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: The sudden death of three calves, one diarrheic calf, and one aborted fetus from four farms in southern Brazil was investigated. Two Histophilus somni-associated syndromes were identified: systemic histophilosis (n=4) and abortion (n=1). The principal pathological findings included vasculitis, meningoencephalitis with thrombosis, necrotizing myocarditis, renal infarctions, hepatic abscesses, and bronchopneumonia. PCR assays were used to amplify specific amplicons of the ovine herpesvirus 2, bovine herpesvirus 1 and −5, Listeria monocytogenes, H. somni, and pestivirus; bovine group A rotavirus (BoRV-A) and bovine coronavirus (BCoV) were investigated in calves with diarrhea. H. somni DNA was amplified in tissues from all calves and the brain of the aborted fetus with pathological alterations consistent with histophilosis. All other PCR assays were negative; BoRV-A and BCoV were not identified. These findings confirm the participation of H. somni in the pathological alterations observed in this study and represent the first description of histophilosis in cattle from Brazil.

A: Histophilus somni-induced infections in cattle from southern Brazil
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Q: 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

A: A prospective prediction tool for understanding CrimeaneCongo haemorrhagic fever dynamics in Turkey
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Q: KEYWORDS Respiratory viruses Laboratory diagnosis Molecular methods Polymerase chain reaction Molecular tests for the detection of respiratory viruses are more sensitive and can detect more viruses than the traditional methods of culture and antigen detection. There are now several molecular assays available, cleared by the Food and Drug Administration, which differ with respect to the viruses detected, instrumentation, throughput, hands-on time, the need for separate nucleic acid extraction, and sensitivity for certain groups of viruses. Issues associated with molecular tests for respiratory viruses include: possible falsenegative results due to sequence variants; the inability of many assays to discriminate rhinoviruses from enteroviruses; the ability to detect viral nucleic acids in asymptomatic individuals; and the increased prevalence of coinfections.

A:
Molecular Detection of Respiratory Viruses
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