Learning to Guide Particle Search for Dynamic Multiobjective Optimization.

Dynamic multiobjective optimization problems (DMOPs) are characterized by multiple objectives that change over time in varying environments. More specifically, environmental changes can be described as various dynamics. However, it is difficult for existing dynamic multiobjective algorithms (DMOAs) to handle DMOPs due to their inability to learn in different environments to guide the search. Besides, solving DMOPs is typically an online task, requiring low computational cost of a DMOA. To address the above challenges, we propose a particle search guidance network (PSGN), capable of directing individuals' search actions, including learning target selection and acceleration coefficient control. PSGN can learn the actions that should be taken in each environment through rewarding or punishing the network by reinforcement learning. Thus, PSGN is capable of tackling DMOPs of various dynamics. Additionally, we efficiently adjust PSGN hidden nodes and update the output weights in an incremental learning way, enabling PSGN to direct particle search at a low computational cost. We compare the proposed PSGN with seven state-of-the-art algorithms, and the excellent performance of PSGN verifies that it can handle DMOPs of various dynamics in a computationally very efficient way.

Association of Zinc Intake, Tobacco Smoke Exposure, With Metabolic Syndrome: Evidence from NHANES 2007-2018.

The objective was to explore the effect modification of zinc (Zn) intake levels on the relationship of tobacco smoke exposure and risk of metabolic syndrome (MetS) in children and adolescents. We used data from 2007-2018 National Health and Nutrition Examination Survey (N = 3701). MetS was considered as main endpoint. Weighted multivariable logistic regression models showed that high cotinine level (≥ 0.05 ng/mL) was associated with increased odds of MetS [odds ratio = 1.54, 95% confidence interval: 1.01, 2.36], and the association between Zn intake levels and MetS did not demonstrate statistical significance. Importantly, the multiplicative interaction term between low Zn intake (≤ 4.89 mg/1000 kcal) and high cotinine level was related to higher odds of MetS (p-value for interaction 0.018). For the group with low Zn intake, high cotinine level was associated with increased odds of MetS. However, there was no significant relationship between cotinine levels and MetS risk in the group with high Zn intake. The effect modification by Zn intake on the relationship of tobacco smoke exposure and risk of MetS is significant in individuals who had a sedentary time of ≥ 6 h, identified as non-Hispanic White, or resided in households with smokers. In short, low Zn intake may potentiate the association of tobacco smoke exposure and MetS risk in children and adolescents.

Online Sparse Representation Clustering for Evolving Data Streams.

Data stream clustering can be performed to discover the patterns underlying continuously arriving sequences of data. A number of data stream clustering algorithms for finding clusters in arbitrary shapes and handling outliers, such as density-based clustering algorithms, have been proposed. However, these algorithms are often limited in their ability to construct and merge microclusters by measuring the Euclidean distances between high-dimensional data objects, e.g., transferring valuable knowledge from historical landmark windows to the current landmark window, and exploiting evolving subspace structures adaptively. We propose an online sparse representation clustering (OSRC) method to learn an affinity matrix for evaluating the relationships among high-dimensional data objects in evolving data streams. We first introduce a low-dimensional projection (LDP) into sparse representation to adaptively reduce the potential negative influence associated with the noise and redundancy contained in high-dimensional data. Then, we take advantage of the l2,1 -norm optimization technique to choose the appropriate number of representative data objects and form a specific dictionary for sparse representation. The specific dictionary is integrated into sparse representation to adaptively exploit the evolving subspace structures of the high-dimensional data objects. Moreover, the data object representatives from the current landmark window can transfer valuable knowledge to the next landmark window. The experimental results based on a synthetic dataset and six benchmark datasets validate the effectiveness of the proposed method compared to that of state-of-the-art methods for data stream clustering.

In silico identification of natural compounds against SARS-CoV-2 main protease from Chinese herbal medicines.

