Long-Term Protection against Virulent Newcastle Disease Virus (NDV) in Chickens Immunized with a Single Dose of Recombinant Turkey Herpesvirus Expressing NDV F Protein.

Newcastle disease (ND) is a significant infectious disease in poultry, causing substantial economic losses in developing countries. To control ND, chickens must be vaccinated multiple times a year. In order to develop an improved vaccine that provides long-term protection, the F gene from genotype VII NDV was inserted into the herpesvirus of turkey (HVT) vaccine virus using CRISPR/Cas9-mediated NHEJ repair and Cre/LoxP technology. The immunogenicity and protective efficacy of the resulting recombinant vaccines were evaluated through antibody assays and virus challenge experiments. Two recombinant vaccines, rHVT-005/006-F and rHVT-US2-F, were generated, both exhibiting growth rates comparable with those of HVT in vitro and consistently expressing the F protein. One-day-old specific pathogen-free (SPF) chickens immunized with 2000 PFU/bird of either rHVT-005/006-F or rHVT-US2-F developed robust humoral immunity and were completely protected against challenge with the NDV F48E8 strain at 4 weeks post-vaccination (wpv). Furthermore, a single dose of these vaccines provided sustained protection for at least 52 wpv. Our study identifies rHVT-005/006-F and rHVT-US2-F as promising ND vaccine candidates, offering long-term protection with a single administration. Moreover, HVT-005/006 demonstrates promise for accommodating additional foreign genes, facilitating the construction of multiplex vaccines.

Metabolomic profiling of Marek's disease virus infection in host cell based on untargeted LC-MS.

Marek's disease (MD) caused by Marek's disease virus (MDV), poses a serious threat to the poultry industry by inducing neurological disease and malignant lymphoma in infected chickens. However, the underlying mechanisms how MDV disrupts host cells and causes damage still remain elusive. Recently, the application of metabolomics has shown great potential for uncovering the complex mechanisms during virus-host interactions. In this study, chicken embryo fibroblasts (CEFs) infected with MDV were subjected to ultrahigh-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS) and multivariate statistical analysis. The results showed that 261 metabolites were significantly altered upon MDV infection, with most changes occurring in amino acid metabolism, energy metabolism, nucleotide metabolism, and lipid metabolism. Notably, MDV infection induces an up-regulation of amino acids in host cells during the early stages of infection to provide the energy and intermediary metabolites necessary for efficient multiplication of its own replication. Taken together, these data not only hold promise in identifying the biochemical molecules utilized by MDV replication in host cells, but also provides a new insight into understanding MDV-host interactions.

SCAMPER: Accurate Type-Specific Prediction of Calcium-Binding Residues Using Sequence-Derived Features.

Understanding molecular mechanisms involved in calcium-protein interactions and modeling corresponding docking rely on the accurate identification of calcium-binding residues (CaBRs). The defects of experimentally annotating protein functions enhances the development of computational approaches that correctly identify calcium-binding interactions. Studies have reported that current methods severely cross-predict residues that interact with other types of molecules (e.g., nucleic acids, proteins, and small ligands) as CaBRs. In this study, a novel predictor named SCAMPER (Selective CAlciuM-binding PrEdictoR) is proposed for the accurate and specific prediction of CaBRs. SCAMPER is designed using newly compiled dataset with complete UniProt sequences and annotations, which include calcium-binding, nucleic acid-binding, protein-binding, and small ligand-binding residues. We use a novel designed two-layer scheme to perform predictions as well as penalize cross-predictions. Empirical tests on an independent test dataset reveals that the proposed method significantly outperforms state-of-the-art predictors. SCAMPER is proved to be capable of distinguishing CaBRs from different types of metal-ion binding residues. We further perform CaBRs predictions on the whole human proteome, and use the results to hypothesize calcium-binding proteins (CaBPs). The latest experimental verified CaBPs and GO analysis prove the accuracy of our predictions. We implement the proposed method and share the data at http://www.inforstation.com/webservers/SCAMPER/.

Identification of Cancer Biomarkers in Human Body Fluids by Using Enhanced Physicochemical-incorporated Evolutionary Conservation Scheme.

