Activity Patterns Research Articles

A residual deep learning network for smartwatch-based user identification using activity patterns in daily living

User identification is a critical aspect of smartwatch security, ensuring that only authorized individuals gain access to sensitive information stored on the device. Conventional methods like passwords and biometrics have limitations, such as the risk of forgetting passwords or the potential for biometric data to be compromised. This research proposes a novel approach for user identification on smartwatches by analyzing activity patterns using a hybrid residual neural network called Att-ResBiLSTM. The proposed method leverages unique patterns of user interactions with their smartwatches, including application usage, typing behavior, and motion sensor data, to create an individualized user profile. Employing a deep learning network specifically designed for wearable devices, the system can reliably and promptly identify users by analyzing their activity patterns. The Att-ResBiLSTM architecture comprises three key components: convolutional layers, ResBiLSTM, and an attention layer. The convolutional layers extract spatial features from the pre-processed data. At the same time, the ResBiLSTM component captures long-term dependencies in the time-series data by combining the advantages of bidirectional long short-term memory (BiLSTM) and residual connections. The attention mechanism enhances the final recognition features by selectively prioritizing the most informative elements of the input data. The Att-ResBiLSTM model is trained and evaluated using a diverse dataset of user activity patterns. Experimental results demonstrate that the proposed approach achieves remarkable accuracy in user identification, with an accuracy rate of 98.29% and the highest F1-score of 98.24%. The research also conducts a comparative analysis to assess the efficacy of accelerometer data versus gyroscope data, revealing that combining both sensor modalities improves user identification performance. The proposed methodology provides a reliable and user-friendly alternative to conventional user authentication techniques for smartwatches. This approach leverages activity patterns and a hybrid residual deep learning network to offer a robust and efficient solution for user identification based on smartwatch data, thereby enhancing the overall security of wearable devices.

Individual differences in prelimbic neural representation of food and cocaine seeking

The prelimbic cortex is involved in operant reward seeking. However, the precise nature of its activity patterns and whether/how they differ between different types of rewards are largely unknown. We use miniscope calcium imaging to observe prelimbic activity during both food and cocaine seeking in freely behaving mice. We find that neurobehavioral representations remain stable across days within individual mice. Unexpectedly, our data reveal significant individual differences: some mice display high similarity in their prelimbic cortex activity patterns for both food and cocaine seeking, while others show no such overlap. These findings suggest that individual differences in the neural mechanisms underlying food and cocaine seeking could be a critical factor to consider when developing future addiction treatment strategies.

Spatial heterogeneity analysis between street network configurations and various service activities: evidence from the Wuhan metropolitan area

China’s economic growth is increasingly being driven by the contemporary service industry in the context of a new economy. This study aims to examine the spatial heterogeneous relationship between various service industry activities and street network design configurations by integrating multisource big data and geospatial analysis to provide insightful implications for human-centered design for compact cities by taking the case study of an inland megacity in central China, Wuhan. Street configurations under the walking/driving modes including closeness, betweenness, severance and efficiency, are characterized from the perspective of spatial design network analysis and angular distance to effectively reflect network shapes and subjective perceptions when navigating through the streets. The point-like, point-axis and ring patterns of various service activities are identified using the kernel density estimation (KDE). Then two sets of densities are analyzed to investigate whether various service activities are spatially associated with specific street metrics and whether spatial stratified heterogeneity exists. The results show that severance and efficiency are two promising indicators to represent the human-scale street design besides the conventional street centrality indices. The spatial mismatch is mainly observed between street metrics and the tourism sector whereas spatial clusters are detected in other types of service activities. Diverse service activities have distinct location preferences for street designs under different transport modes. The walking mode values global closeness and betweenness, while the driving mode values severance and efficiency.

The Toxoplasma gondii homolog of ATPase inhibitory factor 1 is critical for mitochondrial cristae maintenance and stress response.

