A stable iterative method for refining discriminative gene clusters

<p>Abstract</p> <p>Background</p> <p>Microarray technology is often used to identify the genes that are differentially expressed between two biological conditions. On the other hand, since microarray datasets contain a small number of samples and a large number of genes, it is usually desirable to identify small gene subsets with distinct pattern between sample classes. Such gene subsets are highly discriminative in phenotype classification because of their tightly coupling features. Unfortunately, such identified classifiers usually tend to have poor generalization properties on the test samples due to overfitting problem.</p> <p>Results</p> <p>We propose a novel approach combining both supervised learning with unsupervised learning techniques to generate increasingly discriminative gene clusters in an iterative manner. Our experiments on both simulated and real datasets show that our method can produce a series of robust gene clusters with good classification performance compared with existing approaches.</p> <p>Conclusion</p> <p>This backward approach for refining a series of highly discriminative gene clusters for classification purpose proves to be very consistent and stable when applied to various types of training samples.</p

Fabrication of β-cyclodextrin modified mesostructured silica coated multi-walled carbon nanotubes composites and application for paraben removal

In this work, the novel β-cyclodextrin modified mesostructured silica coated multi-walled carbon nanotubes (MWCNTs) composites were synthesized and applied for the removal of parabens in aqueous solution. The prepared MWCNTs/SiO2/β-CD composites were characterized by Fourier transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy and thermogravimetric analysis. The effects of the amount of adsorbent, pH and elution solvents on the removal efficiency of parabens from water solutions were investigated. Under the optimized conditions, over 95% removal efficiency was achieved by using 40 mg of MWCNTs/SiO2/β-CD adsorbents to absorb the parabens from 60 mL of 0.5 μg/mL parabens solutions. The solution pH in the range from 5 to 9 has no influence on the removal efficiency and the parabens sorption capacity of the prepared adsorbents were around 0.75 μg/mg. Furthermore, the stability and reusability studies demonstrated that the prepared MWCNTs/SiO2/β-CD composites are cost-effective adsorbents for the removal of parabens from water with high regeneration efficiency. The composites fabricated in this study could become an attractive candidate for water purification

Establishment of predictive nomogram and web-based survival risk calculator for desmoplastic small round cell tumor: A propensity score-adjusted, population-based study

Desmoplastic small round cell tumor (DSRCT) is a rare undifferentiated malignant soft tissue tumor with a poor prognosis and a lack of consensus on treatment. This study’s objective was to build a nomogram based on clinicopathologic factors and an online survival risk calculator to predict patient prognosis and support therapeutic decision-making. A retrospective cohort analysis of the Surveillance, Epidemiology and End Results (SEER) database was performed for patients diagnosed with DSRCT between 2000 and 2019. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to identify the individual variables related to overall survival (OS) and cancer-specific survival (CSS), as well as to construct online survival risk calculators and nomogram survival models. The nomogram was employed to categorize patients into different risk groups, and the Kaplan-Meier method was utilized to determine the survival rate of each risk category. Propensity score matching (PSM) was used to assess survival with different therapeutic approaches. A total of 374 patients were included, and the median OS and CSS were 25 (interquartile range 21.9-28.1) months and 27 (interquartile range 23.6-30.3) months, respectively. The nomogram models demonstrated high predictive accuracy. PSM found that patients with triple-therapy had better CSS and OS than those who received surgery plus chemotherapy (median survival times: 49 vs 34 months and 49 vs 35 months, respectively). The nomogram successfully predicted the DSRCT patients survival rate. This approach could assist doctors in evaluating prognoses, identifying high-risk populations, and implementing personalized therapy

Novel two-stage fluidized bed-plasma gasification integrated with SOFC and chemical looping combustion for the high efficiency power generation from MSW: a thermodynamic investigation

A novel municipal solid waste (MSW)-based power generation system was proposed in this study, which consists of a bubbling fluidized-bed (BFB)-plasma gasification unit, a high-temperature solid oxide fuel cell (SOFC), a chemical looping combustion (CLC) unit and a heat recovery unit. Process simulation was conducted using Aspen PlusTM and validated by literature data. The energetic and exergetic assessment of the proposed system showed that the net electrical efficiency and exergy efficiency reached 40.9 % and 36.1 %, respectively with 99.3 % of carbon dioxide being captured. It was found that the largest exergy destruction took place in the BFB-Plasma gasification unit (476.5 kW) and accounted for 33.6 % of the total exergy destruction, which is followed by the SOFC (219.1 kW) and then CLC (208.6 kW). Moreover, the effects of key variables, such as steam to fuel ratio (STFR), fuel utilization factor (Uf), current density and air reactor operating temperature, etc., on system performance were carried out and revealed that the system efficiency could be optimized under STFR = 0.5, Uf = 0.8 and air reactor operating temperature of 1000 ºC. Furthermore, the proposed process demonstrated more than 14% improvement in net electrical efficiency in comparison with other MSW incineration and/or gasification to power processes

Identification of disease-related genes and construction of a gene co-expression database in non-alcoholic fatty liver disease

Background: The mechanism of NAFLD progression remains incompletely understood. Current gene-centric analysis methods lack reproducibility in transcriptomic studies.Methods: A compendium of NAFLD tissue transcriptome datasets was analyzed. Gene co-expression modules were identified in the RNA-seq dataset GSE135251. Module genes were analyzed in the R gProfiler package for functional annotation. Module stability was assessed by sampling. Module reproducibility was analyzed by the ModulePreservation function in the WGCNA package. Analysis of variance (ANOVA) and Student’s t-test was used to identify differential modules. The receiver operating characteristic (ROC) curve was used to illustrate the classification performance of modules. Connectivity Map was used to mine potential drugs for NAFLD treatment.Results: Sixteen gene co-expression modules were identified in NAFLD. These modules were associated with multiple functions such as nucleus, translation, transcription factors, vesicle, immune response, mitochondrion, collagen, and sterol biosynthesis. These modules were stable and reproducible in the other 10 datasets. Two modules were positively associated with steatosis and fibrosis and were differentially expressed between non-alcoholic steatohepatitis (NASH) and non-alcoholic fatty liver (NAFL). Three modules can efficiently separate control and NAFL. Four modules can separate NAFL and NASH. Two endoplasmic reticulum related modules were both upregulated in NAFL and NASH compared to normal control. Proportions of fibroblasts and M1 macrophages are positively correlated with fibrosis. Two hub genes Aebp1 and Fdft1 may play important roles in fibrosis and steatosis. m6A genes were strongly correlated with the expression of modules. Eight candidate drugs for NAFLD treatment were proposed. Finally, an easy-to-use NAFLD gene co-expression database was developed (available at https://nafld.shinyapps.io/shiny/).Conclusion: Two gene modules show good performance in stratifying NAFLD patients. The modules and hub genes may provide targets for disease treatment

Comparative study of the gasification of coal and its macerals and prediction of the synergistic effects under typical entrained-bed pulverized coal gasification Conditions

This research is focused on the gasification performance of coal and its corresponding macerals as well as on the interactions among macerals under typical gasification conditions by Aspen Plus modeling. The synergistic coefficient was employed to show the degree of interactions, while the performance indicators including specific oxygen consumption (SOC), specific coal consumption (SCC), cold gas efficiency (CGE), and effective syngas (CO + H2) content were used to evaluate the gasification process. Sensitivity analyses showed that the parent coal and its macerals exhibited different gasification behaviors at the same operating conditions, such as the SOC and SCC decreased in the order of inertinite > vitrinite > liptinite, whereas CGE changed in the order of liptinite > vitrinite > inertinite. The synergistic coefficients of SOC and SCC for the simulated coals were in the range of 0.94–0.97, whereas the synergistic coefficient of CGE was 1.05–1.13. Moreover, it was found that synergistic coefficients of gasification indicators correlated well with maceral contents. In addition, the increase in temperature was found to promote the synergistic coefficients slightly, whilst at an oxygen to coal mass ratio of 0.8 and a steam to coal mass ratio of 0.8, the highest synergistic coefficient was obtained

Boosting Electrocatalytic Nitrate-to-Ammonia Conversion via Plasma Enhanced CuCo Alloy–Substrate Interaction

