Implementation of visualizations using a server-client architecture : Effects on performance measurements

Visualizing large datasets poses challenges in terms of how to create visualization applications with good performance. Due to the amount of data, transfer speed and processing speed may lead to waiting times that cause users to abandon the application. It is therefore important to select methods and techniques that can handle the data in as efficient a way as possible. The aim of this study was to investigate if a server-client architecture had better performance in a visualization web application than a purely client-side architecture in terms of selected performance metrics and network load, and whether the selection of implementation language and tools affected the performance of the server-client architecture implementation. To answer these questions, a visualization application was implemented in three different ways: a purely client-side implementation, a server-client implementation using Node.js for the server, and a server-client implementation using Flask for the server. The results showed that the purely client-side architecture suffered from a very long page loading time and high network load but was able to process data quickly in response to user actions in the application. The server-client architecture implementations could load the page faster, but responding to requests took longer, whereas the amount of data transferred was much lower. Furthermore, the server-client architecture implemented with a Node.js server performed better on all metrics than the application implemented with a Flask server. Overall, when taking all measurements into consideration, the Node.js server architecture may be the best choice among the three when working with a large dataset, although the longer response time compared to the purely client-side architecture may cause the application to seem less responsive

Implementation of visualizations using a server-client architecture : Effects on performance measurements

Visualizing large datasets poses challenges in terms of how to create visualization applications with good performance. Due to the amount of data, transfer speed and processing speed may lead to waiting times that cause users to abandon the application. It is therefore important to select methods and techniques that can handle the data in as efficient a way as possible. The aim of this study was to investigate if a server-client architecture had better performance in a visualization web application than a purely client-side architecture in terms of selected performance metrics and network load, and whether the selection of implementation language and tools affected the performance of the server-client architecture implementation. To answer these questions, a visualization application was implemented in three different ways: a purely client-side implementation, a server-client implementation using Node.js for the server, and a server-client implementation using Flask for the server. The results showed that the purely client-side architecture suffered from a very long page loading time and high network load but was able to process data quickly in response to user actions in the application. The server-client architecture implementations could load the page faster, but responding to requests took longer, whereas the amount of data transferred was much lower. Furthermore, the server-client architecture implemented with a Node.js server performed better on all metrics than the application implemented with a Flask server. Overall, when taking all measurements into consideration, the Node.js server architecture may be the best choice among the three when working with a large dataset, although the longer response time compared to the purely client-side architecture may cause the application to seem less responsive

Domestication and early experiences in chickens : Behavior, stress and gene expression

A number of animal species have undergone domestication, the process of becoming adapted to living in captivity and in proximity to humans. Common for these species is that they have all developed certain traits, including changes to coat color, body size and level of fearfulness. This has been termed the domestic phenotype. Among these traits is also an attenuation of the response to stress, both behaviorally and physiologically. Thus, release of glucocorticoids such as cortisol or corticosterone is lower in domesticated species. However, the underlying mechanism for this is not yet well understood. In this thesis, we have investigated genetic mechanisms for the attenuation of the physiological stress response in ancestral chickens, the Red Junglefowl, and domesticated chickens, the White Leghorn. We found a number of genes that differed in expression between the two breeds in several tissues involved in the stress response. Among the most interesting findings were lower expression of genes involved in production and secretion of ACTH in the pituitary, and in the production of glucocorticoids in the adrenal glands, in the domesticated White Leghorns. We also found higher expression of the glucocorticoid receptor in White Leghorns, indicating that they may have a more efficient negative feedback of the physiological stress response. We then investigated the transcriptome of the chicken pituitary more closely, and we discovered that a number of genes highly involved in several important physiological axes showed differential expression between the ancestral and the domesticated breed. Among these were genes involved in the stress response, the reproductive system, and in metabolism and growth. As these traits are modified in domesticated species, our results suggest that changes to gene expression in the pituitary may be an important underlying factor of the domestic phenotype. A separate aim of this thesis was to investigate effects of hatching time in chickens on their subsequent phenotype. Time of hatching constitutes an early experience that may differ between individuals, and we therefore hypothesized that differences in hatching time would affect chickens later in life. While a number of studies have been performed on hatching time and post-hatch growth, very little work has been done on effects on behavior. We found that the time of hatching had sex-specific effects. Hatching times in females were negatively correlated with body weight, whereas in males, behaviors such as reaction to novelty and spatial learning were affected. As time of hatching is governed by various hormones, including thyroid hormone and corticosterone, we suggest that changes to the levels of these hormones could affect both hatching time and post-hatch phenotypes. Understanding these mechanisms better would be beneficial in terms of production, where batch homogeneity is important, in research on early experiences and the potential for maternal programming, and in evolutionary questions on trade-off between different life strategies

Implementation of visualizations using a server-client architecture : Effects on performance measurements

Visualizing large datasets poses challenges in terms of how to create visualization applications with good performance. Due to the amount of data, transfer speed and processing speed may lead to waiting times that cause users to abandon the application. It is therefore important to select methods and techniques that can handle the data in as efficient a way as possible. The aim of this study was to investigate if a server-client architecture had better performance in a visualization web application than a purely client-side architecture in terms of selected performance metrics and network load, and whether the selection of implementation language and tools affected the performance of the server-client architecture implementation. To answer these questions, a visualization application was implemented in three different ways: a purely client-side implementation, a server-client implementation using Node.js for the server, and a server-client implementation using Flask for the server. The results showed that the purely client-side architecture suffered from a very long page loading time and high network load but was able to process data quickly in response to user actions in the application. The server-client architecture implementations could load the page faster, but responding to requests took longer, whereas the amount of data transferred was much lower. Furthermore, the server-client architecture implemented with a Node.js server performed better on all metrics than the application implemented with a Flask server. Overall, when taking all measurements into consideration, the Node.js server architecture may be the best choice among the three when working with a large dataset, although the longer response time compared to the purely client-side architecture may cause the application to seem less responsive

Olfactory sensitivity of spider monkeys (Ateles geoffroyi) for "green odors"

Primates have traditionally been viewed as having a poorly developed sense of smell. However, in recent years, studies have shown that at least some primate species use olfaction in a number of behaviors, and that they have a high olfactory sensitivity for various chemical classes of odorants. Using a two-choice instrumental conditioning paradigm, the present study assessed olfactor ydetection thresholds of three spider monkeys (Ateles geoffroyi) for eight aliphatic alcohols and aldehydes, known as "green odors". With all odorants, the animals detected concentrations below 1 parts per million, with single individuals performing even better. The type of functional group present systematically affected olfactory detection thresholds, whereas the presence, position and configuration of a double bond did not. Compared to previously tested classes of odorants, thespider monkeys were not particularly sensitive to "green odors". Furthermore, they are lesssensitive for "green odors" compared to humans and mice. The present results suggest that neuroanatomical and genetic comparisons across species are poor predictors of olfactory sensitivity

Behavioral variable loading scores and % variance explained for the four components extracted in the behavioral PCA.

<p>Only loadings with an absolute value >0.4 are shown. Abbreviations are explained in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103040#pone-0103040-t003" target="_blank">Table 3</a>.</p

Weight variable loading scores and variance explained for the two components extracted in the PCA for weight.

<p>Only loadings with an absolute value >0.4 are shown.</p

Means and standard errors of the measured egg and hatch variables for the three different hatch times (N = 60).

<p>Within rows with superscripts, values sharing no common superscript were significantly different.</p

Weight development of a) males (N = 30) and b) females (N = 30) over the experiment.

<p>The graphs show mean±S.E for each hatch group. The solid line arrows indicate significant correlation (p<0.05) between hatch time and weight, whereas the dashed arrows indicate tendency (p<0.1).</p

Hatch group medians (interquartile range) for each of the behavioral variables measured.

<p>Tests used were Open Field (OF), Social Reinstatement (SR), Novel Arena (NA), Novel Arena Retest (reNA) and Tonic Immobility (TI).</p><p>Within rows with superscripts, values sharing no common superscript were significantly different.</p