The production of energy in the form of ATP by the mitochondrial ATP synthase must be tightly controlled. One well-conserved form of regulation is mediated via ATPase inhibitory factor 1 (IF1), which governs ATP synthase activity and gene expression patterns through a cytoprotective process known as mitohormesis. In apicomplexans, the processes regulating ATP synthase activity are not fully elucidated. Using the model apicomplexan Toxoplasma gondii, we found that knockout and overexpression of TgIF1, the structural homolog of IF1, significantly affected gene expression. Additionally, TgIF1 overexpression resulted in the formation of a stable TgIF1 oligomer and increased the presence of higher order ATP synthase oligomers. We also show that parasites lacking TgIF1 exhibit reduced mitochondrial cristae density, and that while TgIF1 levels do not affect growth in conventional culture conditions, they are crucial for parasite survival under hypoxia. Interestingly, TgIF1 overexpression enhances recovery from oxidative stress, suggesting a mitohormetic function. In summary, while TgIF1 does not appear to play a role in ATP synthase regulation under conventional growth conditions, our work uncovers its potential role in adapting to the stressors faced by T. gondii and other apicomplexans throughout their intricate life cycles.

Coexistence of native and invasive species: the case of the Critically Endangered endemic carnivores from Cozumel Island, Mexico

ABSTRACT Species face increasing threats from anthropogenic disturbances to natural habitats, and we need to understand how threatened species are affected. For example, studying animals’ behavior and responses toward co-occurring species provides information on ecological interactions. We explored the patterns and drivers of the spatial use of two native carnivores endemic to Cozumel Island, Mexico, the Cozumel raccoon (Critically Endangered) and the dwarf coati (considered as Threatened in Mexico). We used multi-seasonal occupancy models to assess the effect that environmental variables, or the interactions with the other endemic carnivore, with a potentially competing species (common opossum) and with an invasive predator (dog), have on the activity of each endemic carnivore. We also compared their daily activity patterns. Habitat type was the main factor driving the detection and occupancy probability of both native carnivores that showed low to moderate overlap in daily activity patterns with competing species and with an invasive predator. Neither habitat disturbance nor dogs affected carnivore occupancy, suggesting that both variables have not reached a critical threshold to negatively influence the occurrence of native carnivores. Our results are relevant for understanding the spatiotemporal ecological requirements and constraints of two endangered endemic carnivores and for planning actions for their conservation.

Task Learning through Stimulation-Induced Plasticity in Neural Networks

Synaptic plasticity dynamically shapes the connectivity of neural systems and is key to learning processes in the brain. To what extent the mechanisms of plasticity can be exploited to drive a neural network and make it perform some kind of computational task remains unclear. This question, relevant in a bioengineering context, can be formulated as a control problem on a high-dimensional system with strongly constrained and nonlinear dynamics. We present a self-contained procedure which, through appropriate spatiotemporal stimulations of the neurons, is able to drive rate-based neural networks with arbitrary initial connectivity towards a desired functional state. We illustrate our approach on two different computational tasks: a nonlinear association between multiple input stimulations and activity patterns (representing digit images), and the construction of a continuous attractor encoding a collective variable in a neural population. Our work thus provides a proof of principle for emerging paradigms of computation based on real neurons. Published by the American Physical Society 2024

The Changing Geography of Innovation: Comparing Urban, Suburban and Rural Areas

ABSTRACTUsing novel geocoded patent data for Spain, we analyze the changing spatial pattern of innovative activity at the municipal level from 1995 to 2017 and find that patenting has become increasingly concentrated in urban areas, and more so for the most disruptive innovations. We also find that even though there was a convergence trend before the 2008 Great Recession between suburban and urban core locations, it has since vanished, and stark differences continue to persist. We test for path dependent dynamics along with different determinants of the changing spatial pattern of patenting. Our granular analysis unveils a more nuanced view of the geography of innovation for urban, suburban and rural areas and for different types of inventions according to their degree of radicalness.

In vivo dual-plane 3-photon microscopy: spanning the depth of the mouse neocortex

Cortical computations arise from patterns of neuronal activity that span across all cortical layers and cell types. Three-photon excitation has extended the depth limit of in vivo imaging within the mouse brain to encompass all cortical layers. However, simultaneous three-photon imaging throughout cortical layers has yet to be demonstrated. Here, we combine non-unity magnification remote focusing with adaptive optics to achieve single-cell resolution imaging from two temporally multiplexed planes separated by up to 600 µm. This approach enables the simultaneous acquisition of neuronal activity from genetically defined cell types in any pair of cortical layers across the mouse neocortical column.

Circadian Typology and Physical Activity in Young Adults with Gaming Disorder

Background and Objectives: Exploring daily lifestyle characteristics in individuals with gaming disorder (GD) might identify underlying causes and intervention points. However, integrative and subjective assessments are lacking in studies on this topic. This study aimed to assess circadian typology and physical activity in young adults with GD. Materials and Methods: We recruited 60 participants with GD and 120 age- and sex-matched controls. GD and delayed sleep phase syndrome (DSPS) were diagnosed through structured interviews. Physical activity and sleep patterns were measured using actigraphy. Self-reported measures were chronotype and insomnia by using composite scale of morningness (CSM) and the Pittsburg insomnia rating scale (PIRS). Results: We found that DSPS and eveningness chronotype was more prevalent in the GD group than in the control group. The GD group also contained more participants with insomnia with higher PIRS and a longer time to fall asleep compared with the control group. The GD group had lower physical activity levels for daily calorie expenditure, daily steps, and daily walking distance compared with the control group. No significant differences were observed in body weight and sleep duration between these groups. Conclusions: Individuals with GD, compared to HC, exhibit an eveningness chronotype, poorer sleep quality, lower physical activity, and higher obesity risk, suggesting that lifestyle adjustments like increased exercise and earlier sleep might help modify habitual behaviors and potentially further provide a way to treat GD.

High-resolution biologging of an Atlantic bluefin tuna captured and eaten by a supposed orca

Biologging has been used on a range of wild animals to document spectacular feats of migration and behaviour. We describe the pursuit, capture, and ingestion of an adult Atlantic bluefin tuna (Thunnus thynnus) (175 cm, estimated weight: 81 kg), which was instrumented with a biologging tag, by a predator, most likely an orca (Orcinus orca). The predation event lasted over 19 min, with the tuna exhibiting elevated activity (max acceleration 3.12 g) and a rapid ascent from 126 m at 3.6 m.s− 1 followed by death and handling at the surface. Orca were separately recorded using video tags, capturing and handling tuna cooperatively in a manner consistent with the tuna data. We then present the longest orca accelerometry dataset from the ingested MiniPAT tag, with diel patterns of activity and 77 feeding events. These unique datasets provide insight into the energetic dynamics of two of the ocean’s fastest predators.

POI Recommendation Scheme Based on User Activity Patterns and Category Similarity

The utilization of location-based social networks to provide point-of-interest (POI) recommendation services has been the subject of extensive research in recent years. Various factors that can enhance the precision of POI recommendations were examined in previous studies. However, the factors of a user, including the location and time, were not considered. In this paper, we proposed a POI recommendation scheme in which user activity patterns and the similarity of categories are considered. The proposed scheme is used to organize users based on the activity level and to take into account the characteristics of both the user and location. Furthermore, it provides personalized recommendations by considering the category similarity, time, and location data that were collected from users. We evaluated the performance of the proposed scheme and compared it with that of a currently used scheme. The proposed scheme exhibits precision that is approximately 16% greater than that of the existing scheme.

Association of 'weekend warrior' and other leisure time physical activity patterns with obesity and adiposity: A cross-sectional study.

To investigate the effect of differentphysical activity patterns on obesity. Data from adults aged 17-79 years were extracted from the Hong Kong Territory-Wide Physical Fitness Survey conducted in 2011-2012 and 2021-2022. Moderate to vigorous physical activity (MVPA) patterns were collected through questionnaires and categorized as inactive (no MVPA ≥10 min), insufficiently active (<150 min MVPA/week), weekend warriors (≥150 min MVPA/week from 1 to 2 days) and regularly active (≥150 min MVPA/week from ≥3 days). The association between these activity patterns with obesity risk and body fat percentage was analysed. This study included 9863 obesity data (including valid waist circumference and body mass index) and 7496 body fat data. Compared with the inactive group, the weekend warriors and regularly active individuals had lower risks of general and abdominal obesity, as well as reduced body fat. Furthermore, individuals who were insufficiently active but engaged in ≥3 days of MVPA showed significantly lower body fat and obesity risk than their inactive counterparts. Engaging in physical activity even once or twice a week can positively impact weight control.

Dynamic tuning of neural stability for cognitive control

The brain is thought to execute cognitive control by actively maintaining and flexibly updating patterns of neural activity that represent goals and rules. However, while actively maintaining patterns of activity requires robustness against noise and distractors, updating the activity requires sensitivity to task-relevant inputs. How these conflicting demands can be reconciled in a single neural system remains unclear. Here, we study the prefrontal cortex of monkeys maintaining a covert rule and integrating sensory inputs toward a choice. Following the onset of neural responses, sensory integration evolves with a 70 ms delay. Using a stability analysis and a recurrent neural network model trained to perform the task, we show that this delay enables a transient, system-level destabilization, opening a temporal window to selectively incorporate new information. This mechanism allows robustness and sensitivity to coexist in a neural system and hierarchically updates patterns of neural activity, providing a general framework for cognitive control. Furthermore, it reveals a learned, explicit rule representation, suggesting a reconciliation between the symbolic and connectionist approaches for building intelligent machines.

