Understanding macroscale functionality of metal halide perovskites in terms of nanoscale heterogeneities

Hybrid metal halide perovskites have shown an unprecedented rise as semiconductor building blocks for solar energy conversion and light-emitting applications. Currently, the field moves empirically towards more and more complex chemical compositions, including mixed halide quadruple cation compounds that allow optical properties to be tuned and show promise for better stability. Despite tremendous progress in the field, there is a need for better understanding of mechanisms of efficiency loss and instabilities to facilitate rational optimization of composition. Starting from the device level and then diving into nanoscale properties, we highlight how structural and compositional heterogeneities affect macroscopic optoelectronic characteristics. Furthermore, we provide an overview of some of the advanced spectroscopy and imaging methods that are used to probe disorder and non-uniformities. A unique feature of hybrid halide perovskite compounds is the propensity for these heterogeneities to evolve in space and time under relatively mild illumination and applied electric fields, such as those found within active devices. This introduces an additional challenge for characterization and calls for application of complimentary probes that can aid in correlating the properties of local disorder with macroscopic function, with the ultimate goal of rationally tailoring synthesis towards optimal structures and compositions

Hybrid Composite Coatings for Durable and Efficient Solar Hydrogen Generation under Diverse Operating Conditions

Safe and practical solar-driven hydrogen generators must be capable of efficient and stable operation under diurnal cycling with full separation of gaseous H2 and O2 products. In this study, a novel architecture that fulfills all of these requirements is presented. The approach is inherently scalable and provides versatility for operation under diverse electrolyte and lighting conditions. The concept is validated using a 1 cm2 triple-junction photovoltaic cell with its illuminated photocathode protected by a composite coating comprising an organic encapsulant with an embedded catalytic support. The device is compatible with operation under conditions ranging from 1 m H2SO4 to 1 m KOH, enabling flexibility in selection of semiconductor, electrolyte, membrane, and catalyst. Stable operation at a solar-to-hydrogen conversion efficiency of >10% is demonstrated under continuous operation, as well as under diurnal light cycling for at least 4 d, with simulated sunlight. Operational characteristics are validated by extended time outdoor testing. A membrane ensures products are separated, with nonexplosive gas streams generated for both alkaline and acidic systems. Analysis of operational characteristics under different lighting conditions is enabled by comparison of a device model to experimental data

Catalytic metasurfaces empowered by bound states in the continuum

Photocatalytic platforms based on ultrathin reactive materials facilitate carrier transport and extraction but are typically restricted to a narrow set of materials and spectral operating ranges due to limited absorption and poor energy-tuning possibilities. Metasurfaces, a class of 2D artificial materials based on the electromagnetic design of nanophotonic resonators, allow optical absorption engineering for a wide range of materials. Moreover, tailored resonances in nanostructured materials enable strong absorption enhancement and thus carrier multiplication. Here, we develop an ultrathin catalytic metasurface platform that leverages the combination of loss-engineered substoichiometric titanium oxide (TiO2–x) and the emerging physical concept of optical bound states in the continuum (BICs) to boost photocatalytic activity and provide broad spectral tunability. We demonstrate that our platform reaches the condition of critical light coupling in a TiO2–x BIC metasurface, thus providing a general framework for maximizing light–matter interactions in diverse photocatalytic materials. This approach can avoid the long-standing drawbacks of many naturally occurring semiconductor-based ultrathin films applied in photocatalysis, such as poor spectral tunability and limited absorption manipulation. Our results are broadly applicable to fields beyond photocatalysis, including photovoltaics and photodetectors

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas

Genetic Predisposition to an Impaired Metabolism of the Branched-Chain Amino Acids and Risk of Type 2 Diabetes: A Mendelian Randomisation Analysis

$\textbf{BACKGROUND}$: Higher circulating levels of the branched-chain amino acids (BCAAs; i.e., isoleucine, leucine, and valine) are strongly associated with higher type 2 diabetes risk, but it is not known whether this association is causal. We undertook large-scale human genetic analyses to address this question. $\textbf{METHODS AND FINDINGS}$: Genome-wide studies of BCAA levels in 16,596 individuals revealed five genomic regions associated at genome-wide levels of significance (p < 5 × 10-8). The strongest signal was 21 kb upstream of the PPM1K gene (beta in standard deviations [SDs] of leucine per allele = 0.08, p = 3.9 × 10-25), encoding an activator of the mitochondrial branched-chain alpha-ketoacid dehydrogenase (BCKD) responsible for the rate-limiting step in BCAA catabolism. In another analysis, in up to 47,877 cases of type 2 diabetes and 267,694 controls, a genetically predicted difference of 1 SD in amino acid level was associated with an odds ratio for type 2 diabetes of 1.44 (95% CI 1.26-1.65, p = 9.5 × 10-8) for isoleucine, 1.85 (95% CI 1.41-2.42, p = 7.3 × 10-6) for leucine, and 1.54 (95% CI 1.28-1.84, p = 4.2 × 10-6) for valine. Estimates were highly consistent with those from prospective observational studies of the association between BCAA levels and incident type 2 diabetes in a meta-analysis of 1,992 cases and 4,319 non-cases. Metabolome-wide association analyses of BCAA-raising alleles revealed high specificity to the BCAA pathway and an accumulation of metabolites upstream of branched-chain alpha-ketoacid oxidation, consistent with reduced BCKD activity. Limitations of this study are that, while the association of genetic variants appeared highly specific, the possibility of pleiotropic associations cannot be entirely excluded. Similar to other complex phenotypes, genetic scores used in the study captured a limited proportion of the heritability in BCAA levels. Therefore, it is possible that only some of the mechanisms that increase BCAA levels or affect BCAA metabolism are implicated in type 2 diabetes. $\textbf{CONCLUSIONS}$: Evidence from this large-scale human genetic and metabolomic study is consistent with a causal role of BCAA metabolism in the aetiology of type 2 diabetes.MRC Epidemiology Unit, Fenland study, EPIC-InterAct study, EPIC-Norfolk case-cohort study funding: this study was funded by the United Kingdom’s Medical Research Council through grants MC_UU_12015/1, MC_UU_12015/5, MC_PC_13046, MC_PC_13048 and MR/L00002/1. We acknowledge support from the National Institute for Health Research Biomedical Research Centre. The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement number 115372, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. EPIC-InterAct Study funding: funding for the InterAct project was provided by the EU FP6 programme (grant number LSHM_CT_2006_037197). MRC Human Nutrition Research funding: This research was supported by the Medical Research Council (MC_UP_A090_1006) and Cambridge Lipidomics Biomarker Research Initiative (G0800783). The SABRE study was funded at baseline by the UK Medical Research Council, Diabetes UK and the British Heart Foundation and at follow-up by a programme grant from the Wellcome Trust (WT082464) and British Heart Foundation (SP/07/001/23603); Diabetes UK funded the metabolomics analyses (13/0004774). RJOS, EN, JRZ and AK received funding from the Swedish Research Council, Stockholm County Council, Novo Nordisk Foundation and Diabetes Wellness. DBS is supported by the Wellcome Trust grant number 107064. MIM is a Wellcome Trust Senior Investigator and is supported by the following grants from the Wellcome Trust: 090532 and 098381. IB is supported by the Wellcome Trust grant WT098051

Genetic predisposition to an impaired metabolism of the branched-chain amino acids and risk of type 2 diabetes: a mendelian randomisation analysis

BACKGROUND: Higher circulating levels of the branched-chain amino acids (BCAAs; i.e., isoleucine, leucine, and valine) are strongly associated with higher type 2 diabetes risk, but it is not known whether this association is causal. We undertook large-scale human genetic analyses to address this question. METHODS AND FINDINGS: Genome-wide studies of BCAA levels in 16,596 individuals revealed five genomic regions associated at genome-wide levels of significance (p < 5 × 10-8). The strongest signal was 21 kb upstream of the PPM1K gene (beta in standard deviations [SDs] of leucine per allele = 0.08, p = 3.9 × 10-25), encoding an activator of the mitochondrial branched-chain alpha-ketoacid dehydrogenase (BCKD) responsible for the rate-limiting step in BCAA catabolism. In another analysis, in up to 47,877 cases of type 2 diabetes and 267,694 controls, a genetically predicted difference of 1 SD in amino acid level was associated with an odds ratio for type 2 diabetes of 1.44 (95% CI 1.26-1.65, p = 9.5 × 10-8) for isoleucine, 1.85 (95% CI 1.41-2.42, p = 7.3 × 10-6) for leucine, and 1.54 (95% CI 1.28-1.84, p = 4.2 × 10-6) for valine. Estimates were highly consistent with those from prospective observational studies of the association between BCAA levels and incident type 2 diabetes in a meta-analysis of 1,992 cases and 4,319 non-cases. Metabolome-wide association analyses of BCAA-raising alleles revealed high specificity to the BCAA pathway and an accumulation of metabolites upstream of branched-chain alpha-ketoacid oxidation, consistent with reduced BCKD activity. Limitations of this study are that, while the association of genetic variants appeared highly specific, the possibility of pleiotropic associations cannot be entirely excluded. Similar to other complex phenotypes, genetic scores used in the study captured a limited proportion of the heritability in BCAA levels. Therefore, it is possible that only some of the mechanisms that increase BCAA levels or affect BCAA metabolism are implicated in type 2 diabetes. CONCLUSIONS: Evidence from this large-scale human genetic and metabolomic study is consistent with a causal role of BCAA metabolism in the aetiology of type 2 diabetes

Analysis of synonymous codon usage in Hepatitis A virus

<p>Abstract</p> <p>Background</p> <p>Hepatitis A virus is the causative agent of type A viral hepatitis, which causes occasional acute hepatitis. Nevertheless, little information about synonymous codon usage pattern of HAV genome in the process of its evolution is available. In this study, the key genetic determinants of codon usage in HAV were examined.</p> <p>Results</p> <p>The overall extent of codon usage bias in HAV is high in <it>Picornaviridae</it>. And the patterns of synonymous codon usage are quite different in HAV genomes from different location. The base composition is closely correlated with codon usage bias. Furthermore, the most important determinant that results in such a high codon bias in HAV is mutation pressure rather than natural selection.</p> <p>Conclusions</p> <p>HAV presents a higher codon usage bias than other members of <it>Picornaviridae</it>. Compositional constraint is a significant element that influences the variation of synonymous codon usage in HAV genome. Besides, mutation pressure is supposed to be the major factor shaping the hyperendemic codon usage pattern of HAV.</p

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO