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.

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.

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.

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.

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.

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).

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.

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.

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.

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.