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Background Wearable smart watches provide large amount of real-time data on the environmental state of the users and are useful to determine risk factors for onset and progression of myopia. We aim to evaluate the efficacy of machine learning algorithm in differentiating indoor and outdoor locations as collected by use of smart watches. Methods Real time data on luminance, ultraviolet light levels and number of steps obtained with smart watches from dataset A: 12 adults from 8 scenes and manually recorded true locations. 70% of data was considered training set and support vector machine (SVM) algorithm generated using the variables to create a classification system. Data collected manually by the adults was the reference. The algorithm was used for predicting the location of the remaining 30% of dataset A. Accuracy was defined as the number of correct predictions divided by all. Similarly, data was corrected from dataset B: 172 children from 3 schools and 12 supervisors recorded true locations. Data collected by the supervisors was the reference. SVM model trained from dataset A was used to predict the location of dataset B for validation. Finally, we predicted the location of dataset B using the SVM model self-trained from dataset B. We repeated these three predictions with traditional univariate threshold segmentation method. Results In both datasets, SVM outperformed the univariate threshold segmentation method. In dataset A, the accuracy and AUC of SVM were 99.55% and 0.99 as compared to 95.11% and 0.95 with the univariate threshold segmentation (p < 0.01). In validation, the accuracy and AUC of SVM were 82.67% and 0.90 compared to 80.88% and 0.85 with the univariate threshold segmentation method (p < 0.01). In dataset B, the accuracy and AUC of SVM and AUC were 92.43% and 0.96 compared to 80.88% and 0.85 with the univariate threshold segmentation (p < 0.01). Conclusions Machine learning algorithm allows for discrimination of outdoor versus indoor environments with high accuracy and provides an opportunity to study and determine the role of environmental risk factors in onset and progression of myopia. The accuracy of machine learning algorithm could be improved if the model is trained with the dataset itself.

This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as the voltage of the generator, angle of attack, and position of the rudder surface. A UAV is a typical complex system, and its flight data are typical high-dimensional large sample data sets. In practical applications such as UAV fault detection, the fault data only appear in a small part of the data sets. In this study, representation learning is used to extract the normal features of the flight data and reduce the dimensions of the data. The normal data are used for the training of the Auto-Encoder, and the reconstruction loss is used as the criterion for fault detection. An Improved Auto-Encoder suitable for UAV Flight Data Sets is proposed in this paper. In the Auto-Encoder, we use wavelet analysis to extract the low-frequency signals with different frequencies from the flight data. The Auto-Encoder is used for the feature extraction and reconstruction of the low-frequency signals with different frequencies. To improve the effectiveness of the fault localization at inference, we develop a new fault factor location model, which is based on the reconstruction loss of the Auto-Encoder and edge detection operator. The UAV Flight Data Sets are used for hard-landing detection, and an average accuracy of 91.01% is obtained. Compared with other models, the results suggest that the developed Self-supervised Fault Detection Model for UAVs has better accuracy. Concluding this study, an explanation is provided concerning the proposed model’s good results.

Background. Ten-eleven translocation (TET) enzymes catalyze the oxidation of 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC) promoting demethylation in cells. However, the expression pattern and biologic significance of TET in papillary thyroid carcinoma (PTC) remain unclear. This study aimed to elucidate the biological functions of TET1 and the miRNA and mRNA expression levels in PTC cells with downregulated TET1. Methods. The expression of the TET family in 49 PTC tissues and corresponding tumor-adjacent tissues, as well as PTC cell lines (BCPAP, K1, and TPC-1) and the normal thyroid epithelial cell line (Nthy-ori 3-1), were detected using quantitative real-time polymerase chain reaction. The 5hmC level was detected in PTC tissues and cell lines using immunohistochemistry and dot blot assay, respectively. After silencing the TET1 gene with siRNAs in BCPAP and TPC-1 cells, cell proliferation was detected using EdU assay. Transwell assay was used to investigate cell migration and invasion. miRNA and mRNA expression arrays were conducted in TET1-depleted BCPAP cells. Results. The expression level of TET1 decreased in PTC tissues and cell lines and was consistent with the reduction in the 5hmC level. The knockdown of the TET1 gene promoted cell migration and invasion in BCPAP cells. The expression of miR-7, miR-15/16 cluster, and let-7 family was downregulated, while the expression of let-7e was upregulated after siRNA-TET1 treatment in BCPAP cells. The expression of WNT4, FZD4, CDK6, MCF2L, and EDN1 was upregulated as potential target genes of dysregulated miRNAs. Conclusion. The study showed that TET1 dysfunction inhibited the migration and invasion of BCPAP cells and might have a potential role in the pathogenesis of PTC.

How to optimize the quality of the truss so that the truss meets the load-bearing requirements has always been a key issue of research. In this paper, a genetic algorithm (GA) and finite element analysis (FEA) based optimization method is proposed for the size and topology of a space truss. According to the results of space truss, four kinds of components are divided and their cross-sectional areas are optimized respectively. The coded value on the chromosome in GA is used to represent the truss topology, and the adjacent value represents the adjacent truss structure. The nodal displacement and member stress of the truss are solved by finite element method to ensure the safety of the truss. All work is done using python.

