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Abstract The precise forecasting of air quality is of great significance as an integral component of early warning systems. This remains a formidable challenge owing to the limited information of emission source and the considerable uncertainties inherent in dynamic processes. To improve the accuracy of air quality forecasting, this work proposes a new spatiotemporal hybrid deep learning model based on variational mode decomposition (VMD), graph attention networks (GAT) and bi-directional long short-term memory (BiLSTM), referred to as VMD–GAT–BiLSTM, for air quality forecasting. The proposed model initially employ a VMD to decompose original PM2.5 data into a series of relatively stable sub-sequences, thus reducing the influence of unknown factors on model prediction capabilities. For each sub-sequence, a GAT is then designed to explore deep spatial relationships among different monitoring stations. Next, a BiLSTM is utilized to learn the temporal features of each decomposed sub-sequence. Finally, the forecasting results of each decomposed sub-sequence are aggregated and summed as the final air quality prediction results. Experiment results on the collected Beijing air quality dataset show that the proposed model presents superior performance to other used methods on both short-term and long-term air quality forecasting tasks.

The impact of air pollution on public health is substantial, and accurate long-term predictions of air quality are crucial for early warning systems to address this issue. Air quality prediction has drawn significant attention, bridging environmental science, statistics, and computer science. This paper presents a comprehensive review of the current research status and advances in air quality prediction methods. Deep learning, a novel machine learning approach, has demonstrated remarkable proficiency in identifying complex, nonlinear patterns in air quality data, yet its application in air quality prediction is still relatively nascent. This paper also conducts a systematic analysis and summarizes how cutting-edge deep learning models are applied in air quality prediction. Initially, the historical evolution of air quality prediction methods and datasets is presented. This is followed by an examination of conventional air quality prediction techniques. A thorough comparative analysis of progress made with both traditional and deep learning-based prediction methods is provided. This review particularly focuses on three aspects: temporal modeling, spatiotemporal modeling, and attention mechanisms. Finally, emerging trends in the field of air quality prediction are identified and discussed.

Abstract Norcoclaurine synthase from Thalictrum flavum (TfNCS) demonstrated high stereospecificity and yield in catalyzing the Pictet-Spengler reaction of dopamine with chiral aldehydes, achieving kinetic resolution of aldehydes. However, the mechanism and the factors contributing to the stereoselectivity remain unclear. Herein, by using quantum chemical calculations, the mechanisms of TfNCS-catalyzed reactions of dopamine with both enantiomers of α-methyl-phenylacetaldehyde are studied. The calculations reveal a mechanism mirroring the reaction of natural substrates, for which the deprotonation of the C5−H of the cyclized intermediate is rate-limiting. The calculated overall barriers are 20.1 kcal mol-1 and 21.6 kcal mol-1 for the reactions of (R)- and (S)-α-methyl-phenylacetaldehyde, respectively. The M97 and L72 residues are proposed to be the key residues contributing to the stereospecificity. The obtained detailed information is helpful for designing new variants of TfNCS with extended substrate scope, and also advancing our understanding of TfNCS reactions for potential applications.

IntroductionAs the COVID-19 outbreak escalated into a global pandemic, there was a significant surge in mental health issues worldwide. Over the past three decades, traditional Chinese exercises (TCEs) has gained increasing recognition for its ability to regulate mental well-being. The aim of this study (PROSPERO CRD42024516002) was to comprehensively assess and carry out a meta-analysis on the impact of traditional Chinese exercise on personal mental well-being amidst the COVID-19 pandemic.MethodsLiterature with publication dates from 2020 to 2023 was searched in four databases, including CNKI, Wanfang, Pubmed and Web of science. The literature was selected layer by layer according to the PRIMA guidelines, and then the quality of the included literature was assessed using the Cochrane Risk assessment tool.ResultsA total of 174 articles were screened, and 10 studies met the criteria and were included in the study. The results showed that TCEs had a positive effect on anxiety symptoms [SMD = −1.68, I2 = 98.40, 95%CI = (−2.80, −0.56), p = 0.00] and depressive symptoms [SMD = -1.23, I2 = 89.23, 95%CI = (−1.87, −0.58), p = 0.00]. The data of subgroup analysis showed that Baduanjin exercise had the best effect on reducing anxiety [SMD = −2.29, I2 = 98.3, 95%CI = (−3.69, −0.89), p

