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Neuroinflammation occurs early in Alzheimer’s disease (AD). The initial stage of AD is related to glial dysfunction, which contributes to impairment of Aβ clearance and disruption of synaptic connection. CEBPβ, a member of the CCAAT-enhancer-binding protein (CEBP) family, modulates the expression of inflammation-associated genes, and its expression is elevated in brains undergoing degeneration and injured brains. However, the mechanism underlying CEBPβ-mediated chronic inflammation in AD is unclear. In this study, we observed that increases in the levels of nuclear CEBPβ facilitated the interaction of CEBPβ with the NFκB p65 subunit, increasing the transcription of proinflammatory cytokines in the APP/PS1 mouse brain. Oral administration of nanocarrier-packaged carnosic acid (CA) reduced the aberrant activation of microglia and astrocytes and diminished mature IL-1β, TNFα and IL-6 production in the APP/PS1 mouse brain. CA administration reduced β-amyloid (Aβ) deposition and ameliorated cognitive impairment in APP/PS1 mice. We observed that CA blocked the interaction of CEBPβ with NFκB p65, and chromatin immunoprecipitation revealed that CA reduced the transcription of the NFκB target genes TNFα and IL-6. We confirmed that CA alleviated inflammatory mediator-induced neuronal degeneration and reduced Aβ secretion by inhibiting the CEBPβ-NFκB signalling pathway in vitro. Sulfobutyl ether-beta-cyclodextrin (SBEβCD) was used as the encapsulation agent for the CA-loaded nanocarrier to overcome the poor water solubility and enhance the brain bioavailability of CA. The CA nanoparticles (NPs) had no obvious toxicity. We demonstrated a feasible SBEβCD-based nanodelivery system targeting the brain. Our data provide experimental evidence that CA-loaded NPs are potential therapeutic agents for AD treatment.

In multi-label learning, the use of labels correlation is crucial for the improvement of multi-label learning performance. Most of the existing methods for studying labels correlation usually do not consider the study of feature-space information. Further study is deserved about how to synchronize rich information contained in features-space and labels-space. In this paper, a multi-label learning algorithm of Non-Equilibrium Labels Completion with Mean Shift (i.e. NeLC-MS) was proposed. The aim of this research was to mine the feature hidden information by reconstructing the features space, and introduce non-equilibrium label correlation information so as to better improve the robustness of multi-label learning classification. First, the mean shift clustering method was used to reconstruct the information between features in the feature space to obtain the hidden information between features. Then, the new information entropy was used to measure the correlation between labels which gets the basic labels confidence matrix. Then the basic labels confidence matrix was improved to construct a Non-equilibrium labels completion matrix by the non-equilibrium parameters. Finally, the new training set was constructed by using the reconstructed features space and the Non-equilibrium Labels Completion matrix, and the existing linear classifier was used for predicting the new training set. The experimental results of the proposed algorithm in the opening benchmark multi-label datasets showed that the NeLC-MS algorithm would have some advantages over other comparative multi-label learning algorithms, and the effectiveness of the proposed method was further illustrated by the use of statistical hypothesis test and stability analysis.

Ice algae have successfully adapted to the extreme environmental conditions in the Antarctic, however the underlying mechanisms involved in the regulation and response of thylakoid membranes and chloroplast to low-temperature stress are still not well understood. In this study, changes in pigment concentrations, lipids, fatty acids and pigment protein complexes in thylakoid membranes and chloroplast after exposure to low temperature conditions were investigated using the Antarctic ice algaeChlamydomonassp. ICE-L. Results showed that the chloroplasts ofChlamydomonassp. ICE-L are distributed throughout the cell except in the nuclear region in the form of thylakoid lamellas which exists in the gap between organelles and the starch granules. Also, the structure of mitochondria has no obvious change after cold stress. Concentrations of Chla, Chlb, monogalactosyl diacylglycerol, digalactosyl diacylglycerol and fatty acids were also observed to exhibit changes with temperature, suggesting possible adaptations to cold environments. The light harvesting complex, lutein and β-carotene played an important role for adaptation of ICE-L, and increasing of monogalactosyl diacylglycerol and digalactosyl diacylglycerol improved the overall degree of unsaturation of thylakoid membranes, thereby maintaining liquidity of thylakoid membranes. The pigments, lipids, fatty acids and pigment-protein complexes maintained the stability of the thylakoid membranes and the normal physiological function ofChlamydomonassp. ICE-L.

Do IOT problem definition and research. Research on Internet of things, first research object, Re research alliance,Re study network. Objects are things in the Internet of things, Link is how objects connect to the network, Network is what this network is. Objective function is the key problem. Can start with simple and critical questions. Algorithm is the solution to the problem steps. What is the Internet of things, objects connected to the Internet is the Internet of things, cup networking, car networking. Things better than other networks, is composed of what objects, what composition, what nature, what innovation and superiority. Internet of things four key technologies are widely used, these four technologies are mainly RFID, WSN, M2M, as well as the integration of the two. RFID can be achieved using MATLAB, NS2, Android, WSN can use NS2, OMNET++ implementation, M2M can be developed using JAVA. Therefore, this paper focuses on the advantages of Internet of things than the internet.

The reliability is a key factor for the design and manufacture of large vibrating screen. In the paper, we presented a new large vibrating screen with hyperstatic net-beam structure. Dynamic characteristic of the vibrating screen was researched and dynamic simulation method of large screening machines was explored. We used finite element method (FEM) to analyze dynamic characteristic of large vibrating screen with hyperstatic net-beam structure. Multi natural frequency, natural modes of vibration and dynamic response of the vibrating screen were calculated. The structural size of stiffeners on the side plate was optimized under multiple frequencies constraints and an adaptive optimization criterion was given. The results show that the vibrating screen’s structural strength is increased and the natural frequency of bending deformation is enhanced. The modal frequencies are far from working frequency, and thus the structure is able to avoid resonance effectively and reduce the destructiveness. The maximum transverse displacement of the vibrating screen is 0.13 mm, the maximum difference in vibration amplitude of corresponding points is 0.44mm and the maximum dynamic stress is 16.63MPa. The structural optimization shows that the mass of the side plate is decreased by 194.50kg, the second and third modal frequency is increased by 1.73% and 2.91% respectively and a better optimal effect is received.