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Figure 3 in Improved diagnostic sensitivity of human strongyloidiasis using point-of-care mixed recombinant antigen-based immunochromatography

Figure 3. The purified NIE (a) and SsIR (b) fusion-tagged proteins visualized following electrophoresis through 12% and 10% SDS–PAGE, respectively. The gels were stained with Coomassie Brilliant Blue. Representative dot ELISAs (c) using NIE (1–2), SsIR (3–4), and mixed NIE and SsIR (5–6) proteins as the antigens probed with pooled positive (1, 3, 5) and negative (2, 4, 6) sera. M indicates molecular mass maker.

Figure 1 in Improved diagnostic sensitivity of human strongyloidiasis using point-of-care mixed recombinant antigen-based immunochromatography

Figure 1. Flow diagram of study design for the NIE, SsIR, and NIE-SsIR ICT kits. The SsIR ICT kit procedure was performed as previously described by Boonroumkaew et al. [4].

Figure 2 in Improved diagnostic sensitivity of human strongyloidiasis using point-of-care mixed recombinant antigen-based immunochromatography

Figure 2. The parts of an ICT kit (left) and reference colour card (right) (a). An ICT kit showing a positive result (b) with a band at both control (C) and test (T) lines. An ICT kit showing a negative result (c) with a band only at the C line. The intensity value of the colour image is specified by the red, green, and blue parameters as separate integers from 0 to 255 with the 8-bit representation of a pixel in the image. The intensity values were plotted for C and T lines of the positive (d) and negative (e) ICT kits. The intensity values (d and e) were related to colour band intensity of C and T lines in the strips. S indicates sample hole.

Figure 4 in Improved diagnostic sensitivity of human strongyloidiasis using point-of-care mixed recombinant antigen-based immunochromatography

Figure 4. Representative results of the NIE, SsIR, and NIE-SsIR ICT kits, Positive, positive pooled serum samples; Negative, negative pooled serum samples; Hc, healthy control; Ss, proven strongyloidiasis; Gl, giardiasis; Eh, amoebiasis; Bh, blastocystosis; Ov, opisthorchiasis; Fg, fascioliasis; Ph, paragonimiasis; Tn, taeniasis; Cc, cysticercosis; Se, sparganosis; Hw, hookworm infections; Al, ascariasis; Tt, trichuriasis; Ts, trichinellosis; Ac, angiostrongyliasis; Gs, gnathostomiasis; Cp, capillariasis. The intensity cut-off levels for a positive result of the NIE, SsIR, and NIE-SsIR ICT kits were 1, 1, and>0.5, respectively by the naked eye and <164, <164, and <167, respectively by the in-house strip reader. The "+" and "—" symbols indicated positive and negative results.

opencc-by-4.0Jun 2024View details →

From optimality to prestige: Investigating human-animal interactions at Late Mesolithic Hoge Vaart-A27 (Almere, the Netherlands) using a Prey Choice Model

