Pseudo Components Research Articles

Prediction of sediment concentration based on the MEEMD-ARIMA model in the lower Yellow River

Abstract There are many factors influencing the evolution of sediment concentration, and it is difficult to determine and extract, which brings great difficulties to the high-precision prediction of sediment concentration. Accurate prediction of annual sediment concentration in the lower Yellow River can provide a theoretical basis for flood control and disaster reduction and rational utilization of water and soil resources in the lower Yellow River. For the defects of pseudo-components in data decomposition of Complementary EEMD, the Modified EEMD (MEEMD) method proposed in this paper has the advantage of eliminating pseudo components of IMF and reducing non-stationarity of sediment bearing sequences. Then, combined with the Autoregressive Integrated Moving Average (ARIMA) model with strong approximation ability to the stationary sequence, the MEEMD-ARIMA model for predicting the annual sediment concentration in the lower Yellow River was constructed. Through fitting and predicting the annual sediment concentration in Gaocun Station, it is shown that the model not only considers the evolution of sediment concentration in various frequency domains, but also solves the problem that the ARIMA model requires sequence to be stable, the relative error of prediction is within ±6%, and the prediction accuracy is high, thus providing a new method for the prediction of sediment concentration.

Note on the inconsistent definition assigned in the literature to the heat capacity of the so-called “equilibrium hydrogen” mixture

In classical chemical engineering thermodynamics, constant-pressure heat capacity has a rigorous – non-negotiable – definition. With this communication we aim to make researchers aware of a thermodynamically inconsistent definition assigned, in the literature, to the heat capacity of equilibrium gaseous mixtures of reacting ortho-hydrogen and para-hydrogen (the so-called “equilibrium-hydrogen mixtures”). Along this note we show that in order to define its heat capacity, the equilibrium reacting mixture is inconsistently assimilated to a pseudo pure component having the same enthalpy as the reacting mixture of p-H2 and o-H2 at chemical equilibrium. In so doing, the pseudo-component heat capacity aggregates not only sensible-heat effects but also thermal effects due to compositional changes.

Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses

In this minireview paper it has been elucidated that the proposal of pseudo amino acid components represents a very important milestone for the disciplines of proteome and genome. This has been concluded by observing and analyzing the developments in the following six different sub-disciplines: (1) proteome analysis; (2) genome analysis; (3) protein structural classification; (4) protein subcellular location prediction; (5) post-translational modification (PTM) site prediction; (6) stimulating the birth of the renowned and very powerful 5-steps rule.

Direct hydrothermal treatment of sugarcane juice for 3D oxygen-rich carbon Aerogel/NiCo2O4 based supercapacitor

A direct hydrothermal synthesis is reported to fabricate proficient carbon aerogel via raw sugarcane juice for energy storage. Sugarcane-derived carbon aerogel (SCA) exhibiting 3D interconnected meso/microporous structure reveals enhanced interlayer spacing (3.9 Å) and large surface area (2000 m2 g−1) with low bulk density (0.10 g cm−3). High charge storage (385 F g−1 at 5 mV s−1) and rate performance (5–400 mV s−1) indicate that heteroatom(s) functionalized SCA endures good ion diffusion coefficient (D = 2.1 × 10−11 cm2 s−1). Furthermore, NiCo2O4/SCA nanocomposite is developed sonochemically wherein spinel NiCo2O4 nanoflakes are immobilized over SCA such that the basic skeleton (microporosity) and ion accessibility (D = 1.26 × 10−11 cm2 s−1) of SCA is preserved. Hierarchical deposition of NiCo2O4 over SCA affords fairly high surface area (150 m2 g−1) with specific capacitance 473 F g−1 at 5 mV s−1. The fabricated hybrid supercapacitor: SCA‖NiCo2O4/SCA displays more than twice energy as well power density (25 Wh kg−1, 6245 W kg−1) compared to symmetric cell: SCA‖SCA (10.4 Wh kg−1, 2378 W kg−1) owing to synergism of carbonaceous &pseudo components. The research entails first ever direct use of saccharide juice as carbon aerogel for supercapacitor technologies via facile synthesis without use of any toxic chemicals.

Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components

Lysine formylation is a newly discovered post-translational modification in histones, which plays a crucial role in epigenetics of chromatin function and DNA binding. In this study, a novel bioinformatics tool named CKSAAP_FormSite is proposed to predict lysine formylation sites. An effective feature extraction method, the composition of k-spaced amino acid pairs, is employed to encode formylation sites. Moreover, a biased support vector machine algorithm is proposed to solve the class imbalance problem in the prediction of formylation sites. As illustrated by 10-fold cross-validation, CKSAAP_FormSite achieves an satisfactory performance with an AUC of 0.8234. Therefore, CKSAAP_FormSite can be a useful bioinformatics tool for the prediction of formylation sites. Feature analysis shows that some amino acid pairs, such as ‘KA’, ‘SxxxxK’ and ‘SxxxA’ around formylation sites may play an important role in the prediction. The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of formylation.

Non-isothermal kinetics of pseudo-components of waste biomass

Non-isothermal kinetics involved during the slow pyrolysis of waste biomass (sawdust) was investigated. The slow pyrolysis profile of sawdust was distinguished into three reactions corresponding to each pseudo component (hemicellulose, cellulose & lignin) through the deconvolution process. Kinetic triplets (activation energy, reaction mechanism & pre-exponential factor) were estimated for each pseudo component. The Straink (SR) and Friedman (FR) iso-conversional kinetic models were used to calculate the activation energy of pseudo-cellulose (SR = 162.90 kJ/mol, FR = 165.67 kJ/mol), pseudo-hemicellulose (SR = 156.25 kJ/mol, FR = 158.47 kJ/mol) and pseudo-lignin (SR = 301.62 kJ/mol, FR = 316.72 kJ/mol). The reaction mechanism involved during the slow pyrolysis of waste sawdust was identified through integral master plot method. The results revealed that the two-dimensional contract area (R2) and second order reaction (F2) reaction mechanism dominates slow pyrolysis of pseudo-cellulose and pseudo-hemicellulose, respectively. However, no exact reaction mechanism was observed for pseudo-lignin. The pre-exponential factor was determined with the help of the activation energy and reaction mechanism. Thermodynamic parameters i.e. change in enthalpy (ΔH), entropy (ΔS) and Gibb’s free energy (ΔG) were also determined for pseudo-cellulose (ΔH=157.54kJ/mol, ΔS=-17.615 J/mol and ΔG=168.64 kJ/mol) and pseudo-hemicellulose (ΔH=151.28kJ/mol, ΔS=6.61 J/mol and ΔG=147.12 kJ/mol).

IRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via Chou's 5-step rule and pseudo components

Meiotic recombination plays an important role in the process of genetic diversity generation. “Hotspots” are regions that show a higher rate of meiotic recombination, while the regions with a lower rate are called “cold spots”. It has a great effect on the genome evolution via gene conversion or mutagenesis. According to recent research, recombination is present in uneven distribution across the genome. Many computational methods have been developed using secondary sequence information or physiochemical properties of nucleotide descriptor for the prediction of hotspots and cold spots, which are computationally cheap and fast in performance rather than web-lab experiments, but the correlations between nucleotides pairs at different positions along DNA sequence is often ignored, which conceal a very important predictive information. In this study, we have proposed a deep neural network to predict recombination spots by fusing both the secondary sequence information and physio-chemical derived features. Our deep learning algorithm leverage's deep dense architecture by showing its effectiveness over the state-of-the-art methods with a classification accuracy of 90.04%, sensitivity of 92.21%, specificity of 92.11% and area under the curve of 0.9801. Moreover, it is anticipated, that our model will provide novel insight into basic research, drug designing, academic research and recombination spots studies particularly. All the methodology and python-based source code is publicly available for the users at https://github.com/zaheerkhancs/irSpot_SPI along with publicly accessible web server using the proposed predictor.

ISulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components.

Background:The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification which is known to be associated with regulation of various biological functions and pathological pro-cesses. Thus its identification is necessary to understand its mechanism. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of sulfotyrosine sites.Methodology:In this paper, we present a computational model for the prediction of the sulfotyrosine sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of protein amino acid sequences and various position/composition relative features. These features are in-corporated into PseAAC. The model is validated by jackknife, cross-validation, self-consistency and in-dependent testing.Results:Accuracy determined through validation was 93.93% for jackknife test, 95.16% for cross-validation, 94.3% for self-consistency and 94.3% for independent testing.Conclusion:The proposed model has better performance as compared to the existing predictors, how-ever, the accuracy can be improved further, in future, due to increasing number of sulfotyrosine sites in proteins.

A Computational Model Based on Intelligence Computation for Classifying Enhancers

As a crucial DNA element, enhancer usually can be classified as strong enhancer or weak enhancer. To make some improvement for identifying enhancers and their subsets, the paper constructed an intelligent computational model by extracting features from bi-profile Bayes and pseudo amino acid components. Moreover, the combination of features, jackknife cross validation and Support Vector Machine were tested on the rigorous dataset. It has been achieved that accuracyof 77.30% on identifying enhancers and non-enhancers, and accuracy of 68.22% on classifying strong enhancers and weak enhancers. Eventually, the performance we achieved is satisfactory.

In-depth comparison of the physicochemical characteristics of bio-char derived from biomass pseudo components: Hemicellulose, cellulose, and lignin

Bio-char is a porous, recalcitrant, and highly aromatic carbon-rich material which can be widely used in energy, agriculture, environment, and material industry. In this study, a systematic comparison of the physicochemical characteristics of bio-char derived from three pseudo components of biomass (cellulose char (CC), hemicellulose char (HC), lignin char (LC)) was carried out at different pyrolysis temperatures (250, 350, 450, 550, 650, 750, and 850 °C). The results indicated wide variation in the physicochemical properties and quality of bio-char depending on different pyrolysis temperatures and biomass feedstock composition. Mass and energy yields of CC, HC, and LC decreased with the increase in the pyrolysis temperature, however, LC exhibited higher mass and energy yield than CC and HC because of its better thermal stability. With the increase in the pyrolysis temperature, the carbon content in CC, HC, and LC increased, while the contents of hydrogen and oxygen decreased, because the oxygen and hydrogen containing groups were easily fallen off at higher pyrolysis temperature based on the FTIR analysis. XRD analysis showed that the crystalline structure (Iα-triclinic and Iβ-monoclinic) of cellulose in CC and HC disappeared with the increase in the pyrolysis temperature above 350 °C. However, the graphite structure (002) and (101) in CC, HC, and LC increased. 13C NMR analysis indicated that the carbon structure of alkyl-C, O-alkyl-C, and carboxylic-C gradually decreased in CC, HC and LC as the pyrolysis temperature increased, while the aryl-C increased, indicating the formation of a more polyaromatic graphite-like structure at higher pyrolysis temperature. SEM results revealed that the volume of CC and LC reduced because of the significant particle agglomeration during pyrolysis process. However, the volume of HC sharply increased because of the transformation of hemicellulose into foam-like material at higher temperature. The lower values of mean absolute error strongly suggests that it is feasible to predict the properties (the contents of C, H, O, mass and energy yield) of real biomass derived bio-char based on the properties of three pseudo components derived bio-char.

DForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components

Formylation is a type of post-translational modification that can occur on lysine sites, which plays an irreplaceable role in organism. To better understand the mechanism, it is necessary to identify formylation sites in proteins accurately. Computational method is popular because of its more convenience and higher speed than traditional experimental methods. However, no computational method has been proposed for prediction of lysine formylation. In this study, we developed a predictor named LFPred to identify lysine formylation sites using sequence features (including amino acid composition (AAC), binary profile features (BPF), and amino acid index (AAI)) combined K-nearest neighbor algorithm as classifier. We chose discrete window instead of continuous window according to information entropy. Besides, we took measure to select more reliable negative samples and address the severe imbalance between positive samples and negative samples. Finally, the performance of LFPred is measured with a specificity of 79.9% and a sensibility of 81.4% using jackknife test method, which indicated that our method can be a useful tool for prediction of lysine formylation sites.

