| 1. |
- Li, Yadi, et al.
(author)
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Virtual and In vitro bioassay screening of phytochemical inhibitors from flavonoids and isoflavones against Xanthine oxidase and Cyclooxygenase-2 for gout treatment
- 2013
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In: Chemical Biology and Drug Design. - : John Wiley & Sons. - 1747-0277 .- 1747-0285. ; 81:4, s. 537-544
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Journal article (peer-reviewed)abstract
- Synthetic drugs such as allopurinol and benzbroarone are commonly used to treat the complex pathogenesis of gout, a metabolic disease that results from an inflammation of the joints caused by precipitation of uric acid. We seek to discover novel phytochemicals that could treat gout, by targeting the xanthine oxidase (XO) and cyclooxygenase 2 (COX-2) enzymes. In this study, we report the screening of 9 compounds of flavonoids from the ZINC and PubChem databases (containing 2,092 flavonoids) using the iGEMDOCK software tool against the XO and COX-2 3D protein structures. Each compound was also evaluated by an in vitro bioassay testing the inhibition of XO and COX-2. Myricetin and luteolin were found to be the potential dual inhibitors of XO and COX-2 as demonstrated by IC50: 62.7 and 3.29μg/mL (XO) / 70.8 and 16.38μg/mL (COX-2), respectively. In addition, structure activity relationships and other important factors of the flavonoids binding to the active site of XO and COX-2 were discussed, which is expected for further rational drug design.
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| 2. |
- Wuttke, Matthias, et al.
(author)
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A catalog of genetic loci associated with kidney function from analyses of a million individuals
- 2019
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In: Nature Genetics. - : NATURE PUBLISHING GROUP. - 1061-4036 .- 1546-1718. ; 51:6, s. 957-972
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Journal article (peer-reviewed)abstract
- Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through transancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these,147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
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| 3. |
- Bae, Juhee, et al.
(author)
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Understanding Robust Target Prediction in Basic Oxygen Furnace
- 2021
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In: IEIM 2021. - New York, NY : Association for Computing Machinery (ACM). - 9781450389143 ; , s. 56-62
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Conference paper (peer-reviewed)abstract
- The problem of using machine learning (ML) to predict the process endpoint for a Basic Oxygen Furnace (BOF) process used for steelmaking has been largely studied. However, current research often lacks both the usage of a rich dataset and does not address revealing influential factors that explain the process. The process is complex and difficult to control and has a multi-objective target endpoint with a proper range of heat temperature combined with sufficiently low levels of carbon and phosphorus. Reaching this endpoint requires skilled process operators, who are manually controlling the heat throughout the process by using both implicit and explicit control variables in their decisions. Trained ML models can reach good BOF target prediction results, but it is still a challenge to extract the influential factors that are significant to the ML prediction accuracy. Thus, it becomes a challenge to explain and validate an ML prediction model that claims to capture the process well. This paper makes use of a complex and full production dataset to evaluate and compare different approaches for understanding how the data can determine the process target prediction. One approach is based on the collected process data and the other on the ML approach trained on that data to find the influential factors. These complementary approaches aim to explain the BOF process to reveal actionable information on how to improve process control.
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| 4. |
- Bae, Juhee, et al.
(author)
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Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System
- 2020
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In: Metallurgical and materials transactions. B, process metallurgy and materials processing science. - : Springer. - 1073-5615 .- 1543-1916. ; 51:4, s. 1632-1645
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Journal article (peer-reviewed)abstract
- The steel-making process in a Basic Oxygen Furnace (BOF) must meet a combination of target values such as the final melt temperature and upper limits of the carbon and phosphorus content of the final melt with minimum material loss. An optimal blow end time (cut-off point), where these targets are met, often relies on the experience and skill of the operators who control the process, using both collected sensor readings and an implicit understanding of how the process develops. If the precision of hitting the optimal cut-off point can be improved, this immediately increases productivity as well as material and energy efficiency, thus decreasing environmental impact and cost. We examine the usage of standard machine learning models to predict the end-point targets using a full production dataset. Various causes of prediction uncertainty are explored and isolated using a combination of raw data and engineered features. In this study, we reach robust temperature, carbon, and phosphorus prediction hit rates of 88, 92, and 89 pct, respectively, using a large production dataset. © 2020, The Author(s).
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| 5. |
- He, Haoran, et al.
