Behaviour of dairy cows on organic and non-organic farms

There is an increasing number of organic dairy farms in the UK. The aim of this study is to compare behaviour of dairy cows on organic and non-organic farms. Twenty organic and 20 non-organic farms throughout the UK were visited over two winters (2004/05 and 2005/06). Organic and non-organic farms were paired for housing type, herd size, milk production traits and location. The number of cows feeding was counted every fifteen minutes for 4.5 h after new feed was available post morning milking. Behaviour at the feed-face was recorded for 60 minutes and aggressive interactions between cows were quantified. Farm type had no effect on numbers of cows feeding. There were more interactions between cows feeding at open feed-faces compared to head-bale barriers. At open feed-faces, there were more interactions on organic farms than non-organic. It is possible that organic cows were hungrier than non-organic cows after the arrival of new feed

A novel penalty-based wrapper objective function for feature selection in big data using cooperative co-evolution

The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even with a higher number of features. Feature selection has two purposes: reducing the number of features to decrease computations and improving classification accuracy, which are contradictory but can be achieved using a single objective function. For this very purpose, this paper proposes a penalty-based wrapper objective function. This function can be used to evaluate the FS process using CCEA, hence called Cooperative Co-Evolutionary Algorithm-Based Feature Selection (CCEAFS). An experiment was performed using six widely used classifiers on six different datasets from the UCI ML repository with FS and without FS. The experimental results indicate that the proposed objective function is efficient at reducing the number of features in the final feature subset without significantly reducing classification accuracy. Based on different performance measures, in most cases, naïve Bayes outperforms other classifiers when using CCEAFS

Cooperative co-evolution for feature selection in big data with random feature grouping

© 2020, The Author(s). A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because of some limitations, such as not considering feature interactions, dealing with only an even number of features, and decomposing the dataset statically. In this paper, a novel random feature grouping (RFG) has been introduced with its three variants to dynamically decompose Big Data datasets and to ensure the probability of grouping interacting features into the same subcomponent. RFG can be used in CC-based FS processes, hence called Cooperative Co-Evolutionary-Based Feature Selection with Random Feature Grouping (CCFSRFG). Experiment analysis was performed using six widely used ML classifiers on seven different datasets from the UCI ML repository and Princeton University Genomics repository with and without FS. The experimental results indicate that in most cases [i.e., with naïve Bayes (NB), support vector machine (SVM), k-Nearest Neighbor (k-NN), J48, and random forest (RF)] the proposed CCFSRFG-1 outperforms an existing solution (a CC-based FS, called CCEAFS) and CCFSRFG-2, and also when using all features in terms of accuracy, sensitivity, and specificity

Correction to: Cooperative co‑evolution for feature selection in big data with random feature grouping (Journal of Big Data, (2020), 7, 1, (107), 10.1186/s40537-020-00381-y)

© 2020, The Author(s). Following publication of the original article [1], the author reported that the 2nd author affiliation was incorrect. It should only be “School of Science, Edith Cowan University, Joondalup, WA, Australia”. The affiliation is presented correctly in this correction article. The original article [1] has been corrected

Cerebral blood flow and behavioural effects of caffeine in habitual and non-habitual consumers of caffeine: A near infrared spectroscopy study

Caffeine has been shown to modulate cerebral blood flow, with little evidence of tolerance to these effects following habitual use. However, previous studies have focused on caffeine levels much higher than those found in dietary servings and have compared high caffeine consumers with low consumers rather than 'non-consumers'. The current placebo-controlled double-blind, balanced-crossover study employed near infrared spectroscopy to monitor pre-frontal cerebral-haemodynamics at rest and during completion of tasks that activate the pre-frontal cortex. Twenty healthy young habitual and non-habitual consumers of caffeine received 75mg caffeine or placebo. Caffeine significantly decreased cerebral blood flow but this was subject to a significant interaction with consumption status, with no significant effect being shown in habitual consumers and an exaggerated effect in non-habitual consumers. These findings suggest that caffeine, at levels typically found in a single dietary serving, is able to modulate cerebral blood flow but these effects are subject to tolerance

Qualitative Behavioural Assessment (QBA) as a welfare indicator for farmed Atlantic salmon (Salmo salar) in response to a stressful challenge

