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Stochastic resonance (SR) is the phenomenon wherein the introduction of a suitable level of noise enhances the detection of subthreshold signals in non linear systems. It manifests across various physical and biological systems, including the human brain. Psychophysical experiments have confirmed the behavioural impact of stochastic resonance on auditory, somatic, and visual perception. Aging renders the brain more susceptible to noise, possibly causing differences in the  SR phenomenon between young and elderly individuals. This study investigates the impact of noise on motion detection accuracy throughout the lifespan, with 214 participants ranging in age from 18 to 82. Our objective was to determine the optimal noise level to induce an SR-like response in both young and old populations. Consistent with existing literature, our findings reveal a diminishing advantage with age, indicating that the efficacy of noise addition progressively diminishes. Additionally, as individuals age, peak performance is achieved with lower levels of noise. This study provides the first insight into how SR changes across the lifespan of healthy adults and establishes a foundation for understanding the pathological alterations in perceptual processes associated with aging.

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To explore the influence of state changes on brucellosis, a stochastic brucellosis model with semi-Markovian switchings and diffusion is proposed in this paper. When there is no switching, we introduce a critical value R s and obtain the exponential stability in mean square when R s < 1 by using the stochastic Lyapunov function method. Sudden climate changes can drive changes in transmission rate of brucellosis, which can be modelled by a semi-Markov process. We study the influence of stationary distribution of semi-Markov process on extinction of brucellosis in switching environment including both stable states, during which brucellosis dies out, and unstable states, during which brucellosis persists. The results show that increasing the frequencies and average dwell times in stable states to certain extent can ensure the extinction of brucellosis. Finally, numerical simulations are given to illustrate the analytical results. We also suggest that herdsmen should reduce the densities of animal habitation to decrease the contact rate, increase slaughter rate of animals and apply disinfection measures to kill brucella.

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Human longevity leaders with remarkably long lifespan play a crucial role in the advancement of longevity research. In this paper, we propose a stochastic model to describe the evolution of the age of the oldest person in the world by a Markov process, in which we assume that the births of the individuals follow a Poisson process with increasing intensity, lifespans of individuals are independent and can be characterized by a gamma-Gompertz distribution with time-dependent parameters. We utilize a dataset of the world's oldest person title holders since 1955, and we compute the maximum likelihood estimate for the parameters iteratively by numerical integration. Based on our preliminary estimates, the model provides a good fit to the data and shows that the age of the oldest person alive increases over time in the future. The estimated parameters enable us to describe the distribution of the age of the record holder process at a future time point.

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The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.

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This article aims to analyze a stochastic epidemic model S E I u I r R (Susceptible-exposed-undetected infected-detected infected (reported -recovered) assuming that the transmission rate at which people undetected become detected is perturbed by the Ornstein-Uhlenbeck process. Our first objective is to prove that the stochastic model has a unique positive global solution by constructing a nonnegative Lyapunov function. Afterward, we provide a sufficient criterion to prove the existence of an ergodic stationary distribution of the mode by constructing a suitable series of Lyapunov functions. Subsequently, we establish sufficient conditions for the extinction of the disease. Finally, a series of numerical simulations are carried out to illustrate the theoretical results.

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Mechanisms underlying the effects of ecological disturbance on aquatic ecosystems remain uncertain in subtropical regions. Here, we used a proxy-based approach to explore the community dynamics of testate amoebae (Arcellinida and Euglyphida) in two subtropical deep reservoirs (Tingxi and Shidou) in Xiamen, southeastern China, over a three-year period. Specifically, we employed drought and typhoon events recorded by weather station as proxies for ecological disturbance and chlorophyll-a estimated through fluorometry as a proxy for testate amoeba food. We addressed three questions: (1) Does typhoon-induced ecological disturbance affect the distribution patterns of testate amoebae in subtropical reservoirs? (2) Do typhoon- and drought-induced ecological disturbances affect the testate amoeba community across different water layers of subtropical reservoirs similarly? (3) Do stochastic or deterministic processes shaping the testate amoeba community over time exhibit similar patterns in different water layers of subtropical reservoirs? The typhoon-induced ecological disturbance resulted in pronounced shifts in the distribution patterns of testate amoebae, characterized by lower shell influx in surface waters (11-12 ind. mL-1 d-1) and higher shell influx in middle and bottom waters (12-22 ind. mL-1 d-1). The impact of typhoon-and drought-induced ecological disturbance was more pronounced in surface waters, and its pure explanation accounted for 29.5-35.5 % community variation in a variation partitioning analysis. The effect of stochastic processes revealed by the neutral model increased with water depths, accounting for 63.3-76.5 % of the community variation in the surface, 77.4-82.6 % in the middle, and 82.8-88.1 % in the bottom water. The effect of deterministic processes shown by the null model decreased with water depth and remained relatively low across all water layers. These results suggest contrasting patterns of assembly mechanisms underlying the testate amoeba community responses to ecological disturbance, with the balance perhaps shaped by water depth and the average water residence time in a reservoir.

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Super-resolution single-molecule localization microscopy (SMLM) of presynaptic active zones (AZs) and postsynaptic densities contributed to the observation of protein nanoclusters that are involved in defining functional characteristics and in plasticity of synaptic connections. Among SMLM techniques, direct stochastic optical reconstruction microscopy (dSTORM) depends on organic fluorophores that exert high brightness and reliable photoswitching. While multicolor imaging is highly desirable, the requirements necessary for high-quality dSTORM make it challenging to identify combinations of equally performing, spectrally separated dyes. Red-excited carbocyanine dyes, e.g., Alexa Fluor 647 (AF647) or Cy5, are currently regarded as "gold standard" fluorophores for dSTORM imaging. However, a recent study introduced a set of chemically modified rhodamine dyes, including CF583R, that promise to display similar performance in dSTORM. In this study, we defined CF583R's performance compared to AF647 and CF568 based on a nanoscopic analysis of Bruchpilot (Brp), a nanotopologically well-characterized scaffold protein at Drosophila melanogaster AZs. We demonstrate equal suitability of AF647, CF568 and CF583R for basal AZ morphometry, while in Brp subcluster analysis CF583R outperforms CF568 and is on par with AF647. Thus, the AF647/CF583R combination will be useful in future dSTORM-based analyses of AZs and other subcellularly located marker molecules and their role in physiological and pathophysiological contexts.

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Groundwater pollution risk evaluation is an important basis for developing groundwater protection measures and management strategies, and its accuracy directly affects the effectiveness of protection measures. The heterogeneity of the aquifer significantly affects the transport process of pollutants, increasing the uncertainty of pollutant risk assessment. However, in the actual site, borehole data that reveal aquifer heterogeneity are costly, and only a limited number of borehole data are available, which cannot accurately describe the heterogeneity of the aquifer, thus limiting the accuracy of groundwater pollution risk assessment. In order to overcome the above problems, this paper proposes a groundwater pollution risk assessment framework based on the stochastic and deterministic simulation of aquifer lithology. Based on the statistical characteristics of the change of lithology type in the actual borehole, the framework uses Markov chain to generate some sets of random lithology field and transforms them into heterogeneity parameter field, so as to realize the stochastic assessment of the pollution risk of groundwater resource wells. Furthermore, combined with the pumping test data, the parameter field that is most suitable for the actual situation is selected to evaluate the pollution risk deterministically. Finally, the stochastic and deterministic results are combined to comprehensively evaluate the pollution risk of groundwater resource wells. Through a case study in a river valley plain, the feasibility of the above framework is verified, and good application effects are achieved. This study provides a feasible method for accurately assessing groundwater pollution risk, which is helpful to reduce the impact of uncertain factors on pollution risk assessment, and thus provides a more reliable basis for groundwater management and decision-making.

