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A facile water-activated method is developed for preparing porous graphitic carbon nitride with carbon vacancies by co-pyrolysis of melamine and water at a relatively low temperature under an Ar atmosphere, resulting in an increased specific surface area and the efficient separation of photo-generated electrons and holes. As expected, the optimal catalyst exhibited a high H

Strong light–matter interaction plays a central role in realizing quantum photonic technologies. The entanglement state, which results from the hybridization of excitons and cavity photons, forms the foundation of quantum information science. In this work, an entanglement state is achieved by manipulating the mode coupling between surface lattice resonance and quantum emitter into the strong coupling regime. At the same time, a Rabi splitting of 40 meV is observed. A full quantum model based on the Heisenberg picture is used to describe this unclassical phenomenon, and it perfectly explains the interaction and dissipation process. In addition, the observed concurrency degree of the entanglement state is 0.5, presenting the quantum nonlocality. This work effectively contributes to the understanding of nonclassical quantum effects arising from strong coupling and will intrigue more interesting potential applications in quantum optics.

The regulation of electron distribution of single-atomic metal sites by atomic clusters is an effective strategy to boost their intrinsic activity of oxygen reduction reaction (ORR). Herein we report the construction of single-atomic Mn sites decorated with atomic clusters by an innovative combination of post-adsorption and secondary pyrolysis. The X-ray absorption spectroscopy confirms the formation of Mn sites via Mn-N4 coordination bonding to FeMn atomic clusters (FeMnac/Mn-N4C), which has been demonstrated theoretically to be conducive to the adsorption of molecular O2 and the break of O-O bond during the ORR process. Benefiting from the structural features above, the FeMnac/Mn-N4C catalyst exhibits excellent ORR activity with half-wave potential of 0.79 V in 0.5 M H2SO4 and 0.90 V in 0.1 M KOH as well as preeminent Zn-air battery performance. Such synthetic strategy may open up a route to construct highly active catalysts with tunable atomic structures for diverse applications.

Strong light-matter interaction plays a central role in realizing quantum photonic technologies. The entanglement state, which results from the hybridization of excitons and cavity photons, forms the foundation of quantum information science. In this work, an entanglement state is achieved by manipulating the mode coupling between surface lattice resonance and quantum emitter into the strong coupling regime. At the same time, a Rabi splitting of 40 meV is observed. A full quantum model based on the Heisenberg picture is used to describe this unclassical phenomenon, and it perfectly explains the interaction and dissipation process. In addition, the observed concurrency degree of the entanglement state is 0.5, presenting the quantum non-locality. This work effectively contributes to the understanding of non-classical quantum effects arising from strong coupling and will intrigue more interesting potential applications in quantum optics.

Bound states in the continuum (BICs), manifesting themselves as the collapse of Fano resonance, are observed in many photonic and plasmonic systems. The BICs have been studied systematically through various methods such as the topological photonics analysis, temporal coupled mode theory, multipole decomposition method, and the cavity quantum electrodynamics (CQED) method. Since CQED can give a macroscopic and intrinsic description of light–matter interaction, it is expected to study BIC that participates in strong coupling. What is more, the relation between coupling strength, the Fano parameter, and the asymmetry property of BICs needs to be clarified. In this paper, we investigated the strong coupling between the cavity mode and Bloch-surface plasmon polariton (Bloch-SPP) mode induced by BICs within the plasmonic lattices of the metal-dielectric-metal (MDM) layer. The properties of strong coupling and BIC were revealed theoretically by the quantum model based on the CQED. The increase in the Fano parameters of BICs was proved to facilitate the coupling strength, which was indicated by the monotonically increasing relation between the Fano parameter and the coupling strength. This work may pave the way for flexible modulation and application of BIC in the fields of high-quality plasmonic nanocavity, low-threshold nano-lasers, and quantum information.

We present an ab initio molecular dynamics study of the thermal stability and dynamics behaviors of lattice misalignment structures (LMSs) in the subsurface layers of KH2PO4 (KDP) crystals. The dehydration process at the atomic scale is observed in the LMS system, which is the same as that in a perfect KDP crystal. However, the paths entering the dehydration process are various. The interesting result is that compared with a perfect KDP crystal, many new paths appear in the LMS system, and even in the same paths, the dehydration is more likely to happen in the LMS system. This leads to a dramatic increase in the dehydration numbers in the LMS system, for which the reasons are given in terms of structural deformation and/or uneven distribution of protons. The results elucidate the underlying atomic mechanism of the effect of LMS defects on the thermal stability of KDP material.

