Monitoring muscle activity in pediatric SCI: Insights from sensorized rocking chairs and machine-learning.

Introduction: Activity-based therapy is effective at improving trunk control in children with spinal cord injury. A prototype sensorized rocking chair was developed and confirmed as an activity that activates trunk muscles. This study uses data collected from the chair to predict muscle use during rocking. Methods: The prototype rocking chair included sensors to detect forces, accelerations, as well child and chair movement. Children with spinal cord injury and typically developing children (2-12 years), recruited under an approved IRB protocol, were observed rocking while sensor and electromyography data were collected from arm, leg, and trunk muscles. Features from sensor data were used to predict muscle activation using multiple linear regression, regression learning, and neural network modeling. Correlation analysis examined individual sensor contributions to predictions. Results: Neural network models outperformed regression models. Multiple linear regression predictions significantly correlated (p < 0.05) with targets for four of eleven children with SCI, while decision tree regression predictions correlated for five children. Neural network predictions correlated for all children. Conclusions: Embedded sensors capture useful information about muscle activation, and machine learning techniques can be used to inform therapists. Further work is warranted to refine prediction models and to investigate how well results can be generalized.

Exploring gene regulation and biological processes in insects: Insights from omics data using gene regulatory network models.

Gene regulatory network (GRN) comprises complicated yet intertwined gene-regulator relationships. Understanding the GRN dynamics will unravel the complexity behind the observed gene expressions. Insect gene regulation is often complicated due to their complex life cycles and diverse ecological adaptations. The main interest of this review is to have an update on the current mathematical modelling methods of GRNs to explain insect science. Several popular GRN architecture models are discussed, together with examples of applications in insect science. In the last part of this review, each model is compared from different aspects, including network scalability, computation complexity, robustness to noise and biological relevancy.

Global topology of human connectome is insensitive to early life environments - A prospective longitudinal study of the general population.

The widely acknowledged detrimental impact of early adversity on child development has driven efforts to understand the underlying mechanisms that may mediate these effects within the developing brain. Recent efforts have begun to move beyond associating adversity with the morphology of individual brain regions towards determining if and how adversity might shape their interconnectivity. However, whether adversity effects a global shift in the organisation of whole-brain networks remains unclear. In this study, we assessed this possibility using parental questionnaire and diffusion imaging data from The Avon Longitudinal Study of Parents and Children (ALSPAC, N = 913), a prospective longitudinal study spanning more than 20 years. We tested whether a wide range of adversities-including experiences of abuse, domestic violence, physical and emotional cruelty, poverty, neglect, and parental separation-measured by questionnaire within the first seven years of life were significantly associated with the tractography-derived connectome in young adulthood. We tested this across multiple measures of organisation and using a computational model that simulated the wiring economy of the brain. We found no significant relationships between early exposure to any form of adversity and the global organisation of the structural connectome in young adulthood. We did detect local differences in the medial prefrontal cortex, as well as an association between weaker brain wiring constraints and greater externalising behaviour in adolescence. Our results indicate that further efforts are necessary to delimit the magnitude and functional implications of adversity-related differences in connectomic organization. RESEARCH HIGHLIGHTS: Diverse prospective measures of the early-life environment do not predict the organisation of the DTI tractography-derived connectome in young adulthood Wiring economy of the connectome is weakly associated with externalising in adolescence, but not internalising or cognitive ability Further work is needed to establish the scope and significance of global adversity-related differences in the structural connectome.

Application of Multivariate Regression and Artificial Neural Network Modelling for Prediction of Physicochemical Properties of Grape-Skin Compost.

The reusability of by-products in the food industry is consistent with sustainable and greener production; therefore, the aim of this paper was to evaluate the applicability of multiple linear regression (MLR), piecewise linear regression (PLR) and artificial neural network models (ANN) to the prediction of grape-skin compost's physicochemical properties (moisture, dry matter, organic matter, ash content, carbon content, nitrogen content, C/N ratio, total colour change of compost samples, pH, conductivity, total dissolved solids and total colour change of compost extract samples) during in-vessel composting based on the initial composting conditions (air-flow rate, moisture content and day of sampling). Based on the coefficient of determination for prediction, the adjusted coefficient of determination for calibration, the root-mean-square error of prediction (RMSEP), the standard error of prediction (SEP), the ratio of prediction to deviation (RPD) and the ratio of the error range (RER), it can be concluded that all developed MLR and PLR models are acceptable for process screening. Furthermore, the ANN model developed for predicting moisture and dry-matter content can be used for quality control (RER >11). The obtained results show the great potential of multivariate modelling for analysis of the physicochemical properties of compost during composting, confirming the high applicability of modelling in greener production processes.

