This paper introduces Genesis, the first compiler designed to supportHamiltonian Simulation on hybrid continuous-variable (CV) and discrete-variable(DV) quantum computing systems. Genesis is a two-level compilation system. Atthe first level, it decomposes an input Hamiltonian into basis gates using thenative instruction set of the target hybrid CV-DV quantum computer. At thesecond level, it tackles the mapping and routing of qumodes/qubits to implementlong-range interactions for the gates decomposed from the first level. Ratherthan a typical implementation that relies on SWAP primitives similar toqubit-based (or DV-only) systems, we propose an integrated design ofconnectivity-aware gate synthesis and beamsplitter SWAP insertion tailored forhybrid CV-DV systems. We also introduce an OpenQASM-like domain-specificlanguage (DSL) named CVDV-QASM to represent Hamiltonian in terms ofPauli-exponentials and basic gate sequences from the hybrid CV-DV gate set.Genesis has successfully compiled several important Hamiltonians, including theBose-Hubbard model, $\mathbb{Z}_2-$Higgs model, Hubbard-Holstein model,Heisenberg model and Electron-vibration coupling Hamiltonians, which arecritical in domains like quantum field theory, condensed matter physics, andquantum chemistry. Our implementation is available atGenesis-CVDV-Compiler(https://github.com/ruadapt/Genesis-CVDV-Compiler). <<<

Akanksha Jain, Gilles Gut, Fátima Sanchis-Calleja, Reto Tschannen, Zhisong He, Nicolas Luginbühl, Fides Zenk, Antonius Chrisnandy, Simon Streib, Christoph Harmel, Ryoko Okamoto, Malgorzata Santel, Makiko Seimiya, René Holtackers, Juliane K. Rohland, Sophie Martina Johanna Jansen, Matthias P. Lutolf, J. Gray Camp, Barbara Treutlein <<<

Abstract Brain organoids enable the mechanistic study of human brain development and provide opportunities to explore self-organization in unconstrained developmental systems1–3. Here we establish long-term, live light-sheet microscopy on unguided brain organoids generated from fluorescently labelled human induced pluripotent stem cells, which enables tracking of tissue morphology, cell behaviours and subcellular features over weeks of organoid development4. We provide a novel dual-channel, multi-mosaic and multi-protein labelling strategy combined with a computational demultiplexing approach to enable simultaneous quantification of distinct subcellular features during organoid development. We track actin, tubulin, plasma membrane, nucleus and nuclear envelope dynamics, and quantify cell morphometric and alignment changes during tissue-state transitions including neuroepithelial induction, maturation, lumenization and brain regionalization. On the basis of imaging and single-cell transcriptome modalities, we find that lumenal expansion and cell morphotype composition within the developing neuroepithelium are associated with modulation of gene expression programs involving extracellular matrix pathway regulators and mechanosensing. We show that an extrinsically provided matrix enhances lumen expansion as well as telencephalon formation, and unguided organoids grown in the absence of an extrinsic matrix have altered morphologies with increased neural crest and caudalized tissue identity. Matrix-induced regional guidance and lumen morphogenesis are linked to the WNT and Hippo (YAP1) signalling pathways, including spatially restricted induction of the WNT ligand secretion mediator (WLS) that marks the earliest emergence of non-telencephalic brain regions. Together, our work provides an inroad into studying human brain morphodynamics and supports a view that matrix-linked mechanosensing dynamics have a central role during brain regionalization. <<<

Wen-Yan Cheng, Jian-Feng Li, Yong-Mei Zhu, Xiang-Jie Lin, Li-Jun Wen, Fan Zhang, Yu-Liang Zhang, Ming Zhao, Hai Fang, Sheng-Yue Wang, Xiao-Jing Lin, Niu Qiao, Wei Yin, Jia-Nan Zhang, Yu-Ting Dai, Lu Jiang, Xiao-Jian Sun, Yi Xu, Tong-Tong Zhang, Su-Ning Chen, Hong-Hu Zhu, Zhu Chen, Jie Jin, De-Pei Wu, Yang Shen, Sai-Juan Chen <<<

