Spatial Spread Research Articles

A novel spatio-temporal prediction model of epidemic spread integrating cellular automata with agent-based modeling

Since 2019, major infectious disease outbreaks have placed tremendous pressure on global public health systems, triggering extensive research on the predictive modeling of infectious diseases. Cellular Automaton (CA) is primarily used in the spatial prediction of infectious diseases to establish a model to for simulating the interaction between different regions and the infection risk to simulate the transmission process of the disease and predict its development trend. However, CA models are governed by initial fixed rules and local interactions, and often fail to capture the complex dynamics of epidemic transmission, which are influenced by factors such as public behavior and government intervention. In view of these limitations, we propose a factorial simulation model for the spatial spread of epidemics, the CA-ABM, which divides agents into three categories–public, government, and hospital agents–to comprehensively express the macro factors that affect the development of epidemics. Agent-Based Modeling (ABM) influences the transition rules of the CA through agent choices, constraints and supporting behaviors. Focusing on the COVID-19 pandemic in mainland China from February 6 to March 20, 2020, we simulate its spread. The results showed an average improvement of 8.4 % in prediction accuracy, with few errors, RMSE under 200, and R2 values over 0.9 in most provinces, demonstrating strong macro-scale stability. This approach helps regions to understand influencing factors and enables targeted infection risk assessment and prevention. In addition, scenario analysis based on CA-ABM model changes epidemic decision-making from “prediction-response” to “scenario-response” and provides theoretical reference for future epidemic management.

Online bilateral matching methodology for anti-epidemic resources based on spatial transmission risk

This paper focuses on the online anti-epidemic resource allocation between demanders and suppliers considering the epidemic spatial spread. The spatial crowdsourcing with sharing platform is an effective way for anti-epidemic resource allocation, and a reasonable online matching strategy can improve the efficiency of resource utilization. The paper proposes online matching heuristic strategy (HSTRF-LRLUF strategy) and designs an online batch bilateral matching algorithm for anti-epidemic resources, which considers the impact of grid spatial aggregation and diffusion risk of emerging infectious diseases. The population distribution within grids and the commuting patterns between grids can provide decision support for selecting online matching strategies of anti-epidemic resources. A larger matching time window focusing on the spatial transmission risk (TR) of the epidemic can obtain better matching results. However, with a smaller matching time window, the decision makers can focus on spatial agglomeration risk (OR) or spatial diffusion risk (IR). The paper effectively combines the spatial crowdsourcing model with the anti-epidemic resource allocation to achieve the allocation of emergency resources to individuals. A combined anti-epidemic resource online matching heuristic strategies is designed from the spatial agglomeration risk and the spatial diffusion risk. Decision makers can dynamically adjust the online matching strategies of anti-epidemic resources by evaluating the spatial agglomeration risk, the spatial diffusion risk, and the overall spatial transmission risk based on the real-time spread of the epidemic and the supply of anti-epidemic resources.

Untangling lineage introductions, persistence and transmission drivers of HP-PRRSV sublineage 8.7

Despite a rapid expansion of Porcine reproductive and respiratory syndrome virus (PRRSV) sublineage 8.7 over recent years, very little is known about the patterns of virus evolution, dispersal, and the factors influencing this dispersal. Relying on a national PRRSV surveillance project established over 20 years ago, we expand the available genomic data of sublineage 8.7 from China. We perform independent interlineage and intralineage recombination analyses for the entire study period, which showed a heterogeneous recombination pattern. A series of Bayesian phylogeographic analyses uncover the role of Guangdong as an important infection hub within Asia. The spatial spread of PRRSV is highly linked with a composite of human activities and the heterogeneous provincial distribution of the swine industry, largely propelled by the smaller-scale Chinese rural farming systems in the past years. We sequence all four available modified live vaccines (MLVs) and perform genomic analyses with publicly available data, of which our results suggest a key “leaky” period spanning 2011–2017 with two concurrent amino acid mutations in ORF1a 957 and ORF2 250. Overall, our study provides an in-depth overview of the evolution, transmission dynamics, and potential leaky status of HP-PRRS MLVs, providing critical insights into new MLV development.

