| 1. |
- Nagaraja, Ch., et al.
(författare)
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Opening remarks
- 2016
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Konferensbidrag (refereegranskat)
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| 2. |
- Duggan, D., et al.
(författare)
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Two genome-wide association studies of aggressive prostate cancer implicate putative prostate tumor suppressor gene DAB2IP
- 2007
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Ingår i: Journal of the National Cancer Institute. - : Oxford University Press (OUP). - 0027-8874 .- 1460-2105. ; 99:24, s. 1836-1844
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Tidskriftsartikel (refereegranskat)abstract
- Background: The consistent finding of a genetic susceptibility to prostate cancer suggests that there are germline sequence variants predisposing individuals to this disease. These variants could be useful in screening and treatment. Methods: We performed an exploratory genome-wide association scan in 498 men with aggressive prostate cancer and 494 control subjects selected from a population-based case-control study in Sweden. We combined the results of this scan with those for aggressive prostate cancer from the publicly available Cancer Genetic Markers of Susceptibility (CGEMS) Study. Single-nucleotide polymorphisms (SNPs) that showed statistically significant associations with the risk of aggressive prostate cancer based on two-sided allele tests were tested for their association with aggressive prostate cancer in two independent study populations composed of individuals of European or African American descent using one-sided tests and the genetic model (dominant or additive) associated with the lowest value in the exploratory study. Results: Among the approximately 60000 SNPs that were common to our study and CGEMS, we identified seven that had a similar (positive or negative) and statistically significant (P<.01) association with the risk of aggressive prostate cancer in both studies. Analysis of the distribution of these SNPs among 1032 prostate cancer patients and 571 control subjects of European descent indicated that one, rs1571801, located in the DAB2IP gene, which encodes a novel Ras GTPase-activating protein and putative prostate tumor suppressor, was associated with aggressive prostate cancer (one-sided P value =. 004). The association was also statistically significant in an African American study population that included 210 prostate cancer patients and 346 control subjects (one-sided P value =. 02). Conclusion: A genetic variant in DAB2IP may be associated with the risk of aggressive prostate cancer and should be evaluated further.
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| 3. |
- Lindmark, F, et al.
(författare)
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Interleukin-1 receptor antagonist haplotype associated with prostate cancer risk
- 2005
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Ingår i: British Journal of Cancer. - Umea Univ, Dept Radiat Sci Oncol, S-90187 Umea, Sweden. Wake Forest Univ, Bowman Gray Sch Med, Ctr Human Genom, Winston Salem, NC USA. Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden. Johns Hopkins Med Inst, Dept Urol, Baltimore, MD 21205 USA. Orebro Univ Hosp, Dept Urol & Clin Med, Orebro, Sweden. Univ Hosp, Reg Oncol Ctr, Uppsala, Sweden. : NATURE PUBLISHING GROUP. - 0007-0920 .- 1532-1827. ; 93:4, s. 493-497
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Tidskriftsartikel (refereegranskat)abstract
- IL1-RN is an important anti-inflammatory cytokine that modulate the inflammation response by binding to IL1 receptors, and as a consequence inhibits the action of proinflammatory cytokines IL1 alpha and IL1 beta. In this study, we hypothesise that sequence variants in the IL1-RN gene are associated with prostate cancer risk. The study population, a population-based case - control study in Sweden, consisted of 1383 prostate cancer case patients and 779 control subjects. We first selected 18 sequence variants covering the IL1-RN gene and genotyped these single-nucleotide polymorphisms ( SNPs) in 96 control subjects. Gene-specific haplotypes of IL1-RN were constructed and four haplotype-tagging single-nucleotide polymorphisms (htSNPs) were identified (rs878972, rs315934, rs3087263 and rs315951) that could uniquely describe 495% of the haplotypes. All study subjects were genotyped for the four htSNPs. No significant difference in genotype frequencies between cases and controls were observed for any of the four SNPs based on a multiplicative genetic model. Overall there was no significant difference in haplotype frequencies between cases and controls; however, the prevalence of the most common haplotype (ATGC) was significantly higher among cases (38.7%) compared to controls (33.5%) ( haplotype-specific P = 0.009). Evaluation of the prostate cancer risk associated with carrying the 'ATGC' haplotype revealed that homozygous carriers were at significantly increased risk ( odds ratio (OR) = 1.6, 95% confidence interval (CI) = 1.2 - 2.2), compared to noncarriers, while no significant association was found among subjects heterozygous for the haplotype ( OR = 1.0, 95% CI = 0.8 - 1.2). Restricting analyses to advanced prostate cancer strengthened the association between the 'ATGC' haplotype and disease risk (OR for homozygous carriers vs noncarriers 1.8, 95% CI = 1.3 - 2.5). In conclusion, the results from this study support the hypothesis that inflammation has a role of in the development of prostate cancer, but further studies are needed to identify the causal variants in this region and to elucidate the biological mechanism for this association.
