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As a crucial node of the corticolimbic model, the striatum has been demonstrated in modulating emotional cues in pediatric bipolar disorders (PBD), the striatal distinction in structure and function between PBD-I and PBD-II remains unclear.MRI data of 36 patients in PBD-I, 22 patients in PBD-II and 19 age-gender matched healthy controls (HCs) were processed. Here, we investigated structural and functional alterations of 8 subregions of striatum (bilateral nucleus accumbens, caudate, putamen and globus pallidus) by analyzing MRI data.We found volume reduction of the right pallidum, the significant positive correlation between the number of episodes and the functional connectivity between left pallidum and right caudate in PBD-I patients, abrupted prefrontal-striatal-thalamic functional connectivity in PBD-I group and decreased functional connectivity in PBD-II relative to HCs and PBD-I.Future studies should enroll more subjects and adopt a longitudinal perspective, which could help to discover striatum structural or functional alterations during subject-specific clinical progress in different states.Results of the present study confirmed that structural and functional abnormality of striatum may be helpful in identifying PBD clinical types as distinctive biomarkers. The interruptions of the prefrontal-striatal-thalamic circuits may provide advantageous evidence for expounding the role of striatum in bipolar disorders etiology. Thus, potential mechanisms of dysfunction striatum need to be formulated and reconceptualized with multimodal neuroimaging studies in future.

This study aimed to evaluate the accuracy of deformable image registration (DIR) algorithms using data sets with different levels of ground-truth deformation vector fields (DVFs) and to investigate the correlation between DVF errors and contour-based metrics.Nine pairs of digital data sets were generated through contour-controlled deformations based on 3 anonymized patients' CTs (head and neck, thorax/abdomen, and pelvis) with low, medium, and high deformation intensity for each site using the ImSimQA software. Image pairs and their associated contours were imported to MIM-Maestro, Raystation, and Velocity systems, followed by DIR and contour propagation. The system-generated DVF and propagated contours were compared with the ground-truth data. The correlation between DVF errors and contour-based metrics was evaluated using the Pearson correlation coefficient (r), while their correlation with volumes were calculated using Spearman correlation coefficient (rho).The DVF errors increased with increasing deformation intensity. All DIR algorithms performed well for esophagus, trachea, left femoral, right femoral, and urethral (mean and maximum DVF errors2.50 mm and4.27 mm, respectively; Dice similarity coefficient: 0.93-0.99). Brain, liver, left lung, and bladder showed large DVF errors for all 3 systems (dMost contour-based metrics had no correlation with DVF errors. For adaptive radiation therapy, well-performed contour propagation does not directly indicate accurate dose deformation and summation/accumulation within each contour (determined by DVF accuracy). Tolerance values for DVF errors should vary as the acceptable accuracy for overall adaptive radiation therapy depends on anatomic site, deformation intensity, organ size, and so forth. This study provides benchmark tables for evaluating DIR accuracy in various clinical scenarios.

High-tension glaucoma (HTG) is associated with functional changes in the brain, and elevated intraocular pressure (IOP) is one of the major causes.To evaluate the effects of high IOP on the brain in patients with HTG by using resting-state functional magnetic resonance imaging (rs-fMRI).Thirty-six patients with HTG and 20 age- and gender-matched healthy controls (HCs) were recruited and underwent IOP examination and rs-fMRI scan. Voxel-wise functional connectivity (FC) values were obtained between the Brodmann Area (BA) 17 (primary visual cortex) and the rest of the brain, two-sample t test was performed between HTG group and HCs. Correlation analysis was performed between FC and clinical information.Compared with HCs, HTG patients demonstrated decreased FC between BA 17 and the right precuneus gyrus, decreased FC between BA 17 and the right superior frontal gyrus (SFG) (GRF corrected at voxel level P0.001 and cluster level P0.05, two-tailed). FC between BA 17 and the right SFG showed significantly negative correlation with right eyes' IOP and mean IOP.HTG patients had abnormal FC changes between the visual cortex and multiple functional brain regions related to visual sense, memory consolidation and cognitive processing, which provided image support for the pathophysiology research of HTG, and revealed new targets for the accurate treatment of HTG.

