Dark energy as a mirage

Motivated by the observed cosmic matter distribution, we present the following conjecture: due to the formation of voids and opaque structures, the average matter density on the path of the light from the well-observed objects changes from Omega_M ~ 1 in the homogeneous early universe to Omega_M ~ 0 in the clumpy late universe, so that the average expansion rate increases along our line of sight from EdS expansion Ht ~ 2/3 at high redshifts to free expansion Ht ~ 1 at low redshifts. To calculate the modified observable distance-redshift relations, we introduce a generalized Dyer-Roeder method that allows for two crucial physical properties of the universe: inhomogeneities in the expansion rate and the growth of the nonlinear structures. By treating the transition redshift to the void-dominated era as a free parameter, we find a phenomenological fit to the observations from the CMB anisotropy, the position of the baryon oscillation peak, the magnitude-redshift relations of type Ia supernovae, the local Hubble flow and the nucleosynthesis, resulting in a concordant model of the universe with 90% dark matter, 10% baryons, no dark energy, 15 Gyr as the age of the universe and a natural value for the transition redshift z_0=0.35. Unlike a large local void, the model respects the cosmological principle, further offering an explanation for the late onset of the perceived acceleration as a consequence of the forming nonlinear structures. Additional tests, such as quantitative predictions for angular deviations due to an anisotropic void distribution and a theoretical derivation of the model, can vindicate or falsify the interpretation that light propagation in voids is responsible for the perceived acceleration.Comment: 33 pages, 2 figs; v2: minor clarifications, results unchanged; v3: matches the version published in General Relativity and Gravitatio

RegPhos: a system to explore the protein kinase–substrate phosphorylation network in humans

Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. With the increasing number of experimental phosphorylation sites that has been identified by mass spectrometry-based proteomics, the desire to explore the networks of protein kinases and substrates is motivated. Manning et al. have identified 518 human kinase genes, which provide a starting point for comprehensive analysis of protein phosphorylation networks. In this study, a knowledgebase is developed to integrate experimentally verified protein phosphorylation data and protein–protein interaction data for constructing the protein kinase–substrate phosphorylation networks in human. A total of 21 110 experimental verified phosphorylation sites within 5092 human proteins are collected. However, only 4138 phosphorylation sites (∼20%) have the annotation of catalytic kinases from public domain. In order to fully investigate how protein kinases regulate the intracellular processes, a published kinase-specific phosphorylation site prediction tool, named KinasePhos is incorporated for assigning the potential kinase. The web-based system, RegPhos, can let users input a group of human proteins; consequently, the phosphorylation network associated with the protein subcellular localization can be explored. Additionally, time-coursed microarray expression data is subsequently used to represent the degree of similarity in the expression profiles of network members. A case study demonstrates that the proposed scheme not only identify the correct network of insulin signaling but also detect a novel signaling pathway that may cross-talk with insulin signaling network. This effective system is now freely available at http://RegPhos.mbc.nctu.edu.tw

The effect of inhomogeneous expansion on the supernova observations

We consider an inhomogeneous but spherically symmetric Lemaitre-Tolman-Bondi model to demonstrate that spatial variations of the expansion rate can have a significant effect on the cosmological supernova observations. A model with no dark energy but a local Hubble parameter about 15% larger than its global value fits the supernova data better than the homogeneous model with the cosmological constant. The goodness of the fit is not sensitive to inhomogeneities in the present-day matter density, and our best fit model has Omega_M ~ 0.3, in agreement with galaxy surveys. We also compute the averaged expansion rate, defined by the Buchert equations, of the best fit model and show explicitly that there is no average acceleration.Comment: minor corrections to match the version published in JCA

Bacterial Symbiosis Maintenance in the Asexually Reproducing and Regenerating Flatworm Paracatenula galateia

Bacteriocytes set the stage for some of the most intimate interactions between animal and bacterial cells. In all bacteriocyte possessing systems studied so far, de novo formation of bacteriocytes occurs only once in the host development, at the time of symbiosis establishment. Here, we present the free-living symbiotic flatworm Paracatenula galateia and its intracellular, sulfur-oxidizing bacteria as a system with previously undescribed strategies of bacteriocyte formation and bacterial symbiont transmission. Using thymidine analogue S-phase labeling and immunohistochemistry, we show that all somatic cells in adult worms – including bacteriocytes – originate exclusively from aposymbiotic stem cells (neoblasts). The continued bacteriocyte formation from aposymbiotic stem cells in adult animals represents a previously undescribed strategy of symbiosis maintenance and makes P. galateia a unique system to study bacteriocyte differentiation and development. We also provide morphological and immunohistochemical evidence that P. galateia reproduces by asexual fragmentation and regeneration (paratomy) and, thereby, vertically transmits numerous symbiont-containing bacteriocytes to its asexual progeny. Our data support the earlier reported hypothesis that the symbiont population is subjected to reduced bottleneck effects. This would justify both the codiversification between Paracatenula hosts and their Candidatus Riegeria symbionts, and the slow evolutionary rates observed for several symbiont genes

