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FAST (FAST Analysis of Sequences Toolbox) provides simple, powerful open source command-line tools to filter, transform, annotate and analyze biological sequence data. Modeled after the GNU (GNU's Not Unix) Textutils such as grep, cut, and tr, FAST tools such as fasgrep, fascut, and fastr make it easy to rapidly prototype expressive bioinformatic workflows in a compact and generic command vocabulary. Compact combinatorial encoding of data workflows with FAST commands can simplify the documentation and reproducibility of bioinformatic protocols, supporting better transparency in biological data science. Interface self-consistency and conformity with conventions of GNU, Matlab, Perl, BioPerl, R, and GenBank help make FAST easy and rewarding to learn. FAST automates numerical, taxonomic, and text-based sorting, selection and transformation of sequence records and alignment sites based on content, index ranges, descriptive tags, annotated features, and in-line calculated analytics, including composition and codon usage. Automated content- and feature-based extraction of sites and support for molecular population genetic statistics make FAST useful for molecular evolutionary analysis. FAST is portable, easy to install and secure thanks to the relative maturity of its Perl and BioPerl foundations, with stable releases posted to CPAN. Development as well as a publicly accessible Cookbook and Wiki are available on the FAST GitHub repository at https://github.com/tlawrence3/FAST. The default data exchange format in FAST is Multi-FastA (specifically, a restriction of BioPerl FastA format). Sanger and Illumina 1.8+ FastQ formatted files are also supported. FAST makes it easier for non-programmer biologists to interactively investigate and control biological data at the speed of thought.

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The field of diagnostic and therapeutic radiology has always been characterized by constant innovation and creativity to evolve to its current form. There are numerous imaging techniques that were once prevalent but have become outdated and were replaced by the current examinations and modalities, which improve diagnostic accuracy and patient outcomes. Many of these outdated examinations were first described in the journal Radiology during its first 100 years of existence and were subsequently able to be disseminated across its vast readership to become the standard of care across the nation and the world. These earlier techniques, such as pneumoencephalography as it applies to neuroimaging and neurosurgery; kymography, a predecessor of cardiac imaging; contrast agents such as Thorotrast; and miscellaneous cultural tools, such as the shoe-fitting fluoroscope, left lasting impressions on the current practice of radiology and reflect a small subset of the imaging examinations of our predecessors. Knowledge of historic radiologic examinations and procedures is important to understand how we have arrived at the current practice of radiology we embrace today and how our field can continue to evolve to improve our diagnostic and therapeutic abilities to fit the changing needs of our patients.

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In the past few decades, neural networks have been extensively adopted in various applications ranging from simple synaptic memory coding to sophisticated pattern recognition problems such as scene analysis and robot vision. Moreover, current studies on neuroscience and physiology have reported that in a typical scene segmentation problem our major senses of perception (e.g. vision, olfaction, etc.) are highly chaotic and involved non-linear neural dynamics and oscillations. In this paper, the author proposes an innovative chaotic neural oscillator-namely the Lee-oscillator (Lee's Chaotic Neural Oscillator) to provide a chaotic neural coding and information processing scheme. To illustrate the capability of Lee-oscillators upon pattern association, a chaotic auto-associative network, namely Lee-Associator (Lee's Chaotic Auto-associator) is constructed. Different from classical auto-associators such as the celebrated Hopfield network, which provides time-independent and static pattern association scheme, the Lee-Associator provides a remarkable progressive memory association scheme (what the author called 'Progressive Memory Recalling Scheme, PMRS') during the chaotic memory association. This is exactly consistent with the latest research in psychiatry and perception psychology on dynamic memory recalling schemes, as well as the implications and analogues to human perception as illustrated by the remarkable Rubin-vase experiment on visual psychology.

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