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  • 1
    In: Blood, American Society of Hematology, Vol. 116, No. 7 ( 2010-08-19), p. 1079-1082
    Abstract: X-linked inhibitor of apoptosis (XIAP) deficiency, caused by BIRC4 mutations, is described to cause X-linked lymphoproliferative disease (XLP) phenotypes. However, compared with XLP caused by SLAM-Associated Protein deficiency (SH2D1A mutation), XIAP deficiency was originally observed to be associated with a high incidence of hemophagocytic lymphohistiocytosis (HLH) and a lack of lymphoma, suggesting that classification of XIAP deficiency as a cause of XLP may not be entirely accurate. To further characterize XIAP deficiency, we reviewed our experience with 10 patients from 8 unrelated families with BIRC4 mutations. Nine of 10 patients developed HLH by 8 years of age. Most patients presented in infancy, and recurrent HLH was common. There were no cases of lymphoma. Lymphocyte defects thought to contribute to HLH development in SLAM-Associated Protein deficiency were not observed in XIAP deficiency. We conclude that XIAP deficiency is a unique primary immunodeficiency that is more appropriately classified as X-linked familial hemophagocytic lymphohistiocytosis.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2010
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
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  • 2
    Online Resource
    Online Resource
    Elsevier BV ; 2009
    In:  Marine Pollution Bulletin Vol. 58, No. 2 ( 2009-2), p. 213-221
    In: Marine Pollution Bulletin, Elsevier BV, Vol. 58, No. 2 ( 2009-2), p. 213-221
    Type of Medium: Online Resource
    ISSN: 0025-326X
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2009
    detail.hit.zdb_id: 414337-1
    detail.hit.zdb_id: 2001296-2
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  • 3
    In: Agronomy Journal, Wiley, Vol. 110, No. 6 ( 2018-11), p. 2596-2607
    Abstract: A Machine Learning approach was innovatively used to predict corn EONR. Two features were created to approximate hydrological conditions for modeling EONR. Soil hydrology conditions were found essential in successful modeling in‐season EONR. Determination of in‐season N requirement for corn ( Zea mays L.) is challenging due to interactions of genotype, environment, and management. Machine learning (ML), with its predictive power to tackle complex systems, may solve this barrier in the development of locally based N recommendations. The objective of this study was to explore application of ML methodologies to predict economic optimum nitrogen rate (EONR) for corn using data from 47 experiments across the US Corn Belt. Two features, a water table adjusted available water capacity (AWC wt ) and a ratio of in‐season rainfall to AWC wt (RAWC wt ), were created to capture the impact of soil hydrology on N dynamics. Four ML models—linear regression (LR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO) regression, and gradient boost regression trees (GBRT)—were assessed and validated using “leave‐one‐location‐out” (LOLO) and “leave‐one‐year‐out” (LOYO) approaches. Generally, RR outperformed other models in predicting both at planting and split EONR times. Among the 47 tested sites, for 33 sites the predicted split EONR using RR fell within the 95% confidence interval, suggesting the chance of using the RR model to make an acceptable prediction of split EONR is ∼70%. When RR was used to test split EONR prediction with input weather features surrogated with 10 yr of historical weather data, the model demonstrated robustness (MAE, 33.6 kg ha −1 ; R 2 = 0.46). Incorporating mechanistically derived hydrological features significantly enhanced the ability of the ML procedures to model EONR. Improvement in estimating in‐season soil hydrological status seems essential for success in modeling N demand.
    Type of Medium: Online Resource
    ISSN: 0002-1962 , 1435-0645
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 1471598-3
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  • 4
    In: Journal of Applied Ecology, Wiley, Vol. 57, No. 1 ( 2020-01), p. 77-84
    Abstract: Successful detection of introduced marine pests (IMP) relies upon effective surveillance. However, the expedience of responding following IMP detection is often dependent upon the relationship between regulators and stakeholders. Effective detection of IMP in areas such as commercial ports requires a collaborative approach, as port environments can be highly complex both above and below the water. This complexity can encompass physical, logistical, safety and legislative issues. With this in mind, the aquatic pest biosecurity section within the Department of Primary Industries and Regional Development (DPIRD) developed the State‐Wide Array Surveillance Program (SWASP) in collaboration with Western Australian Port Authorities and port industry stakeholders. The SWASP is primarily based on passive settlement arrays for IMP detection. Arrays are deployed at strategic locations within Ports. Marine growth samples collected from the arrays are processed using Next‐Generation Sequencing (NGS) to identify the presence of IMP within a specific geographical location. Over 8 years, participation in SWASP has grown from 3 to 11 ports, spanning over 11,000 km, from the tropical north to temperate south of Western Australia. The programme has proven to be highly effective as a means of fostering stakeholder involvement and, importantly for IMP surveillance. The growth and success of SWASP has continued primarily because of the commitment and farsightedness of the ports involved. The regular presence of the biosecurity regulator as a partner in SWASP has provided a consistent face for biosecurity and fostered good stakeholder relationships, ensuring there is a reliable and effective ongoing marine surveillance programme for the state. Synthesis and applications. Through a united and collaborative approach to marine biosecurity surveillance, port authorities, industry and biosecurity regulators have developed the State‐Wide Array Surveillance Program (SWASP) and closed a major gap in biosecurity surveillance. The SWASP collaboration uses passive settlement arrays and molecular analyses to provide regular marine pest surveillance from the tropics to temperate regions of Western Australia. The continued commitment has embedded valuable relationships between stakeholder and regulator ensuring ongoing surveillance in marine biosecurity for the state. The Western Australian SWASP example has inspired other jurisdictions around Australia to develop similar collaborative approaches which will have far‐reaching marine biosecurity benefits.
    Type of Medium: Online Resource
    ISSN: 0021-8901 , 1365-2664
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2020408-5
    detail.hit.zdb_id: 410405-5
    SSG: 12
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