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In this paper, we propose an enhanced branch-and-bound (B&B) framework for a class of sequencing problems, which aim to find a permutation of all involved elements to minimize a given objective function. We require that the sequencing problems satisfy three conditions: 1) incrementally computable; 2) monotonic; and 3) overlapping subproblems. Our enhanced B&B framework is built on the classical B&B process by introducing two techniques, i.e., dominance rules and caching search states. Following the enhanced B&B framework, we conduct empirical studies on three typical and challenging sequencing problems, i.e., quadratic traveling salesman problem, traveling repairman problem, and talent scheduling problem. The computational results demonstrate the effectiveness of our enhanced B&B framework when compared to classical B&B and some exact approaches, such as dynamic programming and constraint programming. Additional experiments are carried out to analyze different configurations of the algorithm. [ABSTRACT FROM AUTHOR]

The formation of underwater adhesion is a complicated physiological process and many different types of enzymes are found to be essential apart from structural proteins. Previous studies have shown that various tyrosinases were present in marine adhesives, but little information is available about the over-expression and enzymatic characterization of these enzymes. Specifically, this study first identified four significantly up-regulated tyrosinases in the pedal disc of Haliplanella luciae by means of multi-omics technology, and made preliminary bioinformatics predictions. Sequence alignment showed that the Tyr1_Hl contained six conserved His residues that bind to copper ions, of which a tyrosinase with diphenolase activity named as Tyr1_HlΔ, was expressed in Escherichia coli BL21 (DE3) cells and purified by affinity chromatography. Enzymatic characterization showed that the activity of Tyr1_HlΔ was Cu2+ dependent and maximum catalytic activities were in 20 mM Tris–HCl (pH 8.0) at 37 °C. In summary, we identified novel tyrosinases in the pedal disks of sea anemone for the first time and the Tyr1_HlΔ was successfully recombinant expressed. Our study will provide basis for future exploration of bio-adhesion mechanism and design of bio-adhesives derived from sea anemones. [ABSTRACT FROM AUTHOR]

Many feature selection approaches are proposed in recent years. Most approaches utilize graph-based methods in studying the structure and relationship among data. However, many data relationships may loss during the graph construction, such as the residual relationships. To better preserve the relationships between data, in this paper, we propose a novel unified learning framework - unsupervised feature selection with adaptive residual preserving (UFSARP). The framework unifies feature selection, data reconstruction, and local residual preserving into one unified process, in which these tasks are completed simultaneously. We use the distance of projected data to learn the similarity matrix and simultaneously impose it on the data representation term to enforce that similar samples have similar reconstruction residuals. The use of such learning way has three-fold advantages: (1) The reconstruction residuals aim to maintain the residual relationships between data samples, namely, similar samples have similar residuals, and this helps to reconstruct the original data better; (2) Imposing the similarity matrix on the data representation term encourages similar samples not only have similar reconstruction residuals but also have similar reconstruction coefficients; (3) The similarity matrix and the reconstruction coefficient can be promoted by each other during the learning process. The experimental results show that the proposed algorithm is superior to other similar researches. [ABSTRACT FROM AUTHOR]

Due to its low memory and computational requirements, hashing techniques are widely applied for cross-modal retrieval. However, there are still two unresolved issues: 1) the class-wise similarity of samples for each modality is not well exploited, and 2) most methods ignore the discriminative capacity of modality-specific information. To solve these two issues, we propose a novel supervised cross-modal hashing method called Joint Specifics and Dual-Semantic Hashing Learning for Cross-Modal Retrieval (SDSHL). SDSHL consists of three methods, i.e., Semantic Embedded Triple Matrix Factorization (SETMF), Modality Specific Dual Semantic Learning (MSDSL) and Modality Consistent Dual Semantic Learning (MCDSL). SETMF utilizes triple matrix factorization to fully explore modality features. MSDSL applies clustering to find the class-wise similarity for each modality, preserving modality-specific information well. MCDSL adopts asymmetric distance-distance difference minimization to capture modality-consistent information among modalities. By using SDSHL, the discrepancies between features and labels are reduced, while both modality-specific and modality-consistent information is well preserved in a shared hash code. Comprehensive experimentation on three benchmark datasets demonstrates the superior performance of SDSHL. [ABSTRACT FROM AUTHOR]

