Major regulators of microRNAs biogenesis Dicer and Drosha are down-regulated in endometrial cancer

Alterations in microRNAs expression have been proposed to play role in endometrial cancer pathogenesis. Dicer and Drosha are main regulators of microRNA biogenesis and deregulation of their expression has been indicated as a possible cause of microRNAs alterations observed in various cancers. The objective of this study was to investigate Dicer and Drosha genes expression in endometrial cancer and to analyze the impact of clinicopathological characteristics on their expression. Fresh tissue samples were collected from 44 patients (26 endometroid endometrial carcinoma and 18 controls). Clinical and pathological data were acquired from medical documentation. Dicer and Drosha genes expressions were assessed by qRT-PCR using validated reference genes. Dicer and Drosha expression levels were significantly lower in endometrial cancer samples comparing to controls. Dicer was down-regulated by the factor of 1.54 (p = 0.009) and Drosha gene mean expression value was 1.4 times lower in endometrial cancer group versus control group (p = 0.008). Down-regulation of Dicer significantly correlated with decreased expression of Drosha (coefficient value 0.75). Decreased expression of Drosha correlated with higher histological grade and was influenced by BMI. Lower Dicer expression was found in nulli- and uniparous females comparing to multiparous individuals (p = 0.002). Neither the FIGO stage nor the menstrual status had significant influence on the expression of studied genes. This study revealed for the first time that expression alterations of main regulators of microRNAs biogenesis are present in endometrial cancer tissue and could be potentially responsible for altered microRNAs profiles observed in this malignancy

The regime of the Senate of Free City of Krakow and its chairman during the years 1815-1846

Celem niniejszej pracy było przedstawienie roli, jaką w Wolnym Mieście Krakowie pełnił Senat, istniejący w latach 1815-1846. Praca skupiała się na jego strukturze wewnętrznej, a także kompetencjach wobec władzy ustawodawczej i sądowniczej, oraz organów centralnych Wolnego Miasta. W pracy wykorzystano akty prawne, które zostały uchwalone przez Senat.The purpose of present Bachellor thesis was to depict role, which was fulfilled by the Senate of the Free City of Krakowa, existing between 1815 and 1846. The thesis was concentrated on its interior structure, as well as on its competences towards legislative and judicial power, and central bodies of the Free City. In the thesis were used legal acts, which have been passed by the Senate

The selected legal and economic issues of Poland's accession to the eurozone

Celem pracy było zbadanie zagadnienia przystąpienia Polski do strefy euro pod kątem korzyści i kosztów, jakie może to przynieść gospodarce. Posłużono się w tym celu zarówno analizami teoretycznymi, jak i przykładem Hiszpanii i konkretnych efektów, jakie tam nastąpiły. Wykorzystano metodę analityczną. Wykonano również ankietę na próbie 525 osób. Praca powstała na podstawie dziesięciu źródeł i stu trzydziestu siedmiu opracowań.The aim of this study was to examine the issue of Poland's accession to the eurozone in terms of benefits and costs that it could bring to the economy. To this end, both theoretical analyzes and the example of Spain and the specific effects that followed were used.An analytical method was used. A survey of 525 people was also carried out. The work was based on ten sources and one hundred and thirty-seven studies

Aggregation of GPS, WLAN, and BLE Localization Measurements for Mobile Devices in Simulated Environments

There are multiple available technologies to find the location of a mobile device, such as the Global Positioning System (GPS), Bluetooth Low-Energy beacons (BLE), and Wireless LAN (WLAN) localization. We propose a novel method to estimate the location of a moving device by aggregating information from multiple positioning systems into a single, more precise location estimation. The aggregated location is calculated as the place in which the product of the probability density functions (PDF) of individual methods has the maximum value. The experimental probability density functions of the three analyzed technologies are fitted by gamma distributions based on error histograms found in the literature and measurement data. The location measurements of the individual technologies are provided at different time instants, so the weighted product of the PDFs is used to improve aggregation accuracy. The discrete event-simulation model was used to evaluate the aggregation method with the Gauss–Markov mobility model. Simulations demonstrated that the calculated aggregated location was more accurate than any of the methods taken as the input, and average error was decreased by almost 13% compared to an arithmetic mean of the three considered localization methods, and by more than 36% compared to the single method with the highest accuracy

How to Extract Interesting Information for Identity Verification Process from Spectrograms?

