An Algorithmic Framework for Fairness Elicitation

We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders. We introduce a framework in which pairs of individuals can be identified as requiring (approximately) equal treatment under a learned model, or requiring ordered treatment such as "applicant Alice should be at least as likely to receive a loan as applicant Bob". We provide a provably convergent and oracle efficient algorithm for learning the most accurate model subject to the elicited fairness constraints, and prove generalization bounds for both accuracy and fairness. This algorithm can also combine the elicited constraints with traditional statistical fairness notions, thus "correcting" or modifying the latter by the former. We report preliminary findings of a behavioral study of our framework using human-subject fairness constraints elicited on the COMPAS criminal recidivism dataset

Escherichia coli Isolates That Carry vat, fyuA, chuA, and yfcV Efficiently Colonize the Urinary Tract

Extraintestinal Escherichia coli (ExPEC), a heterogeneous group of pathogens, encompasses avian, neonatal meningitis, and uropathogenic E. coli strains. While several virulence factors are associated with ExPEC, there is no core set of virulence factors that can be used to definitively differentiate these pathotypes. Here we describe a multiplex of four virulence factor-encoding genes, yfcV, vat,fyuA, and chuA, highly associated with uropathogenic E. coli strains that can distinguish three groups of E. coli: diarrheagenic and animal-associated E. colistrains, human commensal and avian pathogenic E. coli strains, and uropathogenic and neonatal meningitis E. coli strains. Furthermore, human intestinal isolates that encode all four predictor genes express them during exponential growth in human urine and colonize the bladder in the mouse model of ascending urinary tract infection in higher numbers than human commensal strains that do not encode the four predictor genes (P = 0.02), suggesting that the presence of the predictors correlates with uropathogenic potential

ICAR: endoscopic skull‐base surgery

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Body size and digestive system shape resource selection by ungulates : a cross-taxa test of the forage maturation hypothesis

The forage maturation hypothesis (FMH) states that energy intake for ungulates is maximised when forage biomass is at intermediate levels. Nevertheless, metabolic allometry and different digestive systems suggest that resource selection should vary across ungulate species. By combining GPS relocations with remotely sensed data on forage characteristics and surface water, we quantified the effect of body size and digestive system in determining movements of 30 populations of hindgut fermenters (equids) and ruminants across biomes. Selection for intermediate forage biomass was negatively related to body size, regardless of digestive system. Selection for proximity to surface water was stronger for equids relative to ruminants, regardless of body size. To be more generalisable, we suggest that the FMH explicitly incorporate contingencies in body size and digestive system, with small-bodied ruminants selecting more strongly for potential energy intake, and hindgut fermenters selecting more strongly for surface water.DATA AVAILABILITY STATEMENT : The dataset used in our analyses is available via Dryad repository (https://doi.org/10.5061/dryad.jsxksn09f) following a year-long embargo from publication of the manuscript. The coordinates associated with mountain zebra data are not provided in an effort to protect critically endangered black rhino (Diceros bicornis) locations. Interested researchers can contact the data owner (Minnesota Zoo) directly for inquiries.https://wileyonlinelibrary.com/journal/elehj2022Mammal Research InstituteZoology and Entomolog

Factors affecting hatch success of hawksbill sea turtles on Long Island, Antigua, West Indies.

Current understanding of the factors influencing hawksbill sea turtle (Eretmochelys imbricata) hatch success is disparate and based on relatively short-term studies or limited sample sizes. Because global populations of hawksbills are heavily depleted, evaluating the parameters that impact hatch success is important to their conservation and recovery. Here, we use data collected by the Jumby Bay Hawksbill Project (JBHP) to investigate hatch success. The JBHP implements saturation tagging protocols to study a hawksbill rookery in Antigua, West Indies. Habitat data, which reflect the varied nesting beaches, are collected at egg deposition, and nest contents are exhumed and categorized post-emergence. We analyzed hatch success using mixed-model analyses with explanatory and predictive datasets. We incorporated a random effect for turtle identity and evaluated environmental, temporal and individual-based reproductive variables. Hatch success averaged 78.6% (SD: 21.2%) during the study period. Highly supported models included multiple covariates, including distance to vegetation, deposition date, individual intra-seasonal nest number, clutch size, organic content, and sand grain size. Nests located in open sand were predicted to produce 10.4 more viable hatchlings per clutch than nests located >1.5 m into vegetation. For an individual first nesting in early July, the fourth nest of the season yielded 13.2 more viable hatchlings than the initial clutch. Generalized beach section and inter-annual variation were also supported in our explanatory dataset, suggesting that gaps remain in our understanding of hatch success. Our findings illustrate that evaluating hatch success is a complex process, involving multiple environmental and individual variables. Although distance to vegetation and hatch success were inversely related, vegetation is an important component of hawksbill nesting habitat, and a more complete assessment of the impacts of specific vegetation types on hatch success and hatchling sex ratios is needed. Future research should explore the roles of sand structure, nest moisture, and local weather conditions

Beta estimates, standard errors and 90% confidence intervals for the covariates included in the top predictive model assessing hawksbill sea turtle hatch success on Long Island, Antigua, West Indies during the nesting seasons from 2003–2008.

a<p>Explanations for abbreviations can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038472#pone-0038472-t001" target="_blank">Table 1</a>.</p><p>The response variable was logit transformed. Reported results were re-fit using restricted maximum likelihood.</p

Model selection results from analyses of hatch success of hawksbill sea turtles nesting on Long Island, Antigua, West Indies during the nesting seasons from 2003–2008.

a<p>Explanations for abbreviations can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038472#pone-0038472-t001" target="_blank">Table 1</a>.</p>b<p>Number of parameters.</p>c<p>Change in Akaike’s Information Criterion.</p>d<p>Relative likelihood of model (i) based on AIC value.</p>*<p>All other models were more than 2 AICc greater than the best supported model.</p><p>Potential covariates in the explanatory model set included all variables listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038472#pone-0038472-t001" target="_blank">Tab 1</a>. The predictive model set did not include categorical terms for nesting-season year (YEAR) and beach section (BeachSec). Models were fit using maximum likelihood and ranked according to differences in Akaike’s information criteria (ΔAIC_c).</p

Effect of Organic Content in Sand on Predicted Hatch Success.

<p>Estimates of hawksbill hatch success (±90% CI) in relation to different proportions of organic content in sand samples collected across the nesting beach (quantiles: 5, 25, 50, 75, 95). We derived estimates from the best-approximating model from our predictive model set. All covariates, other than clutch size, were held constant at their average values (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038472#pone-0038472-t001" target="_blank">Table 1</a>).</p

Vegetative cover’s effects on predicted hatch success.

<p>Estimates of hawksbill hatch success (±90% CI) in relation to nest vegetative cover in four categories: >1.5 m in vegetation, 0.3–1.5 m in vegetation, 0.3 m in veg to 0.3 m in open sand, >0.3 m in open sand. We derived estimates from the best-approximating model from our predictive model set. All covariates, other than vegetation category, were held constant at their average values (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038472#pone-0038472-t001" target="_blank">Table 1</a>).</p