TF-Cluster: A pipeline for identifying functionally coordinated transcription factors via network decomposition of the shared coexpression connectivity matrix (SCCM)

<p>Abstract</p> <p>Background</p> <p>Identifying the key transcription factors (TFs) controlling a biological process is the first step toward a better understanding of underpinning regulatory mechanisms. However, due to the involvement of a large number of genes and complex interactions in gene regulatory networks, identifying TFs involved in a biological process remains particularly difficult. The challenges include: (1) Most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation, making it difficult to recognize TFs for a biological process; (2) Transcription usually involves several hundred genes that generate a combination of intrinsic noise from upstream signaling networks and lead to fluctuations in transcription; (3) A TF can function in different cell types or developmental stages. Currently, the methods available for identifying TFs involved in biological processes are still very scarce, and the development of novel, more powerful methods is desperately needed.</p> <p>Results</p> <p>We developed a computational pipeline called TF-Cluster for identifying functionally coordinated TFs in two steps: (1) Construction of a shared coexpression connectivity matrix (SCCM), in which each entry represents the number of shared coexpressed genes between two TFs. This sparse and symmetric matrix embodies a new concept of coexpression networks in which genes are associated in the context of other shared coexpressed genes; (2) Decomposition of the SCCM using a novel heuristic algorithm termed "Triple-Link", which searches the highest connectivity in the SCCM, and then uses two connected TF as a primer for growing a TF cluster with a number of linking criteria. We applied TF-Cluster to microarray data from human stem cells and <it>Arabidopsis </it>roots, and then demonstrated that many of the resulting TF clusters contain functionally coordinated TFs that, based on existing literature, accurately represent a biological process of interest.</p> <p>Conclusions</p> <p>TF-Cluster can be used to identify a set of TFs controlling a biological process of interest from gene expression data. Its high accuracy in recognizing true positive TFs involved in a biological process makes it extremely valuable in building core GRNs controlling a biological process. The pipeline implemented in Perl can be installed in various platforms.</p

PSSP-RFE: Accurate Prediction of Protein Structural Class by Recursive Feature Extraction from PSI-BLAST Profile, Physical-Chemical Property and Functional Annotations

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets

Overexpressed transferrin receptor implied poor prognosis and relapse in gastrointestinal stromal tumors

Ferroptosis, as a novel-induced programmed cell death, plays critical roles in the pathogenesis of cancers. However, the promising biomarkers of ferroptosis in gastrointestinal stromal tumor (GIST) remain to be elucidated. Herein, the expression of ferroptosis-related genes was analyzed in GIST. Among the 64 ferroptosis-related genes, transferrin receptor (TFRC) expression presented a remarkable upregulation in high-risk patients through Gene Expression Omnibus (GEO) dataset analysis, as well as its significant change after imatinib was treated. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of TFRC-relevant genes revealed that TFRC expression was closely associated with cell growth pathways and metabolism-related pathways. Furthermore, patients at high risk of recurrence were more likely to exhibit high TFRC expression by immunohistochemistry. Additionally, high TFRC expression indicated an undesirable state of patient relapse, which could serve as a powerful significant independent predictor of recurrence-free survival (RFS). In summary, we systematically summarize the expression characteristics and clinical relevance of TFRC and show that TFRC can be used as a prognostic factor, which can be considered a potential therapeutic target in GIST

Combustion and Emissions in a Light-Duty Diesel Engine using Diesel-Water Emulsions and Diesel-Ethanol Blends

The purpose of the investigation presented here was to compare the effects of fuel composition on combustion parameters, emissions and fuel consumption in engine tests and simulations with five fuels: a diesel-water emulsion, a diesel-ethanol blend, a diesel-ethanol blend with EHN (cetane number improver), a Fischer-Tropsch diesel and an ultra-low sulfur content diesel.The engine used in the experiments was a light-duty, single-cylinder, direct injection, common rail diesel engine equipped with a cylinder head and piston from a Volvo NED5 engine. In tests with each fuel the engine was operated at two load points (3 bar IMEP and 10 bar IMEP), and a pilot-main fuel injection strategy was applied under both load conditions. Data were also obtained from 3-D CFD simulations, using the KIVA code, to compare to the experimental results and to further analyze the effects of water and ethanol on combustion.The experimental data indicated that the lower aromatic content of Fischer-Tropsch diesel fuel resulted in reduced soot emissions compared to conventional diesel. Use of Fischer-Tropsch diesel also gave lower NO\dx emissions. The diesel-ethanol blend gave a large reduction in soot emissions, but higher NO\dx emissions than the diesel-water emulsion. The lower heating value of the diesel-water emulsion resulted in increased fuel consumption in comparison to the diesel-ethanol blend and diesel. The addition of the cetane number improver (EHN) to the diesel-ethanol blend further reduced NO\dx emissions

