Self-Assembly of Helical Nanofibrous Chiral Covalent Organic Frameworks.

Despite significant progress on the design and synthesis of covalent organic frameworks (COFs), precise control over microstructures of such materials remains challenging. Herein, two chiral COFs with well-defined one-handed double-helical nanofibrous morphologies were constructed via an unprecedented template-free method, capitalizing on the diastereoselective formation of aminal linkages. Detailed time-dependent experiments reveal the spontaneous transformation of initial rod-like aggregates into the double-helical microstructures. We have further demonstrated that the helical chirality and circular dichroism signal can be facilely inversed by simply adjusting the amount of acetic acid during synthesis. Moreover, by transferring chirality to achiral fluorescent molecular adsorbents, the helical COF nanostructures can effectively induce circularly polarized luminescence with the highest luminescent asymmetric factor (glum ) up to ≈0.01.

Histopathological growth patterns of liver metastasis: updated consensus guidelines for pattern scoring, perspectives and recent mechanistic insights.

The first consensus guidelines for scoring the histopathological growth patterns (HGPs) of liver metastases were established in 2017. Since then, numerous studies have applied these guidelines, have further substantiated the potential clinical value of the HGPs in patients with liver metastases from various tumour types and are starting to shed light on the biology of the distinct HGPs. In the present guidelines, we give an overview of these studies, discuss novel strategies for predicting the HGPs of liver metastases, such as deep-learning algorithms for whole-slide histopathology images and medical imaging, and highlight liver metastasis animal models that exhibit features of the different HGPs. Based on a pooled analysis of large cohorts of patients with liver-metastatic colorectal cancer, we propose a new cut-off to categorise patients according to the HGPs. An up-to-date standard method for HGP assessment within liver metastases is also presented with the aim of incorporating HGPs into the decision-making processes surrounding the treatment of patients with liver-metastatic cancer. Finally, we propose hypotheses on the cellular and molecular mechanisms that drive the biology of the different HGPs, opening some exciting preclinical and clinical research perspectives.

End-diastolic segmentation of intravascular ultrasound images enables more reproducible volumetric analysis of atheroma burden.

BACKGROUND: Volumetric intravascular ultrasound (IVUS) analysis is currently performed at a fixed frame interval, neglecting the cyclic changes in vessel dimensions occurring during the cardiac cycle that can affect the reproducibility of the results. Analysis of end-diastolic (ED) IVUS frames has been proposed to overcome this limitation. However, at present, there is lack of data to support its superiority over conventional IVUS. OBJECTIVES: The present study aims to compare the reproducibility of IVUS volumetric analysis performed at a fixed frame interval and at the ED frames, identified retrospectively using a novel deep-learning methodology. METHODS: IVUS data acquired from 97 vessels were included in the present study; each vessel was segmented at 1 mm interval (conventional approach) and at ED frame twice by an expert analyst. Reproducibility was tested for the following metrics; normalized lumen, vessel and total atheroma volume (TAV), and percent atheroma volume (PAV). RESULTS: The mean length of the analyzed segments was 50.0 ± 24.1 mm. ED analysis was more reproducible than the conventional analysis for the normalized lumen (mean difference: 0.76 ± 4.03 mm3 vs. 1.72 ± 11.37 mm3 ; p for the variance of differences ratio < 0.001), vessel (0.30 ± 1.79 mm3 vs. -0.47 ± 10.26 mm3 ; p < 0.001), TAV (-0.46 ± 4.03 mm3 vs. -2.19 ± 14.39 mm3 ; p < 0.001) and PAV (-0.12 ± 0.59% vs. -0.34 ± 1.34%; p < 0.001). Results were similar when the analysis focused on the 10 mm most diseased segment. The superiority of the ED approach was due to a more reproducible detection of the segment of interest and to the fact that it was not susceptible to the longitudinal motion of the IVUS probe and the cyclic changes in vessel dimensions during the cardiac cycle. CONCLUSIONS: ED IVUS segmentation enables more reproducible volumetric analysis and quantification of TAV and PAV that are established end points in longitudinal studies assessing the efficacy of novel pharmacotherapies. Therefore, it should be preferred over conventional IVUS analysis as its higher reproducibility is expected to have an impact on the sample size calculation for the primary end point.