Hyperdoped silicon (hSi) fabricated via femtosecond laser irradiation has emerged as a promising photoelectric material with strong broadband infrared (IR) absorption. In this work, we measured the optical absorptance of the hSi in the wavelength of 0.3–16.7 µm. Unlike the near to mid wavelength IR absorption, the mid-long wavelength IR (M–LWIR) absorption is heavily dependent on the surface morphology and the dopants. Furthermore, calculations based on coherent potential approximation (CPA) reveal the origin of free carrier absorption, which plays an important role in the M–LWIR absorption. As a result, a more comprehensive picture of the IR absorption mechanism is drawn for the optoelectronic applications of the hSi.

Enhancing light–matter interaction into the strong coupling regime attracts tremendous attention in both theory and experiment, which presents essential significance in research from nano-optics to quantum information. In this work, the entanglement effect is observed in the photons emitted from a plasmonic lattice as the coherent light–matter interaction occurs into the strong coupling regime with a Rabi splitting of 93.4 meV. A full quantum mechanical treatment based on the number state representation is established to reveal the underlying physics of the strong coupling phenomenon, especially the femtosecond dynamics of energy exchange and damping. The entangled split states display oscillating concurrence and negative Wigner quasiprobability distribution function, which demonstrates that this designed plasmonic lattice system can serve as an on-demand entangled photon source for quantum information.

Surface plasmon excitations in metallic hole arrays have been extensively studied in the context of light–matter interaction, since the generated Bloch surface plasmon polariton (Bloch-SPP) exhibits unique properties of nanoscale light confinement, near-field enhancements, and long-range metal surface propagation. In this work, we experimentally demonstrate a plasmonic device that exhibits highly directional emission in visible light; using Ag film with a thickness of 100 nm deposited on a subwavelength porous alumina array as a plasmonic cavity, four-level rhodamine 6G (R6G) is selected as the gain material. It is suggested that a Bloch-SPP with high diffraction orders on a Ag nanohole array can generate a strong local electric field and a high Purcell factor at a nanohole edge. Moreover, directional five-fold enhanced amplified spontaneous emission (ASE) with polarization dependence is observed under a low threshold of 199.9 W/cm2 in the visible light region, which comes from the optical feedback provided by the 2D periodic nanohole array. This work opens up a wide range of applications for real-time tunable wavelength, controlled multimode laser, fluorescence detection, etc.

The evening primrose family, Onagraceae, is a well defined family of the order Myrtales, comprising 22 genera widely distributed from boreal to tropical areas. In this study, we report and characterize the complete chloroplast genome sequences of 13 species in Circaea, Chamaenerion, and Epilobium using a next-generation sequencing method. We also retrieved chloroplast sequences from two other Onagraceae genera to characterize the chloroplast genome of the family. The complete chloroplast genomes of Onagraceae encoded an identical set of 112 genes (with exclusion of duplication), including 78 protein-coding genes, 30 transfer RNAs, and four ribosomal RNAs. The chloroplast genomes are basically conserved in gene arrangement across the family. However, a large segment of inversion was detected in the large single copy region of all the samples of Oenothera subsect. Oenothera. Two kinds of inverted repeat (IR) region expansion were found in Oenothera, Chamaenerion, and Epilobium samples. We also compared chloroplast genomes across the Onagraceae samples in some features, including nucleotide content, codon usage, RNA editing sites, and simple sequence repeats (SSRs). Phylogeny was inferred by the chloroplast genome data using maximum-likelihood (ML) and Bayesian inference methods. The generic relationship of Onagraceae was well resolved by the complete chloroplast genome sequences, showing potential value in inferring phylogeny within the family. Phylogenetic relationship in Oenothera was better resolved than other densely sampled genera, such as Circaea and Epilobium. Chloroplast genomes of Oenothera subsect. Oenothera, which are biparental inheritated, share a syndrome of characteristics that deviate from primitive pattern of the family, including slightly expanded inverted repeat region, intron loss in clpP, and presence of the inversion.

Stickiness is a phenomenon widely observed in drying practices of sewage sludge. This work is aimed at demonstrating the effects of sludge temperature (30–70 °C) and calcium oxide (CaO) on the stic...

