The Contact Interface Electronic Coupling of Cobalt and Zirconia Enables Stable and Highly Efficient 4e- Oxygen Reduction Reaction Catalysis.

Cobalt (Co) is an efficient oxygen reduction reaction (ORR) catalyst but suffers from issues of easy deactivation and instability. Here, it shows that ZrO2 can stabilize Co through interface electron coupling and enables highly efficient 4e- ORR catalysis. Porous carbon nanofibers loaded with dispersed Co-nanodots (≈10 nm, 9.63 wt%) and ZrO2 nanoparticles are synthesized as the catalyst. The electron transfer from the metallic Co to ZrO2 causes interface-oriented electron enrichment that promotes the activation and conversion of O2, improving the efficiency of 4e- transfer. Moreover, the simulation results show that ZrO2 acts like an electron reservoir to store electrons from Co and slowly release them to the interface, solving the easy deactivation problem of Co. The catalyst exhibits a high half-wave potential (E1/2) of 0.84 V, which only decreases by 3.6 mV after 10 000 cycles, showing great stability. Particularly, the enhanced spin polarization of Co in a magnetic field reinforces the interface electron coupling that increases the E1/2 to 0.864 V and decreases the energy barrier of ORR from 0.81 to 0.63 eV, confirming that the proposed strategy is effective for constructing efficient and stable ORR catalysts.

Polydopamine-mediated immobilization of BMP-2 onto electrospun nanofibers enhances bone regeneration.

Dealing with bone defects is a significant challenge to global health. Electrospinning in bone tissue engineering has emerged as a solution to this problem. In this study, we designed a PVDF-b-PTFE block copolymer by incorporating TFE, which induced a phase shift in PVDF fromαtoß, thereby enhancing the piezoelectric effect. Utilizing the electrospinning process, we not only converted the material into a film with a significant surface area and high porosity but also intensified the piezoelectric effect. Then we used polydopamine to immobilize BMP-2 onto PVDF-b-PTFE electrospun nanofibrous membranes, achieving a controlled release of BMP-2. The scaffold's characters were examined using SEM and XRD. To assess its osteogenic effectsin vitro, we monitored the proliferation of MC3T3-E1 cells on the fibers, conducted ARS staining, and measured the expression of osteogenic genes.In vivo, bone regeneration effects were analyzed through micro-CT scanning and HE staining. ELISA assays confirmed that the sustained release of BMP-2 can be maintained for at least 28 d. SEM images and CCK-8 results demonstrated enhanced cell viability and improved adhesion in the experimental group. Furthermore, the experimental group exhibited more calcium nodules and higher expression levels of osteogenic genes, including COL-I, OCN, and RUNX2. HE staining and micro-CT scans revealed enhanced bone tissue regeneration in the defective area of the PDB group. Through extensive experimentation, we evaluated the scaffold's effectiveness in augmenting osteoblast proliferation and differentiation. This study emphasized the potential of piezoelectric PVDF-b-PTFE nanofibrous membranes with controlled BMP-2 release as a promising approach for bone tissue engineering, providing a viable solution for addressing bone defects.

An innovative Zn3In2S6/ZnIn2S4 homojunction photocatalyst with enhanced interfacial charge transfer for the highly efficient degradation of tetracycline under visible radiation.

