Seeing the invisible: from imagined to virtual urban landscapes

Urban ecosystems consist of infrastructure features working together to provide services for inhabitants. Infrastructure functions akin to an ecosystem, having dynamic relationships and interdependencies. However, with age, urban infrastructure can deteriorate and stop functioning. Additional pressures on infrastructure include urbanizing populations and a changing climate that exposes vulnerabilities. To manage the urban infrastructure ecosystem in a modernizing world, urban planners need to integrate a coordinated management plan for these co-located and dependent infrastructure features. To implement such a management practice, an improved method for communicating how these infrastructure features interact is needed. This study aims to define urban infrastructure as a system, identify the systematic barriers preventing implementation of a more coordinated management model, and develop a virtual reality tool to provide visualization of the spatial system dynamics of urban infrastructure. Data was collected from a stakeholder workshop that highlighted a lack of appreciation for the system dynamics of urban infrastructure. An urban ecology VR model was created to highlight the interconnectedness of infrastructure features. VR proved to be useful for communicating spatial information to urban stakeholders about the complexities of infrastructure ecology and the interactions between infrastructure features.https://doi.org/10.1016/j.cities.2019.102559Published versio

Geographically weighted regression models in estimating median home prices in towns of Massachusetts based on an urban sustainability framework

Housing is a key component of urban sustainability. The objective of this study was to assess the significance of key spatial determinants of median home price in towns in Massachusetts that impact sustainable growth. Our analysis investigates the presence or absence of spatial non-stationarity in the relationship between sustainable growth, measured in terms of the relationship between home values and various parameters including the amount of unprotected forest land, residential land, unemployment, education, vehicle ownership, accessibility to commuter rail stations, school district performance, and senior population. We use the standard geographically weighted regression (GWR) and Mixed GWR models to analyze the effects of spatial non-stationarity. Mixed GWR performed better than GWR in terms of Akaike Information Criterion (AIC) values. Our findings highlight the nature and spatial extent of the non-stationary vs. stationary qualities of key environmental and social determinants of median home price. Understanding the key determinants of housing values, such as valuation of green spaces, public school performance metrics, and proximity to public transport, enable towns to use different strategies of sustainable urban planning, while understanding urban housing determinants—such as unemployment and senior population—can help modify urban sustainable housing policies.This research is based upon work supported by the National Science Foundation under Grant No. CNH #1617053, CNH-S and Boston University Initiative on Cities, 2017. (1617053 - National Science Foundation; CNH-S; Boston University Initiative on Cities)https://doi.org/10.3390/su10041026Published versio

Characterizing the spatial determinants and prevention of malaria in Kenya

The United Nations' Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used-Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino-southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.Published versio

Spatializing Coupled Human and Natural System (CHANS)

Human sustainability is one of the most pressing issues of the 21st century. Coupled Human and Natural Systems (CHANS) offers a useful framework to focus on understanding the complex process and pattern that characterizes the dynamical interactions between human and natural systems. This dissertation research integrates the geospatial analysis into the CHANS framework from three perspectives: temporal, spatial, and organizational coupling. Using the temporal coupling aspect, we monitor the risk of deforestation and biodiversity threats from energy investments in Southeast Asia. We assess the energy investment evaluate changes to forest morphology and the risk to biodiversity. In terms of land cover change, we find that hydroelectric power plants tend to have more extensive biodiversity impacts than coal-fired plants, which are usually built within proximity to major population centers. Next, we explore spatial coupling by examining the spatial heterogeneity and homogeneity in home prices across Massachusetts, using Geographically Weighted Regression models with natural and socio-demographic variables. We discovered models that utilized spatial heterogeneity perform better. However, statistical tests of significance are required to determine the model specification to avoid over-fitting. In the fourth chapter, we examined a critical refugium for endangered fish species in East Africa by mapping the organizational dynamics of aquatic vegetation on Lake Kyoga, Uganda. A CHANS organizational coupling involving the natural infrastructure of aquatic vegetation and fishes can adversely impact endangered species and the surrounding human communities. Floating aquatic vegetation can protect the native fishes from predation by Nile Perch by creating hypoxic barriers between water bodies. We developed an algorithm to locate and identify various types of aquatic vegetation. Profiles of lakes are created to examine the spatiotemporal dynamics of refugia. The results are valuable in shaping strategies to conserve both fish species and human livelihoods. The fifth chapter explores emerging technologies, Virtual Reality, in communicating the complex CHANS coupling of green (trees) and gray infrastructure (gas pipelines). This chapter demonstrates the building of 3D urban landscapes from remote sensing data and the emerging use of VR to communicate, educate and empower the stakeholders on sustainability issues related to aging natural gas infrastructure and resulting methane emissions. This dissertation research aims to build a set of methodologies based on extensive geospatial data, spatially explicit models, and tools essential for operationalizing and monitoring CHANS in studies ranging from local to regional scales. Each application builds or revises a new model or algorithm to address a real-world CHANS problem

Leveraging big data and analytics to improve food, energy, and water system sustainability

Published versio

ECA-TFUnet: A U-shaped CNN-Transformer network with efficient channel attention for organ segmentation in anatomical sectional images of canines

