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.

Cryptosporidiosis is one of the most common zoonosis worldwide, causing intestinal infection to both humans and livestock. The purpose of this study was to assess whether the level of anti-C. parvum IgG antibodies transferred through colostrum from dams to newborn calves impacts the susceptibility to cryptosporidiosis. A number of 50 dams and their healthy newborns were included in the study. Colostrum samples were collected within 12 h after birth and anti-C. parvum IgG antibody levels were determined by single radial immunodiffusion. The health condition of the newborns was daily monitored, and fecal samples were collected at first diarrheic episode of a calf. In all dams, the anti-C. parvum IgG antibody concentration in colostrum varied between 570 and 4070 mg/dl; in dams who gave birth to calves with diarrhea and were C. parvum-positive, the antibody concentration in colostrum varied between 680 and 3680 mg/dl (Table 1 ). The point-biserial correlation showed a negative correlation between the levels of anti-C. parvum antibodies and manifestation of clinical cryptosporidiosis (r=-0.425). Our findings highlight the importance of IgG levels in colostrum received by neonatal calves during their first day of life for prevention of C. parvum infection.
Importance of colostrum IgG antibodies level for prevention of infection with Cryptosporidium parvum in neonatal dairy calves

Given the high mortality rate (>50%) and potential danger of intrapersonal transmission, highly pathogenic avian influenza (HPAI) H5N1 epidemics still pose a significant threat to humans. γδ T cells, which participate on the front line of the host immune defense, demonstrate both innate, and adaptive characteristics in their immune response and have potent antiviral activity against various viruses. However, the roles of γδ T cells in HPAI H5N1 viral infection remain unclear. In this study, we found that γδ T cells provided a crucial protective function in the defense against HPAI H5N1 viral infection. HPAI H5N1 viruses could directly activate γδ T cells, leading to enhanced CD69 expression and IFN-γ secretion. Importantly, we found that the trimer but not the monomer of HPAI H5N1 virus hemagglutinin (HA) proteins could directly activate γδ T cells. HA-induced γδ T cell activation was dependent on both sialic acid receptors and HA glycosylation, and this activation could be inhibited by the phosphatase calcineurin inhibitor cyclosporin A but not by the phosphatidylinositol 3-kinase (PI3-K) inhibitors wortmannin and LY294002. Our findings provide a further understanding the mechanism underlying γδ T cell-mediated innate and adoptive immune responses against HPAI H5N1 viral infection, which helps to develop novel therapeutic strategies for the treatment of H5N1 infection in the future.
γδ T Cells Provide Protective Function in Highly Pathogenic Avian H5N1 Influenza A Virus Infection

Prediction of individual life span based on characteristics evaluated at middle-age represents a challenging objective for aging research. In this study, we used machine learning algorithms to construct models that predict life span in a stock of genetically heterogeneous mice. Life-span prediction accuracy of 22 algorithms was evaluated using a cross-validation approach, in which models were trained and tested with distinct subsets of data. Using a combination of body weight and T-cell subset measures evaluated before 2 years of age, we show that the life-span quartile to which an individual mouse belongs can be predicted with an accuracy of 35.3% (60.10%). This result provides a new benchmark for the development of life-span-predictive models, but improvement can be expected through identification of new predictor variables and development of computational approaches. Future work in this direction can provide tools for aging research and will shed light on associations between phenotypic traits and longevity.

How Long Will My Mouse Live? Machine Learning Approaches for Prediction of Mouse Life Span