Quantum generative adversarial learning in photonics.

Quantum generative adversarial networks (QGANs), an intersection of quantum computing and machine learning, have attracted widespread attention due to their potential advantages over classical analogs. However, in the current era of noisy intermediate-scale quantum (NISQ) computing, it is essential to investigate whether QGANs can perform learning tasks on near-term quantum devices usually affected by noise and even defects. In this Letter, using a programmable silicon quantum photonic chip, we experimentally demonstrate the QGAN model in photonics for the first time to our knowledge and investigate the effects of noise and defects on its performance. Our results show that QGANs can generate high-quality quantum data with a fidelity higher than 90%, even under conditions where up to half of the generator's phase shifters are damaged, or all of the generator and discriminator's phase shifters are subjected to phase noise up to 0.04π. Our work sheds light on the feasibility of implementing QGANs on the NISQ-era quantum hardware.

Cross-Parametric Generative Adversarial Network-Based Magnetic Resonance Image Feature Synthesis for Breast Lesion Classification.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contains information on tumor morphology and physiology for breast cancer diagnosis and treatment. However, this technology requires contrast agent injection with more acquisition time than other parametric images, such as T2-weighted imaging (T2WI). Current image synthesis methods attempt to map the image data from one domain to another, whereas it is challenging or even infeasible to map the images with one sequence into images with multiple sequences. Here, we propose a new approach of cross-parametric generative adversarial network (GAN)-based feature synthesis (CPGANFS) to generate discriminative DCE-MRI features from T2WI with applications in breast cancer diagnosis. The proposed approach decodes the T2W images into latent cross-parameter features to reconstruct the DCE-MRI and T2WI features by balancing the information shared between the two. A Wasserstein GAN with a gradient penalty is employed to differentiate the T2WI-generated features from ground-truth features extracted from DCE-MRI. The synthesized DCE-MRI feature-based model achieved significantly (p = 0.036) higher prediction performance (AUC = 0.866) in breast cancer diagnosis than that based on T2WI (AUC = 0.815). Visualization of the model shows that our CPGANFS method enhances the predictive power by levitating attention to the lesion and the surrounding parenchyma areas, which is driven by the interparametric information learned from T2WI and DCE-MRI. Our proposed CPGANFS provides a framework for cross-parametric MR image feature generation from a single-sequence image guided by an information-rich, time-series image with kinetic information. Extensive experimental results demonstrate its effectiveness with high interpretability and improved performance in breast cancer diagnosis.

A Hybrid Strategy for Profile Measurement of Micro Gear Teeth.

A hybrid strategy is proposed to meet the challenge of obtaining the profile of micro gear teeth with a small modulus. Firstly, the contact probe segmentally obtained the falling flank profiles with an auxiliary lifting mechanism to avoid interference when it climbs on the rising slope. Then, the noncontact chromatic confocal displacement sensor efficiently acquired the gear peak positions to carry out the two-point error separation with the gear peak positions from the probe measurement. Finally, actual experiments were carried out to obtain the profile of a harmonic drive flexspline. Compared with the commercial ultraprecise profiler, the proposed method provides measurement results with a deviation of less than 20 µm. In conclusion, the hybrid strategy is feasible and accurate for drawing the micro gear teeth profile without any collision between the measuring probes and the measured workpiece.

Experimental quantum natural gradient optimization in photonics.

Variational quantum algorithms (VQAs) combining the advantages of parameterized quantum circuits and classical optimizers, promise practical quantum applications in the noisy intermediate-scale quantum era. The performance of VQAs heavily depends on the optimization method. Compared with gradient-free and ordinary gradient descent methods, the quantum natural gradient (QNG), which mirrors the geometric structure of the parameter space, can achieve faster convergence and avoid local minima more easily, thereby reducing the cost of circuit executions. We utilized a fully programmable photonic chip to experimentally estimate the QNG in photonics for the first time, to the best of our knowledge. We obtained the dissociation curve of the He-H+ cation and achieved chemical accuracy, verifying the outperformance of QNG optimization on a photonic device. Our work opens up a vista of utilizing QNG in photonics to implement practical near-term quantum applications.

Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram.

Peritoneal recurrence is the most frequent and lethal recurrence pattern in gastric cancer (GC) with serosal invasion after radical surgery. However, current evaluation methods are not adequate for predicting peritoneal recurrence in GC with serosal invasion. Emerging evidence shows that pathomics analyses could be advantageous for risk stratification and outcome prediction. Herein, we propose a pathomics signature composed of multiple pathomics features extracted from digital hematoxylin and eosin-stained images. We found that the pathomics signature was significantly associated with peritoneal recurrence. A competing-risk pathomics nomogram including carbohydrate antigen 19-9 level, depth of invasion, lymph node metastasis, and pathomics signature was developed for predicting peritoneal recurrence. The pathomics nomogram had favorable discrimination and calibration. Thus, the pathomics signature is a predictive indicator of peritoneal recurrence, and the pathomics nomogram may provide a helpful reference for predicting an individual's risk in peritoneal recurrence of GC with serosal invasion.

Floating Ag-NPs@Cu-NW bundles fabricated on copper mesh for highly sensitive SERS detection of uric acid in pretreatment-free urine.

The rapid and sensitive detection of ultra-trace marker molecules from biological samples is of great significance for the wide application of surface-enhanced Raman spectroscopy (SERS) methods in clinical diagnosis and disease monitoring. However, the cumbersome biological sample processing procedures and the poor enrichment of target analytes in hot spots hinder the practical applications of SERS methods. In this paper, we synthesized a novel floating SERS substrate by a simple one-step oxidation process, annealing and in situ chemical etching to form Ag-NPs@Cu-NW bundles on copper mesh (CM). In particular, under spontaneous bottom-up capillary action, the pressure difference at different nanogaps drives uric acid molecules to actively enter hot spots, so that the Ag-NPs@Cu-NW bundle nanostructure with the advantages of a light weight CM is capable of preventing the common coffee-ring effect and enhancing the spatial enrichment of analytes. Therefore, this SERS substrate realizes highly sensitive detection of uric acid at a level of 50 nM in pretreatment-free urine. Currently, this portable, flexible, simple, fast and cost-effective SERS substrate has great potential for early screening and clinical diagnosis of diseases in different biofluids.

Variational Entanglement-Assisted Quantum Process Tomography with Arbitrary Ancillary Qubits.

Quantum process tomography is a pivotal technique in fully characterizing quantum dynamics. However, exponential scaling of the Hilbert space with the increasing system size extremely restrains its experimental implementations. Here, we put forward a more efficient, flexible, and error-mitigated method: variational entanglement-assisted quantum process tomography with arbitrary ancillary qubits. Numerically, we simulate up to eight-qubit quantum processes and show that this tomography with m ancillary qubits (0≤m≤n) alleviates the exponential costs on state preparation (from 4^{n} to 2^{n-m}), measurement settings (at least a 1 order of magnitude reduction), and data postprocessing (efficient and robust parameter optimization). Experimentally, we first demonstrate our method on a silicon photonic chip by rebuilding randomly generated one-qubit and two-qubit unitary quantum processes. Further using the error mitigation method, two-qubit quantum processes can be rebuilt with average gate fidelity enhanced from 92.38% to 95.56%. Our Letter provides an efficient and practical approach to process tomography on the noisy quantum computing platforms.

A Framework for Deep Multitask Learning With Multiparametric Magnetic Resonance Imaging for the Joint Prediction of Histological Characteristics in Breast Cancer.

