Comparative Study of the Efficacy of Autologous Tooth Bone Powder and Inorganic Bovine Bone Powder on the Repair of Jaw Defects.

Context: The main pathological features of jaw cysts are bone defects. Obtaining autologous bone for transplantation repair has been associated with postoperative complications, and the amount of bone that dentist can collect is limited. Studies have found that autologous tooth bone powder is safe and has good bone-formation ability and stability. Objective: The study intended to examine the efficacy of implantation of autologous tooth bone powder and inorganic bovine bone powder, after marsupialization and second-stage curettage for large jaw cysts that dentist can't directly remove by surgery in clinical practice. Design: The research team designed a prospective randomized controlled trial. Setting: The study took place in the Head and Neck Surgery Department at Chongqing University Cancer Hospital in Chongqing, China. Participants: Participants were 60 patients at the hospital between 2016 and 2018 who had mandibular cysts that surgical operation couldn't directly remove by surgery in clinical practice. Intervention: At 4 months after curettage, the research team randomly divided participants into three groups: (1) an intervention group who received implants of autologous tooth bone powder into the bone defects, (2) a positive control group who received implants of inorganic bovine bone powder, and (3) a negative control group who received no implants of any material. Outcome Measures: The research team performed: (1) periodontal probing at a fixed anatomical point for the intervention and both control groups postintervention at one day and 4 months after surgery and recorded the changes in probing depth and (2) computed tomography (CT) scans at baseline one day before and postintervention at 4 months after the implantation to determine changes in the bone mineral density and compared them among the three groups. Results: The change in the height of the intervention group's fixed anatomical point postintervention at 4 months after surgery was significantly smaller than that of the positive control group (P < .05). In the CT scan analysis, the differences between the intervention and negative control groups and between the positive and negative control groups were statistically significant (P < .05); however, the difference between the intervention and positive control groups wasn't significant (P > .05). Conclusions: Autologous tooth bone powder and inorganic bovine bone powder can effectively repair bone defects caused by large jaw cysts and that the repaired effect may be better than that of spontaneous osteogenesis. The autologous tooth bone powder was associated with lower levels of bone loss than those seen with use of inorganic bovine bone powder.

The Detection of Yarn Roll's Margin in Complex Background.

Online detection of yarn roll's margin is one of the key issues in textile automation, which is related to the speed and scheduling of bobbin (empty yarn roll) replacement. The actual industrial site is characterized by uneven lighting, restricted shooting angles, diverse yarn colors and cylinder yarn types, and complex backgrounds. Due to the above characteristics, the neural network detection error is large, and the contour detection extraction edge accuracy is low. In this paper, an improved neural network algorithm is proposed, and the improved Yolo algorithm and the contour detection algorithm are integrated. First, the image is entered in the Yolo model to detect each yarn roll and its dimensions; second, the contour and dimensions of each yarn roll are accurately detected based on Yolo; third, the diameter of the yarn rolls detected by Yolo and the contour detection algorithm are fused, and then the length of the yarn rolls and the edges of the yarn rolls are calculated as measurements; finally, in order to completely eliminate the error detection, the yarn consumption speed is used to estimate the residual yarn volume and the measured and estimated values are fused using a Kalman filter. This method overcomes the effects of complex backgrounds and illumination while being applicable to different types of yarn rolls. It is experimentally verified that the average measurement error of the cylinder yarn diameter is less than 8.6 mm, and the measurement error of the cylinder yarn length does not exceed 3 cm.

A Novel Vectorized Curved Road Representation Based Aerial Guided Unmanned Vehicle Trajectory Planning.

Unmanned vehicles frequently encounter the challenge of navigating through complex mountainous terrains, which are characterized by numerous unknown continuous curves. Drones, with their wide field of view and ability to vertically displace, offer a potential solution to compensate for the limited field of view of ground vehicles. However, the conventional approach of path extraction solely provides pixel-level positional information. Consequently, when drones guide ground unmanned vehicles using visual cues, the road fitting accuracy is compromised, resulting in reduced speed. Addressing these limitations with existing methods has proven to be a formidable task. In this study, we propose an innovative approach for guiding the visual movement of unmanned ground vehicles using an air-ground collaborative vectorized curved road representation and trajectory planning method. Our method offers several advantages over traditional road fitting techniques. Firstly, it incorporates a road star points ordering method based on the K-Means clustering algorithm, which simplifies the complex process of road fitting. Additionally, we introduce a road vectorization model based on the piecewise GA-Bézier algorithm, enabling the identification of the optimal frame from the initial frame to the current frame in the video stream. This significantly improves the road fitting effect (EV) and reduces the model running time (T-model). Furthermore, we employ smooth trajectory planning along the "route-plane" to maximize speed at turning points, thereby minimizing travel time (T-travel). To validate the efficiency and accuracy of our proposed method, we conducted extensive simulation experiments and performed actual comparison experiments. The results demonstrate the superior performance of our approach in terms of both efficiency and accuracy.

