Sequence Constraints Research Articles

Comprehensive multi-omics integration identifies differentially active enhancers during human brain development with clinical relevance

BackgroundNon-coding regulatory elements (NCREs), such as enhancers, play a crucial role in gene regulation, and genetic aberrations in NCREs can lead to human disease, including brain disorders. The human brain is a complex organ that is susceptible to numerous disorders; many of these are caused by genetic changes, but a multitude remain currently unexplained. Understanding NCREs acting during brain development has the potential to shed light on previously unrecognized genetic causes of human brain disease. Despite immense community-wide efforts to understand the role of the non-coding genome and NCREs, annotating functional NCREs remains challenging.MethodsHere we performed an integrative computational analysis of virtually all currently available epigenome data sets related to human fetal brain.ResultsOur in-depth analysis unravels 39,709 differentially active enhancers (DAEs) that show dynamic epigenomic rearrangement during early stages of human brain development, indicating likely biological function. Many of these DAEs are linked to clinically relevant genes, and functional validation of selected DAEs in cell models and zebrafish confirms their role in gene regulation. Compared to enhancers without dynamic epigenomic rearrangement, DAEs are subjected to higher sequence constraints in humans, have distinct sequence characteristics and are bound by a distinct transcription factor landscape. DAEs are enriched for GWAS loci for brain-related traits and for genetic variation found in individuals with neurodevelopmental disorders, including autism.ConclusionThis compendium of high-confidence enhancers will assist in deciphering the mechanism behind developmental genetics of human brain and will be relevant to uncover missing heritability in human genetic brain disorders.

Structure and stability of the designer protein WRAP-T and its permutants

beta -Propeller proteins are common natural disc-like pseudo-symmetric proteins that contain multiple repeats (‘blades’) each consisting of a 4-stranded anti-parallel beta -sheet. So far, 4- to 12-bladed beta -propellers have been discovered in nature showing large functional and sequential variation. Using computational design approaches, we created perfectly symmetric beta -propellers out of natural pseudo-symmetric templates. These proteins are useful tools to study protein evolution of this very diverse fold. While the 7-bladed architecture is the most common, no symmetric 7-bladed monomer has been created and characterized so far. Here we describe such a engineered protein, based on a highly symmetric natural template, and test the effects of circular permutation on its stability. Geometrical analysis of this protein and other artificial symmetrical proteins reveals no systematic constraint that could be used to help in engineering of this fold, and suggests sequence constraints unique to each beta -propeller sub-family.

Mixed integer programming approaches to partial disassembly line balancing and sequencing problem

Product recovery has received greater attention in recent years mainly due to increased environmental awareness of consumers and stricter environmental regulations imposed by governments. In product recovery, disassembly of the product into its constituent parts is the most significant activity and generally performed on a disassembly line. During disassembly, a complete or partial disassembly of the product may be preferred. In complete disassembly, all parts must be disassembled, while partial disassembly allows to disassemble a subset of parts (e.g., the ones with relatively high revenues). This study deals with a partial disassembly line balancing and sequencing (PDLBS) problem considering revenues of parts to be disassembled, general workstation cost, additional cost of workstation(s) with hazardous parts, and cost of direction changes. For the PDLBS problem, a generic mixed integer programming (MIP) model, with the aim of maximizing total profit, is developed. To strengthen the MIP formulation, two sets of valid inequalities are proposed. The computational results show that the MIP model with valid inequalities is able to provide optimal solutions for the PDLBS problems with up to 30 tasks. To obtain near-optimal solutions for large-sized problems, a MIP-based solution approach is proposed. The proposed approach decomposes the entire MIP model into selection and assignment (SA) and sequencing (SEQ) models. The SA model is an exact relaxation of the MIP model (with valid inequalities) obtained by removing all the sequencing variables and constraints. Hence, SA model also produces an efficient upper bound for the PDLBS problem. The SEQ model, accordingly, aims to find an optimal sequence of tasks subject to the fixed selection and assignment of tasks provided by the SA model. The computational results show that the proposed MIP-based solution approach provides efficient solutions with small optimality gaps for large-sized problems.

