Sources and scientific references:
Near-infrared spectroscopy of TON 618 and the widely cited estimate of approximately 66 billion solar masses, based on its Hβ emission-line width and optical luminosity: Shemmer et al. (2004), Near-Infrared Spectroscopy of High-Redshift Active Galactic Nuclei. I. A Metallicity–Accretion Rate Relationship. The Astrophysical Journal.
https://doi.org/10.1086/423607
A later quasar analysis listing an Hβ-based mass estimate of approximately 41 billion solar masses for TON 618, illustrating the dependence of the inferred mass on the adopted calibration: Ge et al. (2019), The Blueshift of C IV Broad Emission Line in QSOs. The Astronomical Journal.
https://doi.org/10.3847/1538-3881/ab0956
How astronomers estimate distant black hole masses from emission-line widths and luminosity, including the calibration and uncertainties of these methods: Vestergaard & Peterson (2006), Determining Central Black Hole Masses in Distant Active Galaxies and Quasars. II. Improved Optical and UV Scaling Relationships. The Astrophysical Journal.
https://doi.org/10.1086/500572
The formation and growth of massive black hole seeds in the early Universe, including accretion, radiation feedback, direct-collapse scenarios, and mergers: Inayoshi, Visbal & Haiman (2020), The Assembly of the First Massive Black Holes. Annual Review of Astronomy and Astrophysics.
https://doi.org/10.1146/annurev-astro… theoretical limit on growth through luminous gas accretion, approximately 50 billion solar masses for typical assumptions and up to 270 billion in extreme cases, with further growth through mergers and possible detection through gravitational lensing: King (2016), How Big Can a Black Hole Grow? Monthly Notices of the Royal Astronomical Society: Letters. https://doi.org/10.1093/mnrasl/slv186 An estimate of the Milky Way’s total stellar mass of approximately 61 billion solar masses, providing the basis for comparing TON 618’s estimated mass with the combined mass of our galaxy’s stars: Licquia & Newman (2015), Improved Estimates of the Milky Way’s Stellar Mass and Star Formation Rate from Hierarchical Bayesian Meta-Analysis. The Astrophysical Journal. https://doi.org/10.1088/0004-637X/806…
A theoretical limit on growth through luminous gas accretion, approximately 50 billion solar masses for typical assumptions and up to 270 billion in extreme cases, with further growth through mergers and possible detection through gravitational lensing: King (2016), How Big Can a Black Hole Grow? Monthly Notices of the Royal Astronomical Society: Letters.
https://doi.org/10.1093/mnrasl/slv186
An estimate of the Milky Way’s total stellar mass of approximately 61 billion solar masses, providing the basis for comparing TON 618’s estimated mass with the combined mass of our galaxy’s stars: Licquia & Newman (2015), Improved Estimates of the Milky Way’s Stellar Mass and Star Formation Rate from Hierarchical Bayesian Meta-Analysis. The Astrophysical Journal.
https://doi.org/10.1088/0004-637X/806…
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Agentic AI for Immune-Related Adverse Event Detection in Cancer Immunotherapy
A new study explores agentic AI for detecting and characterizing immune-related adverse events from clinical notes, highlighting its potential to support faster and more consistent immunotherapy toxicity surveillance.
Efficacy and safety of garetosmab, an activin A-blocking antibody, in fibrodysplasia ossificans progressiva (OPTIMA): a randomised, double-blind, placebo-controlled, phase 3 trial
Garetosmab substantially reduced the number of new heterotopic bone lesions, providing robust evidence of its potential to be an efficacious and generally well tolerated disease-modifying therapy for FOP.
AIs fail to reliably detect AIs in reverse Turing tests
The famous “Turing test” of computer science comes from a 1950 paper written by the British computer scientist (and war hero) Alan Turing. There are many variants, but perhaps the most common involves a human asking questions of “someone” behind a screen. The test is: Can the human consistently determine if the answerer is a human or a machine?
A new paper reverses this situation and asks: Can a large language model (LLM) ask questions that reveal if the hidden respondent is a human or another LLM? The work, by four French scientists from the Unit of Artificial Intelligence of PITOR at Paris Cité University, is published in the journal Computers in Human Behavior Reports.
Chemo-Free Technique Destroys 99% of Cancer Cells in The Lab
Scientists may have found a way to destroy cancer cells without initially relying on chemotherapy, surgery, or radiation.
The approach is called ‘molecular jackhammer’, and it relies on little more than light and a well-established medical molecule.