Animal welfare assessments have struggled to investigate the emotional states of animals while focusing solely on available empirical evidence. Qualitative Behavioural Assessment (QBA) may provide insights into an animal’s subjective experiences without compromising scientific rigor. Rather than assessing explicit, physical behaviours (i.e., what animals are doing, such as swimming or feeding), QBA describes and quantifies the overall expressive manner in which animals execute those behaviours (i.e., how relaxed or agitated they appear). While QBA has been successfully applied to scientific welfare assessments in a variety of species, its application within aquaculture remains largely unexplored. This study aimed to assess QBA’s effectiveness in capturing changes in the emotional behaviour of Atlantic salmon following exposure to a stressful challenge. Nine tanks of juvenile Atlantic salmon were video-recorded every morning for 15 min over a 7-day period, in the middle of which a stressful challenge (intrusive sampling) was conducted on the salmon. The resultant 1-min, 63 video clips were then semi-randomised to avoid predictability and treatment bias for QBA scorers. Twelve salmon-industry professionals generated a list of 16 qualitative descriptors (e.g., relaxed, agitated, stressed) after viewing unrelated video-recordings depicting varying expressive characteristics of salmon in different contexts. A different group of 5 observers, with varied experience of salmon farming, subsequently scored the 16 descriptors for each clip using a Visual Analogue Scale (VAS). Principal Components Analysis (correlation matrix, no rotation) was used to identify perceived patterns of expressive characteristics across the video-clips, which revealed 4 dimensions explaining 74.5% of the variation between clips. PC1, ranging from ‘relaxed/content/positive active’ to ‘unsettled/stressed/spooked/skittish’ explained the highest percentage of variation (37%). QBA scores for video-clips on PC1, PC2, and PC4 achieved good inter- and intra-observer reliability. Linear Mixed Effects Models, controlled for observer variation in PC1 scores, showed a significant difference between PC1 scores before and after sampling (p = 0.03), with salmon being perceived as more stressed afterwards. PC1 scores also correlated positively with darting behaviours (r = 0.42, p < 0.001). These results are the first to report QBA’s sensitivity to changes in expressive characteristics of salmon following a putatively stressful challenge, demonstrating QBA’s potential as a welfare indicator within aquaculture

Generalized Rate-Code Model for Neuron Ensembles with Finite Populations

We have proposed a generalized Langevin-type rate-code model subjected to multiplicative noise, in order to study stationary and dynamical properties of an ensemble containing {\it finite} $N$ neurons. Calculations using the Fokker-Planck equation (FPE) have shown that owing to the multiplicative noise, our rate model yields various kinds of stationary non-Gaussian distributions such as gamma, inverse-Gaussian-like and log-normal-like distributions, which have been experimentally observed. Dynamical properties of the rate model have been studied with the use of the augmented moment method (AMM), which was previously proposed by the author with a macroscopic point of view for finite-unit stochastic systems. In the AMM, original $N$-dimensional stochastic differential equations (DEs) are transformed into three-dimensional deterministic DEs for means and fluctuations of local and global variables. Dynamical responses of the neuron ensemble to pulse and sinusoidal inputs calculated by the AMM are in good agreement with those obtained by direct simulation. The synchronization in the neuronal ensemble is discussed. Variabilities of the firing rate and of the interspike interval (ISI) are shown to increase with increasing the magnitude of multiplicative noise, which may be a conceivable origin of the observed large variability in cortical neurons.Comment: 19 pages, 9 figures, accepted in Phys. Rev. E after minor modification

Effects of high-dose B vitamin complex with vitamin C and minerals on subjective mood and performance in healthy males

A significant proportion of the general population report supplementing their diet with one or more vitamins or minerals, with common reasons for doing so being to combat stress and fatigue and to improve mental functioning. Few studies have assessed the relationship between supplementation with vitamins/minerals and psychological functioning in healthy cohorts of non-elderly adults. The present randomised, placebo-controlled, double-blind, parallel groups trial assessed the cognitive and mood effects of a high-dose B-complex vitamin and mineral supplement (Berocca®) in 215 males aged 30 to 55 years, who were in full-time employment. Participants attended the laboratory prior to and on the last day of a 33-day treatment period where they completed the Profile of Mood States (POMS), Perceived Stress Scale (PSS) and General Health Questionnaire (GHQ-12). Cognitive performance and task-related modulation of mood/fatigue were assessed with the 60 min cognitive demand battery. On the final day, participants also completed the Stroop task for 40 min whilst engaged in inclined treadmill walking and subsequent executive function was assessed. Vitamin/mineral supplementation led to significant improvements in ratings on the PSS, GHQ-12 and the 'vigour' subscale of the POMS. The vitamin/mineral group also performed better on the Serial 3s subtractions task and rated themselves as less 'mentally tired' both pre- and post-completion of the cognitive demand battery. Healthy members of the general population may benefit from augmented levels of vitamins/minerals via direct dietary supplementation. Specifically, supplementation led to improved ratings of stress, mental health and vigour and improved cognitive performance during intense mental processing

An Integration of Chemistry, Biology, and Physics: The Interdisciplinary Laboratory

As a new venture to integrate research and education, a pilot section of a first-year laboratory sequence known as the Interdisciplinary Laboratory (ID Lab) was introduced on the Harvey Mudd campus during the 1999–2000 academic year and continues to be offered. The ID Lab attempts to bridge laboratory experiences from biology, chemistry, and physics for the first-year student. Taught by a team of faculty from these disciplines, the course seeks both to illustrate commonality of investigative methods and laboratory techniques in these sciences and to introduce discipline-specific principles. Experiments with a chemistry component include the Molecular Weight of Macromolecules, the Synthesis and Characterization of Liquid Crystals, A Structure–Activity Investigation of Photosynthetic Electron Transport, a Genetic Map of a Bacterial Plasmid, and The Carbonate Content of Biological Hard Tissue. This article provides some details of the experiments conducted, information on the philosophy and mechanics of the course, and a discussion of student reaction to this innovative and novel course