The Protective Antibodies Induced by a Novel Epitope of Human TNF-α Could Suppress the Development of Collagen-Induced Arthritis

Tumor necrosis factor alpha (TNF-α) is a major inflammatory mediator that exhibits actions leading to tissue destruction and hampering recovery from damage. At present, two antibodies against human TNF-α (hTNF-α) are available, which are widely used for the clinic treatment of certain inflammatory diseases. This work was undertaken to identify a novel functional epitope of hTNF-α. We performed screening peptide library against anti-hTNF-α antibodies, ELISA and competitive ELISA to obtain the epitope of hTNF-α. The key residues of the epitope were identified by means of combinatorial alanine scanning and site-specific mutagenesis. The N terminus (80–91 aa) of hTNF-α proved to be a novel epitope (YG1). The two amino acids of YG1, proline and valine, were identified as the key residues, which were important for hTNF-α biological function. Furthermore, the function of the epitope was addressed on an animal model of collagen-induced arthritis (CIA). CIA could be suppressed in an animal model by prevaccination with the derivative peptides of YG1. The antibodies of YG1 could also inhibit the cytotoxicity of hTNF-α. These results demonstrate that YG1 is a novel epitope associated with the biological function of hTNF-α and the antibodies against YG1 can inhibit the development of CIA in animal model, so it would be a potential target of new therapeutic antibodies

Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data

The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost algorithm directly to imbalanced data since it is designed mainly for processing misclassified samples rather than samples of minority classes. To better process imbalanced data, this paper introduces the indicator Area Under Curve (AUC) which can reflect the comprehensive performance of the model, and proposes an improved AdaBoost algorithm based on AUC (AdaBoost-A) which improves the error calculation performance of the AdaBoost algorithm by comprehensively considering the effects of misclassification probability and AUC. To prevent redundant or useless weak classifiers the traditional AdaBoost algorithm generated from consuming too much system resources, this paper proposes an ensemble algorithm, PSOPD-AdaBoost-A, which can re-initialize parameters to avoid falling into local optimum, and optimize the coefficients of AdaBoost weak classifiers. Experiment results show that the proposed algorithm is effective for processing imbalanced data, especially the data with relatively high imbalances

Effect of H2 on Blast Furnace Ironmaking: A Review

Under the background of “carbon peaking” and “carbon neutralization”, the green transformation of iron and steel enterprises is imminent. The hydrogen-rich smelting technology of blast furnaces is very important for reducing energy consumption and CO2 emission in ironmaking systems, and it is one of the important directions of green and low-carbon development of iron and steel enterprises. In this paper, the research status of the thermal state, reduction mechanism of iron-bearing burden, coke degradation behavior, and formation of the cohesive zone in various areas of blast furnace after hydrogen-rich smelting is summarized, which can make a more clear and comprehensive understanding for the effect of H2 on blast furnace ironmaking. Meanwhile, based on the current research situation, it is proposed that the following aspects should be further studied in the hydrogen-rich smelting of blast furnaces: (1) the utilization rate of hydrogen and degree of substitution for direct reduction, (2) combustion behavior of fuel in raceway, (3) control of gas flow distribution in the blast furnace, (4) operation optimization of the blast furnace

Identification of the mimotope and epitope of hTNF-α.

<p><b>A:</b> Specific phage clones binding to rhTNF-α antibodies were selected by ELISA and competitive ELISA. 1–12 referred to different phage clones. White bars represent negative control, black bars represent screened phage clones (1×10⁹) alone, and gray bars represent screened phage clones (1×10⁹) plus rhTNF-α protein (5 µg). <b>B:</b> Sequence similarity between binding peptides and rhTNF-α. 31, 33, 34, 38, 39, 310, 312 and 314 represent different binding peptides. All the selected binding peptides were homologous to the region 80–91aa of rhTNF-α. <b>C:</b> Development of anti-rhTNF-α antibodies using KLH-312.The interaction between rhTNF-α and sera was tested by ELISA. Results are expressed as OD at 450 nm. Group1: the control group sera immunized with KLH diluted as 1∶500; group2–7: sera immunized with KLH-312 were diluted from 1∶500–1∶16000. The experiment was performed twice. <b>D:</b> The interaction between rhTNF-α and sera was tested by Western Blot. The rhTNF-α protein was applied to SDS-PAGE and transferred to the membrane. And the antisera of peptides were added, the antisera of hTNF-α as the positive control. And the antisera of KLH were taken as the negative control. The antisera are diluted as 1∶500. Lane1: antisera against rhTNF-α; Lane2: antisera against KLH-312; Lane3: antisera against KLH. <b>E:</b> Development of anti rhTNF-α antibodies using KLH-YG1, The interaction between rhTNF-α and sera was tested by ELISA. Results are expressed as OD value at 450 nm. Group1: control group sera immunized with KLH diluted as 1∶500; Group2–7: sera immunized with KLH-YG1 were diluted from 1∶500–1∶16000. The experiment was performed twice. <b>F:</b> The interaction between rhTNF-α and sera was tested by Western Blot. The method was described as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0008920#pone-0008920-g001" target="_blank">figure1D</a>. The antisera are diluted as 1∶500. Lane1: antisera against rhTNF-α; Lane2: antisera against KLH-YG1; Lane3: antisera against KLH.</p

The shotgun scanning code.

<p>For each amino acid, the appropriate shotgun codon ideally encodes only the wild-type amino acid or alanine, but the nature of the genetic code necessitates the occurrence of 2 other amino acids for some shotgun substitutions. Single-letter amino acid and nucleotide abbreviations are used.</p>*<p>DNA degeneracies are represented by the IUB code (K = G/T, M = A/C, N = A/C/G/T, R = A/G, S = G/C, W = A/T, Y = C/T).</p

anti-YG1 Ab could inhibit the cytotoxicity of rhTNF-α.

<p>Various concentrations of antibodies against to YG1 and control antibodies were added to inhibit the cytotoxicity of rhTNF-α. The cytotoxicity was measured by detecting the amounts of cells by MTT. Values represent the mean ± SD for 3 independent tests. * represent P<0.001(anti-YG compared with normal Ab).</p