[DNA Sequence-Specific Ligands. 19. Synthesis, Spectral Properties, Virological and Biochemical Studies of DB3(n) Fluorescent Dimeric Trisbenzimidazoles].

In this work, we synthesized and characterized the properties of a series of new fluorescent DB3(n) narrow-groove ligands. DB3(n) compounds based on dimeric trisbenzimidazoles have the ability to bind to the AT regions of DNA. The synthesis of DB3(n), whose trisbenzimidazole fragments are linked by oligomethylene linkers of different lengths (n = 1, 5, 9), is based on the condensation of the MB3 monomeric trisbenzimidazole with α,ω-alkyldicarboxylic acids. DB3 (n) proved to be effective inhibitors of the catalytic activity of HIV-1 integrase at submicromolar concentrations (0.20-0.30 µM). DB3(n) was found to inhibit the catalytic activity of DNA topoisomerase I at low micromolar concentrations.

Insight into the interaction between trimethoprim and soluble microbial products produced from biological wastewater treatment processes.

Soluble microbial products (SMPs), dissolved organic matter excreted by activated sludge, can interact with antibiotics in wastewater and natural water bodies. Interactions between SMPs and antibiotics can influence antibiotic migration, transformation, and toxicity but the mechanisms involved in such interactions are not fully understood. In this study, integrated spectroscopy approaches were used to investigate the mechanisms involved in interactions between SMPs and a representative antibiotic, trimethoprim (TMP), which has a low biodegradation rate and has been detected in wastewater. The results of liquid chromatography-organic carbon detection-organic nitrogen detection indicated that the SMPs used in the study contained 15% biopolymers and 28% humic-like substances (based on the total dissolved organic carbon concentration) so would have contained sites that could interact with TMP. A linear relationship of fluorescent intensities of tryptophan protein-like substances in SMP was observed (R2>0.99), indicating that the fluorescence enhancement between SMP and TMP occurred. Fourier-transform infrared spectroscopy and X-ray photoelectron spectroscopy indicated that carboxyl, carbonyl, and hydroxyl groups were the main functional groups involved in the interactions. The electrostatic and π-π interactions were discovered by the UV-vis spectra and 1H nuclear magnetic resonance spectra. Structural representations of the interactions between representative SMP subcomponents and TMP were calculated using density functional theory, and the results confirmed the conclusions drawn from the 1H nuclear magnetic resonance spectra. The results help characterize SMP-TMP complexes and will help understand antibiotic transformations in wastewater treatment plants and aquatic environments.

Spectral density-based clustering algorithms for complex networks.

Introduction: Clustering is usually the first exploratory analysis step in empirical data. When the data set comprises graphs, the most common approaches focus on clustering its vertices. In this work, we are interested in grouping networks with similar connectivity structures together instead of grouping vertices of the graph. We could apply this approach to functional brain networks (FBNs) for identifying subgroups of people presenting similar functional connectivity, such as studying a mental disorder. The main problem is that real-world networks present natural fluctuations, which we should consider. Methods: In this context, spectral density is an exciting feature because graphs generated by different models present distinct spectral densities, thus presenting different connectivity structures. We introduce two clustering methods: k-means for graphs of the same size and gCEM, a model-based approach for graphs of different sizes. We evaluated their performance in toy models. Finally, we applied them to FBNs of monkeys under anesthesia and a dataset of chemical compounds. Results: We show that our methods work well in both toy models and real-world data. They present good results for clustering graphs presenting different connectivity structures even when they present the same number of edges, vertices, and degree of centrality. Discussion: We recommend using k-means-based clustering for graphs when graphs present the same number of vertices and the gCEM method when graphs present a different number of vertices.

Strain Streptomyces sp. P-56 Produces Nonactin and Possesses Insecticidal, Acaricidal, Antimicrobial and Plant Growth-Promoting Traits.

Streptomycetes produce a huge variety of bioactive metabolites, including antibiotics, enzyme inhibitors, pesticides and herbicides, which offer promise for applications in agriculture as plant protection and plant growth-promoting products. The aim of this report was to characterize the biological activities of strain Streptomyces sp. P-56, previously isolated from soil as an insecticidal bacterium. The metabolic complex was obtained from liquid culture of Streptomyces sp. P-56 as dried ethanol extract (DEE) and possessed insecticidal activity against vetch aphid (Medoura viciae Buckt.), cotton aphid (Aphis gossypii Glov.), green peach aphid (Myzus persicae Sulz.), pea aphid (Acyrthosiphon pisum Harr.) and crescent-marked lily aphid (Neomyzus circumflexus Buckt.), as well as two-spotted spider mite (Tetranychus urticae). Insecticidal activity was associated with production of nonactin, which was purified and identified using HPLC-MS and crystallographic techniques. Strain Streptomyces sp. P-56 also showed antibacterial and antifungal activity against various phytopathogenic bacteria and fungi (mostly for Clavibacfer michiganense, Alternaria solani and Sclerotinia libertiana), and possessed a set of plant growth-promoting traits, such as auxin production, ACC deaminase and phosphate solubilization. The possibilities for using this strain as a biopesticide producer and/or biocontrol and a plant growth-promoting microorganism are discussed.

