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The B cell receptor (BCR) signals together with a multi-component co-receptor complex to initiate B cell activation in response to antigen binding. Here, we take advantage of peroxidase-catalyzed proximity labeling combined with quantitative mass spectrometry to track co-receptor signaling dynamics in Raji cells from 10 s to 2 h after BCR stimulation. This approach enables tracking of 2,814 proximity-labeled proteins and 1,394 phosphosites and provides an unbiased and quantitative molecular map of proteins recruited to the vicinity of CD19, the signaling subunit of the co-receptor complex. We detail the recruitment kinetics of signaling effectors to CD19 and identify previously uncharacterized mediators of B cell activation. We show that the glutamate transporter SLC1A1 is responsible for mediating rapid metabolic reprogramming and for maintaining redox homeostasis during B cell activation. This study provides a comprehensive map of BCR signaling and a rich resource for uncovering the complex signaling networks that regulate activation.

Abstract Quantum computers are now on the brink of outperforming their classical counterparts. One way to demonstrate the advantage of quantum computation is through quantum random sampling performed on quantum computing devices. However, existing tools for verifying that a quantum device indeed performed the classically intractable sampling task are either impractical or not scalable to the quantum advantage regime. The verification problem thus remains an outstanding challenge. Here, we experimentally demonstrate efficiently verifiable quantum random sampling in the measurement-based model of quantum computation on a trapped-ion quantum processor. We create and sample from random cluster states, which are at the heart of measurement-based computing, up to a size of 4 × 4 qubits. By exploiting the structure of these states, we are able to recycle qubits during the computation to sample from entangled cluster states that are larger than the qubit register. We then efficiently estimate the fidelity to verify the prepared states—in single instances and on average—and compare our results to cross-entropy benchmarking. Finally, we study the effect of experimental noise on the certificates. Our results and techniques provide a feasible path toward a verified demonstration of a quantum advantage.

In head and neck surgery, continuous intraoperative tissue differentiation is of great importance to avoid injury to sensitive structures such as nerves and vessels. Hyperspectral imaging (HSI) with neural network analysis could support the surgeon in tissue differentiation. A 3D Convolutional Neural Network with hyperspectral data in the range of 400 − 1000 nm is used in this work. The acquisition system consisted of two multispectral snapshot cameras creating a stereo-HSI-system. For the analysis, 27 images with annotations of glandular tissue, nerve, muscle, skin and vein in 18 patients undergoing parotidectomy are included. Three patients are removed for evaluation following the leave-one-subjectout principle. The remaining images are used for training, with the data randomly divided into a training group and a validation group. In the validation, an overall accuracy of 98.7% is achieved, indicating robust training. In the evaluation on the excluded patients, an overall accuracy of 83.4% has been achieved showing good detection and identification abilities. The results clearly show that it is possible to achieve robust intraoperative tissue differentiation using hyperspectral imaging. Especially the high sensitivity in parotid or nerve tissue is of clinical importance. It is interesting to note that vein was often confused with muscle. This requires further analysis and shows that a very good and comprehensive data basis is essential. This is a major challenge, especially in surgery.

Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where $\textit{parameterized quantum circuits}$ (PQCs) are used as learning models. These PQC models have a rich structure which suggests that they might be amenable to efficient dequantization via $\textit{random Fourier features}$ (RFF). In this work, we establish necessary and sufficient conditions under which RFF does indeed provide an efficient dequantization of variational quantum machine learning for regression. We build on these insights to make concrete suggestions for PQC architecture design, and to identify structures which are necessary for a regression problem to admit a potential quantum advantage via PQC based optimization.

Recently, the dynamics of quantum systems that involve both unitary evolution and quantum measurements have attracted attention due to the exotic phenomenon of measurement-induced phase transitions. The latter refers to a sudden change in a property of a state of $n$ qubits, such as its entanglement entropy, depending on the rate at which individual qubits are measured. At the same time, quantum complexity emerged as a key quantity for the identification of complex behaviour in quantum many-body dynamics. In this work, we investigate the dynamics of the quantum state complexity in monitored random circuits, where $n$ qubits evolve according to a random unitary circuit and are individually measured with a fixed probability at each time step. We find that the evolution of the exact quantum state complexity undergoes a phase transition when changing the measurement rate. Below a critical measurement rate, the complexity grows at least linearly in time until saturating to a value $e^{\Omega(n)}$. Above, the complexity does not exceed $\operatorname{poly}(n)$. In our proof, we make use of percolation theory to find paths along which an exponentially long quantum computation can be run below the critical rate, and to identify events where the state complexity is reset to zero above the critical rate. We lower bound the exact state complexity in the former regime using recently developed techniques from algebraic geometry. Our results combine quantum complexity growth, phase transitions, and computation with measurements to help understand the behavior of monitored random circuits and to make progress towards determining the computational power of measurements in many-body systems.

