Benzo[a]pyrene-induced DNA adducts and gene expression profiles in target and non-target organs for carcinogenesis in mice

Background: Gene expression changes induced by carcinogens may identify differences in molecular function between target and non-target organs. Target organs for benzo[a]pyrene (BaP) carcinogenicity in mice (lung, spleen and forestomach) and three non-target organs (liver, colon and glandular stomach) were investigated for DNA adducts by 32P-postlabelling, for gene expression changes by cDNA microarray and for miRNA expression changes by miRNA microarray after exposure of animals to BaP. Results: BaP-DNA adduct formation occurred in all six organs at levels that did not distinguish between target and non-target. cDNA microarray analysis showed a variety of genes modulated significantly by BaP in the six organs and the overall gene expression patterns were tissue specific. Gene ontology analysis also revealed that BaP-induced bioactivities were tissue specific; eight genes (Tubb5, Fos, Cdh1, Cyp1a1, Apc, Myc, Ctnnb1 and Cav) showed significant expression difference between three target and three non-target organs. Additionally, several gene expression changes, such as in Trp53 activation and Stat3 activity suggested some similarities in molecular mechanisms in two target organs (lung and spleen), which were not found in the other four organs. Changes in miRNA expression were generally tissue specific, involving, in total, 21/54 miRNAs significantly up- or down-regulated. Conclusions: Altogether, these findings showed that DNA adduct levels and early gene expression changes did not fully distinguish target from non-target organs. However, mechanisms related to early changes in p53, Stat3 and Wnt/β-catenin pathways may play roles in defining BaP organotropism

Microstructure Control and Performance Evolution of Aluminum Alloy 7075 by Nano-Treating.

Nano-treating is a novel concept wherein a low percentage of nanoparticles is used for microstructural control and property tuning in metals and alloys. The nano-treating of AA7075 was investigated to control its microstructure and improve its structural stability for high performance. After treatment with TiC nanoparticles, the grains were significantly refined from coarse dendrites of hundreds of micrometers to fine equiaxial ones smaller than 20 μm. After T6 heat treatment, the grains, with an average size of 18.5 μm, remained almost unchanged, demonstrating an excellent thermal stability. It was found that besides of growth restriction factor by pinning behavior on grain boundries, TiC nanoparticles served as both an effective nucleation agent for primary grains and an effective secondary phase modifier in AA7075. Furthermore, the mechanical properties of nano-treated AA7075 were improved over those of the pure alloy. Thus, nano-treating provides a new method to enhance the performance of aluminum alloys for numerous applications

Prediction of superconducting properties of materials based on machine learning models

The application of superconducting materials is becoming more and more widespread. Traditionally, the discovery of new superconducting materials relies on the experience of experts and a large number of "trial and error" experiments, which not only increases the cost of experiments but also prolongs the period of discovering new superconducting materials. In recent years, machine learning has been increasingly applied to materials science. Based on this, this manuscript proposes the use of XGBoost model to identify superconductors; the first application of deep forest model to predict the critical temperature of superconductors; the first application of deep forest to predict the band gap of materials; and application of a new sub-network model to predict the Fermi energy level of materials. Compared with our known similar literature, all the above algorithms reach state-of-the-art. Finally, this manuscript uses the above models to search the COD public dataset and identify 50 candidate superconducting materials with possible critical temperature greater than 90 K

Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase

Situating at the core of Artificial Intelligence (AI), Machine Learning (ML), and more specifically, Deep Learning (DL) have embraced great success in the past two decades. However, unseen class label prediction is far less explored due to missing classes being invisible in training ML or DL models. In this work, we propose a fuzzy inference system to cope with such a challenge by adopting TSK+ fuzzy inference engine in conjunction with the Curvature-based Feature Selection (CFS) method. The practical feasibility of our system has been evaluated by predicting the positioning labels of networking devices within the realm of the Internet of Things (IoT). Competitive prediction performance confirms the efficiency and efficacy of our system, especially when a large number of continuous class labels are unseen during the model training stage.Comment: Accepted by International Conference on Internet of Things, Big Data and Security (IoTBDS) 202

Entangling ferrimagnetic magnons with an atomic ensemble via opto-magnomechanics

We show how to prepare macroscopic entanglement between an atomic ensemble and a large number of magnons in a ferrimagnetic YIG crystal. Specifically, we adopt an opto-magnomechanical configuration where the magnetostriction-induced magnomechanical displacement couples to an optical cavity via radiation pressure, and the latter further couples to an ensemble of two-level atoms that are placed inside the cavity. We show that by properly driving the cavity and magnon modes, optomechanical entanglement is created which is further distributed to the atomic and magnonic systems, yielding stationary entanglement between atoms and magnons. The atom-magnon entanglement is a result of the combined effect of opto- and magnomechanical cooling and optomechanical parametric down-conversion interactions. A competition mechanism between two mechanical cooling channels is revealed. We further show that genuine tripartite entanglement of three massive subsystems, i.e., atoms, magnons and phonons, can also be achieved in the same system. Our results indicate that the hybrid opto-magnomechanical system may become a promising system for preparing macroscopic quantum states involving magnons, photons, phonons and atoms