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.

Example Input: 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. Ó
Example Output: Wheezing Exacerbations in Early Childhood: Evaluation, Treatment, and Recent Advances Relevant to the Genesis of Asthma

Example Input: Objectives: We aimed to develop a prospective prediction tool on CrimeaneCongo haemorrhagic fever (CCHF) to identify geographic regions at risk. The tool could support public health decision-makers in implementation of an effective control strategy in a timely manner. Methods: We used monthly surveillance data between 2004 and 2015 to predict case counts between 2016 and 2017 prospectively. The Turkish nationwide surveillance data set collected by the Ministry of Health contained 10 411 confirmed CCHF cases. We collected potential explanatory covariates about climate, land use, and animal and human populations at risk to capture spatiotemporal transmission dynamics. We developed a structured Gaussian process algorithm and prospectively tested this tool predicting the future year's cases given past years' cases. Results: We predicted the annual cases in 2016 and 2017 as 438 and 341, whereas the observed cases were 432 and 343, respectively. Pearson's correlation coefficient and normalized root mean squared error values for 2016 and 2017 predictions were (0.83; 0.58) and (0.87; 0.52), respectively. The most important covariates were found to be the number of settlements with fewer than 25 000 inhabitants, latitude, longitude and potential evapotranspiration (evaporation and transpiration). Conclusions: Main driving factors of CCHF dynamics were human population at risk in rural areas, geographical dependency and climate effect on ticks. Our model was able to prospectively predict the numbers of CCHF cases. Our proof-of-concept study also provided insight for understanding possible mechanisms of infectious diseases and found important directions for practice and policy to combat against emerging infectious diseases. Ç. Ak, Clin Microbiol Infect 2020;26:123.e1e123.e7
Example Output: A prospective prediction tool for understanding CrimeaneCongo haemorrhagic fever dynamics in Turkey

Example Input: The objectives of this experiment were to evaluate the effects of alternatives to antibiotic growth promoters (AGP), two group sizes, and their interaction on nursery pig performance to serve as a model for future AGP alternative studies. A 41-d experiment was conducted in a commercial weanto-finish barn; 1,300 piglets weaned at 21 d of age (weaned 2 or 4 d prior to experiment; 6.14 ± 0.18 kg BW; PIC 1050 sows and multiple sire lines) were blocked by sire, sex, and weaning date, then assigned to eight treatments: four dietary treatments each evaluated across two group sizes. The four dietary treatments were: negative control (NC), positive control (PC; NC + in-feed antibiotics), zinc oxide plus a dietary acidifier (blend of fumaric, citric, lactic, and phosphoric acid) (ZA; NC + ZnO + acid), and a Bacillus-based direct-fed-microbial (DFM) plus resistant potato starch (RS) (DR; NC + DFM + RS). The two group sizes were 31 or 11 pigs/pen; floor space was modified so area/pig was equal between the group sizes (0.42 m 2 /pig). There were 7 pens/diet with 11 pigs/pen and 8 pens/diet with 31 pigs/pen. Data were analyzed as a randomized complete block design with pen as the experimental unit. Diagnostic assessment of oral fluids, serum, and tissue samples was used to characterize health status. Pigs experienced natural challenges of acute diarrhea and septicemia in week 1 and porcine reproductive and respiratory syndrome virus (PRRSV) in weeks 4-6. There was a significant interaction between diet and group size for ADG (P = 0.012). PC increased ADG in large and small groups (P < 0.05) and ZA increased ADG only in large groups (P < 0.05). Small groups had improved ADG compared to large groups when fed NC or DR diets (P < 0.05). Similarly, PC increased ADFI (P < 0.05). Compared to NC, ZA improved ADFI in large groups only (P < 0.05; diet × group size: P = 0.015). Pigs fed PC had greater G:F than NC (P < 0.05), and small groups had greater G:F than large groups (P < 0.05). There was no effect of ZA or DR on G:F. Pigs fed PC required fewer individual medical treatments than NC and pigs fed ZA were intermediate (P = 0.024). More pigs were removed from large than small groups (P = 0.049), and there was no effect of diet on removals (P > 0.10). In conclusion, careful study design, protocol implementation, sample collection, and recording of important information allowed us to characterize the health status of this group of pigs and determine treatment effects on growth performance and morbidity.
Example Output:
The effects of group size and subtherapeutic antibiotic alternatives on growth performance and morbidity of nursery pigs: a model for feed additive evaluation 1