Aims: To determine natural compounds with inhibitory effects toward SARS-CoV-2 Mpro from Chinese herbal medicines. Materials & methods: ∼1200 natural compounds from 19 Chinese herbal medicines were collected. Computational methods including molecular docking, drug-likeness assessment, molecular dynamics simulation and molecular mechanics Poisson-Boltzmann surface area analysis were combined to obtain potent inhibitors against SARS-CoV-2 Mpro. Results: Top 20 compounds mainly originated from Ranunculus ternatus and Picrasma quassioides exhibited low binding free energies which below -9.0 kcal/mol. Compounds Japonicone G and Picrasidine T were obtained with favorable drug-likeness. Moreover, the complex of Japonicone G and Mpro had prominent stability. Conclusion: Natural compound Japonicone G is highly promising as a potent inhibitor against SARS-CoV-2 for further study.

Photo-crosslinkable hyaluronic acid microgels with reactive oxygen species scavenging capacity for mesenchymal stem cell encapsulation.

Mesenchymal stem cells (MSCs) have gained increasing attention in various biomedical applications. However, conventional therapeutic approaches, such as direct intravenous injection, are associated with low cell survival due to the shear force during injection and the oxidative stress microenvironments in the lesion area. Herein, a photo-crosslinkable antioxidant hydrogel based on tyramine- and dopamine-modified hyaluronic acid (HA-Tyr/HA-DA) was developed. Meanwhile, human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) were encapsulated in HA-Tyr/HA-DA hydrogel using a microfluidic system to create size-controllable microgels (hUC-MSCs@microgels). The HA-Tyr/HA-DA hydrogel was demonstrated to have good rheology, biocompatibility, and antioxidant properties for cell microencapsulation. The hUC-MSCs encapsulated in microgels showed a high viability and a significantly improved the survival rate under oxidative stress conditions. Therefore, the presented work provides a promising platform for MSCs microencapsulation, which may further improve the stem cell-based biomedical applications.

Improved Stability and Catalytic Efficiency of ω-Transaminase in Aqueous Mixture of Deep Eutectic Solvents.

The efficient biosynthesis of chiral amines at an industrial scale to meet the high demand from industries that require chiral amines as precursors is challenging due to the poor stability and low catalytic efficiency of ω-transaminases (ω-TAs). Herein, this study adopted a green and efficient solvent engineering method to explore the effects of various aqueous solutions of deep eutectic solvents (DESs) as cosolvents on the catalytic efficiency and stability of ω-TA. Binary- and ternary-based DESs were used as cosolvents in enhancing the catalytic activity and stability of a ω-TA variant from Aspergillus terreus (E133A). The enzyme exhibited a higher catalytic activity in a ternary-based DES that was 2.4-fold higher than in conventional buffer. Moreover, the thermal stability was enhanced by a magnitude of 2.7, with an improvement in storage stability. Molecular docking studies illustrated that the most potent DES established strong hydrogen bond interactions with the enzyme's amino acid, which enhanced the catalytic efficiency and improved the stability of the ω-TA. Molecular docking is essential in designing DESs for a specific enzyme.

Discovery of 3CLpro inhibitor of SARS-CoV-2 main protease.

Coronavirus main protease (3CLpro), a special cysteine protease in coronavirus family, is highly desirable in the life cycle of coronavirus. Here, molecular docking, ADMET pharmacokinetic profiles and molecular dynamics (MD) simulation were performed to develop specific 3CLpro inhibitor. The results showed that the 137 compounds originated from Chinese herbal have good binding affinity to 3CLpro. Among these, Cleomiscosin C, (+)-Norchelidonine, Protopine, Turkiyenine, Isochelidonine and Mallotucin A possessed prominent drug-likeness properties. Cleomiscosin C and Turkiyenine exhibited excellent pharmacokinetic profiles. Furthermore, the complex of Cleomiscosin C with SARS-CoV-2 main protease presented high stability. The findings in this work indicated that Cleomiscosin C is highly promising as a potential 3CLpro inhibitor, thus facilitating the development of effective drugs for COVID-19.

In this work, computer aided drug design technology was used to study the main protease 3CLpro of novel coronavirus, and functional small molecules with inhibitory effects on novel coronavirus were screened from the compound library of natural products. The results showed that Cleomiscosin C is highly promising as a potential 3CLpro inhibitor with prominent binding affinity, pharmacokinetic profiles and stability.

