Time Pattern Research Articles

Puzzling Regional Factors of Divorce: the Case of Postcommunist Czechia and Slovakia

The factors influencing divorce remain a relatively under-researched area within (geo)demographic and sociological studies. This study extends the scope of inquiry by conducting a comprehensive examination of the divorce process. We demonstrate the possible way to discover (regional) factors of family dissolution using geostatistical methods, in addition to a demographical approach. In terms of the research background, a comparative perspective is essential. Here, we bring quite a pioneering view. For decades, the two countries involved, Czechia and Slovakia, shared the same legal environment within one state (Czechoslovakia) with a socialist regime. At the same time, many demographic, social, cultural, and religious dissimilarities existed between them. Through a multidimensional analysis, we aim to identify the influence of different groups of divorce factors at the level of local administrative units (LAU 1) during two transformation stages, specifically in three time series: at the beginning of the postsocialist transformation, at the turn of the millennium in 2001, and in 2011. We bring new empirical evidence and underline the role of geographical methods when discovering the time and space patterns of divorce. The most important finding is that regional differences are mostly induced by factors associated with religion, reproductive behavior, economic activity, education, and marital behavior. However, the explainability of regional factors after two decades of societal transformation is lower.

Single photonic reservoir dual-task simultaneous computing based on a semiconductor ring laser with filtered optical feedback

A single photonic reservoir based on a semiconductor ring laser with filtered optical feedback (FOF) is proposed to achieve simultaneous reservoir computing (RC) through multiplexing of the two directional modes and enhance the robustness of RC operations by using a Fabry-Perot filter. Numerical investigations of laser dynamics reveal that the FOF successfully provides RC-preferred dynamically stable, steady state for both directional modes simultaneously. Moreover, the FOF can expand the parameter ranges of the RC-preferred state when the filter bandwidth is smaller than twice the transient response frequency of the laser. Furthermore, such expansion can be enhanced by optimizing the detuning frequency between the filter and the free-running laser. In addition to the advantages in laser dynamics, the positive effects of FOF on RC operations are further verified by benchmark tasks of complex time series prediction and pattern classification. The results show that the robustness of RC operations against parameter fluctuations is significantly enhanced not only in single-task computing independent of task types but also in dual-task simultaneous computing without significant degradation of performances independent of task combinations.

Assessing recovery time of ecosystems in China: insights into flash drought impacts on gross primary productivity

Abstract. Recovery time, referring to the duration that an ecosystem needs to return to its pre-drought condition, is a fundamental indicator of ecological resilience. Recently, flash droughts – characterised by rapid onset and development – have gained increasing attention. Nevertheless, the spatiotemporal patterns in gross primary productivity (GPP) recovery time and the factors influencing it remain largely unknown. In this study, we investigate the recovery time patterns in a terrestrial ecosystem in China based on GPP using a random forest regression model and the SHapley Additive exPlanations (SHAP) method. A random forest regression model was developed to analyse the factors influencing recovery time and establish response functions through partial correlation for typical flash drought recovery periods. The dominant driving factors of recovery time were determined using the SHAP method. The results reveal that the average recovery time across China is approximately 37.5 d, with central and southern regions experiencing the longest durations. Post-flash-drought radiation emerges as the primary environmental factor, followed by the aridity index and post-flash-drought temperature, particularly in semi-arid and sub-humid areas. Temperature exhibits a non-monotonic relationship with recovery time, where both excessively cold and hot conditions lead to longer recovery periods. Herbaceous vegetation recovers more rapidly than woody forests, with deciduous broadleaf forests demonstrating the shortest recovery time. This study provides valuable insights for comprehensive water resource and ecosystem management and contributes to large-scale drought monitoring efforts.

