Characterization of high purity germanium point contact detectors with low net impurity concentration

High Purity germanium point-contact detectors have low energy thresholds and excellent energy resolution over a wide energy range, and are thus widely used in nuclear and particle physics. In rare event searches, such as neutrinoless double beta decay, the point-contact geometry is of particular importance since it allows for pulse-shape discrimination, and therefore for a significant background reduction. In this paper we investigate the pulse-shape discrimination performance of ultra-high purity germanium point contact detectors. It is demonstrated that a minimal net impurity concentration is required to meet the pulse-shape performance requirements

ADC Nonlinearity Correction for the Majorana Demonstrator

Imperfections in analog-to-digital conversion (ADC) cannot be ignored when signal digitization requirements demand both wide dynamic range and high resolution, as is the case for the Majorana Demonstrator 76Ge neutrinoless double-beta decay search. Enabling the experiment's high-resolution spectral analysis and efficient pulse shape discrimination required careful measurement and correction of ADC nonlinearities. A simple measurement protocol was developed that did not require sophisticated equipment or lengthy data-taking campaigns. A slope-dependent hysteresis was observed and characterized. A correction applied to digitized waveforms prior to signal processing reduced the differential and integral nonlinearities by an order of magnitude, eliminating these as dominant contributions to the systematic energy uncertainty at the double-beta decay Q value

Results of the MAJORANA DEMONSTRATOR's Search for Double-Beta Decay of 76Ge to Excited States of 76Se

The MAJORANA DEMONSTRATOR is searching for double-beta decay of 76Ge to excited states (E.S.) in 76Se using a modular array of high purity Germanium detectors. 76Ge can decay into three E.S.s of 76Se. The E.S. decays have a clear event signature consisting of a ββ-decay with the prompt emission of one or two γ-rays, resulting in with high probability in a multi-site event. The granularity of the DEMONSTRATOR detector array enables powerful discrimination of this event signature from backgrounds. Using 21.3 kg-y of isotopic exposure, the DEMONSTRATOR has set world leading limits for each E.S. decay, with 90% CL lower half-life limits in the range of (0.56 2.1) ⋅ 1024 y. In particular, for the 2v transition to the first 0+ E.S. of 76Se, a lower half-life limit of 0.68 ⋅ 1024 at 90% CL was achieved

Surface Chemistry of Almandine Garnet

Almandine garnet is used commonly in abrasive blasting processes to prepare metallic surfaces for painting. However, there is evidence that the process leads to significant amounts of abrasive embedded in the “cleaned” surface, and hence the surface chemistry of garnet will affect the binding of any coatings subsequently applied. In addition, in marine environments seawater aerosol droplets are expected to impact the exposed surface prior to coating application, depositing both water and dissolved inorganic ions. In this work, we provide indepth analysis of the chemistry of the almandine garnet surface using angle-resolved X-ray photoelectron spectroscopy (XPS) and note a correlation for several elemental oxides between literature values of the binding energy of the surface oxygen 1s XPS peak and the basicity of the hydroxyl group that forms on the surface. We also consider the adsorption to almandine garnet powder of seawater-relevant inorganic ions (sodium, magnesium, and calcium): Binding constants have been determined using titration measurements, solution-depletion isotherms, and numerical modeling, with calcium observed to bind more strongly than magnesium. The relevance of Langmuir-type fits to constant-pH adsorption isotherms is discussed. By contrast, sodium either binds very weakly or is effectively inert toward the garnet surface under the experimental conditions. The complex adsorption behavior observed emphasizes the necessity of using multiple techniques to characterize mineral surfaces

Essential versus accessory aspects of cell death: recommendations of the NCCD 2015

Cells exposed to extreme physicochemical or mechanical stimuli die in an uncontrollable manner, as a result of their immediate structural breakdown. Such an unavoidable variant of cellular demise is generally referred to as ‘accidental cell death’ (ACD). In most settings, however, cell death is initiated by a genetically encoded apparatus, correlating with the fact that its course can be altered by pharmacologic or genetic interventions. ‘Regulated cell death’ (RCD) can occur as part of physiologic programs or can be activated once adaptive responses to perturbations of the extracellular or intracellular microenvironment fail. The biochemical phenomena that accompany RCD may be harnessed to classify it into a few subtypes, which often (but not always) exhibit stereotyped morphologic features. Nonetheless, efficiently inhibiting the processes that are commonly thought to cause RCD, such as the activation of executioner caspases in the course of apoptosis, does not exert true cytoprotective effects in the mammalian system, but simply alters the kinetics of cellular demise as it shifts its morphologic and biochemical correlates. Conversely, bona fide cytoprotection can be achieved by inhibiting the transduction of lethal signals in the early phases of the process, when adaptive responses are still operational. Thus, the mechanisms that truly execute RCD may be less understood, less inhibitable and perhaps more homogeneous than previously thought. Here, the Nomenclature Committee on Cell Death formulates a set of recommendations to help scientists and researchers to discriminate between essential and accessory aspects of cell death

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells

Resolution of inflammation: a new therapeutic frontier

Dysregulated inflammation is a central pathological process in diverse disease states. Traditionally, therapeutic approaches have sought to modulate the pro- or anti-inflammatory limbs of inflammation, with mixed success. However, insight into the pathways by which inflammation is resolved has highlighted novel opportunities to pharmacologically manipulate these processes — a strategy that might represent a complementary (and perhaps even superior) therapeutic approach. This Review discusses the state of the art in the biology of resolution of inflammation, highlighting the opportunities and challenges for translational research in this field

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Improving epidemiologic data analyses through multivariate regression modelling

Regression modelling is one of the most widely utilized approaches in epidemiological analyses. It provides a method of identifying statistical associations, from which potential causal associations relevant to disease control may then be investigated. Multivariable regression – a single dependent variable (outcome, usually disease) with multiple independent variables (predictors) – has long been the standard model. Generalizing multivariable regression to multivariate regression – all variables potentially statistically dependent – offers a far richer modelling framework. Through a series of simple illustrative examples we compare and contrast these approaches. The technical methodology used to implement multivariate regression is well established – Bayesian network structure discovery – and while a relative newcomer to the epidemiological literature has a long history in computing science. Applications of multivariate analysis in epidemiological studies can provide a greater understanding of disease processes at the population level, leading to the design of better disease control and prevention programs