The Cosmological Constant and Horava-Lifshitz Gravity

Horava-Lifshitz theory of gravity with detailed balance is plagued by the presence of a negative bare (or geometrical) cosmological constant which makes its cosmology clash with observations. We argue that adding the effects of the large vacuum energy of quantum matter fields, this bare cosmological constant can be approximately compensated to account for the small observed (total) cosmological constant. Even though we cannot address the fine-tuning problem in this way, we are able to establish a relation between the smallness of observed cosmological constant and the length scale at which dimension 4 corrections to the Einstein gravity become significant for cosmology. This scale turns out to be approximately 5 times the Planck length for an (almost) vanishing observed cosmological constant and we therefore argue that its smallness guarantees that Lorentz invariance is broken only at very small scales. We are also able to provide a first rough estimation for the infrared values of the parameters of the theory $\mu$ and $Lambda_w$.Comment: 9 pages, Late

Degradation and forgone removals increase the carbon impact of intact forest loss by 626%

Intact tropical forests, free from substantial anthropogenic influence, store and sequester large amounts of atmospheric carbon but are currently neglected in international climate policy. We show that between 2000 and 2013, direct clearance of intact tropical forest areas accounted for 3.2% of gross carbon emissions from all deforestation across the pantropics. However, full carbon accounting requires the consideration of forgone carbon sequestration, selective logging, edge effects, and defaunation. When these factors were considered, the net carbon impact resulting from intact tropical forest loss between 2000 and 2013 increased by a factor of 6 (626%), from 0.34 (0.37 to 0.21) to 2.12 (2.85 to 1.00) petagrams of carbon (equivalent to approximately 2 years of global land use change emissions). The climate mitigation value of conserving the 549 million ha of tropical forest that remains intact is therefore significant but will soon dwindle if their rate of loss continues to accelerate

Long and repeat-rich intronic sequences favor circular RNA formation under conditions of reduced spliceosome activity

Circular RNAs (circRNAs), an important class of regulatory RNAs, have been shown to be the most prevalent in the brain compared with other tissues. However the processes governing their biogenesis in neurons are still elusive. Moreover, little is known about whether and how different biogenesis factors work in synchrony to generate neuronal circRNAs. To address this question, we pharmacologically inhibited the spliceosome and profiled rat neuronal circRNAs using RNA sequencing. We identified over 100 circRNAs that were up-regulated and a few circRNAs that were down-regulated upon spliceosome inhibition. Bioinformatic analysis revealed that up-regulated circRNAs possess significantly longer flanking introns compared with the un-changed circRNA population. Moreover, the flanking introns of up-regulated circRNAs harbor a higher number of distinct repeat sequences and more reverse complementary motifs compared with the unchanged circRNAs. Taken together, our data demonstrate that the biogenesis of circRNAs containing distinct intronic features becomes favored under conditions of limited spliceosome activity

Automated Discrimination of Pathological Regions in Tissue Images: Unsupervised Clustering vs Supervised SVM Classification

Recognizing and isolating cancerous cells from non pathological tissue areas (e.g. connective stroma) is crucial for fast and objective immunohistochemical analysis of tissue images. This operation allows the further application of fully-automated techniques for quantitative evaluation of protein activity, since it avoids the necessity of a preventive manual selection of the representative pathological areas in the image, as well as of taking pictures only in the pure-cancerous portions of the tissue. In this paper we present a fully-automated method based on unsupervised clustering that performs tissue segmentations highly comparable with those provided by a skilled operator, achieving on average an accuracy of 90%. Experimental results on a heterogeneous dataset of immunohistochemical lung cancer tissue images demonstrate that our proposed unsupervised approach overcomes the accuracy of a theoretically superior supervised method such as Support Vector Machine (SVM) by 8%

The Power of Proofs: New Algorithms for Timed Automata Model Checking (with Appendix)

This paper presents the first model-checking algorithm for an expressive modal mu-calculus over timed automata, $L^{\mathit{rel}, \mathit{af}}_{\nu,\mu}$, and reports performance results for an implementation. This mu-calculus contains extended time-modality operators and can express all of TCTL. Our algorithmic approach uses an "on-the-fly" strategy based on proof search as a means of ensuring high performance for both positive and negative answers to model-checking questions. In particular, a set of proof rules for solving model-checking problems are given and proved sound and complete; we encode our algorithm in these proof rules and model-check a property by constructing a proof (or showing none exists) using these rules. One noteworthy aspect of our technique is that we show that verification performance can be improved with \emph{derived rules}, whose correctness can be inferred from the more primitive rules on which they are based. In this paper, we give the basic proof rules underlying our method, describe derived proof rules to improve performance, and compare our implementation of this model checker to the UPPAAL tool.Comment: This is the preprint of the FORMATS 2014 paper, but this is the full version, containing the Appendix. The final publication is published from Springer, and is available at http://link.springer.com/chapter/10.1007%2F978-3-319-10512-3_9 on the Springer webpag

Targeting lentiviral vectors to antigen-specific immunoglobulins

Gene transfer into B cells by lentivectors can provide an alternative approach to managing B lymphocyte malignancies and autoreactive B cell-mediated autoimmune diseases. These pathogenic B cell Populations can be distinguished by their surface expression of monospecific immunoglobulin. Development of a novel vector system to deliver genes to these specific B cells could improve the safety and efficacy of gene therapy. We have developed an efficient rnethod to target lentivectors to monospecific immunoglobulin-expressing cells in vitro and hi vivo. We were able to incorporate a model antigen CD20 and a fusogenic protein derived from the Sindbis virus as two distinct molecules into the lentiviral Surface. This engineered vector could specifically bind to cells expressing Surface immunoglobulin recognizing CD20 (αCD20), resulting in efficient transduction of target cells in a cognate antigen-dependent manner in vitro, and in vivo in a xenografted tumor model. Tumor suppression was observed in vivo, using the engineered lentivector to deliver a suicide gene to a xenografted tumor expressing αCD20. These results show the feasibility of engineering lentivectors to target immunoglobulin-specific cells to deliver a therapeutic effect. Such targeting lentivectors also Could potentially be used to genetically mark antigen-specific B cells in vivo to study their B cell biology

A note on Friedmann equation of FRW universe in deformed Horava-Lifshitz gravity from entropic force

With entropic interpretation of gravity proposed by Verlinde, we obtain the Friedmann equation of the Friedmann-Robertson-Walker universe for the deformed Ho\v{r}ava-Lifshitz gravity. It is shown that, when the parameter of Ho\v{r}ava-Lifshitz gravity $\omega\rightarrow \infty$, the modified Friedmann equation will go back to the one in Einstein gravity. This results may imply that the entropic interpretation of gravity is effective for the deformed Ho\v{r}ava-Lifshitz gravity.Comment: 9 pages, no figure