Mechanical characteristics of artificial cell walls

Artificial plant cell walls were produced from bacterial cellulose and cell wall constituents. The artificial cell walls were stored at low, medium and high relative humidity, and then subjected to micro-mechanical tests. From chemical composition and microstructure analysis it was found that, among all artificial cell wall materials produced, the most representative analogue of natural apple cell wall was based on bacterial cellulose supplemented with xyloglucan and pectin. Uniaxial tensile tests revealed that the different cell wall materials differed in their mechanical properties; increasing the humidity during storage resulted in a decrease in the value of the secant modulus. The cell wall model material obtained may be used for the simulation of the effect of external factors on the physical and chemical properties of cell walls.     

A CD14 domain with lipopolysaccharide-binding and -neutralizing activity

The interaction of lipopolysaccharide with CD14 plays a key role in signaling that activates an early defense against pathogens but also contributes to the development of sepsis and septic shock. Here we have mapped the entire 356-amino-acid protein with synthetic 20-amino-acid peptides and have identified a new lipopolysaccharide-binding domain with a strong LPS-neutralizing activity. Moreover, analysis of the structure-activity relationship of this peptide, which corresponds to amino acids 81-100 of human CD14, revealed that leucines 87, 91, and 94 are essential for these activities. The functional relevance of these residues was confirmed by cellular expression of mutant CD14 proteins that are no longer able to bind LPS. Furthermore, the peptide provided a basis for the generation of highly soluble analogues with stronger lipopolysaccharide-neutralizing activity.     

Fate of Escherichia coli strains inoculated in model cheese elaborated with or without starter and treated by high hydrostatic pressure

The aim of this research was to study high hydrostatic pressure inactivation of two strains of Escherichia coli (E. coli O59:H21 [CECT 405] and E. coli O157:H7 [CECT 5947]) inoculated in washed-curd model cheese elaborated with and without starter and the ability of these strains for survival, recovery, and growth. Samples were treated at 300, 400, and 500 MPa for 10 min at 20 degrees C and analyzed after the treatment and after 1, 7, and 15 days of storage at 8 degrees C to study the behavior of Escherichia populations. Cheeses elaborated with starter showed the maximum lethality at 400 and 500 MPa, and no significant differences in the baroresistant behavior of either strains were detected, except for E. coli O157:H7 at 400 MPa in cell counts obtained with thin agar layer method medium, where the decrease value was significantly lower. In cheese elaborated without starter, the highest decrease value was observed at 500 MPa, except for E. coli O59:H21 in cell counts obtained with selective culture medium, where the highest decrease value was also found at 400 MPa. The ability to repair and grow was not observed in model cheese elaborated with starter, as cell counts of treated samples decreased after 15 days of storage at 8 degrees C. By contrast, in cheese elaborated without starter, all pressurized samples showed the trend to repair and grow during the storage period in both strains. These results suggest that the presence of starter and low pH values are the main factors that control the ability of Escherichia strains inoculated in this type of cheese and treated by high hydrostatic pressure to recover and grow.     

Generation of a Multicomponent Library of Disulfide Donor-Acceptor Architectures Using Dynamic Combinatorial Chemistry

We describe here the generation of new donor-acceptor disulfide architectures obtained in aqueous solution at physiological pH. The application of a dynamic combinatorial chemistry approach allowed us to generate a large number of new disulfide macrocyclic architectures together with a new type of [2]catenanes consisting of four distinct components. Up to fifteen types of structurally-distinct dynamic architectures have been generated through one-pot disulfide exchange reactions between four thiol-functionalized aqueous components. The distribution of disulfide products formed was found to be strongly dependent on the structural features of the thiol components employed. This work not only constitutes a success in the synthesis of topologically- and morphologically-complex targets, but it may also open new horizons for the use of this methodology in the construction of molecular machines.     

Sampling from repairs of conditional functional dependency violations

Violations of functional dependencies (FDs) and conditional functional dependencies (CFDs) are common in practice, often indicating deviations from the intended data semantics. These violations arise in many contexts such as data integration and Web data extraction. Resolving these violations is challenging for a variety of reasons, one of them being the exponential number of possible repairs. Most of the previous work has tackled this problem by producing a single repair that is nearly optimal with respect to some metric. In this paper, we propose a novel data cleaning approach that is not limited to finding a single repair, namely sampling from the space of possible repairs. We give several motivating scenarios where sampling from the space of CFD repairs is desirable, we propose a new class of useful repairs, and we present an algorithm that randomly samples from this space in an efficient way. We also show how to restrict the space of repairs based on constraints that reflect the accuracy of different parts of the database. We experimentally evaluate our algorithms against previous approaches to show the utility and efficiency of our approach.     

Graph machine learning for design of high-octane fuels

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO₂ emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further autoignition training data.     

Monte Carlo simulation of terpolymerization: Optimizing the simulation and post-processing times

Monte Carlo simulations are a useful and easy way to understand a polymerization reaction process properly. However, achieving reliable results with Monte Carlo simulations can also lead to prohibitive computational times and a considerable amount of data to be processed afterward. The present study analyses the Monte Carlo simulation of a steady-state terpolymerization process to reduce the overall computational time of the simulation and the post-processing of its results. Different sorting algorithms (Bubble, Insertion, Selection, and Tim) and Python libraries (Joblib and Numba) were used. The chain composition distribution and the micro-structures resultant of different scenarios were assessed by processing the simulated mechanism results. The simulation time results indicate the Tim sorting algorithm as the best to use in the post-processing step and the Numba library as the best suited for both the simulation and the post-processing step.     

Human emotion recognition in the significance assessment of property attributes

Journal of Housing and the Built Environment - One of the largest problems in the real estate market analysis, which includes valuation, is determining the significance of individual property...     

A cross-technology benchmark for incremental graph queries

Software and Systems Modeling - To cope with the increased complexity of systems, models are used to capture what is considered the essence of a system. Such models are typically represented as a...