scholarly journals Indication of Electromagnetic Field Exposure via RBF-SVM Using Time-Series Features of Zebrafish Locomotion

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4818
Author(s):  
Yaqing He ◽  
Kim Fung Tsang ◽  
Richard Yuen-Chong Kong ◽  
Yuk-Tak Chow
Keyword(s):  
Time Series ◽  
Environmental Parameters ◽  
Empirical Method ◽  
Support Vector ◽  
The Novel ◽  
Adult Zebrafish ◽  
Field Exposure ◽  
Magnetic Exposure ◽  
Tank Test ◽  
The Impact

This paper introduces a novel model based on support vector machine with radial basis function kernel (RBF-SVM) using time-series features of zebrafish (Danio rerio) locomotion exposed to different electromagnetic fields (EMFs) to indicate the corresponding EMF exposure. A group of 14 adult zebrafish was randomly divided into two groups, 7 in each group; the fish of each group have the novel tank test under a sham or real magnetic exposure of 6.78 MHz and about 1 A/m. Their locomotion in the tests was videotaped to convert into the x, y coordinate time-series of the trajectories for reforming time-series matrices according to different time-series lengths. The time-series features of zebrafish locomotion were calculated by the comparative time-series analyzing framework highly comparative time-series analysis (HCTSA), and a limited number of the time-series features that were most relevant to the EMF exposure conditions were selected using the minimum redundancy maximum relevance (mRMR) algorithm for RBF-SVM classification training. Before this, ambient environmental parameters (AEPs) had little effect on the locomotion performance of zebrafish processed by the empirical method, which had been quantitatively verified by regression using another group of 14 adult zebrafish. The results have demonstrated that the purposed model is capable of accurately indicating different EMF exposures. All classification accuracies can be 100%, and the classification precision of several classifiers based on specific parameters and feature sets with specific dimensions can reach higher than 95%. The speculative reason for this result is that the specified EMF has affected the zebrafish neural aspect, which is then reflected in their behaviors. The outcomes of this study have provided a new indication model for EMF exposures and provided a reference for the investigation of the impact of EMF exposure.

scholarly journals NOS-2 Participates in the Behavioral Effects of Ethanol Withdrawal in Zebrafish

2020 ◽  
Author(s):  
Suianny Nayara da Silva Chaves ◽  
Bruna Patrícia Dutra Costa ◽  
Gabriela Cristini Vidal Gomes ◽  
Monica Lima-Maximino ◽  
Eduardo Pacheco Rico ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Ethanol Withdrawal ◽  
Mechanistic Study ◽  
The Novel ◽  
Adult Zebrafish ◽  
Ethanol Abuse ◽  
Tank Test ◽  
Nitric Oxide Synthase 2 ◽  
Oxide Synthase ◽  
Neurobehavioral Effects

Nitric oxide has been implicated in symptoms of ethanol withdrawal in animal models. Zebrafish have been used as models to study neurobehavioral effects of ethanol (EtOH) withdrawal, but the mechanisms associated with these effects are not yet clear. Adult zebrafish were treated with 1% EtOH for 20 min per day for 8 days, injected with the nitric oxide synthase 2 (NOS-2) inhibitor aminoguanidine (50 mg/kg), and allowed to experience withdrawal (WD) in their hometanks for 7 days. EtOH WD increased anxiety-like behavior in the novel tank test, an effect that was blocked by aminoguanidine. EtOH WD also increased brain levels of nitrite, an effect that was partially blocked by aminoguanidine. These results underline a novel mechanism by which NOS-2 controls anxiety-like responses to ethanol withdrawal, with implications for the mechanistic study of symptoms associated with chronic ethanol abuse.