Vibration Fatigue of FDM 3D Printed Structures: The Use of Frequency Domain Approach.

Additive manufactured structures are replacing the corresponding ones realized with classical manufacturing technique. As for metallic structures, 3D printed components are generally subjected to dynamic loading conditions which can lead to fatigue failure. In this context, it is useful, and sometimes mandatory, to determine the fatigue life of such components through numerical simulation. The methods currently available in literature for the estimation of fatigue life were originally developed for metallic structures and, therefore, it is now necessary to verify their applicability also for components fabricated with different materials. To this end, in the current activity three of the most used spectral methods for the estimation of fatigue life were used to determine the fatigue life of a 3D printed Y-shaped specimen realized in polylactic acid subjected to random loads with the aim of determining their adaptability also for this kind of materials. To certify the accuracy of the numerical prediction, a set of experimental tests were conducted in order to obtain the real fatigue life of the component and to compare the experimental results with those numerically obtained. The obtained outcomes showed there is an excellent match between the numerical and the experimental data, thus certifying the possibility of using the investigated spectral methods to predict the fatigue life of additive manufactured components.

Vitamin B2 concentration in cow milk: Quantification by a new UHPLC method and prediction by visible and near-infrared spectral analysis.

Until now, there are few information on vitamin B2 concentration variability in milk. In this study, a novel analytical method to quantify total vitamin B2 in milk was developed and applied on 676 samples. In parallel, spectral analysis (colorimetry and near infrared spectroscopy) were performed to develop prediction models of vitamin B2 concentration in milk. The analytical method includes an acid and enzymatic extraction followed by vitamin B2 quantification by Ultra High Performance Liquid Chromatography coupled with fluorimetry. Samples analysis showed a wide range of concentration from 0.78 to 4.58 mg/L with a mean of 2.09 ± 0.48 mg/L. Two prediction models based on colorimetric analysis allow estimation of vitamin B2 concentration in milk. Thus, this work shows an analytical method and, for the first time, a prediction method to enable enhancement of researches on vitamin B2 content of milk and its variation factors.

Directed network Laplacians and random graph models.

We consider spectral methods that uncover hidden structures in directed networks. We establish and exploit connections between node reordering via (a) minimizing an objective function and (b) maximizing the likelihood of a random graph model. We focus on two existing spectral approaches that build and analyse Laplacian-style matrices via the minimization of frustration and trophic incoherence. These algorithms aim to reveal directed periodic and linear hierarchies, respectively. We show that reordering nodes using the two algorithms, or mapping them onto a specified lattice, is associated with new classes of directed random graph models. Using this random graph setting, we are able to compare the two algorithms on a given network and quantify which structure is more likely to be present. We illustrate the approach on synthetic and real networks, and discuss practical implementation issues.

Spectral neighbor joining for reconstruction of latent tree Models.

A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model. An important example is phylogenetics, where the tree models the evolutionary lineages of a set of observed organisms. Given a set of independent realizations of the random variables at the leaves of the tree, a key challenge is to infer the underlying tree topology. In this work we develop Spectral Neighbor Joining (SNJ), a novel method to recover the structure of latent tree graphical models. Given a matrix that contains a measure of similarity between all pairs of observed variables, SNJ computes a spectral measure of cohesion between groups of observed variables. We prove that SNJ is consistent, and derive a sufficient condition for correct tree recovery from an estimated similarity matrix. Combining this condition with a concentration of measure result on the similarity matrix, we bound the number of samples required to recover the tree with high probability. We illustrate via extensive simulations that in comparison to several other reconstruction methods, SNJ requires fewer samples to accurately recover trees with a large number of leaves or long edges.

Core-periphery structure in directed networks.

