Spatial Location Research Articles

Mangrove forest ecological function is influenced by the environmental settings and the benthic fauna composition

Benthic communities perform many important roles within mangrove ecosystems and are responsible for facilitating many of the functions attributed to mangrove forests. For instance, they are instrumental in mediating mangrove system productivity, and provide crucial food for juvenile nekton. Despite their importance, very few studies have investigated the benthic community of mangrove forests, and among those the results are inconsistent. This variability manifests in strong location effects, with low organism density and species richness in Indo-Pacific mangroves, compared to West Atlantic sites. These regional difference are confounded by differences in within-region environmental settings (e.g. rainfall, tidal range, spatial location along the coastal mosaic), and this complicates the development of a clear understanding of underlying consistencies. To assess the influence of contrasting environmental influences on the benthic community of mangrove forests we studied the benthic community of Rhizophora stylosa forests at two coastal estuaries and along the shores of two islands within a 75 km radius. The results showed that the community composition differed among settings, with several taxa only occurring at one of the two forests type. Furthermore, Peracarida, a common prey found in the gut of juvenile fish, was only found through the island forests, but never observed within the estuarine forests. This indicates that environmental setting can play a key role in determining the nature of mangrove benthic assemblages and their potential ecological roles. Consequently, caution is required when attributing the ecological roles of mangrove forests without accounting for changes in settings. Additionally, we only investigated a single mangrove species within the same climatic region, meaning that even greater variability is likely when the full range of mangrove types, conditions and areas are assessed. Understanding this variation is important because it implies that mangrove forest restoration projects are unlikely to achieve their desired outcomes unless setting-specific conditions are understood and taken into account.

A new efficient approach of DFN modelling constrained with fracture occurrence and spatial location

Fractures or faults in the subsurface exert a significant impact on fluid flow and engineering activities in that environment. Fracture modelling is one of the crucial techniques, providing essential insights into the mechanisms underlying these impacts. As a useful tool, the Discrete Fracture Network (DFN) method is often utilized to simulate fracture networks and to integrate fracture statistics into 3D numerical models. However, the current DFN modeling technology suffers from low operational efficiency, particularly when handling a substantial quantity of fractures in 3D models. This paper proposes two ways to improve the efficiency and accuracy of modelling fractures: the matrix-based random sampling method (for faster generation of fracture loactions) and the quaternion method (for more accurate description of fractures). These proposed approaches simplify the management of large number of fractures within 3D models. The paper provides a comprehensive description of the proposed methods, accompanied by pseudo-code for the algorithms. The effectiveness of the proposed approach is validated through a practical case study, demonstrating superior computational efficiency and enhanced applicability for large-scale fracture modeling.

A graph self-supervised residual learning framework for domain identification and data integration of spatial transcriptomics

Spatial transcriptomics (ST) technologies allow for comprehensive characterization of gene expression patterns in the context of tissue microenvironment. However, accurately identifying domains with spatial coherence in both gene expression and histology in situ and effectively integrating data from multi-sample remains challenging. Here, we propose ResST, a graph self-supervised residual learning model based on graph neural network and Margin Disparity Discrepancy (MDD) theory. ResST aggregates gene expression, biological effects, spatial location, and morphological information to capture nonlinear relationships between a cell and surrounding cells for spatial domain identification. Also, ResST integrates multiple ST datasets and aligns latent embeddings based on MDD theory for correcting batch effects. Results show that ResST identifies continuous spatial domains at a finer scale in ten ST datasets acquired with different technologies. Moreover, ResST efficiently integrated data from multiple tissue sections vertically or horizontally while correcting batch effects. Overall, ResST demonstrates exceptional performance in analyzing ST datasets.

ImputeHiFI: An Imputation Method for Multiplexed DNA FISH Data by Utilizing Single-Cell Hi-C and RNA FISH Data.