On the characteristics of natural hydraulic dampers: An image-based approach to study the fluid flow behaviour inside the human meniscal tissue.

The meniscal tissue is a layered material with varying properties influenced by collagen content and arrangement. Understanding the relationship between structure and properties is crucial for disease management, treatment development, and biomaterial design. The internal layer of the meniscus is softer and more deformable than the outer layers, thanks to interconnected collagen channels that guide fluid flow. To investigate these relationships, we propose an integrated approach that combines Computational Fluid Dynamics (CFD) with Image Analysis (CFD-IA). We analyze fluid flow in the internal architecture of the human meniscus across a range of inlet velocities (0.1 mm/s to 1.6 m/s) using high-resolution 3D micro-computed tomography scans. Statistical correlations are observed between architectural parameters (tortuosity, connectivity, porosity, pore size) and fluid flow parameters (Re number distribution, permeability). Some channels exhibit Re values of 1400 at an inlet velocity of 1.6 m/s, and a transition from Darcy's regime to a non-Darcian regime occurs around an inlet velocity of 0.02 m/s. Location-dependent permeability ranges from 20-32 Darcy. Regression modelling reveals a strong correlation between fluid velocity and tortuosity at high inlet velocities, as well as with channel diameter at low inlet velocities. At higher inlet velocities, flow paths deviate more from the preferential direction, resulting in a decrease in the concentration parameter by an average of 0.4. This research provides valuable insights into the fluid flow behaviour within the meniscus and its structural influences. 3D models and image stack are available to download at https://doi.org/10.5281/zenodo.10401592. STATEMENT OF SIGNIFICANCE: The meniscus is a highly porous soft tissue with remarkable properties of load transfer and energy absorption. We give insight on the mechanism of energy absorption from high resolution uCT scans, never presented before, and a new method which combine CFD and image. The structure is similar to a sandwich structure with a stiff outside layer and a soft internal layer made of collagen channels oriented in a preferential direction guiding the fluid flow, enabling it to accommodate deformation and dissipate energy, making it a potentially optimized damping system. We investigate architectural/ fluid flow parameters- fluid regimes relationship, which is of interest of the readers working on designing suitable biomimetic systems that can be adopted for replacement.

Peritraumatic physical symptoms and the clinical trajectory of PTSD after a terrorist attack: a network model approach.

Introduction: Following a mass casualty event, such as the Paris terrorist attacks of 13 November 2015, first responders need to identify individuals at risk of PTSD. Physical peritraumatic symptoms involving the autonomic nervous system may be useful in this task.Objective: We sought to determine the trajectory of physical response intensity in individuals exposed to the Paris terrorist attacks using repeated measures, and to examine its associations with PTSD. Using network modelling, we examined whether peritraumatic physical symptom associations differed by PTSD status.Methods: Physical reactions were assessed using the Subjective Physical Reactions Scale at three time points: peritraumatic by retrospective recall, then current at one year (8-18 months) and three years (30-42 months) after the attacks. Interaction networks between peritraumatic physical reactions were compared according to PTSD status.Results: On the one hand, the reported intensity of physical reactions was significantly higher in the PTSD group at all time points. On the other hand, using the dynamic approach, more robust positive interactions between peritraumatic physical reactions were found in the PTSD group one and three years after the attacks. Negative interactions were found in the no-PTSD group at one year. Peritraumatic physical numbness was found to be the most central network symptom in the PTSD group, whereas it was least central in the no-PTSD group.Discussion: Network analysis of the interaction between peritraumatic physical subjective responses, particularly physical numbness, may provide insight into the clinical course of PTSD. Our knowledge of the brain regions involved in dissociation supports the hypothesis that the periaqueductal grey may contribute to the process leading to physical numbing.Conclusions: Our findings highlight the role of peritraumatic somatic symptoms in the course of PTSD. Peritraumatic physical numbness appears to be a key marker of PTSD and its identification may help to improve early triage.

Physical numbness was found to be a central symptom in people developing PTSD in our study examining peritraumatic physical symptoms related to the 2015 Paris terrorist attacks.

Investigating the muti-scaling properties and connectedness of the sovereign bond yields: Hurst exponent and network analysis approach.

Using daily yield data of 14 sovereign bond markets from emerging and developed economies from July 10, 2000, to July 10, 2022, we examine their scaling properties using generalized Hurst exponent and spectral density analysis and investigate the connectedness based on a network analysis approach. We consider the yields of 2-year and 10-year bond yields to investigate the scaling properties for short- and long-term sovereign bonds. This selection also allows us to examine sovereign bond spreads with respect to the USA. We also use regularized partial correlation network analysis to connect different countries in communities based on yields. We find that the scaling behavior of the bond yields for both terms fits well using the Hurst exponent and spectral analysis confirms this finding. Moreover, we also find that even though bonds in both cohorts show anti-persistent behavior except that of the USA, the developed economies' bond yields are relatively less anti-persistent as compared to those of emerging economies. The networks of both the 2-year and 10-year yields indicate community formation in various countries which provides diversification benefits to the investors. Most of the emerging countries are classified into one community in the long-tenure bonds as well but this concentration is more evident in the short-tenure bonds.

An integrated framework for optimal infrastructure planning for decarbonising heating.

This paper presents the HEGIT (Heat, Electricity and Gas Infrastructure and Technology) model for optimal infrastructure planning for decarbonising heating in buildings. HEGIT is an optimisation model based on Mixed Integer Linear Programming. The model co-optimises the integrated operation and capacity expansion planning of electricity and gas grids as well as heating technologies on the consumer side while maintaining the security of supply and subject to different environmental, operational and system-wide constraints. The three main features of the HEGIT model are:•It incorporates an integrated unit commitment and capacity expansion problem for coordinated operation and long-term investment planning of the electricity and gas grids.•It incorporates the flexible operation of heating technologies in buildings and demand response in operation and long-term investment planning of gas and electricity grids.•It incorporates a multi-scale techno-economic representation of heating technologies design features into the whole energy system modelling and capacity planning.These features enable the model to quantify the impacts of different policies regarding decarbonising heating in buildings on the operation and long-term planning of electricity and gas grids, identify the cost-optimal use of available resources and technologies and identify strategies for maximising synergies between system planning goals and minimising trade-offs. Moreover, the multi-scale feature of the model allows for multi-scale system engineering analysis of decarbonising heating, including system-informed heating technology design, identifying optimal operational setups at the consumer end, and assessing trade-offs between consumer investment in heating technologies and infrastructure requirements in different heat decarbonisation pathways.

Meta-Dynamic Network Modelling for Biochemical Networks.

ODE modelling requires accurate knowledge of parameter and state variable values to deliver accurate and robust predictions. Parameters and state variables, however, are rarely static and immutable entities, especially in a biological context. This observation undermines the predictions made by ODE models that rely on specific parameter and state variable values and limits the contexts in which their predictions remain accurate and useful. Meta-dynamic network (MDN) modelling is a technique that can be synergistically integrated into an ODE modelling pipeline to assist in overcoming these limitations. The core mechanic of MDN modelling is the generation of a large number of model instances, each with a unique set of parameters and/or state variable values, followed by the simulation of each to determine how parameter and state variable variation affects protein dynamics. This process reveals the range of possible protein dynamics for a given network topology. Since MDN modelling is integrated with traditional ODE modelling, it can also be used to investigate the underlying causal mechanics. This technique is particularly suited to the investigation of network behaviors in systems that are highly heterogenous or systems wherein the network properties can change over time. MDN is a collection of principles rather than a strict protocol, so in this chapter, we have introduced the core principles using an example, the Hippo-ERK crosstalk signalling network.

Metabolic network reconstruction of Euglena gracilis: Current state, challenges, and applications.

