We implemented the sequential modeling approach in the R package mdmb and provide a worked example to illustrate its application.With the advent of consumer-grade products for presenting an immersive virtual environment (VE), there is a growing interest in utilizing VEs for testing human navigation behavior. Monocrotaline nmr However, preparing a VE still requires a high level of technical expertise in computer graphics and virtual reality, posing a significant hurdle to embracing the emerging technology. To address this issue, this paper presents Delayed Feedback-based Immersive Navigation Environment (DeFINE), a framework that allows for easy creation and administration of navigation tasks within customizable VEs via intuitive graphical user interfaces and simple settings files. Importantly, DeFINE has a built-in capability to provide performance feedback to participants during an experiment, a feature that is critically missing in other similar frameworks. To show the usability of DeFINE from both experimentalists' and participants' perspectives, a demonstration was made in which participants navigated to a hidden goal location with feedback that differentially weighted speed and accuracy of their responses. In addition, the participants evaluated DeFINE in terms of its ease of use, required workload, and proneness to induce cybersickness. The demonstration exemplified typical experimental manipulations DeFINE accommodates and what types of data it can collect for characterizing participants' task performance. With its out-of-the-box functionality and potential customizability due to open-source licensing, DeFINE makes VEs more accessible to many researchers.We systematically tested the Autodock4 docking program for absolute binding free energy predictions using the host-guest systems from the recent SAMPL6, SAMPL7 and SAMPL8 challenges. We found that Autodock4 behaves surprisingly well, outperforming in many instances expensive molecular dynamics or quantum chemistry techniques, with an extremely favorable benefit-cost ratio. Some interesting features of Autodock4 predictions are revealed, yielding valuable hints on the overall reliability of docking screening campaigns in drug discovery projects. The data on hepatocellular carcinoma (HCC) patients without liver cirrhosis is scarce. To study the epidemiology, underlying etiology and fibrosis distribution in noncirrhotic HCC and compare the survival outcomes to cirrhotic HCC. We conducted a retrospective study including all adult patients diagnosed with HCC at two US tertiary academic centers from 2000 to 2015. Univariable and multivariable Cox regression analyses were performed to evaluate the variables associated with patient survival. Two thousand two hundred and thirty-seven HCC patients were included in the final analysis, of which, 13% had no liver cirrhosis. The most common underlying liver disease in non-cirrhotic patients was cryptogenic cause (40%), followed by nonalcoholic fatty liver disease (NAFLD) (25.2%) and hepatitis C (19%). The percentage of F0-F1, F2, and F3 was 72%, 17%, and 11% (cryptogenic cause); 69%, 12%, and 19% (NAFLD); 50%, 17%, and 33% (alcohol); 33%, 39%, and 28% (hepatitis B); 20%, 40%, and 40% (hemochromatosis); and 12%, 40%, and 48% (hepatitis C), respectively. In non-cirrhotic compared to cirrhotic patients, the tumor was more likely to be larger and fell outside Milan criteria (all p < 0.001). Cirrhotic patients had significant shorter survival than non-cirrhotic patients (p < 0.001). On the multivariable analysis, having liver cirrhosis (HR 1.48; 1.21-1.82, p < 0.001), combined viral hepatitis and alcohol use (HR 1.51; 1.23-1.88, p < 0.001), morbid obesity (HR 1.31; 1.01-1.69, p = 0.040) and underweight (HR 2.06; 1.27-3.34, p = 0.004) were associated with worse patient survival. The fibrosis distribution in non-cirrhotic HCC differed among each etiology of liver diseases. Despite more advanced HCC, patients without cirrhosis had significantly longer survival than those with cirrhosis.The fibrosis distribution in non-cirrhotic HCC differed among each etiology of liver diseases. Despite more advanced HCC, patients without cirrhosis had significantly longer survival than those with cirrhosis.Conventional measures of radiologist efficiency, such as the relative value unit, fail to account for variations in the complexity and difficulty of a given study. For lumbar spine MRI (LMRI), an ideal performance metric should account for the global severity of lumbar degenerative disease (LSDD) which may influence reporting time (RT), thereby affecting clinical productivity. This study aims to derive a global LSDD metric and estimate its effect on RT. A 10-year archive of LMRI reports comprising 13,388 exams was reviewed. Objective reporting timestamps were used to calculate RT. A natural language processing (NLP) tool was used to extract radiologist-assigned stenosis severity using a 6-point scale (0 = "normal" to 5 = "severe") at each lumbar level. The composite severity score (CSS) was calculated as the sum of each of 18 stenosis grades. The predictive values of CSS, sex, age, radiologist identity, and referring service on RT were examined with multiple regression models. The NLP tool accurately classified LSDD in 94.8% of cases in a validation set. The CSS increased with patient age and differed between men and women. In a univariable model, CSS was a significant predictor of mean RT (R2 = 0.38, p  25, R2 = 0.15, p = 0.05). Individual radiologist study volume was negatively correlated with mean RT (Pearson's R =  - 0.35, p  less then  0.001). The composite severity score predicts radiologist reporting efficiency in LMRI, providing a quantitative measure of case complexity which may be useful for workflow planning and performance evaluation.Rapid and accurate assessment of endotracheal tube (ETT) location is essential in the intensive care unit (ICU) setting, where timely identification of a mispositioned support device may prevent significant patient morbidity and mortality. This study proposes a series of deep learning-based algorithms which together iteratively identify and localize the position of an ETT relative to the carina on chest radiographs. Using the open-source MIMIC Chest X-Ray (MIMIC-CXR) dataset, a total of 16,000 patients were identified (8000 patients with an ETT and 8000 patients without an ETT). Three different convolutional neural network (CNN) algorithms were created. First, a regression loss function CNN was trained to estimate the coordinate location of the carina, which was then used to crop the original radiograph to the distal trachea and proximal bronchi. Second, a classifier CNN was trained using the cropped inputs to determine the presence or absence of an ETT. Finally, for radiographs containing an ETT, a third regression CNN was trained to both refine the coordinate location of the carina and identify the location of the distal ETT tip.