Local Patterns Research Articles

Spatial nonstationarity and the role of environmental metal exposures on COVID-19 mortality in New Mexico

Worldwide, the COVID-19 pandemic has been influenced by a combination of environmental and sociodemographic drivers. To date, population studies have overwhelmingly focused on the impact of societal factors. In New Mexico, the rate of COVID-19 infection and mortality varied significantly among the state's geographically dispersed, and racially and ethnically diverse populations who are exposed to unique environmental contaminants related to resource extraction industries (e.g. fracking, mining, oil and gas exploration). By looking at local patterns of COVID-19 disease severity, we sought to uncover the spatially varying factors underlying the pandemic. We further explored the compounding role of potential long-term exposures to various environmental contaminants on COVID-19 mortality prior to widespread applications of vaccinations. To illustrate the spatial heterogeneity of these complex associations, we leveraged multiple modeling approaches to account for spatial non-stationarity in model terms. Multiscale geographically weighted regression (MGWR) results indicate that increased potential exposure to fugitive mine waste is significantly associated with COVID-19 mortality in areas of the state where socioeconomically disadvantaged populations were among the hardest hit in the early months of the pandemic. This relationship is paradoxically reversed in global models, which fail to account for spatial relationships between variables. This work contributes both to environmental health sciences and the growing body of literature exploring the implications of spatial nonstationarity in health research.

Formation of linear arrays of holes in self-assembled collagen films

Collagen is one of the main constituents of mammalian extracellular matrix and is used extensively as a coating for tissue culture dishes and medical implants to promote cell growth and proliferation. By modulating the topography of the collagen coating at the nanometer to micrometer length scales, it is possible to achieve spatial control over cell growth and morphology. In this work, we are exploring the self-assembly of a thin collagen film on a glass substrate as a way to create new nanoscale surface features. By controlling the collagen concentration and adding an oscillatory flow, we are able to enrich the collagen film surface with a localized pattern of ripples oriented perpendicular to the flow direction. We propose that these ripples are the result of dewetting of the collagen film that leads to the formation of adjacent holes. We observe that individual holes form with an anisotropic rim due to the microstructure of the deposited collagen fibril network. This intrinsic anisotropy and the oscillatory flow yield new holes being formed in the film next to existing rims. As holes keep growing deeper, the rims extend along the flow direction, and the holes appear rectangular in shape, which gives the linear array of holes the apparent morphology of a ripple. Overall, we are able to create localized ripples at the surface of collagen films that would be difficult to produce via standard nanofabrication techniques.

The Pattern of Copper Release in Copper‐Based Nanoparticles Regulates Tumor Proliferation and Invasiveness in 3D Culture Models

Cancer is a leading cause of death worldwide. Glioblastoma (GBM) is a major challenge in oncology due to its highly invasive nature and limited treatment options. GBM's aggressive migration beyond tumor margins and rapid tumor growth hinders success in patient treatment. Localized therapeutic delivery, such as the use of transition metals like copper, is highlighted as a novel therapeutic agent for many potential biomedical applications. Herein, it is aimed to study the effects of Cu release on the proliferation and invasiveness of cancer cells. To this end, novel copper‐based nanostructures with different release patterns are designed. Using a complex 3D cell culture model to mimic the tumor microenvironment, it is shown that different patterns of copper ion release have a strong impact on GBM progression and invasiveness. The findings highlight the importance of optimizing localized copper release patterns to tailor different tumor treatment strategies. They also show the potential and suitability of 3D microchips as instruments to study the behavior of tumor spheroids. In spite of their limitations, these 3D microdevices enable a controlled and close monitoring of the influence of environmental factors (such as the presence of Cu ions) on the proliferation and invasiveness of the cells, with a better approach to reality compared to 2D models and with a more controlled environment, compared to an in vivo model.

Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis

Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-BiLSTM, which combines attention-based Improved Convolutional Neural Networks (ICNN) and Bidirectional Long Short-Term Memory (BiLSTM) units seamlessly. Our main goal is to improve the accuracy of protein sequence classification by carefully optimizing performance through Bayesian Optimisation. ProtICNN-BiLSTM combines the power of CNN and BiLSTM architectures to effectively capture local and global protein sequence dependencies. In the proposed model, the ICNN component uses convolutional operations to identify local patterns. Captures long-range associations by analyzing sequence data forward and backwards. In advanced biological studies, Bayesian Optimisation optimizes model hyperparameters for efficiency and robustness. The model was extensively confirmed with PDB-14,189 and other protein data. We found that ProtICNN-BiLSTM outperforms traditional categorization models. Bayesian Optimization’s fine-tuning and seamless integration of local and global sequence information make it effective. The precision of ProtICNN-BiLSTM improves comparative protein sequence categorization. The study improves computational bioinformatics for complex biological analysis. Good results from the ProtICNN-BiLSTM model improve protein sequence categorization. This powerful tool could improve medical and biological research. The breakthrough protein sequence classification model is ProtICNN-BiLSTM. Bayesian optimization, ICNN, and BiLSTM analyze biological data accurately.

Liver Cancer Diagnosis: Enhanced Deep Maxout Model with Improved Feature Set

This work proposed a liver cancer classification scheme that includes Preprocessing, Feature extraction, and classification stages. The source images are pre-processed using Gaussian filtering. For segmentation, this work proposes a LUV transformation-based adaptive thresholding-based segmentation process. After the segmentation, certain features are extracted that include multi-texon based features, Improved Local Ternary Pattern (LTP-based features), and GLCM features during this phase. In the Classification phase, an improved Deep Maxout model is proposed for liver cancer detection. The adopted scheme is evaluated over other schemes based on various metrics. While the learning rate is 60%, an improved deep maxout model achieved a higher F-measure value (0.94) for classifying liver cancer; however, the previous method like Support Vector Machine (SVM), Random Forest (RF), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), K-Nearest Neighbor (KNN), Deep maxout, Convolutional Neural Network (CNN), and DL model holds less F-measure value. An improved deep maxout model achieved minimal False Positive Rate (FPR), and False Negative Rate (FNR) values with the best outcomes compared to other existing models for liver cancer classification.

Dynamics of Portevin-Le Chatelier (PLC) bands and fracture behavior of W-tempered 7075 aluminum alloys with different cooling methods

The relationship between Portevin-Le Chatelier (PLC) band propagation, plastic deformation, and failure mechanisms in solution heat-treated 7075-T6 aluminum alloys cooled by water quenching (7075-WQ) or air cooling (7075-AC) is compared using 5052-H32 alloy as a reference. Miniature tension tests, strain rate jump (SRJ) tests, and digital image correlation reveal that 7075-WQ shows diffused band configurations, 7075-AC exhibits localized patterns, and 5052-H32 has relatively stationary bands. These PLC band dynamics, related to geometry and band densities, are supported by fractography and microstructure analyses. The 7075-WQ and 7075-AC alloys exhibit mixed ductile and quasi-brittle fractures, while 5052-H32 shows ductile fractures with cup-and-cone surfaces. The findings provide new insights into the PLC bands and their formation mechanisms in W-tempered 7075 aluminum alloy concerning different cooling methods, supporting the development of thermal-mechanical processing routes for aluminum components with controllable plastic instability.

Machine learning framework for high-resolution air temperature downscaling using LiDAR-derived urban morphological features

Climate models lack the necessary resolution for urban climate studies, requiring computationally intensive processes to estimate high resolution air temperatures. In contrast, Data-driven approaches offer faster and more accurate air temperature downscaling. This study presents a data-driven framework for downscaling air temperature using publicly available outputs from urban climate models, specifically datasets generated by UrbClim. The proposed framework utilized morphological features extracted from LiDAR data. To extract urban morphological features, first a three-dimensional building model was created using LiDAR data and deep learning models. Then, these features were integrated with meteorological parameters such as wind, humidity, etc., to downscale air temperature using machine learning algorithms. The results demonstrated that the developed framework effectively extracted urban morphological features from LiDAR data. Deep learning algorithms played a crucial role in generating three-dimensional models for extracting the aforementioned features. Also, the evaluation of air temperature downscaling results using various machine learning models indicated that the LightGBM model had the best performance with an RMSE of 0.352 °K and MAE of 0.215 °K. Furthermore, the examination of final air temperature maps derived from downscaling showed that the developed framework successfully estimated air temperatures at higher resolutions, enabling the identification of local air temperature patterns at street level. The corresponding source codes are available on GitHub: https://github.com/FatemehCh97/Air-Temperature-Downscaling.

