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.

Background: We present here the recent update of AMS algorithm for identification of post-translational modification (PTM) sites in proteins based only on sequence information, using artificial neural network (ANN) method. The query protein sequence is dissected into overlapping short sequence segments. Ten different physicochemical features describe each amino acid; therefore nine residues long segment is represented as a point in a 90 dimensional space. The database of sequence segments with confirmed by experiments post-translational modification sites are used for training a set of ANNs. The efficiency of the classification for each type of modification and the prediction power of the method is estimated here using recall (sensitivity), precision values, the area under receiver operating characteristic (ROC) curves and leave-one-out tests (LOOCV). The significant differences in the performance for differently optimized neural networks are observed, yet the AMS 3.0 tool integrates those heterogeneous classification schemes into the single consensus scheme, and it is able to boost the precision and recall values independent of a PTM type in comparison with the currently available state-of-the art methods. The standalone version of AMS 3.0 presents an efficient way to indentify post-translational modifications for whole proteomes. The training datasets, precompiled binaries for AMS 3.0 tool and the source code are available at Additional file 1 Supplementary materials for the publication. Supplementary.xls contains detailed results of training and testing of AMS 3.0 tool in MS Excel file format. Additional file 2 PTM predictor software with training data. PTM_Archive.tar.gz includes the predictor software binaries together with all trained ANNs in tar.gz compressed form.
AMS 3.0: prediction of post-translational modifications

Current hemagglutinin (HA)-based seasonal influenza vaccines induce vaccine strain-specific neutralizing antibodies that usually fail to provide protection against mismatched circulating viruses. Inclusion in the vaccine of highly conserved internal proteins such as the nucleoprotein (NP) and the matrix protein 1 (M1) was shown previously to increase vaccine efficacy by eliciting cross-reactive T-cells. However, appropriate delivery systems are required for efficient priming of T-cell responses. In this study, we demonstrated that administration of novel self-amplifying mRNA (SAM 1 ) vectors expressing influenza NP (SAM (NP)), M1 (SAM(M1)), and NP and M1 (SAM(M1-NP)) delivered with lipid nanoparticles (LNP) induced robust polyfunctional CD4 T helper 1 cells, while NP-containing SAM also induced cytotoxic CD8 T cells. Robust expansions of central memory (T CM ) and effector memory (T EM ) CD4 and CD8 T cells were also measured. An enhanced recruitment of NPspecific cytotoxic CD8 T cells was observed in the lungs of SAM(NP)-immunized mice after influenza infection that paralleled with reduced lung viral titers and pathology, and increased survival after homologous and heterosubtypic influenza challenge. Finally, we demonstrated for the first time that the co-administration of RNA (SAM(M1-NP)) and protein (monovalent inactivated influenza vaccine (MIIV)) was feasible, induced simultaneously NP-, M1-and HA-specific T cells and HA-specific neutralizing antibodies, and enhanced MIIV efficacy against a heterologous challenge. In conclusion, systemic administration of SAM vectors expressing conserved internal influenza antigens induced protective immune responses in mice, supporting the SAM 1 platform as another promising strategy for the development of broad-spectrum universal influenza vaccines.
Self-Amplifying mRNA Vaccines Expressing Multiple Conserved Influenza Antigens Confer Protection against Homologous and Heterosubtypic Viral Challenge

Better understanding of aerosol dynamics is an important step for improving personal exposure assessments in indoor environments. Although the limitation of the assumptions in a well-mixed model is well known, there has been very little research reported in the published literature on the discrepancy of exposure assessments between numerical models which take account of gravitational effects and the well-mixed model. A new Eulerian-type drift-flux model has been developed to simulate particle dispersion and personal exposure in a twozone geometry, which accounts for the drift velocity resulting from gravitational settling and diffusion. To validate the numerical model, a small-scale chamber was fabricated. The airflow characteristics and particle concentrations were measured by a phase Doppler Anemometer. Both simulated airflow and concentration profiles agree well with the experimental results. A strong inhomogeneous concentration was observed experimentally for 10 mm aerosols. The computational model was further applied to study a simple hypothetical, yet more realistic scenario. The aim was to explore different levels of exposure predicted by the new model and the well-mixed model. Aerosols are initially uniformly distributed in one zone and subsequently transported and dispersed to an adjacent zone through an opening. Owing to the significant difference in the rates of transport and dispersion between aerosols and gases, inferred from the results, the wellmixed model tends to overpredict the concentration in the source zone, and under-predict the concentration in the exposed zone. The results are very useful to illustrate that the well-mixed assumption must be applied cautiously for exposure assessments as such an ideal condition may not be applied for coarse particles. r

Experimental and numerical study on particle distribution in a two-zone chamber