Time-varying Effects Research Articles

Determinants of South African Asset Market Co-Movement: Evidence from Investor Sentiment and Changing Market Conditions

The co-movement of multi-asset markets in emerging markets has become an important determinant for investors seeking diversified portfolios and enhanced portfolio returns. Despite this, studies have failed to examine the determinants of the co-movement of multi-asset markets such as investor sentiment and changing market conditions. Accordingly, this study investigates the effect of investor sentiment on the co-movement of South African multi-asset markets by introducing alternating market conditions. The Markov regime-switching autoregressive (MS-AR) model and Markov regime-switching vector autoregressive (MS-VAR) model impulse response function are used from 2007 March to January 2024. The findings indicate that investor sentiment has a time-varying and regime-specific effect on the co-movement of South African multi-asset markets. In a bull market condition, investor sentiment positively affects the equity–bond and equity–gold co-movement. In the bear market condition, investor sentiment has a negative and significant effect on the equity–bond, equity–property, bond–gold, and bond–property co-movement. Similarly, in a bull regime, the co-movement of South African multi-asset markets positively responds to sentiment shocks, although this is only observed in the short term. However, in the bear market regime, the co-movement of South African multi-asset markets responds positively and negatively to sentiment shocks, despite this being observed in the long run. These observations provide interesting insights to policymakers, investors, and fund managers for portfolio diversification and risk management strategies. That being, the current policies are not robust enough to reduce asset market integration and reduce sentiment-induced markets. Consequently, policymakers must re-examine and amend current policies according to the findings of the study. In addition, portfolio rebalancing in line with the findings of this study is essential for portfolio diversification.

Visualization of time-varying effects of strain stress field under eccentric charge structure based on digital image correlation technique

This study determines the influence law of the maximum eccentric uncoupling coefficient on the pressure field distribution at the hole wall through theoretical analysis to investigate the time-varying effect of the maximum eccentric uncoupling coefficient on the stress field of an eccentric charge structure. The findings from the theoretical analysis indicate that when the maximum eccentric uncoupling coefficient (K) is 1, the pressure at the ring hole wall exhibits a circular distribution. As K increases, the pressure distribution at the ring hole wall becomes elliptical, with the elliptical eccentricity increasing progressively. This research employs an ultra-high-speed digital imaging system to assess the impact of the maximum eccentric uncoupling coefficient on the attenuation pattern of the stress field within the elastic vibration region. The results demonstrate that the eccentric charge structure’s effect on the strain field is predominantly observed in its time and strength effects. This is evident in the strain field’s time delay on the non-eccentric side and the fact that the strain field’s strength on this side is weaker than on the eccentric side. In the elastic vibration region, the primary type of radial stress is compressive, whereas the circumferential stress is chiefly tensile. As the measurement point’s distance increases, the peak values of both radial compressive and circumferential tensile stresses diminish exponentially. Interestingly, the attenuation index of the radial stress is notably lower than that of the circumferential stress. The change in the K value has a minimal impact on the attenuation indices of both radial and circumferential stresses. At equal distances from the eccentric side, the peak values of both radial and circumferential stresses display a consistent pattern with K and a linear pattern at distances from the eccentric side and in symmetry. The stress field’s intensity and attenuation pattern on the eccentric side remain largely unaffected by variations in K. Conversely, the stress field attenuation on the non-eccentric side is significantly influenced by K value changes, as is the symmetric side.

Risk Factors for Coronary Events After Robotic Hybrid Off-Pump Coronary Revascularization

