Point seeking: a family of dynamic path finding algorithms

In the field of Artificial Intelligence, calculating the best route from one point to another, known as “path finding,” has become a common problem. If an agent cannot effectively navigate through an environment – be it real or virtual – it will often not be able to perform even the most routine tasks. For example, a Martian rover can\u27t collect samples if it can\u27t get to them; meanwhile, a computer game is not much of a challenge if your opponents can\u27t find their way around. The problem of path finding has three basic aspects: map representation, path generation, and locomotion. First, the environment must be interpreted into a form which can be processed algorithmically. Afterward, a path through this environment is planned out. A list of movement instructions or locations to travel to are then produced in order to guide the agent. During both the planning and movement of the agent, an algorithm may consider the agent\u27s limitations with regards to changes in velocity and orientation. Together, these steps serve to move an agent from its initial position to the desired location

Transesophageal echocardiographic evaluation of baboons during microgravity induced by parabolic flight

Transthoracic echocardiography (TTE) is a feasible method to noninvasively examine cardiac anatomy during parabolic flight. However, transducer placement on the chest wall is very difficult to maintain during transition to microgravity. In addition, TTE requires the use of low frequency transponders which limit resolution. Transesophical echocardiography (TEE) is an established imaging technique which obtains echocardiographic information from the esophagus. It is a safe procedure and provides higher quality images of cardiac structure than obtained with TTE. This study is designed to determine whether TEE was feasible to perform during parabolic flight and to determine whether acute central volume responses occur in acute transition to zero gravity by direct visualization of the cardiac chambers

Evidence for increased cardiac compliance during exposure to simulated microgravity

We measured hemodynamic responses during 4 days of head-down tilt (HDT) and during graded lower body negative pressure (LBNP) in invasively instrumented rhesus monkeys to test the hypotheses that exposure to simulated microgravity increases cardiac compliance and that decreased stroke volume, cardiac output, and orthostatic tolerance are associated with reduced left ventricular peak dP/d t. Six monkeys underwent two 4-day (96 h) experimental conditions separated by 9 days of ambulatory activities in a crossover counterbalance design: 1) continuous exposure to 10° HDT and 2) ∼12–14 h per day of 80° head-up tilt and 10–12 h supine (control condition). Each animal underwent measurements of central venous pressure (CVP), left ventricular and aortic pressures, stroke volume, esophageal pressure (EsP), plasma volume, α1- and β1-adrenergic responsiveness, and tolerance to LBNP. HDT induced a hypovolemic and hypoadrenergic state with reduced LBNP tolerance compared with the control condition. Decreased LBNP tolerance with HDT was associated with reduced stroke volume, cardiac output, and peak dP/d t. Compared with the control condition, a 34% reduction in CVP ( P= 0.010) and no change in left ventricular end-diastolic area during HDT was associated with increased ventricular compliance ( P = 0.0053). Increased cardiac compliance could not be explained by reduced intrathoracic pressure since EsP was unaltered by HDT. Our data provide the first direct evidence that increased cardiac compliance was associated with headward fluid shifts similar to those induced by exposure to spaceflight and that reduced orthostatic tolerance was associated with lower cardiac contractility

ROSACE: A Proposed European Design for the Copernicus Ocean Colour System Vicarious Calibration Infrastructure

International audienceThe European Copernicus programme ensures long-term delivery of high-quality, global satellite ocean colour radiometry (OCR) observations from its Sentinel-3 (S3) satellite series carrying the ocean and land colour instrument (OLCI). In particular, the S3/OLCI provides marine water leaving reflectance and derived products to the Copernicus marine environment monitoring service, CMEMS, for which data quality is of paramount importance. This is why OCR system vicarious calibration (OC-SVC), which allows uncertainties of these products to stay within required specifications, is crucial. The European organisation for the exploitation of meteorological satellites (EUMETSAT) operates the S3/OLCI marine ground segment, and envisions having an SVC infrastructure deployed and operated for the long-term. This paper describes a design for such an SVC infrastructure, named radiometry for ocean colour satellites calibration and community engagement (ROSACE), which has been submitted to Copernicus by a consortium made of three European research institutions, a National Metrology Institute, and two small-to medium-sized enterprises (SMEs). ROSACE proposes a 2-site infrastructure deployed in the Eastern and Western Mediterranean Seas, capable of delivering up to about 80 high quality matchups per year for OC-SVC of the S3/OLCI missions

Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.

BackgroundMagnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation.Methods and findingsOur dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson's chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts' specificity in identifying ACL tears (p-value ConclusionsOur deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting

Rapid detection of SARS-CoV-2 RNA in saliva via Cas13.

Rapid nucleic acid testing is central to infectious disease surveillance. Here, we report an assay for rapid COVID-19 testing and its implementation in a prototype microfluidic device. The assay, which we named DISCoVER (for diagnostics with coronavirus enzymatic reporting), involves extraction-free sample lysis via shelf-stable and low-cost reagents, multiplexed isothermal RNA amplification followed by T7 transcription, and Cas13-mediated cleavage of a quenched fluorophore. The device consists of a single-use gravity-driven microfluidic cartridge inserted into a compact instrument for automated running of the assay and readout of fluorescence within 60 min. DISCoVER can detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in saliva with a sensitivity of 40 copies μl-1, and was 94% sensitive and 100% specific when validated (against quantitative PCR) using total RNA extracted from 63 nasal-swab samples (33 SARS-CoV-2-positive, with cycle-threshold values of 13-35). The device correctly identified all tested clinical saliva samples (10 SARS-CoV-2-positive out of 13, with cycle-threshold values of 23-31). Rapid point-of-care nucleic acid testing may broaden the use of molecular diagnostics