We report detection of severe acute respiratory syndrome coronavirus 2 Omicron variant (B.1.1.529) in an asymptomatic, fully vaccinated traveler in a quarantine hotel in Hong Kong, China. The Omicron variant was also detected in a fully vaccinated traveler staying in a room across the corridor from the index patient, suggesting transmission despite strict quarantine precautions.

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The purpose of this study was to develop and assess a new quantitative four-view mammographic image feature based fusion model to predict the near-term breast cancer risk of the individual women after a negative screening mammography examination of interest. The dataset included fully-anonymized mammograms acquired on 870 women with two sequential full-field digital mammography (FFDM) examinations. For each woman, the first “prior” examination in the series was interpreted as negative (not recalled) during the original image reading. In the second “current” examination, 430 women were diagnosed with pathology verified cancers and 440 remained negative (“cancer-free”). For each of four bilateral craniocaudal and mediolateral oblique view images of left and right breasts, we computed and analyzed eight groups of global mammographic texture and tissue density image features. A risk prediction model based on three artificial neural networks was developed to fuse image features computed from two bilateral views of four images. The risk model performance was tested using a ten-fold cross-validation method and a number of performance evaluation indices including the area under the receiver operating characteristic curve (AUC) and odds ratio (OR). The highest AUC = 0.725±0.026 was obtained when the model was trained by gray-level run length statistics (RLS) texture features computed on dense breast regions, which was significantly higher than the AUC values achieved using the model trained by only two bilateral one-view images (p < 0.02). The adjustable OR values monotonically increased from 1.0 to 11.8 as model-generated risk score increases. The regression analysis of OR values also showed a significant increase trend in slope (p < 0.01). As a result, this preliminary study demonstrated that a new four-view mammographic image feature based risk model could provide useful and supplementary image information to help predict the near-term breast cancer risk.

It has recently been demonstrated that two-dimensional photonic band gaps can be realized in systems comprising of a quasiperiodic arrangement of dielectric cylinders. We show that waveguides crafted out of such photonic ''quasicrystals'' can be used to guide light around sharp corners, just as in the case of periodic photonic band-gap systems, but the quasiperiodic systems tend to be more frequency selective. Because of the absence of translational symmetry, these quasiperiodic photonic band-gap structures also display richer defect properties. Spectral gaps for both the TM and TE polarizations in the same frequency range can be realized in metallodielectric configurations. ͓S0163-1829͑99͒10505-8͔

Driven by a host of applications, distributed source coding (DSC) has assumed renewed interest in recent years. Wyner-Ziv coding (WZC), which deals with the rate-distortion problem with side information available only at the decoder, is one case of DSC.In this paper, we focus on successive refinement of the Wyner-Ziv problem described in [1]. Similar to the problem in classic source coding, a successive refinement coding scheme for the Wyner-Ziv problem consists of multi-stage encoders and decoders where each decoder uses all the information generated from previous encoding stages and the side information, which could be different from stage to stage. We call such a scheme successively refinable if the rate-distortion pair associated with any stage falls on the same Wyner-Ziv rate-distortion curve given the corresponding side information. It was shown in [1] that if the side information for all stages are identical, the jointly Gaussian source with squared error distortion measure is successively refinable. We extend successive refinability from jointly Gaussian source to the more general type of sources that the difference between the source and the side information is Gaussian and independent of the side information. As a by-product, we give an alternative proof that the Wyner-Ziv problem for these sources has no rate loss, where this statement was recently shown in [2]. We then propose a layered (successive) coding scheme using nested scalar quantization and Slepian-Wolf coding of bit planes based on LDPC codes for the type of sources described above. Moreover, when ideal Slepian-Wolf coding is assumed, we show that our scheme is practically successively refinable, i.e., there is no performance loss due to layering. For the jointly Gaussian source, our layered coder performs 1.33 to 2.83 dB from the Wyner-Ziv bound for rates ranging from 0.47 to 5.65 bits per sample.The full paper is available at

PURPOSE To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection etc.) as the traditional frequentist Logistic Regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications.

Comprehensive data processing in rtfMRI is possible with a PC, while the number of samples should be considered in real-time GLM.