Carryover effects of varying hay concentration on the transition to silage-based feeding of weaned dairy calves

Objectives of the experiment were to determine effects of dietary hay concentration in a dry total mixed ration and its carryover effects on intake, growth performance, faecal score, and feed efficiency of weaned dairy calves. Eighteen Friesian × Jersey weaned calves (n = 6 calves/treatment) were randomly assigned to three rhodes grass hay treatments (RG13, RG26, and RG39). The experimental diets were rhodes grass hay-based total mixed rations containing 13%, 26%, and 39% chopped hay on a DM basis. The experiment had two phases of four weeks each. In phase 1 (weeks 1–4), weaned calves were fed RG13, RG26, or RG39. Then, in phase 2 (weeks 5–8), all calves were shifted to a maize silage-based diet. All the diets were iso-nitrogenous and were fed ad libitum. Calves were housed in individual pens and had free access to water and feed. Average daily gain and daily dry matter intake were analysed as repeated measures, whereas bodyweight and feed efficiency were analysed using one-way ANOVA. In phases 1 and 2 dry matter intakes were similar. Growth rate decreased linearly with increasing concentration of hay in phase 1. Overall, daily dry matter intake, average daily gain, change in body condition score and structural measurements were not affected by dietary treatments. However, overall feed efficiency was improved for calves fed RG26 compared with RG13 and RG39. Thus, feeding a moderate level of hay had positive impacts on the transition to a silage-based TMR. Keywords: dietary transition, total mixed ration, intake, growth, feed efficiency, body condition score, faecal scor

Memory retention and recall process

Our knowledge is a collection of our experiences, which expands daily as we experience new things. The way we imbue our surroundings and ourselves with meaning depends on the knowledge and understanding we have, and this knowledge depends on our memorization of what we have learned. In daily life, we take in new information and store it in our brain, maintaining it and recalling it depending on our needs. This happens because our brain has the capability of learning new skills and experiences, storing what has been learned and reusing the stored knowledge. These capabilities of storing and reusing experiences and skills are informally known as the human memory system. Everything we do or think depends on our memory, which is active every moment, receiving new information from our senses, updating existing knowledge using focus and attention, retrieving the stored experiences and skills, and planning for future activities that have not occurred yet. Thus far, neuroscientists have been expecting to find specific stores of memory in the brain and discover their exact location to know which type of memory lies where. Unfortunately, because of the great complexity of the human brain system (Figure 10.1 [1]), this concept has not been proved. However, some cognitive and mental functions are found in certain brain areas.</p

Classification of visual and non-visual learners using electroencephalographic alpha and gamma activities

This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students’ EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks. For this purpose, 34 healthy subjects are recruited. The subjects have no background knowledge of the animated learning content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions and each session comprises two parts: (1) Learning task: the subjects are shown the animated learning content for an 8–10 min duration. (2) Memory retrieval task The EEG signals are measured during the leaning task and memory retrieval task in two sessions. The retention time for the first session was 30 min, and 2 months for the second session. The analysis is performed for the EEG measured during the memory retrieval tasks. The study characterizes and differentiates the visual learners from the non-visual learners considering the extracted EEG features, such as the power spectral density (PSD), power spectral entropy (PSE), and discrete wavelet transform (DWT). The PSD and DWT features are analyzed. The EEG PSD and DWT features are computed for the recorded EEG in the alpha and gamma frequency bands over 128 scalp sites. The alpha and gamma frequency band for frontal, occipital, and parietal regions are analyzed as these regions are activated during learning. The extracted PSD and DWT features are then reduced to 8 and 15 optimum features using principal component analysis (PCA). The optimum features are then used as an input to the k-nearest neighbor (k-NN) classifier using the Mahalanobis distance metric, with 10-fold cross validation and support vector machine (SVM) classifier using linear kernel, with 10-fold cross validation. The classification results showed 97% and 94% accuracies rate for the first session and 96% and 93% accuracies for the second session in the alpha and gamma bands for the visual learners and non-visual learners, respectively, for k-NN classifier for PSD features and 68% and 100% accuracies rate for first session and 100% accuracies rate for second session for DWT features using k-NN classifier for the second session in the alpha and gamma band. For PSD features 97% and 96% accuracies rate for the first session, 100% and 95% accuracies rate for second session using SVM classifier and 79% and 82% accuracy for first session and 56% and 74% accuracy for second session for DWT features using SVM classifier. The results showed that the PSDs in the alpha and gamma bands represent distinct and stable EEG signatures for visual learners and non-visual learners during the retrieval of the learned contents.</p

Classification of EEG signals based on pattern recognition approach

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher’s discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven’s Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support VectorMachine (SVM),Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90–7.81Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.</p

EEG spectral analysis and functional connectivity during learning of science concepts

