Improving Photoelectron Counting and Particle Identification in Scintillation Detectors with Bayesian Techniques

Many current and future dark matter and neutrino detectors are designed to measure scintillation light with a large array of photomultiplier tubes (PMTs). The energy resolution and particle identification capabilities of these detectors depend in part on the ability to accurately identify individual photoelectrons in PMT waveforms despite large variability in pulse amplitudes and pulse pileup. We describe a Bayesian technique that can identify the times of individual photoelectrons in a sampled PMT waveform without deconvolution, even when pileup is present. To demonstrate the technique, we apply it to the general problem of particle identification in single-phase liquid argon dark matter detectors. Using the output of the Bayesian photoelectron counting algorithm described in this paper, we construct several test statistics for rejection of backgrounds for dark matter searches in argon. Compared to simpler methods based on either observed charge or peak finding, the photoelectron counting technique improves both energy resolution and particle identification of low energy events in calibration data from the DEAP-1 detector and simulation of the larger MiniCLEAN dark matter detector.Comment: 16 pages, 16 figure

A Pleistocene legacy structures variation in modern seagrass ecosystems

Distribution of Earth’s biomes is structured by the match between climate and plant traits, which in turn shape associated communities and ecosystem processes and services. However, that climate–trait match can be disrupted by historical events, with lasting ecosystem impacts. As Earth’s environment changes faster than at any time in human history, critical questions are whether and how organismal traits and ecosystems can adjust to altered conditions. We quantified the relative importance of current environmental forcing versus evolutionary history in shaping the growth form (stature and biomass) and associated community of eelgrass ( Zostera marina ), a widespread foundation plant of marine ecosystems along Northern Hemisphere coastlines, which experienced major shifts in distribution and genetic composition during the Pleistocene. We found that eelgrass stature and biomass retain a legacy of the Pleistocene colonization of the Atlantic from the ancestral Pacific range and of more recent within-basin bottlenecks and genetic differentiation. This evolutionary legacy in turn influences the biomass of associated algae and invertebrates that fuel coastal food webs, with effects comparable to or stronger than effects of current environmental forcing. Such historical lags in phenotypic acclimatization may constrain ecosystem adjustments to rapid anthropogenic climate change, thus altering predictions about the future functioning of ecosystems

Clinical phenotypes of perinatal depression and time of symptom onset: analysis of data from an international consortium

Background The perinatal period is a time of high risk for onset of depressive disorders and is associated with substantial morbidity and mortality, including maternal suicide. Perinatal depression comprises a heterogeneous group of clinical subtypes, and further refinement is needed to improve treatment outcomes. We sought to empirically identify and describe clinically relevant phenotypic subtypes of perinatal depression, and further characterise subtypes by time of symptom onset within pregnancy and three post-partum periods. Methods Data were assembled from a subset of seven of 19 international sites in the Postpartum Depression: Action Towards Causes and Treatment (PACT) Consortium. In this analysis, the cohort was restricted to women aged 19–40 years with information about onset of depressive symptoms in the perinatal period and complete prospective data for the ten-item Edinburgh postnatal depression scale (EPDS). Principal components and common factor analysis were used to identify symptom dimensions in the EPDS. The National Institute of Mental Health research domain criteria functional constructs of negative valence and arousal were applied to the EPDS dimensions that reflect states of depressed mood, anhedonia, and anxiety. We used k-means clustering to identify subtypes of women sharing symptom patterns. Univariate and bivariate statistics were used to describe the subtypes. Findings Data for 663 women were included in these analyses. We found evidence for three underlying dimensions measured by the EPDS: depressed mood, anxiety, and anhedonia. On the basis of these dimensions, we identified five distinct subtypes of perinatal depression: severe anxious depression, moderate anxious depression, anxious anhedonia, pure anhedonia, and resolved depression. These subtypes have clear differences in symptom quality and time of onset. Anxiety and anhedonia emerged as prominent symptom dimensions with post-partum onset and were notably severe. Interpretation Our findings show that there might be different types and severity of perinatal depression with varying time of onset throughout pregnancy and post partum. These findings support the need for tailored treatments that improve outcomes for women with perinatal depression

A Pleistocene legacy structures variation in modern seagrass ecosystems

Distribution of Earth's biomes is structured by the match between climate and plant traits, which in turn shape associated communities and ecosystem processes and services. However, that climate-trait match can be disrupted by historical events, with lasting ecosystem impacts. As Earth's environment changes faster than at any time in human history, critical questions are whether and how organismal traits and ecosystems can adjust to altered conditions. We quantified the relative importance of current environmental forcing versus evolutionary history in shaping the growth form (stature and biomass) and associated community of eelgrass (Zostera marina), a widespread foundation plant of marine ecosystems along Northern Hemisphere coastlines, which experienced major shifts in distribution and genetic composition during the Pleistocene. We found that eelgrass stature and biomass retain a legacy of the Pleistocene colonization of the Atlantic from the ancestral Pacific range and of more recent within-basin bottlenecks and genetic differentiation. This evolutionary legacy in turn influences the biomass of associated algae and invertebrates that fuel coastal food webs, with effects comparable to or stronger than effects of current environmental forcing. Such historical lags in phenotypic acclimatization may constrain ecosystem adjustments to rapid anthropogenic climate change, thus altering predictions about the future functioning of ecosystems.This work was supported by the US NSF (OCE-1031061, OCE-1336206, OCE0-1336741, OCE-1336905) and the Smithsonian Institution. F.T. was supported by José Castillejo Award CAS14/00177. A.H.E. was supported by the FCT (Foundation for Science and Technology) through Project UIDB/04326/2020 and Contract CEECINST/00114/2018. This is Contribution 106 from the Smithsonian’s MarineGEO and Tennenbaum Marine Observatories Network and Contribution 4105 of the Virginia Institute of Marine Science, College of William & Mary

The circumstellar envelope of the C-rich post-AGB star HD 56126

We present a detailed study of the circumstellar envelope of the post-asymptotic giant branch ``21 micron object'' HD 56126. We build a detailed dust radiative transfer model of the circumstellar envelope in order to derive the dust composition and mass, and the mass-loss history of the star. To model the emission of the dust we use amorphous carbon, hydrogenated amorphous carbon, magnesium sulfide and titanium carbide. We present a detailed parametrisation of the optical properties of hydrogenated amorphous carbon as a function of H/C content. The mid-infrared imaging and spectroscopy is best reproduced by a single dust shell from 1.2 to 2.6 arcsec radius around the central star. This shell originates from a short period during which the mass-loss rate exceeded 10^(-4) M_sun/yr. We find that the strength of the ``21'' micron feature poses a problem for the TiC identification. The low abundance of Ti requires very high absorption cross-sections in the ultraviolet and visible wavelength range to explain the strength of the feature. Other nano-crystalline metal carbides should be considered as well. We find that hydrogenated amorphous carbon in radiative equilibrium with the local radiation field does not reach a high enough temperature to explain the strength of the 3.3-3.4 and 6-9 micron hydrocarbon features relative to the 11-17 micron hydrocarbon features. We propose that the carriers of these hydrocarbon features are not in radiative equilibrium but are transiently heated to high temperature. We find that 2 per cent of the dust mass is required to explain the strength of the ``30'' micron feature, which fits well within the measured atmospheric abundance of Mg and S. This further strengthens the MgS identification of the ``30'' micron feature.Comment: 20 Pages, 10 Figures, accepted for publication in Astronomy and Astrophysic

Barriers to Predicting the Mechanisms and Risk Factors of Non-Contact Anterior Cruciate Ligament Injury

High incidences of non-contact anterior cruciate ligament (ACL) injury, frequent requirements for ACL reconstruction, and limited understanding of ACL mechanics have engendered considerable interest in quantifying the ACL loading mechanisms. Although some progress has been made to better understand non-contact ACL injuries, information on how and why non-contact ACL injuries occur is still largely unavailable. In other words, research is yet to yield consensus on injury mechanisms and risk factors. Biomechanics, video analysis, and related study approaches have elucidated to some extent how ACL injuries occur. However, these approaches are limited because they provide estimates, rather than precise measurements of knee - and more specifically ACL - kinematics at the time of injury. These study approaches are also limited in their inability to simultaneously capture many of the contributing factors to injury

Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU.

OBJECTIVES: To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs). DESIGN: Prospective cohort study. SETTING: A total of 306 ICUs from 24 European countries. PARTICIPANTS: Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81-87 y]; 51.8% male). MEASUREMENTS: Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30-day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves. RESULTS: The 30-day-mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was .80, and the Brier score was .18. At a cut point of 10 or higher (75% of all patients), the model predicts 30-day mortality in 91.1% of all patients who die. CONCLUSION: A predictive model of cumulative events predicts 30-day mortality in patients older than 80 years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision-making capacity