Temporal Correlation Patterns Research Articles

The Spatial Association between Residents’ Leisure Activities and Tourism Activities Using Colocation Pattern Measures: A Case Study of Nanjing, China

With the increasing trend of residents and tourists sharing urban spaces, the boundary between leisure spaces and tourism spaces is gradually being blurred. However, few studies have quantified the spatiotemporal correlation patterns of residents’ leisure activities and tourists’ activities. To fill this gap, this paper takes Nanjing as an example to study the temporal and spatial correlation between residents’ leisure activities and tourists’ activities based on mobile phone signal data. First, through kernel density analysis, it is found that there is a spatial overlap between residents’ leisure activities and tourists’ activities. Then, the spatial and temporal correlation patterns of residents’ leisure activities and tourists’ activities are analyzed through the colocation quotient. According to our findings, (1) residents’ leisure activities and tourists’ activities are not spatially correlated, indicating that they are relatively independent in space both during the week and on weekends. (2) On weekday afternoons, the spatial correlation between residents’ and tourists’ leisure activities is strongest. On weekends, the night is when residents’ leisure activities and tourists’ activities are most closely related. (3) The correlation area is mainly distributed in areas near famous scenic spots, as well as public spaces such as parks and squares. Based on the above analysis, this paper aims to study the resident-tourist interaction in the spatial context to provide directions for improving the attractiveness of cities, urban transportation, services, and marketing strategies.

Hydrogeochemistry of Epikarst Spring During Rainy Seasons in the Ponjong Hydrogeological Subsystem, Gunungsewu Karst Area, Indonesia

Gedaren Spring is an important source of clean water and irrigation water that is located in the transition zone between the Gunungsewu Karst and the Wonosari Basin. Limestone dissolution is the dominant process influencing this epikarst spring’s dynamics, but there has been no temporal hydrogeological testing on its ion content. This hydrogeochemical study was intended to determine the characteristics and quality of Gedaren Spring water during the rainy season. The methods used in the chemical analysis were Schoeller, Piper, and chemographic diagrams, and the results were correlated with spring discharge measurements and rainfall. The chemical analysis showed a positive correlation between discharge and rain, the temporal presence of dominant ions, and temporal correlation patterns between variables. Ca2+ and HCO3- were the dominant cation and anion. The spring water was classified as calciumenriched, calcium, bicarbonate, Ca2+ + Mg2+, and HCO3--type groundwater and as unpolluted groundwater, suggesting good water quality. Chemographic patterns indicated that Mg2+, Cl-, and SO42- ions positively correlated with discharge and rainfall, while Ca2+, HCO3-, Na+, K+, pH, EC, temperature, and TDS negatively correlated with the two variables. Overall, Gedaren Spring has good chemical characteristics and quality during the rainy season has a dominant process of calcium enrichment and is of the type Ca2++Mg2+ and HCO3-.

State-related neural influences on fMRI connectivity estimation

The spatiotemporal structure of functional magnetic resonance imaging (fMRI) signals has provided a valuable window into the network underpinnings of human brain function and dysfunction. Although some cross-regional temporal correlation patterns (functional connectivity; FC) exhibit a high degree of stability across individuals and species, there is growing acknowledgment that measures of FC can exhibit marked changes over a range of temporal scales. Further, FC can co-vary with experimental task demands and ongoing neural processes linked to arousal, consciousness and perception, cognitive and affective state, and brain-body interactions. The increased recognition that such interrelated neural processes modulate FC measurements has raised both challenges and new opportunities in using FC to investigate brain function. Here, we review recent advances in the quantification of neural effects that shape fMRI FC and discuss the broad implications of these findings in the design and analysis of fMRI studies. We also discuss how a more complete understanding of the neural factors that shape FC measurements can resolve apparent inconsistencies in the literature and lead to more interpretable conclusions from fMRI studies.

Temporal Associations of the SARS-CoV-2 NP Antigen and Anti-Spike Total Ig Levels with Laboratory Parameters in a Greek Cohort of Hospitalized COVID-19 Patients.

