Short-term memory in gene induction reveals the regulatory principle behind stochastic IL-4 expression

Combining experiments on primary T cells and mathematical modeling, we characterized the stochastic expression of the interleukin-4 cytokine gene in its physiologic context, showing that a two-step model of transcriptional regulation acting on chromatin rearrangement and RNA polymerase recruitment accounts for the level, kinetics, and population variability of expression.A rate-limiting step upstream of transcription initiation, but occurring at the level of an individual allele, controls whether the interleukin-4 gene is expressed during antigenic stimulation, suggesting that the observed stochasticity of expression is linked to the dynamics of chromatin rearrangement.The computational analysis predicts that the probability to re-express an interleukin-4 gene that has been expressed once is transiently increased. In support, we experimentally demonstrate a short-term memory for interleukin-4 expression at the predicted time scale of several days.The model provides a unifying framework that accounts for both graded and binary modes of gene regulation. Graded changes in expression level can be achieved by controlling transcription initiation, whereas binary regulation acts at the level of chromatin rearrangement and is targeted during the differentiation of T cells that specialize in interleukin-4 production

“I finally felt i had the tools to control these urges”: empowering students to achieve their device use goals with the reduce digital distraction workshop

Digital self-control tools (DSCTs) help people control their time and attention on digital devices, using interventions like distraction blocking or usage tracking. Most studies of DSCTs’ effectiveness have focused on whether a single intervention reduces time spent on a single device. In reality, people may require combinations of DSCTs to achieve more subjective goals across multiple devices. We studied how DSCTs can address individual needs of university students (n = 280), using a workshop where students reflect on their goals before exploring relevant tools. At 1-3 month follow-ups, 95% of respondents still used at least one type of DSCT, typically applied across multiple devices, and there was substantial variation in the tool combinations chosen. We observed a large increase in self reported digital self-control, suggesting that providing a space to articulate goals and self-select appropriate DSCTs is a powerful way to support people who struggle to self-regulate digital device use

The Spectral Energy Distributions and Infrared Luminosities of z \approx 2 Dust Obscured Galaxies from Herschel and Spitzer

Dust-obscured galaxies (DOGs) are a subset of high-redshift (z \approx 2) optically-faint ultra-luminous infrared galaxies (ULIRGs, e.g. L_{IR} > 10^{12} Lsun). We present new far-infrared photometry, at 250, 350, and 500 um (observed-frame), from the Herschel Space Telescope for a large sample of 113 DOGs with spectroscopically measured redshifts. Approximately 60% of the sample are detected in the far-IR, confirming their high IR luminosities, which range from 10^{11.6} Lsun < L_{IR} (8-1000 um) <10^{13.6} Lsun. 90% of the Herschel detected DOGs in this sample are ULIRGs and 30% have L_{IR} > 10^{13} Lsun. The rest-frame near-IR (1 - 3 um) SEDs of the Herschel detected DOGs are predictors of their SEDs at longer wavelengths. DOGs with "power-law" SEDs in the rest-frame near-IR show observed-frame 250/24 um flux density ratios similar to the QSO-like local ULIRG, Mrk 231. DOGs with a stellar "bump" in their rest-frame near-IR show observed-frame 250/24 um flux density ratios similar to local star-bursting ULIRGs like NGC 6240. For the Herschel detected DOGs, accurate estimates (within \approx 25%) of total IR luminosity can be predicted from their rest-frame mid-IR data alone (e.g. from Spitzer observed-frame 24 um luminosities). Herschel detected DOGs tend to have a high ratio of infrared luminosity to rest-frame 8 um luminosity (the IR8= L_{IR}(8-1000 um)/v L_{v}(8 um) parameter of Elbaz et al. 2011). Instead of lying on the z=1-2 "infrared main-sequence" of star forming galaxies (like typical LIRGs and ULIRGs at those epochs) the DOGs, especially large fractions of the bump sources, tend to lie in the starburst sequence. While, Herschel detected DOGs are similar to scaled up versions of local ULIRGs in terms of 250/24 um flux density ratio, and IR8, they tend to have cooler far-IR dust temperatures (20-40 K for DOGs vs. 40-50 K for local ULIRGs). Abridged.Comment: 24 pages, 14 figures, 3 tables, accepted for publication in the Astronomical Journa

LocTree3 prediction of localization

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18 = 80 ± 3% for eukaryotes and a six-state accuracy Q6 = 89 ± 4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3

AGD-library: a library of algorithms for graph drawing

A graph drawing algorithm produces a layout of a graph in two- or three-dimensional space that should be readable and easy to understand. Since the aesthetic criteria differ from one application area to another, it is unlikely that a definition of the 'optimal drawing' of a graph in a strict mathematical sense exists. A large number of graph drawing algorithms taking different aesthetic criteria into account have already been proposed. In this paper we describe the design and implementation of the AGD-Library, a library of Algorithms for Graph Drawing. The library offers a broad range of existing algorithms for two-dimensional graph drawing and tools for implementing new algorithms. The library is written in C++ using the LEDA platform for combinatorial and geometric computing ([16, 17]). The algorithms are implemented independently of the underlying visualization or graphics system by using a generic layout interface. Most graph drawing algorithms place a set of restrictions on the input graphs like planarity or biconnectivity. We provide a mechanism for declaring this precondition for a particular algorithm and checking it for potential input graphs. A drawing model can be characterized by a set of properties of the drawing. We call these properties the postcondition of the algorithm. There is support for maintaining and retrieving the postcondition of an algorithm. (orig.)SIGLEAvailable from TIB Hannover: RR 1912(97-1-019) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDeutsche Forschungsgemeinschaft (DFG), Bonn (Germany)DEGerman

Designing a smartphone exergame for children with cerebral palsy in the home environment

Children with cerebral palsy must perform daily exercise which is a tedious and energy consuming task. Exergames can make this routine more engaging, which can increase the compliance of the patient. This research explores the feasibility of an exergame device called the Squid Monster. The device is the result of a research through design process, and it is designed to be played on smartphones in the home environment. It operates on the smartphone's integrated sensors and two external squeeze sensors, making it accessible and cost-effective. We conceptualize how the design can be supported using a machine learning adaptive difficulty system, aiming to increase flow and therapeutic adherence of the device. Ultimately, guidelines are provided to designers for future work in this field