7 resultados para Behavior Monitoring

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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Most butterfly monitoring protocols rely on counts along transects (Pollard walks) to generate species abundance indices and track population trends. It is still too often ignored that a population count results from two processes: the biological process (true abundance) and the statistical process (our ability to properly quantify abundance). Because individual detectability tends to vary in space (e.g., among sites) and time (e.g., among years), it remains unclear whether index counts truly reflect population sizes and trends. This study compares capture-mark-recapture (absolute abundance) and count-index (relative abundance) monitoring methods in three species (Maculinea nausithous and Iolana iolas: Lycaenidae; Minois dryas: Satyridae) in contrasted habitat types. We demonstrate that intraspecific variability in individual detectability under standard monitoring conditions is probably the rule rather than the exception, which questions the reliability of count-based indices to estimate and compare specific population abundance. Our results suggest that the accuracy of count-based methods depends heavily on the ecology and behavior of the target species, as well as on the type of habitat in which surveys take place. Monitoring programs designed to assess the abundance and trends in butterfly populations should incorporate a measure of detectability. We discuss the relative advantages and inconveniences of current monitoring methods and analytical approaches with respect to the characteristics of the species under scrutiny and resources availability.

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In this study, we present the development and the characterization of a generic platform for cell culture able to monitor extracellular ionic activities (K+, NH4+) for real-time monitoring of cell-based responses, such as necrosis, apoptosis, or differentiation. The platform for cell culture is equipped with an array of 16 silicon nitride micropipet-based ion-selective microelectrodes with a diameter of either 2 or 6 microm. This array is located at the bottom of a 200-microm-wide and 350-microm-deep microwell where the cells are cultured. The characterization of the ion-selective microelectrode arrays in different standard and physiological solutions is presented. Near-Nernstian slopes were obtained for potassium- (58.6 +/- 0.8 mV/pK, n = 15) and ammonium-selective microelectrodes (59.4 +/- 3.9 mV/pNH4, n = 13). The calibration curves were highly reproducible and showed an average drift of 4.4 +/- 2.3 mV/h (n = 10). Long-term behavior and response after immersion in physiological solutions are also presented. The lifetime of the sensors was found to be extremely long with a high recovery rate.

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New directly acting antivirals (DAAs) that inhibit hepatitis C virus (HCV) replication are increasingly used for the treatment of chronic hepatitis C. A marked pharmacokinetic variability and a high potential for drug-drug interactions between DAAs and numerous drug classes have been identified. In addition, ribavirin (RBV), commonly associated with hemolytic anemia, often requires dose adjustment, advocating for therapeutic drug monitoring (TDM) in patients under combined antiviral therapy. However, an assay for the simultaneous analysis of RBV and DAAs constitutes an analytical challenge because of the large differences in polarity among these drugs, ranging from hydrophilic (RBV) to highly lipophilic (telaprevir [TVR]). Moreover, TVR is characterized by erratic behavior on standard octadecyl-based reversed-phase column chromatography and must be separated from VRT-127394, its inactive C-21 epimer metabolite. We have developed a convenient assay employing simple plasma protein precipitation, followed by high-performance liquid chromatography coupled to tandem mass spectrometry (HPLC-MS/MS) for the simultaneous determination of levels of RBV, boceprevir, and TVR, as well as its metabolite VRT-127394, in plasma. This new, simple, rapid, and robust HPLC-MS/MS assay offers an efficient method of real-time TDM aimed at maximizing efficacy while minimizing the toxicity of antiviral therapy.

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Contemporary models of self-regulated learning emphasize the role of distal motivational factors for student's achievement, on the one side, and the proximal role of metacognitive monitoring and control for learning and test outcomes, on the other side. In the present study, two larger samples of elementary school children (9- and 11-year-olds) were included and their mastery-oriented motivation, metacognitive monitoring and control skills were integrated into structural equation models testing and comparing the relative impact of these different constituents for self-regulated learning. For one, results indicate that the factorial structure of monitoring, control and mastery motivation was invariant across the two age groups. Of specific interest was the finding that there were age-dependent structural links between monitoring, control, and test performance (closer links in the older compared to the younger children), with high confidence yielding a direct and positive effect on test performance and a direct and negative effect on adequate control behavior in the achievement test. Mastery-oriented motivation was not found to be substantially associated with monitoring (confidence), control (detection and correction of errors), or test performance underlining the importance of proximal, metacognitive factors for test performance in elementary school children.

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This study focuses on relations between 7- and 9-year-old children’s and adults’ metacognitive monitoring and control processes. In addition to explicit confidence judgments (CJ), data for participants’ control behavior during learning and recall as well as implicit CJs were collected with an eye-tracking device (Tobii 1750). Results revealed developmental progression in both accuracy of implicit and explicit monitoring across age groups. In addition, efficiency of learning and recall strategies increases with age, as older participants allocate more fixation time to critical information and less time to peripheral or potentially interfering information. Correlational analyses, recall performance, metacognitive monitoring, and controlling indicate significant interrelations between all of these measures, with varying patterns of correlations within age groups. Results are discussed in regard to the intricate relationship between monitoring and recall and their relation to performance.

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Behavior is one of the most important indicators for assessing cattle health and well-being. The objective of this study was to develop and validate a novel algorithm to monitor locomotor behavior of loose-housed dairy cows based on the output of the RumiWatch pedometer (ITIN+HOCH GmbH, Fütterungstechnik, Liestal, Switzerland). Data of locomotion were acquired by simultaneous pedometer measurements at a sampling rate of 10 Hz and video recordings for manual observation later. The study consisted of 3 independent experiments. Experiment 1 was carried out to develop and validate the algorithm for lying behavior, experiment 2 for walking and standing behavior, and experiment 3 for stride duration and stride length. The final version was validated, using the raw data, collected from cows not included in the development of the algorithm. Spearman correlation coefficients were calculated between accelerometer variables and respective data derived from the video recordings (gold standard). Dichotomous data were expressed as the proportion of correctly detected events, and the overall difference for continuous data was expressed as the relative measurement error. The proportions for correctly detected events or bouts were 1 for stand ups, lie downs, standing bouts, and lying bouts and 0.99 for walking bouts. The relative measurement error and Spearman correlation coefficient for lying time were 0.09% and 1; for standing time, 4.7% and 0.96; for walking time, 17.12% and 0.96; for number of strides, 6.23% and 0.98; for stride duration, 6.65% and 0.75; and for stride length, 11.92% and 0.81, respectively. The strong to very high correlations of the variables between visual observation and converted pedometer data indicate that the novel RumiWatch algorithm may markedly improve automated livestock management systems for efficient health monitoring of dairy cows.