Background The direct effect of SARS-CoV-2 on the lungs results in increased hospitalization rates of patients with pneumonia. Severe COVID-19 patients often develop ARDS which is associated with poor prognosis. Assessing risk factors for COVID-19 severity is indispensable for implementing and evaluating therapeutic interventions. We investigated the temporal associations between the SARS-CoV-2 antigen (Ag), total Immunoglobulin (Ig) levels, and several laboratory parameters in hospitalized patients with varying degrees of COVID-19 severity. Methods The SARS-CoV-2 nucleocapsid protein (NP) and total Ig Spike (S) protein-specific antibodies were determined for each patient with lateral flow assays through repeated sampling every two days. Hematological and biochemical parameters were evaluated at the same time points. Results 40 Greek COVID-19 patients (31 males, 9 females) with a median age of 59.50 ± 16.21 years were enrolled in the study. The median time from symptom onset to hospitalization was 8.0 ± 4.19 days. A significant negative correlation was observed between the SARS-CoV-2 Ag and total Ig levels. The temporal correlation patterns of the SARS-CoV-2 NP Ag and anti-S total Ig levels with laboratory markers varied among patients with differing degrees of COVID-19 severity. Severe-critical cases had lower SARS-CoV-2 Ag and increased total Ig levels as compared to mild-moderate cases. Conclusions Distinct temporal profiles of the SARS-CoV-2 NP Ag and anti-S total Ig levels may distinguish different groups of COVID-19 severity.

Multi-sensor based landslide monitoring via transfer learning

Landslides are severe geographical activities that result in large quantities of rock and debris flowing down hill-slopes, leading to thousands of casualties and billions of dollars in infrastructure damage every year worldwide. For detecting landslides, on-site sensor systems are widely applied for data collection and many existing statistical process control methods can be adopted for modeling and monitoring. However, the conventional methods may perform poorly or even inapplicable when the sensors have different set-up times and end times, especially when the system includes newly deployed sensors with limited data collected. To make effective use of such new sensors immediately after deployment, we propose a novel multi-sensor based charting scheme for dynamic landslide modeling and monitoring by using transfer learning. A regularized parameter-based transfer learning approach integrated with the ordered LASSO is first proposed to effectively transfer information from old sensors with sufficient historical data to new ones with limited data. The approach considers the similarities not only between the autoregressive coefficients of different sensors, but also between the temporal correlation patterns. A control chart is then proposed for monitoring the newly deployed sensors sequentially based on the generalized likelihood ratio. Extensive simulation results and a real data example of landslide monitoring demonstrate the effectiveness of our proposed method.

Host–parasite dynamics set the ecological theatre for the evolution of state‐ and context‐dependent dispersal in hosts

While host–parasite interactions are ubiquitous, the large‐scale consequences of parasite infections are mainly driven by the spatial context. One trait of pivotal importance for the eco‐evolutionary dynamics of such metapopulations is the spatial behaviour of hosts, that is, their dispersal. It is well established that dispersal is not a random process, rather dispersal is informed and may depend on internal and external factors. In host–parasite metapopulations, dispersal may be a function of a host's infection state, but also of the local context, such as host density or parasite prevalence. Using a dynamical host–parasite metapopulation model of a parasite that reduces host fecundity (sterilizing parasite), we explore whether host dispersal evolves to be state‐ and context‐dependent and what shapes the evolutionarily stable dispersal reaction norms have. We show that state‐dependent dispersal readily evolves in the sense that hosts disperse more when infected, except when infected hosts pay significantly higher dispersal costs. This dispersal bias evolves due to kin selection, which is consistent with previous studies. Most importantly, we show that prevalence‐dependent dispersal evolves, especially when virulence is high and epidemiological dynamics have predictable signatures. The observed evolutionary outcome, a negatively prevalence‐dependent dispersal reaction norm for susceptible hosts at high virulence, seems counter‐intuitive at first. However, our results can be readily explained by the emergent epidemiological dynamics, especially their spatial and temporal correlation patterns. Finally, we show that context‐dependency in dispersal may rely on both, prevalence, and host density cues. Our work provides new insights into the evolution of complex dispersal phenotypes in host–parasite metapopulations as well as on feedbacks with epidemiological dynamics.

