One-Class-Based Intelligent Classifier for Detecting Anomalous Situations During the Anesthetic Process

2020 ◽  
Author(s):  
Alberto Leira ◽  
Esteban Jove ◽  
Jose M Gonzalez-Cava ◽  
José-Luis Casteleiro-Roca ◽  
Héctor Quintián ◽  
...  
Keyword(s):  
Side Effects ◽  
Anomaly Detection ◽  
General Anesthesia ◽  
Closed Loop ◽  
Real Data ◽  
Successful Performance ◽  
Final Model ◽  
Detection Techniques ◽  
Adverse Side Effects ◽  
Operative Recovery

Abstract Closed-loop administration of propofol for the control of hypnosis in anesthesia has evidenced an outperformance when comparing it with manual administration in terms of drug consumption and post-operative recovery of patients. Unlike other systems, the success of this strategy lies on the availability of a feedback variable capable of quantifying the current hypnotic state of the patient. However, the appearance of anomalies during the anesthetic process may result in inaccurate actions of the automatic controller. These anomalies may come from the monitors, the syringe pumps, the actions of the surgeon or even from alterations in patients. This could produce adverse side effects that can affect the patient postoperative and reduce the safety of the patient in the operating room. Then, the use of anomaly detection techniques plays a significant role to avoid this undesirable situations. This work assesses different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining real data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final model presents successful performance.

SARS-CoV-2 Biology Insights, Part I. Genome-wide structure and druggability: literature review (Preprint)

2020 ◽  
Author(s):  
Laura Lafon-Hughes
Keyword(s):  
Side Effects ◽  
Literature Review ◽  
Host Cell ◽  
Nucleoside Analogs ◽  
Viral Proteins ◽  
Therapeutic Strategies ◽  
Adverse Side Effects ◽  
Viral Proteases ◽  
Genome Wide ◽  
Therapeutic Alternatives

BACKGROUND COVID-19 pandemic prompts the study of coronavirus biology and search of putative therapeutic strategies. OBJECTIVE To compare SARS-CoV-2 genome-wide structure and proteins with other coronaviruses, focusing on putative coronavirus-specific or SARS-CoV-2 specific therapeutic designs. METHODS The genome-wide structure of SARS-CoV-2 was compared to that of SARS and other coronaviruses in order to gain insights, doing a literature review through Google searches. RESULTS There are promising therapeutic alternatives. Host cell targets could be modulated to hamper viral replication, but targeting viral proteins directly would be a better therapeutic design, since fewer adverse side effects would be expected. CONCLUSIONS Therapeutic strategies (Figure 1) could include the modulation of host targets (PARPs, kinases) , competition with G-quadruplexes or nucleoside analogs to hamper RDRP. The nicest anti-CoV options include inhibitors of the conserved essential viral proteases and drugs that interfere ribosome slippage at the -1 PRF site.

scholarly journals Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data

2021 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Jessamyn Dahmen ◽  
Diane J. Cook
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Bayesian Optimization ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Health Events ◽  
Warm Start ◽  
Health Related ◽  
Unsupervised Algorithms

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

scholarly journals Brain-specific inhibition of mTORC1 eliminates side effects resulting from mTORC1 blockade in the periphery and reduces alcohol intake in mice

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yann Ehinger ◽  
Ziyang Zhang ◽  
Khanhky Phamluong ◽  
Drishti Soneja ◽  
Kevan M. Shokat ◽  
...  
Keyword(s):  
Side Effects ◽  
Inhibitory Action ◽  
Alcohol Use Disorder ◽  
Alcohol Intake ◽  
Therapeutic Agents ◽  
Adverse Side Effects ◽  
Mtorc1 Activation ◽  
Potential Use ◽  
Heavy Alcohol Intake ◽  
Alcohol Dependent

AbstractAlcohol Use Disorder (AUD) affects a large portion of the population. Unfortunately, efficacious medications to treat the disease are limited. Studies in rodents suggest that mTORC1 plays a crucial role in mechanisms underlying phenotypes such as heavy alcohol intake, habit, and relapse. Thus, mTORC1 inhibitors, which are used in the clinic, are promising therapeutic agents to treat AUD. However, chronic inhibition of mTORC1 in the periphery produces undesirable side effects, which limit their potential use for the treatment of AUD. To overcome these limitations, we designed a binary drug strategy in which male mice were treated with the mTORC1 inhibitor RapaLink-1 together with a small molecule (RapaBlock) to protect mTORC1 activity in the periphery. We show that whereas RapaLink-1 administration blocked mTORC1 activation in the liver, RapaBlock abolished the inhibitory action of Rapalink-1. RapaBlock also prevented the adverse side effects produced by chronic inhibition of mTORC1. Importantly, co-administration of RapaLink-1 and RapaBlock inhibited alcohol-dependent mTORC1 activation in the nucleus accumbens and attenuated alcohol seeking and drinking.

scholarly journals Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration

2021 ◽  
Vol 13 (15) ◽  
pp. 2868
Author(s):  
Yonglin Tian ◽  
Xiao Wang ◽  
Yu Shen ◽  
Zhongzheng Guo ◽  
Zilei Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Closed Loop ◽  
Real Data ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Training Model ◽  
Labor Cost ◽  
Open Loop ◽  
Training Dataset ◽  
Data Annotation

Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual–real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

Efficient Implementation of Hyperspectral Anomaly Detection Techniques on GPUs and Multicore Processors

2014 ◽  
Vol 7 (6) ◽  
pp. 2256-2266 ◽  
Author(s):  
Jose M. Molero ◽  
Ester M. Garzon ◽  
Inmaculada Garcia ◽  
Enrique S. Quintana-Orti ◽  
Antonio Plaza
Keyword(s):  
Anomaly Detection ◽  
Multicore Processors ◽  
Efficient Implementation ◽  
Detection Techniques
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