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Hydrogen is a source of clean energy as it can produce electricity and heat with water as a by-product and no carbon content is emitted when hydrogen is used as burning fuel in a fuel cell. Hydrogen is a potential energy carrier and powerful fuel as it has high flammability, fast flame speed, no carbon content, and no emission of pollutants. Hydrogen production is possible through different technologies by utilizing several feedstock materials, but the main concern in recent years is to reduce the emission of carbon dioxide and other greenhouse gases from energy sectors. Hydrogen production by thermochemical conversion of biomass and greenhouse gases has achieved much attention as researchers have developed several novel thermochemical methods which can be operated with low cost and high efficiency in an environmentally friendly way. This review explained the novel technologies which are being developed for thermochemical hydrogen production with minimum or zero carbon emission. The main concern of this paper was to review the advancements in hydrogen production technologies and to discuss different novel catalysts and novel CO2-absorbent materials which can enhance the hydrogen production rate with zero carbon emission. Recent developments in thermochemical hydrogen production technologies were discussed in this paper. Biomass gasification and pyrolysis, steam methane reforming, and thermal plasma are promising thermochemical processes which can be further enhanced by using catalysts and sorbents. This paper also reviewed the developments and influences of different catalysts and sorbents to understand their suitability for continuous clean industrial hydrogen production.

Background Researchers in the field of bioinformatics often face a challenge of combining several ordered lists in a proper and efficient manner. Rank aggregation techniques offer a general and flexible framework that allows one to objectively perform the necessary aggregation. With the rapid growth of high-throughput genomic and proteomic studies, the potential utility of rank aggregation in the context of meta-analysis becomes even more apparent. One of the major strengths of rank-based aggregation is the ability to combine lists coming from different sources and platforms, for example different microarray chips, which may or may not be directly comparable otherwise. Results The RankAggreg package provides two methods for combining the ordered lists: the Cross-Entropy method and the Genetic Algorithm. Two examples of rank aggregation using the package are given in the manuscript: one in the context of clustering based on gene expression, and the other one in the context of meta-analysis of prostate cancer microarray experiments. Conclusion The two examples described in the manuscript clearly show the utility of the RankAggreg package in the current bioinformatics context where ordered lists are routinely produced as a result of modern high-throughput technologies.

Background It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types. Results We provide a recipe for conducting a differential analysis of networks constructed from microarray data under two experimental settings. At the core of our approach lies a connectivity score that represents the strength of genetic association or interaction between two genes. We use this score to propose formal statistical tests for each of following queries: (i) whether the overall modular structures of the two networks are different, (ii) whether the connectivity of a particular set of \"interesting genes\" has changed between the two networks, and (iii) whether the connectivity of a given single gene has changed between the two networks. A number of examples of this score is provided. We carried out our method on two types of simulated data: Gaussian networks and networks based on differential equations. We show that, for appropriate choices of the connectivity scores and tuning parameters, our method works well on simulated data. We also analyze a real data set involving normal versus heavy mice and identify an interesting set of genes that may play key roles in obesity. Conclusions Examining changes in network structure can provide valuable information about the underlying biochemical pathways. Differential network analysis with appropriate connectivity scores is a useful tool in exploring changes in network structures under different biological conditions. An R package of our tests can be downloaded from the supplementary website http://www.somnathdatta.org/Supp/DNA .

Background We investigated the association of several air pollution measures with postmenopausal breast cancer (BCa) risk. Methods This study included 155,235 postmenopausal women (of which 6146 with BCa) from UK Biobank. Cancer diagnoses were ascertained through the linkage to the UK National Health Service Central Registers. Annual exposure averages were available from 2005, 2006, 2007, and 2010 for NO 2 , from 2007 and 2010 for PM 10 , and from 2010 for PM 2.5 , NO X , PM 2.5–10 and PM 2.5 absorbance. Information on BCa risk factors was collected at baseline. Cox proportional hazards regression was used to evaluate the associations of year-specific and cumulative average exposures with BCa risk, overall and with 2-year exposure lag, while adjusting for BCa risk factors. Results PM 10 in 2007 and cumulative average PM 10 were positively associated with BCa risk (2007 PM 10 : Hazard ratio [HR] per 10 µg/m 3  = 1.18, 95% CI 1.08, 1.29; cumulative average PM 10 : HR per 10 µg/m 3  = 1.99, 95% CI 1.75, 2.27). Compared to women with low exposure, women with higher 2007 PM 10 and cumulative average PM 10 had greater BCa risk (4th vs. 1st quartile HR = 1.15, 95% CI 1.07, 1.24, p-trend = 0.001 and HR = 1.35, 95% CI 1.25, 1.44, p-trend < 0.0001, respectively). No significant associations were found for any other exposure measures. In the analysis with 2-year exposure lag, both 2007 PM 10 and cumulative average PM10 were positively associated with BCa risk (4th vs. 1st quartile HR = 1.19, 95% CI 1.10, 1.28 and HR = 1.29, 95% CI 1.19, 1.39, respectively). Conclusion Our findings suggest a positive association of 2007 PM 10 and cumulative average PM 10 with postmenopausal BCa risk.