Full-Perception Robotic Surgery Environment with Anti-Occlusion Global-Local Joint Positioning.

The robotic surgery environment represents a typical scenario of human-robot cooperation. In such a scenario, individuals, robots, and medical devices move relative to each other, leading to unforeseen mutual occlusion. Traditional methods use binocular OTS to focus on the local surgical site, without considering the integrity of the scene, and the work space is also restricted. To address this challenge, we propose the concept of a fully perception robotic surgery environment and build a global-local joint positioning framework. Furthermore, based on data characteristics, an improved Kalman filter method is proposed to improve positioning accuracy. Finally, drawing from the view margin model, we design a method to evaluate positioning accuracy in a dynamic occlusion environment. The experimental results demonstrate that our method yields better positioning results than classical filtering methods.

Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion.

This paper evaluates the potential application of Raman baselines in characterizing organic deposition. Taking the layered sediments (Stromatolite) formed by the growth of early life on the Earth as the research object, Raman spectroscopy is an essential means to detect deep-space extraterrestrial life. Fluorescence is the main factor that interferes with Raman spectroscopy detection, which will cause the enhancement of the Raman baseline and annihilate Raman information. The paper aims to evaluate fluorescence contained in the Raman baseline and characterize organic sedimentary structure using the Raman baseline. This study achieves spectral image fusion combined with mapping technology to obtain high spatial and spectral resolution fusion images. To clarify that the fluorescence of organic matter deposition is the main factor causing Raman baseline enhancement, 5041 Raman spectra were obtained in the scanning area of 710 µm × 710 µm, and the correlation mechanism between the gray level of the light-dark layer of the detection point and the Raman baseline was compared. The spatial distribution of carbonate minerals and organic precipitations was detected by combining mapping technology. In addition, based on the BI-IHS algorithm, the spectral image fusion of Raman fluorescence mapping and reflection micrograph, polarization micrograph, and orthogonal polarization micrograph are realized, respectively. A fusion image with high spectral resolution and high spatial resolution is obtained. The results show that the Raman baseline can be used as helpful information to characterize stromatolite organic sedimentary structure.

Improved Stability and Catalytic Efficiency of ω-Transaminase in Aqueous Mixture of Deep Eutectic Solvents.

The efficient biosynthesis of chiral amines at an industrial scale to meet the high demand from industries that require chiral amines as precursors is challenging due to the poor stability and low catalytic efficiency of ω-transaminases (ω-TAs). Herein, this study adopted a green and efficient solvent engineering method to explore the effects of various aqueous solutions of deep eutectic solvents (DESs) as cosolvents on the catalytic efficiency and stability of ω-TA. Binary- and ternary-based DESs were used as cosolvents in enhancing the catalytic activity and stability of a ω-TA variant from Aspergillus terreus (E133A). The enzyme exhibited a higher catalytic activity in a ternary-based DES that was 2.4-fold higher than in conventional buffer. Moreover, the thermal stability was enhanced by a magnitude of 2.7, with an improvement in storage stability. Molecular docking studies illustrated that the most potent DES established strong hydrogen bond interactions with the enzyme's amino acid, which enhanced the catalytic efficiency and improved the stability of the ω-TA. Molecular docking is essential in designing DESs for a specific enzyme.

Real-Time Dissecting the Dynamics of Drug Transportation in the Live Brain.

Brain diseases are becoming a more and more serious threat to human health. Many critical properties of the transport mechanisms of drugs in live brains remain poorly understood. In this work, single-particle tracking was used to dissect the transport dynamics of wheat germ agglutinin (WGA) in live brain and characterize the geometry and rheology of the extracellular space (ECS). The results revealed that the movements of WGA were influenced by the specific-binding molecules and the nature of the ECS. We further analyzed the mobility behaviors of WGA globally and quantitatively and found that movement of WGA in brain cells of acute slices was an active transport process associated with actin filaments and microtubules. This work paves the way for studies aiming at characterizing the biophysics of drug transport in the context of live brains, which may contribute to developing potential new therapeutic applications for brain diseases.

