Stochastic molecular descriptors for polymers. 4. Study of complex mixtures with topological indices of mass spectra spiral and star networks: The blood proteome case

The Quantitative Structure-Property Relationships (QSPRs) based on Graph or Network Theory are important for predicting the properties of polymeric systems. In the three previous papers of this series (Polymer 45 (2004) 3845-3853; Polymer 46 (2005) 2791-2798; and Polymer 46 (2005) 6461-6473) we focused on the uses of molecular graph parameters called topological indices (TIs) to link the structure of polymers with their biological properties. However, there has been little effort to extend these TIs to the study of complex mixtures of artificial polymers or biopolymers such as nucleic acids and proteins. In this sense, Blood Proteome (BP) is one of the most important and complex mixtures containing protein polymers. For instance, outcomes obtained by Mass Spectrometry (MS) analysis of BP are very useful for the early detection of diseases and drug-induced toxicities. Here, we use two Spiral and Star Network representations of the MS outcomes and defined a new type of TIs. The new TIs introduced here are the spectral moments (π_k) of the stochastic matrix associated to the Spiral graph and describe non-linear relationships between the different regions of the MS characteristic of BP. We used the MARCH-INSIDE approach to calculate the π_k(SN) of different BP samples and S2SNet to determine several Star graph TIs. In the second step, we develop the corresponding Quantitative Proteome-Property Relationship (QPPR) models using the Linear Discriminant Analysis (LDA). QPPRs are the analogues of QSPRs in the case of complex biopolymer mixtures. Specifically, the new QPPRs derived here may be used to detect drug-induced cardiac toxicities from BP samples. Different Machine Learning classification algorithms were used to fit the QPPRs based on π_k(SN), showing J48 decision tree classifier to have the best performance. These results suggest that the present approach captures important features of the complex biopolymers mixtures and opens new opportunities to the application of the idea supporting classic QSPRs in polymer sciences.     

Kinetic modelling of textural changes in ready-to-eat breakfast cereals during soaking in semi-skimmed milk

Summary Ready‐to‐eat breakfast cereals immersed in milk undergo undesirable changes in texture because of sudden moisture uptake. The textural changes are ascribable to a plasticizing effect of water, which modifies the mechanical strength of products by softening the starch/protein matrix. In this work, some textural parameters of different cereal flakes were derived from the force–displacement curves monitored during 300 s of immersion in milk. Hardness loss, deformability increment and changes in the force–displacement curve profile were calculated and plotted against soaking time. The application of a sugar coating process to a model cereal flake increased the initial product hardness and improved the preservation of the textural parameters during immersion. The Peleg model closely fitted the experimental data, with regression coefficients from 0.967 to 0.999.     

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications - In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble...     

Steel-to-concrete bond after concrete splitting: test results

The definite trend towards the use of large-diameter rebars and the introduction of high-strength steels (f _y=500 to 600 MPa) make it necessary to study the effects of longitudinal splitting on the steel-to-concrete bond. The study of splitting effects requires firstly basic tests to be performed in order to gather experimental information on bond and confinement stresses acting at the bar-to-concrete interface. For this purpose, three series of tests were recently carried out at the Politecnico di Milano. The results make it possible to ascertain a few basic properties of the bond after concrete splitting, and to formulate empirical constitutive laws regarding the stresses and the displacements (bar slip and opening of the splitting crack). All specimens consisted of a short deformed bar embedded in a concrete block, which had a preformed splitting crack in the plane passing through the bar axis: twelve specimens (Tests A and C) were fitted up with a round deformed bar having crescent-shaped lugs (D_b=18 mm); seven specimens (Tests B) were fitted up with a specially machined deformed bar having a rectangular cross-section and straigth, lugs, so that concrete deterioration close to the bar could be investigated at the surface of the specimen, by means of the moiré technique. The tests were carried out at constant slip rate, up to very large slip values ( ); both the ascending and the descending branches of the stress-slip curves were measured, for four different values of the opening of the splitting crack. The agreement among the results of the three series is generally satisfactory and often very good: consequently, constitutive laws regarding the four main variables (crack opening and bar slip, shear and confinement stresses) can be worked out, as will be shown in a companion paper on constitutive relationships and on concrete deterioration at the bar-to-concrete interface.     

