Where exactly is the edge of the Milky Way? That question is harder to answer than one might expect. Since we’re inside of the galaxy itself, it’s obviously hard to judge the “edge” to begin with. But it gets even more complicated when defining what the edge even is — the galaxy simply gets less dense the farther away from the center it goes. A new paper by researchers originally at the University of Malta thinks they have an answer though. The “edge” can be defined as the star-forming region, and in their paper, published in Astronomy & Astrophysics, they very clearly show that “edge” to be between 11.28 and 12.15 kiloparsecs (or about 40,000 light years) from the center.
Even finding that edge was no easy task, though. The researchers had to analyze the ages of over 100,000 giant stars from the data of several different surveys, including APOGEE-DR17, LAMOST-DR3 and Gaia. In the data they found an interesting story about the evolution of the position of stars in the galaxy, and their age.
That relationship can be thought of as a U curve. In this case, the Y axis is age, and the X axis is the distance from the galaxy’s center. A picture (or graph in this case) is worth a thousand words, but in words that simply means that stars closer to the center of the galaxy are older, and get progressively younger out to a certain point, and then start getting older again. That “certain point”, according to the authors, is the end of the galaxy’s star-forming region, and hence, the “edge” of the galaxy.
Fu et al. use Ig-humanized mice expressing the germline CR3022 heavy chain to reveal how somatic hypermutation rapidly adapts this antibody class for broad sarbecovirus recognition. Sequential immunization drives CR3022-like maturation, while structural analyses show that increased affinity and breadth arise from subtle polar and electrostatic refinements.
A quiet revolution is underway in modern medicine: Drug development is aiming to move from managing disease to correcting it through RNA and gene-editing therapies. But delivering these treatments safely and precisely to the right cells remains a major hurdle—especially in hard-to-target organs like the brain and kidneys.
Now, researchers led by a University of Ottawa Faculty of Medicine team offer highly compelling evidence that an elegant, nature-inspired solution lies in ultra-tiny, bubble-like structures called small extracellular vesicles (sEVs). These metabolic messengers, refined over millions of years of evolution, carry RNA—a nucleic acid that is a chemical cousin of DNA—and other molecules between cells.
In a nutshell, the research team’s new findings show that not all sEVs are alike: their cell of origin determines where they travel, with certain vesicles naturally targeting specific tissues in the body.
The Phantom Organ and The “Hard Problem” — I apply MVT to solve David Chalmers’s “Hard Problem” of consciousness-the question of why physical brain processes are accompanied by subjective feelings (qualia).
Nichols’s theory posits that self-referential consciousness and abstract thought in many modern animals are the evolutionary result of the loss of a physical sensory organ: the parietal/pineal eye (the “primal eye”). Nichols maps this transition across three brain states in vertebrate evolution: The E2 State (Finite-State): Early fish, amphibians, and ancestral reptiles (as well as modern “living fossils” like the Tuatara) possessed a functional, light-sensitive median eye on top of their skulls, connected to the pineal gland. This organ directly controlled thermoregulation, circadian rhythms, and basic predator detection in coldblooded (ectothermic) animals. Their brains were “hard-wired,” responding directly to environmental stimuli. The E1 State (Infinite-State): As mammals and birds evolved warmbloodedness (endothermy), external temperature sensing became redundant, and advanced lateral eyes took over visual duties. The primal eye atrophied, leaving behind only the internal pineal gland. Freed from the direct “lock-step” control of the sun, the brain became plastic and self-organising (infinite-state). The E0 State: Some lineages, like certain dinosaurs and modern crocodilians, lost both the median eye and the pineal gland entirely. II. The Phantom Organ and The “Hard Problem” Nichols applies MVT to solve David Chalmers’s “Hard Problem” of consciousness-the question of why physical brain processes are accompanied by subjective feelings (qualia). The Virtual Sensor: Just as an amputee can experience a “phantom limb” because the neural matrix still expects the arm, the E1 mammalian brain experiences a “phantom eye”. The brain was built over millions of years to process a central stream of generic sensory data from the primal eye. Centrally Evoked Mentation: When the physical eye retreated, it left an internal sensory void. The brain compensated by simulating the presence of this lost hub to unscramble data from the other senses. This virtual simulation is the seat of the subjective “I”. III. The Origins of REM Sleep and Dreaming Nichols heavily critiques philosophers like Owen Flanagan, who argue that dreams are useless evolutionary “spandrels” (biological noise). Baseline Architecture: In MVT, Rapid Eye Movement (REM) sleep is the baseline functional state of the new E1 architecture. Because the physical tether to sunlight was severed, the brain uses this “phantom” space to generate internal models.
PD-1/PD-L1 Inhibitors Implications in Common Human Cancers.
Lung cancer: the landscape of lung cancer treatment has been profoundly reshaped by tumor immunotherapy directed at PD-1/PD-L1. Notably, the effectiveness of PD-L1 inhibitors surpasses that of chemotherapy, particularly in advanced non-small cell lung cancer (NSCLC) patients exhibiting elevated PD-L1 levels. This potency is equally evident among patients with previously untreated metastatic squamous NSCLC. Moreover, when considering patients with NSCLC who have undergone prior treatment, a decreased rate of disease progression is more frequently observed in response to PD-1/PD-L1 inhibitors, as opposed to conventional chemotherapy. This observation holds true, particularly for patients with an extensive metastatic burden and an adverse prognosis. In current clinical therapeutics, a strategic alliance between PD-1/PD-L1 immune checkpoint inhibitors and chemotherapeutic agents has emerged as a cornerstone of treatment. This approach attests to the heightened value these inhibitors bring to the therapeutic arsenal. The rapid evolution of anti-PD-1/PD-L1 inhibitors for advanced NSCLC stands as an instrumental factor in enhancing patient outcomes, charting a promising trajectory toward improved prognosis [29,30]. In a recent study, neoadjuvant PD-1 inhibitor sintilimab was administered to individuals with NSCLC. The outcomes revealed that a notable 40.5% of participants achieved a major pathological response, while a commendable 10.8% realized a complete remission at the pathological level [14].
Prostate cancer: currently, PD-1/PD-L1 immune checkpoint inhibitors have ushered substantial clinical advantages for individuals with prostate cancer. A recent study has put forth the notion that synergizing PD-1/PD-L1 checkpoint inhibitors with radiotherapy presents a promising avenue in the management of prostate cancer [31]. However, it is noteworthy that the impact of PD-L1/PD-1 blockade in the context of prostate cancer appears comparatively muted in contrast to its influence on other cancer types. This discrepancy stems from the diminished immunogenicity characterizing prostate cancers [32].
One of the biggest open questions in particle physics today is how the Higgs boson interacts with itself. This “self-coupling” could help explain the evolution of the early universe and the mechanism that gives mass to elementary particles. To try to shed light on this fundamental interaction, the ATLAS Collaboration has recently studied one of the “golden” decay channels of a pair of Higgs bosons, where one Higgs boson decays into two photons and the other into a pair of bottom quarks.
Now online! Comparative Hi-C analysis across 1,025 species reveals that genome architecture has evolved along distinct trajectories, with plants favoring global folding and animals developing checkerboard compartmentalization, yet both converge on spatial organization as a conserved strategy for gene regulation.