Modeling of the intestinal peptide transporter hPepT1 and analysis of its transport capacities by docking and pharmacophore mapping

An early pharmacokinetic screen for peptidomimetic drugs should have the ability to predict molecules with high affinity for intestinal transporters, as peptide-like derivatives are seldom absorbed passively. Hence, the first objective of this study was to generate a reliable model for the structure of the hPepT1 protein, which is the main intestinal transporter involved in the absorption of both dietary peptides and peptidomimetics. The modeling was based on the resolved structure of the homologous bacterial lactose permease LacY using a fragmental strategy. The interaction capacities of the hPepT1 model were explored by docking a set of 50 known ligands. Despite the known predilection of hPepT1 for hydrophobic ligands, docking results unveiled the key role of the polar interactions stabilized by charged termini, especially concerning the ammonium head group. The docking results were further verified by developing a pharmacophore model that confirmed the key features required for optimal hPepT1 affinity. The consistency of the docking results and the agreement with the pharmacophore model afford an encouraging validation for the proposed model and suggest that it can be exploited to design peptide-like molecules with an improved affinity for such a transporter.     

Neurotransmitter receptor activation triggers p27(Kip1 )and p21(CIP1) accumulation and G1 cell cycle arrest in oligodendrocyte progenitors

We examined the pathways that link neurotransmitter receptor activation and cell cycle arrest in oligodendrocyte progenitors. We had previously demonstrated that glutamate receptor activation inhibits oligodendrocyte progenitor proliferation and lineage progression. Here, using purified oligodendrocyte progenitors and cerebellar slice cultures, we show that norepinephrine and the beta-adrenergic receptor agonist isoproterenol also inhibited the proliferation, but in contrast to glutamate, isoproterenol stimulated progenitor lineage progression, as determined by O4 and O1 antibody staining. This antiproliferative effect was specifically attributable to a beta-adrenoceptor-mediated increase in cyclic adenosine monophosphate, since analogs of this cyclic nucleotide mimicked the effects of isoproterenol on oligodendrocyte progenitor proliferation, while alpha-adrenoceptor agonists were ineffective. Despite the opposite effects on lineage progression, both isoproterenol and the glutamate receptor agonist kainate caused accumulation of the cyclin-dependent kinase inhibitors p27(Kip1)and p21(CIP1), and G1 arrest. Studies with oligodendrocyte progenitor cells from INK4a-/- mice indicated that the G1 cyclin kinase inhibitor p16(INK4a) as well as p19(ARF)were not required for agonist-stimulated proliferation arrest. Our results demonstrate that beta-adrenergic and glutamatergic receptor activation inhibit oligodendrocyte progenitor proliferation through a mechanism that may involve p27(Kip1) and p21(CIP1); but while neurotransmitter-induced accumulation of p27(Kip1) is associated with cell cycle arrest, it does not by itself promote oligodendrocyte progenitor differentiation.     

Glass and wine: a good example of the deep relationship between drinkware and beverage

As the choice of the drinkware could deeply affect consumer perception during beverage consumption, an experimental research was carried out by expert testers to investigate the evolution of chemical–physical and sensory profiles of a typical rosé wine maintained in different types of glasses. The aim of this research was to select some parameters to well correlate beverage sensory properties with glass characteristics which can be used for drinkware design. The different sensorial profiles, obtained as a function of the geometric characteristics of glasses, were compared with one another. In order to find any significant parameter useful to represent the time evolution of the wine inside each vessel, the wine poured in all glasses was also characterized at different times from a chemical and physical point of view. As the design parameters are now guided more by aesthetic reasons rather than by functional ones, this new ‘integrated approach’, deriving from the merging of chemical, physical and sensorial data, can be used to design the optimum vessel for the enjoyment of the consumer during assessing sessions. This innovative procedure could be easily adapted for every beverage such as fruit juices, sparkling wines or beer.     

Reliability Target Assessment Based on Integrated Factors Method (IFM): A Real Case Study of a Sintering Plant

The success of a company depends on customer’s satisfaction: quality, price, and service. These three goals depend in particular on R.A.M.S. characteristics: reliability, availability, maintainability, and safety. In the last few years, in order to guarantee high standards of reliability and maintainability, new methodologies and techniques have been developed to estimate the R.A.M.S. targets. In particular, the reliability target represents both the starting and the ending point of R.A.M.S. analysis. The design of the reliability target of a system is a crucial aspect of reliability analysis, as it affects the performance of the system and components. This paper aims to develop a new approach called “IFM Target,” to define the reliability target for complex systems through the integrated factors method, to combine the advantages of usually used approaches, and to overcome some criticalities highlighted in a careful literature analysis. The proposed method has been applied on a sintering system. The results show the effectiveness of the proposed approach.     

Allocating production batches to subcontractors by fuzzy goal programming

Allocating production batches to subcontractors arises frequently in industry. When subcontractors operate different equipment, batch assignment is a complex decision that must take into account both throughput and quality of finished goods. In this paper, a Mixed Integer Linear Goal Programming Model, where productivity uncertainty is taken into account through Fuzzy Set Theory, is developed. Our study was motivated by a real-world application arising in an Italian textile company. Computational results show that this method outperforms the hand-made solutions put to use by the management so far.     

