Collective writing: An inquiry into praxis

This is the second text in the series collectively written by members of the Editors' Collective, which comprises a series of individual and collaborative reflections upon the experience of contributing to the previous and first text written by the Editors' Collective: 'Towards a Philosophy of Academic Publishing.' In the article, contributors reflect upon their experience of collective writing and summarize the main themes and challenges. They show that the act of collective writing disturbs the existing systems of academic knowledge creation, and link these disturbances to the age of the digital reason. They conclude that the collaborative and collective action is a thing of learning-by-doing, and that collective writing seems to offer a possible way forward from the co-opting of academic activities by economics. Through detaching knowledge creation from economy, collaborative and collective writing address the problem of forming new collective intelligences

Rab protein evolution and the history of the eukaryotic endomembrane system

Spectacular increases in the quantity of sequence data genome have facilitated major advances in eukaryotic comparative genomics. By exploiting homology with classical model organisms, this makes possible predictions of pathways and cellular functions currently impossible to address in intractable organisms. Echoing realization that core metabolic processes were established very early following evolution of life on earth, it is now emerging that many eukaryotic cellular features, including the endomembrane system, are ancient and organized around near-universal principles. Rab proteins are key mediators of vesicle transport and specificity, and via the presence of multiple paralogues, alterations in interaction specificity and modification of pathways, contribute greatly to the evolution of complexity of membrane transport. Understanding system-level contributions of Rab proteins to evolutionary history provides insight into the multiple processes sculpting cellular transport pathways and the exciting challenges that we face in delving further into the origins of membrane trafficking specificity

Academics and competing interests in H1N1 influenza media reporting.

BACKGROUND: Concerns have been raised over competing interests (CoI) among academics during the 2009 to 2010 A/H1N1 pandemic. Media reporting can influence public anxiety and demand for pharmaceutical products. We assessed CoI of academics providing media commentary during the early stages of the pandemic. METHODS: We performed a retrospective content analysis of UK newspaper articles on A/H1N1 influenza, examining quoted sources. We noted when academics made a risk assessment of the pandemic and compared this with official estimations. We also looked for promotion or rejection of the use of neuraminidase inhibitors or H1N1-specific vaccine. We independently searched for CoI for each academic. RESULTS: Academics were the second most frequently quoted source after Ministers of Health. Where both academics and official agencies estimated the risk of H1N1, one in two academics assessed the risk as higher than official predictions. For academics with CoI, the odds of a higher risk assessment were 5.8 times greater than those made by academics without CoI (Wald p value=0.009). One in two academics commenting on the use of neuraminidase inhibitors or vaccine had CoI. The odds of CoI in academics promoting the use of neuraminidase inhibitors were 8.4 times greater than for academics not commenting on their use (Fisher's exact p=0.005). CONCLUSIONS: There is evidence of CoI among academics providing media commentary during the early H1N1 pandemic. Heightened risk assessments, combined with advocacy for pharmaceutical products to counter this risk, may lead to increased public anxiety and demand. Academics should declare, and journalists report, relevant CoI for media interviews

Local and Global Convergence of On-Line Learning

We study the performance of an on-line algorithm for learning dichotomies, with a dynamical error-dependent learning rate. The asymptotic scaling form of the solution to the associated Markov equations is derived, assuming certain smoothness conditions. We show that the system converges to the optimal solution and the generalization error vanishes inversely with the number of examples. The system is capable of escaping from local minima, and adapts rapidly to shifts in the target function. The general theory is illustrated for the perceptron and committee machine. Much of learning theory has analyzed the paradigm of batch learning, in which the learner has free access to a fixed set of examples stored in memory. This paradigm leads naturally to an equilibrium statistical mechanical approach based on an energy function that is the learner's error on the training set. The theoretical advantage of this equilibrium formulation is that the learner's performance as a function of the number ..