DTXG-RF-based Intrusion Detection System for Artificial IoT Cyber Attacks

The swift advancement of networking technology and the rising incidence of cyber-attacks have made effective cybersecurity a critical priority. The primary concern with IoT networks is their susceptibility to vulnerabilities. IoT security necessitates the substantial involvement of artificial intelligence as a security technology to mitigate these challenges. Cyberattacks are evolving in sophistication, consequently posing greater obstacles in the precise detection of intrusions. An Intrusion Detection System (IDS) is a device or software application that monitors the activities of network systems for malicious actions or policy breaches and produces reports. The primary objective of an IDS is to efficiently identify attacks. Moreover, it is imperative to identify attacks at an early stage to mitigate their effects. Machine learning models have become increasingly popular in IDSs due to their capacity to process substantial data volumes and identify patterns in real time. Machine learning involves building an algorithm to identify consistent patterns within a dataset. This study aimed to build an IDS using an ensemble machine learning (DTXG-RF) model and compare it with DT, XGBoost, KNN, RF, NB, and CatBoost on the CIC-IoT-2023 and a Ransomware dataset. The results showed that the proposed DTXG-RF outperformed other machine learning models with accuracy reaching 95.06%.

Spatial and temporal predictability drive foraging movements of coastal birds

BackgroundTemporal and spatial predictability of food resources are critical to the foraging efficiency of central place foragers. While site fidelity is often assessed in this context, route fidelity, or the repeated use of the same path while traveling, and temporal aspects of habitat predictability have received less attention. We examined how the use of urban, coastal, and offshore habitats influenced spatiotemporal predictability in the foraging patterns of herring gulls (Larus argentatus) and great black-backed gulls (L. marinus). Since gulls show higher site fidelity when foraging in urban habitats, we predicted that these trips would also show higher route fidelity. Similarly, we predicted that gulls foraging in coastal habitats would adapt the timing of foraging trips relative to tides.MethodsWe analyzed GPS tracks of herring gulls (n = 79) and great black-backed gulls (n = 37)—between 2016–2022 from four nesting colonies whose surrounding areas varied in their degree of urbanization. Fréchet distance, which is defined as the repeated use of the same path while traveling, was used to assess route fidelity, within colonies and between habitat types. We also compared the consistency of foraging trip timing relative to tidal stage and day of week, respectively, across habitat types.ResultsNeither herring nor great black-backed gulls showed higher route fidelity in urban habitats. Herring gulls showed direct travel between urban foraging sites but revisited sites in different orders, suggesting that a mosaic map may be used to navigate between known urban foraging sites. Herring and great black-backed gulls that foraged at coastal sites exhibited patterns in trip timing in relation to the tidal cycle, with foraging primarily occurring at or around low tide. Herring gulls in urban environments foraged more on Fridays and weekends, possibly due to increased or altered human activities on these days.ConclusionsOur results demonstrate the importance of spatial memory and spatiotemporal predictability of gull foraging habitats and highlight the extent to which gulls adjust their movements based on their foraging habitats.

Patterns of physical activity, sedentary behavior, and sleep across pregnancy before and during two COVID pandemic years.

Physical activity is recommended during pregnancy, and high sedentary behavior and poor sleep may increase the risk of pregnancy complications. Activity patterns and sleep were negatively impacted by the COVID pandemic in many segments of the population, but the impact of the pandemic on pregnant people is understudied. We aimed to compare patterns of physical activity, sedentary time, and sleep during pregnancy between a pre-COVID and a COVID-era cohort. Physical activity, sedentary time, and sleep in each trimester of pregnancy were compared between two parallel prospective observational cohorts using identical collection methods. Pre-COVID participants (n=111) were recruited in 2017-2019 and COVID-era participants (n=117) from 2021-2023. Physical activity and sedentary time were measured using the activPAL3 micro accelerometer, and sleep duration was self-reported. Between-cohort comparisons were conducted using linear regression for each behavior in each trimester. Within-COVID-era cohort linear regression analyses assessed whether activity patterns differed as pandemic-era restrictions were eased. Participant demographics were similar between cohorts except for self-reported income. Adjusted mean moderate-to-vigorous physical activity was 57-77 min/week higher in each trimester in pre-COVID compared to COVID-era participants (p<0.001); adjusted mean sedentary time was 0.77-1.13 hours/day lower in each trimester (p<0.01) and sleep duration was 0.8 hours/day lower in the third trimester in the pre-COVID compared to COVID-era cohort (p<0.05). Within the COVID-era cohort, no significant within-trimester differences were detected across the pandemic years. Pregnant participants during the COVID pandemic were less active and more sedentary than their pre-pandemic counterparts, and this trend was still detected years after the pandemic began. A more sedentary lifestyle during pregnancy may have health implications, and prenatal care providers should help pregnant people identify strategies to adopt an active lifestyle in the context of pandemic-era barriers.

