Quantitative Analysis of the MGMT Methylation Status of Glioblastomas in Light of the 2021 WHO Classification.

Glioblastomas with methylation of the promoter region of the O(6)-methylguanine-DNA methyltransferase (MGMT) gene exhibit increased sensitivity to alkylating chemotherapy. Quantitative assessment of the MGMT promoter methylation status might provide additional prognostic information. The aim of our study was to determine a quantitative methylation threshold for better survival among patients with glioblastomas. We included consecutive patients ≥18 years treated at our department between 11/2010 and 08/2018 for a glioblastoma, IDH wildtype, undergoing quantitative MGMT promoter methylation analysis. The primary endpoint was overall survival. A total of 321 patients were included. Median overall survival was 12.6 months. Kaplan-Meier and adjusted Cox regression analysis showed better survival for the groups with 16-30%, 31-60%, and 61-100% methylation. In contrast, survival in the group with 1-15% methylation was similar to those with unmethylated promoter regions. A secondary analysis confirmed this threshold. Better survival is observed in patients with glioblastomas with ≥16% methylation of the MGMT promoter region than with <16% methylation. Survival with tumors with 1-15% methylation is similar to with unmethylated tumors. Above 16% methylation, we found no additional benefit with increasing methylation

Surgical management and outcome of newly diagnosed glioblastoma without contrast enhancement (<i>low-grade appearance</i>):a report of the RANO <i>resect </i>group

BackgroundResection of the contrast-enhancing (CE) tumor represents the standard of care in newly diagnosed glioblastoma. However, some tumors ultimately diagnosed as glioblastoma lack contrast enhancement and have a ‘low-grade appearance’ on imaging (non-CE glioblastoma). We aimed to (a) volumetrically define the value of non-CE tumor resection in the absence of contrast enhancement, and to (b) delineate outcome differences between glioblastoma patients with and without contrast enhancement.MethodsThe RANO resect group retrospectively compiled a global, eight-center cohort of patients with newly diagnosed glioblastoma per WHO 2021 classification. The associations between postoperative tumor volumes and outcome were analyzed. Propensity score-matched analyses were constructed to compare glioblastomas with and without contrast enhancement.ResultsAmong 1323 newly diagnosed IDH-wildtype glioblastomas, we identified 98 patients (7.4%) without contrast enhancement. In such patients, smaller postoperative tumor volumes were associated with more favorable outcome. There was an exponential increase in risk for death with larger residual non-CE tumor. Accordingly, extensive resection was associated with improved survival compared to lesion biopsy. These findings were retained on a multivariable analysis adjusting for demographic and clinical markers. Compared to CE glioblastoma, patients with non-CE glioblastoma had a more favorable clinical profile and superior outcome as confirmed in propensity score analyses by matching the patients with non-CE glioblastoma to patients with CE glioblastoma using a large set of clinical variables.ConclusionsThe absence of contrast enhancement characterizes a less aggressive clinical phenotype of IDH-wildtype glioblastomas. Maximal resection of non-CE tumors has prognostic implications and translates into favorable outcome

The ALFAM2 database on ammonia emission from field-applied manure: Description and illustrative analysis

peer-reviewedAmmonia (NH3) emission from animal manure contributes to air pollution and ecosystem degradation, and the loss of reactive nitrogen (N) from agricultural systems. Estimates of NH3 emission are necessary for national inventories and nutrient management, and NH3 emission from field-applied manure has been measured in many studies over the past few decades. In this work, we facilitate the use of these data by collecting and organizing them in the ALFAM2 database. In this paper we describe the development of the database and summarise its contents, quantify effects of application methods and other variables on emission using a data subset, and discuss challenges for data analysis and model development. The database contains measurements of emission, manure and soil properties, weather, application technique, and other variables for 1895 plots from 22 research institutes in 12 countries. Data on five manure types (cattle, pig, mink, poultry, mixed, as well as sludge and “other”) applied to three types of crops (grass, small grains, maize, as well as stubble and bare soil) are included. Application methods represented in the database include broadcast, trailing hose, trailing shoe (narrow band application), and open slot injection. Cattle manure application to grassland was the most common combination, and analysis of this subset (with dry matter (DM) limited to <15%) was carried out using mixed- and fixed-effects models in order to quantify effects of management and environment on ammonia emission, and to highlight challenges for use of the database. Measured emission in this subset ranged from <1% to 130% of applied ammonia after 48 h. Results showed clear, albeit variable, reductions in NH3 emission due to trailing hose, trailing shoe, and open slot injection of slurry compared to broadcast application. There was evidence of positive effects of air temperature and wind speed on NH3 emission, and limited evidence of effects of slurry DM. However, random-effects coefficients for differences among research institutes were among the largest model coefficients, and showed a deviation from the mean response by more than 100% in some cases. The source of these institute differences could not be determined with certainty, but there is some evidence that they are related to differences in soils, or differences in application or measurement methods. The ALFAM2 database should be useful for development and evaluation of both emission factors and emission models, but users need to recognize the limitations caused by confounding variables, imbalance in the dataset, and dependence among observations from the same institute. Variation among measurements and in reported variables highlights the importance of international agreement on how NH3 emission should be measured, along with necessary types of supporting data and standard protocols for their measurement. Both are needed in order to produce more accurate and useful ammonia emission measurements. Expansion of the ALFAM2 database will continue, and readers are invited to contact the corresponding author for information on data submission. The latest version of the database is available at http://www.alfam.dk