Although multiplexed DNA fluorescence in situ hybridization (FISH) enables tracking the spatial localization of thousands of genomic loci using probes within individual cells, the high rates of undetected probes impede the depiction of 3D chromosome structures. Current data imputation methods neither utilize single-cell Hi-C data, which elucidate 3D genome architectures using sequencing nor leverage multimodal RNA FISH data that reflect cell-type information, limiting the effectiveness of these methods in complex tissues such as the mouse brain. To this end, a novel multiplexed DNA FISH imputation method named ImputeHiFI is proposed, which fully utilizes the complementary structural information from single-cell Hi-C data and the cell type signature from RNA FISH data to obtain a high-fidelity and complete spatial location of chromatin loci. ImputeHiFI enhances cell clustering, compartment identification, and cell subtype detection at the single-cell level in the mouse brain. ImputeHiFI improves the recognition of cell-type-specific loops in three high-resolution datasets. In short, ImputeHiFI is a powerful tool capable of imputing multiplexed DNA FISH data from various resolutions and imaging protocols, facilitating studies of 3D genome structures and functions.

Immune Cell Densities Predict Response to Immune Checkpoint-Blockade in Head and Neck Cancer.

Pre-ICB tumor tissue sections were obtained from 9 responders (complete response, partial response, or stable disease) and 11 non-responders (progressive disease) classified via RECISTv1.1. A custom multi-immunofluorescence (mIF) staining assay was designed, optimized, and applied to characterize tumor cells (pan-cytokeratin), T cells (CD4, CD8), B cells (CD19, CD20), myeloid cells (CD16, CD56, CD163), dendritic cells (LAMP3), fibroblasts (alpha-Smooth Muscle Actin), proliferative status (Ki67) and immunoregulatory molecules (PD1). Spatial metrics were compared among groups. Serial tissue sections were scored for TLS in both H&E and mIF slides. A machine learning model was employed to measure the effect of these metrics on achieving a response to ICB (SD, PR, or CR). A higher density of B lymphocytes (CD20+) was found in responders compared to non-responders to ICB (p=0.022). A positive correlation was observed between mIF and pathologist identification of TLS (R2= 0.66, p-value= <0.0001). TLS trended toward being more prevalent in responders to ICB (p=0.0906). The presence of TLS within 100 um of the tumor was associated with improved overall (p=0.04) and progression-free survival (p=0.03). A multivariate machine learning model identified TLS density as a leading predictor of response to ICB with 80% accuracy. Immune cell densities and TLS spatial location within the tumor microenvironment play a critical role in the immune response to HNSCC and may potentially outperform CPS as a predictor of ICB response.

The puzzle of soil Moisture: Unravelling its dynamics in a watershed with complex physiographic features

Periodic in-situ soil moisture observations are essential for managing agriculture, water resources and validating satellite-based soil moisture products. To facilitate this, a comprehensive soil moisture monitoring network is required that provides an optimal balance between the accuracy of measurement and the associated costs. Therefore, the present study attempts to investigate the spatio-temporal variability of soil moisture over a period of 57 weeks in a watershed spanning 422 km2 with elevation ranging from 739 m to 2914 m. The watershed presents complex physiographic features such as extreme seasonal variability, differential elevations and diverse land use patterns (agricultural, forest and grasslands). The study follows a systematic sampling strategy, using a 2 km x 2 km spatial resolution across 104 grids over the entire watershed area. The soil moisture data is then used to estimate the number of optimal locations from a spatial and temporal perspective. The number of spatial optimal locations and temporal optimality are quantified using the student’s t-distribution test and the random combination method, respectively. Moreover, empirical orthogonal functions (EOFs) coupled with Spearman rank correlation method are used to evaluate the dominant physiographic factors affecting soil moisture variability within the watershed. From a comprehensive set of 71,136 field measurements, this study identified 30 optimal sampling locations necessary to capture the spatial variability of soil moisture within a ± 2 % error band. Within these 30 locations, 8 randomly selected locations can adequately represent the temporal variability of the watershed mean soil moisture within a ± 1 % error band. Furthermore, EOFs coupled with Spearman rank correlation identified elevation and sand content as the dominant physical factors influencing soil moisture variability across the watershed. The insights derived from this study provide a robust framework for carrying out soil moisture studies, especially in an ungauged watershed.