Factors Influencing Emergence Timing Patterns of Long-Tailed Bats in Exotic and Native Forest in New Zealand.

Understanding temporal behavioural patterns in animals can be crucial to their conservation management. Emergence timing in bats, that is, the decision on when to depart day-roosts for foraging, is one such example and is well studied in Northern Hemisphere bats. The emergence timing of New Zealand long-tailed bats (Chalinolobus tuberculatus) is not yet fully understood, including when and where they may be vulnerable to threats. We investigated factors influencing long-tailed bat emergence timing in the Kinleith Forest (exotic plantation, 38° S) and the Eglinton Valley (native beech forest, 45° S). We recorded emergence times during late pregnancy through post-lactation (October to March), to determine whether the month, temperature at sunset, tree density, cloud cover, presence of rain and the number of bats within a roost influenced emergence timing. Most long-tailed bats emerged after sunset in the Kinleith Forest, whilst 80% of first emerging bats departed before sunset in the Eglinton Valley where nights are much shorter in summer, reducing foraging time. Month, temperature at sunset, and roost population size were the most important predictors of emergence timing at both sites. Long-tailed bats in the Kinleith Forest also emerged earlier as tree density increased, a pattern potentially associated with predator defence. The factors influencing long-tailed bat emergence timing are likely context dependent, namely latitude and habitat structure, which has implications for roost protection protocols, timing of bat surveys and interpretation of bat acoustic monitoring data.

Cholinergic neurotransmission in the brain of streptozotocin-induced rat model of sporadic Alzheimer's disease: long-term follow up.

Rats treated intracerebroventricularly with streptozotocin (STZ-icv) develop pathologic features, which resemble those in Alzheimer's disease and have been proposed as a non-transgenic model for sporadic type of the disease (sAD). We aimed to characterize cholinergic transmission in the rat brain as a function of STZ-icv dose and time after the treatment. Acetylcholinesterase (AChE) activity and expression of muscarinic (M1, M4) and nicotinic (α7) receptors, cholin acetyltransferase (ChAT) and glial fibrillary acidic protein (GFAP) were measured in hippocampus (HPC) and parietotemporal cortex (CTX) of STZ-icv and age-matched control rats one week, and one, three, six and nine months after the icv administration of STZ (0.3, 1 and 3mg/kg), respectively. Cholinergic and astroglial changes were found most pronounced with a highest STZ dose in time-dependent manner. The cortex and hippocampus exhibited specific alterations in cholinergic transmission following STZ-icv administration, with either similar or distinct patterns depending on the parameter observed: increased AChE activity in HPC and invariable in CTX; increased M4 and ChAT levels in both regions; substantial cortical M1 level increment and moderate hippocampal M1 decrement; and decreased α7 levels in both regions, with subsequent increase observed only in HPC. Alterations in cerebral cholinergic neurotransmission in STZ-icv rat model were mostly following a threephasic time pattern: acute response (Phase I), complete/partial compensation (Phase II), and reappearance/progression of changes (Phase III). Staging structure of cholinergic changes in STZ-icv rat model might be speculated to partly correlate with cholinergic pathology in clinical AD stages.

Comparison of trends in CPS reports of child maltreatment and child maltreatment-related mortality across time, place and race/ethnicity.

