Quantitative analysis of complex amino acids and RGD peptides by X‐ray photoelectron spectroscopy (XPS)

The C 1 s, N 1 s, and O 1 s core level binding energies (BEs) of the functional groups in amino acids (glycine, aspartic acid, glutamic acid, arginine, and histidine) with varied side‐chains and cell‐binding RGD‐based peptides have been determined and characterized by X‐ray photoelectron spectroscopy with a monochromatic Al K_α source. The zwitterionic nature of the amino acids in the solid state is unequivocally evident from the N 1 s signals of the protonated amine groups and the C 1 s signature of carboxylate groups. Significant adventitious carbon contamination is evident for all samples but can be quantitatively accounted for. No intrinsic differences in the XP spectra are evident between two polymorphs (α and γ) of glycine, indicating that the crystallographic differences have a minor influence on the core level BEs for this system. The two nitrogen centers in the imidazole group of histidine exhibit an N 1 s BE shift that is in line with previously reported data for theophylline and aqueous imidazole solutions, while the nitrogen and carbon chemical shifts reflect the unusual guanidinium chemical environment in arginine. It is shown that the complex envelopes of C 1 s and O 1 s photoemission spectra for short‐chain peptides can be analyzed quantitatively by reference to the less complex XP spectra of the constituent amino acids, provided the peptides are of high enough purity. The distinctive N 1 s photoemission from the amide linkages provides an indicator of peptide formation even in the presence of common impurities, and variations in the relative intensities of N 1 s were found to be diagnostic for each of the three peptides investigated (RGD, RGDS, and RGDSC). Copyright © 2013 John Wiley & Sons, Ltd.     

Assessing calves as carriers of Cryptosporidium and Giardia with zoonotic potential on dairy and beef farms within a water catchment area by mutation scanning

In the present study, we undertook a molecular epidemiological survey of Cryptosporidium and Giardia in calves on three dairy and two beef farms within an open drinking water catchment area (Melbourne, Australia). Faecal samples (n = 474) were collected from calves at two time points (5 months apart) and tested using a PCR‐based mutation scanning‐targeted sequencing phylogenetic approach, employing regions within the genes of small subunit (SSU) of ribosomal RNA (designated partial SSU), 60 kDa glycoprotein (pgp60) and triose phosphate isomerase (ptpi) as genetic markers. Using partial SSU, the C. bovis, C. parvum, C. ryanae and a new genotype of Cryptosporidium were characterised from totals of 74 (15.6%), 35 (7.3%), 37 (7.8%) and 9 (1.9%) samples, respectively. Using pgp60, C. parvum genotype IIa subgenotype A18G3R1 was detected in 29 samples. Using ptpi, G. duodenalis assemblages A and E were detected in totals of 10 (2.1%) and 130 (27.4%) samples, respectively. The present study showed that a considerable proportion of dairy and beef calves in this open water catchment region excreted Cryptosporidium (i.e. subgenotype IIaA18G3R1) and Giardia (e.g. assemblage A) that are consistent with those infecting humans, inferring that they are of zoonotic importance. Future work should focus on exploring, in a temporal and spatial way, whether these parasites occur in the environment and water of the catchment reservoir.     

Identification of ligands that target the HCV-E2 binding site on CD81

Hepatitis C is a global health problem. While many drug companies have active R&D efforts to develop new drugs for treating Hepatitis C virus (HCV), most target the viral enzymes. The HCV glycoprotein E2 has been shown to play an essential role in hepatocyte invasion by binding to CD81 and other cell surface receptors. This paper describes the use of AutoDock to identify ligand binding sites on the large extracellular loop of the open conformation of CD81 and to perform virtual screening runs to identify sets of small molecule ligands predicted to bind to two of these sites. The best sites selected by AutoLigand were located in regions identified by mutational studies to be the site of E2 binding. Thirty-six ligands predicted by AutoDock to bind to these sites were subsequently tested experimentally to determine if they bound to CD81-LEL. Binding assays conducted using surface Plasmon resonance revealed that 26 out of 36 (72 %) of the ligands bound in vitro to the recombinant CD81-LEL protein. Competition experiments performed using dual polarization interferometry showed that one of the ligands predicted to bind to the large cleft between the C and D helices was also effective in blocking E2 binding to CD81-LEL.     

A new high: Cannabis as a budding source of carbon-based materials for electrochemical power sources

Cannabis sativa L., a low-cost, fast-growing herbaceous plant, is seeing a resurgence in widespread cultivation as a result of new policies and product drive. Its biodegradable and environmentally benign nature coupled with its high specific surface area and three-dimensional hierarchal structure makes it an excellent candidate for use as a biomass-derived carbon material for electrochemical power sources. It is proposed that this ‘wonder crop’ could have an important role in the energy transition by providing high-functioning carbon-based materials for electrochemistry. In this article, all instances of C. sativa usage in batteries, fuel cells and supercapacitors are discussed with a focus on highlighting the high capacity, rate capability, capacitance, current density and half-wave potential that can be achieved with its utilisation in the field.     

Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications.     

Vacuum Cerenkov radiation in Lorentz-violating theories without CPT violation

In theories with broken Lorentz symmetry, Cerenkov radiation may be possible even in vacuum. We analyze the Cerenkov emissions that are associated with the least constrained Lorentz-violating modifications of the photon sector, calculating the threshold energy, the frequency spectrum, and the shape of the Mach cone. In order to obtain sensible results for the total power emitted, we must make use of information contained within the theory which indicates at what scale new physics must enter.     

Accelerated formation of the boron–oxygen complex in p‐type Czochralski silicon

This Letter reports on the acceleration of the rate of formation of the boron–oxygen defect in p‐type Czochralski silicon with illumination intensities in excess of 2.1 × 10¹⁷ photons/cm²/s. It is observed that increased light intensities greatly enhance the rate of defect formation, without increasing the saturation concentration of the defect. These results suggest a dependence of the defect formation rate upon the total majority carrier concentration. Finally, a method using temperatures up to 475 K and an illumination intensity of 1.68 × 10¹⁹ photons/cm²/s is shown to result in near‐complete defect formation within seconds. (© 2015 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)     

Photodynamic Therapy Efficacy Enhanced by Dynamics: The Role of Charge Transfer and Photostability in the Selection of Photosensitizers

Progress in the photodynamic therapy (PDT) of cancer should benefit from a rationale to predict the most efficient of a series of photosensitizers that strongly absorb light in the phototherapeutic window (650–800 nm) and efficiently generate reactive oxygen species (ROS=singlet oxygen and oxygen‐centered radicals). We show that the ratios between the triplet photosensitizer–O₂ interaction rate constant (k_D) and the photosensitizer decomposition rate constant (k_d), k_D/k_d, determine the relative photodynamic activities of photosensitizers against various cancer cells. The same efficacy trend is observed in vivo with DBA/2 mice bearing S91 melanoma tumors. The PDT efficacy intimately depends on the dynamics of photosensitizer–oxygen interactions: charge transfer to molecular oxygen with generation of both singlet oxygen and superoxide ion (high k_D) must be tempered by photostability (low k_d). These properties depend on the oxidation potential of the photosensitizer and are suitably combined in a new fluorinated sulfonamide bacteriochlorin, motivated by the rationale.     

Electron (charge) density studies of cellulose models

Introductory material first describes electron density approaches and demonstrates visualization of electron lone pairs and bonding as concentrations of electron density. Then it focuses on the application of Bader’s Quantum Theory of Atoms-in-Molecules (AIM) to cellulose models. The purpose of the work is to identify the various interactions that stabilize cellulose structure. AIM analysis aids study of non-covalent interactions, especially those for which geometric criteria are not well established. The models were in the form of pairs of cellotriose molecules, methylated at the O1 and O4 ends. Based on the unit cell of cellulose Iβ, there were corner–corner, and center–center pairs that correspond to (200) sheets, and corner–center pairings that corresponded to (1–10) and (110) stacks. AIM analysis (or charge-density topology analysis) was applied before and after minimization in vacuum and in continuum solvation. Besides the conventional O–H···O hydrogen bonds, all of which were known from geometric criteria, C–H···O hydrogen bonds (some previously reported), and some O···O and H···H interactions were found. Non-covalent bonds in the (200) sheets were maintained in all calculations with the exception of a weak, bifurcated O6–H···O2′′ bond that was not found in the corner–corner pair model and did not survive minimization. Nor did the O6···O4 interactions on the reducing ends of the triosides. Pairs of molecules along the (110) plane had an equal number (12) of non-covalent bonds compared to the pairs along the (1–10) plane, but the AIM parameters indicated the bonds between the pairs in the (110) plane were weaker. Intra-molecular O–H···O hydrogen bonds survived in these minimized pairs, but the relative chain alignments usually did not.