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.

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.

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.

Dealing with distribution mismatch in semi-supervised deep learning for COVID-19 detection using chest X-ray images: A novel approach using feature densities.

In the context of the global coronavirus pandemic, different deep learning solutions for infected subject detection using chest X-ray images have been proposed. However, deep learning models usually need large labelled datasets to be effective. Semi-supervised deep learning is an attractive alternative, where unlabelled data is leveraged to improve the overall model's accuracy. However, in real-world usage settings, an unlabelled dataset might present a different distribution than the labelled dataset (i.e. the labelled dataset was sampled from a target clinic and the unlabelled dataset from a source clinic). This results in a distribution mismatch between the unlabelled and labelled datasets. In this work, we assess the impact of the distribution mismatch between the labelled and the unlabelled datasets, for a semi-supervised model trained with chest X-ray images, for COVID-19 detection. Under strong distribution mismatch conditions, we found an accuracy hit of almost 30%, suggesting that the unlabelled dataset distribution has a strong influence in the behaviour of the model. Therefore, we propose a straightforward approach to diminish the impact of such distribution mismatch. Our proposed method uses a density approximation of the feature space. It is built upon the target dataset to filter out the observations in the source unlabelled dataset that might harm the accuracy of the semi-supervised model. It assumes that a small labelled source dataset is available together with a larger source unlabelled dataset. Our proposed method does not require any model training, it is simple and computationally cheap. We compare our proposed method against two popular state of the art out-of-distribution data detectors, which are also cheap and simple to implement. In our tests, our method yielded accuracy gains of up to 32%, when compared to the previous state of the art methods. The good results yielded by our method leads us to argue in favour for a more data-centric approach to improve model's accuracy. Furthermore, the developed method can be used to measure data effectiveness for semi-supervised deep learning model training.

Antifungal Biphenyl Derivatives from Sorbus pohuashanensis Leaves Infected by Alternaria tenuissi and Their Effect against Crop Pathogens.

Eight natural biphenyl-type phytoalexins exhibiting antifungal effect were isolated from the leaves of Sorbus pohuashanensis, which invaded by Alternaria tenuissi, and their growth inhibition rate towards A. tenuissi were 50.3 %, 54.0 %, 66.4 %, 58.8 %, 48.5 %, 51.0 %, 33.3 %, and 37.0 %, respectively. In vivo activity assay verified the protective effect of these natural biphenyls on tobacco leaves. The observation of mycelial morphology revealed that these compounds possessed adverse effects on mycelial growth of A. tenuissi. Subsequently, the most potent active compounds, 3',4',5'-trimethoxy[1,1'-biphenyl]-4-ol (3) and 3,4,4',5-tetramethoxy-1,1'-biphenyl (4), were conducted to the further antifungal evaluation and showed significant activity against the other four crop pathogens, Fusarium graminearum, Helminthosporium maydis, Sclerotinia sclerotiorum, and Exserohilum turcicum. Further, the structure-activity relationships and biosynthesis of these compounds were speculated in this work.

Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images.

A key factor in the fight against viral diseases such as the coronavirus (COVID-19) is the identification of virus carriers as early and quickly as possible, in a cheap and efficient manner. The application of deep learning for image classification of chest X-ray images of COVID-19 patients could become a useful pre-diagnostic detection methodology. However, deep learning architectures require large labelled datasets. This is often a limitation when the subject of research is relatively new as in the case of the virus outbreak, where dealing with small labelled datasets is a challenge. Moreover, in such context, the datasets are also highly imbalanced, with few observations from positive cases of the new disease. In this work we evaluate the performance of the semi-supervised deep learning architecture known as MixMatch with a very limited number of labelled observations and highly imbalanced labelled datasets. We demonstrate the critical impact of data imbalance to the model's accuracy. Therefore, we propose a simple approach for correcting data imbalance, by re-weighting each observation in the loss function, giving a higher weight to the observations corresponding to the under-represented class. For unlabelled observations, we use the pseudo and augmented labels calculated by MixMatch to choose the appropriate weight. The proposed method improved classification accuracy by up to 18%, with respect to the non balanced MixMatch algorithm. We tested our proposed approach with several available datasets using 10, 15 and 20 labelled observations, for binary classification (COVID-19 positive and normal cases). For multi-class classification (COVID-19 positive, pneumonia and normal cases), we tested 30, 50, 70 and 90 labelled observations. Additionally, a new dataset is included among the tested datasets, composed of chest X-ray images of Costa Rican adult patients.

A 3D screening approach identifies the compound epitajixanthone hydrate as a new inhibitor of cancer cell growth and invasion.

