MOTIVATION: Constraint-based modeling approaches allow the estimation of maximal in vivo enzyme catalytic rates that can serve as proxies for enzyme turnover numbers. Yet, genome-scale flux profiling remains a challenge in deploying these approaches to catalogue proxies for enzyme catalytic rates across organisms. RESULTS: Here, we formulate a constraint-based approach, termed NIDLE-flux, to estimate fluxes at a genome-scale level by using the principle of efficient usage of expressed enzymes. Using proteomics data from Escherichia coli, we show that the fluxes estimated by NIDLE-flux and the existing approaches are in excellent qualitative agreement (Pearson correlation > 0.9). We also find that the maximal in vivo catalytic rates estimated by NIDLE-flux exhibits a Pearson correlation of 0.74 with in vitro enzyme turnover numbers. However, NIDLE-flux results in a 1.4-fold increase in the size of the estimated maximal in vivo catalytic rates in comparison to the contenders. Integration of the maximum in vivo catalytic rates with publically available proteomics and metabolomics data provide a better match to fluxes estimated by NIDLE-flux. Therefore, NIDLE-flux facilitates more effective usage of proteomics data to estimate proxies for kcatomes. AVAILABILITY AND IMPLEMENTATION: https://github.com/Rudan-X/NIDLE-flux-code. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An efficient and secure environment is necessary for data transmission and storage, especially for large‐column multimedia data. In this study, a novel compression–encryption scheme is presented using a fractal dictionary and Julia set. For the compression in this scheme, fractal dictionary encoding not only reduces time consumption, but also gives good quality image reconstruction. For the encryption in the scheme, the key has large key space and high sensitivity, even to tiny perturbation. Besides, the stream cipher encryption and the diffusion process adopted in this study help spread perturbation in the plaintext, achieving high plain sensitivity and giving an effective resistance to chosen‐plaintext attacks.

Indirect immunofluorescence (IIF) with HEp-2 cells is considered as a powerful, sensitive and comprehensive technique for analyzing antinuclear autoantibodies (ANAs). Fractal dimension can be used on the analysis of image representing and also on the property quantification like texture complexity and spatial occupation. In this study, we apply the fractal theory in the application of HEp-2 cell staining pattern classification, utilizing fractal descriptor firstly in the HEp-2 cell pattern classification with the help of morphological descriptor and pixel difference descriptor. The method is applied to the data set of MIVIA and uses the support vector machine (SVM) classifier. Experimental results show that the fractal descriptor combining with other two descriptors makes the precisions of six patterns more stable, all above 50%, achieving 62.04% overall accuracy at best with relatively simple feature vectors.