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Signaling and Allostery

Abstract

Silver nanoparticles (AgNPs) are nanomaterials obtained by nanotechnology and due to their antimicrobial properties have a major importance in the control of various species of bacteria, fungi and viruses, with applications in medicine, cosmetics or food industry. The goal of the paper was to present the results of the research carried out on rapid extracellular biosynthesis of silver nanoparticles mediated by culture filtrate of lactic acid bacteria Lactobacillus sp. strain LCM5 and to assess the antimicrobial activity. Analysis of transmission electron microscopy (TEM) micrographs evidenced that the size of AgNPs synthesized using culture filtrates of lactic acid bacteria strain LCM5 ranged between 3 and 35 nm diameter, with an average particle size of 13.84±4.56 nm. AgNPs presented a good dispersion, approximately spherical shape, with parallel stripes certifying crystal structure. Frequency distribution revealed that preponderant dimensions of biosynthesized AgNPs were below 20 nm (94%). Antimicrobial activity of AgNPs was variable depending on both species and group of test microorganisms (bacteria or fungi) involved. Diameter of growth inhibition zone of Aspergillus flavus and Aspergillus ochraceus caused by silver nanoparticles synthesized by lactic acid bacteria strain LCM5 were similar (12.39 ± 0.61mm and 12.86 ± 0.78 mm) but significant stronger inhibition was registered against Penicillium expansum (15.87 ± 1.01mm). The effectiveness of biosynthesized silver nanoparticles was more pronounced against Gram-negative bacteria Chromobacterium violaceum with larger zone of inhibition (18 ± 0.69 mm diameter) when compared to those from fungi. Results recommend the silver nanoparticles biosynthesized using culture filtrate of the lactic acid bacteria Lactobacillus sp. strain LCM5 for biotechnological purposes, as promising antimicrobial agents.

Abstract

Hyperparathyroidism-Jaw Tumor (HPT-JT) is an autosomal dominant disorder with variable expression, with an estimated prevalence of 6.7 per 1,000 population. Genetic testing for predisposing CDC73 (HRPT2) mutations has been an important clinical advance, aimed at early detection and/or treatment to prevent advanced disease. The aim of this study is to assess the most deleterious SNPs mutations on CDC73 gene and to predict their influence on the functional and structural levels using different bioinformatics tools. Method: Computational analysis using twelve different in-silico tools including SIFT, PROVEAN, PolyPhen-2, SNAP2, PhD-SNP, SNPs&GO, P-Mut, I-Mutant ,Project Hope, Chimera, COSMIC and dbSNP Short Genetic Variations were used to identify the impact of mutations in CDC73 gene that might be causing jaw tumor. Results: From (733) SNPs identified in the CDC73 gene we found that only Eleven SNPs (G49C, L63P, L64P, D90H, R222G, W231R, P360S, R441C, R441H, R504S and R504H) has deleterious effect on the function and structure of protein and expected to cause the syndrome. Conclusion: Eleven substantial genetic/molecular aberrations in CDC73 gene identified that could serve as diagnostic markers for hyperparathyroidism-jaw tumor (HPT-JT).

Abstract

A new and simple micelles-rich restricted access supramolecular solvent-based liquid phase microextraction method (RASUPRASs-LPME) based on the ion-pair complex formation of phosphate (PO4 3-) ions with ammonium heptamolybdate and malachite green in acidic medium was developed. The phosphate ion concentration after microextraction of the ion-pair complex to the hexagonal aggregates of decanoic acid (DA) was measured with micro-volume UV-Vis spectrophotometer at 625 nm. All analytical parameters which are effective on the method such as acid type and concentration, supramolecular solvent volume, amount of the components forming the complex, sample volume, were optimized. The preconcentration factor (PF), limit of detection (LOD) and limit of quantification (LOQ) for the developed method was found to be 15, 9.6 and 32.1, respectively. The RA-SUPRAs-LPME method was finally applied for the analysis of the phosphate content of different types of water samples.

Abstract

The objective of this work is numerical simulation of the membrane by direct analysis at micro, meso and macro level. This approach includes first a defining and modeling of a basic structural unit, after that simulation of a fragment as a representative element of the membrane structure. Then the results obtained to transfer for the entire membrane module and finally modeling of the membrane as porous media with calculated permeability. The numerical simulation was done with Ansys CFX, using the Darcy’s equation for flow through porous media with configuration of the membrane and second order backward Euler transient scheme for solving the Navier-Stokes equations.

The permeability of the membrane is determined at a micro and macro level by computer simulation for different fluids, which allows to evaluating the influence of the viscosity on the flow passing through the membrane. This micro-macro approach is quite efficient and cost-effective because it saves time and requires less computer capacity and allows direct analysis of the complex structure of the membrane modules.

Abstract

Around 70 infectious agents are possible threats for blood safety.

The risk for blood recipients is increasing because of new emergent agents like West Nile, Zika and Chikungunya viruses, or parasites such as Plasmodium and Trypanosoma cruzi in non-endemic regions, for instance.

Screening programmes of the donors are more and more implemented in several Countries, but these cannot prevent completely infections, especially when they are caused by new agents.

Pathogen inactivation (PI) methods might overcome the limits of the screening and different technologies have been set up in the last years.

This review aims to describe the most widely used methods focusing on their efficacy as well as on the preservation integrity of blood components.