English

Hydrocarbon fluids are made up of defined components which include N2, CO2, H2S, C1, C2, C3, iC4, iC5, and C6  and undefined components known as heavier fractions (Cn+) which include paraffinic, naftenic and aromatic compounds. The hydrocarbons are separated on the surface before they are sent to the market. There are several factors affecting the hydrocarbons surface separation condition which include; pressure, temperature, gas liquid flow rates, surging or slugging tendencies of the feed stream, presence of impurities such as paraffin and sands. This work is limited to the study of the effects of pressure and temperature. To obtain stabilized hydrocarbons phases optimum surface separation, pressure and temperature must be selected. Several empirical models have been developed to obtain optimum surface separation pressure and temperature. However, these models do not consider the full composition of the well stream, and the heavier fractions are most often treated as a single component. This paper presents the estimation of an optimum surface separation-pressure and temperature of crude oil  while including the complete composition of the well stream and extended composition of the heavier fraction. The optimum pressure was estimated through the fluid properties such as oil formation volume factor, gas oil ratio and API gravity.  Optimum pressure and temperature is the one that produces maximum liquid yield (by minimizing oil formation volume factor and gas oil ratio) of maximum quality (by maximizing API gravity).  The fluid properties were predicted by phase equilibrium calculations using Peng Robinson thermodyinamic Model. The optimum pressure was first estimated considering the heavier fraction as single component and second the heavier fraction was splitted in pseudo components, both including the full composition of the well stream. Ahmed splitting method was used to extend the heavier fraction compostion, Kesler and Lee’s correlation was apllied to assign critical properties of the pseudo components. The results indicate that it is possible to estimate more accurately the optimum separation pressure by extending a composition of heavier fraction and accurate values of fluid properties were obtained. Key words: C7+ fraction, splitting scheme, equation of state, Peng-Robison thermodynamic model, optimum separator pressure. &nbsp

IRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components

The 2'-O-methylation transferase is involved in the process of 2'-O-methylation. In catalytic processes, the 2-hydroxy group of the ribose moiety of a nucleotide accept a methyl group. This methylation process is a post-transcriptional modification, which occurs in various cellular RNAs and plays a vital role in regulation of gene expressions at the post-transcriptional level. Through biochemical experiments 2'-O-methylation sites produce good results but these biochemical process and exploratory techniques are very expensive. Thus, it is required to develop a computational method to identify 2'-O-methylation sites. In this work, we proposed a simple and precise convolution neural network method namely: iRNA-PseKNC(2methyl) to identify 2'-O-methylation sites. The existing techniques use handcrafted features, while the proposed method automatically extracts the features of 2'-O-methylation using the proposed convolution neural network model. The proposed prediction iRNA-PseKNC(2methyl) method obtained 98.27% of accuracy, 96.29% of sensitivity, 100% of specificity, and 0.965 of MCC on Home sapiens dataset. The reported outcomes present that our proposed method obtained better outcomes than existing method in terms of all evaluation parameters. These outcomes show that iRNA-PseKNC(2methyl) method might be beneficial for the academic research and drug design.

IRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components

Meiotic recombination plays an important role in the process of genetic evolution. Previous researches have shown that the recombination rates provide important information about the mechanism of recombination study. However, at present, most methods ignore the hidden correlation and spatial autocorrelation of the DNA sequence. In this study, we proposed a predictor called iRSpot-DTS to identify hot/cold spots based on the benchmark datasets. We proposed a feature extraction method called dinucleotide-based spatial autocorrelation(DSA) which can incorporate the original DNA properties and spatial information of DNA sequence. Then it used t-SNE method to remove the noise which outperformed PCA. Finally, we used SAE softmax classifier to do classification which is based on networks and can get more hidden information of DNA sequence, our iRSpot-DTS achieved remarkable performance. Jackknife cross validation tests were done on two benchmark datasets. We achieved state-of-the-art results with 96.61% overall accuracy(OA), 93.16% Matthews correlation coefficient (MCC) and over 95% in Sn and Sp which are the best in this state.

