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.

[Q]: Human rhinovirus (HRV) remains a leading cause of several human diseases including the common cold. Despite considerable research over the last 60 years, development of an effective vaccine to HRV has been viewed by many as unfeasible due, in part, to the antigenic diversity of circulating HRVs in nature. Over 150 antigenically distinct types of HRV are currently known which span three species: HRV A, HRV B, and HRV C. Early attempts to develop a rhinovirus vaccine have shown that inactivated HRV is capable of serving as a strong immunogen and inducing neutralizing antibodies. Yet, limitations to virus preparation and recovery, continued identification of antigenic variants of HRV, and logistical challenges pertaining to preparing a polyvalent preparation of the magnitude required for true efficacy against circulating rhinoviruses continue to prove a daunting challenge. In this review, we describe HRV biology, antigenic diversity, and past and present advances in HRV vaccine design.
[A]: MINI REVIEW Rhinovirus Biology, Antigenic Diversity, and Advancements in the Design of a Human Rhinovirus Vaccine


[Q]: 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.
[A]: AMS 3.0: prediction of post-translational modifications


[Q]: Prohibitins (PHBs) are scaffold proteins that modulate many signaling pathways controlling cell survival, metabolism, and inflammation. Several drugs that target PHBs have been identified and evaluated for various clinical applications. Preclinical and clinical studies indicate that these PHB ligands may be useful in oncology, cardiology, and neurology, as well as against obesity. This review covers the physiological role of PHBs in health and diseases and current developments concerning PHB ligands.
[A]:
Chemistry & Biology Review Prohibitin Ligands in Cell Death and Survival: Mode of Action and Therapeutic Potential