'Being in Being': Contesting the Ontopolitics of Indigeneity Today

This article critiques the shift towards valorizing indigeneity in western thought and contemporary practice. This shift in approach to indigenous ways of knowing and being, historically derided under conditions of colonialism, is a reflection of the ‘ontological turn’ in anthropology. Rather than indigenous peoples simply having an inferior or different understanding of the world to a modernist one, the ‘ontological turn’ suggests their importance is that they constitute different worlds, and that they ‘world’ in a performatively different way. The radical promise is that a different world already exists in potentia and that access to this alternative world is a question of ontology - of being differently: being in being rather than thinking, acting and ‘worlding’ as if we were transcendent or ‘possessive’ subjects. We argue that ontopolitical arguments for the superiority of indigenous ways of being should not be seen as radical or emancipatory resistances to modernist or colonial epistemological and ontological legacies but instead as a new form of neoliberal governmentality, cynically manipulating critical, postcolonial and ecological sensibilities for its own ends. Rather than ‘provincialising’ dominant western hegemonic practices, discourses of ‘indigeneity’ are functioning to extend them, instituting new forms of governing through calls for adaptation and resilience

Improving the production and evaluation of structural models using a Delphi process

Bayes Nets (BNs) are extremely useful for causal and probabilistic modelling in many real-world applications, often built with information elicited from groups of domain experts. But their potential for reasoning and decision support has been limited by two major factors: the need for significant normative knowledge, and the lack of any validated methods or software supporting collaboration. Consequently, we have developed a web-based structured technique – Bayesian Argumentation via Delphi (BARD) – to enable groups of domain experts to receive minimal normative training and then collaborate effectively to produce high-quality BNs. BARD harnesses multiple perspectives on a problem, while minimising biases manifest in freely interacting groups, via a Delphi process: solutions are first produced individually, then shared, followed by an opportunity for individuals to revise their solutions. To test the hypothesis that BNs improve due to Delphi, we conducted an experiment whereby individuals with a little BN training and practice produced structural models using BARD for two Bayesian reasoning problems. Participants then received 6 other structural models for each problem, rated their quality on a 7-point scale, and revised their own models if they wished. Both top-rated and revised models were on average significantly better quality (scored against a gold-standard) than the initial models, with large and medium effect sizes. We conclude that Delphi – and BARD – improves the quality of BNs produced by groups. Further, although rating cannot create new models, rating seems quicker and easier than revision and yielded significantly better models – so, we suggest efficient BN amalgamation should include both

Improving the production and evaluation of structural models using a Delphi process

Bayes Nets (BNs) are extremely useful for causal and probabilistic modelling in many real-world applications, often built with information elicited from groups of domain experts. But their potential for reasoning and decision support has been limited by two major factors: the need for significant normative knowledge, and the lack of any validated methods or software supporting collaboration. Consequently, we have developed a web-based structured technique – Bayesian Argumentation via Delphi (BARD) – to enable groups of domain experts to receive minimal normative training and then collaborate effectively to produce high-quality BNs. BARD harnesses multiple perspectives on a problem, while minimising biases manifest in freely interacting groups, via a Delphi process: solutions are first produced individually, then shared, followed by an opportunity for individuals to revise their solutions. To test the hypothesis that BNs improve due to Delphi, we conducted an experiment whereby individuals with a little BN training and practice produced structural models using BARD for two Bayesian reasoning problems. Participants then received 6 other structural models for each problem, rated their quality on a 7-point scale, and revised their own models if they wished. Both top-rated and revised models were on average significantly better quality (scored against a gold-standard) than the initial models, with large and medium effect sizes. We conclude that Delphi – and BARD – improves the quality of BNs produced by groups. Further, although rating cannot create new models, rating seems quicker and easier than revision and yielded significantly better models – so, we suggest efficient BN amalgamation should include both

A Randomized Clinical Trial Testing the Anti-Inflammatory Effects of Preemptive Inhaled Nitric Oxide in Human Liver Transplantation

<div><p>Decreases in endothelial nitric oxide synthase derived nitric oxide (NO) production during liver transplantation promotes injury. We hypothesized that preemptive inhaled NO (iNO) would improve allograft function (primary) and reduce complications post-transplantation (secondary). Patients at two university centers (Center A and B) were randomized to receive placebo (n = 20/center) or iNO (80 ppm, n = 20/center) during the operative phase of liver transplantation. Data were analyzed at set intervals for up to 9-months post-transplantation and compared between groups. Patient characteristics and outcomes were examined with the Mann-Whitney U test, Student t-test, logistic regression, repeated measures ANOVA, and Cox proportional hazards models. Combined and site stratified analyses were performed. MELD scores were significantly higher at Center B (22.5 vs. 19.5, p<0.0001), surgical times were greater at Center B (7.7 vs. 4.5 hrs, p<0.001) and warm ischemia times were greater at Center B (95.4 vs. 69.7 min, p<0.0001). No adverse metabolic or hematologic effects from iNO occurred. iNO enhanced allograft function indexed by liver function tests (Center B, p<0.05; and p<0.03 for ALT with center data combined) and reduced complications at 9-months (Center A and B, p = 0.0062, OR = 0.15, 95% CI (0.04, 0.59)). ICU (p = 0.47) and hospital length of stay (p = 0.49) were not decreased. iNO increased concentrations of nitrate (p<0.001), nitrite (p<0.001) and nitrosylhemoglobin (p<0.001), with nitrite being postulated as a protective mechanism. Mean costs of iNO were $1,020 per transplant. iNO was safe and improved allograft function at one center and trended toward improving allograft function at the other. ClinicalTrials.gov with registry number 00582010 and the following URL:<a href="http://clinicaltrials.gov/show/NCT00582010" target="_blank">http://clinicaltrials.gov/show/NCT00582010</a>.</p></div

iNO therapy did not affect hospital or ICU length of stay.

<p>Kaplan-Meier plots shown and with P-values from Cox proportional hazards models adjusting for MELD, cold ischemic time, weight, BMI, warm ischemic time or donor age within each center. Placebo = ○, iNO = ▪.</p

Effects of iNO on reperfusion induced injury and PMN accumulation.

<p>Liver biopsy samples were collected pre- (LB1, □) and 1 hr post dual reperfusion (LB2, ▪) and assessed for injury by histopathologic evaluation (Panel A, B) and infiltration of PMN (panel C, D). Data from both UAB and UW cohorts are combined (n = 38). P-values indicated on graph are by paired t-test for panels A and C or by * unpaired t-test (panels B and D).</p

Patient and Surgery Demographics for Center A Cohort.

<p>Values show median (range). P-values calculated from unpaired t-test for placebo vs. iNO. N = 20 except An = 19. Bn = 18.</p

Effects of iNO on liver nitrite levels pre- (LB1) and post-reperfusion (LB2).

<p>Nitrite was measured in paired liver biopsies and data normalized to protein. No significant differences in nitrite levels were observed pre- vs. post-reperfusion or between placebo and iNO treatments.</p

Patient and Surgery Demographics for Center B Cohort.

<p>Values show median (range). P-values calculated from unpaired t-test for placebo vs. iNO.</p