Effects of Aromatic Ammoniums on Methyl Ammonium Lead Iodide Hybrid Perovskite Materials

The introduction of bulky ammoniums into methyl ammonium lead iodide hybrid perovskites (MAPbI3) has emerged as a promising strategy to improve the properties of these materials. In the present work, we studied the effects of several aromatic ammoniums onto the structural, electronic, and optical properties of MAPbI3. Although powder XRD data suggest that the bulky cations are not involved in the bulk phase of the MAPbI3, a surprisingly large effect of the bulky cations onto the photoluminescence properties was observed

Historical data based energy management in a microgrid with a hybrid energy storage system

In a micro-grid, due to potential reverse output profiles of the Renewable Energy Source (RES) and the load, energy storage devices are employed to achieve high self-consumption of RES and to minimize power surplus flowing back into the main grid. This paper proposes a variable charging/discharging threshold method to manage energy storage system. And an Adaptive Intelligence Technique (AIT) is put forward to raise the power management efficiency. A battery-ultra-capacitor hybrid energy storage system (HESS) with merits of high energy and power density is used to evaluate the proposed method with onsite measured RES output data. Compared with the PSO algorithm based on the precise predicted data of the load and the RES, the results show that the proposed method can achieve better load smoothing and maximum self-consumption of the RES without the requirement of precise load and RES forecasting

Discriminating between disease-causing and neutral non-frameshifting micro-INDELs by support vector machines by means of integrated sequence- and structure-based features

poster abstractMicro-INDELs (insertions or deletions of ≤20 bp) constitute the second most frequent class of human gene mutation after single nucleotide variants. A significant portion of exonic INDELs are non-frameshifting (NFS), serving to insert or delete a discrete number of amino-acid residues. Despite the relative abundance of NFS-INDELs, their damaging effect on protein structure and function has gone largely unstudied whilst bioinformatics tools for discriminating between disease-causing and neutral NFS-INDELs remain to be developed. We have developed such a technique (DDIG-in; Detecting DIsease-causing Genetic variations due to INDELs) by comparing the properties of disease-causing NFS-INDELs from the Human Gene Mutation Database (HGMD) with putatively neutral NFS-INDELs from the 1,000 Genomes Project. Having considered 58 different sequence- and structure-based features, we found that predicted disordered regions around the NFS-INDEL region had the highest discriminative capability (disease versus neutral) with an Area Under the receiver-operating characteristic Curve (AUC) of 0.82 and a Matthews Correlation Coefficient (MCC) of 0.56. All features studied were combined by support vector machines (SVM) and selected by a greedy algorithm. The resulting SVM models were trained and tested by ten-fold cross-validation on the microdeletion dataset and independently tested on the microinsertion dataset and vice versa. The final SVM model for determining NFS-INDEL disease-causing probability was built on non-redundant datasets with a protein sequence identity cutoff of 35% and yielded an MCC value of 0.68, an accuracy of 84% and an AUC of 0.89. Predicted disease-causing probabilities exhibited a strong negative correlation with the average minor allele frequency (correlation coefficient, -0.84). DDIG-in, available at http://sparks.informatics.iupui.edu, can be used to estimate the disease-causing probability for a given NFS-INDEL

regSNPs-splicing: a tool for prioritizing synonymous single-nucleotide substitution

While synonymous single-nucleotide variants (sSNVs) have largely been unstudied, since they do not alter protein sequence, mounting evidence suggests that they may affect RNA conformation, splicing, and the stability of nascent-mRNAs to promote various diseases. Accurately prioritizing deleterious sSNVs from a pool of neutral ones can significantly improve our ability of selecting functional genetic variants identified from various genome-sequencing projects, and, therefore, advance our understanding of disease etiology. In this study, we develop a computational algorithm to prioritize sSNVs based on their impact on mRNA splicing and protein function. In addition to genomic features that potentially affect splicing regulation, our proposed algorithm also includes dozens structural features that characterize the functions of alternatively spliced exons on protein function. Our systematical evaluation on thousands of sSNVs suggests that several structural features, including intrinsic disorder protein scores, solvent accessible surface areas, protein secondary structures, and known and predicted protein family domains, show significant differences between disease-causing and neutral sSNVs. Our result suggests that the protein structure features offer an added dimension of information while distinguishing disease-causing and neutral synonymous variants. The inclusion of structural features increases the predictive accuracy for functional sSNV prioritization

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis

Repulsive guidance molecule B inhibits metastasis and is associated with decreased mortality in non-small cell lung cancer