Mathematical formulation and a new metaheuristic for the constrained double-floor corridor allocation problem

A layout plan for a manufacturing system that is designed without any facility constraints will most likely be infeasible when confronted with reality. Additionally, considering that land available for building industrial plants is limited and its cost is high, it is necessary to investigate the layout planning of two and multi-floor facilities. To address these shortages in the scientific literature, we focus on the double-floor corridor allocation problem (DFCAP) which covers a wide range of complex facility constraints, such as fixed floor constraints, fixed row constraints, fixed positioning constraints, mutual floor constraints, mutual row constraints, sequencing constraints and adjacency constraints. For the model mentioned above, we term it as a constrained DFCAP (cDFCAP). A mixed-integer linear programming model is formulated for the cDFCAP. In order to solve larger realistic problems, a constrained metaheuristic with the memetic algorithm framework customised for solving the cDFCAP is introduced in this work. In our algorithm, four problem-specific heuristic rules to construct a set of initial solutions are developed. In addition, an ideal parameter combination for our constrained memetic algorithm is determined through a Taguchi experimental design. Finally, the results of a set of cDFCAP instances with different sizes (n = 10∼80) report that our provided approach is effective for the considered problem.

Sequential optimality conditions for cardinality-constrained optimization problems with applications

Recently, a new approach to tackle cardinality-constrained optimization problems based on a continuous reformulation of the problem was proposed. Following this approach, we derive a problem-tailored sequential optimality condition, which is satisfied at every local minimizer without requiring any constraint qualification. We relate this condition to an existing M-type stationary concept by introducing a weak sequential constraint qualification based on a cone-continuity property. Finally, we present two algorithmic applications: We improve existing results for a known regularization method by proving that it generates limit points satisfying the aforementioned optimality conditions even if the subproblems are only solved inexactly. And we show that, under a suitable Kurdyka–Łojasiewicz-type assumption, any limit point of a standard (safeguarded) multiplier penalty method applied directly to the reformulated problem also satisfies the optimality condition. These results are stronger than corresponding ones known for the related class of mathematical programs with complementarity constraints.

Laplacian integral graphs with a given degreee sequence constraint

Let G be a graph on n vertices. The Laplacian matrix of G, denoted by L(G), is defined as L(G) = D(G) − A(G), where A(G) is the adjacency matrix of G and D(G) is the diagonal matrix of the vertex degrees of G. A graph G is said to be L-integral is all eigenvalues of the matrix L(G) are integers. In this paper, we characterize all L-integral non-bipartite graphs among all connected graphs with at most two vertices of degree larger than or equal to three.

Portfolio-Wide Optimization of Pharmaceutical R&D Activities Using Mathematical Programming

The research and development (R&D) management in any major research pharmaceutical company is constantly faced with the need to make complicated activity scheduling and resource allocation decisions, as they carry out scientific work to develop new therapeutic products. This paper describes how we develop a decision support tool that allows practitioners to determine portfolio-wide optimal schedules in a systematic, quantitative, and largely automated fashion. Our tool is based on a novel mixed-integer linear optimization model that extends archetypal multimode resource-constrained project scheduling models in order to accommodate multiple rich features that are pertinent to the Chemistry, Manufacturing, and Controls (CMC) activities carried out within the pharmaceutical R&D setting. The tool addresses this problem at the operational level, determining schedules that are optimal in light of chosen business objectives under activity sequencing, resource availability, and deadline constraints. Applying the tool on current workload data demonstrates its tractability for practical adoption. We further illustrate how, by utilizing the tool under different input instances, one may conduct various tactical analyses to assess the system’s ability to cope with sudden changes or react to shifting management priorities.

Gender differences in statistical interaction effects of perceived well-being and structural constraints on the intention to attend a game

ABSTRACT Research question: This research examined the effect of perceived well-being from a spectator experience on the intention to attend a sporting event, and the moderating effects of structural constraints on attending a game, depending on gender. Research methods: The authors conducted a questionnaire survey at a professional Japanese basketball game. A total of 172 participants were included (57.6% female). The authors conducted a confirmatory factor analysis to check the reliability and validity of the measurement scales, then tested the hypotheses using two-step hierarchical multiple regression analyses by gender. Results and Findings: The hypotheses testing indicated that spectators who perceived a high degree of well-being from their spectator experience had high intentions to attend a game. Furthermore, for female spectators, the positive effect of perceived well-being decreased when they perceived more constraints before arriving at the event venue, whereas for male spectators, it was reduced when they perceived more constraints at the venue. Implications: This research expanded current understanding of how perceived well-being from a spectator experience can increase intentions to attend a sporting event. Additionally, two sequential structural constraint constructs extended existing constraint studies. Lastly, the identified gender differences in the moderating effects of such structural constraints provided scientific contributions to the field of sport marketing.

