The automatic identification system (AIS) tracks vessel movement by means of electronic exchange of navigation data between vessels, with onboard transceiver, terrestrial, and/or satellite base ...stations. The gathered data contain a wealth of information useful for maritime safety, security, and efficiency. Because of the close relationship between data and methodology in marine data mining and the importance of both of them in marine intelligence research, this paper surveys AIS data sources and relevant aspects of navigation in which such data are or could be exploited for safety of seafaring, namely traffic anomaly detection, route estimation, collision prediction, and path planning.
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
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In many practical transfer learning scenarios, the feature distribution is different across the source and target domains (i.e., nonindependent identical distribution). Maximum mean discrepancy ...(MMD), as a domain discrepancy metric, has achieved promising performance in unsupervised domain adaptation (DA). We argue that the MMD-based DA methods ignore the data locality structure, which, up to some extent, would cause the negative transfer effect. The locality plays an important role in minimizing the nonlinear local domain discrepancy underlying the marginal distributions. For better exploiting the domain locality, a novel local generative discrepancy metric-based intermediate domain generation learning called Manifold Criterion guided Transfer Learning (MCTL) is proposed in this paper. The merits of the proposed MCTL are fourfold: 1) the concept of manifold criterion (MC) is first proposed as a measure validating the distribution matching across domains, and DA is achieved if the MC is satisfied; 2) the proposed MC can well guide the generation of the intermediate domain sharing similar distribution with the target domain, by minimizing the local domain discrepancy; 3) a global generative discrepancy metric is presented, such that both the global and local discrepancies can be effectively and positively reduced; and 4) a simplified version of MCTL called MCTL-S is presented under a perfect domain generation assumption for more generic learning scenario. Experiments on a number of benchmark visual transfer tasks demonstrate the superiority of the proposed MC guided generative transfer method, by comparing with the other state-of-the-art methods. The source code is available in https://github.com/wangshanshanCQU/MCTL.
Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as ...principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error.
This paper studies consensus for input-constrained linear periodic multi-agent systems under undirected graphs. Assume that only the relative output information among agents is known, two types of ...system dynamics are considered. The first type of system dynamics satisfies that the open-loop characteristic multipliers are within the closed unit circle, while the second type is neutrally stable. With the first type of system dynamics, a novel linear protocol is proposed to solve the semi-global consensus problem. With the second type of system dynamics, another simple linear protocol is proposed to solve the global consensus problem. The most outstanding advantages of the proposed protocols are that the considered system dynamics are time-varying and the proposed protocols do not need the a priori global information of the network topology and can thus achieve consensus for arbitrary undirected communication graph.
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Extreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks which need not be neuron-like and perform well in both regression and classification ...applications. In this brief, we propose an ELM with adaptive growth of hidden nodes (AG-ELM), which provides a new approach for the automated design of networks. Different from other incremental ELMs (I-ELMs) whose existing hidden nodes are frozen when the new hidden nodes are added one by one, in AG-ELM the number of hidden nodes is determined in an adaptive way in the sense that the existing networks may be replaced by newly generated networks which have fewer hidden nodes and better generalization performance. We then prove that such an AG-ELM using Lebesgue p -integrable hidden activation functions can approximate any Lebesgue p -integrable function on a compact input set. Simulation results demonstrate and verify that this new approach can achieve a more compact network architecture than the I-ELM.
Extreme learning machine (ELM) has been an important research topic over the last decade due to its high efficiency, easy-implementation, unification of classification and regression, and unification ...of binary and multi-class learning tasks. Though integrating these advantages, existing ELM algorithms pay little attention to optimizing the choice of kernels, which is indeed crucial to the performance of ELM in applications. More importantly, there is the lack of a general framework for ELM to integrate multiple heterogeneous data sources for classification. In this paper, we propose a general learning framework, termed multiple kernel extreme learning machines (MK-ELM), to address the above two issues. In the proposed MK-ELM, the optimal kernel combination weights and the structural parameters of ELM are jointly optimized. Following recent research on support vector machine (SVM) based MKL algorithms, we first design a sparse MK-ELM algorithm by imposing an ℓ1-norm constraint on the kernel combination weights, and then extend it to a non-sparse scenario by substituting the ℓ1-norm constraint with an ℓp-norm (p>1) constraint. After that, a radius-incorporated MK-ELM algorithm which incorporates the radius of the minimum enclosing ball (MEB) is introduced. Three efficient optimization algorithms are proposed to solve the corresponding kernel learning problems. Comprehensive experiments have been conducted on Protein, Oxford Flower17, Caltech101 and Alzheimer׳s disease data sets to evaluate the performance of the proposed algorithms in terms of classification accuracy and computational efficiency. As the experimental results indicate, our proposed algorithms can achieve comparable or even better classification performance than state-of-the-art MKL algorithms, while incurring much less computational cost.
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Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective ...enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas.
One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split.
The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model.
In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch.
Great progress has been achieved in the study of Hippo signaling in regulating tumorigenesis; however, the downstream molecular events that mediate this process have not been completely defined. ...Moreover, regulation of Hippo signaling during tumorigenesis in hepatocellular carcinoma (HCC) remains largely unknown. In the present study, we systematically investigated the relationship between Yes‐associated protein/TEA domain family member (YAP‐TEAD) and hepatocyte nuclear factor 4‐alpha (HNF4α) in the hepatocarcinogenesis of HCC cells. Our results indicated that HNF4α expression was negatively regulated by YAP1 in HCC cells by a ubiquitin proteasome pathway. By contrast, HNF4α was found to directly associate with TEAD4 to compete with YAP1 for binding to TEAD4, thus inhibiting the transcriptional activity of YAP‐TEAD and expression of their target genes. Moreover, overexpression of HNF4α was found to significantly compromise YAP‐TEAD‐induced HCC cell proliferation and stem cell expansion. Finally, we documented the regulatory mechanism between YAP‐TEAD and HNF4α in rat and mouse tumor models, which confirmed our in vitro results. Conclusion: There is a double‐negative feedback mechanism that controls TEAD‐YAP and HNF4α expression in vitro and in vivo, thereby regulating cellular proliferation and differentiation. Given that YAP acts as a dominant oncogene in HCC and plays a crucial role in stem cell homeostasis and tissue regeneration, manipulating the interaction between YAP, TEADs, and HNF4α may provide a new approach for HCC treatment and regenerative medicine. (Hepatology 2017;65:1206‐1221).
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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.
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