Elastic Particle Swarm Optimization for MarSCoDe Spectral Calibration on Tianwen-1 Mars Rover.

China's Tianwen-1 Mars rover carries a laser-induced breakdown spectroscopy (LIBS) payload named MarSCoDe to analyze the mineral and rock composition on Mars. MarSCoDe is expected to experience a wide working temperature range of about 100 °C, which will lead to a spectral shift of up to ∼40 pixels (∼8.13 nm). Even worse, drastic changes in temperature and environment may cause a loss or increase of some spectral lines of an on-board calibration Ti target. An elastic particle swarm optimization (PSO) approach is proposed to fulfill the on-board spectral calibration of MarSCoDe under this harsh condition. Through establishing a standard wavelength set (SWS) and an individual particle wavelength set (PWS), and further elastically selecting a part of PWS to compare with SWS, the problem of spectral shift and number mismatch can be solved gradually with the evolution of the particle swarm. Some tests of standard lamps and Ti with MarSCoDe, placed in a Mars simulation environment chamber (MSEC) in a temperature range of 70 °C, were completed. Compared with the standard spectrum of the Ti target (obtained at 20 °C), the spectral shifts of the first, second, and third channels are approximately 0.33 nm (5 pixels), 0.85 nm (6.4 pixels), and 8.09 nm (39.8 pixels), respectively, at -40 °C before correction; after PSO correction, the spectral shifts are greatly reduced to up to 0.015 nm, and specially for the 626.28 nm line, the spectral shift is reduced from 8.09 nm to about 0 nm. Experimental results demonstrate that the PSO-based approach can not only correct the on-board spectral shift but also solve the number mismatch of spectral lines of MarSCoDe in the harsh working environment of Mars. Further, it can be extended to the on-board calibration of other spectral payloads for deep space exploration.

Association Between Mitochondrial DNA Copy Number and Head and Neck Squamous Cell Carcinoma: A Systematic Review and Dose-Response Meta-Analysis.

BACKGROUND The association between mitochondrial DNA (mtDNA) copy number and head and neck squamous cell carcinoma (HNSCC) risk remains unclear. Therefore, we aimed to evaluate the relationship between mtDNA copy number and HNSCC risk. MATERIAL AND METHODS We searched PubMed, Web of Science, and EMBASE until August 2020. Studies that assessed the association between mtDNA copy number and HNSCC as the outcome of interest were included. We performed a 2-class and dose-response meta-analysis to assess the association between cancer risk and mtDNA. RESULTS Eight articles (2 cohort studies and 6 case-control studies) with a total of 3913 patients were included in our meta-analysis. The overall results showed that mean mtDNA copy number level from 9 studies was 0.71 higher in patients with cancer than in non-cancer controls (the standardized mean differences (SMD) 0.71, 95% CI: 0.28-1.15, P<0.001). However, when 4 studies were pooled by dichotomizing mtDNA copy number at the median value into high- and low-content groups, no significant association between mtDNA content and overall cancer risk was found (odds ratio (OR)=0.87, 95% CI: 0.52-1.44, P=0.584). Furthermore, we observed a non-linear association from 3 studies between increased mtDNA copy number levels (P for nonlinearity <0.001). CONCLUSIONS The elevated mtDNA copy number could predict the risk of HNSCC as a biomarker. Moreover, there was non-linear relationship of risk between HNSCC and mtDNA copy number.

Food-grade γ-aminobutyric acid production by immobilized glutamate decarboxylase from Lactobacillus plantarum in rice vinegar and monosodium glutamate system.

OBJECTIVES: γ-amino butyric acid (GABA) is a non-protein amino acid, considered a potent bioactive compound. This study focused on biosynthesis of food-grade GABA by immobilized glutamate decarboxylase (GAD) from Lactobacillus plantarum in the rice vinegar and monosodium glutamate (MSG) reaction system. RESULTS: The gene encoding glutamate decarboxylase (GadB) from L. plantarum has been heterologously expressed in Lactococcus lactis and biochemically characterized. Recombinant GadB existed as a homodimer, and displayed maximal activity at 40 °C and pH 5.0. The Km value and catalytic efficiency (kcat/Km) of GadB for L-Glu was 22.33 mM and 62.4 mM-1 min-1, respectively, with a specific activity of 24.97 U/mg protein. Then, purified GadB was encapsulated in gellan gum beads. Compared to the free enzyme, immobilized GadB showed higher operational and storage stability. Finally, 9.82 to 21.48 g/L of GABA have been acquired by regulating the amounts of catalyst microspheres ranging from 0.5 to 0.8 g (wet weight) in 0.8 mL of the designed rice vinegar and MSG reaction system. CONCLUSIONS: The method of production GABA by immobilized GadB microspheres mixed in the rice vinegar and MSG reaction system is introduced herein for the first time. Especially, the results obtained here meet the increased interest in the harnessing of biocatalyst to synthesize food-grade GABA.

