Novel Antimicrobial Peptides Designed Using Recurrent Neural Network Reduce Mortality in Experimental Sepsis

2021 ◽  
Author(s):  
Albert Bolatchiev
Keyword(s):  
Neural Network ◽  
Survival Rate ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Intraperitoneal Administration ◽  
Antimicrobial Effect ◽  
Amino Acid Sequences ◽  
Experimental Sepsis ◽  
Sterile Saline

Abstract The amino acid sequences of 198 novel peptides were obtained using a generative long short-term memory recurrent neural network (LTSM RNN). To assess their antimicrobial effect, I synthesized 5 out of 198 generated peptides. The PEP-38 and PEP-137 peptides were active in vitro against carbapenem-resistant isolates of Klebsiella aerogenes (n=12) and K. pneumoniae (n=18). PEP-137 was also active against Pseudomonas aeruginosa (n=17). The remaining three peptides (PEP-36, PEP-136 and PEP-174) showed no antibacterial effect. Then I investigated the effect of PEP-38 and PEP-137 (a single intraperitoneal administration of a 100 µg dose 30 min after infection) on animal survival in an experimental murine model of K. pneumoniae-induced sepsis. As a control, I used two groups of mice: one received sterile saline, and the other received inactive in vitro PEP-36 (a single 100 µg dose). The PEP-36 peptide was shown to provide the highest survival rate (66.7%). PEP-137 showed a survival rate of 50%. PEP-38 was found to be ineffective. The data obtained can be used to develop new antibacterial peptide drugs to combat antibiotic resistance.

scholarly journals Antimicrobial Peptides Epinecidin-1 and Beta-Defesin-3 Are Effective against a Broad Spectrum of Antibiotic-Resistant Bacterial Isolates and Increase Survival Rate in Experimental Sepsis

Antibiotics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 76
Author(s):  
Albert Bolatchiev
Keyword(s):  
Antibacterial Activity ◽  
Survival Rate ◽  
Antimicrobial Peptides ◽  
Lethal Dose ◽  
Experimental Models ◽  
Experimental Sepsis ◽  
Epinephelus Coioides ◽  
Sterile Saline

The antimicrobial peptides human Beta-defensin-3 (hBD-3) and Epinecidin-1 (Epi-1; by Epinephelus coioides) could be a promising tool to develop novel antibacterials to combat antibiotic resistance. The antibacterial activity of Epi-1 + vancomycin against methicillin-resistant Staphylococcus aureus (22 isolates) and Epi-1 + hBD-3 against carbapenem-resistant isolates of Klebsiella pneumoniae (n = 23), Klebsiella aerogenes (n = 17), Acinetobacter baumannii (n = 9), and Pseudomonas aeruginosa (n = 13) was studied in vitro. To evaluate the in vivo efficacy of hBD-3 and Epi-1, ICR (CD-1) mice were injected intraperitoneally with a lethal dose of K. pneumoniae or P. aeruginosa. The animals received a single injection of either sterile saline, hBD-3 monotherapy, meropenem monotherapy, hBD-3 + meropenem, or hBD-3 + Epi-1. Studied peptides showed antibacterial activity in vitro against all studied clinical isolates in a concentration of 2 to 32 mg/L. In both experimental models of murine sepsis, an increase in survival rate was seen with hBD-3 monotherapy, hBD-3 + meropenem, and hBD-3 + Epi-1. For K. pneumoniae-sepsis, hBD-3 was shown to be a promising option in overcoming the resistance of Klebsiella spp. to carbapenems, though more research is needed. In the P. aeruginosa-sepsis model, the addition of Epi-1 to hBD-3 was found to have a slightly reduced mortality rate compared to hBD-3 monotherapy.

scholarly journals Recurrent neural network-based acute concussion classifier using raw resting state EEG data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karun Thanjavur ◽  
Arif Babul ◽  
Brandon Foran ◽  
Maya Bielecki ◽  
Adam Gilchrist ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Resting State ◽  
Short Term Memory ◽  
Characteristic Curve ◽  
Health Concern ◽  
Adolescent Athletes ◽  
Individual Level ◽  
Diffuse Brain Injury ◽  
Eeg Data

AbstractConcussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, the most significant challenge in concussion management is the lack of objective, clinically- accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Here, we report on our efforts to address this challenge. Specifically, we introduce a deep learning long short-term memory (LSTM)-based recurrent neural network that is able to distinguish between non-concussed and acute post-concussed adolescent athletes using only short (i.e. 90 s long) samples of resting state EEG data as input. The athletes were neither required to perform a specific task nor expected to respond to a stimulus during data collection. The acquired EEG data were neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and validated using data from 27 male, adolescent athletes with sports related concussion, benchmarked against 35 non-concussed adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of > 90% and achieved an ensemble median Area Under the Receiver Operating Characteristic Curve (ROC/AUC) equal to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state, raw EEG data. Our concussion classifier represents a promising first step towards the development of an easy-to-use, objective, brain-based, automatic classification of concussion at an individual level.

Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

2021 ◽  
pp. 1-11
Author(s):  
Sang-Ki Jeong ◽  
Dea-Hyeong Ji ◽  
Ji-Youn Oh ◽  
Jung-Min Seo ◽  
Hyeung-Sik Choi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flow Velocity ◽  
Recurrent Neural Network ◽  
Pid Controller ◽  
Short Term Memory ◽  
Ocean Currents ◽  
Ocean Current ◽  
Movement Speed ◽  
Marine Research

In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9].

scholarly journals Research on maintenance spare parts requirement prediction based on LSTM recurrent neural network

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 618-627
Author(s):  
Weixing Song ◽  
Jingjing Wu ◽  
Jianshe Kang ◽  
Jun Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Spare Parts ◽  
Performance Comparison ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Particle Swarm Algorithm

Abstract The aim of this study was to improve the low accuracy of equipment spare parts requirement predicting, which affects the quality and efficiency of maintenance support, based on the summary and analysis of the existing spare parts requirement predicting research. This article introduces the current latest popular long short-term memory (LSTM) algorithm which has the best effect on time series data processing to equipment spare parts requirement predicting, according to the time series characteristics of spare parts consumption data. A method for predicting the requirement for maintenance spare parts based on the LSTM recurrent neural network is proposed, and the network structure is designed in detail, the realization of network training and network prediction is given. The advantages of particle swarm algorithm are introduced to optimize the network parameters, and actual data of three types of equipment spare parts consumption are used for experiments. The performance comparison of predictive models such as BP neural network, generalized regression neural network, wavelet neural network, and squeeze-and-excitation network prove that the new method is effective and provides an effective method for scientifically predicting the requirement for maintenance spare parts and improving the quality of equipment maintenance.

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