An inclusive anatomical network analysis of human craniocerebral topology.

The human brain's complex morphology is spatially constrained by numerous intrinsic and extrinsic physical interactions. Spatial constraints help to identify the source of morphological variability and can be investigated by employing anatomical network analysis. Here, a model of human craniocerebral topology is presented, based on the bony elements of the skull at birth and a previously designed model of the brain. The goal was to investigate the topological components fundamental to the craniocerebral geometric balance, to identify underlying phenotypic patterns of spatial arrangement, and to understand how these patterns might have influenced the evolution of human brain morphology. Analysis of the craniocerebral network model revealed that the combined structure of the body and lesser wings of the sphenoid bone, the parahippocampal gyrus, and the parietal and ethmoid bones are susceptible to sustain and apply major spatial constraints that are likely to limit or channel their morphological evolution. The results also showcase a high level of global integration and efficient diffusion of biomechanical forces across the craniocerebral system, a fundamental aspect of morphological variability in terms of plasticity. Finally, community detection in the craniocerebral system highlights the concurrence of a longitudinal and a vertical modular partition. The former reflects the distinct morphogenetic environments of the three endocranial fossae, while the latter corresponds to those of the basicranium and calvaria.

Integrating decision-making preferences into ecosystem service conservation area identification: A case study of water-related ecosystem services in the Dawen River watershed, China.

The degradation of ecosystems and their services is threatening human wellbeing, making ecosystem service (ES) conservation an urgent necessity. In ES conservation planning, conservation area identification is crucial for the success of conservation initiatives. However, different decision-making preferences have not been fully considered and integrated in ES conservation area identification. This study takes the Dawen River watershed as the study area and considers three water-related ESs to be conserved. We aim to integrate the decision-making preferences of cost-effectiveness, ES sustainable supply, and ES social benefit into identifying ES conservation areas by using conservation cost, ecosystem health, and ES social importance as spatial constraints, respectively. We identified ES conservation area alternatives under the scenarios set according to different decision-making preferences. Specifically, ES conservation targets, i.e., the expected proportion of each ES in conservation areas, are designed to be met where there is low conservation cost (cost-oriented scenario), high ecosystem health (ES sustainable supply scenario), or high ES social importance (ES social benefit scenario). A balanced scenario considering all three decision-making preferences together is further established. The results show that under each scenario, the identified conservation areas can concurrently meet the conservation targets and decision-making preferences. The consideration of different decision-making preferences can greatly influence the spatial distributions of ES conservation areas. Moreover, a severe trade-off between conservation cost and ES social importance is observed under the ES social benefit scenario, and the balanced scenario can achieve a synergy of decision-making preferences. Our study provides a method to integrate the decision-making preference into ES conservation area identification, which can improve the rationality and practicality of ES conservation planning.

Coalescing replication compartments provide the opportunity for recombination between coinfecting herpesviruses.

Homologous recombination (HR) is considered a major driving force of evolution because it generates and expands genetic diversity. Evidence of HR between coinfecting herpesvirus DNA genomes can be found frequently both in vitro and in clinical isolates. Each herpes simplex virus type 1 (HSV-1) replication compartment (RC) derives from a single incoming genome and maintains a specific territory within the nucleus. This raises intriguing questions about where and when coinfecting viral genomes interact. To study the spatiotemporal requirements for intergenomic recombination, we developed an assay with dual-color FISH that enables detection of HR between different pairs of coinfecting HSV-1 genomes. Our results revealed that HR increases intermingling of RCs derived from different genomes. Furthermore, inhibition of RC movement reduces the rate of HR events among coinfecting viruses. Finally, we observed correlation between nuclear size and the number of RCs per nucleus. Our findings suggest that both viral replication and recombination are subject to nuclear spatial constraints. Other DNA viruses and cellular DNA are likely to encounter similar restrictions.-Tomer, E., Cohen, E. M., Drayman, N., Afriat, A., Weitzman, M. D., Zaritsky, A., Kobiler, O. Coalescing replication compartments provide the opportunity for recombination between coinfecting herpesviruses.

A Sequential Two-Stage Track-to-Track Association Method in Asynchronous Bearings-Only Sensor Networks for Aerial Targets Surveillance.