Repulsive guidance molecules (RGMs) are co-receptors of bone morphogenetic proteins (BMPs) and programmed death ligand 2 (PD-L2), and might be involved in lung and other cancers. We evaluated repulsive guidance molecule B (RGMB) expression in 165 non-small cell lung cancer (NSCLC) tumors and 22 normal lung tissue samples, and validated the results in an independent series of 131 samples. RGMB was downregulated in NSCLC (P ≤ 0.001), possibly through promoter hypermethylation. Reduced RGMB expression was observed in advanced-stage tumors (P = 0.017) and in tumors with vascular invasion (P < 0.01), and was significantly associated with poor overall survival (39 vs. 62 months, P < 0.001) and with disease-associated patient mortality (P = 0.015). RGMB knockdown promoted cell adhesion, invasion and migration, in both NSCLC cell lines and an in vivo mouse model, which enhanced metastatic potential. Conversely, RGMB overexpression and secretion suppressed cancer progression. The tumor-suppressing effect of RGMB was exerted through inhibition of the Smad1/5/8 pathway. Our results demonstrate that RGMB is an important inhibitor of NSCLC metastasis and that low RGMB expression is a novel predictor or a poor prognosis

Bazhu Decoction, a Traditional Chinese Medical Formula, Ameliorates Cognitive Deficits in the 5xFAD Mouse Model of Alzheimer's Disease.

Alzheimer’s disease (AD) is the most common neurodegenerative disorder associated with aging. There are currently no effective treatments for AD. Bazhu decoction (BZD), a traditional Chinese medicine (TCM) formula, has been employed clinically to alleviate AD. However, the underlying molecular mechanisms are still unclear. Here we found that middle- and high-doses of BZD ameliorated the behavioral aspects of 5xFAD transgenic mice in elevated plus maze, Y maze and Morris water maze tests. Moreover, BZD reduced the protein levels of BACE1 and PS1, resulting in a reduction of Aβ plaques. We also identified a beneficial effect of BZD on oxidative stress by attenuating MDA levels and SOD activity in the brains of 5xFAD mice. Together, these results indicate that BZD produces a dose-dependent positive effect on 5xFAD transgenic mouse model by decreasing APP processing and Aβ plaques, and by ameliorating oxidative damage. BZD may play a protective role in the cognitive and anxiety impairments and may be a complementary therapeutic option for AD

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Essays on Structural Transformation and Inequality

Chapter 1: In this chapter, I replace the Cobb–Douglas production function in Acemoglu and Guerrieri (2008) with a two-sector model using the CES production technology to analyze how the U.S. capital deepening process must cause unbalanced growth. The U.S. economy is classified into labor- and capital-intensive sectors. My model shows that due to the difference in the two sectors\u27 elasticities of substitution between capital and labor, capital deepening leads to a growing concentration of labor in the labor-intensive sector, but a continuous reallocation of capital away from this sector. This causes sector outputs to increase at different rates. Combined with exogenous technological progress, this unbalanced growth affects the path of factor costs and output prices. Furthermore, the model shows that even as the real interest rate remains relatively constant and wage rate rises, the labor income share in GDP diminishes over time, and the capital income share increases. The ease with which labor can be replaced with capital in the capital-intensive sector and consumers\u27 preferences for how to combine the two sectors\u27 outputs in their final consumption are key parameters influencing the rate of decline of the labor income share. Chapter 2: Land is a crucial production factor in the agricultural sector. An important recent literature argues that a large part of the very large measured cross-country differences in productivity could be accounted for by land misallocation within developing countries and, in particular, that many countries have too few large farms (e.g. Adamopoulos and Restuccia, 2014). These studies measure misallocation against a USA benchmark established by the assumption that the unobserved distribution of farming skills in the population can be inferred from the observed size distribution of farms in the USA, measured at a national level. This paper uses the US Census of Agriculture data to decompose the US farm size distribution by region and land type (e.g. cropland and pastureland). I find important differences in the distribution of farm size across regions and land types, suggesting that any inference about the distribution of unobserved skills should take these factors into account, and that misallocation studies are producing biased results. I also explore whether the top tail of the farm size follows the power law distribution. Chapter 3: This paper studies the relationship between the expansions of manufacturing and service sectors and wage inequalities among employees in US metropolitan areas. Sector expansion is defined as an increase of the number of employees working in the sector. I use the American Community Surveys (ACS) data to conduct my analyses. The metrics that I use to measure wage inequality are wage variance and the 90-50 percentile and 50-10 percentile gaps. In my analyses, I keep the distribution of observed skills across sector size and the price and quantity of skills unchanged since 1989. The basic assumption is that an individuals\u27 wage is the sum of his/her skill group\u27s mean wage and some random residual, where the mean differs across skill groups and locations. I investigate the variance and the 90-50 percentile and 50-10 percentile gaps of mean wage and residual. My finding is that sector expansion is associated with rising wage inequality. However, in service sector, wage inequality is more sensitive to observed skills, and in manufacturing sector wage inequality is more sensitive to unobserved skills