Objectives: The impact of long-term complications after robotic hybrid coronary revascularization (HCR), including persistent angina, repeat revascularization, and myocardial infarction (MI), remains limited. This study aims to determine the risk factors for coronary events after robotic HCR and their time-varying effects on outcomes. Methods: We identified all consecutive patients who underwent robotic HCR at our institution. Baseline characteristics were explored as possible risk factors for angina, MI, and repeat revascularization with stents at any time during the follow-up. Results: A total of 875 patients (mean age 71.1 ± 11.1 years) were included. After a median follow-up of 3.32 years (IQR 1.18–6.34 years), angina occurred in 134 patients (15.3%), repeat revascularization with stents in 139 patients (15.8%), and MI in 36 patients (4.1%). The hazard rates for all outcomes increased with follow-up time, with a notable early rise around two years of follow-up for angina and, to a lesser extent, repeat revascularization. The risk factors were the lack of radial artery graft use, black race, diabetes, obesity, chronic obstructive pulmonary disease, low ejection fraction <50%, severe left main coronary artery stenosis (>50%), and more than three-vessel disease. Conclusions: Optimization of modifiable periprocedural risk factors may positively impact long-term prognosis in patients undergoing robotic HCR.

Time-varying quantitative characterization of reservoir microscopic pore structure based on digital core

The reservoir physical parameters (such as porosity, permeability, phase permeability, etc.) will change in the process of water injection development with the continuous flushing effect of high magnification water drive, which may have adverse effects on development if they cannot be accurately characterized. In this paper, we try to quantitative characterize the time -varying features of pore structure from the microscopic perspective. We used high-resolution 3D micro-CT (MCT) images to supplement the analysis of the physical properties of the rocks, and we used 3D pore network modeling technology to generate a 3D model of the rock samples. Then the relevant static micro-pore parameters are derived to study the changes of rock properties in different periods of development. Pore parameters were obtained to study the changes in porosity, permeability and pore size distribution of the rocks under different expulsion multiples, and to visualize the time-varying pattern of the microstructure of the rock samples under different stages of water injection. Under low-multiple water-driven conditions (10 PV), the physical properties of the rock samples did not change significantly, with an average facies change of only 1.98%. When reaching high multiples of water drive (more than 500PV), the change rate reaches more than 15%. And with the increase of water drive multiples, the distribution of pore radius and throat radius of rock samples showed an obvious right-shift trend, and the pores and throats increased, which was consistent with the results of CT scanning photographs. Equation fitting of the simulation results reveals that the core micro-parameters show an overall logarithmic relationship with the change of water drive. In this paper, a new application of Micro CT is provided to quantify the microstructure of water-injected reservoirs. By adopting this method, the effect of water-drive multiplicity on reservoir structure can be illustrated from a microscopic perspective. Meanwhile, compared with previous studies, this paper further investigates the effect of time-variation based on the qualitative analysis of the effect of time-variation of water-driven in reservoirs, and regresses time-variation curves and formulas through the changes in multiple water-driven states, which fills in the gap of the current lack of quantitative research and provides new understanding and optimization of the development of oilfields. The study will provide a new perspective for understanding and optimizing oilfield development.

The dynamic risk spillover of higher-order moments in the China’s energy market: A time-frequency perspective

ABSTRACT This study employs a risk spillover measurement method based on CEEMDAN-SE, GARCHSK, and TVP-VAR-DY models to assess risk spillover among 11 sub-sectors in China’s energy market, such as coal, oil, thermal power, and new energy vehicles. The energy sector index return is decomposed into intrinsic mode functions (IMFs) and reconstructed into high, medium, and low-frequency sequences. The GARCHSK model calculates conditional mean, variances, skewness, and kurtosis sequences for these sequences, which are then integrated into the TVP-VAR-DY model to evaluate return, volatility, and higher-order moment risk spillovers. Empirical findings highlight significant time-varying spillover effects, with return and volatility spillovers surpassing average total skewness and kurtosis spillovers. The New Energy Vehicle (NEV) and Electric Power Grid (EPG) sectors act as major risk transmitters, while primary risk receivers vary by frequency and order. Regulatory authorities should develop a real-time surveillance mechanism to monitor the transfer of risks between the NEV sector and the EPG sector. It is essential to foster inter-industry cooperation to facilitate better resource allocation and swift response to emerging challenges. Furthermore, policymakers should focus on bolstering the resilience of pivotal sectors by employing dynamic risk management strategies and providing appropriate incentives.

The uneven impact of Medicaid expansion on rural and urban Black, Latino/a, and White mortality.

To determine the differential impact of Medicaid expansion on all-cause mortality between Black, Latino/a, and White populations in rural and urban areas, and assess how expansion impacted mortality disparities between these groups. We employ a county-level time-varying heterogenous treatment effects difference-in-difference analysis of Medicaid expansion on all-cause age-adjusted mortality for those 64 years of age or younger from 2009 to 2019. For all counties within the 50 US States and the District of Columbia, we use restricted-access vital statistics data to estimate Average Treatment Effect on the Treated (ATET) for all combinations of racial and ethnic group (Black, Latino/a, White), rurality (rural, urban), and sex. We then assess aggregate ATET, as well as how the ATET changed as time from expansion increased. Medicaid expansion led to a reduction in all-cause age-adjusted mortality for urban Black populations, but not rural Black populations. Urban White populations experienced mixed effects dependent on years after expansion. Latino/a populations saw no appreciable impact. While no effect was observed for rural Black and Latino/a populations, rural White all-cause age-adjusted mortality unexpectedly increased due to Medicaid expansion. These effects reduced rural- and urban-specific Black-White mortality disparities but did not shrink the rural-urban mortality gap. The mortality-reducing impact of Medicaid expansion has been uneven across racial and ethnic groups and rural-urban status; suggesting that many populations-particularly rural individuals-are not seeing the same benefits as others. It is imperative that states work to ensure Medicaid expansion is being appropriately implemented in rural areas.

Nonparametric Time Varying IV-SVARs: Estimation and Inference

This paper studies the estimation and inference of time-varying impulse response functions in structural vector autoregressions (SVARs) identified with external instruments. Building on kernel estimators that allow for nonparametric time variation, we derive the asymptotic distributions of the relevant quantities. Our estimators are simple and computationally trivial and allow for potentially weak instruments. Simulations suggest satisfactory empirical coverage even in relatively small samples as long as the underlying parameter instabilities are sufficiently smooth. We illustrate the methods by studying the time-varying effects of global oil supply news shocks on US industrial production.

Time-Varying Effects of Financial Uncertainty Shocks on Macroeconomic Fluctuations in Peru

Time-Varying Effects of Financial Uncertainty Shocks on Macroeconomic Fluctuations in Peru

Caregiving Appraisals and Emotional Valence: Moderating Effects of Activity Participation.

The present study examines the extent to which a two-factor model of affect explains how caregiving appraisals experienced by caregivers influence their own well-being. We used data from three waves of Nation Study of Caregiving (NSOC) to conduct latent growth curve models with the time-varying predictors to investigate the effect of between-person (BP) and within-person (WP) caregiving appraisals on positive and negative affect. Furthermore, we simultaneously modeled WP differences in activity participation and affective experience with multilevel modeling. Then, we tested the moderating effect of activity participation in the association between WP caregiving appraisals and emotional valence. We found that BP and WP caregiving negative appraisal also contribute to caregiver positive affect similar to that of negative affect. Time-varying effects of negative appraisals and emotional valence are consistent with the two-factor model. Future longitudinal investigations could target WP and BP activity participation to alleviate caregiving cognitive appraisal among caregivers.

Time-Varying Effects of China’s Agricultural OFDI on the Sustainability of Bilateral Agricultural Trade Within RCEP Countries: Multiple Analyses Based on the Model of Trade Gravity and TVP-VAR

