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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.

BackgroundVision-dependent mechanisms play a role in myopia progression in childhood. Thus, we investigated the distribution of ocular and corneal higher-order aberrations (HOAs) in highly myopic Chinese children and adolescents and the relationship between HOA components and 1-year axial eye growth.MethodsBaseline cycloplegic ocular and corneal HOAs, axial length (AL), spherical equivalent (SE), astigmatism and interpupillary distance (IPD) were determined for the right eyes of 458 highly myopic (SE ≤−5.0D) subjects. HOAs were compared among baseline age groups (≤12 years, 13–15 years and 16–18 years). Ninety-nine subjects completed the 1-year follow-up. Linear mixed model analyses were applied to determine the association between HOA components, other known confounding variables (age, gender, SE, astigmatism and IPD) and axial growth. A comparison with data from an early study of moderate myopia were conducted.ResultsAlmost all ocular HOAs and few corneal HOAs exhibited significant differences between different age groups (all p<0.05). After 1 year, only ocular HOA components was significantly negative associated with a longer AL, including secondary horizontal comatic aberration (p=0.019), primary spherical aberration (p<0.001) and spherical HOA (p=0.026). Comparing with the moderate myopia data, the association of comatic aberration with AL growth was only found in high myopia.ConclusionIn highly myopic children and adolescents, lower levels of annual ocular secondary horizontal comatic aberration changes, besides spherical aberrations, were associated with axial elongation. This suggests that ocular HOA plays a potential role in refractive development in high myopia.

Background Owing to the lack of effective treatment options, early metastasis remains the major cause of pancreatic ductal adenocarcinoma (PDAC) recurrence and mortality. However, the molecular mechanism of early metastasis is largely unknown. We characterized the function of eukaryotic translation initiation factors (eIFs) in epithelial-mesenchymal-transition (EMT) and metastasis in pancreatic cancer cells to investigate whether eIFs and downstream c-MYC affect EMT and metastasis by joint interference. Methods We used The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to analyze eIF4A1 expression in PDAC tissues and further validated the findings with a microarray containing 53 PDAC samples. Expression regulation and pharmacological inhibition of eIF4A1 and c-MYC were performed to determine their role in migration, invasion, and metastasis in pancreatic cancer cells in vitro and in vivo. Results Elevated eIF4A1 expression was positively correlated with lymph node infiltration, tumor size, and indicated a poor prognosis. eIF4A1 decreased E-cadherin expression through the c-MYC/miR-9 axis. Loss of eIF4A1 and c-MYC decreased the EMT and metastasis capabilities of pancreatic cancer cells, whereas upregulation of eIF4A1 attenuated the inhibition of EMT and metastasis induced by c-MYC downregulation. Treatment with the eIF4A1 inhibitor rocaglamide (RocA) or the c-MYC inhibitor Mycro3 either alone or in combination significantly decreased the expression level of EMT markers in pancreatic cancer cells in vitro. However, the efficiency and safety of RocA alone were not inferior to those of the combination treatment in vivo. Conclusion Overexpression of eIF4A1 downregulated E-cadherin expression through the c-MYC/miR-9 axis, which promoted EMT and metastasis of pancreatic cancer cells. Despite the potential feedback loop between eIF4A1 and c-MYC, RocA monotherapy is a promising treatment inhibiting eIF4A1-induced PDAC metastasis.

The camphor tree (Cinnamomum camphora (L.) Presl.) is the representative species of subtropical evergreen broadleaved forests in eastern Asia and an important raw material for essential oil production worldwide. Although MYBs have been comprehensively characterized and their functions have been partially resolved in many plants, it has not been explored in C. camphora. In this study, 121 CcMYBs were identified on 12 chromosomes in the whole genome of C. camphora and found that CcMYBs were mainly expanded by segmental duplication. They were divided into 28 subgroups based on phylogenetic analysis and gene structural characteristics. In the promoter regions, numerous cis-acting elements were related to biological processes. Analysis of RNA sequencing data from seven tissues showed that CcMYBs exhibited different expression profiles, suggesting that they have various roles in camphor tree development. In addition, combined with the correlation analysis of structural genes in the flavonoid synthesis pathway, we identified CcMYBs from three subgroups that might be related to the flavonoid biosynthesis pathway. This study systematically analyzed CcMYBs in C. camphora, which will set the stage for subsequent research on the functions of CcMYBs during their lifetime and provide valuable insights for the genetic improvement of camphor trees.

Staphylococcus aureus (S. aureus), a Gram-positive bacteria, is an incurable cause of hospital and community-acquired infections. Inhibition bacterial virulence is a viable strategy against S. aureus infections based on the multiple virulence factors secreted by S. aureus. Alpha-hemolysin (Hla) plays a crucial role in bacteria virulence without affecting bacterial viability. Here, we identified that 7,8-Dihydroxyflavone (7,8-DHF), a natural compound, was able to decrease the expression of and did not affect the in vitro growth of S. aureus USA300 at a concentration of 32 μg/mL. It was verified by western blot and RT-qPCR that the natural compound could inhibit the transcription and translation of Hla. Further mechanism studies revealed that 7,8-DHF has a negative effect on transcriptional regulator agrA and RNAIII, preventing the upregulation of virulence gene. Cytotoxicity assays showed that 7,8-DHF did not produce significant cytotoxicity to A549 cells. Animal experiments showed that the combination of 7,8-DHF and vancomycin had a more significant therapeutic effect on S. aureus infection, reflecting the synergistic effect of 7,8-DHF with antibiotics. In conclusion, 7,8-DHF was able to target Hla to protect host cells from hemolysis while limiting the development of bacterial resistance.

The iMeta Conference 2024 provides a platform to promote the development of an innovative scientific research ecosystem for microbiome and One Health. The four key components ‐ Technology, Research (Biology), Academic journals, and Social media ‐ form a synergistic ecosystem. Advanced technologies drive biological research, which generates novel insights that are disseminated through academic journals. Social media plays a crucial role in engaging the public and facilitating scientific communication, thus amplifying the impact of research. Together, these elements create a self‐sustaining loop that fosters continuous innovation and collaboration in the field of bioinformatics, biotechnology and microbiome research.

Functional data analysis (FDA) deals with high-resolution data recorded over a continuum, such as time, space or frequency. Device-based assessments of physical activity or sleep are objective yet still prone to measurement error. We present MECfda, an R package that (i) fits scalar-on-function, generalized scalar-on-function, and functional quantile regression models, and (ii) provides bias-corrected estimation when functional covariates are measured with error. By unifying these tools under a consistent syntax, MECfda enables robust inference for FDA applications that involve noisy functional data.