A benchmark of optimization solvers for genome-scale metabolic modeling of organisms and communities

Genome-scale metabolic modeling is a powerful framework for predicting metabolic phenotypes of any organism with an annotated genome. For two decades, this framework has been used for the rational design of microbial cell factories. In the last decade, the range of applications has exploded, and new frontiers have emerged, including the study of the gut microbiome and its health implications and the role of microbial communities in global ecosystems. However, all the critical steps in this framework, from model construction to simulation, require the use of powerful linear optimization solvers, with the choice often relying on commercial solvers for their well-known computational efficiency. In this work, I benchmark a total of six solvers (two commercial and four open source) and measure their performance to solve linear and mixed-integer linear problems of increasing complexity. Although commercial solvers are still the fastest, at least two open-source solvers show comparable performance. These results show that genome-scale metabolic modeling does not need to be hindered by commercial licensing schemes and can become a truly open science framework for solving urgent societal challenges.IMPORTANCEModeling the metabolism of organisms and communities allows for computational exploration of their metabolic capabilities and testing their response to genetic and environmental perturbations. This holds the potential to address multiple societal issues related to human health and the environment. One of the current limitations is the use of commercial optimization solvers with restrictive licenses for academic and non-academic use. This work compares the performance of several commercial and open-source solvers to solve some of the most complex problems in the field. Benchmarking results show that, although commercial solvers are indeed faster, some of the open-source options can also efficiently tackle the hardest problems, showing great promise for the development of open science applications.

Control of the von Neumann Entropy for an Open Two-Qubit System Using Coherent and Incoherent Drives

This article is devoted to developing an approach for manipulating the von Neumann entropy S(ρ(t)) of an open two-qubit system with coherent control and incoherent control inducing time-dependent decoherence rates. The following goals are considered: (a) minimizing or maximizing the final entropy S(ρ(T)); (b) steering S(ρ(T)) to a given target value; (c) steering S(ρ(T)) to a target value and satisfying the pointwise state constraint S(ρ(t))≤S¯ for a given S¯; (d) keeping S(ρ(t)) constant at a given time interval. Under the Markovian dynamics determined by a Gorini-Kossakowski-Sudarshan-Lindblad type master equation, which contains coherent and incoherent controls, one- and two-step gradient projection methods and genetic algorithm have been adapted, taking into account the specifics of the objective functionals. The corresponding numerical results are provided and discussed.

Modeling Human Suboptimal Control: A Review

This review paper provides an overview of the approaches to model neuromuscular control, focusing on methods to identify nonoptimal control strategies typical of populations with neuromuscular disorders or children. Where possible, the authors tightened the description of the methods to the mechanisms behind the underlying biomechanical and physiological rationale. They start by describing the first and most simplified approach, the reductionist approach, which splits the role of the nervous and musculoskeletal systems. Static optimization and dynamic optimization methods and electromyography-based approaches are summarized to highlight their limitations and understand (the need for) their developments over time. Then, the authors look at the more recent stochastic approach, introduced to explore the space of plausible neural solutions, thus implementing the uncontrolled manifold theory, according to which the central nervous system only controls specific motions and tasks to limit energy consumption while allowing for some degree of adaptability to perturbations. Finally, they explore the literature covering the explicit modeling of the coupling between the nervous system (acting as controller) and the musculoskeletal system (the actuator), which may be employed to overcome the split characterizing the reductionist approach.

Brain Tumor Segmentation from MRI Images Using Handcrafted Convolutional Neural Network

Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method's performance.

Analyzing Malaria Disease Using Effective Deep Learning Approach

Medical tools used to bolster decision-making by medical specialists who offer malaria treatment include image processing equipment and a computer-aided diagnostic system. Malaria images can be employed to identify and detect malaria using these methods, in order to monitor the symptoms of malaria patients, although there may be atypical cases that need more time for an assessment. This research used 7000 images of Xception, Inception-V3, ResNet-50, NasNetMobile, VGG-16 and AlexNet models for verification and analysis. These are prevalent models that classify the image precision and use a rotational method to improve the performance of validation and the training dataset with convolutional neural network models. Xception, using the state of the art activation function (Mish) and optimizer (Nadam), improved the effectiveness, as found by the outcomes of the convolutional neural model evaluation of these models for classifying the malaria disease from thin blood smear images. In terms of the performance, recall, accuracy, precision, and F1 measure, a combined score of 99.28% was achieved. Consequently, 10% of all non-dataset training and testing images were evaluated utilizing this pattern. Notable aspects for the improvement of a computer-aided diagnostic to produce an optimum malaria detection approach have been found, supported by a 98.86% accuracy level.

A Time Delay Neural Network Based Technique for Nonlinear Microwave Device Modeling

This paper presents a nonlinear microwave device modeling technique that is based on time delay neural network (TDNN). The proposed technique can accurately model the nonlinear microwave devices when compared to static neural network modeling method. A new formulation is developed to allow for the proposed TDNN model to be trained with DC, small-signal, and large signal data, which can enhance the generalization of the device model. An algorithm is formulated to train the proposed TDNN model efficiently. This proposed technique is verified by GaAs metal-semiconductor-field-effect transistor (MESFET), and GaAs high-electron mobility transistor (HEMT) examples. These two examples demonstrate that the proposed TDNN is an efficient and valid approach for modeling various types of nonlinear microwave devices.

Feasibility Study on Measuring the Particulate Matter Level in the Atmosphere by Means of Yagi-Uda-Like Antennas

In this work, the application of a technique for monitoring changes of the dielectric constant of the atmosphere caused by the presence of pollution is discussed. The method is based on changes in the reflection coefficient of the device induced by these dielectric constant variations of the surrounding medium. To that end, several Yagi–Uda-like antenna designs with different size limitations were simulated by using a Method-of-Moments software and optimized by means of a simulated annealing strategy. It has been found that the larger the optimal elements of the array are allowed to be, the higher the sensitivity reached. Thus, in a trade-off between sensitivity and moderate length (regarding flexibility purposes), the most promising solution has been built. This prototype has been experimentally tested in presence of an artificial aerosol made of PAO (polyalphaolefin) oil and black carbon inclusions of a size of 0.2 μm. As a result, potentials for developing a measurement procedure by means of changes in the characteristic parameters of the antenna led by different concentration levels of suspended particles in the surrounding medium are shown. In this manner, a local mapping of polluted levels could be developed in an easy, real-time, and flexible procedure.

Dosimetric comparison of graphical optimization and inverse planning simulated annealing for brachytherapy of cervical cancer

Purpose

Graphical optimization (GO) and inverse planning simulated annealing (IPSA) are the main treatment planning optimization techniques used in patients undergoing 3D brachytherapy treatment. This study aims to compare the dosimetric difference of plans optimized by GO and IPSA in cervical cancer brachytherapy.

Material and methods

21 cervical cancer patients data sets consisted of computed tomography (CT) and magnetic resonance imaging (MRI), acquired with the Fletcher applicator in situ were transferred to the Oncentra brachytherapy planning system. For each patient, the treatment plan was initially optimized with GO to reach a maximal D₉₀ tumor dose (6 Gy/fraction, 5 fractions), while keeping the dose to organs at risk (OARs) as low as possible. A second plan was then optimized with IPSA on the same CT images and data set (i.e., contours, catheters, and location of dwell points). Targets and OARs dose volume histograms and irradiation time were compared; data were analyzed with paired t-test; p value < 0.05 was considered statistically significant.

Results

The plans with both optimizations meet the clinical requirements. The mean D₉₀ of the clinical target volume was comparable for GO and IPSA. Similar values (p > 0.05) of target V₁₀₀, V₁₅₀, V₂₀₀, HI, and CI were registered for GO and IPSA optimizations. Bladder and rectum D_1cc and D_2cc obtained by GO resulted in larger values than those obtained by IPSA (p = 0.002). V₇₅ for bladder and rectum were slightly higher for IPSA, but without statistical difference (p > 0.05). The irradiation time was comparable (p > 0.05).

Conclusions

In 3D brachytherapy of cervical cancer, GO and IPSA optimizations do not present a significant difference in target dose coverage; nevertheless, IPSA may reduce the maximum dose to normal tissue when compared with GO.

What method to use for protein-protein docking?

A number of well-established servers perform 'free' docking of proteins of known structures. In contrast, template-based docking can start from sequences if structures are available for complexes that are homologous to the target. On the basis of the results of the CAPRI-CASP structure prediction experiments, template-based methods yield more accurate predictions if good templates can be found, but generally fail without such templates. However, free global docking, or focused docking around even poor quality template-based models, can still generate acceptable docked structures in these cases. In accordance with the analysis of a benchmark set, free docking of heterodimers yields acceptable or better predictions in the top 10 models for around 40% of structures. However, it is likely that a combination of template-based and free docking methods can perform better for targets that have template structures available. Another way of improving the reliability of predictions is adding experimental information as restraints, an option built into several docking servers.

Efficiently Improving and Quantifying Robot Accuracy In Situ

The advancement of simulation-assisted robot programming, automation of high-tolerance assembly operations, and improvement of real-world performance engender a need for positionally accurate robots. Despite tight machining tolerances, good mechanical design, and careful assembly, robotic arms typically exhibit average Cartesian positioning errors of several millimeters. Fortunately, the vast majority of this error can be removed in software by proper calibration of the so-called "zero-offsets" of a robot's joints. This research developed an automated, inexpensive, highly portable, in situ calibration method that fine tunes these kinematic parameters, thereby, improving a robot's average positioning accuracy four-fold throughout its workspace. In particular, a prospective low-cost motion capture system and a benchmark laser tracker were used as reference sensors for robot calibration. Bayesian inference produced optimized zero-offset parameters alongside their uncertainty for data from both reference sensors. Relative and absolute accuracy metrics were proposed and applied for quantifying robot positioning accuracy. Uncertainty analysis of a validated, probabilistic robot model quantified the absolute positioning accuracy throughout its entire workspace. Altogether, three measures of accuracy conclusively revealed multi-fold improvement in the positioning accuracy of the robotic arm. Bayesian inference on motion capture data yielded zero-offsets and accuracy calculations comparable to those derived from laser tracker data, ultimately proving this method's viability towards robot calibration.

A New Chaotic System with a Self-Excited Attractor: Entropy Measurement, Signal Encryption, and Parameter Estimation

In this paper, we introduce a new chaotic system that is used for an engineering application of the signal encryption. It has some interesting features, and its successful implementation and manufacturing were performed via a real circuit as a random number generator. In addition, we provide a parameter estimation method to extract chaotic model parameters from the real data of the chaotic circuit. The parameter estimation method is based on the attractor distribution modeling in the state space, which is compatible with the chaotic system characteristics. Here, a Gaussian mixture model (GMM) is used as a main part of cost function computations in the parameter estimation method. To optimize the cost function, we also apply two recent efficient optimization methods: WOA (Whale Optimization Algorithm), and MVO (Multi-Verse Optimizer) algorithms. The results show the success of the parameter estimation procedure.

Optimization of Bioethanol Production Using Whole Plant of Water Hyacinth as Substrate in Simultaneous Saccharification and Fermentation Process

Water hyacinth was used as substrate for bioethanol production in the present study. Combination of acid pretreatment and enzymatic hydrolysis was the most effective process for sugar production that resulted in the production of 402.93 mg reducing sugar at optimal condition. A regression model was built to optimize the fermentation factors according to response surface method in saccharification and fermentation (SSF) process. The optimized condition for ethanol production by SSF process was fermented at 38.87°C in 81.87 h when inoculated with 6.11 ml yeast, where 1.291 g/L bioethanol was produced. Meanwhile, 1.289 g/L ethanol was produced during experimentation, which showed reliability of presented regression model in this research. The optimization method discussed in the present study leading to relatively high bioethanol production could provide a promising way for Alien Invasive Species with high cellulose content.

Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods

Constraint-based models have become popular methods for systems biology as they enable the integration of complex, disparate datasets in a biologically cohesive framework that also supports the description of biological processes in terms of basic physicochemical constraints and relationships. The scope, scale, and application of genome scale models have grown from single cell bacteria to multi-cellular interaction modeling; host-pathogen modeling represents one of these examples at the current horizon of constraint-based methods. There are now a small number of examples of host-pathogen constraint-based models in the literature, however there has not yet been a definitive description of the methodology required for the functional integration of genome scale models in order to generate simulation capable host-pathogen models. Herein we outline a systematic procedure to produce functional host-pathogen models, highlighting steps which require debugging and iterative revisions in order to successfully build a functional model. The construction of such models will enable the exploration of host-pathogen interactions by leveraging the growing wealth of omic data in order to better understand mechanism of infection and identify novel therapeutic strategies.

TROMBONE: T1-relaxation-oblivious mapping of transmit radio-frequency field (B1) for MRI at high magnetic fields

Fast, 3D radio-frequency transmit field (B1) mapping is important for parallel transmission, spatially selective pulse design and quantitative MRI applications. It has been shown that actual flip angle imaging--two interleaved spoiled gradient recalled echo images acquired in steady state with two very short time delays (TR1, TR2)--is an attractive method of B1 mapping. Herein, we describe the TROMBONE method that efficiently integrates actual flip angle imaging with EPI imaging, alleviates very short TR requirement of actual flip angle imaging and through their synergy yields up to 16 times higher precision in B1 estimation in the same experimental time. High precision of TROMBONE can be traded for faster scans. The map of B1 reconstructed from the ratio of intensities of two images is insensitive to longitudinal relaxation time (T1) in the physiologically relevant range. A table of the optimal acquisition protocol parameters for various target experimental conditions is provided.