[4.3.1]Bicyclic FKBP Ligands Inhibit Legionella Pneumophila Infection by LpMip-Dependent and LpMip-Independent Mechanisms**

Legionella pneumophila is the causative agent of Legionnaires’ disease, a serious form of pneumonia. Its macrophage infectivity potentiator (Mip), a member of a highly conserved family of FK506-binding proteins (FKBPs), plays a major role in the proliferation of the gram-negative bacterium in host organisms. In this work, we test our library of >1000 FKBP-focused ligands for inhibition of LpMip. The [4.3.1]-bicyclic sulfonamide turned out as a highly preferred scaffold and provided the most potent LpMip inhibitors known so far. Selected compounds were non-toxic to human cells, displayed antibacterial activity and block bacterial proliferation in cellular infection-assays as well as infectivity in human lung tissue explants. The results confirm [4.3.1]-bicyclic sulfonamides as anti-legionellal agents, although their anti-infective properties cannot be explained by inhibition of LpMip alone.     

New Flower-Shaped Lead(II) Coordination Polymer at the Nano Scale: Synthesis, Structural Characterization and DFT Calculations of [Pb(o-phen)(N3)2] n Containing the Pb-(μ1,1-N3)(μ1,3-N3) Motif

A new nano-flower lead(II) azido coordination polymer from the ligand, orthophenanthroline (o-phen), [Pb(o-phen)(μ_1,1-N₃)(μ_1,3-N₃)] _n (1), was synthesized by a sonochemical method. Compound 1 was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), elemental analyses and IR spectroscopy. Single crystalline material was obtained using a heat gradient applied to a solution of the reagents. The structure of 1 is a coordination polymer of lead(II) containing the Pb-(μ_1,1-N₃)(μ_1,3-N₃) motif, formed of one-dimensional chains. The structure of the 1 was optimized by density functional theory. Structural parameters and IR spectra for 1 are consistent with the crystal structure. Pure phase PbO nanoparticles were obtained by thermolysis of 1 with oleic acid as a surfactant at 180 and 200 °C an air. PbO nanoparticles were characterized by XRD and SEM.     

Biosorption of a reactive textile dye from aqueous solutions utilizing an agro-waste

In the present study a low-cost waste biomass derived from canned food plant, was tested for its ability to remove reactive textile dye from aqueous solutions. The batch biosorption experiments were carried out at various pH, biosorbent dosage, contact time and temperature. Optimum decolorization was observed at pH 2.0 and 1.6 g dm^− 3 of biomass dosage within 20 min. The first-order and the pseudo-second-order kinetics were investigated for the biosorption system. The applicability of the Langmuir and Freundlich isotherm models was examined. The thermodynamic parameters for the biosorption were also calculated. The experimental results in this study indicated that this low-cost biomaterial was an attractive candidate for the removal of textile dye Reactive Red 198 (RR198) from aqueous solutions.     

Kinetic analysis of cellular internalization and expulsion of unstructured D-chirality cell penetrating peptides

Most cell penetrating peptides (CPPs) are unstructured and susceptible to proteolytic degradation. One alternative is to incorporate D-chirality amino acids into unstructured CPPs to allow for enhanced uptake and intracellular stability. This work investigates CPP internalization using a series of time, concentration, temperature, and energy dependent studies, resulting in a three-fold increase in uptake and 50-fold increase in stability of D-chirality peptides over L-chirality counterparts. CPP internalization occurred via a combination of direct penetration and endocytosis, with a percentage of internalized CPP expelling from cells in a time-dependent manner. Mechanistic studies identified that cells exported the intact internalized D-chirality CPPs via an exocytosis independent pathway, analogous to a direct penetration method out of the cells. These findings highlight the potential of a D-chirality CPP as bio-vector in therapeutic and biosensing applications, but also identify a new expulsion method suggesting a relationship between uptake kinetics, intracellular stability, and export kinetics.     

Anti-Idiotype scFv Localizes an Autoepitope in the Globular Domain of C1q

We addressed the issue of C1q autoantigenicity by studying the structural features of the autoepitopes recognized by the polyclonal anti-C1q antibodies present in Lupus Nephritis (LN) sera. We used six fractions of anti-C1q as antigens and selected anti-idiotypic scFv antibodies from the phage library “Griffin.1”. The monoclonal scFv A1 was the most potent inhibitor of the recognition of C1q and its fragments ghA, ghB and ghC, comprising the globular domain gC1q, by the lupus autoantibodies. It was sequenced and in silico folded by molecular dynamics into a 3D structure. The generated 3D model of A1 elucidated CDR similarity to the apical region of gC1q, thus mapping indirectly for the first time a globular autoepitope of C1q. The V_H CDR2 of A1 mimicked the ghA sequence GSEAD suggested as a cross-epitope between anti-DNA and anti-C1q antibodies. Other potential inhibitors of the recognition of C1q by the LN autoantibodies among the selected recombinant antibodies were the monoclonal scFv F6, F9 and A12.     