OBJECTIVE: Cancer is one of the most serious diseases affecting human health. Among all current cancer treatments, early diagnosis and control significantly help increase the chances of cure. Detecting cancer biomarkers in body fluids now is attracting more attention within oncologists. In-silico predictions of body fluid-related proteins, which can be served as cancer biomarkers, open a door for labor-intensive and time-consuming biochemical experiments. METHODS: In this work, we propose a novel method for high-throughput identification of cancer biomarkers in human body fluids. We incorporate physicochemical properties into the weighted observed percentages (WOP) and position-specific scoring matrices (PSSM) profiles to enhance their attributes that reflect the evolutionary conservation of the body fluid-related proteins. The least absolute selection and shrinkage operator (LASSO) feature selection strategy is introduced to generate the optimal feature subset. RESULTS: The ten-fold cross-validation results on training datasets demonstrate the accuracy of the proposed model. We also test our proposed method on independent testing datasets and apply it to the identification of potential cancer biomarkers in human body fluids. CONCLUSION: The testing results promise a good generalization capability of our approach.

Solid-Contact Ion-Selective Electrodes: Response Mechanisms, Transducer Materials and Wearable Sensors.

Wearable sensors based on solid-contact ion-selective electrodes (SC-ISEs) are currently attracting intensive attention in monitoring human health conditions through real-time and non-invasive analysis of ions in biological fluids. SC-ISEs have gone through a revolution with improvements in potential stability and reproducibility. The introduction of new transducing materials, the understanding of theoretical potentiometric responses, and wearable applications greatly facilitate SC-ISEs. We review recent advances in SC-ISEs including the response mechanism (redox capacitance and electric-double-layer capacitance mechanisms) and crucial solid transducer materials (conducting polymers, carbon and other nanomaterials) and applications in wearable sensors. At the end of the review we illustrate the existing challenges and prospects for future SC-ISEs. We expect this review to provide readers with a general picture of SC-ISEs and appeal to further establishing protocols for evaluating SC-ISEs and accelerating commercial wearable sensors for clinical diagnosis and family practice.

A portable fast neutron irradiation system for tumor therapy.

A portable neutron tube was introduced as a small-sized (weight≤14.4 kg, power consumption ≤50W and cost≤ $100,000) neutron accelerator and applied for irradiation therapy on cancer. The effect of growth-inhibiting in vitro by neutrons irradiation on HeLa cells (human cervical cancer cells) was evaluated by colony formation assays, and cell apoptosis was evaluated by Flow Cytometry. A polyethylene protection device as the neutron moderator was designed and connected to the neutron tube to shield normal tissue and organs of the test animals from scatter radiation. Hematology and blood biochemistry were investigated to evaluate the protective effect of polyethylene. U14 (mice cervical cancer cell) tumor-bearing mice were further investigated to determine the tumor suppression effect of neutron irradiation. We found that cells showed a dose-dependent relationship after fast neutrons irradiation at different dose (1.11 Gy, 2.23 Gy, 3.34 Gy and 4.45Gy). Furthermore, in vivo experiments showed that the anti-tumor effect on U14 tumor-bearing mice greatly depended on the neutron irradiation dose. A high dose of fast neutron irradiation (26.73 Gy) could have tumor growth rate only 12.31% compared to 56.07% with control group. All the blood cell counts and blood biochemistry parameters were in the standard value ranges. Immunohistochemistry examinations clearly indicated the apoptosis cells in tumor tissues by the TUNEL assay. This work provides useful evidences on cancer irradiation therapy using fast neutron in pre-clinical study. And the neutron therapy system device has great potential to be a more convenient tool in clinical application with significantly lower power consumption, irradiation toxicity and cost.

HEMEsPred: Structure-Based Ligand-Specific Heme Binding Residues Prediction by Using Fast-Adaptive Ensemble Learning Scheme.

Heme is an essential biomolecule that widely exists in numerous extant organisms. Accurately identifying heme binding residues (HEMEs) is of great importance in disease progression and drug development. In this study, a novel predictor named HEMEsPred was proposed for predicting HEMEs. First, several sequence- and structure-based features, including amino acid composition, motifs, surface preferences, and secondary structure, were collected to construct feature matrices. Second, a novel fast-adaptive ensemble learning scheme was designed to overcome the serious class-imbalance problem as well as to enhance the prediction performance. Third, we further developed ligand-specific models considering that different heme ligands varied significantly in their roles, sizes, and distributions. Statistical test proved the effectiveness of ligand-specific models. Experimental results on benchmark datasets demonstrated good robustness of our proposed method. Furthermore, our method also showed good generalization capability and outperformed many state-of-art predictors on two independent testing datasets. HEMEsPred web server was available at http://www.inforstation.com/HEMEsPred/ for free academic use.

Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme.

BACKGROUND: Bioluminescent proteins (BLPs) widely exist in many living organisms. As BLPs are featured by the capability of emitting lights, they can be served as biomarkers and easily detected in biomedical research, such as gene expression analysis and signal transduction pathways. Therefore, accurate identification of BLPs is important for disease diagnosis and biomedical engineering. In this paper, we propose a novel accurate sequence-based method named PredBLP (Prediction of BioLuminescent Proteins) to predict BLPs. RESULTS: We collect a series of sequence-derived features, which have been proved to be involved in the structure and function of BLPs. These features include amino acid composition, dipeptide composition, sequence motifs and physicochemical properties. We further prove that the combination of four types of features outperforms any other combinations or individual features. To remove potential irrelevant or redundant features, we also introduce Fisher Markov Selector together with Sequential Backward Selection strategy to select the optimal feature subsets. Additionally, we design a lineage-specific scheme, which is proved to be more effective than traditional universal approaches. CONCLUSION: Experiment on benchmark datasets proves the robustness of PredBLP. We demonstrate that lineage-specific models significantly outperform universal ones. We also test the generalization capability of PredBLP based on independent testing datasets as well as newly deposited BLPs in UniProt. PredBLP is proved to be able to exceed many state-of-art methods. A web server named PredBLP, which implements the proposed method, is free available for academic use.

TOX and ADIPOQ Gene Polymorphisms Are Associated with Antipsychotic-Induced Weight Gain in Han Chinese.

To find the genetic markers related to the antipsychotic-induced weight gain (AIWG), we analyzed associations among candidate gene single-nucleotide polymorphisms (SNPs) and quantitative traits of weight changes and lipid profiles in a Chinese Han population. A total of 339 schizophrenic patients, including 86 first-episode patients (FEPs), meeting the entry criteria were collected. All patients received atypical antipsychotic drug monotherapy and hospitalization and were followed for 12 weeks. Forty-three SNPs in 23 candidate genes were calculated for quantitative genetic association with AIWG, performed by PLINK. The TOX gene SNP rs11777927 (P = 0.009) and the ADIPOQ gene SNP rs182052 (P = 0.019) were associated with AIWG (in body mass index, BMI). In addition, the BDNF SNP rs6265 (P = 0.002), BDAF SNP rs11030104 SNP (P = 0.001), and ADIPOQ SNPs rs822396 (P = 0.003) were significantly associated with the change of waist-to-hip ratio (WHR) induced by atypical antipsychotics. These results were still significant after age and gender adjustments. These findings provide preliminary evidence supporting the role of TOX, ADIPOQ and BDNF in weight and WHR gain induced by atypical antipsychotics.

Identification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm.

BACKGROUND: DNA-binding proteins (DBPs) play fundamental roles in many biological processes. Therefore, the developing of effective computational tools for identifying DBPs is becoming highly desirable. RESULTS: In this study, we proposed an accurate method for the prediction of DBPs. Firstly, we focused on the challenge of improving DBP prediction accuracy with information solely from the sequence. Secondly, we used multiple informative features to encode the protein. These features included evolutionary conservation profile, secondary structure motifs, and physicochemical properties. Thirdly, we introduced a novel improved Binary Firefly Algorithm (BFA) to remove redundant or noisy features as well as select optimal parameters for the classifier. The experimental results of our predictor on two benchmark datasets outperformed many state-of-the-art predictors, which revealed the effectiveness of our method. The promising prediction performance on a new-compiled independent testing dataset from PDB and a large-scale dataset from UniProt proved the good generalization ability of our method. In addition, the BFA forged in this research would be of great potential in practical applications in optimization fields, especially in feature selection problems. CONCLUSIONS: A highly accurate method was proposed for the identification of DBPs. A user-friendly web-server named iDbP (identification of DNA-binding Proteins) was constructed and provided for academic use.

Identification of S-glutathionylation sites in species-specific proteins by incorporating five sequence-derived features into the general pseudo-amino acid composition.