This paper aims to analyze the neuronal behavior of the brain during learning of science complex concepts and compare the brain dynamics during learning state with resting state. A sample of 34 healthy participants was recruited and performed two tasks, Raven's Advanced Progressive Matrices (RAPM) intelligence assessment and learning task. Electroencephalography (EEG) signals were recorded during resting state (eyes open) and in learning task. In the learning task, participants study animated contents about human anatomy and physiology for 10 minutes. In the EEG spectral analysis, all six frequency bands showed higher mean power during learning task compared to resting state in the frontal lobe, especially in FP2 and F8 locations. High EEG activity in frontal sites reflects high attention demand and working memory resources for studying the complex science concepts. There is also high connectivity between fronto-temporo-parietal regions indicate that an increase connection network for new memory encoding and it is critical for learning and memory.</p

Classification of cognitive and resting states of the brain using EEG features

Human brain is considered as complex system having different mental states e.g., rest, active or cognitive states. It is well understood fact that brain activity increases with the cognitive load. This paper describes the cognitive and resting state classification based on EEG features. Previously, most of the studies used linear features. EEG signals are non-stationary in nature and have complex dynamics which is not fully mapped by linear methods. Here, we used non-linear feature extraction methods to classify the cognitive and resting states of the human brain. Data acquisition were carried out on eight healthy participants during cognitive state i.e., IQ task and rest conditions i.e., eyes open. After preprocessing, EEG features were extracted using both linear as well as non-linear. Further, these features were passed to the classifier. Results showed that with support vector machine (SVM), we achieved 87.5% classification accuracy with linear and 92.1% classification accuracy with non-linear features.</p

Evaluation of passive polarized stereoscopic 3D display for visual &amp; mental fatigues

Visual and mental fatigues induced by active shutter stereoscopic 3D (S3D) display have been reported using event-related brain potentials (ERP). An important question, that is whether such effects (visual &amp; mental fatigues) can be found in passive polarized S3D display, is answered here. Sixty-eight healthy participants are divided into 2D and S3D groups and subjected to an oddball paradigm after being exposed to S3D videos with passive polarized display or 2D display. The age and fluid intelligence ability of the participants are controlled between the groups. ERP results do not show any significant differences between S3D and 2D groups to find the aftereffects of S3D in terms of visual and mental fatigues. Hence, we conclude that passive polarized S3D display technology may not induce visual and/or mental fatigue which may increase the cognitive load and suppress the ERP components.</p

Brain behavior during reasoning and problem solving task: An EEG study

The capacity of relational reasoning is to find relationships, comprehend implications, analyze novel problems and draw conclusions. This study has investigated brain behavior and dynamic neural activity during Raven's Advance Progressive Matrices (RAPM), which requires strong cognitive reasoning to select a solution, by using an electroencephalogram (EEG). The EEGs were recorded all over the scalps of eight healthy volunteers using the 128-channel Hydro-Cel Geodesic (EGI Inc.) system. Brain activation was dominant during the reasoning and problem solving process in the pre-frontal lobe and parietal lobe as compared to the baseline conditions in all of the subjects, consistently. The theta band (3.5-7.5Hz) was significantly (p&lt;0.025) higher during the reasoning process at the frontal lobe as compared to eye-closed and eye-open conditions. Results showed high frontal theta activity in problem solving, which requires substantial reasoning and thinking skills.</p

Brain behavior during reasoning and problem solving task: An EEG study

The capacity of relational reasoning is to find relationships, comprehend implications, analyze novel problems and draw conclusions. This study has investigated brain behavior and dynamic neural activity during Raven's Advance Progressive Matrices (RAPM), which requires strong cognitive reasoning to select a solution, by using an electroencephalogram (EEG). The EEGs were recorded all over the scalps of eight healthy volunteers using the 128-channel Hydro-Cel Geodesic (EGI Inc.) system. Brain activation was dominant during the reasoning and problem solving process in the pre-frontal lobe and parietal lobe as compared to the baseline conditions in all of the subjects, consistently. The theta band (3.5-7.5Hz) was significantly (p&lt;0.025) higher during the reasoning process at the frontal lobe as compared to eye-closed and eye-open conditions. Results showed high frontal theta activity in problem solving, which requires substantial reasoning and thinking skills.</p

Dynamics of scalp potential and autonomic nerve activity during intelligence test

The main objective of this study was to examine the changes in autonomic nervous system (ANS) and scalp potential during intelligence test (IQ). Electroencephalogram (EEG) and Electrocardiogram (ECG) signals were recorded simultaneously from eight healthy participants during IQ and resting states (eyes–closed and eyes-open). Heart rate (HR) and heart rate variability (HRV) were derived from ECG signal. EEG mean power was computed for five frequency bands (delta, theta, alpha, beta, and gamma) and analyzed in 12 regions across the scalp. The EEG frequency bands showed significant (p&lt;0.025) changes between IQ test and rest states. Delta and theta at frontal (PF, AF, F) and temporal regions (FT, T, TP) and alpha activity at parietal (P), parieto-occipital (PO) and occipital (O) regions were significant. In beta and gamma bands, highly reduced mean power was found at P, PO, and O regions as compared to PF, AF, and F regions in IQ test. HR and low frequency in normalized unit (LFnu) were increased significantly (p&lt;0.05 and p&lt;0.025, respectively) in IQ test. Further, high frequency in normalized unit (HFnu) was decreased (p&lt;0.11). Results showed parallel changes in scalp potential and automatic nervous activity during IQ test compared to rest conditions