Resting-state “physiological networks”

Slow changes in systemic brain physiology can elicit large fluctuations in fMRI time series, which manifest as structured spatial patterns of temporal correlations between distant brain regions. Here, we investigated whether such “physiological networks”—sets of segregated brain regions that exhibit similar responses following slow changes in systemic physiology—resemble patterns associated with large-scale networks typically attributed to remotely synchronized neuronal activity. By analyzing a large group of subjects from the 3T Human Connectome Project (HCP) database, we demonstrate brain-wide and noticeably heterogenous dynamics tightly coupled to either respiratory variation or heart rate changes. We show, using synthesized data generated from physiological recordings across subjects, that these physiologically-coupled fluctuations alone can produce networks that strongly resemble previously reported resting-state networks, suggesting that, in some cases, the “physiological networks” seem to mimic the neuronal networks. Further, we show that such physiologically-relevant connectivity estimates appear to dominate the overall connectivity observations in multiple HCP subjects, and that this apparent “physiological connectivity” cannot be removed by the use of a single nuisance regressor for the entire brain (such as global signal regression) due to the clear regional heterogeneity of the physiologically-coupled responses. Our results challenge previous notions that physiological confounds are either localized to large veins or globally coherent across the cortex, therefore emphasizing the necessity to consider potential physiological contributions in fMRI-based functional connectivity studies. The rich spatiotemporal patterns carried by such “physiological” dynamics also suggest great potential for clinical biomarkers that are complementary to large-scale neuronal networks.

Portfolio analysis and geographical allocation of renewable sources: A stochastic approach

We take inspiration from the Modern Portfolio Theory introduced by Markowitz to propose a simplified strategy for the portfolio management of renewable energy sources based on Gaussian fluctuations with tunable correlations. By analyzing the impact of production fluctuations, we show how – depending on the sources' temporal correlation patterns – a careful geographical allocation of different types of renewal energy sources can reduce both the energy needs for balancing the power system and its uncertainty. The proposed strategy can be easily integrated in a decision support system for the planning of renewable energy sources. Therefore, providing policy/decision makers with an additional tool. We test our strategy on a set of case studies including a real-case based on literature data for solar and wind sources, and discuss how to extend the computation to non-Gaussian sources. The paper shows that in the Markowitz framework an efficient trade-off between production and fluctuations can be easily achieved, and that such framework also leads to important considerations on energy security. In perspective, analysis of time series together with such enriched frameworks would allow for the analysis of multiple realistic renewable generation scenarios helping decisions on the optimal size and spatial allocation of future energy storage facilities.

Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI.

Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) time series, especially during resting state periods, provides a powerful tool to assess human brain functional architecture in health, disease, and developmental states. Recently, the focus of connectivity analysis has shifted toward the subnetworks of the brain, which reveals co-activating patterns over time. Most prior works produced a dense set of high-dimensional vectors, which are hard to interpret. In addition, their estimations to a large extent were based on an implicit assumption of spatial and temporal stationarity throughout the fMRI scanning session. In this paper, we propose an approach called dynamic sparse connectivity patterns (dSCPs), which takes advantage of both matrix factorization and time-varying fMRI time series to improve the estimation power of FC. The feasibility of analyzing dynamic FC with our model is first validated through simulated experiments. Then, we use our framework to measure the difference between young adults and children with real fMRI data set from the Philadelphia Neurodevelopmental Cohort (PNC). The results from the PNC data set showed significant FC differences between young adults and children in four different states. For instance, young adults had reduced connectivity between the default mode network and other subnetworks, as well as hyperconnectivity within the visual system in states 1 and 3, and hypoconnectivity in state 2. Meanwhile, they exhibited temporal correlation patterns that changed over time within functional subnetworks. In addition, the dSCPs model indicated that older people tend to spend more time within a relatively connected FC pattern. Overall, the proposed method provides a valid means to assess dynamic FC, which could facilitate the study of brain networks.

