Search

Predicting the behavior of individuals acting under their own motivation is a challenge shared across multiple scientific fields, from economic to ecological systems. In rivers, fish frequently change their orientation even when stimuli are unchanged, which makes understanding and predicting their movement in time-varying environments near built infrastructure particularly challenging. Cognition is central to fish movement, and our lack of understanding is costly in terms of time and resources needed to design and manage water operations infrastructure that is able to meet the multiple needs of human society while preserving valuable living resources. An open question is how best to cognitively account for the multi-modal, -attribute, -alternative, and context-dependent decision-making of fish near infrastructure. Here, we leverage agent- and individual-based modeling techniques to encode a cognitive approach to mechanistic fish movement behavior that operates at the scale in which water operations river infrastructure is engineered and managed. Our cognitive approach to mechanistic behavior modeling uses a Eulerian-Lagrangian-agent method (ELAM) to interpret and quantitatively predict fish movement and passage/entrainment near infrastructure across different and time-varying river conditions. A goal of our methodology is to leverage theory and equations that can provide an interpretable version of animal movement behavior in complex environments that requires a minimal number of parameters in order to facilitate the application to new data in real-world engineering and management design projects. We first describe concepts, theory, and mathematics applicable to animals across aquatic, terrestrial, avian, and subterranean domains. Then, we detail our application to juvenile Pacific salmonids in the Bay-Delta of California. We reproduce observations of salmon movement and passage/entrainment with one field season of measurements, year 2009, using five simulated behavior responses to 3-D hydrodynamics. Then, using the ELAM model calibrated from year 2009 data, we predict the movement and passage/entrainment of salmon for a later field season, year 2014, which included a novel engineered fish guidance boom not present in 2009. Central to the fish behavior model’s performance is the notion that individuals are attuned to more than one hydrodynamic signal and more than one timescale. We find that multi-timescale perception can disentangle multiplex hydrodynamic signals and inform the context-based behavioral choice of a fish. Simulated fish make movement decisions within a rapidly changing environment without global information, knowledge of which direction is downriver/upriver, or path integration. The key hydrodynamic stimuli are water speed, the spatial gradient in water speed, water acceleration, and fish swim bladder pressure. We find that selective tidal stream transport in the Bay-Delta is a superset of the fish-hydrodynamic behavior repertoire that reproduces salmon movement and passage in dam reservoir environments. From a cognitive movement ecology perspective, we describe how a behavior can emerge from a repertoire of multiple fish-hydrodynamic responses that are each tailored to suit the animal’s recent past experience (localized environmental context). From a movement behavior perspective, we describe how different fish swim paths can emerge from the same local hydrodynamic stimuli. Our findings demonstrate that a cognitive approach to mechanistic fish movement behavior modeling does not always require the maximum possible spatiotemporal resolution for representing the river environmental stimuli although there are concomitant tradeoffs in resolving features at different scales. From a water operations perspective, we show that a decision-support tool can successfully operate outside the calibration conditions, which is a necessary attribute for tools informing future engineering design and management actions in a world that will invariably look different than the past.

This article provides a comprehensive review and best practices for numerically simulating hydraulic flushing for reservoir sediment management. Three sediment flushing types are discussed: drawdown flushing, pressure flushing, and turbidity current venting. The need for reservoir sediment management and the current practices are reviewed. Different hydraulic drawdown types are described in terms of the basic physical processes involved as well as the empirical/analytical assessment tools that may be used. The primary focus has been on the numerical modeling of various hydraulic flushing options. Three model categories are reviewed: one-dimensional (1D), two-dimensional (2D) depth-averaged or layer-averaged, and three-dimensional (3D) computational fluid dynamics (CFD) models. General guidelines are provided on how to select a proper model given the characteristics of the reservoir and the flushing method, as well as specific guidelines for modeling. Case studies are also presented to illustrate the guidelines.