Characterizing Dynamic Changes in the Human Blood Transcriptional Network

Gene expression data generated systematically in a given system over multiple time points provides a source of perturbation that can be leveraged to infer causal relationships among genes explaining network changes. Previously, we showed that food intake has a large impact on blood gene expression patterns and that these responses, either in terms of gene expression level or gene-gene connectivity, are strongly associated with metabolic diseases. In this study, we explored which genes drive the changes of gene expression patterns in response to time and food intake. We applied the Granger causality test and the dynamic Bayesian network to gene expression data generated from blood samples collected at multiple time points during the course of a day. The simulation result shows that combining many short time series together is as powerful to infer Granger causality as using a single long time series. Using the Granger causality test, we identified genes that were supported as the most likely causal candidates for the coordinated temporal changes in the network. These results show that PER1 is a key regulator of the blood transcriptional network, in which multiple biological processes are under circadian rhythm regulation. The fasted and fed dynamic Bayesian networks showed that over 72% of dynamic connections are self links. Finally, we show that different processes such as inflammation and lipid metabolism, which are disconnected in the static network, become dynamically linked in response to food intake, which would suggest that increasing nutritional load leads to coordinate regulation of these biological processes. In conclusion, our results suggest that food intake has a profound impact on the dynamic co-regulation of multiple biological processes, such as metabolism, immune response, apoptosis and circadian rhythm. The results could have broader implications for the design of studies of disease association and drug response in clinical trials

Interdisciplinarity to reconstruct historical introductions: solving the status of cryptogenic crayfish

Anciently introduced species can be confounded with native species because introduction pre-dates the first species inventories or because of the loss of the collective memory of the introductions. The term ‘cryptogenic species’ denotes species of unknown or unclear status (native versus non-native) in a given territory, and disciplinary approaches are often insufficient for solving their true status. Here, we follow an integrative, multidisciplinary approach to solve the status of a cryptogenic species, proposing that building on evidence from multiple disciplines can produce robust and clarifying insights. We undertook an exhaustive review of information on a putatively native crayfish (Austropotamobius italicus) in Spain. The reviewed information included taxonomy, genetics and phylogeography, history, archaeology, linguistics, biogeography, ecology, symbiotic organisms and even gastronomy and pharmacy. The knowledge produced by different scientific disciplines converges to indicate that A. italicus is a non-native species in Spain. Historical documents even identify the first introduction event: crayfish were shipped from Italy to Spain in 1588 as a diplomatic gift from Francesco I de' Medici to King Philip II of Spain. Previous discussions on the status of A. italicus focussed on inconclusive and often confusing genetic results and excluded the rich and clarifying evidence available from other approaches and disciplines. Interdisciplinarity is an often-invoked but rarely implemented practice in an academic environment that increasingly promotes narrow-focussed specialization. Our review shows that the integration of disciplines can surpass disciplinary approaches in solving scientific controversies. Our results have straightforward implications for strategies to conserve biological diversity in Spain and Europe, urging a debate on the appropriateness of devoting conservation efforts to non-native species.Peer Reviewe

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

Signaling pathways play a key role in complex diseases such as cancer, for which the development of novel therapies is a difficult, expensive and laborious task. Computational models that can predict the effect of a new combination of drugs without having to test it experimentally can help in accelerating this process. In particular, network-based dynamic models of these pathways hold promise to both understand and predict the effect of therapeutics. However, their use is currently hampered by limitations in our knowledge of the underlying biochemistry, as well as in the experimental and computational technologies used for calibrating the models. Thus, the results from such models need to be carefully interpreted and used in order to avoid biased predictions. Here we present a procedure that deals with this uncertainty by using experimental data to build an ensemble of dynamic models. The method incorporates steps to reduce overfitting and maximize predictive capability. We find that by combining the outputs of individual models in an ensemble it is possible to obtain a more robust prediction. We report results obtained with this method, which we call SELDOM (enSEmbLe of Dynamic lOgic-based Models), showing that it improves the predictions previously reported for several challenging problems.JRB and DH acknowledge funding from the EU FP7 project NICHE (ITN Grant number 289384). JRB acknowledges funding from the Spanish MINECO project SYNBIOFACTORY (grant number DPI2014-55276-C5-2-R). AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C postdoctoral fellowship ED481B2014/133-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Genome-Wide Analyses of Exonic Copy Number Variants in a Family-Based Study Point to Novel Autism Susceptibility Genes

The genetics underlying the autism spectrum disorders (ASDs) is complex and remains poorly understood. Previous work has demonstrated an important role for structural variation in a subset of cases, but has lacked the resolution necessary to move beyond detection of large regions of potential interest to identification of individual genes. To pinpoint genes likely to contribute to ASD etiology, we performed high density genotyping in 912 multiplex families from the Autism Genetics Resource Exchange (AGRE) collection and contrasted results to those obtained for 1,488 healthy controls. Through prioritization of exonic deletions (eDels), exonic duplications (eDups), and whole gene duplication events (gDups), we identified more than 150 loci harboring rare variants in multiple unrelated probands, but no controls. Importantly, 27 of these were confirmed on examination of an independent replication cohort comprised of 859 cases and an additional 1,051 controls. Rare variants at known loci, including exonic deletions at NRXN1 and whole gene duplications encompassing UBE3A and several other genes in the 15q11–q13 region, were observed in the course of these analyses. Strong support was likewise observed for previously unreported genes such as BZRAP1, an adaptor molecule known to regulate synaptic transmission, with eDels or eDups observed in twelve unrelated cases but no controls (p = 2.3×10−5). Less is known about MDGA2, likewise observed to be case-specific (p = 1.3×10−4). But, it is notable that the encoded protein shows an unexpectedly high similarity to Contactin 4 (BLAST E-value = 3×10−39), which has also been linked to disease. That hundreds of distinct rare variants were each seen only once further highlights complexity in the ASDs and points to the continued need for larger cohorts