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The Long-Term Effect of CBD Crystals and CBD Oil on Depressive-Associated Rat Behaviors

Yang et al. | Dec 18, 2020

The Long-Term Effect of CBD Crystals and CBD Oil on Depressive-Associated Rat Behaviors

Cannabidiol (CBD) is a chemical extracted from cannabis and shown by some studies to alleviate the symptoms of many mental disorders, especially major depressive disorder. The authors hypothesized that chronic treatments with purified CBD through oral administration would relieve depression-associated behaviors in normal healthy rats under adverse conditions. A statistical analysis of the experimental data suggested that long-term consumption of CBD could elicit depression associated symptoms in normal rats without depression. The results imply that people should consume CBD-containing products with extreme caution and highlight the need to carefully monitor the use of CBD in health care products.

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Extroverts as Materialists: Correlating Personality Traits, Materialism, and Spending Behavior

Jackson et al. | Feb 19, 2017

Extroverts as Materialists: Correlating Personality Traits, Materialism, and Spending Behavior

The authors investigated the relationship between personality traits and adolescent materialism, as well as how materialism relates to spending habits. Results indicate that extroversion was positively correlated with materialism, and that adolescents' purchases were affected by the purchasing behaviors of their friends or peers. Moreover, materialistic youth were more likely than non-materialistic youth to spend money on themselves when given a hypothetical windfall of $500.

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The Role of Corresponding Race, Gender, and Species as Incentives for Charitable Giving

Antonides-Jensen et al. | Jul 31, 2019

The Role of Corresponding Race, Gender, and Species as Incentives for Charitable Giving

Inherent bias is often the unconscious driver of human behavior, and the first step towards overcoming these biases is our awareness of them. In this article the authors investigate whether race, gender or species affect the choice of charity by middle class Spaniards. Their conclusions serve as a starting point for further studies that could help charities refine their campaigns in light of these biases effectively transcending them or taking advantage of them to improve their fundraising attempts.

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Recognition of animal body parts via supervised learning

Kreiman et al. | Oct 28, 2023

Recognition of animal body parts via supervised learning
Image credit: Kreiman et al. 2023

The application of machine learning techniques has facilitated the automatic annotation of behavior in video sequences, offering a promising approach for ethological studies by reducing the manual effort required for annotating each video frame. Nevertheless, before solely relying on machine-generated annotations, it is essential to evaluate the accuracy of these annotations to ensure their reliability and applicability. While it is conventionally accepted that there cannot be a perfect annotation, the degree of error associated with machine-generated annotations should be commensurate with the error between different human annotators. We hypothesized that machine learning supervised with adequate human annotations would be able to accurately predict body parts from video sequences. Here, we conducted a comparative analysis of the quality of annotations generated by humans and machines for the body parts of sheep during treadmill walking. For human annotation, two annotators manually labeled six body parts of sheep in 300 frames. To generate machine annotations, we employed the state-of-the-art pose-estimating library, DeepLabCut, which was trained using the frames annotated by human annotators. As expected, the human annotations demonstrated high consistency between annotators. Notably, the machine learning algorithm also generated accurate predictions, with errors comparable to those between humans. We also observed that abnormal annotations with a high error could be revised by introducing Kalman Filtering, which interpolates the trajectory of body parts over the time series, enhancing robustness. Our results suggest that conventional transfer learning methods can generate behavior annotations as accurate as those made by humans, presenting great potential for further research.

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The association between hunting and the feeding and vigilance times of American bison in North Dakota and Montana

McCandless et al. | Mar 30, 2022

The association between hunting and the feeding and vigilance times of American bison in North Dakota and Montana

This study hypothesized that feeding times of bison in the hunted populations would be significantly shorter than that of bison in the nonhunted population and vigilance times would be significantly longer than that of bison in the nonhunted population. Notably, the results found significant differences in feeding and vigilance times of bison in the hunted and non-hunted populations. However, these differences did not support the original hypothesis; bison in hunted populations spent more time feeding and less time vigilant than bison in the non-hunted population. Future studies investigating the association between hunting and bison behaviors could use populations of bison that are hunted more frequently, which may provide different results.

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High school students show some reluctance to COVID-19 guidelines

Basit Abid et al. | Jun 25, 2024

High school students show some reluctance to COVID-19 guidelines
Image credit: The authors

COVID-19 has officially been downgraded from the status of a global health emergency, but have COVID-19 safety practices become a new way of life for students? The authors collected survey data on COVID-19-related knowledge and behaviors of high-school students in Punjab, Pakistan and Santa Clara County, California, USA, so see where high-schoolers stand on pandemic safety today.

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