Core Insights - Jiangsu province achieved a historic best performance in the 14th China Innovation and Entrepreneurship Competition, with 16 companies winning awards, accounting for 35.6% of the national total, and 5 out of 10 finalists being from Jiangsu [1][2][3] Group 1: Award-Winning Companies - Suzhou Yilong Micro Semiconductor Technology Co., Ltd. won the gold medal in the growth enterprise category, focusing on the development of photonic chips for optical communication, with a new chip that offers 2-4 times the speed of current mainstream chips [1] - Suzhou Yuanxiang Micro Technology Co., Ltd. developed a surface dynamic electron microscope for atomic-level defect detection in semiconductor manufacturing, utilizing self-developed technology for non-destructive testing [2] - Suzhou Pairui Biotechnology Co., Ltd. is developing innovative RNA-modifying drugs for cancer treatment, currently conducting Phase II clinical trials in 11 hospitals [2] - Liyang Zhongke Guneng New Energy Technology Co., Ltd. focuses on the development of sulfide solid electrolyte materials for all-solid-state batteries, achieving a 90% reduction in material degradation rate [2][3] Group 2: Technological Innovations - Tianrui Technology (Nantong) Co., Ltd. developed the world's only industrialized non-iridium proton membrane electrolysis catalyst, reducing costs to one-tenth of traditional catalysts [3] - The 16 award-winning companies from Jiangsu collectively hold 302 patents, showcasing their innovative capabilities and contributions to industry development [3] Group 3: Entrepreneurial Ecosystem - The average age of leaders from the 1064 companies that reached the national finals is 41, with a significant number holding advanced degrees and international experience [4] - Companies like Guokewachuang and Yuanxiang Micro have rapidly transitioned from laboratory prototypes to industrial-grade products, demonstrating the importance of combining scientific and entrepreneurial expertise [5][6] - The Jiangsu province has a robust support system for startups, with initiatives that have facilitated over 900 billion yuan in financing and helped nearly 15% of award-winning companies to go public [6] Group 4: Future Development - Companies are optimistic about future growth, with plans for expansion and increased production capabilities, as seen in the recent facility expansions by Suzhou Yilong Micro [7] - The collaborative innovation ecosystem in Jiangsu is seen as a strong support for continuous development and leadership in technology [7]

Core Viewpoint - The article discusses a systematic evaluation of computational tools for detecting multiple types of RNA modifications using Nanopore Direct RNA Sequencing (DRS), highlighting the performance differences and limitations of existing algorithms [1][2]. Group 1: Research Findings - A high-quality, single-base resolution benchmark dataset was constructed as a "gold standard" to evaluate 86 RNA modification detection algorithms based on DRS technology across four dimensions: accuracy, biological relevance, cross-sample generalization ability, and computational efficiency [2]. - The model retraining strategy significantly improved detection performance, with combined training on in vitro transcribed (IVT) RNA and real biological samples enhancing prediction accuracy and generalization, particularly for Ψ, m5C, and A-to-I modifications [4]. - m6A detection tools performed well overall, with models like Dorado and SingleMod excelling in qualitative and quantitative analysis, while most non-m6A modification detection tools struggled with quantitative accuracy and cross-sample generalization [5]. - Biological relevance is a crucial criterion, as ideal detection tools should not only have high accuracy but also align with known biological patterns; some tools showed discrepancies in predicted modification site distributions [5]. - Current tools face challenges in reliably distinguishing different modifications occurring on the same base, leading to "fuzzy predictions," which is a key area for future algorithm optimization [4]. - The retrained models can adapt to the iterative advancements in DRS technology, effectively addressing the challenges posed by changes in signal characteristics with the upgrade from RNA002 to RNA004 [6]. Group 2: Resource Development - To promote field development, the research team launched NaRMBench, an online resource platform that consolidates 12 key performance indicators of each tool into an interactive radar chart, aiding users in selecting and comparing analysis tools [8].