Study of deformation texture in an AZ31 magnesium alloy rolled at wide range of rolling speed and reductions

Having the lowest density among all structural metals, magnesium has opened new horizons for developing commercial alloys with successful use in a wide variety of applications [1-2]. However, the plasticity of Mg is restricted at low temperatures because: (a) only a small number of deformation mechanisms can be activated [3-4], and (b) a preferred crystallographic orientation (texture) develops in wrought alloys, especially in flat-rolled sheets [5-7]. Therefore, manufacturing processes such as rolling and stamping should be performed at elevated temperatures [1, 8]. These barriers to the manufacturing process increase the price of magnesium wrought alloy products and limits the use of Mg to castings [9-10]. As a result, many studies have been conducted to improve formability by investigating the effect of manufacturing process. Therefore the current sheet production techniques, based on DC casting and hot rolling, are basically slow because the demand is easily met [11]. Twin roll casting followed by hot rolling appears to be processing route which can fulfil high volumes and reduced costs. The present authors succeeded in single-pass large draught rolling of various magnesium alloy sheets at low temperature (<473K) by high speed rolling [12]. Based on the data available in those works [13- 17], the sheet obtained by high-speed rolling exhibited a fine-grained microstructure (mean grain size of 2-3 μm), with good mechanical properties. For these advantages, the high speed rolling is a promising process to produce high-quality rolled magnesium alloy sheets at a low cost. For these advantages, the HSR is a promising process to produce high-quality rolled magnesium alloy sheets at a low cost. The goal of this research is thus to investigate the mechanisms responsible for the much higher rollability and the grain refinement after HSR. To do that, in this study, different rolling speeds from 15 to 1000 m/min were employed to twin rolled cast AZ31B magnesium alloy and different reductions

Prostate Motion Modelling Using Biomechanically-Trained Deep Neural Networks on Unstructured Nodes

In this paper, we propose to train deep neural networks with biomechanical simulations, to predict the prostate motion encountered during ultrasound-guided interventions. In this application, unstructured points are sampled from segmented pre-operative MR images to represent the anatomical regions of interest. The point sets are then assigned with point-specific material properties and displacement loads, forming the un-ordered input feature vectors. An adapted PointNet can be trained to predict the nodal displacements, using finite element (FE) simulations as ground-truth data. Furthermore, a versatile bootstrap aggregating mechanism is validated to accommodate the variable number of feature vectors due to different patient geometries, comprised of a training-time bootstrap sampling and a model averaging inference. This results in a fast and accurate approximation to the FE solutions without requiring subject-specific solid meshing. Based on 160,000 nonlinear FE simulations on clinical imaging data from 320 patients, we demonstrate that the trained networks generalise to unstructured point sets sampled directly from holdout patient segmentation, yielding a near real-time inference and an expected error of 0.017 mm in predicted nodal displacement

Phytotoxic, insecticidal and leishmanicidal activities of aerial parts of Polygonatum verticillatum

The aim of the present study was to explore the aerial parts of the Polygonatum verticillatum for variousbiological activities such as phytotoxic, insecticidal and leishmanicidal properties. Outstandingphytotoxicity was observed for the crude extract and its subsequent solvent fractions against Lemnaacquinoctialis Welv at tested doses of 5, 50 and 500 &#236;g/ml. Complete growth inhibition (100%) wasdemonstrated by the crude extract and aqueous fraction at maximum tested dose (500 &#236;g/ml). Amongthe tested insects, moderate insecticidal activity was recorded against Rhyzopertha dominica. However,neither crude extract nor its solvent fraction registered any significant (&gt; 100 &#236;g/ml) leishmanicidalactivity against Leishmania major. Based on the phytotoxicity, the aerial parts of the plant could be asignificant source of natural herbicidal for sustainable weed control

CRISPR/Cas-mediated editing of cis-regulatory elements for crop improvement

To improve future agricultural production, major technological advances are required to increase crop production and yield. Targeting the coding region of genes via the Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated Protein (CRISPR/Cas) system has been well established and has enabled the rapid generation of transgene-free plants, which can lead to crop improvement. The emergence of the CRISPR/Cas system has also enabled scientists to achieve cis-regulatory element (CRE) editing and, consequently, engineering endogenous critical CREs to modulate the expression of target genes. Recent genome-wide association studies have identified the domestication of natural CRE variants to regulate complex agronomic quantitative traits and have allowed for their engineering via the CRISPR/Cas system. Although engineering plant CREs can be advantageous to drive gene expression, there are still many limitations to its practical application. Here, we review the current progress in CRE editing and propose future strategies to effectively target CREs for transcriptional regulation for crop improvement