“An Acre of Land to Plant or a Stick of Wood to Make a Fence or Fire”: An Archaeology of Mohegan Allotment

Abstract Although land loss is among the most profound impacts that settler colonialism had for Indigenous societies across North America, archaeologists rarely study one of the principal colonial mechanisms of land dispossession: allotment. This process forever altered the course of North American history, breaking up collectively held Indigenous lands into lots “owned” by individuals and families while further stressing local Indigenous subsistence patterns, social relations, political organization, and more. Archaeology's long-term, material, and sometimes collaborative vantage stands to offer insights on this process and how it played out for Indigenous peoples in different times and places. As its case study, this article considers the allotment of Mohegan lands in southeastern Connecticut (USA). An archaeology of Mohegan allotment speaks to more than land loss and cultural change. It provides evidence of an enduring and long-term Indigenous presence on the land; of the challenges faced and overcome by Mohegan peoples living through, and with, settler colonialism; and of the nuances of Indigenous-colonial archaeological records. This study also shows the importance of Indigenous and collaborative archaeologies for shedding new light on these challenging but important archaeological traces.

Feasibility analysis of using short-term rainfall time series to evaluate rainwater harvesting systems considering climate change

Employing recent short-term historical rainfall data may enhance the performance of rainwater harvesting systems (RWHs) in response to climate change. However, this assumption lacks extensive research, and the evaluation of RWHs currently relies on long-term historical rainfall time series. This study evaluates the feasibility of this assumption and aims to identify the optimal rainfall time series for evaluating RWH performance under climate change. We evaluated RWHs in residential buildings across 16 Japanese cities utilizing historical rainfall time series of varying lengths and 30-year predicted rainfall time series. The minimum rainfall time series length was obtained based on the similarity index between the evaluation results for historical and future periods. The corresponding optimal series can be determined from the distribution of similarity indices in the minimum length. Finally, we introduce supply pressure indices (SPIs) to summarize the rainfall characteristics of these optimal rainfall time series. Our findings highlight that the minimum rainfall time series length increased from 1 year to 30 years as building non-potable water demand rose and city locations varied. Utilizing rainfall time series incorporating recent rainfall data yielded more dependable evaluation results for RWHs under climate change. These optimal rainfall time series share common characteristics with SPIs ranging from 5.37 to 17.87 mm/d, contingent on the local rainfall patterns. Our study concludes that utilizing recent short-term historical rainfall data is feasible to evaluate and design RWHs under climate change.

Graph Transformer for 3D point clouds classification and semantic segmentation

Recently, graph-based and Transformer-based deep learning have demonstrated excellent performances on various point cloud tasks. Most of the existing graph-based methods rely on static graph, which take a fixed input to establish graph relations. Moreover, many graph-based methods apply maximizing and averaging to aggregate neighboring features, so that only a single neighboring point affects the feature of centroid or different neighboring points own the same influence on the centroid’s feature, which ignoring the correlation and difference between points. Most Transformer-based approaches extract point cloud features based on global attention and lack the feature learning on local neighbors. To solve the above issues of graph-based and Transformer-based models, we propose a new feature extraction block named Graph Transformer and construct a 3D point cloud learning network called GTNet to learn features of point clouds on local and global patterns. Graph Transformer integrates the advantages of graph-based and Transformer-based methods, and consists of Local Transformer that use intra-domain cross-attention and Global Transformer that use global self-attention. Finally, we use GTNet for shape classification, part segmentation and semantic segmentation tasks in this paper. The experimental results show that our model achieves good learning and prediction ability on most tasks. The source code and pre-trained model of GTNet will be released on https://github.com/NWUzhouwei/GTNet.