Purpose This study aimed to investigate radiomic features extracted from magnetic resonance imaging (MRI) scans performed before and after neoadjuvant chemoradiotherapy (nCRT) in predicting response of locally advanced rectal cancer (LARC). Methods and Materials Thirty-nine patients who underwent nCRT for LARC were included, with 294 radiomic features extracted from MRI that was performed before (pre-CRT) and 6 to 8 weeks after completing nCRT (post-CRT). Based on tumor regression grade (TRG), 26 patients were classified as having a histopathologic good response (GR; TRG 0-1) and 13 as non-GR (TRG 2-3). Tumor downstaging (T-downstaging) occurred in 25 patients. Univariate analyses were performed to assess potential radiomic and delta-radiomic predictors for TRG in pathologic complete response (pCR) versus non-pCR, GR versus non-GR, and T-downstaging. The support vector machine-based multivariate model was used to select the best predictors for TRG and T-downstaging. Results We identified 13 predictive features for pCR versus non-pCR, 14 for GR versus non-GR, and 16 for T-downstaging. Pre-CRT gray-level run length matrix nonuniformity, pre-CRT neighborhood intensity difference matrix (NIDM) texture strength, and post-CRT NIDM busyness predicted all 3 treatment responses. The best predictor for GR versus non-GR was pre-CRT global minimum combined with clinical N stage in the multivariate analysis. The best predictor for T-downstaging was the combination of pre-CRT gray-level co-occurrence matrix correlation, NIDM-texture strength, and gray-level co-occurrence matrix variance. The pre-CRT, post-CRT, and delta radiomic-based models had no significant difference in predicting all 3 responses. Conclusions Pre-CRT MRI, post-CRT MRI, and delta radiomic-based models have the potential to predict tumor response after nCRT in LARC. These data, if validated in larger cohorts, can provide important predictive information to aid in clinical decision making.

Purpose The purpose of this study is to evaluate the feasibility of using deformable image registration algorithms to improve high-dose-rate high-risk clinical target volume (HR-CTV) delineation between preapplicator implantation MRI (pre-MRI) and postapplicator implantation CT (post-CT) in the treatment of locally advanced cervical cancer (LACC). Method and materials Twenty-six patients were identified for the study. Regions of interest were segmented on MRI and CT. A HR-CTV was delineated on pre-MRI and compared with the previously contoured HR-CTV on the post-CT. Two commercially available algorithms, ANACONDA (anatomically constrained) and MORFEUS (biomechanical model based) with various controlling structure settings, including the cervix, uterus, etc., were used to deform pre-MRI to post-CT. MRI-to-CT deformed targets are denoted as HR-CTV’. Quantitative deformation metrics include Dice index, distance to agreement, and center of mass displacement. Qualitative clinical usefulness of deformations was scored based on HR-CTV identification on CT images. Results For ANACONDA and MORFEUS deformations, using a cervix controlling region of interest resulted in the highest Dice, lowest distance to agreement, and lowest center of mass displacement for HR-CTV′. With MORFEUS deformations, the deformed HR-CTV’ proved clinically useful in 23 patients. Conclusions Prebrachytherapy implantation MRI can aid target contours for CT-based brachytherapy through ANACONDA or MORFEUS algorithms with appropriate parameter selection for LACC patients.

Support vector machine (SVM) is a popular machine learning method with a solid theoretical foundation, and has shown promising performance on different classification problems. However, it suffers from an expensive training cost, which makes it not be very suitable for the application with a large-scale training set. To this end, as a data pre-processing technique, training set selection (TSS) for SVM has received much attention recently, since it can reduce the size of SVM training set without degrading the performance. In this paper, a subregion division based multi-objective evolutionary algorithm termed SDMOEA-TSS is proposed for SVM training set selection, where objective space is divided into several subregions for effectively searching good solutions. Specifically, in SDMOEA-TSS, a divided based initialization strategy is firstly suggested to initialize the population to locate in different regions of objective space. Then a subregion based evolutionary (including crossover, mutation and update) strategy is developed, which not only makes full use of individuals in each subregion for local search but also maintains the whole population’s global search ability. Empirical studies on 21 public data sets demonstrate the superiority of the proposed algorithm over the state-of-the-arts in terms of both quality and diversity of the selected SVM training subset.

