Nanomechanical probing and strain tuning of the Curie temperature in suspended Cr2Ge2Te6-based heterostructures

Two-dimensional magnetic materials with strong magnetostriction are attractive systems for realizing strain-tuning of the magnetization in spintronic and nanomagnetic devices. This requires an understanding of the magneto-mechanical coupling in these materials. In this work, we suspend thin Cr2Ge2Te6 layers and their heterostructures, creating ferromagnetic nanomechanical membrane resonators. We probe their mechanical and magnetic properties as a function of temperature and strain by observing magneto-elastic signatures in the temperature-dependent resonance frequency near the Curie temperature, TC. We compensate for the negative thermal expansion coefficient of Cr2Ge2Te6 by fabricating heterostructures with thin layers of WSe2 and antiferromagnetic FePS3, which have positive thermal expansion coefficients. Thus we demonstrate the possibility of probing multiple magnetic phase transitions in a single heterostructure. Finally, we demonstrate a strain-induced enhancement of TC in a suspended Cr2Ge2Te6-based heterostructure by 2.5 ± 0.6 K by applying a strain of 0.026% via electrostatic force

S. aureus MscL Is a Pentamer In Vivo but of Variable Stoichiometries In Vitro: Implications for Detergent-Solubilized Membrane Proteins

Detergent-induced rearrangements of membrane-protein subunits explain why two MscL channel stoichiometries have been resolved by X-ray crystallography - but S. aureus MscL is truly a pentamer in vivo

Reperfusion injury following cerebral ischemia: pathophysiology, MR imaging, and potential therapies

INTRODUCTION: Restoration of blood flow following ischemic stroke can be achieved by means of thrombolysis or mechanical recanalization. However, for some patients, reperfusion may exacerbate the injury initially caused by ischemia, producing a so-called “cerebral reperfusion injury”. Multiple pathological processes are involved in this injury, including leukocyte infiltration, platelet and complement activation, postischemic hyperperfusion, and breakdown of the blood–brain barrier. METHODS/RESULTS AND CONCLUSIONS: Magnetic resonance imaging (MRI) can provide extensive information on this process of injury, and may have a role in the future in stratifying patients’ risk for reperfusion injury following recanalization. Moreover, different MRI modalities can be used to investigate the various mechanisms of reperfusion injury. Antileukocyte antibodies, brain cooling and conditioned blood reperfusion are potential therapeutic strategies for lessening or eliminating reperfusion injury, and interventionalists may play a role in the future in using some of these therapies in combination with thrombolysis or embolectomy. The present review summarizes the mechanisms of reperfusion injury and focuses on the way each of those mechanisms can be evaluated by different MRI modalities. The potential therapeutic strategies are also discussed

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Revealing Higher Order Protein Structure Using Mass Spectrometry

International audienceThe development of rapid, sensitive, and accurate mass spectrometric methods for measuring peptides, proteins, and even intact protein assemblies has made mass spectrometry (MS) an extraordinarily enabling tool for structural biology. Here, we provide a personal perspective of the increasingly useful role that mass spectrometric techniques are exerting during the elucidation of higher order protein structures. Areas covered in this brief perspective include MS as an enabling tool for the high resolution structural biologist, for compositional analysis of endogenous protein complexes, for stoichiometry determination, as well as for integrated approaches for the structural elucidation of protein complexes. We conclude with a vision for the future role of MS-based techniques in the development of a multi-scale molecular microscope

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery