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Open AccessArticle

Human Activity Detection Using Smart Wearable Sensing Devices with Feed Forward Neural Networks and PSO

by Raghad Tariq Al_Hassani and Dogu Cagdas Atilla

Appl. Sci. 2023, 13(6), 3716; https://doi.org/10.3390/app13063716 - 14 Mar 2023

Cited by 7 | Viewed by 2355

Hospitals must continually monitor their patients’ actions to lower the chance of accidents, such as patient falls and slides. Human behavior is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating a static link that is used to influence human behavior is challenging, since it is hard to forecast how individuals will think or act in response to a certain event. Mobility tracking depends on intelligent monitoring systems that apply artificial intelligence (AI) applications referred to as “categories”. Because motion sensors, such as gyroscopes and accelerometers, output unconnected data that lack labels, event detection is a vital task. The fall feature parameters of tridimensional accelerometers and gyroscope sensors are presented and used, and the classification technique is based on distinguishing characteristics. This study focuses on the age-old problem of tracking turbulence in motion to improve detection precision. We trained the model, considering that detection accuracy is limited by factors such as the subject’s mass, velocity, and gait style. This is performed by employing an experimental dataset. When we used the sophisticated technique of particle swarm optimization (PSO) in combination with a four-stage forward neural network (4SFNN) to forecast four different types of turbulent motion, we observed that the total prediction accuracy was 98.615% accurate. Full article

(This article belongs to the Special Issue Wireless Communication Optimization in Optical Imaging and Sensing for Connected and Autonomous Vehicles Chain Management)

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26 pages, 8048 KB

Open AccessArticle

Identification of Substrates of Cytoplasmic Peptidyl-Prolyl Cis/Trans Isomerases and Their Collective Essentiality in Escherichia Coli

by Gracjana Klein, Pawel Wojtkiewicz, Daria Biernacka, Anna Stupak, Patrycja Gorzelak and Satish Raina

Int. J. Mol. Sci. 2020, 21(12), 4212; https://doi.org/10.3390/ijms21124212 - 13 Jun 2020

Cited by 8 | Viewed by 4290

Abstract

Protein folding often requires molecular chaperones and folding catalysts, such as peptidyl-prolyl cis/trans isomerases (PPIs). The Escherichia coli cytoplasm contains six well-known PPIs, although a requirement of their PPIase activity, the identity of their substrates and relative enzymatic contribution is unknown. Thus, strains lacking all periplasmic and one of the cytoplasmic PPIs were constructed. Measurement of their PPIase activity revealed that PpiB is the major source of PPIase activity in the cytoplasm. Furthermore, viable Δ6ppi strains could be constructed only on minimal medium in the temperature range of 30–37 °C, but not on rich medium. To address the molecular basis of essentiality of PPIs, proteins that aggregate in their absence were identified. Next, wild-type and putative active site variants of FkpB, FklB, PpiB and PpiC were purified and in pull-down experiments substrates specific to each of these PPIs identified, revealing an overlap of some substrates. Substrates of PpiC were validated by immunoprecipitations using extracts from wild-type and PpiC-H81A strains carrying a 3xFLAG-tag appended to the C-terminal end of the ppiC gene on the chromosome. Using isothermal titration calorimetry, RpoE, RseA, S2, and AhpC were established as FkpB substrates and PpiC’s PPIase activity was shown to be required for interaction with AhpC. Full article

(This article belongs to the Special Issue Molecular Chaperones 3.0)

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