Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery

We introduce a novel deep learning method for detection of individual trees in urban environments using high-resolution multispectral aerial imagery. We use a convolutional neural network to regress a confidence map indicating the locations of individual trees, which are localized using a peak finding algorithm. Our method provides complete spatial coverage by detecting trees in both public and private spaces, and can scale to very large areas. We performed a thorough evaluation of our method, supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. We trained our model on data from Southern California, and achieved a precision of 73.6% and recall of 73.3% using test data from this region. We generally observed similar precision and slightly lower recall when extrapolating to other California climate zones and image capture dates. We used our method to produce a map of trees in the entire urban forest of California, and estimated the total number of urban trees in California to be about 43.5 million. Our study indicates the potential for deep learning methods to support future urban forestry studies at unprecedented scales

Cancer Incidence in World Trade Center Rescue and Recovery Workers, 2001–2008

Background: World Trade Center (WTC) rescue and recovery workers were exposed to a complex mix of pollutants and carcinogens. Objective: The purpose of this investigation was to evaluate cancer incidence in responders during the first 7 years after 11 September 2001. Methods: Cancers among 20,984 consented participants in the WTC Health Program were identified through linkage to state tumor registries in New York, New Jersey, Connecticut, and Pennsylvania. Standardized incidence ratios (SIRs) were calculated to compare cancers diagnosed in responders to predicted numbers for the general population. Multivariate regression models were used to estimate associations with degree of exposure. Results: A total of 575 cancers were diagnosed in 552 individuals. Increases above registry-based expectations were noted for all cancer sites combined (SIR = 1.15; 95% CI: 1.06, 1.25), thyroid cancer (SIR = 2.39; 95% CI: 1.70, 3.27), prostate cancer (SIR = 1.21; 95% CI: 1.01, 1.44), combined hematopoietic and lymphoid cancers (SIR = 1.36; 95% CI: 1.07, 1.71), and soft tissue cancers (SIR = 2.26; 95% CI: 1.13, 4.05). When restricted to 302 cancers diagnosed ≥ 6 months after enrollment, the SIR for all cancers decreased to 1.06 (95% CI: 0.94, 1.18), but thyroid and prostate cancer diagnoses remained greater than expected. All cancers combined were increased in very highly exposed responders and among those exposed to significant amounts of dust, compared with responders who reported lower levels of exposure. Conclusion: Estimates should be interpreted with caution given the short follow-up and long latency period for most cancers, the intensive medical surveillance of this cohort, and the small numbers of cancers at specific sites. However, our findings highlight the need for continued follow-up and surveillance of WTC responders

Individual tree detection in large-scale urban environments using high-resolution multispectral imagery

Systematic maps of urban forests are useful for regional planners and ecologists to understand the spatial distribution of trees in cities. However, manually-created urban forest inventories are expensive and time-consuming to create and typically do not provide coverage of private land. Toward the goal of automating urban forest inventory through machine learning techniques, we performed a comparative study of methods for automatically detecting and localizing trees in multispectral aerial imagery of urban environments, and introduce a novel method based on convolutional neural network regression. Our evaluation is supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. Our method outperforms previous methods, achieving 73.6% precision and 73.3% recall when trained and tested in Southern California, and 76.5% precision 72.0% recall when trained and tested across the entire state. To demonstrate the scalability of the technique, we produced the first map of trees across the entire urban forest of California. The map we produced provides important data for the planning and management of California’s urban forest, and establishes a proven methodology for potentially producing similar maps nationally and globally in the future