Abstract As the population ages, the worldwide prevalence of Alzheimer's disease (AD) as the most common dementia in the elderly is increasing dramatically. However, a long‐term challenge is to achieve rapid and accurate early diagnosis of AD by detecting hallmarks such as amyloid beta (Aβ42). Here, a multi‐channel microfluidic‐based plasmonic fiber‐optic biosensing platform is established for simultaneous detection and differentiation of multiple AD biomarkers. The platform is based on a gold‐coated, highly‐tilted fiber Bragg grating (TFBG) and a custom‐developed microfluidics. TFBG excites a high‐density, narrow‐cladding‐mode spectral comb that overlaps with the broad absorption of surface plasmons for high‐precision interrogation, enabling ultrasensitive monitoring of analytes. In situ detection and in‐parallel discrimination of different forms of Aβ42 in cerebrospinal fluid (CSF) are successfully demonstrated with a detection of limit in the range of ≈30–170 pg mL−1, which is one order of magnitude below the clinical cut‐off level in AD onset, providing high detection sensitivity for early diagnosis of AD. The integration of the TFBG sensor with multi‐channel microfluidics enables simultaneous detection of multiple biomarkers using sub‐µL sample volumes, as well as combining initial binding rate and real‐time response time to differentiate between multiple biomarkers in terms of binding kinetics. With the advantages of multi‐parameter, low consumption, and highly sensitive detection, the sensor represents an urgently needed potentials for large‐scale diagnosis of diseases at early stage.

Through the effective application of Essential Principles of Safety and Performance of Medical Devices and IVD Medical Devices (EP), to continuously improve the corresponding management tools to ensure the safety and effectiveness of medical device in the quality management system, risk management system, evaluation of safety and effectiveness for the supervision departments and manufacturers. The current status of the application of EP and the application issues are analyzed in the study. Take artificial joint products for example, the idea of using EP in quality management system, risk management system and evaluation of safety and effectiveness is investigated, and several thoughts are proposed. Supervision departments should strengthen the unified understanding of EP, develop requirements according to the classification of medical device，and refine specific execution requirements.

In this paper, we apply the classical FKKM lemma to obtain the Ky Fan minimax inequality defined on nonempty non-compact convex subsets in reflexive Banach spaces, and then we apply it to game theory and obtain Nash’s existence theorem for non-compact strategy sets, which can be regarded as a new, simple but interesting application of the FKKM lemma and the Ky Fan minimax inequality, and we can also present another proof about the famous John von Neumann’s existence theorem in two-player zero-sum games. Due to the results of Li, Shi and Chang, the coerciveness in the conclusion can be replaced with the P.S. or G.P.S. conditions.

A marine-derived fungal strain, Aspergillus sp. ITBBc1, was isolated from coral collected from the South China Sea in Hainan province. Intensive chemical investigation of the fermentation extract of this strain afforded four new secondary metabolites (1–4), named megastigmanones A–C and prenylterphenyllin H, along with four known compounds (5–8). Their structures were elucidated by extensive spectroscopic analysis including one-and two-dimensional (1D and 2D) NMR spectroscopy and high-resolution electrospray ionization mass spectrometry (HR-ESI-MS). The modified Mosher’s method was undertaken to determine the absolute configurations of new compounds. The phytotoxic activity test showed that compounds 6–8 exhibited significant antagonistic activity against the germination of Triticum aestivum L. and Oryza sativa L. seeds with a dose-dependent relationship.