Session CCC09: V: General Physics VII

Topological defects induced by MSC-cancer interaction correlate with cancer cell apoptosis

Mesenchymal stem cells (MSC) can target and home into tumors by tracking the chemotactic factors secreted by cancer cells, thus making MSCs ideal candidates for active drug carriers in cancer therapy. Despite some success in preclinical models and clinical trials, little is known about how MSC homing occurs. To gain mechanistic insight into the interactions and response of MSC to cancer cell invasion, we employ a controlled co-culture assay using primary mouse MSC and a neuroblastoma cell line (Neuro2a) as our model cell types. The spindle-shaped MSC collectively develops orientational order and macroscopic patterns disrupted by MSC-Neuro2a interactions, forming topological defects that trigger apoptosis of Neuro2a cells and cell state transition of MSC, respectively. By modeling the MSC layer as an active nematic liquid crystal, we propose a computational framework combining deep learning and Bayesian Inference to explore MSC-cancer cell dynamics. We integrated cell segmentation with Variational Inference methods to predict intercellular aligning interactions similar to a classical XY-like model. To further probe the spatiotemporal correlation between apoptosis signaling and topological defects, we integrated imaging and single-cell transcriptomic profiling, we identified upregulated apoptotic signals in Neuro2a and MSC differentiation toward neural precursors induced by physical interactions. Gene network and ontology analyses revealed that the interactions triggered specific signaling pathways, particularly PTN, Collagen, and FN1 signaling pathways, which may indicate apoptosis in Neuro2a, enhanced adhesion between MSC/Neuro2a, and immunomodulation through MSC. Taken together, our study provides physical insights with molecular perspectives on the interactive motility of stem cells and cancer cells, offering a complementary approach to designing stem cell-based drug delivery strategies for cancer therapy.   

Effect of the Number of Hole Pockets on the Charge-Orbital Nematic Order in FeSe1-xSx System

 We investigate if there is a novel way to distinguish between two different types of nematic orders that exist in systems with multiple degrees of freedom, e.g Fe based superconductors: the charge-orbital Pomeranchuk order and composite spin-nematic order. Both of them break the same Z2 symmetry. We calculate how the onset temperature for each of these two orders  behaves near a quantum critical point for nematicity. Common wisdom says that, near a quantum critical point, they vary as power law. Surprisingly, we find that for Pomeranchuk order the result is more involved. Namely, in the presence of two hole pockets,  there is a perfect cancellation of the power law contributions, and the transition temperature Tp scales as log δ, where δ is the deviation from the quantum critical point.  Once the inner hole pocket sinks below the Fermi level due to e.g.,  spin-orbit coupling, power-law dependence Tp  ~ δ^1/2 is restored.   At the border line, when the inner hole pocket just vanishes, we find a linear dependence  Tp  ~ δ.  For composite spin order the Tp  ~ δ^1/2 dependence holds independent on the geometry of hole pockets.  We propose to use these results as a tool to distinguish between Pomeranchuk and composite spin-nematic order.

  

    

Depth resolved interfacial magnetic spin spiral at (111)-La0.7Sr0.3MnO3/LaFeO3 epitaxial thin films

To probe the emerging switchable magnetic spin spiral at the antiferromagnetic LaFeO₃ (LFO) layer due to interfacial effect with element specificity in (111)-La_0.7Sr_0.3MnO₃ (LSMO)/LaFeO3 epitaxial thin films we utilise a resonant soft X-ray reflectivity (RSXR) technique providing axial sensitivity. This emerging magnetism and magnetic anisotropy control at the ferromagnet (FM) /antiferromagnet (AFM) interface in perovskite transition metal oxides is interesting for spintronics applications. However, studies of magnetic interactions at heterostructure interfaces are often limited by direct measurements, especially of AF spin structures. Here, we present a novel method of probe the spin structure at the AF layer on oxide heterostructures.

