The scalable application of quantum information science will stand on reproducible and controllable high-coherence quantum bits (qubits). Here, we revisit the design and fabrication of the ...superconducting flux qubit, achieving a planar device with broad-frequency tunability, strong anharmonicity, high reproducibility and relaxation times in excess of 40 μs at its flux-insensitive point. Qubit relaxation times T
across 22 qubits are consistently matched with a single model involving resonator loss, ohmic charge noise and 1/f-flux noise, a noise source previously considered primarily in the context of dephasing. We furthermore demonstrate that qubit dephasing at the flux-insensitive point is dominated by residual thermal-photons in the readout resonator. The resulting photon shot noise is mitigated using a dynamical decoupling protocol, resulting in T
≈85 μs, approximately the 2T
limit. In addition to realizing an improved flux qubit, our results uniquely identify photon shot noise as limiting T
in contemporary qubits based on transverse qubit-resonator interaction.
Dynamical error suppression techniques are commonly used to improve coherence in quantum systems. They reduce dephasing errors by applying control pulses designed to reverse erroneous coherent ...evolution driven by environmental noise. However, such methods cannot correct for irreversible processes such as energy relaxation. We investigate a complementary, stochastic approach to reducing errors: Instead of deterministically reversing the unwanted qubit evolution, we use control pulses to shape the noise environment dynamically. In the context of superconducting qubits, we implement a pumping sequence to reduce the number of unpaired electrons (quasiparticles) in close proximity to the device. A 70% reduction in the quasiparticle density results in a threefold enhancement in qubit relaxation times and a comparable reduction in coherence variability.
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Radiological images such as computed tomography (CT) and X-rays render anatomy with intrinsic structures. Being able to reliably locate the same anatomical structure across varying images is a ...fundamental task in medical image analysis. In principle it is possible to use landmark detection or semantic segmentation for this task, but to work well these require large numbers of labeled data for each anatomical structure and sub-structure of interest. A more universal approach would learn the intrinsic structure from unlabeled images. We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM). SAM generates semantic embeddings for each image pixel that describes its anatomical location or body part. To produce such embeddings, we propose a pixel-level contrastive learning framework. A coarse-to-fine strategy ensures both global and local anatomical information are encoded. Negative sample selection strategies are designed to enhance the embedding's discriminability. Using SAM, one can label any point of interest on a template image and then locate the same body part in other images by simple nearest neighbor searching. We demonstrate the effectiveness of SAM in multiple tasks with 2D and 3D image modalities. On a chest CT dataset with 19 landmarks, SAM outperforms widely-used registration algorithms while only taking 0.23 seconds for inference. On two X-ray datasets, SAM, with only one labeled template image, surpasses supervised methods trained on 50 labeled images. We also apply SAM on whole-body follow-up lesion matching in CT and obtain an accuracy of 91%. SAM can also be applied for improving image registration and initializing CNN weights.
A ternary metallic CoNiMo catalyst is electrochemically deposited on a polycrystalline gold (Au) disk electrode using pulse voltammetry, and characterized for hydrogen oxidation reaction (HOR) ...activity by temperature-controlled rotating disk electrode measurements in 0.1 M potassium hydroxide (KOH). The catalyst exhibits the highest HOR activity among all non-precious metal catalysts (e.g., 20 fold higher than Ni). At a sufficient loading, the CoNiMo catalyst is expected to outperform Pt and thus provides a promising low cost pathway for alkaline or alkaline membrane fuel cells. Density functional theory (DFT) calculations and parallel H sub(2)-temperature programmed desorption (TPD) experiments on structurally much simpler model alloy systems show a trend that CoNiMo has a hydrogen binding energy (HBE) similar to Pt and much lower than Ni, suggesting that the formation of multi-metallic bonds modifies the HBE of Ni and is likely a significant contributing factor for the enhanced HOR activity.
Accurate organ-at-risk (OAR) segmentation is critical to reduce radiotherapy complications. Consensus guidelines recommend delineating over 40 OARs in the head-and-neck (H&N). However, prohibitive ...labor costs cause most institutions to delineate a substantially smaller subset of OARs, neglecting the dose distributions of other OARs. Here, we present an automated and highly effective stratified OAR segmentation (SOARS) system using deep learning that precisely delineates a comprehensive set of 42 H&N OARs. We train SOARS using 176 patients from an internal institution and independently evaluate it on 1327 external patients across six different institutions. It consistently outperforms other state-of-the-art methods by at least 3-5% in Dice score for each institutional evaluation (up to 36% relative distance error reduction). Crucially, multi-user studies demonstrate that 98% of SOARS predictions need only minor or no revisions to achieve clinical acceptance (reducing workloads by 90%). Moreover, segmentation and dosimetric accuracy are within or smaller than the inter-user variation.
Automated sepsis alerts in pediatric emergency departments (EDs) can identify patients at risk of sepsis, allowing for earlier intervention with appropriate therapies. The impact of the COVID-19 ...pandemic on the performance of pediatric sepsis alerts is unknown.
