The propagation characteristics of seismic waves in ultra-deep overburden by large-scale shaking table test.

The seismic performance of earth-rock dams on deep overburdens has long been a central focus in dam engineering. Understanding the propagation law of seismic waves within a deep overburden is key to revealing the mechanisms of the dynamic response. In this study, the propagation characteristics of horizontal and vertical seismic waves in a deep overburden were systematically investigated using large-scale shaking table tests, with analysis based on acceleration and pore pressure data combined with stockwell transforms and coherence function methods.

Wearable fall risk assessment by discriminating recessive weak foot individual.

Background: Sensor-based technologies have been widely used in fall risk assessment. To enhance the model's robustness and reliability, it is crucial to analyze and discuss the factors contributing to the misclassification of certain individuals, enabling purposeful and interpretable refinement.

Methods: This study identified an abnormal gait pattern termed "Recessive weak foot (RWF)," characterized by a discontinuous high-risk gait on the weak foot side, observed through weak foot feature space.

Multifunctional 4D printed shape memory composite scaffolds with photothermal and magnetothermal effects for multimodal tumor therapy and bone repair.

Tumor recurrence and bone defects are two key challenges in the surgical treatment of osteosarcoma (OS). Therefore, it is highly necessary to develop a multifunctional scaffold that can simultaneously eradicate tumor cells and promote bone regeneration. Herein, a hierarchically porous shape memory scaffold consisting of hydroxyapatite, silica, poly(D,L-lactide-co-trimethylene carbonate) and FeO(HSP-FeO) is constructed by Pickering emulsion and 4D printing technique.

Self-doping synthesis of nano-TiO with outstanding antibacterial properties under visible light.

Nano-TiO photocatalysis technology has attracted wide attention because of its safety, nontoxicity and long-lasting performance. However, traditional nano-TiO has been greatly limited in its application because its wide band gap can only be activated by ultraviolet light (λ < 387 nm). In this paper, nano-TiO was prepared by self-doping method.

High-Speed and Accurate Diagnosis of Gastrointestinal Disease: Learning on Endoscopy Images Using Lightweight Transformer with Local Feature Attention.

In response to the pressing need for robust disease diagnosis from gastrointestinal tract (GIT) endoscopic images, we proposed FLATer, a fast, lightweight, and highly accurate transformer-based model. FLATer consists of a residual block, a vision transformer module, and a spatial attention block, which concurrently focuses on local features and global attention. It can leverage the capabilities of both convolutional neural networks (CNNs) and vision transformers (ViT).

SFDA: Domain Adaptation With Source Subject Fusion Based on Multi-Source and Single-Target Fall Risk Assessment.

In cross-subject fall risk classification based on plantar pressure, a challenge is that data from different subjects have significant individual information. Thus, the models with insufficient generalization ability can't perform well on new subjects, which limits their application in daily life. To solve this problem, domain adaptation methods are applied to reduce the gap between source and target domain.

Empirical evidence for the neurocognitive effect of nitrous oxide as an adjunctive therapy in patients with treatment resistant depression: A randomized controlled study.

Nitrous oxide (NO) has demonstrated an antidepressant effect for treatment-resistant depression (TRD), but no studies investigated the effects of NO on different cognition domains. This study aimed to test whether NO would display pro-cognitive effects. We conducted a double-blinded, placebo-controlled, randomized controlled trial, 44 patients with TRD were randomized to NO group (one-hour inhalation of 50% NO/50% oxygen) or placebo group (50% air/50% oxygen).

Clinical feasibility of MRI-based synthetic CT imaging in the diagnosis of lumbar disc herniation: a comparative study.

Background: Computed tomography (CT) and magnetic resonance imaging (MRI) are indicated for use in preoperative planning and may complicate diagnosis and place a burden on patients with lumbar disc herniation.

Purpose: To investigate the diagnostic potential of MRI-based synthetic CT with conventional CT in the diagnosis of lumbar disc herniation.

Material And Methods: After obtaining prior institutional review board approval, 19 patients who underwent conventional and synthetic CT imaging were enrolled in this prospective study.

Automatic mammographic breast density classification in Chinese women: clinical validation of a deep learning model.

Background: High breast density is a strong risk factor for breast cancer. As such, high consistency and accuracy in breast density assessment is necessary.

Purpose: To validate our proposed deep learning (DL) model and explore its impact on radiologists on density assessments.

Aberrant DNA methylation and expression of in gastric cardia intestinal metaplasia.

Background: Intestinal metaplasia (IM) of the gastric cardia is an important premalignant lesion. However, there is limited information concerning its epidemiological and molecular features. Herein, we aimed to provide an overview of the epidemiological data for gastric cardiac IM and evaluate the role of EYA transcriptional coactivator and phosphatase 4 (EYA4) as an epigenetic biomarker for gastric cardiac IM.

Efficacy and safety of nitrous oxide for patients with treatment-resistant depression, a randomized controlled trial.

Nitrous oxide (NO), an N-methyl-D-aspartate glutamate receptor antagonist, has demonstrated a rapid-onset antidepressant effect for treatment-resistant depression (TRD) preliminarily in the United States. This study aimed to test the efficacy and safety of NO for TRD patients in China. In this double-blinded, placebo-controlled trial, 44 patients with TRD were randomized to receive a one-hour inhalation of a mixture of either 50% NO/50% oxygen (NO group) or 50% air/50% oxygen (placebo group).

Cross-subject EEG emotion recognition combined with connectivity features and meta-transfer learning.

In recent years, with the rapid development of machine learning, automatic emotion recognition based on electroencephalogram (EEG) signals has received increasing attention. However, owing to the great variance of EEG signals sampled from different subjects, EEG-based emotion recognition experiences the individual difference problem across subjects, which significantly hinders recognition performance. In this study, we presented a method for EEG-based emotion recognition using a combination of a multi-scale residual network (MSRN) and meta-transfer learning (MTL) strategy.