Child maltreatment is a persistent public health problem in the United States. Child Protective Services (CPS) data are the de facto data source for child maltreatment surveillance, despite these data's established limitations. Maltreatment-related mortality could be a complementary source of child maltreatment surveillance data. We calculated trends over time, and patterns across state and by race/ethnicity, comparing child maltreatment report rates to child maltreatment-related mortality rates, between 2005 and 2020. These two measures of maltreatment show different time trends and patterns by state and race/ethnicity. Time trends in maltreatment-related mortality decreased slightly across the study period for all racial/ethnic groups, while maltreatment report rates increased, particularly for Non-Hispanic Black children. Reports and mortality data revealed very different pictures of which states had the highest and lowest maltreatment rates, overall and by race/ethnicity. Only 14 states had report and mortality rates in the same tertile, with less alignment when stratified by race/ethnicity. Patterns in child maltreatment report rates and death rates do not align. Future work should consider additional sources of data to improve maltreatment surveillance. These findings highlight the need to identify a valid and consistent approach to capture patterns of maltreatment in the United States.

Crafting a comprehensive strategy to prevent fraud and enable recovery in mobile insurance in the USA

In the U.S., the rapid expansion of mobile insurance has also created an environment of opportunity and challenge around fraud prevention and recovery. With the ever-increasing sector, fraud in the market can be fought using technological advancements like artificial intelligence (AI), machine learning and blockchain; these help in real time data analysis, pattern recognition and secure transactions. These innovations must however, always be perfected to remain one step ahead of the game and always evolving in fraudulent tactics. This paper discusses the important obstacles facing mobile insurance; namely emerging fraud risks, the necessity for comprehensive regulatory frameworks and lack of consumer awareness. The gaps in current fraud prevention tactics are uncovered by the research and the necessary regulatory updates to address digital identity and mobile platforms are highlighted. This highlights the importance for insurers, lawmakers and consumers to develop a collaborative way to gain trust and foster effective fraud detection and recovery programs. The paper also discusses the limitations of existing fraud detection models, and analyzes the opportunities for new legislation and enforcement mechanisms for reducing the risk of fraud. Also, awareness of consumer education as a key fraud prevention tool is considered important, in that educated consumers would be able to avoid fraudulent schemes. It also highlights the importance of additional research in promising technologies to apply to mobile insurance fraud prevention. Through all of this research, insights are provided into the current state of mobile insurance, challenges of fraud prevention, and actionable recommendations for industry stakeholders that will improve security and help create a more trusting mobile insurance ecosystem.

Machine learning to identify environmental drivers of phytoplankton blooms in the Southern Baltic Sea

Phytoplankton blooms exhibit varying patterns in timing and number of peaks within ecosystems. These differences in blooming patterns are partly explained by phytoplankton:nutrient interactions and external factors such as temperature, salinity and light availability. Understanding these interactions and drivers is essential for effective bloom management and modelling as driving factors potentially differ or are shared across ecosystems on regional scales. Here, we used a 22-year data set (19 years training and 3 years validation data) containing chlorophyll, nutrients (dissolved and total), and external drivers (temperature, salinity, light) of the southern Baltic Sea coast, a European brackish shelf sea, which constituted six different phytoplankton blooming patterns. We employed generalized additive mixed models to characterize similar blooming patterns and trained an artificial neural network within the Universal Differential Equation framework to learn a differential equation representation of these pattern. Applying Sparse Identification of Nonlinear Dynamics uncovered algebraic relationships in phytoplankton:nutrient:external driver interactions. Nutrients availability was driving factor for blooms in enclosed coastal waters; nutrients and temperature in more open regions. We found evidence of hydrodynamical export of phytoplankton, natural mortality or external grazing not explicitly measured in the data. This data-driven workflow allows new insight into driver-differences in region specific blooming dynamics.