Bioactive secondary metabolites from endophytic strains of Neocamarosporium betae collected from desert plants.

Endophytic fungi from desert plants belong to a unique microbial community that has been scarcely investigated chemically and could be a new resource for bioactive natural products. In this study, 13 secondary metabolites (1-13) with diverse carbon skeletons, including a novel polyketide (1) with a unique 5,6-dihydro-4H,7H-2,6-methanopyrano[4,3-d][1,3]dioxocin-7-one ring system and three undescribed polyketides (2, 7, and 11), were obtained from the endophytic fungus Neocamarosporium betae isolated from two desert plant species. Different approaches, including HR-ESI-MS, UV spectroscopy, IR spectroscopy, NMR, and CD, were used to determine the planar and absolute configurations of the compounds. The possible biosynthetic pathways were proposed based on the structural characteristics of compounds 1-13. Compounds 1, 3, 4, and 9 exhibited strong cytotoxicity toward HepG2 cells compared with the positive control. Several metabolites (2, 4-5, 7-9, and 11-13) were phytotoxic to foxtail leaves. The results support the hypothesis that endophytic fungi from special environments, such as desert areas, produce novel bioactive secondary metabolites.

Nano-emulsification essential oil of Monarda didyma L. to improve its preservation effect on postharvest blueberry.

The reduction in blueberry harvest due to pathogen infection was reported to reach 80%. Essential oil (EO) can provide a new way to preserve blueberry. Here, in search for plants volatiles with preservation ability, a novel device was designed for the screening of aromatic plants led to the discovery of hit plant Monarda didyma L. Consequently, antifungi activity of M. didyma EO (MEO) and its nano-emulsion (MNE) were tested. 2 species of pathogenic fungi were isolated from blueberries, namely Alternaria sp. and Colletotrichum sp. were used as the target strains. In the in vitro activity test, the pathogenic were completely inhibited when the EO was 4 µL or 1.0 µL/mL. Compared with EO, MNE exhibited superior antimicrobial activity. Moreover, MNE can cause serious morphological changes and result in a decrease in the rot and weightlessness rate of blueberry. Hence, NME represents a promising agent for the preservation of postharvest blueberry.

Efficient Sparse Representation for Learning With High-Dimensional Data.

Due to the capability of effectively learning intrinsic structures from high-dimensional data, techniques based on sparse representation have begun to display an impressive impact on several fields, such as image processing, computer vision, and pattern recognition. Learning sparse representations isoften computationally expensive due to the iterative computations needed to solve convex optimization problems in which the number of iterations is unknown before convergence. Moreover, most sparse representation algorithms focus only on determining the final sparse representation results and ignore the changes in the sparsity ratio (SR) during iterative computations. In this article, two algorithms are proposed to learn sparse representations based on locality-constrained linear representation learning with probabilistic simplex constraints. Specifically, the first algorithm, called approximated local linear representation (ALLR), obtains a closed-form solution from individual locality-constrained sparse representations. The second algorithm, called ALLR with symmetric constraints (ALLRSC), further obtains a symmetric sparse representation result with a limited number of computations; notably, the sparsity and convergence of sparse representations can be guaranteed based on theoretical analysis. The steady decline in the SR during iterative computations is a critical factor in practical applications. Experimental results based on public datasets demonstrate that the proposed algorithms perform better than several state-of-the-art algorithms for learning with high-dimensional data.

Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance.

In this article, we propose an evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) for handling dynamic multiobjective optimization (DMO) environments. The LP strategy takes into account different levels of progress by different individuals in evolution and historical information to predict the population in the event of environmental changes for a prompt change response. The SDM strategy identifies gaps in population distribution and employs a gap-filling technique to increase population diversity. SDM further guides rational population reproduction with a subspace-based probability model to maintain the balance between population diversity and convergence in every generation of evolution regardless of environmental changes. The proposed algorithm has been extensively studied through comparison with five state-of-the-art algorithms on a variety of test problems, demonstrating its effectiveness in dealing with DMO problems.

Highly-efficient selective recognition and rapid enrichment of chrysin by magnetic surface molecularly imprinted polymer.