Detection of Geometric Temporal Changes in Point Clouds

Detecting geometric changes between two 3D captures of the same location performed at different moments is a critical operation for all systems requiring a precise segmentation between change and no‐change regions. Such application scenarios include 3D surface reconstruction, environment monitoring, natural events management and forensic science. Unfortunately, typical 3D scanning setups cannot provide any one‐to‐one mapping between measured samples in static regions: in particular, both extrinsic and intrinsic sensor parameters may vary over time while sensor noise and outliers additionally corrupt the data. In this paper, we adopt a multi‐scale approach to robustly tackle these issues. Starting from two point clouds, we first remove outliers using a probabilistic operator. Then, we detect the actual change using the implicit surface defined by the point clouds under a Growing Least Square reconstruction that, compared to the classical proximity measure, offers a more robust change/no‐change characterization near the temporal intersection of the scans and in the areas exhibiting different sampling density and direction. The resulting classification is enhanced with a spatial reasoning step to solve critical geometric configurations that are common in man‐made environments. We validate our approach on a synthetic test case and on a collection of real data sets acquired using commodity hardware. Finally, we show how 3D reconstruction benefits from the resulting precise change/no‐change segmentation.     

Critical Flow Method: A New Reliability Allocation Approach for a Thermonuclear System

In the present work, starting from well‐known methodologies, a new reliability allocation method [critical flow method (CFM)] has been proposed. We focused on the most important conventional methods and discussed their limitations in order to motivate the current research. The results show the main common problem of the most conventional reliability allocation methods: they are developed for complex systems with series configurations but not for series–parallel ones. The consequence is an increase of the required units' reliability (series configuration) in order to guarantee the reliability system target. Actually, the design and manufacturing of a subsystem with an extremely low failure rate would consume a considerable amount of economic resources. The proposed method can solve the shortcomings of the conventional methods with a new reliability approach useful to series–parallel configurations in order to obtain an important cost saving. The CFM has been applied to a liquid nitrogen cooling installation in a thermonuclear system, with many series–parallel configurations in order to guarantee the whole safety system. The proposed technique can be applied to working complex systems, and, in general, in the design phase of new installations. By comparing the CFM application results with real parameters, the new technique has been validated. The computational results clearly demonstrate the advantages of the proposed method. In particular, by applying the method to series–parallel configurations, it allocates failure rates higher than conventional methods, with a component cost reduction. Copyright © 2015 John Wiley & Sons, Ltd.     

Heat and mass transfer modeling during continuous flow processing of fluid food by direct steam injection

Fluid food products, in the agri-food industry, are commonly subject to thermal treatments to ensure their safety and quality characteristics. Therefore, these treatments must be accurately selected and monitored to avoid over-processing, as consumer safety and product acceptability must be preserved.     

Soil genotoxicity assessment: a new stategy based on biomolecular tools and plant bioindicators

The setting up of efficient early warning systems is a challenge to research for preventing environmental alteration and human disease. In this paper, we report the development and the field application of a new biomonitoring methodology for assessing soil genotoxicity. In the first part, the use of amplified fragment length polymorphism and flow cytometry techniques to detect DNA damage induced by soils artificially contaminated with heavy metals as potentially genotoxic compounds is explained. Results show that the combination of the two techniques leads to efficient detection of the sublethal genotoxic effect induced in the plant bioindicator by contaminated soil. By contrast, the classic mortality, root, and shoot growth vegetative endpoints prove inappropriate for assessing soil genotoxicity because, although they cause genotoxic damage, some heavy metals do not affect sentinel plant development negatively. The statistical elaboration of the data obtained led to the development of a statistical predictive model which differentiates four different levels of soil genotoxic pollution and can be used everywhere. The second part deals with the application of the biomonitoring protocol in the genotoxic assessment of two areas surrounding a steelworks in northern Italy and the effectiveness of this methodology. In this particular case, in these areas, the predictive model reveals a pollution level strictly correlated to the heavy metal concentrations revealed by traditional chemical analysis.     

Modelling preventive measures and their effect on generation times in emerging epidemics

We present a stochastic epidemic model to study the effect of various preventive measures, such as uniform reduction of contacts and transmission, vaccination, isolation, screening and contact tracing, on a disease outbreak in a homogeneously mixing community. The model is based on an infectivity process, which we define through stochastic contact and infectiousness processes, so that each individual has an independent infectivity profile. In particular, we monitor variations of the reproduction number and of the distribution of generation times. We show that some interventions, i.e. uniform reduction and vaccination, affect the former while leaving the latter unchanged, whereas other interventions, i.e. isolation, screening and contact tracing, affect both quantities. We provide a theoretical analysis of the variation of these quantities, and we show that, in practice, the variation of the generation time distribution can be significant and that it can cause biases in the estimation of reproduction numbers. The framework, because of its general nature, captures the properties of many infectious diseases, but particular emphasis is on COVID-19, for which numerical results are provided.