Identifying time patterns in Huntington’s disease trajectories using dynamic time warping-based clustering on multi-modal data

One of the principal goals of Precision Medicine is to stratify patients by accounting for individual variability. However, extracting meaningful information from Real-World Data, such as Electronic Health Records, still remains challenging due to methodological and computational issues. A Dynamic Time Warping-based unsupervised-clustering methodology is presented in this paper for the clustering of patient trajectories of multi-modal health data on the basis of shared temporal characteristics. Building on an earlier methodology, a new dimension of time-varying clinical and imaging features is incorporated, through an adapted cost-minimization algorithm for clustering on different, possibly overlapping, feature subsets. The model disease chosen is Huntington’s disease (HD), characterized by progressive neurodegeneration. From a wide range of examined user-defined parameters, four case examples are highlighted to demonstrate the identified temporal patterns in multi-modal HD trajectories and to study how these differ due to the combined effects of feature weights and granularity threshold. For each identified cluster, polynomial fits that describe the time behavior of the assessed features are provided for an informative comparison, together with their averaged values. The proposed data-mining methodology permits the stratification of distinct time patterns of multi-modal health data in individuals that share a diagnosis, by employing user-customized criteria beyond the current clinical practice. Overall, this work bears implications for better analysis of individual variability in disease progression, opening doors to personalized preventative, diagnostic and therapeutic strategies.

Evolutionary trajectories of immune escape across cancers.

Immune escape is a critical hallmark of cancer progression and underlies resistance to multiple immunotherapies. However, it remains unclear when the genetic events associated with immune escape occur during cancer development. Here, we integrate functional genomics studies of immunomodulatory genes with a tumor evolution reconstruction approach to infer the evolution of immune escape across 38 cancer types from the Pan-Cancer Analysis of Whole Genomes dataset. Different cancers favor mutations in different immunomodulatory pathways. For example, the antigen presentation machinery is highly mutated in colorectal adenocarcinoma, lung squamous cell carcinoma, and chromophobe renal cell carcinoma, and the protein methylation pathway is highly mutated in bladder transitional cell carcinoma and lung adenocarcinoma. We also observe different timing patterns in multiple immunomodulatory pathways. For instance, mutations impacting genes involved in cellular amino acid metabolism were more likely to happen late in pancreatic adenocarcinoma. Mutations in the glucocorticoid receptor regulatory network pathway tended to occur early, while mutations in the TNF pathways were more likely to occur late in B-cell non-Hodgkin lymphoma. Mutations in the NOD1/2 signaling pathway and DNA binding transcription factor activity tended to happen late in breast adenocarcinoma and ovarian adenocarcinoma. Together, these results delineate the evolutionary trajectories of immune escape in different cancer types and highlight opportunities for improved immunotherapy of cancer.

How do red foxes (Vulpes vulpes) explore their environment? Characteristics of movement patterns in time and space

BackgroundMany animals must adapt their movements to different conditions encountered during different life phases, such as when exploring extraterritorial areas for dispersal, foraging or breeding. To better understand how animals move in different movement phases, we asked whether movement patterns differ between one way directed movements, such as during the transient phase of dispersal or two way exploratory-like movements such as during extraterritorial excursions or stationary movements.MethodsWe GPS collared red foxes in a rural area in southern Germany between 2020 and 2023. Using a random forest model, we analyzed different movement parameters, habitat features—for example landclasses and distances to linear structures—and time variables (season and time of day) within red fox exploratory, transient and stationary movement phases to characterize phase specific movement patterns and to investigate the influence of different variables on classifying the movement phases.ResultsAccording to the classification model, the movement patterns in the different phases were characterized most strongly by the variables persistence velocity, season, step length and distance to linear structures. In extraterritorial areas, red foxes either moved straight with high persistence velocity, close to anthropogenic linear structures during transient movements, or more tortuously containing a higher variance in turning angles and a decrease in persistence velocity during exploratory-like movements. Transient movements mainly took place during autumn, whereas exploratory-like movements were mainly conducted during winter and spring.ConclusionMovement patterns of red foxes differ between transient, exploratory and stationary phases, reflecting displacement, searching and resident movement strategies. Our results signify the importance of the combined effect of using movement, habitat and time variables together in analyzing movement phases. High movement variability may allow red foxes to navigate in extraterritorial areas efficiently and to adapt to different environmental and behavioral conditions.