CLASSIFICATION OF AERIAL PHOTOGRAMMETRIC 3D POINT CLOUDS

We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer

Deep Learning with Data Augmentation for Fruit Counting

Counting the number of fruits in an image is important for orchard management, but is complex due to different challenging problems such as overlapping fruits and the difficulty to create large labeled datasets. In this paper, we propose the use of a data-augmentation technique that creates novel images by adding a number of manually cropped fruits to original images. This helps to increase the size of a dataset with new images containing more fruits and guarantees correct label information. Furthermore, two different approaches for fruit counting are compared: a holistic regression-based approach, and a detection-based approach. The regression-based approach has the advantage that it only needs as target value the number of fruits in an image compared to the detection-based approach where bounding boxes need to be specified. We combine both approaches with different deep convolutional neural network architectures and object-detection methods. We also introduce a new dataset of 1500 images named the Five-Tropical-Fruits dataset and perform experiments to evaluate the usefulness of augmenting the dataset for the different fruit-counting approaches. The results show that the regression-based approaches profit a lot from the data-augmentation method, whereas the detection-based approaches are not aided by data augmentation. Although one detection-based approach finally still works best, this comes with the cost of much more labeling effort.</p

A flexible semi-empirical model for estimating ammonia volatilization from field-applied slurry

This work describes a semi-empirical dynamic model for predicting ammonia volatilization from field-applied slurry. Total volatilization is the sum of first-order transfer from two pools: a "fast" pool representing slurry in direct contact with the atmosphere, and a “slow” one representing fractions less available for emission due to infiltration or other processes. This simple structure is sufficient for reproducing the characteristic course of emission over time. Values for parameters that quantify effects of the following predictor variables on partitioning and transfer rates were estimated from a large data set of emission from cattle and pig slurry (490 field plots in 6 countries from the ALFAM2 database): slurry dry matter, application method, application rate, incorporation (shallow or deep), air temperature, wind speed, and rainfall rate. The effects of acidification were estimated using a smaller dataset. Model predictions generally matched the measured course of emission over time in a reserved data subset used for evaluation, although the model over- or under-estimated emission for many individual plots. Mean error was ca. 12% of applied total ammoniacal nitrogen (and as much as 82% of measured emission) for 72 h cumulative emission, and model efficiency (fraction of observed variation explained by the model) was 0.5–0.7. Most of the explanatory power of the model was related to application method. The magnitude and sign of (apparent) model error varied among countries, highlighting the need to understand why measured emission varies among locations. The new model may be a useful tool for predicting fertilizer efficiency of field-applied slurries, assessing emission factors, and quantifying the impact of mitigation. The model can readily be applied or extended, and is available as an R package (ALFAM2, https://github.com/sashahafner/ALFAM2) or a simple spreadsheet (http://www.alfam.dk)

The ALFAM2 database on ammonia emission from field-applied manure: Description and illustrative analysis

Ammonia (NH3) emission from animal manure contributes to air pollution and ecosystem degradation, and the loss of reactive nitrogen (N) from agricultural systems. Estimates of NH3 emission are necessary for national inventories and nutrient management, and NH3 emission from field-applied manure has been measured in many studies over the past few decades. In this work, we facilitate the use of these data by collecting and organizing them in the ALFAM2 database. In this paper we describe the development of the database and summarise its contents, quantify effects of application methods and other variables on emission using a data subset, and discuss challenges for data analysis and model development. The database contains measurements of emission, manure and soil properties, weather, application technique, and other variables for 1895 plots from 22 research institutes in 12 countries. Data on five manure types (cattle, pig, mink, poultry, mixed, as well as sludge and “other”) applied to three types of crops (grass, small grains, maize, as well as stubble and bare soil) are included. Application methods represented in the database include broadcast, trailing hose, trailing shoe (narrow band application), and open slot injection. Cattle manure application to grassland was the most common combination, and analysis of this subset (with dry matter (DM) limited to <15%) was carried out using mixed- and fixed-effects models in order to quantify effects of management and environment on ammonia emission, and to highlight challenges for use of the database. Measured emission in this subset ranged from <1% to 130% of applied ammonia after 48 h. Results showed clear, albeit variable, reductions in NH3 emission due to trailing hose, trailing shoe, and open slot injection of slurry compared to broadcast application. There was evidence of positive effects of air temperature and wind speed on NH3 emission, and limited evidence of effects of slurry DM. However, random-effects coefficients for differences among research institutes were among the largest model coefficients, and showed a deviation from the mean response by more than 100% in some cases. The source of these institute differences could not be determined with certainty, but there is some evidence that they are related to differences in soils, or differences in application or measurement methods. The ALFAM2 database should be useful for development and evaluation of both emission factors and emission models, but users need to recognize the limitations caused by confounding variables, imbalance in the dataset, and dependence among observations from the same institute. Variation among measurements and in reported variables highlights the importance of international agreement on how NH3 emission should be measured, along with necessary types of supporting data and standard protocols for their measurement. Both are needed in order to produce more accurate and useful ammonia emission measurements. Expansion of the ALFAM2 database will continue, and readers are invited to contact the corresponding author for information on data submission. The latest version of the database is available at http://www.alfam.dk