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Authors = Adam J. Mathews

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18 pages, 4491 KiB  
Article
Spatiotemporal Analysis of Urban Growth and PM2.5 Concentrations in Sylhet, Bangladesh
by Mizanur Rahman, Lei Meng, Adam J. Mathews and Steven Bertman
Atmosphere 2024, 15(11), 1305; https://doi.org/10.3390/atmos15111305 - 30 Oct 2024
Cited by 3 | Viewed by 2653
Abstract
Environmental impacts of urbanization, such as increased air pollution, pose significant challenges for developing countries. This study examines land cover changes and their relationship with particulate matter 2.5 (PM2.5) concentrations in two upazilas of Bangladesh from 2001 to 2019 using GIS [...] Read more.
Environmental impacts of urbanization, such as increased air pollution, pose significant challenges for developing countries. This study examines land cover changes and their relationship with particulate matter 2.5 (PM2.5) concentrations in two upazilas of Bangladesh from 2001 to 2019 using GIS and remote sensing techniques. Results show significant urban expansion (i.e., increase in built-up area) in both upazilas, corresponding with increasing PM2.5 levels. Linear regression reveals correlations between land cover types and PM2.5 levels. Mixed forests and waterbodies tend to be negatively associated with PM2.5 concentrations; on the other hand, built-up and barren land show a positive correlation with PM2.5. The most significant increase in built-up land and PM2.5 was in Madhabpur, indicating urgent environmental and health issues. This study emphasizes the critical role of sustainable urban planning and environmental conservation in mitigating urbanization’s adverse effects on air quality, advocating for preserving natural landscapes to maintain ecological balance, protecting urban health, and providing policymakers with insights to develop strategies addressing urban expansion and air pollution. Full article
(This article belongs to the Special Issue Air Pollution in Asia)
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14 pages, 2532 KiB  
Article
Gluten Induces Subtle Histological Changes in Duodenal Mucosa of Patients with Non-Coeliac Gluten Sensitivity: A Multicentre Study
by Kamran Rostami, Arzu Ensari, Michael N. Marsh, Amitabh Srivastava, Vincenzo Villanacci, Antonio Carroccio, Hamid Asadzadeh Aghdaei, Julio C. Bai, Gabrio Bassotti, Gabriel Becheanu, Phoenix Bell, Camillo Di Bella, Anna Maria Bozzola, Moris Cadei, Giovanni Casella, Carlo Catassi, Carolina Ciacci, Delia Gabriela Apostol Ciobanu, Simon S. Cross, Mihai Danciu, Prasenjit Das, Rachele Del Sordo, Michael Drage, Luca Elli, Alessio Fasano, Ada Maria Florena, Nicola Fusco, James J. Going, Stefano Guandalini, Catherine E. Hagen, David T. S. Hayman, Sauid Ishaq, Hilary Jericho, Melanie Johncilla, Matt Johnson, Katri Kaukinen, Adam Levene, Sarah Liptrot, Laura Lu, Govind K. Makharia, Sherly Mathews, Giuseppe Mazzarella, Roxana Maxim, Khun La Win Myint, Hamid Mohaghegh-Shalmani, Afshin Moradi, Chris J. J. Mulder, Ronnie Ray, Chiara Ricci, Mohammad Rostami-Nejad, Anna Sapone, David S. Sanders, Juha Taavela, Umberto Volta, Marjorie Walker and Mohammad Derakhshanadd Show full author list remove Hide full author list
Nutrients 2022, 14(12), 2487; https://doi.org/10.3390/nu14122487 - 15 Jun 2022
Cited by 28 | Viewed by 8768
Abstract
Background: Histological changes induced by gluten in the duodenal mucosa of patients with non-coeliac gluten sensitivity (NCGS) are poorly defined. Objectives: To evaluate the structural and inflammatory features of NCGS compared to controls and coeliac disease (CeD) with milder enteropathy (Marsh I-II). Methods: [...] Read more.