Some further results for finite‐time stability of impulsive nonlinear systems

This work focuses on Lyapunov‐based finite‐time stability (FTS) theory for impulsive systems. By virtue of a weak Lyapunov inequality condition, which is less restrictive than conditions in most existing results, some further FTS results including settling‐time estimation are derived for impulsive nonlinear systems, under different constraints of impulse time sequences. The impact of impulse jumps on FTS is investigated mathematically from different aspects such as the number and magnitude of impulses, the impulse times, and average impulsive interval (AII) of impulse time sequence. It is found that under relatively weak Lyapunov condition for continuous dynamics, suitable impulses can influence not only the finite‐time convergence (FTC) and settling‐time of a solution, but also the attraction domain of the origin of FTS, which has not been reported in the existing literatures. In particular, stabilizing impulses may change the locality and globality characteristics of FTS. An illustrative example is given to validate the theoretical results.

Smooth topological design of structures with minimum length scale and chamfer/round controls

Topology optimization is a powerful tool for designing high-performance structures. However, the structures resulting from topology optimization usually have complex geometries, which makes them difficult or costly to fabricate. As a result, topology optimization is often used for the conceptual design of product structures. In this paper, a topology optimization method considering manufacturing constraints is proposed under the fixed finite element mesh. The minimum length scale and chamfer/round are controlled as required based on the floating projection topology optimization (FPTO) method, where the linear material interpolation scheme is adopted instead and the material 0/1 distribution is realized by applying sequential constraints on the elemental design variables through the floating projection. The minimum length scale is strictly controlled with the help of the structural skeleton, which is extracted from the structural topology by using a graphic thinning algorithm. Meanwhile, boundary filtering is proposed by using a variable filtering radius to control chamfers and rounds. Two-dimensional and three-dimensional numerical examples demonstrate that the proposed topology optimization algorithm is effective for designing the stiffest structures with smooth boundaries, desired minimum length scale and chamfers/rounds, so as to improve their manufacturability.

A robust MILP and gene expression programming based on heuristic rules for mixed-model multi-manned assembly line balancing

Current dynamic markets require manufacturing industries to organize a robust plan to cope with uncertain demand planning. This work addresses the mixed-model multi-manned assembly line balancing under uncertain demand and aims to optimize the assembly line configuration by a robust mixed-integer linear programming (MILP) model and a robust solution generation mechanism embedded with dispatching rules. The proposed model relaxes the cycle time constraint and designs robust sequencing constraints and objective functions to ensure the line configuration can meet all the demand plans. Furthermore, two solution generation mechanisms, including a task-operator-sequence and an operator-task-sequence, are designed. To quickly find a suitable line configuration, a gene expression programming (GEP) approach with multi-attribute representation is proposed to obtain efficient dispatching rules which are ultimately embedded into the solution generation mechanisms. Experimental results show that solving the proposed MILP model mathematically is effective when tackling small and medium-scale instances. However, for large instances, the dispatching rules generated by the GEP have significant superiority over traditional heuristic rules and those rules mined by a genetic programming algorithm.

CNVxplorer: a web tool to assist clinical interpretation of CNVs in rare disease patients.

Copy Number Variants (CNVs) are an important cause of rare diseases. Array-based Comparative Genomic Hybridization tests yield a ∼12% diagnostic rate, with ∼8% of patients presenting CNVs of unknown significance. CNVs interpretation is particularly challenging on genomic regions outside of those overlapping with previously reported structural variants or disease-associated genes. Recent studies showed that a more comprehensive evaluation of CNV features, leveraging both coding and non-coding impacts, can significantly improve diagnostic rates. However, currently available CNV interpretation tools are mostly gene-centric or provide only non-interactive annotations difficult to assess in the clinical practice. Here, we present CNVxplorer, a web server suited for the functional assessment of CNVs in a clinical diagnostic setting. CNVxplorer mines a comprehensive set of clinical, genomic, and epigenomic features associated with CNVs. It provides sequence constraint metrics, impact on regulatory elements and topologically associating domains, as well as expression patterns. Analyses offered cover (a) agreement with patient phenotypes; (b) visualizations of associations among genes, regulatory elements and transcription factors; (c) enrichment on functional and pathway annotations and (d) co-occurrence of terms across PubMed publications related to the query CNVs. A flexible evaluation workflow allows dynamic re-interrogation in clinical sessions. CNVxplorer is publicly available at http://cnvxplorer.com.

Parallel Constraint Acquisition

Constraint acquisition systems assist the non-expert user in modelling her problem as a constraint network. QUACQ is a sequential constraint acquisition algorithm that generates queries as (partial) examples to be classified as positive or negative. The drawbacks are that the user may need to answer a great number of such examples, within a significant waiting time between two examples, to learn all the constraints. In this paper, we propose PACQ, a portfolio-based parallel constraint acquisition system. The design of PACQ benefits from having several users sharing the same target problem. Moreover, each user is involved in a particular acquisition session, opened in parallel to improve the overall performance of the whole system.We prove the correctness of PACQ and we give an experimental evaluation that shows that our approach improves on QUACQ.

Multilevel Gene Regulation Using Switchable Transcription Terminator and Toehold Switch in Escherichia coli

Nucleic acid-based regulatory components provide a promising toolbox for constructing synthetic biological circuits due to their design flexibility and seamless integration towards complex systems. In particular, small-transcriptional activating RNA (STAR) and toehold switch as regulators of transcription and translation steps have shown a large library size and a wide dynamic range, meeting the criteria to scale up genetic circuit construction. Still, there are limited attempts to integrate the heterogeneous regulatory components for multilevel regulatory circuits in living cells. In this work, inspired by the design principle of STAR, we designed several switchable transcription terminators starting from natural and synthetic terminators. These switchable terminators could be designed to respond to specific RNA triggers with minimal sequence constraints. When combined with toehold switches, the switchable terminators allow simultaneous control of transcription and translation processes to minimize leakage in Escherichia coli. Further, we demonstrated a set of logic gates implementing 2-input AND circuits and multiplexing capabilities to control two different output proteins. This study shows the potential of novel switchable terminator designs that can be computationally designed and seamlessly integrated with other regulatory components, promising to help scale up the complexity of synthetic gene circuits in living cells.

A modified design method of pulse repetition frequency for synthetic aperture radar system based on the single point equivalent squint model

Pulse repetition frequency (PRF) is an essential parameter in synthetic aperture radar (SAR). However, a small PRF value will cause spectrum ambiguity and a large value will increase the computational complexity. In highly squinted SAR imaging with maneuvering platform, the traditional PRF design methods used are airborne and space borne. SAR systems perform with a low precision because of the complex geometric configuration and system deviations. In order to adapt to the maneuvering platform SAR system, a modified design method of PRF is proposed in this paper. First, the azimuth spectrum of echoes is analyzed by establishing the SAR geometry base on the single point equivalent squint (SPES) model. Subsequently, the calculation of azimuth bandwidth is transformed into an optimization problem, and the lower limit of PRF is obtained by the Golden Section Method. Besides, the criteria of spectrum unambiguity and time sequence constraints in PRF design is also discussed in detail. Simulation experiments demonstrate the feasibility and accuracy of the proposed method.

RNA origami design tools enable cotranscriptional folding of kilobase-sized nanoscaffolds.

RNA origami is a framework for the modular design of nanoscaffolds that can be folded from a single strand of RNA, and used to organize molecular components with nanoscale precision. Design of genetically expressible RNA origami, which must cotranscriptionally fold, requires modeling and design tools that simultaneously consider thermodynamics, folding pathway, sequence constraints, and pseudoknot optimization. Here, we describe RNA Origami Automated Design software (ROAD), which builds origami models from a library of structural modules, identifies potential folding barriers, and designs optimized sequences. Using ROAD, we extend the scale and functional diversity of RNA scaffolds, creating 32 designs of up to 2360 nucleotides, five that scaffold two proteins, and seven that scaffold two small molecules at precise distances. Micrographic and chromatographic comparison of optimized and nonoptimized structures validates that our principles for strand routing and sequence design substantially improve yield. By providing efficient design of RNA origami, ROAD may simplify construction of custom RNA scaffolds for nanomedicine and synthetic biology.