Summary: The B cell receptor (BCR) signals together with a multi-component co-receptor complex to initiate B cell activation in response to antigen binding. Here, we take advantage of peroxidase-catalyzed proximity labeling combined with quantitative mass spectrometry to track co-receptor signaling dynamics in Raji cells from 10 s to 2 h after BCR stimulation. This approach enables tracking of 2,814 proximity-labeled proteins and 1,394 phosphosites and provides an unbiased and quantitative molecular map of proteins recruited to the vicinity of CD19, the signaling subunit of the co-receptor complex. We detail the recruitment kinetics of signaling effectors to CD19 and identify previously uncharacterized mediators of B cell activation. We show that the glutamate transporter SLC1A1 is responsible for mediating rapid metabolic reprogramming and for maintaining redox homeostasis during B cell activation. This study provides a comprehensive map of BCR signaling and a rich resource for uncovering the complex signaling networks that regulate activation.

In classical computing, error-correcting codes are well established and are ubiquitous both in theory and practical applications. For quantum computing, error-correction is essential as well, but harder to realize, coming along with substantial resource overheads and being concomitant with needs for substantial classical computing. Quantum error-correcting codes play a central role on the avenue towards fault-tolerant quantum computation beyond presumed near-term applications. Among those, color codes constitute a particularly important class of quantum codes that have gained interest in recent years due to favourable properties over other codes. As in classical computing, $decoding$ is the problem of inferring an operation to restore an uncorrupted state from a corrupted one and is central in the development of fault-tolerant quantum devices. In this work, we show how the decoding problem for color codes can be reduced to a slight variation of the well-known $\texttt{LightsOut}$ puzzle. We propose a novel decoder for quantum color codes using a formulation as a MaxSAT problem based on this analogy. Furthermore, we optimize the MaxSAT construction and show numerically that the decoding performance of the proposed decoder achieves state-of-the-art decoding performance on color codes. The implementation of the decoder as well as tools to automatically conduct numerical experiments are publicly available as part of the $\textit{Munich Quantum Toolkit}$ (MQT) on GitHub.

Tensor networks capture large classes of ground states of phases of quantum matter faithfully and efficiently. Their manipulation and contraction has remained a challenge over the years, however. For most of the history, ground state simulations of two-dimensional quantum lattice systems using (infinite) projected entangled pair states have relied on what is called a time-evolving block decimation. In recent years, multiple proposals for the variational optimization of the quantum state have been put forward, overcoming accuracy and convergence problems of previously known methods. The incorporation of automatic differentiation in tensor networks algorithms has ultimately enabled a new, flexible way for variational simulation of ground states and excited states. In this work we review the state-of-the-art of the variational iPEPS framework, providing a detailed introduction to automatic differentiation, a description of a general foundation into which various two-dimensional lattices can be conveniently incorporated, and demonstrative benchmarking results.

Author(s): Jason B. Whiting [sup.1], Bria Nicole Davies [sup.1], Brady C. Eisert [sup.1], Alyssa Banford Witting [sup.1], Shayne R. Anderson [sup.1] Author Affiliations: (1) grid.253294.b, 0000 0004 1936 9115, School [...], Purpose Intimate partner violence (IPV) impacts the lives of millions, damaging survivors and families. Many survivors are unsure how to get help, and have limited access to resources. In recent years, those affected by IPV have turned online for answers, support, and to share their experiences. This study examined the online dialogue between survivors of violence and those who respond to their posts. Method Data consisted of 451 responses to IPV survivors on the website Reddit in a domestic violence subgroup. Responses were analyzed using a combination of grounded theory coding techniques with content analysis tools to generate categories and subcategories. Results Three main categories emerged from the data, including support, sharing experiences, and sharing information. The subcategories included specific types of help, advice, understanding, and were overwhelmingly positive in their tone and content. Conclusions Findings suggest that online forums may help bridge the gap between survivors and resources. Professionals who work with IPV or create policies can use these findings to understand how to help survivors find support. This may include encouraging survivors to connect with informal supports such as family and friends, or access formal support such as hotlines, therapy, or legal counsel. The findings also suggest that more research is needed to understand what types of questions survivors of violence have, and what outcomes result from online support.

