Magnetic Resonance Signal Amplification by Reversible Exchange of Selective PyFALGEA Oligopeptide Ligands Towards Epidermal Growth Factor Receptors

The biorelevant PyFALGEA oligopeptide ligand, which is selective towards the epidermal growth factor receptor (EGFR), has been successfully employed as a substrate in magnetic resonance signal amplification by reversible exchange (SABRE) experiments. It is demonstrated that PyFALGEA and the iridium catalyst IMes form a PyFALGEA:IMes molecular complex. The interaction between PyFALGEA:IMes and H₂ results in a ternary SABRE complex. Selective 1D EXSY experiments reveal that this complex is labile, which is an essential condition for successful hyperpolarization by SABRE. Polarization transfer from parahydrogen to PyFALGEA is observed leading to significant enhancement of the ¹H NMR signals of PyFALGEA. Different iridium catalysts and peptides are inspected to discuss the influence of their molecular structures on the efficiency of hyperpolarization. It is observed that PyFALGEA oligopeptide hyperpolarization is more efficient when an iridium catalyst with a sterically less demanding NHC ligand system such as IMesBn is employed. Experiments with shorter analogues of PyFALGEA, that is, PyLGEA and PyEA, show that the bulky phenylalanine from the PyFALGEA oligopeptide causes steric hindrance in the SABRE complex, which hampers hyperpolarization with IMes. Finally, a single-scan ¹H NMR SABRE experiment of PyFALGEA with IMesBn revealed a unique pattern of NMR lines in the hydride region, which can be treated as a fingerprint of this important oligopeptide.     

Energy Efficiency Trade-Off Between Duty-Cycling and Wake-Up Radio Techniques in IoT Networks

Wireless Personal Communications - For robust monitoring, control and proper energy management of renewable energy sources (RES), wireless sensing networks (WSNs) are proved to be a vital solution....     

Activity-Directed Synthesis: A Flexible Approach for Lead Generation

Activity-directed synthesis (ADS) is a structure-blind, functional-driven molecular discovery approach. In this Concept, four case studies highlight the general applicability of ADS and showcase its flexibility to support different medicinal chemistry strategies. ADS deliberately harnesses reactions with multiple possible outcomes, and allows many chemotypes to be evaluated in parallel. Resources are focused on bioactive molecules, which emerge in tandem with associated synthetic routes. Some of the future challenges for ADS are highlighted, including the realisation of an autonomous molecular discovery platform. The prospects for ADS to become a mainstream lead generation approach are discussed.     

Evolution of collaboration and optimization of impact: self-organization in multinational research

Scientometrics - The expanding presence of multinational research teams highlights the importance of characterizing the outcomes of international collaboration. Herein, we characterize the...     

A low-phase-noise K-band CMOS VCO

A novel circuit topology for low-phase-noise voltage controlled oscillators (VCOs) is presented in this letter. By employing a PMOS cross-coupled pair with a capacitive feedback, superior circuit performance can be achieved especially at higher frequencies. Based on the proposed architecture, a prototype VCO implemented in a 0.18-/spl mu/m CMOS process is demonstrated for K-band applications. From the measurement results, the VCO exhibits a 510-MHz frequency tuning range at 20GHz. The output power and the phase noise at 1-MHz offset are -3dBm and -111dBc/Hz, respectively. The fabricated circuit consumes a dc power of 32mW from a 1.8-V supply voltage.     

Artificial neural networks and statistical modeling for electronic stress prediction using thermal profiling

Electronic components are constantly under stress due to factors such as signal density, temperature, humidity, and high current and voltage. Relatively little research has emphasized stress-level prediction under voltage stress. The purpose of this paper was to develop an electronic thermal profile model for stress-level prediction utilizing neural network (NN) and statistical approaches, such as multivariate regression models. Electronic components were removed from boards, subjected to different levels of stress, then replaced. An infrared camera was then used to capture information about component temperature changes over time under normal operating and stress conditions. Statistical analysis of the captured images suggests a strong correlation between thermal profiles and voltage stress levels. Artificial neural network (ANN) and statistical approaches were used to model temperature change profiles for components that had been stressed at different levels, and their predictive ability was compared. Separate data sets were used for model development and model verification. ANN prediction rates were around 70%, compared to 30% for the statistical approach. Experiments were also conducted to evaluate the robustness of the ANN model to the presence of noise in the data. Results suggested that the ANN model was able to accommodate the presence of noise. Various backpropagation (BP) learning algorithms were also evaluated and yielded similar average error rates. A 3-2-1 ANN topology performed better than 3-3-1 or 3-2-2-1 topologies, perhaps because the 3-2-1 topology has a higher data sample to nodes ratio than the other topologies.     

Fabrication of 1.55 μm VCSELs on Si using metallic bonding

Using AuGeNiCr multilayered metals as the wafer bonding medium, long-wavelength GaInAsP/InP vertical cavity surface emitting lasers employing Al-oxide/Si as the upper and lower distributed Bragg reflectors were fabricated on Si substrate with the bonding interface formed outside the vertical cavity surface emitting laser cavity. Laser emission at 1.545 μm was measured under pulsed operations near room temperature. The low-temperature metallic bonding process demonstrates a great potential in device fabrication