Optimisation of CH4 and CO2 conversion and selectivity of H2 and CO for the dry reforming of methane by a microwave plasma technique using a Box–Behnken design

A microwave plasma was generated by N2 gas. Synthesis gases (H2 and CO) were produced by the interaction of CH4 and CO2 under plasma conditions at atmospheric pressure. The experimental pilot plant was set up, and the gases were sampled and analysed by gas chromatography–mass spectrometry. The Box–Behnken design (BBD) method was used to find the optimising conditions based on the experimental results. The response surface methodology based on a three-parameter and three-level BBD has been developed to find the effects of independent process parameters, which were represented by the gas flow rates of CH4, CO2, and N2 and their effects on the process performance in terms of CH4, CO2, and N2 conversion and selectivity of H2 and CO. In this work, four models based on quadratic polynomial regression have been determined to understand the connection between the limits of the feed gas flow rate and the performance of the process. The results show that the most important factor influencing the CO2, CH4, and N2 conversion and the selectivity of H2 and CO was “CO2 feed gas flow rate.” At the maximum desirable value of 0.92, the optimum CH4, CO2, and N2 conversion were 84.91%, 44.40%, and 3.37%, respectively, and the selectivities of H2 and CO were 51.31% and 61.17%, respectively. This was achieved at a gas feed flow rate of 0.19, 0.38, and 1.49 L min-1 for CH4, CO2, and N2, respectively

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

<p>Abstract</p> <p>Background</p> <p>Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.</p> <p>Results</p> <p>We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes.</p> <p>Conclusion</p> <p>The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. SpecificDB provides comprehensive information and a user-friendly interface.</p

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

<p>Abstract</p> <p>Background</p> <p>Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.</p> <p>Results</p> <p>We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes.</p> <p>Conclusion</p> <p>The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. SpecificDB provides comprehensive information and a user-friendly interface.</p

Backbone and side-chain 1H, 13C and 15N assignments of the ubiquitin-associated domain of human X-linked inhibitor of apoptosis protein

X-linked inhibitor of apoptosis protein (XIAP), a leading member of the family of inhibitor of apoptosis (IAP) proteins, is considered as the most potent and versatile inhibitor of caspases and apoptosis. It has been reported that XIAP is frequently overexpressed in cancer and its expression level is implicated in contributing to tumorigenesis, disease progression, chemoresistance and poor patient-survival. Therefore, XIAP is one of the leading targets in drug development for cancer therapy. Recently, based on bioinformatics study, a previously unrecognized but evolutionarily conserved ubiquitin-associated (UBA) domain in IAPs was identified. The UBA domain is found to be essential for the oncogenic potential of IAP, to maintain endothelial cell survival and to protect cells from TNF-α-induced apoptosis. Moreover, the UBA domain is required for XIAP to activate NF-κB. In the present study, we report the near complete resonance assignments of the UBA domain-containing region of human XIAP protein. Secondary structure prediction based on chemical shift index (CSI) analysis reveals that the protein is predominately α-helical, which is consistent with the structures of known UBA proteins

Therapeutic DNA vaccine induces broad T cell responses in the gut and sustained protection from viral rebound and AIDS in SIV-infected rhesus macaques.

Immunotherapies that induce durable immune control of chronic HIV infection may eliminate the need for life-long dependence on drugs. We investigated a DNA vaccine formulated with a novel genetic adjuvant that stimulates immune responses in the blood and gut for the ability to improve therapy in rhesus macaques chronically infected with SIV. Using the SIV-macaque model for AIDS, we show that epidermal co-delivery of plasmids expressing SIV Gag, RT, Nef and Env, and the mucosal adjuvant, heat-labile E. coli enterotoxin (LT), during antiretroviral therapy (ART) induced a substantial 2-4-log fold reduction in mean virus burden in both the gut and blood when compared to unvaccinated controls and provided durable protection from viral rebound and disease progression after the drug was discontinued. This effect was associated with significant increases in IFN-γ T cell responses in both the blood and gut and SIV-specific CD8+ T cells with dual TNF-α and cytolytic effector functions in the blood. Importantly, a broader specificity in the T cell response seen in the gut, but not the blood, significantly correlated with a reduction in virus production in mucosal tissues and a lower virus burden in plasma. We conclude that immunizing with vaccines that induce immune responses in mucosal gut tissue could reduce residual viral reservoirs during drug therapy and improve long-term treatment of HIV infection in humans

Tuning ultrafast electron thermalization pathways in a van der Waals heterostructure

Ultrafast electron thermalization - the process leading to Auger recombination, carrier multiplication via impact ionization and hot carrier luminescence - occurs when optically excited electrons in a material undergo rapid electron-electron scattering to redistribute excess energy and reach electronic thermal equilibrium. Due to extremely short time and length scales, the measurement and manipulation of electron thermalization in nanoscale devices remains challenging even with the most advanced ultrafast laser techniques. Here, we overcome this challenge by leveraging the atomic thinness of two-dimensional van der Waals (vdW) materials in order to introduce a highly tunable electron transfer pathway that directly competes with electron thermalization. We realize this scheme in a graphene-boron nitride-graphene (G-BN-G) vdW heterostructure, through which optically excited carriers are transported from one graphene layer to the other. By applying an interlayer bias voltage or varying the excitation photon energy, interlayer carrier transport can be controlled to occur faster or slower than the intralayer scattering events, thus effectively tuning the electron thermalization pathways in graphene. Our findings, which demonstrate a novel means to probe and directly modulate electron energy transport in nanoscale materials, represent an important step toward designing and implementing novel optoelectronic and energy-harvesting devices with tailored microscopic properties.Comment: Accepted to Nature Physic

Fitness Ranking of Individual Mutants Drives Patterns of Epistatic Interactions in HIV-1

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Facilitating Stable Representations: Serial Dependence in Vision

We tested whether the intervening time between multiple glances influences the independence of the resulting visual percepts. Observers estimated how many dots were present in brief displays that repeated one, two, three, four, or a random number of trials later. Estimates made farther apart in time were more independent, and thus carried more information about the stimulus when combined. In addition, estimates from different visual field locations were more independent than estimates from the same location. Our results reveal a retinotopic serial dependence in visual numerosity estimates, which may be a mechanism for maintaining the continuity of visual perception in a noisy environment