The interfacial charge transfer ability is a decisive factor influencing the photocatalytic performance of composite photocatalysts. Compared with heterojunctions that combine two or more semiconductors with different properties, homojunctions that combine two semiconductors with similar properties can accelerate the interfacial charge shift and achieve higher photocatalyticability. In this study, a Zn3In2S6/ZnIn2S4 homojunction photocatalyst (ZIS-5) with a Zn3In2S6 to ZnIn2S4 molar ratio of 5:1 was synthesized by selecting Zn3In2S6 nano-microspheres as the substrate material and growing ZnIn2S4 flocs on the nano-microspheres. The photocatalytic performance of the ZIS-5 homojunction was assessed by using tetracycline (TC) as a typical pollutant. The photocatalytic performance and mineralization rate of the ZIS-5 homojunction were significantly improved compared with those of Zn3In2S6 and ZnIn2S4, and its photocatalytic performance was increased by 10.2% and 20.9%, compared with Zn3In2S6 and ZnIn2S4, respectively, while the mineralization rate was enhanced by 22.78% and 43.28%, respectively. The results of the comparison experiment revealed that the interfacial electron transfer ability of the ZIS-5 homojunction is 1.6 times that of the g-C3N4/ZnIn2S4-5 heterojunction. The density functional theory (DFT) computation and Mott-Schottky plots verified the formation of an internal electric field. The toxicity analysis showed that the ZIS-5 homojunction system effectively reduced the toxicity of TC. This work supplies a valuable route for inventing catalysts with efficient photocatalytic performances.

First-Principles Investigation of Phosphorus-Doped Graphitic Carbon Nitride as Anchoring Material for the Lithium-Sulfur Battery.

The utilization of lithium-sulfur battery is hindered by various challenges, including the "shuttle effect", limited sulfur utilization, and the sluggish conversion kinetics of lithium polysulfides (LiPSs). In the present work, a theoretical design for the viability of graphitic carbon nitride (g-C3N4) and phosphorus-doping graphitic carbon nitride substrates (P-g-C3N4) as promising host materials in a Li-S battery was conducted utilizing first-principles calculations. The PDOS shows that when the P atom is introduced, the 2p of the N atom is affected by the 2p orbital of the P atom, which increases the energy band of phosphorus-doping substrates. The energy bands of PC and Pi are 0.12 eV and 0.20 eV, respectively. When the lithium polysulfides are adsorbed on four substrates, the overall adsorption energy of PC is 48-77% higher than that of graphitic carbon nitride, in which the charge transfer of long-chain lithium polysulfides increase by more than 1.5-fold. It is found that there are powerful Li-N bonds between lithium polysulfides and P-g-C3N4 substrates. Compared with the graphitic carbon nitride monolayer, the anchoring effect of the LiPSs@P-g-C3N4 substrate is enhanced, which is beneficial for inhibiting the shuttle of high-order lithium polysulfides. Furthermore, the catalytic performance of the P-g-C3N4 substrate is assessed in terms of the S8 reduction pathway and the decomposition of Li2S; the decomposition energy barrier of the P-g-C3N4 substrate decrease by 10% to 18%. The calculated results show that P-g-C3N4 can promote the reduction of S8 molecules and Li-S bond cleavage within Li2S, thus improving the utilization of sulfur-active substances and the ability of rapid reaction kinetics. Therefore, the P-g-C3N4 substrates are a promising high-performance lithium-sulfur battery anchoring material.

Identification of plant vacuole proteins by using graph neural network and contact maps.

Plant vacuoles are essential organelles in the growth and development of plants, and accurate identification of their proteins is crucial for understanding their biological properties. In this study, we developed a novel model called GraphIdn for the identification of plant vacuole proteins. The model uses SeqVec, a deep representation learning model, to initialize the amino acid sequence. We utilized the AlphaFold2 algorithm to obtain the structural information of corresponding plant vacuole proteins, and then fed the calculated contact maps into a graph convolutional neural network. GraphIdn achieved accuracy values of 88.51% and 89.93% in independent testing and fivefold cross-validation, respectively, outperforming previous state-of-the-art predictors. As far as we know, this is the first model to use predicted protein topology structure graphs to identify plant vacuole proteins. Furthermore, we assessed the effectiveness and generalization capability of our GraphIdn model by applying it to identify and locate peroxisomal proteins, which yielded promising outcomes. The source code and datasets can be accessed at https://github.com/SJNNNN/GraphIdn .

Protein-protein interaction site prediction by model ensembling with hybrid feature and self-attention.

BACKGROUND: Protein-protein interactions (PPIs) are crucial in various biological functions and cellular processes. Thus, many computational approaches have been proposed to predict PPI sites. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in sequences. Many feature extraction methods rely on the sliding window technique, which simply merges all the features of residues into a vector. The importance of some key residues may be weakened in the feature vector, leading to poor performance. RESULTS: We propose a novel sequence-based method for PPI sites prediction. The new network model, PPINet, contains multiple feature processing paths. For a residue, the PPINet extracts the features of the targeted residue and its context separately. These two types of features are processed by two paths in the network and combined to form a protein representation, where the two types of features are of relatively equal importance. The model ensembling technique is applied to make use of more features. The base models are trained with different features and then ensembled via stacking. In addition, a data balancing strategy is presented, by which our model can get significant improvement on highly unbalanced data. CONCLUSION: The proposed method is evaluated on a fused dataset constructed from Dset186, Dset_72, and PDBset_164, as well as the public Dset_448 dataset. Compared with current state-of-the-art methods, the performance of our method is better than the others. In the most important metrics, such as AUPRC and recall, it surpasses the second-best programmer on the latter dataset by 6.9% and 4.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model, especially, the hybrid feature. We share our code for reproducibility and future research at https://github.com/CandiceCong/StackingPPINet .

Stabilizing High Density Cu Active Sites with ZrO2 Quantum Dots as Chemical Ligand in N-doped Porous Carbon Nanofibers for Efficient ORR.

The emerging transition metal-nitrogen-carbon (MNC) materials are considered as a promising oxygen reduction reaction (ORR) catalyst system to substitute expensive Pt/C catalysts due to their high surface area and potential high catalytic activity. However, MNC catalysts are easy to be attacked by the ORR byproducts that easily lead to the deactivation of metal active sites. Moreover, a high metal loading affects the mass transfer and stability, but a low loading delivers inferior catalytic activity. Here, a new strategy of designing ZrO2 quantum dots and N-complex as dual chemical ligands in N-doped bubble-like porous carbon nanofibers (N-BPCNFs) to stabilize copper (Cu) by forming CuZrO3-x /ZrO2 heterostructures and CuN ligands with a high loading of 40.5 wt.% is reported. While the highly porous architecture design of N-BPCNFs builds a large solidelectrolytegas phase interface and promotes mass transfer. The preliminary results show that the half-wave potential of the catalyst reaches 0.856 V, and only decreases 0.026 V after 10 000 cycles, exhibiting excellent stability. The proposed strategy of stabilizing metal active sites with both heterostructures and CuN ligands is feasible and scalable for developing high metal loading ORR catalyst.

Preparation of lignin nanoparticles via ultra-fast microwave-assisted fractionation of lignocellulose using ternary deep eutectic solvents.

Lignin separation from natural lignocellulose for the preparation of lignin nanoparticles (LNPs) is often challenging owing to the recalcitrant and complex structure of lignocellulose. This paper reports a strategy for the rapid synthesis of LNPs via microwave-assisted lignocellulose fractionation using ternary deep eutectic solvents (DESs). A novel ternary DES with strong hydrogen bonding was prepared using choline chloride, oxalic acid, and lactic acid in a 1:0.5:1 ratio. Efficient fractionation of rice straw (0.5 × 2.0 cm) (RS) was realized by the ternary DES under microwave irradiation (680 W) within only 4 min, and 63.4% of lignin could be separated from the RS to prepare LNPs with a high lignin purity (86.8%), an average particle size of 48-95 nm, and a narrow size distribution. The mechanism of lignin conversion was also investigated, which revealed that dissolved lignin aggregated into LNPs via π-π stacking interactions.

Automatic classification of nerve discharge rhythms based on sparse auto-encoder and time series feature.

BACKGROUND: Nerve discharge is the carrier of information transmission, which can reveal the basic rules of various nerve activities. Recognition of the nerve discharge rhythm is the key to correctly understand the dynamic behavior of the nervous system. The previous methods for the nerve discharge recognition almost depended on the traditional statistical features, and the nonlinear dynamical features of the discharge activity. The artificial extraction and the empirical judgment of the features were required for the recognition. Thus, these methods suffered from subjective factors and were not conducive to the identification of a large number of discharge rhythms. RESULTS: The ability of automatic feature extraction along with the development of the neural network has been greatly improved. In this paper, an effective discharge rhythm classification model based on sparse auto-encoder was proposed. The sparse auto-encoder was used to construct the feature learning network. The simulated discharge data from the Chay model and its variants were taken as the input of the network, and the fused features, including the network learning features, covariance and approximate entropy of nerve discharge, were classified by Softmax. The results showed that the accuracy of the classification on the testing data was 87.5%, which could provide more accurate classification results. Compared with other methods for the identification of nerve discharge types, this method could extract the characteristics of nerve discharge rhythm automatically without artificial design, and show a higher accuracy. CONCLUSIONS: The sparse auto-encoder, even neural network has not been used to classify the basic nerve discharge from neither biological experiment data nor model simulation data. The automatic classification method of nerve discharge rhythm based on the sparse auto-encoder in this paper reduced the subjectivity and misjudgment of the artificial feature extraction, saved the time for the comparison with the traditional method, and improved the intelligence of the classification of discharge types. It could further help us to recognize and identify the nerve discharge activities in a new way.

A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data.

BACKGROUND: Correctly classifying the subtypes of cancer is of great significance for the in-depth study of cancer pathogenesis and the realization of personalized treatment for cancer patients. In recent years, classification of cancer subtypes using deep neural networks and gene expression data has gradually become a research hotspot. However, most classifiers may face overfitting and low classification accuracy when dealing with small sample size and high-dimensional biology data. RESULTS: In this paper, a laminar augmented cascading flexible neural forest (LACFNForest) model was proposed to complete the classification of cancer subtypes. This model is a cascading flexible neural forest using deep flexible neural forest (DFNForest) as the base classifier. A hierarchical broadening ensemble method was proposed, which ensures the robustness of classification results and avoids the waste of model structure and function as much as possible. We also introduced an output judgment mechanism to each layer of the forest to reduce the computational complexity of the model. The deep neural forest was extended to the densely connected deep neural forest to improve the prediction results. The experiments on RNA-seq gene expression data showed that LACFNForest has better performance in the classification of cancer subtypes compared to the conventional methods. CONCLUSION: The LACFNForest model effectively improves the accuracy of cancer subtype classification with good robustness. It provides a new approach for the ensemble learning of classifiers in terms of structural design.

Reverse engineering gene regulatory network based on complex-valued ordinary differential equation model.

BACKGROUND: The growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics. To establish the interactions between genes or proteins and understand the intrinsic mechanisms of biological systems have become an urgent need and study hotspot. RESULTS: In order to forecast gene expression data and identify more accurate gene regulatory network, complex-valued version of ordinary differential equation (CVODE) is proposed in this paper. In order to optimize CVODE model, a complex-valued hybrid evolutionary method based on Grammar-guided genetic programming and complex-valued firefly algorithm is presented. CONCLUSIONS: When tested on three real gene expression datasets from E. coli and Human Cell, the experiment results suggest that CVODE model could improve 20-50% prediction accuracy of gene expression data, which could also infer more true-positive regulatory relationships and less false-positive regulations than ordinary differential equation.

Privacy protection of medical data in social network.