We performed a retrospective cohort study of 59,335 ED visits prior to the pandemic and 51,990 ED visits during the pandemic in an ED with an automated sepsis alert based on systemic inflammatory response syndrome criteria. The sensitivity, specificity, negative predictive value, and positive predictive value of the sepsis algorithm was compared between the pre-pandemic and pandemic phases, and between COVID-19 negative and positive patients during the pandemic phase.
The proportion of ED visits triggering a sepsis alert was 7.0% (n=4,180) prior to and 6.1% (n=3,199) during the pandemic. The number of sepsis alerts triggered per diagnosed case of hypotensive septic shock was 24 in both time periods. There was no difference in the sensitivity (74.1% vs. 72.5%), specificity (93.2% vs. 94.0%), positive predictive value (4.1% vs. 4.1%), or negative predictive value (99.9% vs. 99.9%) of the sepsis alerts between these time periods. The alerts had a lower sensitivity (60% vs. 73.3%) and specificity (87.3% vs. 94.2%) for COVID-19 positive vs. negative patients.
The sepsis alert algorithm evaluated in this study did not result in excess notifications and maintained adequate performance during the COVID-19 pandemic in the pediatric ED setting.
The acquisition of large-scale medical image data, necessary for training machine learning algorithms, is hampered by associated expert-driven annotation costs. Mining hospital archives can address ...this problem, but labels often incomplete or noisy, e.g ., 50% of the lesions in DeepLesion are left unlabeled. Thus, effective label harvesting methods are critical. This is the goal of our work, where we introduce Lesion-Harvester-a powerful system to harvest missing annotations from lesion datasets at high precision. Accepting the need for some degree of expert labor, we use a small fully-labeled image subset to intelligently mine annotations from the remainder. To do this, we chain together a highly sensitive lesion proposal generator (LPG) and a very selective lesion proposal classifier (LPC). Using a new hard negative suppression loss, the resulting harvested and hard-negative proposals are then employed to iteratively finetune our LPG. While our framework is generic, we optimize our performance by proposing a new 3D contextual LPG and by using a global-local multi-view LPC. Experiments on DeepLesion demonstrate that Lesion-Harvester can discover an additional 9,805 lesions at a precision of 90%. We publicly release the harvested lesions, along with a new test set of completely annotated DeepLesion volumes. We also present a pseudo 3D IoU evaluation metric that corresponds much better to the real 3D IoU than current DeepLesion evaluation metrics. To quantify the downstream benefits of Lesion-Harvester we show that augmenting the DeepLesion annotations with our harvested lesions allows state-of-the-art detectors to boost their average precision by 7 to 10%.
The reactivity of secondary organic material (SOM) of variable viscosity, ranging from nonliquid to liquid physical states, was studied. The SOM, produced in aerosol form from terpenoid and aromatic ...precursor species, was reacted with ammonia at variable relative humidity (RH). The ammonium-to-organic mass ratio (M NH4 + /M Org) increased monotonically from <5% RH to a limiting value at a threshold RH, implicating a transition from particle reactivity limited by diffusion at low RH to one limited by other factors at higher RH. For the studied size distributions and reaction times, the transition corresponded to a diffusivity above 10–17.5 ± 0.5 m2 s–1. The threshold RH values for the transition were <5% RH for isoprene-derived SOM, 35–45% RH for SOM derived from α-pinene, toluene, m-xylene, and 1,3,5-trimethylbenzene, and >90% for β-caryophyllene-derived SOM. The transition RH for reactivity differed in all cases from the transition RH of a nonliquid to a liquid state. For instance, for α-pinene-derived SOM the transition for chemical reactivity of 35–45% RH can be compared to the nonliquid to liquid transition of 65–90% RH. These differences imply that chemical transport models of atmospheric chemistry should not use the SOM liquid to nonliquid phase transition as one-to-one surrogates of SOM reactivity.
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Harnessing the microbiota for beneficial outcomes is limited by our poor understanding of the constituent bacteria, as the functions of most of their genes are unknown. Here, we measure the growth of ...a barcoded transposon mutant library of the gut commensal Bacteroides thetaiotaomicron on 48 carbon sources, in the presence of 56 stress-inducing compounds, and during mono-colonization of gnotobiotic mice. We identify 516 genes with a specific phenotype under only one or a few conditions, enabling informed predictions of gene function. For example, we identify a glycoside hydrolase important for growth on type I rhamnogalacturonan, a DUF4861 protein for glycosaminoglycan utilization, a 3-keto-glucoside hydrolase for disaccharide utilization, and a tripartite multidrug resistance system specifically for bile salt tolerance. Furthermore, we show that B. thetaiotaomicron uses alternative enzymes for synthesizing nitrogen-containing metabolic precursors based on ammonium availability and that these enzymes are used differentially in vivo in a diet-dependent manner.
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•Identification of specific phenotypes for 516 Bacteroides thetaiotaomicron genes•A 3-keto-glucoside hydrolase is important for disaccharide utilization•A tripartite multidrug resistance system is important for bile salt resistance•Use of alternate biosynthetic enzymes depends on ammonium availability in the gut
Liu et al. report large-scale, genome-wide fitness assays for the model human gut commensal Bacteroides thetaiotaomicron, both in vitro and in vivo. These data provide insights into the bacterium’s carbohydrate catabolism, stress responses, and adaptation to diet-dependent ammonium availability in the gut.
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