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.

The basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period, in a naive, uninfected population. It is well-known that the estimation of this number may vary due to several methodological issues, including different assumptions and choice of parameters, utilized models, used datasets and estimation period. With the spreading of the novel coronavirus (2019-nCoV) infection, the reproduction number has been found to vary, reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate. Due to significant variations in the control strategies, which have been changing over time, and thanks to the introduction of detection technologies that have been rapidly improved, enabling to shorten the time from infection/symptoms onset to diagnosis, leading to faster confirmation of the new coronavirus cases, our previous estimations on the transmission risk of the 2019-nCoV need to be revised. By using time-dependent contact and diagnose rates, we refit our previously proposed dynamics transmission model to the data available until January 29th , 2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions. We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak. Our updated findings suggest that the best measure is persistent and strict self-isolation. The epidemics will continue to grow, and can peak soon with the peak time depending highly on the public health interventions practically implemented.
An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

on behalf of the Microbes, Allergy, and Asthma Committee Nashville, Tenn; Chicago, Ill; Charlottesville, Va; Manchester, United Kingdom; and Athens, Greece Children who begin wheezing during early childhood are frequently seen by health care providers in primary care, in hospitals, and in emergency departments, and by allergists and pulmonologists. When a young child, such as the 2 yearold patient presented here, is evaluated for wheezing, a frequent challenge for clinicians is to determine whether the symptoms represent transient, viral-induced wheezing or whether sufficient risk factors are present to suspect that the child may experience recurrent wheezing and develop asthma. Most factors that influence prognosis are not mutually exclusive, are interrelated (ie, cofactors), and often represent gene-environment interactions. Many of these risk factors have been, and continue to be, investigated in prospective studies to decipher their relative importance with the goal of developing new therapies and interventions in the future. The etiologies of wheezing in young children, diagnostic methods, treatment, prognostic factors, and potential targets for prevention of the development of asthma are discussed. O
Wheezing Exacerbations in Early Childhood: Evaluation, Treatment, and Recent Advances Relevant to the Genesis of Asthma

The development of the complex network graphs permits us to describe any real system such as social, neural, computer or genetic networks by transforming real properties in topological indices (TIs). This work uses Randic's star networks in order to convert the protein primary structure data in specific topological indices that are used to construct a natural/random protein classification model. The set of natural proteins contains 1046 protein chains selected from the pre-compiled CulledPDB list from PISCES Dunbrack's Web Lab. This set is characterized by a protein homology of 20%, a structure resolution of 1.6 A and R-factor lower than 25%. The set of random amino acid chains contains 1046 sequences which were generated by Python script according to the same type of residues and average chain length found in the natural set. A new Sequence to Star Networks (S2SNet) wxPython GUI application (with a Graphviz graphics back-end) was designed by our group in order to transform any character sequence in the following star network topological indices: Shannon entropy of Markov matrices, trace of connectivity matrices, Harary number, Wiener index, Gutman index, Schultz index, Moreau-Broto indices, Balaban distance connectivity index, Kier-Hall connectivity indices and Randic connectivity index. The model was constructed with the General Discriminant Analysis methods from STATISTICA package and gave training/predicting set accuracies of 90.77% for the forward stepwise model type. In conclusion, this study extends for the first time the classical TIs to protein star network TIs by proposing a model that can predict if a protein/fragment of protein is natural or random using only the amino acid sequence data. This classification can be used in the studies of the protein functions by changing some fragments with random amino acid sequences or to detect the fake amino acid sequences or the errors in proteins. These results promote the use of the S2SNet application not only for protein structure analysis but also for mass spectroscopy, clinical proteomics and imaging, or DNA/RNA structure analysis.
Natural/random protein classification models based on star network topological indices