This work is aimed at demonstrating the effect of hydrothermal treatment on the mechanical dewatering, thermal drying and stickiness of sewage sludge from two different wastewater treatment plants (SS1 and SS2). The analysis of particle size, microstructure and volatile matter content of sludge was conducted to explore the influencing mechanism of hydrothermal treatment. The impact and significance of three factors, i.e. hydrothermal temperature (130–220 °C), retention time (30–75 min) and calcium oxide content (0–5 wt.%), were studied by an orthogonal method. Range and variable analysis were carried out to investigate the significance of the factors. The results indicated that sludge particle size showed a 10–25% reduction after hydrothermal treatment, indicating a degradation of EPS and bacterial cells, which was also confirmed by microstructural analysis. Sludge dewatering performance was markedly promoted by increasing hydrothermal temperature, intention time or CaO content. But CaO showed 1.8–7.8 times higher significance than that of hydrothermal temperature and intention time. On the other hand, both hydrothermal temperature and retention time showed 2–5 times higher significance on chemical oxygen demand of filtrate and drying rate of dewatered sludge cakes than that of CaO. Increasing hydrothermal temperature or retention time led to the increase of the chemical oxygen demand and the decrease of the drying rate in general. Sludge decomposition was also manifested by the change of its volatile matter content which showed a strong positive correlation with the water content of dewatered sludge cakes and a negative correlation with the adhesive and cohesive stress of dewatered sludge cakes. Finally, the economic efficiency of hydrothermal treated sludge dewatering and thermal drying system was calculated and compared with that of conventional direct thermal drying. The total energy consumption of the system is about 25–40% lower than conventional drying for both of sludge, and the operation cost of the system is about 17–30% lower than that of the conventional drying.

The utility of transcriptome data in plant phylogenetics has gained popularity in recent years. However, because RNA degrades much more easily than DNA, the logistics of obtaining fresh tissues has become a major limiting factor for widely applying this method. Here, we used Ranunculaceae to test whether silica-dried plant tissues could be used for RNA extraction and subsequent phylogenomic studies. We sequenced 27 transcriptomes, 21 from silica gel-dried (SD-samples) and six from liquid nitrogen-preserved (LN-samples) leaf tissues, and downloaded 27 additional transcriptomes from GenBank. Our results showed that although the LN-samples produced slightly better reads than the SD-samples, there were no significant differences in RNA quality and quantity, assembled contig lengths and numbers, and BUSCO comparisons between two treatments. Using this data, we conducted phylogenomic analyses, including concatenated- and coalescent-based phylogenetic reconstruction, molecular dating, coalescent simulation, phylogenetic network estimation, and whole genome duplication (WGD) inference. The resulting phylogeny was consistent with previous studies with higher resolution and statistical support. The 11 core Ranunculaceae tribes grouped into two chromosome type clades (T- and R-types), with high support. Discordance among gene trees is likely due to hybridization and introgression, ancient genetic polymorphism and incomplete lineage sorting. Our results strongly support one ancient hybridization event within the R-type clade and three WGD events in Ranunculales. Evolution of the three Ranunculaceae chromosome types is likely not directly related to WGD events. By clearly resolving the Ranunculaceae phylogeny, we demonstrated that SD-samples can be used for RNA-seq and phylotranscriptomic studies of angiosperms.

Adhesion is one of the main features of sewage sludge. This paper aims at reducing sludge-to-wall adhesion through formation of a water layer induced by boundary electro-osmotic pulse (BEOP) which is characterized by distributing anodes and cathodes on one surface and exerting a pulsating current. The effects of the related parameters, including current intensity, exerting time, frequency, duty cycle (DTC), and the ratio of cathodic surface area to anodic surface area (C/A), on the adhesive stress of sewage sludge with different moisture content were thoroughly studied. The results indicated that, under the optimal conditions of BEOP, the adhesive stress of sludge with moisture content of 35%, 45%, 60% and 70% was reduced by 40.4%, 54.5%, 31%, and 24.4%, respectively. The migrations of water, ions and organic matters were also investigated to explore the functional mechanism of BEOP. The results showed that the water migrated from the anode side to the cathode side, whereas the organic matters migrated in the opposite direction. The increment of the sludge moisture content on the cathode surface was reduced with the increase of distance away from the anode. Based on theoretical modeling, the distributions of current density and temperature in sludge cake were obtained. The current flowed from anode to cathode and decayed quickly with the increase of the flowing distance, which well explained the moisture content distribution in sludge cake.

