An evolutionary differential game for regulating the role of monoclonal antibodies in treating signalling pathways in oesophageal cancer.

This work presents a new framework for a competitive evolutionary game between monoclonal antibodies and signalling pathways in oesophageal cancer. The framework is based on a novel dynamical model that takes into account the dynamic progression of signalling pathways, resistance mechanisms and monoclonal antibody therapies. This game involves a scenario in which signalling pathways and monoclonal antibodies are the players competing against each other, where monoclonal antibodies use Brentuximab and Pembrolizumab dosages as strategies to counter the evolutionary resistance strategy implemented by the signalling pathways. Their interactions are described by the dynamical model, which serves as the game's playground. The analysis and computation of two game-theoretic strategies, Stackelberg and Nash equilibria, are conducted within this framework to ascertain the most favourable outcome for the patient. By comparing Stackelberg equilibria with Nash equilibria, numerical experiments show that the Stackelberg equilibria are superior for treating signalling pathways and are critical for the success of monoclonal antibodies in improving oesophageal cancer patient outcomes.

Local-manifold-distance-based regression: an estimation method for quantifying dynamic biological interactions with empirical time series.

Quantifying species interactions based on empirical observations is crucial for ecological studies. Advancements in nonlinear time-series analyses, particularly S-maps, are promising for high-dimensional and non-equilibrium ecosystems. S-maps sequentially perform a local linear model fitting to the time evolution of neighbouring points on the reconstructed attractor manifold, and the coefficients can approximate the Jacobian elements corresponding to interaction effects. However, despite that the advantages in nonlinear forecasting with noise-contaminated data, these methodologies have a limitation in the Jacobian estimation accuracy owing to non-equidistantly stretched local manifolds in the state space. Herein, we therefore introduced a local manifold distance (LMD) concept, a non-equidistant measure based on the multi-faceted state dependency. By integrating LMD with advanced computation techniques, we presented a robust and efficient analytical method, LMD-based regression (LMDr). To validate its advantages in prediction and Jacobian estimation, we analysed synthetic time series of model ecosystems with different noise levels and applied it to an experimental protozoan predator-prey system with established biological information. The robustness to noise was the highest for LMDr, which also showed a better correspondence to expected predator-prey interactions in the protozoan system. Thus, LMDr can be applied to study complex ecological networks under dynamic conditions.

MotionTrack: Learning motion predictor for multiple object tracking.

Significant progress has been achieved in multi-object tracking (MOT) through the evolution of detection and re-identification (ReID) techniques. Despite these advancements, accurately tracking objects in scenarios with homogeneous appearance and heterogeneous motion remains a challenge. This challenge arises from two main factors: the insufficient discriminability of ReID features and the predominant utilization of linear motion models in MOT. In this context, we introduce a novel motion-based tracker, MotionTrack, centered around a learnable motion predictor that relies solely on object trajectory information. This predictor comprehensively integrates two levels of granularity in motion features to enhance the modeling of temporal dynamics and facilitate precise future motion prediction for individual objects. Specifically, the proposed approach adopts a self-attention mechanism to capture token-level information and a Dynamic MLP layer to model channel-level features. MotionTrack is a simple, online tracking approach. Our experimental results demonstrate that MotionTrack yields state-of-the-art performance on datasets such as Dancetrack and SportsMOT, characterized by highly complex object motion.

Design of mechanically intelligent structures: Review of modelling stimuli-responsive materials for adaptive structures.

Smart materials are upcoming in many industries due to their unique properties and wide range of applicability. These materials have the potential to transform traditional engineering practices by enabling the development of more efficient, adaptive, and responsive systems. However, smart materials are characterized by nonlinear behaviour and complex constitutive models, posing challenges in modelling and simulation. Therefore, understanding their mechanical properties is crucial for model-based design. This review aims for advancements in numerically implementing various smart materials, especially focusing on their nonlinear deformation behaviours. Different mechanisms and functionalities, classification, constitutive models and applications of smart materials were analyzed. In addition, different numerical approaches for modelling across scales were investigated. This review also explored the strategies and implementations for mechanically intelligent structures using smart materials. In conclusion, the potential model-based design methodology for the multiple smart material-based structures is proposed, which provides guidance for the future development of mechanically intelligent structures in industrial applications.

