Activity-Based Protein Profiling in Methicillin-Resistant Staphylococcus aureus Reveals the Broad Reactivity of a Carmofur-Derived Probe

Activity-based protein profiling is a powerful chemoproteomic technique to detect active enzymes and identify targets and off-targets of drugs. Here, we report the use of carmofur- and activity-based probes to identify biologically relevant enzymes in the bacterial pathogen Staphylococcus aureus. Carmofur is an anti-neoplastic prodrug of 5-fluorouracil and also has antimicrobial and anti-biofilm activity. Carmofur probes were originally designed to target human acid ceramidase, a member of the NTN hydrolase family with an active-site cysteine nucleophile. Here, we first profiled the targets of a fluorescent carmofur probe in live S. aureus under biofilm-promoting conditions and in liquid culture, before proceeding to target identification by liquid chromatography/mass spectrometry. Treatment with a carmofur-biotin probe led to enrichment of 20 enzymes from diverse families awaiting further characterization, including the NTN hydrolase-related IMP cyclohydrolase PurH. However, the probe preferentially labeled serine hydrolases, thus displaying a reactivity profile similar to that of carbamates. Our results suggest that the electrophilic N-carbamoyl-5-fluorouracil scaffold could potentially be optimized to achieve selectivity towards diverse enzyme families. The observed promiscuous reactivity profile suggests that the clinical use of carmofur presumably leads to inactivation of a number human and microbial enzymes, which could lead to side effects and/or contribute to therapeutic efficacy.     

Experimental study of the effect of fuel types on the performance of spark ignition engine

Different fuels are being used daily in the city of Kirkuk, Iraq for operating vehicles with spark-ignition internal combustion engines. Aiming to address the effects of these fuels on both engines and the environment, this work conducts an experimental study where a single-cylinder, four-stroke small spark ignition engine is employed. Three types of benzene with different octane ratings (low with an additive [85.8%], medium [89.2%], and high [95.6%]) are utilized in the study as they are the most consumed fuel in the area of the study. Moreover, the low-octane fuel will be addressed with a commercial additive. In addition to engine performance, the exhaust gases and sound levels are analyzed as well. Through the outcomes, it is observed that the fuel with higher octane numbers relatively produces better engine performance and pollution. At normal engine speed, the fuel with a medium octane rating, however, has close engine performance results but with worse pollution effects. On the other hand, the engine fails to start with low-octane fuel without the additive. The additive improves the engine performance with the low octane fuel and surprisingly produces fewer pollution gases than the fuel with medium octane number. However, the engine still behaves worse than with the other fuels at normal engine speed.     

Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

Pedestrian detection and tracking are vital elements of today’s surveillance systems, which make daily life safe for humans. Thus, human detection and visualization have become essential inventions in the field of computer vision. Hence, developing a surveillance system with multiple object recognition and tracking, especially in low light and night-time, is still challenging. Therefore, we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night. In particular, we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared (IR) images using machine learning and tracking them using particle filters. Moreover, a random forest classifier is adopted for image segmentation to identify pedestrians in an image. The result of detection is investigated by particle filter to solve pedestrian tracking. Through the extensive experiment, our system shows 93% segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof classes. Moreover, the system achieved a detection accuracy of 90% using multiple template matching techniques and 81% accuracy for pedestrian tracking. Furthermore, our system can identify that the detected object is a human. Hence, our system provided the best results compared to the state-of-art systems, which proves the effectiveness of the techniques used for image segmentation, classification, and tracking. The presented method is applicable for human detection/tracking, crowd analysis, and monitoring pedestrians in IR video surveillance.