Imaging features

Borophenes have sparked considerable interest owing to their fascinating physical characteristics and diverse polymorphism. However, borophene nanoribbons (BNRs) with widths less than 2 nm have not been achieved. Herein, we report the experimental realization of supernarrow BNRs. Combining scanning tunneling microscopy imaging with density functional theory modeling and ab initio molecular dynamics simulations, we demonstrate that, under the applied growth conditions, boron atoms can penetrate the outermost layer of Au(111) and form BNRs composed of a pair of zigzag (2,2) boron rows...

We endeavor on a rarely explored task named thermal infrared video denoising. Perception in the thermal infrared significantly enhances the capabilities of machine vision. Nonetheless, noise in imaging systems is one of the factors that hampers the large-scale application of equipment. Existing thermal infrared denoising methods, primarily focusing on the image level, inadequately utilize time-domain information and insufficiently conduct investigation of system-level mixed noise, presenting the inferior ability in the video-recorded era; while video denoising methods, commonly applied to RGB cameras, exhibit uncertain effectiveness owing to substantial dissimilarities in the noise models and modalities between RGB and thermal infrared images...

High-efficiency deep learning (DL) models are necessary not only to facilitate their use in devices with limited resources but also to improve resources required for training. Convolutional neural networks (ConvNets) typically exert severe demands on local device resources and this conventionally limits their adoption within mobile and embedded platforms. This brief presents work toward utilizing static convolutional filters generated from the space of local binary patterns (LBPs) and Haar features to design efficient ConvNet architectures...

Deep Neural Networks (DNNs) are known to be vulnerable to both backdoor and adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct robustness problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, this paper revealed that there is an intriguing connection between them: (1) planting a backdoor into a model will significantly affect the model's adversarial examples; (2) for an infected model, its adversarial examples have similar features as the triggered images...

With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status...

The issue of traffic congestion poses a significant obstacle to the development of global cities. One promising solution to tackle this problem is intelligent traffic signal control (TSC). Recently, TSC strategies leveraging reinforcement learning (RL) have garnered attention among researchers. However, the evaluation of these models has primarily relied on fixed metrics like reward and queue length. This limited evaluation approach provides only a narrow view of the model's decision-making process, impeding its practical implementation...

Proximal femoral fracture segmentation in computed tomography (CT) is essential in the preoperative planning of orthopedic surgeons. Recently, numerous deep learning-based approaches have been proposed for segmenting various structures within CT scans. Nevertheless, distinguishing various attributes between fracture fragments and soft tissue regions in CT scans frequently poses challenges, which have received comparatively limited research attention. Besides, the cornerstone of contemporary deep learning methodologies is the availability of annotated data, while detailed CT annotations remain scarce...

BACKGROUNDPersistent cough and dyspnea are prominent features of post-acute sequelae of SARS-CoV-2 (also termed 'Long COVID'); however, physiologic measures and clinical features associated with these pulmonary symptoms remain poorly defined. Using longitudinal pulmonary function testing (PFTs) and CT imaging, this study aimed to identify the characteristics and determinants of pulmonary Long COVID.METHODSThis single-center retrospective study included 1,097 patients with clinically defined Long COVID characterized by persistent pulmonary symptoms (dyspnea, cough, and chest discomfort) lasting for ≥1 month after resolution of primary COVID infection...

BACKGROUND: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. OBJECTIVE: This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. METHODS: This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier...

In the article cited above, Fig. 7G mistakenly featured the same images as Fig. 7E due to an error during manuscript preparation. The corresponding graphs and associated data interpretation were not affected, and the conclusions remain unchanged. The correct image for Fig. 7G appears below. The authors apologize for the error. The online version of the article (https://doi.org/10.2337/db22-0745) has been updated with the correct image.

OBJECTIVES: Confocal laser endomicroscopy (CLE) is an optical method that enables microscopic visualization of oral mucosa. Previous studies have shown that it is possible to differentiate between physiological and malignant oral mucosa. However, differences in mucosal architecture were not taken into account. The objective was to map the different oral mucosal morphologies and to establish a "CLE map" of physiological mucosa as baseline for further application of this powerful technology...

