Museum learning: a study of motivation and learning achievement

According to Reinmann-Rothmeier and Mandl (2001), learning environments should provide an ideal balance between constructivist and instructive elements. Most interesting for constructivist learning processes is the combination of the cognitive and the motivational domain. In an empirical study with 207 fifth-graders of the highest stratification level, we evaluated three different approaches to the learning process based on a visit to the Natural History Museum in Berlin: the three approaches were principally characterised by closed, open and mixed tasks. One objective of our study was to as-sess learning achieved through the visit and the effect of different treatments. Another goal was to evaluate the motivation of the three treatment groups. We conducted a pre/post-test study with follow-up measurement. The test instruments consisted of 26 open and closed questions in the cognitive domain and a further 12 items (subdivided into four subsections) for measuring motivation according to self-determination theory. In contrast to our hypotheses based on constructivist theories, open tasks were less successful for gaining knowledge and less intrinsically motivating.     

A note on a quantitative genetic approach for modelling of differentiation tasks

Learning capacities and cognitive abilities of farm animals will become more important and they will have an impact on animal welfare and productivity. Scientific examinations of these aspects presuppose genetic models. This paper presents an approach for modelling differentiation tasks in animal learning on a quantitative genetic basis. The approach links the likelihood function, to make a correct decision, with the two-dimensional density function, capable to be to decide right. The proposed model is able to depict several situations in learning processes of populations. Its parameters characterize the theoretical course of the learning process and also the stage of learning which a population has reached. The approach can be used to estimate parameters of a learning process and of the genetic disposition to learn, to estimate breeding values of learning capacity and to fit models on population's learning capability under selection.     

类别决策中情景标签效应的神经机制

目的:类别决策是人类重要的认知方式之一,情景对类别决策的准确性具有重要影响,但并没有引起足够的重视,对其研究也较少。方法:本研究选取大学生被试,通过ERPs对大学生被试电生理水平探讨情境标签的作用。结果:在基于情境标签下的分类活动激活了更多的大脑区域,基于情景标签下被试分类的潜伏期更短,判断更加快速准确。结论:类别决策过程中情景标签有重要的作用,通过情景标签能提高类别决策能力。     

Application of an unsupervised artificial neural network technique to multivariant surface water quality data

Surface water contamination from agricultural and urban runoff and wastewater discharges from industrial and municipal activities is of major concern to people worldwide. Classical models can be insufficient to visualise the results because the water quality variables used to describe dynamic pollution sources are complex, multivariable, and nonlinearly related. Artificial intelligence techniques with the ability to analyse multivariant water quality data by means of a sophisticated visualisation capacity can offer an alternative to current models. In this study, the Kohonen self-organising feature maps (SOM) neural network was initially applied to analyse the complex nonlinear relationships among multivariable surface water quality variables using the component planes of the variables to determine the complex behaviour of water quality parameters. The dependencies between water quality variables were extracted and interpreted using the pattern analysis visualised in component planes. For further investigation, the k-means clustering algorithm was used to determine the optimal number of clusters by partitioning the maps and utilising the Davies–Bouldin clustering index, leading to seven groups or clusters corresponding to water quality variables. The results reveal that the concentrations of Na, K, Cl, NH₄-N, NO₂-N, o-PO₄, component planes of organic matter (pV), and dissolved oxygen (DO) were significantly affected by seasonal changes, and that the SOM technique is an efficient tool with which to analyse and determine the complex behaviour of multidimensional surface water quality data. These results suggest that this technique could also be applied to other environmentally sensitive areas such as air and groundwater pollution.     

Identification of new candidate drugs for lung cancer using chemical–chemical interactions,chemical–protein interactions and a K-means clustering algorithm

Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical–chemical interactions and chemical–protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.     

Fitting models predicting dates of flowering of temperate-zone trees using simulated annealing

The aim of the present study was to test the four commonly used models to predict the dates of flowering of temperate-zone trees, the spring warming, sequential, parallel and alternating models. Previous studies concerning the performance of these models have shown that they were unable to make accurate predictions based on external data. One of the reasons for such inaccuracy may be wrong estimations of the parameters of each model due to the non-convergence of the optimization algorithm towards their maximum likelihood. We proposed to fit these four models using a simulated annealing method which is known to avoid local extrema of any kind of function, and thus is particularly well adapted to fit budburst models, as their likelihood function presents many local maxima. We tested this method using a phenological dataset deduced from aeropalynological data. Annual pollen spectra were used to estimate the dates of flowering of the populations around the sampling station. The results show that simulated annealing provides a better fit than traditional methods. Despite this improvement, classical models still failed to predict external data. We expect the simulated annealing method to allow reliable comparisons among models, leading to a selection of biologically relevant ones.     

Protections against toxicity in the brains of rat with chronic fluorosis and primary neurons exposed to fluoride by resveratrol involves nicotinic acetylcholine receptors

Protection of Resveratrol (RSV) against the neurotoxicity induced by high level of fluoride was investigated. Sprague-Dawley (SD) rats and their offspring, as well as cultures of primary neurons were divided randomly into four groups: untreated (control); treated with 50 mg RSV/kg/ (once daily by gavage) or (20 M in the cultured medium); exposed to 50 ppm F⁻ in drinking water or 4 mmol/l in the cultured medium; and exposed to fluoride then RSV as above. The adult rats were treated for 7 months and the offspring sacrificed at 28 days of age; the cultured neurons for 48 h. For general characterization, dental fluorosis was assessed and the fluoride content of the urine measured (by fluoride-electrode) in the rates and the survival of cultured neurons monitored with the CCK-8 test. The spatial learning and memory of rats were assessed with the Morris water maze test. The levels of α7 and α4 nicotinic acetylcholine receptors (nAChRs) were quantified by Western blotting; and the activities of superoxide dismutase (SOD) and catalase (CAT), and the levels of malondialdehyde (MDA) and H₂O₂ assayed biochemically. The results showed that chronic fluorosis resulted in the impaired learning and memory in rats and their offspring, and more oxidative stress in both rat brains and cultured neurons, which may be associated the lower levels of α7 and α4 nAChR subunits. Interestingly, RSV attenuated all of these toxic effects by fluorosis, indicating that protection against the neurotoxicity of fluoride by RSV might be in mechanism involved enhancing the expressions of these nAChRs.     

A further food-washing tradition in Japanese macaques (Macaca fuscata)

A case of food-washing tradition is reported in a captive group of Japanese macaques. Two techniques are employed, with one or both hands being used to wash food under water flowing from watering spouts. Processes of diffusion of the behaviour in the group follow the patterns previously described by Japanese scientists observing this species. It is suggested that development of such a behaviour could be related to particular environmental conditions.     

Genomic alignment

As sequencing techniques become increasingly efficient, the average length of a sequence is bound to grow. Traditional sequence-comparison algorithms can either compare DNA or protein, but not a mixture, which is actually a common situation. Most obtained DNA sequences contain coding regions, and it is more reliable to compare the coding regions as protein than just as DNA.A heuristic algorithm is presented that can compare DNA with both coding and noncoding regions, but that also can compare multiple reading frames and determine which exons are homologous.A program, GenAl (Genomic Alignment), was developed that implements the algorithm. Its use is demonstrated on two retroviruses. Correspondence to: J. Hein