Spitzer Warm Mission Transition and Operations

Following the successful dynamic planning and implementation of IRAC Warm Instrument Characterization activities, transition to Spitzer Warm Mission operations has gone smoothly. Operation teams procedures and processes required minimal adaptation and the overall composition of the Mission Operation System retained the same functionality it had during the Cryogenic Mission. While the warm mission scheduling has been simplified because all observations are now being made with a single instrument, several other differences have increased the complexity. The bulk of the observations executed to date have been from ten large Exploration Science programs that, combined, have more complex constraints, more observing requests, and more exo-planet observations with durations of up to 145 hours. Communication with the observatory is also becoming more challenging as the Spitzer DSN antenna allocations have been reduced from two tracking passes per day to a single pass impacting both uplink and downlink activities. While IRAC is now operating with only two channels, the data collection rate is roughly 60% of the four-channel rate leaving a somewhat higher average volume collected between the less frequent passes. Also, the maximum downlink data rate is decreasing as the distance to Spitzer increases requiring longer passes. Nevertheless, with well over 90% of the time spent on science observations, efficiency has equaled or exceeded that achieved during the cryogenic mission

Spitzer Space Telescope observatory planning and scheduling team

Launched as the space infrared telescope facility (SIRTF) in August, 2003 and renamed in early 2004, the Spitzer space telescope is performing an extended series of science observations at wavelengths ranging from 3 to 180 microns. The California Institute of Technology is the home of the Spitzer Science Center (SSC) and operates the science operations system (SOS), which supports science operations of the observatory. A key function supported by the SOS is the long-range planning and short-term scheduling of the observatory. This paper describes the role and function of the SSC observatory planning and scheduling team (OPST), its operational interfaces, processes, and tools

Spitzer operations: scheduling the out years

Spitzer Warm Mission operations have remained robust and exceptionally efficient since the cryogenic mission ended in mid-2009. The distance to the observatory now exceeds 1 AU, making telecommunications increasingly difficult; however, analysis has shown that two-way communication could be maintained through at least 2017 with minimal loss in observing efficiency. The science program continues to emphasize the characterization of exoplanets, time domain studies, and deep surveys, all of which can impose interesting scheduling constraints. Recent changes have significantly improved on-board data compression, which both enables certain high volume observations and reduces Spitzer's demand for competitive Deep Space Network resources

The Hubble Deep Field: Observations, Data Reduction, and Galaxy Photometry

The Hubble Deep Field (HDF) is a Director's Discretionary program on HST in Cycle 5 to image an undistinguished field at high Galactic latitude in four passbands as deeply as reasonably possible. These images provide the most detailed view to date of distant field galaxies and are likely to be important for a wide range of studies in galaxy evolution and cosmology. In order to optimize observing in the time available, a field in the northern continuous viewing zone was selected and images were taken for ten consecutive days, or approximately 150 orbits. Shorter 1-2 orbit images were obtained of the fields immediately adjacent to the primary HDF in order to facilitate spectroscopic follow-up by ground-based telescopes. The observations were made from 18 to 30 December 1995, and both raw and reduced data have been put in the public domain as a community service. We present a summary of the criteria for selecting the field, the rationale behind the filter selection and observing times in each band, and the strategies for planning the observations to maximize the exposure time while avoiding earth-scattered light. Data reduction procedures are outlined, and images of the combined frames in each band are presented. Objects detected in these images are listed in a catalog with their basic photometric parameters.Comment: 37 pages, XX PostScript figures, uses aaspp4.sty astrobib.sty. (Astrobib is available from http://www.stsci.edu/software/TeX.html .) To appear the Astronomical Journal. More info on the Hubble deep field can be found at http://www.stsci.edu/../ftp/observer/hdf/hdf.html . More figures (images) can be found at http://www.stsci.edu/../ftp/observer/hdf/references/williams/ and the full source catalog is available at http://www.stsci.edu/../ftp/observer/hdf/archive/v2catalog

Grain Surface Models and Data for Astrochemistry

AbstractThe cross-disciplinary field of astrochemistry exists to understand the formation, destruction, and survival of molecules in astrophysical environments. Molecules in space are synthesized via a large variety of gas-phase reactions, and reactions on dust-grain surfaces, where the surface acts as a catalyst. A broad consensus has been reached in the astrochemistry community on how to suitably treat gas-phase processes in models, and also on how to present the necessary reaction data in databases; however, no such consensus has yet been reached for grain-surface processes. A team of ∼25 experts covering observational, laboratory and theoretical (astro)chemistry met in summer of 2014 at the Lorentz Center in Leiden with the aim to provide solutions for this problem and to review the current state-of-the-art of grain surface models, both in terms of technical implementation into models as well as the most up-to-date information available from experiments and chemical computations. This review builds on the results of this workshop and gives an outlook for future directions

Hospital outpatient perceptions of the physical environment of waiting areas: the role of patient characteristics on atmospherics in one academic medical center

<p>Abstract</p> <p>Background</p> <p>This study examines hospital outpatient perceptions of the physical environment of the outpatient waiting areas in one medical center. The relationship of patient characteristics and their perceptions and needs for the outpatient waiting areas are also examined.</p> <p>Method</p> <p>The examined medical center consists of five main buildings which house seventeen primary waiting areas for the outpatient clinics of nine medical specialties: 1) Internal Medicine; 2) Surgery; 3) Ophthalmology; 4) Obstetrics-Gynecology and Pediatrics; 5) Chinese Medicine; 6) Otolaryngology; 7) Orthopedics; 8) Family Medicine; and 9) Dermatology. A 15-item structured questionnaire was developed to rate patient satisfaction covering the four dimensions of the physical environments of the outpatient waiting areas: 1) visual environment; 2) hearing environment; 3) body contact environment; and 4) cleanliness. The survey was conducted between November 28, 2005 and December 8, 2005. A total of 680 outpatients responded. Descriptive, univariate, and multiple regression analyses were applied in this study.</p> <p>Results</p> <p>All of the 15 items were ranked as relatively high with a range from 3.362 to 4.010, with a neutral score of 3. Using a principal component analysis' summated scores of four constructed dimensions of patient satisfaction with the physical environments (i.e. visual environment, hearing environment, body contact environment, and cleanliness), multiple regression analyses revealed that patient satisfaction with the physical environment of outpatient waiting areas was associated with gender, age, visiting frequency, and visiting time.</p> <p>Conclusion</p> <p>Patients' socio-demographics and context backgrounds demonstrated to have effects on their satisfaction with the physical environment of outpatient waiting areas. In addition to noticing the overall rankings for less satisfactory items, what should receive further attention is the consideration of the patients' personal characteristics when redesigning more comfortable and customized physical environments of waiting areas.</p

Animal models for COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the aetiological agent of coronavirus disease 2019 (COVID-19), an emerging respiratory infection caused by the introduction of a novel coronavirus into humans late in 2019 (frst detected in Hubei province, China). As of 18 September 2020, SARS-CoV-2 has spread to 215 countries, has infected more than 30 million people and has caused more than 950,000 deaths. As humans do not have pre-existing immunity to SARS-CoV-2, there is an urgent need to develop therapeutic agents and vaccines to mitigate the current pandemic and to prevent the re-emergence of COVID-19. In February 2020, the World Health Organization (WHO) assembled an international panel to develop animal models for COVID-19 to accelerate the testing of vaccines and therapeutic agents. Here we summarize the fndings to date and provides relevant information for preclinical testing of vaccine candidates and therapeutic agents for COVID-19.info:eu-repo/semantics/acceptedVersio

GWAS of Suicide Attempt in Psychiatric Disorders Identifies Association With Major Depression Polygenic Risk Scores

Objective: Over 90% of suicide attempters have a psychiatric diagnosis, however twin and family studies suggest that the genetic etiology of suicide attempt (SA) is partially distinct from that of the psychiatric disorders themselves. Here, we present the largest genome-wide association study (GWAS) on suicide attempt using major depressive disorder (MDD), bipolar disorder (BIP) and schizophrenia (SCZ) cohorts from the Psychiatric Genomics Consortium. Method: Samples comprise 1622 suicide attempters and 8786 non-attempters with MDD, 3264 attempters and 5500 non-attempters with BIP and 1683 attempters and 2946 non-attempters with SCZ. SA GWAS were performed by comparing attempters to non-attempters in each disorder followed by meta-analyses across disorders. Polygenic risk scoring was used to investigate the genetic relationship between SA and the psychiatric disorders. Results: Three genome-wide significant loci for SA were found: one associated with SA in MDD, one in BIP, and one in the meta-analysis of SA in mood disorders. These associations were not replicated in independent mood disorder cohorts from the UK Biobank and iPSYCH. No significant associations were found in the meta-analysis of all three disorders. Polygenic risk scores for major depression were significantly associated with SA in MDD (R2=0.25%, P=0.0006), BIP (R2=0.24%, P=0.0002) and SCZ (R2=0.40%, P=0.0006). Conclusions: This study provides new information on genetic associations and demonstrates that genetic liability for major depression increases risk for suicide attempt across psychiatric disorders. Further collaborative efforts to increase sample size hold potential to robustly identify genetic associations and gain biological insights into the etiology of suicide attempt

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe