Creativity and Songwriting

This study tested a number of theories of creativity in an experiment where a song was written and recorded every day for over 170 days using various techniques and ideas. 15 have been reworked, finalised, and released on an audio CD, attached as Appendix 1. The finished CD contains material from a number of styles and is intended to showcase the gradual progression of the songwriting process and the change in style over time, and explores the question of whether songwriting and creativity in general can be improved through regular practice. It also demonstrates a wide array of skill and fluency in songwriting and creativity gained from a large amount of practice, whilst also exhibiting examples of the material that was written in the daily songwriting practice routine. The audio CD (Appendix 1) is accompanied by a data CD containing 100 recorded demos of songs written over the course of the experiment (Appendix 2) and a thesis explaining the creative process behind selected tracks, complete with a literature review of research into the current understanding of creativity. This is explored from both a psychological viewpoint and a more subjective viewpoint, relating specifically to songwriting. The thesis also attempts to find common ground between psychological practices aimed at improving general creativity, and more specific songwriting techniques, intended to explore how songwriters can produce a higher quality or quantity of work. It addresses such issues as writer’s block, songwriting as a routine, and also the relationship between the number of songs written and the quality of those songs, whilst also autoethnographically detailing the writing process of the songs written over the 170 day period, and the experience of the artist of the effects of the practice routine. The project aimed to determine whether creativity could be improved by following a regimented practice routine over the course of a set period of time (in this case, roughly half a year). Both quantitative and qualitative data have been collected from this experiment and analysed from an autoethnographical perspective, and it has been determined that in this case, the artist’s perceived skill in songwriting has grown due to the amount of time specifically dedicated to it, the regular practice enabling a larger volume of higher quality work to be produced. Secondary research also showed that creativity in general was improved from the exercise, and that this enhanced creativity can be applied more generally than simply to songwriting

Keemei: cloud-based validation of tabular bioinformatics file formats in Google Sheets.

BackgroundBioinformatics software often requires human-generated tabular text files as input and has specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support the concurrent editing of a single spreadsheet by different users working on different platforms. Most of the researchers who enter data are not familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis.Main textWe present Keemei, a Google Sheets Add-on, for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Google's Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports the validation of two widely used tabular bioinformatics formats, the Quantitative Insights into Microbial Ecology (QIIME) sample metadata mapping file format and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others.ConclusionsKeemei will save researchers time and frustration by providing a convenient interface for tabular bioinformatics file format validation. By allowing everyone involved with data entry for a project to easily validate their data, it will reduce the validation and formatting bottlenecks that are commonly encountered when human-generated data files are first used with a bioinformatics system. Simplifying the validation of essential tabular data files, such as sample metadata, will reduce common errors and thereby improve the quality and reliability of research outcomes

Ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses.

BackgroundFungi play critical roles in many ecosystems, cause serious diseases in plants and animals, and pose significant threats to human health and structural integrity problems in built environments. While most fungal diversity remains unknown, the development of PCR primers for the internal transcribed spacer (ITS) combined with next-generation sequencing has substantially improved our ability to profile fungal microbial diversity. Although the high sequence variability in the ITS region facilitates more accurate species identification, it also makes multiple sequence alignment and phylogenetic analysis unreliable across evolutionarily distant fungi because the sequences are hard to align accurately. To address this issue, we created ghost-tree, a bioinformatics tool that integrates sequence data from two genetic markers into a single phylogenetic tree that can be used for diversity analyses. Our approach starts with a "foundation" phylogeny based on one genetic marker whose sequences can be aligned across organisms spanning divergent taxonomic groups (e.g., fungal families). Then, "extension" phylogenies are built for more closely related organisms (e.g., fungal species or strains) using a second more rapidly evolving genetic marker. These smaller phylogenies are then grafted onto the foundation tree by mapping taxonomic names such that each corresponding foundation-tree tip would branch into its new "extension tree" child.ResultsWe applied ghost-tree to graft fungal extension phylogenies derived from ITS sequences onto a foundation phylogeny derived from fungal 18S sequences. Our analysis of simulated and real fungal ITS data sets found that phylogenetic distances between fungal communities computed using ghost-tree phylogenies explained significantly more variance than non-phylogenetic distances. The phylogenetic metrics also improved our ability to distinguish small differences (effect sizes) between microbial communities, though results were similar to non-phylogenetic methods for larger effect sizes.ConclusionsThe Silva/UNITE-based ghost tree presented here can be easily integrated into existing fungal analysis pipelines to enhance the resolution of fungal community differences and improve understanding of these communities in built environments. The ghost-tree software package can also be used to develop phylogenetic trees for other marker gene sets that afford different taxonomic resolution, or for bridging genome trees with amplicon trees.Availabilityghost-tree is pip-installable. All source code, documentation, and test code are available under the BSD license at https://github.com/JTFouquier/ghost-tree

