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Distribution network of smart grid imposes higher requirement on data processing of next-generation consumer electronics devices. Container-based cloud-edge-device collaboration improves quality of service (QoS) by flexible data offloading and parallel data processing. However, the matching among consumer electronics devices, servers, and containers, and container competition further complicates the problem. In this paper, we propose three-dimensional learning-matching-based joint selection algorithm of server and container for multi-service data processing. The formulated problem is firstly reduced to a two-dimensional problem by combination-based dimension reduction. Then, it is solved by the proposed dynamic learning-matching price rising mechanism based on service priority, data size, and upper confidence bound. By avoiding low-priority service preempting container resources of high-priority service, simulation results validate that the proposed algorithm has superior delay reduction performance.

Background: Power distribution Internet of Things is a smart service system in which traditional distribution networks comprehensively apply modern computing technologies, such as cloud computing, mobile Internet of Things, and big data to realize the interconnection of all things in the power system and human-computer interaction. In the power distribution Internet of Things, there are many types of main power distribution equipment, and each type of power distribution equipment is connected with multiple sensor devices. These sensor devices play an important role in the smooth operation of the power distribution Internet of Things, so it is necessary to build a reliability evaluation system for these sensor devices.Objectives: The objective was to evaluate the reliability of the sensor device connected to the main equipment in the power distribution Internet of Things.Methods: Firstly, the G1-CV method (entropy weight method-coefficient of variation method) is used to construct the initial evaluation index system, and the comprehensive weight of each index is determined and ranked. Then the improved TriMap algorithm (ITriMap) is used to reduce the high-dimensional complex index system.Results: Simulation verification results show that the algorithm has a smaller reconstruction error than traditional dimensionality reduction algorithms such as t-SNE, Largr-Vias, UMAP, etc. The reduced index can scientifically and objectively measure the main equipment sensors of the power distribution network.Conclusion: An index reduction method based on the ITriMap algorithm is proposed to evaluate the reliability of sensors. Experimental results show that this algorithm greatly reduces the reconstruction error compared to other traditional methods.Discussion: In the future, on the basis of this article, we intend to construct a sensor reliability evaluation model for the main equipment of the power distribution Internet of Things using the 5- dimensional features after dimensionality reduction as input.

miR-30 is a microRNA frequently overexpressed in human cancers. However, the biological consequence of miR-30 overexpression in cancer has been unclear. In a genetic screen, miR-30 was found to abrogate oncogenic-induced senescence, a key tumor-suppressing mechanism that involves DNA damage responses, activation of p53 and induction of p16INK4A. In cells and mouse models, miR-30 disrupts senescence and promotes cancer by suppressing 2 targets, CHD7 and TNRC6A. We show that while CHD7 is a transcriptional coactivator essential for induction of p16INK4Ain senescent cells, TNRC6A, a miRNA machinery component, is required for expression and functionality of DNA damage response RNAs (DDRNAs) that mediate DNA damage responses and p53 activation by orchestrating histone modifications, chromatin remodeling and recruitment of DNA damage factors at damaged sites. Thus, miR-30 inhibits both p16INK4Aand p53, 2 key senescence effectors, leading to efficient senescence disruption. These findings have identified novel signaling pathways mediating oncogene-induced senescence and tumor-suppression, and revealed the molecular and cellular mechanisms underlying the oncogenic activity of miR-30. Thus, the miR-30/CHD7/TNRC6A pathway is potentially a novel diagnostic biomarker and therapeutic target for cancer.

Background: IsomiR is an isoform of microRNA (miRNA), and its sequences vary from those of a reference miRNA, which arose with the advencements of deep sequencing, high miRNA variability has been detected from the same miRNA precursor. IsomiR exists in four main types formed through the following processes: 5' or 3' trimming, Nucleotide addition, Nucleotide removal, and posttranscriptional RNA editing. Objective: For cancer diagnosis, it needs to explore differential expression profiles which can be used to distinguish cancer and normal cell lines, especially in the isomiR-mRNA regulatory networks, because aberrant isomiR expression profiles may contribute to tumorigenesis. Method: We extracted five features of the isomiR read counts from RNA-SEQ data in TCGA, with a random forest classification algorithm, these features were applied to diagnose six cancers: breast invasive carcinoma, lung adenocarcinoma, squamous-cell carcinoma of the lung, stomach adenocarcinoma, thyroid carcinoma, and uterine corpus endometrial carcinoma. Results: Compare with the classifier libD3C, our method can be utilized to distinguish cancers from their normal counterparts by performance based on sn, sp, ACC and MCC measures. Conclusion: IsomiR can be successfully and effectively used to diagnose cancer through machine learning method from high-throughput data.

Solvent additive processing is important in optimizing an active layer’s morphology and thus improving the performance of organic solar cells (OSCs). In this study, we find that how 1,8-diiodooctane (DIO) additive is removed plays a critical role in determining the film morphology of the bulk heterojunction OSCs in inverted structure based on a porphyrin small molecule. Different from the cases reported for polymer-based OSCs in conventional structures, the inverted OSCs upon the quick removal of the additive either by quick vacuuming or methanol washing exhibit poorer performance. In contrast, the devices after keeping the active layers in ambient pressure with additive dwelling for about 1 h (namely, additive annealing) show an enhanced power conversion efficiency up to 7.78% with a large short circuit current of 19.25 mA/cm2, which are among the best in small molecule-based solar cells. The detailed morphology analyses using UV-vis absorption spectroscopy, grazing incidence X-ray diffraction, resonant soft X-ray scattering, and atomic force microscopy demonstrate that the active layer shows smaller-sized phase separation but improved structure order upon additive annealing. On the contrary, the quick removal of the additive either by quick vacuuming or methanol washing keeps the active layers in an earlier stage of large scaled phase separation.