The clinical management and decision-making process related to breast cancer are based on multiple histological indicators. This study aims to jointly predict the Ki-67 expression level, luminal A subtype and histological grade molecular biomarkers using a new deep multitask learning method with multiparametric magnetic resonance imaging. A multitask learning network structure was proposed by introducing a common-task layer and task-specific layers to learn the high-level features that are common to all tasks and related to a specific task, respectively. A network pretrained with knowledge from the ImageNet dataset was used and fine-tuned with MRI data. Information from multiparametric MR images was fused using the strategy at the feature and decision levels. The area under the receiver operating characteristic curve (AUC) was used to measure model performance. For single-task learning using a single image series, the deep learning model generated AUCs of 0.752, 0.722, and 0.596 for the Ki-67, luminal A and histological grade prediction tasks, respectively. The performance was improved by freezing the first 5 convolutional layers, using 20% shared layers and fusing multiparametric series at the feature level, which achieved AUCs of 0.819, 0.799 and 0.747 for Ki-67, luminal A and histological grade prediction tasks, respectively. Our study showed advantages in jointly predicting correlated clinical biomarkers using a deep multitask learning framework with an appropriate number of fine-tuned convolutional layers by taking full advantage of common and complementary imaging features. Multiparametric image series-based multitask learning could be a promising approach for the multiple clinical indicator-based management of breast cancer.

Construction of Ag nanowire@Au nanoparticle nano nests with densely stacked small gaps for actively trapping molecules to realize diversity SERS detection.

Highly sensitive surface-enhanced Raman spectroscopy (SERS) sensing not only depends on an active substrate with high density of hot spots, but also depends more on whether the molecules can effectively enter the hot spot region. In this paper, a new SERS detection method based on the nano nest model is developed to autonomously capture molecules into hot spots. The nano nest is composed of silver nanowires modified with gold nanoparticles (Ag NW@Au NPs), which not only form high density hot spots between particles or particles-wires, but also have a coupled electromagnetic field enhancement effect. The SERS detection method based nano nest actively traps molecules through the capillary stage, and makes the molecules move toward densely stacked small gaps (hot spots) by capillary action. The above method has been used to detect different kinds of molecules, such as pesticide residues, adenosine triphosphate in culture medium, and antibiotic residues in aquatic products. In addition, an in situ SERS monitoring of allergic reactions was also performed using nano nests with the feature of actively trapping molecules into the hot spots. This nano nest will be able to perform a direct monitoring of biochemical reactions, and more importantly, it can provide a new scheme for SERS detection.

Controlling the Shrinkage of 3D Hot Spot Droplets as a Microreactor for Quantitative SERS Detection of Anticancer Drugs in Serum Using a Handheld Raman Spectrometer.

Quantitative measurement is one of the ultimate targets for surface-enhanced Raman spectroscopy (SERS), but it suffers from difficulties in controlling the uniformity of hot spots and placing the target molecules in the hot spot space. Here, a convenient approach of three-phase equilibrium controlling the shrinkage of three-dimensional (3D) hot spot droplets has been demonstrated for the quantitative detection of the anticancer drug 5-fluorouracil (5-FU) in serum using a handheld Raman spectrometer. Droplet shrinkage, triggered by the shaking of aqueous nanoparticle (NP) colloids with immiscible oil chloroform (CHCl3) after the addition of negative ions and acetone, not only brings the nanoparticles in close proximity but can also act as a microreactor to enhance the spatial enrichment capability of the analyte in plasmonic sites and thereby realize simultaneously controlling 3D hot spots and placing target molecules in hot spots. Moreover, the shrinking process of Ag colloid droplets has been investigated using a high-speed camera, an in situ transmission electron microscope (in situ TEM), and a dark-field microscope (DFM), demonstrating the high stability and uniformity of nanoparticles in droplets. The shrunk Ag NP droplets exhibit excellent SERS sensitivity and reproducibility for the quantitative analysis of 5-FU over a large range of 50-1000 ppb. Hence, it is promising for quantitative analysis of complex systems and long-term monitoring of bioreactions.

Efficacy of Renshen (Radix Ginseng) plus Fuzi (Radix Aconiti Lateralis Preparata) on myocardial infarction by enhancing autophagy in rat.

OBJECTIVE: To elucidate the protective effects of Renshen (Radix Ginseng) and Fuzi (Radix Aconiti Lateralis Preparata) on myocardial infarction (MI) through regulating myocardial autophagy. METHODS: Thirty-one male Sprague-Dawley rats were randomized into five groups (n = 6 or 7 for each). After treatment for 3 weeks, electrocardiogram ( ECG ) and cardiac function were recorded. Hematoxylin and eosin (HE) staining was used to detect pathological changes in the heart. Enzyme-linked immunosorbent assay (ELISA) was used to detect serum B-type brain natriuretic peptide (BNP), cardiac troponin T (cTnT), tumor necrosis factor-α (TNF-α), and serum inflammatory cytokines. Metabolomic analysis was used to identify differential biomarkers of MI in rats. Immunohistochemistry and western blotting were used to detect BNP, cTnT, TNF-α, LC3B, Beclin-1, p62, and adenosine monophosphate activated protein kinase (AMPK) expression in cardiac tissue. RESULTS: Fuzi (Radix Aconiti Lateralis Preparata) alone or Renshen (Radix Ginseng) plus Fuzi (Radix Aconiti Lateralis Preparata) markedly ameliorated cardiac dysfunction and abnormal ECGs, demonstrated by decreases in the heart weight/body weight ratio, BNP, and cTnT. Pro-inflammation cytokine interleukin (IL)-1α significantly decreased and anti-inflammatory cytokine IL-10 significantly increased in Renshen (Radix Ginseng) single or Renshen (Radix Ginseng) plus Fuzi (Radix Aconiti Lateralis Preparata) groups compared with the control group. HE results suggested that co-treatment produced a greater reduction in cardiomyocyte cross-sectional area than Renshen (Radix Ginseng) or Fuzi (Radix Aconiti Lateralis Preparata) alone. Renshen (Radix Ginseng) plus Fuzi (Radix Aconiti Lateralis Preparata) reversed these changes to different degrees in MI rats. Furthermore, Renshen (Radix Ginseng) plus Fuzi (Radix Aconiti Lateralis Preparata) down-regulated LC3B, Beclin-1, and AMPK expression in cardiac tissue and upregulated p62 expression. CONCLUSIONS: Renshen (Radix Ginseng) plus Fuzi (Radix Aconiti Lateralis Preparata) may have a greater effect on heart injury induced by MI in rats than Fuzi (Radix Aconiti Lateralis Preparata) treatment alone, and the underlying mechanism may be associated with the regulation of myocardial autophagy and anti-inflammation effects. These results provide fresh insight into the mechanism of co-treatment with Renshen (Radix Ginseng) and Fuzi (Radix Aconiti Lateralis Preparata) for MI.

Planar diffractive grating for magneto-optical trap application: fabrication and testing.

The design, fabrication, and demonstration of a planar two-dimensional-crossed reflective diffractive grating are proposed to construct a novel optical configuration, to the best of our knowledge, potentially applied for atom cooling and trapping in a magneto-optical trap. Based on the proposed single-beam single-exposure scheme by means of an orthogonal two-axis Lloyd's mirrors interferometer, we rapidly patterned a ∼1µm period grating capable of providing a uniform intensity of the diffracted beams. The key structural parameters of the grating including the array square hole's width and depth were determined, aiming at providing a high energy of the diffracted beams to perform the atom cooling and trapping. To guarantee the diffracted beams to be overlapped possibly, we adopted a polarized beam splitter to guide the optical path of the incident and zero-order diffracted beams. Therefore, one zero-order diffracted beam with a retroreflected mode and four first-order diffracted beams with appropriate optical path constructed a three-dimensional optical configuration of three orthogonal pairs of counterpropagating beams. Finally, three pairs of the counterpropagating cooling laser beams with 9 mm diameter and >10% diffraction efficiencies were achieved, and the circular polarization chirality, purity, and compensation of the desired diffracted beams are further evaluated, which preliminarily validated a high applicability for the magneto-optical trap system.

Construction of Optimal SERS Hotspots Based on Capturing the Spike Receptor-Binding Domain (RBD) of SARS-CoV-2 for Highly Sensitive and Specific Detection by a Fish Model.

It is highly challenging to construct the best SERS hotspots for the detection of proteins by surface-enhanced Raman spectroscopy (SERS). Using its own characteristics to construct hotspots can achieve the effect of sensitivity and specificity. In this study, we built a fishing mode device to detect the receptor-binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at low concentrations in different detection environments and obtained a sensitive SERS signal response. Based on the spatial resolution of proteins and their protein-specific recognition functions, SERS hotspots were constructed using aptamers and small molecules that can specifically bind to RBD and cooperate with Au nanoparticles (NPs) to detect RBD in the environment using SERS signals of beacon molecules. Therefore, two kinds of AuNPs modified with aptamers and small molecules were used in the fishing mode device, which can specifically recognize and bind RBD to form a stable hotspot to achieve high sensitivity and specificity for RBD detection. The fishing mode device can detect the presence of RBD at concentrations as low as 0.625 ng/mL and can produce a good SERS signal response within 15 min. Meanwhile, we can detect an RBD of 0.625 ng/mL in the mixed solution with various proteins, and the concentration of RBD in the complex environment of urine and blood can be as low as 1.25 ng/mL. This provides a research basis for SERS in practical applications for protein detection work.

Investigation of Neurodevelopmental Deficits of 22 q11.2 Deletion Syndrome with a Patient-iPSC-Derived Blood-Brain Barrier Model.

The blood-brain barrier (BBB) is important in the normal functioning of the central nervous system. An altered BBB has been described in various neuropsychiatric disorders, including schizophrenia. However, the cellular and molecular mechanisms of such alterations remain unclear. Here, we investigate if BBB integrity is compromised in 22q11.2 deletion syndrome (also called DiGeorge syndrome), which is one of the validated genetic risk factors for schizophrenia. We utilized a set of human brain microvascular endothelial cells (HBMECs) derived from the induced pluripotent stem cell (iPSC) lines of patients with 22q11.2-deletion-syndrome-associated schizophrenia. We found that the solute permeability of the BBB formed from patient HBMECs increases by ~1.3-1.4-fold, while the trans-endothelial electrical resistance decreases to ~62% of the control values. Correspondingly, tight junction proteins and the endothelial glycocalyx that determine the integrity of the BBB are significantly disrupted. A transcriptome study also suggests that the transcriptional network related to the cell-cell junctions in the compromised BBB is substantially altered. An enrichment analysis further suggests that the genes within the altered gene expression network also contribute to neurodevelopmental disorders. Our findings suggest that neurovascular coupling can be targeted in developing novel therapeutical strategies for the treatment of 22q11.2 deletion syndrome.

General Surface-Enhanced Raman Spectroscopy Method for Actively Capturing Target Molecules in Small Gaps.

Over the past decade, many efforts have been devoted to designing and fabricating substrates for surface-enhanced Raman spectroscopy (SERS) with abundant hot spots to improve the sensitivity of detection. However, there have been many difficulties involved in causing molecules to enter hot spots actively or effectively. Here, we report a general SERS method for actively capturing target molecules in small gaps (hot spots) by constructing a nanocapillary pumping model. The ubiquity of hot spots and the inevitability of molecules entering them lights up all the hot spots and makes them effective. This general method can realize the highly sensitive detection of different types of molecules, including organic pollutants, drugs, poisons, toxins, pesticide residues, dyes, antibiotics, amino acids, antitumor drugs, explosives, and plasticizers. Additionally, in the dynamic detection process, an efficient and stable signal can be maintained for 1-2 min, which increases the practicality and operability of this method. Moreover, a dynamic detection process like this corresponds to the processes of material transformation in some organisms, so the method can be used to monitor transformation processes such as the death of a single cell caused by photothermal stimulation. Our method provides a novel pathway for generating hot spots that actively attract target molecules, and it can achieve general ultratrace detection of diverse substances and be applied to the study of cell behaviors in biological systems.

A highly tunable quadruple quantum dot in a narrow bandgap semiconductor InAs nanowire.

Quantum dots (QDs) made from semiconductors are among the most promising platforms for the development of quantum computing and simulation chips, and they have the advantages of high density integration and compatibility with the standard semiconductor chip fabrication technology compared to other platforms. However, the development of a highly tunable semiconductor multiple QD system still remains a major challenge. Here, we demonstrate the realization of a highly tunable linear quadruple QD (QQD) in a narrow bandgap semiconductor InAs nanowire via a fine finger gate technique. The QQD is studied by electron transport measurements in the linear response regime. Characteristic two-dimensional charge stability diagrams containing four groups of resonant current lines of different slopes are obtained for the QQD. It is shown that these current lines arise from and can be individually assigned to resonant electron transport through the energy levels of different QDs. Benefitting from the excellent gate tunability, we also demonstrate the tuning of the QQD to regimes where the energy levels of two QDs, three QDs and all four QDs are energetically in resonance, respectively, with the Fermi level of the source and drain contacts. A capacitance network model is developed for the linear QQD and the simulated charge stability diagrams based on this model show good agreement with the experiments. Our work provides solid experimental evidence that narrow bandgap semiconductor nanowire multiple QDs could be used as a versatile platform to achieve integrated qubits for quantum computing and to perform quantum simulations of complex many-body systems.

Measurements of anisotropic g-factors for electrons in InSb nanowire quantum dots.

We have measured the Zeeman splitting of quantum levels in few-electron quantum dots (QDs) formed in narrow bandgap InSb nanowires via the Schottky barriers at the contacts under application of different spatially orientated magnetic fields. The effective g-factor tensor extracted from the measurements is strongly anisotropic and level-dependent, which can be attributed to the presence of strong spin-orbit interaction (SOI) and asymmetric quantum confinement potentials in the QDs. We have demonstrated a successful determination of the principal values and the principal axis orientations of the g-factor tensors in an InSb nanowire QD by the measurements under rotations of a magnetic field in the three orthogonal planes. We also examine the magnetic field evolution of the excitation spectra in an InSb nanowire QD and extract a SOI strength of [Formula: see text] ∼ 180 µeV from an avoided level crossing between a ground state and its neighboring first excited state in the QD.

Pseudo-Yang-Lee Edge Singularity Critical Behavior in a Non-Hermitian Ising Model.

The quantum phase transition of a one-dimensional transverse field Ising model in an imaginary longitudinal field is studied. A new order parameter M is introduced to describe the critical behaviors in the Yang-Lee edge singularity (YLES). The M does not diverge at the YLES point, a behavior different from other usual parameters. We term this unusual critical behavior around YLES as the pseudo-YLES. To investigate the static and driven dynamics of M, the (1+1) dimensional ferromagnetic-paramagnetic phase transition ((1+1) D FPPT) critical region, (0+1) D YLES critical region and the (1+1) D YLES critical region of the model are selected. Our numerical study shows that the (1+1) D FPPT scaling theory, the (0+1) D YLES scaling theory and (1+1) D YLES scaling theory are applicable to describe the critical behaviors of M, demonstrating that M could be a good indicator to detect the phase transition around YLES. Since M has finite value around YLES, it is expected that M could be quantitatively measured in experiments.

Modulation of solute diffusivity in brain tissue as a novel mechanism of transcranial direct current stimulation (tDCS).

The breadth of brain disorders and functions reported responsive to transcranial direct current stimulation (tDCS) suggests a generalizable mechanism of action. Prior efforts characterized its cellular targets including neuron, glia and endothelial cells. We propose tDCS also modulates the substance transport in brain tissue. High resolution multiphoton microscopy imaged the spread across rat brain tissue of fluorescently-labeled solutes injected through the carotid artery after tDCS. The effective solute diffusion coefficient of brain tissue (Deff) was determined from the spatio-temporal solute concentration profiles using an unsteady diffusion transport model. 5-10 min post 20 min-1 mA tDCS, Deff increased by ~ 10% for a small solute, sodium fluorescein, and ~ 120% for larger solutes, BSA and Dex-70k. All increases in Deff returned to the control level 25-30 min post tDCS. A mathematical model for Deff in the extracelluar space (ECS) further predicts that this dose of tDCS increases Deff by transiently enhancing the brain ECS gap spacing by ~ 1.5-fold and accordingly reducing the extracellular matrix density. The cascades leading ECS modulation and its impact on excitability, synaptic function, plasticity, and brain clearance require further study. Modulation of solute diffusivity and ECS could explain diverse outcomes of tDCS and suggest novel therapeutic strategies.