Effects of Tillage and Rainfall on Atrazine Residue Levels in Soil

A field study was conducted in 1987 to 1991 to determine the effect of tillage and rainfall on distribution of atrazine in soil. Soil samples (10-cm increments to 50 cm) and crop residue samples were taken at regular intervals after application each year and analyzed for atrazine. Crop residue and living vegetation on no-till plots intercepted 60 to 70% of the applied atrazine; 3 to 16% of the atrazine remained in crop residue 1 to 2 wk later. The amount of atrazine recovered in soil, 1 to 2 wk post-treatment, ranged from 22 to 59 and 47 to 73% of the amount applied for no-till and conventional till, respectively. An average of 2.6 times more atrazine was recovered in the surface 10 cm of soil under conventional till than under no-till for all samplings and years. Total amounts of atrazine in the sampled profile (0- to 50-cm depth) were also generally lower under no-till than conventional till. More leaching below 10 cm occurred under no-till than conventional till, particularly in 1988 and 1990 when rain fell soon after application. Variation in soil atrazine levels among years was related to timing and amount of the first and subsequent rainfall after application

Effects of Tillage and Rainfall on Atrazine Residue Levels in Soil

A field study was conducted in 1987 to 1991 to determine the effect of tillage and rainfall on distribution of atrazine in soil. Soil samples (10-cm increments to 50 cm) and crop residue samples were taken at regular intervals after application each year and analyzed for atrazine. Crop residue and living vegetation on no-till plots intercepted 60 to 70% of the applied atrazine; 3 to 16% of the atrazine remained in crop residue 1 to 2 wk later. The amount of atrazine recovered in soil, 1 to 2 wk post-treatment, ranged from 22 to 59 and 47 to 73% of the amount applied for no-till and conventional till, respectively. An average of 2.6 times more atrazine was recovered in the surface 10 cm of soil under conventional till than under no-till for all samplings and years. Total amounts of atrazine in the sampled profile (0- to 50-cm depth) were also generally lower under no-till than conventional till. More leaching below 10 cm occurred under no-till than conventional till, particularly in 1988 and 1990 when rain fell soon after application. Variation in soil atrazine levels among years was related to timing and amount of the first and subsequent rainfall after application

Parental alcohol use disorders and alcohol use and disorders in offspring: a community study

Background. We examined the association between parental alcohol use disorders and patterns of alcohol consumption and DSM-IV alcohol use disorders in their offspring in a community-based sample of young adults. Methods. Data are based on baseline and 4-year follow-up data of 2427 respondents aged 14–24 at baseline. Alcohol use and disorders in respondents were assessed using the Munich-Composite-International-Diagnostic-Interview with DSM-IV algorithms. Diagnostic information about parents was collected by family history information from the respondents, and by direct interview with one parent (cohort aged 14 to 17 years only). Results. Although the association between maternal and paternal alcohol use disorders and non-problematical drinking in offspring was minimal, there was a strong effect for the transition to hazardous use and for alcohol abuse and dependence; the effect of parental concordance for transition into hazardous use was particularly striking. Maternal history was associated with a higher probability of progression from occasional to regular use, whereas paternal history was associated with progression from regular to hazardous use. Parental alcoholism increased the risk for first onset of hazardous use and alcohol dependence between the ages of 14–17, and for an earlier onset of the alcohol outcomes in offspring. The impact of parental alcohol use disorders was comparable for male and female offspring. Conclusions. Parental alcoholism predicts escalation of alcohol use, development of alcohol use disorders and onset of alcohol outcomes in offspring

Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation

We propose a new deep learning method for tumour segmentation when dealing with missing imaging modalities. Instead of producing one network for each possible subset of observed modalities or using arithmetic operations to combine feature maps, our hetero-modal variational 3D encoder-decoder independently embeds all observed modalities into a shared latent representation. Missing data and tumour segmentation can be then generated from this embedding. In our scenario, the input is a random subset of modalities. We demonstrate that the optimisation problem can be seen as a mixture sampling. In addition to this, we introduce a new network architecture building upon both the 3D U-Net and the Multi-Modal Variational Auto-Encoder (MVAE). Finally, we evaluate our method on BraTS2018 using subsets of the imaging modalities as input. Our model outperforms the current state-of-the-art method for dealing with missing modalities and achieves similar performance to the subset-specific equivalent networks.Comment: Accepted at MICCAI 201

