QOL did not develop in a strictly linear manner following the deterioration of clinical state. This suggests that the evolution of QOL is also determined by other variables relating to the physical and social environment of the patients. Their role seems particularly important for the mild to moderate stages of dementia.

With the development of digital imaging techniques over the last decade, there are now new opportunities to study complex behavioural patterns in fish (e.g. schooling behaviour) and to track a very large number of individuals. These new technologies and methods provide valuable information to fundamental and applied science disciplines such as ethology, animal sociology, animal psychology, veterinary sciences, animal welfare sciences, statistical physics, pharmacology as well as neuro‐ and ecotoxicology. This paper presents a review of fish video multitracking techniques. It describes the possibilities of tracking individuals and groups at different scales, but also outlines the advantages and limitations of the detection methods. The problem of occlusions, during which errors of individual identifications are very frequent, is underlined. This paper summarizes different approaches to improving the quality of individual identification, notably by the development of three‐dimensional tracking, image analysis and probabilistic applications. Finally, implications for fish research and future directions are presented.

QOL of people with dementia is inferior to that of people with MCI and controls. This demonstrates the ADRQL instrument is sufficiently sensitive for evaluating the QOL of people with dementia. Longitudinal studies are needed to specifically examine the rate of QOL evolution throughout the entire dementia process.

SUMMARYObjectives To examine the evolution of quality of life (QOL) in demented subjects at base-line, one and 2 years later and to determine clinical variables associated with QOL. Method Longitudinal study of a cohort of 127 subjects living at home or in a long-term care institution. A QOL measure (Alzheimer Disease Related Quality of Life; ADRQL) was administered three times. In addition, several clinical instruments (MMSE, IADL, ADL and CDR/M) were also administered. Results ADRQL data analysis did not reveal significant modifications of QOL over the 2-year period, whereas results from clinical instruments showed a significant deterioration. On the group, the variations of ADRQL scores were limited, with some improvement after the first year followed by some deterioration after the second year. On the other hand, ADRQL scores fluctuated every year by at least 10 points for more than 50% of subjects. With dementia evolution, it was observed that the clinical variables were more strongly correlated with ADRQL scores and were more significant predictors. This varied from 5.9% (MMSE) in 2002 to 40.01% in 2004 (MMSE and CDR/M). Conclusions QOL did not develop in a strictly linear manner following the deterioration of clinical state. This suggests that the evolution of QOL is also determined by other variables relating to the physical and social environment of the patients. Their role seems particularly important for the mild to moderate stages of dementia.

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Aims: We aimed to examine the association of cognitive decline with quality of life (QoL) in dementia compared to controls and to determine variables associated with QoL. Methods: Every subject was placed within a specific group depending on their designation by the Mini Mental State Examination and evaluated by the Alzheimer’s Disease Related Quality of Life (ADRQL) and clinical assessments. Results: QoL for the mild dementia group was lower (p = 0.08) than that of controls. The very severe dementia group had a significantly lower QoL than the other dementia groups, which all had similar ADRQL scores. The only predictor of ADRQL scores was found to be the behavioral and psychological symptoms of dementia. Conclusion: There is no direct relationship between cognitive decline and QoL.

Coming from the framework of unmarked fry tracking, we compared the capacities, advantages, and disadvantages of two recent video tracking systems: EthoVision 2.3 and a new prototype of multitracking. The EthoVision system has proved to be impressive for tracking a fry using the detection by gray scaling. Detection by subtraction has given less accurate results. Our video multitracking system is able to detect and track more than 100 unmarked fish by gray scaling technique. It permits an analysis at the group level as well as at the individual level. The multitracking program is able to attribute a number to each fish and to follow each one for the whole duration of the track. Our system permits the analysis of the movement of each individual, even if the trajectories of two fish cross each other. This is possible thanks to the theoretical estimation of the trajectory of each fish, which can be compared with the real trajectory (analysis with feedback). However, the period of the track is limited for our system (about 1 min), whereas EthoVision is able to track for numerous hours. In spite of these limitations, these two systems allow an almost continuous automatic sampling of the movement behaviors during the track.

There was a sharp task demarcation between RNs and CAs in the three less frequent task categories. There was no indication that RNs were delegating tasks to CAs.