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Multi-layered graphs are popular in mobility studies because transportation data include multiple modalities, such as railways, buses, and taxis. Another example of a multi-layered graph is the time series of mobility when periodicity is considered. The graphs are analyzed using standard signal processing methods such as singular value decomposition and tensor analysis, which can estimate missing values. However, their feature extraction abilities are insufficient for optimizing mobility networks. This study proposes a method that combines the Wasserstein non-negative matrix factorization (W-NMF) with line graphs to obtain low-dimensional representations of multi-layered graphs. A line graph is defined as the dual graph of a graph, where the vertices correspond to the edges of the original graph, and the edges correspond to the vertices. Thus, the shortest path length between two vertices in the line graph corresponds to the distance between the edges in the original graph. Through experiments using synthetic and benchmark datasets, we show that the performance and robustness of our method are superior to conventional methods. Additionally, we apply our method to real-world taxi origin—destination data as a mobility dataset and discuss the findings.

The zero-suppressed binary decision diagram (ZDD) is a compact data structure widely used for the efficient representation of families of sparse subsets. Its inherent recursive structure also facilitates easy diagram manipulation and family operations. Practical applications generally fall under discrete optimization, such as combinatorial problems and graph theory. Given its utility, summarizing the subsets represented in the diagram using key metrics is of great value as this provides valuable insights into the characteristics of the family. The paper proposes a recursive algorithm to extract information on moments from families represented as a ZDD. Given a value for every element in the universe, the value of a subset is first formulated as the sum of the values of its elements. The moments of a family are then calculated as the mean of the exponentiated subset values, akin to the method of moments. Leveraging the structure of the ZDD, the proposed algorithm recursively traverses a given diagram for efficient moments evaluation via multinomial expansion. Its utility is then demonstrated with three classical problems—power sets, the knapsack problem, and paths in graphs—offering orders of magnitude increase in computational efficiency relative to conventional method. Overall, the proposed algorithm enhances the functionality of the ZDD by introducing an efficient family operation to uncover the distribution of subset values in a represented family.

Behavioral and fMRI experiments using a dot estimation task provide insight into how social interactions create and stabilize perceptual norms as socially shared realities within groups.

BackgroundPhenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge.MethodsWe recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We categorized 65 (13 questionnaire items/day × 5 days) dimensional DRS-R-98 scores using unsupervised machine learning (K-means clustering) to derive a small set of grouped features representing distinct symptoms across all participants. We then reapplied K-means clustering to this set of grouped features to delineate multiple clusters of delirium symptoms.ResultsParticipants were 286 patients, of whom 91 developed delirium defined according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Following the first K-means clustering, we derived four grouped symptom features: (1) mixed motor, (2) cognitive and higher-order thinking domain with perceptual disturbance and thought content abnormalities, (3) acute and temporal response, and (4) sleep–wake cycle disturbance. Subsequent K-means clustering permitted classification of participants into seven subgroups: (i) cognitive and higher-order thinking domain dominant delirium, (ii) prolonged delirium, (iii) acute and brief delirium, (iv) subsyndromal delirium-enriched, (v) subsyndromal delirium-enriched with insomnia, (vi) insomnia, and (vii) fit.ConclusionWe found that patients who have undergone invasive cancer resection can be delineated using unsupervised machine learning into three delirium clusters, two subsyndromal delirium clusters, and an insomnia cluster. Validation of clusters and research into the pathophysiology underlying each cluster will help to elucidate the mechanisms of postoperative delirium after invasive cancer surgery.

The Japan Electric Power Exchange (JEPX) provides a platform for the trading of electric energy in a manner similar to more traditional financial markets. As the number of market agents increase, there is an increasing need for effective price-forecasting models. Electricity price data are observed to exhibit periods of relatively stable, i.e., low-magnitude, low-variance prices interspersed with periods of higher prices accompanied by larger uncertainty. The price data time series therefore exhibits a temporal non-stationarity characteristic that is difficult to capture with typical time series modeling frameworks. In this paper, we implement models for the occurrence of price spike events where spikes are defined as observing prices above a predefined threshold set here at 25 JPY/kWh. This value corresponds to about the 90th percentile of observed prices during peak trading periods. The price spikes time series is observed to be rare events that occur in clusters. We therefore propose to model the data as a Hawkes process whereby the occurrence of a spike event increases the probability of observing more spikes in the period immediately following a price spike event. We test two variations of the classical Hawkes model: the first variation models the change in the magnitude of the underlying intensity as a function of the magnitude of the price spike while the second variation models the change in the decay rate of the underlying intensity as a function of the magnitude of the price spike. An analysis of the performance of the models based on the mean absolute error (MAE) of the spike occurrence probability, a weighted accuracy index, and the Matthews correlation coefficient (MCC) metrics shows the effectiveness of the variable magnitude variation of the Hawkes model in generating short-term forecasts of the occurrence of price spike events. The modified Hawkes model especially outperforms other candidate models as the length of the forecasting horizon increases.

Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated—whether as a subtype of depression, or as a distinct disorder altogethe—interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.

Extraction of complex temporal patterns, such as human behaviors, from time series data is a challenging yet important problem. The double articulation analyzer has been previously proposed by Taniguchi et al. to discover a hierarchical structure that leads to complex temporal patterns. It segments time series into hierarchical state subsequences, with the higher level and the lower level analogous to words and phonemes, respectively. The double articulation analyzer approximates the sequences in the lower level by linear functions. However, it is not suitable to model real behaviors since such a linear function is too simple to represent their non-linearity even after the segmentation. Thus, we propose a new method that models the lower segments by fitting autoregressive functions that allows for more complex dynamics, and discovers a hierarchical structure based on these dynamics. To achieve this goal, we propose a method that integrates the beta process—autoregressive hidden Markov model and the double articulation by nested Pitman-Yor language model. Our results showed that the proposed method extracted temporal patterns in both low and high levels from synthesized datasets and a motion capture dataset with smaller errors than those of the double articulation analyzer.

Physiotherapy is a labor-intensive process that has become increasingly inaccessible. Existing telehealth solutions overcome many of the logistical problems, but they are cumbersome to re-calibrate for the various exercises involved. To facilitate self-exercise efficiently, we developed a framework for personalized physiotherapy exercises. Our approach eliminates the need to re-calibrate for different exercises, using only few user-specific demonstrations available during collocated therapy. Two types of augmented feedback are available to the user for self-correction. The framework's utility was demonstrated for the sit-to-stand task, an important activity of daily living. Although further testing is necessary, our results suggest that the framework can be generalized to the learning of arbitrary motor behaviors.

Emotional contagion is a primitive form of empathy that does not need higher psychological functions. Recent studies reported that emotional contagion exists not only between humans but also among various animal species. The dog (Canis familiaris) is a unique animal and the oldest domesticated species. Dogs have coexisted with humans for more than 30,000 years and are woven into human society as partners bonding with humans. Dogs have acquired human-like communication skills and, likely as a result of the domestication process, the ability to read human emotions; therefore, it is feasible that there may be emotional contagion between human and dogs. However, the higher time-resolution of measurement of emotional contagion between them is yet to be conducted. We assessed the emotional reactions of dogs and humans by heart rate variability (HRV), which reflects emotion, under a psychological stress condition on the owners. The correlation coefficients of heart beat (R-R) intervals (RRI), the standard deviations of all RR intervals (SDNN), and the square root of the mean of the sum of the square of differences between adjacent RR intervals (RMSSD) between dogs and owners were positively correlated with the duration of dog ownership. Dogs’ sex also influenced the correlation coefficients of the RRI, SDNN, and RMSSD in the control condition; female showed stronger values. These results suggest that emotional contagion from owner to dog can occur especially in females and the time sharing the same environment is the key factor in inducing the efficacy of emotional contagion.

Duchenne muscular dystrophy (DMD) is a genetic disorder that results from deficiency of the dystrophin protein. In recent years, DMD pathological models have been created using induced pluripotent stem (iPS) cells derived from DMD patients. In addition, gene therapy using CRISPR-Cas9 technology to repair the dystrophin gene has been proposed as a new treatment method for DMD. However, it is not known whether the contractile function of myotubes derived from gene-repaired iPS cells can be restored. We therefore investigated the maturation of myotubes in electrical pulse stimulation culture and examined the effect of gene repair by observing the contractile behaviour of myotubes. The contraction activity of myotubes derived from dystrophin-gene repaired iPS cells was improved by electrical pulse stimulation culture. The iPS cell method used in this study for evaluating muscle contractile activity is a useful technique for analysing the mechanism of hereditary muscular disease pathogenesis and for evaluating the efficacy of new drugs and gene therapy.

Introduction: Tissue-engineered skeletal muscle constructs should be designed to generate contractile force with directional movement. Because electrical impulses from a somatic nervous system are crucial for in vivo skeletal muscle development, electrical pulse stimulation (EPS) culture as an artificial exercise is essential to fabricate functional skeletal muscle tissues in vitro. To further improve muscle functions, the activation of cell-signaling pathways from myogenic growth factors, such as insulin-like growth factor (IGF)-I, is also important. Because tissue-engineered skeletal muscle constructs should maintain a high cell-dense structure, the expression of an anti-apoptotic factor, such as B-cell lymphoma 2 (Bcl-2), could be effective in preventing cell death. Methods: In the present study, myoblasts were genetically modified with inducible expression units of IGF-I and Bcl-2 genes, and the tissue-engineered skeletal muscle constructs fabricated from the myoblasts were cultured under continuous EPS. Results: Overexpression of IGF-I gene induced muscular hypertrophy in the muscle tissue constructs, and Bcl-2-overexpressing myoblasts formed significantly cell-dense and viable muscle tissue constructs. Furthermore, the combination of IGF-I and Bcl-2 gene transfer with EPS culture highly improved the force generation of the tissue-engineered skeletal muscle constructs. Conclusions: This approach has the potential to yield functional skeletal muscle substitutes with high force generation ability.

Actin and actin-associated proteins migrate within various cell types. To uncover the mechanism of their migration, we analyzed actin waves, which translocate actin and actin-associated proteins along neuronal axons toward the growth cones. We found that arrays of actin filaments constituting waves undergo directional assembly and disassembly, with their polymerizing ends oriented toward the axonal tip, and that the lateral side of the filaments is mechanically anchored to the adhesive substrate. A combination of live-cell imaging, molecular manipulation, force measurement, and mathematical modeling revealed that wave migration is driven by directional assembly and disassembly of actin filaments and their anchorage to the substrate. Actin-associated proteins co-migrate with actin filaments by interacting with them. Furthermore, blocking this migration, by creating an adhesion-free gap along the axon, disrupts axonal protrusion. Our findings identify a molecular mechanism that translocates actin and associated proteins toward the cell’s leading edge, thereby promoting directional cell motility.

Experiments with drug-induced epilepsy in rat brains and epileptic human brain region reveal that focal cooling can suppress epileptic discharges without affecting the brain's normal neurological function. Findings suggest a viable treatment for intractable epilepsy cases via an implantable cooling device. However, precise mechanisms by which cooling suppresses epileptic discharges are still not clearly understood. Cooling experiments in vitro presented evidence of reduction in neurotransmitter release from presynaptic terminals and loss of dendritic spines at post-synaptic terminals offering a possible synaptic mechanism. We show that termination of epileptic discharges is possible by introducing a homogeneous temperature factor in a neural mass model which attenuates the post-synaptic impulse responses of the neuronal populations. This result however may be expected since such attenuation leads to reduced post-synaptic potential and when the effect on inhibitory interneurons is less than on excitatory interneurons, frequency of firing of pyramidal cells is consequently reduced. While this is observed in cooling experiments in vitro, experiments in vivo exhibit persistent discharges during cooling but suppressed in magnitude. This leads us to conjecture that reduction in the frequency of discharges may be compensated through intrinsic excitability mechanisms. Such compensatory mechanism is modelled using a reciprocal temperature factor in the firing response function in the neural mass model. We demonstrate that the complete model can reproduce attenuation of both magnitude and frequency of epileptic discharges during cooling. The compensatory mechanism suggests that cooling lowers the average and the variance of the distribution of threshold potential of firing across the population. Bifurcation study with respect to the temperature parameters of the model reveals how heterogeneous response of epileptic discharges to cooling (termination or suppression only) is exhibited. Possibility of differential temperature effects on post-synaptic potential generation of different populations is also explored.

Abstract Electrical impulses are necessary for proper in vivo skeletal muscle development. To fabricate functional skeletal muscle tissues in vitro, recapitulation of the in vivo niche, including physical stimuli, is crucial. Here, we report a technique to engineer skeletal muscle tissues in vitro by electrical pulse stimulation (EPS). Electrically excitable tissue-engineered skeletal muscle constructs were stimulated with continuous electrical pulses of 0.3 V/mm amplitude, 4 ms width and 1 Hz frequency, resulting in a 4.5-fold increase in force at day 14. In myogenic differentiation culture, the percentage of peak twitch force (%Pt) was determined as the load on the tissue constructs during the artificial exercise induced by continuous EPS. We optimized the stimulation protocol, wherein the tissues were first subjected to 24.5%Pt, which was increased to 50–60%Pt as the tissues developed. This technique may be a useful approach to fabricate tissue-engineered functional skeletal muscle constructs.

When viewing a painting, artists perceive more information from the painting on the basis of their experience and knowledge than art novices do. This difference can be reflected in eye scan paths during viewing of paintings. Distributions of scan paths of artists are different from those of novices even when the paintings contain no figurative object (i.e. abstract paintings). There are two possible explanations for this difference of scan paths. One is that artists have high sensitivity to high-level features such as textures and composition of colors and therefore their fixations are more driven by such features compared with novices. The other is that fixations of artists are more attracted by salient features than those of novices and the fixations are driven by low-level features. To test these, we measured eye fixations of artists and novices during the free viewing of various abstract paintings and compared the distribution of their fixations for each painting with a topological attentional map that quantifies the conspicuity of low-level features in the painting (i.e. saliency map). We found that the fixation distribution of artists was more distinguishable from the saliency map than that of novices. This difference indicates that fixations of artists are less driven by low-level features than those of novices. Our result suggests that artists may extract visual information from paintings based on high-level features. This ability of artists may be associated with artists' deep aesthetic appreciation of paintings.