Background Gene co-expression networks (GCNs) are powerful tools that enable biologists to examine associations between genes during different biological processes. With the advancement of new technologies, such as single-cell RNA sequencing (scRNA-seq), there is a need for developing novel network methods appropriate for new types of data. Results We present a novel sparse Bayesian factor model to explore the network structure associated with genes in scRNA-seq data. Latent factors impact the gene expression values for each cell and provide flexibility to account for common features of scRNA-seq: high proportions of zero values, increased cell-to-cell variability, and overdispersion due to abnormally large expression counts. From our model, we construct a GCN by analyzing the positive and negative associations of the factors that are shared between each pair of genes. Conclusions Simulation studies demonstrate that our methodology has high power in identifying gene-gene associations while maintaining a nominal false discovery rate. In real data analyses, our model identifies more known and predicted protein-protein interactions than other competing network models.

Peer-to-peer (P2P) energy trading facilitates both consumers and prosumers to exchange energy without depending on an intermediate medium. This system makes the energy market more decentralized than before, which generates new opportunities in energy-trading enhancements. In recent years, P2P energy trading has emerged as a method for managing renewable energy sources in distribution networks. Studies have focused on creating pricing mechanisms for P2P energy trading, but most of them only consider energy prices. This is because of a lack of understanding of the pricing mechanisms in P2P energy trading. This paper provides a comprehensive overview of pricing mechanisms for energy and network service prices in P2P energy trading, based on the recent advancements in P2P. It suggests that pricing methodology can be categorized by trading process in two categories, namely energy pricing and network service pricing (NSP). Within these categories, network service pricing can be used to identify financial conflicts, and the relationship between energy and network service pricing can be determined by examining interactions within the trading process. This review can provide useful insights for creating a P2P energy market in distribution networks. This review work provides suggestions and future directions for further development in P2P pricing mechanisms.

Background Metagenomic data of whole genome sequences (WGS) from samples across several cities around the globe may unravel city specific signatures of microbes. Illumina MiSeq sequencing data was provided from 12 cities in 7 different countries as part of the 2018 CAMDA “MetaSUB Forensic Challenge”, including also samples from three mystery sets. We used appropriate machine learning techniques on this massive dataset to effectively identify the geographical provenance of “mystery” samples. Additionally, we pursued compositional data analysis to develop accurate inferential techniques for such microbiome data. It is expected that this current data, which is of higher quality and higher sequence depth compared to the CAMDA 2017 MetaSUB challenge data, along with improved analytical techniques would yield many more interesting, robust and useful results that can be beneficial for forensic analysis. Results A preliminary quality screening of the data revealed a much better dataset in terms of Phred quality score (hereafter Phred score), and larger paired-end MiSeq reads, and a more balanced experimental design, though still not equal number of samples across cities. PCA (Principal Component Analysis) analysis showed interesting clusters of samples and a large amount of the variability in the data was explained by the first three components (~ 70%). The classification analysis proved to be consistent across both the testing mystery sets with a similar percentage of the samples correctly predicted (up to 90%). The analysis of the relative abundance of bacterial “species” showed that some “species” are specific to some regions and can play important roles for predictions. These results were also corroborated by the variable importance given to the “species” during the internal cross validation (CV) run with Random Forest (RF). Conclusions The unsupervised analysis (PCA and two-way heatmaps) of the log2-cpm normalized data and relative abundance differential analysis seemed to suggest that the bacterial signature of common “species” was distinctive across the cities; which was also supported by the variable importance results. The prediction of the city for mystery sets 1 and 3 showed convincing results with high classification accuracy/consistency. The focus of this work on the current MetaSUB data and the analytical tools utilized here can be of great help in forensic, metagenomics, and other sciences to predict city of provenance of metagenomic samples, as well as in other related fields. Additionally, the pairwise analysis of relative abundance showed that the approach provided consistent and comparable “species” when compared with the classification importance variables. Reviewers This article was reviewed by Manuela Oliveira, Dimitar Vassilev, and Patrick Lee.

