Open-source development experiences in scientific software: the HANDE quantum Monte Carlo project

The HANDE quantum Monte Carlo project offers accessible stochastic algorithms for general use for scientists in the field of quantum chemistry. HANDE is an ambitious and general high-performance code developed by a geographically-dispersed team with a variety of backgrounds in computational science. In the course of preparing a public, open-source release, we have taken this opportunity to step back and look at what we have done and what we hope to do in the future. We pay particular attention to development processes, the approach taken to train students joining the project, and how a flat hierarchical structure aids communicationComment: 6 pages. Submission to WSSSPE

Development and Evaluation of a Multifrequency Ultrafast Doppler Spectral Analysis (MFUDSA) Algorithm for Wall Shear Stress Measurement: A Simulation and In Vitro Study

Cardiovascular pathology is the leading cause of death and disability in the Western world, and current diagnostic testing usually evaluates the anatomy of the vessel to determine if the vessel contains blockages and plaques. However, there is a growing school of thought that other measures, such as wall shear stress, provide more useful information for earlier diagnosis and prediction of atherosclerotic related disease compared to pulsed-wave Doppler ultrasound, magnetic resonance angiography, or computed tomography angiography. A novel algorithm for quantifying wall shear stress (WSS) in atherosclerotic plaque using diagnostic ultrasound imaging, called Multifrequency ultrafast Doppler spectral analysis (MFUDSA), is presented. The development of this algorithm is presented, in addition to its optimisation using simulation studies and in-vitro experiments with flow phantoms approximating the early stages of cardiovascular disease. The presented algorithm is compared with commonly used WSS assessment methods, such as standard PW Doppler, Ultrafast Doppler, and Parabolic Doppler, as well as plane-wave Doppler. Compared to an equivalent processing architecture with one-dimensional Fourier analysis, the MFUDSA algorithm provided an increase in signal-to-noise ratio (SNR) by a factor of 4–8 and an increase in velocity resolution by a factor of 1.10–1.35. The results indicated that MFUDSA outperformed the others, with significant differences detected between the typical WSS values of moderate disease progression (p = 0.003) and severe disease progression (p = 0.001). The algorithm demonstrated an improved performance for the assessment of WSS and has potential to provide an earlier diagnosis of cardiovascular disease than current techniques allow

Ozone depletion, greenhouse gases, and climate change

This symposium was organized to study the unusual convergence of a number of observations, both short and long term that defy an integrated explanation. Of particular importance are surface temperature observations and observations of upper atmospheric temperatures, which have declined significantly in parts of the stratosphere. There has also been a dramatic decline in ozone concentration over Antarctica that was not predicted. Significant changes in precipitation that seem to be latitude dependent have occurred. There has been a threefold increase in methane in the last 100 years; this is a problem because a source does not appear to exist for methane of the right isotopic composition to explain the increase. These and other meteorological global climate changes are examined in detail

Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System Technology

Purpose Sprints and accelerations are popular performance indicators in applied sport. The methods used to define these efforts using athlete tracking technology could affect the number of efforts reported. The study aimed to determine the influence of different techniques and settings for detecting high-intensity efforts using Global Positioning System (GPS) data. Methods Velocity and acceleration data of a professional soccer match was recorded via 10-Hz GPS. Velocity data was filtered using either a median or exponential filter. Acceleration data was derived from velocity data over a 0.2 s time interval (with and without an exponential filter applied) and a 0.3 s time interval. High-speed running (≥4.17 m.s-1), sprint (≥7.00 m.s-1) and acceleration (≥2.78 m.s-2) efforts were then identified using minimum effort durations (0.1 to 0.9 s) to assess differences in the total number of efforts reported. Results Different velocity filtering methods resulted in small to moderate differences (Effect Size; 0.28 - 1.09) in the number of high-speed running and sprint efforts detected when minimum duration was <0.5 s and small to very large differences (ES; -5.69 - 0.26) in the number of accelerations when minimum duration was <0.7 s. There was an exponential decline in the number of all efforts as minimum duration increased, regardless of filtering method, with the largest declines in acceleration efforts. Conclusions Filtering techniques and minimum durations substantially affect the number of high-speed running, sprint and acceleration efforts detected with GPS. Changes to how high-intensity efforts are defined affect reported data. Therefore, consistency in data processing is advised

