Conductometric Biosensor Based on Urease, Adsorbed on Silicalite for Determination of Urea in Serum Samples

The method of enzyme adsorption on nano- and microsized zeolites, developed by us, is described. It is notable by such advantages as simple and fast performance, the absence of toxic compounds, high reproducibility and repeatability. The biosensor based on the method developed was applied for urea measurement in samples of blood serum. It was shown that the biosensor could surely distinguish healthy people from people with renal dysfunction. Good results reproducibility was proved at urea determination in real samples of blood serum (RSD = 10%). For these reasons, the biosensors based on enzyme adsorption are more suitable for standardization and production than those based on conventional methods of immobilization. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3547

Macrofungal diversity of Bolu Abant Nature Park (Turkey)

This study was based on materials of macrofungi collected from Bolu Abant Nature Park between 2008 and 2009. As a result of field and laboratory studies, 103 taxa belonging to 34 families were identified. Five (5) taxa belong to Ascomycota and 98 to Basidiomycota

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Generalized Many-Way Few-Shot Video Classification

Few-shot learning methods operate in low data regimes. The aim is to learn with few training examples per class. Although significant progress has been made in few-shot image classification, few-shot video recognition is relatively unexplored and methods based on 2D CNNs are unable to learn temporal information. In this work we thus develop a simple 3D CNN baseline, surpassing existing methods by a large margin. To circumvent the need of labeled examples, we propose to leverage weakly-labeled videos from a large dataset using tag retrieval followed by selecting the best clips with visual similarities, yielding further improvement. Our results saturate current 5-way benchmarks for few-shot video classification and therefore we propose a new challenging benchmark involving more classes and a mixture of classes with varying supervision