<h2>This dataset is from my Research Master's Thesis from Groningen University. </h2> The dataset includes; <ul> <li>The CSV data necessary to recreate all models and graphs from the thesis</li> <li>The R.Script with all codes</li> </ul> Used packages and programming language:  <ul> <li>Kassambara, A. (2023). ggpubr: ‘ggplot2’ Based Publication Ready Plots (R package version 0.6.0) [R; Rstudio]. R Foundation for Statistical Computing.<a href="https://doi.org/%3Chttps://CRAN.R-project.org/package=ggpubr%3E."> <https://CRAN.R-project.org/package=ggpubr>.</a></li> <li>Mei, W., Yu, G., & Greenwell, B. M. (2022). ggtrendline: Add Trendline and Confidence Interval to ‘ggplot’ (R package version 1.0.3) [R; Rstudio]. R Foundation for Statistical Computing.<a href="https://cran.r-project.org/package=ggtrendline"> https://CRAN.R-project.org/package=ggtrendline</a></li> <li>Pedersen, T. L. (2024). patchwork: The Composer of Plots (R package version 1.2.0) [R; RStudio]. R Foundation for Statistical Computing.<a href="https://cran.r-project.org/package=patchwork"> https://CRAN.R-project.org/package=patchwork</a></li> <li>R Core Team. (2022). R: A Language and Environment for Statistical Computing (R version 4.2.1) [R; RStudio]. R Foundation for Statistical Computing.<a href="https://www.r-project.org/"> https://www.R-project.org/</a></li> <li>Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC.</li> <li>Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.<a href="https://ggplot2.tidyverse.org"> https://ggplot2.tidyverse.org</a></li> <li>Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686.<a href="https://doi.org/10.21105/joss.01686"> https://doi.org/10.21105/joss.01686</a></li> </ul> Abstract The research of the faunal assemblage from Hoge Vaart-A27 (Almere, the Netherlands) provides a new perspective on investigating the connections between foraging strategies and socio-cultural dynamics of the Late Mesolithic and Early Swifterbant communities in Northwest Europe. The significance of this topic lies in its potential to help elucidate the broader socio-cultural aspects of foraging and human-animal interactions during this period. Despite extensive research, there remains a gap in comprehending how prestige influenced prey selection alongside optimal foraging strategies. The study aims to address this gap by employing zooarchaeological methods combined with prey choice modelling to investigate prey selection complexities in Flevoland’s transitioning cultural and physical landscape. The methods include analyses of species abundance and detailed examination of red deer, horse, aurochs and wild boar. The key findings reveal that while (optimal) foraging strategies were practised at Hoge Vaart-A27, they were significantly influenced by motivations such as prestige. The results contribute significantly to research by offering a glimpse into how Northwest European foragers could have perceived and interacted with big game beyond subsistence.  Keywords human-animal relationships, prestige, profitability, prey choice, Swifterbant Culture, optimal foraging theory, costly signalling theory

Figure 6. Interface of FFE program-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

Points, Features Extraction and save all input information for the classifier (Features, Ethnic group, Gender and emotion). Figure 6 shows the interface of FFE program.

Figure 7. Samples of MSFDE dataset-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

We performed groups of experiments to study the impact of ethnic group (race) in the accuracy of emotion recognition with three kinds of ethnic groups (Asian, Caucasian as African). So we have three experiments, each experiment has a neural network as a classifier, and each neural network has three layers where there are 16 neurons in the hidden layer except Asian network has 17 neurons (the best result with 17 neurons for Asians).

Figure 3. 46 points are selected on face elements to describe the emotions.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

The number of points and the position of points are not standardized, but it is depending on the features that will be extracted, and used for the classifier. Many researches use various number of points and positions based on their view about the feature to be considered [13] [18] [19]. Figure 3 shows the points we used.

Figure 1. A proposed approach-of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

Our proposed approach uses the face expression to detect the emotions through five steps that shows in Figure 1.

Figure. 2. Examples of Angry from different races. (A,B) African. (C,D) Asian. (E,F) Caucasian.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

We chose 46 points which are distributed over human face image and use these points for features extraction. The choice of these points is to determine the shape of each element of the face (eyes, eyebrows and mouth), because the shape of these elements is changeable for each emotion, but these changes are different for each race as shown in Figure 2.

Figure 4. Distance between eyebrow and eye.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

Based on what we stated above, we need to extract 28 features, which describe the distances between certain points explained in the previous stage, these features are classified into six groups, and each group describes the features of one face element. All features are a vertical distances between two points. Group one contains seven features for mouth, groups two and three contains 14 features for eyes, groups four and five contain six features for eyebrows, and the last group has one feature only which is the distance between the beginning of the eyebrow and the beginning of the eye in same side, this is significant (from point 23 to 15) because it is used to measure the distance of eyebrow from the eye. This feature is shown in Figure 4 by a line.

Figure 5. ANN Structure 4.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

For classification purpose of the emotions, we use ANN of supervised learning based on backpropagation algorithm. Backpropagation neural network architecture is used with its standards learning function with 28 inputs representing the extracted features and 6 outputs representing 6 emotions, happy, sad, angry, fear, shame and disgust. the emotions. We have also a hidden layer with 16 nodes selected after various trails to obtain the best results. The used ANN is depicted in Figure 5.

Figure 4. Extracted bands diagramClassification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Based on that coefficient, different bands have to be extracted. The bands are alpha, beta, theta, gamma, and delta. Figure 4 shown in below which is represent the different extract band diagrams.

opencc-by-4.0Aug 2015View details →

Figure 3. Electrode placement diagram-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Reference electrode placed AFz placed in between AF1 and AF2 electrode and ground electrode Oz is placed between O1 and O2 electrodes. The impedance of the electrode range is 5KΩ. The sampling rate was fixed range between 256 samples per second for all the channels. Figure 3 shown in below which is represent the electrode placement diagram. The recorded EEG signal is used to recognize the different level of emotions.

opencc-by-4.0Aug 2015View details →

Figure 2. Emotion recognition using EEG-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

This section describes that collection of EEG signals for different emotion recognition experiments. The electroencephalography signals of 32 participants were recorded during one minute videos. Based on that participants are rated in terms of valence and arousal, like/dislike, familiarities and dominance. Emotion related ratings are given based on the online self assessment which is 120 one minute extracted music videos, which are rated by 14-16 volunteers based on arousal and valence. The EEG signals were recorded using 64 electrodes, first 62 electrodes are active electrode, one for reference and remaining one is ground electrode. All the electrodes are placed on the scalp which is made up of the Ag/Ag-Cl. Figure 2 shown in below which is represent the basic EEG signal recording methods and emotion analysis process.

opencc-by-4.0Aug 2015View details →

Figure 1. Brain Structure-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

EEG data have collected from desirable subjects. Each and every EEG signal has different kind of bands like Alpha, Beta, Gamma, Theta, and Delta. Each band stores the particular information about the emotions. Alpha band (8-13 Hz) which located in Frontal Occipital, Beta band (13-30 Hz) which located in Frontal Central, Gamma band (30-100 Hz), Theta band (4- 7 Hz) which located in Midline Temp, Delta band (0-4Hz) which located in Frontal Lobe. Before processing the EEG signal and extracting these bands, preprocess the signal and reduce the noise. The basic brain figure is shown in below.

Figure 8. Sample Emotion Classification Result-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

For all the 32 participants the EEG signal has to be sampled and process their emotions. The emotions are depends on the music and video clips. In this paper the video clips are changed from one person to other person. Here hybrid feed forward neural networks with radial basis function; probabilistic neural network classifier is used to classify the emotions from EEG. PNN is very fast and insensitive neural network which provides the optimized classification result. Compare to the multi layer perception neural network it provide accurate result. It classifies the emotion into two different groups like arousal and valence. Figure 8 shows that the model implemented result of emotion classification and person identification.

Figure 7. Neural Network model-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

In Probabilistic Neural Network the operations are organized into a multilayer feed forward neural network with four layers like input layer, hidden layer, pattern layer and output layer. PNN use the Euclidean distance measure the difference between one neuron to other neurons. The actual target values are stored in the hidden neuron and the optimized weighted values are fed into the same category hidden neuron. Then finally the output layer compared the weighted votes of each target values and the target votes are used to predict the emotions.

Figure 10. Sensitivity and specificity of different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Figure10 has shown in sensitivity and specificity of different neural network which is used to explain how exactly the emotions are classified into groups and accuracy value shown in above table 3.

Figure 9. Mean Square Error for different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Figure 9 has shown in different epochs using neural networks with mean square error performance and the Table 3 shows that different bands mean square error values while training the neural networks with particle swarm optimization.