Nowadays, identity verification support systems are becoming more and more popular. Machine learning is one of the leading fields of research from all over the world. However, each classifier needs a large number of samples to be properly trained. Preparing such samples proves to be a big problem for several reasons. One of them is the quality of the recording, another is the problem of feature extraction. In this work, the idea of processing sound samples by using their graphical representation in the form of spectrograms is described. The process removes specific, redundant information from the samples and then performs feature extraction. The proposed technique has been tested for identity verification using convolutional neural networks. The performed tests and obtained results have been described and discussed to indicate numerous advantages and disadvantages of the proposed technique

Obstacle Detection as a Safety Alert in Augmented Reality Models by the Use of Deep Learning Techniques

Augmented reality (AR) is becoming increasingly popular due to its numerous applications. This is especially evident in games, medicine, education, and other areas that support our everyday activities. Moreover, this kind of computer system not only improves our vision and our perception of the world that surrounds us, but also adds additional elements, modifies existing ones, and gives additional guidance. In this article, we focus on interpreting a reality-based real-time environment evaluation for informing the user about impending obstacles. The proposed solution is based on a hybrid architecture that is capable of estimating as much incoming information as possible. The proposed solution has been tested and discussed with respect to the advantages and disadvantages of different possibilities using this type of vision

Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks

In recent years, growing interest in deep learning neural networks has raised a question on how they can be used for effective processing of high-dimensional datasets produced by hyperspectral imaging (HSI). HSI, traditionally viewed as being within the scope of remote sensing, is used in non-invasive substance classification. One of the areas of potential application is forensic science, where substance classification on the scenes is important. An example problem from that area—blood stain classification—is a case study for the evaluation of methods that process hyperspectral data. To investigate the deep learning classification performance for this problem we have performed experiments on a dataset which has not been previously tested using this kind of model. This dataset consists of several images with blood and blood-like substances like ketchup, tomato concentrate, artificial blood, etc. To test both the classic approach to hyperspectral classification and a more realistic application-oriented scenario, we have prepared two different sets of experiments. In the first one, Hyperspectral Transductive Classification (HTC), both a training and a test set come from the same image. In the second one, Hyperspectral Inductive Classification (HIC), a test set is derived from a different image, which is more challenging for classifiers but more useful from the point of view of forensic investigators. We conducted the study using several architectures like 1D, 2D and 3D convolutional neural networks (CNN), a recurrent neural network (RNN) and a multilayer perceptron (MLP). The performance of the models was compared with baseline results of Support Vector Machine (SVM). We have also presented a model evaluation method based on t-SNE and confusion matrix analysis that allows us to detect and eliminate some cases of model undertraining. Our results show that in the transductive case, all models, including the MLP and the SVM, have comparative performance, with no clear advantage of deep learning models. The Overall Accuracy range across all models is 98–100% for the easier image set, and 74–94% for the more difficult one. However, in a more challenging inductive case, selected deep learning architectures offer a significant advantage; their best Overall Accuracy is in the range of 57–71%, improving the baseline set by the non-deep models by up to 9 percentage points. We have presented a detailed analysis of results and a discussion, including a summary of conclusions for each tested architecture. An analysis of per-class errors shows that the score for each class is highly model-dependent. Considering this and the fact that the best performing models come from two different architecture families (3D CNN and RNN), our results suggest that tailoring the deep neural network architecture to hyperspectral data is still an open problem