Urolithin as a converging scaffold linking ellagic acid and coumarin analogues: design of potent protein kinase CK2 inhibitors

Casein kinase 2 (CK2) is a ubiquitous, essential, and highly pleiotropic protein kinase; its abnormally high constitutive activity is suspected to underlie its pathogenic potential in neoplasia and other relevant diseases. Previously, using different in silico screening approaches, two potent and selective CK2 inhibitors were identified by our group: ellagic acid, a naturally occurring tannic acid derivative (K(i)=20 nM) and 3,8-dibromo-7-hydroxy-4-methylchromen-2-one (DBC, K(i)=60 nM). Comparing the crystallographic binding modes of both ellagic acid and DBC, an X-ray structure-driven merging approach was taken to design novel CK2 inhibitors with improved target affinity. A urolithin moiety is proposed as a possible bridging scaffold between the two known CK2 inhibitors, ellagic acid and DBC. Optimization of urolithin A as the bridging moiety led to the identification of 4-bromo-3,8-dihydroxy-benzo[c]chromen-6-one as a novel, potent and selective CK2 inhibitor, which shows a K(i) value of 7 nM against the protein kinase, representing a significant improvement in affinity for the target compared with the two parent fragments.     

Analysis of furan in coffee of different provenance by head-space solid phase microextraction gas chromatography–mass spectrometry: effect of brewing procedures

A simple, sensitive and accurate method for the analysis of furan in roasted coffee has been used based on headspace–solid-phase micro-extraction (HS–SPME) coupled to gas chromatography–mass spectrometry (GC–MS). The extraction was performed using 75-µm carboxen/polydimethylsiloxane fiber. Ionic strength, extraction time and temperature, and desorption time were assessed as the most important parameters affecting the HS–SPME procedure and d ₄-furan was used as the internal standard. The linearity range was in the range 0.0075–0.486 ng g^?1; the LOD and LOQ calculated using the signal-to-noise ratio approach were 0.002 and 0.006 ng g^?1, respectively. The inter- and intra-day precision was 8 and 10%, respectively. The concentration of furan found in batches of roasted coffee powder different producing countries ranged from 57.3 to 587.3 ng g^?1. The mean reduction in furan levels observed when brewing coffee by either infusion, using a moka pot or an expresso machine was 57, 67.5 and 63.3%, respectively.     

Proprotein Convertase Subtilisin Kexin Type 9 Inhibitors Reduce Platelet Activation Modulating ox-LDL Pathways

Background: Proprotein convertase subtilisin kexin type 9 inhibitors (PCSK9i) lower LDL-cholesterol and slow atherosclerosis preventing cardiovascular events. While it is known that circulating PCSK9 enhances platelet activation (PA) and that PCSK9i reduce it, the underlying mechanism is not still clarified. Methods: In a multicenter before–after study in 80 heterozygous familial hypercholesterolemia (HeFH) patients on treatment with maximum tolerated statin dose ± ezetimibe, PA, soluble-NOX2-derived peptide (sNOX2-dp), and oxidized-LDL (ox-LDL) were measured before and after six months of PCSK9i treatment. In vitro study investigates the effects of plasma from HeFH patients before and after PCK9i on PA in washed platelets (wPLTs) from healthy subjects. Results: Compared to baseline, PCSK9i reduced the serum levels of LDL-c, ox-LDL, Thromboxane (Tx) B2, sNOX2-dp, and PCSK9 (p < 0.001). The decrease of TxB2 correlates with that of ox-LDL, while ox-LDL reduction correlated with PCSK9 and sNOX2-dp delta. In vitro study demonstrated that wPLTs resuspended in plasma from HeFH after PCSK9i treatment induced lower PA and sNOX2-dp release than those obtained using plasma before PCSK9i treatment. This reduction was vanished by adding ox-LDL. ox-LDL-induced PA was blunted by CD36, LOX1, and NOX2 inhibition. Conclusions: PCSK9i treatment reduces PA modulating NOX2 activity and in turn ox-LDL formation in HeFH patients.     

An efficient self-timed adder realized using conventional CMOS standard cells

Usually, efficient self-timed adders are realized using the dynamic differential cascode voltage switch logic. This allows the end-completion to be easily detected, but it makes circuit design and testing very complex, compelling the production of full-custom layouts and leading to a very long time before marketing. This paper presents a new 56-bit high-speed self-timed adder realized with conventional AMS 0.35 μm CMOS standard cells. The proposed circuit uses overlapped execution circuits, which exploit the initialization time that always elapses between two consecutive addition operations. Compared to several self-timed adders existing in the literature, the addition circuit proposed here shows brilliant advantages in terms of speed-performance, silicon area occupancy and power dissipation.     

Estimation of the daily global solar radiation; Nepal experience

This work focuses on estimation of global solar radiation and, in particular, it explores the effect of precipitation and temperature on solar radiation profile of Kathmandu (Nepal). An accurate knowledge of solar radiation distribution in each particular geographical location is crucial for the promotion of solar energy technology. The best way of knowing the amount of global solar radiation is to install quality instruments at many locations in the given region. This requires their day to day maintenance, recording and calibration, which is very costly in developing countries like Nepal. Thus, the alternative approach is to correlate meteorological measurements with appropriate models and investigate the key parameters. For the research activities the RadEst program has been used. It includes, for evaluating the daily global solar radiation values at a given latitudes, four models which estimate the atmospheric transmissivity by measurements of daily temperature range and precipitations. The model parameters are fitted in 2 years data by iterative procedures. The values obtained by these models are, then, compared with measured radiation data. The paper reports graphical and statistical evaluations suggesting that among the four models, the Modular DCBB is the best model for Kathmandu area. This result is useful for designing solar panels able to maximize the harvesting of solar energy and to reduce the chronic shortage of hydrocarbon fuel that Nepal imports in a significant amount each year.     

Simulation-based optimization for discharge/loading operations at a maritime container terminal

The discharge/loading process of a single container ship by multiple quay cranes and shuttle vehicles moving back and forth from the quay to the yard and vice versa is focused in this paper. The core problem of this major operational issue reduces to finding the optimal assignment and optimal sequencing (schedule) of bays (jobs) processed by a fixed number of available cranes (machines). Under the classical assumption that machines have no release time and that their processing occurs with continuity, at a constant rate, in literature it has been tackled as a deterministic machine scheduling problem and formulated by integer programming as the quay crane scheduling problem (QCSP). Here, instead, the QCSP is viewed as a decisional step within an uncertain and dynamic logistic process where the quay cranes are the resources to be managed at the best, i.e., by minimizing the time spent waiting for each other due to conflicts, as well as the time wasted for blocking and starvation phenomena due to congestion occurring along the path from the quay area and to the stacking yard and vice versa. We present a simulation-based optimization (SO) model for this wider modeling problem with the objective of finding the schedule which optimizes a classical objective function. The search process for the optimal schedule is accomplished by a simulated annealing (SA) algorithm, while performance estimation of the overall container discharge/loading process is provided by the simulation framework as a whole. Numerical experiments on a real instance are presented for tuning purposes of the SA procedure implemented within the simulator.     

Ab initio calculation of the structural and dynamical properties of layered semiconductors

We present a theoretical first-principles investigation of the structure and lattice dynamics of several layered semiconductors. The equilibrium structure as obtained by minimization of the total energy of the bulk materials is in good agreement with experiment. Furthermore, we have investigated the surface of these materials in order to obtain information on the van der Waals epitaxial growth. We found that the relaxed atomic positions at the surface deviate from the ideal ones in the bulk by less than 1%, which is obviously a consequence of the weak interlayer forces. Additionally, bulk phonon-dispersion curves have been calculated along several high symmetry directions within the density-functional perturbation theory (DFPT). The weak interlayer interaction makes the vibrational properties of the bulk very similar to those of the surface. In fact, our ab initio calculations for the bulk reproduce well both the experimental bulk phonon frequencies obtained by inelastic neutron scattering and the experimental surface phonon dispersion measured with inelastic He-atom scattering (HAS).     

Synthesis and characterization of new poly(arylene ether 1,3,4-oxadiazole)s based on dihydroxynaphthalene isomers

Summary A series of new poly(arylene ether 1,3,4-oxadiazole)s has been obtained starting from a difluorosubstituted monomer containing 1,3,4-oxadiazole rings and some dihydroxynaphthalene isomers. The polymers have been prepared by polycondensation in solution and have been obtained in quantitative yield. They had inherent viscosity from 0.2 to 0.82 dL/g, showed good thermal stability (10% weight loss temperatures in nitrogen and air were above 460 and 450°C, respectively) and high glass transition temperatures (in the range of 197–232°C). The polymers were characterized by elemental and infrared analyses, GPC and wide angle X-ray diffraction. Received: 27 June 2000/Revised version: 2 October 2000/Accepted: 2 October 2000     

Content-based and collaborative techniques for tag recommendation: an empirical evaluation

The rapid growth of the so-called Web 2.0 has changed the surfers’ behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and organizing resources, spread as a model for browsing and retrieving web contents. The success of that collaborative model is justified by two factors: firstly, information is organized in a way that closely reflects the users’ mental model; secondly, the absence of a controlled vocabulary reduces the users’ learning curve and allows the use of evolving vocabularies. Since tags are handled in a purely syntactical way, annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness for complex tasks. Consequently, tag recommenders, with their ability of providing users with the most suitable tags for the resources to be annotated, recently emerged as a way of speeding up the process of tag convergence. The contribution of this work is a tag recommender system implementing both a collaborative and a content-based recommendation technique. The former exploits the user and community tagging behavior for producing recommendations, while the latter exploits some heuristics to extract tags directly from the textual content of resources. Results of experiments carried out on a dataset gathered from Bibsonomy show that hybrid recommendation strategies can outperform single ones and the way of combining them matters for obtaining more accurate results.     

Efficient energy-based embedding models for link prediction in knowledge graphs

We focus on the problem of link prediction in Knowledge Graphs, with the goal of discovering new facts. To this purpose, Energy-Based Models for Knowledge Graphs that embed entities and relations in continuous vector spaces have been largely used. The main limitation in their applicability lies in the parameter learning phase, which may require a large amount of time for converging to optimal solutions. In this article, we first propose an unified view on different Energy-Based Embedding Models. Hence, for improving the model training phase, we propose the adoption of adaptive learning rates. We show that, by adopting adaptive learning rates during training, we can improve the efficiency of the parameter learning process by an order of magnitude, while leading to more accurate link prediction models in a significantly lower number of iterations. We extensively evaluate the proposed learning procedure on a variety of new models: our result show a significant improvement over state-of-the-art link prediction methods on two large Knowledge Graphs, namely WordNet and Freebase.