we must be learning...

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Following the pandemic, the first war, and the failure to convince of a more just peace; a second world war.

Tough to be nice, even for the most powerful people. 🤐 Just saying...

RONNY CHIENG MOCKS TRUMP'S "ISLAMIC REPUBLIC OF JAPAN" BLUNDER! 😂🇯🇵

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Japan, listen 🫳🏾 'Baby doll', 'Matcha cakes'... 😅 Japan, religion is not for everyone 🫳🏾 you'll we'll be alright. Let's just all calm down. Let's just...

Image above: 3rd order rate of change ratio (roc derivative). Grok: The formula is the third time derivative of position (\(\frac{d^3 x}{dt^3}\)), known in physics as "jerk".

Note

The most destructive weapon, in war and peace, is not the gun. Not even the bomb. Though they are both sufficiently horrid — the most destructive weapon at all times, is the pen, of presidents and prime ministers and bankers. Followed closely by 🧠: Muppetry. Cyclically systemic.

I.e., Depressions 🤐 EVERYWHERE.

The economy is a hell of a drug. It's not capitalism, it is the capitalist. "It's the people, stupid". Every cycle.

Still, you will hear about the billionaires, trillionaires (fair enough), and the corporations (reasonable); HOWEVER, they are most often subject to said aforementioned pen 🤐 and tend not to drop bombs and economies.

War is NOT a ladder 🫲🏾 Littlefinger was dangerously mistaken, and "The Master of Coin".

1. The Real Channel of Contagion: The Export Collapse

Japan’s exposure was not financial; it was trade-based. The economy was heavily reliant on external demand, specifically from Western consumers buying high-value manufactured goods like cars and electronics. 👀🫣‼️

2. The Yen Safe-Haven Trap

As global financial markets panicked, international investors rushed to pull their capital out of emerging markets and volatile assets. They piled into the Japanese yen, which was viewed as a stable, "safe-haven" currency due to Japan's status as the world's largest creditor nation.

This massive capital inflow triggered a rapid appreciation of the currency 👀🫣‼️

3. Macroeconomic and Social Fallout

As seen in the GDP data above, Japan's economy contracted sharply—real GDP fell by roughly 5.5% in 2009, a steeper decline than that experienced by the United States where the crisis originated.

The Rise of Precarious Labor 👀🫣‼️‼️

4. Political and Policy Repercussions

The severe economic distress broke decades of political inertia. Frustration over the handling of the crisis led to a landslide defeat for the long-ruling Liberal Democratic Party (LDP) in 2009, bringing the Democratic Party of Japan (DPJ) into power.

The crisis also entrenched Japan’s battle with chronic deflation. The Bank of Japan lowered interest rates effectively to zero and initiated early forms of quantitative easing, setting the stage for the ultra-aggressive "Abenomics" monetary frameworks that would follow a few years later.

Note

Both the quantity and quality were off🤐... Batteries 🫲🏾 🔋🔋s 🫲🏾 & Brains/Bodies 🫲🏾 🧠🏋🏾‍♀️s (i.e., energy and demographics).

Solutions: long required🫲🏾‼️

Tldr: without the aforementioned, no matter how vroom vroom you wanna boom boom, that shit will not work (pun intended). There will be no change, without the solutions: none. Ze-ro 😈 it's a promise. 🫲🏾 Only more depressionary scarcity politics and muppetry. You will not dig yourselves out of a hole with the same mindset and tools — just your futures. See the chart above. Vroom vrrom⁉️🙃😂 ヴルーム (Vurūmu)✋🏾🛑🫲🏾😅 you're already f@ked.

https://japannews.yomiuri.co.jp/society/general-news/20260710-337566/

Zentoshin, a credit card payment service provider👀⁉️⚠️, is suspected of having falsified its financial statements presented to financial institutions and other entities for at least 20 years,👀‼️

it was learned Thursday. 👀‼️⁉️ The amount of the falsification is believed to exceed ¥60 billion.

According to the bankruptcy petition obtained by the Yomiuri Shimbun, the total amount of loans and corporate bonds of the Osaka based firm reached ¥115.1 billion as of May. The document states that it is believed that the company had committed accounting fraud for at least 20 years, according to a representative of Zentoshin.

Regarding the reason, it explains: “In order to maintain and continue borrowing from financial institutions,👀🤐😂🤭...

Zentoshin was necessary to present a favorable financial picture to them, so the company engaged in accounting fraud by preparing and submitting financial statements that differed from its actual financial condition.”

The accounting fraud cited in the petition include four specific types; about ¥17 billion worth of fictitious deposits that inflated the balances in two accounts at major banks; about ¥15.4 billion worth of fictitious receivables; overstatement of goodwill that accounted for about ¥8.8 billion was effectively worthless; and failure to record unpaid advances that is worth of about ¥21.7 billion to such member stores as restaurants.

Although the company’s net assets were recorded as a positive balance of about ¥2.4 billion for the fiscal year ending March 2026, it is reportedly actually in a state of facing insolvency amounting to about ¥60.5 billion. The bankruptcy trustee told the Yomiuri Shimbun, “The excess of liabilities over assets may increase further, and we intend to investigate this.”

The company received a ruling from the Osaka District Court on Monday to enter bankruptcy proceedings.

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Reminder:

Double-entry accounting is a bookkeeping system where every financial transaction is recorded in at least two accounts—as a debit (left side) in one and a credit (right side) in another. This ensures that total debits always equal total credits, keeping the foundational accounting equation balanced (Assets = Liabilities + Equity).

