| 1. |
- Du, Mulong, et al.
(författare)
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Cyp2a6 activity and cigarette consumption interact in smoking-related lung cancer susceptibility
- 2024
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Ingår i: Cancer Research. - : American Association For Cancer Research (AACR). - 0008-5472 .- 1538-7445. ; 84:4, s. 616-625
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Tidskriftsartikel (refereegranskat)abstract
- Cigarette smoke, containing both nicotine and carcinogens, causes lung cancer. However, not all smokers develop lung cancer, highlighting the importance of the interaction between host susceptibility and environmental exposure in tumorigenesis. Here, we aimed to delineate the interaction between metabolizing ability of tobacco carcinogens and smoking intensity in mediating genetic susceptibility to smoking-related lung tumorigenesis. Single-variant and gene-based associations of 43 tobacco carcinogen–metabolizing genes with lung cancer were analyzed using summary statistics and individual-level genetic data, followed by causal inference of Mendelian randomization, mediation analysis, and structural equation modeling. Cigarette smoke–exposed cell models were used to detect gene expression patterns in relation to specific alleles. Data from the International Lung Cancer Consortium (29,266 cases and 56,450 controls) and UK Biobank (2,155 cases and 376,329 controls) indicated that the genetic variant rs56113850 C>T located in intron 4 of CYP2A6 was significantly associated with decreased lung cancer risk among smokers (OR = 0.88, 95% confidence interval = 0.85–0.91, P = 2.18 X 10-16), which might interact (Pinteraction = 0.028) with and partially be mediated (ORindirect = 0.987) by smoking status. Smoking intensity accounted for 82.3% of the effect of CYP2A6 activity on lung cancer risk but entirely mediated the genetic effect of rs56113850. Mechanistically, the rs56113850 T allele rescued the downregulation of CYP2A6 caused by cigarette smoke exposure, potentially through preferential recruitment of transcription factor helicase-like transcription factor. Together, this study provides additional insights into the interplay between host susceptibility and carcinogen exposure in smoking-related lung tumorigenesis.
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| 2. |
- Ma, Tao, et al.
(författare)
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Genomic insights into salt adaptation in a desert poplar
- 2013
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 4, s. 2797-
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Tidskriftsartikel (refereegranskat)abstract
- Despite the high economic and ecological importance of forests, our knowledge of the genomic evolution of trees under salt stress remains very limited. Here we report the genome sequence of the desert poplar, Populus euphratica, which exhibits high tolerance to salt stress. Its genome is very similar and collinear to that of the closely related mesophytic congener, P. trichocarpa. However, we find that several gene families likely to be involved in tolerance to salt stress contain significantly more gene copies within the P. euphratica lineage. Furthermore, genes showing evidence of positive selection are significantly enriched in functional categories related to salt stress. Some of these genes, and others within the same categories, are significantly upregulated under salt stress relative to their expression in another salt-sensitive poplar. Our results provide an important background for understanding tree adaptation to salt stress and facilitating the genetic improvement of cultivated poplars for saline soils.
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| 3. |
- Wen, Guanzhao, et al.
(författare)
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Energy level offsets determine the interplay between charge and energy transfer in all-small-molecule organic solar cells
- 2023
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Ingår i: Chemical Engineering Journal. - 1385-8947. ; 475
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Tidskriftsartikel (refereegranskat)abstract
- All-small-molecule organic solar cells (ASM OSCs) hold great promise in OSCs owing to their defined structures, simple purification, and good reproducibility, but are challenging for further improved efficiency. The energy level strategy has been broadly applied to obtain a better performance; however, a comprehensive understanding of the effects of energy level offset on photoexcitation dynamics in ASM OSCs is rarely studied. Herein, for Y-series molecules (Y6, Y10, Y5, and BTP-4F-12) based ASM OSCs, the effect of energy level offset on charge photogeneration was investigated using steady-state and time-resolved spectroscopies. We found that both energy and charge transfer could occur in blend films. A method to quantitatively analyze the contribution of charge and energy transfer processes was developed. For BTR-Cl:Y6 with the highest LUMO level offset, ∼ 23% of photogenerated excitons in donor dissociated via “energy transfer and the subsequent charge transfer” pathway, suggesting that the energy transfer in blend films should also be considered. And for the hole transfer, the excitons in Y-series molecules can only be effectively dissociated when the HOMO energy level offset is higher than 0.11 eV. Besides, a higher energy level offset would also suppress carrier recombination in ultrafast timescale. These results may shed light on the design of ASM OSCs.
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| 4. |
- Xia, Jianyang, et al.
(författare)
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Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region
- 2017
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Ingår i: Journal of Geophysical Research - Biogeosciences. - 2169-8953. ; 122:2, s. 430-446
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Tidskriftsartikel (refereegranskat)abstract
- Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246±6gCm-2yr-1), most models produced higher NPP (309±12gCm-2yr-1) over the permafrost region during 2000-2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982-2009, there was a twofold discrepancy among models (380 to 800gCm-2yr-1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.
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| 5. |
- Duan, Dongban, et al.
(författare)
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Gadolinium Neutron Capture Reaction-Induced Nucleodynamic Therapy Potentiates Antitumor Immunity
- 2023
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Ingår i: CCS Chemistry. - : Chinese Chemical Society. - 2096-5745. ; 5:11, s. 2589-2602
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Tidskriftsartikel (refereegranskat)abstract
- A nuclear reaction-induced dynamic therapy, denoted as nucleodynamic therapy (NDT), has been invented that triggers immunogenic cell death and successfully treats metastatic tumors due to its unexpected abscopal effect. Gadolinium neutron capture therapy (GdNCT) is binary radiotherapy based on a localized nuclear reaction that produces high-energy radiations (e.g., Auger electrons, γ-rays, etc.) in cancer cells when 157Gd is irradiated with thermal neutrons. Yet, its clinical application has been postponed due to the poor ability of Auger electrons and γ-rays to kill cells. Here, we engineered a 157Gd-porphyrin framework that synergizes GdNCT and dynamic therapy to efficiently produce both •OH and immunogenic 1O2 in cancer cells, thereby provoking a strong antitumor immune response. This study unveils the fact and mechanism that NDT heats tumor immunity. Another unexpected finding is that the Auger electron can be the most effective energy-transfer medium for radiation-induced activation of nanomedicines because its nanoscale trajectory perfectly matches the size of nanomaterials. In mouse tumor models, NDT causes nearly complete regression of both primary and distant tumor grafts. Thus, this 157Gd-porphyrin framework radioenhancer endows GdNCT with the exotic function of triggering dynamic therapy; its application may expand in clinics as a new radiotherapy modality that utilizes GdNCT to provoke whole-body antitumor immune response for treating metastases, which are responsible for 90% of all cancer deaths.
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| 6. |
- Jiang, Chao, et al.
(författare)
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An interpretable framework of data-driven turbulence modeling using deep neural networks
- 2021
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Ingår i: Physics of fluids. - : AIP Publishing. - 1070-6631 .- 1089-7666. ; 33:5
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Tidskriftsartikel (refereegranskat)abstract
- Reynolds-averaged Navier-Stokes simulations represent a cost-effective option for practical engineering applications, but are facing ever-growing demands for more accurate turbulence models. Recently, emerging machine learning techniques have had a promising impact on turbulence modeling, but are still in their infancy regarding widespread industrial adoption. Toward their extensive uptake, this paper presents a universally interpretable machine learning (UIML) framework for turbulence modeling, which consists of two parallel machine learning-based modules to directly infer the structural and parametric representations of turbulence physics, respectively. At each phase of model development, data reflecting the evolution dynamics of turbulence and domain knowledge representing prior physical considerations are converted into modeling knowledge. The data- and knowledge-driven UIML is investigated with a deep residual network. The following three aspects are demonstrated in detail: (i) a compact input feature parameterizing a new turbulent timescale is introduced to prevent nonunique mappings between conventional input arguments and output Reynolds stress; (ii) a realizability limiter is developed to overcome the under-constrained state of modeled stress; and (iii) fairness and noise-insensitivity constraints are included in the training procedure. Consequently, an invariant, realizable, unbiased, and robust data-driven turbulence model is achieved. The influences of the training dataset size, activation function, and network hyperparameter on the performance are also investigated. The resulting model exhibits good generalization across two- and three-dimensional flows, and captures the effects of the Reynolds number and aspect ratio. Finally, the underlying rationale behind prediction is explored.
