Passage Time Research Articles

Risk Factors of Asymptomatic Kidney Stone Passage in Adults with Recurrent Kidney Stones.

Kidney stones are a common health problem and are characterized by a high risk of recurrence. A certain number of kidney stones passed asymptomatically. Data regarding the frequency of asymptomatic spontaneous stone passages are limited. To assess the frequency of asymptomatic spontaneous stone passage and its covariates, we conducted a post-hoc analysis of the prospective randomized NOSTONE trial. All asymptomatic spontaneous stone passages were identified by comparing the total number of kidney stones on low-dose non-contrast CT imaging at the beginning and end of the study, considering symptomatic stone passages and surgical stone removal. The statistical analysis focused on the association of independent variables and the number of asymptomatic spontaneous stone passages using linear regression analyses. Of the 416 randomized patients, 383 with both baseline and end-of-study CT were included in this analysis. The median follow-up period was 35 months, the median patient age was 49 years (IQR:40 - 55), and 20% of the patients were female. A total of 442 stone events occurred in 209 out of 383 (55%) patients: 217 out of 442 (49%) were symptomatic spontaneous stone passages, 67 out of 442 (15%) were surgically removed stones, and 158 out of 442 (36%) were asymptomatic spontaneous stone passages. The median size of asymptomatic stones (2.4 mm; IQR:1.95-3.4) and the size of symptomatic stones (2.15 mm; IQR:1.68-2.79) that passed spontaneously were not significantly different (p=0.37). The number of asymptomatic spontaneous stone passages was significantly associated with a higher number of stones on CT at randomization (P=0.001). Limitations include the lack of data on stone size at the time of passage and overrepresentation of White men. Asymptomatic stone passage was common in patients with recurrent calcium-containing kidney stones. The higher the number of stones at presentation, the more likely it was that a kidney stone would spontaneously pass over time without causing any symptoms.

Persistence of Correlations in Neurotransmitter Transport through the Synaptic Cleft.

The "quantum brain" proposal can revolutionize our understanding of cognition if proven valid. The core of the most common "quantum brain" mechanism is the appearance of correlated neuron triggering induced by quantum correlations between ions. In this work, we examine the preservation of the correlations created in the pre-synaptic neurons through the transfer of neurotransmitters across the synaptic cleft, a critical ingredient for the validity of the "quantum brain" hypothesis. We simulated the transport of two neurotransmitters at two different clefts, with the only assumption that they start simultaneously, and determined the difference in their first passage times. We show that in physiological conditions, the correlations are persistent even if the parameters of the two neurons are different.

Host-derived protein profiles of human neonatal meconium across gestational ages

Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, could provide valuable insights into neonatal health. However, the comprehensive protein profile of meconium across gestational ages remains unclear. Here, we conducted an extensive proteomic analysis of first meconium from 259 newborns across varied gestational ages to delineate protein composition and elucidate its relevance to neonatal diseases. The first meconium samples were collected, with the majority obtained before feeding, and the mean time for the first meconium passage from the anus was 11.9 ± 9.47 h. Our analysis revealed 5370 host-derived meconium proteins, which varied depending on sex and gestational age. Specifically, meconium from preterm infants exhibited elevated concentrations of proteins associated with the extracellular matrix. Additionally, the protein profiles of meconium also exhibited unique variations depending on both specific diseases, including gastrointestinal diseases, congenital heart diseases, and maternal conditions. Furthermore, we developed a machine learning model to predict gestational ages using meconium proteins. Our model suggests that newborns with gastrointestinal diseases and congenital heart diseases may have immature gastrointestinal systems. These findings highlight the intricate relationship between clinical parameters and meconium protein composition, offering potential for a novel approach to assess neonatal gastrointestinal health.