Developing highly efficient earth-abundant catalysts for the electrochemical oxygen evolution reaction (OER) is essential to promote the commercial application of water splitting. Herein boron-doped cobalt-iron bimetal phosphides nanosheets as OER electrocatalysts have been successfully prepared by hydrothermal boronation of bimetallic metal-organic frameworks precursors with the subsequent low-temperature phosphidation. The synergistic effect between non-metal elements (B and P) and metals (Co and Fe) is found to play an important role on tuning the local electronic configuration surrounding the active metal centers. Moreover, the resultant nanosheet structures are beneficial for exposing the catalytically active sites and facilitating the mass/charge transport. As expected, the optimal catalyst exhibits superior catalytic performance toward the OER with long-term durability in 1.0 M KOH, affording a current density of 10 mA cm−2 at low over-potential of 294 mV and small Tafel slope of 49.5 mV dec−1.

Convergent evolution, often viewed as the inevitable outcome of natural selection, has received special attention since the time of Darwin. Clematis is well known for its climbing habit, but it has some shrubby species, known as sect. Fruticella s.l. The shrubby Clematis species are distributed in the dry habitats of Central Asia and adjacent areas showing possible convergent evolution. In this study, we assembled the complete plastome and nuclear ribosomal DNA (nrDNA) sequences of 56 Clematis species, representing most sections and covering most of the shrubby species, to reconstruct their evolutionary histories. Using both maximum likelihood and Bayesian methods, the plastome and nrDNA datasets generated similar, but not identical, phylogenetic relationships, which are better resolved than in previous studies. Then, molecular dating, historical range reconstruction, and character optimization analyses were conducted based on this updated phylogenetic framework. All the morphological characters widely used for taxonomy were shown to have evolved multiple times. Molecular dating inferred that Clematis diverged from its sister in the mid Miocene, and all six major clades of Clematis originated during the late Miocene, with a species radiation during the Pliocene to Pleistocene. The results clearly showed that the shrubby habit evolved independently in four lineages of Clematis in Asia. We also revealed that the shrubby lineages have emerged since the very beginning of Pliocene. Asian monsoon variation in the Pliocene and glacial period fluctuation in the Pleistocene may be the driving forces for the origin and diversification of the shrubby Clematis in Central Asia and adjacent dry areas.

The development of low cost and high efficiency electrocatalyst for hydrogen evolution reaction (HER) is a key step in the realization of hydrogen production from electrolytic water. In this study, we report a feasible strategy to fabricate Cu-doped molybdenum carbide encapsulated within two-dimensional nanosheets assembled hierarchical tubular nitrogen-doped carbon (Cu/Mo2C@HTNC) through the coordination reaction between MoO3 nanorod as template and dopamine in alkaline medium followed by two-step consecutive pyrolysis. The effective Cu doping into Mo2C crystal structure is found to increase the electron density around Mo and catalytic active sites density for optimizing the strength of Mo–H bond, thus facilitating the HER process. As expected, the optimal Cu/Mo2C@HTNC exhibits excellent HER performance with a low over-potential of 113 mV at a current density of 10 mA cm−2 and a small Tafel slope of 55 mV dec−1 as well as excellent stability in 1 M KOH.

The rational design of cost effective and highly efficient oxygen evolution reaction (OER) catalysts plays an extremely important role in promoting the commercial applications of electrochemical water splitting. Herein we reported a sacrificial template strategy for the preparation of borate anion doped Co3O4@ZIF-67 nanocages assembled with nanosheets (B-Co3O4@ZIF-67) by hydrothermal boronation of zeolitic imidazolate framework-67 (ZIF-67). During the preparation process, two different kinds of borate anion sources were found to regulate the morphological structures by tuning the etching rate between ZIF precursors and the borate anion. Moreover, borate anion doping was also found to induce oxygen vacancy defects, which is beneficial for modulating the electronic structure and accelerating electron transport. Meanwhile, the resultant B-Co3O4@ZIF-67 nanocages possess a large specific surface area, which is beneficial for the mass transfer of the electrolyte and exposing more catalytic active sites. Benefiting from the advantages above, the resultant B-Co3O4@ZIF-67 nanocages exhibit impressive OER performance with a small overpotential of 334 mV, a current density of 10 mA cm−2, a small Tafel slope of 73.88 mV dec−1, as well as long-term durability in an alkaline electrolyte.

