Classification of time series by shapelet transformation

Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series. A \emph{shapelet} is a time-series subsequence that allows for TSC based on local, phase-independent similarity in shape. Shapelet-based classification uses the similarity between a shapelet and a series as a discriminatory feature. One benefit of the shapelet approach is that shapelets are comprehensible, and can offer insight into the problem domain. The original shapelet-based classifier embeds the shapelet-discovery algorithm in a decision tree, and uses information gain to assess the quality of candidates, finding a new shapelet at each node of the tree through an enumerative search. Subsequent research has focused mainly on techniques to speed up the search. We examine how best to use the shapelet primitive to construct classifiers. We propose a single-scan shapelet algorithm that finds the best $k$ shapelets, which are used to produce a transformed dataset, where each of the $k$ features represent the distance between a time series and a shapelet. The primary advantages over the embedded approach are that the transformed data can be used in conjunction with any classifier, and that there is no recursive search for shapelets. We demonstrate that the transformed data, in conjunction with more complex classifiers, gives greater accuracy than the embedded shapelet tree. We also evaluate three similarity measures that produce equivalent results to information gain in less time. Finally, we show that by conducting post-transform clustering of shapelets, we can enhance the interpretability of the transformed data. We conduct our experiments on 29 datasets: 17 from the UCR repository, and 12 we provide ourselve

Использование и перераспределение земель сельскохозяйственного назначения в Томском районе

Актуальность темы магистерской диссертацией связана с неэффективностью использования земель СХНО как основы продовольственной безопасности Томской области. Объектом исследования являются земли сельскохозяйственного назначения Томского района Томской области Цель работы – обоснование комплексного подхода к решению сложившихся проблем использования земель СХН и разработка его алгоритма на примере Томского района Томской области. В ходе работы были рассмотрены почвенные и климатические условия Томского района, их влияние на земли СХН.Object of research are the earth of agricultural purpose of the Tomsk region of the Tomsk region The work purpose – justification of an integrated approach to the solution of the developed problems of use of lands of agricultural tenure and development of his algorithm on the example of the Tomsk region of the Tomsk region. During work soil and climatic conditions of the Tomsk area, their influence on lands of SHN have been considered. The analysis of the possible reasons promoting reduction of the areas and decrease in fertility of this category, standard and legal base is carried out, and the existing problems of use and non-use of lands of agricultural purpose on the example of the Tomsk area are also revealed and proved. As a result of research the scheme covering in itself the factor

Structural basis for receptor activity-modifying protein-dependent selective peptide recognition by a G protein-coupled receptor

Association of receptor activity-modifying proteins (RAMP1-3) with the G protein-coupled receptor (GPCR) calcitonin receptor-like receptor (CLR) enables selective recognition of the peptides calcitonin gene-related peptide (CGRP) and adrenomedullin (AM) that have diverse functions in the cardiovascular and lymphatic systems. How peptides selectively bind GPCR:RAMP complexes is unknown. We report crystal structures of CGRP analog-bound CLR:RAMP1 and AM-bound CLR:RAMP2 extracellular domain heterodimers at 2.5 and 1.8 Å resolutions, respectively. The peptides similarly occupy a shared binding site on CLR with conformations characterized by a β-turn structure near their C termini rather than the α-helical structure common to peptides that bind related GPCRs. The RAMPs augment the binding site with distinct contacts to the variable C-terminal peptide residues and elicit subtly different CLR conformations. The structures and accompanying pharmacology data reveal how a class of accessory membrane proteins modulate ligand binding of a GPCR and may inform drug development targeting CLR:RAMP complexes

Testing alternate ecological approaches to seagrass rehabilitation: links to life-history traits

1. Natural resources and ecosystem services provided by the world’s major biomes are increasingly threatened by anthropogenic impacts. Rehabilitation is a common approach to recreating and maintaining habitats, but limitations to the success of traditional techniques necessitate new approaches. 2. Almost one-third of the world’s productive seagrass meadows have been lost in the past 130 years. Using a combined total of three seagrass species at seven sites over 8 years, we experimentally assessed the performance of multiple rehabilitation methods that utilize fundamentally different ecological approaches. 3. First, traditional methods of transplantation were tested and produced varied survival (0–80%) that was site dependent. Secondly, seedling culture and outplanting produced poor survival (2–9%) but reasonable growth. Finally, a novel method that used sand-filled bags of hessian to overcome limitations of traditional techniques by facilitating recruitment and establishment of seedlings in situ produced recruit densities of 150–350 seedlings m-2, with long-term survival (up to 38 months) ranging from 0 to 72 individuals m-2. 4. Results indicate that facilitating seagrass recruitment in situ using hessian bags can provide a new tool to alleviate current limitations to successful rehabilitation (e.g. mobile sediments, investment of time and resources), leading to more successful management and mitigation of contemporary losses. Hessian bags have distinct environmental and economic advantages over other methods tested in that they do not damage existing meadows, are biodegradable, quick to deploy, and cost less per hectare (US$16 737) than the estimated ecosystem value of seagrass meadows (US$27 039 year-1). 5. Synthesis and applications. This research demonstrates how exploring alternate ecological approaches to habitat rehabilitation can expand our collective toolbox for successfully re-creating complex and productive ecosystems, and alleviate the destructive side-effects and low success rates of more traditional techniques. Moreover, new methods can offer economic and environmental solutions to the restrictions placed upon managers of natural resources

Facilitating recruitment of Amphibolis as a novel approach to seagrass rehabilitation in hydrodynamically active waters

Worldwide, 29% of seagrass habitats have been lost over the past century. Compared with large-scale losses, successful restoration programs are usually only small scale (a few hectares). One area of significant seagrass loss (>5200 ha) is Adelaide, South Australia. Improvements to wastewater management have raised the possibility of rehabilitation in this area. Traditional methods of seagrass restoration are expensive and have had limited success owing to high wave energy. We investigated a range of biodegradable substrates, mostly made of hessian (burlap), to enhance Amphibolis recruitment as an alternative. After 5 weeks, 16 514 seedlings, or 157 seedlings m–2, had recruited. Survival declined over the following 12 months to 31.4%, and down to 7.2% after 3 years, in part as a result of breakdown of the hessian, and the wave-exposed nature of the sites. During the initial 12 months, above- and belowground biomass increased 2.6- and 6.4-fold, respectively. The technique may represent a non-destructive, cost-effective (<AU$10 000 ha–1) method to restore Amphibolis over large spatial scales and in areas that are hydrodynamically too active for traditional techniques, thus helping ameliorate some of the large-scale losses of seagrasses that have occurred globally.Rachel J. Wear, Jason E. Tanner and Sonja L. Hoar