Supervised cross-modal hashing has attracted many researchers. In these studies, they seek a common semantic space or directly regress the zero-one label information into the Hamming space. Although they achieve many achievements, they neglect some issues: 1) some methods of the classification task are not suitable for retrieval tasks, since they are lack of learning personalized features of sample; 2) the outcomes of hash retrieval are related to both the length and encoding method of hash codes. Because a sample possess more personalized features than label semantics, in this paper, we propose a novel supervised cross-modal hashing collaboration learning method called discrete Cross-modal Hashing with Relaxation and Label Semantic Guidance (CHRLSG). First, we introduce two relaxation variables as latent spaces. One is used to extract text features and label semantic information collaboratively, and the other is used to extract image features and label semantics collaboratively. Second, the more accurate hash codes are generated from latent spaces, since CHRLSG learns collaboratively feature semantics and label semantics by using labels as the domination and features as the auxiliary. Third, we utilize labels to strengthen the similar relationship of inter-modal samples via keeping the pairwise closeness. Label semantics are made full use of to avoid classification error. Fourth, we introduce class weight to further increase the discrimination of samples that belong to different classes in intra-modal and keep the similarity of samples unchanged. Therefore, CHRLSG model preserves not only the relationship between samples, but also maintains the consistency of label semantic during collaboration optimization. Experimental results of three common benchmark datasets demonstrate that the proposed model is superior to the existing advanced methods. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the leading causes of premature death and disability in people around the world. Therefore, the prevention and treatment of CVDs has become an important subject. In this study, we verified the thrombolytic activities of a nattokinase‐like protease named NK‐01 in vivo. Label‐free liquid chromatography–tandem mass spectrometry (LC‐MS/MS) technique was used in our study. NK‐01 could inhibit the activity of coagulation factors though the up‐regulation of proteinase C inhibitors and protein S. NK‐01 also could inhibit the angiotensinogen conversion to AngII and promote the degradation of kininogen to reduce the blood pressure. In addition, NK‐01 could increase the content of paraoxonase 1, which could prevent atherosclerosis. In our study, we found that NK‐01 cloud effect some key proteins which participant in CVDs associated metabolic processes such as coagulation system, blood pressure, and atherosclerosis. Taken together, the underlying molecular mechanisms for the biological beneficial of NK‐01 were investigated. Our proteomic study will provide further theoretical basis for application of NK in prevention or adjuvant treatment in biomedicine areas. [ABSTRACT FROM AUTHOR]

Although bioactive peptides enhancing bone healing have demonstrated effectiveness in treating bone defects, in vivo instability poses a challenge to their clinical application. Currently reported peptide delivery systems do not meet the demands of bone tissue repair regarding stability and peptide release efficacy. Herein, the self-assembling recombinant chimeric protein (Sbp5–2RGD) is developed by genetic engineering with cell adhesion peptide RGD as the targeted peptide and a newly discovered scallop byssal-derived protein Sbp5–2 that can assemble into wet stable films as the structural domain. In vitro studies show that the Sbp5–2RGD film exhibits excellent extensibility and biocompatibility. In vitro and in vivo degradation experiments demonstrate that the film remains stable due to the layer-by-layer degradation mode, resulting in sustained delivery of RGD in situ for up to 4 weeks. Consequently, the film can effectively promote osteogenesis, which accelerates bone defect healing and the implants osseointegration. Cell-level studies further show that the film up-regulates the expression of genes and proteins (ALP, OCN, OSX, OPN, RUNX2, VEGF) associated with osteogenesis and angiogenesis. Overall, this novel protein film represents an intelligent platform for peptide immobilization, protection, and release through its self-assembly, dense structure, and degradation mode, providing a therapeutic strategy for bone repair. [ABSTRACT FROM AUTHOR]

[Display omitted] • Limitations and major issues of AOB-based PN-A processes are reviewed. • The potential and significance of AOA-based PN-A processes are demonstrated. • Strategies and influencing factors for replacing AOB with AOA are summarized. • Methods and factors influencing AOA-AnAOB coupling are identified. • Four AOA-based OALAND processes are proposed to complement current PN-A processes. The application of ammonia-oxidizing archaea (AOA)-based partial nitrification-anammox (PN-A) for mainstream wastewater treatment has attracted research interest because AOA can maintain higher activity in low-temperature environments and they have higher affinity for oxygen and ammonia–nitrogen compared with ammonia-oxidizing bacteria (AOB), thus facilitating stabilized nitrite production, deep removal of low-ammonia, and nitrite-oxidizing bacteria suppression. Moreover, the low affinity of AOA for ammonia makes them more tolerant to N-shock loading and more efficiently integrated with anaerobic ammonium oxidation (anammox). Based on the limitations of the AOB-based PN-A process, this review comprehensively summarizes the potential and significance of AOA for nitrite supply, then gives strategies and influencing factors for replacing AOB with AOA. Additionally, the methods and key influences on the coupling of AOA and anammox are explored. Finally, this review proposes four AOA-based oxygen- or ammonia-limited autotrophic nitritation/denitrification processes to address the low effluent quality and instability of mainstream PN-A processes. [ABSTRACT FROM AUTHOR]

