Molecular Joint Representation Learning via Multi-modal Information

In recent years, artificial intelligence has played an important role on accelerating the whole process of drug discovery. Various of molecular representation schemes of different modals (e.g. textual sequence or graph) are developed. By digitally encoding them, different chemical information can be learned through corresponding network structures. Molecular graphs and Simplified Molecular Input Line Entry System (SMILES) are popular means for molecular representation learning in current. Previous works have done attempts by combining both of them to solve the problem of specific information loss in single-modal representation on various tasks. To further fusing such multi-modal imformation, the correspondence between learned chemical feature from different representation should be considered. To realize this, we propose a novel framework of molecular joint representation learning via Multi-Modal information of SMILES and molecular Graphs, called MMSG. We improve the self-attention mechanism by introducing bond level graph representation as attention bias in Transformer to reinforce feature correspondence between multi-modal information. We further propose a Bidirectional Message Communication Graph Neural Network (BMC GNN) to strengthen the information flow aggregated from graphs for further combination. Numerous experiments on public property prediction datasets have demonstrated the effectiveness of our model

Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning

Objective: The artificial pancreas (AP) has shown promising potential in achieving closed-loop glucose control for individuals with type 1 diabetes mellitus (T1DM). However, designing an effective control policy for the AP remains challenging due to the complex physiological processes, delayed insulin response, and inaccurate glucose measurements. While model predictive control (MPC) offers safety and stability through the dynamic model and safety constraints, it lacks individualization and is adversely affected by unannounced meals. Conversely, deep reinforcement learning (DRL) provides personalized and adaptive strategies but faces challenges with distribution shifts and substantial data requirements. Methods: We propose a hybrid control policy for the artificial pancreas (HyCPAP) to address the above challenges. HyCPAP combines an MPC policy with an ensemble DRL policy, leveraging the strengths of both policies while compensating for their respective limitations. To facilitate faster deployment of AP systems in real-world settings, we further incorporate meta-learning techniques into HyCPAP, leveraging previous experience and patient-shared knowledge to enable fast adaptation to new patients with limited available data. Results: We conduct extensive experiments using the FDA-accepted UVA/Padova T1DM simulator across three scenarios. Our approaches achieve the highest percentage of time spent in the desired euglycemic range and the lowest occurrences of hypoglycemia. Conclusion: The results clearly demonstrate the superiority of our methods for closed-loop glucose management in individuals with T1DM. Significance: The study presents novel control policies for AP systems, affirming the great potential of proposed methods for efficient closed-loop glucose control.Comment: 12 page

Cross-tropopause transport of surface pollutants during the Beijing July 21 deep convection event

International audienceAir transport from the troposphere to the stratosphere plays an important role in altering the vertical distribution of pollutants in the upper troposphere and lower stratosphere (UTLS). On July 21, 2012, Beijing was hit by an unprecedented extreme rainfall event. In the present study, the Community Multiscale Air Quality Modeling System (CMAQ) is used to simulate the change in vertical profiles of pollutants during this event. The integrated process rate (IPR) method was applied to quantify the relative contributions from different atmospheric processes to the changes in vertical profile of pollutants, and to estimate the vertical transport flux across the tropopause. The results revealed that, in the tropopause layer, during the torrential rainfall event, the values of O 3 decreased by 35%, that of CO increased by 98%, while those of SO 2 , NO 2 , and PM 2.5 increased slightly. Atmospheric transport was the main cause for the change in O 3 values, contributing 32% of the net increase and 99% of the net decrease of O 3 . The calculations showed that the transport masses of CO, O 3 , PM 2.5 , NO 2 , and SO 2 to the stratosphere by this deep convection in 25 hours were 6.0×10 7 kg, 2.4×10 7 kg, 7.9×10 5 kg, 2.2×10 5 kg and 2.7×10 3 kg, respectively, within the ∼300×300 km domain. In the mid-latitudes of the Northern Hemisphere, penetrating deep convective activities can transport boundary layer pollutants into the UTLS layer, which will have a significant impact on the climate of this layer

