Washtenaw County Green Infrastructure Development Analysis

A technical report provided to the Washtenaw County Water Resources Office as a final result of the research conducted by the Winter 2021 CLIMATE 592 course.Rain gardens and other types of green infrastructure help reduce surface water runoff from entering nearby water sources. Our goal is to locate areas that would benefit from the installation of green infrastructure, based on the three priority areas of water quality, equity, and wildlife, in the context of a changing climate. In this report, we show our analysis determining ideal locations in Washtenaw County for the development of green infrastructure based on the three priority areas. We incorporate parameters such as land-use tract areas, poverty status data, roadways, precipitation intensity, degraded creeksheds, etc. in order to calculate preference values that will show where to install green infrastructure. Final maps for each of the three priority areas are shown. We also discuss how climate change may affect the locations we have identified in the long term. Green infrastructure has numerous environmental, social, and financial benefits and therefore it may be beneficial for the Washtenaw County Water Resources Office to consider coordinating with SEMCOG and their green infrastructure efforts to increase resilience and avoid maladaptation.http://deepblue.lib.umich.edu/bitstream/2027.42/176118/1/Climate592_FinalReport_2021.pdfDescription of Climate592_FinalReport_2021.pdf : Technical ReportSEL

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime

Best Practices Recommendations for Diagnostic Immunohistochemistry in Lung Cancer

Since the 2015 WHO classification was introduced into clinical practice, immunohistochemistry (IHC) has figured prominently in lung cancer diagnosis. In addition to distinction of small cell versus non–small cell carcinoma, patients’ treatment of choice is directly linked to histologic subtypes of non–small cell carcinoma, which pertains to IHC results, particularly for poorly differentiated tumors. The use of IHC has improved diagnostic accuracy in the classification of lung carcinoma, but the interpretation of IHC results remains challenging in some instances. Also, pathologists must be aware of many interpretation pitfalls, and the use of IHC should be efficient to spare the tissue for molecular testing. The International Association for the Study of Lung Cancer Pathology Committee received questions on practical application and interpretation of IHC in lung cancer diagnosis. After discussions in several International Association for the Study of Lung Cancer Pathology Committee meetings, the issues and caveats were summarized in terms of 11 key questions covering common and important diagnostic situations in a daily clinical practice with some relevant challenging queries. The questions cover topics such as the best IHC markers for distinguishing NSCLC subtypes, differences in thyroid transcription factor 1 clones, and the utility of IHC in diagnosing uncommon subtypes of lung cancer and distinguishing primary from metastatic tumors. This article provides answers and explanations for the key questions about the use of IHC in diagnosis of lung carcinoma, representing viewpoints of experts in thoracic pathology that should assist the community in the appropriate use of IHC in diagnostic pathology

PD-L1 Testing for Lung Cancer in 2019: Perspective From the IASLC Pathology Committee

The recent development of immune checkpoint inhibitors (ICIs) has led to promising advances in the treatment of patients with NSCLC and SCLC with advanced or metastatic disease. Most ICIs target programmed cell death protein 1 (PD-1) or programmed death ligand 1 (PD-L1) axis with the aim of restoring antitumor immunity. Multiple clinical trials for ICIs have evaluated a predictive value of PD-L1 protein expression in tumor cells and tumor-infiltrating immune cells (ICs) by immunohistochemistry (IHC), for which different assays with specific IHC platforms were applied. Of those, some PD-L1 IHC assays have been validated for the prescription of the corresponding agent for first- or second-line treatment. However, not all laboratories are equipped with the dedicated platforms, and many laboratories have set up in-house or laboratory-developed tests that are more affordable than the generally expensive clinical trial–validated assays. Although PD-L1 IHC test is now deployed in most pathology laboratories, its appropriate implementation and interpretation are critical as a predictive biomarker and can be challenging owing to the multiple antibody clones and platforms or assays available and given the typically small size of samples provided. Because many articles have been published since the issue of the IASLC Atlas of PD-L1 Immunohistochemistry Testing in Lung Cancer, this review by the IASLC Pathology Committee provides updates on the indications of ICIs for lung cancer in 2019 and discusses important considerations on preanalytical, analytical, and postanalytical aspects of PD-L1 IHC testing, including specimen type, validation of assays, external quality assurance, and training

The International Association for the Study of Lung Cancer Global Survey on Programmed Death-Ligand 1 Testing for NSCLC

Introduction: Programmed death-ligand 1 (PD-L1) immunohistochemistry (IHC) is required to determine the eligibility for pembrolizumab monotherapy in advanced NSCLC worldwide and for several other indications depending on the country. Four assays have been approved/ Communauté Européene–In vitro Diagnostic (CV-IVD)–marked, but PD-L1 IHC seems diversely implemented across regions and laboratories with the application of laboratory-developed tests (LDTs). Method: To assess the practice of PD-L1 IHC and identify issues and disparities, the International Association for the Study of Lung Cancer Pathology Committee conducted a global survey for pathologists from January to May 2019, comprising multiple questions on preanalytical, analytical, and postanalytical conditions. Result: A total of 344 pathologists from 64 countries participated with 41% from Europe, 24% from North America, and 18% from Asia. Besides biopsies and resections, cellblocks were used by 75% of the participants and smears by 11%. The clone 22C3 was most often used (69%) followed by SP263 (51%). They were applied as an LDT by 40% and 30% of the users, respectively, and 76% of the participants developed at least one LDT. Half of the participants reported a turnaround time of less than or equal to 2 days, whereas 13% reported that of greater than or equal to 5 days. In addition, quality assurance (QA), formal training for scoring, and standardized reporting were not implemented by 18%, 16%, and 14% of the participants, respectively. Conclusions: Heterogeneity in PD-L1 testing is marked across regions and laboratories in terms of antibody clones, IHC assays, samples, turnaround times, and QA measures. The lack of QA, formal training, and standardized reporting stated by a considerable minority identifies a need for additional QA measures and training opportunities