Sorghum and Sesame Markets Performance under Climate Change in Sudan

Sorghum and sesame are produced under the rain fed sector in Sudan; they are competing each other for agricultural input and resources since they are grown in the same season. The purpose of this paper is to examine sorghum and sesame performance under climate change, including supply, demand, exports and imports and food security variables. The economic performance of the country particularly agriculture; depends on weather conditions especially rainfall. Summer rainfall pattern has been decreasing by 15 to 20 percent in the last forty years. The study has developed a stochastic multi-market model for sorghum and sesame, the model comprices important characteristics of agriculture in Sudan including the dependency of agricultural supply on rainfall. Sorghum and sesame markets have been simulated by calibrating supply and demand functions; the climate scenario simulates sorghum and sesame under climate change of rainfall with a decreasing trend of 20%. The model has introduced prices and rainfall as stochastic variables; it has also incorporated food security indicators, which are directly affected by the agriculture performance. The uncertain stochastic variables in the model are presented in their Cumulative Distribution Functions (CDFs) by the help of BestFit; a software within the @Risk program based on annual observations from 2000-2013. The final results are graphed in the form of Cumulative Distribution Function (CDF). The model scenario simulations revealed that, the impact of the decreasing trend of rainfall would overall lead to considerable losses in sorghum and sesame supplies and hence a deterioration in the country’s external sector and food security situation

A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images

Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by ischemic injuries. The quality of collateral circulation has been established as a key factor in determining the likelihood of a favorable clinical outcome and goes a long way to determine the choice of stroke care model - that is the decision to transport or treat eligible patients immediately. Though there exist several imaging methods and grading criteria for quantifying collateral blood flow, the actual grading is mostly done through manual inspection of the acquired images. This approach is associated with a number of challenges. First, it is time-consuming - the clinician needs to scan through several slices of images to ascertain the region of interest before deciding on what severity grade to assign to a patient. Second, there is a high tendency for bias and inconsistency in the final grade assigned to a patient depending on the experience level of the clinician. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data. First, we formulate a region of interest detection task as a reinforcement learning problem and train a deep learning network to automatically detect the occluded region within the 3D MR perfusion volumes. Second, we extract radiomic features from the obtained region of interest through local image descriptors and denoising auto-encoders. Finally, we apply a convolutional neural network and other machine learning classifiers to the extracted radiomic features to automatically predict the collateral flow grading of the given patient volume as one of three severity classes - no flow (0), moderate flow (1), and good flow (2)..

Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs

An important factor for the development of spinal degeneration, pain and the outcome of spinal surgery is known to be the balance of the spine. It must be analyzed in an upright, standing position to ensure physiological loading conditions and visualize load-dependent deformations. Despite the complex 3D shape of the spine, this analysis is currently performed using 2D radiographs, as all frequently used 3D imaging techniques require the patient to be scanned in a prone position. To overcome this limitation, we propose a deep neural network to reconstruct the 3D spinal pose in an upright standing position, loaded naturally. Specifically, we propose a novel neural network architecture, which takes orthogonal 2D radiographs and infers the spine’s 3D posture using vertebral shape priors. In this work, we define vertebral shape priors using an atlas and a spine shape prior, incorporating both into our proposed network architecture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 0.95, indicating an almost perfect 2D-to-3D domain translation. Validating the reconstruction accuracy of a 3D standing spine on real data is infeasible due to the lack of a valid ground truth. Hence, we design a novel experiment for this purpose, using an orientation invariant distance metric, to evaluate our model’s ability to synthesize full-3D, upright, and patient-specific spine models. We compare the synthesized spine shapes from clinical upright standing radiographs to the same patient’s 3D spinal posture in the prone position from CT