The Catalyst: This trend is highlighted by Anthropic's April 2026 acquisition of Coefficient Bio, an early-stage biotech company with fewer than ten computational biology researchers, no disclosed clinical assets, and no revenue, in an all-stock deal worth roughly $400 million.

Traditional Pharma Exit: Valued based on a specific biological asset and its risk-adjusted net present value moving toward clinical approval.

Tech Company Exit: Valued based on capability. Tech buyers look for a state-of-the-art biological foundation model, a distinctive non-commodity dataset, talent, or a combination of these elements. The target company bridges a critical gap: conventional drug developers lack AI engineering culture, while purely computational AI labs lack wet-lab capacity to generate and model biological data at scale.

Key Takeaway: While both paths require wet-lab and computational expertise, companies aiming for a tech acquisition must invest heavily in high experimental throughput to produce valuable, proprietary, non-commodity data that feeds into an AI data flywheel.

AI research team could streamline clinical trial design | Cornell Chronicle

Cornell Chronicle

AI research team could streamline clinical trial design

By Carla Cantor Weill Cornell Medicine

July 8, 2026

An artificial intelligence system that operates like a collaborative team of medical experts could accelerate clinical trial design, one of the most difficult steps in drug development, according to a new study by Weill Cornell Medicine investigators. The findings, published July 7 in Nature Communications, evaluated the potential of the system, called EmulatRx, to simulate, design and improve clinical trials using real-world patient data.

The researchers predict that combining real-world data with collaborative AI reasoning has the potential to make clinical trials faster, more affordable and more precise, which could ultimately lead to new treatments for patients with higher success rates.

“Randomized clinical trials are the gold standard for determining whether new treatments are safe and effective,” said senior author Fei Wang, associate dean for AI and data science and the Frances and John Loeb Professor of Medical Informatics in the Department of Population Health Sciences at Weill Cornell. “But designing them is a slow, expensive and highly complex process.”

A necessary step before a drug is approved for market, a randomized clinical trial is a rigorous study in which participants are randomly assigned into separate groups to compare the effects of different interventions or treatments. Researchers must decide who qualifies to participate, how treatments are compared, what outcomes to track and whether enough patients can be recruited. These decisions typically require intensive collaboration among trialists, clinicians, statisticians and data specialists.

Wang’s team built EmulatRx 👀 to streamline this decision-making process into one system that can exchange information, identify problems and revise recommendations for a clinical trial.

Haoyang Li, Weishen Pan and Chengxi Zang of the Institute of Artificial Intelligence for Digital Healthin the Department of Population Health Sciences at Weill Cornell, and Suraj Rajendran, a graduate of the Tri-Institutional Computational Biology and Medicine Program, all co-authors of the paper, contributed to the research.

A virtual research team 👀⁉️

EmulatRx is organized around five specialized computational agents 👀‼️that mirror a scientific team. “Because each EmulatRx agent is empowered by a large language model, they can exchange information in natural language and work together much as human experts do,” Wang said.

At the center of the system is a coordinating “Supervisor” agent that manages workflow and integrates outputs.👀 A “Trialist” reviews past studies and extracts key elements – like eligibility criteria, treatments and outcomes – to outline a trial structure.👀 An “Informatician” translates those requirements into queries that can identify appropriate patients in real-world data such as electronic health records.👀 A “Clinician” ensures the design makes medical sense and references published research,👀 while a “Statistician” evaluates potential outcomes and estimates how a treatment might perform using real-world data.

Learning from real-world patients👀

To evaluate EmulatRx, the researchers used de-identified electronic health records from large clinical databases covering both acute conditions (heart failure, septic shock, kidney injury) and chronic diseases (Alzheimer’s and Parkinson’s).👀 These records included diverse populations – such as older adults or patients with multiple conditions – who are often underrepresented in traditional trials.

EmulatRx analyzed the data through “target trial emulation,”👀⁉️ which applies key features of a randomized clinical trial – eligibility criteria, treatment groups, follow-up and outcomes, and causal contrast – to information collected during routine care. 👀‼️ This included searching free-text clinical notes alongside structured information such as diagnosis codes, medications and laboratory results. 👀👍🏾

Using this information, the system identified appropriate patient groups for replicating known findings and investigating differences in treatment effects across patient subgroups that may not have been apparent in the original trials. 👀‼️ For example, the system flagged when a treatment benefited one group but posed risks to another, helping researchers design more precise and safer trials from the outset. 👀🏆🫢👍🏾

Across historical clinical trials, the system reproduced many previously reported treatment effects, suggesting it could help researchers evaluate trial designs before launching expensive studies.

A human in the loop

A central feature of EmulatRx is the ability for researchers to monitor and intervene in its work.👀⁉️ They can follow the agents’ exchanges and review each stage of the analysis,👀 while an expert can pause the process, correct a decision or direct the system to reconsider its approach.👍🏾 EmulatRx can also learn from those corrections, reducing the chance of repeating mistakes.

“It is important to keep a human in the loop, so the system does not go in an unreasonable direction,” Wang said.

Before EmulatRx is ready for clinical or commercial deployment, it will require broader validation across other health systems and types of patient data. The researchers are working on commercial development and hope to make it available for investigator-initiated trials at universities as well.

“We still need randomized controlled trials,” Wang said. 😈‼️“The question is how to design them, so they can be conducted more efficiently and have a higher chance of success.” 😈‼️