TASK DEFINITION: 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.
PROBLEM: Background: Influenza sentinel surveillance in Cote d'Ivoire showed that 70% of Acute Respiratory Infections (ARI) cases remained without etiology. This work aims to describe the epidemiological, clinical, and virological pattern of ARI that tested negative for influenza virus, in children under five years old. Materials and Methods: one thousand and fifty nine samples of patients presenting influenza Like Illness (ILI) or Severe Acute Respiratory Infections (SARI) symptoms were tested for other respiratory viruses using multiplex RT-PCR assays targeting 10 respiratory viruses. Results: The following pathogens were detected as follows, hRV 31,92% (98/307), hRSV 24.4% (75/329), PIV 20.5% (63/307), HCoV 229E 12,05% (37/307), hMPV 6.2% (19/307), HCoVOC43 1.0% (3/307) and EnV 1.0% (3/307). Among the 1,059 specimens analyzed, 917 (86.6%) were ILI samples and 142 (23.4%) were SARI samples. The proportion of children infected with at least one virus was 29.8% (273/917) in ILI cases and 23.9% (34/142) in SARI cases. The most prevalent viruses, responsible for ILI cases were hRV with 35.89% (98/273) and hRSV in SARI cases with 41.2% (14/34) of cases. Among the 1,059 patients, only 22 (2.1%) children presented risk factors related to the severity of influenza virus infection. Conclusion: This study showed that respiratory viruses play an important role in the etiology of ARI in children. For a better understanding of the epidemiology of ARI and improved case management, it would be interesting in this context to expand the surveillance of influenza to other respiratory viruses.

SOLUTION: DETECTION OF NON-INFLUENZA VIRUSES IN ACUTE RESPIRATORY INFECTIONS IN CHILDREN UNDER FIVE-YEAR-OLD IN COTE D'IVOIRE (JANUARY -DECEMBER 2013)

PROBLEM: Interest in bat-borne diseases and parasites has grown in the past decade over concerns for human health. However, the drivers of parasite diversity among bat host species are understudied as are the links between parasite richness and emerging risks. Thus, we aimed at exploring factors that explain macro and microparasite species richness in bats from Southeast Asia, a hotspot of emerging infectious diseases. First, we identified bat species that need increased sampling effort for pathogen discovery. Our approach highlights pathogen investigation disparities among species within the same genus, such as Rhinolophus and Pteropus. Secondly, comparative analysis using independent contrasts method allowed the identification of likely factors explaining parasite and viral diversity of bats. Our results showed a key role of bat distribution shape, an index of the fragmentation of bat distribution, on parasite diversity, linked to a decrease for both viral and endoparasite species richness. We discuss how our study may contribute to a better understanding of the link between parasite species richness and emergence.

SOLUTION: Parasite and viral species richness of Southeast Asian bats: Fragmentation of area distribution matters

PROBLEM: The development of the complex network graphs permits us to describe any real system such as social, neural, computer or genetic networks by transforming real properties in topological indices (TIs). This work uses Randic's star networks in order to convert the protein primary structure data in specific topological indices that are used to construct a natural/random protein classification model. The set of natural proteins contains 1046 protein chains selected from the pre-compiled CulledPDB list from PISCES Dunbrack's Web Lab. This set is characterized by a protein homology of 20%, a structure resolution of 1.6 Å and R-factor lower than 25%. The set of random amino acid chains contains 1046 sequences which were generated by Python script according to the same type of residues and average chain length found in the natural set. A new Sequence to Star Networks (S2SNet) wxPython GUI application (with a Graphviz graphics back-end) was designed by our group in order to transform any character sequence in the following star network topological indices: Shannon entropy of Markov matrices, trace of connectivity matrices, Harary number, Wiener index, Gutman index, Schultz index, Moreau-Broto indices, Balaban distance connectivity index, Kier-Hall connectivity indices and Randic connectivity index. The model was constructed with the General Discriminant Analysis methods from STATISTICA package and gave training/predicting set accuracies of 90.77% for the forward stepwise model type. In conclusion, this study extends for the first time the classical TIs to protein star network TIs by proposing a model that can predict if a protein/fragment of protein is natural or random using only the amino acid sequence data. This classification can be used in the studies of the protein functions by changing some fragments with random amino acid sequences or to detect the fake amino acid sequences or the errors in proteins. These results promote the use of the S2SNet application not only for protein structure analysis but also for mass spectroscopy, clinical proteomics and imaging, or DNA/RNA structure analysis.

SOLUTION:
Natural/random protein classification models based on star network topological indices