Spatial-temporal prediction of air quality based on recurrent neural networks

To predict air quality (PM2.5 concentrations, et al), many parametric regression models have been developed, while deep learning algorithms are used less often. And few of them takes the air pollution emission or spatial information into consideration or predict them in hour scale. In this paper, we proposed a spatial-temporal GRU-based prediction framework incorporating ground pollution monitoring (GPM), factory emissions (FE), surface meteorology monitoring (SMM) variables to predict hourly PM2.5 concentrations. The dataset for empirical experiments was built based on air quality monitoring in Shenyang, China. Experimental results indicate that our method enables more accurate predictions than all baseline models and by applying the convolutional processing to the GPM and FE variables notable improvement can be achieved in prediction accuracy

DEVELOPING MACHINE LEARNING METHODOLOGY FOR PRECISION HEALTH

Precision health has been an increasingly popular solution to improve health care quality and guide the decision making process. This includes precision medicine (at the individual level) and precision public health (at the population level such as communities and institutions). By learning from the available medical data with advanced analytical tools, precision health recommends the treatments that are individualized to each patient or entity to maximize clinical outcomes for each individual. We extend and develop three machine learning methods to improve the estimation of optimal individualized treatment regimes in precision health: the jackknife estimator of value function of precision medicine models compared with zero-order models, doubly robust outcome-weighted estimators with deep neural network structures for complex and large data, and risk-adjusted adverse event monitoring for survival data. First, motivated by a knee osteoarthristis trial, we estimate value functions and select the optimal treatment with the jackknife method whose consistency is established under weak assumptions. Next, we implement deep learning architecture in augmented outcome-weighted learning to increase model flexibility and computation efficiency, especially for high-dimensional data such as medical imaging. Lastly, we develop a risk-adjusted survival model to monitor adverse events and estimate its variance for hierarchical, right-censored data with recurrent events. All three methodologies aim to solve practical, health-related challenges and provide data-driven decision support and operations.Doctor of Philosoph

Self-assembly of Zein-based microcarrier system for colon-targeted oral drug delivery

The advances in pharmaceutical technology allow for the development of various region-selective delivery systems for oral administration to optimize local and systemic therapy. In this paper, micronization associated with a polymorph modification approach was proposed for improving the solubility of hydrophobic drugs for developing a Zein-based colon-targeted delivery system. A microcarrier based on self-assembled structures of Zein was fabricated via a built-in ultrasonic dialysis process, which displayed high payload of a model drug, indomethacin (Indo), with its optimal crystal form. The possible self-assembly mechanism of Zein/Indo forming porous structure in the ultrasonic dialysis process was attributed to the results of intra- and/or intermolecular interactions between Zein and Indo. The designed microspheres, Zein-Indo@PDA, with a surface coating of polydopamine (PDA) not only rendered them enhanced stability and mechanical resistance but also hindered the premature drug release at undesired sites. This innovative formulation design may offer better chances of colon-targeted release

Numerical Simulation of Temperature Field in Ultra-Narrow Arc Welding of Thick-Walled Steam Turbine Valve Body Material

The welding problems of large and thick plates are becoming more prominent as the application of large-scale and thick-plate metal structures grows. Due to the issue of excessive welding deformation between the 60mm thick steam turbine valve body and the pipe joint, a new process method is employed to connect. In this paper, the welding technology of flux strip confined arc ultra-narrow gap is proposed to carry out welding test on the ZG13Cr9Mo2Co1NiVNbNB cast steel test block of steam turbine valve body material with a thickness of 60 mm. The welding temperature field is measured by means of a K-type thermocouple and numerical simulation. The results show that the thermal cycle curve obtained by the homogeneous body heat source simulation is basically consistent with the thermal cycle curve measured during the experiment, and the simulation results of the molten pool morphology are also consistent with the actual macroscopic morphology of the weld

Numerical Simulation of Temperature Field in Ultra-Narrow Arc Welding of Thick-Walled Steam Turbine Valve Body Material

The welding problems of large and thick plates are becoming more prominent as the application of large-scale and thick-plate metal structures grows. Due to the issue of excessive welding deformation between the 60mm thick steam turbine valve body and the pipe joint, a new process method is employed to connect. In this paper, the welding technology of flux strip confined arc ultra-narrow gap is proposed to carry out welding test on the ZG13Cr9Mo2Co1NiVNbNB cast steel test block of steam turbine valve body material with a thickness of 60 mm. The welding temperature field is measured by means of a K-type thermocouple and numerical simulation. The results show that the thermal cycle curve obtained by the homogeneous body heat source simulation is basically consistent with the thermal cycle curve measured during the experiment, and the simulation results of the molten pool morphology are also consistent with the actual macroscopic morphology of the weld