A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains. Thus, there might be negative similarities between different domains so that the negative transfer problem caused by negative similarity may happen. In this paper, we propose a novel method named active transfer learning (ATL) to solve the above problem. Specifically, the orthogonal projection matrix and the weight coefficient vector are introduced to extend maximum mean discrepancy (MMD) so that it can minimize MMD and simultaneously eliminate the negative transfer. To find the informative and discriminative subsets from the source domain, we then propose an information diversity term by using the local geometric structure information of the source samples. Besides, by using the label information of source samples, our method can guarantee the selected subsets as discriminative as possible. Finally, to efficiently implement the proposed method, an alternating optimization approach, which is based on the alternating direction method of multipliers (ADMM), is designed to solve the optimization problem. To demonstrate the effectiveness of the proposed ATL model, experiments are conducted on five real-world data sets. The experimental results show the superiority of our method over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Unsupervised Domain adaptation (UDA) aims to transfer knowledge from the labeled source domain to the unlabeled target domain. Recently, to achieve reliable knowledge learning, progressively labeling (PL) is proposed to select reliable target samples for training. Although PL achieves fruitful results, there are two problems that limit its performance: (a) PL may neglect to filter out the uncertain samples that lie in the classification boundaries, which might select low-quality target samples for training, and lead to error accumulations; and (b) PL might overlook the consistent selection in the sample selection stage during iteration, which might result in unstable sample selection. To cope with these problems, we propose a novel method called Selected Confidence Sample Labeling (SCSL). SCSL consists of three parts: Discriminative Progressively Labeling (DPL), Consistency Strategy (CS), and Differential Learning (DL). First, DPL selects the high-confidence target samples with the maximum probability difference between the highest and the second-highest probabilities of classification. In this way, the uncertain samples are filtered out, while the qualities of the selected target samples are ensured. Second, CS includes a group of consistency strategies to make the selected high-confidence target samples near to the class-centroids. This further improves the confidence degree of the selected target samples, and ensures that the proposed model does not remove or replace the selected target samples during iteration. At last, DL is a bi-strategic training approach that applies CS and top- k fuzzy probability clustering to train the high-confidence and the remaining target samples, respectively. In doing so, all the target samples are trained simultaneously, and the generalization of the model is improved. Extensive experiments on four benchmark datasets with the comparison of several advanced algorithms demonstrate the superiority of SCSL. [ABSTRACT FROM AUTHOR]

The performance of most existing multiobjective evolutionary algorithms deteriorates severely in the face of many-objective problems. Many-objective optimization has been gaining increasing attention, and many new many-objective evolutionary algorithms (MaOEA) have recently been proposed. On the one hand, solution sets with totally different characteristics are obtained by different MaOEAs, since different MaOEAs have different convergence-diversity tradeoff relations. This may suggest the potential usefulness of ensemble approaches of different MaOEAs. On the other hand, the performance of MaOEAs may vary greatly from one problem to another, so that choosing the most appropriate MaOEA is often a non-trivial task. Hence, an MaOEA that performs generally well on a set of problems is often desirable. This study proposes an ensemble of MaOEAs (EMaOEA) for many-objective problems. When solving a single problem, EMaOEA invests its computational budget to its constituent MaOEAs, runs them in parallel and maintains interactions between them by a simple information sharing scheme. Experimental results on 80 benchmark problems have shown that, by integrating the advantages of different MaOEAs into one framework, EMaOEA not only provides practitioners a unified framework for solving their problem set, but also may lead to better performance than a single MaOEA. [ABSTRACT FROM AUTHOR]

The Many to Many (M–M) assignment problem is an important open problem where one task is assigned to many, but different, agents and one agent may undertake many, but different, tasks. The Kuhn–Munkres (K–M) algorithm is a famous and traditional process used in dealing with assignment problems. In this paper, we propose a solution to the M–M assignment problem by improving the K–M algorithm with backtracking (KM B ). To demonstrate the solution's suitability, we prove that the proposed KM B algorithm is valid and that the worst time complexity of the KM B algorithm is O ( ( ∑ L a [ i ] ) 3 ) , where L a [ i ] denotes the maximum number of tasks that can be assigned to agent i . After that, we discuss several critical problems related to the algorithm and provide the necessary and sufficient conditions of solving the M–M assignment problem. Finally, we demonstrate, through experimentation, the validity, practicality and efficiency of the KM B algorithm. [ABSTRACT FROM AUTHOR]