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| 4. |
- Ma, Z., et al.
(författare)
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The role of data analysis in the development of intelligent energy networks
- 2017
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Ingår i: IEEE Network. - : Institute of Electrical and Electronics Engineers Inc.. - 0890-8044 .- 1558-156X. ; 31:5, s. 88-95
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Tidskriftsartikel (refereegranskat)abstract
- Data analysis plays an important role in the development of intelligent energy networks (IENs). This article reviews and discusses the application of data analysis methods for energy big data. The installation of smart energy meters has provided a huge volume of data at different time resolutions, suggesting data analysis is required for clustering, demand forecasting, energy generation optimization, energy pricing, monitoring and diagnostics. The currently adopted data analysis technologies for IENs include pattern recognition, machine learning, data mining, statistics methods, and so on. However, existing methods for data analysis cannot fully meet the requirements for processing the big data produced by IENs, therefore more comprehensive data analysis methods are needed to handle the increasing amount of data and to mine more valuable information.
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| 5. |
- Sun, J L, et al.
(författare)
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Interactions of sequence variants in interieukin-1 receptor-associated kinase4 and the Toll-like receptor 6-1-10 gene cluster increase prostate cancer risk
- 2006
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Ingår i: Cancer Epidemiology, Biomarkers and Prevention. - Wake Forest Univ, Sch Med, Ctr Human Genom, Winston Salem, NC 27109 USA. Wake Forest Univ, Sch Med, Dept Publ Hlth Sci, Winston Salem, NC 27109 USA. Umea Univ, Dept Radiat Sci, Umea, Sweden. Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden. Univ Hosp Uppsala, Reg Oncol Ctr, Uppsala, Sweden. Orebro Univ Hosp, Dept Urol & Clin Med, Orebro, Sweden. Johns Hopkins Med Inst, Dept Urol, Baltimore, MD USA. : AMER ASSOC CANCER RESEARCH. - 1055-9965 .- 1538-7755. ; 15:3, s. 480-485
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Tidskriftsartikel (refereegranskat)abstract
- Chronic or recurrent inflammation has been suggested as a causal factor in several human malignancies, including prostate cancer. Genetic predisposition is also a strong risk factor in the development of prostate cancer. In particular, Toll-like receptors (TLR), especially the TLR6-1-10 gene cluster, are involved in prostate cancer development. Interleukin-1 receptor-associated kinases (IRAK) 1 and 4 are critical components in the TLR signaling pathway. In this large case-control study, we tested two hypotheses: (a) sequence variants in IRAK1 and IRAK4 are associated with prostate cancer risk and (b) sequence variants in IRAK1/4 and TLR1-6-10 interacts and confers a stronger risk to prostate cancer. We analyzed 11 single nucleotide polymorphisms (four in IRAK1 and seven in IRAK4) among 1,383 newly diagnosed prostate cancer patients and 780 population controls in Sweden. Although the single-nucleotide polymorphisms in IRAK1 and IRAK4 alone were not significantly associated with prostate cancer risk, one single-nucleotide polymorphism in IRAK4, when combined with the high-risk genotype at TLR6-1-10, conferred a significant excess risk of prostate cancer. In particular, men with the risk genotype at TLR6-1-10 and IRAK4-7987 CG/CC had an odds ratio of 9.68 (P = 0.03) when compared with men who had wildtype genotypes. Our findings suggest synergistic effects between sequence variants in IRAK4 and the TLR 6-1-10 gene cluster. Although this study was based on a priori hypothesis and was designed to address many common issues facing this type of study, our results need confirmation in even larger studies.