[Work in progress] Scalable, out-of-the box segmentation of individual particles from mineral samples acquired with micro CT

Minerals are indispensable for a functioning modern society. Yet, their supply is limited causing a need for optimizing their exploration and extraction both from ores and recyclable materials. Typically, these processes must be meticulously adapted to the precise properties of the processed particles, an extensive characterization of their shapes, appearances as well as the overall material composition. Current approaches perform this analysis based on bulk segmentation and characterization of particles imaged with a micro CT, and rely on rudimentary postprocessing techniques to separate touching particles. However, due to their inability to reliably perform this separation as well as the need to retrain or reconfigure methods for each new image, these approaches leave untapped potential to be leveraged. Here, we propose ParticleSeg3D, an instance segmentation method that is able to extract individual particles from large micro CT images taken from mineral samples embedded in an epoxy matrix. Our approach is based on the powerful nnU-Net framework, introduces a particle size normalization, makes use of a border-core representation to enable instance segmentation and is trained with a large dataset containing particles of numerous different materials and minerals. We demonstrate that ParticleSeg3D can be applied out-of-the box to a large variety of particle types, including materials and appearances that have not been part of the training set. Thus, no further manual annotations and retraining are required when applying the method to new mineral samples, enabling substantially higher scalability of experiments than existing methods. Our code and dataset are made publicly available

Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images

© 2019, Springer Nature Switzerland AG. Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used

LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation

Deep Learning (DL) models are becoming larger, because the increase in model size might offer significant accuracy gain. To enable the training of large deep networks, data parallelism and model parallelism are two well-known approaches for parallel training. However, data parallelism does not help reduce memory footprint per device. In this work, we introduce Large deep 3D ConvNets with Automated Model Parallelism (LAMP) and investigate the impact of both input's and deep 3D ConvNets' size on segmentation accuracy. Through automated model parallelism, it is feasible to train large deep 3D ConvNets with a large input patch, even the whole image. Extensive experiments demonstrate that, facilitated by the automated model parallelism, the segmentation accuracy can be improved through increasing model size and input context size, and large input yields significant inference speedup compared with sliding window of small patches in the inference. Code is available\footnote{https://monai.io/research/lamp-automated-model-parallelism}.Comment: MICCAI 2020 Early Accepted paper. Code is available\footnote{https://monai.io/research/lamp-automated-model-parallelism

Temporal Dynamics of Preferential Flow to a Subsurface Drain

We conducted a sequential tracer leaching study on a 24.4 by 42.7 m field plot to investigate the temporal behavior of chemical movement to a 1.2-m deep field drain during irrigation and subsequent rainfall events over a 14-d period. The herbicides atrazine [6-chloroN-ethyl-N′-(1-methylethyl)-1,3,5-triazine-2,4-diamine], and alachlor [2-chloro-N-(2,6-diethylphenyl)-N-(methoxymethyl)acetamide] along with the conservative tracer Br were applied to a 1-m wide strip, offset 1.5 m laterally from a subsurface drain pipe, immediately before an 11.3-h long, 4.2-mm h−1 irrigation. Three additional conservative tracers, pentafluorobenzoate (PF), o-trifluoromethylbenzoate (TF), and difluorobenzoate (DF) were applied to the strip during the irrigation at 2-h intervals. Breakthrough of Br and the two herbicides occurred within the first 2-h of irrigation, indicating that a fraction of the solute transport was along preferential flow paths. Retardation and attenuation of the herbicides indicated that there was interaction between the chemicals and the soil lining the preferential pathways. The conservative tracers applied during the later stages of irrigation arrived at the subsurface drain much faster than tracers applied earlier. The final tracer, applied 6 h after the start of irrigation (DF), took only 15 min and 1 mm of irrigation water to travel to the subsurface drain. Model simulations using a two-dimensional, convective, and dispersive numerical model without an explicit preferential flow component failed to reproduce Br tracer concentrations in the drain effluent, confirming the importance of preferential flow. This study showed that preferential flow in this soil is not a uniform process during a leaching event