Spectrum graph-based de-novo sequencing algorithm MaxNovo achieves high peptide identification rates in collisional dissociation MS/MS spectra

We describe MaxNovo, a novel spectrum graph-based peptide de-novo sequencing algorithm integrated into the MaxQuant software. It identifies complete sequences of peptides as well as sequence tags that are incomplete at one or both of the peptide termini. MaxNovo searches for the highest-scoring path in a directed acyclic graph representing the MS/MS spectrum with peaks as nodes and edges as potential sequence constituents consisting of single amino acids or pairs. The raw score is a sum of node and edge weights, plus several reward scores, for instance, for complementary ions or protease compatibility. For search-engine identified peptides, it correlates well with the Andromeda search engine score. We use a particular score normalization and the score difference between the first and second-best solution to define a combined score that integrates all available information. To evaluate its performance, we use a human cell line dataset and take as ground truth all Andromeda-identified MS/MS spectra with an Andromeda score of at least 100. MaxNovo outperforms other software in particular in the high-sensitivity range of precision-coverage plots. We also identify incomplete sequence tags and study their statistical properties. Next, we apply MaxNovo to ion mobility-coupled time of flight data. Here we achieve excellent performance as well, except for potential swaps of the two amino acids closest to the C-terminus, which are not well resolved due to the low end of the mass range in MS/MS spectra in this dataset. We demonstrate the applicability of MaxNovo to palaeoproteomics samples with a Late Pleistocene hominin proteome dataset that was generated using three proteases. Interestingly, we did not use any machine learning in the construction of MaxNovo, but implemented expert domain knowledge directly in the definition of the score. Yet, it performs as good as or better than the leading deep learning-based algorithm.

Flight Level Assignment Using Graph Coloring

This paper models an air traffic optimization problem where, on the one hand, flight operators seek to minimize fuel consumption flying at optimal cruise levels and, on the other hand, air traffic managers aim to keep intersecting airways at as distant as possible flight levels. We study such a problem as a factorized optimization, which is addressed through a spectrum graph coloring model, evaluating the effect that safety constraints have on fuel consumption, and comparing different heuristic approaches for allocation.

PENENTUAN KOMPOSISI KIMIAWI ATMOSFER DAN TEMPERATUR EFEKTIF PADA BINTANG VEGA (α Lyr) DENGAN MENGGUNAKAN METODE SPEKTROSKOPI ABSORPSI

The star spectrum is an information which is though to be a map of the star. The shape of the spectral line actually shows the nature of the star atmosphere, which is the only part researcher can be reached. The intensities of spectral lines will determine the chemical composition of the stars contained in the atmospheres of the stars. The approach used to determine the chemical composition and the effective temperature of the star is spectroscopy. The spectroscopy method had been done in this study using the GAO-ITB RTS telescope system which consisting of the Celestron C11 telescope, NEO-R 1000 spectrograph and CCD SBIG ST-8 camera. The object studied is Vega (α Lyr) star which has a magnitude of 0.00 that the main sequence star of the A0 spectrum class. The image data obtained then be processed using IRAF software to obtain the final spectrum graph with the y-axis representing the flux in erg cm-2s-1 and the x-axis representing the wavelength in units of Å. Furthermore, we match the absorption wavelength on the final spectrum graph to the existing wavelength of the Atomic Spectra Database (ASD) to identify the atmospheric chemical composition of the Vega star. Effective temperature can be calculated by using the Wien shifting laws by find out the maximum wavelength of the final spectrum graph. The most found element in the atmosphere of Vega star was hydrogen (H) that consist of Hε, Hδ, Hγ, Hβ, and Hα. The value of effective temperature of the Vega star is 7136 K that has difference percentage of 22.85 % from the mean temperature of A type star.

RetComm 1.0: Real Time Condition Monitoring of Rotating Machinery Failure

The breakdown of motor proves to be very expensive as it increases downtime on the machines. Development of cost-effective and reliable condition monitoring system for the protection of motors to avoid unexpected breakdowns is necessary. Therefore, RetComm 1.0 is developed as assistant tool for bearing condition diagnosis system. The smartphone accelerometer is used to collect the vibration signal data and send it to computer by using the Android application named Matlab Mobile. The Matlab software is used to implement a program which is the RetComm 1.0 system to analyse the vibration signal and monitor the condition of the bearing. The algorithm used to observe the condition of bearing is trained by using Artificial Neural Network (ANN). In this project, the ANN is trained by using Matlab software. This proposed method is implemented for early diagnosis purposes. The diagnosis process can be done by just attached the smartphone onto the bearing for data collection. In conclusion, the bearing condition can be identified with this system. The bearing condition are shown in text to let the user know the bearing conditions. The raw data and power spectrum graph plotting are to let the user more further to understand the health condition of the bearing.