Autism spectrum disorder knowledge scale: Chinese revision of the general population version.

The general population of China has misconceptions about Autism Spectrum Disorder (ASD). The measurement of ASD knowledge is conducive to conducting widespread scientific publicity. However, China lacks a structurally complete ASD knowledge scale with good reliability and validity. Therefore, this study aimed to introduce a suitable Chinese ASD knowledge scale. Based on 317 participants, this study revised the Chinese version of the Autism Spectrum Disorder Knowledge Scale(ASKSG), assessed its reliability, validity, and psychometric properties, and analyzed the ASD knowledge of the Chinese general population of this subject sample. The results provided support for the Chinese version of the ASKSG as a suitable measure for assessing ASD knowledge and indicated that ASD knowledge in this study's sample was relatively poor, particularly with regard to etiology and epidemiology.

Biopsy or Follow-up: AI Improves the Clinical Strategy of US BI-RADS 4A Breast Nodules Using a Convolutional Neural Network.

OBJECTIVES: To develop predictive nomograms based on clinical and ultrasound features and to improve the clinical strategy for US BI-RADS 4A lesions. METHODS: Patients with US BI-RADS 4A lesions from 3 hospitals between January 2016 and June 2020 were retrospectively included. Clinical and ultrasound features were extracted to establish nomograms CE (based on clinical experience) and DL (based on deep-learning algorithm). The performances of nomograms were evaluated by receiver operator characteristic curves, calibration curves and decision curves. Diagnostic performances with DL of radiologists were analyzed. RESULTS: 1616 patients from 2 hospitals were randomly divided into training and internal validation cohorts at a ratio of 7:3. Hundred patients from another hospital made up external validation cohort. DL achieved more optimized AUCs than CE (internal validation: 0.916 vs. 0.863, P < .01; external validation: 0.884 vs. 0.776, P = .05). The sensitivities of DL were higher than CE (internal validation: 81.03% vs. 72.41%, P = .044; external validation: 93.75% vs. 81.25%, P = .4795) without losing specificity (internal validation: 84.91% vs. 86.47%, P = .353; external validation: 69.14% vs. 71.60%, P = .789). Decision curves indicated DL adds more clinical net benefit. With DL's assistance, both radiologists achieved higher AUCs (0.712 vs. 0.801; 0.547 vs. 0.800), improved specificities (70.93% vs. 74.42%, P < .001; 59.3% vs. 81.4%, P = .004), and decreased unnecessary biopsy rates by 6.7% and 24%. CONCLUSION: DL was developed to discriminate US BI-RADS 4A lesions with a higher diagnostic power and more clinical net benefit than CE. Using DL may guide clinicians to make precise clinical decisions and avoid overtreatment of benign lesions.

Hypothesis Testing for Progressive Kernel Estimation and VCM Framework.

Identifying an appropriate radius for unbiased kernel estimation is crucial for the efficiency of radiance estimation. However, determining both the radius and unbiasedness still faces big challenges. In this paper, we first propose a statistical model of photon samples and associated contributions for progressive kernel estimation, under which the kernel estimation is unbiased if the null hypothesis of this statistical model stands. Then, we present a method to decide whether to reject the null hypothesis about the statistical population (i.e., photon samples) by the F-test in the Analysis of Variance. Hereby, we implement a progressive photon mapping (PPM) algorithm, wherein the kernel radius is determined by this hypothesis test for unbiased radiance estimation. Secondly, we propose VCM+, a reinforcement of Vertex Connection and Merging (VCM), and derive its theoretically unbiased formulation. VCM+ combines hypothesis testing-based PPM with bidirectional path tracing (BDPT) via multiple importance sampling (MIS), wherein our kernel radius can leverage the contributions from PPM and BDPT. We test our new algorithms, improved PPM and VCM+, on diverse scenarios with different lighting settings. The experimental results demonstrate that our method can alleviate light leaks and visual blur artifacts of prior radiance estimate algorithms. We also evaluate the asymptotic performance of our approach and observe an overall improvement over the baseline in all testing scenarios.

Intentional Head-Motion Assisted Locomotion for Reducing Cybersickness.