Estimating the risk and spatial spread of measles in populations with high MMR uptake: Using school-household networks to understand the 2013 to 2014 outbreak in the Netherlands.

Measles outbreaks are still routine, even in countries where vaccination coverage exceeds the guideline of 95%. Therefore, achieving ambitions for measles eradication will require understanding of how unvaccinated children interact with others who are unvaccinated. It is well established that schools and homes are key settings for both clustering of unvaccinated children and for transmission of infection. In this study, we evaluate the potential for contacts between unvaccinated children in these contexts to facilitate measles outbreaks with a focus on the Netherlands, where large outbreaks have been observed periodically since the introduction of mumps, measles and rubella (MMR). We created a network of all primary and secondary schools in the Netherlands based on the total number of household pairs between each school. A household pair are siblings from the same household who attend a different school. We parameterised the network with individual level administrative school and household data provided by the Dutch Ministry for Education and estimates of school level uptake of the MMR vaccine. We analysed the network to establish the relative strength of contact between schools and found that schools associated with low vaccine uptake are highly connected, aided by a differentiated school system in the Netherlands (Coleman homophily index (CHI) = 0.63). We simulated measles outbreaks on the network and evaluated the model against empirical measles data per postcode area from a large outbreak in 2013 (2,766 cases). We found that the network-based model could reproduce the observed size and spatial distribution of the historic outbreak much more clearly than the alternative models, with a case weighted receiver operating characteristic (ROC) sensitivity of 0.94, compared to 0.17 and 0.26 for models that do not account for specific network structure or school-level vaccine uptake, respectively. The key limitation of our framework is that it neglects transmission routes outside of school and household contexts. Our framework indicates that clustering of unvaccinated children in primary schools connected by unvaccinated children in related secondary schools lead to large, connected clusters of unvaccinated children. Using our approach, we could explain historical outbreaks on a spatial level. Our framework could be further developed to aid future outbreak response.

Evil and allies: Opportunistic gulls as both spreaders and sentinels of antibiotic‐resistant bacteria in human‐transformed landscapes

Abstract Human‐transformed residuals, especially those derived from human waste (dumps), farmland, and livestock are involved in the emergence of antibiotic‐resistant bacteria (ARB) in the environment. Wildlife can act as vectors of ARB dispersal through different environments, but also as sentinels to detect the early spread and determine ARB sources. The development of integrated monitoring programmes focused on wildlife would help to anticipate the risks of ARB to humans and livestock. We used the yellow‐legged gull (Larus michahellis) as a model species to investigate and monitor the spatial patterns of ARB dispersal across an extensive farmland region located in northeastern Spain (Lleida). By integrating GPS tracking data and ARB clinical testing for 26 individuals within a network analysis framework, we modelled the risk of spatial pathogen spread through faeces during the bacteria‐transmission latency period (16 days after sample collection). Additionally, we created a connectivity network to determine the main sources of ARB in the area, focusing on three main habitats of special risk for infection: dumps, livestock facilities, and irrigation ponds. Seven individuals were infected by Escherichia coli, with one also co‐infected with Listeria monocytogenes and Salmonella spp. Potential pathogen dispersal distances ranged from 1.13 km to 23.13 km from the breeding colony. Our network analyses revealed 54 main nodes (i.e. high‐risk habitats recurrently visited by tracked gulls) and 1182 links among them. Our findings revealed a high degree of connectivity between the breeding area, located in a shallow lake, and nearby dumps, highlighting them as significant contributors to ARB dispersal. Synthesis and applications: The integration of GPS data, pathogen testing and network analyses can shed further light on pathogen dynamics by creating spatial risk maps and identifying ARB sources. In combination with complementary molecular epidemiology techniques within a One Health framework, our approach can emerge as an important tool for monitoring ARB dynamics within highly human‐transformed ecosystems. This may empower managers for the development of targeted ARB monitoring programmes and effective mitigation strategies, ultimately improving both animal and public health.

Calibration of MAJIS (Moons and Jupiter Imaging Spectrometer): V. Validation with mineral samples and reference materials.

