Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate ...radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.
Background and Aims
The study objective was to compare the effectiveness of microwave ablation (MWA) and laparoscopic liver resection (LLR) on solitary 3–5‐cm HCC over time.
Approach and Results
From ...2008 to 2019, 1289 patients from 12 hospitals were enrolled in this retrospective study. Diagnosis of all lesions were based on histopathology. Propensity score matching was used to balance all baseline variables between the two groups in 2008–2019 (n = 335 in each group) and 2014–2019 (n = 257 in each group) cohorts, respectively. For cohort 2008–2019, during a median follow‐up of 35.8 months, there were no differences in overall survival (OS) between MWA and LLR (HR: 0.88, 95% CI 0.65–1.19, p = 0.420), and MWA was inferior to LLR regarding disease‐free survival (DFS) (HR 1.36, 95% CI 1.05–1.75, p = 0.017). For cohort 2014–2019, there was comparable OS (HR 0.85, 95% CI 0.56–1.30, p = 0.460) and approached statistical significance for DFS (HR 1.33, 95% CI 0.98–1.82, p = 0.071) between MWA and LLR. Subgroup analyses showed comparable OS in 3.1–4.0‐cm HCCs (HR 0.88, 95% CI 0.53–1.47, p = 0.630) and 4.1–5.0‐cm HCCs (HR 0.77, 95% CI 0.37–1.60, p = 0.483) between two modalities. For both cohorts, MWA shared comparable major complications (both p > 0.05), shorter hospitalization, and lower cost to LLR (all p < 0.001).
Conclusions
MWA might be a first‐line alternative to LLR for solitary 3–5‐cm HCC in selected patients with technical advances, especially for patients unsuitable for LLR.
The interface, referred to as the boundary between two phases, has been demonstrated to play a critical role in catalysis. Fundamental understanding of interfacial phenomena occurring in catalysis ...will favor the rational design of high-performance catalysts. With the thriving of nanoscience, the nanointerface has also received tremendous attention in nanocatalysis. In this review, we focus on the recent advances in the delicate design and the fine control of various complex nanomaterials with well-defined interfaces based on progress in nano-synthetic methodologies, including metal-metal oxide, metal-metal, metal-non-oxide and metal in confined spaces. Then the challenging issues in the synthetic control of a nanointerface, based on the authors' experiences, are discussed. Finally, the prospects and outlooks for engineering nanointerfaces for nanocatalysis towards renewable energy are presented.
We focus on recent advances in the delicate design of well-defined nanointerfaces to promote nanocatalysis towards renewable energy.
Background
Accurate glioma grading plays an important role in the clinical management of patients and is also the basis of molecular stratification nowadays.
Purpose/Hypothesis
To verify the ...superiority of radiomics features extracted from multiparametric MRI to glioma grading and evaluate the grading potential of different MRI sequences or parametric maps.
Study Type
Retrospective; radiomics.
Population
A total of 153 patients including 42, 33, and 78 patients with Grades II, III, and IV gliomas, respectively.
Field Strength/Sequence
3.0T MRI/T1‐weighted images before and after contrast‐enhanced, T2‐weighted, multi‐b‐value diffusion‐weighted and 3D arterial spin labeling images.
Assessment
After multiparametric MRI preprocessing, high‐throughput features were derived from patients' volumes of interests (VOIs). The support vector machine‐based recursive feature elimination was adopted to find the optimal features for low‐grade glioma (LGG) vs. high‐grade glioma (HGG), and Grade III vs. IV glioma classification tasks. Then support vector machine (SVM) classifiers were established using the optimal features. The accuracy and area under the curve (AUC) was used to assess the grading efficiency.
Statistical Tests
Student's t‐test or a chi‐square test were applied on different clinical characteristics to confirm whether intergroup significant differences exist.
Results
Patients' ages between LGG and HGG groups were significantly different (P < 0.01). For each patient, 420 texture and 90 histogram parameters were derived from 10 VOIs of multiparametric MRI. SVM models were established using 30 and 28 optimal features for classifying LGGs from HGGs and grades III from IV, respectively. The accuracies/AUCs were 96.8%/0.987 for classifying LGGs from HGGs, and 98.1%/0.992 for classifying grades III from IV, which were more promising than using histogram parameters or using the single sequence MRI.
Data Conclusion
Texture features were more effective for noninvasively grading gliomas than histogram parameters. The combined application of multiparametric MRI provided a higher grading efficiency. The proposed radiomic strategy could facilitate clinical decision‐making for patients with varied glioma grades.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2018;48:1518–1528
Artificial synapses are the key building blocks for low‐power neuromorphic computing that can go beyond the constraints of von Neumann architecture. In comparison with two‐terminal memristors and ...three‐terminal transistors with filament‐formation and charge‐trapping mechanisms, emerging electrolyte‐gated transistors (EGTs) have been demonstrated as a promising candidate for neuromorphic applications due to their prominent analog switching performance. Here, a novel graphdiyne (GDY)/MoS2‐based EGT is proposed, where an ion‐storage layer (GDY) is adopted to EGTs for the first time. Benefitting from this Li‐ion‐storage layer, the GDY/MoS2‐based EGT features a robust stability (variation < 1% for over 2000 cycles), an ultralow energy consumption (50 aJ µm−2), and long retention characteristics (>104 s). In addition, a quasi‐linear conductance update with low noise (1.3%), an ultrahigh Gmax/Gmin ratio (103), and an ultralow readout conductance (<10 nS) have been demonstrated by this device, enabling the implementation of the neuromorphic computing with near‐ideal accuracies. Moreover, the non‐volatile characteristics of the GDY/MoS2‐based EGT enable it to demonstrate logic‐in‐memory functions, which can execute logic processing and store logic results in a single device. These results highlight the potential of the GDY/MoS2‐based EGT for next‐generation low‐power electronics beyond von Neumann architecture.
A novel graphdiyne (GDY)/MoS2‐based electrolyte‐gated transistor using GDY as a Li‐ion‐storage layer is proposed, which features robust stability and flexibility, an ultralow energy consumption, a long retention time, a quasi‐linear weight update with low noise, an ultrahigh Gmax/Gmin ratio, and an ultralow readout conductance. This GDY/MoS2‐based EGT has demonstrated its potential in applications of neuromorphic computing and in‐memory computing.
Severe COVID-19 disease caused by SARS-CoV-2 is frequently accompanied by dysfunction of the lungs and extrapulmonary organs. However, the organotropism of SARS-CoV-2 and the port of virus entry for ...systemic dissemination remain largely unknown. We profiled 26 COVID-19 autopsy cases from four cohorts in Wuhan, China, and determined the systemic distribution of SARS-CoV-2. SARS-CoV-2 was detected in the lungs and multiple extrapulmonary organs of critically ill COVID-19 patients up to 67 days after symptom onset. Based on organotropism and pathological features of the patients, COVID-19 was divided into viral intrapulmonary and systemic subtypes. In patients with systemic viral distribution, SARS-CoV-2 was detected in monocytes, macrophages, and vascular endothelia at blood-air barrier, blood-testis barrier, and filtration barrier. Critically ill patients with long disease duration showed decreased pulmonary cell proliferation, reduced viral RNA, and marked fibrosis in the lungs. Permanent SARS-CoV-2 presence and tissue injuries in the lungs and extrapulmonary organs suggest direct viral invasion as a mechanism of pathogenicity in critically ill patients. SARS-CoV-2 may hijack monocytes, macrophages, and vascular endothelia at physiological barriers as the ports of entry for systemic dissemination. Our study thus delineates systemic pathological features of SARS-CoV-2 infection, which sheds light on the development of novel COVID-19 treatment.
Quantum key distribution (QKD)1,2 offers a long-term solution to secure key exchange. Due to photon loss in transmission, it was believed that the repeaterless key rate is bounded by a linear ...function of the transmittance, O(η) (refs. 3,4), limiting the maximal secure transmission distance5,6. Recently, a novel type of QKD scheme has been shown to beat the linear bound and achieve a key rate performance of O(η) (refs. 7–9). Here, by employing the laser injection technique and the phase post-compensation method, we match the modes of two independent lasers and overcome the phase fluctuation. As a result, the key rate surpasses the linear bound via 302 km and 402 km commercial-fibre channels, over four orders of magnitude higher than existing results5. Furthermore, our system yields a secret key rate of 0.118 bps with a 502 km ultralow-loss fibre. This new type of QKD pushes forward long-distance quantum communication for the future quantum internet.Phase-matching quantum key distribution is implemented with a 502 km ultralow-loss optical fibre. The fluctuations of the laser initial phases and frequencies are suppressed by the laser injection technique and the phase post-compensation method.
Measurement-device-independent quantum key distribution (MDI QKD) removes all detector side channels and enables secure QKD with an untrusted relay. It is suitable for building a star-type quantum ...access network, where the complicated and expensive measurement devices are placed in the central untrusted relay and each user requires only a low-cost transmitter, such as an integrated photonic chip. Here, we experimentally demonstrate a 1.25-GHz silicon photonic chip-based MDI QKD system using polarization encoding. The photonic chip transmitters integrate the necessary encoding components for a standard QKD source. We implement random modulations of polarization states and decoy intensities, and demonstrate a finite-key secret rate of31bit/sover 36-dB channel loss (or 180-km standard fiber). This key rate is higher than state-of-the-art MDI QKD experiments. The results show that silicon photonic chip-based MDI QKD, benefiting from miniaturization, low-cost manufacture, and compatibility with CMOS microelectronics, is a promising solution for future quantum secure networks.
Long-distance entanglement distribution is essential for both foundational tests of quantum physics and scalable quantum networks. Owing to channel loss, however, the previously achieved distance was ...limited to ~100 kilometers. Here we demonstrate satellite-based distribution of entangled photon pairs to two locations separated by 1203 kilometers on Earth, through two satellite-to-ground downlinks with a summed length varying from 1600 to 2400 kilometers. We observed a survival of two-photon entanglement and a violation of Bell inequality by 2.37 ± 0.09 under strict Einstein locality conditions. The obtained effective link efficiency is orders of magnitude higher than that of the direct bidirectional transmission of the two photons through telecommunication fibers.
Silicon (Si) is a promising anode candidate for next-generation lithium-ion batteries (LIBs), but it suffers from poor electronic conductivity and dramatic volume variation during cycling, which ...poses a critical challenge for stable battery operation. To mitigate these issues simultaneously, we propose a “double carbon synergistic encapsulation” strategy, namely thin carbon shell and nitrogen/phosphorus co-doped two-dimensional (2D) carbon sheet dual encapsulate Si nanoparticles (denoted as 2D NPC/C@Si). This double carbon structure can serve as a conductive medium and buffer matrix to accommodate the volume expansion of Si nanoparticles and enable fast electron/ion transport, which promotes the formation of a stable solid electrolyte interphase film during cycling. Through structural advantages, the resulting 2D NPC/C@Si electrode demonstrates a high reversible capacity of 592 mAh·g
−1
at 0.2 A·g
−1
with 90.5% excellent capacity retention after 100 cycles, outstanding rate capability (148 mAh·g
−1
at 8 A·g
−1
), and superior long-term cycling stability (326 mAh·g
−1
at 1 A·g
−1
for 500 cycles, 86% capacity retention). Our findings elucidate the development of high-performance Si@C composite anodes for advanced LIBs.
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