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4. Whole-genome and transcriptome analysis enhances precision cancer treatment options

Author

Pleasance, E., Bohm, A., Williamson, L.M., Nelson, J.M.T., Shen, Y., Bonakdar, M., Titmuss, E., Csizmok, V., Wee, K., Hosseinzadeh, S., Grisdale, C.J., Reisle, C., Taylor, G.A., Lewis, E., Jones, M.R., Bleile, D., Sadeghi, S., Zhang, W., Davies, A., Pellegrini, B., Wong, T., Bowlby, R., Chan, S.K., Mungall, K.L., Chuah, E., Mungall, A.J., Moore, R.A., Zhao, Y., Deol, B., Fisic, A., Fok, A., Regier, D.A., Weymann, D., Schaeffer, D.F., Young, S., Yip, S., Schrader, K., Levasseur, N., Taylor, S.K., Feng, X., Tinker, A., Savage, K.J., Chia, S., Gelmon, K., Sun, S., Lim, H., Renouf, D.J., Jones, S.J.M., Marra, M.A., and Laskin, J.

Published
2022
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7. Scoping future research for air pollution recovery indicators (APRI). (Workshop report)

Author

Perring, M.P., Rowe, E., Arrigoni, E., Bidartondo, M.I., Britton, A.J., Field, C., Gilliam, F., Grenier, M., Hayes, F., Jones, L., Jones, M.R., Kowal, J., Marcham, L., Risser, H., Stevens, C., Suz, L.M., Wilkins, K., Howlett, R., Zappala, S., Simpson, C., Russell, Z., Perring, M.P., Rowe, E., Arrigoni, E., Bidartondo, M.I., Britton, A.J., Field, C., Gilliam, F., Grenier, M., Hayes, F., Jones, L., Jones, M.R., Kowal, J., Marcham, L., Risser, H., Stevens, C., Suz, L.M., Wilkins, K., Howlett, R., Zappala, S., Simpson, C., and Russell, Z.

Abstract

Atmospheric nitrogen (N) pollution is a major and ongoing cause of biodiversity loss across the UK, but in some locations N pollution pressures have been declining. In response to these dynamics, JNCC requested a workshop to help to scope Phase 2 of the Air Pollution Recovery Indicators (APRI) project. The damaging effects of excess N load and of gaseous ammonia on many ecosystems are clear. However, the processes and timescales of ecosystem recovery following a decrease in pollution pressure are less well understood. The APRI project aims to take practical steps to fill this knowledge gap by delivering new scientific research focused on indicators of ecosystem and species recovery from N pollution. In Phase 1, predominantly below-ground responses are being studied at a dry heathland site where experimental additions of N were made between 1998 and 2011, revealing lingering effects on soil chemistry, the soil fungi community and vegetation structure (Kowal et al. 2024). The effect on mycorrhizal fungi, and using these fungi as recovery indicators, is being examined in more detail with recently established assessment methods (Arrigoni et al. 2023). Phase 2 of APRI will consider recovery from N impacts more broadly, e.g. by studying other habitats or species. Further empirical research may be commissioned to better understand recovery pathways from air pollution. A workshop was held on 7–8 November 2023 to help develop an action plan for the remainder of the APRI project. This report summarises the workshop discussion and ensuing work. We note that the focus of the APRI project is on assessing recovery. It is therefore essential to contrast responses of ecosystems subject to decreased pollution pressure with indicators from ecosystems experiencing ongoing pollution. Properties that have been used previously to assess impacts can be used to understand recovery, and novel indicators of ecosystem change are also likely to be useful for assessing recovery. Whatever indica

Published
2024

9. Vitamin B6 catabolism and lung cancer risk: results from the Lung Cancer Cohort Consortium (LC3)

Author

Zuo, H., Ueland, P.M., Midttun, Ø, Tell, G.S., Fanidi, A., Zheng, W., Shu, X., Xiang, Y., Wu, J., Prentice, R., Pettinger, M., Thomson, C.A., Giles, G.G., Hodge, A., Cai, Q., Blot, W.J., Johansson, M., Hultdin, J., Grankvist, K., Stevens, V.L., McCullough, M.L., Weinstein, S.J., Albanes, D., Ziegler, R.G., Freedman, N.D., Caporaso, N.E., Langhammer, A., Hveem, K., Næss, M., Buring, J.E., Lee, I., Gaziano, J.M., Severi, G., Zhang, X., Stampfer, M.J., Han, J., Zeleniuch-Jacquotte, A., Marchand, L.L., Yuan, J., Wang, R., Koh, W., Gao, Y., Ericson, U., Visvanathan, K., Jones, M.R., Relton, C., Brennan, P., and Ulvik, A.

Published
2019
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11. Mouse Model of Neutrophilic Asthma Reveals an Epithelium-Lymphocyte-Neutrophil Communication Circuit Underlying Destructive Allergic Airway Neutrophilia

Author

Shenoy, A.T., primary, Korkmaz, F.T., additional, Lyon De Ana, C., additional, Shao, F.-Z., additional, Odom, C., additional, Barker, K., additional, Ramanujan, A., additional, Goltry, W., additional, Martin, I., additional, Ha, C., additional, Quinton, L.J., additional, Jones, M.R., additional, Fine, A., additional, Chen, F., additional, Belkina, A., additional, and Mizgerd, J.P., additional

Published
2023
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12. UK Eutrophying and Acidifying Atmospheric Pollutants (UKEAP) Annual Report 2022

Author

Twigg, M.M., Braban, C.F., Stephens, A.C.M., Tang, Y.S., Leeson, S.R., Jones, M.R., Simmons, I., Harvey, D., Yeung, K., van Dijk, N., Iwanicka, A., Duarte, F., Nemitz, E., Leaver, D., Andrews, C., Thacker, S., Keenan, P.O., Dos Santos Pereira, M.G., Guyatt, H., Hunt, A., Salisbury, E., Chetiu, N., Grant, H., Warwick, A., Rylett, D., Teagle, S., Lord, W., Bannister, G., Sutton, M.A., Conolly, C., Vincent, K., Sanocka, A., Ritchie, S., Knight, D., Donovan, B., Jackson, T., Dyer, M, Osborne, E., Twigg, M.M., Braban, C.F., Stephens, A.C.M., Tang, Y.S., Leeson, S.R., Jones, M.R., Simmons, I., Harvey, D., Yeung, K., van Dijk, N., Iwanicka, A., Duarte, F., Nemitz, E., Leaver, D., Andrews, C., Thacker, S., Keenan, P.O., Dos Santos Pereira, M.G., Guyatt, H., Hunt, A., Salisbury, E., Chetiu, N., Grant, H., Warwick, A., Rylett, D., Teagle, S., Lord, W., Bannister, G., Sutton, M.A., Conolly, C., Vincent, K., Sanocka, A., Ritchie, S., Knight, D., Donovan, B., Jackson, T., Dyer, M, and Osborne, E.

Published
2023

13. The Precision Toxicology initiative

Author

Colbourne, J.K., Andrews, E., Barnard, M., Čavoški, A., Chaturvedi, A., Epps, D.J.T., Holden, L., Jones, M.R., Li, X., Mueller, F., Ormanin-Lewandowska, A., Orsini, L., Roberts, R., Weber, R.J.M., Zhou, J., Apic, G., Ignasiak, T., Jankovic, K., Krsmanovic, T., Leoni, B., Asole, G., Guigó, R., Marangio, P., Palumbo, E., Perez-Lluch, S., Wucher, V., Vlot, A.H., Anholt, R., Mackay, T., Escher, Beate, Grasse, Nico, Huchthausen, Julia, Massei, Riccardo, Reemtsma, Thorsten, Scholz, Stefan, Schüürmann, Gerrit, Bondesson, M., Cherbas, P., et al., Colbourne, J.K., Andrews, E., Barnard, M., Čavoški, A., Chaturvedi, A., Epps, D.J.T., Holden, L., Jones, M.R., Li, X., Mueller, F., Ormanin-Lewandowska, A., Orsini, L., Roberts, R., Weber, R.J.M., Zhou, J., Apic, G., Ignasiak, T., Jankovic, K., Krsmanovic, T., Leoni, B., Asole, G., Guigó, R., Marangio, P., Palumbo, E., Perez-Lluch, S., Wucher, V., Vlot, A.H., Anholt, R., Mackay, T., Escher, Beate, Grasse, Nico, Huchthausen, Julia, Massei, Riccardo, Reemtsma, Thorsten, Scholz, Stefan, Schüürmann, Gerrit, Bondesson, M., and Cherbas, P., et al.

Abstract

The goal of PrecisionTox is to overcome conceptual barriers to replacing traditional mammalian chemical safety testing by accelerating the discovery of evolutionarily conserved toxicity pathways that are shared by descent among humans and more distantly related animals. An international consortium is systematically testing the toxicological effects of a diverse set of chemicals on a suite of five model species comprising fruit flies, nematodes, water fleas, and embryos of clawed frogs and zebrafish along with human cell lines. Multiple forms of omics and comparative toxicology data are integrated to map the evolutionary origins of biomolecular interactions that are predictive of adverse health effects, to major branches of the animal phylogeny. These conserved elements of adverse outcome pathways (AOPs) and their biomarkers are expected to provide mechanistic insight useful for regulating groups of chemicals based on their shared modes of action. PrecisionTox also aims to quantify risk variation within populations by recognizing susceptibility as a heritable trait that varies with genetic diversity. This initiative incorporates legal experts and collaborates with risk managers to address specific needs within European chemicals legislation, including the uptake of new approach methodologies (NAMs) for setting precise regulatory limits on toxic chemicals.

Published
2023

15. UK guidelines for the management of Stevens-Johnson syndrome/toxic epidermal necrolysis in adults 2016 (print summary – Full guidelines available at http://dx.doi.org/10.1016/j.bjps.2016.01.034)

Author

Creamer, D., Walsh, S.A., Dziewulski, P., Exton, L.S., Lee, H.Y., Dart, J.K.G., Setterfield, J., Bunker, C.B., Ardern-Jones, M.R., Watson, K.M.T., Wong, G.A.E., Philippidou, M., Vercueil, A., Martin, R.V., Williams, G., Shah, M., Brown, D., Williams, P., Mohd Mustapa, M.F., and Smith, C.H.

