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.
We use Pontryagin’s minimum principle to optimize variational quantum algorithms. We show that for a fixed computation time, the optimal evolution has a bang-bang (square pulse) form, both for closed ...and open quantum systems with Markovian decoherence. Our findings support the choice of evolution ansatz in the recently proposed quantum approximate optimization algorithm. Focusing on the Sherrington-Kirkpatrick spin glass as an example, we find a system-size independent distribution of the duration of pulses, with characteristic time scale set by the inverse of the coupling constants in the Hamiltonian. The optimality of the bang-bang protocols and the characteristic time scale of the pulses provide an efficient parametrization of the protocol and inform the search for effective hybrid (classical and quantum) schemes for tackling combinatorial optimization problems. Furthermore, we find that the success rates of our optimal bang-bang protocols remain high even in the presence of weak external noise and coupling to a thermal bath.
Mitigating Traffic Congestion: The Role of Intelligent Transportation Systems
Zhi (Aaron) Cheng, Min-Seok Pang, Paul A. Pavlou
While massive investments in transportation infrastructure, traffic ...congestion remains a major societal and public policy problem. Intelligent transportation systems (ITS) have been proposed as a potential solution to this challenge, but their effectiveness has remained unclear. To examine whether and how ITS affect traffic congestion, we study traffic congestion and the deployment of a large federally supported ITS program in the United States—511 systems—in 99 urban areas between 1994 and 2014. We find that the adoption of 511 systems is associated with a significant decrease in traffic congestion, saving over $4.7 billion dollars and 175 million hours in travel time annually in U.S. cities. 511 systems also reduce about 53 million gallons of fossil fuel consumption and over 10 billion pounds of CO
2
emissions. We show abundant evidence that ITS help individual commuters to make better travel decisions, and that ITS help local governments to develop an urban traffic management capability. We also observe that the traffic-reducing effect of ITS is larger with more actual usage of the online services and when state and local governments incorporate more informative functionalities into the 511 systems. This study informs policymakers of ITS as a cost-effective means to mitigating traffic congestion.
Despite massive investments in transportation infrastructure, traffic congestion remains a major societal and public policy problem. Intelligent transportation systems (ITS) have been proposed as a potential solution to this challenge, but their effectiveness has remained unclear in both research and practice. To understand whether and how ITS affect traffic congestion, we consolidate a unique longitudinal data set on road traffic and the deployment of a large federally supported ITS program in the United States—511 systems—in 99 urban areas between 1994 and 2014. The difference-in-differences estimates show that the adoption of 511 systems is associated with a significant decrease in traffic congestion, saving over $4.7 billion dollars and 175 million hours in travel time annually in U.S. cities. 511 systems also reduce about 53 million gallons of fossil fuel consumption and over 10 billion pounds of CO
2
emissions. We offer two theoretical explanations for this effect: (i) ITS help individual commuters to make better travel decisions, and (ii) ITS help local governments to develop an urban traffic management capability. Empirical evidence supports the underlying theoretical mechanisms and shows that ITS help commuters to schedule travel more efficiently, choose better navigation routes, and optimize their work-trip transportation mode. Second, the effect of ITS is contingent on road supply and public transit services. We also find that the traffic-reducing effect of ITS is larger when commuters use more online services for traffic information and when state governments incorporate more functionalities into their 511 systems. This study contributes to the literature on IT capabilities, public-sector IT value, and the societal impact of IT, while also extending the transportation economics to IT-enabled traffic interventions. Finally, we inform policymakers of ITS as a cost-effective means to mitigating traffic congestion.
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.
Predicting protein subcellular localization is an important and difficult problem, particularly when query proteins may have the multiplex character, i.e., simultaneously exist at, or move between, ...two or more different subcellular location sites. Most of the existing protein subcellular location predictor can only be used to deal with the single-location or "singleplex" proteins. Actually, multiple-location or "multiplex" proteins should not be ignored because they usually posses some unique biological functions worthy of our special notice. By introducing the "multi-labeled learning" and "accumulation-layer scale", a new predictor, called iLoc-Euk, has been developed that can be used to deal with the systems containing both singleplex and multiplex proteins. As a demonstration, the jackknife cross-validation was performed with iLoc-Euk on a benchmark dataset of eukaryotic proteins classified into the following 22 location sites: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centriole, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole, where none of proteins included has ≥25% pairwise sequence identity to any other in a same subset. The overall success rate thus obtained by iLoc-Euk was 79%, which is significantly higher than that by any of the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, iLoc-Euk is freely accessible to the public at the web-site http://icpr.jci.edu.cn/bioinfo/iLoc-Euk. It is anticipated that iLoc-Euk may become a useful bioinformatics tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development Also, its novel approach will further stimulate the development of predicting other protein attributes.
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.
Materials with strong magnetoresistive responses are the backbone of spintronic technology, magnetic sensors, and hard drives. Among them, manganese oxides with a mixed valence and a cubic perovskite ...structure stand out due to their colossal magnetoresistance (CMR). A double exchange interaction underlies the CMR in manganates, whereby charge transport is enhanced when the spins on neighboring Mn3+ and Mn4+ ions are parallel. Prior efforts to find different materials or mechanisms for CMR resulted in a much smaller effect. Here an enormous CMR at low temperatures in EuCd2P2 without manganese, oxygen, mixed valence, or cubic perovskite structure is shown. EuCd2P2 has a layered trigonal lattice and exhibits antiferromagnetic ordering at 11 K. The magnitude of CMR (104%) in as‐grown crystals of EuCd2P2 rivals the magnitude in optimized thin films of manganates. The magnetization, transport, and synchrotron X‐ray data suggest that strong magnetic fluctuations are responsible for this phenomenon. The realization of CMR at low temperatures without heterovalency leads to a new regime for materials and technologies related to antiferromagnetic spintronics.
A change of paradigm in a technologically important phenomenon, the colossal magnetoresistance (CMR), is introduced. Previously, it was believed that the CMR required ferromagnetic ordering and a structural distortion. EuCd2P2 has antiferromagnetic ordering and does not have a structural distortion or mixed valence, yet it exhibits the largest CMR known in a single crystal.
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