A metabolic model, representing all biochemical reactions in a cell, is a prerequisite for several approaches in systems biology used to explore the metabolic phenotype of an organism. Despite the use of Euglena in diverse industrial applications and as a biological model, there is limited understanding of its metabolic network capacity. The unavailability of the completed genome data and the highly complex evolution of Euglena are significant obstacles to the reconstruction and analysis of its genome-scale metabolic model. In this mini-review, we discuss the current state and challenges of metabolic network reconstruction in Euglena gracilis. We have collated and present the available relevant data for the metabolic network reconstruction of E. gracilis, which could be used to improve the quality of the metabolic model of E. gracilis. Furthermore, we deliver the potential applications of the model in metabolic engineering. Altogether, it is supposed that this mini-review would facilitate the investigation of metabolic networks in Euglena and further lay out a direction for model-assisted metabolic engineering.

Utility of Environmental Complexity as a Predictor of Alzheimer's Disease Diagnosis: A Big-Data Machine Learning Approach.

BACKGROUND: Rural-urban differences and spatial navigation deficits have received much attention in Alzheimer's Disease research. While individual environmental and neighborhood factors have been independently investigated, their integrative, multifactorial effects on Alzheimer's diagnosis have not. Here we explore this "environmental complexity" for predictive power in classifying Alzheimer's from cognitively-normal status. METHODS: We utilized data from the National Alzheimer's Coordinating Center (NACC) uniform data set containing annual visits since 2005 and selected individuals with multiple visits and who remained in their zipcode (N = 22,553). We georeferenced each subject with 3-digit zipcodes of their residences since entering the program. We calculated environmental complexity measures using geospatial tools from street networks and landmarks for spatial navigation in subjects' zipcode zones. Zipcode zones were grouped into two cognitive classes (Cognitively-Normal and Alzheimer's-inclined) based on the ratios of AD and dementia subjects to all subjects in an individual zipcode zone. We randomly selected 80% of the data to train a neural network classifier model on environmental complexity measures to predict the cognitive class for each zone, controlling for salient demographic variables. The remaining 20% served as the test set for performance evaluation. RESULTS: Our proposed model reached excellent classification ability on the testing data: 83.87% accuracy, 95.23% precision, 83.33% recall, and 0.8889 F1-score (F1-score=1 for perfect prediction). The most salient features of "Alzheimer's-inclined" zipcode zones included longer street-length average, higher circuity, and slightly fewer points of interest. Most "cognitively-normal" zipcode zones appeared in or near urban areas with high environmental complexity measures. CONCLUSION: Environmental complexity, reflected in frequency and density of street networks and landmarks features, predicted with high precision the cognitive status of 3-digit zipcode zones based on the etiologic diagnoses and observed cognitive impairment of NACC subjects residing in these zones. The zipcode zones vary widely in size (1.6 km2 to 35,241 km2), and large zipcode zones suffer high spatial heterogeneity. Other proven AD risk factors, such as PM2.5, disperse across zones, and so do individual's activities, leading to spatial uncertainty. Nevertheless, the model classifies diagnosis well, establishing the need for prospective experiments to quantify effects of environmental complexity on Alzheimer's development.

A Cross-Country Network Analysis of Resilience Systems in Young Adults.

Multisystemic resilience has been conceptualised as involving a constellation of protective factors which operate at different levels to promote adaptation and thriving despite experiences of adversity. We used network modelling to discover how protective factors at two different systemic levels (intrapersonal strengths and social-ecological resources) interrelate, drawing on survey data from 5283 emerging adults (M = 24.53 years; 52% female) in Brazil, China, Indonesia, Russia, Thailand, the US and Vietnam. Results indicated that the level of connectivity within and between protective factor levels was similar between the countries, but that there was substantial variation in the specific interrelations among protective factors (both within and between levels), including the presence of some country-specific negative interrelations between protective factors at different levels. The findings support the importance of cultural context in studies of resilience, with implications for the development of appropriate resilience-building interventions for this age group.

A pore-scale investigation of the effect of nanoparticle injection on properties of sandy porous media.