BACKGROUND: Accurate prediction of successful sphincter-preserving resection (SSPR) for low rectal cancer enables peer institutions to scrutinize their own performance and potentially avoid unnecessary permanent colostomy. The aim of this study is to evaluate the variation in SSPR and present the first artificial intelligence (AI) models to predict SSPR in low rectal cancer patients. STUDY DESIGN: This was a retrospective post hoc analysis of a multicenter, noninferiority randomized clinical trial (LASRE, NCT XXXXXX) conducted in 22 tertiary hospitals across China...

INTRODUCTION: Retroperitoneum can be the origin of a wide variety of pathologic conditions and potential space for disease spread to other compartments of the abdomen and pelvis. Computed tomography (CT) and magnetic resonance imaging (MRI) are often the initial imaging modalities to evaluate the retroperitoneal pathologies, however given the intrinsic limitations, F18-FDG PET/CT provides additional valuable metabolic information which can change the patient management and clinical outcomes...

Cryptogenic stroke refers to a stroke of undetermined etiology. It accounts for approximately one-fifth of ischemic strokes and has a higher prevalence in younger patients. Embolic stroke of undetermined source (ESUS) refers to a subgroup of patients with nonlacunar cryptogenic strokes in whom embolism is the suspected stroke mechanism. Under the classifications of cryptogenic stroke or ESUS, there is wide heterogeneity in possible stroke mechanisms. In the absence of a confirmed stroke etiology, there is no established treatment for secondary prevention of stroke in patients experiencing cryptogenic stroke or ESUS, despite several clinical trials, leaving physicians with a clinical dilemma...

The progress of research focused on cholangiocytes and the biliary tree during development and following injury is hindered by limited available quantitative methodologies. Current techniques include two-dimensional standard histological cell-counting approaches, which are rapidly performed error-prone and lack architectural context; or three-dimensional analysis of the biliary tree in opacified livers, which introduce technical issues along with minimal quantitation. The present study aims to fill these quantitative gaps with a supervised machine learning model (BiliQML) able to quantify biliary forms in the liver of anti-Keratin 19 antibody-stained whole slide images...

Systemic sclerosis (SSc) with interstitial lung disease (SSc-ILD) lacks curative pharmacological treatments, thus necessitating effective animal models for candidate drug discovery. Existing Bleomycin (BLM)-induced SSc-ILD mouse models feature spatially limited pulmonary fibrosis, spontaneously resolving after 28 days. Here, we present an alternative BLM administration approach in female C57BL/6 mice, combining oropharyngeal aspiration (OA) and subcutaneous mini-pump delivery (pump) of BLM to induce a sustained and more persistent fibrosis, while retaining stable skin fibrosis...

BACKGROUND AND AIMS: The Liver Imaging Reporting and Data System (LI-RADS) offers a standardized approach for imaging hepatocellular carcinoma. However, the diverse styles and structures of radiology reports complicate automatic data extraction. Large language models hold the potential for structured data extraction from free-text reports. Our objective was to evaluate the performance of Generative Pre-trained Transformer (GPT)-4 in extracting LI-RADS features and categories from free-text liver magnetic resonance imaging (MRI) reports...

The primary treatment for glioblastoma (GBM) is removing the tumor mass as defined by magnetic resonance imaging (MRI). However, MRI has limited diagnostic and predictive value. Tumor-associated macrophages (TAMs) are abundant in GBM microenvironment (TME) and are found in peripheral blood (PB). FKBP51 expression, with its canonical and spliced isoforms, is constitutive in immune cells and aberrant in GBM. Spliced FKBP51s supports M2-polarization. To find an immunological signature that combined with MRI could advance in diagnosis, we immunophenotyped the macrophages of TME and PB from 37 GBM patients using FKBP51s and classical M1-M2 markers...

OBJECTIVES: The study focused on developing a reliable real-time venous localization, identification, and visualization framework based upon deep learning (DL) self-parametrized Convolution Neural Network (CNN) algorithm for segmentation of the venous map for both lower and upper limb dataset acquired under unconstrained conditions using near-infrared (NIR) imaging setup, specifically to assist vascular surgeons during venipuncture, vascular surgeries, or Chronic Venous Disease (CVD) treatments...

Brain segmentation from neonatal MRI images is a very challenging task due to large changes in the shape of cerebral structures and variations in signal intensities reflecting the gestational process. In this context, there is a clear need for segmentation techniques that are robust to variations in image contrast and to the spatial configuration of anatomical structures. In this work, we evaluate the potential of synthetic learning, a contrast-independent model trained using synthetic images generated from the ground truth labels of very few subjects...