Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. The major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. To extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extracts different features, fusing them using 1D pooling and cross pooling leads to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that DNN with dropout at input layer converges more quickly when trained using blended features, compared to the same model trained using uni-modal deep features.Comment: 18 pages, 8 figures, published in Electronics MDPI journa

PET imaging of tumor glycolysis downstream of hexokinase through noninvasive measurement of pyruvate kinase M2

Cancer cells reprogram their metabolism to meet increased biosynthetic demands, commensurate with elevated rates of replication. Pyruvate kinase M2 (PKM2) catalyzes the final and rate-limiting step in tumor glycolysis, controlling the balance between energy production and the synthesis of metabolic precursors. We report here the synthesis and evaluation of a positron emission tomography (PET) radiotracer, [(11)C]DASA-23, that provides a direct noninvasive measure of PKM2 expression in preclinical models of glioblastoma multiforme (GBM). In vivo, orthotopic U87 and GBM39 patient-derived tumors were clearly delineated from the surrounding normal brain tissue by PET imaging, corresponding to exclusive tumor-associated PKM2 expression. In addition, systemic treatment of mice with the PKM2 activator TEPP-46 resulted in complete abrogation of the PET signal in intracranial GBM39 tumors. Together, these data provide the basis for the clinical evaluation of imaging agents that target this important gatekeeper of tumor glycolysis

Diagnosis, extent, impacts, and management of subsoil constraints in the northern grains cropping region of Australia

Productivity of grain crops grown under dryland conditions in north-eastern Australia depends on efficient use of rainfall and available soil moisture accumulated in the period preceding sowing. However, adverse subsoil conditions including high salinity, sodicity, nutrient imbalances, acidity, alkalinity, and high concentrations of chloride (Cl) and sodium (Na) in many soils of the region restrict ability of crop roots to access this stored water and nutrients. Planning for sustainable cropping systems requires identification of the most limiting constraint and understanding its interaction with other biophysical factors. We found that the primary effect of complex and variable combinations of subsoil constraints was to increase the crop lower limit (CLL), thereby reducing plant available water. Among chemical subsoil constraints, subsoil Cl concentration was a more effective indicator of reduced water extraction and reduced grain yields than either salinity or sodicity (ESP). Yield penalty due to high subsoil Cl was seasonally variable, with more in-crop rainfall (ICR) resulting in less negative impact. A conceptual model to determine realistic yield potential in the presence of subsoil Cl was developed from a significant positive linear relationship between CLL and subsoil Cl:Since grid sampling of soil to identify distribution of subsoil Cl, both spatially across landscape and within soil profile, is time-consuming and expensive, we found that electromagnetic induction, coupled with yield mapping and remote sensing of vegetation offers potential to rapidly identify possible subsoil Cl at paddock or farm scale.Plant species and cultivars were evaluated for their adaptations to subsoil Cl. Among winter crops, barley and triticale, followed by bread wheat, were more tolerant of high subsoil Cl concentrations than durum wheat. Chickpea and field pea showed a large decrease in yield with increasing subsoil Cl concentrations and were most sensitive of the crops tested. Cultivars of different winter crops showed minor differences in sensitivity to increasing subsoil Cl concentrations. Water extraction potential of oilseed crops was less affected than cereals with increasing levels of subsoil Cl concentrations. Among summer crops, water extraction potential of millet, mungbean, and sesame appears to be more sensitive to subsoil Cl than that of sorghum and maize; however, the differences were significant only to 0.7 m. Among pasture legumes, lucerne was more tolerant to high subsoil Cl concentrations than the others studied.Surface applied gypsum significantly improved wheat grain yield on soils with ESP >6 in surface soil (0–0.10 m). Subsurface applied gypsum at 0.20–0.30 m depth did not affect grain yield in the first year of application; however, there was a significant increase in grain yield in following years. Better subsoil P and Zn partially alleviated negative impact of high subsoil Cl. Potential savings from improved N fertilisation decisions for paddocks with high subsoil Cl are estimated at ~$AU10 million per annum

Impacts of Atomistic Coating on Thermal Conductivity of Germanium Nanowires

By using non-equilibrium molecular dynamics simulations, we demonstrated that thermal conductivity of Germanium nanowires can be reduced more than 25% at room temperature by atomistic coating. There is a critical coating thickness beyond which thermal conductivity of the coated nanowire is larger than that of the host nanowire. The diameter dependent critical coating thickness and minimum thermal conductivity are explored. Moreover, we found that interface roughness can induce further reduction of thermal conductivity in coated nanowires. From the vibrational eigen-mode analysis, it is found that coating induces localization for low frequency phonons, while interface roughness localizes the high frequency phonons. Our results provide an available approach to tune thermal conductivity of nanowires by atomic layer coating.Comment: 24 pages, 5 figure

Choice Models and Customer Relationship Management

Customer relationship management (CRM) typically involves tracking individual customer behavior over time, and using this knowledge to configure solutions precisely tailored to the customers' and vendors' needs. In the context of choice, this implies designing longitudinal models of choice over the breadth of the firm's products and using them prescriptively to increase the revenues from customers over their lifecycle. Several factors have recently contributed to the rise in the use of CRM in the marketplacePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47023/1/11002_2005_Article_5892.pd

Perspectives on multiple category choice

Multiple category choice is a decision process in which an individual selects a number of goods, all of which are nonsubstitutable with respect to consumption. Choices can be made either simultaneously or sequentially. The key feature of multiple category choice is the treatment of the choices as interrelated because each item in the final collection of goods contributes to the achievement of a common behavioral goal. We discuss current and potential applications of psychology, economics and consumer choice theory in developing models of multiple category choice

Multiple Category Decision Making: Review and Synthesis

In many purchase environments, consumers use information from a number of product categories prior to making a decision. These purchase situations create dependencies in choice outcomes across categories. As such, these decision problems cannot be easily modeled using the single-category, single-choice paradigm commonly used by researchers in marketing. We outline a conceptual framework for categorization, and then discuss three types of cross-category dependence: cross-category consideration cross-category learning, and product bundling. We argue that the key to modeling choice dependence across categories is knowledge of the goals driving consumer behavior