As an important natural nutrient-like substance, chrysin is highly desirable in the field of health food. Here, to achieve the specific and efficient adsorption of chrysin, a magnetic surface molecularly imprinted polymer (chrysin/SMIPs) was fabricated by free radical polymerization of methacrylic acid onto the surface of functionalized magnetic Fe3O4 nanoparticles. The obtained chrysin/SMIPs showed favorable binding ability towards chrysin with 35.27 mg·g-1 maximum adsorption capacity in 20 min, prominent reusability with 9 cycles of adsorption-desorption and superior specific recognition with imprinting factor (IF) of 2.87 and selectivity coefficient (K) of 8.74, 5.64 and 8.64 corresponding to the interferents of genistein, daidzein and quercetin, respectively. Moreover, the chrysin/SMIPs showed high recoveries (91.13 % to 95.12 %) and precisions (RSDs from 2.51 % to 1.32 %) in the extraction of chrysin from real propolis samples. Therefore, chrysin/SMIPs is highly promising for the selective enrichment recovery of chrysin in health food.

Two-Stage Sparse Representation Clustering for Dynamic Data Streams.

Data streams are a potentially unbounded sequence of data objects, and the clustering of such data is an effective way of identifying their underlying patterns. Existing data stream clustering algorithms face two critical issues: 1) evaluating the relationship among data objects with individual landmark windows of fixed size and 2) passing useful knowledge from previous landmark windows to the current landmark window. Based on sparse representation techniques, this article proposes a two-stage sparse representation clustering (TSSRC) method. The novelty of the proposed TSSRC algorithm comes from evaluating the effective relationship among data objects in the landmark windows with an accurate number of clusters. First, the proposed algorithm evaluates the relationship among data objects using sparse representation techniques. The dictionary and sparse representations are iteratively updated by solving a convex optimization problem. Second, the proposed TSSRC algorithm presents a dictionary initialization strategy that seeks representative data objects by making full use of the sparse representation results. This efficiently passes previously learned knowledge to the current landmark window over time. Moreover, the convergence and sparse stability of TSSRC can be theoretically guaranteed in continuous landmark windows under certain conditions. Experimental results on benchmark datasets demonstrate the effectiveness and robustness of TSSRC.

The application of dual-heart nursing mode in patients with coronary heart disease and angina pectoris and its impact on anxiety, depression, and quality of life.

To diagnose and treat patients with coronary heart disease and angina pectoris with dual heart care mode and analyze the treatment effect. Three hundred cases meeting the inclusion criteria were equally divided into 3 groups, each containing 50 male and female cases. The patients in experimental group 1 took the dual heart nursing method proposed by the subject; experimental group 2 received betastatins; control group received conventional treatment. After 12 weeks of treatment, Hamilton depression scale scored the 3 groups, and their anxiety and depression scores, clinical manifestations, symptom scores and self-acceptance were analyzed. The chi square value of these data was compared with P, and judge whether they meet the needs and differences of statistical data. Then compare their scores before and after treatment to identify the treatment status. The anxiety and depression scores of experimental group 1 were the lowest among the 3 groups, with the values of 59.62 ±â€…7.925 and 58.64 ±â€…6.416; The total patients who responded effectively to treatment in experimental group 1 accounted for 83%, and the patients who responded effectively to treatment rate was the highest in the 3 groups; The effect of decreasing the score of complications in experimental group 1 was also the most obvious, from 9.07 ±â€…4.28 to 3.14 ±â€…2.07, which was the best in the 3 groups; the self-evaluation of patients in experimental group 1 was the highest among the 3 groups, 89.72 ±â€…4.28. The proposed dual heart care and treatment method can effectively treat coronary heart disease and angina pectoris, and can effectively improve the clinical performance and self-acceptance of patients. It can effectively restore the anxiety and depression of patients after treatment, and then improve patients' life quality, which has the value of popularization and use.

Augmented Sparse Representation for Incomplete Multiview Clustering.