New OpenSees material model for simulating reinforced concrete shear walls subjected to coupled axial tension and cyclic lateral loads

In high-rise buildings, reinforced concrete (RC) shear walls, particularly those forming part of coupled wall systems or core wall systems, may be subjected to coupled axial tension and cyclic lateral loads during strong seismic events. A new two-dimensional material model named the “Fixed Angle Model Considering Crack Sliding” (FAM-CS) was proposed for simulating the nonlinear behavior of RC walls subjected to axial tension and cyclic lateral loads. The proposed model was implemented in the OpenSees platform. Nonlinear constitutive models for shear aggregate interlock effects of concrete and dowel action of rebars were incorporated into the proposed model to represent the shear transfer mechanisms along concrete cracks. The proposed model was validated against the test data of six wall specimens subjected to coupled axial tension and cyclic lateral loads. The results indicated that the proposed model reasonably predicted the lateral strength capacity (an average error of 6.2 %) and the residual strength at the maximum drift loading (an average error of 12.3 %) of the specimens. For comparison, three other commonly used models, the Shear-Flexure Interaction Multiple-Vertical-Line-Element-Model, the multi-layer shell element model and the Cyclic Softened Membrane Model were adopted for the simulation of the specimens. Compared with the three existing OpenSees models, the newly implemented material model provided improved predictions of the wall hysteretic behavior with respect to lateral strength capacity, lateral strength degradation, pinching characteristics and cyclic stiffness degradation. In addition, the proposed model reasonably captured the localized failure pattern as well as the energy dissipation characteristics of the test specimens. Finally, a mesh sensitivity study was conducted to assess the robustness of the proposed model. The results revealed that the proposed model was robust regarding the lateral load-drift responses of RC walls, with a mesh size ranging from approximately one to three times the average crack spacing.

Impact of Intra-Phenotypic Nasal Vestibular Variation on Local Airflow Dynamics.

Many individuals with healthy normal nasal anatomy and function exhibit a prominent notch indentation at the junction of the ala and sidewall, specifically around the anterior-superior region of the unilateral nasal vestibule up to the internal nasal valve. This study evaluates the influence of various sizes of notched indentations at the anterior nasal airway on local airflow pattern. A retrospective study involving 25 healthy individuals, each exhibiting at least one unilateral notched indentation (40 total airways). Each individual's notched indentation was quantified after subject-specific three-dimensional nasal airway reconstruction from radiographic images. Computational fluid dynamics modeling was used to simulate nasal inspiratory airflow in each nasal airway at 15 L/min. Localized airflow distributions passing through the inferior, middle, and superior regions were calculated at 15 cross sections. Notched indentation size ranged 1.75-86.84 mm2 (average = 22.37 mm2). At the anterior airway, notched size significantly correlated with inferior airflow volume (R = 0.32, p = 0.04) but not in the middle (R = 0.21, p = 0.20) or superior (R = 0.06, p = 0.70) regions, whereas middle and superior regional resistance values were significantly correlated with notched size (middle: R = 0.54, p < 0.001; superior: R = 0.41, p = 0.009). Medially, resistance at the middle region significantly correlated with notched size (R = 0.56, p < 0.001). At the posterior airway, airflow distributions through the inferior, middle, and superior regions demonstrated weak correlation with notched size (inferior: R = 0.24, p = 0.14, middle: R = 0.24, p = 0.13; superior:R = 0.03, p = 0.83), whereas resistance was significantly correlated in the middle and inferior regions (middle: R = 0.56, p < 0.001;inferior: R = 0.43, p = 0.006). Anterior nasal airway notched indentation size had significantly stronger influence on localized airflow volume through the anterior-inferior airway than other regions of the nasal passage. N/A Laryngoscope, 2024.

Phenotypic and genotypic characterization of methicillin resistant Staphylococcus aureus associated with pyogenic infections.

Staphylococcal infections are one of the major infectious diseases affecting globally in spite of advances in development of antimicrobial agents. Knowledge and awareness about the local pattern and prevalence of MRSA infections plays a key role in treatment. The aim of this study was to identify MRSA strains by phenotypic and genotypic methods and to analyze the antibiotic susceptibility pattern of MRSA strains from patients attending a tertiary care hospital. This study was conducted over a period of 1 year, where 296 isolates of Staphylococcus aureus were isolated from various clinical specimens. The isolated strains were examined for antibiotic susceptibility by the modified Kirby Bauer disc diffusion method. Methicillin resistance was detected by cefoxitin disk diffusion test. A total of 104 isolates were found to be MRSA and 192 were found to be MSSA. Among the 104 MRSA isolates, 10 strains that were multidrug resistant were subjected to 16S rRNA gene sequencing analysis. All the 10 strains had a 99% match with S. aureus strains that were responsible for causing some serious biofilm mediated clinical manifestations like cystic fibrosis and device mediated infections. The biofilms were quantified using crystal violet staining and their ability to produce biofilms was analyzed using scanning electron microscopy and matched with the Genbank. Hence these phylogenetic analysis aid in treating the patients and combating resistance to antibiotics.

