Method For Large-scale Analysis Research Articles

Investigating Large-Scale Network with Unified Granger Causality Analysis

As the concept of integrating global neuron coupling effect is increasingly accepted, investigating causal connection increasingly requires the intervention of large-scale analysis. In this study, a large-scale brain network analysis was carried out by a description length guided framework, which involves a unified Granger causality analysis (uGCA) method and now integrates the concept of large-scale analysis. This will be helpful to make a more comprehensive determination for causal connection among the global brain regions. Distinct from the conventional GCA, which involves a two-stage scheme consisting of Akaike information criterion or Bayesian information criterion (AIC/BIC) and F -test to obtain a causal effect, a unified guided framework can ensure more reliable results while eliminating some confounding influences among network nodes. Then, we performed large-scale network simulation experiments involving 13 nodes; it was found that our proposal was more accurate and robust in guiding the causal connection investigation of large-scale networks. When it comes to the resting-state fMRI datasets, we studied a 90-node network selected from the Anatomical Automatic Labeling (AAL) template. Then, combining a K-means clustering method, we found that most brain nodes in the connection network obtained by uGCA methods were gathered into the corresponding functional brain regions and functionally related regions cooperated with each other. Compared to conventional GCA, their results were more consistent with clinical and anatomical priors. Moreover, in studies of several large-scale functional networks involving default mode network (DMN), dorsal attention network (DAN), and frontoparietal control network (FCN), the uGCA method more clearly revealed their empirical cooperation. As a brain with numerous nodes and massive connections, a unified large-scale analysis method is of great significance for the integration of causal connections in the whole brain network in the future.

Analysis of Staphylococcus aureus wall teichoic acid glycoepitopes by Fourier Transform Infrared Spectroscopy provides novel insights into the staphylococcal glycocode

Surface carbohydrate moieties are essential for bacterial communication, phage-bacteria and host-pathogen interaction. Most Staphylococcus aureus produce polyribitolphosphate type Wall teichoic acids (WTAs) substituted with α- and/or β-O-linked N-acetyl-glucosamine (α-/β-O-GlcNAc) residues. GlcNAc modifications have attracted particular interest, as they were shown to govern staphylococcal adhesion to host cells, to promote phage susceptibility conferring beta-lactam resistance and are an important target for antimicrobial agents and vaccines. However, there is a lack of rapid, reliable, and convenient methods to detect and quantify these sugar residues. Whole cell Fourier transform infrared (FTIR) spectroscopy could meet these demands and was employed to analyse WTAs and WTA glycosylation in S. aureus. Using S. aureus mutants, we found that a complete loss of WTA expression resulted in strong FTIR spectral perturbations mainly related to carbohydrates and phosphorus-containing molecules. We could demonstrate that α- or β-O-GlcNAc WTA substituents can be clearly differentiated by chemometrically assisted FTIR spectroscopy. Our results suggest that whole cell FTIR spectroscopy represents a powerful and reliable method for large scale analysis of WTA glycosylation, thus opening up a complete new range of options for deciphering the staphylococcal pathogenesis related glycocode.

SAR Matrix Method for Large-Scale Analysis of Compound Structure-Activity Relationships and Exploration of Multitarget Activity Spaces.

As the number of compounds and the volume of bioactivity data rapidly grow, advanced computational methods are required to study structure-activity relationships (SARs) on a large scale. Herein, the SAR matrix (SARM) methodology is described that was designed to systematically extract structural relationships between bioactive compounds from large databases, explore structure-activity relationships, and navigate multitarget activity spaces, which is one of the core tasks in chemogenomics. In addition, the SARM approach was designed to visualize structural and structure-activity relationships, which is often of critical importance for making this information available in an intuitive form for practical applications.

Twenty-Seven Ways a Heat Wave Can Kill You: Deadly Heat in the Era of Climate Change.

Heat waves, sporadic events of extreme heat, pose a threat to human life. Deaths in excess of 70 000 people during the 2003 European heat wave, 10 000 people during the 2010 Russian heat wave, and high death tolls from numerous other heat waves1 are staggering demonstrations that extreme climatic conditions are already exceeding human thermoregulatory capacity. The area of the planet experiencing heat wave conditions similar to those that have killed people is expanding and is currently inhabited by ≈30% of the world’s human population.1 Because the planet will warm another 1°C by 2100 if we curtail greenhouse gases or 3.7°C if we do not, our choices for deadly heat are now between more of it or a lot more of it.1 Here we carried out a systematic synthesis of deadly heat physiological pathways to make the point that the human body is sensitive to heat and that heat waves can harm anyone (even the young and healthy2,3) in ways that extend beyond cardiovascular diseases. We suggest that only the rapid reduction of greenhouse gases paired with large economic investment in adaptation will help us escape the health risks of heat waves. Ambient conditions that prevent body heat dissipation (eg, too hot, too humid, or both) trigger dangerous physiological responses or pathways that have been a topic of considerable medical interest for “military personnel, athletes, and occupations exposed to extreme heat, such as mining.”4 To systematically synthetize these deadly pathways, we searched online databases (ie, PubMed and Google Scholar) using the key words pathophysiology and heat illness , heat stroke , or heat stress . We categorized pathways in a table listing physiological mechanisms by columns and vital organs impacted by rows. We performed secondary searches combining as key words the mechanism (column name) …

P035 A method for large-scale analysis of HLA genetic variation

Aim Less than 7% of HLA genes in IMGT/HLA are reported with full-length sequences. Recent high-throughput advances with NGS offers efficient full-length HLA typing. ∼160,000 HLA loci were reported by HML. A fully automatic annotation pipeline is needed to predict HLA exon–intron architecture and characterize genetic variation within exons and introns. Methods A program hml2fa was created to parse consensus sequences from HML to fasta format, then Clustal-Omega was applied for multiple sequence alignment with references. ExonFinder was created to predict exon–intron structures. UniqueSeq was used to remove sequence duplications for uniqueness analysis. Meanwhile, alignment sequence weights were calculated then bar plotted for validation. Results Sequence weight distribution indicated good alignment coverage especially in exon regions. Uniqueness analysis revealed thousands of full-length HLA variants that are potential novel alleles. In consideration of unique CDS and protein products, ∼50% of the variation was found in exons, resulting in 20–25% non-synonymous changes in class I HLA genes. Further analyses showed that exons 2 and 3 have significant high variability (p Conclusions This annotation pipeline can be used to predict HLA exon–intron structure and explore genetic variation on a large-scale. Surprisingly large numbers of intronic variants may affect HLA function and impact selection of matches for best transplant outcomes. Download : Download high-res image (307KB) Download : Download full-size image

Enhanced large-scale analysis method and its application to multiaxial pipe weld

A large-scale infrared ray telescope is to be built as a next-generation telescope. In this telescope, a large reflector is supported by a truss structure and welded joints that consist of multiple pipes. This paper investigates welding residual stress, which affects fatigue strength and static fracture strength. In the investigation, a novel large-scale analysis method for welding mechanics was developed based on an idealized explicit finite element method (FEM) using the multigrid method. To show the capability of the developed method, it was applied to a fundamental bead-on-plate problem. Residual stress distribution on a multiaxial pipe was studied by using the developed method. The analysis and measurement were compared to show the accuracy of the analysis, and the results showed that the shrinkage of each pipe and residual stress distribution were in good agreement. It was also found that subsequent welding may affect residual stress distribution if the parts being welded are close to each other and that the effect of subsequent welding is small if they are separated. The analysis was finished within almost 10 days. Therefore, the developed multigrid idealized explicit FEM can analyze the welding residual stress of complex structures in a realistic computing time.

Oligotyping reveals community level habitat selection within the genus Vibrio

The genus Vibrio is a metabolically diverse group of facultative anaerobic bacteria, common in aquatic environments and marine hosts. The genus contains several species of importance to human health and aquaculture, including the causative agents of human cholera and fish vibriosis. Vibrios display a wide variety of known life histories, from opportunistic pathogens to long-standing symbionts with individual host species. Studying Vibrio ecology has been challenging as individual species often display a wide range of habitat preferences, and groups of vibrios can act as socially cohesive groups. Although strong associations with salinity, temperature and other environmental variables have been established, the degree of habitat or host specificity at both the individual and community levels is unknown. Here we use oligotyping analyses in combination with a large collection of existing Vibrio 16S ribosomal RNA (rRNA) gene sequence data to reveal patterns of Vibrio ecology across a wide range of environmental, host, and abiotic substrate associated habitats. Our data show that individual taxa often display a wide range of habitat preferences yet tend to be highly abundant in either substrate-associated or free-living environments. Our analyses show that Vibrio communities share considerable overlap between two distinct hosts (i.e., sponge and fish), yet are distinct from the abiotic plastic substrates. Lastly, evidence for habitat specificity at the community level exists in some habitats, despite considerable stochasticity in others. In addition to providing insights into Vibrio ecology across a broad range of habitats, our study shows the utility of oligotyping as a facile, high-throughput and unbiased method for large-scale analyses of publically available sequence data repositories and suggests its wide application could greatly extend the range of possibilities to explore microbial ecology.

Mayday - integrative analytics for expression data

BackgroundDNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files.ResultsWe have rewritten large parts of Mayday's core to make it more efficient and ready for future developments. Among the large number of new plugins are an automated processing framework, dynamic filtering, new and efficient clustering methods, a machine learning module and database connectivity. Extensive manual data analysis can be done using an inbuilt R terminal and an integrated SQL querying interface. Our visualization framework has become more powerful, new plot types have been added and existing plots improved.ConclusionsWe present a major extension of Mayday, a very versatile open-source framework for efficient micro array data analysis designed for biologists and bioinformaticians. Most everyday tasks are already covered. The large number of available plugins as well as the extension possibilities using compiled plugins and ad-hoc scripting allow for the rapid adaption of Mayday also to very specialized data exploration. Mayday is available at http://microarray-analysis.org.

Sequence variation in genes and genomic DNA: methods for large-scale analysis.

The large-scale typing of sequence variation in genes and genomic DNA presents new challenges for which it is not clear that current technologies are sufficiently sensitive, robust, or scalable. This review surveys the current platform technologies: separation-based approaches, which include mass spectrometry; homogeneous assays; and solid-phase/array-based assays. We assess techniques for discovering and typing variation on a large scale, especially that of single-nucleotide polymorphisms. The in-depth focus is the DNA chip/array platform, and some of the published large-scale studies are closely examined. The problem of large-scale amplification is addressed, and emerging technologies for present and future needs are indicated.