mockrobiota: a Public Resource for Microbiome Bioinformatics Benchmarking.

Mock communities are an important tool for validating, optimizing, and comparing bioinformatics methods for microbial community analysis. We present mockrobiota, a public resource for sharing, validating, and documenting mock community data resources, available at http://caporaso-lab.github.io/mockrobiota/. The materials contained in mockrobiota include data set and sample metadata, expected composition data (taxonomy or gene annotations or reference sequences for mock community members), and links to raw data (e.g., raw sequence data) for each mock community data set. mockrobiota does not supply physical sample materials directly, but the data set metadata included for each mock community indicate whether physical sample materials are available. At the time of this writing, mockrobiota contains 11 mock community data sets with known species compositions, including bacterial, archaeal, and eukaryotic mock communities, analyzed by high-throughput marker gene sequencing. IMPORTANCE The availability of standard and public mock community data will facilitate ongoing method optimizations, comparisons across studies that share source data, and greater transparency and access and eliminate redundancy. These are also valuable resources for bioinformatics teaching and training. This dynamic resource is intended to expand and evolve to meet the changing needs of the omics community

Temporal variability is a personalized feature of the human microbiome

Background: It is now apparent that the complex microbial communities found on and in the human body vary across individuals. What has largely been missing from previous studies is an understanding of how these communities vary over time within individuals. To the extent to which it has been considered, it is often assumed that temporal variability is negligible for healthy adults. Here we address this gap in understanding by profiling the forehead, gut (fecal), palm, and tongue microbial communities in 85 adults, weekly over 3 months. Results: We found that skin (forehead and palm) varied most in the number of taxa present, whereas gut and tongue communities varied more in the relative abundances of taxa. Within each body habitat, there was a wide range of temporal variability across the study population, with some individuals harboring more variable communities than others. The best predictor of these differences in variability across individuals was microbial diversity; individuals with more diverse gut or tongue communities were more stable in composition than individuals with less diverse communities. Conclusions: Longitudinal sampling of a relatively large number of individuals allowed us to observe high levels of temporal variability in both diversity and community structure in all body habitats studied. These findings suggest that temporal dynamics may need to be considered when attempting to link changes in microbiome structure to changes in health status. Furthermore, our findings show that, not only is the composition of an individual's microbiome highly personalized, but their degree of temporal variability is also a personalized feature

The effect of six months of Topiramate supplementation use for weight loss on ambulatory blood pressure in abdominally obese males