The current classification of acute myeloid leukemia (AML) relies largely on genomic alterations. Robust identification of clinically and biologically relevant molecular subtypes from nongenomic high-throughput sequencing data remains challenging. We established the largest multicenter AML cohort (n = 655) in China, with all patients subjected to RNA sequencing (RNA-Seq) and 619 (94.5%) to targeted or whole-exome sequencing (TES/WES). Based on an enhanced consensus clustering, eight stable gene expression subgroups (G1–G8) with unique clinical and biological significance were identified, including two unreported (G5 and G8) and three redefined ones (G4, G6, and G7). Apart from four well-known low-risk subgroups including PML::RARA (G1), CBFB::MYH11 (G2), RUNX1::RUNX1T1 (G3), biallelic CEBPA mutations or -like (G4), four meta-subgroups with poor outcomes were recognized. The G5 (myelodysplasia-related/-like) subgroup enriched clinical, cytogenetic and genetic features mimicking secondary AML, and hotspot mutations of IKZF1 (p.N159S) (n = 7). In contrast, most NPM1 mutations and KMT2A and NUP98 fusions clustered into G6–G8, showing high expression of HOXA / B genes and diverse differentiation stages, from hematopoietic stem/progenitor cell down to monocyte, namely HOX -primitive (G7) , HOX -mixed (G8), and HOX -committed (G6). Through constructing prediction models, the eight gene expression subgroups could be reproduced in the Cancer Genome Atlas (TCGA) and Beat AML cohorts. Each subgroup was associated with distinct prognosis and drug sensitivities, supporting the clinical applicability of this transcriptome-based classification of AML. These molecular subgroups illuminate the complex molecular network of AML, which may promote systematic studies of disease pathogenesis and foster the screening of targeted agents based on omics. <<<

Woody Ahern, Jason Yim, Doug Tischer, Saman Salike, Seth M. Woodbury, Donghyo Kim, Indrek Kalvet, Yakov Kipnis, Brian Coventry, Han Raut Altae-Tran, Magnus Bauer, Regina Barzilay, Tommi S. Jaakkola, Rohith Krishna, David Baker <<<

AbstractDe novoenzyme design starts from ideal active site descriptions consisting of constellations of catalytic residue functional groups around reaction transition state(s), and seeks to generate protein structures that can accurately hold the site in place. Highly active enzymes have been designed starting from such descriptions using the generative AI method RFdiffusion [1–3], but there are two current methodological limitations. First, the geometry of the active site can only be specified at the residue level, so for each catalytic residue functional group placed around the reaction transition state, the possible locations of the residue backbone must be enumerated by building side chain rotamers back from the functional group. Second, the location of the catalytic residues along the sequence must be specified in advance, which considerably limits the space of solutions which can be sampled. Here we describe a new deep generative method, Rosetta Fold diffusion 2 (RFdiffusion2), that solves both problems, enabling enzymes to be designed from sequence agnostic descriptions of functional group locations without inverse rotamer generation. We first evaluate RFdiffusion2 on anin silicoenzyme design benchmark of 41 diverse active sites and find that it is able to successfully build proteins scaffolding all 41 sites, compared to 16/41 with prior state-of-the-art deep learning methods. Next, we design enzymes around three diverse catalytic sites and characterize the designs experimentally; in each case we identify active catalysts in testing less than 96 sequences. RFdiffusion2 demonstrates the potential of atomic resolution generative models for the design ofde novoenzymes directly from their reaction mechanisms. <<<

Gayani Senevirathne, Serena C. Fernandopulle, Daniel Richard, Stephanie L. Baumgart, Anika Liv Christensen, Matteo Fabbri, Jakob Höppner, Harald Jüppner, Peishu Li, Vivien Bothe, Nadia Fröbisch, Ian Simcock, Owen J. Arthurs, Alistair Calder, Naomi Freilich, Niamh C. Nowlan, Ian A. Glass, April Craft, Terence D. Capellini <<<

Emily Grist, Peter Dutey-Magni, Marina A. Parry, Larissa Mendes, Ashwin Sachdeva, James A. Proudfoot, Anis A. Hamid, Mazlina Ismail, Sarah Howlett, Stefanie Friedrich, Lia DePaula Oliveira, Laura Murphy, Christopher Brawley, Oluwademilade Dairo, Sharanpreet Lall, Yang Liu, Daniel Wetterskog, Anna Wingate, Karolina Nowakowska, Leila Zakka, Claire L. Amos, Nafisah B. Atako, Victoria Wang, Hannah L. Rush, Robert J. Jones, Hing Leung, William R. Cross, Silke Gillessen, Chris C. Parker, Teresa Marafioti, Alfonso Urbanucci, Matthew Fittall, Edward M. Schaeffer, Daniel E. Spratt, David Waugh, Thomas Powles, Matthew R. Sydes, Felix Y. Feng, Daniel M. Berney, Mahesh K.B. Parmar, Noel W. Clarke, Elai Davicioni, Tamara L. Lotan, Christopher J. Sweeney, Louise C. Brown, Nicholas D. James, Gerhardt Attard <<<