MAJIS (Moons and Jupiter Imaging Spectrometer) is the imaging spectrometer onboard ESA's JUICE (JUpiter and ICy Moons Explorer) spacecraft that operates in the visible and near/mid-infrared between 0.5 and 5.54μm. Before the launch of JUICE in April 2023, MAJIS underwent a comprehensive on-ground calibration campaign in between August and September 2021 in the IAS (Institut d'Astrophysique Spatiale, Université Paris-Saclay) calibration facilities. Among all the operations, calibration sequences using a set of natural mineral samples and synthetic reference materials were acquired in order to characterize MAJIS performances under conditions assumed to be close to certain future observation configurations. Here, we analyze these calibration measurements using comparison with laboratory reference spectra to quantify MAJIS spectral and spatial performances while observing these solid surfaces. We first assess the MAJIS absolute spectral calibration of the visible and near-infrared channel covering half of the wavelength range. We then quantify spectral performances in terms of global spectral slopes, band detection, band shape, and depth retrievals, over most of the spectral range using six mineral samples. We conclude that for most configurations, the MAJIS instrument demonstrates excellent spectral performances compliant with the requirements. MAJIS can, however, be affected by stray light contributions, notably for wavelengths lower than about 1.2μm, and some performances of the instrument may then be significantly impacted depending on viewing conditions. In particular, we have identified cases of spectral contrast reduction up to 40%, absolute spectral shifts up to 2-3nm, and spectral smile variability by +/1nm. Finally, we used the MAJIS internal scanning mirror to test its ability to construct hyperspectral images of a few samples: we present the first band depth maps derived with MAJIS while observing a serpentine/carbonate sample, as well as an evaluation of MAJIS spatial point spread function. Overall, the analysis of MAJIS behavior while observing samples confirms most MAJIS expected performance requirements, while revealing subtle spectral perturbations that may be related to stray light and viewing conditions. These differences will be further investigated in-flight during the cruise, with a solar reflected target such as the Moon, as well as Jupiter before the JUICE orbital insertion.

Spatial spread of Ditylenchus gigas and its interaction with Botrytis fabae on Vicia faba

AbstractDitylenchus gigas Vovlas is the dominant species of Ditylenchus nematode on faba bean in the UK. It is normally reported with Botrytis fabae Sardiña, which causes chocolate spot of faba bean. The aim of the work reported here was to estimate how fast isolated infections of D. gigas may spread spatially and how background infection of the host with B. fabae alters multiplication of D. gigas. Spatial spread in field conditions was measured in plants growing in square grids. After establishment, single plants at the centre of each grid were spray inoculated with D. gigas. In summer 2017, D. gigas spread to a distance of at least 1 m, the edge of each 2 m × 2 m plot. Incidence on shoots decreased very slowly with distance beyond 0.4 m. In a repeat experiment harvested in autumn 2018, D. gigas was detected at distances of up to 160 cm in 4 m × 4 m plots. The yield effect and reproduction rate of D. gigas were measured in glasshouse experiments, alone and following brush inoculation with low (103) or high (106 conidia/mL) doses of B. fabae. At low doses of D. gigas inoculum multiplied by approximately 75‐fold, with lower rates of multiplication as the inoculum dose increased. The reproduction rate of D. gigas was reduced in plants inoculated with B. fabae, especially at high doses of D. gigas. The reduction was approximately proportional to log (B. fabae dose). Seed yield from inoculated plants decreased approximately in proportion to the logarithms of the initial dose of D. gigas and of B. fabae.

High-Resolution Laminar Identification in Macaque Primary Visual Cortex Using Neuropixels Probes.

Laminar electrode arrays allow simultaneous recording of activity of many cortical neurons and assignment to layers using current source density (CSD) analyses. Electrode arrays with 100-micron contact spacing have been used to estimate borders between layer 4 versus superficial or deep layers, but in macaque primary visual cortex (V1) there are far more layers, such as 4A which is only 50-100 microns thick. Neuropixels electrode arrays have 20-micron spacing, and thus could potentially discern thinner layers and more precisely identify laminar borders. Here we show that laminar distributions of CSDs lack consistency and the spatial resolution required for thin layers and accurate layer boundaries. To take full advantage of high density Neuropixels arrays, we have developed approaches based on higher resolution electrical signals and analyses, including spike waveforms and spatial spread, unit density, high-frequency action potential (AP) power spectrum, temporal power change, and coherence spectrum, that afford far higher resolution of laminar distinctions, including the ability to precisely detect the borders of even the thinnest layers of V1.

