Evaluating the New Automatic Method for the Analysis of Absorption Spectra Using Synthetic Spectra

We recently presented a new "artificial intelligence" method for the analysis of high-resolution absorption spectra (Bainbridge and Webb, Mon. Not. R. Astron. Soc. 2017, 468,1639-1670). This new method unifies three established numerical methods: a genetic algorithm (GVPFIT); non-linear least-squares optimisation with parameter constraints (VPFIT); and Bayesian Model Averaging (BMA). In this work, we investigate the performance of GVPFIT and BMA over a broad range of velocity structures using synthetic spectra. We found that this new method recovers the velocity structures of the absorption systems and accurately estimates variation in the fine structure constant. Studies such as this one are required to evaluate this new method before it can be applied to the analysis of large sets of absorption spectra. This is the first time that a sample of synthetic spectra has been utilised to investigate the analysis of absorption spectra. Probing the variation of nature's fundamental constants (such as the fine structure constant), through the analysis of absorption spectra, is one of the most direct ways of testing the universality of physical laws. This "artificial intelligence" method provides a way to avoid the main limiting factor, i.e., human interaction, in the analysis of absorption spectra.Comment: 9 pages, 5 figures, published on 5 April 2017 in Univers

Somatic mutation load of estrogen receptor-positive breast tumors predicts overall survival: an analysis of genome sequence data.

Breast cancer is one of the most commonly diagnosed cancers in women. While there are several effective therapies for breast cancer and important single gene prognostic/predictive markers, more than 40,000 women die from this disease every year. The increasing availability of large-scale genomic datasets provides opportunities for identifying factors that influence breast cancer survival in smaller, well-defined subsets. The purpose of this study was to investigate the genomic landscape of various breast cancer subtypes and its potential associations with clinical outcomes. We used statistical analysis of sequence data generated by the Cancer Genome Atlas initiative including somatic mutation load (SML) analysis, Kaplan-Meier survival curves, gene mutational frequency, and mutational enrichment evaluation to study the genomic landscape of breast cancer. We show that ER(+), but not ER(-), tumors with high SML associate with poor overall survival (HR = 2.02). Further, these high mutation load tumors are enriched for coincident mutations in both DNA damage repair and ER signature genes. While it is known that somatic mutations in specific genes affect breast cancer survival, this study is the first to identify that SML may constitute an important global signature for a subset of ER(+) tumors prone to high mortality. Moreover, although somatic mutations in individual DNA damage genes affect clinical outcome, our results indicate that coincident mutations in DNA damage response and signature ER genes may prove more informative for ER(+) breast cancer survival. Next generation sequencing may prove an essential tool for identifying pathways underlying poor outcomes and for tailoring therapeutic strategies

Microarray analysis using pattern discovery

Analysis of gene expression microarray data has traditionally been conducted using hierarchical clustering. However, such analysis has many known disadvantages and pattern discovery (PD) has been proposed as an alternative technique. In this work, three similar but different PD algorithms – Teiresias, Splash and Genes@Work – were benchmarked for time and memory efficiency on a small yeast cell-cycle data set. Teiresias was found to be the fastest, and best over-all program. However, Splash was more memory efficient. This work also investigated the performance of four methods of discretizing microarray data: sign-of-the-derivative, K-means, pre-set value, and Genes@Work stratification. The first three methods were evaluated on their predisposition to group together biologically related genes. On a yeast cell-cycle data set, sign-of-the-derivative method yielded the most biologically significant patterns, followed by the pre-set value and K-means methods. K-means, preset-value, and Genes@Work were also compared on their ability to classify tissue samples from diffuse large b-cell lymphoma (DLBCL) into two subtypes determined by standard techniques. The Genes@Work stratification method produced the best patterns for discriminating between the two subtypes of lymphoma. However, the results from the second-best method, K-means, call into question the accuracy of the classification by the standard technique. Finally, a number of recommendations for improvement of pattern discovery algorithms and discretization techniques are made

Validation of evidence-Based Fall Prevention Programs for adults with intellectual and/or developmental disorders: a modified otago exercise Program

Introduction: Evidence-based fall prevention (EBFP) programs significantly decrease fall risk, falls, and fall-related injuries in community-dwelling older adults. To date, EBFP programs are only validated for use among people with normal cognition and, therefore, are not evidence-based for adults with intellectual and/or developmental disorders (IDD) such as Alzheimer’s disease and related dementias, cerebral vascular accident, or traumatic brain injury. Background: Adults with IDD experience not only a higher rate of falls than their community-dwelling, cognitively intact peers but also higher rates and earlier onset of chronic diseases, also known to increase fall risk. Adults with IDD experience many barriers to health care and health promotion programs. As the lifespan for people with IDD continues to increase, issues of aging (including falls with associated injury) are on the rise and require effective and efficient prevention. Methods: A modified group-based version of the Otago Exercise Program (OEP) was developed and implemented at a worksite employing adults with IDD in Montana. Participants were tested pre- and post-intervention using the Center for Disease Control and Prevention’s (CDC) Stopping Elderly Accidents Deaths and Injuries (STEADI) tool kit. Participants participated in progressive once weekly, 1-h group exercise classes and home programs over a 7-week period. Discharge planning with consumers and caregivers included home exercise, walking, and an optional home assessment. Results: Despite the limited number of participants (n = 15) and short length of participation, improvements were observed in the 30-s Chair Stand Test, 4-Stage Balance Test, and 2-Minute Walk Test. Additionally, three individuals experienced an improvement in ambulation independence. Participants reported no falls during the study period. Discussion: Promising results of this preliminary project underline the need for further study of this modified OEP among adults with IDD. Future multicenter study should include more participants in diverse geographic regions with longer lengths of participation and follow-up

Concomitant diagnosis of immune deficiency and Pseudomonas sepsis in a 19 month old with ecthyma gangrenosum by host whole-genome sequencing.

X-linked agammaglobulinemia (XLA, OMIM#300300) is a rare monogenic primary immunodeficiency caused by mutations in the Bruton tyrosine kinase (BTK) gene. XLA is characterized by insufficient immunoglobulin levels and susceptibility to life-threatening bacterial infections. We report on a patient that presented with ecthyma gangrenosum and septicemia. Rapid trio whole-genome sequencing (rWGS) revealed an apparently de novo hemizygous pathogenic variant (c.726dupT; p.Ile243TyrfsTer15) in the BTK gene. Metagenomic analysis of rWGS sequences that did not align to the human genome revealed 770 aligned to the Pseudomonas aeruginosa PAO1 genome. The patient was diagnosed with XLA and pseudomonal sepsis

FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology

Summary: Next-generation sequencing can provide insight into protein–DNA association events on a genome-wide scale, and is being applied in an increasing number of applications in genomics and meta-genomics research. However, few software applications are available for interpreting these experiments. We present here an efficient application for use with chromatin-immunoprecipitation (ChIP-Seq) experimental data that includes novel functionality for identifying areas of gene enrichment and transcription factor binding site locations, as well as for estimating DNA fragment size distributions in enriched areas. The FindPeaks application can generate UCSC compatible custom ‘WIG’ track files from aligned-read files for short-read sequencing technology. The software application can be executed on any platform capable of running a Java Runtime Environment. Memory requirements are proportional to the number of sequencing reads analyzed; typically 4 GB permits processing of up to 40 million reads

Spatial variation of hydroclimate in north-eastern North America during the last millennium

Climatic expressions of the Medieval Climate Anomaly (MCA) and the Little Ice Age (LIA) vary regionally, with reconstructions often depicting complex spatial patterns of temperature and precipitation change. The characterisation of these spatial patterns helps advance understanding of hydroclimate variability and associated responses of human and natural systems to climate change. Many regions, including north-eastern North America, still lack well-resolved records of past hydrological change. Here, we reconstruct hydroclimatic change over the past millennium using testate amoeba-inferred peatland water table depth reconstructions obtained from fifteen peatlands across Maine, Nova Scotia, Newfoundland and Québec. Spatial comparisons of reconstructed water table depths reveal complex hydroclimatic patterns that varied over the last millennium. The records suggest a spatially divergent pattern across the region during the Medieval Climate Anomaly and the Little Ice Age. Southern peatlands were wetter during the Medieval Climate Anomaly, whilst northern and more continental sites were drier. There is no evidence at the multi-decadal sampling resolution of this study to indicate that Medieval mega-droughts recorded in the west and continental interior of North America extended to these peatlands in the north-east of the continent. Reconstructed Little Ice Age hydroclimate change was spatially variable rather than displaying a clear directional shift or latitudinal trends, which may relate to local temporary permafrost aggradation in northern sites, and reconstructed characteristics of some dry periods during the Little Ice Age are comparable with those reconstructed during the Medieval Climate Anomaly. The spatial hydroclimatic trends identified here suggest that over the last millennium, peatland moisture balance in north-eastern North America has been influenced by changes in the Polar Jet Stream, storm activities and sea surface temperatures in the North Atlantic as well as internal peatland dynamics

Whole exome capture in solution with 3 Gbp of data

We have developed a solution-based method for targeted DNA capture-sequencing that is directed to the complete human exome. Using this approach allows the discovery of greater than 95% of all expected heterozygous singe base variants, requires as little as 3 Gbp of raw sequence data and constitutes an effective tool for identifying rare coding alleles in large scale genomic studies

Analysis of the prostate cancer cell line LNCaP transcriptome using a sequencing-by-synthesis approach

BACKGROUND: High throughput sequencing-by-synthesis is an emerging technology that allows the rapid production of millions of bases of data. Although the sequence reads are short, they can readily be used for re-sequencing. By re-sequencing the mRNA products of a cell, one may rapidly discover polymorphisms and splice variants particular to that cell. RESULTS: We present the utility of massively parallel sequencing by synthesis for profiling the transcriptome of a human prostate cancer cell-line, LNCaP, that has been treated with the synthetic androgen, R1881. Through the generation of approximately 20 megabases (MB) of EST data, we detect transcription from over 10,000 gene loci, 25 previously undescribed alternative splicing events involving known exons, and over 1,500 high quality single nucleotide discrepancies with the reference human sequence. Further, we map nearly 10,000 ESTs to positions on the genome where no transcription is currently predicted to occur. We also characterize various obstacles with using sequencing by synthesis for transcriptome analysis and propose solutions to these problems. CONCLUSION: The use of high-throughput sequencing-by-synthesis methods for transcript profiling allows the specific and sensitive detection of many of a cell's transcripts, and also allows the discovery of high quality base discrepancies, and alternative splice variants. Thus, this technology may provide an effective means of understanding various disease states, discovering novel targets for disease treatment, and discovery of novel transcripts