Activity Patterns Research Articles

Frequency and Correlates of Physical Activity among Young Adults with Chronic Pain.

Chronic pain affects an estimated 8.5% of young adults in the United States. Physical activity is a promising strategy for behavioral pain management, yet research characterizing prevalence and correlates of physical activity among young adults with chronic pain is scant. The present study sought to characterize physical activity patterns and identify psychosocial predictors of physical activity in this population. Participants were 129 young adults with self-reported chronic pain. Fear-avoidance (experiential avoidance, kinesiophobia), mental health (depression, anxiety) and energy-related (sleep disturbance, fatigue) factors were examined as correlates and predictors of moderate-to-vigorous physical activity (MVPA), walking, and sedentary behavior using generalized linear models. Participants reported an average of 51.0 (IQR=16.75, 100.75) minutes of MVPA and 90.0 (IQR=43, 153) minutes of walking per day. Nearly three-quarters (72.9%) of participants met recommended MVPA guidelines. At baseline, experiential avoidance (95% CI [-0.019, -0.007]), depression (95% CI [-0.100, -0.017]), and fatigue (95% CI [-0.042, -0.007]) were negatively associated with MVPA; depression (95% CI [1.37, 13.71]) was positively associated with sedentary behavior. Experiential avoidance at baseline predicted MVPA at two-week follow-up (95% CI [-0.015, -0.001]). No psychosocial factors were significantly associated with or predictive of walking. On average, young adults with chronic pain in our sample met recommended physical activity guidelines. Experiential avoidance was associated with MVPA at baseline and predicted less MVPA two weeks later. Findings suggest that while pain itself may not prevent engagement in MVPA among this population, a desire to prevent discomfort may be prohibitive.

Representational drift and learning-induced stabilization in the piriform cortex

The brain encodes external stimuli through patterns of neural activity, forming internal representations of the world. Increasing experimental evidence showed that neural representations for a specific stimulus can change over time in a phenomenon called "representational drift" (RD). However, the underlying mechanisms for this widespread phenomenon remain poorly understood. Here, we study RD in the piriform cortex of the olfactory system with a realistic neural network model that incorporates two general mechanisms for synaptic weight dynamics operating at two well-separated timescales: spontaneous multiplicative fluctuations on a scale of days and spike-timing-dependent plasticity (STDP) effects on a scale of seconds. We show that the slow multiplicative fluctuations in synaptic sizes, which lead to a steady-state distribution of synaptic weights consistent with experiments, can induce RD effects that are in quantitative agreement with recent empirical evidence. Furthermore, our model reveals that the fast STDP learning dynamics during presentation of a given odor drives the system toward a low-dimensional representational manifold, which effectively reduces the dimensionality of synaptic weight fluctuations and thus suppresses RD. Specifically, our model explains why representations of already "learned" odors drift slower than unfamiliar ones, as well as the dependence of the drift rate with the frequency of stimulus presentation-both of which align with recent experimental data. The proposed model not only offers a simple explanation for the emergence of RD and its relation to learning in the piriform cortex, but also provides a general theoretical framework for studying representation dynamics in other neural systems.

Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence

ObjectivesThis study aims to build a machine learning (ML) prediction model integrated with explainable artificial intelligence (XAI) to categorize obesity levels from physical activity and dietary patterns. The inclusion of XAI methodologies facilitates a comprehensive understanding of the risk factors influencing the model predictions and thus increases transparency in the identification of obesity risk factors.MethodsSix ML models were used: Bernoulli Naive Bayes, CatBoost, Decision Tree, Extra Trees Classifier, Histogram-based Gradient Boosting and Support Vector Machine. For each model, hyperparameters were tuned by random search methodology and model effectiveness was evaluated by repeated holdout testing. SHAP (SHapley Additive Annotations) and LIME (Local Interpretable Model Independent Annotations) interpretability methods were used to generate local and global feature importance measures.ResultsThe CatBoost model exhibited the highest overall performance and achieved superior results in accuracy, precision, F1 score and AUC metrics. Nonetheless, other models such as Decision Tree and Histogram-based Gradient Boosting also yielded strong and competitive results. The results also highlighted age, weight, height and specific food patterns as key predictors of obesity. In terms of interpretability, LIME showed superior in fidelity, whereas SHAP showed improved sparsity and consistency across models, facilitating a comprehensive understanding of trait importance.ConclusionThis research demonstrates that ML algorithms, when integrated with XAI technologies, can accurately predict obesity levels and explain important contributing risk factors. The use of SHAP and LIME increases model transparency, facilitating the identification of specific lifestyle patterns linked to obesity risk. These findings help to formulate more precise intervention techniques guided by a reliable and understandable predictive framework.

