Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics

Spatial and temporal variation in distribution of mangroves in Moreton Bay, subtropical Australia: a comparison of pattern metrics and change detection analyses based on aerial photographs

Studies of land use change have been increasingly carried out in recent years. Land use change is seen as one of the fundamental factor for the operation of environmental system from local to global scale. There are various methods to study land use change and cellular automata(CA)-based spatial simulation is a popular one. While this method (CA-based spatial simulation) is widely used in study of land use change, there are many aspects that need to be explored regarding its performance. Exploring the effect of spatial resolution on CA-based spatial simulation of land use change is the main objective of this research. Yogyakarta urban area was preferred as research area because of its interesting characteristic. Built up land is continuously increasing while agriculture land tend to decrease. Yogyakarta urban area consisted of the city of Yogyakarta and its suburban areas. Spatial simulation combined with experimental analysis were used as the main methods. CA-based spatial simulation were performed on different scenarios i.e. different spatial resolution of the data input. This study used three different spatial resolution that are 10 m, 50 m, and 75 m. Univariate statistical analysis against empirical data of land-use change was conducted to determine those spatial resolutions. Performance of CA-based spatial simulation was accessed using Kappa Index of Agreement (KIA) and two indices of spatial pattern, i.e. variance to mean ratio (VMR) and Moran's I. This study shows that higher spatial resolution of data input tend to generate a more clustered spatial pattern on the simulated map. The minimum and average value of actual land use change area could be utilized as consideration for determining appropriate spatial resolution. Medium spatial resolution particularly for extended spatial simulation produce more "visually realistic" spatial pattern.

Ecological network resilience, the core capacity of ecosystems to maintain functional stability under external disturbances, is of great significance for ensuring regional ecological security and promoting sustainable development. This study takes the Wuhan Metropolitan Area as the research object. Based on multi-temporal data from 2000 to 2020, we innovatively constructed a comprehensive evaluation system of "water resources–soil conservation–ecosystem quality" to identify ecological source areas. Combined with a three-dimensional resistance surface model of "natural environment–human activities–physical barriers," the Minimum Cumulative Resistance (MCR) model was applied to extract ecological corridors, forming a "source–corridor" spatial frame-work. The gravity model was used to construct the ecological network and analyze its topological structure. Finally, a robustness model was employed to assess the dynamic changes in network resilience. The key findings include: (1) The number of ecological source areas exhibited a "rise-then-decline" trend, with spatial distribution shifting from fragmented to clustered and the global control of core nodes transitioning from decentralized to highly centralized. (2) Ecological corridors displayed a "dense-south-sparse-north, dense-periphery-sparse-center" spatial pattern. Although the number of corridors decreased, the interaction strength and species migration efficiency significantly improved. (3) The ecological network evolved from "expansion" to "quality enhancement," with significant improvements in network density and clustering coefficients, ultimately forming an efficient and stable structure. Based on these findings, strategic recommendations are proposed to optimize the ecological network and enhance environmental quality in the Wuhan Metropolitan Area's composite ecosystem.

Demyelination is the cause of disability in various neurological disorders. It is therefore crucial to understand the molecular regulation of oligodendrocytes, the myelin forming cells in the CNS. Growth factors are known to be essential for the development and maintenance of oligodendrocytes and are involved in the regulation of glial responses in various pathological conditions. We employed the well established murine cuprizone model of toxic demyelination to analyze the expression of 13 growth factors in the CNS during de- and remyelination. The temporal mRNA expression profile during demyelination and the subsequent remyelination were analyzed separately in the corpus callosum and cerebral cortex using laser microdissection and real-time PCR techniques. During demyelination a similar pattern of growth factor mRNA expression was observed in both areas with a strong up-regulation of NRG1 and GDNF and a slight increase of CNTF in the first week of cuprizone treatment. HGF, FGF-2, LIF, IGF-I, and TGF-ß1 were up-regulated mainly during peak demyelination. In contrast, during remyelination there were regional differences in growth factor mRNA expression levels. GDNF, CNTF, HGF, FGF-2, and BDNF were elevated in the corpus callosum but not in the cortex, suggesting tissue differences in the molecular regulation of remyelination in the white and grey matter. To clarify the cellular source we isolated microglia from the cuprizone lesions. GDNF, IGF-1, and FGF mRNA were detected in the microglial fraction with a temporal pattern corresponding to that from whole tissue PCR. In addition, immunohistochemical analysis revealed IGF-1 protein expression also in the reactive astrocytes. CNTF was located in astrocytes. This study identified seven different temporal expression patterns for growth factors in white and grey matter and demonstrated the importance of early tissue priming and exact orchestration of different steps during callosal and cortical de- and remyelination.