A multi-reflection time-of-flight analyzer with a long focus lens

Multi-reflection time-of-flight (MR-ToF) analyzers must control the transversal ion dispersion, orthogonal to the axis of reflection, conventionally via periodic refocusing, or more recently the shaped electrode structure adopted by the Astral analyzer. In principle, the complexity of the dispersion control on every oscillation may be avoided at a cost of a smaller number of oscillations, during which the dispersion doesn't exceed the limit of unrecoverable overlap. A method of dispersion control has been demonstrated experimentally and in simulation, whereby the ion beam is configured by a long focus trans-axial lens made of a pair of quasi-elliptical plates mounted above and below the ion beam, in order to optimize the spatial spread of the ions at a distant detector. The collimation concept was shown to effectively control beam expansion. For experimental confirmation, a prototype Astral analyzer was modified, and resolving power above 70k demonstrated.

Radiation of pain: Psychophysical evidence for a population coding mechanism in humans.

The spread of pain across body locations remains poorly understood but may provide important insights into the encoding of sensory features of noxious stimuli by populations of neurons. In this psychophysical experiment, we hypothesized that more intense noxious stimuli would lead to spread of pain, but more intense light stimuli would not produce perceptual radiation. Fifty healthy volunteers (27 females, 23 males, ages 14-44) participated in this study wherein noxious stimuli (43, 45, 47 and 49°C) were applied to glabrous (hand) and hairy skin (forearm) skin with 5s and 10s durations. Also, visual stimuli displayed on the target bodily area were utilized as a control. Participants provided pain (and light) spatial extent ratings as well as pain (and light) intensity ratings. In the extent rating procedure, participants adjusted the extent of the square displayed on the screen with the extent of pain (or light) which they experienced. Pain extent ratings showed statistically significant radiation of pain indicated by 12.42× greater spatial spread of pain (pain extent) than the area of the stimulation with 49°C (p<0.001), in contrast to visual ratings which closely approximated the size of the stimulus (1.22×). Pain radiation was more pronounced in hairy than glabrous skin (p<0.05) and was more pronounced with longer stimulus duration (p<0.001). Pain intensity explained only 14% of the pain radiation variability. The relative independence of the pain radiation from pain intensity indicates that distinct components of population coding mechanisms may be involved in the spatial representation of pain versus intensity coding.

Exploring infectious disease spread as a function of seasonal and pandemic-induced changes in human mobility.

Community-level changes in population mobility can dramatically change the trajectory of any directly-transmitted infectious disease, by modifying where and between whom contact occurs. This was highlighted throughout the COVID-19 pandemic, where community response and nonpharmaceutical interventions changed the trajectory of SARS-CoV-2 spread, sometimes in unpredictable ways. Population-level changes in mobility also occur seasonally and during other significant events, such as hurricanes or earthquakes. To effectively predict the spread of future emerging directly-transmitted diseases, we should better understand how the spatial spread of infectious disease changes seasonally, and when communities are actively responding to local disease outbreaks and travel restrictions. Here, we use population mobility data from Virginia spanning Aug 2019-March 2023 to simulate the spread of a hypothetical directly-transmitted disease under the population mobility patterns from various months. By comparing the spread of disease based on where the outbreak begins and the mobility patterns used, we determine the highest-risk areas and periods, and elucidate how seasonal and pandemic-era mobility patterns could change the trajectory of disease transmission. Through this analysis, we determine that while urban areas were at highest risk pre-pandemic, the heterogeneous nature of community response induced by SARS-CoV-2 cases meant that when outbreaks were occurring across Virginia, rural areas became relatively higher risk. Further, the months of September and January led to counties with large student populations to become particularly at risk, as population flows in and out of these counties were greatly increased with students returning to school.

The marginal effect of landscapes on urban land surface temperature within local climate zones based on optimal landscape scale

The rising urban land surface temperature (LST) has undoubtedly caused an imminent threat to human habitat. Landscape patterns are among the most significant factors related to LST. However, the marginal effects and thresholds of landscape at optimal grid scale on LST within diverse local climate zones (LCZs) are poorly understood. This study employed the boosted regression trees (BRT) model to evaluate the relative influences and marginal effects of built-up, water, and green landscape metrics on LST at the optimal grid scale across various LCZs. The findings reveal that: (1) The spatial spread trend of LST closely matches the expansion trend of built-up landscapes. (2) Significant variations in average summer LST were observed among LCZs, with LCZ8 (large low-rise) exhibiting the highest temperatures and LCZ11 (dense trees) the lowest. (3) At the optimal scale of 1000 m, the DIVISION metric for water landscapes significantly influenced LCZ6 (open low-rise), LCZ8, and LCZ14 (low plants). (4) In LCZ6 and LCZ8, effective heat mitigation requires increasing water area to 50% and optimizing the water body separation index to 0.88. These findings suggest that heterogeneous landscape heat mitigation strategies should be considered across diverse LCZs in urban areas.

Bayesian Modeling for Nonstationary Spatial Point Process via Spatial Deformations.

Many techniques have been proposed to model space-varying observation processes with a nonstationary spatial covariance structure and/or anisotropy, usually on a geostatistical framework. Nevertheless, there is an increasing interest in point process applications, and methodologies that take nonstationarity into account are welcomed. In this sense, this work proposes an extension of a class of spatial Cox process using spatial deformation. The proposed method enables the deformation behavior to be data-driven, through a multivariate latent Gaussian process. Inference leads to intractable posterior distributions that are approximated via MCMC. The convergence of algorithms based on the Metropolis-Hastings steps proved to be slow, and the computational efficiency of the Bayesian updating scheme was improved by adopting Hamiltonian Monte Carlo (HMC) methods. Our proposal was also compared against an alternative anisotropic formulation. Studies based on synthetic data provided empirical evidence of the benefit brought by the adoption of nonstationarity through our anisotropic structure. A real data application was conducted on the spatial spread of the Spodoptera frugiperda pest in a corn-producing agricultural area in southern Brazil. Once again, the proposed method demonstrated its benefit over alternatives.

Monte Carlo dosimetric analyses on the use of 90Y-IsoPet intratumoral therapy in canine subjects

Objective. To investigate different dosimetric aspects of 90Y-IsoPet™ intratumoral therapy in canine soft tissue sarcomas, model the spatial spread of the gel post-injection, evaluate absorbed dose to clinical target volumes, and assess dose distributions and treatment efficacy. Approach. Six canine cases treated with 90Y-IsoPet™ for soft tissue sarcoma at the Veterinary Health Center, University of Missouri are analyzed in this retrospective study. The dogs received intratumoral IsoPet™ injections, following a grid pattern to achieve a near-uniform dose distribution in the clinical target volume. Two dosimetry methods were performed retrospectively using the Monte Carlo toolkit OpenTOPAS: imaging-based dosimetry obtained from post-injection PET/CT scans, and stylized phantom-based dosimetry modeled from the planned injection points to the gross tumor volume. For the latter, a Gaussian parameter with variable sigma was introduced to reflect the spatial spread of IsoPet™. The two methods were compared using dose-volume histograms (DVHs) and dose homogeneity, allowing an approximation of the closest sigma for the spatial spread of the gel post-injection. In addition, we compared Monte Carlo-based dosimetry with voxel S-value (VSV)-based dosimetry to investigate the dosimetric differences. Main results. Imaging-based dosimetry showed differences between Monte Carlo and VSV calculations in tumor high-density areas with higher self-absorption. Stylized phantom-based dosimetry indicated a more homogeneous target dose with increasing sigma. The sigma approximation of the 90Y-IsoPet™ post-injection gel spread resulted in a median sigma of approximately 0.44 mm across all cases to reproduce the dose heterogeneity observed in Monte Carlo calculations. Significance. The results indicate that dose modeling based on planned injection points can serve as a first-order approximation for the delivered dose in 90Y-IsoPet™ therapy for canine soft tissue sarcomas. The dosimetry evaluation highlights the non-uniformity of absorbed doses despite the gel spread, emphasizing the importance of considering tumor dose heterogeneity in treatment evaluation. Our findings suggest that using Monte Carlo for dose calculation seems more suitable for this type of tumor where high-density areas might play an important role in dosimetry.

An Update to the Stochastically Perturbed Parameterization Scheme of HarmonEPS

Abstract High-resolution, limited-area forecasting is strongly affected by errors in the initial atmospheric state, lateral boundary conditions (LBCs), and physical parameterizations used by numerical weather prediction (NWP) models. These errors need to be accounted for through the introduction of uncertainty in an ensemble prediction system (EPS). One approach to account for model error is to use a stochastically perturbed parameterizations (SPPs) scheme. A first version of the SPP scheme of HARMONIE EPS (HarmonEPS) has been tested, with promising improvements in ensemble spread. However, it introduced systematic biases and deteriorated skill scores for some variables. Here, we investigate the performance of an updated version of the HarmonEPS SPP scheme, which includes (i) the use of uniform distributions, (ii) the correlation of stochastic patterns between key SPP parameters, and (iii) the introduction of four additional parameters, in the microphysics and mass-flux schemes. Two five-parameter SPP-based setups are compared against initial and LBC perturbations setups for five forecast periods: (i) 22–28 March 2019, (ii) 6–12 July 2020, (iii) 20–26 February 2021, (iv) 13–26 January 2021, and (v) 20 May–2 June 2021. We find that SPP-based experiments show better probabilistic metrics for near-surface and cloud-related variables than the non-SPP experiments. The SPP-based ensembles show increased spatial spread (as indicated by dFSS), while maintaining similar spatial skill (as indicated by eFSS) with the non-SPP experiment. In addition, the systematic bias in the ensemble members of the previous SPP iteration has been alleviated with the use of uniform distributions. Finally, the use of microphysical and mass-flux perturbations improves the ensemble scores for cloud-related variables, precipitation, and visibility. Significance Statement Ensemble prediction systems are essential for weather forecasting. Their skill depends on the representation of uncertainties arising from errors in the initial atmospheric state and model physics. In this study, we present an update to the stochastically perturbed parameterizations scheme, which represents uncertainty arising from model errors, of the limited-area HARMONIE ensemble prediction system. The update to the scheme includes the introduction of uniform distributions and correlated perturbations for the perturbed parameters, as well as the addition of four new perturbed parameters. Overall, this revision removes systematic biases in ensemble members, which were present in the previous iteration of the scheme, and shows increased ensemble spread (up to 70%) and reduced model errors (up to 5%) for near-surface and cloud-related variables.

Multiple Satellite Observations of the High‐Latitude Cusp Aurora During Northward IMF Conditions

AbstractCusp auroras poleward of the typical auroral oval are ascribed to high‐latitude lobe reconnection when the Interplanetary Magnetic Field (IMF) Bz is predominantly northward. In this study, we further investigate the ionospheric characteristics of a unique high‐latitude cusp region employing multiple satellite observations. A cusp aurora event with wide spatial spread was observed in the postnoon polar cap region. It was found to be associated with northward IMF Bz and positive By components. The cusp aurora was located from 68° to 86° in magnetic latitude and within 15–17 hr in magnetic local time. This broad coverage in the polar cap indicates direct precipitating particles from the magnetosheath. Particle energy is different between the equatorward and poleward edges of the cusp aurora. The precipitating ions at the equatorward side maintain magnetosheath particle characteristics as expected, while ions with higher energies occurred in the poleward side. Further, the poleward edge of the cusp aurora was nearly situated in the center of a convection shear and was associated with an upward field‐aligned current. These observations suggest a lobe cell circulation, hence we attribute the formation of the cusp aurora to the high‐latitude lobe reconnection. Simultaneous observations in the southern hemisphere indicate the absence of cusp aurora. The auroral presence only in the northern hemisphere is probably due to the combination of large dipole tilt angle and positive IMF Bz, which facilitates the lobe reconnection.