Objective. To examine the effect of six months of Topiramate (TPM) supplementation use for weight loss on ambulatory blood pressure (ABP) in abdominally obese males. Methods. Sixty-eight abdominally obese males (Waist circumference >100 cm) with a BMI >27 and < 35 kg/m2 were assigned to the TPM or placebo group using a randomized pretest-posttest control group design with no behavioral modification. This was followed by a 12-week titration period and a 12-week stabilization period with a dosage up to 400 mg/d. Ambulatory blood pressure was monitored at baseline and at six months using the Spacelabs Medical Model 90207-31. Results. Topiramate supplementation group showed a significant decrease in systolic (5.2 +/- 9.79; p=0.006, -4.6 +/- 10.90; p=0.011 and -6.9 +/- 8.31; 0.003 mmHg) and diastolic (-3.7 +/- 6.13; p =0.006, -3.3 +/- 7.08; p=0.033 and -4.3 +/- 7.13; p=0.006 mmHg) 24-hr, daytime and nighttime ABP respectively. However, after performing an ANCOVA to control for the significant weight loss observed in the TPM supplementation group (-3.19 +/- 5.72 kg; p=0.006), systolic/diastolic 24-hr (p=0.233/0.147), daytime, (p=0.313/0.276) and nighttime (p= 0.108/0.187) changes in ABP were similar between the TPM and placebo group. Furthermore, change in systolic 24-hr ABP was significantly correlated to changes in % body fat (r=0.54; p=0.025), body fat mass (r=0.51; p=0.039) and total abdominal adipose tissue (r=0.46; p=0.043), whereas change in diastolic 24-hr ABP was significantly correlated to changes in total abdominal adipose tissue (c=0.51; p=0.043). Change in visceral adipose tissue (VAT) was not significantly related to changes in systolic/diastolic ABP (r=0.46/0.41; p=0.075/0.113) respectively. Discussion. These results suggest that the reduction in ABP observed in the TPM is mainly secondary to the reduction in body weight, especially the reduction in fat mass and total abdominal adipose tissue and not by the mechanistic abilities of the drug

The interpersonal and intrapersonal diversity of human-associated microbiota in key body sites

The human body harbors 10–100 trillion microbes, mainly bacteria in our gut, which greatly outnumber our own human cells. This bacterial assemblage, referred to as the human microbiota, plays a fundamental role in our well-being. Deviations from healthy microbial compositions (dysbioses) have been linked with important human diseases, including inflammation-linked disorders such as allergies, obesity and inflammatory bowel disease. Characterizing the temporal variations and community membership of the healthy human microbiome is critical in order to accurately identify the significant deviations from normality that could be associated with disease states. However, the diversity of the human microbiome varies between body sites, between individuals, and over time. Environmental differences have also been shown to play a role in shaping the human microbiome in different cultures, requiring that the healthy human microbiome be characterized across lifespans, ethnicities, nationalities, cultures, and geographic locales. In this paper, we summarize our knowledge on the microbial composition of the five best-characterized body sites (gut, skin, oral, airways, and vagina), focusing on inter- and intrapersonal variations and our current understanding of the sources of this variation

Satellite observations of Arctic blowing dust events >82°N

This study reports satellite evidence for the most northerly blown dust activity yet observed on Earth. A systematic inspection of high-resolution satellite imagery identified active dust events and their sources >82°N in Peary Land, Greenland. In the absence of any local weather measurements, for all observed dust activity a focus period in April 2020 with multiple dust plumes, reanalysis climate data found the majority of dust events to be associated with wind speeds exceeding a typical threshold value for blowing sand and dust uplift. Wind direction variability points to dust-raising by cold airflow down-valley winds, likely from nearby ice masses.</p

Keemei: cloud-based validation of tabular bioinformatics file formats in Google Sheets

BACKGROUND: Bioinformatics software often requires human-generated tabular text files as input and has specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support the concurrent editing of a single spreadsheet by different users working on different platforms. Most of the researchers who enter data are not familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis. MAIN TEXT: We present Keemei, a Google Sheets Add-on, for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Google’s Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports the validation of two widely used tabular bioinformatics formats, the Quantitative Insights into Microbial Ecology (QIIME) sample metadata mapping file format and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others. CONCLUSIONS: Keemei will save researchers time and frustration by providing a convenient interface for tabular bioinformatics file format validation. By allowing everyone involved with data entry for a project to easily validate their data, it will reduce the validation and formatting bottlenecks that are commonly encountered when human-generated data files are first used with a bioinformatics system. Simplifying the validation of essential tabular data files, such as sample metadata, will reduce common errors and thereby improve the quality and reliability of research outcomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13742-016-0133-6) contains supplementary material, which is available to authorized users