Securing high resolution grayscale facial captures using a blockwise coevolutionary GA

In biometric systems, reference facial images captured during enrollment are commonly secured using watermarking, where invisible watermark bits are embedded into these images. Evolutionary Computation (EC) is widely used to optimize embedding parameters in intelligent watermarking (IW) systems. Traditional IW methods represent all blocks of a cover image as candidate embedding solutions of EC algorithms, and suffer from premature convergence when dealing with high resolution grayscale facial images. For instance, the dimensionality of the optimization problem to process a 2048 × 1536 pixel grayscale facial image that embeds 1 bit per 8 × 8 pixel block involves 49k variables represented with 293k binary bits. Such Large-Scale Global Optimization problems cannot be decomposed into smaller independent ones because watermarking metrics are calculated for the entire image. In this paper, a Blockwise Coevolutionary Genetic Algorithm (BCGA) is proposed for high dimensional IW optimization of embedding parameters of high resolution images. BCGA is based on the cooperative coevolution between different candidate solutions at the block level, using a local Block Watermarking Metric (BWM). It is characterized by a novel elitism mechanism that is driven by local blockwise metrics, where the blocks with higher BWM values are selected to form higher global fitness candidate solutions. The crossover and mutation operators of BCGA are performed on block level. Experimental results on PUT face image database indicate a 17% improvement of fitness produced by BCGA compared to classical GA. Due to improved exploration capabilities, BCGA convergence is reached in fewer generations indicating an optimization speedup.     

Crystal Structure and Physicochemical Properties of Two Supramolecular Compounds: (C4H8NH2)4[Mo8O26] and (NH4)Na2[AsIIIMo6O21(O2CCH2NH3)3]·8H2O

Two inorganic–organic hybrid supramolecular compounds based on polyoxometalates formulated as (C₄H₈NH₂)₄[Mo₈O₂₆] (1) and (NH₄)Na₂[As^IIIMo₆O₂₁(O₂CCH₂NH₃)₃]·8H₂O (2) have been synthesized by conventional solution method and characterized by infrared, UV–Vis and single-crystal X-ray diffraction analyses. Thermal analysis was performed to study their thermal stability. The atomic arrangement in compound (1) can be described as inorganic layers built by [Mo₈O₂₆]^4?, pyrrolidinium cations are embedded into layers. The fascinating structural feature of compound (2) is that the glycine molecules are bounded to two edge-sharing Mo centers via their carboxylate functionality leading to functionalized heteropolymolybdate [As^IIIMo₆O₂₁(O₂CCH₂NH₃)₃]^3?, extensive net hydrogen bonds between cations and anions contribute to the crystal packing. The electrochemical behavior of compound (2) has been studied.     

A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop

In this paper we address a hybrid flow shop scheduling problem considering the minimization of the sum of the total earliness and tardiness penalties. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. So, we propose an ant colony optimization method to deal with this problem. Our proposed method has several features, including some heuristics that specifically take into account both earliness and tardiness penalties to compute the heuristic information values. The performance of our algorithm is tested by numerical experiments on a large number of randomly generated problems. A comparison with solutions performance obtained by some constructive heuristics is presented. The results show that the proposed approach performs well for this problem.     

Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam

Evaluation of the parameters affecting the shear strength and ductility of steel–concrete composite beam is the goal of this study. This study focuses on predicting the future output of beam’s strength and ductility based on relative inputs using a soft computing scheme, extreme learning machine (ELM). Estimation and prediction results of the ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. Referring to the experimental results, as opposed to the GP and ANN methods, the ELM approach enhanced generalization ability and predictive accuracy. Moreover, achieved results indicated that the developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in shear strength and ductility of steel concrete composite. Furthermore, the experimental results indicate that on the whole, the newflanged algorithm creates good generalization presentation. In comparison to the other widely used conventional learning algorithms, the ELM has a much faster learning ability.     

Automatic detection of depression symptoms in twitter using multimodal analysis

The Journal of Supercomputing - Depression is the most prevalent mental disorder that can lead to suicide. Due to the tendency of people to share their thoughts on social platforms, social data...