Farm-nonfarm linkages in rural sub-Saharan Africa

This paper is an accumulation, over the past 25 years, of a body of detailed work examining the structure of Africa's rural, nonfarm economy. First, it systematically reviews empirical evidence on the nature and magnitude of the African rural, nonfarm economy. It then explores differences across locality and size, across countries and over time, in an effort to assess likely patterns of growth. A subsequent review of key production and consumption parameters allows an estimate of the magnitude of the agricultural growth multipliers in Africa. The paper concludes with a brief discussion of policies and programs that will be necessary if farm-nonfarm growth linkages are to achieve their full potential.Banks&Banking Reform,Agricultural Knowledge&Information Systems,Municipal Financial Management,Environmental Economics&Policies,Economic Theory&Research

Iterative Random Forests to detect predictive and stable high-order interactions

Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order interactions drive gene expression presents a substantial statistical challenge. Building on Random Forests (RF), Random Intersection Trees (RITs), and through extensive, biologically inspired simulations, we developed the iterative Random Forest algorithm (iRF). iRF trains a feature-weighted ensemble of decision trees to detect stable, high-order interactions with same order of computational cost as RF. We demonstrate the utility of iRF for high-order interaction discovery in two prediction problems: enhancer activity in the early Drosophila embryo and alternative splicing of primary transcripts in human derived cell lines. In Drosophila, among the 20 pairwise transcription factor interactions iRF identifies as stable (returned in more than half of bootstrap replicates), 80% have been previously reported as physical interactions. Moreover, novel third-order interactions, e.g. between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order relationships that are candidates for follow-up experiments. In human-derived cells, iRF re-discovered a central role of H3K36me3 in chromatin-mediated splicing regulation, and identified novel 5th and 6th order interactions, indicative of multi-valent nucleosomes with specific roles in splicing regulation. By decoupling the order of interactions from the computational cost of identification, iRF opens new avenues of inquiry into the molecular mechanisms underlying genome biology

Measuring reproducibility of high-throughput experiments

Reproducibility is essential to reliable scientific discovery in high-throughput experiments. In this work we propose a unified approach to measure the reproducibility of findings identified from replicate experiments and identify putative discoveries using reproducibility. Unlike the usual scalar measures of reproducibility, our approach creates a curve, which quantitatively assesses when the findings are no longer consistent across replicates. Our curve is fitted by a copula mixture model, from which we derive a quantitative reproducibility score, which we call the "irreproducible discovery rate" (IDR) analogous to the FDR. This score can be computed at each set of paired replicate ranks and permits the principled setting of thresholds both for assessing reproducibility and combining replicates. Since our approach permits an arbitrary scale for each replicate, it provides useful descriptive measures in a wide variety of situations to be explored. We study the performance of the algorithm using simulations and give a heuristic analysis of its theoretical properties. We demonstrate the effectiveness of our method in a ChIP-seq experiment.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS466 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Refining interaction search through signed iterative Random Forests

Advances in supervised learning have enabled accurate prediction in biological systems governed by complex interactions among biomolecules. However, state-of-the-art predictive algorithms are typically black-boxes, learning statistical interactions that are difficult to translate into testable hypotheses. The iterative Random Forest algorithm took a step towards bridging this gap by providing a computationally tractable procedure to identify the stable, high-order feature interactions that drive the predictive accuracy of Random Forests (RF). Here we refine the interactions identified by iRF to explicitly map responses as a function of interacting features. Our method, signed iRF, describes subsets of rules that frequently occur on RF decision paths. We refer to these rule subsets as signed interactions. Signed interactions share not only the same set of interacting features but also exhibit similar thresholding behavior, and thus describe a consistent functional relationship between interacting features and responses. We describe stable and predictive importance metrics to rank signed interactions. For each SPIM, we define null importance metrics that characterize its expected behavior under known structure. We evaluate our proposed approach in biologically inspired simulations and two case studies: predicting enhancer activity and spatial gene expression patterns. In the case of enhancer activity, s-iRF recovers one of the few experimentally validated high-order interactions and suggests novel enhancer elements where this interaction may be active. In the case of spatial gene expression patterns, s-iRF recovers all 11 reported links in the gap gene network. By refining the process of interaction recovery, our approach has the potential to guide mechanistic inquiry into systems whose scale and complexity is beyond human comprehension

Interferometric data for a shock-wave/boundary-layer interaction

An experimental study of the axisymmetric shock-wave / boundary-layer strong interaction flow generated in the vicinity of a cylinder-cone intersection was conducted. The study data are useful in the documentation and understanding of compressible turbulent strong interaction flows, and are part of a more general effort to improve turbulence modeling for compressible two- and three-dimensional strong viscous/inviscid interactions. The nominal free stream Mach number was 2.85. Tunnel total pressures of 1.7 and 3.4 atm provided Reynolds number values of 18 x 10(6) and 36 x 10(6) based on model length. Three cone angles were studied giving negligible, incipient, and large scale flow separation. The initial cylinder boundary layer upstream of the interaction had a thickness of 1.0 cm. The subsonic layer of the cylinder boundary layer was quite thin, and in all cases, the shock wave penetrated a significant portion of the boundary layer. Owing to the thickness of the cylinder boundary layer, considerable structural detail was resolved for the three shock-wave / boundary-layer interaction cases considered. The primary emphasis was on the application of the holographic interferometry technique. The density field was deduced from an interferometric analysis based on the Able transform. Supporting data were obtained using a 2-D laser velocimeter, as well as mean wall pressure and oil flow measurements. The attached flow case was observed to be steady, while the separated cases exhibited shock unsteadiness. Comparisons with Navier-Stokes computations using a two-equation turbulence model are presented

*-DCC: A platform to collect, annotate, and explore a large variety of sequencing experiments.

BackgroundOver the past few years the variety of experimental designs and protocols for sequencing experiments increased greatly. To ensure the wide usability of the produced data beyond an individual project, rich and systematic annotation of the underlying experiments is crucial.FindingsWe first developed an annotation structure that captures the overall experimental design as well as the relevant details of the steps from the biological sample to the library preparation, the sequencing procedure, and the sequencing and processed files. Through various design features, such as controlled vocabularies and different field requirements, we ensured a high annotation quality, comparability, and ease of annotation. The structure can be easily adapted to a large variety of species. We then implemented the annotation strategy in a user-hosted web platform with data import, query, and export functionality.ConclusionsWe present here an annotation structure and user-hosted platform for sequencing experiment data, suitable for lab-internal documentation, collaborations, and large-scale annotation efforts