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Abstract Predicting the blood–brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improve the accuracy and shorten the time for new drug development. The primary goal of this research is to develop artificial intelligence (AI) computing models and novel deep learning architectures capable of predicting whether molecules can permeate the human blood–brain barrier (BBB). The in silico (computational) and in vitro (experimental) results were validated by the Natural Products Research Laboratories (NPRL) at China Medical University Hospital (CMUH). The transformer-based MegaMolBART was used as the simplified molecular input line entry system (SMILES) encoder with an XGBoost classifier as an in silico method to check if a molecule could cross through the BBB. We used Morgan or Circular fingerprints to apply the Morgan algorithm to a set of atomic invariants as a baseline encoder also with an XGBoost classifier to compare the results. BBB permeability was assessed in vitro using three-dimensional (3D) human BBB spheroids (human brain microvascular endothelial cells, brain vascular pericytes, and astrocytes). Using multiple BBB databases, the results of the final in silico transformer and XGBoost model achieved an area under the receiver operating characteristic curve of 0.88 on the held-out test dataset. Temozolomide (TMZ) and 21 randomly selected BBB permeable compounds (Pred scores = 1, indicating BBB-permeable) from the NPRL penetrated human BBB spheroid cells. No evidence suggests that ferulic acid or five BBB-impermeable compounds (Pred scores

Abstract The emergence of severe acute respiratory syndrome (SARS) underscored the importance of influenza detection and response in China. From 2004, the Chinese National Influenza Center (CNIC) and the United States Centers for Disease Control and Prevention (USCDC) initiated Cooperative Agreements to build capacity in influenza surveillance in China. From 2004 to 2014, CNIC and USCDC collaborated on the following activities: 1) developing human technical expertise in virology and epidemiology in China; 2) developing a comprehensive influenza surveillance system by enhancing influenza-like illness (ILI) reporting and virological characterization; 3) strengthening analysis, utilization and dissemination of surveillance data; and 4) improving early response to influenza viruses with pandemic potential. Since 2004, CNIC expanded its national influenza surveillance and response system which, as of 2014, included 408 laboratories and 554 sentinel hospitals. With support from USCDC, more than 2500 public health staff from China received virology and epidemiology training, enabling > 98% network laboratories to establish virus isolation and/or nucleic acid detection techniques. CNIC established viral drug resistance surveillance and platforms for gene sequencing, reverse genetics, serologic detection, and vaccine strains development. CNIC also built a bioinformatics platform to strengthen data analysis and utilization, publishing weekly on-line influenza surveillance reports in English and Chinese. The surveillance system collects 200,000–400,000 specimens and tests more than 20,000 influenza viruses annually, which provides valuable information for World Health Organization (WHO) influenza vaccine strain recommendations. In 2010, CNIC became the sixth WHO Collaborating Centre for Influenza. CNIC has strengthened virus and data sharing, and has provided training and reagents for other countries to improve global capacity for influenza control and prevention. The collaboration’s successes were built upon shared mission and values, emphasis on long-term capacity development and sustainability, and leadership commitment.

In this study, NO2 columns from the US EPA Models-3/CMAQ model simulations carried out using the 2001 ACE-ASIA (Asia Pacific Regional Aerosol Characterization Experiment) emission inventory over East Asia were compared with the GOME-derived NO2 columns. There were large discrepancies between the CMAQ-predicted and GOME-derived NO2 columns in the fall and winter seasons. In particular, while the CMAQ-predicted NO2 columns produced larger values than the GOME-derived NO2 columns over South Korea for all four seasons, the CMAQ-predicted NO2 columns produced smaller values than the GOME-derived NO2 columns over North China for all seasons with the exception of summer (summer anomaly). It is believed that there might be some error in the NOx emission estimates as well as uncertainty in the NOx chemical loss rates over North China and South Korea. Regarding the latter, this study further focused on the biogenic VOC (BVOC) emissions that were strongly coupled with NOx chemistry during summer in East Asia. This study also investigated whether the CMAQ-modeled NO2/NOx ratios with the possibly overestimated isoprene emissions were higher than those with reduced isoprene emissions. Although changes in both the NOx chemical loss rates and NO2/NOx ratios from CMAQ-modeling with the different isoprene emissions affected the CMAQ-modeled NO2 levels, the effects were found to be limited, mainly due to the low absolute levels of NO2 in summer. Seasonal variations of the NOx emission fluxes over East Asia were further investigated by a set of sensitivity runs of the CMAQ model. Although the results still exhibited the summer anomaly possibly due to the uncertainties in both NOx-related chemistry in the CMAQ model and the GOME measurements, it is believed that consideration of both the seasonal variations in NOx emissions and the correct BVOC emissions in East Asia are critical. Overall, it is estimated that the NOx emissions are underestimated by ~57.3% in North China and overestimated by ~46.1% in South Korea over an entire year. In order to confirm the uncertainty in NOx emissions, the NOx emissions over South Korea and China were further investigated using the ACE-ASIA, REAS (Regional Emission inventory in ASia), and CAPSS (Clean Air Policy Support System) emission inventories. The comparison between the CMAQ-calculated and GOME-derived NO2 columns indicated that both the ACE-ASIA and REAS inventories have some uncertainty in NOx emissions over North China and South Korea, which can also lead to some errors in modeling the formation of ozone and secondary aerosols in South Korea and North China.

This paper presents a novel numerical procedure based on the combination of an edge-based smoothed finite element (ES-FEM) with a phantom-node method for 2D linear elastic fracture mechanics. In the standard phantom-node method, the cracks are formulated by adding phantom nodes, and the cracked element is replaced by two new superimposed elements. This approach is quite simple to implement into existing explicit finite element programs. The shape functions associated with discontinuous elements are similar to those of the standard finite elements, which leads to certain simplification with implementing in the existing codes. The phantom-node method allows modeling discontinuities at an arbitrary location in the mesh. The ES-FEM model owns a close-to-exact stiffness that is much softer than lower-order finite element methods (FEM). Taking advantage of both the ES-FEM and the phantom-node method, we introduce an edge-based strain smoothing technique for the phantom-node method. Numerical results show that the proposed method achieves high accuracy compared with the extended finite element method (XFEM) and other reference solutions.