title: Metal complexes of sporidesmin D and dimethylgliotoxin, investigated by electrospray ionisation mass spectrometry

authors: Woodcock, Jane C.

subjects: Electrospray mass spectrometry

abstract: Electrospray ionisation mass spectrometry (ES-MS) has been used to probe the coordination chemistry of metabolites such as sporidesmin D (spdD), found in the saprophytic fungus Pithomyces chartarum, and the related bisdethiobis(methylthio)gliotoxin (dimethylgliotoxin, Megtx). SpdD forms complexes of the type [spdD+M(MeCN)]+ and [2spdD+M]+ (M=Cu, Ag) and, at higher cone voltages, [spdD+M]+. The bis(ligand) ion [2spdD+M]+ was observed at very high cone voltages, indicating it has appreciable stability; the proposed structure of this species has a four-coordinate metal ion with two bidentate spdD ligands, coordinated through their SMe groups. 1H NMR titrations of spdD with K+, Ag+ and Cu+ provided additional evidence for complex formation with the soft metals. SpdD forms only relatively weak complexes with Zn2+, Cd2+, Co2+ and Mn2+, in keeping with the known reduced tendency of these metals to form stable thioether complexes. ES-MS studies of Megtx showed similar results to spdD, with stable adducts formed with Cu+ and Ag+ ions. The X-ray crystal structure of spdD is also reported.

text: Metal complexes of sporidesmin D and dimethylgliotoxin, investigated by electrospray ionisation mass spectrometry Electrospray ionisation mass spectrometry (ES-MS) has been used to probe the coordination chemistry of metabolites such as sporidesmin D (spdD), found in the saprophytic fungus Pithomyces chartarum, and the related bisdethiobis(methylthio)gliotoxin (dimethylgliotoxin, Megtx). SpdD forms complexes of the type [spdD+M(MeCN)]+ and [2spdD+M]+ (M=Cu, Ag) and, at higher cone voltages, [spdD+M]+. The bis(ligand) ion [2spdD+M]+ was observed at very high cone voltages, indicating it has appreciable stability; the proposed structure of this species has a four-coordinate metal ion with two bidentate spdD ligands, coordinated through their SMe groups. 1H NMR titrations of spdD with K+, Ag+ and Cu+ provided additional evidence for complex formation with the soft metals. SpdD forms only relatively weak complexes with Zn2+, Cd2+, Co2+ and Mn2+, in keeping with the known reduced tendency of these metals to form stable thioether complexes. ES-MS studies of Megtx showed similar results to spdD, with stable adducts formed with Cu+ and Ag+ ions. The X-ray crystal structure of spdD is also reported.

year: 2001-04-01

title: What have scholars retrieved from Walsh and Ungson (1991)? A citation context study

authors: Anderson, Marc Howard

subjects: citation analysis

abstract: Organizational memory is fundamentally important to organizational learning. The seminal work on organizational memory is Walsh and Ungson’s article published in the Academy of Management Review in 1991. More than 300 articles have cited this classic work, but a simple citation count reveals nothing about the nature of what has been retrieved. We examine this issue through a citation context analysis (i.e. content analysis) of the citations that citing authors have made to this classic article. Our analysis provides a richer understanding of which knowledge claims made by Walsh and Ungson have been retrieved and have had the greatest impact on later work in the area of organizational memory, and also what criticisms have been leveled against their claims. Through this analysis and a review of the citing articles that contain the largest number of citations to Walsh and Ungson, we identify several important directions for future research.

text: What have scholars retrieved from Walsh and Ungson (1991)? A citation context study Organizational memory is fundamentally important to organizational learning. The seminal work on organizational memory is Walsh and Ungson’s article published in the Academy of Management Review in 1991. More than 300 articles have cited this classic work, but a simple citation count reveals nothing about the nature of what has been retrieved. We examine this issue through a citation context analysis (i.e. content analysis) of the citations that citing authors have made to this classic article. Our analysis provides a richer understanding of which knowledge claims made by Walsh and Ungson have been retrieved and have had the greatest impact on later work in the area of organizational memory, and also what criticisms have been leveled against their claims. Through this analysis and a review of the citing articles that contain the largest number of citations to Walsh and Ungson, we identify several important directions for future research.

year: 2010

title: Data and model bias in artificial intelligence for healthcare applications in New Zealand

authors: Yogarajan, Vithya

abstract: Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

text: Data and model bias in artificial intelligence for healthcare applications in New Zealand Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

year: 2022-12-22