Empirical networks often exhibit different meso-scale structures, such as community and core-periphery structures. Core-periphery structure typically consists of a well-connected core and a periphery that is well connected to the core but sparsely connected internally. Most core-periphery studies focus on undirected networks. We propose a generalization of core-periphery structure to directed networks. Our approach yields a family of core-periphery block model formulations in which, contrary to many existing approaches, core and periphery sets are edge-direction dependent. We focus on a particular structure consisting of two core sets and two periphery sets, which we motivate empirically. We propose two measures to assess the statistical significance and quality of our novel structure in empirical data, where one often has no ground truth. To detect core-periphery structure in directed networks, we propose three methods adapted from two approaches in the literature, each with a different trade-off between computational complexity and accuracy. We assess the methods on benchmark networks where our methods match or outperform standard methods from the literature, with a likelihood approach achieving the highest accuracy. Applying our methods to three empirical networks-faculty hiring, a world trade dataset and political blogs-illustrates that our proposed structure provides novel insights in empirical networks.

On the accuracy and applicability of a new implicit Taylor method and the high-order spectral method on steady nonlinear waves.

This paper presents an investigation and discussion of the accuracy and applicability of an implicit Taylor (IT) method versus the classical higher-order spectral (HOS) method when used to simulate two-dimensional regular waves. This comparison is relevant, because the HOS method is in fact an explicit perturbation solution of the IT formulation. First, we consider the Dirichlet-Neumann problem of determining the vertical velocity at the free surface given the surface elevation and the surface potential. For this problem, we conclude that the IT method is significantly more accurate than the HOS method when using the same truncation order, M, and spatial resolution, N, and is capable of dealing with steeper waves than the HOS method. Second, we focus on the problem of integrating the two methods in time. In this connection, it turns out that the IT method is less robust than the HOS method for similar truncation orders. We conclude that the IT method should be restricted to M = 4, while the HOS method can be used with M ≤ 8. We systematically compare these two options and finally establish the best achievable accuracy of the two methods as a function of the wave steepness and the water depth.

A Mathieu function boundary spectral method for scattering by multiple variable poro-elastic plates, with applications to metamaterials and acoustics.

Many problems in fluid mechanics and acoustics can be modelled by Helmholtz scattering off poro-elastic plates. We develop a boundary spectral method, based on collocation of local Mathieu function expansions, for Helmholtz scattering off multiple variable poro-elastic plates in two dimensions. Such boundary conditions, namely the varying physical parameters and coupled thin-plate equation, present a considerable challenge to current methods. The new method is fast, accurate and flexible, with the ability to compute expansions in thousands (and even tens of thousands) of Mathieu functions, thus making it a favourable method for the considered geometries. Comparisons are made with elastic boundary element methods, where the new method is found to be faster and more accurate. Our solution representation directly provides a sine series approximation of the far-field directivity and can be evaluated near or on the scatterers, meaning that the near field can be computed stably and efficiently. The new method also allows us to examine the effects of varying stiffness along a plate, which is poorly studied due to limitations of other available techniques. We show that a power-law decrease to zero in stiffness parameters gives rise to unexpected scattering and aeroacoustic effects similar to an acoustic black hole metamaterial.

Spectral Jaccard Similarity: A New Approach to Estimating Pairwise Sequence Alignments.

Pairwise sequence alignment is often a computational bottleneck in genomic analysis pipelines, particularly in the context of third-generation sequencing technologies. To speed up this process, the pairwise k-mer Jaccard similarity is sometimes used as a proxy for alignment size in order to filter pairs of reads, and min-hashes are employed to efficiently estimate these similarities. However, when the k-mer distribution of a dataset is significantly non-uniform (e.g., due to GC biases and repeats), Jaccard similarity is no longer a good proxy for alignment size. In this work, we introduce a min-hash-based approach for estimating alignment sizes called Spectral Jaccard Similarity, which naturally accounts for uneven k-mer distributions. The Spectral Jaccard Similarity is computed by performing a singular value decomposition on a min-hash collision matrix. We empirically show that this new metric provides significantly better estimates for alignment sizes, and we provide a computationally efficient estimator for these spectral similarity scores.

Diagnosis of thalassemia using fluorescence spectroscopy, auto-analyzer, and hemoglobin electrophoresis - A prospective study.

