Repeated misdiagnosis of small intestine bronchogenic cyst: a case report

Bronchogenic cysts are uncommon congenital malformations of the respiratory system. These cysts can be categorized as intrapulmonary, mediastinal, or ectopic. Ectopic bronchogenic cysts, which lack distinctive clinical and imaging features, are particularly challenging to diagnose. This study presents a 48-year-old woman having a small intestinal bronchogenic cyst. She was repeatedly misdiagnosed as having an ovarian chocolate cyst or a cystic mass of bladder origin three years ago. However, no cyst was found during the operation. Half a year prior to presenting at our hospital, the patient developed frequent urination, prompting her to seek further treatment. We eventually discovered a cyst in the small intestine. The histological evaluation of the specimen showed a bronchogenic cyst. Small intestine bronchogenic cysts are extremely rare and easily misdiagnosed. It should be considered as one of the differential diagnoses of pelvic cysts. Particularly, when intraoperative exploration of the pelvic cavity fails to detect any cysts, consideration should be given to the possibility of small intestine bronchogenic cysts

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Motivated Emotion Regulation Across Cultures

Emotion regulation is linked to adaptive psychological outcomes. To engage in such regulation, people must be motivated to do it. Given that people in different countries vary in how they think about unpleasant emotions, we expected motivation to decrease unpleasant emotions to differ across countries. Furthermore, given that emotion regulation strategies operate in the service of motivation, we expected people who are less motivated to decrease unpleasant emotions to use emotion regulation strategies less across countries. To test these predictions, we conducted two studies during the COVID-19 pandemic: Study 1 in 2020 (N = 1,329) and Study 2 in 2021 (N = 1,279). We assessed the motivation to decrease unpleasant emotions and the use of emotion regulation strategies among members of East Asian countries (i.e., Japan, South Korea, and China) and Western countries (i.e., USA, UK, and Germany). Because we found substantial variation within these two broader cultural categories, we examined motivation and overall strategy use in emotion regulation at the country level. In both studies, motivation to decrease unpleasant emotions was the lowest in Japan and relatively high in the United States. As expected, across countries, weaker motivation to decrease unpleasant emotions was associated with using emotion regulation strategies less. We discuss implications of our findings for understanding cultural differences in motivated emotion regulation