Search

Every late autumn, fluttering poplar leaves scatter throughout the campus and city streets. In this work, poplar leaves were used as the raw material, while H 3 PO 4 and KOH were used as activators and urea was used as the nitrogen source to prepare biomass based-activated carbons (ACs) to capture CO 2 . The pore structures, functional groups and morphology, and desorption performance of the prepared ACs were characterized; the CO 2 adsorption, regeneration, and kinetics were also evaluated. The results showed that H 3 PO 4 and urea obviously promoted the development of pore structures and pyrrole nitrogen (N-5), while KOH and urea were more conductive to the formation of hydroxyl (-OH) and ether (C-O) functional groups. At optimal operating conditions, the CO 2 adsorption capacity of H 3 PO 4 - and KOH-activated poplar leaves after urea treatment reached 4.07 and 3.85 mmol/g, respectively, at room temperature; both showed stable regenerative behaviour after ten adsorption-desorption cycles.

Accurate recognition of progressive mild cognitive impairment (MCI) is helpful to reduce the risk of developing Alzheimer's disease (AD). However, it is still challenging to extract effective biomarkers from multivariate brain structural magnetic resonance imaging (MRI) features to accurately differentiate the progressive MCI from stable MCI. We develop novel biomarkers by combining subspace learning methods with the information of AD as well as normal control (NC) subjects for the prediction of MCI conversion using multivariate structural MRI data. Specifically, we first learn two projection matrices to map multivariate structural MRI data into a common label subspace for AD and NC subjects, where the original data structure and the one-to-one correspondence between multiple variables are kept as much as possible. Afterwards, the multivariate structural MRI features of MCI subjects are mapped into a common subspace according to the projection matrices. We then perform the self-weighted operation and weighted fusion on the features in common subspace to extract the novel biomarkers for MCI subjects. The proposed biomarkers are tested on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results indicate that our proposed biomarkers outperform the competing biomarkers on the discrimination between progressive MCI and stable MCI. And the improvement from the proposed biomarkers is not limited to a particular classifier. Moreover, the results also confirm that the information of AD and NC subjects is conducive to predicting conversion from MCI to AD. In conclusion, we find a good representation of brain features from high-dimensional MRI data, which exhibits promising performance for predicting conversion from MCI to AD., Competing Interests: The authors declare no competing interests., (Copyright © 2021 Ying Li et al.)

Large-scale optimization with high dimensionality and high computational cost becomes ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed evolutionary computation algorithms is imperative. To this end, this paper proposes a distributed swarm optimizer based on a special master-slave model. Specifically, in this distributed optimizer, the master is mainly responsible for communication with slaves, while each slave iterates a swarm to traverse the solution space. An asynchronous and adaptive communication strategy based on the request-response mechanism is especially devised to let the slaves communicate with the master efficiently. Particularly, the communication between the master and each slave is adaptively triggered during the iteration. To aid the slaves to search the space efficiently, an elite-guided learning strategy is especially designed via utilizing elite particles in the current swarm and historically best solutions found by different slaves to guide the update of particles. Together, this distributed optimizer asynchronously iterates multiple swarms to collaboratively seek the optimum in parallel. Extensive experiments on a widely used large-scale benchmark set substantiate that the distributed optimizer could: 1) achieve competitive effectiveness in terms of solution quality as compared to the state-of-the-art large-scale methods; 2) accelerate the execution of the algorithm in comparison with the sequential one and obtain almost linear speedup as the number of cores increases; and 3) preserve a good scalability to solve higher dimensional problems.

