“Only the orangutans get a life jacket”: Uncommoning responsibility in a global conservation nexus

In an era of mass extinction, who gets a life jacket, who is left to drown or swim—and on what basis? This article addresses these questions by analyzing how tropes and practices of responsibility are variously enacted, reworked, contested, and refused across the global nexus of orangutan conservation. Drawing on multisited, collaborative ethnography, we trace the mutually constitutive relation between multiple orangutan figures and commons imaginaries at different nodes of conservation—from environmental activism in the Global North to NGO-villager encounters in rural Borneo. In so doing, we “uncommon” international conservation's encompassing planetary imaginaries, showing how dominant portrayals of the orangutan as a global responsibility are translated and fragmented in different settings. We further contemplate what an analytic of responsibility might bring to ongoing discussions about the “commoning” planetary epoch in which conservation is increasingly embedded: the Anthropocene.European Research Council Starting Grant 758494, Arcus Foundation Great Apes Program, Brunel University Londo

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods

Potential damaging mutation in LRP5 from genome sequencing of the first reported chimpanzee with the Chiari malformation

The genus Pan is the closest related to humans (Homo sapiens) and it includes two species: Pan troglodytes (chimpanzees) and Pan paniscus (bonobos). Different characteristics, some of biomedical aspect, separate them from us. For instance, some common human medical conditions are rare in chimpanzees (menopause, Alzheimer disease) although it is unclear to which extent longevity plays an active role in these differences. However, both humans and chimpanzees present similar pathologies, thus, understanding traits in chimpanzees can help unravel the molecular basis of human conditions. Here, we sequenced the genome of Nico, a central chimpanzee diagnosed with a particular biomedical condition, the Chiari malformation. We performed a variant calling analysis comparing his genome to 25 whole genomes from healthy individuals (bonobos and chimpanzees), and after predicting the effects of the genetic variants, we looked for genes within the OMIM database. We found a novel, private, predicted as damaging mutation in Nico in LRP5, a gene related to bone density alteration pathologies, and we suggest a link between this mutation and his Chiari malformation as previously shown in humans. Our results reinforce the idea that a comparison between humans and chimpanzees can be established in this genetic frame of common diseases.M.S.-M. is supported by the Ministerio de Economía y Competitividad, Spain (Maria de Maetzu grant MDM-2014-0370-16-3). M.d.M. is supported by a Formació de personal Investigador fellowship from Generalitat de Catalunya (FI_B01111). J.H.-R. is supported by the Ministerio de Economía y Competitividad, Spain (FPI grant BES-2013-064333). C.F. is supported by La Caixa Foundation. A.N. is funded by MINECO BFU2015-68649-P. T.M.-B. is supported by MINECO BFU2014-55090- P (FEDER), U01 MH106874 grant, Howard Hughes International Early Career, La Caixa Foundation and Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya