Plus: Meta is pausing an AI training program that tracks workers' keystrokes. It's a bit of a schlep to get to the top of ASML's newest machine. It's about the size of a double-decker bus, weighs more than 150 tons, and costs $400 million. But if you want to make the world's most powerful chips, a lithography system like this is essential. The AI era needs ever faster chips, and ASML's machines make that possible. They pattern chip features with extreme-ultraviolet light, or EUV--radiation outside the visible spectrum, produced by shooting lasers at tiny molten drops of tin tens of thousands of times a second.

A central challenge in organ transplantation is the extremely low acceptance rate of donor organ offers--typically in the single digits--leading to high discard rates and suboptimal use of available grafts. Current acceptance models embedded in allocation systems are non-causal, trained on observational data, and fail to generalize to policy-relevant counterfactuals. This limits their reliability for both policy evaluation and simulator-based optimization. In this work, we reframe organ offer acceptance as a counterfactual prediction problem and propose a method to learn from routinely recorded--but often overlooked--refusal explanations. These refusal reasons act as direction-only counterfactual signals: for example, a refusal reason such as "old donor age" implies acceptance might have occurred had the donor been younger. We formalize this setting and introduce CLEXNET, a novel causal model that learns policy-invariant representations via balanced training and an explanation-guided augmentation loss. On both synthetic and semi-synthetic data, CLEXNET outperforms existing acceptance models in predictive performance, generalization, and calibration, offering a robust drop-in improvement for simulators and allocation policy evaluation. Beyond transplantation, our approach provides a general method for incorporating human direction-only explanations as a form of model supervision, improving performance in settings where only observational data is available.

Widely adopted evaluation metrics for sparse-view CT reconstruction, such as Structural Similarity Index Measure and Peak Signal-to-Noise Ratio, prioritize pixel-wise fidelity but often fail to capture the completeness of critical anatomical structures, particularly small or thin regions that are easily missed. To address this limitation, we propose a suite of novel anatomy-aware evaluation metrics designed to assess structural completeness across anatomical structures, including large organs, small organs, intestines, and vessels. Building on these metrics, we introduce CARE, a Completeness-Aware Reconstruction Enhancement framework that incorporates structural penalties during training to encourage anatomical preservation of significant structures. CARE is model-agnostic and can be seamlessly integrated into analytical, implicit, and generative methods.

The free Human Organ Atlas gives users an up-close-and-personal look at 56 human organs. The Human Organ Atlas portal is open-access and includes the kidneys, brain, heart, and more. Breakthroughs, discoveries, and DIY tips sent six days a week. If watching is giving you a renewed interest in the human body in all of its gory glory, there's a new tool that will help satisfy your curiosity. An international team of scientists developed an open-access 3D portal where users can explore human organs in detail.

Hormones, shifting organs, and a growing baby can leave permanent marks. Breakthroughs, discoveries, and DIY tips sent six days a week. We've all seen the photos: a celebrity gives birth on Tuesday and looks flawless in a bikini by Thursday. Meanwhile, the rest of us are still trying to figure out what happened to our feet. The truth is pregnancy causes some surprising long-term changes to your body, regardless of whether you're a celebrity or a soccer mom.

The results of our survey are shown in Table 10.