Brain-Controlled Augmented Hearing for Spatially Moving Conversations in Multi-Talker Environments.

Focusing on a specific conversation amidst multiple interfering talkers is challenging, especially for those with hearing loss. Brain-controlled assistive hearing devices aim to alleviate this problem by enhancing the attended speech based on the listener's neural signals using auditory attention decoding (AAD). Departing from conventional AAD studies that relied on oversimplified scenarios with stationary talkers, a realistic AAD task that involves multiple talkers taking turns as they continuously move in space in background noise is presented. Invasive electroencephalography (iEEG) data are collected from three neurosurgical patients as they focused on one of the two moving conversations. An enhanced brain-controlled assistive hearing system that combines AAD and a binaural speaker-independent speech separation model is presented. The separation model unmixes talkers while preserving their spatial location and provides talker trajectories to the neural decoder to improve AAD accuracy. Subjective and objective evaluations show that the proposed system enhances speech intelligibility and facilitates conversation tracking while maintaining spatial cues and voice quality in challenging acoustic environments. This research demonstrates the potential of this approach in real-world scenarios and marks a significant step toward developing assistive hearing technologies that adapt to the intricate dynamics of everyday auditory experiences.

A Microrobotic Design for the Spontaneous Tracing of Isochemical Contours in the Environment

Microrobotic platforms hold significant potential to advance a variety of fields, from medicine to environmental sensing. Herein, minimally functional robotic entities modeled on readily achievable state‐of‐the‐art features in a modern lab or cleanroom are computationally simulated. Inspired by Dou and Bishop (Phys Rev Res. 2019;1(3):1–5), it is shown that the simple combination of unidirectional steering connected to a single environmental (chemical) sensor along with constant propulsion gives rise to highly complex functions of significant utility. Such systems can trace the contours orthogonal to arbitrary chemical gradients in the environment. Also, pairs of such robots that are additionally capable of emitting the same chemical signal are shown to exhibit coupled relative motion. When the pair has unidirectional steering in opposite directions within the 2D plane (i.e., counter‐rotating), they move in parallel trajectories to each other. Alternatively, when steering is in the same direction (corotation), the two move in the same epicyclical trajectory. In this way, the chirality of the unidirectional steering produces two distinct emergent phenomena. The behavior is understood as a ratchet mechanism that exploits the differential in the radii of curvature corresponding to different spatial locations. Applications to environmental detection, remediation, and monitoring are discussed.

Optimality of multisensory integration while compensating for uncertain visual target information with artificial vibrotactile cues during reach planning

BackgroundPlanning and executing movements requires the integration of different sensory modalities, such as vision and proprioception. However, neurological diseases like stroke can lead to full or partial loss of proprioception, resulting in impaired movements. Recent advances focused on providing additional sensory feedback to patients to compensate for the sensory loss, proving vibrotactile stimulation to be a viable option as it is inexpensive and easy to implement. Here, we test how such vibrotactile information can be integrated with visual signals to estimate the spatial location of a reach target.MethodsWe used a center-out reach paradigm with 31 healthy human participants to investigate how artificial vibrotactile stimulation can be integrated with visual-spatial cues indicating target location. Specifically, we provided multisite vibrotactile stimulation to the moving dominant arm using eccentric rotating mass (ERM) motors. As the integration of inputs across multiple sensory modalities becomes especially relevant when one of them is uncertain, we additionally modulated the reliability of visual cues. We then compared the weighing of vibrotactile and visual inputs as a function of visual uncertainty to predictions from the maximum likelihood estimation (MLE) framework to decide if participants achieve quasi-optimal integration.ResultsOur results show that participants could estimate target locations based on vibrotactile instructions. After short training, combined visual and vibrotactile cues led to higher hit rates and reduced reach errors when visual cues were uncertain. Additionally, we observed lower reaction times in trials with low visual uncertainty when vibrotactile stimulation was present. Using MLE predictions, we found that integration of vibrotactile and visual cues followed optimal integration when vibrotactile cues required the detection of one or two active motors. However, if estimating the location of a target required discriminating the intensities of two cues, integration violated MLE predictions.ConclusionWe conclude that participants can quickly learn to integrate visual and artificial vibrotactile information. Therefore, using additional vibrotactile stimulation may serve as a promising way to improve rehabilitation or the control of prosthetic devices by patients suffering loss of proprioception.

Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills.

Health professional education stands to gain substantially from collective efforts toward building video databases of skill performances in both real and simulated settings. An accessible resource of videos that demonstrate an array of performances - both good and bad-provides an opportunity for interdisciplinary research collaborations that can advance our understanding of movement that reflects technical expertise, support educational tool development, and facilitate assessment practices. In this paper we raise important ethical and legal considerations when building and sharing health professions education data. Collective data sharing may produce new knowledge and tools to support healthcare professional education. We demonstrate the utility of a data-sharing culture by providing and leveraging a database of cardio-pulmonary resuscitation (CPR) performances that vary in quality. The CPR skills performance database (collected for the purpose of this research, hosted at UK Data Service's ReShare Repository) contains videos from 40 participants recorded from 6 different angles, allowing for 3D reconstruction for movement analysis. The video footage is accompanied by quality ratings from 2 experts, participants' self-reported confidence and frequency of performing CPR, and the demographics of the participants. From this data, we present an Automatic Clinical Assessment tool for Basic Life Support that uses pose estimation to determine the spatial location of the participant's movements during CPR and a deep learning network that assesses the performance quality.

A multivariate and wavelet-based analysis on the wall pressure fluctuations for propellers in ground effect

This study, conducted at the University of Bristol’s aeroacoustic facility, explores the aeroacoustic characteristics of a 10-inch APC propeller in pusher configuration within ground effect conditions. Tested at a rotational speed of 7000 r/min, the propeller’s performance was evaluated at varying distances from the ground. Wall pressure fluctuations near the propeller were measured at eight radial points, complemented by far-field acoustic measurements. Consistent with previous research, distinctive thrust and torque behaviours were observed when operating both in and out of ground effect. Notably, a significant noise increase of approximately 5 dB was observed on the ground’s reflected side, while a reduction of about 14 dB was detected on the shielded side. Higher-order statistical analysis such as skewness and kurtosis revealed substantial effects of tip vortex impingement in ground effect conditions. This study applies two-point statistics and the Corcos model to analyze propeller-induced wall pressure fluctuations. The coherence function effectively captures the intricate dynamics of pressure variations across different spatial locations and frequencies. This approach offers novel insights into the behaviour of boundary layers and flow-induced pressures in the context of propellers operating in the ground effect. Wavelet decomposition was utilized to differentiate the influences of the boundary layer and the acoustic field. This comprehensive analysis highlights the intricate dynamics of propeller operation in ground effect, contributing valuable insights to the field of aeroacoustic research.

Human–object interaction detection algorithm based on graph structure and improved cascade pyramid network

Aiming at the problem of insufficient use of human–object interaction (HOI) information and spatial location information in images, we propose a human–object​ interaction detection network based on graph structure and improved cascade pyramid. This network is composed of three branches, namely, graph branch, human–object branch and human pose branch. In graph branch, we propose a Graph-based Interactive Feature Generation Algorithm (GIFGA) to address the inadequate utilization of interaction information. GIFGA constructs an initial dense graph model by taking humans and objects as nodes and their interaction relationships as edges. Then, by traversing each node, the graph model is updated to generate the final interaction features. In human pose branch, we propose an Improved Cascade Pyramid Network (ICPN) to tackle the underutilization of spatial location information. ICPN extracts human pose features and maps both the object bounding boxes and extracted human pose maps onto the global feature map to capture the most discriminative interaction-related region features within the global context. Finally, the features from the three branches are fed into a Multi-Layer Perceptron (MLP) for fusion and then classified for recognition. Experimental results demonstrate that our network achieves mAP of 54.93% and 28.69% on the V-COCO and HICO-DET datasets, respectively.

