Integrated Calendar

<p>Moiré materials built on transition metal dichalcogenide semiconductors have emerged as a tunable platform for simulating the Hubbard model on a triangular lattice. A natural question arises: Can the platform be tuned to yield a phase diagram similar to that in high-temperature cuprate superconductors? In this talk, I will discuss the emergence of “high-temperature” superconductivity near the Mott transition in a triangular moiré lattice with intermediate coupling strength. The emergent doping-temperature phase diagram looks remarkably similar to that in cuprate superconductors. I will also discuss the evolution of the phase diagram by tuning the band structure of the material by gating. The results could provide a new angle for understanding the phenomenon of high-temperature superconductivity in strongly correlated materials.&nbsp;</p><p>&nbsp;</p>

<p>Understanding why some patients respond to immune checkpoint therapy while others do not remains a critical challenge in cancer research. This talk will explore three key studies that shed light on this question. First, we uncover metabolic predictors of response to checkpoint blockade therapy, revealing how tumor and immune cell metabolism shape treatment outcomes. Next, we present a single-cell meta-analysis of T cell clonal dynamics, highlighting their role in immunotherapy success. Finally, we introduce scXpand, a machine-learning approach for predicting T cell clonality from scRNA-seq, offering a novel tool to enhance immunotherapy research and precision medicine.</p>

Given a distribution of images, how can we can decompose the data into a set of underlying components? In this talk, I'll present an approach that decomposes images into a underlying composable energy functions. I'll illustrate how energy functions allow us to represent both global components of an image, such as lighting as well as local components such as objects. I'll further show how we leverage pretrained vision models to infer these components. Finally, I'll illustrate how discover components can be recombined to form a variety of images substantially different than those seen at training time.

Speaker's bio:

Yilun Du is an incoming assistant professor at Harvard and is currently a senior research scientist at Google Deepmind. He received has PhD and BS from MIT and was supported by a NSF graduate fellowship.

<p>In my talk I will introduce a novel framework that applies a principle from information theory, that of Minimum Description Length (MDL), to understand how regulation of human gene expression across organs, tissues is shaped by regulatory architecture.</p><p>Examination of expression patterns of human genes across the body reveals an intriguing duality: While many genes are expressed in only one tissue, others, known as “housekeeping genes”, are ubiquitously expressed in essentially every tissue. Yet, interestingly, a considerable portion of the genes are on the mid-range, deliberately expressed in many tissues but are also absent in many others.</p><p>Intuitively, in human language terms, specifying the expression program of the genes on the two ends of the spectrum requires a short description – e.g. “expressed in all tissues”, or “expressed only in brain”. Yet specifying the expression of genes in the middle of the scale requires longer description, or a longer MDL, having to specify in each tissue if the gene is expressed or not, and at what level. We sought to measure regulatory complexity of each human gene and examine if the MDL principle predicts and explains regulatory complexity. Our findings lend support to the MDL principle’s prediction. Our measure of regulatory complexity of a gene’s expression pattern can be predicted by quantifying its regulatory information content. In the talk we shall discuss evolutionary implications to the development of multi-cellularity.</p><p><strong>FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR&nbsp;WEBSITE:&nbsp;https://www.biosoftweizmann.com/</strong></p><p>&nbsp;</p>