As a selective and reversible protein post-translational modification, S-glutathionylation generates mixed disulfides between glutathione (GSH) and cysteine residues, and plays an important role in regulating protein activity, stability, and redox regulation. To fully understand S-glutathionylation mechanisms, identification of substrates and specific S-Glutathionylated sites is crucial. Experimental identification of S-glutathionylated sites is labor-intensive and time consuming, so establishing an effective computational method is much desirable due to their convenient and fast speed. Therefore, in this study, a new bioinformatics tool named SSGlu (Species-Specific identification of Protein S-glutathionylation Sites) was developed to identify species-specific protein S-glutathionylated sites, utilizing support vector machines that combine multiple sequence-derived features with a two-step feature selection. By 5-fold cross validation, the performance of SSGlu was measured with an AUC of 0.8105 and 0.8041 for Homo sapiens and Mus musculus, respectively. Additionally, SSGlu was compared with the existing methods, and the higher MCC and AUC of SSGlu demonstrated that SSGlu was very promising to predict S-glutathionylated sites. Furthermore, a site-specific analysis showed that S-glutathionylation intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S-glutathionylation mechanism and guide the related experimental validation. For public access, SSGlu is freely accessible at http://59.73.198.144:8080/SSGlu/.

A novel conformational B-cell epitope prediction method based on mimotope and patch analysis.

A B-cell epitope is a group of residues on the surface of an antigen that stimulates humoral immune responses. Identifying B-cell epitopes is important for effective vaccine design. Predicting epitopes by experimental methods is expensive in terms of time, cost and effort; therefore, computational methods that have a low cost and high speed are widely used to predict B-cell epitopes. Recently, epitope prediction based on random peptide library screening has been viewed as a promising method. Some novel software and web-based servers have been proposed that have succeeded in some test cases. Herein, we propose a novel epitope prediction method based on amino acid pairs and patch analysis. The method first divides antigen surfaces into overlapping patches based on both radius (R) and number (N), then predict epitopes based on Amino Acid Pairs (AAPs) from mimotopes and the surface patch. The proposed method yields a mean sensitivity of 0.53, specificity of 0.77, ACC of 0.75 and F-measure of 0.45 for 39 test cases. Compared with mimotope-based methods, patch-based methods and two other prediction methods, the sensitivity of the new method offers a certain improvement. Our findings demonstrate that this proposed method was successful for patch and AAPs analysis and allowed for conformational B-cell epitope prediction.

In vitro dynamic degradation behavior of new magnesium alloy for orthopedic applications.

We report on methodologies for use in the design of a biodegradable Mg alloy appropriate for load-bearing but temporary orthopedic implant applications. Comparative studies of Mg-5Ca and Mg-5Ca-1Zn were conducted to explore the effects of a combination of minor alloying and hot extrusion, on the alloy's mechanical properties and corrosion resistance. The extruded Mg-5Ca-1Zn exhibited high ultimate compressive strength of 385 MPa and suffered no significant structural degradation even after immersion in simulated body fluid for 30 days. Mg-5Ca-1Zn alloy showed the mechanical strength and controlled corrosion rate to be considered as an ideal candidate for biodegradable orthopedic implant material.

Biodegradability engineering of biodegradable Mg alloys: tailoring the electrochemical properties and microstructure of constituent phases.

Crystalline Mg-based alloys with a distinct reduction in hydrogen evolution were prepared through both electrochemical and microstructural engineering of the constituent phases. The addition of Zn to Mg-Ca alloy modified the corrosion potentials of two constituent phases (Mg + Mg2Ca), which prevented the formation of a galvanic circuit and achieved a comparable corrosion rate to high purity Mg. Furthermore, effective grain refinement induced by the extrusion allowed the achievement of much lower corrosion rate than high purity Mg. Animal studies confirmed the large reduction in hydrogen evolution and revealed good tissue compatibility with increased bone deposition around the newly developed Mg alloy implants. Thus, high strength Mg-Ca-Zn alloys with medically acceptable corrosion rate were developed and showed great potential for use in a new generation of biodegradable implants.

Abnormality of glycometabolism related factors in non-psychotic offspring of schizophrenic patients.

