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.

Input: Consider Input: Npl4 is likely to be the most upstream factor recognizing Lys48-linked polyubiquitylated substrates in the proteasomal degradation pathway in yeast. Along with Ufd1, Npl4 forms a heterodimer (UN), and functions as a cofactor for the Cdc48 ATPase. Here, we report the crystal structures of yeast Npl4 in complex with Lys48-linked diubiquitin and with the Npl4binding motif of Ufd1. The distal and proximal ubiquitin moieties of Lys48-linked diubiquitin primarily interact with the C-terminal helix and N-terminal loop of the Npl4 C-terminal domain (CTD), respectively. Mutational analysis suggests that the CTD contributes to linkage selectivity and initial binding of ubiquitin chains. Ufd1 occupies a hydrophobic groove of the Mpr1/Pad1 N-terminal (MPN) domain of Npl4, which corresponds to the catalytic groove of the MPN domain of JAB1/MPN/Mov34 metalloenzyme (JAMM)-family deubiquitylating enzyme. This study provides important structural insights into the polyubiquitin chain recognition by the Cdc48-UN complex and its assembly.

Output: Structural insights into ubiquitin recognition and Ufd1 interaction of Npl4


Input: Consider Input: Molecular technology has given us a greater insight into the aetiology of disease, the functioning of the immune system and the mode of action of veterinary pathogens. The knowledge gained has been used to develop new vaccines with specific, reactive antigens which elicit protective immune mediated responses (humoral and/or cell mediated) in the host. These vaccines should not burden the immune system by initiating responses against non-essential antigens. However, the efficacy of these vaccines is only as good as the delivery technology or route used to present them to the immune system. Some vaccines, traditionally given by the parenteral route, are now given by the natural route; either orally or intranasally. Two major advantages, often interrelated, are the rapid onset of immunity and stimulation of the local, mucosal immunity. These new technologies are now making an impact on current vaccine development. The balance has to be found between what is technologically feasible and what will provide at least as good a protective immunity as current, conventional vaccines. As new and emerging diseases appear globally, new opportunities arise for molecular and conventional technologies to be applied to both the development and delivery of novel vaccines, as well as the improvement of vaccines in current use. #

Output: Overview of new vaccines and technologies


Input: Consider Input: The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a viral respiratory disease that is spreading worldwide necessitating to have an accurate diagnosis system that accurately predicts infections. As data mining classifiers can greatly assist in enhancing the prediction accuracy of diseases in general. In this paper, classifier model performance for two classification types: (1) binary and (2) multi-class were tested on a MERS-CoV dataset that consists of all reported cases in Saudi Arabia between 2013 and 2017. A cross-validation model was applied to measure the accuracy of the Support Vector Machine (SVM), Decision Tree, and k-Nearest Neighbor (k-NN) classifiers. Experimental results demonstrate that SVM and Decision Tree classifiers achieved the highest accuracy of 86.44% for binary classification based on healthcare personnel class. On the other hand, for multiclass classification based on city class, the decision tree classifier had the highest accuracy among the remaining classifiers; although it did not reach a satisfactory accuracy level (42.80%). This work is intended to be a part of a MERS-CoV prediction system to enhance the diagnosis of MERS-CoV disease.


Output: Selecting Accurate Classifier Models for a MERS-CoV Dataset