Electrocatalytic conversion of widely distributed nitrate from industrial wastewater into value-added ammonia was proposed as an attractive and sustainable alternative to harvesting green ammonia. Herein, CuCo alloys were facilely synthesized for nitrate conversion, while nonthermal Ar-plasma was employed to enhance the adhesion strength between the electrocatalyst and substrate interface via regulating the surface hydrophobicity and roughness. Based on Ar-plasma treatment, a high ammonia yield rate (5129.29 μg cm-2 h-1) was achieved using Cu30Co70 electrocatalyst -0.47 V vs RHE, while nearly 100% of Faradaic efficiency was achieved using Cu50Co50 electrocatalyst at -0.27 V vs RHE (reversible hydrogen electrode). Validated by in situ spectroscopy and density functional theory calculations, the high activity of the CuCo alloy was derived from the regulation of Co to weaken the strong adsorption capacity of Cu and the shift of the d-band center to lower the energy barrier, while Ar-plasma modification promoted the formation of *NO to boost nitrate conversion

A simple coupled ANNs-RSM approach in modeling product distribution of Fischer-Tropsch synthesis using a microchannel reactor with Ru-promoted Co/Al2O3 catalyst

A simple approach using hybrid artificial neural networks (ANNs)-response surface methodology (RSM) was developed to model the detailed product distribution using Ru-promoted cobalt-based catalyst with Al2O3 as the support in a microchannel reactor for Fischer-Tropsch (FT) synthesis. Using the independent process parameters for training, the established model is capable of predicting hydrocarbon production distributions, ie, paraffin formation rate (C2-C15) and olefin to paraffin ratio (OPR C2-C15) within acceptable uncertainties. The ANNs-RSM model and comprehensive mechanistic model using the Langmuir-Hinshelwood-Hougen-Watson (LHHW) approach were compared to identify a few inherent advantages of the proposed model in modeling complex FT synthesis. The proposed ANNs-RSM model shows its appealing merits, ie, faster converge and higher accuracy (less than ±10% uncertainties was achieved from ANNs-RSM model, while LHHW model achieved less than ±15% uncertainties except a few exceptional high errors uncertainties at certain experimental conditions). The statistical significances to the product distributions and hydrocarbon formation rate during FT synthesis could be easily identified and quantitatively analyzed by the established ANNs-RSM model. The future effective alternative of kinetic study of the complex system such as FT synthesis in the microstructured reactor might follow the methods of using empirical reliable approach for process optimization together with mechanistic kinetic study for detailed reaction pathway discrimination

Liquid-Phase Exfoliated Silicon Nanosheets: Saturable Absorber for Solid-State Lasers

As a newly-developed two-dimensional (2D) material of group-IVA, few-layer silicon (Si) nanosheets were prepared by the liquid phase exfoliation (LPE) method. Its non-linear saturable adsorption property was investigated by 532 and 1064 nm nanosecond lasers. Using Si nanosheets as the saturable absorber (SA), passive Q-switched all-solid-state lasers were demonstrated for the first time. For different laser emissions of Nd3+ at 0.9, 1.06, and 1.34 &micro;m, the narrowest Q-switched pulse widths were 200.2, 103.7, and 110.4 ns, corresponding to the highest peak powers of 2.76, 2.15, and 1.26 W. The results provide a promising SA for solid-state pulsed lasers and broaden the potential application range of Si nanosheets in ultrafast photonics and optoelectronics

A POCS Algorithm Based on Text Features for the Reconstruction of Document Images at Super-Resolution

In order to address the problem of the uncertainty of existing noise models and of the complexity and changeability of the edges and textures of low-resolution document images, this paper presents a projection onto convex sets (POCS) algorithm based on text features. The current method preserves the edge details and smooths the noise in text images by adding text features as constraints to original POCS algorithms and converting the fixed threshold to an adaptive one. In this paper, the optimized scale invariant feature transform (SIFT) algorithm was used for the registration of continuous frames, and finally the image was reconstructed under the improved POCS theoretical framework. Experimental results showed that the algorithm can significantly smooth the noise and eliminate noise caused by the shadows of the lines. The lines of the reconstructed text are smoother and the stroke contours of the reconstructed text are clearer, and this largely eliminates the text edge vibration to enhance the resolution of the document image text