For eachstimulus,ourextractedcomputational mechanisms areconsistent withprior scientific literature, and in one case yields a new mechanistic hypothesis.

The abundance of fine-grained spatio-temporal data, such as traffic sensor networks, offers vast opportunities for scientific discovery. However, inferring causal relationships from such observational data remains challenging, particularly due to unobserved confounders that are specific to units (e.g., geographical locations) yet influence outcomes over time. Most existing methods for spatio-temporal causal inference assume that all confounders are observed, an assumption that is often violated in practice. In this paper, we introduce Spatio-Temporal Hierarchical Causal Models (ST-HCMs), a novel graphical framework that extends hierarchical causal modeling to the spatio-temporal domain. At the core of our approach is the Spatio-Temporal Collapse Theorem, which shows that a complex ST-HCM converges to a simpler flat causal model as the amount of subunit data increases. This theoretical result enables a general procedure for causal identification, allowing ST-HCMs to recover causal effects even in the presence of unobserved, time-invariant unit-level confounders, a scenario where standard non-hierarchical models fail. We validate the effectiveness of our framework on both synthetic and real-world datasets, demonstrating its potential for robust causal inference in complex dynamic systems.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. One of the chief concerns of systems neuroscientists is characterising how individual neurons respond to sensory stimuli. Since the stimulus space is often huge, data is always limited, and neurons are fundamentally noisy, the statistical challenges involved with this characterisation have spurned a vibrant field of computational neuroscience. This paper considers a particular form of this task, where recordings are made of local populations of neurons, at some intermediate point in the processing hierarchy in the brain (e.g. Such recordings are very common already, and also are growing in number and fidelity.

This manuscript aims to attack an interesting problem, namely how could one obtain mechanistic insights from the CNN model fit to the neural responses. The writing is generally clear, although it would benefit to tone down some of the statements to more accurately reflect the real contributions. Overall, the manuscript could be an interesting contribution to the field. However, I am skeptical about various claims made in the paper. The main issues I have with this manuscript are three-fold: 1.the results is rather incremental relatively to ref [2] and [9,10].

Deep learning has made significant progress in protein structure prediction, advancing the development of computational biology. However, despite the high accuracy achieved in predicting single-chain structures, a significant number of large homo-oligomeric assemblies exhibit internal symmetry, posing a major challenge in structure determination. The performances of existing deep learning methods are limited since the symmetrical protein assembly usually has a long sequence, making structural computation infeasible. In addition, multiple identical subunits in symmetrical protein complex cause the issue of supervision ambiguity in label assignment, requiring a consistent structure modeling for the training. To tackle these problems, we propose a protein folding framework called SGNet to model protein-protein interactions in symmetrical assemblies. SGNet conducts feature extraction on a single subunit and generates the whole assembly using our proposed symmetry module, which largely mitigates computational problems caused by sequence length. Thanks to the elaborate design of modeling symmetry consistently, we can model all global symmetry types in quaternary protein structure prediction. Extensive experimental results on a benchmark of symmetrical protein complexes further demonstrate the effectiveness of our method.

We map the surface of Pluto using an unsupervised machine learning technique using the near-infrared observations of the LEISA/Ralph instrument onboard NASA's New Horizons spacecraft. The principal component reduced Gaussian mixture model was implemented to investigate the geographic distribution of the surface units across the dwarf planet. We also present the likelihood of each surface unit at the image pixel level. Average I/F spectra of each unit were analyzed -- in terms of the position and strengths of absorption bands of abundant volatiles such as N${}_{2}$, CH${}_{4}$, and CO and nonvolatile H${}_{2}$O -- to connect the unit to surface composition, geology, and geographic location. The distribution of surface units shows a latitudinal pattern with distinct surface compositions of volatiles -- consistent with the existing literature. However, previous mapping efforts were based primarily on compositional analysis using spectral indices (indicators) or implementation of complex radiative transfer models, which need (prior) expert knowledge, label data, or optical constants of representative endmembers. We prove that an application of unsupervised learning in this instance renders a satisfactory result in mapping the spatial distribution of ice compositions without any prior information or label data. Thus, such an application is specifically advantageous for a planetary surface mapping when label data are poorly constrained or completely unknown, because an understanding of surface material distribution is vital for volatile transport modeling at the planetary scale. We emphasize that the unsupervised learning used in this study has wide applicability and can be expanded to other planetary bodies of the Solar System for mapping surface material distribution.

The new work, from MIT's Center for Bits and Atoms (CBA), builds on years of research, including recent studies demonstrating that objects such as a deformable airplane wing and a functional racing car could be assembled from tiny identical lightweight pieces -- and that robotic devices could be built to carry out some of this assembly work. Now, the team has shown that both the assembler bots and the components of the structure being built can all be made of the same subunits, and the robots can move independently in large numbers to accomplish large-scale assemblies quickly. The new work is reported in the journal Nature Communications Engineering, in a paper by CBA doctoral student Amira Abdel-Rahman, Professor and CBA Director Neil Gershenfeld, and three others. A fully autonomous self-replicating robot assembly system capable of assembling larger structures, including larger robots, and planning the best construction sequence is still years away, Gershenfeld says. But the new work makes important strides toward that goal, including working out the complex tasks of when to build more robots and how big to make them, as well as how to organize swarms of bots of different sizes to build a structure efficiently without crashing into each other.

Researchers at MIT have made significant steps toward creating robots that could practically and economically assemble nearly anything, including things much larger than themselves, from vehicles to buildings to larger robots. The new system involves large, usable structures built from an array of tiny identical subunits called voxels (the volumetric equivalent of a 2-D pixel). Researchers at MIT have made significant steps toward creating robots that could practically and economically assemble nearly anything, including things much larger than themselves, from vehicles to buildings to larger robots. The new work, from MIT's Center for Bits and Atoms (CBA), builds on years of research, including recent studies demonstrating that objects such as a deformable airplane wing and a functional racing car could be assembled from tiny identical lightweight pieces -- and that robotic devices could be built to carry out some of this assembly work. Now, the team has shown that both the assembler bots and the components of the structure being built can all be made of the same subunits, and the robots can move independently in large numbers to accomplish large-scale assemblies quickly.

Before getting into what polymers are on a molecular level, let's see some familiar materials that are good examples. Some examples of polymers include: plastic, nylon, rubber, wood, protein, and DNA. In this case, we will focus primarily on synthetic polymers like plastic and nylon. At the molecular level, polymers are composed of long chains of repeating molecules. The molecule that repeats in this chain is known as a monomer (or subunit).

One of the most important molecules in the brain doesn't work quite the way scientists thought it did, according to new work by researchers at Columbia University Vagelos College of Physicians and Surgeons and Carnegie Mellon University. The results, published April 20 in Nature, may aid the development of a new generation of more effective neurological and psychiatric therapies with fewer side effects. The new research takes a close look at glutamate, the most prevalent neurotransmitter in the brain. Glutamate binds to receptors on brain cells, which opens a channel into the cell, allowing ions to pass through to propagate an electrical signal. "The way the brain works is through communication between neurons, and these are the main receptors which allow this communication," says Alexander Sobolevsky, Ph.D., associate professor of biochemistry and molecular biophysics at Columbia and senior author on the paper.