Published
2016
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20. Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Author

Dareng, E.O., Tyrer, J.P., Barnes, D.R., Jones, M.R., Yang, X, Aben, K.K.H., Adank, M.A., Agata, S., Andrulis, I.L., Anton-Culver, H., Antonenkova, N.N., Aravantinos, G., Arun, B.K., Augustinsson, A., Balmaña, J., Bandera, E.V., Barkardottir, R.B., Barrowdale, D., Beckmann, M.W., Beeghly-Fadiel, A., Benitez, J., Bermisheva, M., Bernardini, M.Q., Bjorge, L., Black, A., Bogdanova, N.V., Bonanni, B., Borg, A., Brenton, J.D., Budzilowska, A., Butzow, R., Buys, S.S., Cai, H., Caligo, M.A., Campbell, I., Cannioto, R., Cassingham, H., Chang-Claude, J., Chanock, S.J., Chen, K., Chiew, Y.E., Chung, W.K., Claes, K.B.M., Colonna, S., Cook, L.S., Couch, F.J., Daly, M.B., Dao, F., Davies, E., Hoya, M. de la, Putter, R. de, Dennis, J., DePersia, A., Devilee, P., Diez, O., Ding, Y.C., Doherty, J.A., Domchek, S.M., Dörk, T., Bois, A. du, Dürst, M., Eccles, D.M., Eliassen, H.A., Engel, C., Evans, G.D., Fasching, P.A., Flanagan, J.M., Fortner, R.T., Machackova, E., Friedman, E., Ganz, P.A., Garber, J., Gensini, F., Giles, G.G., Glendon, G., Godwin, A.K., Goodman, M.T., Greene, M.H., Gronwald, J., Hahnen, E., Haiman, C.A., Håkansson, N., Hamann, U., Hansen, T.V., Harris, H.R., Hartman, M, Heitz, F., Hildebrandt, M.A., Høgdall, E., Høgdall, C.K., Hopper, J.L., Huang, R.Y., Huff, C., Hulick, P.J., Huntsman, D.G., Imyanitov, E.N., Isaacs, C., Jakubowska, A., James, P.A., Kiemeney, L.A.L.M., Altena, A.M. van, Janavicius, R., Antoniou, A.C., Pharoah, P.D., Dareng, E.O., Tyrer, J.P., Barnes, D.R., Jones, M.R., Yang, X, Aben, K.K.H., Adank, M.A., Agata, S., Andrulis, I.L., Anton-Culver, H., Antonenkova, N.N., Aravantinos, G., Arun, B.K., Augustinsson, A., Balmaña, J., Bandera, E.V., Barkardottir, R.B., Barrowdale, D., Beckmann, M.W., Beeghly-Fadiel, A., Benitez, J., Bermisheva, M., Bernardini, M.Q., Bjorge, L., Black, A., Bogdanova, N.V., Bonanni, B., Borg, A., Brenton, J.D., Budzilowska, A., Butzow, R., Buys, S.S., Cai, H., Caligo, M.A., Campbell, I., Cannioto, R., Cassingham, H., Chang-Claude, J., Chanock, S.J., Chen, K., Chiew, Y.E., Chung, W.K., Claes, K.B.M., Colonna, S., Cook, L.S., Couch, F.J., Daly, M.B., Dao, F., Davies, E., Hoya, M. de la, Putter, R. de, Dennis, J., DePersia, A., Devilee, P., Diez, O., Ding, Y.C., Doherty, J.A., Domchek, S.M., Dörk, T., Bois, A. du, Dürst, M., Eccles, D.M., Eliassen, H.A., Engel, C., Evans, G.D., Fasching, P.A., Flanagan, J.M., Fortner, R.T., Machackova, E., Friedman, E., Ganz, P.A., Garber, J., Gensini, F., Giles, G.G., Glendon, G., Godwin, A.K., Goodman, M.T., Greene, M.H., Gronwald, J., Hahnen, E., Haiman, C.A., Håkansson, N., Hamann, U., Hansen, T.V., Harris, H.R., Hartman, M, Heitz, F., Hildebrandt, M.A., Høgdall, E., Høgdall, C.K., Hopper, J.L., Huang, R.Y., Huff, C., Hulick, P.J., Huntsman, D.G., Imyanitov, E.N., Isaacs, C., Jakubowska, A., James, P.A., Kiemeney, L.A.L.M., Altena, A.M. van, Janavicius, R., Antoniou, A.C., and Pharoah, P.D.