The modified Allen test (MAT) is a widely used tool to examine the perfusion capacities of the arteries (ulnar or radial) of the hand. In clinical practice, MAT is utilized to ensure that in the event of occlusion, remaining vessels guarantee sufficient hand perfusion. This work shows that perfusion assessment via imaging photoplethysmography (iPPG) can be used to assess MAT. We captured the hands of seven subjects with a standard RGB camera while performing a MAT. Out of these captured video recordings, the iPPG signal was extracted via plane-orthogonal-to-skin transformation. Based on these extracted signals, we derive the heart rate, signal-to-noise ratio, DC parameter, and correlation with a reference iPPG signal to determine if an Allen test was pathological or inconspicuous. For all seven subjects, the results of our MAT assessment were identical to the medical expert’s assessment. This paper presents a procedure and initial thresholds for objectively evaluating and quantifying MAT.We show how globally and locally calculated parameters can be used to assess the perfusion of the investigated hand. This analysis makes it possible to quantify the exact point in time when the perfusion is again sufficient. If a reperfusion problem occurs, we are able to localize the spatial regions of occurrence. The presented hardware setup is affordable and can be easily installed and used in every doctor’s office.

In image-guided surgery, imaging systems such as (stereo-)endoscopes allow intra-operative 3D reconstructions in form of point clouds. However, endoscopes provide only a narrow field of view, resulting in a confined point cloud. In this paper, we present an analysis and registration pipeline for confined point clouds acquired by stereo endoscopes into a fused representation. For a coarse registration, TEASER is applied, while a refinement is conducted utilizing point-to-plane ICP. The pipeline is tested on two datasets: acquired point clouds of a head phantom using a EinsteinVision® 3.0 endoscope and point clouds from the Stereo Correspondence And Reconstruction of Endoscopic Data challenge. The results for both datasets show that 3D reconstructions of anatomical structures by utilizing stereo-endoscopes and point cloud registration is a promising contactless and radiation-free method. However, non-rigid deformations are not yet incorporated and evaluation of the method compared to reference data is challenging.

Spectral imaging allows to analyze optical tissue properties that are invisible to the naked eye. Different spectral setups have been introduced in the last years to allow intraoperative image-guidance. All systems suffer from different disadvantages, such as lack of real-time capability, limited spectral coverage or low resolution. We present a novel approach using two multispectral snapshot cameras covering different spectral ranges as a stereo-system. The proposed method allows for continuous hyperspectral imaging of the surgical scene, while also enabling 3D scene reconstruction. The study demonstrates the feasibility of this approach and its potential applications in real-time monitoring and tissue identification of surgical interventions. The paper also discusses the technical challenges and future directions for improving the imaging system.

Surgical phase recognition is an important aspect of surgical workflow analysis, as it allows an automatic analysis of the performance and efficiency of surgical procedures. A big challenge for training a neural network for surgical phase recognition is the availability of training data and the large (visual) variability in procedures of different surgeons. Hence, a network must be able to generalize to new data. In this paper, we present an adaptation of a Temporal Convolutional Network for surgical phase recognition in order to ensure the generalization of the network to new scenes with different conditions on the example of cholecystectomy. We used publicly available datasets of 104 surgeries from four different centers for training. The results showed that the network was able to generalize to new scenes and we obtained recognition results with accuracy up to 82% on our own six captured surgeries, performed in a different hospital. This performance is similar for test data from the hospitals of the training data, suggesting that the network can well generalize to new surgical rooms and surgeons. The findings have important implications for the development of automated surgical decision support systems that can be applied in a variety of real-world surgical settings.

Hyperspectral imaging is an emerging technology that has gained significant attention in the medical field due to its ability to provide precise and accurate imaging of biological tissues. The current methods of hyperspectral imaging, such as filter-wheel, snapshot, line-scanning, and push-broom cameras have limitations such as low spatial and spectral resolution, slow acquisition time. New developments on the field of light field cameras show the potential to overcome these limitations. In this paper, we use a novel hyperspectral lightfield camera and try to combine the capability of hyperspectral and 3D analysis. For this purpose we calibrate our system and test it during two ENT-surgeries to show its potential for improving surgical outcomes. The micro-lenses of the camera map 66 spectral sub-images onto the sensor allowing to reconstruct the spectral behavior of the captured scene in the spectral range of 350-1000nm. In addition, we use the sensor data to apply a 3D camera calibration pipeline to allow 3D surface reconstruction. We captured 26 calibration images and achieved calibration results in accordance to stated company data. The best calibration showed a re-projection error of 0.55 px. Further, we tested the camera during a parotidectomy and a neck-dissection. The extracted reflectance spectra of the selected venal and arterial regions correspond perfectly to the spectrum of oxygenated and deoxygenated hemoglobin. For the first time, up to our knowledge, a hyperspectral lightfield camera has been used during a surgery. We were able to continuously capture images and analyze the reconstructed spectra of specific tissue types. Further, we are able to use the sensor data of the micro-lens projections to calibrate the multilens camera system for later intraoperative measurement tasks.