ranking, graphic, structural, segmentation, comparative. A marketing study of the product portfolio was conducted, its structure was formed, and a macro-contour of RPhM drugs used for the treatment of gastroduodenitis in adolescents was developed. Research methods: content analysis, ranking, graphic, structural, segmentation, comparative. A marketing study of the product portfolio was conducted, its structure was formed, and a macro-contour of RPhM drugs used for the treatment of gastroduodenitis in adolescents was developed.

Immunocapture LC-MS methods for pharmacokinetics of large molecule drugs.

Implementation of immunocapture LC-MS methods to characterize the pharmacokinetic profile of large molecule drugs has become a widely used technique over the past decade. As the pharmaceutical industry strives for speediness into clinical development without jeopardizing quality, robust assays with generic application across the pipeline are becoming instrumental in bioanalysis, especially in early-stage development. This review highlights the capabilities and challenges involved in hybrid immunocapture LC-MS techniques and its continued applications in nonclinical and clinical pharmacokinetic assay design. This includes a comparison of LC-MS-based approaches to conventional ligand-binding assays and the driving demands in large molecule drug portfolios including growing sensitivity requirements and the unique challenges of new modalities requiring innovation in the bioanalytical laboratory.

Viscosity solution for optimal liquidation problems with randomly-terminated horizon

In this paper, we study an optimal liquidation problem of a stressed asset, for which its value is modeled by a geometric Brownian motion. The default time of the stressed asset, therefore, becomes stochastic and predictable. Hence we deal with the optimal liquidation problem in a randomly-terminated horizon. We consider the liquidation of a large single-asset portfolio with the aim of minimizing a combination of volatility risk and transaction costs arising from permanent and temporary market impacts. We prove that the value function is the unique viscosity solution to the Hamilton–Jacobi–Bellman (HJB) equation of the considered optimal liquidation problem.

Genome-scale metabolic model of the versatile bacterium Paracoccus denitrificans Pd1222.

A genome scale metabolic model of the bacterium Paracoccus denitrificans has been constructed. The model containing 972 metabolic genes, 1,371 reactions, and 1,388 unique metabolites has been reconstructed. The model was used to carry out quantitative predictions of biomass yields on 10 different carbon sources under aerobic conditions. Yields on C1 compounds suggest that formate is oxidized by a formate dehydrogenase O, which uses ubiquinone as redox co-factor. The model also predicted the threshold methanol/mannitol uptake ratio, above which ribulose biphosphate carboxylase has to be expressed in order to optimize biomass yields. Biomass yields on acetate, formate, and succinate, when NO3- is used as electron acceptor, were also predicted correctly. The model reconstruction revealed the capability of P. denitrificans to grow on several non-conventional substrates such as adipic acid, 1,4-butanediol, 1,3-butanediol, and ethylene glycol. The capacity to grow on these substrates was tested experimentally, and the experimental biomass yields on these substrates were accurately predicted by the model.IMPORTANCEParacoccus denitrificans has been broadly used as a model denitrifying organism. It grows on a large portfolio of carbon sources, under aerobic and anoxic conditions. These characteristics, together with its amenability to genetic manipulations, make P. denitrificans a promising cell factory for industrial biotechnology. This paper presents and validates the first functional genome-scale metabolic model for P. denitrificans, which is a key tool to enable P. denitrificans as a platform for metabolic engineering and industrial biotechnology. Optimization of the biomass yield led to accurate predictions in a broad scope of substrates.

The Deep Promotion Time Cure Model.

We propose a novel method for predicting time-to-event data in the presence of cure fractions based on flexible survival models integrated into a deep neural network (DNN) framework. Our approach allows for nonlinear relationships and high-dimensional interactions between covariates and survival and is suitable for large-scale applications. To ensure the identifiability of the overall predictor formed of an additive decomposition of interpretable linear and nonlinear effects and potential higher-dimensional interactions captured through a DNN, we employ an orthogonalization layer. We demonstrate the usefulness and computational efficiency of our method via simulations and apply it to a large portfolio of U.S. mortgage loans. Here, we find not only a better predictive performance of our framework but also a more realistic picture of covariate effects.

Stochastic PDEs for large portfolios with general mean-reverting volatility processes

Stochastic PDEs for large portfolios with general mean-reverting volatility processes

Green finance: Evidence from large portfolios and networks during financial crises and recessions

AbstractIn this article, we study the relevance of green finance from a portfolio and a network perspective. The estimates are derived from a regularized graphical model, which allows us to deal with two important issues. First, we refer to the curse of dimensionality, as we focus on a relatively large set of companies. Second, we explicitly take into account the heavy‐tailed distributions of financial time series, which reflect the impact of crises and recessions. Focusing on a time interval spanning across well‐known tail events, from the US subprime crisis to the recent outbreak of the COVID‐19 pandemic, we show that the selected green stocks offer a relevant contribution to the minimization of the overall portfolio risk. Moreover, they outperform the gray assets in terms of risk, profitability, and risk‐adjusted return in a statistically significant way. These findings are consistent with the estimates obtained from the network analysis. Indeed, the gray stocks exhibit a greater connection within the dynamic networks and, then, are more exposed to the risk of a greater propagation of negative spillover effects during stressed periods. Interestingly, the relevance of the green stocks increases when moving from the standard Gaussian to the leptokurtic setting. The policy implications suggested by these results induce policymakers to undertake synergistic interventions with private finance aimed at supporting a green economy and environmentally responsible companies.

Capturing Data on Antimicrobial Resistance Patterns and Trends in Use in Regions of Asia (CAPTURA).

In 2015, the UK government established the Fleming Fund with the aim to address critical gaps in surveillance of antimicrobial resistance (AMR) in low- and middle-income countries in Asia and Africa. Among a large portfolio of grants, the Capturing Data on Antimicrobial Resistance Patterns and Trends in Use in Regions of Asia (CAPTURA) project was awarded with the specific objective of expanding the volume of historical data on AMR, consumption (AMC), and use (AMU) in the human healthcare sector across 12 countries in South and Southeast Asia. Starting in early 2019, the CAPTURA consortium began working with local governments and >100 relevant data-holding facilities across the region to identify, assess for quality, prioritize, and subsequently retrieve data on AMR, AMC, and AMU. Relevant and shared data were collated and analyzed to provide local overviews for national stakeholders as well as regional context, wherever possible. From the vast information resource generated on current surveillance capacity and data availability, the project has highlighted gaps and areas for quality improvement and supported comprehensive capacity-building activities to optimize local data-collection and -management practices. The project has paved the way for expansion of surveillance networks to include both the academic and private sector in several countries and has actively engaged in discussions to promote data sharing at the local, national, and regional levels. This paper describes the overarching approach to, and emerging lessons from, the CAPTURA project, and how it contributes to other ongoing efforts to strengthen national AMR surveillance in the region and globally.

Are Manufacturing Patents to Blame for Biosimilar Market Launch Delays?

ObjectivesBiosimilar market launch delays are likely costing healthcare systems billions of dollars and preventing patients accessing affordable biologic therapies sooner. Many claim these delays are mostly caused by originator biologics’ large patent portfolios asserted during litigation against biosimilar developers, particularly that the manufacturing patents filed after the originator is approved is an important driver of these delays. Our objective was to investigate the accuracy of these claims. MethodsWe reviewed US Court document submissions for litigation data, including the details of patents asserted against biosimilar owners, and collated biosimilar market launch dates from publicly available databases. ResultsWe find that, although approximately half of all patents asserted in litigation were manufacturing patents, a greater proportion of composition, active pharmaceutical ingredient, and treatment patents are associated with longer market launch delays, whereas a greater proportion of manufacturing patents are associated with shorter market launch delays. ConclusionsOur results suggest that manufacturing patents were having less of an impact on market launch delays than other types of patents. Our findings have implications for both biosimilar and originator developers, as well as patent policy and its association with healthcare accessibility.

Configuring alliance portfolios for digital innovation

We examine how firms configure alliance portfolios—that is, networks of partnering firms—in order to exchange, share, or codevelop the capabilities they require to engage in digital innovation. We analyze data from 550 U.S. firms and the strategic alliances they formed within and across industrial sectors to study how the configuration of alliance portfolios in terms of size, degree of exploration, internationality, and competition affects the volume and quality of digital patents. We find that alliances appear to be an effective means, yet alliances for digital innovations require a different configuration when compared with alliances for non-digital innovations. Large and explorative alliance portfolios help with the creation of digital innovations while international alliances and alliances involving competitors do not. We discuss the implications of these findings for research on digital innovation and alliances. We also distill practical advice to executives charged with making strategic decisions about inter-firm partnerships.

Net-Zero Policy vs Energy Security: The Impact on GCC Countries

Gulf Cooperation Council countries have accumulated large oil portfolio revenues. However, the world economy is seeking to reduce carbon emissions, and in turn, its reliance on fossil fuel resources through investments in renewable energyresources. The aim of this research is to analyze oil portfolio risk from anexporters’perspective, highlighting how relevant determinants, such as the increasing penetration of renewables in the importer counterparties, and financial and policy uncertainty, increase the volatility of oil export portfolios.We construct oil portfolios for four Gulf Cooperation Council countries (Kuwait, Oman, Saudi Arabia, United Arab Emirates) from 2008 to 2018, and compute volatility spillovers à la Diebold and Yilmaz. Then, the effects of policy and economic variables on volatility spillover indices are estimated using different panel linear regression models. We find rising renewable market shares significantly affects oil export portfolio risks and reduces adverse impacts on importing countries of oil market fluctuations.

A Semi-Static Replication Method for Bermudan Swaptions under an Affine Multi-Factor Model

We present a semi-static replication algorithm for Bermudan swaptions under an affine, multi-factor term structure model. In contrast to dynamic replication, which needs to be continuously updated as the market moves, a semi-static replication needs to be rebalanced on just a finite number of instances. We show that the exotic derivative can be decomposed into a portfolio of vanilla discount bond options, which mirrors its value as the market moves and can be priced in closed form. This paves the way toward the efficient numerical simulation of xVA, market, and credit risk metrics for which forward valuation is the key ingredient. The static portfolio composition is obtained by regressing the target option’s value using an interpretable, artificial neural network. Leveraging the universal approximation power of neural networks, we prove that the replication error can be arbitrarily small for a sufficiently large portfolio. A direct, a lower bound, and an upper bound estimator for the Bermudan swaption price are inferred from the replication algorithm. Additionally, closed-form error margins to the price statistics are determined. We practically study the accuracy and convergence of the method through several numerical experiments. The results indicate that the semi-static replication approaches the LSM benchmark with basis point accuracy and provides tight, efficient error bounds. For in-model simulations, the semi-static replication outperforms a traditional dynamic hedge.

How Does Market Cap Play Its Role in Returns during COVID-19? The Case of Norway

This research investigates the role of the large, medium, and small market cap portfolios in returns during the COVID-19 pandemic, around the ‘lockdown’ policy in March 2020 based on the Norwegian market. The main results suggest that during the event window, the medium and small portfolios are impacted more negatively than the large. During the before-event days, the large portfolio is slightly negatively affected, but it tends to be better after the event. The medium and small portfolios are not adversely affected during before the event, but they are considerably negatively affected after the event. The small portfolio is affected more severely negatively than the medium. The small portfolio is the most volatile of the three during the event window. In addition, there are opportunities to earn extremely positive abnormal returns (from 2.5%) after the event by holding the small and medium portfolios, but not for the large. It seems that more opportunities to earn extremely positive abnormal returns for the small portfolio than the medium.

Two-phase selection of representative contracts for valuation of large variable annuity portfolios

A computationally appealing methodology for the valuation of large variable annuities portfolios is a metamodelling framework that evaluates a small set of representative contracts, fits a predictive model based on these computed values, and then extrapolates the model to estimate the values of the remaining contracts. This paper proposes a new two-phase procedure for selecting representative contracts. The representatives from the first phase are determined using contract attributes as in existing metamodelling approaches, but those in the second phase are chosen by utilizing the information contained in the values of the representatives from the first phase. Two numerical studies confirm that our two-phase selection procedure improves upon conventional approaches from the existing literature.

P09-05: Endocrine Disruption: Screening Assessment of Properties of a Large Portfolio of Petroleum-Related UVCB Substances

P09-05: Endocrine Disruption: Screening Assessment of Properties of a Large Portfolio of Petroleum-Related UVCB Substances

Sponsor Perspectives on the Impact of the COVID-19 Pandemic on Interventional Cancer Clinical Trial Protocols and Data Quality.

The onset of the COVID-19 pandemic created major disruptions in the conduct of cancer clinical trials. In response, regulators and sponsors allowed modifications to traditional trial processes to enable clinical research and care to continue. We systematically evaluated how these mitigation strategies affected data quality and overall trial conduct. This study used surveys and live interviews. Forty-one major industry and National Cancer Institute Network groups (sponsors) overseeing anticancer treatment trials open in the United States from January 2015 to May 2022 were invited to participate. Descriptive statistics were used for survey data summaries. Key themes from interviews were identified. Twenty sponsors (48.8%; 15 industry and five Network groups) completed the survey; 11/20 (55.0%) participated in interviews. Sponsors predominantly (n = 12; 60.0%) reported large (≥11 trials) portfolios of phase II and/or phase III trials. The proportion of sponsors reporting a moderate (9) or substantial (8) increase in protocol deviations in the initial pandemic wave versus the pre-pandemic period was 89.5% (17/19); the proportion reporting a substantial increased dropped from 42.1% (n = 8/19) in the initial wave to 15.8% (n = 3/19) thereafter. The most commonly adopted mitigation strategies were remote distribution of oral anticancer therapies (70.0%), remote adverse event monitoring (65.0%), and remote consenting (65.0%). Most respondents (15/18; 83.3%) reported that the pandemic had minimal (n = 14) or no impact (n = 1) on overall data integrity. Despite nearly all sponsors observing a temporary increase in protocol deviations, most reported the pandemic had minimal/no impact on overall data integrity. The COVID-19 pandemic accelerated an emerging trend toward greater flexibility in trial conduct, with potential benefits of reduced burden on trial participants and sites and improved patient access to research.