Abstract

Item does not contain fulltext, Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

Published
2022

21. Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci

Author

DeVries, A.A.F., Dennis, J., Tyrer, J.P., Peng, P.C., Coetzee, S.G., Reyes, A.L., Plummer, J.T., Davis, B.D., Chen, S.S., Dezem, F.S., Aben, K.K.H., Anton-Culver, H., Antonenkova, N.N., Beckmann, M.W., Beeghly-Fadiel, A., Berchuck, A., Bogdanova, N.V., Bogdanova-Markov, N., Brenton, J.D., Butzow, R., Campbell, I., Chang-Claude, J., Chenevix-Trench, G., Cook, L.S., Defazio, A., Doherty, J.A., Dörk, T., Eccles, D.M., Eliassen, A.H., Fasching, P.A., Fortner, R.T., Giles, G.G., Goode, E.L., Goodman, M.T., Gronwald, J., Håkansson, N., Hildebrandt, M.A., Huff, C., Huntsman, D.G., Jensen, A., Kar, S., Karlan, B.Y., Khusnutdinova, E.K., Kiemeney, L.A.L.M., Kjaer, S.K., Kupryjanczyk, J., Labrie, M., Lambrechts, D., Le, N.D., Lubiński, J., May, T., Menon, U., Milne, R.L., Modugno, F., Monteiro, Ana, Moysich, K.B., Odunsi, K., Olsson, H., Pearce, C.L., Pejovic, T., Ramus, S.J., Riboli, E., Riggan, M.J., Romieu, I., Sandler, D.P., Schildkraut, J.M., Setiawan, V.W., Sieh, W., Song, H., Sutphen, R., Terry, K.L., Thompson, P.J., Titus, L., Tworoger, S.S., Nieuwenhuysen, E. Van, Edwards, D.V., Webb, P.M., Wentzensen, N., Whittemore, A.S., Wolk, A., Wu, A.H., Ziogas, Argyrios, Freedman, M.L., Lawrenson, K., Pharoah, P.D., Easton, D.F., Gayther, S.A., Jones, M.R., DeVries, A.A.F., Dennis, J., Tyrer, J.P., Peng, P.C., Coetzee, S.G., Reyes, A.L., Plummer, J.T., Davis, B.D., Chen, S.S., Dezem, F.S., Aben, K.K.H., Anton-Culver, H., Antonenkova, N.N., Beckmann, M.W., Beeghly-Fadiel, A., Berchuck, A., Bogdanova, N.V., Bogdanova-Markov, N., Brenton, J.D., Butzow, R., Campbell, I., Chang-Claude, J., Chenevix-Trench, G., Cook, L.S., Defazio, A., Doherty, J.A., Dörk, T., Eccles, D.M., Eliassen, A.H., Fasching, P.A., Fortner, R.T., Giles, G.G., Goode, E.L., Goodman, M.T., Gronwald, J., Håkansson, N., Hildebrandt, M.A., Huff, C., Huntsman, D.G., Jensen, A., Kar, S., Karlan, B.Y., Khusnutdinova, E.K., Kiemeney, L.A.L.M., Kjaer, S.K., Kupryjanczyk, J., Labrie, M., Lambrechts, D., Le, N.D., Lubiński, J., May, T., Menon, U., Milne, R.L., Modugno, F., Monteiro, Ana, Moysich, K.B., Odunsi, K., Olsson, H., Pearce, C.L., Pejovic, T., Ramus, S.J., Riboli, E., Riggan, M.J., Romieu, I., Sandler, D.P., Schildkraut, J.M., Setiawan, V.W., Sieh, W., Song, H., Sutphen, R., Terry, K.L., Thompson, P.J., Titus, L., Tworoger, S.S., Nieuwenhuysen, E. Van, Edwards, D.V., Webb, P.M., Wentzensen, N., Whittemore, A.S., Wolk, A., Wu, A.H., Ziogas, Argyrios, Freedman, M.L., Lawrenson, K., Pharoah, P.D., Easton, D.F., Gayther, S.A., and Jones, M.R.

Abstract

Item does not contain fulltext, BACKGROUND: Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort. METHODS: Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer-related cell types. RESULTS: We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P < .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P < .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types. CONCLUSIONS: CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention.

Published
2022

23. LOX-1 Limits Inflammatory Lung Injury During Pneumonia, Regulating Alveolar Macrophage Immunometabolic Function

Author

Korkmaz, F.T., primary, Shenoy, A.T., additional, Symer, E., additional, Baird, L., additional, Odom, C., additional, Arafa, E.I., additional, Na, E., additional, Dimbo, E., additional, Molina-Arocho, W., additional, Brudner, M., additional, Standiford, T.J., additional, Mehta, J., additional, Sawamura, T., additional, Jones, M.R., additional, Traber, K., additional, Mizgerd, J.P., additional, and Quinton, L.J., additional

Published
2022
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25. Mouse Model of Late-Onset Neutrophilic Asthma Reveals Novel Cellular and Molecular Circuits Underlying Destructive Airway Neutrophilia

Author

Shenoy, A.T., primary, Korkmaz, F.T., additional, Lyon De Ana, C., additional, Odom, C., additional, Barker, K., additional, Goltry, W., additional, Martin, I., additional, Shao, F.-Z., additional, Jones, M.R., additional, Quinton, L.J., additional, Fine, A., additional, Chen, F., additional, Belkina, A., additional, and Mizgerd, J.P., additional

Published
2022
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26. Lung Mesenchymal Cell Responses to Pneumonia

Author

Soucy, A.M., primary, Brune, J.E., additional, Shenoy, A.T., additional, Korkmaz, F.T., additional, Campbell, J.D., additional, Goltry, W.N., additional, Martin, I.M.C., additional, Traber, K.E., additional, Jones, M.R., additional, Quinton, L.J., additional, Frevert, C.W., additional, and Mizgerd, J.P., additional

Published
2022
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27. Physics-informed machine learning for structural health monitoring

Author

Cross, E.J., Gibson, S.J., Jones, M.R., Pitchforth, D.J., Zhang, S., Rogers, T.J., Cury, A., Ribeiro, D., Ubertini, F., and Todd, M.D.

Abstract

The use of machine learning in structural health monitoring is becoming more common, as many of the inherent tasks (such as regression and classification) in developing condition-based assessment fall naturally into its remit. This chapter introduces the concept of physics-informed machine learning, where one adapts ML algorithms to account for the physical insight an engineer will often have of the structure they are attempting to model or assess. The chapter will demonstrate how grey-box models, that combine simple physics-based models with data-driven ones, can improve predictive capability in an SHM setting. A particular strength of the approach demonstrated here is the capacity of the models to generalize, with enhanced predictive capability in different regimes. This is a key issue when life-time assessment is a requirement, or when monitoring data do not span the operational conditions a structure will undergo. The chapter will provide an overview of physics-informed ML, introducing a number of new approaches for grey-box modelling in a Bayesian setting. The main ML tool discussed will be Gaussian process regression, and we will demonstrate how physical assumptions/models can be incorporated through constraints, through the mean function and kernel design, and finally in a state-space setting. A range of SHM applications will be demonstrated, from loads monitoring tasks for off-shore and aerospace structures, through to performance monitoring for long-span bridges.

Published
2022

30. A summary of the updated report on the incidence and epidemiological trends of keratinocyte cancers in the UK 2013–2018

Author

Kwiatkowska, M., primary, Ahmed, S., additional, Ardern‐Jones, M.R., additional, Bhatti, L.A., additional, Bleiker, T.O., additional, Gavin, A., additional, Hussain, S., additional, Huws, D.W., additional, Irvine, L., additional, Langan, S.M., additional, Millington, G.W.M., additional, Mitchell, H., additional, Murphy, R., additional, Paley, L., additional, Proby, C.M., additional, Thomson, C.S., additional, Thomas, R., additional, Turner, C., additional, Vernon, S., additional, and Venables, Z.C., additional

Published
2021
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31. Simulated Impacts of Climate Change on Water Use and Yield of Irrigated Sugarcane in South Africa

Author

Jones, M.R, Singels, A, and Ruane, A. C

Subjects
Meteorology And Climatology
Abstract

Reliable predictions of climate change impacts on water use, irrigation requirements and yields of irrigated sugarcane in South Africa (a water-scarce country) are necessary to plan adaptation strategies. Although previous work has been done in this regard, methodologies and results vary considerably. The objectives were (1) to estimate likely impacts of climate change on sugarcane yields, water use and irrigation demand at three irrigated sugarcane production sites in South Africa (Malelane, Pongola and La Mercy) for current (1980-2010) and future (2070-2100) climate scenarios, using an approach based on the Agricultural Model Inter-comparison and Improvement Project (AgMIP) protocols; and (2) to assess the suitability of this methodology for investigating climate change impacts on sugarcane production. Future climate datasets were generated using the Delta downscaling method and three Global Circulation Models (GCMs) assuming atmospheric CO2 concentration [CO2] of 734 ppm(A2 emissions scenario). Yield and water use were simulated using the DSSAT-Canegro v4.5 model. Irrigated cane yields are expected to increase at all three sites (between 11 and 14%), primarily due to increased interception of radiation as a result of accelerated canopy development. Evapotranspiration and irrigation requirements increased by 11% due to increased canopy cover and evaporative demand. Sucrose yields are expected to decline because of increased consumption of photo-assimilate for structural growth and maintenance respiration. Crop responses in canopy development and yield formation differed markedly between the crop cycles investigated. Possible agronomic implications of these results include reduced weed control costs due to shortened periods of partial canopy, a need for improved efficiency of irrigation to counter increased demands, and adjustments to ripening and harvest practices to counter decreased cane quality and optimize productivity. Although the Delta climate data downscaling method is considered robust, accurate and easily-understood, it does not change the future number of rain-days per month. The impacts of this and other climate data simplifications ought to be explored in future work. Shortcomings of the DSSAT-Canegro model include the simulated responses of phenological development, photosynthesis and respiration processes to high temperatures, and the disconnect between simulated biomass accumulation and expansive growth. Proposed methodology refinements should improve the reliability of predicted climate change impacts on sugarcane yield.

Published
2015

33. Dynamic Stark Effect in Two-Dimensional Spectroscopy Revealing Modulation of Ultrafast Charge Separation in Bacterial Reaction Centers by an Inherent Electric Field

Author

Ma, F., Romero, E., Jones, M.R., Novoderezhkin, V.I., Yu, L.-J., Van Grondelle, R., Ma, F., Romero, E., Jones, M.R., Novoderezhkin, V.I., Yu, L.-J., and Van Grondelle, R.

Abstract

Despite extensive study, mysteries remain regarding the highly efficient ultrafast charge separation processes in photosynthetic reaction centers (RCs). In this work, transient Stark signals were found to be present in ultrafast two-dimensional electronic spectra recorded for purple bacterial RCs at 77 K. These arose from the electric field that is inherent to the intradimer charge-transfer intermediate of the bacteriochlorophyll pair (P), PA+PB-. By comparing three mutated RCs, a correlation was found between the efficient formation of PAA+PB- and a fast charge separation rate. Importantly, the energy level of P∗ was changed due to the Stark shift, influencing the driving force for P∗ → P+BA- electron transfer and hence its rate. Furthermore, the orientation and amplitude of the inherent electric field varied in different ways upon different mutation, leading to contrasting changes in the rates. This mechanism of modulation provides a solution to a long-lasting inconsistency between experimental observations and activation energy theory.

Published
2021
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34. Corrigendum to ‘SJS/TEN 2019: From science to translation’ [J. Dermatol. Sci. 98/1 (2020) 2–12]

Author

Chang, W-C, Abe, R., Anderson, P., Anderson, W., Ardern-Jones, M.R., Beachkofsky, T.M., Bellón, T., Biala, A.K., Bouchard, C., Cavalleri, G.L., Chapman, N., Chodosh, J., Choi, H.K., Cibotti, R.R., Divito, S.J., Dewar, K., Dehaeck, U., Etminan, M., Forbes, D., Fuchs, E., Goldman, J.L., Holmes, J.H., Hope, E.A., Hung, S-L, Hsieh, C-L, Iovieno, A., Jagdeo, J., Kim, M.K., Koelle, D.M., Lacouture, M.E., Le Pallec, S., Lehloenya, R.J., Lim, R., Lowe, A., McCawley, J., Micheletti, R.G., Mockenhaupt, M., Niemeyer, K., Norcross, M.A., Oboh, D., Olteanu, C., Pasieka, H.B., Peters, J., Pirmohamed, M., Rieder, M., Saeed, H.N., Shear, N.H., Shieh, C., Straus, S., Sukasem, C., Sung, C., Trubiano, J.A., Tsou, S-Y, Ueta, M., Volpi, S., Wan, C., Wang, H., Wang, Z-Q, Weintraub, J., Whale, C., Wheatley, L.M., Whyte-Croasdaile, S., Williams, K.B., Wright, G., Yeung, S.N., Zhou, L., Chung, W-H, Phillips, E.J., Carleton, B.C., Chang, W-C, Abe, R., Anderson, P., Anderson, W., Ardern-Jones, M.R., Beachkofsky, T.M., Bellón, T., Biala, A.K., Bouchard, C., Cavalleri, G.L., Chapman, N., Chodosh, J., Choi, H.K., Cibotti, R.R., Divito, S.J., Dewar, K., Dehaeck, U., Etminan, M., Forbes, D., Fuchs, E., Goldman, J.L., Holmes, J.H., Hope, E.A., Hung, S-L, Hsieh, C-L, Iovieno, A., Jagdeo, J., Kim, M.K., Koelle, D.M., Lacouture, M.E., Le Pallec, S., Lehloenya, R.J., Lim, R., Lowe, A., McCawley, J., Micheletti, R.G., Mockenhaupt, M., Niemeyer, K., Norcross, M.A., Oboh, D., Olteanu, C., Pasieka, H.B., Peters, J., Pirmohamed, M., Rieder, M., Saeed, H.N., Shear, N.H., Shieh, C., Straus, S., Sukasem, C., Sung, C., Trubiano, J.A., Tsou, S-Y, Ueta, M., Volpi, S., Wan, C., Wang, H., Wang, Z-Q, Weintraub, J., Whale, C., Wheatley, L.M., Whyte-Croasdaile, S., Williams, K.B., Wright, G., Yeung, S.N., Zhou, L., Chung, W-H, Phillips, E.J., and Carleton, B.C.

Abstract

The authors regret not all contributing authors correctly acknowledged funding. Jonathan Peter’s IMARI-Africa project (AFRISCAR) is part of the EDCTP2 programme supported by the European Union (grant number TMA2017SF-1981 ). The authors would like to apologise for any inconvenience caused.

Published
2021

36. Using the ultrasonic wave transmission method to study the setting behavior of foamed concrete

Author

Wei, She, Yunsheng, Zhang, and Jones, M.R.

Subjects
Ultrasonic waves -- Usage, Foamed materials -- Analysis -- Properties -- Thermal properties, Fly ash -- Usage, Business, Construction and materials industries
Abstract

ABSTRACT The objective of this paper is to investigate the possibility of using ultrasonic wave transmission method to study the setting behavior of foamed concretes (FC), and relate ultrasonic wave [...]

Published
2014
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37. Three-dimensional numerical modeling and simulation of the thermal properties of foamed concrete

Author

Wei, She, Yunsheng, Zhang, and Jones, M.R.

Subjects
Concrete -- Thermal properties -- Analysis -- Models, Business, Construction and materials industries
Abstract

ABSTRACT In this paper, a three-dimensional method was developed for modeling the heat transfer of foamed concretes with a large range of densities (300-1700 kg/[m.sup.3]). A random generation method was [...]

Published
2014
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39. LOX-1 Regulates Alveolar Macrophage Responsiveness and Limits Inflammatory Lung Injury During Pneumonia

Author

Korkmaz, F.T., primary, Shenoy, A.T., additional, Symer, E., additional, Baird, L., additional, Odom, C., additional, Arafa, E.I., additional, Na, E., additional, Dimbo, E., additional, Molina-Arocho, W., additional, Brudner, M., additional, Standiford, T.J., additional, Mehta, J., additional, Sawamura, T., additional, Jones, M.R., additional, Mizgerd, J.P., additional, Traber, K., additional, and Quinton, L.J., additional

Published
2021
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40. Lung Mesenchymal Cell Responses to Pneumococcal Pneumonia

Author

Soucy, A.M., primary, Campbell, J.D., additional, Hu, T., additional, Korkmaz, F.T., additional, Shenoy, A.T., additional, Goltry, W., additional, Martin, I.M.C., additional, Traber, K.E., additional, Jones, M.R., additional, Quinton, L.J., additional, and Mizgerd, J.P., additional

Published
2021
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41. Epithelial MHC-II Drives Antigen Presentation, Checkpoint Display, and CD4+ TRM Cell Locations and Functions in the Lung

Author

Shenoy, A.T., primary, Arafa, E.I., additional, Lyon De Ana, C., additional, Barker, K., additional, Korkmaz, F.T., additional, Ramanujan, A., additional, Etesami, N., additional, Martin, I.M.C., additional, Tilton, B., additional, Hinds, A., additional, Goltry, W.N., additional, Kathuria, H., additional, Jones, M.R., additional, Quinton, L.J., additional, Belkina, A., additional, and Mizgerd, J.P., additional

Published
2021
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46. Opportunities for coupled electrochemical and ion-exchange technologies to remove recalcitrant micropollutants in water

Author

Martins, V.L., Ogden, M.D., Jones, M.R., Trowsdale, S.A., Hall, P.J., and Jensen, H.S.

Abstract

Micropollutants are found in many waters at concentrations that are concerning for living and environmental systems. They are usually characterised as being persistent and are generally difficult to remove from the water using traditional techniques. In this work, we investigate a treatment technology that couples electrooxidation of micropollutants with subsequent absorption of charged products and remaining compounds through a mixed strong acid and strong base ion exchange resin. The results clearly show that carbon fibre is a promising electrode material. Electrooxidation of the drug Ibuprofen using carbon fibre in a coulombic efficiency of 13 mC/ppm removed 71% of the compound after two hours (down to 29 ppm). The addition of sodium chloride led to a near doubling of the pseudo-first order reaction rate from 1.7 to 3.0 10-4 s-1. A mix of Ibuprofen and the pesticide Diuron showed similarly promising results and while the overall oxidation decreased the positive effect of sodium chloride was present. Strikingly, coupling electrooxidation with a mixed bed ion exchange resin removed both compounds, decreasing levels of Diuron to below the limit of detection (18 ppb) and Ibuprofen down to 0.8 ppm. The approach shows potential as a treatment technology for the removal of complex pollutants in water.

Published
2020

47. A polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by phenomewide association studies

Author

Joo, Y.Y. (Yoonjung Yoonie), Actkins, K. (Ky'Era), Pacheco, J.A. (Jennifer A.), Basile, A.O. (Anna O.), Carroll, R. (Robert), Crosslin, D.R. (David), Day, F.R. (Felix), Denny, J.C. (Joshua C.), Edwards, D.R.V. (Digna R. Velez), Hakonarson, H. (Hakon), Harley, J.B. (John B.), Hebbring, S.J. (Scott J.), Ho, K. (Kevin), Jarvik, G.P. (Gail), Jones, M.R. (Michelle), Karaderi, T. (Tugce), Mentch, F.D. (Frank D.), Meun, C. (Cindy), Namjou, B. (Bahram), Pendergrass, S.A. (Sarah), Ritchie, M.D. (Marylyn D.), Stanaway, I.B. (Ian B.), Urbanek, M. (Margrit), Walunas, T.L. (Theresa L.), Smith, M. (Maureen), Chisholm, R.L. (Rex L.), Kho, M.M.L. (Marcia), Davis, L. (Lea), Geoffrey Hayes, M. (M.), Joo, Y.Y. (Yoonjung Yoonie), Actkins, K. (Ky'Era), Pacheco, J.A. (Jennifer A.), Basile, A.O. (Anna O.), Carroll, R. (Robert), Crosslin, D.R. (David), Day, F.R. (Felix), Denny, J.C. (Joshua C.), Edwards, D.R.V. (Digna R. Velez), Hakonarson, H. (Hakon), Harley, J.B. (John B.), Hebbring, S.J. (Scott J.), Ho, K. (Kevin), Jarvik, G.P. (Gail), Jones, M.R. (Michelle), Karaderi, T. (Tugce), Mentch, F.D. (Frank D.), Meun, C. (Cindy), Namjou, B. (Bahram), Pendergrass, S.A. (Sarah), Ritchie, M.D. (Marylyn D.), Stanaway, I.B. (Ian B.), Urbanek, M. (Margrit), Walunas, T.L. (Theresa L.), Smith, M. (Maureen), Chisholm, R.L. (Rex L.), Kho, M.M.L. (Marcia), Davis, L. (Lea), and Geoffrey Hayes, M. (M.)