Altered patterning of neural activity in a neuropathology.

The dynamics of neural circuits and their role in mediating cellular and organismal phenomena remain poorly understood, despite numerous efforts to dissect these processes through precise instantaneous measurements or longer-time averages and approximations. We use an alternative approach: we investigate these dynamics at the system's mesoscale by analyzing spike trains and waveforms. These extended activity patterns carry robust, tractable information, are highly responsive to physiological specifics, and enable detailed tracking of circuit behavior. In particular, this methodology allows for characterizing the functionality of tau-pathology-afflicted hippocampal circuits and identifying circuit-level abnormalities that are missed by through traditional analyses. In healthy mice, spike flows and field undulations exhibit strong coupling with the animal's location, speed, and acceleration. In contrast, neural activity in tau-mice is profoundly disorganized: brainwave cadence becomes decoupled from locomotion, spatial selectivity is lost, and spike flow is disrupted. Importantly, these alterations emerge early and progressively worsen with age, revealing a gradual dissociation of the hippocampal circuit from spatial behavior.

Spectral Components of Honey Bee Sound Signals Recorded Inside and Outside the Beehive: An Explainable Machine Learning Approach to Diurnal Pattern Recognition

This study investigates the impact of microphone placement on honey bee audio monitoring for time-of-day classification, a key step toward automated activity monitoring and anomaly detection. Recognizing the time-dependent nature of bee behavior, we aimed to establish a baseline diurnal pattern recognition method. A custom apparatus enabled simultaneous audio acquisition from internal (brood frame, protected from propolization) and external hive locations. Sound signals were preprocessed using Power Spectral Density (PSD). Extra Trees and Convolutional Neural Network (CNN) classifiers were trained to identify diurnal activity patterns. Analysis focused on feature importance, particularly spectral characteristics. Interestingly, Extra Trees performance varied significantly. While achieving near-perfect accuracy (98–99%) with internal recordings, its accuracy was considerably lower (61–72%) with external recordings, even lower than CNNs trained on the same data (76–87%). Further investigation using Extra Trees and feature selection methods using Mean Decrease Impurity (MDI) and Recursive Feature Elimination with Cross-Validation (RFECV) revealed the importance of the 100–600 Hz band, with peaks around 100 Hz and 300 Hz. These findings inform future monitoring setups, suggesting potential for reduced sampling frequencies and underlining the need for monitoring of sound inside the beehive in order to validate methods being tested.

Habitual physical activity and sedentary behavior among women with and without premenopausal bilateral oophorectomy: an exploratory study

Objective: To explore potential differences in physical activity and sedentary behavior volumes and patterns among postmenopausal women with and without premenopausal bilateral oophorectomy (PBO). Methods: Women with a history of PBO (n = 50) and age-matched postmenopausal referent women (n = 50) were recruited. Participants wore accelerometers on both ankles for 7 days. Volume metrics of sedentary behavior and physical activity, such as step counts, active time, and sedentary time, as well as the sedentary behavior and habitual physical activity distribution, and accumulation patterns, were quantified from the accelerometer data and compared between groups. Results: Metrics indicating volume of sedentary behavior and physical activity were not statistically different between groups. PBO was significantly associated with higher variability in stepping bout time (P = 0.022), indicating a potentially more complex walking pattern. In addition, PBO was significantly associated with lower variability in sedentary break time (P = 0.012), and lower activity time Gini index (Z = −2.428, P = 0.015). This suggests that women with PBO may have broken up their sedentary time with shorter and less variable activity bouts, and because they had relatively shorter average daily active time, they might be at a higher risk of subsequent adverse health outcomes such as low bone mineral density. Conclusions: Although there were no differences in overall activity volume, some differences in activity patterns emerged between women with PBO and referent women. The study highlights the need for longitudinal research to understand how physical activity and sedentary behavior patterns evolve in postmenopausal women with a history of PBO.

