The global outbreak of coronavirus disease 2019 (COVID-19) has spread to more than 200 countries worldwide, leading to severe health and socioeconomic consequences. As such, the topic of monitoring and predicting epidemics has been attracting a lot of interest. Previous work reported search volumes from Google Trends are beneficial in decoding influenza dynamics, implying its potential for COVID-19 prediction. Therefore, a predictive model using the Wiener methods was built based on epidemic-related search queries from Google Trends, along with climate variables, aiming to forecast the dynamics of the weekly COVID-19 incidence in Washington, DC, USA. The Wiener model, which shares the merits of interpretability, low computation costs, and adaptation to nonlinear fluctuations, was used in this study. Models with multiple sets of features were constructed and further optimized by the highest weight selecting strategy. Furthermore, comparisons to the other two commonly used prediction models based on the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) were also performed. Our results showed the predicted COVID-19 trends significantly correlated with the actual (rho$=$0.88,$p $$<$0.0001), outperforming those with ARIMA and LSTM approaches, indicating Google Trends data as a useful tool in terms of COVID-19 prediction. Also, the model using 20 search queries with the highest weighting outperformed all other models, supporting the highest weight feature selection as a feasible criterion. Google Trends search query data can be used to forecast the outbreak of COVID-19, which might assist health policymakers to allocate health care resources and taking preventive strategies.

The performance of license plate detection has greatly improved with the development of deep learning. However, two challenges remain. First, in unconstrained scenarios, such as rotation and uneven lighting, license plate detection still faces significant challenges. Secondly, traditional horizontal bounding boxes are not suitable for representing multi-oriented license plates. And quadrilateral boxes can effectively represent them though, they cannot avoid the confusion caused by sequential label points. To address these challenges, we propose a license plate detection method called SvRetina-LPD. Specifically, we design an inception residual fusion pyramid network, which fuses multiple layers of features thoroughly. Then, by integrating spatial attention and channel attention, the inception residual multi-dimensional attention network is developed to weaken the noise and highlight the features of the license plate. Finally, we introduce a sliding vertex head network that incorporates a sliding vertex branch and regresses four length ratios to represent the relative sliding offsets of the corner points of the quadrilateral to the corresponding sides of the bounding rectangle. This method can accurately detect multi-directional license plate regions and effectively avoids the sequential label points. Extensive experimental tests were conducted on datasets such as CCPD, AOLP, and CLPD. On the CCPD test set, SvRetina-LPD demonstrated a high accuracy of 97.9%, surpassing existing methods. In addition, SvRetina-LPD also demonstrated excellent detection accuracy in other subsets of CCPD, including various challenging scenarios, demonstrating its ability to accurately detect multi-directional license plates in unconstrained scenarios.

OBJECTIVE: This paper presents a novel method to quantify cardiopulmonary dynamics for automatic sleep apnea detection by integrating the synchrosqueezing transform (SST) algorithm with the standard cardiopulmonary coupling (CPC) method. METHODS: Simulated data were designed to validate the reliability of the proposed method, with varying levels of signal bandwidth and noise contamination. Real data were collected from the Physionet sleep apnea database, consisting of 70 single-lead ECGs with expert-labeled apnea annotations on a minute-by-minute basis. Three different signal processing techniques applied to sinus interbeat interval and respiratory time series include short-time Fourier transform, continuous Wavelet transform, and synchrosqueezing transform, respectively. Subsequently, the CPC index was computed to construct sleep spectrograms. Features derived from such spectrogram were used as input to five machine- learning-based classifiers including decision trees, support vector machines, k-nearest neighbors, etc. Results: The simulation results showed that the SST-CPC method is robust to both noise level and signal bandwidth, outperforming Fourier-based and Wavelet-based approaches. Meanwhile, the SST-CPC spectrogram exhibited relatively explicit temporal-frequency biomarkers compared with the rest. Furthermore, by integrating SST-CPC features with common-used heart rate and respiratory features, accuracies for per-minute apnea detection improved from 72% to 83%, validating the added value of CPC biomarkers in sleep apnea detection. CONCLUSION: The SST-CPC method improves the accuracy of automatic sleep apnea detection and presents comparable performances with those automated algorithms reported in the literature. SIGNIFICANCE: The proposed SST-CPC method enhances sleep diagnostic capabilities, and may serve as a complementary tool to the routine diagnosis of sleep respiratory events.