EFFECT OF BEVEL DIRECTION OF THE ENDOTRACHEAL TUBE ON THE PROPORTION OF CASES DEVELOPING EPISTAXIS DURING NASOTRACHEAL INTUBATION-A RANDOMIZED CONTROLLED INTERVENTIONAL STUDY

Objective: Nasotracheal intubation is associated with a number of complications, most commonly nasal trauma or epistaxis. The aim of the study is to determine the effect of conventional versus cephalad direction of the bevel of endotracheal tube on the development of epistaxis during nasotracheal intubation. Methods: A total of 74 adults aged 18 to 50 y posted for surgeries requiring nasotracheal intubation were randomly divided into group A (Conventional) and group B (Interventional). After induction of anesthesia, in group a bevel direction of thermo softened PVC endotracheal tube was towards the nasal septum in the nasal passage and cephalad in group B, later nasotracheal intubation was completed with direct laryngoscopy and Magill forceps. After five minutes direct laryngoscopy was done to check for presence of epistaxis and its severity. Degree of resistance, nasal passage time, intubation time and haemodynamic parameters (heart rate, blood pressures) were also assessed and compared. Results: The incidence of epistaxis was significantly lower in group B (Interventional) than group A (Conventional) [15 vs 27; p-value = 0.005], severity of epistaxis [0/1/2] were also significantly lower in group B [22/12/3] than group A [10/15/12] [p value = 0.001]. There was significant difference in haemodynamic parameters between both groups at 1 min till 7 min post intubation. There was no significant difference in degree of resistance, nasal passage time and mean intubation time. Conclusion: The cephalad direction of the bevel of endotracheal tube in nasal passage during nasotracheal intubation decreases the chances of developing epistaxis and its severity. Hemodynamic parameters are also more stable in cephalad direction.

Gaussian and Lévy noises excited delayed tumor growth model: first-passage behavior and stochastic resonance

In this paper, we analyze the dynamical behavior of a delayed tumor growth model under the joint effect of Gaussian white noise and Lévy noise by studying the mean first passage time (MFPT) and stochastic resonance (SR). Firstly, the tumor growth model under the joint effect of Gaussian white noise, Lévy noise and time delay is introduced. Then, the Lévy noise sequence is simulated by Janicki-Weron algorithm, and the MFPT and signal-to-noise ratio(SNR) of the system are simulated by using fourth-order stochastic Runge–Kutta algorithm. The effects of noise parameters, time delay and periodic signal parameters on MFPT, SR are discussed in detail, respectively. In addition, we find the phenomenon of noise enhanced stability. The results of the study can help to select the optimal regulatory parameters in the tumor growth model and promote the treatment of tumors.

Tunable intracellular transport on converging microtubule morphologies

A common type of cytoskeletal morphology involves multiple microtubules converging with their minus ends at the microtubule organizing center (MTOC). The cargo-motor complex will experience ballistic transport when bound to microtubules or diffusive transport when unbound. This machinery allows for sequestering and subsequent dispersal of dynein-transported cargo. The general principles governing dynamics, efficiency, and tunability of such transport in the MTOC vicinity are not fully understood. To address this, we develop a one-dimensional model that includes advective transport toward an attractor (such as the MTOC) and diffusive transport that allows particles to reach absorbing boundaries (such as cellular membranes). We calculated the mean first passage time (MFPT) for cargo to reach the boundaries as a measure of the effectiveness of sequestering (large MFPT) and diffusive dispersal (low MFPT). We show that the MFPT experiences a dramatic growth, transitioning from a low to high MFPT regime (dispersal to sequestering) over a window of cargo on-/off-rates that is close to in vivo values. Furthermore, increasing either the on-rate (attachment) or off-rate (detachment) can result in optimal dispersal when the attractor is placed asymmetrically. Finally, we also describe a regime of rare events where the MFPT scales exponentially with motor velocity and the escape location becomes exponentially sensitive to the attractor positioning. Our results suggest that structures such as the MTOC allow for the sensitive control of the spatial and temporal features of transport and corresponding function under physiological conditions.

Conceptualization of a functional random walker for the analysis of socio-technical systems

The Functional Resonance Analysis Method (FRAM) has been pointed out by several prior studies as an effective approach for modeling socio-technical systems. Despite previous contributions, FRAM still suffers from limited tractability in case of large models such as a difficulty of carrying out reliable and insightful analysis. This paper addresses this gap by proposing an approach inspired by network theory to enhance traditional FRAM analyses, namely the functional random walker (FRW). The definition of FRW complements the notion of random walks accounting for the variability being transferred inside the system from one function to another, offering a cost-effective means of exploring FRAM instantiations. Five design criteria are presented to reproduce such analysis with any FRAM model, along with two indicators, namely the probability of visiting nodes and the mean passage time. These two metrics permit to study the FRAM model both at function level (how a single function behaves within the model) and at system level (how multiple functions interact with each other). A walk-through application of FRW is presented for a healthcare setting. This application revealed how the FRW sheds light on systems’ strengths and vulnerabilities, and highlighted underlying knowledge, otherwise hidden in cluttered visual representations of FRAM instantiations.

Pricing airbag option via first passage time approach

Airbag option is a new structured product that has been highly favored by investors in the over-the-counter derivatives market. It provides protections against the downside loss to investors like the ‘airbag’ in a car crash. In contrast with its popularity, there is little literature on the pricing of airbag options, especially from the mathematical finance point of view. In this paper, we study the pricing of airbag options using the first passage time approach. We propose two innovative new types of airbag options, namely airbag-TB and airbag-ER, which enhance the downside loss protections by integrating a time-varying barrier and early redemption features respectively in the payoffs. Explicit and closed-form pricing formulas for the original and the two new types of airbag options are derived under the mixed-exponential jump diffusion (MEJD) model, double exponential jump diffusion (DEJD) model and Black-Scholes (BS) model. In doing so, we significantly improve the Hybrid model proposed by Jaimungal and Sigloch (Incorporating risk and ambiguity aversion into a hybrid model of default. Math. Finance, 2012, 22, 57–81) by incorporating a stochastic redemption intensity model and more general underlying asset dynamics. We further demonstrate the performance of our pricing formulas and the two new types of airbag options with numerical examples based on Monte Carlo simulation, which verifies the accuracy and efficiency of our pricing formulas.

Markov Chains for User Queries Mining: Image Retrieval Application using Internet

Abstract: We suggest a brand-new technique for automatically annotating, indexing, and retrieving photos based on annotations. We introduce the novel technique, which we term Markovian Semantic Indexing (MSI), within the framework of an image retrieval system in online mode. In the event that such a system exists, the queries entered by the users are utilised to build an Aggregate Markov Chain (AMC), which defines the significance between the terms the system sees. The photographs are automatically annotated based on the queries entered by the users. Next, depending on the annotation of each image and the keyword relevance recorded in the AMC, a stochastic distance between them is presented. The suggested distance is given geometric meanings, and its relationship to a grouping in the keyword space is examined. The optimality qualities of the suggested distance are demonstrated by the use of a novel Markovian state similarity measure, the mean first cross passage time (CPT). Images are represented as points in a vector space, and MSI is used to calculate how similar two images are. In Annotation-Based Image Retrieval (ABIR) tasks, it is demonstrated that the novel method outperforms Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in terms of Precision against Recall and has some theoretical advantages.

The mean exiting time for the biased correlated random walk characterized by a system of first-order PDEs

A biased correlated random walk (BCRW) is a stochastic process that models individual movement and other similar practical phenomena. This paper studies the mean exiting time of a BCRW in a finite interval, subject to a system of first-order PDEs. The interval has two scenarios: (i) both ends are absorbing; and (ii) one end is absorbing and the other end is reflecting. We obtain an exact formula for the mean exiting time of a particle as a function of its speed, turning rates, and initial position. Using the mean exiting time, we also derive the mean first passage time for a particle to reach the boundary. Our approach enables us to infer relevant parameters from experimental data, or to manipulate the mean exiting time and the first passage time by choosing the initial position of the particle suitably. The unbiased case is a special case of the biased case when the right-turning rate equals the left-turning rate.

Cover-time Gumbel fluctuations in finite-range, symmetric, irreducible random walks on torus

In this paper, we provide the mathematical foundation for an explicit and universal feature of cover time for a large class of random work processes, which was previously observed by Chupeau et al (2015 Nat. Phys. 11 844–7). Specifically, we rigorously establish that the fluctuations of the cover time, normalized by the mean first passage time, follow a Gumbel distribution, for finite-range, symmetric, irreducible random walks on a torus of dimension three or higher. The result contributes to a better understanding of cover-time behavior in random search processes, especially on the efficiency of exhaustive searches. Our approach builds upon the work of Belius (2013 Probab. Theory Relat. Fields 157 635–89) on cover times for simple random walks, leveraging a strong coupling between the random walk and random interlacements.

Abdominal wound length influences the postoperative serum level of interleukin-6 and recovery of flatus passage among patients with colorectal cancer.

A mini-laparotomy for colorectal cancer (CRC) has been reported to shorten postoperative ileus (POI) and hospital stay. Interleukin-6 (IL-6) plays a role in intestinal tissue inflammation, leading to POI. This study investigated the effects of abdominal wounds and IL-6 levels on POI in patients having CRC surgery. Forty-three patients with CRC underwent bowel resection. Serum samples were collected preoperatively and at 2, 24, and 48 h after surgery for cytokine quantification by ELISA. Clinical data, including time from surgery to first passage of flatus and postoperative hospital stay, demographic and pathological data, and routine blood tests, were compared statistically with abdominal wound length and the postoperative increments of cytokines (designated as Δ). The length of the abdominal wound showed a significant correlation with clinical variables (length of operation time, time of first flatus passage, and length of postoperative hospital stay) and cytokine variables (IL-6(Δ2 h), IL-8(Δ2 h) and IL-10(Δ2 h). Linear regression analysis showed that the abdominal wound length significantly influenced the operation time, time of first flatus passage, and length of postoperative hospital stay (p < 0.001). The length of the abdominal wound showed a significant influence on the IL-6(Δ2 h) and IL-8(Δ2 h) (p < 0.001, respectively) but no influence on IL-10(Δ2 h). IL-6(Δ2 h), but not IL-8(Δ2 h), significantly influenced the time to first flatus passage and length of hospital stay (p = 0.007, p = 0.006, respectively). The mini-laparotomy approach (wound length <7 cm) led to significantly shortened operation time, time of first flatus passage, length of postoperative stay (p = 0.004, p = 0.003, p = 0.006, respectively) as well as reduced postoperative increment of IL-6(Δ2 h) (p = 0.015). The mini-laparotomy for anterior resection surgery significantly influenced operation time, time of first passage of flatus, length of postoperative stay, and IL-6(Δ2 h). Our study is the first to report the complex interaction among the length of the abdominal wound, IL-6 serum level, recovery of the first passage of flatus, and postoperative hospital stay. These results suggest that smaller abdominal wounds and smaller postoperative IL-6 increments were associated with faster recovery of flatus passage and shorter hospital stays.

Information flow and catalytic dyad in SARS-CoV2 main protease Mpro enzyme using embedded discrete Markov chains and centrality measures

Abstract In this work, we use a network representation of the globular crystalline structure of a given protein as a graph structure with N nodes and E edges in order to analyze quantitatively the flow information and to identify key sites within the globular structure. Each node nj represents a $ C_{\alpha}^{i}$ carbon of the main backbone whereas the node’s degree ki is a measure of its physical interactions. In order to identify biologically relevant and active nodes, we compute local per residue closeness $ C_{c}(i)$, betweenness $ C_{b}(i)$ and eigenvector centralities $ C_{e}(i)$. Further analysis is done by embedding a stochastic dynamic discrete Markov chain in order to evaluate the dynamics of a set of normal random walkers (NRW’s) within the network. From this, we compute the mean first passage time matrix M and the stationary occupation probability vector ψi for each node. These two measures provide very useful information on the dynamical process embed within the $ C_{\alpha}$ network. We apply this to SARS-CoV2 Mpro main protease which is a key enzyme in the virus replication cycle. In particular, we focus our attention to the properties of the catalytic dyad integrated by His41-Cys145 in Mpro main protease since this active site has been under intense scrutiny as a pharmaceutical target. In addition, our results show the existence of additional relevant aminoacids that might play a fundamental role on signal propagation and allosteric pathways in SARS-CoV main proteases.

First passage times of charge transport and entropy change

All real physical processes, including of the first-passage time, occur with a change in entropy. This circumstance is not taken into account when studying the first-passage time, but is illustrated in this article using the example of electron transfer through a metallic double dot. The statistics of the first-passage time of a random process N(t) for electrons transferred through a metallic double dot is considered. The expressions for the average first-passage time are compared with and without taking into account the change in entropy during this time. External influences on the average value of the first-passage time are considered for the case of DC bias voltage.

Estimation of land surface temperature (LST) using single-channel and multi-band methods in Sablan mountainous region

Studying Land Surface Temperature (LST) provides various scientific, environmental, and practical advantages. It aids climate monitoring, urban heat island studies, environmental assessments, agriculture, urban planning, infrastructure development, and remote sensing. Algorithm selection depends on research constraints and goals, considering different algorithm strengths and limitations. In this study, the LST around the Sabalan volcanic peak was estimated using Landsat 8 satellite images captured by the OLI and TIRS, employing the Single Channel JM&S and Split Window algorithms. The obtained surface temperatures from both algorithms were converted to air temperature. Subsequently, the pixel air temperatures were compared to the corresponding synoptic station air temperatures of the studied region, considering the satellite's passage time. This comparison was executed using regression analysis (R2 and correlation coefficients) and statistical indices such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to validate the data. The results indicated R2 values and correlation coefficients of 0.04 and 0.97, respectively, for the Single Channel algorithm, and 0.12 and 0.69 for the SW algorithm, signifying a positive relationship and close alignment between these datasets. Furthermore, the examination of the results revealed root mean square error and mean absolute error with minimum errors of 4.20, 3.88, and 8.14, 8.97 °C in the SW method. By assessing the difference between mean LST and the synoptic station air temperatures, the SW approach exhibited lower temperature discrepancies across all stations and lower estimation errors compared to the Single Channel method, thus displaying higher accuracy and closer conformity to the actual temperature.

Kinetic Network in Milestoning: Clustering, Reduction, and Transition Path Analysis.

We present a reduction of the Milestoning (ReM) algorithm to analyze the high-dimensional Milestoning kinetic network. The algorithm reduces the Milestoning network to low dimensions but preserves essential kinetic information, such as local residence time, exit time, and mean first passage time between any two states. This is achieved in three steps. First, nodes (milestones) in the high-dimensional Milestoning network are grouped into clusters based on the metastability identified by an auxiliary continuous-time Markov chain. Our clustering method is applicable not only to time-reversible networks but also to nonreversible networks generated from practical simulations with statistical fluctuations. Second, a reduced network is established via network transformation, containing only the core sets of clusters as nodes. Finally, transition pathways are analyzed in the reduced network based on the transition path theory. The algorithm is illustrated using a toy model and a solvated alanine dipeptide in two and four dihedral angles.

Optimizing Flow Conditions and Fish Passage Success in Vertical Slot Fishways: Lessons from Fish Behavior Observations

This study investigates the behavior of chubs (Squalius cephalus) of mid-body length (9.7–15.6 cm) with respect to turbulent flow conditions in a pool representing an experimental vertical slot fishway. Velocity and turbulence were characterized using PIV data. The influence of turbulent flow on fish behavior was assessed through the number of successful fish passage attempts, the associated passage times, and the spatial distribution of fish in the pool. Turbulence conditions were modified by the addition of one or three vertical rigid cylinders inside the pool. The results show that these adaptations may facilitate the passage of chubs. Results provide valuable insights and information to understand the relationship between fish behavior and hydraulic conditions, especially in the context of improving the design of fishways.

Simulation of urban surface temperature and surface heat balance in the Tokyo metropolitan area

This study aimed to propose a method for simulating ground surface temperature and surface heat balance using thermal infrared remote sensing and an urban surface heat balance model and to conduct a simulation in the Tokyo metropolitan area. Previous studies have typically employed ground surface temperatures obtained through satellite remote sensing to analyze urban thermal environments. However, these studies were limited to using surface temperature data available only at the time of satellite passage. Therefore, in this study, we conducted a simulation for the Tokyo metropolitan area by integrating a one-dimensional heat balance model with ground surface temperatures observed by satellites. The novelty of this study lies in the utilization of parameters related to heat balance obtained through calculations of physical processes, as opposed to relying on empirical formulas or parameters. Consequently, realistic ground surface characteristics, including parameters such as evaporation efficiency and thermal inertia essential for the simulation, were derived by this combined method. Furthermore, diurnal variations in ground surface temperature distributions were accurately represented by using realistic surface temperature data based on satellite remote sensing, considering the effect of heat storage in urban areas—an aspect challenging to reproduce using conventional local meteorological models.

Delay Segmented Tristable Stochastic Resonance System Driven by Non-Gaussian Colored Noise and Its Application in Bearing Fault Detection

Abstract This study investigates the Delayed Segmented Tristable Stochastic Resonance (DSTSR) system under the influence of additive non-Gaussian colored noise. The research employs an improved segmented tristable potential function, wherein the equilibrium points and barrier heights can be independently controlled by parameters. Simultaneously, the segmented function on both sides reduces the restrictions of higher-order terms on the walls of the potential wells. The equivalent Langevin equation for the DSTSR system is obtained using the path integral method, the unified colored noise approximation method, and the small-delay approximation. Subsequently, the theoretical expressions for the steady-state probability density, mean first passage time (MFPT), and Signal-to-Noise Ratio (SNR) are derived from the resulting equations, and the impact of variations in system parameters on these performance metrics is discussed. Additionally, Monte Carlo simulations for MFPT are conducted to verify the accuracy of the theoretical derivations. Combining the results from the theoretical section and the impact of parameters on system performance, the article employs an adaptive genetic algorithm to optimize system parameters. This algorithm is then applied to simulation experiments and bearing fault detection. In the simulation experiments, the DSTSR system is compared with other systems. The results indicate that the DSTSR system exhibits the highest SNR improvement. Furthermore, in bearing fault detection under non-Gaussian colored noise, the DSTSR system shows higher spectral amplitude and SNR at the fault frequency compared to the tristable stochastic resonance system and the segmented tristable stochastic resonance system without time delay feedback. This suggests that stochastic resonance can effectively detect weak signals in non-Gaussian non-white noise scenarios, and the introduction of time delay contributes to the occurrence of stochastic resonance to a certain extent.

A non-oriented first passage percolation model and statistical invariance by time reversal

We introduce and study a non-oriented first passage percolation model having a property of statistical invariance by time reversal. This model is defined in a graph having directed edges and the passage times associated with each set of outgoing edges from a given vertex are distributed according to a generalized Bernoulli–Exponential law and i.i.d. among vertices. We derive the statistical invariance property by time reversal through a zero-temperature limit of the random walk in Dirichlet environment model.