China’s agricultural OFDI has achieved a new promotion of regional opening to the RCEP agricultural field, and the bilateral agricultural trade cooperation has great potential. China’s agricultural OFDI has been steadily increasing in recent years. As significant trade partners for Chinese agricultural products, RCEP countries represent an important region for China’s agricultural OFDI initiatives. However, existing studies have paid little attention to the trade effect and dynamic impact of China’s agricultural OFDI on agricultural products of RCEP countries. Therefore, based on the panel data of agricultural trade between China and RCEP countries from 2004 to 2019, this paper uses the trade gravity model and TVP-VAR model to investigate the time-varying effects of China’s agricultural OFDI on agricultural trade of RCEP countries. The findings are as follows: (1) The impact of China’s agricultural OFDI on bilateral agricultural trade between China and RCEP countries has significant time-varying characteristics, and there are short-, medium-, and long-term positive impacts most of the time. (2) When the bilateral agricultural trade reaches a certain level, the role of China’s agricultural OFDI is limited, but the overall positive and significant impact remains, showing a strong trade effect. (3) The positive impact on China’s agricultural OFDI has a heterogeneous impact on RCEP countries with different income levels and geographical connections, but the overall positive externality effect is significant. The research conclusions expand the research perspective on the trade effect of agricultural OFDI to a certain extent and provide management and policy implications for agricultural enterprises to “move fast” and promote the sustainability of bilateral agricultural trade between China and RCEP countries through government efforts.

Interpretable AI-driven causal inference to uncover the time-varying effects of PM2.5 and public health interventions on COVID-19 infection rates

Interpretable AI-driven causal inference to uncover the time-varying effects of PM2.5 and public health interventions on COVID-19 infection rates

The solar radiation induced spatiotemporal temperature distribution and time-varying temperature effect in long-span ballastless track continuous beam-arch bridge

The influence of a time-varying temperature environment on the mechanical performance and geometric alignment of long-span ballastless track continuous beam-arch bridge is significant. However, the structural temperature loads are often simplified into a single mode, making it difficult to accurately simulate the evolution of geometric alignment of long-span ballastless track continuous beam-arch bridge under time-varying temperature effects. To explore the spatiotemporal distribution characteristics of temperature fields and the evolution patterns of track geometric alignment in long-span ballastless track continuous beam-arch bridge, a refined analysis model for the time-varying temperature field is established incorporating ray tracing methods and real-time shadow techniques. Combining the temperature experiment results, the spatiotemporal distribution characteristic and evolution patterns of temperature across the bridge system and key sections are explored. Building on this foundation, the evolution of track geometric alignment on the bridge is analyzed, and the measures for controlling track geometric alignment are proposed. The research findings indicate that the surface temperature of the structure undergoes periodic changes throughout the day, and the orientation of the bridge significantly influences the temperature field of the beam-track structure. The vertical displacement extremes of the ballastless track on the bridge are positively correlated with the structural temperature. The track maintenance moments affect the track alignment on the bridge, but the extreme deformation of the ballastless track and the temporal variation pattern of the structural system temperature remain consistent. To minimize irregularities in the ballastless track geometric alignment on the bridge, adjustments to the track position during high-speed railway maintenance window should choose the moments close to the daily average temperature, aiming to decrease the effects of time-varying temperature on track irregularity.

P70 The 1-year all-cause drug survival of bimekizumab compared with other biologics in psoriasis: a nationwide real-world cohort study