The evolution of grain boundary character distribution in a cold-deformed nickel-based superalloy during the electropulsing treatment (EPT) was studied. The results revealed that both coherent Σ3 boundaries (Σ3c) and incoherent Σ3 boundaries (Σ3ic) were formed in the recrystallized structures mainly by growth accidents during EPT, while the fraction of Σ3ic boundaries became much lower at the higher EPT temperatures. Meanwhile, the fraction of Σ3 boundaries increased with the increasing EPT temperatures till 850 °C, and decreased again at 900 °C. In addition, the high mobility of grain boundary during EPT could promote the formation of stacking errors at some locations of high angle grain boundaries (HAGBs), and thus promoting the nucleation of annealing twins. With the increasing EPT time, the fraction of Σ3 boundaries increased slightly, and the unstable Σ3ic boundaries were transformed into stable Σ3c boundaries, in which intersecting migrating SRX fronts were easier to be extended. On the other hand, a lot of low-Σ CSL grain boundaries were formed in the pre-deformed alloy with the strain of 0.1 during EPT, which was mainly attributed to the development of large grain-clusters. As the strain was large enough, the energy supplied by electropulsing could enable the cold-rolled alloy to effectively utilize the stored energy on the formation of Σ3 boundaries. Besides that, the evolution of Σ9 and Σ27 boundaries was analyzed in detail as well as the corrosion resistance of the studied alloy after different treatments.

Evolutionary algorithms have shown prominent performance in solving various kinds of multi-objective optimization problems (MOPs), but most of them only use single operator that is often sensitive to the characteristics of problems. As different operators have different search patterns, a proper combination of multiple operators can be more efficient and robust than using one single operator in solving complex problems. However, how to ensemble multiple operators based on their performances in optimization process is a challenging task. In the machine learning field, it is well known that AdaBoost can effectively ensemble multiple classifiers by giving the proper weights based on their classification errors. Inspired by this ensemble way, we propose a multi-operator ensemble (MOE) strategy based on multiple subpopulations for evolutionary multi-objective optimization. In the proposed strategy, the survival rate of each subpopulation after environmental selection is used to evaluate the performance of the operator, and then a credit assignment scheme is developed by using the weight update method in AdaBoost. Based on these credits, an emigration-immigration mechanism is designed to update the subpopulation that can adaptively reward or punish the computational resources for operators. Experimental results on three complex test suites demonstrate that the proposed MOE can significantly improve the performance of multi-objective evolutionary algorithms on different types of MOPs.

Purpose To identify factors associated with MRI-to-CT image deformation accuracy and modes of failure for MRI-optimized intracavitary high-dose-rate treatment of locally advanced cervical cancer. Methods and Materials Twenty-six patients with locally advanced cervical cancer had preimplantation MRI registered and deformed to postimplantation CT images using anatomically constrained and biomechanical model–based deformable image registration (DIR) algorithms. Cervix (primary) and cervix plus 10-mm margin (secondary) were used as controlling regions of interest for deformation. High-risk clinical target volume defined on pre-MRI was propagated to CT and evaluated for clinical utility in optimizing target volumes using scores 0 (low performing) to 4 (high performing). Quantitative evaluation of deformation performance included Dice index, distance to agreement, center of mass (COM) differences, cervical/uterus volume, and geometric change in organ position for MR-projected structures. Statistical analysis was performed to identify predictors of clinical utility and modes of failure. Results Anatomically constrained and biomechanical model–based deformable image registration algorithms achieved clinical utility >3 in 65% and 81% of patients, respectively. This improved to 81% and 85%, respectively, if cervix plus margin was used to drive deformations. Total COM displacement (cervix plus uterus) had the highest sensitivity in predicting low from high clinical utility in optimizing target volumes. Deformation failure (low clinical utility) resulted from high COM displacement, high cervical volume change, and retroverted uterine anatomy. Conclusions MRI-to-CT deformable image registration using a cervix-controlling region of interest can aid clinical target delineation in cervical brachytherapy and potentially improve brachytherapy implant quality and clinical workflow. Deformation failures warrant further study and prospective deformation validation.

The measure called partial area under the curve (pAUC) has attracted increasing interest in recent years due to the wide range of real-world applications it can be used for, such as biomarker selection and pedestrian detection. Compared with AUC, pAUC only acts upon a partial (instead of a full) area under the ROC curve. In this paper, a multi-objective evolutionary algorithm termed MOPA is proposed to optimize the pAUC in an arbitrary false positive range. Within this range, two important components are proposed to focus on a particular region within the AUC. First, a new metric (K-FPR), is proposed that is created by considering the partial range of the false positive rate (FPR). This is combined with the true positive rate (TPR) to provide the two optimized objectives of MOPA. Second, a preference-based multi-objective evolutionary algorithm is developed within the framework of a recently proposed algorithm (AR-MOEA), by which the search is concentrated on a partial area under the ROC curve. Numerical experiments on different data sets demonstrate the competitiveness of the proposed method in comparison with state-of-the-art algorithms.