Lateral inhibition in V1 controls neural & perceptual contrast sensitivity.

Lateral inhibition is a central principle for sensory system function. It is thought to operate by the activation of inhibitory neurons that restrict the spatial spread of sensory excitation. Much work on the role of inhibition in sensory systems has focused on visual cortex; however, the neurons, computations, and mechanisms underlying cortical lateral inhibition remain debated, and its importance for visual perception remains unknown. Here, we tested how lateral inhibition from PV or SST neurons in mouse primary visual cortex (V1) modulates neural and perceptual sensitivity to stimulus contrast. Lateral inhibition from PV neurons reduced neural and perceptual sensitivity to visual contrast in a uniform subtractive manner, whereas lateral inhibition from SST neurons more effectively changed the slope (or gain) of neural and perceptual contrast sensitivity. A neural circuit model identified spatially extensive lateral projections from SST neurons as the key factor, and we confirmed this with anatomy and direct subthreshold measurements of a larger spatial footprint for SST versus PV lateral inhibition. Together, these results define cell-type specific computational roles for lateral inhibition in V1, and establish their unique consequences on sensitivity to contrast, a fundamental aspect of the visual world.

Predicting heavy metal transport in groundwater around Lemna dumpsite: implications for residence utilizing borehole water in Cross River State, Nigeria

Groundwater is considered the most important natural resource to mankind. Groundwater constitutes an important part of the hydrological cycle and is more prone to pollution. Dumpsite located in close proximity to groundwater resources is highly susceptible to leachates pollution. Predicting the susceptibility of groundwater pollution is crucial to address industry-standard codes for groundwater flow, contaminant transport, local to regional-scale water quality, and source water protection issues. Therefore, predicting heavy metal transport in groundwater around Lemna dumpsite in Cross River State, Nigeria, was examined. Soil samples were purposively collected with a soil Auger, along a straight line at (5 m, 25 m and 50 m) in the dumpsite. Water samples were purposively collected from five (5) boreholes close to Lemna dumpsite. The study utilized pumping test method to obtain data for the analysis of heavy metal transport in groundwater. Data analysis of the laboratory results of soil and borehole water quality focuses on arsenic, lead, cadmium, chromium, nickel, and mercury. Paired sample t test was used to analyse the soil and borehole water quality. Visual Modflow was also used to analyse the solute transport of heavy metals in groundwater around Lemna dumpsite. The paired sample t test of the analysis of heavy metals in soil exhibited a significant difference (p < 0.05) compared to National Environmental Standard Regulation and Enforcement Agency limits. The paired sample t test of the analysis of heavy metals in borehole water exhibited a significant difference (p < 0.05) compared to World Health Organization limits. The significant level indicates contamination of the soil and borehole water. The findings revealed a spatial spread of 259.2000 m2/day, with the contaminant travelling up to 94,608 m2/year. The extent of heavy metals concentration exhibited a maximum of 0.991 mg/l to a minimum of (− 6.72 × 10–18 mg/l), with concentrations decreasing as the plume extend. The study recommends the need for remediation and stringent monitoring to mitigate heavy metal contamination of boreholes near Lemna dumpsite.

A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis–NIR–SWIR hyperspectral imaging

The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis–NIR, 400–1000 nm) and short-wave infrared (SWIR, 1000–1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis–NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis–NIR spectral data.

Dynamics of diseases spreading on networks in the forms of reaction-diffusion systems

In the face of persistent threats posed by infectious diseases, despite remarkable medical advancements, understanding and efficiently controlling their spatial spread through mathematical modeling remain imperative. Networked reaction-diffusion systems offer a promising avenue to effectively delineate population discrete distribution and individual movement heterogeneity. However, the dynamics of spatial diseases within these systems and the formulation of optimal control strategies are currently undergoing vigorous development. In this letter, we illustrate the dynamics of spatial disease spread in networked reaction-diffusion systems through the lens of optimal control, considering various network complexities from pairwise networks to higher-order networks. It then emphasizes their applicability in designing effective spatial disease control strategies across diverse network complexities. Finally, we discuss the existing challenges.