scholarly journals Variation of the Performance of Machine-Learning Based Image Classifier in Automated Detection of Itch-Induced Scratch

2020 ◽  
Author(s):  
Chuan Liu ◽  
Sheng-Xiang Yan ◽  
Xiao-Bo Wu ◽  
Zhi-Jun Zhang ◽  
Wei Li
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Laboratory Animal ◽  
Automated Detection ◽  
Laboratory Animals ◽  
Primary Role ◽  
Data Recording ◽  
Non Invasive ◽  
Overall Performance ◽  
First Time

A 'little brother' of pain, itch is an unpleasant sensation that creates a specific urge to scratch. To date, various machine-learning based image classifiers (MBICs) have been proposed for quantitative analysis of itch-induced scratch behaviour of laboratory animals in an automated, non-invasive, inexpensive and real-time manner. In spite of MBICs' advantages, the overall performances (accuracy, sensitivity and specificity) of current MBIC approaches remains inconsistent, with their values varying from ~50% to ~99%, for which the reasons underlying have yet to be investigated further, both computationally and experimentally. To look into the variation of the performance of MBICs in automated detection of itch-induced scratch, this article focuses on the experimental data recording step, and reports here for the first time that MBICs' overall performance is inextricably linked to the sharpness of experimentally recorded video of laboratory animal scratch behaviour. This article furthermore demonstrates for the first time that a linearly correlated relationship exists between video sharpness and overall performance (accuracy and specificity, but not sensitivity) of MBICs, and highlight the primary role of experimental data recording in rapid, accurate and consistent quantitative assessment of laboratory animal itch.

scholarly journals Report on first certificate course on applied physiology of pain, analgesia, anesthesia, and euthanasia for laboratory animals held in Sri Lanka

2018 ◽  
Vol 42 (2) ◽  
pp. 263-266
Author(s):  
Mangala Gunatilake
Keyword(s):  
Sri Lanka ◽  
Laboratory Animal ◽  
Pain Perception ◽  
Anesthesia Machine ◽  
Laboratory Animals ◽  
Comparative Biology ◽  
Human Beings ◽  
Unpleasant Sensation ◽  
Applied Physiology ◽  
First Time

Similar to human beings, pain is an unpleasant sensation experienced by animals as well. There is no exception when the animals are subjected to experimental procedures. Our duty as researchers/scientists is to prevent or minimize the pain in animals so as to lessen their suffering and distress during experimental procedures. The basics of the physiology of pain and pain perception, analgesia, anesthesia, and euthanasia of laboratory animals were included to complete the program, before the practical part was attempted and before advanced topics, such as comparison of anesthetic combinations, were discussed. Therefore, this course was organized in Sri Lanka for the first time in collaboration with the Comparative Biology Centre of Newcastle University, UK. During this course, we were able to demonstrate how an anesthesia machine could be used in laboratory animal anesthesia for the first time in the country. None of the animal houses in the country were equipped with an anesthesia machine at the time of conducting the course.

scholarly journals Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep

2020 ◽  
Vol 7 (1) ◽  
pp. 190824
Author(s):  
Jasmeet Kaler ◽  
Jurgen Mitsch ◽  
Jorge A. Vázquez-Diosdado ◽  
Nicola Bollard ◽  
Tania Dottorini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Best Practice ◽  
Sampling Frequency ◽  
Automated Detection ◽  
Automated System ◽  
Objective Measures ◽  
Learning Approaches ◽  
Random Forest Algorithm ◽  
First Time ◽  
Rapid Treatment

Lameness in sheep is the biggest cause of concern regarding poor health and welfare among sheep-producing countries. Best practice for lameness relies on rapid treatment, yet there are no objective measures of lameness detection. Accelerometers and gyroscopes have been widely used in human activity studies and their use is becoming increasingly common in livestock. In this study, we used 23 datasets (10 non-lame and 13 lame sheep) from an accelerometer- and gyroscope-based ear sensor with a sampling frequency of 16 Hz to develop and compare algorithms that can differentiate lameness within three different activities (walking, standing and lying). We show for the first time that features extracted from accelerometer and gyroscope signals can differentiate between lame and non-lame sheep while standing, walking and lying. The random forest algorithm performed best for classifying lameness with an accuracy of 84.91% within lying, 81.15% within standing and 76.83% within walking and overall correctly classified over 80% sheep within activities. Both accelerometer- and gyroscope-based features ranked among the top 10 features for classification. Our results suggest that novel behavioural differences between lame and non-lame sheep across all three activities could be used to develop an automated system for lameness detection.

scholarly journals A water destructible SnS2 QD/PVA film based transient multifunctional sensor and machine learning assisted stimulus identification for non-invasive personal care diagnostics

2020 ◽  
Vol 1 (8) ◽  
pp. 2818-2830
Author(s):  
Naveen Bokka ◽  
Venkatarao Selamneni ◽  
Parikshit Sahatiya
Keyword(s):  
Machine Learning ◽  
Water Soluble ◽  
Personal Care ◽  
Non Invasive ◽  
Stimulus Identification ◽  
First Time ◽  
Sensor Strain ◽  
Multifunctional Sensor

We demonstrate, for the first time, a transient, flexible multifunctional sensor (strain, pressure, and breath) using a water soluble SnS2-QD/PVA film.

scholarly journals Enhancing the Accuracy of Non-Invasive Glucose Sensing in Aqueous Solutions Using Combined Millimeter Wave and Near Infrared Transmission

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3275
Author(s):  
Helena Cano-Garcia ◽  
Rohit Kshirsagar ◽  
Roberto Pricci ◽  
Ahmed Teyeb ◽  
Fergus O’Brien ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Glucose Sensing ◽  
Machine Learning Algorithms ◽  
Infrared Transmission ◽  
Non Invasive ◽  
Measurement Results ◽  
Transmission Data ◽  
First Time ◽  
Single Modality

We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Synthesis of a silymarin-gold nanoparticle conjugate and analysis of its liver-protecting activity

2021 ◽  
Vol 22 ◽  
Author(s):  
Sergey Staroverov ◽  
Sergey Kozlov ◽  
Alexander Fomin ◽  
Konstantib Gabalov ◽  
Alexey Volkov ◽  
...  
Keyword(s):  
Gold Nanoparticles ◽  
Liver Disease ◽  
Carbon Tetrachloride ◽  
Glutathione Depletion ◽  
Laboratory Animals ◽  
Experimental Hepatitis ◽  
Acute Liver Disease ◽  
The Mean ◽  
Extent Of Damage ◽  
First Time

Background: The liver disease problem prompts investigators to search for new methods of liver treatment. Introduction: Silymarin (Sil) protects the liver by reducing the concentration of free radicals and the extent of damage to the cell membranes. A particularly interesting method to increase the bioavailability of Sil is to use synthesized gold nanoparticles (AuNPs) as reagents. The study considered whether it was possible to use the silymarin-AuNP conjugate as a potential liver-protecting drug. Method: AuNPs were conjugated to Sil and examine the liver-protecting activity of the conjugate. Experimental hepatitis and hepatocyte cytolysis after carbon tetrachloride actionwere used as a model system, and the experiments were conducted on laboratory animals. Result: For the first time, silymarin was conjugated to colloidal gold nanoparticles (AuNPs). Electron microscopy showed that the resultant preparations were monodisperse and that the mean conjugate diameter was 18–30 nm ± 0.5 nm (mean diameter of the native nanoparticles, 15 ± 0.5 nm). In experimental hepatitis in mice, conjugate administration interfered with glutathione depletion in hepatocytes in response to carbon tetrachloride was conducive to an increase in energy metabolism, and stimulated the monocyte–macrophage function of the liver. The results were confirmed by the high respiratory activity of the hepatocytes in cell culture. Conclusion: We conclude that the silymarin-AuNP conjugate holds promise as a liver-protecting agent in acute liver disease caused by carbon tetrachloride poisoning.

Simulation of Laminar Liquid Jets in Gaseous Crossflow Before the Onset of Primary Breakup

2011 ◽  
Author(s):  
C.-L. Ng ◽  
K. A. Sallam
Keyword(s):  
Experimental Data ◽  
Fuel Injection ◽  
Liquid Jets ◽  
Liquid Jet ◽  
Jet Breakup ◽  
Primary Breakup ◽  
Density Ratios ◽  
First Time ◽  
Two Sides ◽  
Reduced Pressures

The deformation of laminar liquid jets in gaseous crossflow before the onset of primary breakup is studied motivated by its application to fuel injection in jet afterburners and agricultural sprays, among others. Three crossflow Weber numbers that represent three different liquid jet breakup regimes; column, bag, and shear breakup regimes, were studied at large liquid/gas density ratios and small Ohnesorge numbers. In each case the liquid jet was simulated from the jet exit and ended before the location where the experimental data indicated the onset of breakup. The results show that in column and bag breakup, the reduced pressures along the sides of the jet cause the liquid to move to the sides of the jet and enhance the jet deformation. In shear breakup, the flattened upwind surface pushes the liquid towards the two sides of the jet and causing the gaseous crossflow to separate near the edges of the liquid jet thus preventing further deformation before the onset of breakup. It was also found out that in shear breakup regime, the liquid phase velocity inside the liquid jet was large enough to cause onset of ligament formation along the jet side, which was not the case in the column and bag breakup regimes. In bag breakup, downwind surface waves were observed to grow along the sides of the liquid jet triggered a complimentary experimental study that confirmed the existence of those waves for the first time.

scholarly journals Raman spectroscopic detection of interleukin-10 and angiotensin converting enzyme

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhang ◽  
Frederieke A. M. van der Mee ◽  
Roel J. Erckens ◽  
Carroll A. B. Webers ◽  
Tos T. J. M. Berendschot
Keyword(s):  
Angiotensin Converting Enzyme ◽  
Biomedical Applications ◽  
Interleukin 10 ◽  
Spectral Features ◽  
Converting Enzyme ◽  
Raman Spectroscopic ◽  
Non Invasive ◽  
Confocal Raman ◽  
First Time ◽  
Enzyme Characteristic

AbstractIn this report we present a confocal Raman system to identify the unique spectral features of two proteins, Interleukin-10 and Angiotensin Converting Enzyme. Characteristic Raman spectra were successfully acquired and identified for the first time to our knowledge, showing the potential of Raman spectroscopy as a non-invasive investigation tool for biomedical applications.

scholarly journals Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Sen Yang ◽  
Yaping Zhang ◽  
Siu-Yeung Cho ◽  
Ricardo Correia ◽  
Stephen P. Morgan
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Deep Learning ◽  
Absolute Error ◽  
Learning Model ◽  
Physiological Measurement ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Blood Pressure Estimation ◽  
Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

Sign in / Sign up

Export Citation Format

Share Document