Cross-modal retrieval has recently attracted much interest along with the rapid development of multimodal data, and effectively utilizing the complementary relationship of different modal data and eliminating the heterogeneous gap as much as possible are the two key challenges. In this paper, we present a novel network model termed cross-modal Dual Subspace learning with Adversarial Network (DSAN). The main contributions are as follows: (1) Dual subspaces (visual subspace and textual subspace) are proposed, which can better mine the underlying structure information of different modalities as well as modality-specific information. (2) An improved quadruplet loss is proposed, which takes into account the relative distance and absolute distance between positive and negative samples, together with the introduction of the idea of hard sample mining. (3) Intra-modal constrained loss is proposed to maximize the distance of the most similar cross-modal negative samples and their corresponding cross-modal positive samples. In particular, feature preserving and modality classification act as two antagonists. DSAN tries to narrow the heterogeneous gap between different modalities, and distinguish the original modality of random samples in dual subspaces. Comprehensive experimental results demonstrate that, DSAN significantly outperforms 9 state-of-the-art methods on four cross-modal datasets., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Hashing could significantly accelerate large-scale image search by transforming the high-dimensional features into binary Hamming space, where efficient similarity search can be achieved with very fast Hamming distance computation and extremely low storage cost. As an important branch of hashing methods, multi-view hashing takes advantages of multiple features from different views for binary hash learning. However, existing multi-view hashing methods are either based on shallow models which fail to fully capture the intrinsic correlations of heterogeneous views, or unsupervised deep models which suffer from insufficient semantics and cannot effectively exploit the complementarity of view features. In this paper, we propose a novel Deep Collaborative Multi-view Hashing (DCMVH) method to deeply fuse multi-view features and learn multi-view hash codes collaboratively under a deep architecture. DCMVH is a new deep multi-view hash learning framework. It mainly consists of 1) multiple view-specific networks to extract hidden representations of different views, and 2) a fusion network to learn multi-view fused hash code. DCMVH associates different layers with instance-wise and pair-wise semantic labels respectively. In this way, the discriminative capability of representation layers can be progressively enhanced and meanwhile the complementarity of different view features can be exploited effectively. Finally, we develop a fast discrete hash optimization method based on augmented Lagrangian multiplier to efficiently solve the binary hash codes. Experiments on public multi-view image search datasets demonstrate our approach achieves substantial performance improvement over state-of-the-art methods.

Linear discriminant analysis (LDA) is the most widely used supervised dimensionality reduction approach. After removing the null space of the total scatter matrix S t via principal component analysis (PCA), the LDA algorithm can avoid the small sample size problem. Most existing supervised dimensionality reduction methods extract the principal component of data first, and then conduct LDA on it. However, "most variance" is very often the most important, but not always in PCA. Thus, this two-step strategy may not be able to obtain the most discriminant information for classification tasks. Different from traditional approaches which conduct PCA and LDA in sequence, we propose a novel method referred to as joint principal component and discriminant analysis (JPCDA) for dimensionality reduction. Using this method, we are able to not only avoid the small sample size problem but also extract discriminant information for classification tasks. An iterative optimization algorithm is proposed to solve the method. To validate the efficacy of the proposed method, we perform extensive experiments on several benchmark data sets in comparison with some state-of-the-art dimensionality reduction methods. A large number of experimental results illustrate that the proposed method has quite promising classification performance.