Timing based clustering of childhood BMI trajectories reveals differential maturational patterns; Study in the Northern Finland Birth Cohorts 1966 and 1986.

Children's biological age does not always correspond to their chronological age. In the case of BMI trajectories, this can appear as phase variation, which can be seen as shift, stretch, or shrinking between trajectories. With maturation thought of as a process moving towards the final state - adult BMI, we assessed whether children can be divided into latent groups reflecting similar maturational age of BMI. The groups were characterised by early factors and time-related features of the trajectories. We used data from two general population birth cohort studies, Northern Finland Birth Cohorts 1966 and 1986 (NFBC1966 and NFBC1986). Height (n = 6329) and weight (n = 6568) measurements were interpolated in 34 shared time points using B-splines, and BMI values were calculated between 3 months to 16 years. Pairwise phase distances of 2999 females and 3163 males were used as a similarity measure in k-medoids clustering. We identified three clusters of trajectories in females and males (Type 1: females, n = 1566, males, n = 1669; Type 2: females, n = 1028, males, n = 973; Type 3: females, n = 405, males, n = 521). Similar distinct timing patterns were identified in males and females. The clusters did not differ by sex, or early growth determinants studied. Trajectory cluster Type 1 reflected to the shape of what is typically illustrated as the childhood BMI trajectory in literature. However, the other two have not been identified previously. Type 2 pattern was more common in the NFBC1966 suggesting a generational shift in BMI maturational patterns.

Strategies for Real-time Adjustment of Stimulation Patterns in Closed-loop Deep Brain Stimulation for Parkinsons Disease: A Comprehensive Review

Parkinsons disease (PD) is a prevalent neurodegenerative disorder that poses significant healthcare challenges worldwide. Deep brain stimulation (DBS) has proven effective for managing PD, particularly for patients with inadequate medication response. However, traditional high-frequency DBS systems lack the capability to adjust stimulation patterns in real time based on patient responses. In contrast, closed-loop DBS systems, which adjust stimulation based on recorded biomarkers, present a promising alternative that allows more flexible, on-demand electrical pulses delivery, offering possibility of achieving better therapeutic outcomes with less invasive stimulus. This review article explored various strategies for real-time adjustment of stimulation parameters in closed-loop DBS primarily based on electrophysiological biomarkers, particularly local field potentials (LFPs). We categorize these strategies into four categories: simple on-demand methods, control methods in systems theory, optimization or machine learning algorithms, and desynchronization approaches. By synthesizing existing literature, we provide a comprehensive overview of these strategies, highlighting contributions from multiple disciplines and the prospect of optimizing treatment for Parkinsons disease using closed-loop approach.

Freshwater life-cycle timing of Pacific salmon and steelhead (Oncorhynchus spp.) in Canada

Information on species’ phenology and distribution is essential for assessing and mitigating exposure to pressures that vary over space and time, such as development projects or climate changes. Pacific salmon (Oncorhynchus spp.) have complex life cycles that span freshwater and marine environments and unfold over several years and many thousands of kilometers. Currently, there is no central repository of life-cycle timing for salmon and steelhead populations throughout their Canadian range, hindering conservation and recovery efforts. To fill this gap, we have compiled timing data for fry migration from incubation to rearing grounds, juvenile migration from rearing grounds to the ocean, adult entry into rivers, and spawning for salmon and steelhead from British Columbia and the Yukon, Canada. For each species, we analysed the compiled data using linear models to describe patterns in population-specific timing across latitude, distance from the ocean, river gradient, and life-history types. Although we found significant data gaps in remote regions, our compiled dataset represents the best-available information on life-history timing that can inform environmental planning and salmon conservation actions

Parameter identifiability in evidence-accumulation models: The effect of error rates on the diffusion decision model and the linear ballistic accumulator.