Schizophrenic patients appear to have higher rates of abnormality of glycometabolism. It has been suggested that these could be secondary to the use of antipsychotics. However, findings indicate that abnormal glucose metabolism may be potentially involved in the pathogenesis of schizophrenia. The present study examined the fasting plasma levels of glycometabolism related factors in healthy offspring of schizophrenic patients. Our objective was to test the hypothesis that abnormal glucose metabolism might be potentially involved in the pathogenesis of schizophrenia. Thirty-two healthy offspring of schizophrenic inpatients who met DSM-IV criteria for schizophrenia and 37 comparison subjects were recruited. Fasting plasma levels of glucose, insulin, insulin-like growth factor 1 (IGF-1), growth hormone (GH), leptin and cortisol were tested for all subjects. Compared with controls, offspring of schizophrenic patients had significantly higher mean plasma insulin and insulin resistance, and lower mean plasma IGF-1 level. Meanwhile, the mean fasting plasma levels of glucose, GH, leptin and cortisol did not differ significantly between offspring of schizophrenic patients and healthy comparison subjects. Offspring of schizophrenic patients showed significant differences from comparison subjects in glycometabolism related factors. Abnormal glucose metabolism might be potentially involved in the pathogenesis of schizophrenia.

Hexaaqua-zinc(II) bis-(2,4,5-tricarboxybenzoate) 4,5-diaza-fluoren-9-one disolvate dihydrate.

The asymmetric unit of the title complex, [Zn(H(2)O)(6)](C(10)H(5)O(8))(2)·2C(11)H(6)N(2)O·2H(2)O, contains one half of the complex cation with the Zn(II) ion located on an inversion center, a monovalent 2,4,5-tricarboxybenzoate (1,2,4,5-BTC) counter-anion, a 4,5-diaza-fluoren-9-one (DAFO) mol-ecule and an uncoordinated water mol-ecule. In the crystal structure, O-H⋯O and O-H⋯N hydrogen bonds link the cations, anions and water mol-ecules into a three-dimensional network.

Ion-responsive behavior of ionic-liquid surfactant aggregates with applications in controlled release and emulsification.

We propose a simple but efficient, rapid, and quantitative ion-responsive micelle system based on counter-anion exchange of a surfactant with an imidazolium unit. The ion-exchange reaction results in the amphiphilic-to-hydrophobic transition of the imidazolium salt, leading to the destruction of the micelles, which has been successfully applied to controlled release and emulsification. The proposed design offers a novel alternative stimulus to control these smart physical aggregates besides pH, temperature and light-with extra advantages. Our finding greatly benefits both fundamental research and industry.

Size-controlled synthesis of monodispersed gold nanoparticles stabilized by polyelectrolyte-functionalized ionic liquid.

The size-controlled synthesis of monodispersed gold nanoparticles (AuNPs) stabilized by polyelectrolyte-functionalized ionic liquid (PFIL) is described. The resulting AuNPs' size, with a narrow distribution, can be tuned by the concentration of HAuCl(4). Such PFIL-stabilized AuNPs (PFIL-AuNPs) showed a high stability in water at room temperature for at least one month; they were also quite stable in solutions of pH 7-13 and high concentration of NaCl. In addition, the PFIL-AuNPs exhibited obvious electrocatalytical activity toward ß-nicotinamide adenine dinucleotide (NADH for short, a cofactor in enzymatic reactions of NAD(+)/NADH(-)-dependent dehydrogenases) oxidation, suggesting a potential application for bioelectroanalysis.

Enriched environment treatment counteracts enhanced addictive and depressive-like behavior induced by prenatal chronic stress.

Prenatal stress can cause many long-term behavior changes in offspring, but whether prenatal stress can alter addictive behavior in offspring and postnatal enriched environment treatment (EE) can restore these changes are unknown. We reported here that prenatal chronic stress (10 unpredictable, 1 s, 0.8 mA foot-shocks per day during gestational days 13-19) enhanced morphine-induced (10 mg/kg, s.c., per day, 6 consecutive days) place preference. Moreover, prenatal chronic stress caused higher depressive-like behavior in forced swimming test in adult offspring. However, enriched environment housing treatment on postnatal days 22-52 counteracted both the abnormal behaviors alterations. This work observed a phenomenon that might contribute to the understanding of clinically important interactions among addiction, prenatal stress and enriched environment treatment. Postnatal enriched environment treatment might be an important therapeutic intervention in preventing the prenatal stress-induced addictive disorders.