The influence of street facades on user behavior: insights from Nguyen Van Binh Book Street

Abstract Vibrant public spaces are critical to a city's livability and identity, with built environment features such as spatial function and façade design significantly shaping user behavior and activity patterns. Grounded in Environmental-Behavior Studies, this research investigates the interplay between façade characteristics, functional spatial organization, and user behavior in public street spaces functioning as pocket parks. The case study focuses on Nguyen Van Binh Book Street in Ho Chi Minh City, a former roadway repurposed as a thematic pedestrian space promoting reading culture and serving as an open green public hub. The study identifies five key façade characteristics that inform an evaluation framework for aesthetic preferences in urban design. It also reveals three distinct patterns linking façade features to user behavior and spatial activity distribution. These findings highlight the importance of visual and spatial coherence in enhancing public engagement. The research offers actionable insights for urban policymakers and planners, emphasizing the integration of façade strategies in street space governance and sustainable urban development. It further provides practical guidance for designing culturally resonant, multifunctional public spaces in Vietnamese and Southeast Asian urban contexts.

Impact of COVID-19 on physical activity patterns in non-professional populations in Asia: a mini review of pre-, during, and post-pandemic periods

This mini-narrative review examines the impact of the COVID-19 pandemic on physical activity (PA) patterns across Asian countries, including regions such as Hong Kong, South Korea, Japan, and Southeast Asia. Pre-pandemic (before 2019), Asia experienced gradually increasing PA participation rates, characterized predominantly by outdoor activities, gym workouts, and organized group exercises, driven by growing fitness awareness and the availability of facilities for exercise. During the pandemic (2020–2022), widespread declines in regular exercise occurred due to restrictions, causing a substantial shift toward indoor, home-based, and online-based PA. Low PA adversely affects cardiovascular health, immune function, obesity, metabolic conditions, and psychological well-being. Although home-based exercise modalities partially mitigated these impacts, their effectiveness remained limited compared to pre-pandemic routines. In the post-pandemic period (2022–2025), PA in Asia partially recovered, with some regions, such as Hong Kong and South Korea, reporting PA levels surpassing pre-pandemic baselines due to widespread adoption of hybrid exercise models. This recovery has fostered lasting changes toward hybrid exercise models, combining traditional and digital modalities, resulting in positive health outcomes across the cardiovascular, immune, metabolic, and psychological domains. Future public health strategies should emphasize flexible, diverse, and accessible exercise options, and further research should explore the sustainability and implications of these evolving exercise behaviors.

Stratification of insect diversity and daily activity patterns in the West African virgin forest Taï assessed by entomological Lidar

Abstract Lidar technology was utilized to non-destructively explore the diverse insect life in the Taï virgin forest of Côte d’Ivoire. This study employed an entomological Lidar scanned at different elevation angles, combined with traditional insect trapping at different canopy heights, to investigate the composition and spatial–temporal distribution of insects within the forest canopy. We detected stratified patterns of insect activity at various canopy heights, revealing differences in the composition of Lidar signals reflecting divergent species composition with height and time-of-day, indicative of height-dependent insect biodiversity. The optical properties of captured insects, including wing specularity and polarimetric response, were analyzed and correlated with Lidar signals, revealing distinct insect clusters and activity patterns at different canopy heights. These findings show the potential of using Lidar to non-invasively assess insect diversity continuously across complex canopies to uncover species composition in ecosystems with high species diversity.

Beyond Expectations: Anomalies in Financial Statements and Their Application in Modelling

The increasing complexity of financial reporting has enabled the implementation of innovative accounting practices that often obscure a company’s actual performance. This project seeks to uncover manipulative behaviours by constructing an anomaly detection model that utilises unsupervised machine learning techniques. We examined a dataset of 149,566 Slovak firms from 2016 to 2023, which included 12 financial parameters. Utilising TwoSteps and K-means clustering in IBM SPSS, we discerned patterns of normative financial activity and computed an abnormality index for each firm. Entities with the most significant deviation from cluster centroids were identified as suspicious. The model attained a silhouette score of 1.0, signifying outstanding clustering quality. We discovered a total of 231 anomalous firms, predominantly concentrated in sectors C (32.47%), G (13.42%), and L (7.36%). Our research indicates that anomaly-based models can markedly enhance the precision of fraud detection, especially in scenarios with scarce labelled data. The model integrates intricate data processing and delivers an exhaustive study of the regional and sectoral distribution of anomalies, thereby increasing its relevance in practical applications.

Personalizing brain stimulation: continual learning for sleep spindle detection

Objective.Personalized stimulation, in which algorithms used to detect neural events adapt to a user's unique neural characteristics, may be crucial to enable optimized and consistent stimulation quality for both fundamental research and clinical applications. Precise stimulation of sleep spindles-transient patterns of brain activity that occur during non rapid eye movement sleep that are involved in memory consolidation-presents an exciting frontier for studying memory functions; however, this endeavour is challenged by the spindles' fleeting nature, inter-individual variability, and the necessity of real-time detection.Approach.We tackle these challenges using a novel continual learning framework. Using a pre-trained model capable of both online classification of sleep stages and spindle detection, we implement an algorithm that refines spindle detection, tailoring it to the individual throughout one or more nights without manual intervention.Main results.Our methodology achieves accurate, subject-specific targeting of sleep spindles and enables advanced closed-loop stimulation studies. While fine-tuning alone offers minimal benefits for single nights, our approach combining weight averaging demonstrates significant improvement over multiple nights, effectively mitigating catastrophic forgetting.Significance.This work represents an important step towards signal-level personalization of brain stimulation that can be applied to different brain stimulation paradigms including closed-loop brain stimulation, and to different neural events. Applications in fundamental neuroscience may enhance the investigative potential of brain stimulation to understand cognitive processes such as the role of sleep spindles in memory consolidation, and may lead to novel therapeutic applications.

Study protocol for a multi-session randomized sham-controlled trial of PCC- and amygdala-targeted neurofeedback for the treatment of PTSD.

Post-traumatic stress disorder (PTSD) is marked by distressing and often chronic symptoms, including the reliving and re-experiencing of trauma memories, avoidance, negative alterations in cognition and mood, heightened arousal and reactivity, and dissociation. Current psychotherapies and pharmacotherapies may yield suboptimal results for many individuals with PTSD, underscoring the need for new approaches. Recent neuroimaging research highlights functional disruptions in brainstem, cerebellar, limbic, and cortical networks underlying PTSD. Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NFB) is an emerging intervention that has directly targeted limbic (i.e., the amygdala) and cortical (i.e., the posterior cingulate [PCC]) regions and has shown promising initial findings in PTSD. However, key research gaps remain, such as the need for rigorous randomized controlled trials (RCTs) to establish clinical efficacy and neurophysiological specificity, determine optimal brain targets, and evaluate dose-response relationships. This double-blind, multi-session RCT investigates whether targeting distinct brain regions via rt-fMRI-NFB yields differential therapeutic effects in individuals with PTSD (n = 72). Participants will be randomly assigned to PCC-targeted rt-fMRI-NFB, amygdala-targeted rt-fMRI-NFB, or a sham-control group. Each participant will complete three rt-fMRI-NFB sessions over three weeks, with clinical assessments at baseline, after each session, and at a one-month follow-up. The sham group will receive a 'yoked' feedback signal from a random participant in one of the experimental groups. The primary outcome is PTSD symptom severity, measured using the PTSD Checklist for DSM-5 (PCL-5). Secondary outcomes include depressive symptoms, emotion regulation difficulties, dissociation, anxiety, interoceptive awareness, sleep quality, and state PTSD symptoms during trauma provocation. Neural outcomes will also be examined, focusing on brain activation and connectivity patterns. Additionally, qualitative interviews and actigraphy will assess participants' subjective experiences and track sleep and physical activity patterns. This trial aims to address critical research gaps by evaluating the therapeutic potential of rt-fMRI-NFB targeting the PCC and amygdala in PTSD. By employing a wide range of data collection methods, this study will provide valuable insights into the clinical and neural effects of rt-fMRI-NFB. This study will be the first to investigate the phenomenological dimension and physiological impacts of rt-fMRI-NFB in this population. Taken together, these findings are expected to contribute to the development of targeted neurofeedback interventions and clarify the therapeutic mechanisms underlying rt-fMRI-NFB for PTSD. This study has been registered with ClinicalTrials.gov under the trial registration number NCT05456958. It was initially registered on July 13th, 2022, and most recently updated on October 9th, 2024.

Tick-wildlife host-pathogen network interactions in Northern Africa

Ticks are hosts and vectors of zoonotic pathogens, posing a critical threat to public health and the conservation of animal host populations, especially in Northern Africa. Tick-host-pathogen interactions are driven by tick spatial distribution and abundance, and the influence of biotic (animal hosts) and abiotic (environmental conditions) factors. The objectives of this study, conducted in the Maamora Forest (Northwest Morocco), were: (i) description of seasonal interactions network between off-host questing ticks and the wild hosts, rabbits (Oryctolagus cuniculus) and addax (Addax nasomaculatus), (ii) analysis of density-dependent and environmental effects in questing and on-rabbit ticks, and (iii) identification of tick-borne pathogens in questing and on-addax ticks. Results showed that questing and on-rabbit ticks (Hyalomma lusitanicum, Rhipicephalus pusillus, and H. aegyptium) presented significant spatial and seasonal differences. Questing ticks were highly abundant in summer, but infestation on rabbits was higher in spring. Spatially, areas with contact between rabbits and ungulates showed the highest tick infestations during summer. Ticks from rabbits were density-dependent and had a positive relationship with questing ticks. Addax was infested by H. lusitanicum ticks. Tick network of interactions resulted in the presence of Coxiella burnetii in both questing and in addax ticks (17–27%), and Rickettsia aeschlimannii in H. lusitanicum questing ticks (4%). These results support that ticks represent a challenge for human and animal health, as well as ecosystems in Northern Africa, emphasizing the need for long-term studies on their network of interactions, seasonal activity patterns, and tick-borne pathogens in wildlife.

Tick-wildlife host-pathogen network interactions in Northern Africa

Ticks are hosts and vectors of zoonotic pathogens, posing a critical threat to public health and the conservation of animal host populations, especially in Northern Africa. Tick-host-pathogen interactions are driven by tick spatial distribution and abundance, and the influence of biotic (animal hosts) and abiotic (environmental conditions) factors. The objectives of this study, conducted in the Maamora Forest (Northwest Morocco), were: (i) description of seasonal interactions network between off-host questing ticks and the wild hosts, rabbits (Oryctolagus cuniculus) and addax (Addax nasomaculatus), (ii) analysis of density-dependent and environmental effects in questing and on-rabbit ticks, and (iii) identification of tick-borne pathogens in questing and on-addax ticks. Results showed that questing and on-rabbit ticks (Hyalomma lusitanicum, Rhipicephalus pusillus, and H. aegyptium) presented significant spatial and seasonal differences. Questing ticks were highly abundant in summer, but infestation on rabbits was higher in spring. Spatially, areas with contact between rabbits and ungulates showed the highest tick infestations during summer. Ticks from rabbits were density-dependent and had a positive relationship with questing ticks. Addax was infested by H. lusitanicum ticks. Tick network of interactions resulted in the presence of Coxiella burnetii in both questing and in addax ticks (17–27%), and Rickettsia aeschlimannii in H. lusitanicum questing ticks (4%). These results support that ticks represent a challenge for human and animal health, as well as ecosystems in Northern Africa, emphasizing the need for long-term studies on their network of interactions, seasonal activity patterns, and tick-borne pathogens in wildlife.