Developmental Trends of Black Spruce Fibre Attributes in Maturing Plantations

This study assessed the temporal developmental patterns of commercially relevant fibre attributes (tracheid length and diameters, wall thickness, specific surface area, wood density, microfibril angle, fibre coarseness, and modulus of elasticity) and their interrelationships within maturing black spruce (Picea mariana(Mill.) B.S.P.) plantations. A size-based stratified random sample procedure within 5 semimature plantations located in the Canadian Boreal Forest Region was used to select 50 trees from which radial cross-sectional xylem sequences at breast-height (1.3 m) were cut and analyzed. Statistically, the graphical and linear correlation analyses indicated that the attributes exhibited significant (p≤0.05) relationships among themselves and with morphological tree characteristics. Relative variation of each annually measured attribute declined with increasing size class (basal area quintile). The transitional shifts in temporal correlation patterns occurring at the time of approximate crown closure where suggestive of intrinsic differences in juvenile and mature wood formation processes. The temporal cumulative development patterns of all 8 of the annually measured attributes varied systematically with tree size and exhibited the most rapid rates of change before the trees reached a cambial age of 20 years. At approximately 50 years after establishment, plantation mean attribute values were not dissimilar from those reported for more mature natural-origin stands.

Geospatial and Temporal Dynamics of Application Usage in Cellular Data Networks

Significant geospatial and temporal correlations, in terms of traffic volume and application access, exist in cellular network usage as shown in recent studies on cellular network measurement. Such geospatial and temporal correlation patterns provide local optimization opportunities to cellular network operators for handling the explosive growth in the traffic volume observed in recent years. To the best of our knowledge, in this paper, we provide the first fine-grained joint characterization of the geospatial and temporal dynamics of application usage in a 3G cellular data network. Our analysis is based on two simultaneously collected traces from the radio access network (containing location records) and the core network (containing traffic records) of a tier-1 cellular network in the United States. To better understand the application usage in our data, we first cluster cell locations based on their application distributions and then study the geospatial and temporal dynamics of application usage across different geographical regions. The results of our measurement study present cellular network operators with fine-grained insights that can be leveraged to tune network parameter settings for better network performance and user experience.

Temporal correlation patterns in pre-seismic electromagnetic emissions reveal distinct complexity profiles prior to major earthquakes

In the last years, continuous recordings of electromagnetic emissions from geophysical observatories have been recognized to exhibit characteristic fluctuation patterns prior to some major earthquakes. To further evaluate and quantify these findings, this work presents a detailed assessment of the time-varying correlation properties of such emissions during the preparatory phases preceding some recent earthquakes in Greece and Italy. During certain stages before the earthquakes’ occurrences, the electromagnetic variability profiles are characterized by a marked increase in the degree of organization of fluctuations, which allow developing hypotheses about the underlying physical mechanisms. Based on the preparatory phases of selected seismic events, the information provided by different statistical properties characterizing complementary aspects of the time-varying complexity based on temporal correlations is systematically assessed. The obtained results allow further insights into different pre-seismic stages based on the variability of electromagnetic emissions, which are probably associated with distinct geophysical processes.

Graded defragmentation of cortical neuronal firing during recovery of consciousness in rats

State-dependent neuronal firing patterns reflect changes in ongoing information processing and cortical function. A disruption of neuronal coordination has been suggested as the neural correlate of anesthesia. Here, we studied the temporal correlation patterns of ongoing spike activity, during a stepwise reduction of the volatile anesthetic desflurane, in the cerebral cortex of freely moving rats. We hypothesized that the recovery of consciousness from general anesthesia is accompanied by specific changes in the spatiotemporal pattern and correlation of neuronal activity. Sixty-four contact microelectrode arrays were chronically implanted in the primary visual cortex (contacts spanning 1.4-mm depth and 1.4-mm width) for recording of extracellular unit activity at four steady-state levels of anesthesia (8–2% desflurane) and wakefulness. Recovery of consciousness was defined as the regaining of the righting reflex (near 4%). High-intensity firing (HI) periods were segmented using a threshold (200-ms) representing the minimum in the neurons’ bimodal interspike interval histogram under anesthesia. We found that the HI periods were highly fragmented in deep anesthesia and gradually transformed to a near-continuous firing pattern at wakefulness. As the anesthetic was withdrawn, HI periods became longer and increasingly correlated among the units both locally and across remote recording sites. Paradoxically, in 4 of 8 animals, HI correlation was also high at the deepest level of anesthesia (8%) when local field potentials (LFP) were burst-suppressed. We conclude that recovery from desflurane anesthesia is accompanied by a graded defragmentation of neuronal activity in the cerebral cortex. Hypersynchrony during deep anesthesia is an exception that occurs only with LFP burst suppression.