High-Fat Diet Leads to Reduced Protein O-GlcNAcylation and Mitochondrial Defects Promoting the Development of Alzheimer’s Disease Signatures

The disturbance of protein O-GlcNAcylation is emerging as a possible link between altered brain metabolism and the progression of neurodegeneration. As observed in brains with Alzheimer’s disease (AD), flaws of the cerebral glucose uptake translate into reduced protein O-GlcNAcylation, which promote the formation of pathological hallmarks. A high-fat diet (HFD) is known to foster metabolic dysregulation and insulin resistance in the brain and such effects have been associated with the reduction of cognitive performances. Remarkably, a significant role in HFD-related cognitive decline might be played by aberrant protein O-GlcNAcylation by triggering the development of AD signature and mitochondrial impairment. Our data support the impairment of total protein O-GlcNAcylation profile both in the brain of mice subjected to a 6-week high-fat-diet (HFD) and in our in vitro transposition on SH-SY5Y cells. The reduction of protein O-GlcNAcylation was associated with the development of insulin resistance, induced by overfeeding (i.e., defective insulin signaling and reduced mitochondrial activity), which promoted the dysregulation of the hexosamine biosynthetic pathway (HBP) flux, through the AMPK-driven reduction of GFAT1 activation. Further, we observed that a HFD induced the selective impairment of O-GlcNAcylated-tau and of O-GlcNAcylated-Complex I subunit NDUFB8, thus resulting in tau toxicity and reduced respiratory chain functionality respectively, highlighting the involvement of this posttranslational modification in the neurodegenerative process.     

Microstructure Evolution and Tensile Properties of a Selectively Laser Melted CoNi-Base Superalloy

Metallurgical and Materials Transactions A - A high $$\gamma ^{\prime }$$ volume fraction CoNi-base superalloy with roughly equal amounts of cobalt and nickel was successfully processed through...     

Low-Thermal-Conductivity Rare-Earth Zirconates for Potential Thermal-Barrier-Coating Applications

Rare-earth zirconates have been identified as a class of low-thermal-conductivity ceramics for possible use in thermal barrier coatings (TBCs) for gas-turbine engine applications. To document and compare the thermal conductivities of important rare-earth zirconates, we have measured the thermal conductivities of the following hot-pressed ceramics: (i) Gd₂Zr₂O₇ (pyrochlore phase), (ii) Gd₂Zr₂O₇ (fluorite phase), (iii) Gd_2.58Zr_1.57O₇ (fluorite phase), (iv) Nd₂Zr₂O₇ (pyrochlore phase), and (v) Sm₂Zr₂O₇ (pyrochlore phase). We have also measured the thermal conductivity of pressureless-sintered 7 wt% yttria-stabilized zirconia (7YSZ)—the commonly used composition in current TBCs. All rare-earth zirconates investigated here showed nearly identical thermal conductivities, all of which were ∼30% lower than the thermal conductivity of 7YSZ in the temperature range 25°–700°C. This finding is discussed qualitatively with reference to thermal-conductivity theory.     

Self-scaling cooperative discovery of service compositions in unstructured P2P networks

We propose an efficient technique for improving the performance of automatic and cooperative compositions in unstructured Peer-to-Peer networks during service discovery. The technique exploits a probabilistic forwarding algorithm that uses different sources of knowledge, such as network density and service grouping, to reduce the amount of messages exchanged in the network. The technique, analysed in several network configurations by using a simulator to observe resolution time, recall and message overhead, presents good performance especially in dense and large-scale service networks.     

A bio-inspired predictive sensory-motor coordination scheme for robot reaching and preshaping

This paper presents a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach an object while pre-shaping the fingers to the required grasp configuration and while predicting the tactile image that will be perceived after grasping. A model for sensory-motor coordination derived from studies in humans inspired the development of this scheme. A peculiar feature of this model is the prediction of the tactile image. The implementation of the proposed scheme is based on a neuro-fuzzy module that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for reaching, selects the best-suited hand configuration, and predicts the tactile feedback. The implementation of the scheme on a humanoid robot allowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control.