BACKGROUND: Hemoglobinopathies (HgP) are prevalent in certain regions of the world. The World Health Organization estimated that 5% of the world's population is a carrier of the potentially pathological hemoglobin (Hb) gene. METHODS: This study aimed to compare the performance of fluorescence spectroscopy, a simple and inexpensive method, with that of conventional techniques for diagnosing thalassemia. The red blood cell (RBC) counts and levels of Hb, HbA, HbA2, and HbS were estimated via conventional methods of complete blood count and Hb electrophoresis to diagnose thalassemia. RESULTS: The RBCs and Hb, particularly the average values of HbA and HbA2, were lower in patients with thalassemia than in the normal controls. These hematologic parameters were also analyzed via fluorescence spectroscopybased on fluorescent biomolecules including tyrosine (275 nm), tryptophan (290 nm), nicotinamide adenine dinucleotide (NADH) (370 nm), flavin adenine dinucleotide (FAD) (450 nm), and porphyrin (585-635 nm). In thalassemia patients, all these parameters were above the normal range, primarily due to abnormal depression of NADH and elevation of FAD. CONCLUSION: Thalassemia canbe diagnosed via a fluorescent spectral method with an accuracy of 85% for blinded groups. This method may be useful for screening patients and reducing the cost of diagnosis in many rural countries.

Graph convolutional networks: a comprehensive review.

Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, it is often very challenging to solve the learning problems on graphs, because (1) many types of data are not originally structured as graphs, such as images and text data, and (2) for graph-structured data, the underlying connectivity patterns are often complex and diverse. On the other hand, the representation learning has achieved great successes in many areas. Thereby, a potential solution is to learn the representation of graphs in a low-dimensional Euclidean space, such that the graph properties can be preserved. Although tremendous efforts have been made to address the graph representation learning problem, many of them still suffer from their shallow learning mechanisms. Deep learning models on graphs (e.g., graph neural networks) have recently emerged in machine learning and other related areas, and demonstrated the superior performance in various problems. In this survey, despite numerous types of graph neural networks, we conduct a comprehensive review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models. First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph convolutional network models in details. Then, we categorize different graph convolutional networks according to the areas of their applications. Finally, we present several open challenges in this area and discuss potential directions for future research.

Characterization of interactions between a metabolic uncoupler O-chlorophenol and extracellular polymeric substances of activated sludge.

Metabolic uncouplers are widely used for the in-situ reduction of excess sludge from activated sludge systems. However, the interaction mechanism between the metabolic uncouplers and extracellular polymeric substances (EPS) of activated sludge is unknown yet. In this study, the interactions between a typical metabolic uncoupler, o-chlorophenol (oCP), and the EPS extracted from activated sludge were explored using a suite of spectral methods. The binding constants calculated for the four peaks of three-dimensional excitation-emission matrix fluorescence were in a range of 1.24-1.76 × 103 L/mol, implying that the tyrosine protein-like substances governed the oCP-EPS interactions. Furthermore, the results of Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy and 1H nuclear magnetic resonance indicated that the carboxyl, carbonyl, amine, and hydroxyl groups of EPS were the main functional groups involved in the formation of the oCP-EPS complex. The results of this study are useful for understanding the interactions between metabolic uncouplers and the EPS of activated sludge as well as their fates in biological wastewater treatment systems.

Dynamics reconstruction and classification via Koopman features.

Knowledge discovery and information extraction of large and complex datasets has attracted great attention in wide-ranging areas from statistics and biology to medicine. Tools from machine learning, data mining, and neurocomputing have been extensively explored and utilized to accomplish such compelling data analytics tasks. However, for time-series data presenting active dynamic characteristics, many of the state-of-the-art techniques may not perform well in capturing the inherited temporal structures in these data. In this paper, integrating the Koopman operator and linear dynamical systems theory with support vector machines, we develop an ovel dynamic data mining framework to construct low-dimensional linear models that approximate the nonlinear flow of high-dimensional time-series data generated by unknown nonlinear dynamical systems. This framework then immediately enables pattern recognition, e.g., classification, of complex time-series data to distinguish their dynamic behaviors by using the trajectories generated by the reduced linear systems. Moreover, we demonstrate the applicability and efficiency of this framework through the problems of time-series classification in bioinformatics and healthcare, including cognitive classification and seizure detection with fMRI and EEG data, respectively. The developed Koopman dynamic learning framework then lays a solid foundation for effective dynamic data mining and promises a mathematically justified method for extracting the dynamics and significant temporal structures of nonlinear dynamical systems.

A 3D spatial spectral integral equation method for electromagnetic scattering from finite objects in a layered medium.

The generalization of a two-dimensional spatial spectral volume integral equation to a three-dimensional spatial spectral integral equation formulation for electromagnetic scattering from dielectric objects in a stratified dielectric medium is explained. In the spectral domain, the Green function, contrast current density, and scattered electric field are represented on a complex integration manifold that evades the poles and branch cuts that are present in the Green function. In the spatial domain, the field-material interactions are reformulated by a normal-vector field approach, which obeys the Li factorization rules. Numerical evidence is shown that the computation time of this method scales as O ( N log N ) on the number of unknowns. The accuracy of the method for three numerical examples is compared to a finite element method reference.

