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]: Low cost, high throughput, easy quantitation, and rich descriptions have made gene expression profiling generated by DNA microarrays an optimal approach for describing host transcriptional responses (HTRs). However, efforts to characterize the landscape of HTRs to diverse pathogens are far from offering a comprehensive view. Here, we developed an HTR Connectivity Map based on systematic assessment of pairwise similarities of HTRs to 50 clinically important human pathogens using 1353 gene-expression profiles generated from >60 human cells/tissues. These 50 pathogens were further partitioned into eight robust "HTR communities" (i.e., groups with more consensus internal HTR similarities). These communities showed enrichment in specific infection attributes and differential gene expression patterns. Using query signatures of HTRs to external pathogens, we demonstrated four distinct modes of HTR associations among different pathogens types/class, and validated the reliability of the HTR community divisions for differentiating and categorizing pathogens from a host-oriented perspective. These findings provide a first-generation HTR Connectivity Map of 50 diverse pathogens, and demonstrate the potential for using annotated HTR community to detect functional associations among infectious pathogens.
[A]: Inferring Infection Patterns Based on a Connectivity Map of Host Transcriptional Responses


[Q]: Background. Bacteria are found in 50%-90% of cases of acute otitis media (AOM) with or without otorrhea, and viruses are found in 20%-49% of cases. However, for at least 15% of patients with AOM, the microbiological etiology is never determined. Our aim was to specify the full etiology of acute middle ear infection by using modern microbiological methods concomitantly for bacterial and viral detection. Methods. The subjects were 79 young children having AOM with new onset (!48 h) of otorrhea through a tympanostomy tube. Middle ear fluid samples were suctioned from the middle ear through the tympanostomy tube. Bacteria were sought by culture and polymerase chain reaction; viruses were analyzed by culture, antigen detection, and polymerase chain reaction. Results. At least 1 respiratory tract pathogen was noted in 76 children (96%). Bacteria were found in 73 cases (92%), and viruses were found in 55 (70%). In 52 patients (66%), both bacteria and viruses were found. Bacteria typical of AOM were detected in 86% of patients. Picornaviruses accounted for 60% of all viral findings. Conclusions. In the great majority of children, AOM is a coinfection with bacteria and viruses. The patent tympanostomy tube does not change the spectrum of causative agents in AOM. A microbiological etiology can be established in practically all cases.
[A]: Microbiology of Acute Otitis Media in Children with Tympanostomy Tubes: Prevalences of Bacteria and Viruses


[Q]: With the features of extremely high selectivity and efficiency in catalyzing almost all the chemical reactions in cells, enzymes play vitally important roles for the life of an organism and hence have become frequent targets for drug design. An essential step in developing drugs by targeting enzymes is to identify drug-enzyme interactions in cells. It is both time-consuming and costly to do this purely by means of experimental techniques alone. Although some computational methods were developed in this regard based on the knowledge of the three-dimensional structure of enzyme, unfortunately their usage is quite limited because threedimensional structures of many enzymes are still unknown. Here, we reported a sequence-based predictor, called "iEzy-Drug, " in which each drug compound was formulated by a molecular fingerprint with 258 feature components, each enzyme by the Chou's pseudo amino acid composition generated via incorporating sequential evolution information and physicochemical features derived from its sequence, and the prediction engine was operated by the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iEzy-Drug via rigorous cross-validations was about 91%. Moreover, to maximize the convenience for the majority of experimental scientists, a user-friendly web server was established, by which users can easily obtain their desired results.
[A]:
iEzy-Drug: A Web Server for Identifying the Interaction between Enzymes and Drugs in Cellular Networking