Combustion and Emissions in a Light-Duty Diesel Engine using Diesel-Water Emulsions and Diesel-Ethanol Blends

The purpose of the investigation presented here was to compare the effects of fuel composition on combustion parameters, emissions and fuel consumption in engine tests and simulations with five fuels: a diesel-water emulsion, a diesel-ethanol blend, a diesel-ethanol blend with EHN (cetane number improver), a Fischer-Tropsch diesel and an ultra-low sulfur content diesel.The engine used in the experiments was a light-duty, single-cylinder, direct injection, common rail diesel engine equipped with a cylinder head and piston from a Volvo NED5 engine. In tests with each fuel the engine was operated at two load points (3 bar IMEP and 10 bar IMEP), and a pilot-main fuel injection strategy was applied under both load conditions. Data were also obtained from 3-D CFD simulations, using the KIVA code, to compare to the experimental results and to further analyze the effects of water and ethanol on combustion.The experimental data indicated that the lower aromatic content of Fischer-Tropsch diesel fuel resulted in reduced soot emissions compared to conventional diesel. Use of Fischer-Tropsch diesel also gave lower NO\dx emissions. The diesel-ethanol blend gave a large reduction in soot emissions, but higher NO\dx emissions than the diesel-water emulsion. The lower heating value of the diesel-water emulsion resulted in increased fuel consumption in comparison to the diesel-ethanol blend and diesel. The addition of the cetane number improver (EHN) to the diesel-ethanol blend further reduced NO\dx emissions

Combustion and emissions in a light-duty diesel engine using diesel-water emulsion and diesel-ethanol blends

The purpose of the investigation presented here was to compare the effects of fuel composition on combustion parameters, emissions and fuel consumption in engine tests and simulations with five fuels: a Diesel-water emulsion, a Diesel-ethanol blend, a Diesel-ethanol blend with EHN (cetane number improver), a Fischer-Tropsch Diesel and an ultra-low sulfur content Diesel. The engine used in the experiments was a light duty, single cylinder, direct injection, common rail Diesel engine equipped with a cylinder head and piston from a Volvo NED5 engine. In tests with each fuel the engine was operated at two load points (3 bar IMEP and 10 bar IMEP), and a pilot-main fuel injection strategy was applied under both load conditions. Data were also obtained from 3-D CFD simulations, using the KIVA code, to compare to the experimental results and to further analyze the effects of water and ethanol on combustion. The experimental data indicated that the lower aromatic content of Fischer-Tropsch Diesel fuel resulted in reduced soot emissions compared to conventional Diesel. Use of Fischer-Tropsch Diesel also gave lower NOx emissions. The Diesel-ethanol blend gave a large reduction in soot emissions, but higher NOx emissions than the Diesel-water emulsion. The lower heating value of the Diesel-water emulsion resulted in increased fuel consumption in comparison to the Diesel-ethanol blend and Diesel. The addition of the cetane number improver (EHN) to the Diesel-ethanol blend further reduced NOx emissions

Deterministic Light-to-Voltage Conversion with a Tunable Two-Dimensional Diode

Heterojunctions accompanied by energy barriers are of significant importance in two-dimensional materials-based electronics and optoelectronics. They provide more functional device performance, compared with their counterparts with uniform channels. Multimodal optoelectronic devices could be accomplished by elaborately designing band diagrams and architectures of the two-dimensional junctions. Here, we demonstrate deterministic light-to-voltage conversion based on strong dielectric screening effect in a tunable two-dimensional Schottky diode based on semiconductor/metal heterostructure, where the resultant photovoltage is dependent on the intensity of light input but independent of gate voltage. The converted photovoltage across the diode is independent of gate voltage under both monochromatic laser and white light illumination. In addition, the Fermi level of two-dimensional semiconductor area on dielectric SiO2 is highly gate-dependent, leading to the tunable rectifying effect of this heterostructure, whichcorporates a vertical Schottky junction and a lateral homojunction. As a result, a constant open-circuit voltage of ∼0.44 V and a hybrid “photovoltaic + photoconduction” photoresponse behavior are observed under 1 μW illumination of 403 nm laser, in addition to an electrical rectification ratio up to nearly 104. The scanning photocurrent mappings under different bias voltages indicate that the switchable operation mode (photovoltaic, photoconduction, or hybrid) depends on the bias-dependent effective energy barrier at the two-dimensional semiconductor–metal interface. This approach provides a facile and reliable solution for deterministic on-chip light-to-voltage conversion and optical-to-electrical interconnects.Peer reviewe