The potential anesthetic neurotoxicity on the neonate is an important focus of research investigation in the field of pediatric anesthesiology. It is essential to understand how these anesthetics may affect the development and growth of neonatal immature and vulnerable brains. Functional magnetic resonance imaging (fMRI) has suggested that using anesthetics result in reduced functional connectivity may consider as core sequence for the neurotoxicity and neurodegenerative changes in the developed brain. Anesthetics either directly impact the primary structures and functions of the brain or indirectly alter the hemodynamic parameters that contribute to cerebral blood flow (CBF) in neonatal patients. We hypothesis that anesthetic agents may either decrease the brain functional connectivity in neonatal patients or animals, which was observed by fMRI. This review will summarize the effect and mechanism of anesthesia on the rapid growth and development infant and neonate brain with fMRI through functional connectivity. It is possible to provide the new mechanism of neuronal injury induced by anesthetics and objective imaging evidence in animal developing brain.

Abstract Background Single cell transcriptome sequencing has become an increasingly valuable technology for dissecting complex biology at a resolution impossible with bulk sequencing. However, the gap between the technical expertise required to effectively work with the resultant high dimensional data and the biological expertise required to interpret the results in their biological context remains incompletely addressed by the currently available tools. Results Single Cell Explorer is a Python-based web server application we developed to enable computational and experimental scientists to iteratively and collaboratively annotate cell expression phenotypes within a user-friendly and visually appealing platform. These annotations can be modified and shared by multiple users to allow easy collaboration between computational scientists and experimental biologists. Data processing and analytic workflows can be integrated into the system using Jupyter notebooks. The application enables powerful yet accessible features such as the identification of differential gene expression patterns for user-defined cell populations and convenient annotation of cell types using marker genes or differential gene expression patterns. Users are able to produce plots without needing Python or R coding skills. As such, by making single cell RNA-seq data sharing and querying more user-friendly, the software promotes deeper understanding and innovation by research teams applying single cell transcriptomic approaches. Conclusions Single cell explorer is a freely-available single cell transcriptomic analysis tool that enables computational and experimental biologists to collaboratively explore, annotate, and share results in a flexible software environment and a centralized database server that supports data portal functionality.

Aims To evaluate the type 2 diabetes mellitus (T2DM) risk of individuals with different types of dyslipidaemia and compare the predictive value of distinct lipid parameters in predicting T2DM.Methods We conducted a secondary analysis of data from the China Health and Retirement Longitudinal Study (CHARLS). 17 708 individuals over 45 years old were interviewed, and 11 847 blood samples were collected at the baseline survey (2011–2012). Outcome of T2DM was confirmed during two follow-up surveys (2013–2014 and 2015–2016). The HRs and 95% CI of T2DM associated with dyslipidaemia were estimated by Cox proportional hazards regressions model. The discriminatory value of eight lipid parameters were compared by the area under the receiver operating characteristic (ROC) curve (AUC).Results A total of 7329 participants were enrolled in our analysis; during the mean follow-up time of 3.4 years, 387 (5.28%) participants developed new-onset diabetes. Compared with participants in normal lipid levels, the T2DM risk of those with hypercholesterolaemia, hypertriglyceridaemia and low high-density lipoprotein cholesterol (HDL-C) were significantly increased (HRs (95% CI) were 1.48 (1.11 to 1.96), 1.92 (1.49 to 2.46) and 1.67 (1.35 to 2.07), respectively). The AUCs of non-HDL-C (0.685, 95% CI 0.659 to 0.711), triglyceride (TG) (0.684, 95% CI 0.658 to 0.710), total cholesterol (TC)/HDL-C (0.685, 95% CI 0.659 to 0.712) and TG/HDL-C (0.680, 95% CI 0.654 to 0.706) were significantly (p

Flow structures at a river diversion juncture are complex and have been studied extensively. Their impact on the juvenile salmon entrainment into the side channel, however, is less investigated, and based mostly on empiricism. In this study, a Eulerian fish tracking model is developed and used in conjunction with a 3D flow solver to quantitatively evaluate the implications of complex flow characteristics at typical junctures on fish entrainment. First, the flow model is validated with the available experimental data, key flow structures are examined using the results, and their implications for fish entrainment are discussed. Next, the numerical fish tracking model is used to show that the cross-sectional fish distribution immediately upstream of a juncture is an important factor that controls fish entrainment efficiency. Fish entrainment efficiency curves are developed for different flow diversion ratios and fish distribution patterns and used to shed light on the reasons behind some field-observed fish entrainment patterns. Further, the model is used to show that the secondary flow in a river bend may have a significant impact on fish entrainment at flow junctures, in agreement with field observations. Finally, a submerged vane is demonstrated to be a potential management option to locally generate secondary flows upstream of a juncture to achieve the desired fish entrainment property.

The near-shore and estuary environment is characterized by complex natural processes. A prominent feature is the wind-generated waves, which transfer energy and lead to various phenomena not observed where the hydrodynamics is dictated only by currents. Over the past several decades, numerical models have been developed to predict the wave and current state and their interactions. Most models, however, have relied on the two-model approach in which the wave model is developed independently of the current model and the two are coupled together through a separate steering module. In this study, a new wave model is developed and embedded in an existing two-dimensional (2D) depth-integrated current model, SRH-2D. The work leads to a new wave–current model based on the one-model approach. The physical processes of the new wave model are based on the latest third-generation formulation in which the spectral wave action balance equation is solved so that the spectrum shape is not pre-imposed and the non-linear effects are not parameterized. New contributions of the present study lie primarily in the numerical method adopted, which include: (a) a new operator-splitting method that allows an implicit solution of the wave action equation in the geographical space; (b) mixed finite volume and finite difference method; (c) unstructured polygonal mesh in the geographical space; and (d) a single mesh for both the wave and current models that paves the way for the use of the one-model approach. An advantage of the present model is that the propagation of waves from deep water to shallow water in near-shore and the interaction between waves and river inflows may be carried out seamlessly. Tedious interpolations and the so-called multi-model steering operation adopted by many existing models are avoided. As a result, the underlying interpolation errors and information loss due to matching between two meshes are avoided, leading to an increased computational efficiency and accuracy. The new wave model is developed and verified using a number of cases. The verified near-shore wave processes include wave shoaling, refraction, wave breaking and diffraction. The predicted model results compare well with the analytical solution or measured data for all cases.

Objectives: Our aim was to retrospectively evaluate the benefit of levosimendan in certain complicated congenital heart procedures such as the pediatric anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA) with moderate or severe cardiac dysfunction and its repair.Study Design: We enrolled 40 pediatric patients with ALCAPA and moderate or severe left ventricular dysfunction. Patients who had a preoperative left ventricular ejection fraction (LVEF) of 50% or less and had undergone the surgical correction of their coronary artery through cardiopulmonary bypass met the criteria of our study. Twenty patients were given 0.1–0.2 μg/kg/min levosimendan at the induction of anesthesia, which lasted for 24 h. The remaining 20 patients were not given levosimendan.Results: The mean preoperative LVEF in the levosimendan group was significantly lower than that in the non-levosimendan group (22.5 ± 10.7% vs. 31.8 ± 8.1%, p = 0.004). On postoperative day 7, the LVEF in the levosimendan group was still significantly lower (27.1 ± 8.9% vs. 37.5 ± 11.0%, p = 0.002). There was no significant difference in ΔLVEF detected on day 7 [median 30.8%, interquartile range (IQR) −4.4 to 63.5% vs. median 15.1%, IQR −3.5 to 40.0%, p = 0.560] or at follow-up of about 180 days (median 123.5%, IQR 56.1–222.6% vs. median 80.0%, IQR 36.4–131.3%, p = 0.064). There was no significant difference between the two groups in postoperative vasoactive-inotropic score (VIS) at any of the time points of 1, 6, 12, 24, and 48 h (p = 0.093). Three patients had to be supported by extracorporeal membrane oxygenation when difficulty appeared in weaning off cardiopulmonary bypass because of low cardiac output in the non-levosimendan group, but no patient needed extracorporeal membrane oxygenation after levosimendan infusion (p = 0.231). The length of intensive care unit stay (median 10.5 days, IQR 7.3–39.3 days vs. median 4.0 days, IQR 2.0–10.0 days, p = 0.002) and duration of mechanical ventilation (median 146.0 h, IQR 76.5–888.0 h vs. median 27.0 h, IQR 11.0–75.0 h, p = 0.002) were revealed to be longer in the levosimendan group. Peritoneal dialysis occurred in eight patients (40%) in the levosimendan group and two patients (10%) in the non-levosimendan group (p = 0.028). No significant difference was revealed in all-cause mortality within 180 days, which occurred in two patients (10%) in the levosimendan group and one (5%) in the non-levosimendan group (p = 1.00).Conclusion: Levosimendan's unique pharmacological properties have strong potential for cardiac function recovery among pediatric patients with ALCAPA with impaired left ventricular function who have undergone surgical repair.However, any improvement from levosimendan on postoperative outcomes or mortality was not substantiated by this study and must be investigated further.

Three-dimensional (3D) hydrostatic-pressure-assumption numerical models are widely used for environmental flows with free surfaces and phase interfaces. In this study, a new flow and sediment transport model is developed, aiming to be general and more flexible than existing models. A general set of governing equations are used for the flow and suspended sediment transport, an improved solution algorithm is proposed, and a new mesh type is developed based on the unstructured polygonal mesh in the horizontal plane and a terrain-following sigma mesh in the vertical direction. The new flow model is verified first with the experimental cases, to ensure the validity of flow and free surface predictions. The model is then validated with cases having the suspended sediment transport. In particular, turbidity current flows are simulated to examine how the model predicts the interface between the fluid and sediments. The predicted results agree well with the available experimental data for all test cases. The model is generally applicable to all open-channel flows, such as rivers and reservoirs, with both flow and suspended sediment transport issues.

OBJECTIVE: To investigate the risk factors involved in radial-femoral artery pressure gradient after cardiac surgery. METHODS: In this retrospective study, we reviewed 412 cardiac surgeries with both femoral artery pressure and radial artery pressure monitoring before cardiopulmonary bypass. 138 patients had radial-femoral artery pressure gradient after cardiopulmonary bypass (group P) but 263 were not (group N). Their hemodynamic data and other demographic data were analyzed. RESULTS: Phenylephrine usage was 1.7±1.1 mg in group N and 2.9±1.2 mg in group P (P

The subcellular localization of specific RNA molecules promotes localized cellular activity across a variety of species and cell types. The misregulation of this RNA targeting can result in developmental defects, and mutations in proteins that regulate this process are associated with multiple diseases. For the vast majority of localized RNAs, however, the mechanisms that underlie their subcellular targeting are unknown, partly due to the difficulty associated with profiling and quantifying subcellular RNA populations. To address this challenge, we developed Halo-seq, a proximity labeling technique that can label and profile local RNA content at virtually any subcellular location. Halo-seq relies on a HaloTag fusion protein localized to a subcellular space of interest. Through the use of a radical-producing Halo ligand, RNAs that are near the HaloTag fusion are specifically labeled with spatial and temporal control. Labeled RNA is then specifically biotinylated in vitro via a click reaction, facilitating its purification from a bulk RNA sample using streptavidin beads. The content of the biotinylated RNA is then profiled using high-throughput sequencing. In this article, we describe the experimental and computational procedures for Halo-seq, including important benchmark and quality control steps. By allowing the flexible profiling of a variety of subcellular RNA populations, we envision Halo-seq facilitating the discovery and further study of RNA localization regulatory mechanisms. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Visualization of HaloTag fusion protein localization Basic Protocol 2: In situ copper-catalyzed cycloaddition of fluorophore via click reaction Basic Protocol 3: In vivo RNA alkynylation and extraction of total RNA Basic Protocol 4: In vitro copper-catalyzed cycloaddition of biotin via click reaction Basic Protocol 5: Assessment of RNA biotinylation by RNA dot blot Basic Protocol 6: Enrichment of biotinylated RNA using streptavidin beads and preparation of RNA-seq library Basic Protocol 7: Computational analysis of Halo-seq data.