Abstract Motivation The 3D organization of the genome plays a crucial role in various biological processes. Hi-C technology is widely used to investigate chromosome structures by quantifying 3D proximity between genomic regions. While numerous computational tools exist for detecting differences in Hi-C data between conditions, a comprehensive review and benchmark comparing their effectiveness is lacking. Results This study offers a comprehensive review and benchmark of 10 generic tools for differential analysis of Hi-C matrices at the interaction count level. The benchmark assesses the statistical methods, usability, and performance (in terms of precision and power) of these tools, using both real and simulated Hi-C data. Results reveal a striking variability in performance among the tools, highlighting the substantial impact of preprocessing filters and the difficulty all tools encounter in effectively controlling the false discovery rate across varying resolutions and chromosome sizes. Availability The complete benchmark is available at https://forgemia.inra.fr/scales/replication-chrocodiff using processed data deposited at https://doi.org/10.57745/LR0W9R. Contact nathalie.vialaneix@inrae.fr <<<

Weiran Feng, Erik Ladewig, Matthew Lange, Nazifa Salsabeel, Huiyong Zhao, Young Sun Lee, Anuradha Gopalan, Hanzhi Luo, Wenfei Kang, Ning Fan, Eric Rosiek, Elisa de Stanchina, Yu Chen, Brett S. Carver, Christina S. Leslie, Charles L. Sawyers <<<

Abstract Despite the high prevalence of ERG transcription factor translocations in prostate cancer, the mechanism of tumorigenicity remains poorly understood. Using lineage tracing, we find the tumor-initiating activity of ERG resides in a subpopulation of murine basal cells that coexpress luminal genes (BasalLum) and not in the larger population of ERG+ luminal cells. Upon ERG activation, BasalLum cells give rise to highly proliferative intermediate (IM) cells with stem-like features that coexpress basal, luminal, hillock and club marker genes, before transitioning to Krt8+ luminal cells. Transcriptomic analysis of ERG+ human prostate cancers confirms the presence of rare ERG+ BasalLum cells, as well as IM cells whose presence is associated with a worse prognosis. Single-cell analysis revealed a chromatin state in ERG+ IM cells enriched for STAT3 transcription factor binding sites and elevated expression of the KMT2A/MLL1 and DOT1L, all three of which are essential for ERG-driven tumorigenicity in vivo. In addition to providing translational opportunities, this work illustrates how single-cell approaches combined with lineage tracing can identify cancer vulnerabilities not evident from bulk analysis. <<<

Abstract Stress is widely considered to negatively impact hippocampal function, thus impairing episodic memory. However, the hippocampus is not merely the seat of episodic memory. Rather, it also (via distinct circuitry) supports statistical learning. On the basis of rodent work suggesting that stress may impair the hippocampal pathway involved in episodic memory while sparing or enhancing the pathway involved in statistical learning, we developed a behavioral experiment to investigate the effects of acute stress on both episodic memory and statistical learning in humans. Participants were randomly assigned to one of three conditions: stress (socially evaluated cold pressor) immediately before learning, stress ∼15 min before learning, or no stress. In the learning task, participants viewed a series of trial-unique scenes (allowing for episodic encoding of each image) in which certain scene categories reliably followed one another (allowing for statistical learning of associations between paired categories). Memory was assessed 24 hr later to isolate stress effects on encoding/learning rather than retrieval. We found modest support for our hypothesis that acute stress can amplify statistical learning: Only participants stressed ∼15 min in advance exhibited reliable evidence of learning across multiple measures. Furthermore, stress-induced cortisol levels predicted statistical learning retention 24 hr later. In contrast, episodic memory did not differ by stress condition, although we did find preliminary evidence that acute stress promoted memory for statistically predictable information and attenuated competition between statistical and episodic encoding. Together, these findings provide initial insights into how stress may differentially modulate learning processes within the hippocampus. <<<

Clinical trials show that strategies to elicit the production of broadly neutralizing antibodies have a promising start <<<

Indi P. Joore, Sawsan Shehata, Irena Muffels, Jose Castro-Alpízar, Elena Jiménez-Curiel, Emilia Nagyova, Natacha Levy, Ziqin Tang, Kimberly Smit, Wilbert P. Vermeij, Richard Rodenburg, Raymond Schiffelers, Edward E.S. Nieuwenhuis, Peter M. van Hasselt, Sabine A. Fuchs, Martijn A. J. Koppens <<<