Calocybe indicia extract modifies GABA and Serotonin levels to Alleviate anxiety in experimental adult Zebrafish models

2021 ◽  
pp. 4789-4794
Author(s):  
Sarah Andrea Wilson ◽  
Anushree Nagaraj ◽  
Lalitha Vaidyanathan
Keyword(s):  
Biochemical Parameters ◽  
Anxiolytic Effect ◽  
The Novel ◽  
Adult Zebrafish ◽  
Tissue Weight ◽  
Mao A ◽  
Before And After ◽  
Tank Test ◽  
Gaba Content ◽  
Calocybe Indica

Zebrafish (Danio rerio) was used as a model to study anxiety due to its physiological homology to humans. The pathophysiology of anxiety, even though still unclear, has been extensively studied in Zebrafish. Anxiety was induced by withdrawal after exposure to 0.5% ethanol, which proved to be anxiogenic, validated through the novel tank test. The light/dark test revealed that exposure to 0.5% ethanol had anxiolytic effects. The milky mushroom, Calocybe indica was used to treat anxiety since its anti-hypertensive effects have already been reported. Biochemical parameters such as GABA and MAO (A&B) were measured before and after treatment with different concentrations of C. indica and standard anxiolytic drug, Fluoxetine to compare and confirm the anxiolytic effect. The GABA content was found to be 119.9±1.99 mmoles/g tissue weight after treatment with 50 µg C. indica which was comparable to the normal group values (100±4.12). MAO (A&B) activity decreased which in turn increased serotonin levels with 25µg of C. indica. 25µg and 100µg concentration of the extract of C. indica was found to be optimum in reducing the level of anxiety.

scholarly journals Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

2019 ◽  
Vol 11 (21) ◽  
pp. 2512 ◽  
Author(s):  
Nicolas Karasiak ◽  
Jean-François Dejoux ◽  
Mathieu Fauvel ◽  
Jérôme Willm ◽  
Claude Monteil ◽  
...  
Keyword(s):  
Time Series ◽  
Tree Species ◽  
Cross Validation ◽  
Satellite Image ◽  
Support Vector ◽  
Validation Data ◽  
Spatial Instability ◽  
Classification Errors ◽  
Leave One Out ◽  
The Impact

Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on 10 Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the Support Vector Machine (SVM) algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy (OA)) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

scholarly journals Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak

2021 ◽  
Vol 7 ◽  
pp. e746
Author(s):  
Muhammad Naeem ◽  
Jian Yu ◽  
Muhammad Aamir ◽  
Sajjad Ahmad Khan ◽  
Olayinka Adeleye ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Comparative Analysis ◽  
Prediction Models ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Support Vector ◽  
Absolute Percentage Error ◽  
Statistical Measures ◽  
The Impact

Background Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread. Methods In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic. Results Statistical measures—Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE)—are used for model accuracy. The values of MAPE for the best-selected models for confirmed, recovered and deaths cases are 0.003, 0.006 and 0.115, respectively, which falls under the category of highly accurate forecasts. In addition, we computed 15 days ahead forecast for the daily deaths, recovered, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting the decision-making of evolving short-term policies.

An acetylcholinesterase inhibitor, donepezil, increases anxiety and cortisol levels in adult zebrafish

2020 ◽  
Vol 34 (12) ◽  
pp. 1449-1456 ◽  
Author(s):  
Ana CVV Giacomini ◽  
Barbara W Bueno ◽  
Leticia Marcon ◽  
Naiara Scolari ◽  
Rafael Genario ◽  
...  
Keyword(s):  
Acetylcholinesterase Inhibitor ◽  
Model Organism ◽  
Acetylcholinesterase Activity ◽  
Acetylcholinesterase Inhibition ◽  
Whole Body ◽  
The Novel ◽  
Adult Zebrafish ◽  
Tank Test ◽  
Cortisol Levels ◽  
Brain Acetylcholinesterase

Background: A potent acetylcholinesterase inhibitor, donepezil is a cognitive enhancer clinically used to treat neurodegenerative diseases. However, its complete pharmacological profile beyond cognition remains unclear. The zebrafish ( Danio rerio) is rapidly becoming a powerful novel model organism in neuroscience and central nervous system drug screening. Aim: Here, we characterize the effects of 24-h donepezil administration on anxiety-like behavioral and endocrine responses in adult zebrafish. Methods: We evaluated zebrafish anxiety-like behaviors in the novel tank, the light-dark and the shoaling tests, paralleled by assessing brain acetylcholinesterase activity and whole-body cortisol levels. Results: Overall, donepezil dose-dependently decreased zebrafish locomotor activity in the novel tank test and reduced time in light in the light-dark test, likely representing hypolocomotion and anxiety-like behaviors. Donepezil predictably decreased brain acetylcholinesterase activity, also increasing whole-body cortisol levels, thus further linking acetylcholinesterase inhibition to anxiety-like behavioral and endocrine responses. Conclusion: Collectively, these findings suggest negative modulation of zebrafish affective behavior by donepezil, support the key role of cholinergic mechanisms in behavioral regulation in zebrafish, and reinforce the growing utility of zebrafish models for studying complex behavioral processess and their neuroendocrine and neurochemical regulation.

