Formal models of the extension activity of DNA polymerase enzymes

The study of formal language operations inspired by enzymatic actions on DNA is part of ongoing efforts to provide a formal framework and rigorous treatment of DNA-based information and DNA-based computation. Other studies along these lines include theoretical explorations of splicing systems, insertion-deletion systems, substitution, hairpin extension, hairpin reduction, superposition, overlapping concatenation, conditional concatenation, contextual intra- and intermolecular recombinations, as well as template-guided recombination. First, a formal language operation is proposed and investigated, inspired by the naturally occurring phenomenon of DNA primer extension by a DNA-template-directed DNA polymerase enzyme. Given two DNA strings u and v, where the shorter string v (called the primer) is Watson-Crick complementary and can thus bind to a substring of the longer string u (called the template) the result of the primer extension is a DNA string that is complementary to a suffix of the template which starts at the binding position of the primer. The operation of DNA primer extension can be abstracted as a binary operation on two formal languages: a template language L1 and a primer language L2. This language operation is called L1-directed extension of L2 and the closure properties of various language classes, including the classes in the Chomsky hierarchy, are studied under directed extension. Furthermore, the question of finding necessary and sufficient conditions for a given language of target strings to be generated from a given template language when the primer language is unknown is answered. The canonic inverse of directed extension is used in order to obtain the optimal solution (the minimal primer language) to this question. The second research project investigates properties of the binary string and language operation overlap assembly as defined by Csuhaj-Varju, Petre and Vaszil as a formal model of the linear self-assembly of DNA strands: The overlap assembly of two strings, xy and yz, which share an overlap y, results in the string xyz. In this context, we investigate overlap assembly and its properties: closure properties of various language families under this operation, and related decision problems. A theoretical analysis of the possible use of iterated overlap assembly to generate combinatorial DNA libraries is also given. The third research project continues the exploration of the properties of the overlap assembly operation by investigating closure properties of various language classes under iterated overlap assembly, and the decidability of the completeness of a language. The problem of deciding whether a given string is terminal with respect to a language, and the problem of deciding if a given language can be generated by an overlap assembly operation of two other given languages are also investigated

Conditional Image Synthesis by Generative Adversarial Modeling

Recent years, image synthesis has attracted more interests. This work explores the recovery of details (low-level information) from high-level features. The generative adversarial nets (GAN) has led to the explosion of image synthesis. Moving away from those application-oriented alternatives, this work investigates its intrinsic drawbacks and derives corresponding improvements in a theoretical manner.Based on GAN, this work further investigates the conditional image synthesis by incorporating an autoencoder (AE) to GAN. The GAN+AE structure has been demonstrated to be an effective framework for image manipulation. This work emphasizes the effectiveness of GAN+AE structure by proposing the conditional adversarial autoencoder (CAAE) for human facial age progression and regression. Instead of editing on the image level, i.e., explicitly changing the shape of face, adding wrinkle, etc., this work edits the high-level features which implicitly guide the recovery of images towards expected appearance.While GAN+AE being prevalent in image manipulation, its drawbacks lack exploration. For example, GAN+AE requires a weight to balance the effects of GAN and AE. An inappropriate weight would generate unstable results. This work provides an insight to such instability, which is due to the interaction between GAN and AE. Therefore, this work proposes the decoupled learning (GAN//AE) to avoid the interaction between them and achieve a robust and effective framework for image synthesis. Most existing works used GAN+AE structure could be easily adapted to the proposed GAN//AE structure to boost their robustness. Experimental results demonstrate the correctness and effectiveness of the provided derivation and proposed methods, respectively.In addition, this work extends the conditional image synthesis to the traditional area of image super-resolution, which recovers the high-resolution image according the low-resolution counterpart. Diverting from such traditional routine, this work explores a new research direction | reference-conditioned super-resolution, in which a reference image containing desired high-resolution texture details is used besides the low-resolution image. We focus on transferring the high-resolution texture from reference images to the super-resolution process without the constraint of content similarity between reference and target images, which is a key difference from previous example-based methods

IV Congreso de Lingüística General. Cádiz, del 3 al 6 de abril de 2000. Vol. I

Durante los días 3 al 6 de abril del año 2000 tuvo lugar en la Universidad de Cádiz el IV Congreso de Lingüística General, organizado por el área de Lingüística General de esta Universidad.325 págs

