Self-regulation shift theory (SRST) provides a nonlinear dynamical systems-based theory of posttraumatic psychological adaptation. SRST has two central tenets: (1) coping self-efficacy and posttraumatic stress symptoms (PTSS) dynamically coevolve, such that changes in one correspond with changes in the other, and (2) that this coevolution produces critical transitions in psychological state for a subset of trauma survivors, including a self-determination violation (a decrease in coping self-efficacy paired with a rapid increase in PTSS) and/or a self-determination resurgence (an increase in coping self-efficacy paired with a rapid decrease in PTSS). Though existing evidence generally supports these tenets, SRST has yet to be robustly evaluated using nonlinear dynamical systems-based analytic approaches. This study evaluated these tenets using six weeks of ecological momentary assessment data from 81 hurricane survivors reporting continued hurricane-related stress 5-8.5 months after Hurricane Florence. Coping self-efficacy and PTSS velocities were estimated using functional data analysis, and generalized additive modelling was then used to evaluate dynamic coevolution. Results suggested that coping self-efficacy and PTSS velocities coevolved for 91-96% of participants. Inspection of gradient plots suggested approximately 6% of survivors exhibited nonlinear transitions consistent with SRST's self-determination violation and an additional 6% exhibited nonlinear transitions consistent with SRST's self-determination resurgence. Results provide preliminary support for SRST's tenets and suggest that interventions targeting either PTSS or coping self-efficacy may generate cascading benefits across the psychological system. It also adds to the growing body of literature suggesting nonlinear dynamical systems-based approaches can offer considerable insight into posttraumatic psychological processes.

Over 30 years, the National Cotton Council's National Cottonseed Treatment program has conducted coordinated seed treatment trials across the U.S., resulting in a comprehensive dataset of over 500 field trials. After data curation compiled from annual reports published in the Cotton Beltwide Proceedings, we used data from 409 field trials to develop beta regression models for upland cotton (Gossypium hirsutum L.) seedling emergence that integrates environmental and pathogen variables and to evaluate the efficacy of seed treatments. The minimum air temperature nine days post-planting and the cumulative precipitation three days post-planting were identified as key environmental predictors of emergence. The successful isolation of Pythium spp. from cotton roots was significantly associated with reduced emergence. Seed treatments containing at least three active ingredients targeting both true fungi and oomycetes improved emergence by an average of 10.2% and significantly reduced the risk of falling below the critical threshold of 35,000 emerged plants per hectare, often cited as the population to maintain yield. The resulting model introduces a benchmark tool with an associated error rate of approximately 16 to 20%, enabling future refinement. These findings highlight the long-term value of multi-state field trials and support the development of data-driven tools to guide planting or scouting decisions and optimize seed treatment strategies.

Multimodal analgesic strategies designed to minimise perioperative opioid exposure are fundamental components of enhanced recovery after surgery (ERAS) pathways. Despite widespread implementation of ERAS protocols, the optimal analgesic regimen remains undefined, as the individual contributions of specific agents to overall analgesic efficacy and opioid-sparing effects are not fully elucidated. Intravenous lidocaine, a widely utilised local anaesthetic, possesses both analgesic and anti-inflammatory properties and has been associated with improved gastrointestinal recovery. This study seeks to pragmatically evaluate the impact of incorporating perioperative intravenous lidocaine infusion into established ERAS pathways on postoperative functional recovery.

Cellular functions are dynamically regulated by electrical, chemical, and mechanical signals. Optical methods provide high spatial resolution and flexibility for detecting and understanding cellular signals, but most existing approaches rely on fluorescent reporters, which are often limited by photobleaching, phototoxicity, and potential perturbation of native cellular behaviors. In recent years, materials-based optical detection has emerged as a promising strategy, which transduces subtle cellular signals into measurable optical readouts without introducing fluorescent labels or the need for genetic modification. However, this field is still at a nascent stage, as many materials platforms are constrained by limited detection sensitivity, specificity, spatiotemporal resolution, and compatibility with different biological systems. Here, we discuss recent advances in optical methods for detecting cellular signals harnessing cell-material interfaces. In particular, we connect the structure-property relationships of polymers, liquid crystals, carbon-based materials, two-dimensional materials, quantum materials, and plasmonic metals to their optical detection mechanisms. We then summarize the applications of these materials in optical detection of bioelectric, biomechanical, and biochemical signals. We anticipate that this review will bridge materials science with cell biology, and guide the rational design of next-generation material-based optical detection platforms for minimally invasive, high-sensitivity interrogation of cellular signals.

Problematic smartphone use (PSU) is an emerging behavioral concern linked to impaired cognitive control and heightened social sensitivity, with fear of missing out (FoMO) increasingly recognized as a core psychological driver. However, the neurobiological mechanisms underlying PSU remain unclear. In this study, we investigated the neural architecture of PSU by integrating functional connectome gradient mapping, psychological mediation, and transcriptomic annotation. PSU severity was associated with altered principal gradients of functional connectivity, including increased connectivity gradients in the frontal pole and angular gyrus, and decreased connectivity gradients in the superior parietal lobule. Inter-subject representational similarity analyses further revealed that PSU-related gradient alterations were distributed across the default mode, frontoparietal, dorsal and ventral attention, sensorimotor, and visual networks. Mediation analyses demonstrated that FoMO significantly mediated the association between gradient 1 alterations in the bilateral superior parietal lobule and PSU severity. Transcriptomic analysis identified 1273 genes spatially aligned with the neural gradients, enriched for synaptic and glial functions, with distinct developmental expression profiles. Notably, hub genes such as BDNF, SYN1, and MRPS11 pointed to neuroplasticity and developmental mechanisms. Together, these findings provide a multilevel framework for understanding PSU, bridging neural gradients, molecular architecture, and psychological vulnerability.

In this study we test how sexual assault survivors on a college campus engage their social networks and how this relational context contributes to psychopathology. We conducted egocentric social network analysis of N = 423 sexual assault survivors and their N = 3,863 network members. We examine how characteristics of the network (e.g., number of other survivors, number of disclosure recipients, presence of unsupportive responders) are associated with posttraumatic stress disorder, depression, and anxiety symptoms. We also examine what network-, survivor-, and network member-level factors are associated with a network member receiving a sexual assault disclosure and the supportiveness of that network member's response. We found that characteristics of the network were associated with increased psychopathology. Participant and network member factors were associated with both the likelihood that a network member received a disclosure and with the perceived supportiveness of the network member. Finally, survivors with minoritized identities were less likely to disclose to or report supportive responses from network members who did not share their identity (i.e., different race, heterosexual). Findings from this study help to further elucidate the social context through which survivors are accessing support and demonstrate an association between network composition and psychopathology. Further, findings from this study have implications for supporter-driven postsexual assault prevention. (PsycInfo Database Record (c) 2026 APA, all rights reserved).