A dynamic sequential decision-making model on MRI real-time scheduling with simulation-based optimization.

Magnetic resonance imaging (MRI) is widely used in diagnostic medicine and contributes significantly to US health care spending. Scheduling MRI jobs involves uncertainties (e.g., patient arrival time, scanning time, and preparation time) that can lead to excessive delays and high costs in MRI operations. This study addresses real-time decision making in use of MRI scanners based on job assignment and sequencing decisions that override the appointment schedule. The decisions are made using real-time information of the waiting patients, the utilization status of the MRI scanners, and the partially revealed uncertainties of scanning times of current patients. A sequential decision-making framework and a simulation-based solution method are proposed to utilize massive real-time information and match the use of MRI rescheduling in practice. The results are then compared with a real case in a large midwestern academic medical center in the US. This study illustrates that the proposed method reduces patient waiting time by 21.7% and improves utilization of MRI scanners by 23.0%. An optimality gap of 13.6% is provided when compared to off line scheduling methods based on a mixed integer programming (MIP) model. The number of simulation replications in this approach uses the ranking and selection method, which not only reduces solution time, but also provides solution quality guarantees wherein the probability of errors in the proposed method for one day is less than 0.1%. In 100 randomly generated workday experiments, all of the scheduling decisions given by the proposed method perform better than current policy, with an average reduction of 17.93 minutes in each patient's waiting time and an improvement of scanner utilization by 7.20%.

A Continuous Volatility Forecasting Model Based on Neural Differential Equations and Scale-Similarity.

Volatility forecasting is a problem in finance that attracts the attention of both academia and industry. While existing approaches typically utilize a discrete-time latent process that governs the volatility to forecast its future level, volatility is considered to evolve continuously, which makes discrete-time modeling inevitably lose some critical information about the evolution of volatility. In this article, a novel neural-network-based model, Continuous Volatility Forecasting Model, CVFM is proposed to tackle this problem. First, CVFM introduces a continuous-time latent process, whose evolution is modeled with neural differential equations (NDEs), to govern volatility, which effectively captures the continuous evolutionary behavior of volatility in a data-driven way. Second, a scale-similarity-based mechanism is designed to calibrate the evolution equation of the latent process with real-world observations in the absence of high-frequency data. CVFM is tested on six real-world stock index datasets. The main experimental results show that CVFM can significantly outperform existing models in terms of both forecasting accuracy and high-volatility recognition.

Semi-supervised bipartite graph construction with active EEG sample selection for emotion recognition.

Electroencephalogram (EEG) signals are derived from the central nervous system and inherently difficult to camouflage, leading to the recent popularity of EEG-based emotion recognition. However, due to the non-stationary nature of EEG, inter-subject variabilities become obstacles for recognition models to well adapt to different subjects. In this paper, we propose a novel approach called semi-supervised bipartite graph construction with active EEG sample selection (SBGASS) for cross-subject emotion recognition, which offers two significant advantages. Firstly, SBGASS adaptively learns a bipartite graph to characterize the underlying relationships between labeled and unlabeled EEG samples, effectively implementing the semantic connection for samples from different subjects. Secondly, we employ active sample selection technique in this paper to reduce the impact of negative samples (outliers or noise in the data) on bipartite graph construction. Drawing from the experimental results with the SEED-IV data set, we have gained the following three insights. (1) SBGASS actively rejects negative labeled samples, which helps mitigate the impact of negative samples when constructing the optimal bipartite graph and improves the model performance. (2) Through the learned optimal bipartite graph in SBGASS, the transferability of labeled EEG samples is quantitatively analyzed, which exhibits a decreasing tendency as the distance between each labeled sample and the corresponding class centroid increases. (3) Besides the improved recognition accuracy, the spatial-frequency patterns in emotion recognition are investigated by the acquired projection matrix.

Degradation of Cell Wall Polysaccharides during Traditional and Tank Fermentation of Chinese Liupao Tea.

The increase of polysaccharides in the dark tea pile process is thought to be connected to the cell wall polysaccharides' breakdown. However, the relationship between tea polysaccharides (TPSs) and tea cell wall polysaccharides has not been further explored. In this study, the structural changes in the cell wall polysaccharides [e.g., cellulose, hemicellulose (HC), and pectin] in Liupao tea were characterized before and after traditional fermentation and tank fermentation. Additionally, the degradation mechanism of tea cell wall polysaccharides during fermentation was assessed. The results showed that cellulose crystallinity decreased by 11.9-49.6% after fermentation. The molar ratio of monosaccharides, such as arabinose, rhamnose, and glucose in HC, was significantly reduced, and the molecular weight decreased. The esterification degree and linearity of water-soluble pectin (WSP) were reduced. TPS content increases during pile fermentation, which may be due to HC degradation and the increase in WSP caused by cell wall structure damage. Microorganisms were shown to be closely associated with the degradation of cell wall polysaccharides during fermentation according to correlation analyses. Traditional fermentation had a greater effect on the cellulose structure, while tank fermentation had a more noticeable impact on HC and WSP.

Chemical energy assisted self-assembling of a porphyrin-substituted benzoic acid in complex environments.

Energy input plays a crucial role in the assembling of matter. In our present study, we utilize EDC as a chemical fuel to drive the molecular assembling of POR-COOH. POR-COOH will react with EDC to generate the intermediate POR-COOEDC first, which was well-solvated by solvent molecules. During the subsequent hydrolysis process, EDU and oversaturated POR-COOH molecules at high energy states will be formed and thus allowed the self-assembling of POR-COOH into 2D nanosheets. This chemical energy assisted assembling process could be performed not only under mild conditions with high spatial accuracy but also with high selectivity in complex environments.