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| 7. |
- Li, Zhao, et al.
(författare)
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Non-uniform seasonal warming regulates vegetation greening and atmospheric CO2 amplification over northern lands
- 2018
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Ingår i: Environmental Research Letters. - : IOP Publishing. - 1748-9326. ; 13:12
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Tidskriftsartikel (refereegranskat)abstract
- The enhanced vegetation growth by climate warming plays a pivotal role in amplifying the seasonal cycle of atmospheric CO2 at northern lands (>50° N) since 1960s. However, the correlation between vegetation growth, temperature and seasonal amplitude of atmospheric CO2 concentration have become elusive with the slowed increasing trend of vegetation growth and weakened temperature control on CO2 uptake since late 1990s. Here, based on in situ atmospheric CO2 concentration records from the Barrow observatory site, we found a slowdown in the increasing trend of the atmospheric CO2 amplitude from 1990s to mid-2000s. This phenomenon was associated with the paused decrease in the minimum CO2 concentration ([CO2]min), which was significantly correlated with the slowdown of vegetation greening and growing-season length extension. We then showed that both the vegetation greenness and growing-season length were positively correlated with spring but not autumn temperature over the northern lands. Furthermore, such asymmetric dependences of vegetation growth upon spring and autumn temperature cannot be captured by the state-of-art terrestrial biosphere models. These findings indicate that the responses of vegetation growth to spring and autumn warming are asymmetric, and highlight the need of improving autumn phenology in the models for predicting seasonal cycle of atmospheric CO2 concentration.
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| 8. |
- Luo, Yiqi, et al.
(författare)
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Transient dynamics of terrestrial carbon storage : Mathematical foundation and its applications
- 2017
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Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4170 .- 1726-4189. ; 14:1, s. 145-161
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Tidskriftsartikel (refereegranskat)abstract
- Terrestrial ecosystems have absorbed roughly 30 % of anthropogenic CO2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is time-dependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times. Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a three-dimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationally feasible so that both C flux-and pool-related datasets can be used to better constrain model predictions of land C sequestration. Overall, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.
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| 9. |
- Ma, Yining, et al.
(författare)
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Lane Change Analysis and Prediction Using Mean Impact Value Method and Logistic Regression Model
- 2021
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Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. ; 2021-September, s. 1346-1352
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Konferensbidrag (refereegranskat)abstract
- The analysis and estimation of lane change (LC) behavior are essential for autonomous vehicles (AVs) to predict other vehicles' intentions and avoid accidents. Since the LC intention is easily affected by various features, the feature selection and LC modeling greatly influence the prediction accuracy and interpretability. Therefore, a binary logistic regression LC model with a mean impact value (MIV) method to select features is proposed for accurate prediction. First, the related features are classified as individual, microscopic, and macroscopic levels. Then they are ranked and analyzed by the MIV method. Next, the closely related features are selected and used as input to the logistic regression model for LC intention prediction. As a result, a highly interpretable LC model is built with a prediction performance of around 80%. This paper benefits the quantification and explanation of the influences of different levels' features on LC intention and lays a solid foundation for the AVs to predict the LC behavior.
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