(author)
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Deciphering microbiomes dozens of meters under our feet and their edaphoclimatic and spatial drivers
- 2024
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In: Global Change Biology. - 1354-1013. ; 30:1
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Journal article (peer-reviewed)abstract
- Microbes inhabiting deep soil layers are known to be different from their counterpart in topsoil yet remain under investigation in terms of their structure, function, and how their diversity is shaped. The microbiome of deep soils (>1 m) is expected to be relatively stable and highly independent from climatic conditions. Much less is known, however, on how these microbial communities vary along climate gradients. Here, we used amplicon sequencing to investigate bacteria, archaea, and fungi along fifteen 18-m depth profiles at 20–50-cm intervals across contrasting aridity conditions in semi-arid forest ecosystems of China's Loess Plateau. Our results showed that bacterial and fungal α diversity and bacterial and archaeal community similarity declined dramatically in topsoil and remained relatively stable in deep soil. Nevertheless, deep soil microbiome still showed the functional potential of N cycling, plant-derived organic matter degradation, resource exchange, and water coordination. The deep soil microbiome had closer taxa–taxa and bacteria–fungi associations and more influence of dispersal limitation than topsoil microbiome. Geographic distance was more influential in deep soil bacteria and archaea than in topsoil. We further showed that aridity was negatively correlated with deep-soil archaeal and fungal richness, archaeal community similarity, relative abundance of plant saprotroph, and bacteria–fungi associations, but increased the relative abundance of aerobic ammonia oxidation, manganese oxidation, and arbuscular mycorrhizal in the deep soils. Root depth, complexity, soil volumetric moisture, and clay play bridging roles in the indirect effects of aridity on microbes in deep soils. Our work indicates that, even microbial communities and nutrient cycling in deep soil are susceptible to changes in water availability, with consequences for understanding the sustainability of dryland ecosystems and the whole-soil in response to aridification. Moreover, we propose that neglecting soil depth may underestimate the role of soil moisture in dryland ecosystems under future climate scenarios.
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| 6. |
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| 7. |
- Li, Hong-Quan, et al.
(author)
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Weak type (1,1) of some operators for the Laplacian with drift
- 2016
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In: Mathematische Zeitschrift. - : Springer Science and Business Media LLC. - 0025-5874 .- 1432-1823. ; 282:3, s. 623-633
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Journal article (peer-reviewed)abstract
- Let $\Delta_{v} = \Delta + 2v\cdot \nabla $ be the Laplacian with drift in $\R^n$. Here $v$ is any nonzero vector. Then $\Delta_{v}$ has a self-adjoint extension in $L^2(\mu)$ for the measure $d\mu(x) = e^{2 \langle v, x \rangle}dx$. Clearly, this measure has exponential volume growth with respect to the Euclidean metric. We prove the weak type (1,1) boundedness of the corresponding Riesz transforms and the heat maximal operator, with respect to $\mu$. These operators were already known to be bounded on $L^p(\mu),\;1
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| 8. |
- Schlosser, Pascal, et al.
(author)
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Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine
- 2023
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In: Nature Genetics. - 1546-1718. ; 55:6, s. 995-1008
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Journal article (peer-reviewed)abstract
- The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite-disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments.
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| 9. |
- Wang, Xiaohuan, et al.
(author)
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Using Machine Learning Method to Discover Hygrothermal Transfer Patterns from the Outside of the Wall to Interior Bamboo and Wood Composite Sheathing
- 2022
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In: Buildings. - : MDPI AG. - 2075-5309. ; 12:7, s. 898-898
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Journal article (peer-reviewed)abstract
- To identify hygrothermal transfer patterns of exterior walls is a crucial issue in the design, assessment, and construction of buildings. Temperature and relative humidity, as sensor monitoring data, were collected from the outside of the wall to interior bamboo and wood composite sheathing over the year in Huangshan Mountain District, Anhui Province, China. Combining the machine learning method of reservoir computing (RC) with agglomerative hierarchical clustering (AHC), a novel clustering framework was built for better extraction of the characteristics of hygrothermal transfer on the time series data. The experimental results confirmed the hypothesis that the change in the temperature and relative humidity of the outside of the wall (RHT12) dominated the change of the interior sheathing (RHT11). The delay time between two adjacent peaks in temperature was 1 to 2 h, while that in relative humidity was 1 to 4 h from the outside of the wall to interior bamboo and wood composite sheathing. There was no significant difference in temperature peak delay time between April and July. Temperature peak delay time was 50 to 120 min. However, relative humidity peak delay time was 100 to 240 min in April, whereas it was 20 to 120 min in July. The impact formed a relatively linear relationship between outdoor temperature and relative humidity peak delay time. The hygrothermal transfer patterns were characterized effectively by the peak delays. The discovery of the hygrothermal transfer patterns for the bamboo and wood composite walls using the machine learning method will facilitate the development of energy-efficient and durable bamboo and wood composite wall materials and structures.
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