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.

Faeces samples from 218, 1 to 30-day-old, diarrheic dairy calves in 65 dairy herds were screened for the presence of Cryptosporidium and concurrent infections with rotavirus, coronavirus, F5 Escherichia coli and Salmonella spp. Calves were grouped according to their age as follows: 1±7, 8±14, 15±21 and 22±30 days. Cryptosporidium infection was detected in 43.8%, 71.9%, 63.2% and 6.9% of the calves in the respective age groups. Significant differences in the detection rate of Cryptosporidium were found between the age group 22±30 days and all other age groups, and between the age group 1±7 days and the age groups 8±14 days and 15±21 days. Cryptosporidium was the only enteropathogen detected in 60 of the 114 (52.6%) diarrheic calves. Concurrent infections with other enteropathogen(s) were detected in 64.3%, 46.3%, 39.5% and 0% of the Cryptosporidium-infected calves in the age groups 1±7, 8±14, 15±21 and 22±30 days, respectively. A significant age-associated decrease in the detection rate of mixed infections (p < 0.05) was found. The detection rates of the other enteropathogens considered in calves with Cryptosporidium infection were 87% for rotavirus, 11.1% for coronavirus, 27.8% for F5 E. coli and 1.8% for Salmonella. #
Cryptosporidium and concurrent infections with other major enterophatogens in 1 to 30-day-old diarrheic dairy calves in central Spain

Although the majority of cases of Legionnaires' disease (LD) are caused by Legionella pneumophila, an increasing number of other Legionella species have been reported to cause human disease. There are no clinical presentations unique to LD and hence accurate laboratory tests are required for early diagnosis. Therefore, we designed a real-time PCR assay that targets the 23S-5S rRNA intergenic spacer region (23S-5S PCR) and allows for detection of all Legionella species and discrimination of L. pneumophila from other Legionella species. In total, 271 isolates representing 50 Legionella species were tested and the assay was validated using 39 culture-positive and 110 culture-negative patient specimens collected between 1989 and 2006. PCR-positive results were obtained with all 39 culture-positive samples (100% sensitivity). Specimens that tested positive according to 23S-5S PCR, but were culture-negative, were further analysed by DNA sequencing of the amplicon or the macrophage infectivity potentiator (mip) gene. In addition to L. pneumophila, Legionella longbeachae, Legionella cincinnatiensis and Legionella micdadei were identified in the specimens. The assay showed a 7-log dynamic range displaying a sensitivity of 7.5 CFU/mL or three genome equivalents per reaction. Sixty-one specimens containing viruses or bacteria other than Legionellae were negative according to 23S-5S PCR, demonstrating its specificity. Use of this assay should contribute to the earlier detection of respiratory disease caused by Legionella species, as well as to increased rates of detection.
Dual detection of Legionella pneumophila and Legionella species by real-time PCR targeting the 23S-5S rRNA gene spacer region

Background: Several methodological approaches have been used to estimate distance in health service research. In this study, focusing on cardiac catheterization services, Euclidean, Manhattan, and the less widely known Minkowski distance metrics are used to estimate distances from patient residence to hospital. Distance metrics typically produce less accurate estimates than actual measurements, but each metric provides a single model of travel over a given network. Therefore, distance metrics, unlike actual measurements, can be directly used in spatial analytical modeling. Euclidean distance is most often used, but unlikely the most appropriate metric. Minkowski distance is a more promising method. Distances estimated with each metric are contrasted with road distance and travel time measurements, and an optimized Minkowski distance is implemented in spatial analytical modeling. Methods: Road distance and travel time are calculated from the postal code of residence of each patient undergoing cardiac catheterization to the pertinent hospital. The Minkowski metric is optimized, to approximate travel time and road distance, respectively. Distance estimates and distance measurements are then compared using descriptive statistics and visual mapping methods. The optimized Minkowski metric is implemented, via the spatial weight matrix, in a spatial regression model identifying socioeconomic factors significantly associated with cardiac catheterization. The Minkowski coefficient that best approximates road distance is 1.54; 1.31 best approximates travel time. The latter is also a good predictor of road distance, thus providing the best single model of travel from patient's residence to hospital. The Euclidean metric and the optimal Minkowski metric are alternatively implemented in the regression model, and the results compared. The Minkowski method produces more reliable results than the traditional Euclidean metric. Conclusion: Road distance and travel time measurements are the most accurate estimates, but cannot be directly implemented in spatial analytical modeling. Euclidean distance tends to underestimate road distance and travel time; Manhattan distance tends to overestimate both. The optimized Minkowski distance partially overcomes their shortcomings; it provides a single model of travel over the network. The method is flexible, suitable for analytical modeling, and more accurate than the traditional metrics; its use ultimately increases the reliability of spatial analytical models.

Comparison of distance measures in spatial analytical modeling for health service planning