The lead halide-based perovskite solar cells have attracted much attention in the photovoltaic industry due to their high efficiency, easy manufacturing, lightweight, and low cost. However, these lead halide-based perovskite solar cells are not manufactured commercially due to lead-based toxicity. To investigate lead-free inorganic perovskite solar cells (PSCs), we investigated a novel Cs3Bi2I9-based perovskite configuration in SCAPS-1D software using different hole transport layers (HTLs). At the same time, WS2 is applied as an electron transport layer (ETL). Comparative analysis of the various design configurations reveals that ITO/WS2/Cs3Bi2I9/PEDOT:PSS/Au offers the best performance with 20.12% of power conversion efficiency (PCE). After optimizing the thickness, bandgap, defect density, and carrier density, the efficiency of the configuration is increased from 20.12 to 24.91%. Improvement in other performance parameters such as short circuit current (17.325 mA/cm2), open circuit voltage (1.5683 V), and fill factor (91.66%) are also observed after tuning different attributes. This investigation indicates the potential application of Cs3Bi2I9 as a lead-free and stable perovskite material that can contribute to improving the renewable energy sector.

Background Composition of microbial communities can be location-specific, and the different abundance of taxon within location could help us to unravel city-specific signature and predict the sample origin locations accurately. In this study, the whole genome shotgun (WGS) metagenomics data from samples across 16 cities around the world and samples from another 8 cities were provided as the main and mystery datasets respectively as the part of the CAMDA 2019 MetaSUB “Forensic Challenge”. The feature selecting, normalization, three methods of machine learning, PCoA (Principal Coordinates Analysis) and ANCOM (Analysis of composition of microbiomes) were conducted for both the main and mystery datasets. Results Features selecting, combined with the machines learning methods, revealed that the combination of the common features was effective for predicting the origin of the samples. The average error rates of 11.93 and 30.37% of three machine learning methods were obtained for main and mystery datasets respectively. Using the samples from main dataset to predict the labels of samples from mystery dataset, nearly 89.98% of the test samples could be correctly labeled as “mystery” samples. PCoA showed that nearly 60% of the total variability of the data could be explained by the first two PCoA axes. Although many cities overlapped, the separation of some cities was found in PCoA. The results of ANCOM, combined with importance score from the Random Forest, indicated that the common “family”, “order” of the main-dataset and the common “order” of the mystery dataset provided the most efficient information for prediction respectively. Conclusions The results of the classification suggested that the composition of the microbiomes was distinctive across the cities, which could be used to identify the sample origins. This was also supported by the results from ANCOM and importance score from the RF. In addition, the accuracy of the prediction could be improved by more samples and better sequencing depth.

Background SepIVA has been reported to be an essential septation factor in Mycolicibacterium smegmatis and Mycobacterium tuberculosis . It is a coiled-coil protein with similarity to DivIVA, a protein necessary for polar growth in members of the phylum Actinomycetota. Orthologues of SepIVA are broadly distributed among actinomycetes, including in Streptomyces spp. Results To clarify the role of SepIVA and its potential involvement in cell division in streptomycetes, we generated sepIVA deletion mutants in Streptomyces venezuelae and found that sepIVA is dispensable for growth, cell division and sporulation. Further, mNeonGreen-SepIVA fusion protein did not localize at division septa, and we found no evidence of involvement of SepIVA in cell division. Instead, mNeonGreen-SepIVA was accumulated at the tips of growing vegetative hyphae in ways reminiscent of the apical localization of polarisome components like DivIVA. Bacterial two-hybrid system analyses revealed an interaction between SepIVA and DivIVA. The results indicate that SepIVA is associated with polar growth. However, no phenotypic effects of sepIVA deletion could be detected, and no evidence was observed of redundancy with the other DivIVA-like coiled-coil proteins Scy and FilP that are also associated with apical growth in streptomycetes. Conclusions We conclude that S. venezuelae SepIVA, in contrast to the situation in mycobacteria, is dispensable for growth and viability. The results suggest that it is associated with polar growth rather than septum formation.