Femtosecond (fs)-laser hyperdoped silicon has aroused great interest for applications in infrared photodetectors due to its special properties. Crystallinity and optical absorption influenced by co-hyperdoped nitrogen in surface microstructured silicon, prepared by fs-laser irradiation in gas mixture of SF6/NF3 and SF6/N2 were investigated. In both gas mixtures, nitrogen and sulfur were incorporated at average concentrations above 1019 atoms/cm3 in the 20–400 nm surface layer. Different crystallinity and optical absorption properties were observed for samples microstructured in the two gas mixtures. For samples prepared in SF6/N2, crystallinity and light absorption properties were similar to samples formed in SF6. Significant differences were observed amongst samples formed in SF6/NF3, which possess higher crystallinity and strong sub-band gap absorption. The differing crystallinity and light absorption rates between the two types of nitrogen co-hyperdoped silicon were attributed to different nitrogen configurations in the doped layer. This was induced by fs-laser irradiating silicon in the two N-containing gas mixtures.

The improvement of computational methods greatly benefits the investigation of miRNAs. Our study validates the features in miRNA identification with an independent dataset, and provides researchers with common practices of the development of predictive models. A total number of 84 representative features, which occurred in researches of miRNAs classification, have been extracted and divided into four feature sets, i.e. complex network feature set (NET), structural feature set (STRUC), thermodynamic feature set (THERMO), and hybrid feature set (TOTAL). Systematic analysis is carried out on network, structural, thermodynamic and hybrid features. The dominant features are discriminated from uninformative features in both single and hybrid sets, on the basis of permutation importance strategy. Random forest models are constructed using only informative network, structural, thermodynamic and hybrid variables, resulting in area under the receiver operating curve (AUC) values of 0.9611, 0.9563, 0.9351, and 0.9469, respectively, based on validated datasets. The result suggests that the best performance could be got by using features derived from complex network. These results would be invaluable in understanding biological mechanism and function of miRNAs. All the data and scripts used in this article are freely available for download at http://cic.scu.edu.cn/bioinformatics/Extended_miRNA.zip .

We used the coherent potential approximation to investigate the band structures of group-IV semiconductor alloys, including Si(x)Ge(1-x), Ge(1-y)Sn(y) and Si(x)Ge(1-x-y)Sn(y). The calculations for Si(x)Ge(1-x) prove the reliability and accuracy of the method we used. For Ge(1-y)Sn(y), the direct band gap optical bowing parameter we obtained is 2.37 eV and the indirect-direct band gap transition point is at y = 0.067, both consistent with the existing experimental data. For Si(x)Ge(1-x-y)Sn(y), with the increase of the Si concentration, the compositional dependency of the band gap becomes complex. An indirect-direct band gap transition is found in Si(x)Ge(1-x-y)Sn(y) in the range of 0x≤ 0.20, and the indirect-direct crossover line in the compositional space has the quadratic form of y = 3.4x(2) + 1.11x + 0.07, not the linear form as suggested before. Furthermore, for the Ge lattice-matched alloy Ge(1-x()Si0.79Sn0.21)(X), our results show that those with 0.18X0.253 have band gaps larger than 0.8 eV at room temperature.

We employ supercells to investigate the electronic structure of hyperdoped silicon with periodic S and random distribution of S, respectively and reveal disordering of impurities on the band structure of hyperdoped silicon cannot be ignored.

Background MicroRNAs (miRNAs) play a key role in regulating various biological processes such as participating in the post-transcriptional pathway and affecting the stability and/or the translation of mRNA. Current methods have extracted feature information at different levels, among which the characteristic stem-loop structure makes the greatest contribution to the prediction of putative miRNA precursor (pre-miRNA). We find that none of these features alone is capable of identifying new pre-miRNA accurately. Results In the present work, a pre-miRNA stem-loop secondary structure is translated to a network, which provides a novel perspective for its structural analysis. Network parameters are used to construct prediction model, achieving an area under the receiver operating curves (AUC) value of 0.956. Moreover, by repeating the same method on two independent datasets, accuracies of 0.976 and 0.913 are achieved, respectively. Conclusions Network parameters effectively characterize pre-miRNA secondary structure, which improves our prediction model in both prediction ability and computation efficiency. Additionally, as a complement to feature extraction methods in previous studies, these multifaceted features can reflect natural properties of miRNAs and be used for comprehensive and systematic analysis on miRNA.

