The emergence of deep learning has impacted numerous machine learning based applications and research. The reason for its success lies in two main advantages: 1) it provides the ability to learn very complex non-linear relationships between features and 2) it allows one to leverage information from unlabeled data that does not belong to the problem being handled. This paper presents a transfer learning procedure for cancer classification, which uses feature selection and normalization techniques in conjunction with stacked sparse auto-encoders on gene expression data. While classifying any two tumor types, data of other tumor types were used in unsupervised manner to improve the feature representation. The performance of our algorithm was tested on 36 two-class benchmark datasets from the GEMLeR repository. On performing statistical tests, it is clearly ascertained that our algorithm statistically outperforms several generally used cancer classification approaches. The deep learning based molecular disease classification can be used to guide decisions made on the diagnosis and treatment of diseases, and therefore may have important applications in precision medicine.
The structural features of water-in-oil microemulsions of Triton X-100, 1-hexanol, and water in cyclohexane have been studied by NMR relaxation and ESR spin probe methods. The mobility of the water molecules, the extent of hydration of the surfactant, and the mobility of the poly(oxyethy1ene) segment and the nonpolar segment of the amphiphile have been monitored and analyzed in terms of the molecular processes occurring in the microemulsion system. Comparison of these results with those on simple reverse micelles in nonpolar mediia indicates considerable similarities between water-in-oil microemulsions and reverse micelles.
IntroductionMicelles are formed when surfactant molecules aggregate in aqueous solution, with their nonpolar tails associating through the hydrophobic effect. The hydrophobic core of the micelle thus formed can solubilize significant amounts of nonpolar molecules. Tanfordl has discussed the basic features of micellar aggregation, and Menger2 has recently reviewed the structure, fluidity, and spectroscopic properties of micelles in agueous medium.Several arnphiphiles are able to aggregate when dispersed even in nonpolar organic solvents, to produce reverse micelles, wherein the structural organization is the inverse of that of aqueous micelles. The polar core of these reverse micelles is able to solubilize significant amounts of water. These reverse micellar systems are thought to resemble pockets of water included in bioaggregates such as membranes, the rnitochondrial matrix, etc., and considerable attention has been paid to reverse micellar systems in recent yearsa3There is yet another class of aggregates, commonly referred to as microemulsions, wherein microdroplets (size 10-100 nm) of a hydrocarbon are solubilized in water (the
Unmanned Aerial Vehicles (UAVs) equipped with vision capabilities have become popular in recent years. Many applications have especially been employed object detection techniques extracted from the information captured by an onboard camera. However, object detection on UAVs requires high performance, which has a negative effect on the result. In this article, we propose a deep feature pyramid architecture with a modified focal loss function, which enables it to reduce the class imbalance. Moreover, the proposed method employed an end to end object detection model running on the UAV platform for real-time application. To evaluate the proposed architecture, we combined our model with Resnet and MobileNet as a backend network, and we compared it with RetinaNet and HAL-RetinaNet. Our model produced a performance of 30.6 mAP with an inference time of 14 fps. This result shows that our proposed model outperformed RetinaNet by 6.2 mAP.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.