Background: Histological changes induced by gluten in the duodenal mucosa of patients with non-coeliac gluten sensitivity (NCGS) are poorly defined. Objectives: To evaluate the structural and inflammatory features of NCGS compared to controls and coeliac disease (CeD) with milder enteropathy (Marsh I-II). Methods: Well-oriented biopsies of 262 control cases with normal gastroscopy and histologic findings, 261 CeD, and 175 NCGS biopsies from 9 contributing countries were examined. Villus height (VH, in μm), crypt depth (CrD, in μm), villus-to-crypt ratios (VCR), IELs (intraepithelial lymphocytes/100 enterocytes), and other relevant histological, serologic, and demographic parameters were quantified. Results: The median VH in NCGS was significantly shorter (600, IQR: 400–705) than controls (900, IQR: 667–1112) (p < 0.001). NCGS patients with Marsh I-II had similar VH and VCR to CeD [465 µm (IQR: 390–620) vs. 427 µm (IQR: 348–569, p = 0·176)]. The VCR in NCGS with Marsh 0 was lower than controls (p < 0.001). The median IEL in NCGS with Marsh 0 was higher than controls (23.0 vs. 13.7, p < 0.001). To distinguish Marsh 0 NCGS from controls, an IEL cut-off of 14 showed 79% sensitivity and 55% specificity. IEL densities in Marsh I-II NCGS and CeD groups were similar. Conclusion: NCGS duodenal mucosa exhibits distinctive changes consistent with an intestinal response to luminal antigens, even at the Marsh 0 stage of villus architecture. Full article
(This article belongs to the Section Clinical Nutrition)
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13 pages, 4031 KiB  
Article
Assessment of Image-Based Point Cloud Products to Generate a Bare Earth Surface and Estimate Canopy Heights in a Woodland Ecosystem
by Jennifer L. R. Jensen and Adam J. Mathews
Remote Sens. 2016, 8(1), 50; https://doi.org/10.3390/rs8010050 - 8 Jan 2016
Cited by 113 | Viewed by 15189
Abstract
We examine the utility of Structure from Motion (SfM) point cloud products to generate a digital terrain model (DTM) and estimate canopy heights in a woodland ecosystem in the Texas Hill Country, USA. Very high spatial resolution images were acquired with a Canon [...] Read more.
We examine the utility of Structure from Motion (SfM) point cloud products to generate a digital terrain model (DTM) and estimate canopy heights in a woodland ecosystem in the Texas Hill Country, USA. Very high spatial resolution images were acquired with a Canon PowerShot A800 digital camera mounted on an unmanned aerial system. Image mosaicking and dense point matching were accomplished using Agisoft PhotoScan. The resulting point cloud was classified according to ground/non-ground classes and used to interpolate a high resolution DTM which was both compared to a DTM from an existing lidar dataset and used to model vegetation height for fifteen 20 × 20 m plots. Differences in the interpolated DTM surfaces demonstrate that the SfM surface overestimates lidar-modeled ground height with a mean difference of 0.19 m and standard deviation of 0.66 m. Height estimates obtained solely from SfM products were successful with R2 values of 0.91, 0.90, and 0.89 for mean, median, and maximum canopy height, respectively. Use of the lidar DTM in the analyses resulted in R2 values of 0.90, 0.89, and 0.89 for mean, median, and maximum canopy height. Our results suggest that SfM-derived point cloud products function as well as lidar data for estimating vegetation canopy height for our specific study area. Full article
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20 pages, 3079 KiB  
Article
Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud
by Adam J. Mathews and Jennifer L. R. Jensen
Remote Sens. 2013, 5(5), 2164-2183; https://doi.org/10.3390/rs5052164 - 7 May 2013
Cited by 216 | Viewed by 18668
Abstract
This study explores the use of structure from motion (SfM), a computer vision technique, to model vine canopy structure at a study vineyard in the Texas Hill Country. Using an unmanned aerial vehicle (UAV) and a digital camera, 201 aerial images (nadir and [...] Read more.
This study explores the use of structure from motion (SfM), a computer vision technique, to model vine canopy structure at a study vineyard in the Texas Hill Country. Using an unmanned aerial vehicle (UAV) and a digital camera, 201 aerial images (nadir and oblique) were collected and used to create a SfM point cloud. All points were classified as ground or non-ground points. Non-ground points, presumably representing vegetation and other above ground objects, were used to create visualizations of the study vineyard blocks. Further, the relationship between non-ground points in close proximity to 67 sample vines and collected leaf area index (LAI) measurements for those same vines was also explored. Points near sampled vines were extracted from which several metrics were calculated and input into a stepwise regression model to attempt to predict LAI. This analysis resulted in a moderate R2 value of 0.567, accounting for 57 percent of the variation of LAISQRT using six predictor variables. These results provide further justification for SfM datasets to provide three-dimensional datasets necessary for vegetation structure visualization and biophysical modeling over areas of smaller extent. Additionally, SfM datasets can provide an increased temporal resolution compared to traditional three-dimensional datasets like those captured by light detection and ranging (lidar). Full article
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