Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or contexts. In parallel, quantum computing has demonstrated to be able to output complex wave functions with a few number of gate operations, which could generate distributions that are hard for a classical computer to produce. Here we propose a hybrid quantum-classical convolutional neural network (QCCNN), inspired by convolutional neural networks (CNNs) but adapted to quantum computing to enhance the feature mapping process. QCCNN is friendly to currently noisy intermediate-scale quantum computers, in terms of both number of qubits as well as circuit's depths, while retaining important features of classical CNN, such as nonlinearity and scalability. We also present a framework to automatically compute the gradients of hybrid quantum-classical loss functions which could be directly applied to other hybrid quantum-classical algorithms. We demonstrate the potential of this architecture by applying it to a Tetris dataset, and show that QCCNN can accomplish classification tasks with learning accuracy surpassing that of classical CNN with the same structure.

Tumor-selective contrast agents have the potential to aid in the diagnosis and treatment of cancer using noninvasive imaging modalities such as magnetic resonance imaging (MRI). Such contrast agents can consist of magnetic nanoparticles incorporating functionalities that respond to cues specific to tumor environments. Genetically engineering magnetotactic bacteria to display peptides has been investigated as a means to produce contrast agents that combine the robust image contrast effects of magnetosomes with the transgenic-targeting peptides displayed on their surface. This work reports the first use of magnetic nanoparticles that display genetically encoded pH low insertion peptide (pHLIP), a long peptide intended to enhance MRI contrast by targeting the extracellular acidity associated with the tumors. To demonstrate the modularity of this versatile platform to incorporate diverse targeting ligands by genetic engineering, we also incorporated the cyclic αv integrin-binding peptide iRGD into separate magnetosomes. Specifically, we investigate their potential for enhanced binding and tumor imaging both in vitro and in vivo. Our experiments indicate that these tailored magnetosomes retain their magnetic properties, making them well suited as T2 contrast agents, while exhibiting an increased binding compared to the binding in wild-type magnetosomes.

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Objectives. To examine what changes are caused in the activity of the vastus medialis oblique (VMO) and vastus lateralis (VL) at the time of sling-based exercises in patients with patellofemoral pain syndrome (PFPS) and compare the muscular activations in patients with PFPS among the sling-based exercises. Methods. This was a cross-over study. Sling-based open and closed kinetic knee extension and hip adduction exercises were designed for PFPS, and electromyography was applied to record maximal voluntary contraction during the exercises. The VMO and VL activations and VMO : VL ratios for the three exercises were analyzed and compared. Results. Thirty male (age = 21.19 ± 0.68 y) and 30 female (age = 21.12 ± 0.74 y) patients with PFPS were recruited. VMO activations during the sling-based open and closed kinetic knee extension exercises were significantly higher (P = 0.04 and P = 0.001) than those during hip adduction exercises and VMO : VL ratio for the sling-based closed kinetic knee extension and hip adduction exercises approximated to 1. Conclusions. The sling-based closed kinetic knee extension exercise produced the highest VMO activation. It also had an appropriate VMO : VL ratio similar to sling-based hip adduction exercise and had beneficial effects on PFPS.

A varying oxygen environment is known to affect cellular function in disease as well as activity of various therapeutics. For transient structures, whether they are unconstrained therapeutic transplants, migrating cells during tumor metastasis, or cell populations induced by an immunological response, the role of oxygen in their fate and function is known to be pivotal albeit not well understood in vivo. To address such a challenge in the case of generation of a bioartificial pancreas, we have combined fluorine magnetic resonance imaging and unsupervised machine learning to monitor over time the spatial arrangement and the oxygen content of implants encapsulating pancreatic islets that are unconstrained in the intraperitoneal (IP) space of healthy and diabetic mice. Statistically significant trends in the postimplantation temporal dependence of oxygen content between aggregates of 0.5-mm or 1.5-mm alginate microcapsules were identified in vivo by looking at their dispersity as well as arrangement in clusters of different size and estimating oxygen content on a pixelby-pixel basis from thousands of 2D images. Ultimately, we found that this dependence is stronger for decreased implant capsule size consistent with their tendency to also induce a larger immunological response. Beyond the bioartificial pancreas, this work provides a framework for the simultaneous spatiotemporal tracking and oxygen sensing of other cell populations and biomaterials that change over time to better understand and improve therapeutic design across diverse applications such as cellular transplant therapy, treatments preventing metastatic formation, and modulators for improving immunologic response, for all of which oxygen is a major mechanistic component. magnetic resonance imaging | oxygen sensing | cellular therapy | diabetes | implants

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The quality of street space has attracted attention. It is important to understand the needs of different population groups for street space quality, especially the rapidly growing elderly group. Improving the quality of street space is conducive to promoting the physical leisure activities of the elderly to benefit to their health. Therefore, it is important to evaluate street space quality for the elderly. The existing studies, on the one hand, are limited by the sample size of traditional survey data, which is hard to apply on a large scale; on the other hand, there is a lack of consideration for factors that reveal the quality of street space from the perspective of the elderly. This paper takes Guangzhou as an example to evaluate the quality of street space. First, the sample street images were scored by the elderly on a small scale; then the regression analysis was used to extract the street elements that the elderly care about. Last, the street elements were put into the random forest model to assess street space quality io a large scale. It was found that the green view rate and sidewalks are positively correlated with satisfaction, and the positive effect increases in that order. Roads, buildings, sky, vehicles, walls, ceilings, glass windows, runways, railings, and rocks are negatively correlated with satisfaction, and the negative effect increases in that order. The mean satisfaction score of the quality of street space for the elderly’s recreational physical activities in three central districts of Guangzhou (Yuexiu, Liwan, and Haizhu) is 2.6, among which Xingang street gets the highest quality score (2.92), and Hailong street has the lowest quality score (2.32). These findings are useful for providing suggestions to governors and city designers for street space optimization.

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