Housekeeping gene xanthine oxidoreductase is necessary for milk fat droplet enveloping and secretion: gene sharing in the lactating mammary gland.

Journal ArticleXanthine oxidoreductase (XOR) is the rate-limiting enzyme in purine catabolism occurring in most cell types. However, this housekeeping gene is expressed at very high levels in a number of mammalian tissues including the lactating mammary epithelium, suggesting additional roles for XOR in these tissues. Mice with targeted disruption of XOR were generated to assess these potential additional roles. XOR-/- mice are runted and do not live beyond 6 wk of age. Strikingly, however, XOR+/- females, although of healthy appearance and normal fertility, are unable to maintain lactation and their pups die of starvation 2 wk postpartum. Histological and whole-mount analyses showed that in XOR+/- females the mammary epithelium collapses, resulting in premature involution of the mammary gland. Electron microscopy showed that XOR is specifically required for enveloping milk fat droplets with the apical plasma membrane prior to secretion from the lactating mammary gland. We present evidence that XOR may have primarily a structural role, as a membrane-associated protein, in milk fat droplet secretion and thus XOR provides another example of "gene sharing". About 5% of women experience primary lactation insufficiency. The above observations suggest that human females suffering from xanthinuria, a deficiency in XOR, are potential candidates for lactation problems

The geodynamo for non-geophysicists

The geodynamo usually appears as a somewhat intimidating subject. Its understanding seems to require the intricate theory of magnetohydrodynamics. The solution of the corresponding equations can only be achieved numerically. It seems to be a subject for the specialist. We show that one can understand the basics of the functioning of the geodynamo solely by using the well-known laws of electrodynamics. The topic is not only important for geophysicists. The same physics is the cause for the magnetic fields of sun-like stars, of the very strong fields of neutron stars, and also of the cosmic magnetic fields

Xanthine oxidoreductase is central to the evolution and function of the innate immune system.

Journal ArticleThe housekeeping enzyme xanthine oxidoreductase (XOR) has been studied intensively over the past 100 years, yet the complexity of its in vivo function is still poorly understood. A large body of literature focuses on the different catalyltic forms of XOR and their importance in the synthesis of reactive oxygen and reactive nitrogen species, which are involved in many disease processes. More recently, various protective physiological roles of XOR have been recognized. We summarize for the first time that XOR is a component of the innate immune system. Because XOR is involved in multiple features of innate immunity we suggest that it is central to the evolution and function of this ancient defense system. We present evidence suggesting that XOR is a direct downstream target of the evolutionarily conserved Toll-like receptor-NF-kB pathway and discuss that numerous forms of post-translational modification of XOR could provide intrinsic molecular switches that make XOR an ideal component of various fast innate immune responses

Managing leadership and cultural change at Beak and Johnston: a work in progress

types: ArticleThis is the author’s pre-refereed version of the following article: Methodological Approaches for Interviewing Elites, Global Business and Organizational Excellence, Volume 33, Issue 6, September/October 2014, Pages: 43–50 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/joe.21572/abstractAs it strives to attain its goal of becoming an AUD$1 billion company by 2020, Beak and Johnston (B&J), a family-owned food processing business based in Sydney, Australia, has embarked on a transformative leadership and cultural enhancement process. The company’s founder and CEO, David Beak, is trying to improve the capabilities of his senior strategy team while stepping back from the business. To that end, the company has adopted a leadership development approach with an eye on succession planning in which workers at all levels are encouraged to speak openly. It also is fostering cultural change with a less hierarchical organizational structure in which factory managers are empowered with greater levels of responsibility and accountability. The insights revealed in this candid snapshot of an organization in transition offer valuable lessons on the essential elements of an effective change effort

Community governance in Vanuatu through a critical institutionalist lens

For many rural Vanuatu communities, local collective action institutions are the only option to govern services and natural resources. To address this need, the Vanuatu government is introducing generic committee structures, which rarely work as expected. This model of introducing committees has become the foundation of many projects aimed at improving local governance in developing countries despite widespread accounts of poor performance. This thesis explores the overall question: What does the application of critical institutionalism through the lens of institutional bricolage reveal about community governance in Vanuatu? Drawing on critical institutionalist literature and the concept of institutional bricolage, I use the case of three rural communities in Vanuatu to analyse mechanisms behind different forms of institutional change. I explore challenges to institutional design and ways to facilitate the development of enduring and equitable institutions. Data were collected through storian conversations with community members (27f, 28m) and key stakeholder interviews (4f, 13m) and analysed using reflexive thematic analysis. Chapter 4 uses an institutional bricolage lens to unpack how the study communities adapted or rejected introduced water governance arrangements in response to diverse local contexts. Chapter 5 explores the underlying mechanisms that shape autonomous change processes in traditional institutions. It reveals the centrality of two established institutional bricolage processes – elite capture and leakage of meaning – in opening up and closing down spaces for change. Chapter 6 identifies the phenomenon of ghost committees that do not exist in practice yet are referred to as if they were performing their intended roles. I argue that a feedback loop between ghost committees and discourse in favour of the committee model contributes to the persistence of both committees and the model. Chapter 7 draws on the preceding analytical chapters to argue that institutional bricolage and agonistic methods can be combined to support communities in developing enduring and equitable institutions

Exploration of Computational Methods for Classification of Movement Intention During Human Voluntary Movement from Single Trial EEG

Objective: To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Methods: Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. Results: The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Conclusions: Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training. Significance: Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy

Advances in Neural Information Processing Systems

Imitation learning enables high-fidelity, vision-based learning of policies within rich, photorealistic environments. However, such techniques often rely on traditional discrete-time neural models and face difficulties in generalizing to domain shifts by failing to account for the causal relationships between the agent and the environment. In this paper, we propose a theoretical and experimental framework for learning causal representations using continuous-time neural networks, specifically over their discrete-time counterparts. We evaluate our method in the context of visual-control learning of drones over a series of complex tasks, ranging from short- and long-term navigation, to chasing static and dynamic objects through photorealistic environments. Our results demonstrate that causal continuous-time deep models can perform robust navigation tasks, where advanced recurrent models fail. These models learn complex causal control representations directly from raw visual inputs and scale to solve a variety of tasks using imitation learning