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| 6. |
- Sun, J L, et al.
(författare)
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Sequence variants in toll-like receptor gene cluster (TLR6-TLR1-TLR10) and prostate cancer risk
- 2005
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Ingår i: Journal of the National Cancer Institute. - Wake Forest Univ, Sch Med, Ctr Human Genom, Winston Salem, NC 27157 USA. Umea Univ, Dept Radiat Sci & Oncol, Umea, Sweden. Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden. Orebro Univ Hosp, Dept Urol & Clin Med, Orebro, Sweden. Univ Uppsala Hosp, Reg Oncol Ctr, Uppsala, Sweden. Johns Hopkins Sch Med, Dept Urol, Baltimore, MD USA. : OXFORD UNIV PRESS INC. - 0027-8874 .- 1460-2105. ; 97:7, s. 525-532
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Tidskriftsartikel (refereegranskat)abstract
- Background: Chronic inflammation plays an important role in several human cancers and may be involved in the etiology of prostate cancer. Toll-like receptors (TLRs) are important in the innate immune response to pathogens and in cross-talk between innate immunity and adaptive immunity. Our previous finding of an association of TLR4 gene sequence variants and prostate cancer risk provides evidence for a role of TLRs in prostate cancer. In this study, we investigated whether sequence variants in the TLR6-TLR1-TLR10 gene cluster, residing within a 54-kb region on 4p14, were associated with prostate cancer risk. Methods: We selected 32 single-nucleotide polymorphisms (SNPs) covering these three genes and genotyped these SNPs in 96 control subjects from the Cancer Prostate in Sweden (CAPS) population-based prostate cancer case-control study. Five distinct haplotype blocks were inferred at this region, and we identified 17 haplotype-tagging SNPs (htSNPs) that could uniquely describe < 95% of the haplotypes. These 17 htSNPs were then genotyped in the entire CAPS study population (1383 case subjects and 780 control subjects). Odds ratios of prostate cancer for the carriers of a variant allele versus those with the wild-type allele were estimated using unconditional logistic regression. Results: The allele frequencies of 11 of the 17 SNPs were statistically significantly different between case and control subjects (P = .04-.001), with odds ratios for variant allele carriers (homozygous or heterozygous) compared with wild-type allele carriers ranging from 1.20 (95% confidence interval [CI] = 1.00 to 1.43) to 1.38 (95% CI = 1.12 to 1.70). Phylogenetic tree analyses of common haplotypes identified a clade of two evolutionarily related haplotypes that are statistically significantly associated with prostate cancer risk. These two haplotypes contain all the risk alleles of these 11 associated SNPs. Conclusion: The observed multiple associated SNPs at the TLR6-TLR1-TLR10 gene cluster were dependent and suggest the presence of a founder prostate cancer risk variant on this haplotype background. The TLR6-TLR1-TLR10 gene cluster may play a role in prostate cancer risk, although further functional studies are needed to pinpoint the disease-associated variants in this gene cluster.
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| 7. |
- Xie, J., et al.
(författare)
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SEA : A Combined Model for Heat Demand Prediction
- 2018
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Ingår i: Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018. - : Institute of Electrical and Electronics Engineers Inc.. - 9781538660669 ; , s. 71-75
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Konferensbidrag (refereegranskat)abstract
- Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decompose the series into seasonal and trend components. Then, predicting the seasonal and trend components respectively, and combining their predictions together as the heat demand prediction is a possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network (ENN) and the autoregressive integrated moving average (ARIMA) model, which are commonly applied to heat demand prediction. ENN and ARIMA are used to predict seasonal and trend components, respectively. Experimental results demonstrate that the proposed SEA model has a promising performance.
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| 8. |
- Du, J., et al.
