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.

Influenza infection and pneumonia are known to cause much of their mortality by inducing acute respiratory distress syndrome (ARDS), which is the most severe form of acute lung injury (ALI). Angiotensin-converting enzyme 2 (ACE2), which is a negative regulator of angiotensin II in the renin-angiotensin system, has been reported to have a crucial role in ALI. Downregulation of ACE2 is always associated with the ALI or ARDS induced by avian influenza virus, severe acute respiratory syndrome-coronavirus, respiratory syncytial virus and sepsis. However, the molecular mechanism of the decreased expression of ACE2 in ALI is unclear. Here we show that avian influenza virus H5N1 induced the upregulation of miR-200c-3p, which was then demonstrated to target the 3′-untranslated region of ACE2. Then, we found that nonstructural protein 1 and viral RNA of H5N1 contributed to the induction of miR-200c-3p during viral infection. Additionally, the synthetic analog of viral double-stranded RNA (poly (I:C)), bacterial lipopolysaccharide and lipoteichoic acid can all markedly increase the expression of miR-200c-3p in a nuclear factor-κB-dependent manner. Furthermore, markedly elevated plasma levels of miR-200c-3p were observed in severe pneumonia patients. The inhibition of miR-200c-3p ameliorated the ALI induced by H5N1 virus infection in vivo, indicating a potential therapeutic target. Therefore, we identify a shared mechanism of viral and bacterial lung infection-induced ALI/ARDS via nuclear factor-κB-dependent upregulation of miR-200c-3p to reduce ACE2 levels, which leads increased angiotensin II levels and subsequently causes lung injury.
miRNA-200c-3p is crucial in acute respiratory distress syndrome

Estimating temporal changes in a target population from phylogenetic or count data is an important problem in ecology and epidemiology. Reliable estimates can provide key insights into the climatic and biological drivers influencing the diversity or structure of that population and evidence hypotheses concerning its future growth or decline. In infectious disease applications, the individuals infected across an epidemic form the target population. The renewal model estimates the effective reproduction number, R, of the epidemic from counts of its observed cases. The skyline model infers the effective population size, N, underlying a phylogeny of sequences sampled from that epidemic. Practically, R measures ongoing epidemic growth while N informs on historical caseload. While both models solve distinct problems, the reliability of their estimates depends on p-dimensional piecewise-constant functions. If p is misspecified, the model might underfit significant changes or overfit noise and promote a spurious understanding of the epidemic, which might misguide intervention policies or misinform forecasts. Surprisingly, no transparent yet principled approach for optimising p exists. Usually, p is heuristically set, or obscurely controlled via complex algorithms. We present a computable and interpretable p-selection method based on the minimum description length (MDL) formalism of information theory. Unlike many standard model selection techniques, MDL accounts for the additional statistical complexity induced by how parameters interact. As a result, our method optimises p so that R and N estimates properly adapt to the available data. It also outperforms comparable Akaike and Bayesian information criteria on several classification problems. Our approach requires some knowledge of the parameter space and exposes the similarities between renewal and skyline models.
Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models

To obtain information about the occurrence and genotype distribution of G. intestinalis and C. parvum in Austrian cattle, faecal samples from diarrhoeic calves younger than 180 days of age originating from 70 farms were examined. Of the 177 faecal samples, 27.1% were positive for Giardia cysts (immunofluorescence microscopy) and 55.4% for Cryptosporidium oocysts (phase-contrast microscopy). Positive samples were characterized by nested PCR for Giardia, 83.3% (triosephosphate isomerase; tpi) and 89.6% (β-giardin; bg) were positive, while the Cryptosporidium nested PCR returned 92.5% (60-kDa glycoprotein) positive results. Sequence analysis revealed one assemblage A-positive sample and 30 (bg) respectively 29 (tpi) assemblage E-positive samples for G. intestinalis. For C. parvum four subtypes within the IIa family (IIaA15G2R1, n = 29; IIaA19G2R2, n = 3; IIaA21G2R1, n = 2; IIaA14G1R1, n = 1) could be differentiated. Validation of two immunochromatographic point-of-care tests resulted in a sensitivity of 29.2% and 77.6%; a specificity of 98.4% and 91.1% for the detection of Giardia intestinalis and Cryptosporidium parvum, respectively. Results confirm the widespread occurrence of both protozoa in diarrhoeic calves in Austria.

Molecular characterization of Giardia intestinalis and Cryptosporidium parvum from calves with diarrhoea in Austria and evaluation of point-of-care tests