Oral traditions as a fount of ethnomathematical knowledge: A bibliometric analysis of contemporary research trends

This study mapped the contemporary research landscape surrounding “oral traditions as a fount of ethnomathematics knowledge” through a bibliometric analysis employed as a methodological procedure. The main problem addressed in this study is the lack of a comprehensive consideration of evolving research trends and intellectual structure within ethnomathematics, i.e., the role of oral traditions as a source of knowledge. Oral traditions, defined as the cultural knowledge, art, ideas, and practices transmitted verbally across generations, offer a rich repository of ethnomathematical insights. 713 publications were identified from the Dimensions database between 2014 and 2024. This bibliometric analysis focused on identifying key research themes, authors, publishers, keywords, or key terms distributions within the corpus of extracted publications, as well as temporal trends in publication activity and citation patterns. The findings revealed a growing interest in the field, focusing on (a) educational context, (b) cultural studies, and (c) the intersection of mathematics and language. The analysis also identified key research themes, such as (a) the role of language and culture in shaping mathematical thought, (b) the development of ethnomathematical practices, and (c) the pedagogical implications of incorporating ethnomathematics into formal education. Additionally, 11 influential scholars who are driving the field forward were identified. According to the term distribution analysis, of the 6,092 terms, 316 met the threshold (minimum number of occurrences of the term: 6). For each 316 terms, a relevance score has been calculated. Based on this score, the most relevant terms have been selected. The VOSviewer’s default setting (60% of the most relevant terms) resulted in selecting 190 terms. Based on the given criteria, there is a result of 5 distinctive clusters related to the query that emerged. This study provides theoretical insights into the conceptual foundations of ethnomathematics derived from oral traditions and empirical evidence for informing future research directions and pedagogical practices in mathematics education.

Global banks and the picking order in internal capital markets: Do locational activity patterns matter?

This paper examines whether global banks’ liquidity reallocations via internal capital markets are driven by the locational activity patterns of their foreign branches. Using aggregated data of foreign branches of US global banks located in 52 countries, we advance evidence of a picking order that favors locations where branches are more heavily engaged in lending activities. Specifically, we find that internal liquidity support to branches in host countries with prominent local lending activities is especially significant during a contraction in local deposits.In jurisdictions where branches have higher shares of market-based activities and off-balance sheet exposures, we do not observe a significant increase in internal liquidity support following local funding contractions.

Preconfigured cortico-thalamic neural dynamics constrain movement-associated thalamic activity

Neural preconfigured activity patterns (nPAPs), conceptualized as organized activity parcellated into groups of neurons, have been proposed as building blocks for cognitive and sensory processing. However, their existence and function in motor networks have been scarcely studied. Here, we explore the possibility that nPAPs are present in the motor thalamus (VL/VM) and their potential contribution to motor-related activity. To this end, we developed a preparation where VL/VM multiunitary activity could be robustly recorded in mouse behavior evoked by primary motor cortex (M1) optogenetic stimulation and forelimb movements. VL/VM-evoked activity was organized as rigid stereotypical activity patterns at the single and population levels. These activity patterns were unable to dynamically adapt to different temporal architectures of M1 stimulation. Moreover, they were experience-independent, present in virtually all animals, and pairs of neurons with high correlations during M1-stimulation also presented higher correlations during spontaneous activity, confirming their preconfigured nature. Finally, subpopulations expressing specific M1-evoked patterns also displayed specific movement-related patterns. Our data demonstrate that the behaviorally related identity of specific neural subpopulations is tightly linked to nPAPs.