(författare)
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A theory-guided deep-learning method for predicting power generation of multi-region photovoltaic plants
- 2023
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Ingår i: Engineering applications of artificial intelligence. - : Elsevier Ltd. - 0952-1976 .- 1873-6769. ; 118
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Tidskriftsartikel (refereegranskat)abstract
- Recently, clean solar energy has aroused wide attention due to its excellent potential for electricity production. A highly accurate prediction of photovoltaic power generation (PVPG) is the basis of the production and transmission of electricity. However, the current works neglect the regional correlation characteristics of PVPG and few studies propose an effective framework by incorporating prior knowledge for more physically reasonable results. In this work, a hybrid deep learning framework is proposed for simultaneously capturing the spatial correlations among different regions and temporal dependency patterns with various importance. The scientific theory and domain knowledge are incorporated into the deep learning model to make the predicted results possess physical reasonability. Subsequently, the theory-guided and attention-based CNN-LSTM (TG-A-CNN-LSTM) is constructed for PVPG prediction. In the training process, data mismatch and boundary constraint are incorporated into the loss function, and the positive constraint is utilized to restrict the output of the model. After receiving the parameters of the neural network, a TG-A-CNN-LSTM model, whose predicted results obey the physical law, is constructed. A real energy system in five regions is used to verify the accuracy of the proposed model. The predicted results indicate that TG-A-CNN-LSTM can achieve higher precision of PVPG prediction than other prediction models, with RMSE being 11.07, MAE being 4.98, and R2 being 0.94, respectively. Moreover, the performance of prediction models with sparse data is tested to illustrate the stability and robustness of TG-A-CNN-LSTM.
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| 9. |
- Ma, Z., et al.
(författare)
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Deep Neural Network-based Impacts Analysis of Multimodal Factors on Heat Demand Prediction
- 2020
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Ingår i: IEEE Transactions on Big Data. - : IEEE. - 2372-2096 .- 2332-7790. ; 6:3, s. 594-605
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Tidskriftsartikel (refereegranskat)abstract
- Prediction of heat demand using artificial neural networks has attracted enormous research attention. Weather conditions, such as direct solar irradiance and wind speed, have been identified as key parameters affecting heat demand. This paper employs an Elman neural network to investigate the impacts of direct solar irradiance and wind speed on the heat demand from the perspective of the entire district heating network. Results of the overall mean absolute percentage error (MAPE) show that direct solar irradiance and wind speed have quite similar impacts. However, the involvement of direct solar irradiance can clearly reduce the maximum absolute deviation when only involving direct solar irradiance and wind speed, respectively. In addition, the simultaneous involvement of both wind speed and direct solar irradiance does not show an obvious improvement of MAPE. Moreover, the prediction accuracy can also be affected by other factors like data discontinuity and outliers.
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| 10. |
- Wang, F., et al.
(författare)
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Performance and economic assessments of integrating geothermal energy into coal-fired power plant with CO2 capture
- 2017
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Ingår i: Energy. - : Elsevier. - 0360-5442 .- 1873-6785. ; 119, s. 278-287
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Tidskriftsartikel (refereegranskat)abstract
- A novel carbon capture and storage system integrated with geothermal energy was proposed to reduce energy consumption in the post-combustion CO2 capture (PCC) process. Geothermal energy at medium temperature was used to provide the heat required for solvent regeneration. A technical and economic assessment was conducted based on a 300 MWe coal-fired power plant. Additionally, the integrated system was also compared with a stand-alone geothermal power (GP) plant to evaluate individual advantages. Both an enhanced geothermal system (EGS) and a hot sedimentary aquifer (HSA) reservoir were selected to identify the effect of geological properties and heat characteristics on system performance. The results indicated that the geothermal-assisted post-combustion CO2 capture (GPCC) plant exhibited better performance than the PCC plant. The net plant average efficiency increased 5.56% and 4.42% in the EGS scenario and HSA scenario, respectively. Furthermore, the net incremental geothermal efficiency obtained corresponded to 21.34% and 20.35% in the EGS scenario and HSA scenario, respectively. The economic assessment indicated that the GPCC systems in both the EGS scenario and HSA scenario had lower marginal cost of electricity (70.84 $/MWh and 101.06 $/MWh) when compared with that of the stand-alone GP systems (151.09 $/MWh and 101.95 $/MWh).
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