We present an efficient locomotion technique that can reduce cybersickness through aligning the visual and vestibular induced self-motion illusion. Our locomotion technique stimulates proprioception consistent with the visual sense by intentional head motion, which includes both the head's translational movement and yaw rotation. A locomotion event is triggered by the hand-held controller together with an intended physical head motion simultaneously. Based on our method, we further explore the connections between the level of cybersickness and the velocity of self motion through a series of experiments. We first conduct Experiment 1 to investigate the cybersickness induced by different translation velocities using our method and then conduct Experiment 2 to investigate the cybersickness induced by different angular velocities. Our user studies from these two experiments reveal a new finding on the correlation between translation/angular velocities and the level of cybersickness. The cybersickness is greatest at the lowest velocity using our method, and the statistical analysis also indicates a possible U-shaped relation between the translation/angular velocity and cybersickness degree. Finally, we conduct Experiment 3 to evaluate the performances of our method and other commonly-used locomotion approaches, i.e., joystick-based steering and teleportation. The results show that our method can significantly reduce cybersickness compared with the joystick-based steering and obtain a higher presence compared with the teleportation. These advantages demonstrate that our method can be an optional locomotion solution for immersive VR applications using commercially available HMD suites only.

Diagnostic accuracy of cryptococcal antigen test in pulmonary cryptococcosis: a protocol for a systematic review and meta-analysis.

BACKGROUND: The cryptococcal antigen (CrAg) test was proposed as a rapid diagnostic tool to identify cryptococcal meningitis in patients suffering from AIDS. Several studies have demonstrated its diagnostic performance in cryptococcal meningitis. However, the diagnostic performance of the CrAg test in serum or bronchoalveolar lavage fluid in patients with pulmonary cryptococcosis remains uncertain. Therefore, the purpose of this systematic review is to summarise the evidence concerning diagnostic performance of the CrAg test in patients with pulmonary cryptococcosis. METHODS AND ANALYSIS: Databases such as PubMed, EMBASE, Cochrane Database of Systematic Reviews, Web of Science, ClinicalTrials.gov, International Clinical Trials Registry Platform, Wanfang Database and China National Knowledge Infrastructure will be searched systematically. The titles and abstracts will be reviewed by two independent reviewers. The Quality Assessment of Diagnostic Accuracy Studies 2 tool will be used to evaluate the risk of bias and clinical applicability of each study. Potential sources of heterogeneity will be investigated through visual inspection of the paired forest plots and summary receiver operating characteristic plots. The pooled summary statistics for the area under the curve, sensitivities, specificities, likelihood ratios and diagnostic ORs with 95% CI will be reported. ETHICS AND DISSEMINATION: The underlying study is based on published articles thus does not require ethical approval. The findings of the systematic review and meta-analysis will be published in a peer-reviewed journal and disseminated in various scientific conferences and seminars. PROSPERO REGISTRATION NUMBER: CRD42022373321.

Multi-Constraint Latent Representation Learning for Prognosis Analysis Using Multi-Modal Data.

The Cox proportional hazard model has been widely applied to cancer prognosis prediction. Nowadays, multi-modal data, such as histopathological images and gene data, have advanced this field by providing histologic phenotype and genotype information. However, how to efficiently fuse and select the complementary information of high-dimensional multi-modal data remains challenging for Cox model, as it generally does not equip with feature fusion/selection mechanism. Many previous studies typically perform feature fusion/selection in the original feature space before Cox modeling. Alternatively, learning a latent shared feature space that is tailored for Cox model and simultaneously keeps sparsity is desirable. In addition, existing Cox-based models commonly pay little attention to the actual length of the observed time that may help to boost the model's performance. In this article, we propose a novel Cox-driven multi-constraint latent representation learning framework for prognosis analysis with multi-modal data. Specifically, for efficient feature fusion, a multi-modal latent space is learned via a bi-mapping approach under ranking and regression constraints. The ranking constraint utilizes the log-partial likelihood of Cox model to induce learning discriminative representations in a task-oriented manner. Meanwhile, the representations also benefit from regression constraint, which imposes the supervision of specific survival time on representation learning. To improve generalization and alleviate overfitting, we further introduce similarity and sparsity constraints to encourage extra consistency and sparseness. Extensive experiments on three datasets acquired from The Cancer Genome Atlas (TCGA) demonstrate that the proposed method is superior to state-of-the-art Cox-based models.

Identification of Enterocytozoon bieneusi in an HIV-negative bronchiectasis patient with respiratory infection based on metagenomic next-generation sequencing: A case report.

Bronchiectasis is often caused by serious infections. Infections caused by Enterocytozoon bieneusi (E. bieneusi) is most common in the immunocompromised host, such as HIV-positive patients. Herein, we reported an HIV-negative patient with bronchiectasis infected with E. bieneusi, which diagnosed by mNGS and validated by Sanger sequencing. During the treatment of albendazole, the patient gradually recovered. This is the first report of a case of respiratory E. bieneusi infection in a bronchiectasis patient. This finding highlights the efficacy of mNGS for pathogen diagnosis in bronchiectasis patients and the potential treatment option of albendazole for bronchiectasis patients with E. bieneusi infection.

Own-Age Effects in a Face-Emotion Recognition Intervention for Children With ASD--Evidence From Eye Movements.

Background: The own-age effect is the phenomenon in which individuals perceive and recognize faces of their own age better than others in terms of cognitive processing. Previous eye movement studies on children with autism spectrum disorders (ASD) have reported that children with ASD have an attentional bias toward own-age faces and own-age scenes. Methods: The present study used own-age faces as the intervention material and examined the application of the own-age effect in the emotional recognition of faces in ASD. The length of the intervention was 12 weeks, and 2 sessions were conducted each week. Results: The results revealed that the own-age face intervention group gazed at children's faces significantly more often than before the intervention, gazed at children's angry faces significantly longer than before the intervention, and gazed at adults' happy faces significantly longer and more often than before the intervention; the other-age faces intervention group did not differ significantly from the preintervention in gazing at children's and adults' faces after the intervention. Conclusion: The results suggest that own-age faces as teaching materials can better promote the emotion recognition ability of children with ASD than other-age faces.

Multi-Attribute Attention Network for Interpretable Diagnosis of Thyroid Nodules in Ultrasound Images.

Ultrasound (US) is the primary imaging technique for the diagnosis of thyroid cancer. However, accurate identification of nodule malignancy is a challenging task that can elude less-experienced clinicians. Recently, many computer-aided diagnosis (CAD) systems have been proposed to assist this process. However, most of them do not provide the reasoning of their classification process, which may jeopardize their credibility in practical use. To overcome this, we propose a novel deep learning (DL) framework called multi-attribute attention network (MAA-Net) that is designed to mimic the clinical diagnosis process. The proposed model learns to predict nodular attributes and infer their malignancy based on these clinically-relevant features. A multi-attention scheme is adopted to generate customized attention to improve each task and malignancy diagnosis. Furthermore, MAA-Net utilizes nodule delineations as nodules spatial prior guidance for the training rather than cropping the nodules with additional models or human interventions to prevent losing the context information. Validation experiments were performed on a large and challenging dataset containing 4554 patients. Results show that the proposed method outperformed other state-of-the-art methods and provides interpretable predictions that may better suit clinical needs.

Boundary-rendering network for breast lesion segmentation in ultrasound images.

Breast Ultrasound (BUS) has proven to be an effective tool for the early detection of cancer in the breast. A lesion segmentation provides identification of the boundary, shape, and location of the target, and serves as a crucial step toward accurate diagnosis. Despite recent efforts in developing machine learning algorithms to automate this process, problems remain due to the blurry or occluded edges and highly irregular nodule shapes. Existing methods often produce over-smooth or inaccurate results, failing the need of identifying detailed boundary structures which are of clinical interest. To overcome these challenges, we propose a novel boundary-rendering framework that explicitly highlights the importance of boundary for automated nodule segmentation in BUS images. It utilizes a boundary selection module to automatically focuses on the ambiguous boundary region and a graph convolutional-based boundary rendering module to exploit global contour information. Furthermore, the proposed framework embeds nodule classification via semantic segmentation and encourages co-learning across tasks. Validation experiments were performed on different BUS datasets to verify the robustness of the proposed method. Results show that the proposed method outperforms states-of-art segmentation approaches (Dice=0.854, IOU=0.919, HD=17.8) in nodule delineation, as well as obtains a higher classification accuracy than classical classification models.

Extracting keyframes of breast ultrasound video using deep reinforcement learning.

Ultrasound (US) plays a vital role in breast cancer screening, especially for women with dense breasts. Common practice requires a sonographer to recognize key diagnostic features of a lesion and record a single or several representative frames during the dynamic scanning before performing the diagnosis. However, existing computer-aided diagnosis tools often focus on the final diagnosis process while neglecting the influence of the keyframe selection. Moreover, the lesions could have highly-irregular shapes, varying sizes, and locations during the scanning. The recognition of diagnostic characteristics associated with the lesions is challenging and also faces severe class imbalance. To address these, we proposed a reinforcement learning-based framework that can automatically extract keyframes from breast US videos of unfixed length. It is equipped with a detection-based nodule filtering module and a novel reward mechanism that can integrate anatomical and diagnostic features of the lesions into keyframe searching. A simple yet effective loss function was also designed to alleviate the class imbalance issue. Extensive experiments illustrate that the proposed framework can benefit from both innovations and is able to generate representative keyframe sequences in various screening conditions.

AW3M: An auto-weighting and recovery framework for breast cancer diagnosis using multi-modal ultrasound.

