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ABSTRACT Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulating metabolism, proliferation, apoptosis, subcellular trafficking, and other crucial physiological processes. Phosphorylation prediction in a microbial organism can assist in understanding pathogenesis and host–pathogen interaction, drug and antibody design, and antimicrobial agent development. Experimental methods for predicting phosphorylation sites are costly, slow, and tedious. Hence low‐cost and high‐speed computational approaches are highly desirable. This paper presents a new deep learning tool called DeepPhoPred for predicting microbial phospho‐serine (pS), phospho‐threonine (pT), and phospho‐tyrosine (pY) sites. DeepPhoPred incorporates a two‐headed convolutional neural network architecture with the squeeze and excitation blocks followed by fully connected layers that jointly learn significant features from the peptide's structural and evolutionary information to predict phosphorylation sites. Our empirical results demonstrate that DeepPhoPred significantly outperforms the existing microbial phosphorylation site predictors with its highly efficient deep‐learning architecture. DeepPhoPred as a standalone predictor, all its source codes, and our employed datasets are publicly available athttps://github.com/faisalahm3d/DeepPhoPred. 

Abstract Physics-based modeling aids in designing efficient data center power and cooling systems. These systems have traditionally been modeled independently under the assumption that the inherent coupling of effects between the systems has negligible impact. This study tests the assumption through uncertainty quantification of models for a typical 300 kW data center supplied through either an alternating current (AC)-based or direct current (DC)-based power distribution system. A novel calculation scheme is introduced that couples the calculations of these two systems to estimate the resultant impact on predicted power usage effectiveness (PUE), computer room air conditioning (CRAC) return temperature, total system power requirement, and system power loss values. A two-sample z-test for comparing means is used to test for statistical significance with 95% confidence. The power distribution component efficiencies are calibrated to available published and experimental data. The predictions for a typical data center with an AC-based system suggest that the coupling of system calculations results in statistically significant differences for the cooling system PUE, the overall PUE, the CRAC return air temperature, and total electrical losses. However, none of the tested metrics are statistically significant for a DC-based system. The predictions also suggest that a DC-based system provides statistically significant lower overall PUE and electrical losses compared to the AC-based system, but only when coupled calculations are used. These results indicate that the coupled calculations impact predicted general energy efficiency metrics and enable statistically significant conclusions when comparing different data center cooling and power distribution strategies.