scholarly journals Artificial-Intelligence-Based Time-Series Intervention Models to Assess the Impact of the COVID-19 Pandemic on Tomato Supply and Prices in Hyderabad, India

Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1878
Author(s):  
Gayathri Chitikela ◽  
Meena Admala ◽  
Vijaya Kumari Ramalingareddy ◽  
Nirmala Bandumula ◽  
Gabrijel Ondrasek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Negative Impact ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Time Series Models ◽  
Intervention Model ◽  
Intervention Models ◽  
The Impact

This study’s objective was to assess the impact of the COVID-19 pandemic on tomato supply and prices in Gudimalkapur market in Hyderabad, India. The lockdown imposed by the government of India from 25 March 2020 to 30 June 2020 particularly affected the supply chain of perishable agricultural products, including tomatoes as one of the major vegetable crops in the study area. The classical time series models such as autoregressive integrated moving average (ARIMA) intervention models and artificial intelligence (AI)-based time-series models namely support vector regression (SVR) intervention and artificial neural network (ANN) intervention models were used to predict tomato supplies and prices in the studied market. The modelling results show that the pandemic had a negative impact on supply and a positive impact on tomato prices. Moreover, the ANN intervention model outperformed the other models in both the training and test data sets. The superior performance of the ANN intervention model could be due to its ability to account for the nonlinear and complex nature of the data with exogenous intervention variable.

scholarly journals NOS-2 Participates in the Behavioral Effects of Ethanol Withdrawal in Zebrafish

2020 ◽  
Author(s):  
Suianny Nayara da Silva Chaves ◽  
Bruna Patrícia Dutra Costa ◽  
Gabriela Cristini Vidal Gomes ◽  
Monica Lima-Maximino ◽  
Eduardo Pacheco Rico ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Ethanol Withdrawal ◽  
Mechanistic Study ◽  
The Novel ◽  
Adult Zebrafish ◽  
Ethanol Abuse ◽  
Tank Test ◽  
Nitric Oxide Synthase 2 ◽  
Oxide Synthase ◽  
Neurobehavioral Effects

Nitric oxide has been implicated in symptoms of ethanol withdrawal in animal models. Zebrafish have been used as models to study neurobehavioral effects of ethanol (EtOH) withdrawal, but the mechanisms associated with these effects are not yet clear. Adult zebrafish were treated with 1% EtOH for 20 min per day for 8 days, injected with the nitric oxide synthase 2 (NOS-2) inhibitor aminoguanidine (50 mg/kg), and allowed to experience withdrawal (WD) in their hometanks for 7 days. EtOH WD increased anxiety-like behavior in the novel tank test, an effect that was blocked by aminoguanidine. EtOH WD also increased brain levels of nitrite, an effect that was partially blocked by aminoguanidine. These results underline a novel mechanism by which NOS-2 controls anxiety-like responses to ethanol withdrawal, with implications for the mechanistic study of symptoms associated with chronic ethanol abuse.

scholarly journals NOS-2 Participates in the Behavioral Effects of Ethanol Withdrawal in Zebrafish

2019 ◽  
Author(s):  
Suianny Nayara da Silva Chaves ◽  
Bruna Patrícia Dutra Costa ◽  
Gabriela Cristini Vidal Gomes ◽  
Monica Lima-Maximino ◽  
Eduardo Pacheco Rico ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Ethanol Withdrawal ◽  
Mechanistic Study ◽  
The Novel ◽  
Adult Zebrafish ◽  
Ethanol Abuse ◽  
Tank Test ◽  
Nitric Oxide Synthase 2 ◽  
Oxide Synthase ◽  
Neurobehavioral Effects