Two hydrazones derived from 1-aryl-3-(p-substituted phenyl)prop-2-en-1-one: synthesis, crystal structure, Hirshfeld surface analysis and in vitro biological properties.

Two chalcones were synthesized by the aldolic condensation of enolizable aromatic ketones with substituted benzaldehydes under Claisen-Schmidt reaction conditions and then treated with 2,4-dinitrophenylhydrazine to yield their corresponding hydrazones. The two (E,Z)-2,4-dinitrophenylhydrazone structures, namely (Z)-1-(2,4-dinitrophenyl)-2-[(E)-3-(4-methylphenyl)-1-phenylallylidene]hydrazine, C22H18N4O4, (H1), and (Z)-1-[(E)-3-(4-chlorophenyl)-1-(naphthalen-1-yl)allylidene]-2-(2,4-dinitrophenyl)hydrazine, C25H17ClN4O4, (H2), were isolated by recrystallization and characterized by FT-IR, UV-Vis, single-crystal and powder X-ray diffraction methods. The UV-Vis spectra of the hydrazones have been studied in two organic solvents of different polarity. It was found that (H2) has a molar extinction coefficient larger than 40000. Single-crystal X-ray diffraction analysis reveals that the molecular zigzag chains of (H1) and (H2) are interconnected through noncovalent contacts. A quantitative analysis of the intermolecular interactions in the crystal structures has been performed using Hirshfeld surface analysis. All the synthesized chalcones and hydrazones were evaluated for their antibacterial and antioxidant activities. Results indicate that the studied compounds show significant activity against Gram negative Escherichia coli strain and the chalcone 3-(4-methylphenyl)-1-phenylprop-2-en-1-one, (C1), was the most effective. In addition, only hydrazone (H1) displayed a moderate DPPH (2,2-diphenyl-1-picryl hydrazyl) scavenging efficiency.

Synthesis and biological evaluation of the new 1,3-dimethylxanthine derivatives with thiazolidine-4-one scaffold.

BACKGROUND: The xanthine structure has proved to be an important scaffold in the process of developing a wide variety of biologically active molecules such as bronchodilator, hypoglycemiant, anticancer and anti-inflammatory agents. It is known that hyperglycemia generates reactive oxygen species which are involved in the progression of diabetes mellitus and its complications. Therefore, the development of new compounds with antioxidant activity could be an important therapeutic strategy against this metabolic syndrome. RESULTS: New thiazolidine-4-one derivatives with xanthine structure have been synthetized as potential antidiabetic drugs. The structure of the synthesized compounds was confirmed by using spectral methods (FT-IR, 1H-NMR, 13C-NMR, 19F-NMR, HRMS). Their antioxidant activity was evaluated using in vitro assays: DPPH and ABTS radical scavenging ability and phosphomolybdenum reducing antioxidant power assay. The developed compounds showed improved antioxidant effects in comparison to the parent compound, theophylline. In the case of both series, the intermediate (5a-k) and final compounds (6a-k), the aromatic substitution, especially in para position with halogens (fluoro, chloro), methyl and methoxy groups, was associated with an increase of the antioxidant effects. CONCLUSIONS: For several thiazolidine-4-one derivatives the antioxidant effect of was superior to that of their corresponding hydrazone derivatives. The most active compound was 6f which registered the highest radical scavenging activity.Graphical abstractDesign and synthesis of new thiazolidine-4-one derivatives.

Biological control via "ecological" damping: An approach that attenuates non-target effects.

In this work we develop and analyze a mathematical model of biological control to prevent or attenuate the explosive increase of an invasive species population, that functions as a top predator, in a three-species food chain. We allow for finite time blow-up in the model as a mathematical construct to mimic the explosive increase in population, enabling the species to reach "disastrous", and uncontrollable population levels, in a finite time. We next improve the mathematical model and incorporate controls that are shown to drive down the invasive population growth and, in certain cases, eliminate blow-up. Hence, the population does not reach an uncontrollable level. The controls avoid chemical treatments and/or natural enemy introduction, thus eliminating various non-target effects associated with such classical methods. We refer to these new controls as "ecological damping", as their inclusion dampens the invasive species population growth. Further, we improve prior results on the regularity and Turing instability of the three-species model that were derived in Parshad et al. (2014). Lastly, we confirm the existence of spatiotemporal chaos.