Cancer incidence in World Trade Center rescue and recovery workers, 2001-2008

Background: World Trade Center (WTC) rescue and recovery workers were exposed to a complex mix of pollutants and carcinogens. Objective: The purpose of this investigation was to evaluate cancer incidence in responders during the first 7 years after 11 September 2001. Methods: Cancers among 20,984 consented participants in the WTC Health Program were identified through linkage to state tumor registries in New York, New Jersey, Connecticut, and Pennsylvania. Standardized incidence ratios (SIRs) were calculated to compare cancers diagnosed in responders to predicted numbers for the general population. Multivariate regression models were used to estimate associations with degree of exposure. Results: A total of 575 cancers were diagnosed in 552 individuals. Increases above registry-based expectations were noted for all cancer sites combined (SIR = 1.15; 95% CI: 1.06, 1.25), thyroid cancer (SIR = 2.39; 95% CI: 1.70, 3.27), prostate cancer (SIR = 1.21; 95% CI: 1.01, 1.44), combined hematopoietic and lymphoid cancers (SIR = 1.36; 95% CI: 1.07, 1.71), and soft tissue cancers (SIR = 2.26; 95% CI: 1.13, 4.05). When restricted to 302 cancers diagnosed = 6 months after enrollment, the SIR for all cancers decreased to 1.06 (95% CI: 0.94, 1.18), but thyroid and prostate cancer diagnoses remained greater than expected. All cancers combined were increased in very highly exposed responders and among those exposed to significant amounts of dust, compared with responders who reported lower levels of exposure. Conclusion: Estimates should be interpreted with caution given the short follow-up and long latency period for most cancers, the intensive medical surveillance of this cohort, and the small numbers of cancers at specific sites. However, our findings highlight the need for continued follow-up and surveillance of WTC responders

Post-hospitalization experiences of older adults diagnosed with diabetes: “It was daunting!”

Multimorbidity combined with geriatric syndromes in older adults with diabetes exacerbate their risks for poor post-discharge outcomes. The purpose of this study was to examine self-described hospital-to-home transition challenges encountered by older adults with a diagnosis of diabetes within the first 30 days following discharge. The qualitative responses for this paper emerged from a larger mixed methods study (n = 96) in which participants provided free responses specifying transition challenges during follow-up telephone interviews on the 7th day (n = 67) and 30th day (n = 55) post-discharge. Using inductive content analysis techniques four major themes emerged: a) “The daily stuff is difficult”; b) engineering care at home is complex; c) “life is very difficult”; and d) managing complex health problems is difficult. Findings suggest existing system-level metrics such as readmission rates fail to capture the complex and dynamic interplay of personal, family and social factors which complicate hospital-to-home transitions of older adults with pre-existing diabetes

Eosinophil Depletion with Benralizumab for Eosinophilic Esophagitis

Background: Benralizumab is an eosinophil-depleting anti-interleukin-5 receptor α monoclonal antibody. The efficacy and safety of benralizumab in patients with eosinophilic esophagitis are unclear. Methods: In a phase 3, multicenter, double-blind, randomized, placebo-controlled trial, we assigned patients 12 to 65 years of age with symptomatic and histologically active eosinophilic esophagitis in a 1:1 ratio to receive subcutaneous benralizumab (30 mg) or placebo every 4 weeks. The two primary efficacy end points were histologic response (≤6 eosinophils per high-power field) and the change from baseline in the score on the Dysphagia Symptom Questionnaire (DSQ; range, 0 to 84, with higher scores indicating more frequent or severe dysphagia) at week 24. Results: A total of 211 patients underwent randomization: 104 were assigned to receive benralizumab, and 107 were assigned to receive placebo. At week 24, more patients had a histologic response with benralizumab than with placebo (87.4% vs. 6.5%; difference, 80.8 percentage points; 95% confidence interval [CI], 72.9 to 88.8; P<0.001). However, the change from baseline in the DSQ score did not differ significantly between the two groups (difference in least-squares means, 3.0 points; 95% CI, -1.4 to 7.4; P = 0.18). There was no substantial between-group difference in the change from baseline in the Eosinophilic Esophagitis Endoscopic Reference Score, which reflects endoscopic abnormalities. Adverse events were reported in 64.1% of the patients in the benralizumab group and in 61.7% of those in the placebo group. No patients discontinued the trial because of adverse events. Conclusions: In this trial involving patients 12 to 65 years of age with eosinophilic esophagitis, a histologic response (≤6 eosinophils per high-power field) occurred in significantly more patients in the benralizumab group than in the placebo group. However, treatment with benralizumab did not result in fewer or less severe dysphagia symptoms than placebo. (Funded by AstraZeneca; MESSINA ClinicalTrials.gov number, NCT04543409.)