Nitric oxide has been implicated in symptoms of ethanol withdrawal in animal models. Zebrafish have been used as models to study neurobehavioral effects of ethanol (EtOH) withdrawal, but the mechanisms associated with these effects are not yet clear. Adult zebrafish were treated with 1% EtOH for 20 min per day for 8 days, injected with the nitric oxide synthase 2 (NOS-2) inhibitor aminoguanidine (50 mg/kg), and allowed to experience withdrawal (WD) in their hometanks for 7 days. EtOH WD increased anxiety-like behavior in the novel tank test, an effect that was blocked by aminoguanidine. EtOH WD also increased brain levels of nitrite, an effect that was partially blocked by aminoguanidine. These results underline a novel mechanism by which NOS-2 controls anxiety-like responses to ethanol withdrawal, with implications for the mechanistic study of symptoms associated with chronic ethanol abuse.

scholarly journals Adult zebrafish anaesthesia: a study of efficacy and behavioural recovery of different anaesthetics

2021 ◽  
Author(s):  
Sara Jorge ◽  
Jorge M Ferreira ◽  
I Anna S Olsson ◽  
Ana M Valentim
Keyword(s):  
Fish Behaviour ◽  
The Novel ◽  
Clove Oil ◽  
Adult Zebrafish ◽  
Long Term Effects ◽  
Fish Welfare ◽  
Equilibrium Response ◽  
Tank Test ◽  
Fish Activity

AbstractThe use of proper anaesthesia in zebrafish research is essential to ensure fish welfare and data reliability. However, anaesthesia long-term side effects remain poorly understood. The purpose of this study was to assess anaesthesia quality and recovery in adult zebrafish using different anaesthetic protocols and to determine possible long-term effects on the fish activity and anxiety-like behaviours after anaesthesia.Mixed sex adult AB zebrafish were randomly assigned to 5 different groups (control, 175mg/L MS222, 45 mg/L clove oil, 2 mg/L etomidate and 5mg/L propofol combined with 150mg/L lidocaine) and placed in the respective anaesthetic bath. Time to lose the equilibrium, response to touch and to tail pinch stimuli, and recovery after anaesthesia administration were evaluated. In addition, after stopping anaesthesia, respiratory rate, activity and anxiety-like behaviours in the novel tank test were studied.Overall, all protocols proved to be adequate for zebrafish anaesthesia research as they showed full recovery at 1h, and only etomidate had minor effects on fish behaviour in the novel tank, a validated test for anxiety.

scholarly journals Evaluation of Machine Learning Predictions of a Highly Resolved Time Series of Chlorophyll-a Concentration

2021 ◽  
Vol 11 (16) ◽  
pp. 7208
Author(s):  
Felipe de Luca Lopes de Amorim ◽  
Johannes Rick ◽  
Gerrit Lohmann ◽  
Karen Helen Wiltshire
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Support Vector Machine ◽  
Chlorophyll A ◽  
Environmental Parameters ◽  
Machine Learning Algorithms ◽  
Validation Dataset ◽  
Support Vector ◽  
Environmental Status

Pelagic chlorophyll-a concentrations are key for evaluation of the environmental status and productivity of marine systems, and data can be provided by in situ measurements, remote sensing and modelling. However, modelling chlorophyll-a is not trivial due to its nonlinear dynamics and complexity. In this study, chlorophyll-a concentrations for the Helgoland Roads time series were modeled using a number of measured water and environmental parameters. We chose three common machine learning algorithms from the literature: the support vector machine regressor, neural networks multi-layer perceptron regressor and random forest regressor. Results showed that the support vector machine regressor slightly outperformed other models. The evaluation with a test dataset and verification with an independent validation dataset for chlorophyll-a concentrations showed a good generalization capacity, evaluated by the root mean squared errors of less than 1 µg L−1. Feature selection and engineering are important and improved the models significantly, as measured in performance, improving the adjusted R2 by a minimum of 48%. We tested SARIMA in comparison and found that the univariate nature of SARIMA does not allow for better results than the machine learning models. Additionally, the computer processing time needed was much higher (prohibitive) for SARIMA.

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