The Effect of Neurocognitive Training on Biomechanical Risk Factors Related to Anterior Cruciate Ligament Injury in Athletes: A Narrative Review.

CONTEXT: The best current evidence supports the effectiveness of neuromuscular training in reducing the risk of injury; however, the rate of anterior cruciate ligament (ACL) injuries is still high. Neurocognitive training (NT) has successfully improved biomechanical risk factors, but they have been considered in only a few studies. OBJECTIVE: To review the literature to determine the effect of NT on biomechanical risk factors related to ACL injury in athletes. EVIDENCE ACQUISITION: We searched PubMed, Google Scholar, Scopus, Science Direct, and the Physiotherapy Evidence Database from inception to August 2011. We included randomized controlled trials that used motor learning approaches and injury prevention programs to investigate kinematic and kinetic risk factors related to ACL injury. The quality of each clinical trial study was evaluated by the Physiotherapy Evidence Database scale. The eligibility criteria were checked based on the PICOS (population, intervention, comparison, outcome, and study type) framework. EVIDENCE SYNTHESIS: A total of 9 studies were included in the final analysis. Motor learning approaches include internal and external focus of attention, dual tasks, visual motor training, self-control feedback, differential learning, and linear and nonlinear pedagogy, combined with exercise programs. In most of the studies that used NT, a significant decrease in knee valgus; tibial abduction and external rotation; ground reaction force; and an increase in knee-, trunk-, hip-, and knee-flexion moment was observed. CONCLUSION: In classical NT, deviation from the ideal movement pattern especially emphasizing variability and self-discovery processes is functional in injury prevention and may mitigate biomechanical risk factors of ACL injuries in athletes. Practitioners are advised to use sport-specific cognitive tasks in combination with neuromuscular training to simulate loads of the competitive environment. This may improve ACL injury risk reduction and rehabilitation programs.

Energy and synchronization between two neurons with nonlinear coupling.

Consensus and synchronous firing in neural activities are relative to the physical properties of synaptic connections. For coupled neural circuits, the physical properties of coupling channels control the synchronization stability, and transient period for keeping energy diversity. Linear variable coupling results from voltage coupling via linear resistor by consuming certain Joule heat, and electric synapse coupling between neurons derives from gap junction connection under special electrophysiological condition. In this work, a voltage-controlled electric component with quadratic relation in the i-v (current-voltage) is used to connect two neural circuits composed of two variables. The energy function obtained by using Helmholtz theorem is consistent with the Hamilton energy function converted from the field energy in the neural circuit. Chaotic signals are encoded to approach a mixed signal within certain frequency band, and then its amplitude is adjusted to excite the neuron for detecting possible occurrence of nonlinear resonance. External stimuli are changed to trigger different firing modes, and nonlinear coupling activates changeable coupling intensity. It is confirmed that nonlinear coupling behaves functional regulation as hybrid synapse, and the synchronization transition between neurons can be controlled for reaching possible energy balance. The nonlinear coupling is helpful to keep energy diversity and prevent synchronous bursting because positive and negative feedback is switched with time. As a result, complete synchronization is suppressed and phase lock is controlled between neurons with energy diversity.

Geographical Patterns in Mortality Impacts Due To Heatwaves of Different Characteristics in Spanish Cities.

The impact of heatwaves (HWs) on human health is a topic of growing interest due to the global magnification of these phenomena and their substantial socio-economic impacts. As for other countries of Southern Europe, Spain is a region highly affected by heat and its increase under climate change. This is observed in the mean values and the increasing incidence of extreme weather events and associated mortality. Despite the vast knowledge on this topic, it remains unclear whether specific types and characteristics of HW are particularly harmful to the population and whether this shows a regional interdependency. The present study provides a comprehensive analysis of the relationship between HW characteristics and mortality in 12 Spanish cities. We used separated time series analysis in each city applying a quasi-Poisson regression model and distributed lag linear and non-linear models. Results show an increase in the mortality risk under HW conditions in the cities with a lower HW frequency. However, this increase exhibits remarkable differences across the cities under study not showing any general pattern in the HW characteristics-mortality association. This relationship is shown to be complex and strongly dependent on the local properties of each city pointing out the crucial need to examine and understand on a local scale the HW characteristics and the HW-mortality relationship for an efficient design and implementation of prevention measures.

