7,8-Dihydro-8-oxoguanosine Lesions Inhibit the Theophylline Aptamer or Change Its Selectivity

Aptamers are attractive constructs due to their high affinity/selectivity towards a target. Here 7,8-dihydro-8-oxoguanosine (8-oxoG) has been used, due in part to its unique H-bonding capabilities (Watson–Crick or Hoogsteen), to expand the “RNA alphabet”. Its impact on the theophylline RNA aptamer was explored by modifying its binding pocket at positions G11, G25, or G26. Structural probing, with RNases A and T₁, showed that modification at G11 leads to a drastic structural change, whereas the G25-/G26-modified analogues exhibited cleavage patterns similar to that of the canonical construct. The recognition properties towards three xanthine derivatives were then explored through thermophoresis. Modifying the aptamer at position G11 led to binding inhibition. Modification at G25, however, changed the selectivity towards theobromine (K_d≈160 μm ), with a poor affinity for theophylline (K_d>1.5 mm ) being observed. Overall, 8-oxoG can have an impact on the structures of aptamers in a position-dependent manner, leading to altered target selectivity.     

Acromioclavicular joint cyst and rotator cuff tear

The role of a depressor factor, atrial natriuretic peptide, in the development of arterial hypertension in adolescents with pubertal hypothalamic syndrome was studied in 52 patients and 13 healthy males aged 13-24 years. The duration of disease was 2-11 years. Radioimmunological methods were used to measure plasma atrial natriuretic peptide, plasma renin activity, and serum aldosterone. Patients with borderline arterial hypertension were found to have a significant reduction in their atrial natriuretic peptide levels, and this correlated directly with the renin-aldosterone system, demonstrating insufficiency of the depressor system in patients with pubertal hypothalamic syndrome and the involvement of atrial natriuretic peptide in the development of arterial hypertension, along with disturbances in the functional relationship between atrial natriuretic peptide and the renin-aldosterone system.     

A study on the stability of superconducting YBa2Cu3O7−x phase

The thermal stability of the superconducting phase of nominal composition YBa₂Cu₃O_7–x -sintered pellets has been studied with respect to different temperatures (ranging from 300 to 950° C), time (ranging from 1 to 72 h), oxygen partial pressure (from 4 Pa to 1 atm) and carbon dioxide partial pressure (from 10^–4 Pa to 1 atm). Annealed samples were characterized by X-ray diffraction analysis, optical microscopy, and resistive measurements of the superconductive transition temperature. A stability field of the orthorhombic and tetragonal phases was obtained, showing a region of coexistance. The decomposition of the 1 2 3 phase is found to be strongly influenced by the presence of a small amount of CO₂ (1 p.p.m.) in the sintering atmosphere. A sintering process is proposed to avoid the formation of by-products.     

Liquid-phase hot-pressing and WC-particle reinforcement of SiC-Si composites

The sintering behaviour of SiC-Si and SiC-Si-WC composites hot-pressed via silicon liquid phase has been studied. The results show that the increase of silicon makes the sintered materials densify at lower temperature and the grain growth is restricted due to the effect of the liquid. The strength of SiC-25 vol.% Si samples is higher than the strength obtained for SiC-5 vol.% Si with the same hot-pressing conditions. The addition of WC particles improves relative densities and strengths of SiC-Si ceramics.     

Comparing and experimenting machine learning techniques for code smell detection

Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.     

Real-Time Global Illumination for VR Applications

Real-time global illumination in VR systems enhances scene realism by incorporating soft shadows, reflections of objects in the scene, and color bleeding. The Virtual Light Field (VLF) method enables real-time global illumination rendering in VR. The VLF has been integrated with the Extreme VR system for real-time GPU-based rendering in a Cave Automatic Virtual Environment.     

Model-based cold-start speed control scheme for spark ignition engines

This paper presents a model-based control scheme to the cold-start speed control in spark ignition (SI) engines. The multi-variable control algorithm is developed with the purpose of improving the transient performance of the starting engine speed: the control inputs are the fuel injection, the throttle and the spark advance (SA), while the engine speed and the air mass flow rate are the measured signals. The fuel injection is performed with a dual sampling rate system: the cycle-based fuel injection command is individually adjusted for each cylinder by using a TDC (top dead center)-based air charge estimation. The desired performance for speed regulation is achieved by using a coordinated control of SA and throttle operation. The speed error convergence of the closed loop system is proved for simplified, second-order model with a time-delay, and the robustness with respect to parameter uncertainties is investigated. The performance and the robustness with respect to modeling uncertainties of the proposed control scheme are tested using an industrial engine simulator with six cylinders.     

An adaptive learning regulator for uncertain minimum phase systems with undermodeled unknown exosystems

The design of an adaptive learning regulator is addressed for uncertain minimum phase linear systems (with known bounds, known upper bound on system order, known relative degree, known high frequency gain sign) and for unknown exosystems (with unknown order, uncertain frequencies). On the basis of a known bound on system uncertainties and a known bound on the modeled exosystem frequencies, a new adaptive output error feedback control algorithm is proposed which guarantees exponential convergence of both the output and the control input errors into residual bounds which decrease as the exosystem modeling error decreases. Exponential convergence of both errors to zero is obtained when the regulator exactly models all exosystem excited frequencies, while asymptotic convergence of both errors to zero is achieved when the actual exosystem is overmodeled by the regulator. The new algorithm generalizes existing learning controllers since, in the case of periodic references and/or disturbances, the knowledge of the period is not required.