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AI model predicts B cell response to advance personalized cancer vaccines

KAIST announced on the 2nd that a team led by Professor Jeong Kyun Choi of the Department of Bio and Brain Engineering, in a joint study with the company ‘Neogene Logic,’ has developed a new AI model to predict neoantigens—a key element in developing personalized cancer vaccines—and has identified the importance of B cells in cancer immunotherapy. The research findings were published in the international journal *Science Advances* on December 3.

Neoantigens are protein fragments derived from cancer cell mutations that serve as unique markers distinguishing only cancer cells. Moderna and BioNTech developed their COVID-19 vaccines using the messenger ribonucleic acid (mRNA) platform secured during their research on neoantigen-based cancer vaccines. Currently, global pharmaceutical companies are actively conducting clinical trials for cancer vaccines.

The problem is that most existing cancer vaccine technologies focus solely on T-cell-centered immune responses. B cells, along with T cells, play a key role in the immune system, and recent studies have increasingly demonstrated their importance in anti-cancer immune activity.

AI-Based Cancer Models in Oncology: From Diagnosis to ADC Drug Prediction

Introduction Artificial intelligence (AI) has been influencing the way oncology has been practiced. Major issues constituting a bottleneck are the lack of data for training purposes, confidentiality preventing development, or the absence of transparency in clarifying how models operate to generate decisions. Novel Models With explainable AI, trust and utilization barriers among clinicians, researchers, and patients can be removed. With the implementation of federated learning, multiple institutions could contribute to crucial dataset’s learning information. Precise diagnosis and prescription of the right drug are essential in preventing unnecessary life losses, and economic burden to the underling system.

Just 1.2 billion years after the Big Bang, galaxies were already shaped by where they lived

A large protocluster of galaxies that existed 12.6 billion years ago, first discovered with the Subaru Telescope, has been examined in detail using the James Webb Space Telescope (JWST). The study found that galaxies in crowded regions are more extended than similar galaxies in less dense environments. The results, published in The Astrophysical Journal Letters, show that even when the universe was only 1.2 billion years old, environment was already influencing how galaxies grow.

In today’s universe, galaxies are not spread evenly through space. They have gathered into groups, and those groups form enormous galaxy clusters containing hundreds or even thousands of galaxies. But these giant structures did not exist at the beginning of the universe.

In the early universe, slightly denser regions of matter gradually grew under gravity and eventually developed into galaxy clusters. These “seeds” of galaxy clusters are known as protoclusters.

After 100 years, scientists finally uncover hidden rule behind cosmic rays

A mysterious new cosmic pattern discovered by the DAMPE space telescope may finally crack the century-old mystery of cosmic rays. Scientists studying mysterious ultra-powerful cosmic rays have uncovered a surprising hidden pattern that could finally help explain where these particles come from. Using the DAMPE space telescope, researchers found that cosmic ray particles—from tiny protons to heavy iron nuclei—all begin fading away more sharply at the exact same point, hinting at a universal rule governing their behavior across the galaxy.

For more than 100 years, scientists have been trying to understand cosmic rays, incredibly powerful particles that travel across the universe at extreme energies. Despite decades of research, many questions about where they come from and how they are accelerated remain unanswered. Now, researchers working with the DAMPE (Dark Matter Particle Explorer) space telescope have uncovered an important new clue. Their findings, published in Nature, reveal a common feature shared by these mysterious particles and could help scientists better understand their origins.

Cosmic rays are the highest energy particles ever observed in nature. They carry far more energy than particles produced by even the most advanced accelerators on Earth. Scientists believe they are created by some of the universe’s most violent events, including supernova explosions, jets from black holes, and pulsars.

A single real-world datapoint may stop AI model collapse, analysis suggests

New work explaining the inner workings of artificial intelligence could provide a way around the threat of AI “model collapse,” potentially averting growing numbers of AI hallucinations in the future.

First coined in 2024, “model collapse” refers to a scenario where an AI model trained on AI-produced data ceases to provide accurate results, instead producing inaccurate “gibberish” because of the poor quality of its training data.