Among the global Earth system changes, human-induced land-cover and land-use changes (LULCC) play significant roles in sustainability of ecosystems. Suburban sprawl, for example, is one of the major components of LULCC that develops urban impervious surface area (ISA) across the landscapes. Increasing ISA is a major environmental issue that has profound impacts on hydrology and climate. Coastal state of Rhode Island has experienced problems associated with urban runoffs due to the alteration of ISA on the landscape. Precise and accurate coverage and location of ISA are key parameters in ISA related environmental studies. Due to the limitations of existing coarse spatial resolution ISA data and current methods for extraction of ISA from high spatial resolution remote sensing imageries, this study developed an algorithm of multiple agent segmentation and classification (MASC) to extract ISA information from high spatial resolution remote sensing imageries. The MASC model includes submodels of segmentation, shadow-effect, MANOV A-based classification, and post-classification. Based on the MASC model, this study built the 1-meter high spatial resolution ISA dataset for the state of Rhode Island, and revealed its spatial patterns. The result indicates that, as of 2004, 10% of the state land has been covered by ISA. The major population centers and historical cities, such as the Providence, Woonsocket, and Newport, have ISA over 30%. The heavily settled suburban communities have ISA between 10% and 30%. Only 17 out of 39 towns in the state have less than 10% ISA. The average ISA for the coastal towns is 14%. Because most stream quality indicators are predicted to decline when watershed ISA exceeds 10%, the results from this study serve as an alarming indicator for managing the state's watershed and coastal ecosystems. It is important to develop scientific basis for observing and modeling watershed hydrologic dynamic with emphasis on how the hydrologic processes are affected by the spatial heterogeneity of ISA, and to improve the understanding of influence of ISA on hydrologic cycle in watersheds. This developed a distributed object-oriented rainfall-runoff simulation (DORS) model that enhances capability and performance of hydrologic modeling with incorporation of high spatial resolution ISA information. With the innovative process of object-oriented spatial units, the DORS model can reduce data volume, increase computational efficiency, strengthen representation of watersheds and utilize the data in variable scales. The DORS model provides a framework to integrate remote sensing data and the derived products in different scale for the simulation of

Groundwater storage anomalies projection by optimized deep learning refines groundwater management in typical arid basins

Reference evapotranspiration is a key element in agricultural management, particularly in a changing global environment, and represents an important requirement for the effective planning, monitoring, and management of water resources. However, accurate evapotranspiration estimation requires spatially well-distributed continuous meteorological data to capture regional variations, and reanalysis datasets are valuable tools for this purpose. In this context, this study aimed to assess the performance of the Copernicus European Regional ReAnalysis (CERRA) dataset in the western Iberian Peninsula, focusing on Portugal and Galicia (Spain). Meteorological data (air temperature, relative humidity, solar radiation, and wind speed) from several surface stations were used to analyze the differences between the observations and CERRA hindcasts. The reference evapotranspiration ( ET o ) was then computed for both datasets to estimate CERRA’s consistency and accuracy. The results revealed that CERRA data strongly correlated with the observational data, accurately capturing the spatial and temporal atmospheric patterns. Daily air temperature was the most accurately represented variable, followed by relative humidity, solar radiation, and wind speed. ET o estimates from the CERRA dataset were closely aligned with observations. The high spatial resolution of CERRA enabled an accurate representation of the regional climatic variations, addressing the weaknesses found in other reanalysis datasets, particularly in coastal areas influenced by land‒sea interactions. The findings of this study indicate that CERRA is a highly valuable database for climate studies to validate the results of regional climate models with high resolution. These models are essential for developing effective adaptation and mitigation strategies to address agricultural planning and management in response to climate-related challenges. Graphical Abstract In this study, the performance of the Copernicus European Regional ReAnalysis (CERRA) dataset in replicating atmospheric variables and reference evapotranspiration (ET o ) for agrometeorological applications in the western Iberian Peninsula was assessed. CERRA hindcasts were compared with meteorological observations (minimum and maximum air temperature, relative humidity, wind speed, and solar radiation) from surface stations using Taylor diagrams, box plots, scatter plots, and the Kling–Gupta efficiency ( KGE ) metric for validation. The ET o was subsequently computed for both datasets. The results indicate strong correlations between CERRA and observational data, with CERRA effectively reproducing spatial and temporal patterns. ET o estimates from the CERRA dataset closely align with observations. This study emphasizes the ability of CERRA to accurately represent regional climatic variations because of its high spatial resolution, overcoming the limitations of other reanalysis datasets, particularly in coastal zones. The results suggest that CERRA is a valuable asset for climate studies, validation of high-resolution regional climate models, water resource management, and agricultural planning.