Researchers have realized that microRNAs (miRNAs) play significant roles in the pathogenesis of various diseases. Although many computational models have been proposed to predict the associations between miRNAs and diseases, prediction performance could still be improved. In this paper, we propose a novel self-weighted, multi-kernel, multi-label learning (SwMKML) method to predict disease-related miRNAs. SwMKML adaptively learns two optimal kernel matrices for both miRNAs and diseases from multiple kernels constructed from known miRNA-disease associations. Moreover, the miRNA-disease associations predicted from both spaces are updated simultaneously based on a multi-label framework. Compared with four state-of-the-art computational models, SwMKML achieved best results of 95.5%, 93.1%, and 84.1% in global leave-one-out cross-validation, 5-fold cross-validation, and overall prediction accuracy, respectively. A case study conducted on head and neck neoplasms further identified two potential prognostic biomarkers, hsa-mir-125b-1 and hsa-mir-125b-2, for the disease. SwMKML is freely available at Github, and we anticipate that it may become an effective tool for potential miRNA-disease association prediction., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Semisupervised learning aims to leverage both labeled and unlabeled data to improve performance, where most of them are graph-based methods. However, the graph-based semisupervised methods are not capable for large-scale data since the computational consumption on the construction of graph Laplacian matrix is huge. On the other hand, the substantial unlabeled data in training stage of semisupervised learning could cause large uncertainties and potential threats. Therefore, it is crucial to enhance the robustness of semisupervised classification. In this paper, a novel large-scale robust semisupervised learning method is proposed in the framework of capped l 2,p -norm. This strategy is superior not only in computational cost because it makes the graph Laplacian matrix unnecessary, but also in robustness to outliers since the capped l 2,p -norm used for loss measurement. An efficient optimization algorithm is exploited to solve the nonconvex and nonsmooth challenging problem. The complexity of the proposed algorithm is analyzed and discussed in theory detailedly. Finally, extensive experiments are conducted over six benchmark data sets to demonstrate the effectiveness and superiority of the proposed method.

Privacy intrusion has become a major bottleneck for current trust-aware social sensing, since online social media allows anybody to largely disclose their personal information due to the proliferation of the Internet of Things (IoT). State-of-the-art social sensing still suffers from severe privacy threats since it collects users' personal data and disclosure behaviors, which could raise user privacy concerns due to data integration for personalization. In this paper, we propose a trust-aware model, called the User and Item Similarity Model with Trust in Diverse Kinds (UISTD), to enhance the personalization of social sensing while reducing users' privacy concerns. UISTD utilizes user-to-user similarities and item-to-item similarities to generate multiple kinds of personalized items with common tags. UISTD also applies a modified k -means clustering algorithm to select the core users among trust relationships, and the core users' preferences and disclosure behaviors will be regarded as the predicted disclosure pattern. The experimental results on three real-world data sets demonstrate that target users are more likely to: (1) follow the core users' interests on diverse kinds of items and disclosure behaviors, thereby outperforming the compared methods; and (2) disclose more information with lower intrusion awareness and privacy concern.

Spectral clustering (SC) has been proven to be effective in various applications. However, the learning scheme of SC is suboptimal in that it learns the cluster indicator from a fixed graph structure, which usually requires a rounding procedure to further partition the data. Also, the obtained cluster number cannot reflect the ground truth number of connected components in the graph. To alleviate these drawbacks, we propose a rank-constrained SC with flexible embedding framework. Specifically, an adaptive probabilistic neighborhood learning process is employed to recover the block-diagonal affinity matrix of an ideal graph. Meanwhile, a flexible embedding scheme is learned to unravel the intrinsic cluster structure in low-dimensional subspace, where the irrelevant information and noise in high-dimensional data have been effectively suppressed. The proposed method is superior to previous SC methods in that: 1) the block-diagonal affinity matrix learned simultaneously with the adaptive graph construction process, more explicitly induces the cluster membership without further discretization; 2) the number of clusters is guaranteed to converge to the ground truth via a rank constraint on the Laplacian matrix; and 3) the mismatch between the embedded feature and the projected feature allows more freedom for finding the proper cluster structure in the low-dimensional subspace as well as learning the corresponding projection matrix. Experimental results on both synthetic and real-world data sets demonstrate the promising performance of the proposed algorithm.

MiRNAs are small non-coding regulatory RNAs which are associated with multiple diseases. Increasing evidence has shown that miRNAs play important roles in various biological and physiological processes. Therefore, the identification of potential miRNA-disease associations could provide new clues to understanding the mechanism of pathogenesis. Although many traditional methods have been successfully applied to discover part of the associations, they are in general time-consuming and expensive. Consequently, computational-based methods are urgently needed to predict the potential miRNA-disease associations in a more efficient and resources-saving way. In this paper, we propose a novel method to predict miRNA-disease associations based on Locality-constrained Linear Coding (LLC). Specifically, we first reconstruct similarity networks for both miRNAs and diseases using LLC and then apply label propagation on the similarity networks to get relevant scores. To comprehensively verify the performance of the proposed method, we compare our method with several state-of-the-art methods under different evaluation metrics. Moreover, two types of case studies conducted on two common diseases further demonstrate the validity and utility of our method. Extensive experimental results indicate that our method can effectively predict potential associations between miRNAs and diseases.