Assessment of oxygen kinetic parameters for closely related ammonia-oxidizing bacteria.

The reaction kinetics of lithotrophic ammonia-oxidizing bacteria (AOB) are strongly dependent on dissolved oxygen (DO) as their metabolism is an aerobic process. In this study, we estimate the kinetic parameters, including the oxygen affinity constant (Km[O2]) and the maximum oxygen consumption rate (Vmax[O2]), of different AOB species, by fitting the data to the Michaelis-Menten equation using non-linear regression analysis. An example for three different species of Nitrosomonas bacteria (N. europaea, N. eutropha, and N. mobilis) in monoculture is given, finding a Km[O2] of 0.25±0.05 mg L-1, 0.47±0.09 mg L-1, and 0.28±0.08 mg L-1, and a Vmax[O2] of 0.07±0.04 pg h-1cell-1, 0.25±0.06 pg h-1cell-1, and 0.02±0.001 pg h-1cell-1 for Nitrosomonas europaea, Nitrosomonas eutropha, and Nitrosomonas mobilis, respectively. This study shows that of the analyzed AOB, N. europaea has the highest affinity towards oxygen and N. eutropha the lowest affinity towards oxygen, indicating that the former can convert ammonia even under low DO conditions. These results improve the understanding of the ecophysiology of ammonia-oxidizing bacteria in the environment. The accuracy of mathematically modelled ammonia oxidation can be improved, allowing the implementation of better management practices to restore the nitrogen cycle in natural and engineered water systems.

Resist and recover: Introducing a spring theory for modeling disaster resilience.

This paper presents a new approach for quantitatively modeling the resilience of a system that has been disrupted by a sudden-impact event. It introduces a new theoretical model that explicitly incorporates representations of the enabling and inhibiting forces that are inherent within postdisruption recovery behavior. Based on a new, more comprehensive measure of resilience that is able to capture both negative and positive deviations in performance, a generic mass-spring system is then used to illustrate the applicability of the theoretical model. The interplay between the enabling and inhibiting forces that is revealed by the new model provides a new theoretical basis for understanding the complexity of resilience and disaster recovery. With the addition of the new resilience measure, it lends support for defining and characterizing a new type of resilient behavior: unstable resilience.

Fractional-order state space reconstruction: a new frontier in multivariate complex time series.

This paper presents a novel approach to the phase space reconstruction technique, fractional-order phase space reconstruction (FOSS), which generalizes the traditional integer-order derivative-based method. By leveraging fractional derivatives, FOSS offers a novel perspective for understanding complex time series, revealing unique properties not captured by conventional methods. We further develop the multi-span transition entropy component method (MTECM-FOSS), an advanced complexity measurement technique that builds upon FOSS. MTECM-FOSS decomposes complexity into intra-sample and inter-sample components, providing a more comprehensive understanding of the dynamics in multivariate data. In simulated data, we observe that lower fractional orders can effectively filter out random noise. Time series with diverse long- and short-term memory patterns exhibit distinct extremities at different fractional orders. In practical applications, MTECM-FOSS exhibits competitive or superior classification performance compared to state-of-the-art algorithms when using fewer features, indicating its potential for engineering tasks.

Depletion Flocculation of High Internal Phase Pickering Emulsion Inks: A Colloidal Engineering Approach to Develop 3D Printed Porous Scaffolds with Tunable Bioactive Delivery.

Flocculation is a type of aggregation where the surfaces of approaching droplets are still at distances no closer than a few nanometers while still remaining in close proximity. In a high internal-phase oil-in-water (O/W) emulsion, the state of flocculation affects the bulk flow behavior and viscoelasticity, which can consequently control the three-dimensional (3D)-printing process and printing performance. Herein, we present the assembly of O/W Pickering high-internal-phase emulsions (Pickering-HIPEs) as printing inks and demonstrate how depletion flocculation in such Pickering-HIPE inks can be used as a facile colloidal engineering approach to tailor a porous 3D structure suitable for drug delivery. Pickering-HIPEs were prepared using different levels of cellulose nanocrystals (CNCs), co-stabilized using "raw" submicrometer-sized sustainable particles from a biomass-processing byproduct. In the presence of this sustainable particle, the higher CNC contents facilitated particle-induced depletion flocculation, which led to the formation of a mechanically robust gel-like ink system. Nonetheless, the presence of adsorbed particles on the surface of droplets ensured their stability against coalescence, even in such a highly aggregated system. The gel structures resulting from the depletion phenomenon enabled the creation of high-performance printed objects with tunable porosity, which can be precisely controlled at two distinct levels: first, by introducing voids within the internal structure of filaments, and second, by generating cavities (pore structures) through the elimination of the water phase. In addition to printing efficacy, the HIPEs could be applied for curcumin delivery, and in vitro release kinetics demonstrated that the porous 3D scaffolds engineered for the first time using depletion-flocculated HIPE inks played an important role in 3D scaffold disintegration and curcumin release. Thus, this study offers a unique colloidal engineering approach of using depletion flocculation to template 3D printing of sustainable inks to generate next-generation porous scaffolds for personalized drug deliveries.

Robust Edge States of Quasi-1D Material Ta2NiSe7 and Applications in Saturable Absorbers.

The helical edge states (ESs) protected by underlying Z2 topology in two-dimensional topological insulators (TIs) arouse upsurges in saturable absorptions thanks to the strong photon-electron coupling in ESs. However, limited TIs demonstrate clear signatures of topological ESs at liquid nitrogen temperatures, hindering the applications of such exotic quantum states. Here, we demonstrate the existence of one-dimensional (1D) ESs at the step edge of the quasi-1D material Ta2NiSe7 at 78 K by scanning tunneling microscopy. Such ESs are rather robust against the irregularity of the edges, suggesting a possible topological origin. The exfoliated Ta2NiSe7 flakes were used as saturable absorbers (SAs) in an Er-doped fiber laser, hosting a mode-locked pulse with a modulation depth of up to 52.6% and a short pulse duration of 225 fs, far outstripping existing TI-based SAs. This work demonstrates the existence of robust 1D ESs and the superior SA performance of Ta2NiSe7.

Empathy and Autism: Establishing the Structure and Different Manifestations of Empathy in Autistic Individuals Using the Perth Empathy Scale.

PURPOSE: There is a common mischaracterisation that autistic individuals have reduced or absent empathy. Measurement issues may have influenced existing findings on the relationships between autism and empathy, and the structure of the empathy construct in autism remains unclear. METHODS: The present study sought to address these gaps by examining the structure and psychometric properties of the Perth Empathy Scale (PES) in autistic individuals (N = 239) compared to non-autistic individuals (N = 690). RESULTS: Our moderated non-linear factor analysis revealed that the multidimensional empathy construct manifested similarly in autistic and non-autistic individuals, with the PES displaying good validity and reliability. Moreover, the results revealed that autistic individuals reported reduced cognitive empathy and reduced affective empathy for positive and negative emotions. However, there was greater heterogeneity of empathic tendencies in the autistic sample, indicating that these mean differences may not be generalisable for all autistic individuals. CONCLUSION: The present study highlights that the PES is suitable for assessing empathy across autistic and non-autistic individuals. This work with the PES also provides greater nuance to our understanding of empathy and autism, and based on these findings, we propose the empathy heterogeneity hypothesis of autism as a new way of describing empathy in autism.

Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques.

Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn. The number of verified coronavirus cases is increasing daily, putting the whole human race at danger and putting strain on medical experts to eradicate the disease as rapidly as possible. As a consequence, it is vital to predict the upcoming coronavirus positive patients in order to plan actions in the future. Furthermore, it has been discovered all across the globe that asymptomatic coronavirus patients play a significant part in the disease's transmission. This prompted us to incorporate similar examples in order to accurately forecast trends. A typical strategy for analysing the rate of pandemic infection is to use time-series forecasting technique. This would assist us in developing better decision support systems. To anticipate COVID-19 active cases for a few countries, we recommended a hybrid model utilizing a fuzzy time series (FTS) model mixed with a non-linear growth model. The coronavirus positive case outbreak has been evaluated for Italy, Brazil, India, Germany, Pakistan, and Myanmar through June 5, 2020 in phase-1, and January 15, 2022 in phase-2, and forecasts active cases for the next 26 and 14 days respectively. The proposed framework fitting effect outperforms individual logistic growth and the fuzzy time series techniques, with R-scores of 0.9992 in phase-1 and 0.9784 in phase-2. The proposed model provided in this article may be utilised to comprehend a country's epidemic pattern and assist the government in developing better effective interventions.

Quantifying instability in neurological disorders EEG based on phase space DTM function.

Classifying individuals with neurological disorders and healthy subjects using EEG is a crucial area of research. The current feature extraction approach focuses on the frequency domain features in each of the EEG frequency bands and functional brain networks. In recent years, researchers have discovered and extensively studied stability differences in the electroencephalograms (EEG) of patients with neurological disorders. Based on this, this paper proposes a feature descriptor to characterize EEG instability. The proposed method starts by forming a signal point cloud through Phase Space Reconstruction (PSR). Subsequently, a pseudo-metric space is constructed, and pseudo-distances are calculated based on the consistent measure of the point cloud. Finally, Distance to Measure (DTM) Function are generated to replace the distance function in the original metric space. We calculated the relative distances in the point cloud by measuring signal similarity and, based on this, summarized the point cloud structures formed by EEG with different stabilities after PSR. This process demonstrated that Multivariate Kernel Density Estimation (MKDE) based on a Gaussian kernel can effectively separate the mappings of different stable components within the signal in the phase space. The two average DTM values are then proposed as feature descriptors for EEG instability.In the validation phase, the proposed feature descriptor is tested on three typical neurological disorders: epilepsy, Alzheimer's disease, and Parkinson's disease, using the Bonn dataset, CHB-MIT, the Florida State University dataset, and the Iowa State University dataset. DTM values are used as feature inputs for four different machine learning classifiers, and The results show that the best classification accuracy of the proposed method reaches 98.00 %, 96.25 %, 96.71 % and 95.34 % respectively, outperforming commonly used nonlinear descriptors. Finally, the proposed method is tested and analyzed using noisy signals, demonstrating its robustness compared to other methods.

Multivalued nonlinear dominated mappings on a closed ball and associated numerical illustrations with applications to nonlinear integral and fractional operators.

In the context of complete strong b -metric-like spaces, we prove new multi-fixed point solutions for the pair of multivalued, dominated operators that fulfill the generalized nonlinear contractions on a closed ball. We employ a mix of two different types of mappings in our approach: one is a class of multi-dominated mappings, while the other is a weaker class of strictly increasing mappings. Additionally, some new fixed-point results concerning the multi-graph-dominated structure in graph contraction are presented. To validate the hypothesis of acquired results, a few sample cases are presented. A numerical experiment has been carried out to approximate the fixed point. In addition, to demonstrate the originality of our findings, we proposed simple and efficient solutions to the system of fractional differential equations and nonlinear Volterra-type integral equations.

Development of measured hydrodynamic information-based flood early warning system for small streams.

In general, small stream basins, characterized by narrow channels and steep slopes, face heightened vulnerability to climate change-induced flooding, posing challenges for evacuation procedures. With the increasing intensity of floods and typhoons in recent years, urgent measures are necessary to mitigate damage in such areas. This research endeavors to address these challenges by developing a novel small stream flood early warning system (SSFEWS) tailored to small streams and piloting its application. The proposed system integrates real-time hydrodynamic data collection, flood probability forecasting, and proactive warning issuance through an amalgamation of IoT-based sensor networks, statistical models leveraging measurement data, a robust constrained nonlinear optimization algorithm (RCNOA), and four-parameter logistic method (4PL). Moreover, system accuracy and reliability are enhanced by an automated iterative process that continuously refines forecasting model parameters via a user-defined rainfall-discharge nomograph and rating curve using RCNOA and 4PL. The developed SSFEWS is expected to contribute to the safety of the community as well as prevent possible small stream-related casualties by enabling efficient disaster response. © 2024 Elsevier Ltd. All rights reserved.

Fast and silent MRI using nonlinear gradient fields at the ultrasonic gradient switching frequency of 20 kHz with a Point Spread Function framework reconstruction.

PURPOSE: To demonstrate the feasibility of using a nonlinear gradient field for spatial encoding at the ultrasonic switching frequency of 20 kHz and present a framework to reconstruct data acquired in this way. METHODS: Nonlinear encoding at 20 kHz was realized by using a single-axis silent gradient insert for imaging in the periphery, that, is the nonlinear region, of the gradient field. The gradient insert induces a rapidly oscillating gradient field in the phase-encode direction, which enables nonlinear encoding when combined with a Cartesian readout from the linear whole-body gradients. Data from a 2D gradient echo sequence were reconstructed using a point spread function (PSF) framework. Accelerated scans were also simulated via retrospective undersampling (R = 1 to R = 8) to determine the effectiveness of the PSF-framework for accelerated imaging. RESULTS: Using a nonlinear gradient field switched at 20 kHz and the PSF-framework resulted in images of comparable quality to images from conventional Cartesian linear encoding. At increased acceleration factors (R ≤ 8), the PSF-framework outperformed linear SENSE reconstructions by improved controlling of aliasing artifacts. CONCLUSION: Using the PSF-framework, images of comparable quality to conventional SENSE reconstructions are possible via combining traditional linear and ultrasonic oscillating nonlinear encoding fields. Using nonlinear gradient fields relaxes the demand for strictly linear gradient fields, enabling much higher slew rates with a reduced risk of peripheral nerve stimulation or cardiac stimulation, which could aid in extension to ultrasonic whole-body MRI. The lack of aliasing artifacts also highlights the potential of accelerated imaging using the PSF-framework.

Leveraging Random Effects in Cistrome-Wide Association Studies for Decoding the Genetic Determinants of Prostate Cancer.

Cistrome-wide association studies (CWAS) are pivotal for identifying genetic determinants of diseases by correlating genetically regulated cistrome states with phenotypes. Traditional CWAS typically develops a model based on cistrome and genotype data to associate predicted cistrome states with phenotypes. The random effect cistrome-wide association study (RECWAS), reevaluates the necessity of cistrome state prediction in CWAS. RECWAS utilizes either a linear model or marginal effect for initial feature selection, followed by kernel-based feature aggregation for association testing is introduced. Through simulations and analysis of prostate cancer data, a thorough evaluation of CWAS and RECWAS is conducted. The results suggest that RECWAS offers improved power compared to traditional CWAS, identifying additional genomic regions associated with prostate cancer. CWAS identified 102 significant regions, while RECWAS found 50 additional significant regions compared to CWAS, many of which are validated. Validation encompassed a range of biological evidence, including risk signals from the GWAS catalog, susceptibility genes from the DisGeNET database, and enhancer-domain scores. RECWAS consistently demonstrated improved performance over traditional CWAS in identifying genomic regions associated with prostate cancer. These findings demonstrate the benefits of incorporating kernel methods into CWAS and provide new insights for genetic discovery in complex diseases.

Advanced temperature control in ethanol fermentation using a PSO-PID controller with split-range control strategy.

Global energy demand is experiencing a notable surge due to growing energy security. Renewable energy sources, like ethanol, are becoming more viable. In the present study, the application of a PSO-PID (Particle Swarm Optimization - Proportional Integral Derivative) controller with a split-range control strategy was suggested for the regulation of temperature within the fermentation system. To optimize performance, a POS-PID controller with a split-range arrangement utilizing two control valves for hot and cold utilities was constructed. The study began by examining the open-loop dynamic response of the system to inlet temperature and concentration disturbances during ethanol production fermentation. Subsequently, a transfer function model was developed through linearization at the steady-state operating point. The split-range controller structure, implemented by optimizing the PSO-PID controller parameters using PSO, effectively demonstrated temperature control in simulations of a nonlinear model. In this investigation, the ethanol fermentation system was modeled as a CSTR using a modified Monod equation for microbial growth kinetics. Various dynamic behavioral disturbances were explored and verified in the model with plant data in this study. The simulation model results were validated through plant data. The proposed method showed superior closed-loop performance with respect to errors, with the actuators proving to be effective than other reported methods for temperature control.