Thousands of RNA species display nonuniform distribution within cells. However, quantification of the spatial patterns adopted by individual RNAs remains difficult, in part by a lack of quantitative tools for subcellular transcriptome analysis. In this study, we describe an RNA proximity labeling method that facilitates the quantification of subcellular RNA populations with high spatial specificity. This method, termed Halo-seq, pairs a light-activatable, radical generating small molecule with highly efficient Click chemistry to efficiently label and purify spatially defined RNA samples. We compared Halo-seq with previously reported similar methods and found that Halo-seq displayed a higher efficiency of RNA labeling, indicating that it is well suited to the investigation of small, precisely localized RNA populations. We then used Halo-seq to quantify nuclear, nucleolar and cytoplasmic transcriptomes, characterize their dynamic nature following perturbation, and identify RNA sequence features associated with their composition. Specifically, we found that RNAs containing AU-rich elements are relatively enriched in the nucleus. This enrichment becomes stronger upon treatment with the nuclear export inhibitor leptomycin B, both expanding the role of HuR in RNA export and generating a comprehensive set of transcripts whose export from the nucleus depends on HuR.

Runoff and sediment movement induced by storms have serious economic, environmental, and social impacts around the world. Hydrological models have become useful decision support tools for watershed management. The development of process-based distributed models is complex as it involves a range of disciplines and spans multiple spatial and temporal scales. In this study, a process-based and mesh-distributed model, suitable for both event-based and continuous simulations, is developed. The watershed is conceptualized in three distinct zones: a surface overland region, an unsaturated subsurface zone, and a groundwater zone. Overland flow is governed by the 2D diffusive wave equation with an optional 1D channel network solver; water in the unsaturated zone is modelled through mass conservation and vertical dominance of the processes; and saturated groundwater flow is governed by the 2D Dupuit equation. Soil erosion and transport are governed by the multi-size, non-equilibrium equations incorporating soil entrainment, transport, and deposition. The model is driven by meteorological input, land use and soil type properties. The numerical approach is a generalization of existing models to overcome some current modelling shortcomings. New contributions of the model are highlighted and model verification and validation are reported against benchmark cases. The model is then applied to the Cache Creek watershed in California, USA. Satisfactory agreement with the measured data is obtained with both the runoff flow rate and sediment load. [ABSTRACT FROM AUTHOR]

An integrated current–wave–sediment model is developed for coastal and estuary applications. The new model aims to improve the existing ones in both the physical process representation and the numerical techniques. Two areas of improvements are emphasized: the numerical procedure and a new general sediment sub-model. The numerical procedure adopts the one-model one-mesh approach to improve the model accuracy, efficiency and user friendliness. One model is developed which includes three major sub-models: current flow, wave dynamics and sediment transport. The three are tightly coupled during the solution process by exchanging data among sub-models within the same time step. Further, one unstructured geophysical mesh is adopted for all three sub-models and the mesh allows the most flexible polygonal shapes with an arbitrary number of sides. The current flow sub-model is an extension of the existing river hydraulic model (SRH-2D), the wave sub-model follows the third-generation theory implemented in SWAN which solves the multi-frequency multi-direction wave action balance equation and the sediment sub-model is a new development adopting a general multi-size non-equilibrium sediment transport formulation but tailored for coastal applications. In this paper, the theory, the governing equations and the numerical methods are presented; the new model is then verified and validated using selected experimental cases. It is shown that the new model may predict the current–wave–sediment dynamics well. In addition, model sensitivity results are also discussed to shed light on future needs. [ABSTRACT FROM AUTHOR]