Grid codes vs. multi-scale, multi-field place codes for space

Introduction

Recent work on bats flying over long distances has revealed that single hippocampal cells have multiple place fields of different sizes. At the network level, a multi-scale, multi-field place cell code outperforms classical single-scale, single-field place codes, yet the performance boundaries of such a code remain an open question. In particular, it is unknown how general multi-field codes compare to a highly regular grid code, in which cells form distinct modules with different scales.

Methods

In this work, we address the coding properties of theoretical spatial coding models with rigorous analyses of comprehensive simulations. Starting from a multi-scale, multi-field network, we performed evolutionary optimization. The resulting multi-field networks sometimes retained the multi-scale property at the single-cell level but most often converged to a single scale, with all place fields in a given cell having the same size. We compared the results against a single-scale single-field code and a one-dimensional grid code, focusing on two main characteristics: the performance of the code itself and the dynamics of the network generating it.

Results

Our simulation experiments revealed that, under normal conditions, a regular grid code outperforms all other codes with respect to decoding accuracy, achieving a given precision with fewer neurons and fields. In contrast, multi-field codes are more robust against noise and lesions, such as random drop-out of neurons, given that the significantly higher number of fields provides redundancy. Contrary to our expectations, the network dynamics of all models, from the original multi-scale models before optimization to the multi-field models that resulted from optimization, did not maintain activity bumps at their original locations when a position-specific external input was removed.

Discussion

Optimized multi-field codes appear to strike a compromise between a place code and a grid code that reflects a trade-off between accurate positional encoding and robustness. Surprisingly, the recurrent neural network models we implemented and optimized for either multi- or single-scale, multi-field codes did not intrinsically produce a persistent "memory" of attractor states. These models, therefore, were not continuous attractor networks.

Multiscale embedded printing of engineered human tissue and organ equivalents

Creating tissue and organ equivalents with intricate architectures and multiscale functional feature sizes is the first step toward the reconstruction of transplantable human tissues and organs. Existing embedded ink writing approaches are limited by achievable feature sizes ranging from hundreds of microns to tens of millimeters, which hinders their ability to accurately duplicate structures found in various human tissues and organs. In this study, a multiscale embedded printing (MSEP) strategy is developed, in which a stimuli-responsive yield-stress fluid is applied to facilitate the printing process. A dynamic layer height control method is developed to print the cornea with a smooth surface on the order of microns, which can effectively overcome the layered morphology in conventional extrusion-based three-dimensional bioprinting methods. Since the support bath is sensitive to temperature change, it can be easily removed after printing by tuning the ambient temperature, which facilitates the fabrication of human eyeballs with optic nerves and aortic heart valves with overhanging leaflets on the order of a few millimeters. The thermosensitivity of the support bath also enables the reconstruction of the full-scale human heart on the order of tens of centimeters by on-demand adding support bath materials during printing. The proposed MSEP demonstrates broader printable functional feature sizes ranging from microns to centimeters, providing a viable and reliable technical solution for tissue and organ printing in the future.

A Sound and Vibration Fusion Method for Fault Diagnosis of Rolling Bearings under Speed-Varying Conditions

The fault diagnosis of rolling bearings is critical for the reliability assurance of mechanical systems. The operating speeds of the rolling bearings in industrial applications are usually time-varying, and the monitoring data available are difficult to cover all the speeds. Though deep learning techniques have been well developed, the generalization capacity under different working speeds is still challenging. In this paper, a sound and vibration fusion method, named the fusion multiscale convolutional neural network (F-MSCNN), was developed with strong adaptation performance under speed-varying conditions. The F-MSCNN works directly on raw sound and vibration signals. A fusion layer and a multiscale convolutional layer were added at the beginning of the model. With comprehensive information, such as the input, multiscale features are learned for subsequent classification. An experiment on the rolling bearing test bed was carried out, and six datasets under various working speeds were constructed. The results show that the proposed F-MSCNN can achieve high accuracy with stable performance when the speeds of the testing set are the same as or different from the training set. A comparison with other methods on the same datasets also proves the superiority of F-MSCNN in speed generalization. The diagnosis accuracy improves by sound and vibration fusion and multiscale feature learning.

Correlation between interfacial layer properties and physical stability of food emulsions: current trends, challenges, strategies, and further perspectives

Emulsions are thermodynamically unstable systems that tend to separate into two immiscible phases over time. The interfacial layer formed by the emulsifiers adsorbed at the oil-water interface plays an important role in the emulsion stability. The interfacial layer properties of emulsion droplets have been considered the cutting-in points that influence emulsion stability, a traditional motif of physical chemistry and colloid chemistry of particular significance in relation to the food science and technology sector. Although many attempts have shown that high interfacial viscoelasticity may contribute to long-term emulsion stability, a universal relationship for all cases between the interfacial layer features at the microscopic scale and the bulk physical stability of the emulsion at the macroscopic scale remains to be established. Not only that, but integrating the cognition from different scales of emulsions and establishing a unified single model to fill the gap in awareness between scales also remain challenging. In this review, we present a comprehensive overview of recent progress in the general science of emulsion stability with a peculiar focus on interfacial layer characteristics in relation to the formation and stabilization of food emulsions, where the natural origin and edible safety of emulsifiers and stabilizers are highly requested. This review begins with a general overview of the construction and destruction of interfacial layers in emulsions to highlight the most important physicochemical characteristics of interfacial layers (formation kinetics, surface load, interactions among adsorbed emulsifiers, thickness and structure, and shear and dilatational rheology), and their roles in controlling emulsion stability. Subsequently, the structural effects of a series of typically dietary emulsifiers (small-molecule surfactants,proteins, polysaccharides, protein-polysaccharide complexes, and particles) on oil-water interfaces in food emulsions are emphasized. Finally, the main protocols developed for modifying the structural characteristics of adsorbed emulsifiers at multiple scales and improving the stability of emulsions are highlighted. Overall, this paper aims to comprehensively study the literature findings in the past decade and find out the commonality of multi-scale structures of emulsifiers, so as to deeply understand the common characteristics and emulsification stability behaviour of adsorption emulsifiers with different interfacial layer structures. It is difficult to say that there has been significant progress in the underlying principles and technologies in the general science of emulsion stability over the last decade or two. However, the correlation between interfacial layer properties and physical stability of food emulsions promotes revealing the role of interfacial rheological properties in emulsion stability, providing guidance on controlling the bulk properties by tuning the interfacial layer functionality.

Environmental Controls to Soil Heavy Metal Pollution Vary at Multiple Scales in a Highly Urbanizing Region in Southern China

Natural and anthropogenic activities affect soil heavy metal pollution at different spatial scales. Quantifying the spatial variability of soil pollution and its driving forces at different scales is essential for pollution mitigation opportunities. This study applied a multivariate factorial kriging technique to investigate the spatial variability of soil heavy metal pollution and its relationship with environmental factors at multiple scales in a highly urbanized area of Guangzhou, South China. We collected 318 topsoil samples and used five types of environmental factors for the attribution analysis. By factorial kriging, we decomposed the total variance of soil pollution into a nugget effect, a short-range (3 km) variance and a long-range (12 km) variance. The distribution of patches with a high soil pollution level was scattered in the eastern and northwestern parts of the study domain at a short-range scale, while they were more clustered at a long-range scale. The correlations between the soil pollution and environmental factors were either enhanced or counteracted across the three distinct scales. The predictors of soil heavy metal pollution changed from the soil physiochemical properties to anthropogenic dominated factors with the studied scale increase. Our study results suggest that the soil physiochemical properties were a good proxy to soil pollution across the scales. Improving the soil physiochemical properties such as increasing the soil organic matter is essentially effective across scales while restoring vegetation around pollutant sources as a nature-based solution at a large scale would be beneficial for alleviating local soil pollution.

Association Between Natural/Built Campus Environment and Depression Among Chinese Undergraduates: Multiscale Evidence for the Moderating Role of Socioeconomic Factors After Controlling for Residential Self-Selection

Aim

Evidence on the association between natural-built environments and depression is largely derived from the general population and prone to residential self-selection bias because of the nature of cross-sectional research design. Despite emerging adulthood, which includes the university years, is a critical stage for forming life-long health habits, studies on this topic focusing on undergraduate students are limited. The current study aims to illustrate the underlying mechanisms for how the campus-based environments affect depression in undergraduate students.

Methods

Based on a nationwide representative analytical sample of 22,009 Chinese undergraduates in 2018, we examined participants' reports of depression and campus-centered natural/built environments within multiple buffer sizes including 0.5, 1.0, and 2.5 km. After disentangling residential self-selection, we explored the moderating role of the socioeconomic attributes of undergraduates. The depression outcome was measured by the nine-item Patient Health Questionnaire (PHQ9). Indicators of exposure to green and blue space, transportation infrastructure, and food environments were objectively assessed using different circular buffers around each campus address.

Results

Modeling results indicated that campus neighborhoods with more scattered trees (0.5 km), water (0.5, 1.0, and 2.5 km), and street intersections (1.0 and 2.5 km) were protective against depression. In contrast, those living near denser distributions of outlets serving take-away sweets and fast food (0.5, 1.0, and 2.5 km) were susceptible to depression. These associations were modified by undergraduates' socioeconomic attributes (e.g., grade, Hukou status, and ethnicity) and varied according to geographical scales and exposure metrics.

Conclusion

To deliver effective environmental interventions to curb the prevalence of depression among undergraduate students, further planning policies should focus on the careful conception of the campus-based environment, especially regarding different spatial scales.

Submillimetre mechanistic designs of termite-built structures

Termites inhabit complex underground mounds of intricate stigmergic labyrinthine designs with multiple functions as nursery, food storage and refuge, while maintaining a homeostatic microclimate. Past research studied termite building activities rather than the actual material structure. Yet, prior to understanding how multi-functionality shaped termite building, a thorough grasp of submillimetre mechanistic architecture of mounds is required. Here, we identify for Nasutitermes exitiosus via granulometry and Fourier transform infrared spectroscopy analysis, preferential particle sizes related to coarse silts and unknown mixtures of organic/inorganic components. High-resolution micro-computed X-ray tomography and microindentation tests reveal wall patterns of filigree laminated layers and sub-millimetre porosity wrapped around a coarse-grained inner scaffold. The scaffold geometry, which is designed of a lignin-based composite and densely biocementitious stercoral mortar, resembles that of trabecula cancellous bones. Fractal dimension estimates indicate multi-scaled porosity, important for enhanced evaporative cooling and structural stability. The indentation moduli increase from the outer to the inner wall parts to values higher than those found in loose clays and which exceed locally the properties of anthropogenic cementitious materials. Termites engineer intricately layered biocementitious composites of high elasticity. The multiple-scales and porosity of the structure indicate a potential to pioneer bio-architected lightweight and high-strength materials.

Text Detection Using Multi-Stage Region Proposal Network Sensitive to Text Scale

Recently, attention has surged concerning intelligent sensors using text detection. However, there are challenges in detecting small texts. To solve this problem, we propose a novel text detection CNN (convolutional neural network) architecture sensitive to text scale. We extract multi-resolution feature maps in multi-stage convolution layers that have been employed to prevent losing information and maintain the feature size. In addition, we developed the CNN considering the receptive field size to generate proposal stages. The experimental results show the importance of the receptive field size.

An adaptive plan for prioritizing road sections for fencing to reduce animal mortality

Mortality of animals on roads is a critical threat to many wildlife populations and is poised to increase strongly because of ongoing and planned road construction. If these new roads cannot be avoided, effective mitigation measures will be necessary to stop biodiversity decline. Fencing along roads effectively reduces roadkill and is often used in combination with wildlife passages. Because fencing the entire road is not always possible due to financial constraints, high-frequency roadkill areas are often identified to inform the placement of fencing. We devised an adaptive fence-implementation plan to prioritize road sections for fencing. In this framework, areas along roads of high, moderate, and low levels of animal mortality (respectively, roadkill hotspots, warmspots, and coldspots) are identified at multiple scales (i.e., in circles of different diameters [200-2000 m] in which mortality frequency is measured). Fence deployment is based on the relationship between the amount of fencing being added to the road, starting with the strongest roadkill hotspots, and potential reduction in road mortality (displayed in mortality-reduction graphs). We applied our approach to empirical and simulated spatial patterns of wildlife-vehicle collisions. The scale used for analysis affected the number and spatial extent of roadkill hot-, warm-, and coldspots. At fine scales (e.g., 200 m), more hotspots were identified than at coarse scales (e.g., 2000 m), but combined the fine-scale hotspots covered less road and less fencing was needed to reduce road mortality. However, many short fences may be less effective in practice due to a fence-end effect (i.e., animals moving around the fence more easily), resulting in a trade-off between few long and many short fences, which we call the FLOMS (few-long-or-many-short) fences trade-off. Thresholds in the mortality-reduction graphs occurred for some roadkill patterns, but not for others. Thresholds may be useful to consider when determining road-mitigation targets. The existence of thresholds at multiple scales and the FLOMS trade-off have important implications for biodiversity conservation.

Distinctive Spatial and Laminar Organization of Single Axons from Lateral Pulvinar in the Macaque

Pulvino-cortical (PC) projections are a major source of extrinsic input to early visual areas in the macaque. From bulk injections of anterograde tracers, these are known to terminate in layer 1 of V1 and densely in the middle cortical layers of extrastriate areas. Finer, single axon analysis, as reviewed here for projections from the lateral pulvinar (PL) in two macaque monkeys (n = 25 axons), demonstrates that PL axons have multiple arbors in V2 and V4, and that these are spatially separate and offset in different layers. In contrast, feedforward cortical axons, another major source of extrinsic input to extrastriate areas, are less spatially divergent and more typically terminate in layer 4. Functional implications are briefly discussed, including comparisons with the better investigated rodent brain.

Generalization of the Zabolotskaya equation to all incompressible isotropic elastic solids

We study elastic shear waves of small but finite amplitude, composed of an anti-plane shear motion and a general in-plane motion. We use a multiple scales expansion to derive an asymptotic system of coupled nonlinear equations describing their propagation in all isotropic incompressible nonlinear elastic solids, generalizing the scalar Zabolotskaya equation of compressible nonlinear elasticity. We show that for a general isotropic incompressible solid, the coupling between anti-plane and in-plane motions cannot be undone and thus conclude that linear polarization is impossible for general nonlinear two-dimensional shear waves. We then use the equations to study the evolution of a nonlinear Gaussian beam in a soft solid: we show that a pure (linearly polarized) shear beam source generates only odd harmonics, but that introducing a slight in-plane noise in the source signal leads to a second harmonic, of the same magnitude as the fifth harmonic, a phenomenon recently observed experimentally. Finally, we present examples of some special shear motions with linear polarization.

Pulse dynamics in reaction-diffusion equations with strong spatially localized impurities

In this article, a general geometric singular perturbation framework is developed to study the impact of strong, spatially localized, nonlinear impurities on the existence, stability and bifurcations of localized structures in systems of linear reaction-diffusion equations. By taking advantage of the multiple-scale nature of the problem, we derive algebraic conditions determining the existence and stability of pinned single- and multi-pulse solutions. Our methods enable us to explicitly control the spectrum associated with a (multi-)pulse solution. In the scalar case, we show how eigenvalues may move in and out of the essential spectrum and that Hopf bifurcations cannot occur. By contrast, even a pinned 1-pulse solution can undergo a Hopf bifurcation in a two-component system of linear reaction-diffusion equations with (only) one impurity.This article is part of the theme issue 'Stability of nonlinear waves and patterns and related topics'.