Nanoremediation is a new groundwater remediation technology in which nanoparticles (NPs) are injected into the sub-surface to promote in-situ degradation of aquifer contaminants. Although nanoremediation is an effective process to eliminate contaminants in-situ, its success relies on sufficiently mobile NPs that can reach the contaminated zones and remain there to facilitate chemical degradation of contaminants. Therefore, understanding the main parameters that control the mobility and retention of NPs in saturated porous media is a key component of designing a successful nanoremediation process. This work presents the outcome of a pore-scale study of nZVI NP (zero-valent iron) transport in sandy porous media using the non-destructive 3D imaging technique, X-ray computed micro-tomography (X-ray micro-CT). We investigate the effect of grain size (fine, coarse, carbonate and mixed sand) and composition (carbonate vs sand grains) on the mobility and retention of NPs in sand columns. To achieve this, we used four columns packed with grains of different sizes and compositions. The main contribution of this work is, therefore, to understand the effect of NP injection on the structural and geometric properties of sandy porous media and to identify the main pore-scale mechanisms controlling NP transport and entrapment. Our experiment shows that the pore geometries change because of NP injection. Pore clogging is evidenced through pore size and throat size distribution displaying a shift to the left with a noticeable reduction in pore connectivity in all the columns. The porosity and permeability of the columns studied display significant reduction as result of the NP injection.

Mechanisms underlying divergent relationships between Ca2+ and YAP/TAZ signalling.

Yes-associated protein (YAP) and its homologue TAZ are transducers of several biochemical and biomechanical signals, integrating multiplexed inputs from the microenvironment into higher level cellular functions such as proliferation, differentiation and migration. Emerging evidence suggests that Ca2+ is a key second messenger that connects microenvironmental input signals and YAP/TAZ regulation. However, studies that directly modulate Ca2+ have reported contradictory YAP/TAZ responses: in some studies, a reduction in Ca2+ influx increases the activity of YAP/TAZ, while in others, an increase in Ca2+ influx activates YAP/TAZ. Importantly, Ca2+ and YAP/TAZ exhibit distinct spatiotemporal dynamics, making it difficult to unravel their connections from a purely experimental approach. In this study, we developed a network model of Ca2+ -mediated YAP/TAZ signalling to investigate how temporal dynamics and crosstalk of signalling pathways interacting with Ca2+ can alter the YAP/TAZ response, as observed in experiments. By including six signalling modules (e.g. GPCR, IP3-Ca2+ , kinases, RhoA, F-actin and Hippo-YAP/TAZ) that interact with Ca2+ , we investigated both transient and steady-state cell response to angiotensin II and thapsigargin stimuli. The model predicts that stimuli, Ca2+ transients and frequency-dependent relationships between Ca2+ and YAP/TAZ are primarily mediated by cPKC, DAG, CaMKII and F-actin. Simulation results illustrate the role of Ca2+ dynamics and CaMKII bistable response in switching the direction of changes in Ca2+ -induced YAP/TAZ activity. A frequency-dependent YAP/TAZ response revealed the competition between upstream regulators of LATS1/2, leading to the YAP/TAZ non-monotonic response to periodic GPCR stimulation. This study provides new insights into underlying mechanisms responsible for the controversial Ca2+ -YAP/TAZ relationship observed in experiments. KEY POINTS: YAP/TAZ integrates biochemical and biomechanical inputs to regulate cellular functions, and Ca2+ acts as a key second messenger linking cellular inputs to YAP/TAZ. Studies have reported contradictory Ca2+ -YAP/TAZ relationships for different cell types and stimuli. A network model of Ca2+ -mediated YAP/TAZ signalling was developed to investigate the underlying mechanisms of divergent Ca2+ -YAP/TAZ relationships. The model predicts context-dependent Ca2+ transient, CaMKII bistable response and frequency-dependent activation of LATS1/2 upstream regulators as mechanisms governing the Ca2+ -YAP/TAZ relationship. This study provides new insights into the underlying mechanisms of the controversial Ca2+ -YAP/TAZ relationship to better understand the dynamics of cellular functions controlled by YAP/TAZ activity.

Boredom is the root of all evil-or is it? A psychometric network approach to individual differences in behavioural responses to boredom.