(111) pc-LSMO/LFO fully single crystalline heterostructures were epitaxially grown on (101) DyScO3 substrates by pulsed laser deposition as our model system to probe the spin alignment at the antiferromagnetic LaFeO3 layer. Vibrating sample magnetometer measurements confirmed bulk ferromagnetism. RSXR data was collected at Advanced Light Source beamline 4.0.2 at Lawrence Berkeley Lab using linear polarized X- rays at 600-800 eV, with applied magnetic field parallel to the sample surface within the scattering plane using permanent magnet. Data analysis was carried out with RemagX using the magnetic matrix formalism.

RSXR data were recorded at the Fe L3 edge (708.32 eV) for the bilayer along LSMO easy and hard axis. From fitting the preliminary data on the bilayered samples, the model with a spin spiral reproduced the spin structure of the system with a weakly pronounced cross-over at Qz = 0.125 nm-1. Furthermore, a characteristic top/dip at Qz = 0.18 nm-1 in the asymmetry is reversed for the easy and hard axis, which can only be attained by axial switching indicating a directional transition in spin spirals. This suggests a translation of ferromagnetic momenta from the LSMO layer to LFO due to exchange coupling at the interface, which is controllable by rotating the applied field. We demonstratethat RSXR with linearly polarised light presents a unique means of resolving FM and AFM spins at buried interfaces in multistructures, providing valuable details about the spin texture that are necessary in order to exploit emerging interfacial phenomena towards applications.   

Creation and Characterization of Self-Assembled Quantum Dot Foams

Self-assembly of nanoparticles (NP) into micron scale hollow structures of various morphologies can be achieved using the Isotropic-Nematic (I-N) phase transition of Liquid Crystals as a template [1]. This year a new structure was created which we had previously not seen with this system. It is a three-dimensional foam with hexagonal cells. The structure of this foam looks very similar to human epithelial cells, the cells that form protective and functional layers on the environment interfacing surfaces of organs. Recreating and characterizing this foam to control the self-assembly of it can lead to the creation of foams with different properties. In this study we recreate the cellular foam using a variety of experimental parameters to control cell size and use techniques applied to studies of epithelial cells to analyze and quantify the characteristics of the new structure.

 

Robust room-temperature Valley Polarization in graphite-filtered WS2 monolayers

Transition metal dichalcogenide (TMD) monolayers (1L) in the 2H-phase are 2D semiconductors with two valleys in their band structure that can be selectively populated using circularly polarized light. The choice of the substrate is an essential factor for the optoelectronic properties and for achieving a high degree of valley polarization at room temperature (RT). In this work, we investigate the RT valley polarization of 1L-WS₂ on different substrates. A polarization degree of 27% is measured from neutral excitons in 1L-WS₂/graphite at RT, under resonant excitation. Using photochlorination doping, we modulate the polarization of the neutral exciton emission from 27% to 38% for this system. We show that the valley polarization strongly depends on the interplay between doping and the choice of the supporting layer of TMDs. Time-resolved photoluminescence measurements, corroborated by a rate equation model accounting for the bright exciton population in the presence of a dark exciton reservoir, support our findings. These results suggest a pathway towards engineering valley polarization and exciton lifetimes in TMDs, by controlling the carrier density and/or the dielectric environment at ambient conditions.   

Identifying optimal cycles in quantum thermal machines with reinforcement-learning

Driven quantum thermal machines, such as heat engines and refrigerators, are quantum devices that allow us to control the conversion between heat and work at the micro-nano scale through time-dependent controls. Their performance is mainly characterized by their power, efficiency, and power fluctuations. However, optimizing such quantities is challenging: in finite-time, the state can be driven far from equilibrium, and the space of all possible time-dependent cycles is exponentially large. While general results have been found in the slow and fast driving regime – general finite-time optimization schemes are currently lacking.

We introduce a general framework based on Reinforcement Learning to discover optimal cycles for quantum thermal machines [1]. Our method makes no assumptions on the shape or speed of the cycle. We employ our method to maximize tradeoffs between high power, high efficiency, and low power fluctuations in simple models of quantum heat engines and refrigerators [2-3], finding cycles that outperform previous proposals made in literature, such as Otto cycles, and showing that such cycles mitigate the detrimental effect of generation of coherence, also known as "quantum friction". The method can be generalized to only observe the heat currents, thus potentially applicable to experimental devices [3].

 

[1] P.A. Erdman and F. Noé, NPJ Quantum Inf. 8, 1 (2022).

[2] P.A. Erdman et al., arXiv:2207.13104 (2022).

[3] P.A. Erdman and F. Noé, arXiv:2204.04785 (2022).   

Lens-shaped nematic liquid crystal droplets with negative dielectric anisotropy in electric and magnetic fields

Recently plano-convex spherical lens-shaped liquid crystal sessile droplets with positive dielectric anisotropy were studied in magnetic and/or AC electric fields. Here we present experimental observations in magnetic, AC electric, combined fields and theoretical considerations of plano-convex lens-shaped nematic liquid crystal droplets with negative dielectric anisotropy. In purely magnetic field our results are the same as for positive materials, i.e., an inversion wall forms normal to the field that moves toward the periphery. In contrast to previous observations on nematic liquid crystals with positive dielectric anisotropy, in electric fields applied normal to the base plane, we do not find any wall formation, but only a radial director structure with a central defect line along the field. Above a threshold electric field, the radial symmetry becomes twisted due to elastic constant anisotropy. Applying a magnetic field perpendicular to the vertical electric field, a twisted inversion wall forms together with a vertical central defect line. When the electric field is applied parallel to the base plane of the droplet, a homeotropic central region forms along the electric field. When this field is applied together with a magnetic field applied in the same direction, the homeotropic central region becomes perpendicular to the applied field.

    

Estimating outcome probabilities of linear optical circuits and its applications

We propose polynomial-time estimation schemes for outcome probabilities of linear optical circuits by using $s$-parameterized quasiprobability distributions. We exploit the Gaussian factors' interchangeability between the quasiprobability distributions of the input state and the measurement operator, which is an intrinsic property of a linear optical circuit. By performing Monte-Carlo sampling, the precision of the additive error estimation continuously changes as the classicality of the Gaussian input state. Furthermore, we find conditions for efficient multiplicative error estimations of the outcome probabilities by using the sampling from log-concave functions. Our results provide quantum-inspired efficient estimating algorithms for various matrix functions, e.g., permanent and hafnian, within additive or multiplicative errors for a certain class of matrices. For the hafnian, we give an efficient algorithm of $|Haf(R)|^2$ for an $M imes M$ complex symmetric matrix $R$ within an additive error $epsilon(a||R||)^M$, with $a simeq 1.502$.

    Our method can also efficiently approximate any outcome probability with any marginal within an additive error of a Gaussian boson sampling circuit with photon-number detectors under a moderate transmission rate as $eta simeq 0.764$. For Gaussian boson sampling circuits with threshold detectors, our scheme is applicable even for the noiseless case, so we can efficiently simulate those circuits for sparse output distribution.   

Development of Vickers hardness prediction models via microstructural quantification and machine learning approaches

Hardness is an important property in superalloys for high-temperature applications. In this talk, I will discuss about data-driven approaches to predict the Vickers hardness in Co- and Ni-based superalloys using machine learning (ML). Conventional and advanced image processing tools are implemented to quantify the microstructural variations with composition and processing conditions. Two different and noble image processing methods are implemented to quantify the scanning electron microscopy (SEM) images of Co- and Ni-based superalloys into descriptors for ML models. The conventional approach extracts geometrical features such as volume fraction, area, and perimeter of the phases from the microstructures. Whereas, the advanced approach uses statistics based 2-point correlations and principal component analysis (PCA) to quantify the microstructural variations. These microstructural descriptors combined with alloy compositions and processing conditions are used to develop Gaussian process regression (GPR) models to predict Vickers hardness. Both the methods, reveal a very good prediction of Vickers hardness with a higher R² greater than 95% and lower rmse less than 0.16 HV. Further analysis of the model presents numerous in-sights into structure-property relationships, which will be also discussed. The ML models developed can be generalized for any mechanical property of interest and can be utilized for accelerated development of new generation of high temperature superalloys.