Daiichi Sankyo subsidiary American Regent is launching a recall of three lots of generic epinephrine as it becomes one of the latest manufacturers to face headaches over particulate contamination in its drug products.

BREAKING: Unrealized losses on domestic bond holdings for Japan's 4 largest life insurers rose +7% in Q2 2026, to a record $96 billion. All 4 insurers, Nippon Life, Daiichi Life, Sumitomo Life, and Meiji Yasuda, reported increases in paper losses. This marks the 7th consecutive quarterly increase, with unrealized losses more than tripling over the period. Japanese life insurers typically hold government bonds and other debt securities until maturity to match their long-term insurance obligations. However, a potential surge in customer policy cancellations could force them to liquidate those holdings to meet payouts, putting pressure on both investment portfolios and earnings. This comes as 30-year Japanese government bond yields surged above +4.0% in May for the first time since the bonds were introduced in 1999, driven by concerns that Prime Minister Takaichi's administration may increase fiscal spending. Pressure on Japan's financial institutions is intensifying.

Robotics deep dive: WHY JAPAN LOST THE HUMANOID LEAD Honda began humanoid research in 1986. P2 walked unaided in December 1996, the first bipedal robot to do it without a tether. ASIMO arrived in November 2000, 120cm and 43kg, down from P3's 160cm and 130kg. Nobody could ever buy one. That is the pattern, and it repeats across all three programmes. Sony's QRIO was developed, marketed and never sold. On 26 January 2006, inside Howard Stringer's restructuring, Sony ended AIBO and stopped QRIO in the same announcement. Toyota's first humanoid, unveiled in 2004, played the trumpet; it performed at the 2005 Aichi Expo. ASIMO's seventh and last generation shipped in 2011. Development stopped in 2018, reported by Nikkei, NHK and Kyodo rather than announced as a product decision. Then March 2011, iRobot announced on the 18th that it was sending PackBots and Warriors to Fukushima Daiichi. American PackBots went into the reactor buildings in April. Quince, built by Chiba Institute of Technology, Tohoku University and the International Rescue System Institute, got inside in June, two months later. The version that circulates is that Japan had no robots. That part is wrong: JAERI had built a radiation-hardened machine after the 1999 Tokai criticality accident and it was on site. Japan's machines had never been proven in the field, because Japanese makers are barred from exporting military robots, while PackBot and Talon had spent a decade in Iraq and Afghanistan. Japan's transmit-power limits did not lift for the emergency either. Procurement, export control and radio spectrum. None of that is an engineering failure. SCHAFT, a University of Tokyo spinout, won the DARPA Trials in December 2013 with 27 points, three weeks after Google bought it for a reported $20M. It did not compete in the June 2015 finals, won by Korea's Team KAIST. Alphabet closed SCHAFT in November 2018 after failing to find a buyer. Honda's Takahide Yoshiike wrote the epitaph himself: "instead of getting hung up on developing ASIMO as a perfect package of all functions, we would like to provide value to society as soon as possible by focusing first on individual functions." Japan kept the part that had customers. Nabtesco puts its own share of the precision reduction gears inside medium and large robot joints at about 60%. The demonstrations left, the gearboxes stayed.

🔬 "It feels like having a team of 50 people doing all the work in a day." A Gemini-based multi-agent system generates, debates, and evolves scientific hypotheses, even surfacing a drug candidate that blocks 91% of scarring responses in liver fibrosis. Title: Co-Scientist: A multi-agent AI partner to accelerate research URL: 📝 Overview Co-Scientist is a collaborative multi-agent AI built on Gemini that generates, critiques, and refines novel scientific hypotheses. By automating the hypothesis generation and evaluation cycle, it acts as an AI research partner that accelerates breakthrough discovery. ❓ Challenges Solved Amid information overload and increasingly complex problems, researchers struggle to form breakthrough hypotheses. Connecting scattered facts across vast literature to identify promising research directions is hard. 💡 Methodology & Proposed Approach It organizes specialized agents into three phases. ・Generation phase: a Generation agent proposes novel hypotheses grounded in literature and data, and a Proximity agent clusters them to ensure diverse exploration ・Debate phase: a Reflection agent critiques as a virtual peer reviewer, and a Ranking agent prioritizes via pairwise comparison and Elo-based tournaments ・Evolution phase: an Evolution agent continuously refines and combines top hypotheses, and a Meta-review agent synthesizes final research proposals ・Most of the compute goes to verification, cross-checking claims against ChEMBL, UniProt, web search, and specialized tools like AlphaFold 🎯 Use Cases It is applied across life sciences: antimicrobial resistance, plant immunity, liver fibrosis treatment discovery, ALS mechanisms, cellular aging reversal, infectious-disease protein identification, metabolic disease, and aging biology. 📊 Results ・In liver fibrosis, it identified a drug candidate that blocks 91% of scarring-linked responses ・In cellular aging, it generated genetic leads that rejuvenated cells in the lab and cut screening analysis from months to days ・Over 100 institutions tested it, with collaborators including Stanford, MIT, Cambridge, and Calico ・Enterprise versions are deployed at organizations like Daiichi Sankyo, Bayer Crop Science, and U.S. National Labs #AIforScience# #AIAgents#