Automated organ segmentation in anatomical sectional images of canines is crucial for clinical applications and the study of sectional anatomy. The manual delineation of organ boundaries by experts is a time-consuming and laborious task. However, semi-automatic segmentation methods have shown low segmentation accuracy. Deep learning-based CNN models lack the ability to establish long-range dependencies, leading to limited segmentation performance. Although Transformer-based models excel at establishing long-range dependencies, they face a limitation in capturing local detail information. To address these challenges, we propose a novel ECA-TFUnet model for organ segmentation in anatomical sectional images of canines. ECA-TFUnet model is a U-shaped CNN-Transformer network with Efficient Channel Attention, which fully combines the strengths of the Unet network and Transformer block. Specifically, The U-Net network is excellent at capturing detailed local information. The Transformer block is equipped in the first skip connection layer of the Unet network to effectively learn the global dependencies of different regions, which improves the representation ability of the model. Additionally, the Efficient Channel Attention Block is introduced to the Unet network to focus on more important channel information, further improving the robustness of the model. Furthermore, the mixed loss strategy is incorporated to alleviate the problem of class imbalance. Experimental results showed that the ECA-TFUnet model yielded 92.63% IoU, outperforming 11 state-of-the-art methods. To comprehensively evaluate the model performance, we also conducted experiments on a public dataset, which achieved 87.93% IoU, still superior to 11 state-of-the-art methods. Finally, we explored the use of a transfer learning strategy to provide good initialization parameters for the ECA-TFUnet model. We demonstrated that the ECA-TFUnet model exhibits superior segmentation performance on anatomical sectional images of canines, which has the potential for application in medical clinical diagnosis

Fibronectin leucine-rich transmembrane protein 2 drives monocyte differentiation into macrophages via the UNC5B-Akt/mTOR axis

Upon migrating into the tissues, hematopoietic stem cell (HSC)-derived monocytes differentiate into macrophages, playing a crucial role in determining innate immune responses towards external pathogens and internal stimuli. However, the regulatory mechanisms underlying monocyte-to-macrophage differentiation remain largely unexplored. Here we divulge a previously uncharacterized but essential role for an axon guidance molecule, fibronectin leucine-rich transmembrane protein 2 (FLRT2), in monocyte-to-macrophage maturation. FLRT2 is almost undetectable in human monocytic cell lines, human peripheral blood mononuclear cells (PBMCs), and mouse primary monocytes but significantly increases in fully differentiated macrophages. Myeloid-specific deletion of FLRT2 (Flrt2ΔMyel) contributes to decreased peritoneal monocyte-to-macrophage generation in mice in vivo, accompanied by impaired macrophage functions. Gain- and loss-of-function studies support the promoting effect of FLRT2 on THP-1 cell and human PBMC differentiation into macrophages. Mechanistically, FLRT2 directly interacts with Unc-5 netrin receptor B (UNC5B) via its extracellular domain (ECD) and activates Akt/mTOR signaling. In vivo administration of mTOR agonist MYH1485 reverses the impaired phenotypes observed in Flrt2ΔMyel mice. Together, these results identify FLRT2 as a novel pivotal endogenous regulator of monocyte differentiation into macrophages. Targeting the FLRT2/UNC5B-Akt/mTOR axis may provide potential therapeutic strategies directly relevant to human diseases associated with aberrant monocyte/macrophage differentiation

Fully endoscopic transforaminal discectomy for thoracolumbar junction disc herniation with or without calcification under general anesthesia: Technical notes and preliminary outcomes

ObjectiveTo evaluate the feasibility, safety, and outcomes of percutaneous endoscopic transforaminal discectomy (PETD) for thoracolumbar junction disc herniation (TLDH) with or without calcification.MethodsThis study included 12 patients diagnosed with TLDH with or without calcification who met the inclusion criteria and underwent surgery for PETD from January 2019 to December 2021. The mean patient age, operation time, hospitalization time, time in bed, and complications were recorded. Patients were followed up for at least 9 months. Visual analog scale (VAS) scores for low-back and leg or thoracic radicular pain and modified Japanese Orthopedic Association score (m-JOA) scores were preoperatively evaluated, at 1 day and 3, 6, and 12 months postoperatively or at last follow-up. The modified MacNab criteria were used to evaluate clinical efficacy at 12 months postoperatively or at last follow-up.ResultsThe mean patient age, operation time, hospitalization time, and time in bed were 53 ± 13.9 years, 101.3 ± 9.2 min, 4.5 ± 1.3 days, and 18.0 ± 7.0 h, respectively. The mean VAS scores of low-back and leg or thoracic radicular pain improved from 5.8 ± 1.5 and 6.5 ± 1.4 to 2.0 ± 0.9 and 1.3 ± 0.5, respectively (P < 0.05). The m-JOA score improved from 7.5 ± 1.2 to 10.0 ± 0.7 (P < 0.05). The overall excellent–good rate of the modified MacNab criteria was 83.3%. No severe complications occurred.ConclusionFully endoscopic transforaminal discectomy and ventral decompression under general anesthesia is a safe, feasible, effective, and minimally invasive method for treating herniated discs with or without calcification at thoracolumbar junction zone