Integrated Si3N4 microresonator-based quantum light sources with high brightness using a subtractive wafer-scale platform.

The silicon nitride (Si3N4) platform, demonstrating a moderate third-order optical nonlinearity and a low optical loss compared with those of silicon, is suitable for integrated quantum photonic circuits. However, it is challenging to develop a crack-free, wafer-scale, thick Si3N4 platform in a single deposition run using a subtractive complementary metal-oxide-semiconductor (CMOS)-compatible fabrication process suitable for dispersion-engineered quantum light sources. In this paper, we demonstrate our unique subtractive fabrication process by introducing a stress-release pattern prior to the single Si3N4 film deposition. Our Si3N4 platform enables 950 nm-thick and 8 µm-wide microring resonators supporting whispering-gallery modes for quantum light sources at 1550 nm wavelengths. We report a high photon-pair generation rate of ∼1.03 MHz/mW2, with a high spectral brightness of ∼5×106 pairs/s/mW2/GHz. We demonstrate the first heralded single-photon measurement on the Si3N4 platform, which exhibits a high quality of conditional self-correlation gH(2)(0) of 0.008 ± 0.003.

Effects of peat on plant growth and lead and zinc phytostabilization from lead-zinc mine tailing in southern China: Screening plant species resisting and accumulating metals.

In order to investigate the toxicity-resistance of eighteen Chinese native plants in lead (Pb)-zinc (Zn) mine tailings, we categorized their resistance to Pb and Zn, and tested their potential for phytoremediation effectiveness of Pb and Zn. Fourteen woody plant species belonging to 12 families, and 4 herbaceous species belonging to 4 families, were grown in pots with mixtures of 100% tailing +0% peat (CK), 90% tailing +10% peat (A1), and 80% tailing + 20% peat (A2), respectively. Plant height and biomass, chlorophyll content, and Pb and Zn contents of non-rhizosphere spoil mixtures and plant tissues were measured. Fifteen of the plants grew in all three spoil mixtures. Both A1 and A2 had higher plant height and biomass increment and chlorophyll contents than CK. The content of Pb and Zn in plant shoots and roots was CK > A1 > A2. The value of BCF less than 0.1, compared to 1, was a more precise classification basis for plants excluding metals. Screening for Pb and Zn resistant plants and their bioremediation potential produced the following candidate species: Sapium sebiferum, Salix matsudana, Hibiscus cannabinus, Corchorus capsularis, Ricinus communis, and Populus nigra. These species were highly Pb and Zn tolerant species, with notable growth characteristics and capacities to bioaccumulate Pb and Zn from the mine tailings. Compared to CK, the removal of Pb and Zn from non-rhizosphere spoil increased by an average of 9.64% and 9.6%, respectively in A1, but decreased in A2. The results indicated candidate species and 10% peat addition in the tailing were significant in phytoremediation of Pb and Zn regarding environmental safety.

Accumulation and subcellular distribution of heavy metal in Paulownia fortunei cultivated in lead-zinc slag amended with peat.

The contamination of toxic heavy metals was a major issue of concern in the last century. A fast-growing metal-accumulating woody plant is a promising approach for the remediation of toxic heavy metal. In this study, the transportation of heavy metals (Pb, Zn, Cu, and Cd) in Paulownia fortunei cultivated in lead-zinc slag amended with different mass ratios of peat (CK: 0; T1: 10%; T2: 20%; T3: 30%) was investigated, as well as the subcellular distribution of Pb, Zn, Cu, and Cd in Paulownia fortunei. The results showed that the accumulation content of Pb, Zn, Cu, and Cd in Paulownia fortunei were increased with peat amendment, which was in the range of 4.216 ∼ 6.853, 20.905 ∼ 23.017, 1.898 ∼ 2.572, and 0.530 ∼ 0.616 mg/pot, respectivly. The experimental group with 30% dose of peat showed the best performance on the accumulation content of Pb, Zn, Cu, and Cd, with increase rates (compared to control) of 4.088, 10.573, 1.360, and 0.294 mg/pot, respectively. The bioconcentration, translocation and transfer quantity factor of Pb, Zn, Cu, and Cd were less than 1. Fixation of cell wall and compartmentalization of vacuolar appeared to play an important role in reducing the toxicity of Pb, Zn, Cu, and Cd.

Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images.

Purpose: Optical coherence tomography (OCT) is an emerging imaging tool in healthcare with common applications in ophthalmology for detection of retinal diseases, as well as other medical domains. The noise in OCT images presents a great challenge as it hinders the clinician's ability to diagnosis in extensive detail. Approach: In this work, a region-based, deep-learning, denoising framework is proposed for adaptive cleaning of noisy OCT-acquired images. The core of the framework is a hybrid deep-learning model named transformer enhanced autoencoder rendering (TEAR). Attention gates are utilized to ensure focus on denoising the foreground and to remove the background. TEAR is designed to remove the different types of noise artifacts commonly present in OCT images and to enhance the visual quality. Results: Extensive quantitative evaluations are performed to evaluate the performance of TEAR and compare it against both deep-learning and traditional state-of-the-art denoising algorithms. The proposed method improved the peak signal-to-noise ratio to 27.9 dB, CNR to 6.3 dB, SSIM to 0.9, and equivalent number of looks to 120.8 dB for a dental dataset. For a retinal dataset, the performance metrics in the same sequence are: 24.6, 14.2, 0.64, and 1038.7 dB, respectively. Conclusions: The results show that the approach verifiably removes speckle noise and achieves superior quality over several well-known denoisers.

Denoising of Optical Coherence Tomography Images in Ophthalmology Using Deep Learning: A Systematic Review.

Imaging from optical coherence tomography (OCT) is widely used for detecting retinal diseases, localization of intra-retinal boundaries, etc. It is, however, degraded by speckle noise. Deep learning models can aid with denoising, allowing clinicians to clearly diagnose retinal diseases. Deep learning models can be considered as an end-to-end framework. We selected denoising studies that used deep learning models with retinal OCT imagery. Each study was quality-assessed through image quality metrics (including the peak signal-to-noise ratio-PSNR, contrast-to-noise ratio-CNR, and structural similarity index metric-SSIM). Meta-analysis could not be performed due to heterogeneity in the methods of the studies and measurements of their performance. Multiple databases (including Medline via PubMed, Google Scholar, Scopus, Embase) and a repository (ArXiv) were screened for publications published after 2010, without any limitation on language. From the 95 potential studies identified, a total of 41 were evaluated thoroughly. Fifty-four of these studies were excluded after full text assessment depending on whether deep learning (DL) was utilized or the dataset and results were not effectively explained. Numerous types of OCT images are mentioned in this review consisting of public retinal image datasets utilized purposefully for denoising OCT images (n = 37) and the Optic Nerve Head (ONH) (n = 4). A wide range of image quality metrics was used; PSNR and SNR that ranged between 8 and 156 dB. The minority of studies (n = 8) showed a low risk of bias in all domains. Studies utilizing ONH images produced either a PSNR or SNR value varying from 8.1 to 25.7 dB, and that of public retinal datasets was 26.4 to 158.6 dB. Further analysis on denoising models was not possible due to discrepancies in reporting that did not allow useful pooling. An increasing number of studies have investigated denoising retinal OCT images using deep learning, with a range of architectures being implemented. The reported increase in image quality metrics seems promising, while study and reporting quality are currently low.

[Evolution and implementation pathways from desertification prevention and control to sandy land management and use in China]. / 中国从防沙治沙到管沙用沙的演变过程与实现路径.

The construction of ecological civilization emphasizes holistic protection of "mountain-water-forest-farmland-lake-grassland-sand", which has become an important concept of desertification prevention projects in arid and semi-arid areas of China. In the past, sandy land management and use have been neglected in desertification prevention and control, in that the links have not been effectively connected and the long-term and efficient desertification prevention has not been realized. Therefore, combining Qian Xuesen's understanding of "deserticulture", we comprehensively discussed the "long-term achievements" of China's desertification control miracle from the perspective of the historical evolution of the interaction of technology and practice, and the strategic development of policy guidance. Further, we defined the concepts of desertification prevention, desertification control, and sandy land management and use. We analyzed the coupling and coordination relationship between the four links and the scientific principle based on the development of ecological industry chain. Finally, we put forward the policy and market realization pathways, with efficient sandy land management as the core, desertification prevention as the basis, desertification control as the channel, and long-term sandy land use as the foundation. We expected to provide theoretical and practical guidance for creating a new miracle of China's desertification prevention and control.

