Towards Precision Dermatology: Emerging Role of Proteomic Analysis of the Skin

Background: The skin is the largest organ in the human body and serves as a multilayered protective shield from the environment as well as a sensor and thermal regulator. However, despite its importance, many details about skin structure and function at the molecular level remain incompletely understood. Recent advances in liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics have enabled the quantification and characterization of the proteomes of a number of clinical samples, including normal and diseased skin. Summary: Here, we review the current state of the art in proteomic analysis of the skin. We provide a brief overview of the technique and skin sample collection methodologies as well as a number of recent examples to illustrate the utility of this strategy for advancing a broader understanding of the pathology of diseases as well as new therapeutic options. Key Messages: Proteomic studies of healthy skin and skin diseases can identify potential molecular biomarkers for improved diagnosis and patient stratification as well as potential targets for drug development. Collectively, efforts such as the Human Skinatlas offer improved opportunities for enhancing clinical practice and patient outcomes

Dynamic Range Majority Data Structures

Given a set $P$ of coloured points on the real line, we study the problem of answering range $\alpha$-majority (or "heavy hitter") queries on $P$. More specifically, for a query range $Q$, we want to return each colour that is assigned to more than an $\alpha$-fraction of the points contained in $Q$. We present a new data structure for answering range $\alpha$-majority queries on a dynamic set of points, where $\alpha \in (0,1)$. Our data structure uses O(n) space, supports queries in $O((\lg n) / \alpha)$ time, and updates in $O((\lg n) / \alpha)$ amortized time. If the coordinates of the points are integers, then the query time can be improved to $O(\lg n / (\alpha \lg \lg n) + (\lg(1/\alpha))/\alpha))$. For constant values of $\alpha$, this improved query time matches an existing lower bound, for any data structure with polylogarithmic update time. We also generalize our data structure to handle sets of points in d-dimensions, for $d \ge 2$, as well as dynamic arrays, in which each entry is a colour.Comment: 16 pages, Preliminary version appeared in ISAAC 201

Optimal Color Range Reporting in One Dimension

Color (or categorical) range reporting is a variant of the orthogonal range reporting problem in which every point in the input is assigned a \emph{color}. While the answer to an orthogonal point reporting query contains all points in the query range $Q$, the answer to a color reporting query contains only distinct colors of points in $Q$. In this paper we describe an O(N)-space data structure that answers one-dimensional color reporting queries in optimal $O(k+1)$ time, where $k$ is the number of colors in the answer and $N$ is the number of points in the data structure. Our result can be also dynamized and extended to the external memory model

Caregiving, Metabolic Syndrome Indicators, and 1-year Decline in Walking Speed: Results of Caregiver-SOF

BACKGROUND Chronic stress may lead to health decline through metabolic syndrome. Thus, persons in stressful caregiving situations who also have more indicators of metabolic syndrome may experience more decline than other caregivers or noncaregivers. METHODS The sample included 921 women (338 caregivers and 583 noncaregivers) from the Caregiver-Study of Osteoporotic Fractures study. Participants had home-based baseline and 1-year follow-up interviews between 1999 and 2003. At baseline, caregivers were categorized as long term (³4 years) versus short term (<4 years), and caring for someone with Alzheimer's disease/dementia or not. A metabolic risk composite score was the sum of four indicators: body mass index ³30, and diagnosis or using medications for hypertension, diabetes, or high cholesterol. Walking speed (m/second) was measured at both interviews. RESULTS Walking speed declined for the total sample (adjusted mean = −0.005 m/second, ±0.16) over an average of 1.04 years (±0.16). Overall, caregiving was not associated with decline. Increasing metabolic risk score was associated with greater decline for the total sample and long-term and dementia caregivers, but not other caregivers or noncaregivers. Metabolic risk score modified the adjusted associations between years of caregiving and dementia caregiving with walking speed decline (p values for interaction terms were 0.039 and 0.057, respectively). The biggest declines were in long-term caregivers and dementia caregivers who also had 3–4 metabolic indicators (−0.10 m/second and −0.155 m/second, respectively). CONCLUSIONS Walking speed declined the most among older women who had both stressful caregiving situations and more metabolic syndrome indicators, suggesting these caregiver subgroups may have increased risk of health decline.AG18037, AG05407, AR35582, AG05394, AR35584, and AR3558

On dualization in products of forests, in

Abstract. Let P = P1 ×...×Pn be the product of n partially ordered sets, each with an acyclic precedence graph in which either the in-degree or the out-degree of each element is bounded. Given a subset A⊆P,it is shown that the set of maximal independent elements of A in P can be incrementally generated in quasi-polynomial time. We discuss some applications in data mining related to this dualization problem

Computing discriminating and generic words

International audienceWe study the following three problems of computing generic or discriminating words for a given collection of documents. Given a pattern P and a threshold d, we want to report (i) all longest extensions of P which occur in at least d documents, (ii) all shortest extensions of P which occur in less than d documents, and (iii) all shortest extensions of P which occur only in d selected documents. For these problems, we propose efficient algorithms based on suffix trees and using advanced data structure techniques. For problem (i), we propose an optimal solution with constant running time per output word

13-Series resolvins mediate the leukocyte-platelet actions of atorvastatin and pravastatin in inflammatory arthritis

This work was supported by funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program (Grant 677542), a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant 107613/Z/15/Z), and the Barts Charity (Grant MGU0343). This work was also funded, in part, by Medical Research Council Advance Course Masters (Grant MR/J015741/1). The authors declare no conflicts of interest

Blood pro-resolving mediators are linked with synovial pathology and are predictive of DMARD responsiveness in rheumatoid arthritis.

Biomarkers are needed for predicting the effectiveness of disease modifying antirheumatic drugs (DMARDs). Here, using functional lipid mediator profiling and deeply phenotyped patients with early rheumatoid arthritis (RA), we observe that peripheral blood  specialized pro-resolving mediator (SPM) concentrations are linked with both DMARD responsiveness and disease pathotype. Machine learning analysis demonstrates that baseline plasma concentrations of resolvin D4, 10S, 17S-dihydroxy-docosapentaenoic acid, 15R-Lipoxin (LX)A4 and n-3 docosapentaenoic-derived Maresin 1 are predictive of DMARD responsiveness at 6 months. Assessment of circulating SPM concentrations 6-months after treatment initiation establishes that differences between responders and non-responders are maintained, with a decrease in SPM concentrations in patients resistant to DMARD therapy. These findings elucidate the potential utility of  plasma SPM concentrations as biomarkers of DMARD responsiveness in RA

Longest Increasing Subsequence under Persistent Comparison Errors

We study the problem of computing a longest increasing subsequence in a sequence $S$ of $n$ distinct elements in the presence of persistent comparison errors. In this model, every comparison between two elements can return the wrong result with some fixed (small) probability $p$, and comparisons cannot be repeated. Computing the longest increasing subsequence exactly is impossible in this model, therefore, the objective is to identify a subsequence that (i) is indeed increasing and (ii) has a length that approximates the length of the longest increasing subsequence. We present asymptotically tight upper and lower bounds on both the approximation factor and the running time. In particular, we present an algorithm that computes an $O(\log n)$-approximation in time $O(n\log n)$, with high probability. This approximation relies on the fact that that we can approximately sort $n$ elements in $O(n\log n)$ time such that the maximum dislocation of an element is at most $O(\log n)$. For the lower bounds, we prove that (i) there is a set of sequences, such that on a sequence picked randomly from this set every algorithm must return an $\Omega(\log n)$-approximation with high probability, and (ii) any $O(\log n)$-approximation algorithm for longest increasing subsequence requires $\Omega(n \log n)$ comparisons, even in the absence of errors