Improving the physicochemical and pharmacokinetic properties of olaparib through cocrystallization strategy.

Olaparib (OLA) is the first PARP inhibitor worldwide used for the treatment of ovarian cancer. However, the oral absorption of OLA is extremely limited by its poor solubility. Herein, pharmaceutical cocrystallization strategy was employed to optimize the physicochemical and pharmacokinetic properties. Four cocrystals of OLA with oxalic acid (OLA-OA), malonic acid (OLA-MA), fumaric acid (OLA-FA) and maleic acid (OLA-MLA) were successfully discovered and characterized. Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy confirmed the formation of cocrystals rather than salts, and the possible hydrogen bonding patterns were analyzed through molecular surface electrostatic potential calculations. The in vitro and in vivo evaluations indicate that all of the cocrystals demonstrate significantly improved dissolution performance, oral absorption and tabletability compared to pure OLA. Among them, OLA-FA exhibit sufficient stability and the most increased Cmax and AUC0-24h values that were 11.6 and 6.1 times of free OLA, respectively, which has great potential to be developed into the improved solid preparations of OLA.

Effect of pile fermentation on the cells of Chinese Liupao tea: The first record of cell wall of Liupao tea on transmission electron microscope.

Fermentation often degrades the cell wall of dark tea, changes the carbohydrate components in the cell wall of tea, and thus affects the quality of tea. However, the lack of ultrastructural details limits our knowledge on the effect of fermentation on tea cell walls. Morphological studies of cell structures are important; thus, the cell wall of Liupao tea was analyzed under transmission electron microscopy for the first time, and the effects of different raw materials and fermentation methods on the cell wall and main carbohydrates of tea were compared. Overall, fermentation degrades the cell wall of Liupao tea under the action of microorganisms. Interestingly, the middle lamella degrades obviously, whereas the primary wall is complete. The decrease in hemicellulose and increase in water-soluble pectin (WSP) were remarkable, whereas the changes in cellulose and WSP were considerably correlated with the increase in tea polysaccharide (TPS). The results suggest that cell wall degradation might be related to the increase in TPS.

Screening and characterization of mixotrophic sulfide oxidizing bacteria for odorous surface water bioremediation.

Eight species of mixotrophic sulfide oxidizing bacteria (SOB) were isolated from activated sludge and identified using 16S rRNA sequence analysis. The effects of organic substances, dissolved oxygen (DO) and nitrate on sulfide oxidation and bacterial growth were studied in this work. The results showed that Paracoccus sp. (N1), Pseudomonas sp. (N2) and Pseudomonas sp. (S4) have strong adaptability to environments with low DO and high concentrations of organic substance. An SOB additive was optimized in artificial, odorous water. The optimized SOB additive is ablendof 80% N1 and 20% N2 bacteria solution with absorbance equal to 0.5 at a wavelength of 600 nm (OD600), and the optimal dose of the additive is 20 ml/L. Oxidation-reduction potential (ORP), ammonia-nitrogen (NH3-N) and released H2S in an odorous river were measured with and without SOB additive, and the results indicated that the optimized SOB additive has excellent performance for odorous river bioremediation.

Assessing the Impact of Lifestyle Interventions on Diabetes Prevention in China: A Modeling Approach.

China's diabetes epidemic is getting worse. People with diabetes in China usually have a lower body weight and a different lifestyle profile compared to their counterparts in the United States (US). More and more evidence show that certain lifestyles can possibly be spread from person to person, leading some to propose considering social influence when establishing preventive policies. This study developed an innovative agent-based model of the diabetes epidemic for the Chinese population. Based on the risk factors and related complications of diabetes, the model captured individual health progression, quantitatively described the peer influence of certain lifestyles, and projected population health outcomes over a specific time period. We simulated several hypothetical interventions (i.e., improving diet, controlling smoking, improving physical activity) and assessed their impact on diabetes rates. We validated the model by comparing simulation results with external datasets. Our results showed that improving physical activity could result in the most significant decrease in diabetes prevalence compared to improving diet and controlling smoking. Our model can be used to inform policymakers on how the diabetes epidemic develops and help them compare different diabetes prevention programs in practice.

Removal of dissolved sulfides in aqueous solution by activated sludge: mechanism and characteristics.

Activated sludge recycling has been developed as a novel technique to directly prevent volatile sulfides emission from wastewater influents. In this study, mechanisms and characteristics of dissolved sulfides removal in aqueous solution by activated sludge were investigated. When DO content in water was 0.49mg/L, 70% of removed dissolved sulfides were released back from the activated sludge by lowering pH to 1. The SEM/EDS result revealed that removed sulfur was fixed in activated sludge and the XPS result showed that fixed sulfur had an oxidation state of -2. FTIR results showed that primary amine group (R-NH2) could be one of the radical groups bonding sulfides. All these results verified that sulfides removal by activated sludge is primarily attributed to adsorption, rather than biodegradation, under low DO conditions in 40min. The equilibrium isotherm data fit the Langmuir isotherm model well. The maximum adsorption capacity (q0) ranged in 25-38mg/g at temperatures of 10-40°C. The adsorption kinetic data fit the pseudo-second-order model well. The amounts of adsorbed sulfides at equilibrium (qe) were positively proportional to temperature, initial sulfides concentration and agitation speed. These results indicate that sulfides adsorption could be a chemical sorption or ion exchange process.