Efficient Peak-Labeling Algorithms for Whole-Sample Mass Spectrometry Proteomics

Whole-sample mass spectrometry (MS) proteomics allows for a parallel measurement of hundreds of proteins present in a variety of biospecimens. Unfortunately, the association between MS signals and these proteins is not straightforward. The need to interpret mass spectra demands the development of methods for accurate labeling of ion species in such profiles. To aid this process, we have developed a new peak-labeling procedure for associating protein and peptide labels with peaks. This computational method builds upon characteristics of proteins expected to be in the sample, such as the amino sequence, mass weight, and expected concentration within the sample. A new probabilistic score that incorporates this information is proposed. We evaluate and demonstrate our method's ability to label peaks first on simulated MS spectra and then on MS spectra from human serum with a spiked-in calibration mixture.     

Assessment of cortical bone fracture resistance curves by fusing artificial neural networks and linear regression

Bone injures (BI) represents one of the major health problems, together with cancer and cardiovascular diseases. Assessment of the risks associated with BI is nontrivial since fragility of human cortical bone is varying with age. Due to restrictions for performing experiments on humans, only a limited number of fracture resistance curves (R-curves) for particular ages have been reported in the literature. This study proposes a novel decision support system for the assessment of bone fracture resistance by fusing various artificial intelligence algorithms. The aim was to estimate the R-curve slope, toughness threshold and stress intensity factor using the two input parameters commonly available during a routine clinical examination: patients age and crack length. Using the data from the literature, the evolutionary assembled Artificial Neural Network was developed and used for the derivation of Linear regression (LR) models of R-curves for arbitrary age. Finally, by using the patient (age)-specific LR models and diagnosed crack size one could estimate the risk of bone fracture under given physiological conditions. Compared to the literature, we demonstrated improved performances for estimating nonlinear changes of R-curve slope (R² = 0.82 vs. R² = 0.76) and Toughness threshold with ageing (R² = 0.73 vs. R² = 0.66).     

Dynamics of pigment pattern formation in the zebrafish, Brachydanio rerio. III. Effect of anteroposterior location of three-day lateral line melanophores on colonization by the second wave of melanophores

The mode of colonization of the lateral line melanophore band of the zebrafish, Brachydanio rerio, by the second wave of melanophores has been investigated. This stripe forms in two consecutive stages. First, there is an initial migration and reorientation of pigment cells in an anteroposterior wave into the site to form an interrupted stripe. Following this, a round of melanophores differentiates directly at the site and fills in the gaps between the initial cells. An analysis of the distributions of initial and second wave melanophores along the stripe site has shown that both groups of cells are selective as to localization. Initial wave melanophores colonize more anterior somite areas than do second wave melanophores. However, both groups of cells exhibit preferential colonization of the same anterior sites. It is suggested that second wave melanophores attempt to colonize the same somite areas of the stripe as the initial wave of melanophores but are forced to move to more posterior locations due to the presence of initial wave melanophores anteriorly. Observations were also made on later stages of development of the lateral line melanophore band. These melanophores retain the ability to migrate. Some of them reorient out onto the flank and contribute to the juvenile flank pigment pattern.