Combined Description of N‾N\bf{\overline{N}N} Scattering and Annihilation With A Hadronic Model

A model for the nucleon-antinucleon interaction is presented which is based on meson-baryon dynamics. The elastic part is the $G$-parity transform of the Bonn $NN$ potential. Annihilation into two mesons is described in terms of microscopic baryon-exchange processes including all possible combinations of $\pi,\eta,\rho,\omega,a_0,f_0,a_1,f_1,a_2,f_2,K,K^*$. The remaining annihilation part is taken into account by a phenomenological energy- and state independent optical potential of Gaussian form. The model enables a simultaneous description of nucleon-antinucleon scattering and annihilation phenomena with fair quality.Comment: revised version, REVTEX, 9 pages, 10 figures available from this URL ftp://ikp113.ikp.kfa-juelich.de/pub/kph140/nucl-th.9411014.u

Small but significant excess mortality compared with the general population for long-term survivors of breast cancer in the Netherlands

Background: Coinciding with the relatively good and improving prognosis for patients with stage I-III breast cancer, late recurrences, new primary tumours and late side-effects of treatment may occur. We gained insight into prognosis for long-term breast cancer survivors. Patients and methods: Data on all 205 827 females aged 15-89 diagnosed with stage I-III breast cancer during 1989-2008 were derived from the Netherlands Cancer Registry. Conditional 5-year relative survival was calculated for every subsequent year from diagnosis up to 15 years. Results: For stage I, conditional 5-year relative survival remained ~95% up to 15 years after diagnosis (a stable 5-year excess mortality rate of 5%). For stage II, excess mortality remained 10% for those aged 15-44 or 45-59 and 15% for those aged 60-74. For stage III, excess mortality decreased from 35% at diagnosis to 10% at 15 years for those aged 15-44 or 45-59, and from ~40% to 30% for those aged ≥60. Conclusions: Patients with stage I or II breast cancer had a (very) good long-term prognosis, albeit exhibiting a small but significant excess mortality at least up to 15 years after diagnosis

Exploring rumen microbe-derived fibre-degrading activities for improving feed digestibility

Ruminal fibre degradation is mediated by a complex community of rumen microbes, and its efficiency is crucial for optimal dairy productivity. Enzymes produced by rumen microbes are primarily responsible for degrading the complex structural polysaccharides that comprise fibre in the plant cell walls of feed materials. Because rumen microbes have evolved with their ruminant hosts over millions of years to perform this task, their enzymes are hypothesised to be optimally suited for activity at the temperature, pH range, and anaerobic environment of the rumen. However, fibre-rich diets are not fully digested, which represents a loss in potential animal productivity. Thus, there is opportunity to improve fibre utilisation through treating feeds with rumen microbe-derived fibrolytic enzymes and associated activities that enhance fibre degradation. This research aims to gain a better understanding of the key rumen microbes involved in fibre degradation and the mechanisms they employ to degrade fibre, by applying cultivation-based and culture-independent genomics approaches to rumen microbial communities of New Zealand dairy cattle. Using this knowledge, we aim to identify new opportunities for improving fibre degradation to enhance dairy productivity. Rumen content samples were taken over the course of a year from a Waikato dairy production herd. Over 1,000 rumen bacterial cultures were obtained from the plant-adherent fraction of the rumen contents. Among these cultures, two, 59 and 103 potentially new families, genera and species of rumen bacteria were identified, respectively. Many of the novel strains are being genome sequenced within the Hungate 1000 rumen microbial reference genome programme, which is providing deeper insights into the range of mechanisms used by the individual strains for fibre degradation. This information has been used to guide the selection of rumen bacterial strains with considerable potential as fibrolytic enzyme producers in vitro, with the intent of developing the strains so that their enzymes may be used as feed pre-treatments for use on farm. Culture-independent metagenomic approaches were also used to explore the activities involved in fibre degradation from the rumen microbial communities. Functional screening has revealed a range of novel enzymes and a novel fibre disrupting activity. Enrichment for the cell-secreted proteins from the community revealed evidence of a diverse range of cellulosomes, which are cell-surface associated multi-enzyme complexes that efficiently degrade plant cell wall polysaccharides. Biochemical and structural characterisation of these proteins has been conducted. In conclusion, cultivation and culture-independent genomic approaches have been applied to New Zealand bovine rumen microbial communities, and have provided considerable new insights into ruminal fibre degradation processes. Novel activities and bacterial species that display desirable activities on fibrous substrates in vitro are now being explored for their potential to improve ruminal fibre degradation, to allow the development of new technologies that will enhance dairy productivity

Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy

Methane (CH) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH. To address this limitation, we developed novel CH prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH production (g CH/animal·d, ANIM-B models) and CH yield (g CH/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH emissions from sheep, providing valuable insights for future research and mitigation strategies.Te authors gratefully acknowledge funding for this project from the USDA National Institute of Food and Agriculture (Award number: 2014-67003-21979). Te animal and microbial data originated from a study funded by the Pastoral Greenhouse Gas Research Consortium (www.pggrc.co.nz)

Parameters of the Magnetic Flux inside Coronal Holes

Parameters of magnetic flux distribution inside low-latitude coronal holes (CHs) were analyzed. A statistical study of 44 CHs based on Solar and Heliospheric Observatory (SOHO)/MDI full disk magnetograms and SOHO/EIT 284\AA images showed that the density of the net magnetic flux, $B_{{\rm net}}$, does not correlate with the associated solar wind speeds, $V_x$. Both the area and net flux of CHs correlate with the solar wind speed and the corresponding spatial Pearson correlation coefficients are 0.75 and 0.71, respectively. A possible explanation for the low correlation between $B_{{\rm net}}$ and $V_x$ is proposed. The observed non-correlation might be rooted in the structural complexity of the magnetic field. As a measure of complexity of the magnetic field, the filling factor, $f(r)$, was calculated as a function of spatial scales. In CHs, $f(r)$ was found to be nearly constant at scales above 2 Mm, which indicates a monofractal structural organization and smooth temporal evolution. The magnitude of the filling factor is 0.04 from the Hinode SOT/SP data and 0.07 from the MDI/HR data. The Hinode data show that at scales smaller than 2 Mm, the filling factor decreases rapidly, which means a mutlifractal structure and highly intermittent, burst-like energy release regime. The absence of necessary complexity in CH magnetic fields at scales above 2 Mm seems to be the most plausible reason why the net magnetic flux density does not seem to be related to the solar wind speed: the energy release dynamics, needed for solar wind acceleration, appears to occur at small scales below 1 Mm.Comment: 6 figures, approximately 23 pages. Accepted in Solar Physic

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Search for dark matter in events with heavy quarks and missing transverse momentum in pp collisions with the ATLAS detector

This article reports on a search for dark matterpair production in association with bottom or top quarks in20.3fb−1ofppcollisions collected at√s=8TeVbytheATLAS detector at the LHC. Events with large missing trans-verse momentum are selected when produced in associationwith high-momentum jets of which one or more are identifiedas jets containingb-quarks. Final states with top quarks areselected by requiring a high jet multiplicity and in some casesa single lepton. The data are found to be consistent with theStandard Model expectations and limits are set on the massscale of effective field theories that describe scalar and tensorinteractions between dark matter and Standard Model par-ticles. Limits on the dark-matter–nucleon cross-section forspin-independent and spin-dependent interactions are alsoprovided. These limits are particularly strong for low-massdark matter. Using a simplified model, constraints are set onthe mass of dark matter and of a coloured mediator suitableto explain a possible signal of annihilating dark matter