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The significance of HER-1 and CA 125 lies in their ability to guide cancer diagnosis, treatment, and monitoring, improving personalized care and enhancing prognostic accuracy. The utilization of HER-1 and CA 125 as screening biomarkers for the anticipation of early-stage cancer and monitoring cancer progression is expanding due to the invasive and costly nature of present techniques. In this study, a novel stochastic sensor was developed for the simultaneous determination of HER-1 and CA 125 in whole blood, saliva, and gastric tumor tissue samples using a fast, easy, inexpensive, and portable method. The stochastic sensor was prepared by electropolymerization of cysteine on the surface of the Au-TiO2@rGO/SPCE sensor. The Au-TiO2@rGO nanocomposite was synthesized using a simple chemical reduction process. The proposed sensor showed wide linear concentration ranges and very low limits of quantification (LOQ). The concentration ranges were from 3.9 × 10-14 to 3.9 × 10-8 µg mL-1, with a LOQ of 3.9 × 10-14 µg mL-1 for HER-1. For CA 125, the concentration ranges were from 8.3 × 10-14 to 8.3 × 10-10 U mL-1, with a LOQ of 8.3 × 10-14 U mL-1. Both biomarkers exhibit precise discrimination in different biological samples, with recoveries above 96.78% and RSD values below 0.04%. With a confidence level of 99%, the Student t-test revealed that there is no statistically significant difference between the outcomes obtained by using the poly-Cys/Au-TiO2@rGO/SPCE sensor for screening examinations of biological samples. This was determined because the results were not significantly different from one another.

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Background: TheraBracelet is peripheral vibrotactile stimulation applied to the affected upper extremity via a wristwatch-like wearable device during daily activities and therapy to improve upper limb function. The objective of this study was to examine feasibility of using TheraBracelet for a child with hemiplegic cerebral palsy. Methods: A nine-year-old male with cerebral palsy was provided with TheraBracelet to use during daily activities in the home and community settings for 1.5 years while receiving standard care physical/occupational therapy. Results: The child used TheraBracelet independently and consistently except during summer vacations and elbow-to-wrist orthotic use from growth spurt-related contracture. The use of TheraBracelet did not impede or prevent participation in daily activities. No study-related adverse events were reported by the therapist, child, or parent. Conclusion: Future research is warranted to investigate TheraBracelet as a propitious therapeutic device with focus on potential impact of use to improve the affected upper limb function in daily activities in children with hemiplegic cerebral palsy.

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Plasmodium vivax is the most geographically widespread malaria parasite. P. vivax has the ability to remain dormant (as a hypnozoite) in the human liver and subsequently reactivate, which makes control efforts more difficult. Given the majority of P. vivax infections are due to hypnozoite reactivation, targeting the hypnozoite reservoir with a radical cure is crucial for achieving P. vivax elimination. Stochastic effects can strongly influence dynamics when disease prevalence is low or when the population size is small. Hence, it is important to account for this when modelling malaria elimination. We use a stochastic multiscale model of P. vivax transmission to study the impacts of multiple rounds of mass drug administration (MDA) with a radical cure, accounting for superinfection and hypnozoite dynamics. Our results indicate multiple rounds of MDA with a high-efficacy drug are needed to achieve a substantial probability of elimination. This work has the potential to help guide P. vivax elimination strategies by quantifying elimination probabilities for an MDA approach.

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We develop a provably efficient importance sampling scheme that estimates exit probabilities of solutions to small-noise stochastic reaction-diffusion equations from scaled neighborhoods of a stable equilibrium. The moderate deviation scaling allows for a local approximation of the nonlinear dynamics by their linearized version. In addition, we identify a finite-dimensional subspace where exits take place with high probability. Using stochastic control and variational methods we show that our scheme performs well both in the zero noise limit and pre-asymptotically. Simulation studies for stochastically perturbed bistable dynamics illustrate the theoretical results.

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Stochastic delayed modeling has a significant non-pharmaceutical intervention to control transmission dynamics of infectious diseases and its results are close to the reality of nature. The covid-19 has been controlled globally but there is still a threat and appears in different variants like omicron and SARS-CoV-2 etc. globally. This article, considered pattern a mathematical model based on Susceptible, Infected, and recovered populations with highly nonlinear incidence rates. we studied the dynamics of the coronavirus model; a newly proposed version is a stochastic delayed model that is based on nonlinear stochastic delayed differential equations (SDDEs). Transition probabilities and parametric perturbation methods were used for the construction of the stochastic delayed model. The fundamental properties like positivity, boundedness, existence and uniqueness, and stability results of equilibria of the model with certain conditions of reproduction number are studied regularly. Also, the extinction and persistence of disease are studied with the help of well-known theorems. The numerical methods used to find a visualization of results due to the complexity of stochastic delayed differential equations. Furthermore, for computational analysis, we implemented existing methods in the literature and compared their results with the proposed method like nonstandard finite difference for stochastic delayed model. The proposed method restores all dynamical properties of the model with a free choice of time steps.

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Exploring the temporal dynamics of biological communities can offer valuable insights into the underlying mechanisms driving changes in biodiversity in the context of short and long-term effects of climate fluctuations. However, an understanding of how temporal shifts in climatic fluctuations influence the spatial patterns of the temporary ecological processes remains unexplored. This study examined the relative importance of temporary deterministic and stochastic processes (i.e., the influence of environmental filtering compared to stochastic variation within the same community) on community dynamics across watersheds in 15 rivers of the European Iberian Peninsula using 21 years of data. This study was divided into two time periods (i.e., 1997-2006 and 2007-2017). The climatic differences between the periods included decreasing levels and heightened variability of precipitation. Additionally, there were declining minimum temperatures and rising maximum temperatures, accompanied by reduced fluctuations in both minimum and maximum temperatures. Water quality and its variations also occur along an elevation pattern and changed over the time period studied. Spatial patterns of the relative importance of the ecological processes shifted between the two decades. The significance of stochastic processes increased with elevation in the earlier period, although no clear elevation pattern emerged in the later period. At the same time, the importance of deterministic processes decreased with elevation in the earlier period, and there was no clear pattern of elevation in the later period. An understanding of the patterns in community dynamics existing at various elevations over time can lay the groundwork for predicting and mitigating the impacts of short-term climate changes on biodiversity and guide appropriate conservation actions.

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In this paper sharp lower error bounds for numerical methods for jump-diffusion stochastic differential equations (SDEs) with discontinuous drift are proven. The approximation of jump-diffusion SDEs with non-adaptive as well as jump-adapted approximation schemes is studied and lower error bounds of order 3/4 for both classes of approximation schemes are provided. This yields optimality of the transformation-based jump-adapted quasi-Milstein scheme.

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Understanding the factors driving the maintenance of long-term biodiversity in changing environments is essential for improving restoration and sustainability strategies in the face of global environmental change. Biodiversity is shaped by both niche and stochastic processes, however the strength of deterministic processes in unpredictable environmental regimes is highly debated. Since communities continuously change over time and space-species persist, disappear or (re)appear-understanding the drivers of species gains and losses from communities should inform us about whether niche or stochastic processes dominate community dynamics. Applying a nonparametric causal discovery approach to a 30-year time series containing annual abundances of benthic invertebrates across 66 locations in New Zealand rivers, we found a strong negative causal relationship between species gains and losses directly driven by predation indicating that niche processes dominate community dynamics. Despite the unpredictable nature of these system, environmental noise was only indirectly related to species gains and losses through altering life history trait distribution. Using a stochastic birth-death framework, we demonstrate that the negative relationship between species gains and losses can not emerge without strong niche processes. Our results showed that even in systems that are dominated by unpredictable environmental variability, species interactions drive continuous community assembly.

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Lung cancer is the most common causes of death among all cancer-related diseases. A lung scan examination of the patient is the primary diagnostic technique. This scan analysis pertains to an MRI, CT, or X-ray. The automated classification of lung cancer is difficult due to the involvement of multiple steps in imaging patients' lungs. In this manuscript, human lung cancer classification and comprehensive analysis using different machine learning techniques is proposed. Initially, the input images are gathered using lung cancer dataset. The proposed method processes these images using image-processing techniques, and further machine learning techniques are utilized for categorization. Seven different classifiers including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), multinomial naive Bayes (MNB), stochastic gradient descent (SGD), random forest (RF), and multi-layer perceptron (MLP) classifier are used, which classifies the lung cancer as malignant and benign. The performance of the proposed approach is examined using performances metrics, like positive predictive value, accuracy, sensitivity, and f-score are evaluated. Among them, the performance of the MLP classifier provides 25.34%, 45.39%, 15.39%, 41.28%, 22.17%, and 12.12% higher accuracy than other KNN, SVM, DT, MNB, SGD, and RF respectively. RESEARCH HIGHLIGHTS: Lung cancer is a leading cause of cancer-related death. Imaging (MRI, CT, and X-ray) aids diagnosis. Automated classification of lung cancer faces challenges due to complex imaging steps. This study proposes human lung cancer classification using diverse machine learning techniques. Input images from lung cancer dataset undergo image processing and machine learning. Classifiers like k-nearest neighbors, support vector machine, decision tree, multinomial naive Bayes, stochastic gradient descent, random forest, and multi-layer perceptron (MLP) classify cancer types; MLP excels in accuracy.