With unique advantages, the small-molecule anticancer drugs have recently gained growing attention. Particular strategies, exemplified by high-throughput screening, fragment-based drug discovery, virtual screening and knowledge-based design, have been developed to identify active compounds. However, such screens generally rely on sophisticated and expensive instrumentations. Herein, we developed a simple spheroids 3D culture system to enable direct screening of small molecules with reliable results. Using this system, we screened 27 fungal natural products and three fungal crude extracts for their inhibitory effects on cancer cell growth, and invasion. We identified that the compound M23 (epitajixanthone hydrate, a derivative of prenylxanthone) and the crude extracts (MPT-191) from the fungi Taxus chinensis showed potential anticancer activity. The effect of epitajixanthone hydrate on cancer cell growth and invasion were further confirmed by the assays of cells viability, trans-well migration and invasion, colony formation and cells reattachment. Overall, Epitajixanthone hydrate was identified as an effective inhibitor of cancer cell growth and invasion by our simple and fast screening platform.

Cytotoxic Activity of Inositol Angelates and Tirucallane-Type Alkaloids from Amoora Dasyclada.

Three new inositol angelate compounds (1-3) and two new tirucallane-type alkaloids (4 and 5) were isolated from the Amoora dasyclada, and their structures were established mainly by means of combination of 1D and 2D nuclear magnetic resonance and HR-ESI-MS. Based on cytotoxicity testing, compounds 4 and 5 exhibited significant cytotoxic activity against human cancer cell line HepG2 with IC50 value at 8.4 and 13.2 µM. In addition, compounds 4 and 5 also showed remarkable growth inhibitory activity to Artemia salina larvae.

Chemical composition and larvicidal activity of essential oils from Peganum harmala, Nepeta cataria and Phellodendron amurense against Aedes aegypti (Diptera: Culicidae).

Essential oils from aerial parts of the herbs Peganum harmala and Nepeta cataria, and leaves of the tree Phellodendron amurense were analyzed by GC-FID and GC-MS, and their larvicidal activities were assayed on the early fourth instar larvae of Aedes aegypti. The major constituents of the oils were limonene (14.5%) and thymol (11.5%) in P. harmala, thymol (46.5%), 4aα,7α,7aß-nepetalactone (18.3%) and 4aα,7ß,7aα-neptalactone (19.7%) in N. cataria, eugenol (14.5%) andγ-eudesmol (9.5%) in P. amurense.The oil of N. cataria had a strong larvicidal activity (LC50 < 50 µg/mL; LC90 < 86.8 µg/mL) on A. aegypti while the remaining oils showed a moderated killing effect. The larvicidal activity of N. cataria oil was associated to the contents of 1,8-cineol, camphor, 4aα,7α,7aß-Nepetalactone, 4aα,7ß,7aα-Nepetalactone and thymol. Our results indicate that the oil of N. catariadeserves to be used as a source of larvicidal agents against A. aegypti.

Aureonitols A and B, Two New C13 -Polyketides from Chaetomium globosum, an Endophytic Fungus in Salvia miltiorrhiza.

Two new C13 -polyketides, aureonitols A and B (1 and 2), along with five known compounds (3-7), were isolated from the solid fermentation culture of the plant endophytic fungus Chaetomium globosum from the aerial parts of Salvia miltiorrhiza. The structures and absolute configurations of 1 and 2 were determined by comprehensive spectroscopic data analysis and computed methods. Compound 5 was found to display the remarkable antimicrobial activities against four multidrug-resistant bacteria (Enterococcus faecalis, Enterococcus faecium, Staphylococcus aureus, and Staphylococcus epidermidis) with MIC values of 3.13-6.25 µg/mL (ciprofloxacin: 0.78-1.56 µg/mL), and also against all tested fungal strains with MIC values of 3.13-25 µg/mL (ketoconazole: 0.78-12.50 µg/mL).

Field comparison of the effectiveness of agricultural and nonagricultural organic wastes for aided phytostabilization of a Pb-Zn mine tailings pond in Hunan Province, China.

To date, very few attempts have been made to systematically compare the effectiveness of agricultural and nonagricultural organic wastes for aided phytostabilization of mine tailings under field conditions. In this study, we performed a field trial to compare the effectiveness of three agricultural organic wastes: chicken manure (CM), crop straw (CS), and spent mushroom compost (SMC), with that of three nonagricultural organic wastes, municipal sludge (MS), medicinal herb residues (MHR), and sweet sorghum vinasse (SSV) for aided phytostabilization of a Pb-Zn mine tailings pond in Hunan Province, China. Eight plant species naturally established in the vicinity of the mine were selected and seeded onto trial plots. It was found that the CM-amended plots had the highest (p < 0.05) vegetation cover (86%) and biomass production (881 g m-2), compared to other treatments. CM was also one of the best amendments in terms of improving soil nutrient status, increasing activities of soil enzymes, and immobilizing soil Pb. In addition, CM-amended plots were characterized by their higher microbial diversity and distinct microbial community structure as compared to the control plots. MS was the second best amendment in promoting vegetation cover (71%) and biomass production (461 g m-2), and it performed as well as CM for improving nutrient status, immobilizing heavy metals, and increasing the activities of enzymes in the mine tailings. Suggestions for further lines of research are made in order to develop future investigations.

Isolation, stereochemical study, and cytotoxic activity of isobenzofuran derivatives from a marine Streptomyces sp.