Interval Inspired Approach Based on Temporal Sequence Constraints to Place Recognition

Place recognition is an essential task in many robotics applications. Recognizing if the robot is crossing an already visited place may be used to improve its localization and map estimation. A place recognition strategy must be as accurate as possible, despite the challenges related to environment dynamicity. It should avoid generating false positives since even a few erroneous matches may be enough to cause the degradation of the Simultaneous Localization and Mapping (SLAM) process. We propose a novel approach for place recognition inspired by interval analysis theory. Our approach models the known world as a set of intervals based on the robot’s observations. The search to determine whether the current robot location is new or known begins as the robot explores its surroundings. Our approach has three main steps. First, it selects a set of nearest neighbors based on the similarity between the current robot observation and the intervals composing the known world. In the second step, our approach uses temporal constraints to select one element of the set. And finally, the third step is to sweep the selected interval looking for the query best match. We evaluate our proposal by dealing with visual place recognition using only image information and demonstrate its effectiveness using some challenging public datasets.

A Model-Driven-to-Sample-Driven Method for Rural Road Extraction

Road extraction in rural areas is one of the most fundamental tasks in the practical application of remote sensing. In recent years, sample-driven methods have achieved state-of-the-art performance in road extraction tasks. However, sample-driven methods are prohibitively expensive and laborious, especially when dealing with rural roads with irregular curvature changes, narrow widths, and diverse materials. The template matching method can overcome these difficulties to some extent and achieve impressive road extraction results. This method also has the advantage of the vectorization of road extraction results, but the automation is limited. Straight line sequences can be substituted for curves, and the use of the color space can increase the recognition of roads and nonroads. A model-driven-to-sample-driven road extraction method for rural areas with a much higher degree of automation than existing template matching methods is proposed in this study. Without prior samples, on the basis of the geometric characteristics of narrow and long roads and using the advantages of straight lines instead of curved lines, the road center point extraction model is established through length constraints and gray mean contrast constraints of line sequences, and the extraction of some rural roads is completed through topological connection analysis. In addition, we take the extracted road center point and manual input data as local samples, use the improved line segment histogram to determine the local road direction, and use the panchromatic and hue, saturation, value (HSV) space interactive matching model as the matching measure to complete the road tracking extraction. Experimental results show that, for different types of data and scenarios on the premise, the accuracy and recall rate of the evaluation indicators reach more than 98%, and, compared with other methods, the automation of this algorithm has increased by more than 40%.

Ultraconserved enhancer function does not require perfect sequence conservation.

Ultraconserved enhancer sequences show perfect conservation between human and rodent genomes, suggesting that their functions are highly sensitive to mutation. However, current models of enhancer function do not sufficiently explain this extreme evolutionary constraint. We subjected 23 ultraconserved enhancers to different levels of mutagenesis, collectively introducing 1,547 mutations, and examined their activities in transgenic mouse reporter assays. Overall, we find that the regulatory properties of ultraconserved enhancers are robust to mutation. Upon mutagenesis, nearly all (19/23, 83%) still functioned as enhancers at one developmental stage, as did most of those tested again later in development (5/9, 56%). Replacement of endogenous enhancers with mutated alleles in mice corroborated results of transgenic assays, including the functional resilience of ultraconserved enhancers to mutation. Our findings show that the currently known activities of ultraconserved enhancers do not necessarily require the perfect conservation observed in evolution and suggest that additional regulatory or other functions contribute to their sequence constraint.

Extrapolated sequential constraint method for variational inequality over the intersection of fixed-point sets

This paper deals with the solving of variational inequality problem where the constrained set is given as the intersection of a number of fixed-point sets. To this end, we present an extrapolated sequential constraint method. At each iteration, the proposed method is updated based on the ideas of a hybrid conjugate gradient method used to accelerate the well-known hybrid steepest descent method, and an extrapolated cyclic cutter method for solving a common fixed-point problem. We prove strong convergence of the method under some suitable assumptions of step-size sequences. We finally show the numerical efficiency of the proposed method compared to some existing methods.