Emotion, an essential aspect in inferring human psychological states, is featured by entangled oscillators operating at multiple frequencies and montages. However, the dynamics of mutual interactions among rhythmic activities in EEGs under various emotional expressions are unclear. To this end, a novel method named variational phase-amplitude coupling is proposed to quantify the rhythmic nesting structure in EEGs under emotional processing. The proposed algorithm lies in variational mode decomposition, featured by its robustness to noise artifacts and its merit in avoiding the mode-mixing problem. This novel method reduces the risk of spurious coupling compared to that with ensemble empirical mode decomposition or iterative filter when evaluated by simulations. An atlas of cross-couplings in EEGs under eight emotional processing is established. Mainly, α activity in the anterior frontal region serves as a critical sign for neutral emotional state, whereas γ amplitude seems to be linked with both positive and negative emotional states. Moreover, for those γ-amplitude-related couplings under neutral emotional state, the frontal lobe is associated with lower phase-given frequencies while the central lobe is attached to higher ones. The γ-amplitude-related coupling in EEGs is a promising biomarker for recognizing mental states. We recommend our method as an effective tool in characterizing the entangled multifrequency rhythms in brain signals for emotion neuromodulation.

Age is an essential factor in modern diagnostic procedures. However, assessment of the true biological age (BA) remains a daunting task due to the lack of reference ground-truth labels. Current BA estimation approaches are either restricted to skeletal images or rely on non-imaging modalities that yield a whole-body BA assessment. However, various organ systems may exhibit different aging characteristics due to lifestyle and genetic factors. In this initial study, we propose a new framework for organ-specific BA estimation utilizing 3D magnetic resonance image (MRI) scans. As a first step, this framework predicts the chronological age (CA) together with the corresponding patient-dependent aleatoric uncertainty. An iterative training algorithm is then utilized to segregate atypical aging patients from the given population based on the predicted uncertainty scores. In this manner, we hypothesize that training a new model on the remaining population should approximate the true BA behavior. We apply the proposed methodology on a brain MRI dataset containing healthy individuals as well as Alzheimer’s patients. We demonstrate the correlation between the predicted BAs and the expected cognitive deterioration in Alzheimer’s patients.

The concept of biological age (BA) - although important in clinical practice - is hard to grasp mainly due to the lack of a clearly defined reference standard. For specific applications, especially in pediatrics, medical image data are used for BA estimation in a routine clinical context. Beyond this young age group, BA estimation is mostly restricted to whole-body assessment using non-imaging indicators such as blood biomarkers, genetic and cellular data. However, various organ systems may exhibit different aging characteristics due to lifestyle and genetic factors. Thus, a whole-body assessment of the BA does not reflect the deviations of aging behavior between organs. To this end, we propose a new imaging-based framework for organ-specific BA estimation. In this initial study we focus mainly on brain MRI. As a first step, we introduce a chronological age (CA) estimation framework using deep convolutional neural networks (Age-Net). We quantitatively assess the performance of this framework in comparison to existing state-of-the-art CA estimation approaches. Furthermore, we expand upon Age-Net with a novel iterative data-cleaning algorithm to segregate atypical-aging patients (BA [Formula: see text] CA) from the given population. We hypothesize that the remaining population should approximate the true BA behavior. We apply the proposed methodology on a brain magnetic resonance image (MRI) dataset containing healthy individuals as well as Alzheimer's patients with different dementia ratings. We demonstrate the correlation between the predicted BAs and the expected cognitive deterioration in Alzheimer's patients. A statistical and visualization-based analysis has provided evidence regarding the potential and current challenges of the proposed methodology.