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This study aims to develop a digital retrieval system for art museums to solve the problems of inaccurate information and low retrieval efficiency in the digital management of cultural heritage. By introducing an improved Genetic Algorithm (GA), digital management and access efficiency are enhanced, to bring substantial optimization and innovation to the digital management of cultural heritage. Based on the collection of art museums, this study first integrates the collection's images, texts, and metadata with multi-source intelligent information to achieve a more accurate and comprehensive description of digital content. Second, a GA is introduced, and a GA 2 Convolutional Neural Network (GA2CNN) optimization model combining domain knowledge is proposed. Moreover, the convergence speed of traditional GA is improved to adapt to the characteristics of cultural heritage data. Lastly, the Convolutional Neural Network (CNN), GA, and GA2CNN are compared to verify the proposed system's superiority. The results show that in all models, the sample output results' actual value is 2.62, which represents the real data observation results. For sample number 5, compared with the actual value of 2.62, the predicted values of the GA2CNN and GA models are 2.6177 and 2.6313, and their errors are 0.0023 and 0.0113. The CNN model's predicted value is 2.6237, with an error of 0.0037. It can be found that the network fitting accuracy after optimization of the GA2CNN model is high, and the predicted value is very close to the actual value. The digital retrieval system integrated with the GA2CNN model has a good performance in enhancing retrieval efficiency and accuracy. This study provides technical support for the digital organization and display of cultural heritage and offers valuable references for innovative exploration of museum information management in the digital era. [ABSTRACT FROM AUTHOR]

Aiming at the high dimensional problem of pathological image morphometric features and the small amount of applied samples in medical field, this paper presents the ReliefF-HEPSO head and neck cancer pathological image feature selection algorithm. The algorithm constructs a framework of multi-level dimensionality reduction. Firstly, according to the correlation of features and categories, the ReliefF algorithm is used to determine different feature weights to achieve initial dimensionality reduction. Secondly, the evolutionary neural strategy (ENS) is used to enrich the particle populations diversity of the binary particle swarm optimization (BPSO). A hybrid binary evolutionary particle swarm optimization (HEPSO) algorithm is proposed to automatically search for the best feature subsets of candidate feature subsets. Compared with 7 feature selection algorithms, the experiment proves that the algorithm can effectively screen out the morphological features of high correlation pathology images, achieve rapid dimensionality reduction, and obtain higher classification performance with fewer features.

The ocean surface current influences the roughness of the sea surface, subsequently affecting the scatterometer's measurement of wind speed. In this study, the effect of surface currents on ASCAT-retrieved winds is investigated based on in-situ observations of both surface winds and currents from 40 open ocean moored buoys in the tropical and mid-latitude oceans. A total of 28,803 data triplets, consisting of buoy-observed wind vectors, current vectors, and ASCAT Level 2 wind vectors, were collected from the dataset spanning over 10 years. It is found that the bias between scatterometer-retrieved wind speed and buoy-observed wind speed is negatively correlated with the ocean surface current speed. The wind speed bias is approximately 0.96 times the magnitude of the downwind surface current. The root-mean-square error between the ASCAT wind speeds and buoy observations is reduced by about 15% if rectification with ocean surface currents is involved. Therefore, it is essential to incorporate surface current information into wind speed calibration, particularly in regions with strong surface currents. [ABSTRACT FROM AUTHOR]

Dysregulated microRNAs (miRNAs) are involved in carcinoma progression, metastasis, and poor prognosis. We demonstrated that in nasopharyngeal carcinoma (NPC), transactivated MIR106A-5p promotes a malignant phenotype by functioning as a macroautophagy/autophagy suppressor by targeting BTG3 (BTG anti-proliferation factor 3) and activating autophagy-regulating MAPK signaling. MIR106A-5p expression was markedly increased in NPC cases based on quantitative real-time PCR, miRNA microarray, and TCGA database analysis findings. Moreover, MIR106A-5p was correlated with advanced stage, recurrence, and poor clinical outcomes in NPC patients. In addition to three-dimensional cell culture assays, zebrafish and BALB/c mouse tumor models revealed that overexpressed MIR106A-5p targeted BTG3 and accelerated the NPC malignant phenotype by inhibiting autophagy. BTG3 promoted autophagy, and its expression was correlated with poor prognosis in NPC. Attenuation of autophagy, mediated by the MIR106A-5p-BTG3 axis, occurred because of MAPK pathway activation. MIR106A-5p overexpression in NPC was due to increased transactivation by EGR1 and SOX9. Our findings may lead to novel insights into the pathogenesis of NPC. Abbreviations: ACTB: actin beta; ATG: autophagy-related; ATG5: autophagy related 5; BLI: bioluminescence; BTG3: BTG anti-proliferation factor 3; CASP3: caspase 3; ChIP: chromatin immunoprecipitation; CQ: chloroquine; Ct: threshold cycle; DAPI: 4ʹ,6-diamidino-2-phenylindole; DiL: 1,1ʹ-dioctadecyl-3,3,3ʹ,3ʹ-tetramethylindocarbocyanine perchlorate; EBSS: Earle's balanced salt solution; EGR1: early growth response 1; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GEO: Gene Expression Omnibus; GFP: green fluorescent protein; IF: immunofluorescence; IHC: immunohistochemistry; ISH: in situ hybridization; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; MIR106A-5p: microRNA 106a-5p; miRNAs: microRNAs; MKI67: marker of proliferation ki-67; mRNA: messenger RNA; MTOR: mechanistic target of rapamycin kinase; NPC: nasopharyngeal carcinoma; qRT-PCR: quantitative real-time PCR; siRNA: small interfering RNA; SOX9: SRY-box transcription factor 9; SQSTM1: sequestosome 1; TCGA: The Cancer Genome Atlas; WB: western blot. [ABSTRACT FROM AUTHOR]