Sketch-Supervised Histopathology Tumour Segmentation: Dual CNN-Transformer With Global Normalised CAM.

Deep learning methods are frequently used in segmenting histopathology images with high-quality annotations nowadays. Compared with well-annotated data, coarse, scribbling-like labelling is more cost-effective and easier to obtain in clinical practice. The coarse annotations provide limited supervision, so employing them directly for segmentation network training remains challenging. We present a sketch-supervised method, called DCTGN-CAM, based on a dual CNN-Transformer network and a modified global normalised class activation map. By modelling global and local tumour features simultaneously, the dual CNN-Transformer network produces accurate patch-based tumour classification probabilities by training only on lightly annotated data. With the global normalised class activation map, more descriptive gradient-based representations of the histopathology images can be obtained, and inference of tumour segmentation can be performed with high accuracy. Additionally, we collect a private skin cancer dataset named BSS, which contains fine and coarse annotations for three types of cancer. To facilitate reproducible performance comparison, experts are also invited to label coarse annotations on the public liver cancer dataset PAIP2019. On the BSS dataset, our DCTGN-CAM segmentation outperforms the state-of-the-art methods and achieves 76.68 % IOU and 86.69 % Dice scores on the sketch-based tumour segmentation task. On the PAIP2019 dataset, our method achieves a Dice gain of 8.37 % compared with U-Net as the baseline network.

SASAN: Spectrum-Axial Spatial Approach Networks for Medical Image Segmentation.

Ophthalmic diseases such as central serous chorioretinopathy (CSC) significantly impair the vision of millions of people globally. Precise segmentation of choroid and macular edema is critical for diagnosing and treating these conditions. However, existing 3D medical image segmentation methods often fall short due to the heterogeneous nature and blurry features of these conditions, compounded by medical image clarity issues and noise interference arising from equipment and environmental limitations. To address these challenges, we propose the Spectrum Analysis Synergy Axial-Spatial Network (SASAN), an approach that innovatively integrates spectrum features using the Fast Fourier Transform (FFT). SASAN incorporates two key modules: the Frequency Integrated Neural Enhancer (FINE), which mitigates noise interference, and the Axial-Spatial Elementum Multiplier (ASEM), which enhances feature extraction. Additionally, we introduce the Self-Adaptive Multi-Aspect Loss (LSM), which balances image regions, distribution, and boundaries, adaptively updating weights during training. We compiled and meticulously annotated the Choroid and Macular Edema OCT Mega Dataset (CMED-18k), currently the world's largest dataset of its kind. Comparative analysis against 13 baselines shows our method surpasses these benchmarks, achieving the highest Dice scores and lowest HD95 in the CMED and OIMHS datasets. Our code is publicly available at https://github.com/IMOP-lab/SASAN-Pytorch.

Stimulus-guided adaptive transformer network for retinal blood vessel segmentation in fundus images.

Automated retinal blood vessel segmentation in fundus images provides important evidence to ophthalmologists in coping with prevalent ocular diseases in an efficient and non-invasive way. However, segmenting blood vessels in fundus images is a challenging task, due to the high variety in scale and appearance of blood vessels and the high similarity in visual features between the lesions and retinal vascular. Inspired by the way that the visual cortex adaptively responds to the type of stimulus, we propose a Stimulus-Guided Adaptive Transformer Network (SGAT-Net) for accurate retinal blood vessel segmentation. It entails a Stimulus-Guided Adaptive Module (SGA-Module) that can extract local-global compound features based on inductive bias and self-attention mechanism. Alongside a light-weight residual encoder (ResEncoder) structure capturing the relevant details of appearance, a Stimulus-Guided Adaptive Pooling Transformer (SGAP-Former) is introduced to reweight the maximum and average pooling to enrich the contextual embedding representation while suppressing the redundant information. Moreover, a Stimulus-Guided Adaptive Feature Fusion (SGAFF) module is designed to adaptively emphasize the local details and global context and fuse them in the latent space to adjust the receptive field (RF) based on the task. The evaluation is implemented on the largest fundus image dataset (FIVES) and three popular retinal image datasets (DRIVE, STARE, CHASEDB1). Experimental results show that the proposed method achieves a competitive performance over the other existing method, with a clear advantage in avoiding errors that commonly happen in areas with highly similar visual features. The sourcecode is publicly available at: https://github.com/Gins-07/SGAT.

