Genome-wide algorithm for detecting CNV associations with diseases

<p>Abstract</p> <p>Background</p> <p>SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP data using the marker intensities. However, these algorithms lack specificity to detect small CNVs owing to the high false positive rate when calling CNVs based on the intensity values. Therefore, the resulting association tests lack power even if the CNVs affecting disease risk are common. An alternative procedure called PennCNV uses information from both the marker intensities as well as the genotypes and therefore has increased sensitivity.</p> <p>Results</p> <p>By using the hidden Markov model (HMM) implemented in PennCNV to derive the probabilities of different copy number states which we subsequently used in a logistic regression model, we developed a new genome-wide algorithm to detect CNV associations with diseases. We compared this new method with association test applied to the most probable copy number state for each individual that is provided by PennCNV after it performs an initial HMM analysis followed by application of the Viterbi algorithm, which removes information about copy number probabilities. In one of our simulation studies, we showed that for large CNVs (number of SNPs ≥ 10), the association tests based on PennCNV calls gave more significant results, but the new algorithm retained high power. For small CNVs (number of SNPs <it><</it>10), the logistic algorithm provided smaller average p-values (e.g., <it>p </it>= 7.54<it>e </it>- 17 when relative risk <it>RR </it>= 3.0) in all the scenarios and could capture signals that PennCNV did not (e.g., <it>p </it>= 0.020 when <it>RR </it>= 3.0). From a second set of simulations, we showed that the new algorithm is more powerful in detecting disease associations with small CNVs (number of SNPs ranging from 3 to 5) under different penetrance models (e.g., when <it>RR </it>= 3.0, for relatively weak signals, <it>power </it>= 0.8030 comparing to 0.2879 obtained from the association tests based on PennCNV calls). The new method was implemented in software GWCNV. It is freely available at <url>http://gwcnv.sourceforge.net</url>, distributed under a GPL license.</p> <p>Conclusions</p> <p>We conclude that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than the existing HMM algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.</p

Magnetic γ-Fe2O3-Loaded Attapulgite Sorbent for Hg0 Removal in Coal-Fired Flue Gas

A magnetically recoverable composite mercury removal sorbent was produced by introducing magnetic γ-Fe2O3 into attapulgite (ATT) (xFe1ATT) via the co-precipitation method and used to remove Hg0 in the simulated coal-fired power plant flue gas. The as-prepared 0.5Fe1ATT sorbent was characterized by X-ray diffraction, Brunauer–Emmett–Teller, transmission electron microscopy, vibrating sample magnetometer, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy analyses. The results showed that the Hg0 removal performance of the composite of γ-Fe2O3 and ATT was significantly promoted in comparison to pure γ-Fe2O3 and ATT individually. A relatively high magnetization value and good Hg0 removal performance were obtained by the sample of 0.5Fe1ATT. O2 could enhance Hg0 removal activity via the Mars–Maessen mechanism. NO displayed a significant promotion effect on Hg0 removal as a result of the formation of active species, such as NO2 and NO+. SO2 inhibited the removal of Hg0 as a result of its competition adsorption against Hg0 for the active sites and the sulfation of the sorbent. However, the introduction of NO could obviously alleviate the adverse effect of SO2 on the Hg0 removal capability. H2O showed a prohibitive effect on Hg0 removal as a result of its competition with Hg0 for the active sites. The findings of this study are of fundamental importance to the development of efficient and economic magnetic mercury sorbents for Hg0 removal from coal-fired boiler flue gases

Desulfurization using limestone during sludge incineration in a fluidized bed furnace: Increased risk of particulate matter and heavy metal emissions

Incineration of sludge can be an effective method to minimise waste whilst producing useful heat. However, incineration can cause secondary pollution issues due to the emission of SO2, therefore a set of experiments of sludge incineration in a bubble bed furnace were conducted with limestone addition to study desulfurization of sludge incineration flue gas. As expected, over 93% emission of SO2 was reduced with limestone addition, and that of CO and NOx were increased and decreased respectively when the fuel feeding rate raised. The distribution of fly ash was also increased by raising the fuel feeding rate due to increasing fragmentation of the ash. However, distributions of PM2.5 and heavy metals in submicron particles have dramatically increased with limestone desulfurization. The mechanism was revealed by SEM and EDS statistical analysis, indicating that the reaction between aluminosilicate and calcium made particles agglomerate and eutectic mixtures form, these larger ash particles were found to divide between collection as cyclone ash and fragmentation into finer particles that bypassed the cyclone. Those fine particles provided more surface area for heavy metal condensation. Furthermore, it was found that the reaction mechanism for semi-volatile metals involved them being released from the sludge and forming PM1 particles due to the vaporization-condensation mechanism, leading to higher emission of PM1 and distribution of heavy metals in PM1. Thus, it should be considered that there may actually be higher emission risks of PM and heavy metal emissions when aiming to desulfurize a flue gas using Ca-based minerals in certain circumstance

Modelling of redox flow battery electrode processes at a range of length scales : a review