Functional accounts of boredom propose that boredom serves as an impartial signal to change something about the current situation, which should give rise to adaptive and maladaptive behaviour alike. This seemingly contrasts with research on boredom proneness, which has overwhelmingly shown associations with maladaptive behaviour. To shed light on this discrepancy, we disentangled boredom proneness from individual differences in (i) the urge to avoid and escape boredom and (ii) adaptive and maladaptive ways of dealing with boredom by developing corresponding trait scales. In a study with N = 636 participants, psychometric network modelling revealed tight associations between boredom proneness and less adaptive and (especially) more maladaptive ways of dealing with boredom. However, its associations with the urge to avoid and escape boredom were rather weak. Importantly, a higher urge to avoid and escape boredom was linked not only to more maladaptive but also to more adaptive ways of dealing with boredom. This pattern of results was robust across various specific behaviours that have previously been linked to boredom. Our findings provide novel evidence for functional accounts of boredom from an individual difference perspective, cautioning against a shallow view of boredom as being associated with purely maladaptive behaviour.

Financial interbanking networks resilience under shocks propagation.

The concept of resilience-i.e., the ability of a unified structure to absorb shocks-is of high relevance in the context of network modelling and analysis, mainly when referred to finance. This paper starts from this premise, and deals with the resilience of a financial interbanking system. At this aim, we firstly introduce a new measure of the resilience of a network, by taking into full consideration the influence of the topology of the network and the weights of its links in the shocks propagation; then, we build one financial network model related to the quarterly-based interbanking sector, whose weights are calibrated on high quality empirical data; lastly, we compute the resilience measure of the considered networks. A discussion of the results is provided, by considering both finance and network theory perspectives.

Comparing the Real-World Performance of Exponential-family Random Graph Models and Latent Order Logistic Models for Social Network Analysis.

Exponential-family Random Graph models (ERGM) are widely used in social network analysis when modelling data on the relations between actors. ERGMs are typically interpreted as a snapshot of a network at a given point in time or in a final state. The recently proposed Latent Order Logistic model (LOLOG) directly allows for a latent network formation process. We assess the real-world performance of these models when applied to typical networks modelled by researchers. Specifically, we model data from an ensemble of articles in the journal Social Networks with published ERGM fits, and compare the ERGM fit to a comparable LOLOG fit. We demonstrate that the LOLOG models are, in general, in qualitative agreement with the ERGM models, and provide at least as good a model fit. In addition they are typically faster and easier to fit to data, without the tendency for degeneracy that plagues ERGMs. Our results support the general use of LOLOG models in circumstances where ERGMs are considered.

A network analysis on the relationship between loneliness and schizotypy.

BACKGROUND: Schizotypy is a multidimensional personality trait related to the heightened risk for the development of schizophrenia spectrum disorders. While it has been suggested that loneliness may be associated with schizotypy in general, whether it relates to the specific schizotypal traits differentially remains unknown. Besides, as loneliness often co-occurs with depression and anxiety, it is important to delineate its relationship with schizotypy in consideration of these co-occurring emotional disturbances. METHODS: A demographically diverse sample of young people was obtained from multiple sources. The validated sample consisted of 2089 participants (68.4% female, age range: 18-30). The structural relationship between loneliness and schizotypy was modelled using a network analytic approach. The Gaussian graphical model with loneliness and nine schizotypal traits as nodes was first estimated without, and then with adjustment for the levels of depressive and anxiety symptoms. Edges were estimated as unique associations between nodes. RESULTS: 'Suspiciousness', 'odd beliefs or magical thinking', 'no close friends', 'constricted affect' and 'excessive social anxiety' were linked to loneliness directly. Loneliness was found to be more strongly associated with 'suspiciousness' and 'no close friends' than other schizotypal traits. After adjustment for the levels of depressive and anxiety symptoms, the above direct edges remained robust. LIMITATIONS: The use of cross-sectional data indicated only undirected associations between variables. CONCLUSIONS: Loneliness was more strongly linked to some schizotypal traits than others, with the relationships maintaining above and beyond the effects of anxiety and depression. These findings warrant further investigation of the specific relationships between loneliness and individual schizotypal traits.