Lesion Detection in Optical Coherence Tomography with Transformer-Enhanced Detector.

Optical coherence tomography (OCT) is an emerging imaging tool in healthcare with common applications in ophthalmology for the detection of retinal diseases and in dentistry for the early detection of tooth decay. Speckle noise is ubiquitous in OCT images, which can hinder diagnosis by clinicians. In this paper, a region-based, deep learning framework for the detection of anomalies is proposed for OCT-acquired images. The core of the framework is Transformer-Enhanced Detection (TED), which includes attention gates (AGs) to ensure focus is placed on the foreground while identifying and removing noise artifacts as anomalies. TED was designed to detect the different types of anomalies commonly present in OCT images for diagnostic purposes and thus aid clinical interpretation. Extensive quantitative evaluations were performed to measure the performance of TED against current, widely known, deep learning detection algorithms. Three different datasets were tested: two dental and one CT (hosting scans of lung nodules, livers, etc.). The results showed that the approach verifiably detected tooth decay and numerous lesions across two modalities, achieving superior performance compared to several well-known algorithms. The proposed method improved the accuracy of detection by 16-22% and the Intersection over Union (IOU) by 10% for both dentistry datasets. For the CT dataset, the performance metrics were similarly improved by 9% and 20%, respectively.

Automated photographic analysis of inferior oblique overaction based on deep learning.

Background: Inferior oblique overaction (IOOA) is a common ocular motility disorder. This study aimed to propose a novel deep learning-based approach to automatically evaluate the amount of IOOA. Methods: This prospective study included 106 eyes of 72 consecutive patients attending the strabismus clinic in a tertiary referral hospital. Patients were eligible for inclusion if they were diagnosed with IOOA. IOOA was clinically graded from +1 to +4. Based on photograph in the adducted position, the height difference between the inferior corneal limbus of both eyes was manually measured using ImageJ and automatically measured by our deep learning-based image analysis system with human supervision. Correlation coefficients, Bland-Altman plots and mean absolute deviation (MAD) were analyzed between two different measurements of evaluating IOOA. Results: There were significant correlations between automated photographic measurements and clinical gradings (Kendall's tau: 0.721; 95% confidence interval: 0.652 to 0.779; P<0.001), between automated and manual photographic measurements [intraclass correlation coefficients (ICCs): 0.975; 95% confidence interval: 0.963 to 0.983; P<0.001], and between two-repeated automated photographic measurements (ICCs: 0.998; 95% confidence interval: 0.997 to 0.999; P<0.001). The biases and MADs were 0.10 [95% limits of agreement (LoA): -0.45 to 0.64] mm and 0.26±0.14 mm between automated and manual photographic measurements, and 0.01 (95% LoA: -0.14 to 0.16) mm and 0.07±0.04 mm between two-repeated automated photographic measurements, respectively. Conclusions: The automated photographic measurements of IOOA using deep learning technique were in excellent agreement with manual photographic measurements and clinical gradings. This new approach allows objective, accurate and repeatable measurement of IOOA and could be easily implemented in clinical practice using only photographs.

Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy.