Mutations in the mitochondrial genome can cause maternally inherited diseases, cancer, and aging-related conditions. Recent technological progress now enables the creation and correction of mutations in the mitochondrial genome, but it remains relatively unknown how patients with primary mitochondrial disease can benefit from this technology. Here, we demonstrate the potential of the double-stranded DNA deaminase toxin A-derived cytosine base editor (DdCBE) to develop disease models and therapeutic strategies for mitochondrial disease in primary human cells. Introduction of the m.15150G > A mutation in liver organoids resulted in organoid lines with varying degrees of heteroplasmy and correspondingly reduced ATP production, providing a unique model to study functional consequences of different levels of heteroplasmy of this mutation. Correction of the m.4291T > C mutation in patient-derived fibroblasts restored mitochondrial membrane potential. DdCBE generated sustainable edits with high specificity and product purity. To prepare for clinical application, we found that mRNA-mediated mitochondrial base editing resulted in increased efficiency and cellular viability compared to DNA-mediated editing. Moreover, we showed efficient delivery of the mRNA mitochondrial base editors using lipid nanoparticles, which is currently the most advanced non-viral in vivo delivery system for gene products. Our study thus demonstrates the potential of mitochondrial base editing to not only generate unique in vitro models to study these diseases, but also to functionally correct mitochondrial mutations in patient-derived cells for future therapeutic purposes. <<<

Robotic surgery has become increasingly popular over the last 30 years. This techniqueis particularly attractive due to its minimally invasive nature, high precision comparedto open and laparoscopic techniques, less postoperative pain, shorter hospital stay forpatients, and faster recovery. For an anesthesiologist, robot-assisted operations involvenumerous challenges resulting from the surgical technique. The most important problems during anesthesia include changes in physiology resulting from the developmentof pneumoperitoneum and a steep Trendelenburg position. This review discusses problems that may be encountered by an anesthesiologist performing anesthesia duringrobotic surgery. <<<

We construct $\varepsilon$-approximate unitary $k$-designs on $n$ qubits incircuit depth $O(\log k \log \log n k / \varepsilon)$. The depth isexponentially improved over all known results in all three parameters $n$, $k$,$\varepsilon$. We further show that each dependence is optimal up toexponentially smaller factors. Our construction uses $\tilde{{O}}(nk)$ ancillaqubits and ${O}(nk)$ bits of randomness, which are also optimal up to $\log(nk)$ factors. An alternative construction achieves a smaller ancilla count$\tilde{{O}}(n)$ with circuit depth ${O}(k \log \log nk/\varepsilon)$. Toachieve these efficient unitary designs, we introduce a highly-structuredrandom unitary ensemble that leverages long-range two-qubit gates and low-depthimplementations of random classical hash functions. We also develop a newanalytical framework for bounding errors in quantum experiments involving manyqueries to random unitaries. As an illustration of this framework'sversatility, we provide a succinct alternative proof of the existence ofpseudorandom unitaries. <<<

AbstractBackgroundAdvances in cancer biology are increasingly dependent on integration of heterogeneous datasets. Large-scale efforts have systematically mapped many aspects of cancer cell biology; however, it remains challenging for individual scientists to effectively integrate and understand this data.ResultsWe have developed a new data retrieval and indexing framework that allows us to integrate publicly available data from different sources and to combine publicly available data with new or bespoke datasets. Our approach, which we have named the cancer data integrator (CanDI), is straightforward to implement, is well documented, and is continuously updated which should enable individual users to take full advantage of efforts to map cancer cell biology. We show that CanDI empowered testable hypotheses of new synthetic lethal gene pairs, genes associated with sex disparity, and immunotherapy targets in cancer.ConclusionsCanDI provides a flexible approach for large-scale data integration in cancer research enabling rapid generation of hypotheses. The CanDI data integrator is available athttps://github.com/GilbertLabUCSF/CanDI. <<<

A major challenge in the burgeoning field of quantum simulation forhigh-energy physics is the realization of scalable $2+1$D lattice gaugetheories on state-of-the-art quantum hardware, which is an essential steptowards the overarching goal of probing $3+1$D quantum chromodynamics on aquantum computer. Despite great progress, current experimental implementationsof $2+1$D lattice gauge theories are mostly restricted to relatively smallsystem sizes and two-level representations of the gauge and electric fields.Here, we propose a resource-efficient method for quantum simulating $2+1$Dspin-$S$ $\mathrm{U}(1)$ quantum link lattice gauge theories with dynamicalmatter using qudit-based quantum processors. By integrating out the matterfields through Gauss's law, we reformulate the quantum link model in a purelyspin picture compatible with qudit encoding across arbitrary spatialdimensions, eliminating the need for ancillary qubits and reducing resourceoverhead. Focusing first on the spin-$1/2$ case, we construct explicit circuitsfor the full Hamiltonian and demonstrate through numerical simulations that thefirst-order Trotterized circuits accurately capture the quench dynamics even inthe presence of realistic noise levels. Additionally, we introduce a generalmethod for constructing coupling-term circuits for higher-spin representations$S>1/2$. Compared to conventional qubit encodings, our framework significantlyreduces the number of quantum resources and gate count. Our approachsignificantly enhances scalability and fidelity for probing nonequilibriumphenomena in higher-dimensional lattice gauge theories, and is readily amenableto implementation on state-of-the-art qudit platforms. <<<