In this article, the different approaches reported in the literature for modelling electrode processes in redox flow batteries (RFBs) are reviewed. RFB models vary widely in terms of computational complexity, research scalability and accuracy of predictions. Development of RFB models have been quite slow in the past, but in recent years researchers have reported on a range of modelling approaches for RFB system optimisation. Flow and transport processes, and their influence on electron transfer kinetics, play an important role in the performance of RFBs. Macro-scale modelling, typically based on a continuum approach for porous electrode modelling, have been used to investigate current distribution, to optimise cell design and to support techno-economic analyses. Microscale models have also been developed to investigate the transport properties within porous electrode materials. These microscale models exploit experimental tomographic techniques to characterise three-dimensional structures of different electrode materials. New insights into the effect of the electrode structure on transport processes are being provided from these new approaches. Modelling flow, transport, electrical and electrochemical processes within the electrode structure is a developing area of research, and there are significant variations in the model requirements for different redox systems, in particular for multiphase chemistries (gas–liquid, solid–liquid, etc.) and for aqueous and non-aqueous solvents. Further development is essential to better understand the kinetic and mass transport phenomena in the porous electrodes, and multiscale approaches are also needed to enable optimisation across the relevent length scales

Genome-wide algorithm for detecting CNV associations with diseases

SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). The quality of the inferences about copy number can be affected by many factors including batch effects, DNA sample preparation, signal processing, and analytical approach. Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP genotyping data. However, these algorithms lack specificity to detect small CNVs due to the high false positive rate when calling CNVs based on the intensity values. Association tests based on detected CNVs therefore lack power even if the CNVs affecting disease risk are common. In this research, by combining an existing Hidden Markov Model (HMM) and the logistic regression model, a new genome-wide logistic regression algorithm was developed to detect CNV associations with diseases. We showed that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than an existing popular algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV

Investigation of elemental mercury removal from coal-fired flue gas over MIL101-Cr

In this work, the MIL101-Cr sorbent with a large BET surface area was prepared and used to remove Hg0 from the simulated coal-fired boiler flue gas. The chemical and physical properties of the prepared sorbent were characterized by X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET) and X-ray photoelectron spectroscopy (XPS). A range of experiments was conducted in a fixed-bed reactor to investigate the effects of reaction temperature, Hg0 inlet concentration, gas hourly space velocity (GHSV) and flue gas composition on the Hg0 removal for the prepared sorbent. The mechanisms and kinetics of the Hg0 adsorption were also studied. The results showed that the MIL101-Cr sorbent achieved the Hg0 removal efficiency of more than 85% for 4 h at 200 oC under the condition of a relatively high Hg0 inlet concentration (203 μg/m3) and large GHSV (8 105 h-1). The O2 in the flue gas was found to be beneficial to Hg0 removal. The NO in the flue gas favoured Hg0 removal both in the presence and absence of O2. The SO2 in the flue gas notably inhibited Hg0 adsorption in the absence of O2, whereas a low concentration of SO2 slightly inhibited Hg0 removal in the presence of O2. However, high concentrations of SO2 in the flue gas still significantly weakened the Hg0 removal ability even in the presence of O2 due to the competitive adsorption of SO2 with Hg0 on the sorbent and the sulfation of the sorbent. A simultaneous presence of O2 and NO in the flue gas could overcome the adverse impact of SO2 on the Hg0 adsorption. The H2O in the flue gas could have a minor influence on Hg0 removal as a result of the competitive adsorptions between Hg0 and H2O. The XPS analysis indicated that the surface Cr3+, oxygen species and C=O group in MIL101-Cr acted as the active adsorption/oxidation sites for Hg0. The Hg0 removal by MIL101-Cr belonged to chemisorption and could be described by the pseudo-second-order model. The equilibrium adsorption capacity calculated for the sorbent amounted to 25656 μg/g at 200 oC, which indicated that MIL101-Cr could be used as a promising sorbent to remove Hg0 from coal-fired boiler flue gases

Chemical Characteristics of Ash Formed from the Combustion of Shoe Manufacturing Waste in a 2.5 MWth Circulating Fluidized Bed Combustor

© 2019, Springer Nature B.V. Abstract: The ash formation behaviour and associated compositional characteristics of the combustion of shoe manufacturing waste (SMW) in a 2.5MWth pilot-scale circulating fluidized bed combustor (CFBC) were investigated to better understand the combustion behaviour and to find effective management strategy for the disposal of the ash streams produced. The compositional characterisations for the ashes produced from the pilot demonstrations showed the presence of a variety of trace heavy metals both in the fly ash and bottom ash. A pronounced uneven partitioning behaviour was observed on the distribution of these heavy metals between the fly ash and bottom ash, and it was found that all the heavy metals except chromium were preferentially enriched in the fly ash, with the contents of lead and cadmium in the fly ash being over 11 and 6 times higher than in the bottom ash. Leaching tests demonstrated that the concentrations of most of the aforementioned metals present in the leachates from the fly ash and bottom ash could meet the permissible limits for landfill disposal but with lead as an exception with its concentration in the fly ash leachates being over 2 times higher than the limit. The total PCDD and PCDF contents both in the fly ash and bottom ash were also much below the legal limit. To further understand the ash behaviour, the slagging and fouling tendency during SMW combustion in the CFBC was examined by use of the characterisation of the ash mineralogy and the results indicated that the SMW ash likely had low tendencies for slagging and fouling. The best two valorization routes for the SMW bottom ash and fly ash were recommended, one being to use them to produce colloidal silica medium to obtain safe inert filler and the other being to use them as the raw materials in the cement industry. Graphic Abstract: [Figure not available: see fulltext.]