A Bayesian Nonparametric Approach to Modeling Motion Patterns

The most difficult—and often most essential— aspect of many interception and tracking tasks is constructing motion models of the targets to be found. Experts can often provide only partial information, and fitting parameters for complex motion patterns can require large amounts of training data. Specifying how to parameterize complex motion patterns is in itself a difficult task. In contrast, nonparametric models are very flexible and generalize well with relatively little training data. We propose modeling target motion patterns as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides a flexible representation for each individual motion pattern, while the DP assigns observed trajectories to particular motion patterns. Both automatically adjust the complexity of the motion model based on the available data. Our approach outperforms several parametric models on a helicopter-based car-tracking task on data collected from the greater Boston area

The ChatGPT Artificial Intelligence Chatbot: How Well Does It Answer Accounting Assessment Questions?

ChatGPT, a language-learning model chatbot, has garnered considerable attention for its ability to respond to users’ questions. Using data from 14 countries and 186 institutions, we compare ChatGPT and student performance for 28,085 questions from accounting assessments and textbook test banks. As of January 2023, ChatGPT provides correct answers for 56.5 percent of questions and partially correct answers for an additional 9.4 percent of questions. When considering point values for questions, students significantly outperform ChatGPT with a 76.7 percent average on assessments compared to 47.5 percent for ChatGPT if no partial credit is awarded and 56.5 percent if partial credit is awarded. Still, ChatGPT performs better than the student average for 15.8 percent of assessments when we include partial credit. We provide evidence of how ChatGPT performs on different question types, accounting topics, class levels, open/closed assessments, and test bank questions. We also discuss implications for accounting education and research

Emergency department spirometric volume and base deficit delineate risk for torso injury in stable patients

BACKGROUND: We sought to determine torso injury rates and sensitivities associated with fluid-positive abdominal ultrasound, metabolic acidosis (increased base deficit and lactate), and impaired pulmonary physiology (decreased spirometric volume and PaO(2)/FiO(2)). METHODS: Level I trauma center prospective pilot and post-pilot study (2000–2001) of stable patients. Increased base deficit was < 0.0 in ethanol-negative and ≤ -3.0 in ethanol-positive patients. Increased lactate was > 2.5 mmol/L in ethanol-negative and ≥ 3.0 mmol/L in ethanol-positive patients. Decreased PaO(2)/FiO(2 )was < 350 and decreased spirometric volume was < 1.8 L. RESULTS: Of 215 patients, 66 (30.7%) had a torso injury (abdominal/pelvic injury n = 35 and/or thoracic injury n = 43). Glasgow Coma Scale score was 14.8 ± 0.5 (13–15). Torso injury rates and sensitivities were: abdominal ultrasound negative and normal base deficit, lactate, PaO(2)/FiO(2), and spirometric volume – 0.0% & 0.0%; normal base deficit and normal spirometric volume – 4.2% & 4.5%; chest/abdominal soft tissue injury – 37.8% & 47.0%; increased lactate – 39.7% & 47.0%; increased base deficit – 41.3% & 75.8%; increased base deficit and/or decreased spirometric volume – 43.8% & 95.5%; decreased PaO(2)/FiO(2 )– 48.9% & 33.3%; positive abdominal ultrasound – 62.5% & 7.6%; decreased spirometric volume – 73.4% & 71.2%; increased base deficit and decreased spirometric volume – 82.9% & 51.5%. CONCLUSIONS: Trauma patients with normal base deficit and spirometric volume are unlikely to have a torso injury. Patients with increased base deficit or lactate, decreased spirometric volume, decreased PaO(2)/FiO(2), or positive FAST have substantial risk for torso injury. Increased base deficit and/or decreased spirometric volume are highly sensitive for torso injury. Base deficit and spirometric volume values are readily available and increase or decrease the suspicion for torso injury

Frequent mutation of histone-modifying genes in non-Hodgkin lymphoma

Follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL) are the two most common non-Hodgkin lymphomas (NHLs). Here we sequenced tumour and matched normal DNA from 13 DLBCL cases and one FL case to identify genes with mutations in B-cell NHL. We analysed RNA-seq data from these and another 113 NHLs to identify genes with candidate mutations, and then re-sequenced tumour and matched normal DNA from these cases to confirm 109 genes with multiple somatic mutations. Genes with roles in histone modification were frequent targets of somatic mutation. For example, 32% of DLBCL and 89% of FL cases had somatic mutations in MLL2, which encodes a histone methyltransferase, and 11.4% and 13.4% of DLBCL and FL cases, respectively, had mutations in MEF2B, a calcium-regulated gene that cooperates with CREBBP and EP300 in acetylating histones. Our analysis suggests a previously unappreciated disruption of chromatin biology in lymphomagenesis

Population pharmacokinetics of S(−)-carvedilol in healthy volunteers after administration of the immediate-release (IR) and the new controlled-release (CR) dosage forms of the racemate

Carvedilol is a β1-, β1-, and α1-adrenoreceptor blocker indicated for treatment of hypertension and mild-tosevere congestive heart failure. The objective of this study was to develop and evaluate a single population model that describesS(−)-carvedilol pharmacokinetics from both the immediate-release (IR) and the new controlled-release dosage forms of the racemate. Carvedilol IR data (1270 measurements) were obtained from 2 open-label studies (50 mg/25 mg Q12 hours for 2 doses). Carvedilol CR data (2058 measurements) were obtained from an open-label, nonrandomized, dose-rising (10, 20, 40, and 80 mg), 4-period balanced crossover study. All data were simultaneously analyzed using NONMEM V. Leverage analysis and internal evaluations were conducted for the final model. A 2-compartment model with first-order absorption and elimination provided the best fit. The model included different absorption rates (KAs) for the CR and IR morning (IRAM) and evening (IRPM) doses; incorporating change-points at certain times. Estimates of KAs indicated that the absorption was slower at equivalent times and extended for CR relative to IR carvedilol. Oral clearance ofS(−)-carvedilol was 149 L/h. The IRPM and the CR doses had bioavailability (Frel) of 0.80 and 0.76, respectively, relative to the IRAM dose. The inter-subject variability in KAs was lower for the CR dosage form than the original IR dosage form. Estimation of interoccasion variability on KAs and Frel for the CR dosage form improved the fit. The model performed well in simulation and leverage analysis indicated its robustness. The model will be a useful tool for future simulation studies