In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Ex Input:
Ubiquitination, a post-translational modification, mediates diverse cellular functions including endocytic transport of molecules. Kaposi's sarcoma-associated herpesvirus (KSHV), an enveloped herpesvirus, enters endothelial cells primarily through clathrin-mediated endocytosis. Whether ubiquitination and proteasome activity regulates KSHV entry and endocytosis remains unknown. We showed that inhibition of proteasome activity reduced KSHV entry into endothelial cells and intracellular trafficking to nuclei, thus preventing KSHV infection of the cells. Three-dimensional (3-D) analyses revealed accumulation of KSHV particles in a cytoplasmic compartment identified as EEA1+ endosomal vesicles upon proteasome inhibition. KSHV particles are colocalized with ubiquitin-binding proteins epsin and eps15. Furthermore, ubiquitination mediates internalization of both KSHV and one of its receptors integrin b1. KSHV particles are colocalized with activated forms of the E3 ligase c-Cbl. Knock-down of c-Cbl or inhibition of its phosphorylation reduced viral entry and intracellular trafficking, resulting in decreased KSHV infectivity. These results demonstrate that ubiquitination mediates internalization of both KSHV and one of its cognate receptors integrin b1, and identify c-Cbl as a potential E3 ligase that facilitates this process.

Ex Output:
Associated Herpesvirus in Endothelial Cells


Ex Input:
Background: We present here the recent update of AMS algorithm for identification of post-translational modification (PTM) sites in proteins based only on sequence information, using artificial neural network (ANN) method. The query protein sequence is dissected into overlapping short sequence segments. Ten different physicochemical features describe each amino acid; therefore nine residues long segment is represented as a point in a 90 dimensional space. The database of sequence segments with confirmed by experiments post-translational modification sites are used for training a set of ANNs. The efficiency of the classification for each type of modification and the prediction power of the method is estimated here using recall (sensitivity), precision values, the area under receiver operating characteristic (ROC) curves and leave-one-out tests (LOOCV). The significant differences in the performance for differently optimized neural networks are observed, yet the AMS 3.0 tool integrates those heterogeneous classification schemes into the single consensus scheme, and it is able to boost the precision and recall values independent of a PTM type in comparison with the currently available state-of-the art methods. The standalone version of AMS 3.0 presents an efficient way to indentify post-translational modifications for whole proteomes. The training datasets, precompiled binaries for AMS 3.0 tool and the source code are available at Additional file 1 Supplementary materials for the publication. Supplementary.xls contains detailed results of training and testing of AMS 3.0 tool in MS Excel file format. Additional file 2 PTM predictor software with training data. PTM_Archive.tar.gz includes the predictor software binaries together with all trained ANNs in tar.gz compressed form.

Ex Output:
AMS 3.0: prediction of post-translational modifications


Ex Input:
In commercial poultry production there are a number of diseases which are of particular importance due to the heavy economic losses that can arise if a flock becomes infected. The development of an automated and rapid disease detection system would therefore be of considerable benefit to both production and animal welfare. This study represents an intelligence device for diagnosing avian diseases by using Data-mining methods and Dempster-Shafer evidence theory (D-S). 14-day-old chickens were divided into four groups. Each group was deliberately infected with a disease: Newcastle Disease (ND), Bronchitis Virus (BV), Avian Influenenza (AI), and the last group was considered as control samples. Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) were used to process the chicken's sound signals in frequency and time-frequency domains, respectively. In order to achieve information, 25 statistical features from frequency domains, and 75 statistical features from time-frequency domains were extracted. During dimensionality reduction stage, the best features of the sound signals were selected, using improved distance evaluation (IDE) method. The chicken's sound signals were analyzed in two consecutive days after virus infection. Support vector machine (SVM) was used as the classifier in this study. The first classification was done with SVM and based on sound features in frequency and time-frequency domains with accuracy of 41.35 and 83.33%, respectively. The accuracy of the method based on D-S infusion of sound data reached 91.15%. The developed model based on achievement result could diagnose Newcastle Disease, Bronchitis Virus and Avian Influenza from sound signals.

Ex Output:

Original papers