Traditional spatial co-location pattern mining attempts to find the subsets of spatial features whose instances are frequently located together in some regions. Most previous studies take the prevalence of co-locations as the interestingness measure. However, it is more meaningful to take the utility value of each instance into account in spatial co-location pattern mining in some cases. In this paper, we present a new interestingness measure for mining high utility co-location patterns from spatial data sets with instance-specific utilities. In the new interestingness measure, we take the intra-utility and inter-utility into consideration to capture the global influence of each feature in co-locations. We present a basic algorithm for mining high utility co-locations. In order to reduce high computational cost, some pruning strategies are given to improve the efficiency. The experiments on synthetic and real-world data sets show that the proposed method is effective and the pruning strategies are efficient.

Colinearity in the Xenopus laevis Hox-2 complex

Landform is an essential aspect for environmental and disaster studies. Automated landform classification has been developed due to the importance role of landrom for many studies. Automated landform classification can be applied for general purposes. TPI (Topographic Position Index) is one of automated landform classification method. TPI measures the difference between center elevation and mean elevation in its surroundings within certain radius. This study used SRTM data with 90 meters resolution and ASTER GDEM data with 30 meters resolution for the south part of Yogyakarta. Data processing is conducted by using SAGA GIS. The research documented here aims to clarify how TPI support the landform classification thus for practical use can be utilized effectively for analysis any aspect related to landform classification. Generally, automated landform classification for two datas results the same spatial pattern. Study area is mostly classified as plains. But feature number of landform in ASTER GDEM data is larger than STRM data. Because ASTER GDEM data has higher spatial resolution so that the result is more detail. Based on Tobler`s Law, ASTER GDEM work best for 1:50.000 scale, while SRTM fit for 1:180.000 scale.

There is often need to measure aggregation levels of spatial patterns within a single map class in landscape ecological studies. The contagion index (CI), shape index (SI), and probability of adjacency of the same class (Qi), all have certain limits when measuring aggregation of spatial patterns. We have developed an aggregation index (AI) that is class specific and independent of landscape composition. AI assumes that a class with the highest level of aggregation (AID 1) is comprised of pixels sharing the most possible edges. A class whose pixels share no edges (completely disaggregated) has the lowest level of aggregation (AID 0). AI is similar to SI and Qi ,b ut it calculates aggregation more precisely than the latter two. We have evaluated the performance of AI under varied levels of (1) aggregation, (2) number of patches, (3) spatial resolutions, and (4) real species distribution maps at various spatial scales. AI was able to produce reasonable results under all these circumstances. Since it is class specific, it is more precise than CI, which measures overall landscape aggregation. Thus, AI provides a quantitative basis to correlate the spatial pattern of a class with a specific process. Since AI is a ratio variable, map units do not affect the calculation. It can be compared between classes from the same or different landscapes, or even the same classes from the same landscape under different resolutions.

Data mining is the process of extracting implicit, valuable, and interesting information from large sets of data. Visualization is the process of visually exploring data for pattern and trend analysis, and it is a common method of browsing spatial datasets to look for patterns. However the growing volume of spatial datasets make it difficult for humans to browse such datasets in their entirety, and data mining algorithms are needed to filter out large uninteresting parts of spatial datasets. We construct a web-based visualization software package for observing the summarization of spatial patterns and temporal trends. We also present data mining algorithms for filtering out vast parts of datasets for spatial outlier patterns. The algorithms were implemented and tested with a real-world set of Minneapolis-St. Paul (Twin Cities) traffic data.

