CREATING TOUCHPANEL GRAPHICS FOR CONTROL SYSTEMS

More often than system designers would like to admit, a discrepancy lies between the implementation of audiovisual control systems and their apparent ease of use to a novice or casual user. System designers and programmers are often hampered by the software tools provided by industry manufacturers and cannot reliably create desirable graphical interfaces that match the level of systems they are asked to program and install. Popular consumer trends in portable touchscreen devices, pioneered on devices such as the Apple iPhone, light a way forward into a new era of elegantly solving the audiovisual control system graphical user interface problem. Since expensive specialized hardware can be replaced by readily available consumer devices and a wide variety of tools exists with which to create content, possible alternatives to the current methods of designing the graphical user interface for the audiovisual system are ripe for discovery. Using the latest release of Autodesk Maya 2011, with features such as Python and Pymel, we have developed scripts to generate graphical user interface content for use with audiovisual control systems hardware. Also explored is the potential for a standalone development environment such that audiovisual designers and programmers are not required to operate Maya or adjust scripts to generate content. Given this new level of control over the graphical user interface, coupled with the flexibility of the control system central processor programming, a truly powerful, intuitive, and groundbreaking control interface can finally be realized

Reducing Complex Visualizations for Analysis

Data visualization provides a means to present known information in a format that is easily consumable and does not generally require specialized training. It is also well-suited to aid an analyst in discovering previously unknown information [1]. This is possible because visualization techniques can be used to highlight internal relationships and structures within the data, and present them in a graphical manner. Using visualization during the preliminary analysis phase can provide a pathway to enable an analyst to discover patterns or anomalies within the data that might otherwise go undiscovered as humans have an innate ability to visually identify patterns and anomalies. \ \ Even when an analyst has identified a pattern or anomaly within the data, creating an algorithm that allows for automated detection of other occurrences of the same, or similar, patterns is a non-trivial task. While humans are innately skilled at pattern recognition, computers are not, and patterns that might be obvious for a human to identify might be difficult for a computer to detect even when assisted by a skilled analyst [2]. This paper describes a method of taking a complex visualization, and reducing it into several smaller components in order to facilitate computer analysis of the analyst-identified patterns or anomalies in the data. From there, a detection scheme can be generated through an analyst-supervised data analysis process in order to find more occurrences in a larger dataset

Reflecting on non-reflective action: An exploratory think-aloud study of self-report habit measures

Objectives. Within health psychology, habit – the tendency to enact action automatically as a learned response to contextual cues – is most commonly quantified using the ‘Self-Report Habit Index’, which assesses behavioural automaticity, or measures combining self-reported behaviour frequency and context stability. Yet, the use of self-report to capture habit has proven controversial. This study used ‘think aloud’ methods to investigate problems experienced when completing these two measures. Design. Cross-sectional survey with think-aloud study. Methods. Twenty student participants narrated their thoughts while completing habit measures applied to four health-related behaviours (active commuting, unhealthy snacking, and one context-free and one context-specific variant of alcohol consumption). Data were coded using thematic analysis procedures. Results. Problems were found in 10% of responses. Notable findings included participants lacking confidence in reporting automaticity, struggling to recall behaviour or cues, differing in interpretations of ‘commuting’, and misinterpreting items. Conclusions. While most responses were unproblematic, and further work is needed to investigate habit self-reports among larger and more diverse samples, findings nonetheless question the sensitivity of the measures, and the conceptualisation of habit underpinning common applications of them. We offer suggestions to minimise these problems

StreptomeDB:a resource for natural compounds isolated from <i>Streptomyces</i> species

Bacteria from the genus Streptomyces are very important for the production of natural bioactive compounds such as antibiotic, antitumour or immunosuppressant drugs. Around two-thirds of all known natural antibiotics are produced by these bacteria. An enormous quantity of crucial data related to this genus has been generated and published, but so far no freely available and comprehensive database exists. Here, we present StreptomeDB (http://www.pharmaceutical-bioinformatics.de/streptomedb/). To the best of our knowledge, this is the largest database of natural products isolated from Streptomyces. It contains >2400 unique and diverse compounds from >1900 different Streptomyces strains and substrains. In addition to names and molecular structures of the compounds, information about source organisms, references, biological role, activities and synthesis routes (e.g. polyketide synthase derived and non-ribosomal peptides derived) is included. Data can be accessed through queries on compound names, chemical structures or organisms. Extraction from the literature was performed through automatic text mining of thousands of articles from PubMed, followed by manual curation. All annotated compound structures can be downloaded from the website and applied for in silico screenings for identifying new active molecules with undiscovered properties

Mucus-degrading Bacteroides link carbapenems to aggravated graft-versus-host disease

View full abstracthttps://openworks.mdanderson.org/leading-edge/1009/thumbnail.jp

Semi-automated assembly of high-quality diploid human reference genomes

The current human reference genome, GRCh38, represents over 20 years of effort to generate a high-quality assembly, which has benefitted society. However, it still has many gaps and errors, and does not represent a biological genome as it is a blend of multiple individuals. Recently, a high-quality telomere-to-telomere reference, CHM13, was generated with the latest long-read technologies, but it was derived from a hydatidiform mole cell line with a nearly homozygous genome. To address these limitations, the Human Pangenome Reference Consortium formed with the goal of creating high-quality, cost-effective, diploid genome assemblies for a pangenome reference that represents human genetic diversity. Here, in our first scientific report, we determined which combination of current genome sequencing and assembly approaches yield the most complete and accurate diploid genome assembly with minimal manual curation. Approaches that used highly accurate long reads and parent-child data with graph-based haplotype phasing during assembly outperformed those that did not. Developing a combination of the top-performing methods, we generated our first high-quality diploid reference assembly, containing only approximately four gaps per chromosome on average, with most chromosomes within ±1% of the length of CHM13. Nearly 48% of protein-coding genes have non-synonymous amino acid changes between haplotypes, and centromeric regions showed the highest diversity. Our findings serve as a foundation for assembling near-complete diploid human genomes at scale for a pangenome reference to capture global genetic variation from single nucleotides to structural rearrangements