Pancytopenia: a rare cause for a common presentation

Filariasis is a major public health hazard in tropical and subtropical countries and is endemic among the Indian population. Asymptomatic microfilariaemia, elephantiasis, acute adenolymphangitis, hydrocoele and chronic lymphatic disease are its common manifestations. We hereby report a case of microfilaria found in the bone marrow presenting as pancytopenia. There was no classical feature of elephantiasis or lymphoedema present.

A Deep-Learning-Based Oil-Well-Testing Stage Interpretation Model Integrating Multi-Feature Extraction Methods

The interpretation of well-testing data is a key means of decision-making support for oil and gas field development. However, conventional processing methods have many problems, such as the stochastic nature of the data, feature redundancies, the randomness of the initial weights or thresholds, and fluctuations in the generalization ability with slight changes in the network parameters. These result in a poor ability to characterize data features and a low generalization ability of the interpretation models. We propose a new integrated well-testing interpretation model based on a multi-feature extraction method and deep mutual information classifiers (MFE-DMIC). This model can avoid the low model classification accuracy caused by the simple training structures, lacking of redundancy elimination, and the non-optimal classifier configuration parameters. First, we obtained the initial features according to four classical feature extraction methods. Then, we eliminated feature redundancies using a deep belief network and united the maximum information coefficient method to achieve feature purification. Finally, we calculated the interpretation results using a hybrid particle swarm optimization–support vector machine classification system. We used 572 well-testing field samples, including five working stages, for model training and testing. The results show that the MFE-DMIC model had the highest total stage classification accuracy of 98.18% as well as the least of features (nine) compared with the classical feature extraction and classification methods and their combinations. The proposed model can reduce the efforts of oil analysts and allow accurate labeling of samples to be predicted.

ABOUT OPTIMAL CLONING AND ENTANGLEMENT

The main non-classical feature of quantum key distribution (QKD) is that it is characterized by a trade-off relation that limits the information possibly gained by a spy and the quality of the transmission line between the authorized users. In particular, perfect cloning is impossible, due to this trade-off, while optimal imperfect cloning saturates the trade-off relation. We investigate by numerical methods the deep nature of this trade-off relation, in the case of optimal cloning, and find that it reveals a subtle interplay between fidelity and entanglement.