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Consumer attitudes and behaviors are fundamentally dynamic processes; thus, understanding consumer dynamics is crucial for truly understanding consumer behaviors and for firms to formulate appropriate actions. Recent history in empirical marketing research has enjoyed increasingly richer consumer data as the result of technology and firms’ conscious data collection efforts. Richer data, in turn, have propelled the development and application of quantitative methods in modeling consumer dynamics, and have contributed to the understanding of complex dynamic behaviors across many domains. In this paper, we discuss the sources of consumer dynamics and how our understanding in this area has improved over the past four decades. Accordingly, we discuss several commonly used empirical methods for conducting dynamics research. Finally, as the data evolution continues into new forms and new environments, we identify cutting-edge trends and domains, and offer directions for advancing the understanding of consumer dynamics in these emerging areas.

Natural systems are often complex and dynamic (i.e. nonlinear), making them difficult to understand using linear statistical approaches. Linear approaches are fundamentally based on correlation. Thus, they are ill-posed for dynamical systems, where correlation can occur without causation, and causation may also occur in the absence of correlation. “Mirage correlation” (i.e., the sign and magnitude of the correlation change with time) is a hallmark of nonlinear systems that results from state dependency. State dependency means that the relationships among interacting variables change with different states of the system. In recent decades, nonlinear methods that acknowledge state dependence have been developed. These nonlinear statistical methods are rooted in state space reconstruction, i.e. lagged coordinate embedding of time series data. These methods do not assume any set of equations governing the system but recover the dynamics from time series data, thus called empirical dynamic modeling (EDM). EDM bears a variety of utilities to investigating dynamical systems. Here, we provide a step-by-step tutorial for EDM applications with rEDM, a free software package written in the R language. Using model examples, we aim to guide users through several basic applications of EDM, including (1) determining the complexity (dimensionality) of a system, (2) distinguishing nonlinear dynamical systems from linear stochastic systems, and quantifying the nonlinearity (i.e. state dependence), (3) determining causal variables, (4) forecasting, (5) tracking the strength and sign of interaction, and (6) exploring the scenario of external perturbation. These methods and applications can be used to provide a mechanistic understanding of dynamical systems.

A method for modelling time-varying dynamic stability in a natural marine fish community finds that seasonal patterns in community stability are driven by species diversity and interspecific interactions. Fish interactions and ecosystem stability Ecological theory suggests that ecosystem stability—the ability of an ecosystem to persist through perturbations—is influenced by changes in the interactions between different species. Masayuki Ushio and colleagues use a 12-year observational dataset of species interactions in a marine fish community in Maizuru Bay, Japan, to examine the link between fluctuations in interspecific interactions and community stability. They find that short-term changes in the interaction network influence the overall community dynamics, with weak interactions and higher species diversity promoting community stability. Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time 1 , 2 , 3 . Although this theory has experimental support 2 , 4 , 5 , evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) 6 and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series 6 , 7 , 8 , 9 and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

1. Understanding the relationship between biodiversity and ecosystem functioning (BEF) is a central topic in ecology. Multi-trait-based functional diversity has been proposed to improve mechanistic understanding of the BEF relationship; however, how trait-based functional diversity affects ecosystem functioning and processes has rarely been addressed in aquatic ecosystems. 2. Here, we examined the causal relationships between three phytoplankton functional diversity indices (FAD2, functional diversity based on dendrograms [FDc], FRic) and Shannon diversity index versus resource use efficiency for nitrogen (RUEN), phosphorus (RUEP) and silicate (RUESi), with monthly long-term datasets from the marine (Western English Channel, 2000-2014) and freshwater (Lake Kasumigaura, 1984-2012) ecosystems. 3. We employed Convergent Cross Mapping (CCM), a novel method developed for identifying causality for nonlinear dynamical systems; this is in contrast to linear approaches that cannot distinguish causality from correlation. CCM found that FDc is the most robust functional diversity index among the selected functional diversity indices (FAD2, FDc, FRic) in predicting phytoplankton resource use efficiency and exhibited a much stronger causal effect than the Shannon index. 4. Furthermore, scenario exploration analysis indicates that most causal effects from phytoplankton diversity indices on resource use efficiency (RUEN, RUEP and RUESi) are on average positive, and FDc exhibited the most consistent positive causal effects on phytoplankton resource efficiency in both marine and freshwater ecosystems. Thus, increasing FDc can enhance phytoplankton resource use efficiency in aquatic ecosystems. 5. Synthesis. Our results show significant causal effects of functional diversity on phytoplankton resource use efficiency in both marine and freshwater ecosystems. Among all selected functional diversity indices, functional diversity based on dendrogram is the most robust functional diversity index in promoting phytoplankton resource efficiency. Our study provides empirical evidences in natural aquatic systems that trait-based functional diversity represents better species niche partitioning than the Shannon index and thereafter enhances resource use efficiency. This finding can improve our understanding on trophic transfer and nutrient cycling in aquatic ecosystems.

Abrupt changes in system states and dynamical behaviors are often observed in natural systems; such phenomena, named regime shifts, are explained as transitions between alternative steady states (more generally, attractors). Various methods have been proposed to detect regime shifts from time series data, but a generic detection method with theoretical linkage to underlying dynamics is lacking. Here, we provide a novel method named Nested-Library Analysis (NLA) to retrospectively detect regime shifts using empirical dynamic modeling (EDM) rooted in theory of attractor reconstruction. Specifically, NLA determines the time of regime shift as the cutting point at which sequential reduction of the library set (i.e., the time series data used to reconstruct the attractor for forecasting) optimizes the forecast skill of EDM. We illustrate this method on a chaotic model of which changing parameters present a critical transition. Our analysis shows that NLA detects the change point in the model system and outperforms existing approaches based on statistical characteristics. In addition, NLA empirically detected a real-world regime shift event revealing an abrupt change of Pacific Decadal Oscillation index around the mid-1970s. Importantly, our method can be easily generalized to various systems because NLA is equation-free and requires only a single time series.

