The association of ambient PM2.5 with school absence and symptoms in schoolchildren: a panel study

BACKGROUND: Children are a susceptible population to exposure of ambient fine particulate air pollution (PM2.5), and the associated symptoms are sensitive prevalent indicators of morbidity. However, few studies to date investigate the association between PM2.5 exposure and school absence and symptoms. METHODS: In a panel study including 20,291 observations in 615 schoolchildren 8-13 years of age, we asked the participants to record their school absence and symptoms on every school day from 17 November to 31 December 2014 in Jinan, China. We used the generalized linear mixed effects models to examine the adverse effects of ambient PM2.5 on school absence and symptoms, adjusting for covariates including meteorological and individual factors. RESULTS: The 3-day moving average of PM2.5 was significantly associated with school absence (1.37; 95% CI: 1.07-1.74) and increases in symptoms of the throat (1.03; 95% CI: 1.00-1.05), nose (1.03; 95% CI: 1.01-1.06), and skin (1.09; 95% CI: 1.06-1.12). High PM2.5 exposure also increased the risks of individual symptoms, especially for cough (1.02; 95% CI: 1.00-1.04), sneezing (1.03; 95% CI: 1.00-1.07), and stuffy nose (1.09; 95% CI: 1.02-1.17). CONCLUSION: High PM2.5 exposure is a risk factor for the health of schoolchildren. Allocation of medical resources for children should take into account the ambient PM2.5 concentrations and be proportioned accordingly

Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification

Failure to recognize samples from the classes unseen during training is a major limit of artificial intelligence (AI) in real-world implementation of retinal anomaly classification. To resolve this obstacle, we propose an uncertainty-inspired open-set (UIOS) model which was trained with fundus images of 9 common retinal conditions. Besides the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external testing set and non-typical testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicted high uncertainty scores, which prompted the need for a manual check, in the datasets of rare retinal diseases, low-quality fundus images, and non-fundus images. This work provides a robust method for real-world screening of retinal anomalies

Exploring The Potential of GPT-3-based Large Language Model For Melody Generation

<p>Here, we provide the dataset used, all generated melodies and melodies used to conduct subjective listening test.</p&gt

An end-to-end fault diagnostics method based on convolutional neural network for rotating machinery with multiple case studies

International audienc

Online Surface Roughness Prediction for Assembly Interfaces of Vertical Tail Integrating Tool Wear under Variable Cutting Parameters

Monitoring surface quality during machining has considerable practical significance for the performance of high-value products, particularly for their assembly interfaces. Surface roughness is the most important metric of surface quality. Currently, the research on online surface roughness prediction has several limitations. The effect of tool wear variation on surface roughness is seldom considered in machining. In addition, the deterioration trend of surface roughness and tool wear differs under variable cutting parameters. The prediction models trained under one set of cutting parameters fail when cutting parameters change. Accordingly, to timely monitor the surface quality of assembly interfaces of high-value products, this paper proposes a surface roughness prediction method that considers the tool wear variation under variable cutting parameters. In this method, a stacked autoencoder and long short-term memory network (SAE–LSTM) is designed as the fundamental surface roughness prediction model using tool wear conditions and sensor signals as inputs. The transfer learning strategy is applied to the SAE–LSTM such that the surface roughness online prediction under variable cutting parameters can be realized. Machining experiments for the assembly interface (using Ti6Al4V as material) of an aircraft’s vertical tail are conducted, and monitoring data are used to validate the proposed method. Ablation studies are implemented to evaluate the key modules of the proposed model. The experimental results show that the proposed method outperforms other models and is capable of tracking the true surface roughness with time. Specifically, the minimum values of the root mean square error and mean absolute percentage error of the prediction results after transfer learning are 0.027 μm and 1.56%, respectively

EXPLORING THE POTENTIAL OF LARGE LANGUAGE MODEL FOR MELODY GENERATION

<p>Here, we provide four sets of MIDI files generated in the experiments and corresponding ground-truth MIDI files. </p&gt

Effect of Sodium Selenite Concentration and Culture Time on Extracellular and Intracellular Metabolite Profiles of Epichloë sp. Isolated from Festuca sinensis in Liquid Culture

Selenium (Se) is not only an essential trace element critical for the proper functioning of an organism, but it is also an abiotic stressor that affects an organism’s growth and metabolite profile. In this study, Epichloë sp. from Festuca sinensis was exposed to increasing concentrations of Na2SeO3 (0, 0.1, and 0.2 mmol/L) in a liquid media for eight weeks. The mycelia and fermentation broth of Epichloë sp. were collected from four to eight weeks of cultivation. The mycelial biomass decreased in response to increased Se concentrations, and biomass accumulation peaked at week five. Using gas chromatography-mass spectrometry (GC-MS), approximately 157 and 197 metabolites were determined in the fermentation broth and mycelia, respectively. Diverse changes in extracellular and intracellular metabolites were observed in Epichloë sp. throughout the cultivation period in Se conditions. Some metabolites accumulated in the fermentation broth, while others decreased after different times of Se exposure compared to the control media. However, some metabolites were present at lower concentrations in the mycelia when cultivated with Se. The changes in metabolites under Se conditions were dynamic over the experimental period and were involved in amino acids, carbohydrates, organic acids, fatty acids, and nucleotides. Based on these results, we conclude that selenite concentrations and culture time influence the growth, extracellular and intracellular metabolite profiles of Epichloë sp. from F. sinensis

A Transcriptomic and Metabolomic Study on the Biosynthesis of Iridoids in <i>Phlomoides rotata</i> from the Qinghai–Tibet Plateau

Phlomoides rotata is a traditional Chinese herbal medicine that grows in the Qinghai–Tibet Plateau region at a 3100–5000 m altitude. Iridoid compounds are the main active compounds of the P. rotata used as medical ingredients and display anti-inflammatory, analgesic, and hepatoprotective properties. To better understand the biological mechanisms of iridoid compounds in this species, we performed a comprehensive analysis of the transcriptome and metabolome of P. rotata leaves from four different regions (3540–4270 m). Global metabolome profiling detected 575 metabolites, and 455 differentially accumulated metabolites (DAMs) were detected in P. rotata leaves from the four regions. Eight major DAMs related to iridoid metabolism in P. rotata leaves were investigated: shanzhiside methyl ester, 8-epideoxyloganic acid, barlerin, shanzhiside, geniposide, agnuside, feretoside, and catalpin. In addition, five soil physical and chemical indicators in P. rotata rhizosphere soils were analyzed. Four significant positive correlations were observed between alkaline nitrogen and geniposide, exchangeable calcium and geniposide, available potassium and shanzhiside, and available phosphorus and shanzhiside methyl ester. The transcriptome data showed 12 P. rotata cDNA libraries with 74.46 Gb of clean data, which formed 29,833 unigenes. Moreover, 78.91% of the unigenes were annotated using the eight public databases. Forty-one candidate genes representing 23 enzymes involved in the biosynthesis of iridoid compounds were identified in P. rotata leaves. Moreover, the DXS1, IDI1, 8-HGO1, and G10H2 genes associated with iridoid biosynthesis were specifically expressed in P. rotata. The integration of transcriptome and metabolome analyses highlights the crucial role of soil physical and chemical indicators and major gene expression related to iridoid metabolism pathways in P. rotata from different areas. Our findings provide a theoretical foundation for exploring the molecular mechanisms underlying iridoid compound accumulation in P. rotata

Design and performance research of single rubber cylinder for high pressure gas injection packer

In order to solve the problem of &quot;gas explosion&quot; at the end of common rubber cylinder in the process of high temperature, high pressure and gas drive operation, the rubber cylinder with new structure suitable for 51/2 in casing pipe is developed. The &quot;M&quot; type single rubber cylinder structure is adopted in the new structure rubber cylinder, and the &quot;gas explosion&quot; problem of the end gas in the low-pressure side is solved by setting the double-layer staggered slotted steel cover to prevent outburst. The finite element method is used to simulate the setting of the rubber cylinder, and the structural parameters of the new rubber cylinder are obtained by single factor analysis and orthogonal optimization, simulation test and seal test were carried out to verify the sealing performance of the rubber cylinder. According to the actual working condition, the simulation test results and seal test results show that the sealing capacity of the packer reaches 50 MPa under the temperature resistance of 120℃, and the end steel cover is fully opened to wrap the rubber cylinder, which meets the operation requirements of high temperature and high pressure gas injection packer