Understanding Motivations for Dating App Use Among Gay and Bisexual Men: Validation, Latent Profile Analysis, and Differences in Health Outcomes.

While there has been a proliferation in gay dating app (GDA) use in China, research into their potential effects on health outcomes, particularly mental health outcomes, among gay and bisexual men is lacking. The motivations for GDA use are diverse, and understanding users' motivation profiles may provide a necessary starting point for exploring the heterogeneous effects of GDA use on health outcomes. A cross-sectional survey of the motivations for GDA use and other health outcome variables (i.e., condom use frequency, self-stigma, and subjective emptiness) was conducted among 366 Chinese gay and bisexual men. The results of exploration structure equation modeling indicate that the GDA Use Motivation Scale, with a four-factor first-order model, had strong psychometric properties. Then, latent profile analysis (LPA) based on the mean scores of four aspects of motivation was performed. The results of the LPA revealed the existence of four profiles: "Weak motivations" (30.9%), "Differentiated motivations" (17.8%), "Moderate motivations" (30.3%), and "Strong motivations" (21.0%). Differences in health outcomes among the motivation profiles were found by using the Bolck-Croon-Hagenaars approach. Overall, most participants (60.1%) tended to use condoms consistently, regardless of how strong their GDA use motivation was; however, stronger GDA use motivations were associated with higher levels of self-stigma and subjective emptiness. We call for more research to focus on the real needs behind and motivations for GDA use so that all such app users' voices can be heard, as well as to raise awareness about the potential health risks associated with GDA use among Chinese gay and bisexual men.

Measuring character strengths as possible protective factors against suicidal ideation in older Chinese adults: a cross-sectional study.

BACKGROUND: Suicide is a global issue among the elderly. The number of older people committing suicide is proliferating, and the elderly suicide rate is the highest among all age groups in China. A better understanding of the possible protective factors against suicidal ideation is necessary to facilitate prevention and intervention efforts. The objectives of the present study are threefold. First, this study aims to examine the psychometric properties of the three-dimensional inventory of character strengths (TICS) with a sample of older adults. Second, this study intends to investigate correlations among suicide ideation, wellbeing, and character strengths. Third, the study seeks to explore the possible protective roles of the three character strengths and wellbeing in explaining suicidal ideation among older adults. METHODS: A cross-sectional study comprising 308 older adults aged at least 50 years old from nursing homes was conducted. Four questionnaires, namely, the TICS, the Geriatric Suicide Ideation Scale-10 items, the Brief Inventory of Thriving, and the Satisfaction with Life Scale, were used. Exploratory structural equation modeling, intraclass correlation coefficients, partial correlations, and sets of hierarchical regressions were adopted to estimate and report the results. RESULTS: TICS could be used to assess the character strengths (i.e., caring, inquisitiveness, and self-control) among older adults with an acceptable goodness-of-fit (chi square = 157.30, df = 63, p < 0.001, CFI = 0.94, TLI = 0.90, RMSEA = 0.07, 90% CI = [0.06, 0.08]). Wellbeing and character strengths exhibited a negative association with suicidal ideation among older adults. Moreover, character strengths showed an independently cross-sectional relationship with suicidal ideation, explaining 65.1% of the variance of suicidal ideation after controlling for the wellbeing and demographics. CONCLUSION: This study indicated that character strengths were associated with low levels of suicidal ideation. Therefore, the protective factors against suicidal ideation among older adults should be given additional attention.

Sexual Self-Identification Offsets and Self-Stigma Moderates: Expanding the Rejection-Identification Model to Examine Stigma's Effects on Well-Being Among Gay and Bisexual Men.

