The Dynamics of Poor Systems of Galaxies

We assemble and observe a sample of poor galaxy systems that is suitable for testing N-body simulations of hierarchical clustering (Navarro, Frenk, & White 1997; NFW) and other dynamical halo models (e.g., Hernquist 1990). We (1) determine the parameters of the density profile rho(r) and the velocity dispersion profile sigma(R), (2) separate emission-line galaxies from absorption-line galaxies, examining the model parameters and as a function of spectroscopic type, and (3) for the best-behaved subsample, constrain the velocity anisotropy parameter, beta, which determines the shapes of the galaxy orbits. The NFW universal profile and the Hernquist (1990) model both provide good descriptions of the spatial data. In most cases an isothermal sphere is ruled out. Systems with declining sigma(R) are well-matched by theoretical profiles in which the star-forming galaxies have predominantly radial orbits (beta > 0); many of these galaxies are probably falling in for the first time. There is significant evidence for spatial segregation of the spectroscopic classes regardless of sigma(R).Comment: 36 pages, 20 figures, and 5 tables. To appear in the Astrophysical Journa

Chaotic scattering on surfaces and collisional damping of collective modes

The damping of hot giant dipole resonances is investigated. The contribution of surface scattering is compared with the contribution from interparticle collisions. A unified response function is presented which includes surface damping as well as collisional damping. The surface damping enters the response via the Lyapunov exponent and the collisional damping via the relaxation time. The former is calculated for different shape deformations of quadrupole and octupole type. The surface as well as the collisional contribution each reproduce almost the experimental value, therefore we propose a proper weighting between both contributions related to their relative occurrence due to collision frequencies between particles and of particles with the surface. We find that for low and high temperatures the collisional contribution dominates whereas the surface damping is dominant around the temperatures $\sqrt{3}/2\pi$ of the centroid energy.Comment: PRC su

SETD2 haploinsufficiency for microtubule methylation is an early driver of genomic instability in renal cell carcinoma

Loss of the short arm of chromosome 3 (3p) occurs early in >95% of clear cell renal cell carcinoma (ccRCC). Nearly ubiquitous 3p loss in ccRCC suggests haploinsufficiency for 3p tumor suppressors as early drivers of tumorigenesis. We previously reported methyltransferase SETD2, which trimethylates H3 histones on lysine 36 (H3K36me3) and is located in the 3p deletion, to also trimethylate microtubules on lysine 40 (aTubK40me3) during mitosis, with aTubK40me3 required for genomic stability. We now show that monoallelic, Setd2-deficient cells retaining H3K36me3, but not aTubK40me3, exhibit a dramatic increase in mitotic defects and micronuclei count, with increased viability compared with biallelic loss. In SETD2-inactivated human kidney cells, rescue with a pathogenic SETD2 mutant deficient for microtubule (aTubK40me3), but not histone (H3K36me3) methylation, replicated this phenotype. Genomic instability (micronuclei) was also a hallmark of patient-derived cells from ccRCC. These data show that the SETD2 tumor suppressor displays a haploinsufficiency phenotype disproportionately impacting microtubule methylation and serves as an early driver of genomic instability. Significance: Loss of a single allele of a chromatin modifier plays a role in promoting oncogenesis, underscoring the growing relevance of tumor suppressor haploinsufficiency in tumorigenesis

Stellar structure and compact objects before 1940: Towards relativistic astrophysics

Since the mid-1920s, different strands of research used stars as "physics laboratories" for investigating the nature of matter under extreme densities and pressures, impossible to realize on Earth. To trace this process this paper is following the evolution of the concept of a dense core in stars, which was important both for an understanding of stellar evolution and as a testing ground for the fast-evolving field of nuclear physics. In spite of the divide between physicists and astrophysicists, some key actors working in the cross-fertilized soil of overlapping but different scientific cultures formulated models and tentative theories that gradually evolved into more realistic and structured astrophysical objects. These investigations culminated in the first contact with general relativity in 1939, when J. Robert Oppenheimer and his students George Volkoff and Hartland Snyder systematically applied the theory to the dense core of a collapsing neutron star. This pioneering application of Einstein's theory to an astrophysical compact object can be regarded as a milestone in the path eventually leading to the emergence of relativistic astrophysics in the early 1960s.Comment: 83 pages, 4 figures, submitted to the European Physical Journal

Mapping the fragmentation of acetylene with femtosecond resolution pump probe at LCLS using 2, 3, and 4 particle coincidences.

A three-layer delay line anode detector has been used in x-ray pump x-ray probe time-resolved measurement at LCLS. We used ~10 fs long pulses to initiate and probe ultrafast dynamics in the dication of acetylene. The dynamics are discerned from the temporal evolution of multi-particle coincidences

Ultrafast isomerization initiated by X-ray core ionization

Rapid proton migration is a key process in hydrocarbon photochemistry. Charge migration and subsequent proton motion can mitigate radiation damage when heavier atoms absorb X-rays. If rapid enough, this can improve the fidelity of diffract-before-destroy measurements of biomolecular structure at X-ray-free electron lasers. Here we study X-ray-initiated isomerization of acetylene, a model for proton dynamics in hydrocarbons. Our time-resolved measurements capture the transient motion of protons following X-ray ionization of carbon K-shell electrons. We Coulomb-explode the molecule with a second precisely delayed X-ray pulse and then record all the fragment momenta. These snapshots at different delays are combined into a ‘molecular movie’ of the evolving molecule, which shows substantial proton redistribution within the first 12 fs. We conclude that significant proton motion occurs on a timescale comparable to the Auger relaxation that refills the K-shell vacancy

Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy, we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. This opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

<p>Abstract</p> <p>Background</p> <p>The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes.</p> <p>Results</p> <p>We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality.</p> <p>Conclusion</p> <p>We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality.</p

Improving care for people with heart failure in Uganda: serial in-depth interviews with patients' and their health care professionals

Abstract Background The short prognosis of patients with advanced heart failure (HF) and the associated multidimensional distress as illustrated in literature from high income countries necessitates the integration of palliative care into the care of advanced HF patients to address these needs and improve their quality of life. These needs, which are subjective, have not been described from the patients’ and health care professionals’(HPs) view point in the Ugandan setting, a low income country with a different socio-cultural context. This study aimed at bridging this gap in knowledge by eliciting patients’ and HPs’ views of HF patients’ needs over the course of their illness to enable generalists, cardiologists and palliative care clinicians to develop guidelines to provide patient-centred realistic care in Uganda. Methods Serial qualitative in-depth interviews were conducted with HF patients who were purposively sampled and recruited in Mulago National Referral Hospital (MNRH) until thematic saturation. In-depth interviews were conducted at three time points with intervals of 3 month between interviews over the course of their illness in the hospital and their home context. One-off interviews were conducted with HPs that manage HF in MNRH. We used a grounded theory approach in data analysis. The Uganda National Council of science and technology approved the research. Results Forty-eight interviews were conducted with 21 patients and their carers and eight interviews with their HPs. Multidimensional needs including physical, psychological, social, spiritual and information needs were identified. These highlighted the underpinning need to have normal functioning, control, to cope and adapt to a changed life and to find meaning. Spiritual needs were less recognised by HPs than the other multidimensional needs. Information needs were commonly unmet. Patients and HPs suggested improvements in care that were congruent with the recommendations in chronic disease care and the six pillars of the WHO health systems strengthening approach. Conclusion Management of HF in Uganda requires an approach that targets multidimensional needs, embraces multidisciplinary care and strengthens health systems which are all important tenets of palliative care