Background: We aimed to propose a deep learning-based approach to automatically measure eyelid morphology in patients with thyroid-associated ophthalmopathy (TAO). Methods: This prospective study consecutively included 74 eyes of patients with TAO and 74 eyes of healthy volunteers visiting the ophthalmology department in a tertiary hospital. Patients diagnosed as TAO and healthy volunteers who were age- and gender-matched met the eligibility criteria for recruitment. Facial images were taken under the same light conditions. Comprehensive eyelid morphological parameters, such as palpebral fissure (PF) length, margin reflex distance (MRD), eyelid retraction distance, eyelid length, scleral area, and mid-pupil lid distance (MPLD), were automatically calculated using our deep learning-based analysis system. MRD1 and 2 were manually measured. Bland-Altman plots and intraclass correlation coefficients (ICCs) were performed to assess the agreement between automatic and manual measurements of MRDs. The asymmetry of the eyelid contour was analyzed using the temporal: nasal ratio of the MPLD. All eyelid features were compared between TAO eyes and control eyes using the independent samples t-test. Results: A strong agreement between automatic and manual measurement was indicated. Biases of MRDs in TAO eyes and control eyes ranged from -0.01 mm [95% limits of agreement (LoA): -0.64 to 0.63 mm] to 0.09 mm (LoA: -0.46 to 0.63 mm). ICCs ranged from 0.932 to 0.980 (P<0.001). Eyelid features were significantly different in TAO eyes and control eyes, including MRD1 (4.82±1.59 vs. 2.99±0.81 mm; P<0.001), MRD2 (5.89±1.16 vs. 5.47±0.73 mm; P=0.009), upper eyelid length (UEL) (27.73±4.49 vs. 25.42±4.35 mm; P=0.002), lower eyelid length (LEL) (31.51±4.59 vs. 26.34±4.72 mm; P<0.001), and total scleral area (SATOTAL) (96.14±34.38 vs. 56.91±14.97 mm2; P<0.001). The MPLDs at all angles showed significant differences in the 2 groups of eyes (P=0.008 at temporal 180°; P<0.001 at other angles). The greatest temporal-nasal asymmetry appeared at 75° apart from the midline in TAO eyes. Conclusions: Our proposed system allowed automatic, comprehensive, and objective measurement of eyelid morphology by only using facial images, which has potential application prospects in TAO. Future work with a large sample of patients that contains different TAO subsets is warranted.

POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation.

Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS2 to automatically segment IVUS images in a user-friendly environment.

CARDIAN: a novel computational approach for real-time end-diastolic frame detection in intravascular ultrasound using bidirectional attention networks.

Introduction: Changes in coronary artery luminal dimensions during the cardiac cycle can impact the accurate quantification of volumetric analyses in intravascular ultrasound (IVUS) image studies. Accurate ED-frame detection is pivotal for guiding interventional decisions, optimizing therapeutic interventions, and ensuring standardized volumetric analysis in research studies. Images acquired at different phases of the cardiac cycle may also lead to inaccurate quantification of atheroma volume due to the longitudinal motion of the catheter in relation to the vessel. As IVUS images are acquired throughout the cardiac cycle, end-diastolic frames are typically identified retrospectively by human analysts to minimize motion artefacts and enable more accurate and reproducible volumetric analysis. Methods: In this paper, a novel neural network-based approach for accurate end-diastolic frame detection in IVUS sequences is proposed, trained using electrocardiogram (ECG) signals acquired synchronously during IVUS acquisition. The framework integrates dedicated motion encoders and a bidirectional attention recurrent network (BARNet) with a temporal difference encoder to extract frame-by-frame motion features corresponding to the phases of the cardiac cycle. In addition, a spatiotemporal rotation encoder is included to capture the IVUS catheter's rotational movement with respect to the coronary artery. Results: With a prediction tolerance range of 66.7 ms, the proposed approach was able to find 71.9%, 67.8%, and 69.9% of end-diastolic frames in the left anterior descending, left circumflex and right coronary arteries, respectively, when tested against ECG estimations. When the result was compared with two expert analysts' estimation, the approach achieved a superior performance. Discussion: These findings indicate that the developed methodology is accurate and fully reproducible and therefore it should be preferred over experts for end-diastolic frame detection in IVUS sequences.

Sex Difference in Global Burden of Major Depressive Disorder: Findings From the Global Burden of Disease Study 2019.

Objectives: Globally, major depressive disorder (MDD) is considered to be a leading cause of disability. In this article, we aim to investigate the sex difference in global burden of MDD by year, age, and socioeconomic development, utilizing disability-adjusted life-years (DALYs). Methods: Global and national sex-specific DALY estimates caused by MDD from 1990 to 2019 and in different age groups were obtained from the Global Burden of Disease (GBD) Study 2019. Human development index (HDI) was used as an indicator of national socioeconomic development. Spearman correlation and linear regression analyses were performed to explore the relationship between national socioeconomic development and sex difference in MDD burden. Results: Sex difference in global burden of MDD persisted between 1990 and 2019, with age-standardized DALY rates being 352 among males vs. 593 among females in 1990 and 354 vs. 564 in 2019. Females had higher burden of MDD than males at the same age. Disability-adjusted life-years numbers and rates among both sexes rapidly increased with age for those aged 10-24 years, along with gradually enlarging sex difference. Age-standardized DALY rates among females were higher than that among males for each HDI-based country group (P < 0.001). National age-standardized DALY rates among both sexes were negatively related to HDI. However, female-to-male age-standardized DALY rate ratios were positively associated with HDI (Spearman r = 0.383, P < 0.001; standardized ß = 0.300, P < 0.001). Conclusion: Although some improvement in sex difference in global burden of MDD has been achieved, it still persists in the past three decades, with females bearing more burden than males. To reduce sex difference in global MDD burden, more attention should be paid to young people and people in developed countries. The findings highlight the importance of making sex-specific health policy to manage mental impairment caused by MDD.

Multimodal Gait Recognition for Neurodegenerative Diseases.

In recent years, single modality-based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognized that each of the established approaches has different strengths and weaknesses. As an important motor symptom, gait disturbance is usually used for diagnosis and evaluation of diseases; moreover, the use of multimodality analysis of the patient's walking pattern compensates for the one-sidedness of single modality gait recognition methods that only learn gait changes in a single measurement dimension. The fusion of multiple measurement resources has demonstrated promising performance in the identification of gait patterns associated with individual diseases. In this article, as a useful tool, we propose a novel hybrid model to learn the gait differences between three neurodegenerative diseases, between patients with different severity levels of Parkinson's disease, and between healthy individuals and patients, by fusing and aggregating data from multiple sensors. A spatial feature extractor (SFE) is applied to generating representative features of images or signals. In order to capture temporal information from the two modality data, a new correlative memory neural network (CorrMNN) architecture is designed for extracting temporal features. Afterward, we embed a multiswitch discriminator to associate the observations with individual state estimations. Compared with several state-of-the-art techniques, our proposed framework shows more accurate classification results.

Automated Measurement of Ocular Movements Using Deep Learning-Based Image Analysis.

PURPOSE: Clinical assessment of ocular movements is essential for the diagnosis and management of ocular motility disorders. This study aimed to propose a deep learning-based image analysis to automatically measure ocular movements based on photographs and to investigate the relationship between ocular movements and age. METHODS: 207 healthy volunteers (414 eyes) aged 5-60 years were enrolled in this study. Photographs were taken in the cardinal gaze positions. Ocular movements were manually measured based on a modified limbus test using ImageJ and automatically measured by our deep learning-based image analysis. Correlation analyses and Bland-Altman analyses were conducted to assess the agreement between manual and automated measurements. The relationship between ocular movements and age were analyzed using generalized estimating equations. RESULTS: The intraclass correlation coefficients between manual and automated measurements of six extraocular muscles ranged from 0.802 to 0.848 (P < 0.001), and the bias ranged from -0.63 mm to 0.71 mm. The average measurements were 8.62 ± 1.07 mm for superior rectus, 7.77 ± 1.24 mm for inferior oblique, 6.99 ± 1.23 mm for lateral rectus, 6.71 ± 1.22 mm for medial rectus, 6.81 ± 1.20 mm for inferior rectus, and 6.63 ± 1.37 mm for superior oblique, respectively. Ocular movements in each cardinal gaze position were negatively related to age (P < 0.05). CONCLUSIONS: The automated measurements of ocular movements using a deep learning-based approach were in excellent agreement with the manual measurements. This new approach allows objective assessment of ocular movements and shows great potential in the diagnosis and management of ocular motility disorders.