The technique involves using aminocyanine molecules – synthetic dyes already widely used in medical imaging – and stimulating them with near-infrared light.
Ultracold atoms simulate hard-to-observe matter-antimatter pair production
According to quantum electrodynamics, the theory describing interactions between light and electrically charged particles, a sufficiently strong electric field can, in principle, convert some of its energy into particles and their antimatter partners. A particle’s antimatter partner has the same mass and, for electrically charged particles, the opposite electric charge.
The creation of particle–antiparticle pairs via an electric field and from the vacuum is known as the Schwinger effect. As the production of these pairs is strongly suppressed at electric field strengths that can be realized in a laboratory setting, observing this effect experimentally has so far proved challenging.
Researchers at the University of Science and Technology of China and other institutions have now introduced an experiment that could be used to simulate and study this rare particle production process. Their experiment, outlined in a paper published in Physical Review Letters, entails representing particles and electric fields through the arrangement and motion of ultracold rubidium atoms trapped in an optical lattice, a laser light-based pattern that can trap atoms.
Genetic Causes and Modifiers of Autism Spectrum Disorder
Autism Spectrum Disorder (ASD) is one of the most prevalent neurodevelopmental disorders, affecting an estimated 1 in 59 children. ASD is highly genetically heterogeneous and may be caused by both inheritable and de novo gene variations. In the past decade, hundreds of genes have been identified that contribute to the serious deficits in communication, social cognition, and behavior that patients often experience. However, these only account for 10–20% of ASD cases, and patients with similar pathogenic variants may be diagnosed on very different levels of the spectrum. In this review, we will describe the genetic landscape of ASD and discuss how genetic modifiers such as copy number variation, single nucleotide polymorphisms, and epigenetic alterations likely play a key role in modulating the phenotypic spectrum of ASD patients.
Hidden role of a cancer treatment target could explain toxic side effects
Wee1 inhibitors are a promising new cancer treatment, but serious side effects threaten their adoption in the clinic.
Now, researchers from the MRC Laboratory of Medical Sciences (LMS), the University of Oxford, and UCL have discovered a previously unknown role for Wee1 that could explain these effects.
The findings could realign the drugs’ clinical progress, helping to identify patients most likely to benefit from treatment and guiding the development of safer, more targeted therapies.
Enabling Multi-Agent Penetration Testing Environments for Software Vulnerability Detection
In light of the ongoing challenge posed by unknown vulnerabilities in software solutions, the prompt identification and rectification of these weaknesses are of vital importance. Traditional penetration testing, although thorough, typically demands substantial human resources and time commitments. To contend with this issue, we propose an innovative penetration testing framework that incorporates natural language processing, utilizing large language model (LLM) driven multi-agent systems to automate and expedite vulnerability detection. By integrating insights from established exploit signatures and AI-driven fuzzing techniques, our framework, LLMAZD, preemptively examines target software within a controlled setting, effectively identifying both zero-day and one-day exploits, as well as system vulnerabilities, in a cost-effective manner. The framework demonstrates versatility in detecting multiple vulnerability classes, including SQL injection, Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), file upload vulnerabilities, and other different types, all while maintaining efficiency and cost-effectiveness throughout the testing process. Extensive evaluations on more than 20 CVE test cases reveal that the framework substantially enhances detection capabilities, reduces the overall burden of testing, and offers a practical pathway towards more secure software ecosystems.
Cyber-Security, Large Language Models (LLMs), Zero-Day Exploit, Malware, Vulnerability, Exposure, Multi-Agent Systems, Penetration Testing
Juan Enriquez on Right/Wrong: How Technology Transforms Our Ethics
In October 2020, two years before ChatGPT, I sat down with Juan Enriquez for 90 minutes to talk about right and wrong.
Not tech #ethics. Ethics.
Juan’s argument is unsettling in the best possible way: much of what we feel certain is right today rests on the technology we happen to have. Change the technology and the moral ground shifts underneath us, usually before anyone notices.
Which raises a question I still can’t shake. If our grandchildren will judge some of our certainties the way we judge our grandparents’, which ones will it be? And what happens when we hand moral decisions to machines trained on today’s certainties?
Juan has given nine TED talks, sailed around the world on an expedition that multiplied the number of known genes a hundredfold, and sat on the peace commission that negotiated a ceasefire with the Zapatistas. He also calls himself a very curious and optimistic Cro-Magnon.
We went from Catholic ethics to humility and forgiveness, from homo evolutis and #transhumanism to veganism, techno-solutionism, #AI and the singularity. Plus why those who can make you believe absurdities can make you commit atrocities.