BETULA: Numerically Stable CF-Trees for BIRCH Clustering

BIRCH clustering is a widely known approach for clustering, that has influenced much subsequent research and commercial products. The key contribution of BIRCH is the Clustering Feature tree (CF-Tree), which is a compressed representation of the input data. As new data arrives, the tree is eventually rebuilt to increase the compression. Afterward, the leaves of the tree are used for clustering. Because of the data compression, this method is very scalable. The idea has been adopted for example for k-means, data stream, and density-based clustering. Clustering features used by BIRCH are simple summary statistics that can easily be updated with new data: the number of points, the linear sums, and the sum of squared values. Unfortunately, how the sum of squares is then used in BIRCH is prone to catastrophic cancellation. We introduce a replacement cluster feature that does not have this numeric problem, that is not much more expensive to maintain, and which makes many computations simpler and hence more efficient. These cluster features can also easily be used in other work derived from BIRCH, such as algorithms for streaming data. In the experiments, we demonstrate the numerical problem and compare the performance of the original algorithm compared to the improved cluster features

Conservation of the role of INNER NO OUTER in development of unitegmic ovules of the Solanaceae despite a divergence in protein function

The P-SlINO::SlINO-GFP transgene continues to be expressed after fertilization during the onset of fruit development. A-C: Ovules from P-SlINO::SlINO-GFP plants. D, E: Ovules from control plants. Images A (confocal) and B (DIC overlaid with GFP channel) show expression in the outer cell layer in an ovule post-anthesis. C-E are images of the surface cells of the integument of ovules taken from 3–4 mm fruits. C and D are images taken on an epifluorescence microscope (Axioplan) using a Chroma GFP filter set 41017 (Chroma, Bellows Falls, VT). E is a dark-field image of the same ovule in D. These images show expression is present in developing fruit. Scale bar in B represents 20 μm, scale bar in E represents 20 μm in C-E. (TIF 4435 kb

Recombinase technology: applications and possibilities

The use of recombinases for genomic engineering is no longer a new technology. In fact, this technology has entered its third decade since the initial discovery that recombinases function in heterologous systems (Sauer in Mol Cell Biol 7(6):2087–2096, 1987). The random insertion of a transgene into a plant genome by traditional methods generates unpredictable expression patterns. This feature of transgenesis makes screening for functional lines with predictable expression labor intensive and time consuming. Furthermore, an antibiotic resistance gene is often left in the final product and the potential escape of such resistance markers into the environment and their potential consumption raises consumer concern. The use of site-specific recombination technology in plant genome manipulation has been demonstrated to effectively resolve complex transgene insertions to single copy, remove unwanted DNA, and precisely insert DNA into known genomic target sites. Recombinases have also been demonstrated capable of site-specific recombination within non-nuclear targets, such as the plastid genome of tobacco. Here, we review multiple uses of site-specific recombination and their application toward plant genomic engineering. We also provide alternative strategies for the combined use of multiple site-specific recombinase systems for genome engineering to precisely insert transgenes into a pre-determined locus, and removal of unwanted selectable marker genes

Sensing the fuels: glucose and lipid signaling in the CNS controlling energy homeostasis

The central nervous system (CNS) is capable of gathering information on the body’s nutritional state and it implements appropriate behavioral and metabolic responses to changes in fuel availability. This feedback signaling of peripheral tissues ensures the maintenance of energy homeostasis. The hypothalamus is a primary site of convergence and integration for these nutrient-related feedback signals, which include central and peripheral neuronal inputs as well as hormonal signals. Increasing evidence indicates that glucose and lipids are detected by specialized fuel-sensing neurons that are integrated in these hypothalamic neuronal circuits. The purpose of this review is to outline the current understanding of fuel-sensing mechanisms in the hypothalamus, to integrate the recent findings in this field, and to address the potential role of dysregulation in these pathways in the development of obesity and type 2 diabetes mellitus

Genomic-based-breeding tools for tropical maize improvement

Maize has traditionally been the main staple diet in the Southern Asia and Sub-Saharan Africa and widely grown by millions of resource poor small scale farmers. Approximately, 35.4 million hectares are sown to tropical maize, constituting around 59% of the developing worlds. Tropical maize encounters tremendous challenges besides poor agro-climatic situations with average yields recorded <3 tones/hectare that is far less than the average of developed countries. On the contrary to poor yields, the demand for maize as food, feed, and fuel is continuously increasing in these regions. Heterosis breeding introduced in early 90 s improved maize yields significantly, but genetic gains is still a mirage, particularly for crop growing under marginal environments. Application of molecular markers has accelerated the pace of maize breeding to some extent. The availability of array of sequencing and genotyping technologies offers unrivalled service to improve precision in maize-breeding programs through modern approaches such as genomic selection, genome-wide association studies, bulk segregant analysis-based sequencing approaches, etc. Superior alleles underlying complex traits can easily be identified and introgressed efficiently using these sequence-based approaches. Integration of genomic tools and techniques with advanced genetic resources such as nested association mapping and backcross nested association mapping could certainly address the genetic issues in maize improvement programs in developing countries. Huge diversity in tropical maize and its inherent capacity for doubled haploid technology offers advantage to apply the next generation genomic tools for accelerating production in marginal environments of tropical and subtropical world. Precision in phenotyping is the key for success of any molecular-breeding approach. This article reviews genomic technologies and their application to improve agronomic traits in tropical maize breeding has been reviewed in detail