UbiSitePred: A novel method for improving the accuracy of ubiquitination sites prediction by using LASSO to select the optimal Chou's pseudo components

Ubiquitination is an essential process in protein post-translational modification, which plays a crucial role in cell life activities, such as proteasomal degradation, transcriptional regulation, and DNA damage repair. Therefore, recognition of ubiquitination sites is a crucial step to understand the molecular mechanisms of ubiquitination. However, the experimental verification of numerous ubiquitination sites is time-consuming and costly. To alleviate these issues, a computational approach is needed to predict ubiquitination sites. This paper proposes a new method called UbiSitePred for predicting ubiquitination sites combined least absolute shrinkage and selection operator (LASSO) feature selection and support vector machine. First, we use binary encoding (BE), pseudo-amino acid composition (PseAAC), the composition of k-spaced amino acid pairs (CKSAAP), position-specific propensity matrices (PSPM) to extract the sequence feature information; thus, the initial feature space is obtained. Secondly, LASSO is applied to remove the feature redundancy information and selects the optimal feature subset. Finally, the optimal feature subset is input into the support vector machine (SVM) to predict the ubiquitination sites. Five-fold cross-validation shows that UbiSitePred model can achieve a better prediction performance compared with other methods, the AUC values for Set1, Set2, and Set3 are 0.9998, 0.8887, and 0.8481, respectively. Notably, the UbiSitePred has overall accuracy rates of 98.33%, 81.12%, and 76.90%, respectively. The results demonstrate that the proposed method is significantly superior to other state-of-the-art prediction methods and provide a new idea for the prediction of other post-translational modification sites of proteins. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/UbiSitePred/.

Predicting protein–protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach

Research on protein–protein interactions (PPIs) not only helps to reveal the nature of life activities but also plays a driving role in understanding the mechanisms of disease activity and the development of effective drugs. The rapid development of machine learning provides new opportunities and challenges for understanding the mechanism of PPIs. It plays an important role in the field of proteomics research. In recent years, an increasing number of computational methods for predicting PPIs have been developed. This paper proposes a new method for predicting PPIs based on multi-information fusion. First, the pseudo-amino acid composition (PseAAC), auto-covariance (AC) and encoding based on grouped weight (EBGW) methods are used to extract the features of protein sequences, and the extracted three groups of feature vectors were fused. Secondly, the fused feature vectors are denoised by two-dimensional (2-D) wavelet denoising. Finally, the denoised feature vectors are input to the support vector machine (SVM) classifier to predict the PPIs. The ACC of PPIs of Helicobacter pylori (H. pylori) and Saccharomyces cerevisiae (S. cerevisiae) datasets were 95.97% and 95.55% by 5-fold cross-validation test and compared with other prediction methods. The experimental results show that the proposed multi-information fusion prediction method can effectively improve the prediction performance of PPIs. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/PPIs-WDSVM/.

Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions

The animal toxin proteins are one of the disulfide rich small peptides that detected in venomous species. They are used as pharmacological tools and therapeutic agents in medicine for the high specificity of their targets. The successful analysis and prediction of toxin proteins may have important signification for the pharmacological and therapeutic researches of toxins. In this study, significant differences were found between the toxins and the non-toxins in amino acid compositions and several important biological properties. The random forest was firstly proposed to predict the animal toxin proteins by selecting 400 pseudo amino acid compositions and the dipeptide compositions of reduced amino acid alphabet as the input parameters. Based on dipeptide composition of reduced amino acid alphabet with 13 reduced amino acids, the best overall accuracy of 85.71% was obtained. These results indicated that our algorithm was an efficient tool for the animal toxin prediction.

Application of Improved Singular Spectrum Decomposition Method for Composite Fault Diagnosis of Gear Boxes.

