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In sports, advance sensing technologies generate massive amount of unstructured telemedicine data that need to be refined for accurate diagnosis of underlying diseases. For accurate prediction of diseases and classification of athletes' data, deep learning algorithms are frequently used at the cloud. However, the transmission of raw data of athletes to the cloud faces numerous challenges. Among them, security and privacy are a major challenge in view of the sensitive and personal information present within the unstructured data. In this paper, first we present a data block scrambling algorithm (without key management) for secured transmission and storage of ECG (electrocardiogram) data of table tennis players at the cloud. A small piece of original data stored at the cloud is used for scrambling the massive amount of remaining ECG data. The secured telemedicine data is then imported into Hadoop Distributed File System for data management, which is read by Spark framework to form Resilient Distributed Datasets. Finally, a deep learning approach is used that extracts useful features, learns the related information, and weights and sums the feature vectors at different layers for classification. Theoretical analysis proves that our proposed approach is highly robust and resilient to brute force attacks and at the same time has a much better accuracy, sensitivity, and specificity as compared to the existing approaches. [ABSTRACT FROM AUTHOR]

Laboratory identification of carbapenemase-producing clinical isolates is crucial to limit the spread of the bacteria. In this study, we shall first develop the matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) assay in automatic identification of carbapenemase producers. A total of 143 well-characterized isolates were studied. After an incubation of bacteria with meropenem trihydrate, the mixture was centrifuged and the supernatant analyzed by MALDI-TOF MS. A genetic algorithm model with ClinProTools software was built using spectra of 43 carbapenemase-positive isolates and 40 carbapenemase-negative isolates after 2 h of incubation. This model was externally validated using 60 test isolates. All spectra of supernatants of the carbapenemase-negative isolates showed peak profiles comparable to that of pure meropenem ( m/ z 384.159, 406.140, and 428.122 of its two sodium salt variants) regardless of the incubation time tested. For the carbapenemase-positive isolates, the specific peak for meropenem at m/ z 384.159 disappeared during the incubation time, two products of meropenem degradation were identified with m/ z 358.18 (the decarboxylated product) and 380.161 (sodium salt of the decarboxylated product), and other degradation products were observed (native molecule with disrupted amide bond with m/ z 402.169, three sodium salt variants with m/ z 424.151, 446.133, and 468.115). Sixty test isolates were 100 % correctly classified as carbapenemase positive and carbapenemase negative with the genetic algorithm model. MALDI-TOF MS coupled with ClinProTools is capable of rapidly, accurately, and automatically identifying carbapenemase producers. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

The prediction of secondary structure is an important topic in the field of bioinformatics, even if the methods have matured, and development of the algorithms is a far less active area than a decade ago. Accurate prediction is very useful to biologists in its own right, but it is worth pointing out that it is also an essential component of tertiary structure prediction, which in contrast is far from solved and continues to be a highly active area of research. In addition, sequence comparison methods have more recently incorporated local structure tracks. The extra information utilized by the new methods has led to considerable improvements in fold recognition and alignment accuracy. In this paper, a novel method for protein secondary structure prediction is presented. Using evolutionary information contained in amino acid's physicochemical properties, position-specific scoring matrix generated by PSI-BLAST and HMMER3 profiles as input to hybrid back propagation system, secondary structure can be predicted at significantly increased accuracy. Based on knowledge discovery theory based on inner cognitive mechanism (KDTICM) theory, we have constructed a compound pyramid model approach, which is composed of four layers of the intelligent interface and integrated in several ways, such as hybrid back propagation method (HBP), modified knowledge discovery in databases (KDD*), hybrid SVM method (HSVM) and so on. Experiments on three standard datasets (RS126, CB513 and CASP8) show that CPM is capable of producing the higher Q and SOV scores than that achieved by existing widely used schemes such as PSIPRED, PHD, Predator, as well as previously developed prediction methods. On the RS126 and CB513 datasets, it achieves a Q and SOV99 score are considerably higher than the best reported scores, respectively. It is also tested on target proteins of critical assessment of protein structure prediction experiment (CASP8) and achieves better results than the traditional methods, including the popular PSIPRED method over overall prediction accuracy. Available: . [ABSTRACT FROM AUTHOR]