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 Q]: lord mayor and aldermen of the city of London put into place a set of orders "concerning the infection of the plague," which was then sweeping through the population. He intended that these actions would be "very expedient for preventing and avoiding of infection of sickness" (1). At that time, London faced a public health crisis, with an inadequate scientific base in that the role of rats and their fleas in disease transmission was unknown. Nonetheless, this crisis was faced with good intentions by the top medical and political figures of the community. Daniel Defoe made an observation that could apply to many public health interventions then and today, "This shutting up of houses was at first counted a very cruel and unchristian method but it was a public good that justified a private mischief" (1). Then, just as today, a complex relationship existed between the science of public health and the practice of public health and politics. We address the relationship between science, public health, and politics, with a particular emphasis on infectious diseases. Science, public health, and politics are not only compatible, but all three are necessary to improve the public's health. The progress of each area of public health is related to the strength of the other areas. The effect of politics in public health becomes dangerous when policy is dictated by ideology. Policy is also threatened when it is solely determined by science, devoid of considerations of social condition, culture, economics, and public will. When using the word "politics," we refer not simply to partisan politics but to the broader set of policies and sys-tems. Although ideology is used in many different ways, in this case, it refers to individual systems of belief that may color a person's attitudes and actions and that are not necessarily based on scientific evidence (2).
[EX A]: Plagues, Public Health, and Politics 1

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

[EX Q]: Serum or plasma proteases have been associated with various diseases including cancer, inflammation, or reno-cardiovascular diseases. We aimed to investigate whether the enzymatic activities of serum proteases are associated with the estimated glomerular filtration rate (eGFR) in patients with different stages of chronic kidney disease (CKD). Our study population comprised 268 participants of the ''Greifswald Approach to Individualized Medicine'' (GANI_MED) cohort. Enzymatic activity of aminopeptidase A, aminopeptidase B, alanyl (membrane) aminopeptidase, insulin-regulated aminopeptidase, puromycin-sensitive aminopeptidase, leucine aminopeptidase 3, prolyl-endopeptidase (PEP), dipeptidyl peptidase 4 (DPP4), angiotensin Iconverting enzyme, and angiotensin I-converting enzyme 2 (ACE2) proteases was measured in serum. Linear regression of the respective protease was performed on kidney function adjusted for age and sex. Kidney function was modeled either by the continuous Modification of Diet in Renal Disease (MDRD)-based eGFR or dichotomized by eGFR < 15 mL/min/1.73 m 2 or <45 mL/min/1.73 m 2 , respectively. Results with a false discovery rate below 0.05 were deemed statistically significant. Among the 10 proteases investigated, only the activities of ACE2 and DPP4 were correlated with eGFR. Patients with lowest eGFR exhibited highest DPP4 and ACE2 activities. DPP4 and PEP were correlated with age, but all other serum protease activities showed no associations with age or sex. Our data indicate that ACE2 and DPP4 enzymatic activity are associated with the eGFR in patients with CKD. This finding distinguishes ACE2 and DPP4 from other serum peptidases analyzed and clearly indicates that further analyses are warranted to identify the precise role of these serum ectopeptidases in the pathogenesis of CKD and to fully elucidate underlying molecular mechanisms.
[EX A]:
Original Research Serum protease activity in chronic kidney disease patients: The GANI_MED renal cohort Impact statement