Fall Risk Assessment for the Elderly Based on Weak Foot Features of Wearable Plantar Pressure.

The high fall rate of the elderly brings enormous challenges to families and the medical system; therefore, early risk assessment and intervention are quite necessary. Compared to other sensor-based technologies, in-shoe plantar pressure sensors, effectiveness and low obtrusiveness are widely used for long-term fall risk assessments because of their portability. While frequently-used bipedal center-of-pressure (COP) features are derived from a pressure sensing platform, they are not suitable for the shoe system or pressure insole owing to the lack of relative position information.

MhNet: Multi-scale spatio-temporal hierarchical network for real-time wearable fall risk assessment of the elderly.

Continuous fall risk assessment and real-time high falling risk warning are extremely necessary for the elderly, to protect their lives and ensure their quality of life. Wearable in-shoe pressure sensors have the potential to achieve these targets, due to their adequate wearing comfort. However, it is a great challenge to remove the individual differences of foot pressure data and identify the accurate fall risk from fewer gait cycles to realize real-time warning.

The added value of an artificial intelligence system in assisting radiologists on indeterminate BI-RADS 0 mammograms.

Objectives: To investigate the value of an artificial intelligence (AI) system in assisting radiologists to improve the assessment accuracy of BI-RADS 0 cases in mammograms.

Methods: We included 34,654 consecutive digital mammography studies, collected between January 2011 and January 2019, among which, 1088 cases from 1010 unique patients with initial BI-RADS 0 assessment who were recalled during 2 years of follow-up were used in this study. Two mid-level radiologists retrospectively re-assessed these BI-RADS 0 cases with the assistance of an AI system developed by us previously.

Prediction of Clinical Outcomes With EEG Microstate in Patients With Major Depressive Disorder.

The difficulty in timely evaluating patient response to antidepressants has brought great challenge to the treatment of major depressive disorder (MDD). Some studies found that the electroencephalogram (EEG) microstates might be a reliable marker to evaluate patient response to treatment. The present study aims to evaluate the relationship between EEG microstate parameters and MDD symptoms before and after treatment to identify predictive biological markers for patient response.

MommiNet-v2: Mammographic multi-view mass identification networks.

Many existing approaches for mammogram analysis are based on single view. Some recent DNN-based multi-view approaches can perform either bilateral or ipsilateral analysis, while in practice, radiologists use both to achieve the best clinical outcome. MommiNet is the first DNN-based tri-view mass identification approach, which can simultaneously perform bilateral and ipsilateral analysis of mammographic images, and in turn, can fully emulate the radiologists' reading practice.

Mechanical Characterization and Thermodynamic Analysis of Laser-Polished Landscape Design Products Using 3D Printing.

Recent innovations in 3D printing technologies and processes have influenced how landscape products are designed, built, and developed. In landscape architecture, reduced-size models are 3D-printed to replicate full-size structures. However, high surface roughness usually occurs on the surfaces of such 3D-printed components, which requires additional post-treatment.

Shape-stabilized phase change material with highly thermal conductive matrix developed by one-step pyrolysis method.

Metal microspheres doping porous carbon (MMPC), which was prepared using in-situ pyrolysis reduction strategy, could enhance the thermal conductivity of shape-stabilized phase change material (ss-PCM) prepared by MMPC as the matrix. However, in previous studies that were reported, the preparation of MMPC needed to synthesize porous carbon by pyrolysis firstly, and then porous carbon adsorbed metal ions was pyrolyzed again to obtain MMPC, which was tedious and energy-prodigal. In this study, a one-step pyrolysis strategy was developed for the synthesis of MMPC through the pyrolyzation of wheat bran adsorbed copper ions, and the copper microspheres doping wheat bran biochar (CMS-WBB) was prepared.

To Align Multimodal Lumbar Spine Images via Bending Energy Constrained Normalized Mutual Information.

To align multimodal images is important for information fusion, clinical diagnosis, treatment planning, and delivery, while few methods have been dedicated to matching computerized tomography (CT) and magnetic resonance (MR) images of lumbar spine. This study proposes a coarse-to-fine registration framework to address this issue. Firstly, a pair of CT-MR images are rigidly aligned for global positioning.

Erratum to "Automatic Segmentation of Ultrasound Tomography Image".

[This corrects the article DOI: 10.1155/2017/2059036.].

Efficient Segmentation of a Breast in B-Mode Ultrasound Tomography Using Three-Dimensional GrabCut (GC3D).

As an emerging modality for whole breast imaging, ultrasound tomography (UST), has been adopted for diagnostic purposes. Efficient segmentation of an entire breast in UST images plays an important role in quantitative tissue analysis and cancer diagnosis, while major existing methods suffer from considerable time consumption and intensive user interaction. This paper explores three-dimensional GrabCut (GC3D) for breast isolation in thirty reflection (B-mode) UST volumetric images.

Iterative image-domain ring artifact removal in cone-beam CT.

Ring artifacts in cone beam computed tomography (CBCT) images are caused by pixel gain variations using flat-panel detectors, and may lead to structured non-uniformities and deterioration of image quality. The purpose of this study is to propose a method of general ring artifact removal in CBCT images. This method is based on the polar coordinate system, where the ring artifacts manifest as stripe artifacts.

A shallow convolutional neural network for blind image sharpness assessment.

Blind image quality assessment can be modeled as feature extraction followed by score prediction. It necessitates considerable expertise and efforts to handcraft features for optimal representation of perceptual image quality. This paper addresses blind image sharpness assessment by using a shallow convolutional neural network (CNN).