Microstructure Engineering of Metal-Halide Perovskite Films for Efficient Solar Cells

Photovoltaic (PV) devices with metal-halide perovskite films, namely perovskite solar cells, have become a rapidly rising star due to low cost of raw materials, simple solution processability, and swiftly increased power conversion efficiency (PCE). The PCEs so far certified have gone beyond 22% for perovskite solar cells and 23.6% for tandem devices with single crystalline silicon solar cells, which offer a promising PV technology for practical applications. In principle, performance of perovskite solar cells are largely dominated by the optoelectronic properties and stability of metal-halide perovskite films, which are determined by the microstructure features of the films in turns. In this chapter, we will describe the recently developed strategies on microstructure engineering of metal-halide perovskite films for efficient perovskite solar cells

Energy Generation Scheduling in Microgrids Involving Temporal-Correlated Renewable Energy

In this paper, a cost minimization problem is formulated to intelligently schedule energy generations for microgrids equipped with unstable renewable sources and energy storages. In such systems, the uncertain renewable energy will impose unprecedented scheduling challenges. To cope with the fluctuate nature of the renewable energy, an uncertainty model based on renewable energies’ moment statistics is developed. Specifically, we obtain the mean vector and second-order moment matrix according to predictions and field measurements and then define uncertainty set to confine the renewable energy generation. The uncertainty model allows the renewable energy generation distributions to fluctuate within the uncertainty set. We develop chance constraint approximations and robust optimization approaches based on a Chebyshev inequality framework to firstly transform and then solve the scheduling problem. Numerical results based on real-world data traces evaluate the performance bounds of the proposed scheduling scheme. It is shown that the temporal correlation information of the renewable energy within a proper time span can effectively reduce the conservativeness of the solution. Moreover, detailed studies on the impacts of different factors on the proposed scheme provide some interesting insights which shall be useful for the policy making for the future microgrids

Bacterial Peptidoglycan Triggers Candida albicans Hyphal Growth by Directly Activating the Adenylyl Cyclase Cyr1p

SummaryHuman serum potently induces hyphal development of the polymorphic fungal pathogen Candida albicans, a phenotype that contributes critically to infections. The fungal adenylyl cyclase Cyr1p is a key component of the cAMP/PKA-signaling pathway that controls diverse infection-related traits, including hyphal morphogenesis. However, identity of the serum hyphal inducer(s) and its fungal sensor remain unknown. Our initial analyses of active serum fractions revealed signs of bacterial peptidoglycan (PGN)-like molecules. Here, we show that several purified and synthetic muramyl dipeptides (MDPs), subunits of PGN, can strongly promote C. albicans hyphal growth. Analogous to PGN recognition by the mammalian sensors Nod1 and Nod2 through their leucine-rich-repeat (LRR) domain, we show that MDPs activate Cyr1p by directly binding to its LRR domain. Given the abundance of PGN in the intestine, a natural habitat and invasion site for C. albicans, our findings have important implications for the mechanisms of infection by this pathogen

Next-generation DNA sequencing-based assay for measuring allelic expression imbalance (AEI) of candidate neuropsychiatric disorder genes in human brain

<p>Abstract</p> <p>Background</p> <p>Common genetic variants that regulate gene expression are widely suspected to contribute to the etiology and phenotypic variability of complex diseases. Although high-throughput, microarray-based assays have been developed to measure differences in mRNA expression among independent samples, these assays often lack the sensitivity to detect rare mRNAs and the reproducibility to quantify small changes in mRNA expression. By contrast, PCR-based allelic expression imbalance (AEI) assays, which use a "marker" single nucleotide polymorphism (mSNP) in the mRNA to distinguish expression from pairs of genetic alleles in individual samples, have high sensitivity and accuracy, allowing differences in mRNA expression greater than 1.2-fold to be quantified with high reproducibility. In this paper, we describe the use of an efficient PCR/next-generation DNA sequencing-based assay to analyze allele-specific differences in mRNA expression for candidate neuropsychiatric disorder genes in human brain.</p> <p>Results</p> <p>Using our assay, we successfully analyzed AEI for 70 candidate neuropsychiatric disorder genes in 52 independent human brain samples. Among these genes, 62/70 (89%) showed AEI ratios greater than 1 ± 0.2 in at least one sample and 8/70 (11%) showed no AEI. Arranging log₂AEI ratios in increasing order from negative-to-positive values revealed highly reproducible distributions of log₂AEI ratios that are distinct for each gene/marker SNP combination. Mathematical modeling suggests that these log₂AEI distributions can provide important clues concerning the number, location and contributions of <it>cis</it>-acting regulatory variants to mRNA expression.</p> <p>Conclusions</p> <p>We have developed a highly sensitive and reproducible method for quantifying AEI of mRNA expressed in human brain. Importantly, this assay allowed quantification of differential mRNA expression for many candidate disease genes entirely missed in previously published microarray-based studies of mRNA expression in human brain. Given the ability of next-generation sequencing technology to generate large numbers of independent sequencing reads, our method should be suitable for analyzing from 100- to 200-candidate genes in 100 samples in a single experiment. We believe that this is the appropriate scale for investigating variation in mRNA expression for defined sets candidate disorder genes, allowing, for example, comprehensive coverage of genes that function within biological pathways implicated in specific disorders. The combination of AEI measurements and mathematical modeling described in this study can assist in identifying SNPs that correlate with mRNA expression. Alleles of these SNPs (individually or as sets) that accurately predict high- or low-mRNA expression should be useful as markers in genetic association studies aimed at linking candidate genes to specific neuropsychiatric disorders.</p