Exploring out-of-equilibrium quantum magnetism and thermalization in a spin-3 many-body dipolar lattice system

Understanding quantum thermalization through entanglement build-up in isolated quantum systems addresses fundamental questions on how unitary dynamics connects to statistical physics. Here, we study the spin dynamics and approach towards local thermal equilibrium of a macroscopic ensemble of S = 3 spins prepared in a pure coherent spin state, tilted compared to the magnetic field, under the effect of magnetic dipole-dipole interactions. The experiment uses a unit filled array of 104 chromium atoms in a three dimensional optical lattice, realizing the spin-3 XXZ Heisenberg model. The buildup of quantum correlation during the dynamics, especially as the angle approaches pi/2, is supported by comparison with an improved numerical quantum phase-space method and further confirmed by the observation that our isolated system thermalizes under its own dynamics, reaching a steady state consistent with the one extracted from a thermal ensemble with a temperature dictated from the system's energy. This indicates a scenario of quantum thermalization which is tied to the growth of entanglement entropy. Although direct experimental measurements of the Renyi entropy in our macroscopic system are unfeasible, the excellent agreement with the theory, which can compute this entropy, does indicate entanglement build-up.Comment: 12 figure

Quantum Convolutional Neural Networks

We introduce and analyze a novel quantum machine learning model motivated by convolutional neural networks. Our quantum convolutional neural network (QCNN) makes use of only $O(\log(N))$ variational parameters for input sizes of $N$ qubits, allowing for its efficient training and implementation on realistic, near-term quantum devices. The QCNN architecture combines the multi-scale entanglement renormalization ansatz and quantum error correction. We explicitly illustrate its potential with two examples. First, QCNN is used to accurately recognize quantum states associated with 1D symmetry-protected topological phases. We numerically demonstrate that a QCNN trained on a small set of exactly solvable points can reproduce the phase diagram over the entire parameter regime and also provide an exact, analytical QCNN solution. As a second application, we utilize QCNNs to devise a quantum error correction scheme optimized for a given error model. We provide a generic framework to simultaneously optimize both encoding and decoding procedures and find that the resultant scheme significantly outperforms known quantum codes of comparable complexity. Finally, potential experimental realization and generalizations of QCNNs are discussed.Comment: 12 pages, 11 figures. v2: New application to optimizing quantum error correction codes, added sample complexity analysis, more details for experimental realizations, and other minor revision

Experimental Quantum Hamiltonian Learning

Efficiently characterising quantum systems, verifying operations of quantum devices and validating underpinning physical models, are central challenges for the development of quantum technologies and for our continued understanding of foundational physics. Machine-learning enhanced by quantum simulators has been proposed as a route to improve the computational cost of performing these studies. Here we interface two different quantum systems through a classical channel - a silicon-photonics quantum simulator and an electron spin in a diamond nitrogen-vacancy centre - and use the former to learn the latter's Hamiltonian via Bayesian inference. We learn the salient Hamiltonian parameter with an uncertainty of approximately $10^{-5}$. Furthermore, an observed saturation in the learning algorithm suggests deficiencies in the underlying Hamiltonian model, which we exploit to further improve the model itself. We go on to implement an interactive version of the protocol and experimentally show its ability to characterise the operation of the quantum photonic device. This work demonstrates powerful new quantum-enhanced techniques for investigating foundational physical models and characterising quantum technologies