Aiming at the problem that the composite fault signal of the gearbox is weak and the fault characteristics are difficult to extract under strong noise environment, an improved singular spectrum decomposition (ISSD) method is proposed to extract the composite fault characteristics of the gearbox. Singular spectrum decomposition (SSD) has been proved to have higher decomposition accuracy and can better suppress modal mixing and pseudo component. However, noise has a great influence on it, and it is difficult to extract weak impact components. In order to improve the limitations of SSD, we chose the minimum entropy deconvolution adjustment (MEDA) as the pre-filter of the SSD to preprocess the signal. The main function of the minimum entropy deconvolution adjustment is to reduce noise and enhance the impact component, which can make up for the limitations of SSD. However, the ability of MEDA to reduce noise and enhance the impact signal is greatly affected by its parameter, the filter length. Therefore, to improve the shortcomings of MEDA, a parameter adaptive method based on Cuckoo Search (CS) is proposed. First, construct the objective function as the adaptive function of CS to optimize the MEDA algorithm. Then, the pre-processed signal is decomposed into singular spectral components (SSC) by SSD, and the meaningful components are selected by Correlation coefficient. For the existing modal mixing phenomenon, the SSC component is reconstructed to eliminate the misjudgment of the result. Then, the frequency spectrum analysis is performed to obtain the frequency information for fault diagnosis. Finally, the effectiveness and superiority of ISSD are validated by simulation signals and applying to compound faults of a Gear box test rig.

Activity of water in pyrolysis oil—Experiments and modelling

Fast pyrolysis is an option to convert lignocellulosic biomass into a large fraction of liquid bio-oil besides some char/ash and non-condensable gas. When ash-rich material is used, this usually results in a higher water content of the pyrolysis oil and a possible phase separation. To avoid phase separation, fractional condensation systems have been developed. Choosing appropriate condensation temperatures is essential to adjust the desired water content and bio-oil composition. For the modelling of such systems, detailed knowledge about the phase equilibrium of bio-oils is necessary, especially when aerosol formation is considered. This study presents the development of a model that is capable of describing the highly non-ideal behavior of these complex biomass derived mixtures.Straw based bio-oils from the bioliq® fast pyrolysis process are investigated. The required vapor liquid equilibrium data is measured in a phase equilibrium cell. The results are used for the development of suitable surrogate mixtures by studying about 200 possibly suited components to represent the heavy fraction of the bio-oil. For the calculation of the activity coefficients the gE-model Modified UNIFAC is used. Experimental and calculated data is presented and compared, with the focus on the activity of water as a key property for the thermodynamic behavior. The measured activity of water validates its strong non-ideal behavior in bio-oil. For the bio-oil investigated in this work, and according to the predictions of UNIFAC, a pseudo component formed by 3 groups of aromatic alcohol and 12 aromatic carbons (as in 3,4,4′-biphenyltriol) shows the thermodynamic behavior that best represents the unknown fraction of bio-oil. Depending on the choice of the surrogate mixture, the results can differ more than 100%. This demonstrates the importance of a carefully chosen model as an essential tool for simulating fractional condensation systems.

Initiation of Flash Boiling of Multicomponent Miscible Mixtures with Application to Transportation Fuels and Their Surrogates

This paper presents methodologies to predict thermodynamic conditions that initiate flash boiling by spontaneous nucleation of liquids consisting of hundreds of miscible liquids and their lower order surrogate mixtures. The methods are illustrated with a kerosene-based fuel and a seven-component surrogate for it. The predictions are compared to measurements of nucleation temperatures obtained from a pulse-heating technique that rapidly heats a microscale platinum film immersed in a pool of the test fluid. Nucleation temperatures are predicted using a generalized corresponding states principle (GCSP), and a modification of classical nucleation theory that considers the mixture as a pseudo single component fluid (PSCF). The intent is to offer a simple means to predict the initiation of flash boiling that can have important consequences for fuel efficiency in combustion engines. We show that contact angle has a strong effect such that predicted and measured spontaneous nucleation temperatures agree for a giv...