Incomplete multiview data are collected from multiple sources or characterized by multiple modalities, where the features of some samples or some views may be missing. Incomplete multiview clustering (IMVC) aims to partition the data into different groups by taking full advantage of the complementary information from multiple incomplete views. Most existing methods based on matrix factorization or subspace learning attempt to recover the missing views or perform imputation of the missing features to improve clustering performance. However, this problem is intractable due to a lack of prior knowledge, e.g., label information or data distribution, especially when the missing views or features are completely damaged. In this article, we proposed an augmented sparse representation (ASR) method for IMVC. We first introduce a discriminative sparse representation learning (DSRL) model, which learns the sparse representations of multiple views as applied to measure the similarity of the existing features. The DSRL model explores complementary and consistent information by integrating the sparse regularization item and a consensus regularization item, respectively. Simultaneously, it learns a discriminative dictionary from the original samples. The sparsity constrained optimization problem in the DSRL model can be efficiently solved by the alternating direction method of multipliers (ADMM). Then, we present a similarity fusion scheme, namely, a sparsity augmented fusion of sparse representations, to obtain a sparsity augmented similarity matrix across different views for spectral clustering. Experimental results on several datasets demonstrate the effectiveness of the proposed ASR method for IMVC.

Synthesis and Antibacterial Activity Evaluation of Biphenyl and Dibenzofuran Derivatives as Potential Antimicrobial Agents against Antibiotic-Resistant Bacteria.

The escalating prevalence of antibiotic-resistant bacteria has led to a serious global public health problem; therefore, there is an urgent need for the development of structurally innovative antibacterial agents. In our study, a series of biphenyl and dibenzofuran derivatives were designed and synthesized by Suzuki-coupling and demethylation reactions in moderate to excellent yields (51-94% yield). Eleven compounds exhibited potent antibacterial activities against the prevalent antibiotic-resistant Gram-positive and Gram-negative pathogens, among which compounds 4'-(trifluoromethyl)-[1,1'-biphenyl]-3,4,5-triol (6i) and 5-(9H-carbazol-2-yl) benzene-1,2,3-triol (6m) showed the most potent inhibitory activities against methicillin-resistant Staphylococcus aureus and multidrug-resistant Enterococcus faecalis with MIC (minimum inhibitory concentration) values as low as 3.13 and 6.25 µg/mL, respectively. Compounds 3',5'-dimethyl-[1,1'-biphenyl]-3,4,4',5-tetraol (6e), 4'-fluoro-[1,1'-biphenyl]-3,4,5-triol (6g), and 4'-(trifluoromethyl)-[1,1'-biphenyl]-3,4,5-triol (6i) showed comparable inhibitory activities with ciprofloxacin to Gram-negative bacterium carbapenems-resistant Acinetobacter baumannii. Study of the structure-activity relationship indicated that a strong electron-withdrawing group on the A ring and hydroxyl groups on the B ring of biphenyls were beneficial to their antibacterial activities, and for benzo-heterocycles, N-heterocycle exhibited optimal antibacterial activity. These results can provide novel structures of antibacterial drugs chemically different from currently known antibiotics and broaden prospects for the development of effective antibiotics against antibiotic-resistant bacteria.

Electroactive shape memory polyurethane composites reinforced with octadecyl isocyanate-functionalized multi-walled carbon nanotubes.

Shape memory polymers (SMPs) have a wide range of potential applications in many fields. In particular, electrically driven SMPs have attracted increasing attention due to their unique electrical deformation behaviors. Carbon nanotubes (CNTs) are often used as SMP conductive fillers because of their excellent electrical conductivities. However, raw CNTs do not disperse into the polymer matrix well. This strictly limits their use. In this study, to improve their dispersion performance characteristics in the polymer matrix, hydroxylated multi-walled carbon nanotubes (MWCNT-OHs) were functionalized with octadecyl isocyanate (i-MWCNTs). Polyurethane with shape memory properties (SMPU) was synthesized using polycaprolactone diol (PCL-diol), hexamethylene diisocyanate (HDI), and 1,4-butanediol (BDO) at a 1:5:4 ratio. Then, electroactive shape memory composites were developed by blending SMPU with i-MWCNTs to produce SMPU/i-MWCNTs. The functionalized i-MWCNTs exhibited better dispersibility characteristics in organic solvents and SMPU composites than the MWCNT-OHs. The addition of i-MWCNTs reduced the crystallinity of SMPU without affecting the original chemical structure. In addition, the hydrogen bond index and melting temperature of the SMPU soft segment decreased significantly, and the thermal decomposition temperatures of the composites increased. The SMPU/i-MWCNT composites exhibited conductivity when the i-MWCNT content was 0.5 wt%. This conductivity increased with the i-MWCNT content. In addition, when the i-MWCNT content exceeded 1 wt%, the composite temperature could increase beyond 60°C within 140 s and the temporary structure could be restored to its initial state within 120 s using a voltage of 30 eV. Therefore, the functionalized CNTs exhibit excellent potential for use in the development of electroactive shape memory composites, which may be used in flexible electronics and other fields.

