Search

In order to prevent drugs from being captured and degraded by the acidic environment of organelles, such as lysosomes, after entering cells, this study designed and synthesized a novel carrier amphiphilic polypeptide (DGRHHHLLLAAAA), designated P13, for use as a tumor-targeting drug delivery vehicle. The P13 peptide was synthesized by the solid phase synthesis method, and its self-assembly behavior and drug-loading capacity in aqueous solution were studied and characterized in vitro. Doxorubicin (DOX) was loaded by dialysis method, and P13 and DOX were mixed at a mass ratio of 6:1 to form regular rounded globules. The acid–base buffering capacity of P13 was investigated determined by acid–base titration. The results revealed that P13 had excellent acid–base buffering capacity, a critical micelle concentration value of about 0.000 21 g l−1, and the particle size of P13-Dox nanospheres was 167 nm. The drug encapsulation efficiency and drug loading capacity of micelles were 20.40 ± 1.21% and 21.25 ± 2.79%, respectively. At the concentration of 50 μg ml−1 of P13-DOX , the inhibition rate was 73.35%. The results of the in vivo antitumor activity assay in mice showed that P13-DOX also exhibited excellent inhibitory effect on tumor growth, compared with the tumor weight of 1.1 g in the control group, the tumor weight in the P13-DOX-treated group was only 0.26 g. Additionally, the results of hematoxylin and eosin staining of the organs showed that P13-DOX had no damaging effect on normal tissues. The novel amphiphilic peptide P13 with proton sponge effect designed and prepared in this study is expected to be a promising tumor-targeting drug carrier with excellent application potential.

In this paper, we propose a novel photonic approach for generating arbitrary waveform. The approach is based on the property of real-time Fourier transform in the temporal Talbot effect, where the spectrum of the modulating analog signal is converted into the output time-domain waveform in each period. We present a concise and strict theoretical framework to reveal the relationship of real-time Fourier transform between the optical signals before and after the dispersion. A proof-of-concept experiment is implemented to validate the presented theoretical model. We propose to generate symmetrical or asymmetrical arbitrary waveforms by using double-sideband or single-sideband modulation, respectively, which is verified by simulation results. It is shown that the given approach can be used to generate a repetition-rate multiplied optical pulse train with arbitrary waveform by simply using a multi-tone RF signal with appropriate frequencies and powers.

We describe a simplified approach to simulating Raman spectra from ab initio molecular dynamics (AIMD) calculations. The protocol relies on on-the-fly calculations of approximate molecular polarizabilities using the well-known sum over orbitals (as opposed to states) method. This approach bypasses the more accurate but computationally expensive approach to calculating molecular polarizabilities along AIMD trajectories, i.e., solving the coupled perturbed Hartree–Fock/Kohn–Sham equations. We demonstrate the advantages and limitations of our method through a few case studies targeting molecular systems of interest to surface- and/or tip-enhanced Raman spectroscopy practitioners.

Tip-enhanced Raman (TER) spectral images of 4-thiobenzonitrile-coated Au nanorods map the spatial profiles and trace the resonances of dipolar and multipolar plasmonic modes that are characteristic of the imaged particles. For any particular rod, we observe sequential transitions between high-order modes at low frequency shifts and lower-order modes at higher frequencies. We also notice that higher-order modes (up to

The effect of air and foam liquid alternating injection frequency on corrosion of oil casing steel in air-foam flooding process for enhanced oil recovery (EOR) was investigated in an autoclave. The characteristics of alternating corrosion in air-foam flooding were different from the conventional corrosion. The surface micro-topography of corrosion scale was observed by scanning electron microscopy (SEM) at various alternating injection frequencies (F = 0, 1, 2, 4 and 6). In addition, energy dispersive spectrometry (EDS) and X-ray diffraction (XRD) were used to analyze the composition of the corrosion product when alternating injection frequency was F = 0, 1 and 6. The results showed that the corrosion rate was increased significantly with the increase of alternating injection frequency. Meanwhile, pitting gradually developed into localized corrosion, which then slowly led to general corrosion. Moreover, the main corrosion products were Fe(OH)3, FeOOH, and Fe3O4 with alternating injection frequency for F = 0, F = 1 and F = 6, respectively. In this study, the alternating corrosion in air-foam flooding was investigated systematically, which would provide theoretical guidance for further research of air-foam corrosion.

Fracturing flowback fluid (FFF) is one of the most toxic effluents in oil and gas fields. The catalysts prepared by excessive impregnation were used wet air oxidation (CWAO) of FFFs at oxygen partial pressure (PO 2 ) of 2.5 MPa and temperature of 250 °C. Catalysts were characterized by H 2 -TPR, XRD, SEM and EDS. Results revealed that the activated carbon (AC) supported Cu Cr had a better catalytic performance due to superior dispersion, lower reduction temperature, and strong interaction between Cu and Cr. Using the optimal 5%Cu1%Cr/AC catalyst (loading 10 g/L), about 97% chemical oxygen demand (COD) can be removed.

DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. With the rapid development of next generation of sequencing technique, the number of protein sequences is unprecedentedly increasing. Thus it is necessary to develop computational methods to identify the DNA-binding proteins only based on the protein sequence information. In this study, a novel method called iDNA-KACC is presented, which combines the Support Vector Machine (SVM) and the auto-cross covariance transformation. The protein sequences are first converted into profile-based protein representation, and then converted into a series of fixed-length vectors by the auto-cross covariance transformation with Kmer composition. The sequence order effect can be effectively captured by this scheme. These vectors are then fed into Support Vector Machine (SVM) to discriminate the DNA-binding proteins from the non DNA-binding ones. iDNA-KACC achieves an overall accuracy of 75.16% and Matthew correlation coefficient of 0.5 by a rigorous jackknife test. Its performance is further improved by employing an ensemble learning approach, and the improved predictor is called iDNA-KACC-EL. Experimental results on an independent dataset shows that iDNA-KACC-EL outperforms all the other state-of-the-art predictors, indicating that it would be a useful computational tool for DNA binding protein identification. .

This study evaluated the potential of bioflocculant production from a Klebsiella strain using brewery wastewater as nutrients. The bioflocculant named OS-1B produced by Klebsiella sp. OS-1 exhibited a good flocculating activity to kaolin clay suspension (around 95%), when the diluted brewery wastewater with 7.2 mg/L total nitrogen and 1013 mg/L CODCr was used as a nitrogen source. Glucose (15 g/L) is the most favorable carbon source for Klebsiella sp. OS-1 in bioflocculant production from brewery wastewater. The yielded bioflocculant is pH tolerant and thermally stable, suggesting its good industrial potential. OS-1B mainly comprises polysaccharide (69.4%) and protein (24.5%). Fourier-transform infrared spectra indicate the presence of hydroxyl, carboxyl, esters and amino groups in the bioflocculant molecules. Combined with the results of zeta potential measurements, bridging is suggested as the main flocculation mechanism for OS-1B flocculation with kaolin. Overall, brewery wastewater can be used as a substrate to produce bioflocculants.

Motivation Coexisting in a DNA system, meiosis and recombination are two indispensible aspects for cell reproduction and growth. With the avalanche of genome sequences emerging in the post-genomic age, it is an urgent challenge to acquire the information of DNA recombination spots because it can timely provide very useful insights into the mechanism of meiotic recombination and the process of genome evolution. Results To address such a challenge, we have developed a predictor, called iRSpot-EL, by fusing different modes of pseudo K-tuple nucleotide composition and mode of dinucleotide-based auto-cross covariance into an ensemble classifier of clustering approach. Five-fold cross tests on a widely used benchmark dataset have indicated that the new predictor remarkably outperforms its existing counterparts. Particularly, far beyond their reach, the new predictor can be easily used to conduct the genome-wide analysis and the results obtained are quite consistent with the experimental map. Availability and Implementation For the convenience of most experimental scientists, a user-friendly web-server for iRSpot-EL has been established at http://bioinformatics.hitsz.edu.cn/iRSpot-EL/, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. Supplementary information Supplementary data are available at Bioinformatics online.

DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. With the rapid development of next generation of sequencing technique, the number of protein sequences are unprecedentedly increasing. Thus it is necessary to develop computational methods to identify the DNA-binding protein from the protein sequence information. In this study, a novel method is presented which combines the support vector machine and the auto-cross covariance transformation. The protein sequence represented in the form of amino acids or the physical-chemical properties of amino acids are converted into a series of fixed-length vectors by Kmer composition and the auto-cross covariance transformation. The sequence order effect can be effectively capture by this scheme. These vectors are then inputted to support vector machine to discriminate the DNA-binding proteins from the non DNA-binding ones. The proposed method achieves the overall accuracy of 75.23% and Matthew correlation coefficient of 0.5 by a rigorous jackknife test. The independent test shows that the proposed method outperforms most of the existing methods. These results demonstrate that the proposed method provides the state-of-the-art performance for the prediction of DNA-binding proteins.

DNA-binding proteins play an important role in most cellular processes. Therefore, it is necessary to develop an efficient predictor for identifying DNA-binding proteins only based on the sequence information of proteins. The bottleneck for constructing a useful predictor is to find suitable features capturing the characteristics of DNA binding proteins. We applied PseAAC to DNA binding protein identification and PseAAC was further improved by incorporating the evolutionary information by using profile-based protein representation. Finally, Combined with Support Vector Machines (SVMs), a predictor called iDNAPro-PseAAC was proposed. Experimental results on an updated benchmark dataset showed that iDNAPro-PseAAC outperformed some state-of-the-art approaches and it can achieve stable performance on an independent dataset. By using an ensemble learning approach to incorporate more negative samples (non-DNA binding proteins) in the training process, the performance of iDNAPro-PseAAC was further improved. The web server of iDNAPro-PseAAC is available at http://bioinformatics.hitsz.edu.cn/iDNAPro-PseAAC/.

The microRNA (miRNA), a small non-coding RNA molecule, plays an important role in transcriptional and post-transcriptional regulation of gene expression. Its abnormal expression, however, has been observed in many cancers and other disease states, implying that the miRNA molecules are also deeply involved in these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Most existing methods in this regard were based on the strategy in which RNA samples were formulated by a vector formed by their Kmer components. But the length of Kmers must be very short; otherwise, the vector's dimension would be extremely large, leading to the "high-dimension disaster" or overfitting problem. Inspired by the concept of "degenerate energy levels" in quantum mechanics, we introduced the "degenerate Kmer" (deKmer) to represent RNA samples. By doing so, not only we can accommodate long-range coupling effects but also we can avoid the high-dimension problem. Rigorous jackknife tests and cross-species experiments indicated that our approach is very promising. It has not escaped our notice that the deKmer approach can also be applied to many other areas of computational biology. A user-friendly web-server for the new predictor has been established at http://bioinformatics.hitsz.edu.cn/miRNA-deKmer/, by which users can easily get their desired results.