An enhanced topologically significant directed random walk in cancer classification using gene expression datasets

Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Divergent growth of Norway spruce on Babia Góra Mountain in the western Carpathians

Growth divergence – i.e. the expression of divergent growth trends of neighboring trees – has certain implications for dendrochronological research, for instance in the context of climate reconstructions but also in terms of estimating net ecosystem productivity. Thus, understanding the underlying mechanisms is essential to extend our fundamental dendroecological knowledge. In this context, the Picea genus plays an important role since several of its species were reported to exhibit growth divergence. Here, we investigate a well sampled Norway spruce (Picea abies (L.) Karst) data set for growth divergence comprising ring-width and Blue Intensity measurements from seven sites on Babia Góra Mountain, at the border between Poland and Slovakia. By means of Principal Component Gradient Analysis, inter-series correlations, and climate growth relationships, we are able to show that I) Norway spruce on Babia Góra expressed growth divergence since the 1970s, II) the definition of groups increased the strength of population signals and the stability of climate-growth relationships, and III) Blue Intensity appeared as a more robust proxy for environmental conditions. We discuss soil heterogeneity, genetics, and air pollution as possible underlying mechanisms, thereby indicating further research avenues to obtain a better understanding of growth divergence.     

Environmental factors affecting the distribution of aquatic invertebrates in temporary ponds in Mammoth Cave National Park,Kentucky, USA

Despite a recent surge of interest in temporary lentic systems, a strong theory linking the biota to its environment has not emerged. Using data from 10 temporary ponds at Mammoth Cave National Park, Kentucky, USA, we investigated how invertebrate communities were structured along environmental gradients, both between and within ponds. Samples were collected with a benthic corer in winter and spring, and a sweep net in spring. Six between-pond and two within-pond datasets were created. Between-pond analyses yielded significant CCA’s with only one of the six data sets. The ranges of environmental variables (EV’s) within ponds were often similar to the ranges of EV’s when averaged and compared between ponds. Some taxa were aggregated in a single pond, and richness increased with pond area. The theory that richness increases with hydroperiod did not apply to these systems. Within-pond analyses yielded more consistent relationships, with both CCA’s being significant. Sample depth was the best predictor of invertebrate richness and abundance, with most taxa preferring shallow habitats. Richness and abundance were higher in both shallow ponds and shallow areas of deep ponds than in deep areas of deep ponds. Standardizing sample depth may be an effective way to remove this gradient as a confounding variable in future research. The presence of within-pond gradients, possibly coupled with the limited dispersal and random colonization of tolerant taxa, makes between-pond comparisons difficult. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorised users. Handling editor: S. Declerck     

Pollen productivity estimates of key European plant taxa for quantitative reconstruction of past vegetation: a review

Information on the spatial distribution of past vegetation on local, regional and global scales is increasingly used within climate modelling, nature conservancy and archaeology. It is possible to obtain such information from fossil pollen records in lakes and bogs using the landscape reconstruction algorithm (LRA) and its two models, REVEALS and LOVE. These models assume that reliable pollen productivity estimates (PPEs) are available for the plant taxa involved in the quantitative reconstructions of past vegetation, and that PPEs are constant through time. This paper presents and discusses the PPEs for 15 tree and 18 herb taxa obtained in nine study areas of Europe. Observed differences in PPEs between regions may be explained by methodological issues and environmental variables, of which climate and related factors such as reproduction strategies and growth forms appear to be the most important. An evaluation of the PPEs at hand so far suggests that they can be used in modelling applications and quantitative reconstructions of past vegetation, provided that consideration of past environmental variability within the region is used to inform selection of PPEs, and bearing in mind that PPEs might have changed through time as a response to climate change. Application of a range of possible PPEs will allow a better evaluation of the results.     

Frequency-dependent conspecific attraction to food patches

In many ecological situations, resources are difficult to find but become more apparent to nearby searchers after one of their numbers discovers and begins to exploit them. If the discoverer cannot monopolize the resources, then others may benefit from joining the discoverer and sharing their discovery. Existing theories for this type of conspecific attraction have often used very simple rules for how the decision to join a discovered resource patch should be influenced by the number of individuals already exploiting that patch. We use a mechanistic, spatially explicit model to demonstrate that individuals should not necessarily simply join patches more often as the number of individuals exploiting the patch increases, because those patches are likely to be exhausted soon or joining them will intensify future local competition. Furthermore, we show that this decision should be sensitive to the nature of the resource patches, with individuals being more responsive to discoveries in general and more tolerant of larger numbers of existing exploiters on a patch when patches are resource-rich and challenging to locate alone. As such, we argue that this greater focus on underlying joining mechanisms suggests that conspecific attraction is a more sophisticated and flexible tactic than currently appreciated.     

Computational approaches toward the design of pools for the in vitro selection of complex aptamers

It is well known that using random RNA/DNA sequences for SELEX experiments will generally yield low-complexity structures. Early experimental results suggest that having a structurally diverse library, which, for instance, includes high-order junctions, may prove useful in finding new functional motifs. Here, we develop two computational methods to generate sequences that exhibit higher structural complexity and can be used to increase the overall structural diversity of initial pools for in vitro selection experiments. Random Filtering selectively increases the number of five-way junctions in RNA/DNA pools, and Genetic Filtering designs RNA/DNA pools to a specified structure distribution, whether uniform or otherwise. We show that using our computationally designed DNA pool greatly improves access to highly complex sequence structures for SELEX experiments (without losing our ability to select for common one-way and two-way junction sequences).