SU-E-J-26: Real-Time Dynamic Management of the Correlation Between Internal Tumor Movement and External Abdominal Motion

Purpose: To reduce radiation exposure, internal/external correlation has been applied to derive tumor position based on radiation free external motion tracking. However, the reliability and stability of the correlation is a big concern. This study will characterize the temporal correlation patterns and provide a dynamic approach to build reliable correlation with minimum internal imaging. Methods: Retrospective analysis has been performed on simultaneously collected 3D internal and 1D external motion of eight patients with 51 treatment days and 165 beams. Both static and dynamic correlations are built through linear and quadratic functions. Static approach compares and summarized the correlation patterns of different granularities (different patients, treatment fractions, beams, and breathing cycles). Dynamic approach studies the temporal correlation patterns and derives tumor positions by adjusting three parameters: internal imaging frequency, previous acquired tumor positions, and future tumor positions. The correlation coefficient and the difference between derived and truly observed tumor positions are used to evaluate the accuracy. Results: The statistical analysis showed that the internal/external correlation is patient specific. Overall, the quadratic correlation outperforms the linear correlation. For the 8 patients, three patients have steady internal/external correlation (1 SD <0.35%), three with medium variation (<0.65%), and two with significant changes (<2.50%). The temporal changes of the last two patients showed that considerable correlation variations exist from day to day, beam to beam, and breathing cycle to cycle during the same beam delivery. Different combinations of the three parameters for the dynamic approach showed the most recent breathing patterns are of more importance in building a reliable correlation and internal imaging frequency of 1 Hz is sufficient for most patients. Conclusions: This study addressed several major concerns for establishing reliable internal/ external correlations. Dynamic correlation with proper settings will yield improved tumor tracking precision with minimal internal imaging. In part supported by NIH/NCI 1R21CA130849

Deriving daily snapshots of the Earth's gravity field from GRACE L1B data using Kalman filtering

Different GRACE data analysis centers provide temporal variations of the Earth's gravity field as monthly, 10‐daily or weekly mean fields. These solutions are derived independently for each time span, i.e., no correlation between the analyzed batches is considered. Following this procedure, an increase in temporal resolution is accompanied by a loss in accuracy. To avoid this problem, a new approach is followed, which takes into account the temporal correlations of the gravity field variations thus enabling the enhancement of the temporal resolution up to daily snapshots. The GRACE Level‐1B (L1B) instrument data processing is performed within the framework of a Kalman filter estimation procedure, where the information about the temporal correlation patterns can be derived from geophysical models. The WaterGAP hydrological model was analyzed to derive the required information in terms of an empirical auto‐covariance function. First results are presented and compared to GFZ‐RL04 monthly and weekly gravity field solutions.

Which solar EUV indices are best for reconstructing the solar EUV irradiance?

The solar EUV irradiance is of key importance for space weather. Most of the time, however, surrogate quantities such as EUV indices have to be used by lack of continuous and spectrally resolved measurements of the irradiance. The ability of such proxies to reproduce the irradiance from different solar atmospheric layers is usually investigated by comparing patterns of temporal correlations. We consider instead a statistical approach. The TIMED/SEE experiment, which has been continuously operating since February 2002, allows for the first time to compare in a statistical manner the EUV spectral irradiance to five EUV proxies: the sunspot number, the f10.7, Ca K, and Mg II indices, and the He I equivalent width. Using multivariate statistical methods, we represent in a single graph the measure of relatedness between these indices and various strong spectral lines. The ability of each index to reproduce the EUV irradiance is discussed; it is shown why so few lines can be effectively reconstructed from them. All indices exhibit comparable performance, apart from the sunspot number, which is the least appropriate. No single index can satisfactorily describe both the level of variability on time scales beyond 27 days, and relative changes of irradiance on shorter time scales.

Neural Dynamics and Cognitive Synthesis

Nervous systems facing complex environments have to balance two seemingly opposing requirements. First, there is a need quickly and reliably to extract important features from sensory inputs. This is accomplished by functionally segregated (specialized) sets of neurons, e.g. those found in different cortical areas. Second, there is a need to generate coherent perceptual and cognitive states allowing an organism to respond to objects and events, representing conjunctions of numerous individual features. This is accomplished by functional integration of the activity of specialized neurons through their dynamic interactions. These interactions produce patterns of temporal correlations or functional connectivity involving distributed neuronal populations, both within and across cortical areas. Empirical and computational studies suggesting that changes in functional connectivity may underlie specific perceptual and cognitive states involving the integration of information across specialized areas of the brain offer new insights into the linkage between neural dynamics and cognitive synthesis.

Exact BER for M-QAM with MRC and Imperfect Channel Estimation in Rician Fading Channels

In this paper, we study the effect of imperfect channel estimation (ICE) on the performance of M-level quadrature amplitude modulation (M-QAM) with maximum ratio combining (MRC) and pilot-symbol assisted modulation (PSAM) in generalized Rician fading channels. By expressing the bit error rate (BER) of MRC diversity M-QAM in terms of the distribution of new decision variables, we derive novel, exact, and easy-to-evaluate BER expressions for diversity M-QAM with channel estimation errors. Our results include versatile system and fading channel parameters (e.g., arbitrary spatial and temporal correlation patterns among the diversity branches), and are valid for arbitrary linear channel estimators and square and rectangular M -QAM with different constellation sizes. In addition, we evaluate the performance of minimum mean-squared error (MMSE)- and sinc-interpolator-based channel estimators with PSAM, and provide some new insights into the performance of M-QAM with PSAM in generalized fading channels

Limitations of Statistical Measures of Error in Assessing the Accuracy of Continuous Glucose Sensors

Various statistical methods are commonly used to assess the accuracy of near-continuous glucose sensors. The performance and reliability of these methods have not been well described. We used computer simulation to describe the behavior of several statistical measures including error grid analysis, receiver operating characteristics, correlation, and repeated measures under varying conditions. Actual data from an inpatient accuracy study conducted by the Diabetes Research in Children Network (DirecNet) were also used to demonstrate these limitations. Sensors that were made artificially inaccurate by randomly shuffling the pairings to reference values still fell in Zone A or B 78% of the time for the Clarke grid and 79% of the time for the modified grid. Area under the curve values for these shuffled pairs averaged 64% for hypoglycemia and 68% for hyperglycemia. Continuous error grid analysis resulted in 75% of shuffled pairs designated as "Accurate Readings" or "Benign Errors." Correlation analysis gave inconsistent results for sensors simulated to have identical accuracies with values ranging from 0.50 to 0.96. Simplistic repeated-measures analyses accounting for subject effects, but ignoring temporal correlation patterns substantially inflated the probability of falsely obtaining a statistically significant result. In simulations where the null hypothesis was correct, 23% of observed P values were <0.05 and 12% of observed P values were <0.01. Commonly used statistical methods can give overly optimistic and/or inconsistent notions of sensor accuracy if results are not placed in proper context. Novel techniques are needed to assess the accuracy of near-continuous glucose sensors.

Classes of network connectivity and dynamics

AbstractMany kinds of complex systems exhibit characteristic patterns of temporal correlations that emerge as the result of functional interactions within a structured network. One such complex system is the brain, composed of numerous neuronal units linked by synaptic connections. The activity of these neuronal units gives rise to dynamic states that are characterized by specific patterns of neuronal activation and co‐activation. These patterns, called functional connectivity, are possible neural correlates of perceptual and cognitive processes. Which functional connectivity patterns arise depends on the anatomical structure of the underlying network, which in turn is modified by a broad range of activity‐dependent processes. Given this intricate relationship between structure and function, the question of how patterns of anatomical connectivity constrain or determine dynamical patterns is of considerable theoretical importance. The present study develops computational tools to analyze networks in terms of their structure and dynamics. We identify different classes of network, including networks that are characterized by high complexity. These highly complex networks have distinct structural characteristics such as clustered connectivity and short wiring length similar to those of large‐scale networks of the cerebral cortex. © 2002 Wiley Periodicals, Inc.