Antisense Oligonucleotide-Based Rescue of Aberrant Splicing Defects Caused by 15 Pathogenic Variants in ABCA4

The discovery of novel intronic variants in the ABCA4 locus has contributed significantly to solving the missing heritability in Stargardt disease (STGD1). The increasing number of variants affecting pre-mRNA splicing makes ABCA4 a suitable candidate for antisense oligonucleotide (AON)-based splicing modulation therapies. In this study, AON-based splicing modulation was assessed for 15 recently described intronic variants (three near-exon and 12 deep-intronic variants). In total, 26 AONs were designed and tested in vitro using a midigene-based splice system. Overall, partial or complete splicing correction was observed for two variants causing exon elongation and all variants causing pseudoexon inclusion. Together, our results confirm the high potential of AONs for the development of future RNA therapies to correct splicing defects causing STGD1.     

The Causes and Potential Injurious Effects of Elevated Serum Leptin Levels in Chronic Kidney Disease Patients

Leptin is an adipokine that regulates appetite and body mass and has many other pleiotropic functions, including regulating kidney function. Increased evidence shows that chronic kidney disease (CKD) is associated with hyperleptinemia, but the reasons for this phenomenon are not fully understood. In this review, we focused on potential causes of hyperleptinemia in patients with CKD and the effects of elevated serum leptin levels on patient kidney function and cardiovascular risk. The available data indicate that the increased concentration of leptin in the blood of CKD patients may result from both decreased leptin elimination from the circulation by the kidneys (due to renal dysfunction) and increased leptin production by the adipose tissue. The overproduction of leptin by the adipose tissue could result from: (a) hyperinsulinemia; (b) chronic inflammation; and (c) significant lipid disturbances in CKD patients. Elevated leptin in CKD patients may further deteriorate kidney function and lead to increased cardiovascular risk.     

Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach

The problem of finding optimal set of users for influencing others in the social network has been widely studied. Because it is NP-hard, some heuristics were proposed to find sub-optimal solutions. Still, one of the commonly used assumption is the one that seeds are chosen on the static network, not the dynamic one. This static approach is in fact far from the real-world networks, where new nodes may appear and old ones dynamically disappear in course of time. The main purpose of this paper is to analyse how the results of one of the typical models for spread of influence - linear threshold - differ depending on the strategy of building the social network used later for choosing seeds. To show the impact of network creation strategy on the final number of influenced nodes - outcome of spread of influence, the results for three approaches were studied: one static and two temporal with different granularities, i.e. various number of time windows. Social networks for each time window encapsulated dynamic changes in the network structure. Calculation of various node structural measures like degree or betweenness respected these changes by means of forgetting mechanism - more recent data had greater influence on node measure values. These measures were, in turn, used for node ranking and their selection for seeding. All concepts were applied to experimental verification on five real datasets. The results revealed that temporal approach is always better than static and the higher granularity in the temporal social network while seeding, the more finally influenced nodes. Additionally, outdegree measure with exponential forgetting typically outperformed other time-dependent structural measures, if used for seed candidate ranking.     

Quality assessment for a visual and automatic license plate recognition

Video transmission and analysis is often utilized in applications outside of the entertainment sector, and generally speaking this class of video is used to perform specific tasks. Examples of these applications include security and public safety. The Quality of Experience (QoE) concept for video content used for entertainment differs significantly from the QoE of surveillance video used for recognition tasks. This is because, in the latter case, the subjective satisfaction of the user depends on achieving a given functionality. Recognizing the growing importance of video in delivering a range of public safety services, we focused on developing critical quality thresholds in license plate recognition tasks based on videos streamed in constrained networking conditions. Since the number of surveillance cameras is still growing it is obvious that automatic systems will be used to do the tasks. Therefore, the presented research includes also analysis of automatic recognition algorithms.     

MapReduce approach to relational influence propagation in complex networks

The relational label propagation problem for large data sets using MapReduce programming model was considered in the paper. The method we propose estimates class probability in relational domain in the networks. The method was examined on large real telecommunication data set. The results indicated that it could be used successfully to classify networks’ nodes and, thanks to that, new offerings or tariffs might be proposed to customers who belong to other providers. Moreover, basic properties of relational label propagation were examined and reported.     

Alcohol-induced changes of {beta}-lactoglobulin - retinol-binding stoichiometry

It has been demonstrated using CD that ethanol induces importantsecondary structure changes of ß-lactoglobulin. CDspectra indicate that ß-lactoglobulin secondary structure,which is mainly composed of ß-strands, becomes mostly-helical under the influence of the solvent polarity changes.The midpoint of ß-strand/-helix transition in ß-lactoglobulinis observed at dielectric constant {small tilde}60 (35% ethanol;v/v). According to CD measurements, the ethanol-dependent secondarystructure changes are reversible. The alkylation of lysines-NH₂ in ß-lactoglobulin weakens the central ß-barrelstructure, since the ß-strand/-helix transition midpointof alkylated ß-lactoglobulin is shifted to lower ethanolconcentration (25% ethanol; v/v). ß-Lactoglobulinstructural changes are triggering the dissociation of the ß-lactoglobulin- retinol complex as judged from complete quenching of its fluorescencein ethanol concentration >30% (v/v). However, in 20% ethanol(v/v), ß-lactoglobulin still retains most of its nativesecondary structure as shown by CD and, in this condition, oneß-lactoglobulin molecule binds an additional secondretinol molecule. This suggests that the highly populated speciesobserved around 20% ethanol (v/v) might represent an intermediatestate able to bind two molecules of retinol.     

An approach to reduce energy consumption and performance losses on heterogeneous servers using power capping

Journal of Scheduling - Rapid growth of demand for remote computational power, along with high energy costs and infrastructure limits, has led to treating power usage as a primary constraint in...     

A two‐level detector of short‐term unique changes in time series based on a similarity method

In the paper, a novel two‐level algorithm of time‐series change detection is presented. In the first level, to identify non‐stationary sequences in a processed signal, preliminary detection of events is performed with a short‐term prediction comparison. In the second stage, to confirm the changes detected in the first level, a similarity method aimed at identification of unique changes is employed. The detection of changes in a non‐stationary time series is discussed, implemented algorithms are described and the results produced on a sample four financial time series are shown. General conditions for implementing the proposed algorithm as an immune‐like event detector are discussed.     

Regular Languages of Thin Trees

An infinite tree is called thin if it contains only countably many infinite branches. Thin trees can be seen as intermediate structures between infinite words and infinite trees. In this work we investigate properties of regular languages of thin trees. Our main tool is an algebra suitable for thin trees. Using this framework we characterize various classes of regular languages: commutative, open in the standard topology, and definable in weak MSO logic among all trees. We also show that in various meanings thin trees are not as rich as all infinite trees. In particular we observe a collapse of the parity index to the level (1, 3) and a collapse of the topological complexity to co-analytic sets. Moreover, a gap property is shown: a regular language of thin trees is either weak MSO-definable among all trees or co-analytic-complete.     

Computing all efficient solutions of the biobjective minimum spanning tree problem

A common way of computing all efficient (Pareto optimal) solutions for a biobjective combinatorial optimisation problem is to compute first the extreme efficient solutions and then the remaining, non-extreme solutions. The second phase, the computation of non-extreme solutions, can be based on a “k-best” algorithm for the single-objective version of the problem or on the branch-and-bound method. A k-best algorithm computes the k-best solutions in order of their objective values. We compare the performance of these two approaches applied to the biobjective minimum spanning tree problem. Our extensive computational experiments indicate the overwhelming superiority of the k-best approach. We propose heuristic enhancements to this approach which further improve its performance.