Polysaccharopeptides derived from Coriolus versicolor potentiate the S-phase specific cytotoxicity of Camptothecin (CPT) on human leukemia HL-60 cells

<p>Abstract</p> <p>Background</p> <p>Polysaccharopeptide (PSP) from <it>Coriolus versicolor </it>(<it>Yunzhi</it>) is used as a supplementary cancer treatment in Asia. The present study aims to investigate whether PSP pre-treatment can increase the response of the human leukemia HL-60 cells to apoptosis induction by Camptothecin (CPT).</p> <p>Methods</p> <p>We used bivariate bromodeoxyuridine/propidium iodide (BrdUrd/PI) flow cytometry analysis to measure the relative movement (RM) of the BrdUrd positively labeled cells and DNA synthesis time (Ts) on the HL-60 cell line. We used annexin V/PI flow cytometry analysis to quantify the viable, necrotic and apoptotic cells. The expression of cyclin E and cyclin B1 was determined with annexin V/PI flow cytometry and western blotting. Human peripheral blood mononuclear cells were used to test the cytotoxicity of PSP and CPT.</p> <p>Results</p> <p>PSP reduced cellular proliferation; inhibited cells progression through both S and G₂phase, reduced ³H-thymidine uptake and prolonged DNA synthesis time (Ts) in HL-60 cells. PSP-pretreated cells enhanced the cytotoxicity of CPT. The sensitivity of cells to the cytotoxic effects of CPT was seen to be the highest in the S-phase and to a small extent of the G₂phase of the cell cycle. On the other hand, no cell death (measured by annexin V/PI) was evident with the normal human peripheral blood mononuclear cells with treatment of either PSP or CPT.</p> <p>Conclusion</p> <p>The present study shows that PSP increases the sensitization of the HL-60 cells to undergo effective apoptotic cell death induced by CPT. The pattern of sensitivity of cancer cells is similar to that of HL-60 cells. PSP rapidly arrests and/or kills cells in S-phase and did not interfere with the anticancer action of CPT. PSP is a potential adjuvant to treat human leukemia as rapidly proliferating tumors is characterized by a high proportion of S-phase cells.</p

Chinese Synesthesia Detection: New Dataset and Models

In this paper, we introduce a new task called synesthesia detection, which aims to extract the sensory word of a sentence, and to predict the original and synesthetic sensory modalities of the corresponding sensory word. Synesthesia refers to the description of perceptions in one sensory modality through concepts from other modalities. It involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought and action, which makes it become a bridge between figurative linguistic phenomenon and abstract cognition, and thus be helpful to understand the deep semantics. To address this, we construct a large-scale human-annotated Chinese synesthesia dataset, which contains 7,217 annotated sentences accompanied by 187 sensory words. Based on this dataset, we propose a family of strong and representative baseline models. Upon these baselines, we further propose a radical-based neural network model to identify the boundary of the sensory word, and to jointly detect the original and synesthetic sensory modalities for the word. Through extensive experiments, we observe that the importance of the proposed task and dataset can be verified by the statistics and progressive performances. In addition, our proposed model achieves state-of-the-art results on the synesthesia dataset

Discovery of 21 New Changing-look AGNs in Northern Sky

The rare case of changing-look (CL) AGNs, with the appearance or disappearance of broad Balmer emission lines within a few years, challenges our understanding of the AGN unified model. We present a sample of 21 new CL AGNs at $0.08<z<0.58$, which doubles the number of such objects known to date. These new CL AGNs were discovered by several ways, from (1) repeat spectra in the SDSS, (2) repeat spectra in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and SDSS, and (3) photometric variability and new spectroscopic observations. We use the photometric data from surveys, including the SDSS imaging survey, the Pan-STARRS1, the DESI Legacy imaging survey, the Wide-field Infrared Survey Explorer (WISE), the Catalina Real-time Transient Survey, and the Palomar Transient Factory. The estimated upper limits of transition timescale of the CL AGNs in this sample spans from 0.9 to 13 years in the rest frame. The continuum flux in the optical and mid-infrared becomes brighter when the CL AGNs turn on, or vice versa. Variations of more than 0.2 mag in $W1$ band were detected in 15 CL AGNs during the transition. The optical and mid-infrared variability is not consistent with the scenario of variable obscuration in 10 CL AGNs at more than $3\sigma$ confidence level. We confirm a bluer-when-brighter trend in the optical. However, the mid-infrared WISE colors $W1-W2$ become redder when the objects become brighter in the $W1$ band, possibly due to a stronger hot dust contribution in the $W2$ band when the AGN activity becomes stronger. The physical mechanism of type transition is important for understanding the evolution of AGNs.Comment: Accepted for publication in Ap