Undescribed specialised metabolites from the endophytic fungus Emericella sp. XL029 and their antimicrobial activities.

The fungus Emericella sp. XL029 isolated from leaves of Panax notoginseng was investigated for agents with potential antibacterial and antifungal activities using a one strain-many compounds (OSMAC) strategy. Fifteen compounds, including seven undescribed structures, were obtained from this species. Their structures were confirmed by extensive spectroscopic data, single-crystal X-ray crystallography and quantum chemistry calculations. Emerlactam A exhibited better antibacterial activity against multidrug-resistant Enterococcus faecium and antifungal activity against Helminthosporium maydis, with an MIC value of 12.5 µg/mL. Quiannulatic acid displayed significant antibacterial activity against multidrug-resistant Enterococcus faecium and multidrug-resistant Enterococcus faecalis with MIC values of 1.56 µg/mL and 3.13 µg/mL, respectively. 5-alkenylresorcinol exhibited significant antifungal activity against all tested phytopathogenic fungi with MIC values ranging from 6.25 to 12.5 µg/mL.

Birth weight influences differently on systolic and diastolic blood pressure in children and adolescents aged 8-15.

AIM: Globally, hypertension is one of the main threats to public health and a significant risk factor predisposing individuals to various cardiovascular conditions. Hypertension in the young is particularly complex and challenging. Accumulating evidence has implicated that low birth weight is vital for elevated blood pressure, and birth weight was negatively correlated with blood pressure. However, fewer studies with conflicting results have addressed the associations between birth weight and blood pressure in children and adolescents, and there is no relevant research conducted in the NHANES population. The principal objective of this project was to investigate the relationship between birth weight and blood pressure in children and adolescents in NHANES. METHODS: A total of 7600 subjects aged 8 to15 were enrolled in the present study from the National Health and Nutrition Examination Survey (NHANES) 2007-2018. Outcome variables were systolic blood pressure(SBP) and diastolic blood pressure(DBP). Birth weight was regarded as an independent variable. EmpowerStats software and R (version 3.4.3) were performed to examine the association between birth weight and SBP or DBP. RESULTS: Birth weight was negatively correlated with SBP in the fully-adjusted model(ß = -0.02, 95%CI: -0.04 to -0.04, p = 0.0013), especially in non-Hispanic White (ß = -0.03, 95%CI: -0.06 to -0.00,p = 0.0446), aged between 13 to 15(ß = -0.03, 95%CI: -0.04 to -0.01, p = 0.0027), and male individuals(ß = -0.03, 95%CI: -0.05 to -0.01, p = 0.0027). However, there was no unidirectional association between birth weight and DBP in the fully adjusted model(ß = -0.01, 95%CI: -0.03 to 0.02, p = 0.5668) and in sub-analysis. An inverted U-shaped and J-shaped relationship was uncovered between birth weight and DBP in those aged 13 or above and Mexican Americans, respectively. The inflection point calculated by a recursive algorithm of birth weight in these groups was all 105 oz. CONCLUSIONS: The current study identified that birth weight was negatively related to SBP but not significantly related to DBP in children and adolescents aged 8 to 15, highlighting different potential mechanisms behind high SBP and high DBP in the young. However, an inverted U-shaped and J-shaped relationship between birth weight and DBP was observed, suggesting that targeted intervention measures should be taken for different groups of people rather than generalizations.