Jędrzej Trajer

Effect of pregabalin on fear-based conditioned avoidance learning and spatial learning in a mouse model of scopolamine-induced amnesia

Abstract Objectives: Cognitive deficits are one of the frequent symptoms accompanying epilepsy or its treatment. Methods: In this study, the effect on cognition of intraperitoneally administered antiepileptic drug, pregabalin (10 mg/kg), was investigated in scopolamine-induced memory-impaired mice in the passive avoidance task and Morris water maze task. The effect of scopolamine and pregabalin on animals’ locomotor activity was also studied. Results: In the retention phase of the passive avoidance task, pregabalin reversed memory deficits induced by scopolamine (p < 0.05). During the acquisition phase of the Morris water maze pregabalin-treated memory-impaired mice performed the test with longer escape latencies than the vehicle-treated mice (significant at p < 0.05 on Day 5, and at p < 0.001 on Day 6). There were no differences in this parameter between the scopolamine-treated control group and pregabalin-treated memory-impaired mice, which indicated that pregabalin had no influence on spatial learning in this task. During the probe trial a significant difference (p < 0.05) was observed in terms of the mean number of target crossings between vehicle-treated mice and pregabalin-treated memory-impaired mice but there was no difference between the scopolamine-treated control group and mice treated with pregabalin + scopolamine. Pregabalin did not influence locomotor activity increased by scopolamine. Discussion: In passive avoidance task, pregabalin reversed learning deficits induced by scopolamine. In the Morris water maze, pregabalin did not influence spatial learning deficits induced by scopolamine. These results are relevant for epileptic patients treated with pregabalin and those who use it for other therapeutic indications (anxiety, pain).

Wild boar meat sensory attributes contributing general meat quality

Abstract The purpose of the study was to establish relationship between different sensory attributes of wild boar meat, as well as to develop a prediction model of sensory attributes demanded. The sensory analysis of 40 samples of wild boar meat (the loin) was performed. For wild boar meat, tenderness, juiciness, colour, taste, aroma, and off-flavours are significantly correlated with general quality of meat, assessed by the sensory panel. The results from the study indicate that wild boar meat reveals characteristic sensory traits; however, texture as well as off-flavours do not play an important role in creating general quality, but the most important factors influencing the general quality of wild boar meat included juiciness, colour, taste, and aroma.

Multi-objective optimization of convective drying of apple cubes

Abstract The effect of drying temperature and air velocity on apple quality parameters, such as color difference (CD), volume ratio (VR) and water absorption capacity (WAC) in convective drying was experimentally studied. Optimization of drying conditions was carried out in the range of air temperatures from 50 to 70 °C and air velocity from 0.01 to 6 m s−1. A novel algorithm of multi-objective optimization, based on artificial neural network (ANN), genetic algorithm (GA) and Pareto optimization was developed. Three optimization objectives included simultaneous minimization of CD, maximization of VR and maximization of WAC. Objective functions for CD, VR and WAC were developed by using ANN training on the experimental dataset of apple drying at 50, 60 and 70 °C. Pareto optimal set was developed with elitist non-dominated sorting genetic algorithm (NSGA II). Unique Pareto optimal solution within specified constraints was found at air temperature 65 °C and velocity 1 m s−1. This mode of apple drying resulted in CD = 5.24, VR = 49.66% and WAC = 0.488. Experimental verification showed that maximum error of modelling did not exceed 3.24%.

Neural Modelling for the Analysis of Changes in Selected Features of Plant Products

The work investigates possibilities of plant products quality assessment by means of neural networks. A quick method of plant products assessment was proposed based on the correlations occurring between selected features of plant products and neural modelling. This approach facilitates sustainable agricultural production, which often requires making decisions based on approximate but quick assessment of the quality of produced or processed products. The method of quality assessment is presented using changes in the features of pumpkin being dried as an example. Changes in selected features of chemical composition and colour were analysed, including correlations between them. Initial analysis involved cluster analysis, which allowed for grouping data into cases characterized by similar quality. Based on the analysis, a neural model was developed, which, based on easily obtainable features, allowed for classification of products according to their quality features. This approach was positively verified based on the results of chemical composition and quality assessment performed using statistical analysis of data. INTRODUCTION Methods used for the assessment of plant materials include organoleptic and laboratory tests. The first method involves assessing a given object by using one’s own senses: sight, smell, taste, feel or hearing, while the second mainly involves assessing an object by means of appropriate equipment and analysis of physicochemical and microbiological features. Both methods are usually time-consuming and expensive. Therefore, attempts are made to improve the assessment process. In order to achieve this, other, easily obtainable features of the product are used, e.g. image features such as geometry, colour and texture, which may be correlated with other features of plant products, or dependencies between ultrasonic wave propagation and selected features of the product, Ratajski, et al (2014). This forms the base for a neural model, which allows for the assessment of product quality based on these easily measurable features. MATERIALS AND METHODS A database containing 39 cases of research results for three different varieties of dried pumpkin: Ambar, Amazonka and Justynka was used for the analysis, Sojak et al (2016), Król (2017). The pumpkins were dried by three methods: convection, tunnel and hybrid method. Input data used for the analysis of changes in pumpkin features being studied were chemical composition (dry mass, total and reducing sugars, lutein, lycopene and beta carotene) as well as colour discriminant in the CIE system L, a, b, Hunter (1948). The dataset was analysed using cluster analysis in order to find and classify similar cases, homogeneous in terms of features. Objects belonging to the same group should be as similar as possible to one another and as different as possible from objects belonging to other groups. The classification was based on k-means algorithm, Hartigan (1975). Chemical composition and colour discriminants parameters were used as classification variables.

Apple Tree Yield Analysis Using Data Mining

The paper analyses an orcharding farm that specializes in apple trees production. Based on the data for the period of 2008-2014, the authors analysed the main factors that might have impact on apple yield. A computer system for assessment of apple trees cultivation efficiency that aids in making appropriate decisions allowing for obtaining the highest yield, was proposed. The system was developed using selected Data Mining techniques such as cluster analysis and Kohonen networks. The system may be useful for decision support in sustainable horticulture production, and thus contributes to the development of sustainable agriculture. Although its quality is acceptable it still requires improvement using a bigger dataset.

The Character of Heat Exchange in the Element Powered by Renewable Energy

Among other renewable resources of energy, solar energy has a small but dynamically increasing share. Solar irradiance is characterized by large variability, especially in the 24-hour cycle. Therefore, machines that use solar energy work in transient states. The character of heat exchange for the plate solar collector and the heat exchanger working in the hybrid system was investigated. The character of heat exchange turned out to be oscillatory for both machines mentioned above. The investigations of the heat exchanger model in laboratory conditions have been carried out to analyse the observed phenomenon in detail. The investigations confirmed the previously observed oscillatory character of heat exchange. As this phenomenon is unfavourable, working out methods of process control to stabilize the operation of these machines is highly recommended.