A techmque for proving correctness of programs manipulating data structures Is proposed. Its three major components are (i) an abstract representation for the data structures called free state description (FSD), (n) a...
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The concept of history is introduced to the variables of a programming language. At each execution of assignment statement for a variable x , the current value of x is saved to x 〈1〉, that of x 〈1〉 to x 〈2〉 and ...
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The concept of history is introduced to the variables of a programming language. At each execution of assignment statement for a variable x , the current value of x is saved to x 〈1〉, that of x 〈1〉 to x 〈2〉 and so on automatically. With the aid of this facility, a programmer will not worry about bookkeeping of saving the content of a variable and will make fewer errors in programming. A possible implementation method for the history on a conventional computer is also discussed.
Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such ...
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Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on standardized recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular, do not require cell-cell communication channels, and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed automated workflows that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare. Automated frameworks to systematically implement robust history-dependent genetic programs in cellular populations.
Letters to the Editor and author responses for previously published Computer articles from Bob Colwell, David Patterson, Jeremy Gibbons, Andreas Stefik, and Stefan Hanenberg.
Letters to the Editor and author responses for previously published Computer articles from Bob Colwell, David Patterson, Jeremy Gibbons, Andreas Stefik, and Stefan Hanenberg.
GEDAN KEN is an experimental programming language with the following characteristics. (1) Any value which is permitted in some context of the language is permissible in any other meaningful context. In particular, fun...
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GEDAN KEN is an experimental programming language with the following characteristics. (1) Any value which is permitted in some context of the language is permissible in any other meaningful context. In particular, functions and labels are permissible results of functions and values of variables. (2) Assignment and indirect addressing are formalized by introducing values, called references, which in turn possess other values. The assignment operation always affects the relation between some reference and its value. (3) All compound data structures are treated as functions. (4) Type declarations are not permitted. The functional approach to data structures and the use of references insure that any process which accepts some data structure will accept any logically equivalent structure, regards less of its internal representation. More generally, any data structure may be implicit; i.e. it may be specified by giving an arbitrary algorithm for computing or accessing its components. The existence of label variables permits the construction of co-routines, quasi-parallel processes, and other unorthodox control mechanisms. A variety of programming examples illustrates the generality of the language. Limitations and possible extensions are discussed briefly. [ABSTRACT FROM AUTHOR]
The article presents the Fortran programming language standards. Its development was started in May 1962 and became the first programming language standardized in the U.S. in March 1966. Studies and application of the...
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The article presents the Fortran programming language standards. Its development was started in May 1962 and became the first programming language standardized in the U.S. in March 1966. Studies and application of the standards indicated the need for maintenance of these specifications and an effort to prepare an initial set of clarifying interpretations has been initiated in 1967. Included in this presentation are the nature of the needed maintenance, corrections to the standard specifications and completed interpretations.
Unprecedented technological advances in single-cell RNA-sequencing (scRNA-seq) technology have now made it possible to profile genome-wide expression in single cells at low cost and high throughput. There is substanti...
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Unprecedented technological advances in single-cell RNA-sequencing (scRNA-seq) technology have now made it possible to profile genome-wide expression in single cells at low cost and high throughput. There is substantial ongoing effort to use scRNA-seq measurements to identify the “cell types” that form components of a complex tissue, akin to taxonomizing species in ecology. Cell type classification from scRNA-seq data involves the application of computational tools rooted in dimensionality reduction and clustering, and statistical analysis to identify molecular signatures that are unique to each type. As datasets continue to grow in size and complexity, computational challenges abound, requiring analytical methods to be scalable, flexible, and robust. Moreover, careful consideration needs to be paid to experimental biases and statistical challenges that are unique to these measurements to avoid artifacts. This chapter introduces these topics in the context of cell-type identification, and outlines an instructive step-by-step example bioinformatic pipeline for researchers entering this field. less
The use of major histocompatibility complex (MHC) class I binding peptides for immunotherapeutic purposes has shown promising results in recent years. The identification of such peptides mostly starts with predicting ...
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The use of major histocompatibility complex (MHC) class I binding peptides for immunotherapeutic purposes has shown promising results in recent years. The identification of such peptides mostly starts with predicting MHC-binding peptides, given a protein of interest. An accurate prediction method can reduce the number of peptides that needs to be tested experimentally. This protocol describes in this describes how support vector machines (SVMs) can be used for predicting MHC class I binding peptides. Focus is given on data representation, the concept of cross-validation, and how optimal SVM-specific parameters are obtained. less
Real-time deformability cytometry (RT-DC) is a microfluidic technique that allows to capture and evaluate morphology and rheology of up to 1000 cells/s in a constricted channel. The cells are deformed without mec...
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Real-time deformability cytometry (RT-DC) is a microfluidic technique that allows to capture and evaluate morphology and rheology of up to 1000 cells/s in a constricted channel. The cells are deformed without mechanical contact by hydrodynamic forces and are quantified in real-time without the need of additional handling or staining procedures. Segmented pictures of the cells are stored and can be used for further analysis. RT-DC is sensitive to alterations of the cytoskeleton, which allows, e.g., to show differences in cell cycle phases, identify different subpopulations in whole blood and to study mechanical stiffening of cells entering a dormant state. The abundance of the obtainable parameters and the interpretation as mechanical readout is an analytical challenge that needs standardization. Here, we will provide guidelines for measuring and post-processing of RT-DC data. less
The oocytes, embryos, and cell-free lysates of the frog Xenopus laevis have emerged as powerful models for quantitative proteomic experiments. In the accompanying paper (Chapter 13) we describe how to prepare samples ...
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The oocytes, embryos, and cell-free lysates of the frog Xenopus laevis have emerged as powerful models for quantitative proteomic experiments. In the accompanying paper (Chapter 13) we describe how to prepare samples and acquire multiplexed proteomics spectra from those. As an illustrative example we use a 10-stage developmental time series from the egg to stage 35 (just before hatching). Here, we outline how to convert the ~700,000 acquired mass spectra from this time series into protein expression dynamics for ~9000 proteins. We first outline a preliminary quality-control analysis to discover any errors that occurred during sample preparation. We discuss how peptide and protein identification error rates are controlled, and how peptide and protein species are quantified. Our analysis relies on the freely available MaxQuant proteomics pipeline. Finally, we demonstrate how to start interpreting this large dataset by clustering and gene-set enrichment analysis. less
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