Neural Dynamics of Strategic Early Predictive Saccade Behavior in Target Arrival Estimation

Background/Objectives: Accurately predicting the arrival position of a moving target is essential in sports and daily life. While predictive saccades are known to enhance performance, the neural mechanisms underlying the timing of these strategies remain unclear. This study investigated how the timing of saccadic strategies—executed early versus late—affects cortical activity patterns, as measured by electroencephalography (EEG). Methods: Sixteen participants performed a task requiring them to predict the arrival position and timing of a parabolically moving target that became occluded midway through its trajectory. Based on eye movement behavior, participants were classified into an Early Saccade Strategy Group (SSG) or a Late SSG. EEG signals were analyzed in the low beta band (13–15 Hz) using the Hilbert transform. Group differences in eye movements and EEG activity were statistically assessed. Results: No significant group differences were observed in final position or response timing errors. However, time-series analysis showed that the Early SSG achieved earlier and more accurate eye positioning. EEG results revealed greater low beta activity in the Early SSG at electrode sites FC6 and P8, corresponding to the frontal eye field (FEF) and middle temporal (MT) visual area, respectively. Conclusions: Early execution of predictive saccades was associated with enhanced cortical activity in visuomotor and motion-sensitive regions. These findings suggest that early engagement of saccadic strategies supports more efficient visuospatial processing, with potential applications in dynamic physical tasks and digitally mediated performance domains such as eSports.

Combined effects of physical activity, cognitive activity, and dietary patterns on cognitive impairment in older adults with consideration of APOE genotype: a cohort study.

This study aims to evaluate the combination effects of physical activity (PA), cognitive activity (CA), and dietary patterns impacts cognitive impairment among older adults, and whether these associations differ by apolipoprotein E (APOE) genotype. A total of 18,909 adults aged 65 years or older at baseline were enrolled from 1998 to 2014 years. PA and CA was assessed using scores that reflected participation in various activities, and a total activity (TA) score was calculated as the sum of PA and CA scores. Diet quality was assessed with the SHE-index. Participants were categorized into lifestyle groups based on combinations of activity levels and SHE-index. The APOE genotype was categorized as APOEε4 carriers versus non-carriers. Cognitive function was evaluated using the Mini-Mental State Examination at baseline and follow-up. Adjusted Cox proportional hazards models was used to estimate the association and interaction between lifestyle and APOE genotype on the risk of cognitive impairment. During a mean follow-up time of 5.27 ± 3.67 years, 5713 participants developed cognitive impairment. Participants with higher levels of TA, PA, or CA, combined with a healthy diet, consistently exhibited the lowest risk of cognitive impairment, with hazard ratios (HR) of 0.65 [95% confidence intervals (CIs): 0.60-0.71] for TA, 0.72 (95% CI: 0.66-0.78) for PA, and 0.73 (95% CI: 0.67-0.79) for CA. This protective effect remained consistent across APOEε4 genotype groups, with HRs ranging from 0.54 to 0.67 in carriers and 0.63 to 0.69 in non-carriers for higher TA, PA, or CA combined with a healthy diet. In addition, participants engaging in high PA, high CA, and a healthy diet simultaneously showed the greatest protection against cognitive impairment, with HRs of 0.46 (95% CI: 0.28-0.76) in APOEε4 carriers and 0.47 (95% CI: 0.37-0.58) in non-carriers. The greatest decrease in cognitive impairment was observed among older Chinese adults adhering to both high activity levels and healthy diet, The protective effect was even stronger when high physical activity, high cognitive activity, and a healthy diet were maintained simultaneously, and this trend was consistent regardless of APOEε4 status.

Uncovering consumption inequalities by integrating household activity patterns in UK input-output tables

Abstract Understanding inequality through the lens of what households do – and the products and time their activities require – offers a richer perspective than conventional income-based inequality metrics. The study demonstrates this by analysing detailed consumption activities for the UK (2005, 2015) and their total (direct and upstream) use of household-produced products, market products, and human time. We expanded the national input-output tables with household production and detailed final consumption activities, the latter receiving product flows from market and household production activities. Household production outputs were preferentially valued by a market-equivalent value. For household types differentiated by income and composition, the tables include time use across the full 24-hour day, divided on 129 market production, 22 household production, and 33 consumption activities. The subsequent analyses provide a comprehensive understanding of households’ products and time use, with policy-relevant findings to promote more equitable consumption patterns. E.g., including the value of household production reduces inequality in consumption, although household production time and value are increasing with income. Furthermore, consumption activities differ in their total input requirements and in the degree to which these requirements vary by household type. Community activities (e.g., socialising) shows relatively equal uses of products across household types while the use in outgoing leisure (e.g., dining out) increases notably more with income. The upstream production time required for one minute of consumption increases notably with income across consumption activities, excluding basic consumption (e.g., sleeping). Considering household and market input requirements of distinct consumption activities can refine policies promoting more equitable consumption.