Effect of polarization on spectroscopic characterization of laser produced aluminium plasma

Laser-induced breakdown spectroscopy (LIBS) is a well-established technique widely used in fundamental research and diverse practical fields. Polarization-resolved LIBS, a variant of this technique, aims to improve the sensitivity, which is a critical aspect in numerous scientific domains. In our recent work we demonstrated that the degree of polarization (DOP) in the emission depends on the spatial location and time in a nano second laser generated aluminium plasma1. Present study investigates the effect of polarized emission on the estimation of plasma parameters. The plasma parameters are estimated using the conventional spectroscopic methods such as Boltzmann plot and line intensity ratio for the estimation of electron temperature and Stark broadening for estimating the electron density. The estimated plasma temperature using Boltzmann plot method shows large errors in electron temperature for the locations where DOP is higher. However, the electron density estimated using the Stark width does not show such variation. The observed ambiguity in temperature estimation using the Boltzmann plot method appears to be a consequence of deviation from expected Maxwell Boltzmann distribution of population of the involved energy levels. These findings highlight the need of assessing the DOP of the plasma before selecting the polarization for PRLIBS or temperature estimation using Boltzmann plots in elemental analysis.

Deciphering the spatiotemporal transcriptional landscape of intestinal diseases (Review).

The intestines are the largest barrier organ in the human body. The intestinal barrier plays a crucial role in maintaining the balance of the intestinal environment and protecting the intestines from harmful bacterial invasion. Single‑cell RNA sequencing technology allows the detection of the different cell types in the intestine in two dimensions and the exploration of cell types that have not been fully characterized. The intestinal mucosa is highly complex in structure, and its proper functioning is linked to multiple structures in the proximal‑distal intestinal and luminal‑mucosal axes. Spatial localization is at the core of the efforts to explore the interactions between the complex structures. Spatial transcriptomics (ST) is a method that allows for comprehensive tissue analysis and the acquisition of spatially separated genetic information from individual cells, while preserving their spatial location and interactions. This approach also prevents the loss of fragile cells during tissue disaggregation. The emergence of ST technology allows us to spatially dissect enzymatic processes and interactions between multiple cells, genes, proteins and signals in the intestine. This includes the exchange of oxygen and nutrients in the intestine, different gradients of microbial populations and the role of extracellular matrix proteins. This regionally precise approach to tissue studies is gaining more acceptance and is increasingly applied in the investigation of disease mechanisms related to the gastrointestinal tract. Therefore, this review summarized the application of ST in gastrointestinal diseases.

The effects of Gurney flap on the aerodynamic characteristics of two airfoils in non-continuum regimes

Gurney flap is a simple and effective high-lift device that has the potential to reduce the takeoff-and-landing distance for transport aircraft. It is also a good option for enhancing the airfoil cruising characteristics. However, its effectiveness in non-continuum regimes has rarely been studied. The Unified Gas Kinetic Scheme algorithm, which is valid for all flow regimes, is further developed and used to simulate the flow field around a cambered airfoil and a high-speed quadrilateral airfoil with and without the Gurney flap in the supersonic non-continuum flow regimes. The distribution function shapes at different spatial locations are provided and analyzed. The effects of angle of attack, free-stream Knudsen number, free-stream Mach number, Gurney flap height, and Gurney flap mounting angle on the aerodynamic characteristics of the two airfoils are studied. The results show that the lift and drag of the airfoil with the attached Gurney flap are increased. The pressure rise on the lower surface of the airfoil is the primary factor contributing to the increase in lift. The overall increase in drag mainly comes from the Gurney flap, which also generates a small amount of negative lift. When the angle of attack is less than the critical angle of attack, the Gurney flap can increase the lift-to-drag ratio of the airfoil and improve its aerodynamic performance. The critical angle of attack decreases with the increase in the free-stream Knudsen number and the free-stream Mach number. It decreases with increasing Gurney flap height and increases with increasing Gurney flap mounting angle.

Trajectory analysis of Bacillus subtilis in micro-droplets.

In order to study Bacillus subtilis biofilm formation in microdroplets, we use microfluidics technology to make the droplets and confocal microscopy to capture bacterial movement and biofilm formation in the droplets. We develop a multi-target tracking methodology, using a YOLOv5 detector to identify cells and a DeepSORT algorithm to track cell movements. We find that Bacillus subtilis bacteria with autonomous migration and biofilm-forming ability prefer to cluster and swarm near the microdroplet surface, rather than in the droplet interior. Bacterial mobility depends on phenotype and spatial location within the droplet. The motile cells move about 3.5 times faster than the matrix-producing cells. When the cells are near the wall of the droplet, the direction of the motion of motile cells is along that wall. When the cells are inside the droplet, the direction of the motion of motile cells is disordered, i.e., there is no clear directional or goal-oriented movement. This contrast increases the cell contact probability and facilitates the formation of a Bacillus subtilis biofilm in the droplet. Furthermore, we develop a mathematical model to describe the motion behavior of Bacillus subtilis in microdroplets, which is useful for exploring the influence of motility on biofilm formation.

Spatiotemporal weighted neural network reveals surface seawater pCO2 distributions and underlying environmental mechanisms in the North Pacific Ocean

The North Pacific Ocean plays a pivotal role as a carbon sink within the global carbon cycle. However, a comprehensive understanding of the spatiotemporal dynamics of carbon dioxide concentration and its determinants in this domain remains elusive due to its vast dimensions and the intricacies of influencing factors, with previous research on carbon dioxide partial pressure in the North Pacific Ocean also being relatively scarce. While prevalent machine learning methodologies have been extensively applied to predict the partial pressure of ocean carbon dioxide (pCO2), their limited interpretability has impeded substantial progress in elucidating the underlying mechanisms. This study introduces a gridded spatiotemporal neural network weighted regression (GSTNNWR) model to illuminate temporal and spatial relationships among relevant environmental variables and pCO2. The GSTNNWR model achieves high-precision and high-resolution forecasts of surface pCO2 in the North Pacific Ocean, demonstrating commendable performance (R2 = 0.863 and RMSE=15.123 µatm). Simultaneously, we obtain a quantitative characterization of how various environmental factors influence pCO2 across different temporal and spatial scales. Results show a dominant positive effect of temperature on the pCO2, with an averaged normalized coefficient of 0.28, and variability in the effects of chlorophyll and salinity on the pCO2 at different spatial and temporal locations and temperatures, whose average normalized coefficients are −0.10 and −0.04.The findings of our study will provide insights into the mechanisms and interactions within the North Pacific carbon cycle, contributing to a better understanding of ocean carbon sink formation and the dynamic regulation of the North Pacific carbon cycle.

The Environments of Fast Radio Bursts Viewed Using Adaptive Optics

We present observations from the Gemini Multi-Conjugate Adaptive Optics System/Gemini South Adaptive Optics Imager at Gemini South of five fast radio burst (FRB) host galaxies of FRBs with subarcsecond localizations. We examine and quantify the spatial distributions and locations of the FRBs with respect to their host galaxy light distributions, finding a median host-normalized offset of 2.09 half-light radii (r e ) and the trend that these FRBs occur in fainter regions of their host galaxies. When combined with the FRB host galaxy sample from Mannings et al., we find that FRBs are statistically distinct from Ca-rich transients in terms of light at the source location and from SGRBs and LGRBs in terms of host-normalized offset. We further find that most FRBs are in regions of elevated local stellar mass surface densities in comparison to the mean global values of their hosts. This, along with the finding that the FRB locations trace the distribution of stellar mass, points toward a possible similarity of the environments of CCSNe and FRBs. We also find that four out of five FRB hosts exhibit distinct spiral arm features, and the bursts originating from such hosts tend to appear on or close to their host’s spiral structure, with a median distance of 0.53 ± 0.27 kpc. With many well-localized FRB detections looming on the horizon, we will be able to better characterize the properties of FRB environments relative to their host galaxies and other transient classes. Such insights may only require us to double the number of FRBs with subarcsecond localizations.