Recently, more clinicians have realized the diagnostic value of multi-modal ultrasound in breast cancer identification and began to incorporate Doppler imaging and Elastography in the routine examination. However, accurately recognizing patterns of malignancy in different types of sonography requires expertise. Furthermore, an accurate and robust diagnosis requires proper weights of multi-modal information as well as the ability to process missing data in practice. These two aspects are often overlooked by existing computer-aided diagnosis (CAD) approaches. To overcome these challenges, we propose a novel framework (called AW3M) that utilizes four types of sonography (i.e. B-mode, Doppler, Shear-wave Elastography, and Strain Elastography) jointly to assist breast cancer diagnosis. It can extract both modality-specific and modality-invariant features using a multi-stream CNN model equipped with self-supervised consistency loss. Instead of assigning the weights of different streams empirically, AW3M automatically learns the optimal weights using reinforcement learning techniques. Furthermore, we design a light-weight recovery block that can be inserted to a trained model to handle different modality-missing scenarios. Experimental results on a large multi-modal dataset demonstrate that our method can achieve promising performance compared with state-of-the-art methods. The AW3M framework is also tested on another independent B-mode dataset to prove its efficacy in general settings. Results show that the proposed recovery block can learn from the joint distribution of multi-modal features to further boost the classification accuracy given single modality input during the test.

Highly Stable 3D Ti3C2Tx MXene-Based Foam Architectures toward High-Performance Terahertz Radiation Shielding.

Terahertz technology promises broad applications, which calls for terahertz electromagnetic interference (EMI) shielding materials to alleviate radiation pollution. 2D transition metal carbides and/or nitrides (MXenes) with metallic conductivity are promising for EMI shielding, but simultaneously realizing light weight, high stability, and foldability in a MXene shielding material to meet the requirements of increasingly popular portable and wearable equipment has remained a great challenge. Herein, an ion-diffusion-induced gelation method is demonstrated to synthesize free-standing, light-weight, foldable, and highly stable MXene foams, in which MXene sheets are cross-linked by multivalent metal ions and graphene oxide to form an oriented porous structure. The method is highly efficient, controllable, and versatile for scalable generation of functional 3D MXene structures with arbitrary shapes and synergistic properties. The distinctive cross-linked porous structure endows the light-weight MXene foam with good foldability, outstanding durability and stability in wet environments, and an excellent terahertz shielding effectiveness of 51 dB at a small thickness of 85 µm. This work not only provides an insight for the on-target design of high-performance terahertz shielding materials but also expands the applications of MXenes in 3D macroscopic form.

Multi-task learning for quality assessment of fetal head ultrasound images.

It is essential to measure anatomical parameters in prenatal ultrasound images for the growth and development of the fetus, which is highly relied on obtaining a standard plane. However, the acquisition of a standard plane is, in turn, highly subjective and depends on the clinical experience of sonographers. In order to deal with this challenge, we propose a new multi-task learning framework using a faster regional convolutional neural network (MF R-CNN) architecture for standard plane detection and quality assessment. MF R-CNN can identify the critical anatomical structure of the fetal head and analyze whether the magnification of the ultrasound image is appropriate, and then performs quality assessment of ultrasound images based on clinical protocols. Specifically, the first five convolution blocks of the MF R-CNN learn the features shared within the input data, which can be associated with the detection and classification tasks, and then extend to the task-specific output streams. In training, in order to speed up the different convergence of different tasks, we devise a section train method based on transfer learning. In addition, our proposed method also uses prior clinical and statistical knowledge to reduce the false detection rate. By identifying the key anatomical structure and magnification of the ultrasound image, we score the ultrasonic plane of fetal head to judge whether it is a standard image or not. Experimental results on our own-collected dataset show that our method can accurately make a quality assessment of an ultrasound plane within half a second. Our method achieves promising performance compared with state-of-the-art methods, which can improve the examination effectiveness and alleviate the measurement error caused by improper ultrasound scanning.

Synthesis and Gas-Sensing Property of Highly Self-assembled Tungsten Oxide Nanosheets.

We report the synthesis of tungsten oxide (WO3) nanosheets using a simple yet efficient hydrothermal technique free of surfactantand template. The WO3 nano-sheets are self-assembled as well to form ordered one-dimensional chain nanostructure. A comprehensive microscopic characterization reveals that the nano-sheets have triangular and circular two different shape edges, dislocation and stacking faults are also observed, which should have implications for our understanding of catalytic activity of ceria. We also propose a growth mechanism for the nano-sheets. As a result of this unique morphology, this WO3 nano-sheets are found to show excellent gas-sensing properties which can use as promising sensor materials detecting ethanol with low concentration.