Evolutionary patterns in sound production across fishes

Published versio

Publisher Correction: An engineered human Fc domain that behaves like a pH-toggle switch for ultra-long circulation persistence.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Synthesis, characterization and photovoltaic properties of poly(thiophenevinylene-alt-benzobisoxazole)swz

Herein we report the synthesis of two solution processible, conjugated polymers containing the benzobisoxazole moiety. The polymers were characterized using 1 H NMR, UV-Vis and fluorescence spectroscopy. Thermal gravimetric analysis shows that the polymers do not exhibit significant weight loss until approximately 300 1C under nitrogen. Cyclic voltammetry shows that the polymers have reversible reduction waves with estimated LUMO levels at À3.02 and À3.10 eV relative to vacuum and optical bandgaps of 2.04-2.17 eV. Devices based on blends of the copolymers and [6,6]-phenyl C61 butyric acid methyl ester (PCBM) exhibited modest power conversion efficiencies. Theoretical models reveal that there is poor electron delocalization along the polymer backbone, leading to poor performance. However, the energy levels of these polymers indicate that the incorporation of benzobisoxazoles into the polymer backbone is a promising strategy for the synthesis of new materials

Lithium-Doped Two-Dimensional Perovskite Scintillator for Wide-Range Radiation Detection

Two-dimensional lead halide perovskites have demonstrated their potential as high-performance scintillators for X- and gamma-ray detection, while also being low-cost. Here we adopt lithium chemical doping in two-dimensional phenethylammonium lead bromide (PEA)2PbBr4 perovskite crystals to improve the properties and add functionalities with other radiation detections. Li doping is confirmed by X-ray photoemission spectroscopy and the scintillation mechanisms are explored via temperature dependent X-ray and thermoluminescence measurements. Our 1:1 Li-doped (PEA)2PbBr4 demonstrates a fast decay time of 11 ns (80%), a clear photopeak with an energy resolution of 12.4%, and a scintillation yield of 11,000 photons per MeV under 662 keV gamma-ray radiation. Additionally, our Li-doped crystal shows a clear alpha particle/gamma-ray discrimination and promising thermal neutron detection through 6Li enrichment. X-ray imaging pictures with (PEA)2PbBr4 are also presented. All results demonstrate the potential of Li-doped (PEA)2PbBr4 as a versatile scintillator covering a wide radiation energy range for various applications

ProxiMAX randomisation:a new technology for non-degenerate saturation mutagenesis of contiguous codons

Back in 2003, we published ‘MAX’ randomisation, a process of non-degenerate saturation mutagenesis using exactly 20 codons (one for each amino acid) or else any required subset of those 20 codons. ‘MAX’ randomisation saturates codons located in isolated positions within a protein, as might be required in enzyme engineering, or else on one face of an alpha-helix, as in zinc finger engineering. Since that time, we have been asked for an equivalent process that can saturate multiple, contiguous codons in a non-degenerate manner. We have now developed ‘ProxiMAX’ randomisation, which does just that: generating DNA cassettes for saturation mutagenesis without degeneracy or bias. Offering an alternative to trinucleotide phosphoramidite chemistry, ProxiMAX randomisation uses nothing more sophisticated than unmodified oligonucleotides and standard molecular biology reagents. Thus it requires no specialised chemistry, reagents nor equipment and simply relies on a process of saturation cycling comprising ligation, amplification and digestion for each cycle. The process can encode both unbiased representation of selected amino acids or else encode them in pre-defined ratios. Each saturated position can be defined independently of the others. We demonstrate accurate saturation of up to 11 contiguous codons. As such, ProxiMAX randomisation is particularly relevant to antibody engineering

Metabolic Reprogramming of Macrophages: GLUCOSE TRANSPORTER 1 (GLUT1)-MEDIATED GLUCOSE METABOLISM DRIVES A PROINFLAMMATORY PHENOTYPE

Glucose is a critical component in the proinflammatory response of macrophages (MΦs). However, the contribution of glucose transporters (GLUTs) and the mechanisms regulating subsequent glucose metabolism in the inflammatory response are not well understood. Because MΦs contribute to obesity-induced inflammation, it is important to understand how substrate metabolism may alter inflammatory function. We report that GLUT1 (SLC2A1) is the primary rate-limiting glucose transporter on proinflammatory-polarized MΦs. Furthermore, in high fat diet-fed rodents, MΦs in crown-like structures and inflammatory loci in adipose and liver, respectively, stain positively for GLUT1. We hypothesized that metabolic reprogramming via increased glucose availability could modulate the MΦ inflammatory response. To increase glucose uptake, we stably overexpressed the GLUT1 transporter in RAW264.7 MΦs (GLUT1-OE MΦs). Cellular bioenergetics analysis, metabolomics, and radiotracer studies demonstrated that GLUT1 overexpression resulted in elevated glucose uptake and metabolism, increased pentose phosphate pathway intermediates, with a complimentary reduction in cellular oxygen consumption rates. Gene expression and proteome profiling analysis revealed that GLUT1-OE MΦs demonstrated a hyperinflammatory state characterized by elevated secretion of inflammatory mediators and that this effect could be blunted by pharmacologic inhibition of glycolysis. Finally, reactive oxygen species production and evidence of oxidative stress were significantly enhanced in GLUT1-OE MΦs; antioxidant treatment blunted the expression of inflammatory mediators such as PAI-1 (plasminogen activator inhibitor 1), suggesting that glucose-mediated oxidative stress was driving the proinflammatory response. Our results indicate that increased utilization of glucose induced a ROS-driven proinflammatory phenotype in MΦs, which may play an integral role in the promotion of obesity-associated insulin resistance

POT1 mutations predispose to familial melanoma

Deleterious germline variants in CDKN2A account for around 40% of familial melanoma cases, and rare variants in CDK4, BRCA2, BAP1 and the promoter of TERT have also been linked to the disease. Here we set out to identify new high-penetrance susceptibility genes by sequencing 184 melanoma cases from 105 pedigrees recruited in the UK, The Netherlands and Australia that were negative for variants in known predisposition genes. We identified families where melanoma cosegregates with loss-of-function variants in the protection of telomeres 1 gene (POT1), with a proportion of family members presenting with an early age of onset and multiple primary tumors. We show that these variants either affect POT1 mRNA splicing or alter key residues in the highly conserved oligonucleotide/oligosaccharide-binding (OB) domains of POT1, disrupting protein-telomere binding and leading to increased telomere length. These findings suggest that POT1 variants predispose to melanoma formation via a direct effect on telomeres.D.J.A., C.D.R.-E., Z.D., J.Z.L., J.C.T., M.P. and T.M.K. were supported by Cancer Research UK and the Wellcome Trust (WT098051). C.D.R.-E. was also supported by the Consejo Nacional de Ciencia y Tecnología of Mexico. K.A.P. and A.M.D. were supported by Cancer Research UK (grants C1287/A9540 and C8197/A10123) and by the Isaac Newton Trust. N.K.H. was supported by a fellowship from the National Health and Medical Research Council of Australia (NHMRC). L.G.A. was supported by an Australia and New Zealand Banking Group Limited Trustees PhD scholarship. A.L.P. is supported by Cure Cancer Australia. The work was funded in part by the NHMRC and Cancer Council Queensland. The work of N.A.G. was in part supported by the Dutch Cancer Society (UL 2012-5489). M.H., J.A.N.-B. and D.T.B. were supported by Cancer Research UK (programme awards C588/A4994 and C588/A10589 and the Genomics Initiative). C.L.-O., A.J.R. and V.Q. are funded by the Spanish Ministry of Economy and Competitiveness through the Instituto de Salud Carlos III (ISCIII), the Red Temática de Investigación del Cáncer (RTICC) del ISCIII and the Consolider-Ingenio RNAREG Consortium. C.L.-O. is an investigator with the Botín Foundation.This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/ng.294

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects