Inflammation and tissue repair markers distinguish the nodular sclerosis and mixed cellularity subtypes of classical Hodgkin's lymphoma

Background: Classical Hodgkin's lymphoma (cHL), although a malignant disease, has many features in common with an inflammatory condition. The aim of this study was to establish the molecular characteristics of the two most common cHL subtypes, nodular sclerosis (NS) and mixed cellularity (MC), based on molecular profiling and immunohistochemistry, with special reference to the inflammatory microenvironment. Methods: We analysed 44 gene expression profiles of cHL whole tumour tissues, 25 cases of NS and 19 cases of MC, using Affymetrix chip technology and immunohistochemistry. Results: In the NS subtype, 152 genes showed a significantly higher expression, including genes involved in extracellular matrix (ECM) remodelling and ECM deposition similar to wound healing. Among these were SPARC, CTSK and COLI. Immunohistochemistry revealed that the NS-related genes were mainly expressed by macrophages and fibroblasts. Fifty-three genes had a higher expression in the MC subtype, including several inflammation-related genes, such as C1Qα, C1Qβ and CXCL9. In MC tissues, the C1Q subunits were mainly expressed by infiltrating macrophages. Conclusions and interpretations: We suggest that the identified subtype-specific genes could reflect different phases of wound healing. Our study underlines the potential function of infiltrating macrophages in shaping the cHL tumour microenvironment

Nonreciprocal Phonon Propagation in a Metallic Chiral Magnet

The phonon magnetochiral effect (MChE) is the nonreciprocal acoustic and thermal transports of phonons caused by the simultaneous breaking of the mirror and time-reversal symmetries. So far, the phonon MChE has been observed only in a ferrimagnetic insulator Cu2OSeO3, where the nonreciprocal response disappears above the Curie temperature of 58 K. Here, we study the nonreciprocal acoustic properties of a room-temperature ferromagnet Co9Zn9Mn2 for unveiling the phonon MChE close to the room temperature. Surprisingly, the nonreciprocity in this metallic compound is enhanced at higher temperatures and observed up to 250 K. This clear contrast between insulating Cu2OSeO3 and metallic Co9Zn9Mn2 suggests that metallic magnets have a mechanism to enhance the nonreciprocity at higher temperatures. From the ultrasound and microwave-spectroscopy experiments, we conclude that the magnitude of the phonon MChE of Co9Zn9Mn2 mostly depends on the magnon bandwidth, which increases at low temperatures and hinders the magnon-phonon hybridization. Our results suggest that the phonon nonreciprocity could be further enhanced by engineering the magnon band of materials.Comment: 6 pages, 4 figures, 1 tabl

Metastable skyrmion lattices governed by magnetic disorder and anisotropy in β\beta-Mn-type chiral magnets

Magnetic skyrmions are vortex-like topological spin textures often observed in structurally chiral magnets with Dzyaloshinskii-Moriya interaction. Among them, Co-Zn-Mn alloys with a $\beta$-Mn-type chiral structure host skyrmions above room temperature. In this system, it has recently been found that skyrmions persist over a wide temperature and magnetic field region as a long-lived metastable state, and that the skyrmion lattice transforms from a triangular lattice to a square one. To obtain perspective on chiral magnetism in Co-Zn-Mn alloys and clarify how various properties related to the skyrmion vary with the composition, we performed systematic studies on Co$_{10}$Zn$_{10}$, Co$_9$Zn$_9$Mn$_2$, Co$_8$Zn$_8$Mn$_4$ and Co$_7$Zn$_7$Mn$_6$ in terms of magnetic susceptibility and small-angle neutron scattering measurements. The robust metastable skyrmions with extremely long lifetime are commonly observed in all the compounds. On the other hand, preferred orientation of a helimagnetic propagation vector and its temperature dependence dramatically change upon varying the Mn concentration. The robustness of the metastable skyrmions in these materials is attributed to topological nature of the skyrmions as affected by structural and magnetic disorder. Magnetocrystalline anisotropy as well as magnetic disorder due to the frustrated Mn spins play crucial roles in giving rise to the observed change in helical states and corresponding skyrmion lattice form.Comment: 70 pages, 19 figure

Disordered skyrmion phase stabilized by magnetic frustration in a chiral magnet

Magnetic skyrmions are vortex-like topological spin textures often observed to form a triangular-lattice skyrmion crystal in structurally chiral magnets with Dzyaloshinskii-Moriya interaction. Recently $\beta$-Mn structure-type Co-Zn-Mn alloys were identified as a new class of chiral magnet to host such skyrmion crystal phases, while $\beta$-Mn itself is known as hosting an elemental geometrically frustrated spin liquid. Here we report the intermediate composition system Co$_7$Zn$_7$Mn$_6$ to be a unique host of two disconnected, thermal-equilibrium topological skyrmion phases; one is a conventional skyrmion crystal phase stabilized by thermal fluctuations and restricted to exist just below the magnetic transition temperature $T_\mathrm{c}$, and the other is a novel three-dimensionally disordered skyrmion phase that is stable well below $T_\mathrm{c}$. The stability of this new disordered skyrmion phase is due to a cooperative interplay between the chiral magnetism with Dzyaloshinskii-Moriya interaction and the frustrated magnetism inherent to $\beta$-Mn.Comment: 57 pages, 16 figure

Task-adaptive physical reservoir computing

Reservoir computing is a neuromorphic architecture that potentially offers viable solutions to the growing energy costs of machine learning. In software-based machine learning, neural network properties and performance can be readily reconfigured to suit different computational tasks by changing hyperparameters. This critical functionality is missing in ``physical" reservoir computing schemes that exploit nonlinear and history-dependent memory responses of physical systems for data processing. Here, we experimentally present a `task-adaptive' approach to physical reservoir computing, capable of reconfiguring key reservoir properties (nonlinearity, memory-capacity and complexity) to optimise computational performance across a broad range of tasks. As a model case of this, we use the temperature and magnetic-field controlled spin-wave response of Cu$_2$OSeO$_3$ that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to a host of different physical reservoir responses. We quantify phase-tunable reservoir performance, characterise their properties and discuss the correlation between these in physical reservoirs. This task-adaptive approach overcomes key prior limitations of physical reservoirs, opening opportunities to apply thermodynamically stable and metastable phase control across a wide variety of physical reservoir systems, as we show its transferable nature using above(near)-room-temperature demonstration with Co$_{8.5}$Zn$_{8.5}$Mn$_{3}$ (FeGe).Comment: Main manuscript: 14 pages, 5 figures. Supplementary materials: 13 pages, 10 figure

Task-adaptive physical reservoir computing

Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in 'physical' reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a 'task-adaptive' approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu2OSeO3 that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to different computational reservoir responses. The task-adaptive approach is applicable to a wide variety of physical systems, which we show in other chiral magnets via above (and near) room-temperature demonstrations in Co8.5Zn8.5Mn3 (and FeGe)

Task-adaptive physical reservoir computing

Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in ‘physical’ reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a ‘task-adaptive’ approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu2OSeO3 that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to different computational reservoir responses. The task-adaptive approach is applicable to a wide variety of physical systems, which we show in other chiral magnets via above (and near) room-temperature demonstrations in Co8.5Zn8.5Mn3 (and FeGe)