Successful track-to-track association (TTTA) in a multisensor and multitarget scenario is predicated on a reasonable likelihood function to evaluate the similarity of asynchronous mono tracks. To deal with the lack of synchronous data and prior knowledge of the targets in practical applications, this paper investigates a global optimization method with a novel likelihood function constructed by finite asynchronous measurements with joint temporal and spatial constraints (JTSC). For a scenario with more than two independent sensors, a sequential two-stage strategy is proposed to calculate the similarity of multiple asynchronous mono tracks. For the first stage, based on the temporal features of measurements from different sensors, a pairwise fusion model to estimate the position of the target with two mono tracks is established based on the asynchronous crossing location approach. For the other stage, to evaluate the similarity of the outputs, a pairwise similarity model is constructed by searching for the optimal matching points by setting temporal and spatial constraints. Thus, the likelihood of multiple asynchronous tracks is obtained. Simulations are performed to verify that the proposed method can achieve favorable performance without data-synchronization, and also demonstrate the superiority over the methods based on hinge angle differences (HADs) in some scenarios.

Spatially differentiated strategies for reducing nitrate loads from agriculture in two Danish catchments.

Nutrient loss from agriculture is the largest source of diffuse water pollution in Denmark. To reduce nutrient loads a number of solutions have been implemented, but this has been insufficient to achieve the environmental objectives without unacceptable repercussions for agricultural production. This has substantiated the need to develop a new approach to achieve nitrogen (N) load reduction to the aquatic environments with lower costs to farmers. The new approach imply targeting N leaching mitigation to those parts of the landscape which contribute most to the N-loadings. This would involve either reducing the source loading or enhancing the natural reduction (denitrification) of N after it is leached from the root zone of agricultural crops. In this study, a new method of spatially differentiated analysis for two Danish catchments (Odense and Norsminde) was conducted that reach across the individual farms to achieve selected N-load reduction targets. It includes application of cover crops within current crop rotations, set-a-side application on high N-load areas, and changes in agricultural management based on maps of N-reduction available for two different spatial scales, considering soil type and farm boundaries as spatial constraints. In summary, the results revealed that considering spatial constraints for changes in agricultural management will affect the effectiveness of N-load reduction, and the highest N-load reduction was achieved where less constraints were considered. The results also showed that the range of variation in land use, soil types, and N-reduction potential influence the reduction of N-loadings that can originate from critical source areas. The greater the spatial variation the greater the potential for N load reduction through targeting of measures. Therefore, the effectiveness of spatially differentiated measures in term of set-a-side area in Odense catchment were relatively greater compared to Norsminde catchment. The results also showed that using a fine spatial N-reduction map provides greater potential for N load reductions compared to using sub-catchment scale N-reduction maps.

Scenario prediction of emerging coastal city using CA modeling under different environmental conditions: a case study of Lingang New City, China.

The world's coastal regions are experiencing rapid urbanization coupled with increased risk of ecological damage and storm surge related to global climate and sea level rising. This urban development issue is particularly important in China, where many emerging coastal cities are being developed. Lingang New City, southeast of Shanghai, is an excellent example of a coastal city that is increasingly vulnerable to environmental change. Sustainable urban development requires planning that classifies and allocates coastal lands using objective procedures that incorporate changing environmental conditions. In this paper, we applied cellular automata (CA) modeling based on self-adaptive genetic algorithm (SAGA) to predict future scenarios and explore sustainable urban development options for Lingang. The CA model was calibrated using the 2005 initial status, 2015 final status, and a set of spatial variables. We implemented specific ecological and environmental conditions as spatial constraints for the model and predicted four 2030 scenarios: (a) an urban planning-oriented Plan Scenario; (b) an ecosystem protection-oriented Eco Scenario; (c) a storm surge-affected Storm Scenario; and (d) a scenario incorporating both ecosystem protection and the effects of storm surge, called the Ecostorm Scenario. The Plan Scenario has been taken as the baseline, with the Lingang urban area increasing from 45.8 km(2) in 2015 to 66.8 km(2) in 2030, accounting for 23.9 % of the entire study area. The simulated urban land size of the Plan Scenario in 2030 was taken as the target to accommodate the projected population increase in this city, which was then applied in the remaining three development scenarios. We used CA modeling to reallocate the urban cells to other unconstrained areas in response to changing spatial constraints. Our predictions should be helpful not only in assessing and adjusting the urban planning schemes for Lingang but also for evaluating urban planning in coastal cities elsewhere.

Robotic versus non-robotic instruments in spatially constrained operating workspaces: a pre-clinical randomized crossover study.

OBJECTIVE: To compare the effectiveness of robotic and non-robotic laparoscopic instruments in spatially constrained workspaces. MATERIALS AND METHODS: Surgeons performed intracorporeal sutures with various instruments within three different cylindrical workspace sizes. Three pairs of instruments were compared: 3-mm non-robotic mini-laparoscopy instruments; 5-mm robotic instruments; and 8-mm robotic instruments. Workspace diameters were 4, 6 and 8 cm, with volumes of 50, 113 and 201 cm(3) respectively. Primary outcomes were validated objective task performance scores and instrument workspace breach counts. RESULTS: A total of 23 participants performed 276 suture task repetitions. The overall median task performance scores for the 3-, 5- and 8-mm instruments were 421, 398 and 402, respectively (P = 0.12). Task scores were highest (best) for the 3-mm non-robotic instruments in all workspace sizes. Scores were significantly lower when spatial constraints were imposed, with median task scores for the 4-, 6- and 8-cm diameter workspaces being 388, 415 and 420, respectively (P = 0.026). Significant indirect relationships were seen between boundary breaches and workspace size (P < 0.001). Higher breach counts occurred with the robotic instruments. CONCLUSIONS: Smaller workspaces limit the performance of both robotic and non-robotic instruments. In operating workspaces <200 cm(3) , 3-mm non-robotic instruments are better suited for advanced bimanual operative tasks such as suturing. Future robotic instruments need further optimization if this technology is to be uniquely advantageous for clinical roles that involve endoscopic access to workspace-restricted anatomical areas.

Temporal and spatial constraints of action effect on sensory binding.

Previous studies have shown that asynchrony in perceived changes in the visual attributes of an object is attenuated when the object is the target of a manual reaching action (e.g. Corveleyn et al. in J Vis, 2012. doi: 10.1167/12.11.20 ). In the present study, we examined the temporal and spatial constraints associated with the effect of action on sensory binding. Participants performed a temporal order judgment task which required them to judge which changed first, the position or the colour of a visual stimulus, either while performing a concurrent motor task (manual acquisition of a visual target) or not (perceptual task). In Experiment 1, the fixed-attribute change (colour or position) occurred 0, 250, 500 or 1000 ms following the end of the motor action or the presentation of an auditory cue, while the variable-attribute change (position or colour) occurred randomly within an interval of ±200 ms from the fixed-attribute change. In Experiment 2, the visual stimulus was presented at a distance of 0, 2, 4 or 8 cm from a central fixation cross which was the target in the motor task. The fixed attribute (colour or position) changed 700 ms after an auditory cue (perceptual task) or when the hand reached the visual target (motor task). The variable-attribute change (position or colour) again occurred within an interval of ±200 ms from the fixed-attribute change. Statistical analysis of the point of subjective simultaneity revealed that performing a motor action reduced the perceived temporal asynchrony in the perceptual task, but only when the visual changes occurred less than 500 ms (for the fixed attribute) following movement execution (Exp. 1) and at a distance of less than 4 cm from the movement endpoint (Exp. 2). These results indicate that action-induced sensory binding requires temporal contiguity and spatial congruency between the endpoint of the action and its visual consequences.

Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis.

Multiplex immunofluorescence (MxIF) is an advanced molecular imaging technique that can simultaneously provide biologists with multiple (i.e., more than 20) molecular markers on a single histological tissue section. Unfortunately, due to imaging restrictions, the more routinely used hematoxylin and eosin (H&E) stain is typically unavailable with MxIF on the same tissue section. As biological H&E staining is not feasible, previous efforts have been made to obtain H&E whole slide image (WSI) from MxIF via deep learning empowered virtual staining. However, the tiling effect is a long-lasting problem in high-resolution WSI-wise synthesis. The MxIF to H&E synthesis is no exception. Limited by computational resources, the cross-stain image synthesis is typically performed at the patch-level. Thus, discontinuous intensities might be visually identified along with the patch boundaries assembling all individual patches back to a WSI. In this work, we propose a deep learning based unpaired high-resolution image synthesis method to obtain virtual H&E WSIs from MxIF WSIs (each with 27 markers/stains) with reduced tiling effects. Briefly, we first extend the CycleGAN framework by adding simultaneous nuclei and mucin segmentation supervision as spatial constraints. Then, we introduce a random walk sliding window shifting strategy during the optimized inference stage, to alleviate the tiling effects. The validation results show that our spatially constrained synthesis method achieves a 56% performance gain for the downstream cell segmentation task. The proposed inference method reduces the tiling effects by using 50% fewer computation resources without compromising performance. The proposed random sliding window inference method is a plug-and-play module, which can be generalized for other high-resolution WSI image synthesis applications. The source code with our proposed model are available at https://github.com/MASILab/RandomWalkSlidingWindow.git.

Modularity and community detection in human brain morphology.

Humans possess morphologically complex brains, which are spatially constrained by their many intrinsic and extrinsic physical interactions. Anatomical network analysis can be used to study these constraints and their implications. Modularity is a key issue in this framework, namely, the presence of groups of elements that undergo morphological evolution in a concerted way. An array of community detection algorithms was tested on a previously designed anatomical network model of the human brain in order to provide a detailed assessment of modularity in this context. The algorithms that provide the highest quality partitions also reveal general phenotypic patterns underlying the topology of human brain morphology. Taken together, the community detection algorithms highlight the simultaneous presence of a longitudinal and a vertical modular partition of the brain's topology, the combination of which matches the organization of the enveloping braincase. Specifically, the longitudinal organization is in line with the different morphogenetic environments of the three endocranial fossae, while the vertical arrangement corresponds to the distinct developmental processes associated with the cranial base and vault, respectively. The results are robust and have the potential to be compared with equivalent network models of other species. Furthermore, they suggest a degree of concerted topological reciprocity in the spatial organization of brain and skull elements, and posit questions about the extent to which geometrical constraints of the cranial base and the modular partition of the corresponding brain regions may channel both evolutionary and developmental trajectories.

A comparative anatomical network analysis of the human and chimpanzee brains.

Spatial interactions among anatomical elements help to identify topological factors behind morphological variation and can be investigated through network analysis. Here, a whole-brain network model of the chimpanzee (Pan troglodytes, Blumenbach 1776) is presented, based on macroanatomical divisions, and compared with a previous equivalent model of the human brain. The goal was to contrast which regions are essential in the geometric balance of the brains of the two species, to compare underlying phenotypic patterns of spatial variation, and to understand how these patterns might have influenced the evolution of human brain morphology. The human and chimpanzee brains share morphologically complex inferior-medial regions and a topological organization that matches the spatial constraints exerted by the surrounding braincase. These shared topological features are interesting because they can be traced back to the Chimpanzee-Human Last Common Ancestor, 7-10 million years ago. Nevertheless, some key differences are found in the human and chimpanzee brains. In humans, the temporal lobe, particularly its deep and medial limbic aspect (the parahippocampal gyrus), is a crucial node for topological complexity. Meanwhile, in chimpanzees, the cerebellum is, in this sense, more embedded in an intricate spatial position. This information helps to interpret brain macroanatomical change in fossil hominids.

A wrap-around movement path randomization method to distinguish social and spatial drivers of animal interactions.

Studying the spatial-social interface requires tools that distinguish between social and spatial drivers of interactions. Testing hypotheses about the factors determining animal interactions often involves comparing observed interactions with reference or 'null' models. One approach to accounting for spatial drivers of social interactions in reference models is randomizing animal movement paths to decouple spatial and social phenotypes while maintaining environmental effects on movements. Here, we update a reference model that detects social attraction above the effect of spatial constraints. We explore the use of our 'wrap-around' method and compare its performance to the previous approach using agent-based simulations. The wrap-around method provides reference models that are more similar to the original tracking data, while still distinguishing between social and spatial drivers. Furthermore, the wrap-around approach results in fewer false-positives than its predecessor, especially when animals do not return to one place each night but change movement foci, either locally or directionally. Finally, we show that interactions among GPS-tracked griffon vultures (Gyps fulvus) emerge from social attraction rather than from spatial constraints on their movements. We conclude by highlighting the biological situations in which the updated method might be most suitable for testing hypotheses about the underlying causes of social interactions. This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Shaping dynamical neural computations using spatiotemporal constraints.

Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinary progress has been made in bridging the gap between how we understand biological versus artificial computation, including how insights gained from one can translate to the other. Research has revealed that neurobiology is a key determinant of brain network architecture, which gives rise to spatiotemporally constrained patterns of activity that underlie computation. Here, we discuss how neural systems use dynamics for computation, and claim that the biological constraints that shape brain networks may be leveraged to improve the implementation of artificial neural networks. To formalize this discussion, we consider a natural artificial analog of the brain that has been used extensively to model neural computation: the recurrent neural network (RNN). In both the brain and the RNN, we emphasize the common computational substrate atop which dynamics occur-the connectivity between neurons-and we explore the unique computational advantages offered by biophysical constraints such as resource efficiency, spatial embedding, and neurodevelopment.

The Effect of Claustrophobic Tendencies on Digital Spatial Preferences.

In digital environments, the demand for larger devices (e.g., larger smartphones) has been growing continuously, indicating users' spatial needs in digital interfaces. This study explores the need for space in digital interfaces in relation to claustrophobic tendencies. The findings from two studies consistently report that (1) stronger claustrophobic tendencies toward physical spatial constraints are positively associated with a stronger need for digital space. The results also demonstrate that (2) people with elevated claustrophobic tendencies and a stronger need for digital space perceive stronger spatial constraints on digital interfaces, and (3) claustrophobic tendencies and need for digital space have stronger effects on spatial constraints with a more complex grid design. Interestingly, the findings suggest that (4) claustrophobic tendencies are more closely associated with spatial needs from attentive tasks (e.g., reading a long document), than device-related spatial needs (e.g., large screen preferences), implying that such claustrophobic tendencies are more likely to influence cognitive tasks on digital devices. Overall, the findings indicate that claustrophobic tendencies may be utilized beyond medical purposes and may assist researchers and business practitioners understand users' spatial needs in fast-changing digital environments.

Measuring Competitiveness at NUTS3 Level and Territorial Partitioning of the Italian Provinces.

In this paper we propose a dashboard of indicators of territorial attractiveness at NUTS3 level in the framework of the EU Regional Competitiveness Index (RCI). Then, the Fuzzy C-Medoids Clustering model with multivariate data and contiguity constraints is applied for partitioning the Italian provinces (NUTS3). The novelty is the territorial level analized, and the identification of the elementary indicators at the basis of the construction of the eleven composite competitiveness pillars. The positioning of the Italian provinces is deeply analyzed. The clusters obtained with and without contraints are compared. The obtained partition may play an important role in the design of policies at the NUTS3 level, a route already considered by the Italian government. The analysis developed and the related set of indicators at NUTS3 level constitute an information base that could be effectively used for the implementation of the National Recovery and Resilience Plan (NRRP).

Microbial biogeography of acid mine drainage sediments at a regional scale across southern China.

Investigations of microbial biogeography in extreme environments provide unique opportunities to disentangle the roles of environment and space in microbial community assembly. Here, we reported a comprehensive microbial biogeographic survey of 90 acid mine drainage (AMD) sediment samples from 18 mining sites of various mineral types across southern China. We found that environmental selection was strong in determining the AMD habitat species pool. However, microbial alpha diversity was primarily explained by mining sites rather than environmental factors, and microbial beta diversity correlated more strongly with geographic than environmental distance at both large and small spatial scales. Particularly, the presence/absence of widespread AMD habitat generalists was only correlated with geographic distance and independent of environmental variation. These distance-decay patterns suggested that spatial processes played a more important role in determining microbial compositional variation across space; which could be explained by the reinforced impacts of dispersal limitation in less fluid, spatially structured sediment habitat with diverse pre-existing communities. In summary, our findings suggested that the deterministic assembling and spatial constraints interact to shape microbial biogeography in AMD sediments; and provided implications that spatial processes should be considered when predicting microbial dynamics in response to severe environmental change across large spatial scales.

Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks.

The accurate diagnosis of autism spectrum disorder (ASD), a common mental disease in children, has always been an important task in clinical practice. In recent years, the use of graph neural network (GNN) based on functional brain network (FBN) has shown powerful performance for disease diagnosis. The challenge to construct "ideal" FBN from resting-state fMRI data remained. Moreover, it remains unclear whether and to what extent the non-Euclidean structure of different FBNs affect the performance of GNN-based disease classification. In this paper, we proposed a new method named Pearson's correlation-based Spatial Constraints Representation (PSCR) to estimate the FBN structures that were transformed to brain graphs and then fed into a graph attention network (GAT) to diagnose ASD. Extensive experiments on comparing different FBN construction methods and classification frameworks were conducted on the ABIDE I dataset (n = 871). The results demonstrated the superiority of our PSCR method and the influence of different FBNs on the GNN-based classification results. The proposed PSCR and GAT framework achieved promising classification results for ASD (accuracy: 72.40%), which significantly outperformed competing methods. This will help facilitate patient-control separation, and provide a promising solution for future disease diagnosis based on the FBN and GNN framework.

A spatial constraint to model and extract texture components in Multivariate Curve Resolution of near-infrared hyperspectral images.

This article highlights the importance of properly taking into account spatial structures and features to better resolve near-infrared (NIR) hyperspectral images by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), especially when highly mixed components (in terms of spatial and spectral overlap) underlying the systems under study are dealt with. As in the NIR domain these components can explain both chemical properties and physical phenomena, their improved unravelling can therefore represent an alternative or a complement to more standard approaches for, e.g., spectral data preprocessing. These points will be illustrated through the comprehensive analysis of a complex real-world forensic case-study where texture characterization is crucial for the sake of a more appropriate resolution.

Effect of image processing constraints on the extent of rotational ambiguity in MCR-ALS of hyperspectral images.

Hyperspectral imaging is a way to explore the spatial and spectral information of the different compounds in chemical or biological samples. In addition, multivariate curve resolution - alternating least squares (MCR-ALS) can be used to extract this information based on the bilinearity assumption. However, it is well-known that using proper constraints can reduce the amount of uncertainty in the results of MCR, which is called rotational ambiguity. In MCR-ALS analysis of hyperspectral images, different image processing techniques, such as model fitting, image segmentation or sparse image recovery can be applied as spatial constraints. In this contribution, we aim to investigate how the use of these spatial constraints limits the extent of rotational ambiguity of MCR-ALS solutions. For this purpose, we evaluate the extent of rotational ambiguity and use Borgen plots to visualize it. We show on simulations and real hyperspectral imaging data that accuracy of the results is improved when spatial constraints are applied.

Mapping of Protein Interfaces in Live Cells Using Genetically Encoded Crosslinkers.

Understanding the topology of protein-protein interactions is a matter of fundamental importance in the biomedical field. Biophysical approaches such as X-ray crystallography and nuclear magnetic resonance can investigate in detail only isolated protein complexes that are reconstituted in an artificial environment. Alternative methods are needed to investigate protein interactions in a physiological context, as well as to characterize protein complexes that elude the direct structural characterization. We describe here a general strategy to investigate protein interactions at the molecular level directly in the live mammalian cell, which is based on the genetic incorporation of photo- and chemical crosslinking noncanonical amino acids. First a photo-crosslinking amino acid is used to map putative interaction surfaces and determine which positions of a protein come into proximity of an associated partner. In a second step, the subset of residues that belong to the binding interface are substituted with a chemical crosslinker that reacts selectively with proximal cysteines strategically placed in the interaction partner. This allows determining inter-molecular spatial constraints that provide the basis for building accurate molecular models. In this chapter, we illustrate the detailed application of this experimental strategy to unravel the binding modus of the 40-mer neuropeptide hormone Urocortin1 to its class B G-protein coupled receptor, the corticotropin releasing factor receptor type 1. The approach is in principle applicable to any protein complex independent of protein type and size, employs established techniques of noncanonical amino acid mutagenesis, and is feasible in any molecular biology laboratory.