Behind the Cod Curtain: A Perspective on the Political Economy of the Atlantic Groundfish Fishery

This article addresses the collapse of Atlantic groundfish stocks in terms of its significant social and economic impact. How had so many people become dependent on this modest resource? What circumstances contributed to creating a hidden underemployed class in the fishing industry? The analysis adds to the thesis that public support of unproductive industry and income support systems underlie the current crisis, creating barriers to a viable future for the Atlantic Fishery. The authors draw on comparisons with the economy of the former Soviet Union where central planning of an economy based on state owned common property failed to harness market forces and proved unsustainable. They suggest that the common property problem can be addressed by enhancing the security of access to the resource that individuals or groups enjoy, and by increasing user group responsibility for conservation and sustainable exploitation practices. They also advocate the elimination of direct and indirect subsidies to capital and labour which support excessive capacity and ultimately undermine the industry

By-passing the Kohn-Sham equations with machine learning

Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields, ranging from materials science to biochemistry to astrophysics. Machine learning holds the promise of learning the kinetic energy functional via examples, by-passing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing either larger systems or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. Both improved accuracy and lower computational cost with this method are demonstrated by reproducing DFT energies for a range of molecular geometries generated during molecular dynamics simulations. Moreover, the methodology could be applied directly to quantum chemical calculations, allowing construction of density functionals of quantum-chemical accuracy

Moonlight On The Highway

Illustration of a man driving a car in the moonlight towards a house on a hill. A photograph of The Lombardos is inset in the lower right corner.https://scholarsjunction.msstate.edu/cht-sheet-music/2555/thumbnail.jp

Double-labelling immunohistochemistry for MGMT and a “cocktail” of non-tumourous elements is a reliable, quick and easy technique for inferring methylation status in glioblastomas and other primary brain tumours

BACKGROUND: Our aim was to develop a new protocol for MGMT immunohistochemistry with good agreement between observers and good correlation with molecular genetic tests of tumour methylation. We examined 40 primary brain tumours (30 glioblastomas and 10 oligodendroglial tumours) with our new technique, namely double-labelling immunohistochemistry for MGMT and a "cocktail" of non-tumour antigens (CD34, CD45 and CD68). We compared the results with single-labelling immunohistochemistry for MGMT and methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA, a recognised molecular genetic technique which we applied as the gold-standard for the methylation status). RESULTS: Double-labelling immunohistochemistry for MGMT produced a visual separation of tumourous and non-tumourous elements on the same histological slide, making it quick and easy to determine whether tumour cell nuclei were MGMT-positive or MGMT-negative (and thereby infer the methylation status of the tumour). We found good agreement between observers (kappa 0.76) and within observer (kappa 0.84). Furthermore, double-labelling showed good specificity (80%), sensitivity (73.33%), positive predictive value (PPV, 83.33%) and negative predictive value (NPV, 68.75%) compared to MS-MLPA. Double-labelling was quicker and easier to assess than single-labelling and it outperformed quantitative computerised image analysis of MGMT single-labelling in terms of sensitivity, specificity, PPV and NPV. CONCLUSIONS: Double-labelling immunohistochemistry for MGMT and a cocktail of non-tumourous elements provides a "one look" method for determining whether tumour cell nuclei are MGMT-positive or MGMT-negative. This can be used to infer the methylation status of the tumour. There is good observer agreement and good specificity, sensitivity, PPV and NPV compared to a molecular gold-standard

ResearchFanshawe Magazine Issue 3

https://first.fanshawec.ca/researchfanshawemag/1002/thumbnail.jp

POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors

Motivation: At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences. Frequently the most accurate classifiers are obtained by training support vector machines (SVMs) with complex sequence kernels. However, a cumbersome shortcoming of SVMs is that their learned decision rules are very hard to understand for humans and cannot easily be related to biological facts