Core Viewpoint - The FDA approval of the Jewel Patch-WCD wearable defibrillator marks a significant advancement in providing a non-invasive solution for high-risk patients facing sudden cardiac arrest (SCA) [1][3]. Group 1: Product Features - Jewel Patch-WCD is designed with a low-profile, waterproof patch that allows for continuous ECG monitoring without disrupting daily activities, including sleep and showering [3][6]. - The device is intended for temporary high-risk patients, including those recovering from myocardial infarction or with severe cardiovascular diseases, with an estimated 500,000 patients in the US and Europe benefiting annually [6][9]. - The device has demonstrated high compliance, with an average daily wear time exceeding 23 hours during clinical trials, successfully identifying and treating arrhythmias without any reported deaths or serious complications [6][9]. Group 2: Technology and Innovation - Jewel Patch-WCD utilizes a machine learning-based arrhythmia recognition system, achieving a low inappropriate shock rate of 0.36 per 100 patients per month, significantly better than many similar devices [7][9]. - The device integrates with the Jewel mobile app for near real-time data transmission to healthcare providers, facilitating remote monitoring and personalized cardiovascular care [7][9]. Group 3: Company Background - Element Science, founded by Dr. Uday Kumar in 2011, focuses on developing next-generation wearable technologies for high-risk cardiac patients, particularly in preventing SCA [8]. - The company has raised $145 million in Series C funding, positioning itself as one of the fastest-growing startups in the wearable medical device sector [8].

Core Findings - An international research team, including Israeli researchers, discovered that psychological stress during pregnancy can "reprogram" key molecular pathways in fetuses, affecting newborns' stress response systems in different ways based on gender [1][2] - The study involved over 120 mother-infant pairs recruited in Germany from 2016 to 2018, focusing on pregnant women with high perceived stress levels, which often go unmonitored [2][3] - The research highlighted significant molecular changes in female infants, particularly the near-complete disappearance of choline tRNA fragments, which regulate genes responsible for synthesizing acetylcholine, a neurotransmitter [1] - Male infants exhibited higher levels of acetylcholinesterase, an enzyme that breaks down acetylcholine, indicating an imbalance in their stress response systems from birth [1] - Machine learning algorithms were utilized to analyze choline tRNA fragment characteristics in newborn girls, achieving up to 95% accuracy in determining exposure to maternal stress during pregnancy, paving the way for early diagnostic and intervention tools [1] Research Implications - The study emphasizes the importance of maternal mental health during pregnancy, particularly for women who perceive high stress but are not diagnosed with depression or anxiety, as their stress can still impact both themselves and their newborns [2] - The findings suggest potential avenues for early diagnosis and support for pregnant women experiencing high levels of stress [2]

Core Insights - A machine learning algorithm developed by a team at Columbia University can infer the atomic structure of materials by observing patterns produced by nanocrystals, addressing a century-old challenge in materials science [1] Group 1: Implications for Drug Development - The breakthrough in nanocrystal structure analysis is expected to accelerate new drug development processes [1] Group 2: Clean Energy Materials - The new algorithm may also facilitate the development of clean energy materials, potentially impacting the renewable energy sector positively [1] Group 3: Cultural Heritage Research - This advancement could enhance research in cultural heritage, providing new methods for analyzing and preserving historical materials [1]

作者｜冬梅 字节 Top Seed 启动 2026 届招聘，瞄准顶尖博士 4 月 27 日，字节跳动 Seed 在其官微上发布了一则招聘启示，宣布正式启动 2026 届 Top Seed 大模型顶尖人才校招计划， 研究课题包括大语言模型、机器学习算法和系统、多模态生成、多模态理解、语音等方向，基本覆盖大模型研究各个领域， 计划招募约 30 位顶尖应届博士。 值得一提的是，本届 Top Seed 强调不限专业背景，更关注研究潜力，希望寻找具有极强技术信仰与热情、具备出色研究能 力、富有好奇心和驱动力的年轻研究者。 值得注意的是，字节跳动在此次招聘启事中还透露了几位刚毕业的同学已经做出了一些有影响力的研究。 比如，Z 同学构建并开源了首个多语言代码修复基准 Multi-SWE-bench，在 SWE-bench 基础上，首次覆盖 Python 之外的 Java、TypeScript、C、C++、Go、Rust 和 JavaScript 七种编程语言，1632 个真实修复任务，是真正面向"全栈工程"的评测 基准，其数据均来自 GitHub issue，历时近一年构建，以尽可能准确测评和提高大模型高阶编程智能水平。 ...

Group 1 - The core point of the article is that Zhejiang Jinke Tom Cat (300459) has signed a capital increase agreement to invest in Aurion11 Limited, aiming to expand its mobile internet programmatic advertising business [1][2] - Outfit7 will hold 60% of Aurion11 Limited after the investment, with the remaining 40% held by four founding shareholders from the original Outfit7 team [1] - Aurion11 Limited focuses on the commercialization of mobile internet advertising technology services, utilizing AI, machine learning algorithms, and big data analysis to enhance advertising value for third parties [1] Group 2 - The company has over 14 years of experience in programmatic advertising technology since the early development of mobile internet [1] - The investment aims to leverage Outfit7's accumulated knowledge, experience, technical reserves, and client resources to explore providing advertising technology services to third-party mobile application companies [1] - The company has established a strong competitive edge in programmatic advertising monetization and has long-term partnerships with major global advertising service providers such as Google, Meta, and TikTok [2]