Microflora in soils of desert regions

Desert soil samples, collected using aseptic techniques, are low in organic matter and cation exchange capacity. Aerobic and microaerophilic bacteria are most abundant, next are algae and molds. Chemical and physical properties are determined by standard procedures, including the Kjeldahl method and the use of Munsell soil color charts

Abundance of microflora in soils of desert regions

Physical, chemical, and microbiological properties of desert soil

Legacies in Black and White: The Racial Composition of the Legacy Pool

Selective universities regularly employ policies that favor children of alumni (known as legacies') in undergraduate admissions. Since alumni from selective colleges and universities have, historically, been disproportionately white, admissions policies that favor legacies have disproportionately benefited white students. For this reason, legacy policies lead to additional costs in terms of reductions in racial diversity. As larger numbers of minority students graduate from colleges and universities and have children, however, the potential pool of legacy applicants will change markedly in racial composition. This analysis begins with a review of the history and objectives of the preference for children of alumni in undergraduate admissions. We then consider the specific case of the University of Virginia and employ demographic techniques to predict the racial composition of the pool of potential legacy applicants to the University. Significant changes in the racial composition of classes that graduated from the University of Virginia from the late 1960s through the 1970s foreshadow similar changes in the characteristics of alumni children maturing through the next two decades.

Minimizing Polarization and Disagreement in Social Networks

The rise of social media and online social networks has been a disruptive force in society. Opinions are increasingly shaped by interactions on online social media, and social phenomena including disagreement and polarization are now tightly woven into everyday life. In this work we initiate the study of the following question: given $n$ agents, each with its own initial opinion that reflects its core value on a topic, and an opinion dynamics model, what is the structure of a social network that minimizes {\em polarization} and {\em disagreement} simultaneously? This question is central to recommender systems: should a recommender system prefer a link suggestion between two online users with similar mindsets in order to keep disagreement low, or between two users with different opinions in order to expose each to the other's viewpoint of the world, and decrease overall levels of polarization? Our contributions include a mathematical formalization of this question as an optimization problem and an exact, time-efficient algorithm. We also prove that there always exists a network with $O(n/\epsilon^2)$ edges that is a $(1+\epsilon)$ approximation to the optimum. For a fixed graph, we additionally show how to optimize our objective function over the agents' innate opinions in polynomial time. We perform an empirical study of our proposed methods on synthetic and real-world data that verify their value as mining tools to better understand the trade-off between of disagreement and polarization. We find that there is a lot of space to reduce both polarization and disagreement in real-world networks; for instance, on a Reddit network where users exchange comments on politics, our methods achieve a $\sim 60\,000$-fold reduction in polarization and disagreement.Comment: 19 pages (accepted, WWW 2018

An Introduction to Programming for Bioscientists: A Python-based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables, numerous exercises, and 19 pages of Supporting Information; currently in press at PLOS Computational Biolog

On the distribution of multiplicatively dependent vectors

In this paper, we study the distribution of multiplicatively dependent vectors. For example, although they have zero Lebesgue measure, they are everywhere dense both in $\R^n$ and \C^n. We also study this property in a more detailed manner by considering the covering radius of such vectors.Comment: 19 page