Incident data, a form of big data frequently used in urban studies, are characterized by point features with high spatial and temporal resolution and categorical values. In contrast to panel data, such spatial data pooled over time reflect multi-directional spatial effects but only unidirectional temporal effects, which are challenging to analyze. This paper presents an innovative approach to address this challenge – a geographically and temporally weighted co-location quotient which includes global and local computation, a method to calculate a spatiotemporal weight matrix and a significance test using Monte Carlo simulation. This new approach is used to identify spatio-temporal crime patterns across Greater Manchester in 2016 from open source recorded crime data. The results show that this approach is suitable for the analysis and visualization of spatio-temporal dependence and heterogeneity in categorical spatial data pooled over time. It is particularly useful for detecting symmetrical spatio-temporal co-location patterns and mapping local clusters. The method also addresses the unbalanced temporal scale problem caused by unidirectional temporal data representation and explores potential impacts. The empirical evidence of the spatiotemporal crime patterns might usefully be deployed to inform the development of criminological theory by helping to disentangle the relationships between crime and the urban environment.

Analysis of time-evolving data is crucial to understand the functioning of dynamic systems such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) data collected during a task may reveal spatial regions of interest, and how they evolve during the task. However, capturing underlying spatial patterns as well as their change in time is challenging. The traditional approach in fMRI data analysis is to assume that underlying spatial regions of interest are static. In this article, using fractional amplitude of low-frequency fluctuations (fALFF) as an effective way to summarize the variability in fMRI data collected during a task, we arrange time-evolving fMRI data as a subjects by voxels by time windows tensor, and analyze the tensor using a tensor factorization-based approach called a PARAFAC2 model to reveal spatial dynamics. The PARAFAC2 model jointly analyzes data from multiple time windows revealing subject-mode patterns, evolving spatial regions (also referred to as networks) and temporal patterns. We compare the PARAFAC2 model with matrix factorization-based approaches relying on independent components, namely, joint independent component analysis (ICA) and independent vector analysis (IVA), commonly used in neuroimaging data analysis. We assess the performance of the methods in terms of capturing evolving networks through extensive numerical experiments demonstrating their modeling assumptions. In particular, we show that (i) PARAFAC2 provides a compact representation in all modes, i.e., subjects, time, and voxels, revealing temporal patterns as well as evolving spatial networks, (ii) joint ICA is as effective as PARAFAC2 in terms of revealing evolving networks but does not reveal temporal patterns, (iii) IVA's performance depends on sample size, data distribution and covariance structure of underlying networks. When these assumptions are satisfied, IVA is as accurate as the other methods, (iv) when subject-mode patterns differ from one time window to another, IVA is the most accurate. Furthermore, we analyze real fMRI data collected during a sensory motor task, and demonstrate that a component indicating statistically significant group difference between patients with schizophrenia and healthy controls is captured, which includes primary and secondary motor regions, cerebellum, and temporal lobe, revealing a meaningful spatial map and its temporal change.

Many applications of wireless sensor networks, such as object tracking and disaster monitoring, require effective and efficient techniques to detect events of interest in the physical world. Despite the simplicity in implementation, the existing threshold-based approach to event detection suffers from a limited expressive power and a poor tolerance to missing values and errors in sensor data. In this thesis, we present three novel database approaches to event detection for sensor networks: (i) a pattern-based approach that abstracts events as temporal patterns on individual sensor nodes, (ii) a contour map-based approach that represent events as spatial shapes in the sensor value distribution across nodes, and (iii) a model-based approach that specifies events as regression models over spatial regions in the sensor network. The key observation of all three approaches is that, because an event generates a particular kind of sensor data distribution, the problem of event detection can be converted into that of matching this distribution. In each of the three proposed approaches, we formalize the event specification and develop efficient matching algorithms. The experimental results on both real-world and synthetic data sets demonstrate the effectiveness and efficiency of our proposed approaches.