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This study evaluated the antimicrobial and desiccation resistance of Cronobacter sakazakii and Cronobacter malonaticus isolates from powdered infant formula and processing environments. The antimicrobial susceptibility tests showed that the 70 Cronobacter strains, representing 19 sequence types, were susceptible to the most of the antibiotics except for amoxicillin-clavulanate, ampicillin, and cefazolin. Furthermore, the growth of six C. sakazakii and two C. malonaticus strains from different sequence types (STs) in hyperosmotic media was measured. The growth of the two C. sakazakii strains (CE1 and CE13) from the neonatal pathovars ST4 and ST8, were significantly higher (p < 0.05) than that of other strains. C. malonaticus strain CM35 (ST201) was the slowest grower in all strains, and most could not grow in more than 8% NaCl solution. Also the survival of these strains under desiccation conditions was followed for 1 year. The viable count of Cronobacter spp. under desiccation conditions was reduced on average by 3.02 log cycles during 1 year, with CE13 (ST8) being the most desiccation resistant strain. These results will improve our understanding of the persistence of the two closely related species C. sakazakii and C. malonaticus which are of concern for neonatal and adult health.

In Body Area Networks (BANs), big data collected by wearable sensors usually contain sensitive information, which is compulsory to be appropriately protected. Previous methods neglected privacy protection issue, leading to privacy exposure. In this paper, a differential privacy protection scheme for big data in body sensor network is developed. Compared with previous methods, this scheme will provide privacy protection with higher availability and reliability. We introduce the concept of dynamic noise thresholds, which makes our scheme more suitable to process big data. Experimental results demonstrate that, even when the attacker has full background knowledge, the proposed scheme can still provide enough interference to big sensitive data so as to preserve the privacy.

Odorant-binding proteins (OBPs) play a fundamental role in insect olfaction. In recent years, Galeruca daurica (Joannis) (Coleoptera: Chrysomelidae) has become one of the most important insect pests in the Inner Mongolian grasslands of China. This pest only feeds on the species of Allium plants, implying the central role of olfaction in its search for specific host plants. However, the olfaction-related proteins have not been investigated in this beetle. In this study, we identified 29 putative OBP genes, namely GdauOBP1-29, from the transcriptome database of G. daurica assembled in our laboratory by using RNA-Seq. All 29 genes had the full-length open reading frames except GdauOBP29, encoding proteins in length from 119 to 202 amino acids with their predicted molecular weights from 12 to 22 kDa with isoelectric points from 3.88 to 8.84. Predicted signal peptides consisting of 15-22 amino acid residues were found in all except GdauOBP6, GdauOBP13 and GdauOBP29. The amino acid sequence identity between the 29 OBPs ranged 8.33-71.83%. GdauOBP1-12 belongs to the Classic OBPs, while the others belong with the Minus-C OBPs. Phylogenetic analysis indicated that GdauOBPs are the closest to CbowOBPs from Colaphellus bowringi. RT-PCR and qRT-PCR analyses showed that all GdauOBPs were expressed in adult antennae, 11 of which with significant differences in their expression levels between males and females. Most GdauOBPs were also expressed in adult heads (without antennae), thoraxes, abdomens, legs and wings. Moreover, the expression levels of the GdauOBPs varied during the different development stages of G. daurica with most GdauOBPs expressed highly in the adult antennae but scarcely in eggs and pupae. These results provide insights for further research on the molecular mechanisms of chemical communications in G. daurica.

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At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals.

At present, the clinical diagnosis of mild cognitive impairment (MCI) patients becomes the important approach of evaluating early Alzheimer’s disease. The methods of EEG signal coupling and synchronization act as a key role in evaluating and diagnosing MCI patients. Recently, these coupling and synchronization methods were used to analyze the EEG signals of MCI patients according to different angles, and many important discoveries have been achieved. However, considering that every method is single-faceted in solving problems, these methods have various deficiencies when analyzing EEG signals of MCI patients. This paper reviewed in detail the coupling and synchronization analysis methods, analyzed their advantages and disadvantages, and proposed a few research questions needed to solve in the future. Also, the principles and best performances of these methods were described. It is expected that the performance analysis of these methods can provide the theoretical basis for the method selection of analyzing EEG signals of MCI patients and the future research directions.