Learning image quality assessment by reinforcing task amenable data selection

In this paper, we consider a type of image quality assessment as a task-specific measurement, which can be used to select images that are more amenable to a given target task, such as image classification or segmentation. We propose to train simultaneously two neural networks for image selection and a target task using reinforcement learning. A controller network learns an image selection policy by maximising an accumulated reward based on the target task performance on the controller-selected validation set, whilst the target task predictor is optimised using the training set. The trained controller is therefore able to reject those images that lead to poor accuracy in the target task. In this work, we show that the controller-predicted image quality can be significantly different from the task-specific image quality labels that are manually defined by humans. Furthermore, we demonstrate that it is possible to learn effective image quality assessment without using a ``clean'' validation set, thereby avoiding the requirement for human labelling of images with respect to their amenability for the task. Using $6712$, labelled and segmented, clinical ultrasound images from $259$ patients, experimental results on holdout data show that the proposed image quality assessment achieved a mean classification accuracy of $0.94\pm0.01$ and a mean segmentation Dice of $0.89\pm0.02$, by discarding $5\%$ and $15\%$ of the acquired images, respectively. The significantly improved performance was observed for both tested tasks, compared with the respective $0.90\pm0.01$ and $0.82\pm0.02$ from networks without considering task amenability. This enables image quality feedback during real-time ultrasound acquisition among many other medical imaging applications

Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels

When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels. In this study, we evaluate the utility of a pre-screening classification network prior to the segmentation network. Experimental results demonstrate that such a classifier, minimising frame classification errors, was able to directly impact the number of false positive and false negative frames. Importantly, the segmentation accuracy on the classifier-selected frames, that would be segmented, remains comparable to or better than those from standalone segmentation networks. Interestingly, the efficacy of the pre-screening classifier was affected by the sampling methods for training labels from multiple observers, a seemingly independent problem. We show experimentally that a previously proposed approach, combining random sampling and consensus labels, may need to be adapted to perform well in our application. Furthermore, this work aims to share practical experience in developing a machine learning application that assists highly variable interventional imaging for prostate cancer patients, to present robust and reproducible open-source implementations, and to report a set of comprehensive results and analysis comparing these practical, yet important, options in a real-world clinical application

Adaptable image quality assessment using meta-reinforcement learning of task amenability

The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability potentially provides an objective measure of task-specific image quality. To predict task amenability, an IQA agent is trained using reinforcement learning (RL) with a simultaneously optimised task predictor, such as a classification or segmentation neural network. In this work, we develop transfer learning or adaptation strategies to increase the adaptability of both the IQA agent and the task predictor so that they are less dependent on high-quality, expert-labelled training data. The proposed transfer learning strategy re-formulates the original RL problem for task amenability in a meta-reinforcement learning (meta-RL) framework. The resulting algorithm facilitates efficient adaptation of the agent to different definitions of image quality, each with its own Markov decision process environment including different images, labels and an adaptable task predictor. Our work demonstrates that the IQA agents pre-trained on non-expert task labels can be adapted to predict task amenability as defined by expert task labels, using only a small set of expert labels. Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19.7 % % and 29.6 % % expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100 % % expert labels

Prostate Motion Modelling Using Biomechanically-Trained Deep Neural Networks on Unstructured Nodes

In this paper, we propose to train deep neural networks with biomechanical simulations, to predict the prostate motion encountered during ultrasound-guided interventions. In this application, unstructured points are sampled from segmented pre-operative MR images to represent the anatomical regions of interest. The point sets are then assigned with point-specific material properties and displacement loads, forming the un-ordered input feature vectors. An adapted PointNet can be trained to predict the nodal displacements, using finite element (FE) simulations as ground-truth data. Furthermore, a versatile bootstrap aggregating mechanism is validated to accommodate the variable number of feature vectors due to different patient geometries, comprised of a training-time bootstrap sampling and a model averaging inference. This results in a fast and accurate approximation to the FE solutions without requiring subject-specific solid meshing. Based on 160,000 nonlinear FE simulations on clinical imaging data from 320 patients, we demonstrate that the trained networks generalise to unstructured point sets sampled directly from holdout patient segmentation, yielding a near real-time inference and an expected error of 0.017 mm in predicted nodal displacement