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Path planning for multiple unmanned aerial vehicles (UAVs) is crucial in collaborative operations and is commonly regarded as a complicated, multi-objective optimization problem. However, traditional approaches have difficulty balancing convergence and diversity, as well as effectively handling constraints. In this study, a directional evolutionary non-dominated sorting dung beetle optimizer with adaptive stochastic ranking (DENSDBO-ASR) is developed to address these issues in collaborative multi-UAV path planning. Two objectives are initially formulated: the first one represents the total cost of length and altitude, while the second represents the total cost of threat and time. Additionally, an improved multi-objective dung beetle optimizer is introduced, which integrates a directional evolutionary strategy including directional mutation and crossover, thereby accelerating convergence and enhancing global search capability. Furthermore, an adaptive stochastic ranking mechanism is proposed to successfully handle different constraints by dynamically adjusting the comparison probability. The effectiveness and superiority of DENSDBO-ASR are demonstrated by the constrained problem functions (CF) test, the Wilcoxon rank sum test, and the Friedman test. Finally, three sets of simulated tests are carried out, each including different numbers of UAVs. In the most challenging scenario, DENSDBO-ASR successfully identifies feasible paths with average values of the two objective functions as low as 637.26 and 0. The comparative results demonstrate that DENSDBO-ASR outperforms the other five algorithms in terms of convergence accuracy and population diversity, making it an exceptional optimization approach to path planning challenges.

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Alpha-synuclein (aSyn) aggregates in the central nervous system are the main pathological hallmark of Parkinson's disease (PD). ASyn aggregates have also been detected in many peripheral tissues, including the skin, thus providing a novel and accessible target tissue for the detection of PD pathology. Still, a well-established validated quantitative biomarker for early diagnosis of PD that also allows for tracking of disease progression remains lacking. The main goal of this research was to characterize aSyn aggregates in skin biopsies as a comparative and quantitative measure for PD pathology. Using direct stochastic optical reconstruction microscopy (dSTORM) and computational tools, we imaged total and phosphorylated-aSyn at the single molecule level in sweat glands and nerve bundles of skin biopsies from healthy controls (HCs) and PD patients. We developed a user-friendly analysis platform that offers a comprehensive toolkit for researchers that combines analysis algorithms and applies a series of cluster analysis algorithms (i.e., DBSCAN and FOCAL) onto dSTORM images. Using this platform, we found a significant decrease in the ratio of the numbers of neuronal marker molecules to phosphorylated-aSyn molecules, suggesting the existence of damaged nerve cells in fibers highly enriched with phosphorylated-aSyn molecules. Furthermore, our analysis found a higher number of aSyn aggregates in PD subjects than in HC subjects, with differences in aggregate size, density, and number of molecules per aggregate. On average, aSyn aggregate radii ranged between 40 and 200 nm and presented an average density of 0.001-0.1 molecules/nm2. Our dSTORM analysis thus highlights the potential of our platform for identifying quantitative characteristics of aSyn distribution in skin biopsies not previously described for PD patients while offering valuable insight into PD pathology by elucidating patient aSyn aggregation status.

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This study presents findings that demonstrate the possibility of simplifying neural networks by inducing multifunctionality through separate manipulation within a single material. Herein, two-terminal memristor W/ZnTe/W devices implemented a multifunctional memristor comprising a selector, synapse, and a neuron using an ovonic threshold switching material. By setting the low-current level (µA) in the forming process, a stable memory-switching operation is achieved, and the capacity to implement a synapse is demonstrated based on paired-pulse facilitation/depression, potentiation/depression, spike-amplitude-dependent plasticity, and spike-number-dependent plasticity outcomes. Based on synaptic behavior, the Modified National Institute of Standards and Technology database image classification accuracy is up to 90%. Conversely, by setting the high-current level (mA) in the forming process, the stable bipolar threshold switching operation and good selector characteristics (300 ns switching speed, free-drift, recovery properties) are demonstrated. In addition, a stochastic neuron is implemented using the stochastic switching response in the positive voltage region. Utilizing stochastic neurons, it is possible to create a generative restricted Boltzmann machine model.