Using actigraphy to assess chronotype: Simpler is better

ABSTRACT Actigraphy provides a unique method for objectively measuring sleep activity patterns, but confusion remains about how to use actigraphy data to determine chronotype. To determine the most suitable parameter, this study made a systematic comparison of actigraphy-derived parameters: the average midpoint of sleep of all record days (aMS-acti), cosine parameter (Bathyphase), and non-parametric parameter (L5-mid) in terms of the consistency with subjective chronotype parameters, test–retest reliability, and external validity. More importantly, we proposed multiple Munich Chronotype Questionnaire (MCTQ)-based actigraphy parameters: considering the difference between weekday (MSW-acti) with weekends (MSF-acti) and the sleep debt (MSFsc-acti). The study collected 5 days of actigraphy and scale data from 1,055 young adults, 138 of whom participated in the retest 2 years later. The results showed that, in terms of consistency with subjective chronotype, aMS-acti generally performed better than other actigraphy parameters. In addition, aMS-acti had the highest test–retest reliability and was more closely related to the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), and Self-Rating Depression Scale (SDS). The results suggest that the simplest parameter (aMS-acti) is superior to traditional cosine and non-parametric parameters and MCTQ-derived parameters for short-term assessment of chronotype.

Chisco: An EEG-based BCI dataset for decoding of imagined speech

The rapid advancement of deep learning has enabled Brain-Computer Interfaces (BCIs) technology, particularly neural decoding techniques, to achieve higher accuracy and deeper levels of interpretation. Interest in decoding imagined speech has significantly increased because its concept akin to “mind reading”. However, previous studies on decoding neural language have predominantly focused on brain activity patterns during human reading. The absence of imagined speech electroencephalography (EEG) datasets has constrained further research in this field. We present the Chinese Imagined Speech Corpus (Chisco), including over 20,000 sentences of high-density EEG recordings of imagined speech from healthy adults. Each subject’s EEG data exceeds 900 minutes, representing the largest dataset per individual currently available for decoding neural language to date. Furthermore, the experimental stimuli include over 6,000 everyday phrases across 39 semantic categories, covering nearly all aspects of daily language. We believe that Chisco represents a valuable resource for the fields of BCIs, facilitating the development of more user-friendly BCIs.

Macro-scale patterns in functional connectivity associated with ongoing thought patterns and dispositional traits

Complex macro-scale patterns of brain activity that emerge during periods of wakeful rest provide insight into the organisation of neural function, how these differentiate individuals based on their traits, and the neural basis of different types of self-generated thoughts. Although brain activity during wakeful rest is valuable for understanding important features of human cognition, its unconstrained nature makes it difficult to disentangle neural features related to personality traits from those related to the thoughts occurring at rest. Our study builds on recent perspectives from work on ongoing conscious thought that highlight the interactions between three brain networks – ventral and dorsal attention networks, as well as the default mode network. We combined measures of personality with state-of-the-art indices of ongoing thoughts at rest and brain imaging analysis and explored whether this ‘tri-partite’ view can provide a framework within which to understand the contribution of states and traits to observed patterns of neural activity at rest. To capture macro-scale relationships between different brain systems, we calculated cortical gradients to describe brain organisation in a low-dimensional space. Our analysis established that for more introverted individuals, regions of the ventral attention network were functionally more aligned to regions of the somatomotor system and the default mode network. At the same time, a pattern of detailed self-generated thought was associated with a decoupling of regions of dorsal attention from regions in the default mode network. Our study, therefore, establishes that interactions between attention systems and the default mode network are important influences on ongoing thought at rest and highlights the value of integrating contemporary perspectives on conscious experience when understanding patterns of brain activity at rest.

Shadows in the forest: Uncovering unusual colouration records in mammals from the Ecuadorian Tropical Andes.

Variations in colouration patterns have been reported in numerous wildlife species, particularly birds. However, the increased use of camera traps for wildlife monitoring has enabled the detection of elusive species and phenotypic variations that might otherwise go undetected. Here, we compiled records of unusual colouration patterns in terrestrial mammals, documented through camera-trap studies over a 12-year period in the Llanganates-Sangay Connectivity Corridor, in the Tropical Andes of Ecuador. We identified colour variations in seven species of terrestrial mammals, including disorders, such as melanism, white spotting/ piebaldism, xanthocromism and progressive greying. Notably, we reported a high prevalence of melanism in wild populations of the clouded oncilla, along with observations on the species' activity patterns. Approximately half of the recorded clouded oncillas were melanistic. We detected significant differences in activty patterns between melanistic and non-melanistc clouded oncilla, with melanistic morphs showing a peak of activity between 3 a.m. and before dawn. The proportion of melanistic individuals suggests that melanism is widespread throughout the corridor. However, its impact on the species' fitness remains unclear.