BACKGROUND: Protection of privacy data published in the health care field is an important research field. The Health Insurance Portability and Accountability Act (HIPAA) in the USA is the current legislation for privacy protection. However, the Institute of Medicine Committee on Health Research and the Privacy of Health Information recently concluded that HIPAA cannot adequately safeguard the privacy, while at the same time researchers cannot use the medical data for effective researches. Therefore, more effective privacy protection methods are urgently needed to ensure the security of released medical data. METHODS: Privacy protection methods based on clustering are the methods and algorithms to ensure that the published data remains useful and protected. In this paper, we first analyzed the importance of the key attributes of medical data in the social network. According to the attribute function and the main objective of privacy protection, the attribute information was divided into three categories. We then proposed an algorithm based on greedy clustering to group the data points according to the attributes and the connective information of the nodes in the published social network. Finally, we analyzed the loss of information during the procedure of clustering, and evaluated the proposed approach with respect to classification accuracy and information loss rates on a medical dataset. RESULTS: The associated social network of a medical dataset was analyzed for privacy preservation. We evaluated the values of generalization loss and structure loss for different values of k and a, i.e. [Formula: see text] = {3, 6, 9, 12, 15, 18, 21, 24, 27, 30}, a = {0, 0.2, 0.4, 0.6, 0.8, 1}. The experimental results in our proposed approach showed that the generalization loss approached optimal when a = 1 and k = 21, and structure loss approached optimal when a = 0.4 and k = 3. CONCLUSION: We showed the importance of the attributes and the structure of the released health data in privacy preservation. Our method achieved better results of privacy preservation in social network by optimizing generalization loss and structure loss. The proposed method to evaluate loss obtained a balance between the data availability and the risk of privacy leakage.

Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA.

BACKGROUND: Apoptosis, also called programmed cell death, refers to the spontaneous and orderly death of cells controlled by genes in order to maintain a stable internal environment. Identifying the subcellular location of apoptosis proteins is very helpful in understanding the mechanism of apoptosis and designing drugs. Therefore, the subcellular localization of apoptosis proteins has attracted increased attention in computational biology. Effective feature extraction methods play a critical role in predicting the subcellular location of proteins. RESULTS: In this paper, we proposed two novel feature extraction methods based on evolutionary information. One of the features obtained the evolutionary information via the transition matrix of the consensus sequence (CTM). And the other utilized the evolutionary information from PSSM based on absolute entropy correlation analysis (AECA-PSSM). After fusing the two kinds of features, linear discriminant analysis (LDA) was used to reduce the dimension of the proposed features. Finally, the support vector machine (SVM) was adopted to predict the protein subcellular locations. The proposed CTM-AECA-PSSM-LDA subcellular location prediction method was evaluated using the CL317 dataset and ZW225 dataset. By jackknife test, the overall accuracy was 99.7% (CL317) and 95.6% (ZW225) respectively. CONCLUSIONS: The experimental results show that the proposed method which is hopefully to be a complementary tool for the existing methods of subcellular localization, can effectively extract more abundant features of protein sequence and is feasible in predicting the subcellular location of apoptosis proteins.

A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data.

BACKGROUND: Cancer subtype classification attains the great importance for accurate diagnosis and personalized treatment of cancer. Latest developments in high-throughput sequencing technologies have rapidly produced multi-omics data of the same cancer sample. Many computational methods have been proposed to classify cancer subtypes, however most of them generate the model by only employing gene expression data. It has been shown that integration of multi-omics data contributes to cancer subtype classification. RESULTS: A new hierarchical integration deep flexible neural forest framework is proposed to integrate multi-omics data for cancer subtype classification named as HI-DFNForest. Stacked autoencoder (SAE) is used to learn high-level representations in each omics data, then the complex representations are learned by integrating all learned representations into a layer of autoencoder. Final learned data representations (from the stacked autoencoder) are used to classify patients into different cancer subtypes using deep flexible neural forest (DFNForest) model.Cancer subtype classification is verified on BRCA, GBM and OV data sets from TCGA by integrating gene expression, miRNA expression and DNA methylation data. These results demonstrated that integrating multiple omics data improves the accuracy of cancer subtype classification than only using gene expression data and the proposed framework has achieved better performance compared with other conventional methods. CONCLUSION: The new hierarchical integration deep flexible neural forest framework(HI-DFNForest) is an effective method to integrate multi-omics data to classify cancer subtypes.

HSCVFNT: Inference of Time-Delayed Gene Regulatory Network Based on Complex-Valued Flexible Neural Tree Model.