Some have warned that high-quality text data to train systems like Large Language Models (LLMs) is set to run out as early as this year, and so data produced by models themselves has taken a larger training role—inviting the threat of model collapse.

Abstract: Address correspondence to: Koji Haratani, Department of Medical Oncology, Kindai University Faculty of Medicine, 377–2 Ohno-higashi, Osaka-Sayama, Osaka 589‑8511, Japan

Phone: 81.72.366.0221; Email: [email protected] or [email protected].

Reading brachycephalic dogs’ facial expressions requires extra cognitive processing by humans

People often look to dogs’ behavior, especially their facial expressions, for indications of their states of mind. Numerous studies show that this is a popular interpretation strategy. However, modern dog breeds vary greatly in size and structure, and few studies have explored how breed-specific morphology might affect humans’ ability to assess visual cues from the faces of different breeds of dogs.

Now, for the first time, a collaborative research team including scientists from Israel, Czechia, and Hungary has used eye-tracking to compare the visual attention patterns of humans observing photographs of normocephalic and brachycephalic dogs. A research paper detailing the team’s findings appears in Frontiers in Veterinary Science.

A More Accurate Prediction of Band-Gap Energies

Temperature is a tuning knob for semiconductor-band-gap energies, which in turn play a key role in the performance of optoelectronic devices. But computational tools for predicting this temperature dependence from first principles struggle to capture the influence of one main factor: many-electron effects in electron–phonon interactions. Xiaoxun Gong at the University of California, Berkeley, and colleagues now demonstrate a computational framework that properly accounts for these effects [1]. Their framework could aid the design of materials and devices with precisely tailored electronic and optical properties.

Theoretical calculations consistently underestimate the strength of electron–phonon interactions and how they modify band gaps at different temperatures. Previous studies indicated that this discrepancy likely stems from insufficient treatment of many-electron effects. To quantify the role of electron–phonon interactions more accurately, Gong and his colleagues have proposed a new framework that breaks down the total temperature-dependent modification of the band gap into various contributions. Within this framework, they analyzed electron–phonon interactions using a many-body perturbation theory, in which electrons’ energies and their perturbation by phonons are captured by the “GW” approximation.

To test their framework, the researchers computed the band gaps of diamond, silicon, and gallium phosphide at different temperatures. They found that the temperature-dependent band-gap modification was enhanced using the GW-based perturbation theory—especially compared to a description based on density-functional theory (DFT), the workhorse tool for first-principles electronic calculations. The new predictions for all three materials showed excellent agreement with previous measurements.

How dual-comb spectroscopy works and why it could reshape precision sensing

Spectroscopy has many applications, ranging from fundamental tests of quantum electrodynamics and investigations of molecular structure to environmental sensing, biomedical diagnostics and industrial monitoring. A highly promising spectroscopic instrument that has the potential to transform the field has emerged over the years: the dual-comb spectrometer, which relies on the interference of two mode-locked ultrafast lasers that produce broad frequency combs composed of evenly spaced narrow spectral lines.

A frequency comb is a spectrum of phase-coherent sharp laser lines that are evenly spaced. Such combs based on femtosecond mode-locked lasers, as pioneered at the Max-Planck Institute of Quantum Optics in the 1990s, have revolutionized measurements of frequency and time. In frequency metrology, a laser comb acts as a ruler in frequency space that conveniently links microwave and optical frequencies, and/or measures a large separation between two optical frequencies.

In the past two decades, frequency combs have found new applications. One of them is dual-comb spectroscopy. Dual-comb spectroscopy addresses the challenge of combining wide spectral coverage with high resolution and accuracy by using two optical frequency combs with slightly different repetition frequencies to map optical spectra directly into the radio-frequency domain. The method relies on time-domain interferometry and avoids mechanical scanning, enabling precise, rapid, and broadband measurements. Dual-comb spectroscopy has been implemented across the electromagnetic spectrum, from the terahertz to the visible range, with ongoing efforts towards the ultraviolet range.

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