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MRI antenna can boost image quality and shorten scan times—without changing existing machines

Magnetic resonance imaging (MRI) is one of medicine’s most powerful diagnostic tools. But certain tissues deep inside the body—including brain regions and delicate structures of the eye and orbit that are of particular relevance for ophthalmology—are difficult to image clearly. The problem is not the scanner itself, but the hardware that sends and receives radio signals.

Now, researchers led by Nandita Saha, a doctoral student in the Experimental Ultrahigh Field Magnetic Resonance lab of Professor Thoralf Niendorf at the Max Delbrück Center have developed an advanced materials-based MRI antenna that overcomes these limitations—delivering enhanced images more quickly and that can be used in existing MRI machines. The research was published in Advanced Materials.

Niendorf and his team worked closely with researchers at Rostock University Medical Center, combining expertise in MRI physics with clinical ophthalmology and translational imaging. The Rostock team is also supporting clinical validation of the technology.

Relationship Between Hematoma Location and Underlying Small Vessel Disease in Cerebellar Intracerebral Hemorrhage

Background and ObjectivesIn supratentorial intracerebral hemorrhage (ICH), hematoma location serves as a useful proxy for the underlying cerebral small vessel disease (cSVD) subtype, especially in the context of the Boston criteria. Whether this framework…

‘Learn-to-Steer’ method improves AI’s ability to understand spatial instructions

Researchers from the Department of Computer Science at Bar-Ilan University and from NVIDIA’s AI research center in Israel have developed a new method that significantly improves how artificial intelligence models understand spatial instructions when generating images—without retraining or modifying the models themselves. Image-generation systems often struggle with simple prompts such as “a cat under the table” or “a chair to the right of the table,” frequently placing objects incorrectly or ignoring spatial relationships altogether. The Bar-Ilan research team has introduced a creative solution that allows AI models to follow such instructions more accurately in real time.

The new method, called Learn-to-Steer, works by analyzing the internal attention patterns of an image-generation model, effectively offering insight into how the model organizes objects in space. A lightweight classifier then subtly guides the model’s internal processes during image creation, helping it place objects more precisely according to user instructions. The approach can be applied to any existing trained model, eliminating the need for costly retraining.

The results show substantial performance gains. In the Stable Diffusion SD2.1 model, accuracy in understanding spatial relationships increased from 7% to 54%. In the Flux.1 model, success rates improved from 20% to 61%, with no negative impact on the models’ overall capabilities.

Emerging mechanisms of psilocybin-induced neuroplasticity

Psilocybin, a serotonergic psychedelic, is gaining attention for its rapid and sustained therapeutic effects in depression and other hard-to-treat neuropsychiatric conditions, potentially through its capacity to enhance neuronal plasticity. While its neuroplastic and therapeutic effects are commonly attributed to serotonin 2A (5-HT2A) receptor activation, emerging evidence reveals a more nuanced pharmacological profile involving multiple serotonin receptor subtypes and nonserotonergic targets such as TrkB. This review integrates current findings on the molecular interactome of psilocin (psilocybin active metabolite), emphasizing receptor selectivity, biased agonism, and intracellular receptor localization.

In defense of artificial suffering

Perhaps our last line of defense.

Philosophical Studies — The ability to suffer, in the case of artificial entities, is often viewed as a moral turning point—once detected, there is no going back, and the moral landscape is irreversibly altered. The presence of entities capable of suffering imposes moral and legal obligations on humans. It is therefore unsurprising that many have urged caution in pursuing artificial suffering, with some even proposing a moratorium. In this paper, however, I argue that the emergence of artificial suffering need not entail moral disaster. On the contrary, I defend its development and contend that it may be a necessary feature of superintelligent robots. I suggest that artificial suffering could be essential for enabling human-like ethics in machines, bridging the retribution gap, and functioning as a control mechanism to mitigate existential risks. Rather than constraining research in this area, I maintain that work on artificial suffering should be actively intensified.

Emerging and underrecognized viral triggers of autoimmune inflammatory rheumatic disease flares

In this Review, the authors summarize the potential role of emerging viruses in autoimmune rheumatic diseases (AIRDs). They describe the association between viruses and AIRD flare ups, the putative mechanisms linking AIRD to viral infections and hormone modulation of viral pathogenesis and autoimmune diseases.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge

We introduce Layered Self-Supervised Knowledge Distillation (LSSKD) framework for training compact deep learning models. Unlike traditional methods that rely on pre-trained teacher networks, our approach appends auxiliary classifiers to intermediate feature maps, generating diverse self-supervised knowledge and enabling one-to-one transfer across different network stages. Our method achieves an average improvement of 4.54\% over the state-of-the-art PS-KD method and a 1.14% gain over SSKD on CIFAR-100, with a 0.32% improvement on ImageNet compared to HASSKD. Experiments on Tiny ImageNet and CIFAR-100 under few-shot learning scenarios also achieve state-of-the-art results. These findings demonstrate the effectiveness of our approach in enhancing model generalization and performance without the need for large over-parameterized teacher networks. Importantly, at the inference stage, all auxiliary classifiers can be removed, yielding no extra computational cost. This makes our model suitable for deploying small language models on affordable low-computing devices. Owing to its lightweight design and adaptability, our framework is particularly suitable for multimodal sensing and cyber-physical environments that require efficient and responsive inference. LSSKD facilitates the development of intelligent agents capable of learning from limited sensory data under weak supervision.

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