Large-Scale Multiobjective Evolutionary Algorithm Guided by Low-Dimensional Surrogates of Scalarization Functions.

Recently, computationally intensive multiobjective optimization problems have been efficiently solved by surrogate-assisted multiobjective evolutionary algorithms. However, most of those algorithms can only handle no more than 200 decision variables. As the number of decision variables increases further, unreliable surrogate models will result in a dramatic deterioration of their performance, which makes large-scale expensive multiobjective optimization challenging.

Nanopore sequencing improves construction of customized CRISPR-based gene activation libraries.

Clustered regularly interspaced short palindromic repeats (CRISPR)-based screening has emerged as a powerful tool for identifying new gene targets for desired cellular phenotypes. The construction of guide RNA (gRNA) pools largely determines library quality and is usually performed using Golden Gate assembly or Gibson assembly. To date, library construction methods have not been systematically compared, and the quality check of each batch has been slow.

Phase Separation Modulates the Formation and Stabilities of DNA Guanine Quadruplex.

In the presence of monovalent alkali metal ions, G-rich DNA sequences containing four runs of contiguous guanines can fold into G-quadruplex (G4) structures. Recent studies showed that these structures are located in critical regions of the human genome and assume important functions in many essential DNA metabolic processes, including replication, transcription, and repair. However, not all potential G4-forming sequences are actually folded into G4 structures in cells, where G4 structures are known to be dynamic and modulated by G4-binding proteins as well as helicases.

Targeted Proteomic Analysis of Small GTPases in Radioresistant Breast Cancer Cells.

Radiation therapy benefits more than 50% of all cancer patients and cures 40% of them, where ionizing radiation (IR) deposits energy to cells and tissues, thereby eliciting DNA damage and resulting in cell death. Small GTPases are a superfamily of proteins that play critical roles in cell signaling. Several small GTPases, including RAC1, RHOB, and RALA, were previously shown to modulate radioresistance in cancer cells.

Performance Indicator-Based Adaptive Model Selection for Offline Data-Driven Multiobjective Evolutionary Optimization.

A number of real-world multiobjective optimization problems (MOPs) are driven by the data from experiments or computational simulations. In some cases, no new data can be sampled during the optimization process and only a certain amount of data can be sampled before optimization starts. Such problems are known as offline data-driven MOPs.

Improved Functional Expression of Cytochrome P450s in Through Screening a cDNA Library From .

Cytochrome P450 enzymes (P450s) are a superfamily of heme-thiolate proteins widely existing in various organisms and play a key role in the metabolic network and secondary metabolism. However, the low expression levels and activities have become the biggest challenge for P450s studies. To improve the functional expression of P450s in , an cDNA library was expressed in the betaxanthin-producing yeast strain, which functioned as a biosensor for high throughput screening.

A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems.

Many real-world optimization problems can be solved by using the data-driven approach only, simply because no analytic objective functions are available for evaluating candidate solutions. In this paper, we address a class of expensive data-driven constrained multiobjective combinatorial optimization problems, where the objectives and constraints can be calculated only on the basis of a large amount of data. To solve this class of problems, we propose using random forests (RFs) and radial basis function networks as surrogates to approximate both objective and constraint functions.

Multimodal Optimization Enhanced Cooperative Coevolution for Large-Scale Optimization.

Cooperative coevolutionary (CC) algorithms decompose a problem into several subcomponents and optimize them separately. Such a divide-and-conquer strategy makes CC algorithms potentially well suited for large-scale optimization. However, decomposition may be inaccurate, resulting in a wrong division of the interacting decision variables into different subcomponents and thereby a loss of important information about the topology of the overall fitness landscape.

Maximizing geographical efficiency: An analysis of the configuration of Colorado's trauma system.

Background: Trauma center designation in excess of need risks dilution of experience, reduction in research and training opportunities, and increased costs. The objective of this study was to evaluate the use of a novel data-driven approach (whole-system mathematical modeling of patient flow) to compare the configuration of an existing trauma system with a mathematically optimized design, using the State of Colorado as a case study.

Methods: Geographical network analysis and multiobjective optimization, 105,448 patients injured in the State of Colorado between 2009 and 2013, who met the criteria for inclusion in the state-mandated trauma registry maintained by the Colorado Department of Public Health and Environment were included.

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems.

Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems.

Diversity Assessment in Many-Objective Optimization.

Maintaining diversity is one important aim of multiobjective optimization. However, diversity for many-objective optimization problems is less straightforward to define than for multiobjective optimization problems. Inspired by measures for biodiversity, we propose a new diversity metric for many-objective optimization, which is an accumulation of the dissimilarity in the population, where an L -norm-based ( ) distance is adopted to measure the dissimilarity of solutions.

Access to specialist care: Optimizing the geographic configuration of trauma systems.

Background: The optimal geographic configuration of health care systems is key to maximizing accessibility while promoting the efficient use of resources. This article reports the use of a novel approach to inform the optimal configuration of a national trauma system.

Methods: This is a prospective cohort study of all trauma patients, 15 years and older, attended to by the Scottish Ambulance Service, between July 1, 2013, and June 30, 2014.

Regularity Model for Noisy Multiobjective Optimization.

Regularity models have been used in dealing with noise-free multiobjective optimization problems. This paper studies the behavior of a regularity model in noisy environments and argues that it is very suitable for noisy multiobjective optimization. We propose to embed the regularity model in an existing multiobjective evolutionary algorithm for tackling noises.

Feasibility and utility of population-level geospatial injury profiling: prospective, national cohort study.

Background: Geospatial analysis is increasingly being used to evaluate the design and effectiveness of trauma systems, but there are no metrics to describe the geographic distribution of incidents. The aim of this study, therefore, was to evaluate the feasibility and utility of using spatial analysis to characterize, at scale, the geospatial profile of an injured population.

Methods: This is a prospective national cohort study of all trauma patients attended to by the Scottish Ambulance Service in a complete year (between July 1, 2013, and June 30, 2014).

Optimizing trauma system design: the GEOS (Geospatial Evaluation of Systems of Trauma Care) approach.

Background: Trauma systems have been shown to reduce death and disability from injury but must be appropriately configured. A systematic approach to trauma system design can help maximize geospatial effectiveness and reassure stakeholders that the best configuration has been chosen.

Methods: This article describes the GEOS [Geospatial Evaluation of Systems of Trauma Care] methodology, a mathematical modeling of a population-based data set, which aims to derive geospatially optimized trauma system configurations for a geographically defined setting.

A memetic optimization strategy based on dimension reduction in decision space.

There can be a complicated mapping relation between decision variables and objective functions in multi-objective optimization problems (MOPs). It is uncommon that decision variables influence objective functions equally. Decision variables act differently in different objective functions.

Corner Sort for Pareto-Based Many-Objective Optimization.

Nondominated sorting plays an important role in Pareto-based multiobjective evolutionary algorithms (MOEAs). When faced with many-objective optimization problems multiobjective optimization problems (MOPs) with more than three objectives, the number of comparisons needed in nondominated sorting becomes very large. In view of this, a new corner sort is proposed in this paper.