Over the last decade, studies examining the cognitive abilities of fish have increased, using a broad range of approaches. One of the foci has been to test the ability of fish to discriminate quantities of items and to determine whether fish can solve tasks solely on the basis of numerical information. This study is the first to investigate this ability in two elasmobranch species. All animals were trained in two-alternative forced-choice visual experiments and then examined in transfer tests, to determine if previously gained knowledge could be applied to new tasks. Results show that the grey bamboo shark () and the ocellate river stingray () can discriminate quantities based on numerical information alone, while continuous variables were controlled for. Furthermore, the data indicates that similar magnitudes and limits for quantity discrimination exist as in other animals. However, the high degree of intraspecific variation that was observed as well as the low rate of animals proving to be successful suggest that the ability to discriminate quantities may not be as important to these species as to some other vertebrate and invertebrate species tested so far.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8466846 | PMC |
http://dx.doi.org/10.3390/ani11092634 | DOI Listing |
Am J Ind Med
January 2025
Icahn School of Medicine at Mount Sinai, Selikoff Centers for Occupational Health, New York, New York, USA.
Background: Housecleaning work has been characterized as precarious employment with unstable work hours, arbitrary and low pay and benefits, and exposures to chemical, physical, and psychosocial stressors. Understanding how interpersonal power dynamics between workers and clients, a component of precarious work, contributes to work exposures can inform and improve prevention programs.
Methods: We used reflexive thematic analysis of data from seven focus groups with Latinx immigrant housecleaners in New York City to explore workers' experience of interpersonal power dynamics with their clients-whom they referred to as their "employers"-and its influences on working conditions.
Neural Netw
January 2025
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430070, Hubei, China.
In the Imbalanced Multivariate Time Series Classification (ImMTSC) task, minority-class instances typically correspond to critical events, such as system faults in power grids or abnormal health occurrences in medical monitoring. Despite being rare and random, these events are highly significant. The dynamic spatial-temporal relationships between minority-class instances and other instances make them more prone to interference from neighboring instances during classification.
View Article and Find Full Text PDFAnal Methods
January 2025
Shaanxi Province Key Laboratory of New Drugs and Chinese Medicine Foundation Research, Pharmacy College, Shaanxi University of Chinese Medicine, No. 1, Shiji Avenue, Xi Xian New District, Xi'an City, 712046 Xianyang, Shaanxi Province, China.
Aim: this study aimed to examine the effect of different storage times (0, 7, 24, 57, and 119 days) on the volatile components of Shenling Baizhu powder across different preparation processes (Pharmacopoeia, ultra-micro pulverization-pulverization, and microparticle design methods). The findings offer insights to guide quality control measures for Shenling Baizhu powder.
Methods: gas chromatography-ion mobility spectrometry (GC-IMS) was employed to ascertain the volatile components in Shenling Baizhu powder at various storage times across different preparation processes.
Forensic Sci Int Genet
January 2025
Bundeskriminalamt, Wiesbaden, Germany; International Commission on Missing Persons, The Hague, The Netherlands.
The ReAct (Recovery, Activity) project is an ENFSI (European Network of Forensic Science Institutes) supported initiative comprising a large consortium of laboratories. Here, the results from more than 23 laboratories are presented. The primary purpose was to design experiments simulating typical casework circumstances; collect data and to implement Bayesian networks to assess the value (i.
View Article and Find Full Text PDFSci Rep
January 2025
Key Laboratory of Earth Exploration and Information Techniques of Ministry of Education, Chengdu University of Technology, Chengdu, 610059, Sichuan, China.
Ground Penetrating Radar (GPR) has been widely used to detect highway pavement structures. In recent years, deep learning techniques have achieved significant success in image recognition, which is potentially relevant for interpreting ground-penetrating radar data. This is because the various types of damage develop at different levels and in different quantities.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!