Abstract

Plants have been seen to possess the potential to be excellent biological matrices to serve as a basis for investigating the presence of promising therapeutic agents for cancer treatment. Several successful anti-cancer medicines - or their analogues - nowadays in use are plant derived and many more are under clinical trials. Under current circumstances, the purpose of this work was to test aqueous and ethanolic extracts of five aromatic and medicinal plants from arid zones on some tumor cell lines. These plants, Cymbopogon schoenanthus (L.) Spreng, Crithmum maritimum (L.) Spreng, Hammada scoparia (Pomel) Iljin, Retama raetam (Forssk.) and Zizyphus lotus (L.) Desf., widely used in Tunisian ethnomedicine, were assessed for their phenolic compounds, antioxidants and anticancer activities in aqueous and ethanol extracts. Total polyphenols, flavonoid and tannin contents were determined colorimetrically and some of these molecules were identified using RP-HPLC. A significant difference on phenolic contents and composition were found among the investigated plants. Cymbopogon schoenanthus was the richest in phenolic compounds (approx. 72%) with quercetine-3-o-rhamnoside (approx. 33%) as main contributor. For all the tested plants, the highest antioxidant capacity was detected in the ethanolic extracts rather than in the aqueous ones. The highest antiproliferative potential was observed for the ethanolic extracts. Hammada scoparia, Retama raetam and Zizyphus lotus exhibited important antiproliferative effect that reached 67% at a 1% extract concentration. Taken together, the present study supports the potential development of chemotherapeutic agents from, at least, four of the five studied Tunisian ethnomedicinal plants.

Abstract

The problem of determining which nucleotides of an RNA sequence are paired or unpaired in the secondary structure of an RNA, which we call RNA state inference, can be studied by different machine learning techniques. Successful state inference of RNA sequences can be used to generate auxiliary information for data-directed RNA secondary structure prediction. Typical tools for state inference, such as hidden Markov models, exhibit poor performance in RNA state inference, owing in part to their inability to recognize nonlocal dependencies. Bidirectional long short-term memory (LSTM) neural networks have emerged as a powerful tool that can model global nonlinear sequence dependencies and have achieved state-of-the-art performances on many different classification problems.

This paper presents a practical approach to RNA secondary structure inference centered around a deep learning method for state inference. State predictions from a deep bidirectional LSTM are used to generate synthetic SHAPE data that can be incorporated into RNA secondary structure prediction via the Nearest Neighbor Thermodynamic Model (NNTM). This method produces predicted secondary structures for a diverse test set of 16S ribosomal RNA that are, on average, 25 percentage points more accurate than undirected MFE structures. Accuracy is highly dependent on the success of our state inference method, and investigating the global features of our state predictions reveals that accuracy of both our state inference and structure inference methods are highly dependent on the similarity of pairing patterns of the sequence to the training dataset. Availability of a large training dataset is critical to the success of this approach. Code available at https://github.com/dwillmott/rna-state-inf.

Abstract

Recently, persistent homology has had tremendous success in biomolecular data analysis. It works by examining the topological relationship or connectivity of a group of atoms in a molecule at a variety of scales, then rendering a family of topological representations of the molecule. However, persistent homology is rarely employed for the analysis of atomic properties, such as biomolecular flexibility analysis or B-factor prediction. This work introduces atom-specific persistent homology to provide a local atomic level representation of a molecule via a global topological tool. This is achieved through the construction of a pair of conjugated sets of atoms and corresponding conjugated simplicial complexes, as well as conjugated topological spaces. The difference between the topological invariants of the pair of conjugated sets is measured by Bottleneck and Wasserstein metrics and leads to an atom-specific topological representation of individual atomic properties in a molecule. Atom-specific topological features are integrated with various machine learning algorithms, including gradient boosting trees and convolutional neural network for protein thermal fluctuation analysis and B-factor prediction. Extensive numerical results indicate the proposed method provides a powerful topological tool for analyzing and predicting localized information in complex macromolecules.

Abstract

In forests, edaphic microbial communities are involved in litter decomposition and soil forming processes, with major contribution to humification, especially bacteria and fungi being responsible for the main ecosystem services fulfilled by the soil. Research has been carried out aiming to characterize the structure and diversity of microbial communities in the Rendzic Leptosols (WRB) under natural deciduous forest from Visterna, Babadag Plateau and to assess their contribution to ecosystem services provided by soil. The paper presents the results of quantitative estimations and taxonomic composition of soil and litter communities of heterotrophic bacteria and fungi, identification of cellulolytic species, as well as the microbial biomass and global physiological activities expressed as soil respiration potential. More than a half of bacterial species were common in litter and soil (SI=57.14%) and were represented by dominant species of fluorescent or non-fluorescent pseudomonads and Bacillus subtilis but no similarity was found between the two fungal communities. Fungal populations included cosmopolitan species, such as antagonists and strong cellulolytic representatives of genera Penicillium, Trichoderma, Mortierella, Chaetomium, Epicoccum, Aspergillus. Microbial density and microbial biomass presented the highest values in the litter (684 mg C x kg-1 d.s.) and in surface horizon Am1 of soil profile than in the bottom layers. The highest diversity was found in Am1 horizon (0-5 cm) H’=1.983 bits and ε=0.869 for cellulolytic community. Soil respiration reflected the intense physiological activity of microbiome, with high values associated to numerous effectives of bacteria and fungi especially in surface horizon. Microorganisms identified contribute to formation of soil by recycling of nutrients, cellulose decomposition, the synthesis of stable organic matter (humic acids), aggregation of soil particles, biological control of pathogens by antagonistic activity. They improve plant uptake of water and nutrients by forming symbioses (ectomycorrhizae), thus modelling the structure of vegetation.