In vivo Observation of Tree Drought Response with Low-Field NMR and Neutron Imaging

Using a simple low-field NMR system, we monitored water content in a livingtree in a greenhouse over two months. By continuously running thesystem, we observed changes in tree water content on a scale of halfan hour. The data showed a diurnal change in water content consistentboth with previous NMR and biological observations. Neutron imaging experiments showthat our NMR signal is primarily due to water being rapidly transported through the plant, and not to other sources of hydrogen, such as water in cytoplasm, or water in cell walls. After accountingfor the role of temperature in the observed NMR signal, we demonstratea change in the diurnal signal behavior due to simulated drought conditionsfor the tree. These results illustrate the utility of our system toperform noninvasive measurements of tree water content outside of a temperature controlled environment

1952: Abilene Christian College Bible Lectures - Full Text

Delivered in the Auditorium of Abilene Christian College, February, 1952 ABILENE, TEXAS PRICE, $3.00 firm foundation publishing house Box 77 Austin Cl, Texa

Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions

IMPORTANCE Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk. OBJECTIVE To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription. DESIGN, SETTING, AND PARTICIPANTS A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples. EXPOSURES Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation. MAIN OUTCOMES AND MEASURES Opioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator-type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity. RESULTS Beneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2%[142 180] of the cohort), medium-risk (18.6%[34 579] of the cohort), and high-risk (5.2%[9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome. CONCLUSIONS AND RELEVANCE Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.NIH/National Institute on Drug Abuse [R01DA044985]; Pharmaceutical Research and Manufacturers of America Foundation Research Starter AwardOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Physiotherapy and occupational therapy vs no therapy in mild to moderate Parkinson disease: a randomized clinical trial

IMPORTANCE It is unclear whether physiotherapy and occupational therapy are clinically effective and cost-effective in Parkinson disease (PD). OBJECTIVE To perform a large pragmatic randomized clinical trial to evaluate the clinical effectiveness of individualized physiotherapy and occupational therapy in PD. DESIGN, SETTING, AND PARTICIPANTS The PD REHAB Trial was a multicenter, open-label, parallel group, controlled efficacy trial. A total of 762 patients with mild to moderate PD were recruited from 38 sites across the United Kingdom. Recruitment took place between October 2009 and June 2012, with 15 months of follow-up. INTERVENTIONS Participants with limitations in activities of daily living (ADL) were randomized to physiotherapy and occupational therapy or no therapy. MAIN OUTCOMES AND MEASURES The primary outcome was the Nottingham Extended Activities of Daily Living (NEADL) Scale score at 3 months after randomization. Secondary outcomes were health-related quality of life (assessed by Parkinson Disease Questionnaire–39 and EuroQol-5D); adverse events; and caregiver quality of life. Outcomes were assessed before trial entry and then 3, 9, and 15 months after randomization. RESULTS Of the 762 patients included in the study (mean [SD] age, 70 [9.1] years), 381 received physiotherapy and occupational therapy and 381 received no therapy. At 3 months, there was no difference between groups in NEADL total score (difference, 0.5 points; 95%CI, −0.7 to 1.7; P = .41) or Parkinson Disease Questionnaire–39 summary index (0.007 points; 95%CI, −1.5 to 1.5; P = .99). The EuroQol-5D quotient was of borderline significance in favor of therapy (−0.03; 95%CI, −0.07 to −0.002; P = .04). The median therapist contact time was 4 visits of 58 minutes over 8 weeks. Repeated-measures analysis showed no difference in NEADL total score, but Parkinson Disease Questionnaire–39 summary index (diverging 1.6 points per annum; 95%CI, 0.47 to 2.62; P = .005) and EuroQol-5D score (0.02; 95%CI, 0.00007 to 0.03; P = .04) showed small differences in favor of therapy. There was no difference in adverse events. CONCLUSIONS AND RELEVANCE Physiotherapy and occupational therapy were not associated with immediate or medium-term clinically meaningful improvements in ADL or quality of life in mild to moderate PD. This evidence does not support the use of low-dose, patient-centered, goal-directed physiotherapy and occupational therapy in patients in the early stages of PD. Future research should explore the development and testing of more structured and intensive physical and occupational therapy programs in patients with all stages of PD

CLO: The cell line ontology

Abstract Background Cell lines have been widely used in biomedical research. The community-based Cell Line Ontology (CLO) is a member of the OBO Foundry library that covers the domain of cell lines. Since its publication two years ago, significant updates have been made, including new groups joining the CLO consortium, new cell line cells, upper level alignment with the Cell Ontology (CL) and the Ontology for Biomedical Investigation, and logical extensions. Construction and content Collaboration among the CLO, CL, and OBI has established consensus definitions of cell line-specific terms such as ‘cell line’, ‘cell line cell’, ‘cell line culturing’, and ‘mortal’ vs. ‘immortal cell line cell’. A cell line is a genetically stable cultured cell population that contains individual cell line cells. The hierarchical structure of the CLO is built based on the hierarchy of the in vivo cell types defined in CL and tissue types (from which cell line cells are derived) defined in the UBERON cross-species anatomy ontology. The new hierarchical structure makes it easier to browse, query, and perform automated classification. We have recently added classes representing more than 2,000 cell line cells from the RIKEN BRC Cell Bank to CLO. Overall, the CLO now contains ~38,000 classes of specific cell line cells derived from over 200 in vivo cell types from various organisms. Utility and discussion The CLO has been applied to different biomedical research studies. Example case studies include annotation and analysis of EBI ArrayExpress data, bioassays, and host-vaccine/pathogen interaction. CLO’s utility goes beyond a catalogue of cell line types. The alignment of the CLO with related ontologies combined with the use of ontological reasoners will support sophisticated inferencing to advance translational informatics development.http://deepblue.lib.umich.edu/bitstream/2027.42/109554/1/13326_2013_Article_185.pd

Potential Use of a Serpin from Arabidopsis for Pest Control

Although genetically modified (GM) plants expressing toxins from Bacillus thuringiensis (Bt) protect agricultural crops against lepidopteran and coleopteran pests, field-evolved resistance to Bt toxins has been reported for populations of several lepidopteran species. Moreover, some important agricultural pests, like phloem-feeding insects, are not susceptible to Bt crops. Complementary pest control strategies are therefore necessary to assure that the benefits provided by those insect-resistant transgenic plants are not compromised and to target those pests that are not susceptible. Experimental GM plants producing plant protease inhibitors have been shown to confer resistance against a wide range of agricultural pests. In this study we assessed the potential of AtSerpin1, a serpin from Arabidopsis thaliana (L). Heynh., for pest control. In vitro assays were conducted with a wide range of pests that rely mainly on either serine or cysteine proteases for digestion and also with three non-target organisms occurring in agricultural crops. AtSerpin1 inhibited proteases from all pest and non-target species assayed. Subsequently, the cotton leafworm Spodoptera littoralis Boisduval and the pea aphid Acyrthosiphon pisum (Harris) were fed on artificial diets containing AtSerpin1, and S. littoralis was also fed on transgenic Arabidopsis plants overproducing AtSerpin1. AtSerpin1 supplied in the artificial diet or by transgenic plants reduced the growth of S. littoralis larvae by 65% and 38%, respectively, relative to controls. Nymphs of A. pisum exposed to diets containing AtSerpin1 suffered high mortality levels (LC50 = 637 µg ml−1). The results indicate that AtSerpin1 is a good candidate for exploitation in pest control