Gene regulatory network (GRN) inference can understand the growth and development of animals and plants, and reveal the mystery of biology. Many computational approaches have been proposed to infer GRN. However, these inference approaches have hardly met the need of modeling, and the reducing redundancy methods based on individual information theory method have bad universality and stability. To overcome the limitations and shortcomings, this thesis proposes a novel algorithm, named HSCVFNT, to infer gene regulatory network with time-delayed regulations by utilizing a hybrid scoring method and complex-valued flexible neural network (CVFNT). The regulations of each target gene can be obtained by iteratively performing HSCVFNT. For each target gene, the HSCVFNT algorithm utilizes a novel scoring method based on time-delayed mutual information (TDMI), time-delayed maximum information coefficient (TDMIC) and time-delayed correlation coefficient (TDCC), to reduce the redundancy of regulatory relationships and obtain the candidate regulatory factor set. Then, the TDCC method is utilized to create time-delayed gene expression time-series matrix. Finally, a complex-valued flexible neural tree model is proposed to infer the time-delayed regulations of each target gene with the time-delayed time-series matrix. Three real time-series expression datasets from (Save Our Soul) SOS DNA repair system in E. coli and Saccharomyces cerevisiae are utilized to evaluate the performance of the HSCVFNT algorithm. As a result, HSCVFNT obtains outstanding F-scores of 0.923, 0.8 and 0.625 for SOS network and (In vivo Reverse-Engineering and Modeling Assessment) IRMA network inference, respectively, which are 5.5%, 14.3% and 72.2% higher than the best performance of other state-of-the-art GRN inference methods and time-delayed methods.

Polysulfide intercalation in bilayer-structured graphitic C3N4: a first-principles study.

Lithium-sulfur (Li-S) batteries have attracted increasing attention due to their high theoretical capacity, being a promising candidate for portable electronics, electric vehicles and large-scale energy storage. The interactions of bilayer structured graphitic C3N4 (bi-C3N4) with S8, lithium polysulfides (LiPSs), 1,3-dioxolane, 1,2-dimethoxyethane and tetrahydrofuran ether-based solvents have been studied using first-principles calculations. It has been found that the (micropore-scale) interlayer of bi-C3N4 shows intimate contact and strong binding with S8 and LiPSs due to the formation of chemical Li-N bonds. The incorporation of soluble LiPSs by the wrinkled layers of bi-C3N4 with 5.5-7.2 Å interlayer pores can suppress the shuttling effect. The interlayer ultramicropores with interlayer distances of <4 Å can accommodate the small Li2S2 and Li2S molecules, and impede the irreversible reaction between the solvents and the LiPSs. The calculated energy gap of bi-C3N4 decreases to be narrow during lithiation. Our results can provide a guideline for promoting the electrochemical performance of microporous g-C3N4/sulfur composites for Li-S batteries.

Sensor Fault and Delay Tolerant Control for Networked Control Systems Subject to External Disturbances.

In this paper, the problem of sensor fault and delay tolerant control problem for a class of networked control systems under external disturbances is investigated. More precisely, the dynamic characteristics of the external disturbance and sensor fault are described as the output of exogenous systems first. The original sensor fault and delay tolerant control problem is reformulated as an equivalence problem with designed available system output and reformed performance index. The feedforward and feedback sensor fault tolerant controller (FFSFTC) can be obtained by utilizing the solutions of Riccati matrix equation and Stein matrix equation. Based on the designed fault diagnoser, the proposed FFSFTC is further reconstructed to compensate for the sensor fault and delayed measurement effects. Finally, numerical examples are provided to illustrate the effectiveness of our proposed FFSFTC with different cases with various types of sensor faults, measurement delays and external disturbances.

The 6-item Kessler psychological distress scale to survey serious mental illness among Chinese undergraduates: Psychometric properties and prevalence estimate.