Phylogenetics in the Genomic Era

Molecular phylogenetics was born in the middle of the 20th century, when the advent of protein and DNA sequencing offered a novel way to study the evolutionary relationships between living organisms. The first 50 years of the discipline can be seen as a long quest for resolving power. The goal – reconstructing the tree of life – seemed to be unreachable, the methods were heavily debated, and the data limiting. Maybe for these reasons, even the relevance of the whole approach was repeatedly questioned, as part of the so-called molecules versus morphology debate. Controversies often crystalized around long-standing conundrums, such as the origin of land plants, the diversification of placental mammals, or the prokaryote/eukaryote divide. Some of these questions were resolved as gene and species samples increased in size. Over the years, molecular phylogenetics has gradually evolved from a brilliant, revolutionary idea to a mature research field centred on the problem of reliably building trees. This logical progression was abruptly interrupted in the late 2000s. High-throughput sequencing arose and the field suddenly moved into something entirely different. Access to genome-scale data profoundly reshaped the methodological challenges, while opening an amazing range of new application perspectives. Phylogenetics left the realm of systematics to occupy a central place in one of the most exciting research fields of this century – genomics. This is what this book is about: how we do trees, and what we do with trees, in the current phylogenomic era. One obvious, practical consequence of the transition to genome-scale data is that the most widely used tree-building methods, which are based on probabilistic models of sequence evolution, require intensive algorithmic optimization to be applicable to current datasets. This problem is considered in Part 1 of the book, which includes a general introduction to Markov models (Chapter 1.1) and a detailed description of how to optimally design and implement Maximum Likelihood (Chapter 1.2) and Bayesian (Chapter 1.4) phylogenetic inference methods. The importance of the computational aspects of modern phylogenomics is such that efficient software development is a major activity of numerous research groups in the field. We acknowledge this and have included seven "How to" chapters presenting recent updates of major phylogenomic tools – RAxML (Chapter 1.3), PhyloBayes (Chapter 1.5), MACSE (Chapter 2.3), Bgee (Chapter 4.3), RevBayes (Chapter 5.2), Beagle (Chapter 5.4), and BPP (Chapter 5.6). Genome-scale data sets are so large that statistical power, which had been the main limiting factor of phylogenetic inference during previous decades, is no longer a major issue. Massive data sets instead tend to amplify the signal they deliver – be it biological or artefactual – so that bias and inconsistency, instead of sampling variance, are the main problems with phylogenetic inference in the genomic era. Part 2 covers the issues of data quality and model adequacy in phylogenomics. Chapter 2.1 provides an overview of current practice and makes recommendations on how to avoid the more common biases. Two chapters review the challenges and limitations of two key steps of phylogenomic analysis pipelines, sequence alignment (Chapter 2.2) and orthology prediction (Chapter 2.4), which largely determine the reliability of downstream inferences. The performance of tree building methods is also the subject of Chapter 2.5, in which a new approach is introduced to assess the quality of gene trees based on their ability to correctly predict ancestral gene order. Analyses of multiple genes typically recover multiple, distinct trees. Maybe the biggest conceptual advance induced by the phylogenetic to phylogenomic transition is the suggestion that one should not simply aim to reconstruct “the” species tree, but rather to be prepared to make sense of forests of gene trees. Chapter 3.1 reviews the numerous reasons why gene trees can differ from each other and from the species tree, and what the implications are for phylogenetic inference. Chapter 3.2 focuses on gene trees/species trees reconciliation methods that account for gene duplication/loss and horizontal gene transfer among lineages. Incomplete lineage sorting is another major source of phylogenetic incongruence among loci, which recently gained attention and is covered by Chapter 3.3. Chapter 3.4 concludes this part by taking a user’s perspective and examining the pros and cons of concatenation versus separate analysis of gene sequence alignments. Modern genomics is comparative and phylogenetic methods are key to a wide range of questions and analyses relevant to the study of molecular evolution. This is covered by Part 4. We argue that genome annotation, either structural or functional, can only be properly achieved in a phylogenetic context. Chapters 4.1 and 4.2 review the power of these approaches and their connections with the study of gene function. Molecular substitution rates play a key role in our understanding of the prevalence of nearly neutral versus adaptive molecular evolution, and the influence of species traits on genome dynamics (Chapter 4.4). The analysis of substitution rates, and particularly the detection of positive selection, requires sophisticated methods and models of coding sequence evolution (Chapter 4.5). Phylogenomics also offers a unique opportunity to explore evolutionary convergence at a molecular level, thus addressing the long-standing question of predictability versus contingency in evolution (Chapter 4.6). The development of phylogenomics, as reviewed in Parts 1 through 4, has resulted in a powerful conceptual and methodological corpus, which is often reused for addressing problems of interest to biologists from other fields. Part 5 illustrates this application potential via three selected examples. Chapter 5.1 addresses the link between phylogenomics and palaeontology; i.e., how to optimally combine molecular and fossil data for estimating divergence times. Chapter 5.3 emphasizes the importance of the phylogenomic approach in virology and its potential to trace the origin and spread of infectious diseases in space and time. Finally, Chapter 5.5 recalls why phylogenomic methods and the multi-species coalescent model are key in addressing the problem of species delimitation – one of the major goals of taxonomy. It is hard to predict where phylogenomics as a discipline will stand in even 10 years. Maybe a novel technological revolution will bring it to yet another level? We strongly believe, however, that tree thinking will remain pivotal in the treatment and interpretation of the deluge of genomic data to come. Perhaps a prefiguration of the future of our field is provided by the daily monitoring of the current Covid-19 outbreak via the phylogenetic analysis of coronavirus genomic data in quasi real time – a topic of major societal importance, contemporary to the publication of this book, in which phylogenomics is instrumental in helping to fight disease

Verilog HDL: digital design and modeling

PREFACE INTRODUCTION History of HDL Verilog HDL IEEE Standard Features Assertion Levels OVERVIEW Design Methodologies Modulo-16 Synchronous Counter Four-Bit Ripple Adder Modules and Ports Designing a Test Bench for Simulation Construct Definitions Introduction to Dataflow Modeling Two-Input Exclusive-OR Gate Four 2-Input AND Gates With Delay Introduction to Behavioral Modeling Three-Input OR Gate Four-Bit Adder Modulo-16 Synchronous Counter Introduction to Structural Modeling Sum-of-Products Implementation Full Adder Four-Bit Ripple Adder Introduction to Mixed-Design Modeling Full Adder Problems LANGUAGE ELEMENTS Comments Identifiers Keywords Bidirectional Gates Charge Storage Strengths CMOS Gates Combinational Logic Gates Continuous Assignment Data Types Module Declaration MOS Switches Multiple-Way Branching Named Event Parameters Port Declaration Procedural Constructs Procedural Continuous Assignment Procedural Flow Control Pull Gates Signal Strengths Specify Block Tasks and Functions Three-State Gates Timing Control User-Defined Primitives Value Set Data Types Net Data Types Register Data Types Compiler Directives Problems EXPRESSIONS Operands Constant Parameter Net Register Bit-Select Part-Select Memory Element Operators Arithmetic Logical Relational Equality Bitwise Reduction Shift Conditional Concatenation Replication Problems GATE-LEVEL MODELING Multiple-Input Gates Gate Delays Inertial Delay Transport Delay Module Path Delay Additional Design Examples Iterative Networks Priority Encoder Problems USER-DEFINED PRIMITIVES Defining a User-Defined Primitive Combinational User-Defined Primitives Map-Entered Variables Sequential User-Defined Primitives Level-Sensitive User-Defined Primitives Edge-Sensitive User-Defined Primitives Problems DATAFLOW MODELINGContinuous Assignment Three-Input AND Gate Sum Of Products Reduction Operators Octal-To-Binary Encoder Four-To-One Multiplexer Four-To-One Multiplexer Using The Conditional Operator Four-Bit Adder Carry Lookahead Adder Asynchronous Sequential Machine Pulse-Mode Asynchronous Sequential Machine Implicit Continuous Assignment Delays Problems BEHAVIORAL MODELINGProcedural Constructs Initial Statement Always Statement Procedural Assignments Intrastatement Delay Interstatement Delay Blocking Assignments Nonblocking Assignments Conditional Statement Case Statement Loop Statements For Loop While Loop Repeat Loop Forever Loop Block Statements Sequential Blocks Parallel Blocks Procedural Continuous Assignment Assign . . . Deassign Force . . . Release Problems STRUCTURAL MODELING Module Instantiation Ports Unconnected Ports Port Connection Rules Design Examples Gray-To-Binary Code Converter BCD-To-Decimal Decoder Modulo-10 Counter Adder/Subtractor Four-Function ALU Adder and High-Speed Shifter Array Multiplier Moore-Mealy Synchronous Sequential Machine Moore Synchronous Sequential Machine Moore Asynchronous Sequential Machine Moore Pulse-Mode Asynchronous Sequential Machine Problems TASKS AND FUNCTIONS Tasks Task Declaration Task Invocation Functions Function Declaration Function Invocation Problems ADDITIONAL DESIGN EXAMPLES Johnson Counter Counter-Shifter Universal Shift Register Hamming Code Error Detection and Correction Booth Algorithm Moore Synchronous Sequential Machine Mealy Pulse-Mode Asynchronous Sequential Machine Mealy One-Hot Machine BCD Adder/Subtractor BCD Addition BCD Subtraction Pipelined RISC Processor Instruction Cache Instruction Unit Decode Unit Execution Unit Register File Data Cache RISC CPU Top System Top Problems APPENDIX A Event Queue Event Handling for Dataflow Constructs Event Handling for Blocking Assignments Event Handling for Nonblocking Assignments Event Handline for Mixed Blocking and Nonblocking Assignments APPENDIX B Verilog Project Procedure APPENDIX C Answers to Selected Problems Overview Language Elements Expressions Gate Level Modeling User-Defined Primitives Dataflow Modeling Behavioral Modeling Structural Modeling Tasks and Functions Additional Design Examples INDEX