A new 1,3-dihydroisobenzofuran derivative (), together with its epimer (), was isolated from marine Streptomyces sp. W007. The structure of the two compounds was established by extensive spectroscopic analysis and comparison with reported data. The absolute configurations of and were determined by a combination of experimental and computational means, including J-coupling analysis and nuclear Overhauser effect spectroscopy (NOESY) spectra, nuclear magnetic resonance (NMR) calculations, electronic circular dichroism (ECD), and optical rotation (OR) calculations. Compound had no cytotoxicity against human lung adenocarcinoma cell line A549, while compound exhibited weak activity, suggesting that the biological activity depends on the configuration of a single chirality center.

An adaptive multi-swarm optimizer for dynamic optimization problems.

The multipopulation method has been widely used to solve dynamic optimization problems (DOPs) with the aim of maintaining multiple populations on different peaks to locate and track multiple changing optima simultaneously. However, to make this approach effective for solving DOPs, two challenging issues need to be addressed. They are how to adapt the number of populations to changes and how to adaptively maintain the population diversity in a situation where changes are complicated or hard to detect or predict. Tracking the changing global optimum in dynamic environments is difficult because we cannot know when and where changes occur and what the characteristics of changes would be. Therefore, it is necessary to take these challenging issues into account in designing such adaptive algorithms. To address the issues when multipopulation methods are applied for solving DOPs, this paper proposes an adaptive multi-swarm algorithm, where the populations are enabled to be adaptive in dynamic environments without change detection. An experimental study is conducted based on the moving peaks problem to investigate the behavior of the proposed method. The performance of the proposed algorithm is also compared with a set of algorithms that are based on multipopulation methods from different research areas in the literature of evolutionary computation.

ETEA: a Euclidean minimum spanning tree-based evolutionary algorithm for multi-objective optimization.

The Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in space where the edge weight between each pair of points is their Euclidean distance. Since the generation of an EMST is entirely determined by the Euclidean distance between solutions (points), the properties of EMSTs have a close relation with the distribution and position information of solutions. This paper explores the properties of EMSTs and proposes an EMST-based evolutionary algorithm (ETEA) to solve multi-objective optimization problems (MOPs). Unlike most EMO algorithms that focus on the Pareto dominance relation, the proposed algorithm mainly considers distance-based measures to evaluate and compare individuals during the evolutionary search. Specifically, in ETEA, four strategies are introduced: (1) An EMST-based crowding distance (ETCD) is presented to estimate the density of individuals in the population; (2) A distance comparison approach incorporating ETCD is used to assign the fitness value for individuals; (3) A fitness adjustment technique is designed to avoid the partial overcrowding in environmental selection; (4) Three diversity indicators-the minimum edge, degree, and ETCD-with regard to EMSTs are applied to determine the survival of individuals in archive truncation. From a series of extensive experiments on 32 test instances with different characteristics, ETEA is found to be competitive against five state-of-the-art algorithms and its predecessor in providing a good balance among convergence, uniformity, and spread.

[Chemical constituents of Hyptis rhomboidea and their antifungal activity].

The present work is to investigate the chemical constitutions of Hyptis rhomboidea and their antifungal activities. The compounds were isolated by Toyopearl HW-40, Sephadex LH-20, MCI-Gel CHP-20, RP-18, PTLC and silica column chromatographic methods and subjected to evaluate some monomers antifungal activity of eight kinds of plant pathogenic bacteria. Eleven compounds were isolated and identified as ethyl caffeate (1), ursolic acid (2), oleanolic acid (3), vanillactic acid (4), methyl rosmarinate (5), kaempferol 3-O-alpha-L-rhamnopyranosyl-(1 --> 6) -beta-D-glucopyranoside (6), kaempferol 3-O-alpha-L-rhamnopyranosyl-(1 --> 6)-beta-D-glucopyranoside (7), ilexgenin A (8), beta-amyrin (9), kaempferol 3-O-beta-D-glucopyranoside (astrgalin, 10) and cholest-5-ene-3beta, 4beta-diol (11). Compound 1 showed the strongest inhibitory effect on Sclerotinia sclerotiorum with the MIC 16.2 mg x L(-1), and compound 5 showed the strongest inhibitory effect on S. minor and Exserohilum turcicum with MIC 16.2, 8.1 mg x L(-1), respectively. All compounds were isolated from the H. rhomboidea for the first time, and compounds 1 and 5 showed antifungal activity.

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.

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.

Cytotoxic azaphilone alkaloids from Chaetomium globosum TY1.

Three novel azaphilone alkaloids, namely chaetomugilides A-C (1-3), together with three related compounds (4-6) were isolated from the methanol extract of Chaetomium globosum TY1, an endophytic fungus isolated from Ginkgo biloba. Their structures were elucidated on the basis of extensive 1D and 2D NMR as well as HRESI-MS spectroscopic data analysis. The isolated compounds exhibited highly cytotoxic activities against human cancer cell line HePG2 with the IC50 values range from 1.7 to 53.4µM.

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.

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.