Background: Optimized abnormalities of individual brain network may allow earlier detection of mild cognitive impairment (MCI) and accurate prediction of its conversion to Alzheimer's disease (AD). Currently, most studies constructed individual networks based on region-to-region correlation without employing multi-region information. In order to develop the potential discriminative power of network and provide supportive evidence for feasibility of individual metabolic network study, we propose a new approach to extract features from network with indirect relation based on 18 F-FDG PET data., New Method: Direct relation based individual network is first constructed using Gaussian kernel function. After that, the lattice-close-degree in fuzzy mathematics is applied to reflect region-to-region indirect relation using the direct relations of regions and their common neighbors. The proposed approach has been evaluated on 199 MCI subjects and 166 normal controls (NC) using SVM classifier., Results: The indirect relation based network features significantly promote classification performance in separating MCI from normal controls (NC) as well as MCI converters from non-converters. Specially, further improvements can be obtained by combining indirect relation features with ADAS-cog scores. Moreover, the discriminative regions we found are consistent with previous studies, indicating the efficacy of our constructed network in identifying correct biomarkers for diagnosing MCI and predicting its conversion., Comparison With Existing Method(s): More accurate MCI identification of PET data can be achieved by features of network with indirect relation., Conclusions: This work provides a new way to investigate brain network from metabolic perspective for accurate identification of MCI., (Copyright © 2018 Elsevier B.V. All rights reserved.)

miRNAs are a class of small noncoding RNAs that are associated with a variety of complex biological processes. Increasing studies have shown that miRNAs have close relationships with many human diseases. The prediction of the associations between miRNAs and diseases has thus become a hot topic. Although traditional experimental methods are reliable, they could only identify a limited number of associations as they are time-consuming and expensive. Consequently, great efforts have been made to effectively predict reliable disease-related miRNAs based on computational methods. In this study, we present a novel approach to predict the potential microRNA-disease associations based on sparse neighbourhood. Specifically, our method takes advantage of the sparsity of the miRNA-disease association network and integrates the sparse information into the current similarity matrices for both miRNAs and diseases. To demonstrate the utility of our method, we applied global LOOCV, local LOOCV and five-fold cross-validation to evaluate our method, respectively. The corresponding AUCs are 0.936, 0.882 and 0.934. Three types of case studies on five common diseases further confirm the performance of our method in predicting unknown miRNA-disease associations. Overall, results show that SNMDA can predict the potential associations between miRNAs and diseases effectively., (© 2018 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.)

This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similarity that the structure of their search space is usually in the shape of a permutation tree, this paper proposes a generic multiobjective set-based particle swarm optimization methodology based on decomposition, termed MS-PSO/D. In order to coordinate with the property of permutation-based MOCOPs, MS-PSO/D utilizes an element-based representation and a constructive approach. Through this, feasible solutions under constraints can be generated step by step following the permutation-tree-shaped structure. And problem-related heuristic information is introduced in the constructive approach for efficiency. In order to address the multiobjective optimization issues, the decomposition strategy is employed, in which the problem is converted into multiple single-objective subproblems according to a set of weight vectors. Besides, a flexible mechanism for diversity control is provided in MS-PSO/D. Extensive experiments have been conducted to study MS-PSO/D on two permutation-based MOCOPs, namely the MOTSP and the MOPSP. Experimental results validate that the proposed methodology is promising.

Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is proposed to randomly divide the whole dimensions into segments. During update, variables in different segments are evolved by learning from different exemplars while the ones in the same segment are evolved by the same exemplar. Second, to accelerate search speed and enhance search diversity, a predominant learning strategy is also proposed, which lets several predominant particles guide the update of a particle with each predominant particle responsible for one segment of dimensions. By combining these two learning strategies together, SPLSO evolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities. Extensive experiments are conducted on two large-scale benchmark function sets to investigate the influence of each algorithmic component and comparisons with several state-of-the-art meta-heuristic algorithms dealing with large-scale problems demonstrate the competitive efficiency and effectiveness of the proposed optimizer. Further the scalability of the optimizer to solve problems with dimensionality up to 2000 is also verified.

The popular performance profiles and data profiles for benchmarking deterministic optimization algorithms are extended to benchmark stochastic algorithms for global optimization problems. A general confidence interval is employed to replace the significance test, which is popular in traditional benchmarking methods but suffering more and more criticisms. Through computing confidence bounds of the general confidence interval and visualizing them with performance profiles and (or) data profiles, our benchmarking method can be used to compare stochastic optimization algorithms by graphs. Compared with traditional benchmarking methods, our method is synthetic statistically and therefore is suitable for large sets of benchmark problems. Compared with some sample-mean-based benchmarking methods, e.g., the method adopted in black-box-optimization-benchmarking workshop/competition, our method considers not only sample means but also sample variances. The most important property of our method is that it is a distribution-free method, i.e., it does not depend on any distribution assumption of the population. This makes it a promising benchmarking method for stochastic optimization algorithms. Some examples are provided to illustrate how to use our method to compare stochastic optimization algorithms.

A strain WHAK1, identified as Aspergillus niger, was isolated from Yichang phosphate mines in Hubei province of China. The fungus developed a phosphate solubilization zone on modified National Botanical Research Institute's phosphate growth (NBRIP) agar medium, supplemented with tricalcium phosphate. The fungus was applied in a repeated-batch fermentation process in order to test its effect on solubilization of rock phosphate (RP). The results showed that A. niger WHAK1 could effectively solubilize RP in NBRIP liquid medium and released soluble phosphate in the broth, which can be illustrated by the observation of scanning electron microscope, energy-dispersive X-ray microanalysis, and Fourier transform infrared spectroscopy. Acidification of the broth seemed to be the major mechanism for RP solubilization by the fungus. Indeed, multiple organic acids (mainly gluconic acid) were detected in the broth by high-performance liquid chromatography analysis. These organic acids caused a significant drop of pH and an obvious rise of titratable acidity in the broth. The fungus also exhibited high levels of tolerance against temperature, pH, salinity, and desiccation stresses, although a significant decline in the fungal growth and release of soluble phosphate was marked under increasing intensity of stress parameters. Further, the fungus was introduced into the soil supplemented with RP to analyze its effect on plant growth and phosphate uptake of wheat plants. The result revealed that inoculation of A. niger WHAK1 significantly increased the growth and phosphate uptake of wheat plants in the RP-amended soil compared to the control soil.

3-(1H-benzo[d][1,2,3]triazol-1-yl)-1-(4-ethylphenyl)-1-oxopropan-2-yl-4-ethyl-benzoate (BEOE) has been synthesized and characterized by elemental analysis, IR, UV-vis and fluorescence spectroscopy. Its crystal structure has also been determined by X-ray single crystal diffraction. For the title compound, density functional theory (DFT) calculations of the structure and vibrational frequencies have been performed at B3LYP/6-31G* level of theory. Based on the vibration analysis, thermodynamic properties of the title compound have been calculated. The correlative equations between the thermodynamic properties and temperatures have also been listed. By using TD-DFT method, electron spectra of the title compound have been predicted, which suggests the B3LYP/6-31G* method can approximately simulate the electron spectra for the system presented here., (Copyright 2009 Elsevier B.V. All rights reserved.)