title: The sex or the head? Feminine voices and academic women through the work of Hélène Cixous

authors: Locke, Kirsten

subjects: academic writing

abstract: Hélène Cixous is perhaps best known for her paper, ‘The Laugh of the Medusa’ (1976) and her literary contributions outside academia. In this paper, we pick up a lesser known Cixous text, ‘Le Sexe ou la tête?’ that offers an interesting and provocative perspective on the traps associated with being feminine in a masculine environment. As we converse with Cixous, weaving our own words and experiences with hers, we link her work more closely with the feminine in modern-day academia. We suggest that Cixous’s remarks on decapitation and voice offer a way forward for academic women to be; to speak; to recognise the double jeopardy of decapitation in the university; and to use laughter as a strategic, powerful, political act of resistance and subversion against oppressive masculine power structures. We draw on and seek to enact Cixous’s notion of écriture feminine—a disruptive style of writing that provides a mode of being, speaking, and writing that subverts the power of masculine norms in order to be heard and to bring possibilities for change.

text: The sex or the head? Feminine voices and academic women through the work of Hélène Cixous Hélène Cixous is perhaps best known for her paper, ‘The Laugh of the Medusa’ (1976) and her literary contributions outside academia. In this paper, we pick up a lesser known Cixous text, ‘Le Sexe ou la tête?’ that offers an interesting and provocative perspective on the traps associated with being feminine in a masculine environment. As we converse with Cixous, weaving our own words and experiences with hers, we link her work more closely with the feminine in modern-day academia. We suggest that Cixous’s remarks on decapitation and voice offer a way forward for academic women to be; to speak; to recognise the double jeopardy of decapitation in the university; and to use laughter as a strategic, powerful, political act of resistance and subversion against oppressive masculine power structures. We draw on and seek to enact Cixous’s notion of écriture feminine—a disruptive style of writing that provides a mode of being, speaking, and writing that subverts the power of masculine norms in order to be heard and to bring possibilities for change.

year: 2023-06-09

title: Modelling single cell electroporation with bipolar pulse: Simulating dependance of electroporated fractional pore area on the bipolar field frequency

authors: Talele, Sadhana

subjects: electroporation

abstract: Electroporation EP, in which external electric field pulses create transient pores in a cell membrane, is an important technique for delivery of DNA and drugs into the cell. To enable entry of DNA into cells, the pores should have sufficiently large radii, remain open long enough for the DNA chain to enter the cell, and should not cause membrane rupture. A numerical model for a single spherical cell electroporated by application of direct and/or alternating external electric field pulses has been developed. The model is used to calculate the transmembrane potential, the number of pores and the the fraction of area occupied by the pores (fractional pore area FPA) in response to the various electric field pulses. Presented here are simulation results used to compare the extent of electroporation (fractional pore area FPA) in response to eletric field pulses of different frequencies in a range of extracellular conductivity for two cell raii. It is also observed that a 1 MHz bipolar sinusoidal applied electric field pulse reduces the relative difference in fractional pore area between the two cell sizes compared to a 100 kHz pulse.

text: Modelling single cell electroporation with bipolar pulse: Simulating dependance of electroporated fractional pore area on the bipolar field frequency Electroporation EP, in which external electric field pulses create transient pores in a cell membrane, is an important technique for delivery of DNA and drugs into the cell. To enable entry of DNA into cells, the pores should have sufficiently large radii, remain open long enough for the DNA chain to enter the cell, and should not cause membrane rupture. A numerical model for a single spherical cell electroporated by application of direct and/or alternating external electric field pulses has been developed. The model is used to calculate the transmembrane potential, the number of pores and the the fraction of area occupied by the pores (fractional pore area FPA) in response to the various electric field pulses. Presented here are simulation results used to compare the extent of electroporation (fractional pore area FPA) in response to eletric field pulses of different frequencies in a range of extracellular conductivity for two cell raii. It is also observed that a 1 MHz bipolar sinusoidal applied electric field pulse reduces the relative difference in fractional pore area between the two cell sizes compared to a 100 kHz pulse.

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