The Primarily Undergraduate Nanomaterials Cooperative: A New Model for Supporting Collaborative Research at Small Institutions on a National Scale

The Primarily Undergraduate Nanomaterials Cooperative (PUNC) is an organization for research-active faculty studying nanomaterials at Primarily Undergraduate Institutions (PUIs), where undergraduate teaching and research go hand-in-hand. In this perspective, we outline the differences in maintaining an active research group at a PUI compared to an R1 institution. We also discuss the work of PUNC, which focuses on community building, instrument sharing, and facilitating new collaborations. Currently consisting of 37 members from across the United States, PUNC has created an online community consisting of its Web site (nanocooperative.org), a weekly online summer group meeting program for faculty and students, and a Discord server for informal conversations. Additionally, in-person symposia at ACS conferences and PUNC-specific conferences are planned for the future. It is our hope that in the years to come PUNC will be seen as a model organization for community building and research support at primarily undergraduate institutions

Green Criminology Before ‘Green Criminology’: Amnesia and Absences

Although the first published use of the term ‘green criminology’ seems to have been made by Lynch (Green criminology. Aldershot, Hampshire, 1990/2006), elements of the analysis and critique represented by the term were established well before this date. There is much criminological engagement with, and analysis of, environmental crime and harm that occurred prior to 1990 that deserves acknowledgement. In this article, we try to illuminate some of the antecedents of green criminology. Proceeding in this way allows us to learn from ‘absences’, i.e. knowledge that existed but has been forgotten. We conclude by referring to green criminology not as an exclusionary label or barrier but as a symbol that guides and inspires the direction of research

Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.</p> <p>Results</p> <p>This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|H_i), which is used as confidence level. The unit network with higher P(D|H_i) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|H_i), which is a unique property of the proposed algorithm.</p> <p>The algorithm is evaluated with synthetic and <it>Saccharomyces cerevisiae </it>expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.</p> <p>The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and <it>Saccharomyces cerevisiae </it>expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.</p> <p>Conclusion</p> <p>From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.</p