RNA secondary structure prediction using large margin methods

The secondary structure of RNA is essential for its biological role. Recently, Do, Woods, Batzoglou, (ISMB 2006) proposed a probabilistic approach that generalizes SCFGs using conditional maximum likelihood to estimate the model parameters. We propose an alternative approach to parameter estimation which is based on an SVM-like large margin method

Expectation adjustment in the housing market: insights from the Scottish auction system

This paper examines price expectation adjustment of house buyers and sellers to rapid changes in the housing market using data from Scotland where houses are sold through 'first-price sealed-bid' auctions. These auctions provide more information on market signals, incentives and the behaviour of market participants than private treaty sales. This paper therefore provides a theoretical framework for analysing revealed preference data generated from these auctions. We specifically focus on the analysis of the selling to asking price difference, the 'bid-premium'. The bid-premium is shown to be affected by expectations of future price movements, market duration and high bidding frequency. The bid-premium reflects consumers' expectations, adapting to market conditions more promptly than asking price setting behaviour and final sale prices. The volatile conditions of the recent housing market bubble are fully reflected in the bid-premium, whereas the asking and sale prices are much less prone to rapid movements

Kernel Methods for Predictive Sequence Analysis

This tutorial is meant for a broad audience: Students, researchers, biologists and computer scientist interested in (a) an overview of general and efficient algorithms for statistical learning used in computational biology, (b) sequence kernels for the problems such as promoter or splice site detection. No specific knowledge will be required since the tutorial is self-contained and most fundamental concepts are introduced during the course

Towards the Inference of Graphs on Ordered Vertexes

We propose novel methods for machine learning of structured output spaces. Specifically, we consider outputs which are graphs with vertices that have a natural order. We consider the usual adjacency matrix representation of graphs, as well as two other representations for such a graph: (a) decomposing the graph into a set of paths, (b) converting the graph into a single sequence of nodes with labeled edges. For each of the three representations, we propose an encoding and decoding scheme. We also propose an evaluation measure for comparing two graphs

The Nernst effect in high-TcT_c superconductors

The observation of a large Nernst signal $e_N$ in an extended region above the critical temperature $T_c$ in hole-doped cuprates provides evidence that vortex excitations survive above $T_c$. The results support the scenario that superfluidity vanishes because long-range phase coherence is destroyed by thermally-created vortices (in zero field), and that the pair condensate extends high into the pseudogap state in the underdoped (UD) regime. We present a series of measurements to high fields $H$ which provide strong evidence for this phase-disordering scenario.Comment: 21 pages, 28 figure

PALMA: Perfect Alignments using Large Margin Algorithms

Despite many years of research on how to properly align sequences in the presence of sequencing errors, alternative splicing and micro-exons, the correct alignment of mRNA sequences to genomic DNA is still a challenging task. We present a novel approach based on large margin learning that combines kernel based splice site predictions with common sequence alignment techniques. By solving a convex optimization problem, our algorithm -- called PALMA -- tunes the parameters of the model such that the true alignment scores higher than all other alignments. In an experimental study on the alignments of mRNAs containing artificially generated micro-exons, we show that our algorithm drastically outperforms all other methods: It perfectly aligns all 4358 sequences on an hold-out set, while the best other method misaligns at least 90 of them. Moreover, our algorithm is very robust against noise in the query sequence: when deleting, inserting, or mutating up to 50 of the query sequence, it still aligns 95 of all sequences correctly, while other methods achieve less than 36 accuracy. For datasets, additional results and a stand-alone alignment tool see http://www.fml.mpg.de/raetsch/projects/palma

Season of the year influences infection rates following total hip arthroplasty

To research the influence of season of the year on periprosthetic joint infections. METHODS We conducted a retrospective review of the entire Medicare files from 2005 to 2014. Seasons were classified as spring, summer, fall or winter. Regional variations were accounted for by dividing patients into four geographic regions as per the United States Census Bureau (Northeast, Midwest, West and South). Acute postoperative infection and deep periprosthetic infections within 90 d after surgery were tracked. RESULTS In all regions, winter had the highest incidence of periprosthetic infections (mean 0.98%, SD 0.1%) and was significantly higher than other seasons in the Midwest, South and West (P \u3c 0.05 for all) but not the Northeast (P = 0.358). Acute postoperative infection rates were more frequent in the summer and were significantly affected by season of the year in the West. CONCLUSION Season of the year is a risk factor for periprosthetic joint infection following total hip arthroplasty (THA). Understanding the influence of season on outcomes following THA is essential when risk-stratifying patients to optimize outcomes and reduce episode of care costs. © The Author(s) 2017

Strongly Coupled Matter-Field and Non-Analytic Decay Rate of Dipole Molecules in a Waveguide

The decay rate \gam of an excited dipole molecule inside a waveguide is evaluated for the strongly coupled matter-field case near a cutoff frequency \ome_c without using perturbation analysis. Due to the singularity in the density of photon states at the cutoff frequency, we find that \gam depends non-analytically on the coupling constant $\ggg$ as $\ggg^{4/3}$. In contrast to the ordinary evaluation of \gam which relies on the Fermi golden rule (itself based on perturbation analysis), \gam has an upper bound and does not diverge at \ome_c even if we assume perfect conductance in the waveguide walls. As a result, again in contrast to the statement found in the literature, the speed of emitted light from the molecule does not vanish at \ome_c and is proportional to $c\ggg^{2/3}$ which is on the order of $10^3 \sim 10^4$ m/s for typical dipole molecules.Comment: 4 pages, 2 figure

Inhibition of DNA damage response at telomeres improves the detrimental phenotypes of Hutchinson–Gilford Progeria Syndrome

Hutchinson–Gilford progeria syndrome (HGPS) is a genetic disorder characterized by premature aging features. Cells from HGPS patients express progerin, a truncated form of Lamin A, which perturbs cellular homeostasis leading to nuclear shape alterations, genome instability, heterochromatin loss, telomere dysfunction and premature entry into cellular senescence. Recently, we reported that telomere dysfunction induces the transcription of telomeric non-coding RNAs (tncRNAs) which control the DNA damage response (DDR) at dysfunctional telomeres. Here we show that progerin-induced telomere dysfunction induces the transcription of tncRNAs. Their functional inhibition by sequence-specific telomeric antisense oligonucleotides (tASOs) prevents full DDR activation and premature cellular senescence in various HGPS cell systems, including HGPS patient fibroblasts. We also show in vivo that tASO treatment significantly enhances skin homeostasis and lifespan in a transgenic HGPS mouse model. In summary, our results demonstrate an important role for telomeric DDR activation in HGPS progeroid detrimental phenotypes in vitro and in vivo