Characteristic Evolution and Matching

I review the development of numerical evolution codes for general relativity based upon the characteristic initial value problem. Progress in characteristic evolution is traced from the early stage of 1D feasibility studies to 2D axisymmetric codes that accurately simulate the oscillations and gravitational collapse of relativistic stars and to current 3D codes that provide pieces of a binary black hole spacetime. Cauchy codes have now been successful at simulating all aspects of the binary black hole problem inside an artificially constructed outer boundary. A prime application of characteristic evolution is to extend such simulations to null infinity where the waveform from the binary inspiral and merger can be unambiguously computed. This has now been accomplished by Cauchy-characteristic extraction, where data for the characteristic evolution is supplied by Cauchy data on an extraction worldtube inside the artificial outer boundary. The ultimate application of characteristic evolution is to eliminate the role of this outer boundary by constructing a global solution via Cauchy-characteristic matching. Progress in this direction is discussed.Comment: New version to appear in Living Reviews 2012. arXiv admin note: updated version of arXiv:gr-qc/050809

Incubation of ovine scrapie with environmental matrix results in biological and biochemical changes of PrPSc over time

Ovine scrapie can be transmitted via environmental reservoirs. A pool of ovine scrapie isolates were incubated on soil for one day or thirteen months and eluted prion was used to challenge tg338 mice transgenic for ovine PrP. After one-day incubation on soil, two PrPSc phenotypes were present: G338 or Apl338ii. Thirteen months later some divergent PrPSc phenotypes were seen: a mixture of Apl338ii with either G338 or P338, and a completely novel PrPSc deposition, designated Cag338. The data show that prolonged ageing of scrapie prions within an environmental matrix may result in changes in the dominant PrPSc biological/biochemical properties

PeakRegressor Identifies Composite Sequence Motifs Responsible for STAT1 Binding Sites and Their Potential rSNPs

How to identify true transcription factor binding sites on the basis of sequence motif information (e.g., motif pattern, location, combination, etc.) is an important question in bioinformatics. We present “PeakRegressor,” a system that identifies binding motifs by combining DNA-sequence data and ChIP-Seq data. PeakRegressor uses L1-norm log linear regression in order to predict peak values from binding motif candidates. Our approach successfully predicts the peak values of STAT1 and RNA Polymerase II with correlation coefficients as high as 0.65 and 0.66, respectively. Using PeakRegressor, we could identify composite motifs for STAT1, as well as potential regulatory SNPs (rSNPs) involved in the regulation of transcription levels of neighboring genes. In addition, we show that among five regression methods, L1-norm log linear regression achieves the best performance with respect to binding motif identification, biological interpretability and computational efficiency

Ancient hydrothermal seafloor deposits in Eridania basin on Mars

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/. The file attached is the Published/publisher’s pdf version of the article

Carrion Beetles Visiting Pig Carcasses during Early Spring in Urban, Forest and Agricultural Biotopes of Western Europe

Carrion beetles are important in terrestrial ecosystems, consuming dead mammals and promoting the recycling of organic matter into ecosystems. Most forensic studies are focused on succession of Diptera while neglecting Coleoptera. So far, little information is available on carrion beetles postmortem colonization and decomposition process in temperate biogeoclimatic countries. These beetles are however part of the entomofaunal colonization of a dead body. Forensic entomologists need databases concerning the distribution, ecology and phenology of necrophagous insects, including silphids. Forensic entomology uses pig carcasses to surrogate human decomposition and to investigate entomofaunal succession. However, few studies have been conducted in Europe on large carcasses. The work reported here monitored the presence of the carrion beetles (Coleoptera: Silphidae) on decaying pig carcasses in three selected biotopes (forest, crop field, urban site) at the beginning of spring. Seven species of Silphidae were recorded: Nicrophorus humator (Gleditsch), Nicrophorus vespillo (L.), Nicrophorus vespilloides (Herbst), Necrodes littoralis L., Oiceoptoma thoracica L., Thanatophilus sinuatus (Fabricius), Thanatophilus rugosus (L.). All of these species were caught in the forest biotope, and all but O. thoracica were caught in the agricultural biotope. No silphids were caught in the urban site

Postcopulatory sexual selection

The female reproductive tract is where competition between the sperm of different males takes place, aided and abetted by the female herself. Intense postcopulatory sexual selection fosters inter-sexual conflict and drives rapid evolutionary change to generate a startling diversity of morphological, behavioural and physiological adaptations. We identify three main issues that should be resolved to advance our understanding of postcopulatory sexual selection. We need to determine the genetic basis of different male fertility traits and female traits that mediate sperm selection; identify the genes or genomic regions that control these traits; and establish the coevolutionary trajectory of sexes

Understanding Weather and Hospital Admissions Patterns to Inform Climate Change Adaptation Strategies in the Healthcare Sector in Uganda

Background: Season and weather are associated with many health outcomes, which can influence hospital admission rates. We examined associations between hospital admissions (all diagnoses) and local meteorological parameters in Southwestern Uganda, with the aim of supporting hospital planning and preparedness in the context of climate change. Methods: Hospital admissions data and meteorological data were collected from Bwindi Community Hospital and a satellite database of weather conditions, respectively (2011 to 2014). Descriptive statistics were used to describe admission patterns. A mixed-effects Poisson regression model was fitted to investigate associations between hospital admissions and season, precipitation, and temperature. Results: Admission counts were highest for acute respiratory infections, malaria, and acute gastrointestinal illness, which are climate-sensitive diseases. Hospital admissions were 1.16 (95% CI: 1.04, 1.31; p = 0.008) times higher during extreme high temperatures (i.e., >95th percentile) on the day of admission. Hospital admissions association with season depended on year; admissions were higher in the dry season than the rainy season every year, except for 2014. Discussion: Effective adaptation strategy characteristics include being low-cost and quick and practical to implement at local scales. Herein, we illustrate how analyzing hospital data alongside meteorological parameters may inform climate-health planning in low-resource contexts

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration

Characteristic Evolution and Matching

I review the development of numerical evolution codes for general relativity based upon the characteristic initial value problem. Progress is traced from the early stage of 1D feasibility studies to 2D axisymmetric codes that accurately simulate the oscillations and gravitational collapse of relativistic stars and to current 3D codes that provide pieces of a binary black spacetime. A prime application of characteristic evolution is to compute waveforms via Cauchy-characteristic matching, which is also reviewed.Comment: Published version http://www.livingreviews.org/lrr-2005-1

Role of the monocarboxylate transporter MCT1 in the uptake of lactate during active recovery

Purpose We assessed the role of monocarboxylate transporter 1 (MCT1) on lactate clearance during an active recovery after high-intensity exercise, by comparing genetic groups based on the T1470A (rs1049434) MCT1 polymorphism, whose influence on lactate transport has been proven. Methods Sixteen young male elite field hockey players participated in this study. All of them completed two 400 m maximal run tests performed on different days, followed by 40 min of active or passive recovery. Lactate samples were measured immediately after the tests, and at min 10, 20, 30 and 40 of the recoveries. Blood lactate decreases were calculated for each 10-min period. Participants were distributed into three groups according to the T1470A polymorphism (TT, TA and AA). Results TT group had a lower blood lactate decrease than AA group during the 10?20 min period of the active recovery (p = 0.018). This period had the highest blood lactate for the whole sample, significantly differing from the other periods (p ? 0.003). During the passive recovery, lactate declines were constant except for the 0?10-min period (p ? 0.003), suggesting that liver uptake is similar in all the genetic groups, and that the difference seen during the active recovery is mainly due to muscle lactate uptake. Conclusions These differences according to the polymorphic variant T1470A suggest that MCT1 affects the plasma lactate decrease during a crucial period of active recovery, where the maximal lactate amount is cleared (i.e. 10?20 min period)