From Ribbons to Networks: Hierarchical Organization of DNA-Grafted Supramolecular Polymers

DNA-grafted supramolecular polymers (SPs) allow the programmed organization of DNA in a highly regular, one-dimensional array. Oligonucleotides are arranged along the edges of pyrene-based helical polymers. Addition of complementary oligonucleotides triggers the assembly of individual nanoribbons resulting in the development of extended supramolecular networks. Network formation is enabled by cooperative coaxial stacking interactions of terminal GC base pairs. The process is accompanied by structural changes in the pyrene polymer core that can be followed spectroscopically. Network formation is reversible, and disassembly into individual ribbons is realized either via thermal denaturation or by addition of a DNA separator strand.

Characterization of Chelonus inanitus polydnavirus segments: sequences and analysis, excision site and demonstration of clustering

Polydnaviruses (genera Ichnovirus and Bracovirus) have a segmented genome of circular double-stranded DNA molecules, replicate in the ovary of parasitic wasps and are essential for successful parasitism of the host. Here we show the first detailed analysis of various segments of a bracovirus, the Chelonus inanitus virus (CiV). Four segments were sequenced and two of them, CiV12 and CiV14, were found to be closely related while CiV14.5 and CiV16.8 were unrelated. CiV12, CiV14.5 and CiV16.8 are unique while CiV14 occurs also nested in another larger segment. All four segments are predicted to contain genes and predictions could be substantiated in most cases. Comparison with databases revealed no significant similarities at either the nucleotide or amino acid level. Inverted repeats with identities between 77% and 92% and lengths between 26 bp and 100 bp were found on all segments outside of predicted genes. Hybridization experiments indicate that CiV12 and CiV14 are both flanked by other virus segments, suggesting that proviral CiV segments are clustered in the genome of the wasp. The integration/excision site of CiV14 was analysed and compared to that of CiV12. On both termini of proviral CiV12 and CiV14 as well as in the excised circular molecule and the rejoined DNA a very similar repeat of 14 bp was found. A model to illustrate where the terminal repeats might recombine to yield the circular molecule is presented. Excision of CiV12 and CiV14 is restricted to the female and sets in at a very specific time-point in pupal-adult development.

Space-time clustering of childhood cancers in Switzerland: A nationwide study.

The aetiology of childhood cancers remains largely unknown. It has been hypothesized that infections may be involved and that mini-epidemics thereof could result in space-time clustering of incident cases. Most previous studies support spatio-temporal clustering for leukaemia, while results for other diagnostic groups remain mixed. Few studies have corrected for uneven regional population shifts which can lead to spurious detection of clustering. We examined whether there is space-time clustering of childhood cancers in Switzerland identifying cases diagnosed at age <16 years between 1985 and 2010 from the Swiss Childhood Cancer Registry. Knox tests were performed on geocoded residence at birth and diagnosis separately for leukaemia, acute lymphoid leukaemia (ALL), lymphomas, tumours of the central nervous system, neuroblastomas and soft tissue sarcomas. We used Baker's Max statistic to correct for multiple testing and randomly sampled time-, sex- and age-matched controls from the resident population to correct for uneven regional population shifts. We observed space-time clustering of childhood leukaemia at birth (Baker's Max p = 0.045) but not at diagnosis (p = 0.98). Clustering was strongest for a spatial lag of <1 km and a temporal lag of <2 years (Observed/expected close pairs: 124/98; p Knox test = 0.003). A similar clustering pattern was observed for ALL though overall evidence was weaker (Baker's Max p = 0.13). Little evidence of clustering was found for other diagnostic groups (p > 0.2). Our study suggests that childhood leukaemia tends to cluster in space-time due to an etiologic factor present in early life.

T cell adhesion regulation from clustering GM1 lipid rafts

Lipid rafts are small laterally mobile cell membrane structures that are highly enriched in lymphocyte signaling molecules. Lipid rafts can form from the assembly of specialized lipids and proteins through hydrophobic associations from saturated acyl chains. GM1 gangliosides are a common lipid raft component and have been shown to be essential in many T cell functions. Current lipid raft theory hypothesizes that certain aspects of T cell signaling can be initiated from the coalescence of these signaling-enriched lipid rafts to sites of receptor engagement. We have described how the specific aggregation of GM1 lipid rafts can cause a reorganization of cell surface molecular associations which include dynamic associations of β1 integrins with GM1 lipid rafts. These associations had pronounced effects on T cell adhesive and migratory states. We show that GM1 lipid raft aggregation can dramatically inhibit T cell migration and chemotaxis on the extracellular matrix constituent fibronectin. This inhibition of migration function was shown to be dependent on the src kinase Lck and PKC-regulated F-actin polymerization to extending pseudopods. Furthermore, GM1 lipid raft clustering could activate T cell adhesion-strengthening mechanisms. These include an increase in cellular rigidity, the creation of polymerized cortical F-actin structures, the induction of high affinity integrin states, an increase in surface area and symmetry of the contact plane, and resistance to shear flow detachment while adherent to fibronectin. This indicates that GM1 lipid raft aggregation defines a novel stimulus to regulate lymphocyte motility and cellular adhesion which could have important implications in T cell homing mechanisms. ^

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^

Gene clustering of the small leucine -rich repeat gene family

The small leucine-rich repeat proteoglycans (or SLRPs) are a group of extracellular proteins (ECM) that belong to the leucine-rich repeat (LRR) superfamily of proteins. The LRR is a protein folding motif composed of 20–30 amino acids with leucines in conserved positions. LRR-containing proteins are present in a broad spectrum of organisms and possess diverse cellular functions and localization. In mammals, the SLRPs are abundant in connective tissues, such as bones, cartilage, tendons, skin, and blood vessels. We have discovered a new member of the class I small leucine rich repeat proteoglycan (SLRP) family which is distinct from the other class I SLRPs since it possesses a unique stretch of aspartate residues at its N-terminus. For this reason, we called the molecule asporin. The deduced amino acid sequence is about 50% identical (and 70% similar) to decorin and biglycan. However, asporin does not contain a serine/glycine dipeptide sequence required for the assembly of O-linked glycosaminoglycans and is probably not a proteoglycan. The tissue expression of asporin partially overlaps with the expression of decorin and biglycan. During mouse embryonic development, asporin mRNA expression was detected primarily in the skeleton and other specialized connective tissues; very little asporin message was detected in the major parenchymal organs. The mouse asporin gene structure is similar to that of biglycan and decorin with 8 exons. The asporin gene is localized to human chromosome 9q22-9g21.3 where asporin is part of a SLRP gene cluster that includes ECM2, osteoadherin, and osteoglycin. This gene cluster of four LRR-encoding genes is embedded in a 238 kilobase intron of another novel gene named Tes9orf that is expressed primarily in the testes of the adult mouse. The SLRP genes are not present in Drosophila or C. elegans , but reside in three separate gene clusters in the puffer fish, mice and humans. Targeted disruption of individual mouse SLRP genes display minor connective tissue defects such as skin fragility, tendon laxity, minor growth plate defects, and mild osteoporosis. However, double and triple knockouts of SLRP genes exacerbate these phenotypes. Both the double epiphycan/biglycan and the triple PRELP/fibromodulin/biglycan knockout mice exhibit premature osteoarthritis. ^

Environmental variables and multivariate seabed classification maps via k means clustering of Potter Cove, Antarctica, link to input and result files in GeoTIFF format

This study subdivides the Potter Cove, King George Island, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis includes in total 42 different environmental variables, interpolated based on samples taken during Australian summer seasons 2010/2011 and 2011/2012. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared and the most reasonable method has been applied. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested and 4, 7, 10 as well as 12 were identified as reasonable numbers for clustering the Potter Cove. Especially the results of 10 and 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.

Generating Scale-free Networks with Adjustable Clustering Coefficient Via Random Walks

This paper presents an algorithm for generating scale-free networks with adjustable clustering coefficient. The algorithm is based on a random walk procedure combined with a triangle generation scheme which takes into account genetic factors; this way, preferential attachment and clustering control are implemented using only local information. Simulations are presented which support the validity of the scheme, characterizing its tuning capabilities.

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection

Image Segmentation Using Ant System-based Clustering Algorithm

Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data.

Turbulence models of gravitational clustering.

Large-scale structure formation can be modeled as a nonlinear process that transfers energy from the largest scales to successively smaller scales until it is dissipated, in analogy with Kolmogorov’s cascade model of incompressible turbulence. However, cosmic turbulence is very compressible, and vorticity plays a secondary role in it. The simplest model of cosmic turbulence is the adhesion model, which can be studied perturbatively or adapting to it Kolmogorov’s non-perturbative approach to incompressible turbulence. This approach leads to observationally testable predictions, e.g., to the power-law exponent of the matter density two-point correlation function.

Two way clustering of Microarray Data using a Hybrid Approach

The Microarray technique is rather powerful, as it allows to test up thousands of genes at a time, but this produces an overwhelming set of data files containing huge amounts of data, which is quite difficult to pre-process, separate, classify and correlate for interesting conclusions to be extracted. Modern machine learning, data mining and clustering techniques based on information theory, are needed to read and interpret the information contents buried in those large data sets. Independent Component Analysis method can be used to correct the data affected by corruption processes or to filter the uncorrectable one and then clustering methods can group similar genes or classify samples. In this paper a hybrid approach is used to obtain a two way unsupervised clustering for a corrected microarray data.