Impact of different image reconstructions on PET quantification in non-small cell lung cancer: a comparison of adenocarcinoma and squamous cell carcinoma

OBJECTIVE: Positron emission tomography (PET) using 18F-fluordeoxyglucose (F-FDG) is an established imaging modality for tumor staging in patients with non-small cell lung cancer (NSCLC). There is a growing interest in using F-FDG PET for therapy response assessment in NSCLC which relies on quantitative PET parameters such as standardized uptake values (SUV). Different reconstruction algorithms in PET may affect SUV. We sought to determine the variation of SUV in patients with NSCLC when using ordered subset expectation maximization (OSEM) and block sequential regularized expectation maximization (BSREM) in latest-generation digital PET/CT, including a subanalysis for adenocarcinoma and squamous cell carcinoma. METHODS: A total of 58 patients (34 = adenocarcinoma, 24 = squamous cell carcinoma) that underwent a clinically indicated F-FDG PET/CT for staging were reviewed. PET images were reconstructed with OSEM and BSREM reconstruction with noise penalty strength β-levels of 350, 450, 600, 800 and 1200. Lung tumors maximum standardized uptake value (SUV) were compared. RESULTS: Lung tumors SUV were significantly lower in adenocarcinomas compared to squamous cell carcinomas in all reconstructions evaluated (all p 0.05). There was a statistically significant difference of the relative increase of SUV in adenocarcinoma (mean + 34.8%) and squamous cell carcinoma (mean 23.4%), when using BSREM instead of OSEM (p < 0.05). CONCLUSIONS: In NSCLC the relative change of SUV when using BSREM instead of OSEM is significantly higher in adenocarcinoma as compared to squamous cell carcinoma. ADVANCES IN KNOWLEDGE: The impact of BSREM on SUV may vary in different histological subtypes of NSCLC. This highlights the importance for careful standardisation of β-value used for serial F-FDG PET scans when following-up NSCLC patients

Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier

Sen2-Agri is a software system that was developed to facilitate the use of multi-temporal satellite data for crop classification with a random forest (RF) classifier in an operational setting. It automatically ingests and processes Sentinel-2 and LandSat 8 images. Our goal was to provide practitioners with recommendations for the best sample size and composition. The study area was located in the Yaqui Valley in Mexico. Using polygons of more than 6000 labeled crop fields, we prepared data sets for training, in which the nine crops had an equal or proportional representation, called Equal or Ratio, respectively. Increasing the size of the training set improved the overall accuracy (OA). Gains became marginal once the total number of fields approximated 500 or 40 to 45 fields per crop type. Equal achieved slightly higher OAs than Ratio for a given number of fields. However, recall and F-scores of the individual crops tended to be higher for Ratio than for Equal. The high number of wheat fields in the Ratio scenarios, ranging from 275 to 2128, produced a more accurate classification of wheat than the maximal 80 fields of Equal. This resulted in a higher recall for wheat in the Ratio than in the Equal scenarios, which in turn limited the errors of commission of the non-wheat crops. Thus, a proportional representation of the crops in the training data is preferable and yields better accuracies, even for the minority crops

Strategic & Applied Research & Coordination in Action: Climate Services for Resilient Development (CSRD) in South Asia

A global partnership that is aligned with the Global Framework for Climate Services, Climate Services for Resilient Development (CSRD) works to link climate science, data streams, decision support tools, and training with decision-makers in developing countries. CSRD is led by the United States Government and is supported by the UK Government Department for International Development (DFID), UK Meteorological Office, ESRI, Google, the Inter-American Development Bank, the Asian Development Bank, and the American Red Cross. Led by the International Maize and Wheat Improvement Center (CIMMYT), the CSRD initiative in South Asia works with partners to conduct applied research and facilitate the use of climate information to reduce risk for smallholder farmers. This report details activities of the CSRD project in South Asia during 2018, with emphasis on the second half of 201

Climate Services for Resilient Development in South Asia and Bangladesh: Semi-Annual and Inception Period Report April 2017

Developing countries are at considerable risk from climate variability and climate change, both of which threaten poverty reduction and development efforts. The Climate Services for Resilient Development (CSRD) partnership is led by the United States Government has developed a consortium of global leaders in science, technology and development finance to assist at-risk nations to adapt to these problems. CSRD is aligned with the the Global Framework for Climate Services and works in Bangladesh, Ethiopia, and Colombia to creating and provide timely and useful climate data, information, tools, and services. Within South Asia, efforts to develop agricultural climate services under CSRD are led by the International Maize and Wheat Improvement Center (CIMMYT). CSRD in turn works to support Investment Options Paper (IOP) for Climate Services for Resilient Development in Bangladesh, compiled by the Asian Development Bank (ADB) in 2016. CSRD’s core objectives are to prepare farmers, extension services, and agricultural policy makers with actionable climate information and crop management advisories to reduce agricultural production risks and to increase the resilience of smallholder farming communities. This report summarizes CSRD activities, achievements, and challenges during the project’s inception phase (from the end of November 2017 through April of 2017)

Climate Services for Resilient Development in South Asia Mid-Term Report, January - June 2018

Aligned with the Global Framework for Climate Services, Climate Services for Resilient Development (CSRD) is a global partnership that works to link climate science, data streams, decision support tools, and training with decision-makers in developing countries. CSRD is led by the United States Government and is supported by the UK Government Department for International Development (DFID), UK Meteorological Office, ESRI, Google, the Inter-American Development Bank, the Asian Development Bank, and the American Red Cross. Led by the International Maize and Wheat Improvement Center (CIMMYT), the CSRD initiative in South Asia implements applied research and facilitates an expanding network of partners assure that actionable climate information and crop management advisories can be generated, refined, and delivered to smallholder farmers. This report details activities of the CSRD project in South Asia during the first six months of 2018

Yield-independent variation in grain nitrogen and phosphorus concentration among Ethiopian wheats

New semiDwarf wheat (Triticum aestivum L.) cultivars and new land management practices for Vertisols are being introduced in Ethiopia. Our objectives were to (i) determine the variation of N and P contents and concentration in the grain and whether these are related to grain yield, (ii) test cultivar response to different fertility levels, and (iii) assess component traits of N and P yield. Five bread wheat cultivars and three durum wheat (Triticum durum Desf.) cultivars were sown in Ex1 at three locations in Ethiopia on two dates. In Ex2, seven of these cultivars were grown on a P-deficient soil at four N levels (0, 20.5, 41, 61.5 kg N ha-1) and four P levels (0, 10, 20, 30 kg P ha-1); in Ex3, two cultivars were grown in all possible combinations of the same four N and P levels. Grain yields did not differ among cultivars, but significant variations were found for total shoot N and P, grain N and P yield, and grain N and P concentration. Cultivar differences in these traits were fairly consistent across the treatments and were corroborated by Ex3. The N and P concentrations in the grain were not related to grain yield (r=0.36 NS for N; r=0.28 NS for P). There was a positive association between grain N and P concentrations in Ex1 (r=0.66; P=0.001). However, postanthesis accumulation of N was more closely related to postanthesis dry matter accumulation (r=0.84; P<0.05) than to the postanthesis accumulation of P (r=0.56 NS). Total shoot P varied by as much as 50 percent. Thus, cultivar choice is an important factor determining removal of P from the soil

Satellite imagery for high-throughput phenotyping in breeding plots

Advances in breeding efforts to increase the rate of genetic gains and enhance crop resilience to climate change have been limited by the procedure and costs of phenotyping methods. The recent rapid development of sensors, image-processing technology, and data-analysis has provided opportunities for multiple scales phenotyping methods and systems, including satellite imagery. Among these platforms, satellite imagery may represent one of the ultimate approaches to remotely monitor trials and nurseries planted in multiple locations while standardizing protocols and reducing costs. However, the deployment of satellite-based phenotyping in breeding trials has largely been limited by low spatial resolution of satellite images. The advent of a new generation of high-resolution satellites may finally overcome these limitations. The SkySat constellation started offering multispectral images at a 0.5 m resolution since 2020. In this communication we present a case study on the use of time series SkySat images to estimate NDVI from wheat and maize breeding plots encompassing different sizes and spacing. We evaluated the reliability of the calculated NDVI and tested its capacity to detect seasonal changes and genotypic differences. We discuss the advantages, limitations, and perspectives of this approach for high-throughput phenotyping in breeding programs