OBJECTIVE: To evaluate the psychometric properties of the 6-item Kessler psychological distress scale (K6) in screening for serious mental illness (SMI) among undergraduates in a major comprehensive university in China. METHOD: The K6 was self-completed by 8289 randomly sampled participants. A group of them (n=222) were re-assessed using K6 and interviewed using the Chinese version of Composite International Diagnostic Interview 3.1 (CIDI-3.1). RESULTS: The test-retest reliability of the K6 scale was 0.79, the Cronbach's alpha was 0.84, and its area under the receiver operating curve (AUC) for diagnosing CIDI-3.1 SMI was 0.85 (95% CI=0.80-0.90). For the optimal cut-off of K6 (12/13), the sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and classification accuracy (AC) were 0.83, 0.79, 0.60, 0.93, and 0.80, respectively. The 12-month prevalence of SMI was estimated as 3.97% using this optimal cut-off. Binary logistic regression analysis (including gender, ethnicity, grade, number of siblings and family residency location) showed that only family residency location in rural areas compared to urban areas was significantly associated with more SMI. CONCLUSIONS: This study documented the value of using the K6 for detecting SMI in Chinese undergraduate populations and supported its cross-cultural reliability and validity.

[Research on maternal health behaviors for Yi-nationality women in poor rural areas based on the theory of reasoned action].

OBJECTIVE: To analyze the status of maternal health behaviors and it's risk factors for Yi-nationality women in poor rural areas of Sichuan province. METHODS: In 2012, multi-stage stratified cluster sampling method was used to select 14 villages of two poor counties in Liangshan Yi-nationality autonomous prefecture Sichuan province. At least 10 women who have infants aged 0-12 months were selected in each simple villages, a total of 284. The structured questionnaire was developed on the basis of the theory of reasoned action. Yi-nationality female college students were trained as investigators. Research indicators included prenatal care rate, hospital delivery rate, postpartum examination rate, socio-demographic characteristics, maternal health care knowledge. χ² test was used to compare the differences of above indicators among different groups. The structural equation model were used to statistical analyze. RESULTS: In the 284 subject women, 51.7% (147/284) women owned more than 2 children, 41.6% (118/284) women were more than 30 years old, 87.3% (248/284) women were illiteracy. The prenatal care rate was 69.7% (197/284), the hospital delivery rate was 26.8% (76/284), and the postnatal check rate was 22.9% (65/284). The influence factors of maternal health behaviors included the number of children, age and education (χ² were 10.92, 13.24, 9.58; P values were 0.027, 0.004, 0.008, respectively).The structural equation model analysis results showed that the maternal health behaviors were directly or indirectly affected by subjective norms (ß = 0.236, P < 0.001), women's cognition (ß = 0.226, P = 0.020) and women's attitudes on maternal health behavior (ß = 0.157, P = 0.001). Among subjective norms, women have high compliance to their husbands (ß = 0.850, P < 0.001), their peers (ß = 0.708, P < 0.001), and their mothers-in-law (ß = 0.636, P < 0.001). CONCLUSION: There were still serious problems in maternal health behaviors for Yi-nationality women in poor rural areas. The main factors included not only the women's cognition and attitudes for maternal health, but also the attitudes of important social relationships.

Depolymerization and decolorization of kraft lignin by bacterium Comamonas sp. B-9.

There is no commercial or industrial-scale process for the remediation of black liquor using microorganisms to date. One of the most important causes is that most microorganisms are not able to use lignin as their principal metabolic carbon or energy source. The bacterial strain Comamonas sp. B-9 has shown remarkable ability to degrade kraft lignin and decolorize black liquor using lignin as its principal metabolic carbon and energy source. This report looks at the depolymerization and decolorization of kraft lignin by Comamonas sp. B-9. The degradation, decolorization, and total carbon removal reached 45, 54, and 47.3%, respectively, after 7 days treatment. Comamonas sp. B-9 was capable of depolymerizing kraft lignin effectively as analyzed by gel permeation chromatography and decolorization via degrading benzene ring structures as shown using Fourier transform infrared spectroscopy analysis.