Due to the wide applications in imbalanced learning, directly optimizing AUC has gained increasing interest in recent years. Compared with traditional batch learning methods, which often suffer from poor scalability, it is more challenging to design the efficient AUC maximizing algorithm for large-scale data set, especially when dimension of data is also high. To address the issue, in this paper, an adaptive stochastic gradient method for AUC maximization, termed AMAUC, is proposed. Specifically, the algorithm adopts the framework of mini-batch, and uses projection gradient method for the inner optimization. To further improve the performance, an adaptive learning rate updating strategy is also suggested, where the second order gradient information is utilized to provide the feature-wise updating. Empirical studies on the benchmark and high-dimensional data sets with large scale demonstrate the efficiency and effectiveness of the proposed AMAUC.

The 1D Ag3PO4 sensitized SrTiO3 nanowires are prepared by simple route of electrospinning-in situ deposition technique. The results of the thermogravimetry (TG), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive Spectrometer (EDS), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and UV–Visible diffuse reflectance spectroscopy (UV–Vis) indicate that the Ag3PO4 nanoparticles has been deposited on the surface of the SrTiO3 nanowires successfully. Experimental results showed that compared with pure SrTiO3, the as-prepared 1D Ag3PO4 sensitized SrTiO3 nanowires exhibit obvious enhancement of photocatalytic performance and stability. Especially, the Ag3PO4/SrTiO3 (3AS sample) had a satisfactory photocatalytic activity for degrading methylene blue (MB) more than 98% under visible light irradiation. As to pure SrTiO3 and Ag3PO4, only 9.8% and 49% of MB was decomposed after 35 min irradiation respectively. Furthermore, the mechanism of the enhancing photocatalytic activity could be ascribed to the nano-heterojunction of the Ag3PO4/SrTiO3, the visible light response of the Ag3PO4, and the 1D structure of the nanowires.

Mining interesting itemsets with both high support and utility values from transactional database is an important task in data mining. In this paper, we consider the two measures support and utility in a unified framework from a multi-objective view. Specifically, the task of mining frequent and high utility itemsets is modeled as a multi-objective problem. Then, a multi-objective itemset mining algorithm is proposed for solving the transformed problem, which can provide multiple itemsets recommendation for decision-makers in only one run. One key advantage of the proposed multi-objective algorithm is that it does not need to specify the prior parameters such as minimal support threshold min_sup and minimal utility threshold min_uti , which brings much convenience to users. The experimental results on several real datasets demonstrate the effectiveness of the proposed algorithm. In addition, comparison results show that the proposed algorithm can provide more diverse yet frequent and high utility itemsets in only one run.

Feature selection (FS) is an NP-hard combinatorial optimization problem, which aims to select the most relevant features from a large number of candidates. Recently, the FS in classification has been handled as a bi-objective optimization problem, where both the classification error and the number of selected features are minimized simultaneously. Despite of the effectiveness of existing multi-objective optimization (MOO) algorithms on FS, the convergence toward the Pareto front and the distribution over the Pareto front still deserve to be improved. To this end, this paper proposes a new multi-objective chemical reaction optimization algorithm for FS, termed MOFSCRO. In the proposed MOFSCRO, the preference information related to the problem is firstly extracted from the population. Then, three evolutionary operators based on the preference information are suggested to guide the population evolution, with the purpose of enhancing the population convergence and diversity. Experimental results on 12 benchmark data sets demonstrate the superiority of the proposed MOFSCRO over seven state-of-the-art FS algorithms in terms of both the classification error and the number of selected features. In comparison to five MOO algorithms, MOFSCRO also shows better performance according to the metric of inverted generational distance.

