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Assisted reproductive technologies (ART) have significantly impacted fertility treatment worldwide through innovations such as in vitro fertilization (IVF) and in vitro maturation (IVM). IVM holds promise as a technology for fertility treatment in women who cannot or do not wish to undergo conventional controlled ovarian hyperstimulation (COH). However, IVM has historically shown highly variable performance in maturing oocytes and generating oocytes with strong developmental capacity. Furthermore, novel IVM approaches are usually highly limited to use in cycles lacking human chorionic gonadotropin (hCG) triggers, which is not standard practice in fertility treatment. We recently reported the development of ovarian support cells (OSCs) generated from human induced pluripotent stem cells (hiPSCs) that recapitulate dynamic ovarian function in vitro. Here we investigate the potential of these OSCs in an IVM co-culture system to improve the maturation of human cumulus-enclosed immature oocytes retrieved from abbreviated gonadotropin stimulated cycles. We reveal that OSC-IVM significantly improves maturation rates compared to existing IVM systems. Most importantly, we demonstrate that OSC-assisted IVM oocytes are capable of robust euploid blastocyst formation, a key marker of their clinical utility. Together, these findings demonstrate a novel approach to IVM with broad applicability to modern ART practice.

Artificial Intelligence (AI) has played an increasingly crucial part in our daily lives in recent years. Convolutional neural network (CNN) in medical image processing has lately received a lot of interest. With the introduction of modern endoscopic technologies, the doctor could be able to diagnose a patient more accurately. Consequently, it becomes crucial and advantageous to use computer-aided support during procedures. This paper proposes a framework for automatic classification of Upper Gastrointestinal tract diseases that consists of two main parts: a Convolutional Neural Network model (ResNet-50) with Focal Loss application and Data Augmentation techniques that consists of Geometric Transformation (GeoT), Brightness and Contrast Transformation (BaC), which learn hidden features of various Upper GI diseases and anatomical landmark classes, as well as solving the imbalanced dataset problem. As a result, the classification result is improved, with 98.61% of Accuracy on a self-collected dataset from Hanoi Medical University Hospital and Institute of Gastroenterology and Hepatology. Additionally, the proposed method is capable of using in real-time applications by classifying every frame in the video streams.

Research Article| January 01, 1941 The effect of earthquakes on fire-alarm systems* F. B. Philbrick F. B. Philbrick The Gamewell Company, 1151 South Broadway, Los Angeles, California Search for other works by this author on: GSW Google Scholar Author and Article Information F. B. Philbrick The Gamewell Company, 1151 South Broadway, Los Angeles, California Publisher: Seismological Society of America First Online: 03 Mar 2017 Online Issn: 1943-3573 Print Issn: 0037-1106 Copyright © 1941, by the Seismological Society of America Bulletin of the Seismological Society of America (1941) 31 (1): 1–8. https://doi.org/10.1785/BSSA0310010001 Article history First Online: 03 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation F. B. Philbrick; The effect of earthquakes on fire-alarm systems. Bulletin of the Seismological Society of America 1941;; 31 (1): 1–8. doi: https://doi.org/10.1785/BSSA0310010001 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of the Seismological Society of America Search Advanced Search This content is PDF only. Please click on the PDF icon to access. First Page Preview Close Modal You do not have access to this content, please speak to your institutional administrator if you feel you should have access.