SmoGSI: smoothed multiscale iterative geostatistical seismic inversion

Iterative geostatistical seismic inversion is a vital technique for estimating subsurface properties. However, a conventional single-scale strategy faces challenges in preserving large-scale geological features due to the limited restoration of the type of data template, and conventional multiscale strategies face the challenge in that it is easy to lose the large-scale structure that was previously preserved. This paper introduces a novel smoothed multiscale strategy aimed at overcoming these limitations, which comprises two components: Simulated annealing at the coarsest scale and smooth conversation between two scale grids. This approach offers a smoother way to simultaneously retain large-scale and small-scale structures, improving the overall accuracy of the subsurface property estimations. To validate the effectiveness of our approach, we apply it to both synthetic and real examples. The results show that the simulated annealing strategy at the coarsest scale grid explores the prior space and finds the best large structures to avoid the generated models trapping in the local minimal. Meanwhile, the smooth conversation strategy between two scale grids helps us avoid the damage of the coarser structure. It can be explained that a large weight is assigned to the coarse structure at the beginning of the conversation of two grid scale, reducing the likelihood of the replacing proposed local small-scale geological patterns, which prone to be accepted in the conventional multiscale strategies. The combination of the two strategies used in the proposed smoothed multiscale strategy displays a significant improvement in subsurface property estimation accuracy compared to traditional multiscale strategies. This innovation can have far-reaching implications, benefiting a wide range of geophysical applications and contributing to more accurate and informed decision-making in geological and hydrogeological assessments.

Gaussian-filtered Local Difference Pattern with kernel representation for person-independent facial expression recognition robust to noise and resolution

Gaussian-filtered Local Difference Pattern with kernel representation for person-independent facial expression recognition robust to noise and resolution

Recurrent selection shapes the genomic landscape of differentiation between a pair of host-specialized haplodiploids that diverged with gene flow.

Understanding the genetics of adaptation and speciation is critical for a complete picture of how biodiversity is generated and maintained. Heterogeneous genomic differentiation between diverging taxa is commonly documented, with genomic regions of high differentiation interpreted as resulting from differential gene flow, linked selection and reduced recombination rates. Disentangling the roles of each of these non-exclusive processes in shaping genome-wide patterns of divergence is challenging but will enhance our knowledge of the repeatability of genomic landscapes across taxa. Here, we combine whole-genome resequencing and genome feature data to investigate the processes shaping the genomic landscape of differentiation for a sister-species pair of haplodiploid pine sawflies, Neodiprion lecontei and Neodiprion pinetum. We find genome-wide correlations between genome features and summary statistics are consistent with pervasive linked selection, with patterns of diversity and divergence more consistently predicted by exon density and recombination rate than the neutral mutation rate (approximated by dS). We also find that both global and local patterns of FST, dXY and π provide strong support for recurrent selection as the primary selective process shaping variation across pine sawfly genomes, with some contribution from balancing selection and lineage-specific linked selection. Because inheritance patterns for haplodiploid genomes are analogous to those of sex chromosomes, we hypothesize that haplodiploids may be especially prone to recurrent selection, even if gene flow occurred throughout divergence. Overall, our study helps fill an important taxonomic gap in the genomic landscape literature and contributes to our understanding of the processes that shape genome-wide patterns of genetic variation.

Modeling the Spatial Dynamics and Human Mobility in Zika Virus Transmission: A Review

Spatial dynamics and human mobility significantly influence the transmission dynamics of vector-borne diseases like Zika virus. This paper reviews advancements in mathematical modeling that integrate spatial factors and human movement to enhance our understanding of disease transmission dynamics. Traditional SEIR (Susceptible-Exposed-Infectious-Recovered) models have been foundational but lack spatial heterogeneity and human mobility considerations. Recent studies have addressed these limitations by developing spatially explicit models that incorporate local interactions, environmental conditions, and human mobility patterns. By synthesizing findings from these studies, we identify strengths, limitations, and future research directions for improving predictive modeling of Zika virus transmission. Key insights include the amplifying effect of local interactions on disease spread and the critical role of human mobility networks in shaping transmission pathways. Addressing remaining challenges, such as refining spatial modeling techniques and integrating real-time data sources, will enhance the accuracy and applicability of spatial disease models in informing public health strategies.

A Retrospective Study (2019-2023) on the Prevalence and Antimicrobial Resistance of Isolates from Canine Clinical Samples Submitted to the University Veterinary Hospital in Stara Zagora, Bulgaria.

The identification of local susceptibility patterns is important for the elaboration of effective local antimicrobial use guidelines and improvement in treatment outcomes. This retrospective study investigated the prevalence of microbial pathogens in dogs over a five-year period (2019-2023) and their antimicrobial resistance patterns with an emphasis on multidrug-resistant strains on the basis of 896 swab samples submitted to the microbiological laboratory at the University Veterinary Hospital, Stara Zagora, Bulgaria. A total of 1247 strains-1046 bacteria and 201 yeasts-were isolated. An increased proportion of Staphylococcus spp. as an agent of infections in dogs along with significant decrease in the share of Streptococcus spp. (from 16.2% in 2019 to 7.7% in 2023) was found. The occurrence of Staphylococcus spp. in otitis externa increased from 53.4% in 2019 to 84.5% in 2023 (p < 0.0001). The resistance of Staphylococcus spp. isolates to amoxicillin/clavulanic acid and cephalexin increased significantly in 2023 vs. 2022. At the same time, increased susceptibility to amikacin was observed in 2023 vs. 2019. For Enterobacteriaceae, significantly decreased resistance against amikacin and marbofloxacin was demonstrated in 2023 compared to 2019. Multidrug resistance (MDR) was present in 405 of 1046 bacterial isolates (38.7%). More than 50% of streptococci and pseudomonads were MDR. Of the MDR staphylococci, 41.7% were isolated from skin lesions and 28.3% were isolated from otitis. More than half of the strains resistant to seven, eight and nine groups of antimicrobial drugs (AMDs) were from wounds/abscesses. The results highlighted the importance of regular local monitoring of the spread of bacterial strains in veterinary clinics and their susceptibility to AMDs with regard to successful therapy outcomes and control on MDR spread.

Drivers of Intraspecific Variation in Thermal Traits and Their Importance for Resilience to Global Change in Amphibians.

Intraspecific variation can be as great as variation across species, but the role of intraspecific variation in driving local and large-scale patterns is often overlooked, particularly in the field of thermal biology. In amphibians, which depend on environmental conditions and behavior to regulate body temperature, recognizing intraspecific thermal trait variation is essential to comprehensively understanding how global change impacts populations. Here, we examine the drivers of micro- and macrogeographical intraspecific thermal trait variation in amphibians. At the local scale, intraspecific variation can arise via changes in ontogeny, body size, and between the sexes, and developmental plasticity, acclimation, and maternal effects may modulate predictions of amphibian performance under future climate scenarios. At the macrogeographic scale, local adaptation in thermal traits may occur along latitudinal and elevational gradients, with seasonality and range-edge dynamics likely playing important roles in patterns that may impact future persistence. We also discuss the importance of considering disease as a factor affecting intraspecific variation in thermal traits and population resilience to climate change, given the impact of pathogens on thermal preferences and critical thermal limits of hosts. Finally, we make recommendations for future work in this area. Ultimately, our goal is to demonstrate why it is important for researchers to consider intraspecific variation to determine the resilience of amphibians to global change.

Water fluxes pattern growth and identity in shoot meristems

In multicellular organisms, tissue outgrowth creates a new water sink, modifying local hydraulic patterns. Although water fluxes are often considered passive by-products of development, their contribution to morphogenesis remains largely unexplored. Here, we mapped cell volumetric growth across the shoot apex in Arabidopsis thaliana. We found that, as organs grow, a subpopulation of cells at the organ-meristem boundary shrinks. Growth simulations using a model that integrates hydraulics and mechanics revealed water fluxes and predicted a water deficit for boundary cells. In planta, a water-soluble dye preferentially allocated to fast-growing tissues and failed to enter the boundary domain. Cell shrinkage next to fast-growing domains was also robust to different growth conditions and different topographies. Finally, a molecular signature of water deficit at the boundary confirmed our conclusion. Taken together, we propose that the differential sink strength of emerging organs prescribes the hydraulic patterns that define boundary domains at the shoot apex.