Abstract Bimekizumab was approved by the European Medicines Agency for the treatment of moderate-to-severe plaque psoriasis in August 2021. Bimekizumab is the only biologic that exerts specific inhibition of interleukin (IL)-17A and IL-17F. Few real-world studies have assessed the drug survival of bimekizumab. We assessed the 1-year all-cause drug survival of the tumour necrosis factor (TNF)-α inhibitor adalimumab, the IL-12/23 inhibitor ustekinumab, the IL-17 inhibitors secukinumab, ixekizumab, brodalumab, and bimekizumab, and the IL-23 inhibitor guselkumab. We used data on adult patients with psoriasis that were extracted from a nationwide prospective psoriasis database in February 2024. Survival functions were estimated using the Kaplan–Meier method. Hazard ratios (HRs) were estimated using a Cox proportional hazards model adjusted for the a priori-defined covariates age, sex, body weight, psoriatic arthritis (PsA), and previous number of biologics. Proportional hazards and linearity were assessed using the cumulative residuals. If the proportionality assumption was violated, we explored the time-varying effects of covariates. We included 4165 patients with 6554 treatment series. There were 3064 treatment series with adalimumab, 1498 with ustekinumab, 856 with secukinumab, 555 with ixekizumab, 195 with brodalumab, 192 with bimekizumab, and 194 with guselkumab. The crude 1-year all-cause drug survival was highest for bimekizumab (83.7%) followed by ustekinumab (79.2%), guselkumab (76.5%), ixekizumab (73.9%), adalimumab (71.4%), secukinumab (70.1%), and brodalumab (60.3%). In the Cox proportional hazards model, we found that bimekizumab had a significantly lower discontinuation hazard than all the other investigated biologics except guselkumab [HR, 1.26; 95% confidence interval (CI), 0.76–2.09]. Compared with bimekizumab, the HR was 1.55 (95% CI, 1.00–2.39) for ixekizumab, 1.56 (95% CI, 1.03–2.38) for ustekinumab, 2.25 (95% CI, 1.48–3.43) for secukinumab, 2.61 (95% CI, 1.65–4.13) for brodalumab, and 2.65 (95% CI, 1.75–4.01) for adalimumab. The analysis found that sex and PsA had time-varying effects. The hazard was 135% higher for females compared with males (HR, 2.35; 95% CI, 1.95–2.84) in the first 100 days and, among those still on treatment after 100 days, the HR decreased to 1.57 (95% CI, 1.40–1.78). In the first 100 days, PsA was protective (HR, 0.69; 95% CI, 0.56–0.86) but PsA had no protective effect for those still on treatment after 100 days (HR, 0.98; 95% CI, 0.86–1.11). In conclusion, bimekizumab had the highest 1-year all-cause drug survival compared with adalimumab, ustekinumab, secukinumab, ixekizumab, and brodalumab but not guselkumab. Data on the long-term drug survival of bimekizumab, as well as the drug survival associated with effectiveness and safety, are needed.

Pengaruh Waktu Tinggal Kadar COD, BOD, TSS dan Amonia Pada Limbah Cair Industri Tekstil dengan Metode Constructed Wetland Menggunakan Tanaman Melati Air (Echinodorus Palaefolius)

Textile waste has damaged the environment with various very high pollutants, such as high dissolved solids, COD, BOD, TSS and Ammonia. The high pollutant load in textile waste causes damage to aquatic ecosystems and affects human health if not managed properly. Constructed wetland is one of the efficient, effective and appropriate liquid waste treatment technologies because it uses a variety of vegetation. The study of the constructed wetland system using water jasmine plants (Echinodorus palaefolius) aims to reduce COD, BOD, TSS and Ammonia pollutants as an effect of detention time variations. Detention time includes 3,5,7 days. The results of the study showed that COD, BOD, TSS and Ammonia levels were affected by detention time. The efficiency of reducing COD levels on days 3, 5, and 7 was 58.38%, 54.30% and 55.45% respectively with concentrations of 397 mg/L, 430 mg/L and 425 mg/L. At BOD levels of 5.12%, 9.40% and 47.69% with concentrations of 111 mg/L, 106 mg/L and 61.2 mg/L. At TSS levels of 54.96%, 60.30% and 63.35% with concentrations of 59 mg/L, 52 mg/L and 48 mg/L. At Ammonia levels of 49.30%, 49.65% and 56.25% with concentrations of 1.46 mg/L, 1.45 mg/L and 1.26 mg/L. The results of textile wastewater quality standards are in accordance with the Minister of Environment Regulation No. 5 of 2014.

Seismic performance of continuous rigid-frame bridges affected by flood-induced scour