Background and Objective There are various artificial markers in ultrasound images of thyroid nodules, which have impact on subsequent processing and computer-aided diagnosis. The purpose of this study was to develop an approach to automatically remove artifacts and restore ultrasound images of thyroid nodules. Methods Fifty ultrasound images with manually induced artifacts were selected from publicly available and self-collected datasets. A combined approach was developed which consisted of two steps, artifacts detection and removal of the detected artifacts. Specifically, a novel edge-connection algorithm was used for artifact detection, detection accuracy and false discovery rate were used to evaluate the performance of artifact detection approaches. Criminisi algorithm was used for image restoration with peak signal-to-noise ratio (PSNR) and mean gradient difference to evaluate its performance. In addition, computation complexity was evaluated by execution time of relevant algorithms. Results Results revealed that the proposed joint approach with edge-connection and Criminisi algorithm could achieve automatic artifacts removal. Mean detection accuracy and mean false discovery rate of the proposed edge-connection algorithm for the 50 ultrasound images were 0.86 and 1.50. Mean PSNR of the 50 restored images by Criminisi algorithm was 36.64 dB, and mean gradient difference of the restored images was -0.002 compared with the original images. Conclusions The proposed combined approach had a good detection accuracy for different types of manually induced artifacts, and could significantly improve PSNR of the ultrasound images. The proposed combined approach may have potential use for the repair of ultrasound images with artifacts.

Positive and unlabeled (PU) learning has attracted increasing interests in recent years. Despite that a number of PU learning algorithms have been proposed, most of them are subject to some assumptions about unlabeled sample distribution and objective functions, which makes them difficult to be adopted for real applications. To this end, in this paper, an evolutionary multi-objective approach, namely MOEA-PUL, is suggested for PU learning, whose aim is to build a PU classifier without any prior assumption for data distribution and objective functions. To be specific, the PU learning is formulated as a bi-objective optimization problem, where the true positive rate (TPR) and a new metric, termed unlabeled accuracy rate (UAR) are used as the two objectives. A multi-objective evolutionary algorithm is proposed to solve this bi-objective optimization problem, under the framework of NSGA-II, where a PU similarity based initialization strategy and an elite label based learning strategy are developed. Empirical studies on 12 datasets demonstrate the superiority of MOEA-PUL over the existing PU learning algorithms.

In this work, arylamine branches N4-[4-[Bis(4-methoxyphenyl)-amino]phenyl]-N1,N1-bis(4-methoxyphenyl)-1,4-benzenediamine are introduced to the 1,6- and 2,7-positions of pyrene core to afford two novel HTMs coded PYR16 and PYR27. The influence of different positions of arylamine units on the optical and electronic properties and the performance of PSCs are investigated. The perovskite solar cells (PSCs) using dopant-free PYR16 exhibite a PCE of 17.00%, which is higher than that of PYR27 (14.67%). Furthermore, the PSCs based on PYR16 obtain better stability than those using PYR27 by maintaining 98% of the initial values after 1080 h at 80 ℃ in an ambient environment in the dark and 85% of initial values after 672 h under continuous sunlight soaking in an ambient environment at 45–50 ℃.

Retired lithium-ion batteries from electric vehicles provide considerable social, economic and environmental benefits in second-use applications especially for energy storage. However, the effective capacity estimation is a key technique and difficulty for second-life of large-scale retired cells. To address the issue, this paper provides a fast novel capacity estimation method from a new perspective of current distribution within parallel-connected cells. Firstly, using the parallel-connected cells analytical model, the correlation between capacity and current distribution is deduced. According to the correlation, the capacity of each cell is simultaneously estimated for the n parallel-connected cells by measured the current curves in only one complete discharge cycle, which is more efficient than one after another. Then an experimental device and procedure are designed to verify the method. Results demonstrate that the method is feasible and accurate. Meanwhile, through comparing the results of packs with three different battery types, the main factors affecting accuracy are found including open circuit voltage, parameter difference of cell and loads. Finally, comparison of different methods shows that the method has the advantages of high efficiency, low calculation, low accumulative errors and good consistency of state-of-charge, which is helpful for regrouping of retired cells and promoting second-life battery applications.

Dynamic vehicle routing problem (DVRP) has attracted increasing attention due to its wide applications in logistics. Compared with the static vehicle routing problem, DVRP is characterized by the prior unknown customer requests dynamically appearing in route execution. Nevertheless, the newly appeared customers pose a great challenge to route optimizer, since the optimized route may be contrarily of bad quality when including the new customers that are far from planned routes in route planning. To address this issue, in this paper we propose a demand coverage diversity based metaheuristic, termed ACO-CD, in the framework of ant colony algorithm. In ACO-CD, a demand coverage diversity adaptation method is suggested to maintain the diversity of covered customers in routes so that the optimizer can effectively response to the newly appeared customer requests. Experimental results on 27 DVRP test instances demonstrate the effectiveness of the proposed demand coverage diversity adaptation method and the superiority of the proposed ACO-CD over four state-of-the-art DVRP algorithms in terms of solution quality.