Combustion characteristics and kinetic analysis of oil sludge with CaO additive.

Combustion of High-sulfur oil sludge (OS) blended with CaO can significantly reduce the emission of sulfur gas pollutants, but its combustion and kinetic characteristics need to be further studied. TGA experiments showed the combustion characteristics of OS were significantly changed after adding CaO. As reflected by comprehensive combustion index (S), the combustion performance of OS decreased after adding CaO, and significantly improved with the increase of heating rate. The kinetic parameters of the main combustion process of OS with CaO were calculated by the iso-conversion methods of Friedman, FWO, and Starink, respectively. Kinetic analysis results indicated the energy required for OS combustion with CaO first increased and then decreased with deepening of reaction degree. The thermodynamic parameters of ΔH, ΔG and ΔS were determined on the basis of kinetics. The negative ΔH, positive ΔG, and negative ΔS validated the combustion of OS with CaO was an exothermic and nonspontaneous process.

MTTFsite: cross-cell type TF binding site prediction by using multi-task learning.

MOTIVATION: The prediction of transcription factor binding sites (TFBSs) is crucial for gene expression analysis. Supervised learning approaches for TFBS predictions require large amounts of labeled data. However, many TFs of certain cell types either do not have sufficient labeled data or do not have any labeled data. RESULTS: In this paper, a multi-task learning framework (called MTTFsite) is proposed to address the lack of labeled data problem by leveraging on labeled data available in cross-cell types. The proposed MTTFsite contains a shared CNN to learn common features for all cell types and a private CNN for each cell type to learn private features. The common features are aimed to help predicting TFBSs for all cell types especially those cell types that lack labeled data. MTTFsite is evaluated on 241 cell type TF pairs and compared with a baseline method without using any multi-task learning model and a fully shared multi-task model that uses only a shared CNN and do not use private CNNs. For cell types with insufficient labeled data, results show that MTTFsite performs better than the baseline method and the fully shared model on more than 89% pairs. For cell types without any labeled data, MTTFsite outperforms the baseline method and the fully shared model by more than 80 and 93% pairs, respectively. A novel gene expression prediction method (called TFChrome) using both MTTFsite and histone modification features is also presented. Results show that TFBSs predicted by MTTFsite alone can achieve good performance. When MTTFsite is combined with histone modification features, a significant 5.7% performance improvement is obtained. AVAILABILITY AND IMPLEMENTATION: The resource and executable code are freely available at http://hlt.hitsz.edu.cn/MTTFsite/ and http://www.hitsz-hlt.com:8080/MTTFsite/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cross-Cell-Type Prediction of TF-Binding Site by Integrating Convolutional Neural Network and Adversarial Network.

Transcription factor binding sites (TFBSs) play an important role in gene expression regulation. Many computational methods for TFBS prediction need sufficient labeled data. However, many transcription factors (TFs) lack labeled data in cell types. We propose a novel method, referred to as DANN_TF, for TFBS prediction. DANN_TF consists of a feature extractor, a label predictor, and a domain classifier. The feature extractor and the domain classifier constitute an Adversarial Network, which ensures that learned features are common features across different cell types. DANN_TF is evaluated on five TFs in five cell types with a total of 25 cell-type TF pairs and compared to a baseline method which does not use Adversarial Network. For both data augmentation and cross-cell-type prediction, DANN_TF performs better than the baseline method on most cell-type TF pairs. DANN_TF is further evaluated by an additional 13 TFs in the five cell types with a total of 65 cell-type TF pairs. Results show that DANN_TF achieves significantly higher AUC than the baseline method on 96.9% pairs of the 65 cell-type TF pairs. This is a strong indication that DANN_TF can indeed learn common features for cross-cell-type TFBS prediction.

A Single Mutation Increases the Thermostability and Activity of Aspergillus terreus Amine Transaminase.