Early-time resonances in the three-dimensional wall-bounded axisymmetric Euler and related equations

We investigate the complex-time analytic structure of solutions of the three-dimensional (3D)-axisymmetric, wall-bounded, incompressible Euler equations, by starting with the initial data proposed in Luo and Hou [“Potentially singular solutions of the 3D axisymmetric Euler equations,” Proc. Natl. Acad. Sci. U. S. A. 111(36), 12968–12973 (2014)], to study a possible finite-time singularity. We use our pseudospectral Fourier–Chebyshev method [Kolluru et al., “Insights from a pseudospectral study of a potentially singular solution of the three-dimensional axisymmetric incompressible Euler equation,” Phys. Rev. E 105(6), 065107 (2022)], with quadruple-precision arithmetic, to compute the time-Taylor-series coefficients of the flow fields, up to a high order. We show that the resulting approximations display early-time resonances; the initial spatial location of these structures is different from that for the tygers, which we have obtained in Kolluru et al. [“Insights from a pseudospectral study of a potentially singular solution of the three-dimensional axisymmetric incompressible Euler equation,” Phys. Rev. E 105(6), 065107 (2022)]. We then perform asymptotic analysis of the Taylor-series coefficients, by using generalized ratio methods, to extract the location and nature of the convergence-limiting singularities and demonstrate that these singularities are distributed around the origin, in the complex-t2 plane, along two curves that resemble the shape of an eye. We obtain similar results for the one-dimensional (1D) wall-approximation (of the full 3D-axisymmetric Euler equation) called the 1D Hou–Luo model, for which we use Fourier-pseudospectral methods to compute the time-Taylor-series coefficients of the flow fields. Our work examines the link between tygers, in Galerkin-truncated pseudospectral studies, and early-time resonances, in truncated time-Taylor expansions of solutions of partial differential equations, such as those we consider.

Assessing the Potential of Environmental DNA for Monitoring Nature‐Like Bypasses: Erroneous Surveillance Owing to DNA Flow‐Through

ABSTRACTNature‐like bypasses refer to fishways that simulate natural streams. Apart from facilitating fish migrations, bypasses possess the capacity to enhance biodiversity in dammed rivers. Feasibility of environmental DNA (eDNA) as a tool for bypass assessments is unknown. This study investigated fish eDNA in 10 bypasses and their main channels. Initially, the relative DNA flow‐through was estimated in bypasses. Subsequently, the impact of environmental factors and bypasses on fish assemblages was evaluated, and the robustness of the eDNA and electrofishing methods was assessed pertaining to bypass monitoring. The eDNA flow‐through was computed using an equation to estimate the residual DNA at specified distances downstream of the source site. The relative DNA flow‐through was lowest in the longest bypass with low flow rate and highest in the shortest bypass with higher flow rate and was dependent on the DNA decay rate coefficient used. The redundancy analysis revealed significant effects of spatial location, agriculture, catchment area, and bypass length on the species composition. The within‐river analyses indicated significant and nonsignificant bypass effects on species composition and total species richness, respectively. Higher richness and DNA abundance of migratory and threatened species were observed in the bypasses than in the main channels. The eDNA samples displayed higher species richness compared to electrofishing. The species composition of the bypass eDNA samples was intermediate between that of the main channel eDNA and bypass electrofishing samples, which further corroborated performance of eDNA flow‐through in bypasses. Therefore, bypass eDNA samples represented variable mixtures of local and main channel assemblages, indicating relatively low robustness of eDNA for quantitative and spatially accurate bypass assessments. Nevertheless, these results demonstrate practical applicability of eDNA in surveying the presence of desired species and evidence of the benefits of bypasses in supporting biodiversity and species threatened by damming.