Ovarian cancer (OVCA) arises from three cellular origins, namely surface epithelial cells, germ cells, and stromal cells. More than 85% of OVCAs are EOCs (epithelial ovarian carcinomas), which are the most lethal gynecological malignancies. Cancer stem/progenitor cells (CSPCs) are considered to be cancer promoters due to their capacity for unlimited self-renewal and drug resistance. Androgen receptor (AR) belongs to the nuclear receptor superfamily and can be activated through binding to its ligand androgens. Studies have reported an association between AR expression and EOC carcinogenesis, and AR is suggested to be involved in proliferation, migration/invasion, and stemness. In addition, alternative AR activating signals, including both ligand-dependent and ligand-independent, are involved in OVCA progression. Although some clinical trials have previously been conducted to evaluate the effects of anti-androgens in EOC, no significant results have been reported. In contrast, experimental studies evaluating the effects of anti-androgen or anti-AR reagents in AR-expressing EOC models have demonstrated positive results for suppressing disease progression. Since AR is involved in complex signaling pathways and may be expressed at various levels in OVCA, the aim of this article was to provide an overview of current studies and perspectives regarding the relevance of androgen/AR roles in OVCA.

Ample evidence has indicated shifts in distribution of fish populations in response to environmental stress. However, most studies focused at the whole population scale. This neglects the spatial dynamics between groups of different body size (body size groups), that fundamentally shapes the spatial structure of a population. Here, we explored the mechanisms that modulate spatial dynamics of body size groups, and applied our analyses to three North Sea fish populations which experienced severe declines in biomass from 1977 to 2019: Atlantic cod Gadus morhua, haddock Melanogrammus aeglefinus and whiting Merlangius merlangius. All three populations exhibited strong declines in the overlapped area between body size groups in winter over 43 years, yet their mechanisms differed. These declines were either due to 1) different magnitudes of contraction of the distribution area of body size groups; and/or 2) different speeds and directions of spatial shift among various body size groups, both increasing spatial segregation within populations. These patterns were either associated with ocean warming, and/or declining population biomass, and such associations often varied according to distinct body size groups. Increasing spatial segregation between size groups of a population likely hampers life‐cycle connectivity and stability to local perturbations. Our analytical approach provides a powerful tool for identifying vulnerable populations under environmental stress and can be generalized to study a variety of size/age structured populations at various ecosystem types.

Objectives: This systematically scoping review aims to evaluate the therapeutic potential and clinical benefits of specific Lactiplantibacillus plantarum (L. plantarum) strains in human health, identifying their strain-specific effects across various medical conditions. Methods: Following the PRISMA for Scoping Reviews (PRISMA-ScR) guidelines and employing the PICO framework, a comprehensive literature search was conducted in the PubMed and Embase databases to identify relevant studies published up to December 2023. Inclusion criteria were rigorously applied to ensure the selection of high-quality studies focusing on the clinical application of distinct L. plantarum stains. Results: This review analyzed several unique strains of L. plantarum across 69 studies, identifying several therapeutic benefits. L. plantarum 299v effectively improved gastrointestinal symptoms, enhanced oral health, and reduced systemic inflammation. L. plantarum IS-10506 exhibited notable immunomodulatory effects, especially in managing atopic dermatitis. L. plantarum LB931 showed promise in decreasing pathogenic colonization, supporting women’s vaginal health. Additionally, L. plantarum CCFM8724 demonstrated potential in reducing early childhood caries, highlighting its promise in pediatric oral care. Conclusions: The therapeutic potential of L. plantarum is extensive, with certain strains exhibiting promising clinical benefits for specific health concerns. The findings of this review advocate for the integration of L. plantarum strains into clinical practice, emphasizing the need for further research to elucidate their mechanisms of action, optimal dosages, and long-term safety profiles.

Our study aimed to investigate the incidence, risk factors and time to occurrence of malignancy in patients with dermatomyositis (DM) and polymyositis (PM). The electronic medical records of 1100 patients with DM and 1164 patients with PM were studied between January 2001 and May 2019. Malignancies after myositis were diagnosed in 61 (5.55%) patients with DM and 38 (3.26%) patients with PM. The cumulative incidence of malignancies in patients with DM were significantly higher than patients with PM (hazard ratio = 1.78, log-rank p = 0.004). Patients with DM had a greater risk of developing malignancy than those with PM at 40–59 years old (p = 0.01). Most malignancies occurred within 1 year after the initial diagnosis of DM (n = 35; 57.38%). Nasopharyngeal cancer (NPC) was the most common type of malignancy in patients with DM (22.95%), followed by lung, and breast cancers. In patients with PM, colorectal, lung and hepatic malignancies were the top three types of malignancy. The risk factors for malignancy included old age (≥ 45 years old) and low serum levels of creatine phosphokinase (CPK) for patients with DM and male sex and low serum levels of CPK for patients with PM. Low serum levels of CPK in patients with myositis with malignancy represented a low degree of muscle destruction/inflammation, which might be attributed to activation of the PD-L1 pathway by tumor cells, thus inducing T-cell dysfunction mediating immune responses in myofibers. A treatment and follow-up algorithm should explore the occurrence of malignancy in different tissues and organs and suggested annual follow-ups for at least 5.5 years to cover the 80% cumulative incidence of malignancy in patients with DM and PM.