Enhancing the thermostability of (R)-selective amine transaminases (AT-ATA) will expand its application in the asymmetric synthesis of chiral amines. In this study, mutual information and coevolution networks of ATAs were analyzed by the Mutual Information Server to Infer Coevolution (MISTIC). Subsequently, the amino acids most likely to influence the stability and function of the protein were investigated by alanine scanning and saturation mutagenesis. Four stabilized mutants (L118T, L118A, L118I, and L118V) were successfully obtained. The best mutant, L118T, exhibited an improved thermal stability with a 3.7-fold enhancement in its half-life (t1/2) at 40 °C and a 5.3 °C increase in T5010 compared to the values for the wild-type protein. By the differential scanning fluorimetry (DSF) analysis, the best mutant, L118T, showed a melting temperature (Tm) of 46.4 °C, which corresponded to a 5.0 °C increase relative to the wild-type AT-ATA (41.4 °C). Furthermore, the most stable mutant L118T displayed the highest catalytic efficiency among the four stabilized mutants.

CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.

BACKGROUND: Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structure prediction is becoming increasingly urgent. RESULTS: We present a novel deep learning based model, referred to as CNNH_PSS, by using multi-scale CNN with highway. In CNNH_PSS, any two neighbor convolutional layers have a highway to deliver information from current layer to the output of the next one to keep local contexts. As lower layers extract local context while higher layers extract long-range interdependencies, the highways between neighbor layers allow CNNH_PSS to have ability to extract both local contexts and long-range interdependencies. We evaluate CNNH_PSS on two commonly used datasets: CB6133 and CB513. CNNH_PSS outperforms the multi-scale CNN without highway by at least 0.010 Q8 accuracy and also performs better than CNF, DeepCNF and SSpro8, which cannot extract long-range interdependencies, by at least 0.020 Q8 accuracy, demonstrating that both local contexts and long-range interdependencies are indeed useful for prediction. Furthermore, CNNH_PSS also performs better than GSM and DCRNN which need extra complex model to extract long-range interdependencies. It demonstrates that CNNH_PSS not only cost less computer resource, but also achieves better predicting performance. CONCLUSION: CNNH_PSS have ability to extracts both local contexts and long-range interdependencies by combing multi-scale CNN and highway network. The evaluations on common datasets and comparisons with state-of-the-art methods indicate that CNNH_PSS is an useful and efficient tool for protein secondary structure prediction.

The pretreatment neutrophil-lymphocyte ratio may predict prognosis of patients with liver cancer: A systematic review and meta-analysis.

BACKGROUND: At present, several studies have reported that the pretreatment neutrophil-lymphocyte ratio (NLR) may be associated with the prognosis of liver cancer. Nevertheless, their conclusions remain controversial. Thus, we performed a meta-analysis of 54 studies to evaluate the prognostic value of NLR. METHOD: Databases including PubMed, Embase, Cochrane Library, and Web of Science were searched to July 2017. RESULT: A total of 54 studies including 12 979 patients were included in this meta-analysis. Elevated NLR had a close relationship with the overall survival (OS) (HR 1.52; 95% CI 1.39-1.67), recurrence-free survival (RFS) (HR 1.84; 95% CI 1.48-2.30), and disease-free survival (DFS) (HR 1.71; 95% CI 1.39-2.11) of liver cancer, respectively. In addition, elevated NLR was associated with the presence of tumor vascular invasion (OR 2.35; 95% CI 1.93-2.86), multiple tumors (OR 1.38; 95% CI 1.15-1.66), alpha-fetoprotein ≥ 400 ng/mL (OR 1.51; 95% CI 1.15-1.98), presence of HbsAg (+) (OR 0.68; 95% CI 0.51-0.90), and cirrhosis (OR: 0.59; 95% CI 0.44-0.80). CONCLUSION: This meta-analysis indicated that elevated NLR may be an effective and noninvasive indicator for prognosis of patients with liver cancer.

Improving thermostability of (R)-selective amine transaminase from Aspergillus terreus through introduction of disulfide bonds.