A variety of different evidence-accumulation models (EAMs) account for common response time and accuracy patterns in two-alternative forced choice tasks by assuming that subjects collect and sum information from their environment until a response threshold is reached. Estimates of model parameters mapped to components of this decision process can be used to explain the causes of observed behavior. However, such explanations are only meaningful when parameters can be identified, that is, when their values can be uniquely estimated from data generated by the model. Prior studies suggest that parameter identifiability is poor when error rates are low but have not systematically compared this issue across different EAMs. We conducted a simulation study investigating the identifiability and estimation properties of model parameters at low error rates in the two most popular EAMs: The diffusion decision model (DDM) and the linear ballistic accumulator (LBA). We found poor identifiability at low error rates for both models but less so for the DDM and for a larger number of trials. The DDM also showed better identifiability than the LBA at low trial numbers for a design with a manipulation of response caution. Based on our results, we recommend tasks with error rates between 15% and 35% for small, and between 5% and 35% for large trial numbers. We explain the identifiability problem in terms of trade-offs caused by correlations between decision-threshold and accumulation-rate parameters and discuss why the models differ in terms of their estimation properties.

Individualized temporal patterns drive human sleep spindle timing

Sleep spindles are cortical electrical oscillations considered critical for memory consolidation and sleep stability. The timing and pattern of sleep spindles are likely to be important in driving synaptic plasticity during sleep as well as preventing disruption of sleep by sensory and internal stimuli. However, the relative importance of factors such as sleep depth, cortical up/down-state, and temporal clustering in governing sleep spindle dynamics remains poorly understood. Here, we analyze sleep data from 1,025 participants, statistically modeling the simultaneous influences of multiple factors on moment-to-moment spindle production using a point process-generalized linear model framework. Results reveal fingerprint-like timing patterns, characterized by a refractory period followed by a period of increased spindle activity, which are highly individualized yet consistent night-to-night, with increased variability with age. Strikingly, short-term (<15 s) temporal patterns of past spindle history are the main determinant of spindle timing, accounting for over 70% of the statistical deviance-surpassing the contribution of factors such as cortical up/down-state (slow oscillation phase), sleep depth, and long-term history (15 to 90 s, including ~50 s infraslow activity). Short-term history has a statistically significant influence in over 98% of the population, suggesting it is a near-universal feature of spindle activity. Short-term history and slow oscillation phase exert independent effects on spindle timing. Our results establish a robust statistical framework to examine abnormalities in sleep spindle timing observed in neurological disorders and aging, as well as the relationship between individualized sleep spindle timing, cognition, and sleep stability.

All-optical mapping of Ca2+ transport and homeostasis in dendrites

Calcium mediates many important signals in dendrites. However, the basic transport properties of calcium in dendrites have been difficult to measure: how far and how fast does a local influx of calcium propagate? We developed an all-optical system for simultaneous targeted Ca2+ import and Ca2+ concentration mapping. We co-expressed a blue light-activated calcium selective channelrhodopsin, CapChR2, with a far-red calcium sensor, FR-GECO1c, in cultured rat hippocampal neurons, and used patterned optogenetic stimulation to introduce calcium into cells with user-defined patterns of space and time. We determined a mean steady-state length constant for Ca2+ transport ϕ ∼ 5.8 μm, a half-life for return to baseline t1/2 ∼ 1.7 s, and an effective diffusion coefficient D ∼ 20 μm2/s, though there were substantial differences in Ca2+ dynamics between proximal and distal dendrites. At high Ca2+ concentration, distal dendrites showed nonlinear activation of Ca2+ efflux, which we pharmacologically ascribed to the NCX1 antiporter. Genetically encoded tools for all-optical mapping of Ca2+ transport and handling provide a powerful capability for studying this important messenger.