Spaces of Order: An African Poetics of Space

“Spaces of Order” argues that the African novel should be studied as a revolutionary form characterized by aesthetic innovations that are not comprehensible in terms of the novel’s European archive of forms. It does this by mapping an African spatial order that undermines the spatial problematic at the formal and ideological core of the novel—the split between a private, subjective interior, and an abstract, impersonal outside. The project opens with an examination of spatial fragmentation as figured in the “endless forest” of Amos Tutuola’s The Palmwine Drinkard (1952). The second chapter studies Chinua Achebe’s Things Fall Apart (1958) as a fictional world built around a peculiar category of space, the “evil forest,” which constitutes an African principle of order and modality of power. Chapter three returns to Tutuola via Ben Okri’s The Famished Road (1991) and shows how the dispersal of fragmentary spaces of exclusion and terror within the colonial African city helps us conceive of political imaginaries outside the nation and other forms of liberal political communities. The fourth chapter shows Nnedi Okorafor—in her 2014 science-fiction novel Lagoon—rewriting Things Fall Apart as an alien-encounter narrative in which Africa is center-stage of a planetary, multi-species drama. Spaces of Order is a study of the African novel as a new logic of world making altogether.

Bayesian variable selection in structured high-dimensional covariate spaces with applications in genomics

We consider the problem of variable selection in regression modeling in high-dimensional spaces where there is known structure among the covariates. This is an unconventional variable selection problem for two reasons: (1) The dimension of the covariate space is comparable, and often much larger, than the number of subjects in the study, and (2) the covariate space is highly structured, and in some cases it is desirable to incorporate this structural information in to the model building process. We approach this problem through the Bayesian variable selection framework, where we assume that the covariates lie on an undirected graph and formulate an Ising prior on the model space for incorporating structural information. Certain computational and statistical problems arise that are unique to such high-dimensional, structured settings, the most interesting being the phenomenon of phase transitions. We propose theoretical and computational schemes to mitigate these problems. We illustrate our methods on two different graph structures: the linear chain and the regular graph of degree k. Finally, we use our methods to study a specific application in genomics: the modeling of transcription factor binding sites in DNA sequences. © 2010 American Statistical Association.

Clinician judgment vs formal scales for predicting intracerebral hemorrhage outcomes.

OBJECTIVE: To compare the performance of formal prognostic instruments vs subjective clinical judgment with regards to predicting functional outcome in patients with spontaneous intracerebral hemorrhage (ICH). METHODS: This prospective observational study enrolled 121 ICH patients hospitalized at 5 US tertiary care centers. Within 24 hours of each patient's admission to the hospital, one physician and one nurse on each patient's clinical team were each asked to predict the patient's modified Rankin Scale (mRS) score at 3 months and to indicate whether he or she would recommend comfort measures. The admission ICH score and FUNC score, 2 prognostic scales selected for their common use in neurologic practice, were calculated for each patient. Spearman rank correlation coefficients (r) with respect to patients' actual 3-month mRS for the physician and nursing predictions were compared against the same correlation coefficients for the ICH score and FUNC score. RESULTS: The absolute value of the correlation coefficient for physician predictions with respect to actual outcome (0.75) was higher than that of either the ICH score (0.62, p = 0.057) or the FUNC score (0.56, p = 0.01). The nursing predictions of outcome (r = 0.72) also trended towards an accuracy advantage over the ICH score (p = 0.09) and FUNC score (p = 0.03). In an analysis that excluded patients for whom comfort care was recommended, the 65 available attending physician predictions retained greater accuracy (r = 0.73) than either the ICH score (r = 0.50, p = 0.02) or the FUNC score (r = 0.42, p = 0.004). CONCLUSIONS: Early subjective clinical judgment of physicians correlates more closely with 3-month outcome after ICH than prognostic scales.