Purpose Studies have shown that circRNA is involved in the occurrence and development of human cancers. However, it remains unclear that the contribution of circRNA in thyroid carcinoma and its role in the process of tumorigenesis. Methods The expression profile of circRNA-miRNA-mRNA in thyroid carcinoma was detected by RNA sequencing and verified by qRT-PCR. The characteristics of circGLIS3 were verified by RNase R and actinomycin assays, subcellular fractionation, and fluorescence in situ hybridization. The functions of circGLIS3 and AIF1L were detected by wound healing, transwell, 3D culture and Western blot. RNA Immunoprecipitation (RIP), RNA pulldown and dual-luciferase reporter assays were used to verify the target genes of circGLIS3 and downstream miRNAs. Functional rescue experiments were performed by transfecting miRNA mimics or siRNA of target genes. Finally, metastatic mouse models were used to investigate circGLIS3 function in vivo. Results In this study, we discovered a novel circRNA (has_circ_0007368, named as circGLIS3) by RNA sequencing. CircGLIS3 was down-regulated in thyroid carcinoma tissues and cells line, and was negatively associated with malignant clinical features of thyroid carcinoma. Functional studies found that circGLIS3 could inhibit the migration and invasion of thyroid carcinoma cells, and was related to the EMT process. Mechanistically, circGLIS3 can upregulate the expression of the AIF1L gene by acting as a miR-146b-3p sponge to inhibit the progression of thyroid carcinoma. Conclusion Our study identified circGLIS3 as a novel tumor suppressor in thyroid cancer, indicating the potential of circGLIS3 as a promising diagnostic and prognostic marker for thyroid cancer.

Aortic aneurysm and dissection (AAD) are life-threatening conditions without effective medications. Impaired contractility of vascular smooth muscle cells (VSMCs) is strongly linked to AAD, but the role of calcium channel blockers (CCBs), which directly inhibits VSMC contractility, in AAD remains unclear. Here we showed data from 501,878 initially AAD-free participants in UK Biobank. Over a median follow-up of 13.5 years, CCB users had higher AAD risk (HR = 1.31) than hypertensive patients not receiving antihypertensive treatment. In mouse models of AAD, CCBs significantly aggravated aortic stiffness and AAD development. For patients with type B aortic dissection who underwent endovascular repair, CCBs limited AAD regression compared with other antihypertensives. Moreover, silencing of protein kinase cGMP-dependent 1 (PRKG1) significantly mitigated CCB-aggravated AAD progression. These findings suggest that CCBs may increase AAD risk and post-stent surgery prognosis, highlighting the need for caution when prescribing CCBs to hypertensive patients at risk for AAD. Aortic aneurysm and dissection are lethal vascular diseases lacking effective medical therapy. Here the authors show that calcium channel blocker use increases AAD risk, worsens disease progression in models and patients, and may impair post-repair outcomes, warranting caution in at-risk hypertension.

The orange-spotted grouper (Epinephelus coioides) is an important mariculture fish in China. However, in recent years, with the rapid development of aquaculture activities, outbreaks of viral diseases have affected the grouper aquaculture industry, causing severe economic losses. In the present study, we isolated and identified a virus from diseased, orange-spotted groupers from an aquaculture farm in Hainan Province, China. The isolated virus was identified as orange-spotted grouper iridovirus, hence named the orange-spotted grouper iridovirus Hainan strain (OSGIV-HN-2018-001). OSGIV-HN-2018-001 induces a cytopathic effect after the infection of mandarin fish (Siniperca chuatsi) brain clonal passage (SBC) cells. In addition, the cytoplasm of the OSGIV-HN-2018-001-infected SBC cells was found to contain a large number of hexagonal virus particles with a diameter of approximately 134 nm. Using the Illumina NovaSeq system, we assembled the sequence data and annotated the complete genome of OSGIV-HN-2018-001 (GenBank accession number: PP974677), which consisted of 110,699 bp and contained 122 open reading frames (ORFs). Phylogenetic tree analysis showed that OSGIV-HN-2018-001 was most closely related to ISKNV-ASB-23. The cumulative mortality rate of groupers infected with OSGIV-HN-2018-001 reached 100% on day 8. The spleens were enlarged and blackened after the dissection of the dying groupers. These results contribute to the understanding of the molecular regulatory mechanism of the iridovirus infection and provide a basis for iridovirus prevention.

The homeostasis of the microbiome in lower respiratory tract is crucial in sustaining normal physiological functions of the lung. Different pulmonary diseases display varying degrees of microbiome imbalance; however, the specific variability and clinical significance of their microbiomes remain largely unexplored. In this study, we delineated the pathogen spectrum and commensal microorganisms in the lower respiratory tract of various pulmonary diseases using metagenomic sequencing. We analyzed the disparities and commonalities of the microbial features and examined their correlation with disease characteristics. We observed distinct pathogen profiles and a diversity in lower airway microbiome in patients diagnosed with cancer, interstitial lung disease, bronchiectasis, common pneumonia, Nontuberculous mycobacteria (NTM) pneumonia, and severe pneumonia. This study illustrates the utility of Metagenomic Next-generation Sequencing (mNGS) in identifying pathogens and analyzing the lower respiratory microbiome, which is important for understanding the microbiological aspect of pulmonary diseases and essential for their early and precise diagnosis.

PurposeThe aim of the study was to investigate the differential effects of interpersonal relationships across functions on product and service innovation, and to examine the moderating role of market competition.Design/methodology/approachThis study was based on a survey of senior and middle managers from 149 pharmaceutical firms in China.FindingsInterpersonal relationships between employees across functions (IR-E) have a stronger impact on product innovation than do interpersonal relationships between managers across functions (IR-M), but IR-M have a stronger impact on service innovation. Market competition strengthens the effects of IR-M on both product and service innovation, but it attenuates the effect of IR-E on service innovation.Originality/valueAlthough the effects of interpersonal relationships across functions are crucial to cross-functional interactions, these effects have received little attention in the literature. By identifying the potential “backfiring” effect of dual-level interpersonal relationships, this study contributes to knowledge of cross-functional relationships. It also deepens understanding of the relationship between cross-functional relationships and organizational innovation, especially in the service setting.