First Report of a Common Dolphin (Delphinus delphis) Feeding Alongside Indo‐Pacific Humpback Dolphins (Sousa chinensis) in the Beibuwan Gulf, China

ABSTRACTResearch on mixed‐species groups of cetaceans has increased worldwide in recent decades; however, their drivers are not well understood. Although the common dolphin (Delphinus delphis) and Indo‐Pacific humpback dolphin (Sousa chinensis) do not form a sympatric community and exhibit different patterns of diet and activity, we observed, for the first time, a mixed‐species group in the Shatian waters of the Beibuwan Gulf, China (a typical habitat for the latter species) on the 3rd and 6th of May 2022. On both occasions, two humpback dolphins were closely accompanied by a common dolphin identified by its distinctive body features. During an approximately 6‐h period of observation, the mixed group exhibited a concentrated use of a very small area in shallow waters and proximity to the coast. Behavior analysis showed that foraging was the most frequently observed activity, followed by socializing, traveling, and resting, which may provide confirmation of the foraging advantage hypothesis for the mixed species group. Our findings are the first to show that the common dolphin and Indo‐Pacific humpback dolphin are mixed‐species groups that expand the known mixed groups of cetaceans.

Associations Between Device-Measured Physical Activity Patterns and 8-Year Incidence of Cardiovascular Events in Well-Functioning Older Adults: Variations by Age.

Moderate-to-vigorous physical activity (MVPA) protects against cardiovascular disease (CVD) but less is known about how different physical activity (PA) patterns influence CVD risk. This study examined these associations considering age-related differences. Data from 423 CVD-free participants from the Swedish SNAC-K study were analyzed. Accelerometer data was collected from 2016 to 2019 and CVD events (fatal and non-fatal) over an 8-year period were obtained from national registers. Cox regression models were employed to assess associations between PA patterns-including steps per day, sit-to-stand transitions, daily time in light PA (LPA) and MVPA, and walking events (i.e. daily number of LPA or MVPA walking bouts)-and CVD risk. Cubic splines evaluated non-linear relationships, and interactions between PA and age-group (=66 vs. ≥80 years) were explored. Over a mean follow-up period of 5.6 years, 125 participants (30%) experienced at least one CVD event. In the total sample, each additional 10 MVPA walking events reduced CVD risk (HR 0.90, 95% CI 0.82-0.98, p=0.019). Among the oldest-old, each additional 100 LPA walking events (HR 0.61, 95% CI 0.39-0.95, p=0.027) and each additional 10 MVPA walking events (HR 0.87, 95% CI 0.76-0.98, p=0.024) reduced CVD risk. No clear associations were observed in younger-old adults. In this cohort of generally well-functioning older adults, frequent daily engagement in MVPA was associated with a reduced risk of incident CVD. Among the oldest-old, recurrent participation in both LPA and MVPA was linked to lower CVD risk, highlighting the importance of frequent daily PA.

Retinal waves reveal axial biases in modular patterns of cortical activity that predict future orientation preferences.

Spontaneous activity before sensory onset is thought to guide the formation of functional neural circuits. In visual cortex, spontaneous activity prior to experience exhibits modular patterns that resemble future visually evoked orientation selective responses. However, the factors at this early stage that build the initial network interactions that support the orientation tuning of modular responses at eye opening remain unknown. Here we provide the first evidence that retinal waves could play an important role by shaping the modular biases in patterns of intracortical connectivity and lay the foundation for orientation selective responses. We demonstrate that slow propagating waves in developing cortex that are dependent on retinal activity recruit specific modular patterns during their movement across the cortical surface, resulting in strikingly elongated patterns of modular coactivity that predict visually-evoked orientation responses at eye opening. Thus axial biases in spontaneous patterns of co-activity are present before the onset of visual experience and could serve as the seed for the developmental emergence of modular orientation representations.