A Review of Multiscale Computational Methods in Polymeric Materials

Polymeric materials display distinguished characteristics which stem from the interplay of phenomena at various length and time scales. Further development of polymer systems critically relies on a comprehensive understanding of the fundamentals of their hierarchical structure and behaviors. As such, the inherent multiscale nature of polymer systems is only reflected by a multiscale analysis which accounts for all important mechanisms. Since multiscale modelling is a rapidly growing multidisciplinary field, the emerging possibilities and challenges can be of a truly diverse nature. The present review attempts to provide a rather comprehensive overview of the recent developments in the field of multiscale modelling and simulation of polymeric materials. In order to understand the characteristics of the building blocks of multiscale methods, first a brief review of some significant computational methods at individual length and time scales is provided. These methods cover quantum mechanical scale, atomistic domain (Monte Carlo and molecular dynamics), mesoscopic scale (Brownian dynamics, dissipative particle dynamics, and lattice Boltzmann method), and finally macroscopic realm (finite element and volume methods). Afterwards, different prescriptions to envelope these methods in a multiscale strategy are discussed in details. Sequential, concurrent, and adaptive resolution schemes are presented along with the latest updates and ongoing challenges in research. In sequential methods, various systematic coarse-graining and backmapping approaches are addressed. For the concurrent strategy, we aimed to introduce the fundamentals and significant methods including the handshaking concept, energy-based, and force-based coupling approaches. Although such methods are very popular in metals and carbon nanomaterials, their use in polymeric materials is still limited. We have illustrated their applications in polymer science by several examples hoping for raising attention towards the existing possibilities. The relatively new adaptive resolution schemes are then covered including their advantages and shortcomings. Finally, some novel ideas in order to extend the reaches of atomistic techniques are reviewed. We conclude the review by outlining the existing challenges and possibilities for future research.

Bifurcation Control of an Electrostatically-Actuated MEMS Actuator with Time-Delay Feedback

The parametric excitation system consisting of a flexible beam and shuttle mass widely exists in microelectromechanical systems (MEMS), which can exhibit rich nonlinear dynamic behaviors. This article aims to theoretically investigate the nonlinear jumping phenomena and bifurcation conditions of a class of electrostatically-driven MEMS actuators with a time-delay feedback controller. Considering the comb structure consisting of a flexible beam and shuttle mass, the partial differential governing equation is obtained with both the linear and cubic nonlinear parametric excitation. Then, the method of multiple scales is introduced to obtain a slow flow that is analyzed for stability and bifurcation. Results show that time-delay feedback can improve resonance frequency and stability of the system. What is more, through a detailed mathematical analysis, the discriminant of Hopf bifurcation is theoretically derived, and appropriate time-delay feedback force can make the branch from the Hopf bifurcation point stable under any driving voltage value. Meanwhile, through global bifurcation analysis and saddle node bifurcation analysis, theoretical expressions about the system parameter space and maximum amplitude of monostable vibration are deduced. It is found that the disappearance of the global bifurcation point means the emergence of monostable vibration. Finally, detailed numerical results confirm the analytical prediction.

From a meso- to micro-scale connectome: array tomography and mGRASP

Mapping mammalian synaptic connectivity has long been an important goal of neuroscience because knowing how neurons and brain areas are connected underpins an understanding of brain function. Meeting this goal requires advanced techniques with single synapse resolution and large-scale capacity, especially at multiple scales tethering the meso- and micro-scale connectome. Among several advanced LM-based connectome technologies, Array Tomography (AT) and mammalian GFP-Reconstitution Across Synaptic Partners (mGRASP) can provide relatively high-throughput mapping synaptic connectivity at multiple scales. AT- and mGRASP-assisted circuit mapping (ATing and mGRASPing), combined with techniques such as retrograde virus, brain clearing techniques, and activity indicators will help unlock the secrets of complex neural circuits. Here, we discuss these useful new tools to enable mapping of brain circuits at multiple scales, some functional implications of spatial synaptic distribution, and future challenges and directions of these endeavors.

Bird and mammal species composition in distinct geographic regions and their relationships with environmental factors across multiple spatial scales

Global patters of species distributions and their underlying mechanisms are a major question in ecology, and the need for multi-scale analyses has been recognized. Previous studies recognized climate, topography, habitat heterogeneity and disturbance as important variables affecting such patterns. Here we report on analyses of species composition – environment relationships among different taxonomic groups in two continents, and the components of such relationships, in the contiguous USA and Australia. We used partial Canonical Correspondence Analysis of occurrence records of mammals and breeding birds from the Global Biodiversity Information Facility, to quantify relationships between species composition and environmental variables in remote geographic regions at multiple spatial scales, with extents ranging from 10⁵ to 10⁷ km² and sampling grids from 10 to 10,000 km². We evaluated the concept that two elements contribute to the impact of environmental variables on composition: the strength of species' affinity to an environmental variable, and the amount of variance in the variable. To disentangle these two elements, we analyzed correlations between resulting trends and the amount of variance contained in different environmental variables to isolate the mechanisms behind the observed relationships. We found that climate and land use-land cover are responsible for most explained variance in species composition, regardless of scale, taxonomic group and geographic region. However, the amount of variance in species composition attributed to land use / land cover (LULC) was closely related to the amount of intrinsic variability in LULC in the USA, but not in Australia, while the effect of climate on species composition was negatively correlated to the variability found in the climatic variables. The low variance in climate, compared to LULC, suggests that species in both taxonomic groups have strong affinity to climate, thus it has a strong effect on species distribution and community composition, while the opposite is true for LULC.

The influence of natural and anthropic environmental variables on the structure and spatial distribution along longitudinal gradient of macroinvertebrate communities in southern Brazilian streams

Southern Brazilian rivers and streams have been intensively affected by human activities, especially agriculture and the release of untreated domestic sewage. However, data about the aquatic macroinvertebrates in these streams are scarce and limited to only certain groups. In addition, studies focusing on the structure and spatial distribution of these communities are lacking. This study analyzed the effects of natural and anthropic variables on the community structure of macroinvertebrates along a longitudinal gradient in three microbasins located in a region of landscape transition in the state of Rio Grande do Sul, Brazil. Sampling was conducted in the Vacacaí-Mirim River (August 2008) and in the Ibicuí-Mirim and Tororaipí rivers (August 2009) following an environmental gradient including 1(st), 2(nd), 3(rd), and 4(th) order segments. Local natural factors that were analyzed include water temperature, pH, electrical conductivity, dissolved oxygen, substrate granulometry, and the presence of aquatic vegetation. Anthropic variables that were analyzed include including bank erosion, land use, urbanization, riparian deforestation, and fine sediments input. A total of 42 families and 129 taxa were found, with predominance of environmentally tolerant taxa. Geological context (landscape transition and large hydrographic basins) tended to influence natural environmental factors along the rivers' longitudinal gradients. However, changes in anthropic variables were not affected by these geological differences and therefore did not correlate with patterns of spatial distribution in macroinvertebrate communities. Only 1(st) order stream segments showed a community composition with high richness of taxa intolerant to anthropic disturbance. Richness as a whole tended to be higher in 3(rd) to 4(th) order set of segments, but this trend was a result of local anthropic environmental disturbances. Future inventories conducted in similar landscape transition regions of Brazil, for conservation purposes, must consider stream segments of different orders, microbasins, and major basins in order to obtain data that faithfully reflect the regional diversity. Additionally, it is necessary to consider environmental gradients of land use and anthropic impacts in order to suggest appropriate strategies for conserving the environmental integrity of streams.