To improve the thermostability of (R)-selective amine transaminase from Aspergillus terreus (AT-ATA), we used computer software Disulfide by Design and Modelling of Disulfide Bonds in Proteins to identify mutation sites where the disulfide bonds were most likely to form. We obtained three stabilized mutants (N25C-A28C, R131C-D134C, M150C-M280C) from seven candidates by site-directed mutagenesis. Compared to the wild type, the best two mutants N25C-A28C and M150C-M280C showed improved thermal stability with a 3.1- and 3.6-fold increase in half-life (t1/2 ) at 40 °C and a 4.6 and 5.1 °C increase in T5010 . In addition, the combination of mutant R131C-D134C and M150C-M280C displayed the largest shift in thermostability with a 4.6-fold increase in t1/2 at 40 °C and a 5.5 °C increase in T5010 . Molecular dynamics simulation indicated that mutations of N25C-A28C and M150C-M280C lowered the overall root mean square deviation for the overall residues at elevated temperature and consequently increased the protein rigidity. The stabilized mutation of R131C-D134C was in the region of high mobility and on the protein surface, and the disulfide bond constraints the flexibility of loop 121-136.

Adsorption of Cu(II) and Zn(II) Ions from Aqueous Solution by Gel/PVA-Modified Super-Paramagnetic Iron Oxide Nanoparticles.

Super-paramagnetic iron oxide nanoparticles (SPIONs)/gelatin (gel)/polyvinyl alcohol (PVA) nanoparticles were designed and synthesized by the co-precipitation method and further modified with gel and PVA. These nanoparticles were used for the removal of Cu(II) and Zn(II) from aqueous solutions. The adsorbents were rich in different functional groups for chemisorption and showed effective adsorption properties. The adsorption of Cu(II) and Zn(II) on the SPIONs/gel and SPIONs/gel/PVA materials were investigated with respect to pH, adsorption kinetics, and adsorption isotherms. The adsorption data was fitted to the Langmuir, Freundlich, and Sips models at the optimum pH 5.2 (±0.2) over 60 min; SPIONs/gel showed maximum adsorption capacities of 47.594 mg/g and 40.559 mg/g for Cu(II) and Zn(II); SPIONs/gel/PVA showed those of 56.051 mg/g and 40.865 mg/g, respectively. The experimental data fitted the pseudo-second-order model, indicating that the process followed chemical monolayer adsorption. In addition, the SPIONs/gel/PVA showed better stability and Cu(II) adsorption efficiency than SPIONs/gel.

EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation.

BACKGROUND: Prediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the relationships of evolutionary information between residues. RESULTS: In this paper, we first propose a novel residue encoding method, referred to as the Position Specific Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode residues by utilizing the relationships of evolutionary information between residues. PDNA-62 and PDNA-224 are used to evaluate PSSM-RT and two existing PSSM encoding methods by five-fold cross-validation. Performance evaluations indicate that PSSM-RT is more effective than previous methods. This validates the point that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction. An ensemble learning classifier (EL_PSSM-RT) is also proposed by combining ensemble learning model and PSSM-RT to better handle the imbalance between binding and non-binding residues in datasets. EL_PSSM-RT is evaluated by five-fold cross-validation using PDNA-62 and PDNA-224 as well as two independent datasets TS-72 and TS-61. Performance comparisons with existing predictors on the four datasets demonstrate that EL_PSSM-RT is the best-performing method among all the predicting methods with improvement between 0.02-0.07 for MCC, 4.18-21.47% for ST and 0.013-0.131 for AUC. Furthermore, we analyze the importance of the pair-relationships extracted by PSSM-RT and the results validates the usefulness of PSSM-RT for encoding DNA-binding residues. CONCLUSIONS: We propose a novel prediction method for the prediction of DNA-binding residue with the inclusion of relationship of evolutionary information and ensemble learning. Performance evaluation shows that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction and ensemble learning can be used to address the data imbalance issue between binding and non-binding residues. A web service of EL_PSSM-RT ( http://hlt.hitsz.edu.cn:8080/PSSM-RT_SVM/ ) is provided for free access to the biological research community.

Vesicle formation by proton transfer driven short-tailed fatty acids of C4-C8 chain length in water.

Ultrashort single-chain fatty acids self-assemble to form vesicles under certain proton-driven conditions. The protonation provides a larger charge area around the hydrophilic carbonyl headgroups, and proton shift as the key driving parameter was studied. The ultrashort fatty acids (C4-C8) formed stable unilamellar vesicles predominantly through out the whole range of tested pH levels (6.5-9.5). A proton-driven self-assembly process and effects on the phase transition were characterized by dynamic light scattering, transmission electron microscopy and cryo-transmission electron microscopy. In particular, we studied in greater detail the molecular packing characteristics of FA vesicles for geometric reasons and the protonation effect changes the molecular surface charge and further carboxylic acid headgroup motion. This study enhances the understanding of the physicochemical specificity of these membrane vesicles, and may facilitate the alteration of membrane function caused by FAs.