Numerical investigation of the impact of urban trees on O3–NOx–VOCs chemistry and pollutant dispersion in a typical street canyon

Urban greening is one of major factors that influences flow, turbulence and air quality in street canyons. This paper aims to investigate the impact of urban trees on O3-NOx-VOCs (ozone -nitrogen oxides - volatile organic compounds) chemistry and pollutant dispersion in street canyons by Computational Fluid Dynamics (CFD). The Atmospheric Photolysis calculation framework (i.e., APFoam), which includes complex O3-NOx-VOCs chemistry into CFD, is employed to carry out the numerical simulations. The validation of the APFoam modelling results has been completed prior to further modelling works, including turbulent airflow, pollutant dispersion, and photochemical reactions. The influence of aerodynamic effects, biogenic VOC (BVOCs) emission and dry deposition of urban trees on air quality in a typical two-dimensional (2D) street canyon with aspect ratio H/W = 1 (where H is the building height and W the street width) are thoroughly examined. Moreover, the source contribution on ozone (O3) creation and the human health risk are also analyzed. Results show that, inside the street canyon, aerodynamic effects of trees have a greater impact on photochemical pollutant concentrations than BVOCs emission and dry deposition, the latter showing the smallest impact. In particular, the aerodynamic effects cause a wind reduction by 35%-45% at pedestrian level and subsequently an increase of nitrogen monoxide (NO) and nitrogen dioxide (NO2) concentrations by 95% and 66% near the ground, respectively, and an O3 concentration decrease by 35%. Further, the BVOCs emitted from trees, the vehicle VOCs and the background VOCs contribute 15%, 67%, and 9% to O3 production, respectively. These findings further suggest that the APFoam is an effective and promising tool which allows us to investigate the influencing mechanisms of trees on photochemical pollutant dispersion and urban air quality for the purpose of developing sustainable urban policy

The impact of marine shipping and its DECA control on air quality in the Pearl River Delta, China

Marine trade has significantly expanded over the past decades aiding to the economic development of the maritime countries, yet, this has been associated with a considerable increase in pollution emission from shipping operation. This study aims at considering both sides of the spectrum at the same time, which is including both public and shipping business. Of the key significance would be to optimize the operation of the shipping industry, such that its impact on air pollution is minimized, without, however, significant escalation of its cost, and therefore to protect the whole seaborne trade. To do this, we considered the impacts of three control strategies, including the current emission control area (ECA) design, as well two additional ones. Thus the first scenario (DECA1) was based on the China's domestic emission control area (DECA), which was set up in 2016. The DECA1 scale was only 12 nautical miles, which was much smaller than the emission control areas in US or Europe. We defined the second scenario (DECA2), by stretching the zone to 200 nautical miles towards the ocean, modeling it on the ECA in North America. The third scenario (DECA3), on the other hand, expanded the 12 nautical miles control zone along the whole coastline. To investigate the impact of shipping emissions on air quality, a shipping emission calculation model and an air quality simulation model were used, and Pearl River Delta (PRD), China was chosen to serve as a case study. The study demonstrated that in 2013 marine shipping emissions contributed on average 0.33 and 0.60 μg·m− 3, respectively to the land SO2 and PM2.5 concentrations in the PRD, and that the concentrations were high along the coastline. The DECA1 policy could effectively reduce SO2 and PM2.5 concentrations in the port regions, and the average reduction in the land area were 9.54% and 2.7%, respectively. Compared with DECA1, DECA2 would not measurably improve the air quality, while DECA3 would effectively decrease the pollution in the entire coast area. Thus, instead of expanding emission control area far to the ocean, it is more effective to control emissions along the coastline to secure the best air quality and lower the health impacts. By doing this, 19 million dollars of fuel cost could be saved per year. The saved cost could help the ship owners to endure, considering the current low profits of the seaborne trade, and thus to protect the overall growth of the economy