Abstract

Context: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated tobe unidentified in clinical practice. Objective: Utilizing polygenic risk prediction, we aim to identify the phenome-widecomorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventivetreatment.Design, Patients, and Methods: Leveraging the electronic health records (EHRs) of 124 852individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores(PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). Weevaluated its predictive capability across different ancestries and perform a PRS-based phenomewide association study (PheWAS) to assess the phenomic expression of the heightened risk ofPCOS.Results: The integrated polygenic prediction improved the average performance (pseudo-R2)for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null modelacross European, African, and multi-ancestry participants respectively. The subsequent PRSpowered PheWAS identified a high level of shared biology between PCOS and a range ofmetabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity","type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension",and "sleep apnea" reaching phenome-wide significance.Conclusions: Our study has expanded the methodological utility of PRS in patient stratificationand risk prediction, especially in a multifactorial condition like PCOS, across different geneticorigins. By utilizing the individual genome-phenome data available from the EHR, our approachalso demonstrates that polygenic prediction by PRS can provide valuable opportunities todiscover the pleiotropic phenomic network associated with PCOS pathogenesis.Abbreviations: AA, African ancestry; ANOVA, analysis of variance; BMI, body mass index; EA,European ancestry; EHR, electronic health records; eMERGE, electronic Medical Records andGenomi

Published
2020
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48. SJS/TEN 2019: From Science to Translation

Author

Chang, W-C, Abe, R., Anderson, P., Anderson, W., Ardern-Jones, M.R., Beachkofsky, T.M., Bellón, T., Biala, A.K., Bouchard, C., Cavalleri, G.L., Chapman, N., Chodosh, J., Choi, H.K., Cibotti, R.R., Divito, S.J., Dewar, K., Dehaeck, U., Etminan, M., Forbes, D., Fuchs, E., Goldman, J.L., Holmes, J.H., Hope, E.A., Hung, S-I, Hsieh, C-L, Iovieno, A., Jagdeo, J., Kim, M.K., Koelle, D.M., Lacouture, M.E., Le Pallec, S., Lehloenya, R.J., Lim, R., Lowe, A., McCawley, J., Micheletti, R.G., Mockenhaupt, M., Niemeyer, K., Norcross, M.A., Oboh, D., Olteanu, C., Pasieka, H.B., Peter, J., Pirmohamed, M., Rieder, M., Saeed, H.N., Shear, N.H., Shieh, C., Straus, S., Sukasem, C., Sung, C., Trubiano, J.A., Tsou, S-Y, Ueta, M., Volpi, S., Wan, C., Wang, H., Wang, Z-Q, Weintraub, J., Whale, C., Wheatley, L.M., Whyte-Croasdaile, S., Williams, K.B., Wright, G., Yeung, S.N., Zhou, L., Chung, W-H, Phillips, E.J., Carleton, B.C., Chang, W-C, Abe, R., Anderson, P., Anderson, W., Ardern-Jones, M.R., Beachkofsky, T.M., Bellón, T., Biala, A.K., Bouchard, C., Cavalleri, G.L., Chapman, N., Chodosh, J., Choi, H.K., Cibotti, R.R., Divito, S.J., Dewar, K., Dehaeck, U., Etminan, M., Forbes, D., Fuchs, E., Goldman, J.L., Holmes, J.H., Hope, E.A., Hung, S-I, Hsieh, C-L, Iovieno, A., Jagdeo, J., Kim, M.K., Koelle, D.M., Lacouture, M.E., Le Pallec, S., Lehloenya, R.J., Lim, R., Lowe, A., McCawley, J., Micheletti, R.G., Mockenhaupt, M., Niemeyer, K., Norcross, M.A., Oboh, D., Olteanu, C., Pasieka, H.B., Peter, J., Pirmohamed, M., Rieder, M., Saeed, H.N., Shear, N.H., Shieh, C., Straus, S., Sukasem, C., Sung, C., Trubiano, J.A., Tsou, S-Y, Ueta, M., Volpi, S., Wan, C., Wang, H., Wang, Z-Q, Weintraub, J., Whale, C., Wheatley, L.M., Whyte-Croasdaile, S., Williams, K.B., Wright, G., Yeung, S.N., Zhou, L., Chung, W-H, Phillips, E.J., and Carleton, B.C.

Abstract

Stevens-Johnson syndrome and toxic epidermal necrolysis (SJS/TEN) are potentially life-threatening, immune-mediated adverse reactions characterized by widespread erythema, epidermal necrosis, and detachment of skin and mucosa. Efforts to grow and develop functional international collaborations and a multidisciplinary interactive network focusing on SJS/TEN as an uncommon but high burden disease will be necessary to improve efforts in prevention, early diagnosis and improved acute and long-term management. SJS/TEN 2019: From Science to Translation was a 1.5-day scientific program held April 26-27, 2019, in Vancouver, Canada. The meeting successfully engaged clinicians, researchers, and patients and conducted many productive discussions on research and patient care needs.

Published
2020

49. Corrigendum to ‘Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset’ [Field Crops Research, 244 (2019) 107622]

Author

Jones, M.R., primary, Singels, A., additional, Chinorumba, S., additional, Patton, A., additional, Poser, C., additional, Singh, M., additional, Martiné, J., additional, Christina, M., additional, Shine, J., additional, Annandale, J., additional, and Hammer, G.L., additional

Published
2021
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