Relationship Between Outdoor Air Pollutant Exposure and Premature Delivery in China- Systematic Review and Meta-Analysis

Objective: Preterm birth (PTB) is considered as a public health problem and one of the main risk factors related to the global disease burden. The purpose of this study aims to explore the influence of exposure to major air pollutants at different pregnancies on PTB.Methods: The relationship between air pollutants and PTB in China was collected from cohort studies and case-control studies published before 30 April 2022. Meta-analysis was carried out with STATA 15.0 software.Results: A total of 2,115 papers were retrieved, of which 18 papers met the inclusion criteria. The comprehensive effect of pollutant exposure and PTB were calculated. PM2.5 during entire pregnancy and O3 exposure during third trimester were positively associated with preterm birth. Every 10 μg/m3 increase in the average concentration of PM2.5 during the whole pregnancy will increase the risk of premature delivery by 4%, and every 10 μg/m3 increase in the average concentration of O3 in the third trimester will increase the risk of premature delivery by 1%.Conclusion: Exposure to PM2.5 entire prenatal pregnancy and O3 in third trimester is associated with an increased risk of preterm birth occurrence

On the Effectiveness of Speech Self-supervised Learning for Music

Self-supervised learning (SSL) has shown promising results in various speech and natural language processing applications. However, its efficacy in music information retrieval (MIR) still remains largely unexplored. While previous SSL models pre-trained on music recordings may have been mostly closed-sourced, recent speech models such as wav2vec2.0 have shown promise in music modelling. Nevertheless, research exploring the effectiveness of applying speech SSL models to music recordings has been limited. We explore the music adaption of SSL with two distinctive speech-related models, data2vec1.0 and Hubert, and refer to them as music2vec and musicHuBERT, respectively. We train $12$ SSL models with 95M parameters under various pre-training configurations and systematically evaluate the MIR task performances with 13 different MIR tasks. Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech. However, we identify the limitations of such existing speech-oriented designs, especially in modelling polyphonic information. Based on the experimental results, empirical suggestions are also given for designing future musical SSL strategies and paradigms

MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training

Self-supervised learning (SSL) has recently emerged as a promising paradigm for training generalisable models on large-scale data in the fields of vision, text, and speech. Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored. This is primarily due to the distinctive challenges associated with modelling musical knowledge, particularly its tonal and pitched characteristics of music. To address this research gap, we propose an acoustic Music undERstanding model with large-scale self-supervised Training (MERT), which incorporates teacher models to provide pseudo labels in the masked language modelling (MLM) style acoustic pre-training. In our exploration, we identified a superior combination of teacher models, which outperforms conventional speech and audio approaches in terms of performance. This combination includes an acoustic teacher based on Residual Vector Quantization - Variational AutoEncoder (RVQ-VAE) and a musical teacher based on the Constant-Q Transform (CQT). These teachers effectively guide our student model, a BERT-style transformer encoder, to better model music audio. In addition, we introduce an in-batch noise mixture augmentation to enhance the representation robustness. Furthermore, we explore a wide range of settings to overcome the instability in acoustic language model pre-training, which allows our designed paradigm to scale from 95M to 330M parameters. Experimental results indicate that our model can generalise and perform well on 14 music understanding tasks and attains state-of-the-art (SOTA) overall scores. The code and models are online: https://github.com/yizhilll/MERT

The predictive powers of plasma trefoil factor 3 or its related micro RNAs for patients with hepatocellular carcinoma

Abstract Background Earlier diagnosis is beneficial for the prognosis of hepatocellular carcinoma (HCC). Alpha fetoprotein (AFP) is the most widely used biomarker for HCC, but its sensitivity and specificity are only 60 and 90%, respectively. Therefore, it is of great clinical significance to identify early prognostic biomarkers for HCC, especially a blood-based biomarker as it offers several advantages over tissue-based biomarkers. Trefoil factor 3 (TFF3), a novel secretory protein, was over-expressed in HCC tissues, indicating it might be a blood-based biomarker for HCC. In addition, circulating microRNAs have been investigated as biomarkers for HCC, indicating that miR-7-5p and miR-203a-3p, which are reported or predicted to target TFF3, also hold promise as blood-based biomarkers for HCC. Methods We enrolled 43 patients who were firstly diagnosed HCC and matched 47 control subjects without HCC. The levels of TFF3, miR-7-5p and miR-203a-3p were tested in the plasma of HCC patients. Moreover, we assayed the correlation of TFF3 with its related micro RNAs, miR-7-5p and miR-203a-3p, and evaluated their predictive powers for HCC. Results Decrease of TFF3 was associated with increase of miR-203a-3p in the plasma of HCC patients and they displayed potent predictive powers for HCC diagnosis. However, there was no significant change of plasma miR-7-5p between HCC and control group. Conclusion Decrease of TFF3 correlated with increase of miR-203a-3p in the plasma of HCC patients and they could be additional biomarkers to improve sensitivity and specificity in the diagnosis of HCC

An In situ assembled WO3-TiO2 vertical heterojunction for enhanced Z-scheme photocatalytic activity

The face-to-face contact of a vertical heterojunction is beneficial to charge interaction in photocatalysis. However, constructing a vertical heterojunction with uncompromised redox ability still remains a challenge. Herein, we report the successful synthesis of a WO3-TiO2 vertical heterojunction via establishing an internal electric field across the interface. Experimental investigation and computational simulations reveal that strong electric coupling occurs at the WO3-TiO2 interface forming an internal electric field. The internal electric field induces a Z-scheme charge-carrier transfer through the heterojunction under light irradiation, which leads to effective charge separation and maintains high reaction potentials of charge-carriers. The improved photocatalytic activity of the WO3-TiO2 heterojunction is proved by enhanced generation of reactive oxygen species and accelerated Escherichia coli (E. coli) disinfection. This study provides new insights into understanding and designing Z-scheme heterogeneous photocatalysts.</p

Simultaneously Tuning Charge Separation and Oxygen Reduction Pathway on Graphitic Carbon Nitride by Polyethylenimine for Boosted Photocatalytic Hydrogen Peroxide Production

The synthesis of hydrogen peroxide (H2O2) from H2O and O2 by metal-free photocatalysts (e.g., graphitic carbon nitride, C3N4) is a potentially promising approach to generate H2O2. However, the photocatalytic H2O2 generation activity of the pristine C3N4 in pure H2O is poor due to unpropitious rapid charge recombination and unfavorable selectivity. Herein, we report a facile method to boost the photocatalytic H2O2 production by grafting cationic polyethylenimine (PEI) molecules onto C3N4. Experimental results and density functional theory (DFT) calculations demonstrate PEI can tune the local electronic environment of C3N4. The unique intermolecular electronic interaction in PEI/C3N4 not only improves the electron-hole separation but also promotes the two-electron O2 reduction to H2O2 via the sequential two-step single-electron reduction route. With the synergy of improved charge separation and high selectivity of two-electron O2 reduction, PEI/C3N4 exhibits an unexpectedly high H2O2 generation activity of 208.1 μmol g-1 h-1, which is 25-fold higher than that of pristine C3N4. This study establishes a paradigm of tuning the electronic property of C3N4 via functional molecules for boosted photocatalysis activity and selectivity.</p