River-crossing bridges in high-intensity seismic regions can be vulnerable to the combined action of earthquake and flood hazards; in particular, flood-induced scour of the substructure can have a notable influence on the seismic behavior of bridge structures. Currently, long-span continuous rigid-frame bridges are widely used to cross large rivers worldwide, yet related studies on their seismic performance under flood-induced scour have been lacking. This paper focuses on the effects of time-varying flood-induced scour on the seismic performance of long-span continuous rigid-frame bridges incorporating complex thin-walled reinforced concrete piers with pile group foundations. A detailed finite element (FE) model for a representative bridge structure is developed and the scour depth risk of the bridge is evaluated under several flood events for different return periods. The influence of flood-induced scour on the pile-soil interaction is examined throughout the bridge service life. Detailed seismic fragility assessment is carried out through nonlinear time-history analyses using a large suite of 100 seismic records. Particular focus is given to evaluate the time-varying scour effects on the pile and soil deformations as well as on the component and system level seismic fragility functions. It is shown that the total flood-induced scour depth exhibits a nonlinear increasing trend with the increase in service time, particularly in the initial 10 years of the bridge service life. The deformations of the scoured piles, within a depth of 20 m, are also shown to increase significantly with the increase in service time. The results indicate that the scour has a pronounced effect on both the component and system level seismic fragility functions. Importantly, although the piles (or pile groups) are typically designed to remain elastic under seismic loading, they are shown to be subjected to significant inelastic demands and governing damage levels as a result of time-varying flood-induced scour effect. Overall, this study provides a methodology to assess the time-varying seismic fragility of the scoured long-span rigid-frame bridges with complex thin-walled reinforced concrete piers, which can enhance the multi-hazard time-varying seismic resilience assessment for bridge structures with similar configurations.

Parallel layered scheme-based integrated orbit-attitude-vibration coupled dynamics and control for large-scale spacecraft.

This paper investigates an integrated model-control scheme for large-scale spacecraft, focusing on orbit-attitude-vibration dynamics subject to strong time-varying coupling characteristics. The proposed scheme aims to achieve cooperative modeling and control for orbit maintenance, attitude stabilization and vibration suppression simultaneously. An integrated dynamic model is established using the Absolute Nodal Coordinate Formulation and Lagrangian mechanics, where time-varying coupling terms are preserved to enhance model integrity, contrasting with the reduction and decoupling methods commonly adopted in existing literature. To address the time-varying coupling effect among the orbit, attitude, and vibration degrees of freedom, a parallel layered scheme is proposed to enable joint integrated modeling-control design while avoiding complex dynamic calculations and enhancing control precision. The scheme is directly applicable to the un-simplified time-varying coupling dynamic model. Specifically, within this parallel layered scheme, vibration control, owing to its relative independence, is separated from orbit-attitude dynamics through equivalent dynamic conversion. Consequently, a disturbance observer-based terminal sliding mode controller is developed to stabilize the orbit and attitude with vibration suppression achieved automatically via active feedback mechanism. Finally, numerical simulations of large-scale spacecraft system are conducted to demonstrate the effectiveness and performance of the proposed approach.

Time-Varying Spillover Effects of Carbon Prices on China’s Financial Risks

As China’s financial markets become increasingly integrated and the carbon market undergoes financialization, the impact of carbon emission price fluctuations on financial markets has emerged as a key area of systemic risk research. This study employs the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model and the optimal Copula function to investigate the dynamic correlation between carbon prices and China’s financial markets. Building on this, the Monte Carlo simulation and Copula CoVaR models are used to explore the spillover effects of carbon price volatility on China’s financial markets. The findings reveal the following: (1) Carbon price fluctuations generate spillover effects on all financial markets, but the intensity varies across different markets. The foreign exchange market experiences the strongest spillover effect, followed by the bond market, while the stock and money markets are relatively less affected. (2) The optimal Copula functions differ between the carbon market and China’s financial markets, indicating heterogeneous characteristics across regional markets. (3) There is a degree of interdependence between the carbon market and various sub-markets in China’s financial system. The carbon market has the strongest positive correlation with the commodity market and a relatively high negative correlation with the real estate market. These findings underscore the importance of integrating carbon price volatility into financial risk management frameworks. For policymakers, it highlights the need to consider market stability measures when crafting carbon emission regulations. Market managers can leverage these insights to develop strategies that mitigate risk spillover effects, while investors can use this analysis to inform their portfolio diversification and risk assessment processes.

Machines vs. humans: The evolving role of artificial intelligence in livestreaming e-commerce

As the capability of artificial intelligence (AI) improves, online retailers are exploring AI-based agents to communicate with viewers in live streaming, which is referred to as AI stremer. However, it is unclear where, what, and when the implementation of AI stremer is more effective than human beings in live-streaming e-commerce. To explore the dynamic interrelationships and temporal evolution between AI and human streamers and viewer engagement, this study examined the evolving role of AI streamers in live-streaming e-commerce. We utilised the linear mixed model (LMM) and the time-varying effect model (TVEM) to examine whether AI and human streamers differ in both monetary and non-monetary engagement activities. Additionally, we investigated how these differences change over time and whether such changes are consistent across different consumption contexts. The dataset consists of 924,036 products from 21,190 live streaming shows in 123 live broadcasting rooms over a period of four months was used in this study. The results suggest that AI streamers can substitute for humans in monetary activities in the context of utilitarian consumption but not in hedonic consumption. However, the substitute effect of AI may gradually diminish over time. In addition, in a hedonic context, AI exhibits an increasing effect on viewer engagement over time.

Longitudinal Microbiome-based Interpretable Machine Learning for Identification of Time-Varying Biomarkers in Early Prediction of Disease Outcomes.

Information generated from longitudinally-sampled microbial data has the potential to illuminate important aspects of development and progression for many human conditions and diseases. Identifying microbial biomarkers and their time-varying effects can not only advance our understanding of pathogenetic mechanisms, but also facilitate early diagnosis and guide optimal timing of interventions. However, longitudinal predictive modeling of highly noisy and dynamic microbial data (e.g., metagenomics) poses analytical challenges. To overcome these challenges, we introduce a robust and interpretable machine-learning-based longitudinal microbiome analysis framework, LP-Micro, that encompasses: (i) longitudinal microbial feature screening via a polynomial group lasso, (ii) disease outcome prediction implemented via machine learning methods (e.g., XGBoost, deep neural networks), and (iii) interpretable association testing between time points, microbial features, and disease outcomes via permutation feature importance. We demonstrate in simulations that LP-Micro can not only identify incident disease-related microbiome taxa but also offers improved prediction accuracy compared to existing approaches. Applications of LP-Micro in two longitudinal microbiome studies with clinical outcomes of childhood dental disease and weight loss following bariatric surgery yield consistently high prediction accuracy. The identified critical early predictive time points are informative and aligned with clinical expectations.

Pancreatic cancer risk after benign gallbladder disease: A Swedish population-based cohort study

AimThe purpose of this nationwide registry-based cohort study was to outline pancreatic cancer risk after benign gallbladder disease (GBD). Anatomically adjacent cancers were investigated to address incidental findings. MethodsWe included all Swedes aged 20–79 years with histologically confirmed GBD (cholecystitis and/or cholecystectomy) in 1992–2016 and five matched non-exposed population comparators. Follow-up started one month after GBD and incidence rates (IR) and hazard ratios (HR) with 95 % confidence intervals (CI) up to 15 years after GBD were estimated using Poisson and Cox regression, respectively. Fully adjusted models included sex, age, year, education, type 2 diabetes, obesity, smoking-, and alcohol-related disorders. Analyses were stratified by follow-up and flexible parametric models applied to assess time-varying effects. Interaction models were used to identify patient groups at risk. Results680 and 1890 incident pancreatic cancers were detected over 15 years in 130907 GBD exposed and 571618 non-exposed, respectively. An excess pancreatic cancer risk was mainly seen within the first 2 years; IR: 84 [95 % CI 73,95] versus 31 [95 % CI 27,34] per 100000 person-years corresponding to an HR of 2.74 [95 % CI 2.31,3.25]. The same pattern was noted for duodenal cancer while primary liver cancer risk was elevated across follow-up. An initial extrahepatic biliary cancer risk elevation shifted to a reduction over time. The 2-year pancreatic cancer risk was augmented in younger (age 20–49) individuals, HR 7.64 [95 % CI 3.73,15.65]. ConclusionOur findings urge more studies on the clinical follow-up the first years after cholecystitis to detect early pancreatic cancer.