Cloud Absorption Radiometer Autonomous Navigation System - CANS

CAR (cloud absorption radiometer) acquires spatial reference data from host aircraft navigation systems. This poses various problems during CAR data reduction, including navigation data format, accuracy of position data, accuracy of airframe inertial data, and navigation data rate. Incorporating its own navigation system, which included GPS (Global Positioning System), roll axis inertia and rates, and three axis acceleration, CANS expedites data reduction and increases the accuracy of the CAR end data product. CANS provides a self-contained navigation system for the CAR, using inertial reference and GPS positional information. The intent of the software application was to correct the sensor with respect to aircraft roll in real time based upon inputs from a precision navigation sensor. In addition, the navigation information (including GPS position), attitude data, and sensor position details are all streamed to a remote system for recording and later analysis. CANS comprises a commercially available inertial navigation system with integral GPS capability (Attitude Heading Reference System AHRS) integrated into the CAR support structure and data system. The unit is attached to the bottom of the tripod support structure. The related GPS antenna is located on the P-3 radome immediately above the CAR. The AHRS unit provides a RS-232 data stream containing global position and inertial attitude and velocity data to the CAR, which is recorded concurrently with the CAR data. This independence from aircraft navigation input provides for position and inertial state data that accounts for very small changes in aircraft attitude and position, sensed at the CAR location as opposed to aircraft state sensors typically installed close to the aircraft center of gravity. More accurate positional data enables quicker CAR data reduction with better resolution. The CANS software operates in two modes: initialization/calibration and operational. In the initialization/calibration mode, the software aligns the precision navigation sensors and initializes the communications interfaces with the sensor and the remote computing system. It also monitors the navigation data state for quality and ensures that the system maintains the required fidelity for attitude and positional information. In the operational mode, the software runs at 12.5 Hz and gathers the required navigation/attitude data, computes the required sensor correction values, and then commands the sensor to the required roll correction. In this manner, the sensor will stay very near to vertical at all times, greatly improving the resulting collected data and imagery. CANS greatly improves quality of resulting imagery and data collected. In addition, the software component of the system outputs a concisely formatted, high-speed data stream that can be used for further science data processing. This precision, time-stamped data also can benefit other instruments on the same aircraft platform by providing extra information from the mission flight

The use of a T-maze to measure cognitive–motor function in cats (Felis catus)

Few tests have been developed to test the cognitive and motor capabilities of domestic cats, in spite of the suitability of cats for specific studies of neuroanatomy, infectious diseases, development, aging, and behavior. The present study evaluated a T-maze apparatus as a sensitive and reliable measure of cognition and motor function of cats. Eighteen purpose-bred, specific-pathogen-free, male, neutered domestic shorthair cats (Felis catus), 1-2 years of age, were trained and tested to a T-maze protocol using food rewards. The test protocol consisted of positional discrimination training (left arm or right arm) to criterion followed by two discrimination reversal tests. The two reversal tests documented the ability of the subjects to respond to a new reward location, and switch arms of the T-maze. Data were collected on side preference, number of correct responses, and latency of responses by the subjects. Aided by a customized computer program (CanCog Technologies), data were recorded electronically as each cat progressed from the start box to the reward arm. The protocol facilitated rapid training to a high and consistent level of performance during the discrimination training. This learning was associated with a decrease in the latency to traverse the maze to a mean of 4.80 ± 0.87 s indicating strong motivation and consistent performance. When the rewarded side was reversed in the test phase, cats required more trials to reach criterion, as expected, but again showed reliable learning. The latency to reward in the first session of reversal increased 86% from the first to the last trial indicating that it may provide a useful index of cognitive processing. Latencies subsequently decreased as the new reversal paradigm was learned. This paradigm provides a relatively rapid and reliable test of cognitive motor performance that can be used in various settings for evaluation of feline cognitive and motor function

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Our field is called automated theorem proving because traditionally it has been concerned with the art of finding proofs automatically. In the beginning, researchers were motivated by the wish to build computer systems that can automatically solve difficult mathematical problems. When searching for a difficult proof, it is acceptable for a system to consume all resources and not to recognise false theorems. However, in the last years one has become aware of the fact that for applications, one also needs to be able to efficiently identify non-theorems. For example, automated theorem proving systems are now being used as assistants which must automatically solve easy subtasks in large, interactive projects. For such problems, the requirements to the automated theorem prover are different: The input problems are not terribly hard, usually contain additional irrelevant information, and often they are not provable. In case the subgoal is incorrect, it is not acceptable to simply remain silent and consume all resources in an interactive system. In this year’s DISPROVING workshop, we have again collected an interesting range of papers covering both theory and practice of disproving. Most of the papers do not only discuss theoretical contributions, but also working implementations. This demonstrates that the area of disproving is both theoretically interesting and practically relevant

Mycophenolate mofetil withdrawal in patients with systemic lupus erythematosus: a multicentre, open-label, randomised controlled trial

Mycophenolate mofetil is an immunosuppressant commonly used to treat systemic lupus erythematosus (SLE) and lupus nephritis. It is a known teratogen associated with significant toxicities, including an increased risk of infections and malignancies. Mycophenolate mofetil withdrawal is desirable once disease quiescence is reached, but the timing of when to do so and whether it provides a benefit has not been well-studied. We aimed to determine the effects of mycophenolate mofetil withdrawal on the risk of clinically significant disease reactivation in patients with quiescent SLE on long-term mycophenolate mofetil therapy. This multicenter, open-label, randomised trial was conducted in 19 centres in the USA. Eligible patients were aged between 18 and 70 years old, met the American College of Rheumatology (ACR) 1997 SLE criteria, and had a clinical SLEDAI score of less than 4 at screening. Mycophenolate mofetil therapy was required to be stable or decreasing for 2 years or more if initiated for renal indications, or for 1 year or more for non-renal indications. Participants were randomly allocated in a 1:1 ratio to a withdrawal group, who tapered off mycophenolate mofetil over 12 weeks, or a maintenance group who maintained their baseline dose (1-3g per day) for 60 weeks. Adaptive random allocation ensured groups were balanced for study site, renal versus non-renal disease, and baseline mycophenolate mofetil dose (≥2 g per day vs <2 g per day). Clinically significant disease reactivation by week 60 following random allocation, requiring increased doses or new immunosuppressive therapy was the primary endpoint, in the modified intention-to-treat population (all randomly allocated participants who began study-provided mycophenolate mofetil). Non-inferiority was evaluated using an estimation-based approach. The trial was registered at ClinicalTrials.gov (NCT01946880) and is completed. Between Nov 6, 2013, and April 27, 2018, 123 participants were screened, of whom 102 were randomly allocated to the maintenance group (n=50) or the withdrawal group (n=52). Of the 100 participants included in the modified intention-to-treat analysis (49 maintenance, 51 withdrawal), 84 (84%) were women, 16 (16%) were men, 40 (40%) were White, 41 (41%) were Black, and 76 (76%) had a history of lupus nephritis. The average age was 42 (SD 12·7). By week 60, nine (18%) of 51 participants in the withdrawal group had clinically significant disease reactivation, compared to five (10%) of 49 participants in the maintenance group. The risk of clinically significant disease reactivation was 11% (95% CI 5-24) in the maintenance group and 18% (10-32) in the withdrawal group. The estimated increase in the risk of clinically significant disease reactivation with mycophenolate mofetil withdrawal was 7% (one-sided upper 85% confidence limit 15%). Similar rates of adverse events were observed in the maintenance group (45 [90%] of 50 participants) and the withdrawal group (46 [88%] of 52 participants). Infections were more frequent in the mycophenolate mofetil maintenance group (32 [64%]) compared with the withdrawal group (24 [46%]). Mycophenolate mofetil withdrawal is not significantly inferior to mycophenolate mofetil maintenance. Estimates for the rates of disease reactivation and increases in risk with withdrawal can assist clinicians in making informed decisions on withdrawing mycophenolate mofetil in patients with stable SLE. The National Institute of Allergy and Infectious Diseases and the National Institute of Arthritis and Musculoskeletal and Skin Diseases