Graphical abstractDisplay Omitted Highlights? In this study, we constructed random forest models based on local sequence-structure feature to identify miRNA in four species. ? We achieved the accuracies of 86.51% for Homo sapiens, 81.66% for Ornithorhynchus anatinus, 82.33% for Mus musculus and 85.71% for Schmidtea mediterranea, respectively. ? We implemented the importance analysis of all feature elements by using the conditional feature importance strategy. ? We concluded that the GC base pair is crucial to the selection of guide strand. MicroRNA (miRNA) is the negative regulator of gene expression, also known as guide strand of transient miRNA:miRNA* duplex. It is critical in maintaining the normal physiological processes such as development, differentiation, and apoptosis in many organisms. With increasing miRNA data, it is desirable to design methods to identify guide strand based on machine learning algorithms. In this study, the random forest models based on local sequence-structure features were proposed to identify miRNA in four species. The accuracies achieved were 86.51% for Homo sapiens, 81.66% for Ornithorhynchus anatinus, 82.33% for Mus musculus and 85.71% for Schmidtea mediterranea, respectively. Furthermore, the important analysis of feature elements was carried out by using the conditional feature importance strategy. The analysis results revealed that most of the significant elements were related to guanine-cytosine (GC) base pair. We believed that our method could be beneficial to annotate the function of miRNA and help the further understanding of the RNA interference mechanism.

Background The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues. Results We found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively. Conclusions The satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.

Owing to their potential for systematic analysis, complex networks have been widely used in proteomics. Representing a protein structure as a topology network provides novel insight into understanding protein folding mechanisms, stability and function. Here, we develop a new feature to reveal correlations between residues using a protein structure network. In an original attempt to quantify the effects of several key residues on catalytic residues, a power function was used to model interactions between residues. The results indicate that focusing on a few residues is a feasible approach to identifying catalytic residues. The spatial environment surrounding a catalytic residue was analyzed in a layered manner. We present evidence that correlation between residues is related to their distance apart most environmental parameters of the outer layer make a smaller contribution to prediction and ii catalytic residues tend to be located near key positions in enzyme folds. Feature analysis revealed satisfactory performance for our features, which were combined with several conventional features in a prediction model for catalytic residues using a comprehensive data set from the Catalytic Site Atlas. Values of 88.6 for sensitivity and 88.4 for specificity were obtained by 10-fold cross-validation. These results suggest that these features reveal the mutual dependence of residues and are promising for further study of structure-function relationship.

RNA-protein interactions play a pivotal role in various biological processes, such as mRNA processing, protein synthesis, assembly, and function of ribosome. In this work, we have introduced a computational method for predicting RNA-binding sites in proteins based on support vector machines by using a variety of features from amino acid sequence information including position-specific scoring matrix (PSSM) profiles, physicochemical properties and predicted solvent accessibility. Considering the influence of the surrounding residues of an amino acid and the dependency effect from the neighboring amino acids, a sliding window and a smoothing window are used to encode the PSSM profiles. The outer fivefold cross-validation method is evaluated on the data set of 77 RNA-binding proteins (RBP77). It achieves an overall accuracy of 88.66% with the Matthew's correlation coefficient (MCC) of 0.69. Furthermore, an independent data set of 39 RNA-binding proteins (RBP39) is employed to further evaluate the performance and achieves an overall accuracy of 82.36% with the MCC of 0.44. The result shows that our method has good generalization abilities in predicting RNA-binding sites for novel proteins. Compared with other previous methods, our method performs well on the same data set. The prediction results suggest that the used features are effective in predicting RNA-binding sites in proteins. The code and all data sets used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/Predict_RBP.rar .

Membrane transporters are critical in living cells. Therefore, the discrimination of the types of membrane proteins based on their functions is of great importance both for helping genome annotation and providing a supplementary role to experimental researchers to gain insight into membrane proteins' function. There are a lot of computational methods to facilitate the identification of the functional types of membrane proteins. However, in these methods, the local sequence environment was not integrated into the constructed model. In this study, we described a new strategy to predict the functional types of membrane proteins using a model based on auto covariance and position-specific scoring matrix. The novelty of the presented approach is considering the distribution of different positions of functional conservation sites in protein sequences. Thereby, this model adequately takes into account the long-range correlation between such sites during sequential evolution. Fivefold cross-validation test shows that this method greatly improves the prediction accuracy and achieves an acceptable prediction accuracy of 87.51%. The result indicates that the current approach might be an effective tool for predicting the functional types of membrane proteins only using the primary sequences. The code and dataset used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/predict_membrane.zip.