The megakaryocytic transcription factor ARID3A suppresses leukemia pathogenesis

Given the plasticity of hematopoietic stem and progenitor cells, multiple routes of differentiation must be blocked in the the pathogenesis of acute myeloid leukemia, the molecular basis of which is incompletely understood. We report that posttranscriptional repression of the transcription factor ARID3A by miR-125b is a key event in the pathogenesis of acute megakaryoblastic leukemia (AMKL). AMKL is frequently associated with trisomy 21 and GATA1 mutations (GATA1s), and children with Down syndrome are at a high risk of developing the disease. The results of our study showed that chromosome 21-encoded miR-125b synergizes with Gata1s to drive leukemogenesis in this context. Leveraging forward and reverse genetics, we uncovered Arid3a as the main miR-125b target behind this synergy. We demonstrated that, during normal hematopoiesis, this transcription factor promotes megakaryocytic differentiation in concert with GATA1 and mediates TGFβ-induced apoptosis and cell cycle arrest in complex with SMAD2/3. Although Gata1s mutations perturb erythroid differentiation and induce hyperproliferation of megakaryocytic progenitors, intact ARID3A expression assures their megakaryocytic differentiation and growth restriction. Upon knockdown, these tumor suppressive functions are revoked, causing a blockade of dual megakaryocytic/erythroid differentiation and subsequently of AMKL. Inversely, restoring ARID3A expression relieves the arrest of megakaryocytic differentiation in AMKL patient-derived xenografts. This work illustrates how mutations in lineage-determining transcription factors and perturbation of posttranscriptional gene regulation can interact to block multiple routes of hematopoietic differentiation and cause leukemia. In AMKL, surmounting this differentiation blockade through restoration of the tumor suppressor ARID3A represents a promising strategy for treating this lethal pediatric disease

Past answers to present concerns: the relevance of the premodern past for 21st century policy planners; comments on the state of the field

How is history relevant to the present, or indeed the future? Governments around the world have used history to inform planning and decision-making in various fields for years, but more recently it has taken on a renewed importance as governments grapple with increasingly complex challenges arising from the impacts of climatic change. Yet identifying ?lessons from the past? is not straightforward. Especially in the case of big questions about historical structures and social processes, establishing precise causal relationships is complex and interpretive, making consensus difficult among specialists. A second major challenge arises over the uses of history. Historical precedent can and does play a role in some contexts in helping formulate new strategies for addressing local environmental challenges. At the national level policy-makers and politicians often look to the past for inspiration, guidance, or justification. In both respects, the cases and examples chosen are often highly selective and tend to align with pre-existing assumptions. This article briefly reviews these challenges within the context of climate change and associated environmental and sustainability issues, comments on recent work in the field, and suggests some ways forward for historians.1 Introduction: The Challenge 2 Nuancing the Past: Continuity, Rupture, Agency, and Belief 3 Ways Forward I: Expert Elicitation and Qualitative–Quantitative Data Integration 4 Ways Forward II: Case Studies of Policy Influence 4.1 Case Study 1: The Eastern Mediterranean 4.2 Case Study 2: The North Atlantic Islands 5 Ongoing Challenges 6 Conclusio

Mechanisms of Progression of Myeloid Preleukemia to Transformed Myeloid Leukemia in Children with Down Syndrome

Myeloid leukemia in Down syndrome (ML-DS) clonally evolves from transient abnormal myelopoiesis (TAM), apreleukemic condition in DS newborns. To define mechanisms of leukemic transformation, we combinedexome and targeted resequencing of 111 TAM and 141 ML-DS samples with functional analyses. TAM requirestrisomy 21 and truncating mutations in GATA1; additional TAM variants are usually not pathogenic. By contrast,in ML-DS, clonal and subclonal variants are functionally required. We identified a recurrent and oncogenic hot-spot gain-of-function mutation in myeloid cytokine receptor CSF2RB. By a multiplex CRISPR/Cas9 screen in anin vivo murine TAM model, we tested loss-of-function of 22 recurrently mutated ML-DS genes. Loss of 18different genes produced leukemias that phenotypically, genetically, and transcriptionally mirrored ML-D

Comparison of gene expression profiles in core biopsies and corresponding surgical breast cancer samples

INTRODUCTION: Gene expression profiling has been successfully used to classify breast cancer into clinically distinct subtypes, and to predict the risk of recurrence and treatment response. The aim of this study was to investigate whether the gene expression profile (GEP) detected in a core biopsy (CB) is representative for the entire tumor, since CB is an important tool in breast cancer diagnosis. Moreover, we investigated whether performing CBs prior to the surgical excision could influence the GEP of the respective tumor. METHODS: We quantified the RNA expression of 60 relevant genes by quantitative real-time PCR in paired CBs and surgical specimens from 22 untreated primary breast cancer patients. Subsequently, expression data were compared with independent GEPs obtained from tumors of 317 patients without preceding CB. RESULTS: In 82% of the cases the GEP detected in the CB correlated very well with the corresponding profile in the surgical sample (r(s )≥ 0.95, p < 0.001). Gene-by-gene analysis revealed four genes significantly elevated in the surgical sample compared to the CB; these comprised genes mainly involved in inflammation and the wound repair process as well as in tumor invasion and metastasis. CONCLUSION: A GEP detected in a CB are representative for the entire tumor and is, therefore, of clinical relevance. The observed alterations of individual genes after performance of CB deserve attention since they might impact the clinical interpretation with respect to prognosis and therapy prediction of the GEP as detected in the surgical specimen following CB performance

RUNX1 isoform disequilibrium promotes the development of trisomy 21 associated myeloid leukemia

Gain of chromosome 21 (Hsa21) is among the most frequent aneuploidies in leukemia. However, it remains unclear how partial or complete amplifications of Hsa21 promote leukemogenesis and why children with Down syndrome (i.e. trisomy 21) are particularly at risk of leukemia development. Here, we propose that RUNX1 isoform disequilibrium with RUNX1A bias is key to Down syndrome-associated myeloid leukemia (ML-DS). Starting with Hsa21-focused CRISPR-Cas9 screens, we uncovered a strong and specific RUNX1 dependency in ML-DS cells. Expression of the RUNX1A isoform is elevated in ML-DS patients, and mechanistic studies using murine ML-DS models and patient-derived xenografts (PDXs) revealed that excess RUNX1A synergizes with the pathognomonic Gata1s mutation during leukemogenesis by displacing RUNX1C from its endogenous binding sites and inducing oncogenic programs in complex with the MYC cofactor MAX. These effects were reversed by restoring the RUNX1A:RUNX1C equilibrium in PDXs in vitro and in vivo. Moreover, pharmacological interference with MYC:MAX dimerization using MYCi361 exerted strong anti-leukemic effects. Thus, our study highlights the importance of alternative splicing in leukemogenesis, even on a background of aneuploidy, and paves the way for the development of specific and targeted therapies for ML-DS, as well as for other leukemias with Hsa21 aneuploidy or RUNX1 isoform disequilibrium

Holocene hydro-climatic variability in the Mediterranean: A synthetic multi-proxy reconstruction

Here we identify and analyze proxy data interpreted to reflect hydro-climatic variability over the last 10,000 years from the Mediterranean region to (1) outline millennial and multi-centennial-scale trends and (2) identify regional patterns of hydro-climatic variability. A total of 47 lake, cave, and marine records were transformed to z-scores to allow direct comparisons between sites, put on a common time scale, and binned into 200-year time slices. Six different regions were identified based on numerical and spatial analyzes of z-scores: S Iberia and Maghreb, N Iberia, Italy, the Balkans, Turkey, and the Levant, and the overall hydro-climate history of each region was reconstructed. N Iberia is largely decoupled from the five other regions throughout the Holocene. Wetter conditions occur in the five other regions between 8500 and 6100 yr BP. After 6000 yr BP, climate oscillated until around 3000 ± 300 yr BP, which seems to have been the overall driest period in the eastern Mediterranean and North Africa. In contrast, Italy and N Iberia seem to have remained wetter during this period. In addition, non-metric multidimensional scaling (nMDS) was applied to 18 long, continuous climate z-score records that span the majority of the Holocene. nMDS axes 1 and 2 illustrate the main trends in the z-score data. The first axis captures a long-term development of drier condition in the Mediterranean from 7900 to 3700 yr BP. Rapid shifts occur in nMDS axis 2 at 6700–6300 BP, 4500–4300 BP, and 3500–3300 BP indicating centennial-scale climate change. Our synthesis highlights a dominant south/east versus north/west Mediterranean hydro-climate dipole throughout the Holocene and therefore confirms that there was no single climate trajectory characterizing the whole Mediterranean basin during the last 10 millennia