COAL GASIFICATION CHARACTERISTICS IN A 2MWth SECOND-GENERATION PFB GASIFIER

ABSTRACT Coal gasification process and equipment feasibility research w ere carried out in a 2 MW thermal input pressurized spout-fluid bed pilot-scale gasifier and a long-time-run test was performed to study the effects of operating parameters on coal partial gasification behaviors. The test results have demonstrated the feasibility of the gasifier to provide suitable fuel gas and residual char for downstream system of 2G PFBC-CC. The concentration of methane decreased at higher gasification temperature due to the secondary cracking of methane while the carbon conversion increased, and the concentration of hydrogen increased with an increase of steam flow rate. The main experimental results were compared with those of pilot-scale facilities in the world

Biomonitoring Study of Deoxynivalenol Exposure in Chinese Inhabitants

Objective: To investigate the levels of a deoxynivalenol (DON) biomarker in the urine of subjects living in two China provinces with different geographic locations and dietary patterns, and estimate their dietary DON exposures and health risks. Methods: First morning urine samples were collected on three consecutive days from 599 healthy subjects—301 from Henan province and 298 from Sichuan province—to analyze the total DON concentrations (tDON) after β-glucuronidase hydrolysis using a high-performance liquid chromatography tandem mass spectrometry-based method. The consumption of cereal foods in the previous 24 h before each urine collection was recorded using a duplicate diet method. DON exposure levels were estimated based on the urinary tDON concentrations. Results: Total DON were detected in 100% and 92% of the urine samples from Henan and Sichuan, respectively. Mean urinary tDON concentrations were 52.83 ng/mL in Henan subjects and 12.99 ng/mL in Sichuan subjects, respectively. The tDON levels were significantly higher in the urine of Henan subjects than that of the Sichuan subjects (p < 0.001). Urinary tDON levels were significantly different among age groups in both areas (Henan: p < 0.001; Sichuan: p = 0.026) and were highest in adolescents aged 13–17 years, followed by children aged 7–12 years. Based on the DON biomarker and exposure conversion reported by the European Food Safety Authority (EFSA), the mean estimated dietary intakes of DON were 1.82 μg/kg bw/day in Henan subjects and 0.45 μg/kg bw/day in Sichuan subjects. A total of 56% of Henan subjects and 12% of Sichuan subjects were estimated to exceed the PMTDI of 1 μg/kg bw/day. Consistent with urinary tDON levels, the highest estimated dietary DON intakes were also in children and adolescents aged 7–17 years. For all kinds of wheat-based foods except dumplings, the consumptions were significantly higher in Henan than those in Sichuan. The mean consumption of steamed buns was 8.4-fold higher in Henan (70.67 g/d) than that in Sichuan (8.45 g/d). The mean consumption of noodles in Henan (273.91 g/d) was 3.6-fold higher than that in Sichuan (75.87 g/d). Conclusions: The levels of urinary DON biomarker and the estimated dietary DON intakes in Henan province were high and concerning, especially for children and adolescents. The overall exposure level of Sichuan inhabitants was low

Emodin Alleviates Liver Fibrosis of Mice by Reducing Infiltration of Gr1 hi

Emodin, as a major active component of Rheum palmatum L. and Polygonum cuspidatum, has been reported to have antifibrotic effect. However, the mechanism of emodin on antifibrotic effect for liver fibrosis was still obscure. In the present study, we aimed to investigate whether emodin can alleviate carbon tetrachloride- (CCl4-) induced liver fibrosis through reducing infiltration of Gr1hi monocytes. Liver fibrosis was induced by intraperitoneal CCl4 injection in mice. Mice in the emodin group received emodin treatment by gavage. Pretreatment with emodin significantly protected mice from liver inflammation and fibrosis revealed by the decreased elevation of serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST), as well as reduced hepatic necrosis and fibrosis by analysis of hematoxylin-eosin (HE) staining, Masson staining, α-smooth muscle actin (α-SMA), and collagen-I immunohistochemistry staining. Further, compared to CCl4 group, mice in the emodin group showed significantly less intrahepatic infiltration of Gr1hi monocytes. Moreover, emodin significantly inhibited hepatic expression of interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), transforming growth factor-β1 (TGF-β1), granulin (GRN), monocyte chemoattractant protein 1 (MCP-1), and chemokine ligand 7 (CCL7), which was in line with the decreased numbers of intrahepatic Gr1hi monocytes. In conclusion, emodin can alleviate the degree of liver fibrosis by reducing infiltration of Gr1hi monocytes. These results suggest that emodin is a promising candidate to prevent and treat liver fibrosis

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: the Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology

Simulating drip irrigation in large-scale and high-resolution ecohydrological models: From emitters to the basin

Drip irrigation is deemed as a solution to the water conflict between agricultural and ecological needs in arid regions, but assessing hydrological impacts of drip irrigation remains challenging. This study developed a new approach to simulating drip irrigation at a basin scale, with the configuration of irrigation system explicitly represented and the emitter-scale wetted soil volume directly modeled, and incorporated the approach into HEIFLOW, a fully distributed and physically based ecohydrological model. The improved model, named HEIFLOW-drip, was used to study the potential implementation of drip irrigation in the Zhangye basin in Northwest China. Due to the recycling effect of irrigation return flow, the basin-scale water-saving efficiency (WSE) of drip irrigation fully implemented in the irrigated farmlands is 16.8%, less than half of its field-scale WSE (36.5%). This discrepancy indicates that accounting the water saved in fields may lead to notable overestimation of the basin-wide water saving by drip irrigation in basins with strong surface water-groundwater interactions. If fully implemented, drip irrigation would significantly alter the regional water balance, increasing the stream outflow by approximately 30%, while causing a decline in groundwater level. This tradeoff can be alleviated by tuning the management parameters of drip irrigation, which can achieve a synergistic effect of increasing the streamflow while preventing dramatic groundwater depletion. Another effective strategy is to implement drip irrigation in part of the basin. Given the intricate impacts of drip irrigation on hydrological processes, the determination of the scale and location of drip irrigation should be thought through at the basin level. Further analyses indicate the importance of proper management of the water saved by drip irrigation, which is crucial to prevent the paradox of irrigation efficiency. The findings of this study have great implications for addressing the complex water-food-ecosystem nexus in arid endorheic river basins

Resurgence of syphilis: focusing on emerging clinical strategies and preclinical models

Abstract Syphilis, a sexually transmitted disease (STD) caused by Treponema pallidum (T. pallidum), has had a worldwide resurgence in recent years and remains a public health threat. As such, there has been a great deal of research into clinical strategies for the disease, including diagnostic biomarkers and possible strategies for treatment and prevention. Although serological testing remains the predominant laboratory diagnostic method for syphilis, it is worth noting that investigations pertaining to the DNA of T. pallidum, non-coding RNAs (ncRNAs), chemokines, and metabolites in peripheral blood, cerebrospinal fluid, and other bodily fluids have the potential to offer novel perspectives on the diagnosis of syphilis. In addition, the global spread of antibiotic resistance, such as macrolides and tetracyclines, has posed significant challenges for the treatment of syphilis. Fortunately, there is still no evidence of penicillin resistance. Hence, penicillin is the recommended course of treatment for syphilis, whereas doxycycline, tetracycline, ceftriaxone, and amoxicillin are viable alternative options. In recent years, efforts to discover a vaccine for syphilis have been reignited with better knowledge of the repertoire of T. pallidum outer membrane proteins (OMPs), which are the most probable syphilis vaccine candidates. However, research on therapeutic interventions and vaccine development for human subjects is limited due to practical and ethical considerations. Thus, the preclinical model is ideal for conducting research, and it plays an important role in clinical transformation. Different preclinical models have recently emerged, such as in vitro culture and mouse models, which will lay a solid foundation for clinical treatment and prevention of syphilis. This review aims to provide a comprehensive summary of the most recent syphilis tactics, including detection, drug resistance treatments, vaccine development, and preclinical models in clinical practice

MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules

Abstract Artificial intelligence (AI)-based molecular design methods, especially deep generative models for generating novel molecule structures, have gratified our imagination to explore unknown chemical space without relying on brute-force exploration. However, whether designed by AI or human experts, the molecules need to be accessibly synthesized and biologically evaluated, and the trial-and-error process remains a resources-intensive endeavor. Therefore, AI-based drug design methods face a major challenge of how to prioritize the molecular structures with potential for subsequent drug development. This study indicates that common filtering approaches based on traditional screening metrics fail to differentiate AI-designed molecules. To address this issue, we propose a novel molecular filtering method, MolFilterGAN, based on a progressively augmented generative adversarial network. Comparative analysis shows that MolFilterGAN outperforms conventional screening approaches based on drug-likeness or synthetic ability metrics. Retrospective analysis of AI-designed discoidin domain receptor 1 (DDR1) inhibitors shows that MolFilterGAN significantly increases the efficiency of molecular triaging. Further evaluation of MolFilterGAN on eight external ligand sets suggests that MolFilterGAN is useful in triaging or enriching bioactive compounds across a wide range of target types. These results highlighted the importance of MolFilterGAN in evaluating molecules integrally and further accelerating molecular discovery especially combined with advanced AI generative models