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Animal cognitive abilities are frequently quantified in strictly controlled settings, with laboratory-reared subjects. Results from these studies have merit for clarifying proximate mechanisms of performance and the potential upper limits of certain cognitive abilities. Researchers often assume that performance on laboratory-based assessments accurately represents the abilities of wild conspecifics, but this is infrequently tested. In this experiment, we quantified the performance of wild and captive corvid subjects on an extractive foraging task. We found that performance was not equivalent, and wild subjects were faster at problem-solving to extract the food reward. By contrast, there was no difference in the time it took for captive and wild solvers to repeat the behaviour to get additional food rewards (learning speed). Our findings differ from the few other studies that have statistically compared wild and captive performance on assessments of problem-solving and learning. This indicates that without explicitly testing it, we cannot assume that captive animal performance on experimental tasks can be generalized to the species as a whole. To better understand the causes and consequences of a variety of animal cognitive abilities, we should measure performance in the social and physical environment in which the ability in question evolved.
animal cognition, animal captivity, ecological validity, corvids
Captive jays exhibit reduced problem-solving performance compared to wild conspecifics
Kelsey B. McCune,1 Piotr Jablonski,2,3 Sang-im Lee,2,4 and Renee R. Ha1
Three new species of the genus Plato from caves in the states of Para and Minas Gerais, Brazil, are described. P. novalima sp. n., from Minas Gerais, is the first record of the genus in the southeastern region of Brazil. P. ferriferus sp. n. and P. striatus sp. n., from Carajas, Para, north of Brazil, are also described. The former is an extremely abundant species, whereas the latter has only one known male specimen. Cuacuba gen. n. is proposed and represented by two new species, C. mariana sp. n. (type species) and C. morrodopilar sp. n., both from the state of Minas Gerais. Morphology of genitalia in Cuacuba gen. n. is similar to other Theridiosomatidae genera and is herein discussed. None of the proposed species presents troglomorphic adaptations. They are widespread, abundant inside caves in different and large karst areas, and each genus prefers different lithologies.
biospeleology, Neotropical region, taxonomy
Three new species of the spider genus Plato and the new genus Cuacuba from caves of the states of Para and Minas Gerais, Brazil (Araneae, Theridiosomatidae)
Pedro H. Prete,1 Igor Cizauskas,1 and Antonio D. Brescovit1
A critical decision-step in the emergency treatment of ischemic stroke is whether or not to administer thrombolysis — a treatment that can result in good recovery, or deterioration due to symptomatic intracranial haemorrhage (SICH). Certain imaging features based upon early computerized tomography (CT), in combination with clinical variables, have been found to predict SICH, albeit with modest accuracy. In this proof-of-concept study, we determine whether machine learning of CT images can predict which patients receiving tPA will develop SICH as opposed to showing clinical improvement with no haemorrhage. Clinical records and CT brains of 116 acute ischemic stroke patients treated with intravenous thrombolysis were collected retrospectively (including 16 who developed SICH). The sample was split into training (n = 106) and test sets (n = 10), repeatedly for 1760 different combinations. CT brain images acted as inputs into a support vector machine (SVM), along with clinical severity. Performance of the SVM was compared with established prognostication tools (SEDAN and HAT scores; original, or after adaptation to our cohort). Predictive performance, assessed as area under receiver-operating-characteristic curve (AUC), of the SVM (0.744) compared favourably with that of prognostic scores (original and adapted versions: 0.626-0.720; p < 0.01). The SVM also identified 9 out of 16 SICHs, as opposed to 1-5 using prognostic scores, assuming a 10% SICH frequency (p < 0.001). In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods.
Stroke, Thrombolysis, Prediction, Machine learning, Imaging
Prediction of stroke thrombolysis outcome using CT brain machine learning
Paul Bentley,⁎ Jeban Ganesalingam, Anoma Lalani Carlton Jones, Kate Mahady, Sarah Epton, Paul Rinne, Pankaj Sharma, Omid Halse, Amrish Mehta, and Daniel Rueckert