Search results for "113"
showing 10 items of 853 documents
Insulin resistance is associated with altered amino acid metabolism and adipose tissue dysfunction in normoglycemic women
2016
AbstractInsulin resistance is associated adiposity, but the mechanisms are not fully understood. In this study, we aimed to identify early metabolic alterations associated with insulin resistance in normoglycemic women with varying degree of adiposity. One-hundred and ten young and middle-aged women were divided into low and high IR groups based on their median HOMA-IR (0.9 ± 0.4 vs. 2.8 ± 1.2). Body composition was assessed using DXA, skeletal muscle and liver fat by proton magnetic resonance spectroscopy, serum metabolites by nuclear magnetic resonance spectroscopy and adipose tissue and skeletal muscle gene expression by microarrays. High HOMA-IR subjects had higher serum branched-chain …
Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy
2017
Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introducedan adaptive burst analysis methodwhich enhances the analysis power for neuronal networks with highly varying firing dynamics. The adaptation is based on single channels analyzing each element of a network separately. Such kind of analysis was adequate for the assessment of local behavior, where the analysis focuses on the neuronal activity in the vicinity of a single electrode. However, the assessment of the whole network may be hampered, if parts of the network are analyzed using different rules. Here, we test how using multiple channels and measurement time points affect adaptive b…
Revealing community structures by ensemble clustering using group diffusion
2018
We propose an ensemble clustering approach using group diffusion to reveal community structures in data. We represent data points as a directed graph and assume each data point belong to single cluster membership instead of multiple memberships. The method is based on the concept of ensemble group diffusion with a parameter to represent diffusion depth in clustering. The ability to modulate the diffusion-depth parameter by varying it within a certain interval allows for more accurate construction of clusters. Depending on the value of the diffusion-depth parameter, the presented approach can determine very well both local clusters and global structure of data. At the same time, the ability …
Excessive daytime sleepiness is associated with an increased frequency of falls and sarcopenia.
2021
Background:\ud \ud This cross-sectional study aimed to examine associations between excessive daytime sleepiness (EDS) with falls and falls related conditions in older adults.\ud \ud Methods:\ud \ud To assess EDS, the Epworth Sleepiness Scale was used, with a score of ≥11/24 points indicating EDS. Number of falls and fall history (at least one) in the last year were recorded. Timed Up and Go test (TUG) was used to assess fall risk. Sarcopenia was defined by SARC-F tool. A grip strength score of the dominant hand, measured with a hand-grip dynamometer, less than 16 kg in females and 27 kg in males was accepted as dynapenia. Frailty status was defined by five dimensions including shrinking, e…
Newly Digitized Database Reveals the Lives and Families of Forced Migrants from Finnish Karelia
2017
Studies on displaced persons often suffer from a lack of data on the long-term effects of forced migration. A register created during 1960s and published as a book series ‘Siirtokarjalaisten tie’ in 1970 documented the lives of individuals who fled the southern Karelian district of Finland after its first and second occupation by the Soviet Union in 1940 and 1944. To realize the potential value of these data for scientific research, we have recently scanned the register using optical character recognition (OCR) software, and developed proprietary computer code to extract these data. Here we outline the steps involved in the digitization process, and present an overview of the Migration Kare…
Taxonomic Classification for Living Organisms Using Convolutional Neural Networks
2017
Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential f…
Experimental virus evolution in cancer cell monolayers, spheroids, and tissue explants
2021
Viral laboratory evolution has been used for different applications, such as modeling viral emergence, drug-resistance prediction, and therapeutic virus optimization. However, these studies have been mainly performed in cell monolayers, a highly simplified environment, raising concerns about their applicability and relevance. To address this, we compared the evolution of a model virus in monolayers, spheroids, and tissue explants. We performed this analysis in the context of cancer virotherapy by performing serial transfers of an oncolytic vesicular stomatitis virus (VSV-Δ51) in 4T1 mouse mammary tumor cells. We found that VSV-Δ51 gained fitness in each of these three culture systems, and t…
Recycling a genre for news automation: The production of Valtteri the Election Bot
2020
Abstract The amount of available digital data is increasing at a tremendous rate. These data, however, are of limited use unless converted into a user-friendly form. We took on this task and built a natural language generation (NLG) driven system that generates journalistic news stories about elections without human intervention. In this paper, after presenting an overview of state-of-the-art technologies in NLG, we explain systematically how we identified and then recontextualized the determinant aspects of the genre of an online news story in the algorithm of our NLG software. In the discussion, we introduce the key results of a user test we carried out and some improvements that these re…
Containers in Software Development: A Systematic Mapping Study
2019
Over the past decade, continuous software development has become a common place in the field of software engineering. Containers like Docker are a lightweight solution that developers can use to deploy and manage applications. Containers are used to build both component-based architectures and microservice architectures. Still, practitioners often view containers only as way to lower resource requirements compared to virtual machines. In this paper, we conducted a systematic mapping study to find information on what is known of how containers are used in software development. 56 primary studies were selected into this paper and they were categorized and mapped to identify the gaps in the cu…
From face-to-face to blended learning using ICT
2016
This study examines the development of the education model created in connection with the Master Studies in Mathematical Information Technology. The model has developed from the first stage, where there was only face-to-face teaching supported with Learning Management System, to a stage where studying is possible also fully in online and students may choose themselves how much to take advantage of technology in their studies. The examination of the development of the education model is made from the viewpoints of accessibility, increased role of technology and interaction. In earlier studies, the education model has been evaluated for example from the viewpoints of changes in the participat…