Gender and sexual minorities still face stigma-related stress from all areas of society. The rejection-identification model (RIM) proposes that some stigmatized individuals may respond to the negative effects of stigma on well-being by enhancing their self-identification. However, this does not apply to all gender and sexual minorities. Grounded in minority stress theory and the RIM, this study examined how stigma-related stressors (i.e. perceived stigma and self-stigma) and their associated mechanisms impact sexual self-identification and subjective well-being. A total of 366 Chinese gay and bisexual men were included in the study. The results showed that sexual self-identification, as a protective factor, mediated the association between perceived stigma and subjective well-being. Furthermore, the indirect effect of the mediation model was moderated by self-stigma, such that the indirect effect of perceived stigma on subjective well-being through sexual self-identification was the highest among Chinese gay and bisexual men with low self-stigma. Given that research into the mental health and well-being of Chinese gay and bisexual men is still in its infancy, our findings are important and may help in developing and improving socially and psychologically sensitive counseling services or intervention strategies for these populations.

Bilateral Supervision Network for Semi-Supervised Medical Image Segmentation.

Massive high-quality annotated data is required by fully-supervised learning, which is difficult to obtain for image segmentation since the pixel-level annotation is expensive, especially for medical image segmentation tasks that need domain knowledge. As an alternative solution, semi-supervised learning (SSL) can effectively alleviate the dependence on the annotated samples by leveraging abundant unlabeled samples. Among the SSL methods, mean-teacher (MT) is the most popular one. However, in MT, teacher model's weights are completely determined by student model's weights, which will lead to the training bottleneck at the late training stages. Besides, only pixel-wise consistency is applied for unlabeled data, which ignores the category information and is susceptible to noise. In this paper, we propose a bilateral supervision network with bilateral exponential moving average (bilateral-EMA), named BSNet to overcome these issues. On the one hand, both the student and teacher models are trained on labeled data, and then their weights are updated with the bilateral-EMA, and thus the two models can learn from each other. On the other hand, pseudo labels are used to perform bilateral supervision for unlabeled data. Moreover, for enhancing the supervision, we adopt adversarial learning to enforce the network generate more reliable pseudo labels for unlabeled data. We conduct extensive experiments on three datasets to evaluate the proposed BSNet, and results show that BSNet can improve the semi-supervised segmentation performance by a large margin and surpass other state-of-the-art SSL methods.

NKUT: Dataset and Benchmark for Pediatric Mandibular Wisdom Teeth Segmentation.

Germectomy is a common surgery in pediatric dentistry to prevent the potential dangers caused by impacted mandibular wisdom teeth. Segmentation of mandibular wisdom teeth is a crucial step in surgery planning. However, manually segmenting teeth and bones from 3D volumes is time-consuming and may cause delays in treatment. Deep learning based medical image segmentation methods have demonstrated the potential to reduce the burden of manual annotations, but they still require a lot of well-annotated data for training. In this paper, we initially curated a Cone Beam Computed Tomography (CBCT) dataset, NKUT, for the segmentation of pediatric mandibular wisdom teeth. This marks the first publicly available dataset in this domain. Second, we propose a semantic separation scale-specific feature fusion network named WTNet, which introduces two branches to address the teeth and bones segmentation tasks. In WTNet, We design a Input Enhancement (IE) block and a Teeth-Bones Feature Separation (TBFS) block to solve the feature confusions and semantic-blur problems in our task. Experimental results suggest that WTNet performs better on NKUT compared to previous state-of-the-art segmentation methods (such as TransUnet), with a maximum DSC lead of nearly 16%.

What triggers depressive symptoms among gay and bisexual men? A sequential mediation model of sexual minority-related stigma and self-criticism.

A growing number of studies has shown that gay and bisexual men are more likely to experience adverse mental health status than their heterosexual counterparts. Stigma-related stress and self-criticism are believed likely to exacerbate depressive symptoms among gay and bisexual men. This research used cross-sectional findings to illustrate the mediating role of internalized self-stigma and self-criticism in the association between perceived public stigma and depressive symptoms. A total of 317 Chinese gay and bisexual men (267 gays and 50 bisexuals with a mean age of 25.29) were invited to participate in this study from October to November 2021. Sociodemographic characteristics, perceived public stigma, internalized self-stigma, self-criticism and depressive symptoms were measured through self-reported online questionnaires. The results indicated that the association between perceived public stigma and depressive symptoms was sequentially mediated by internalized self-stigma and self-criticism. The findings indicated that public stigma, as a distal stressor, was perceived and internalized as self-stigma by gay and bisexual men. This self-stigma may pose a risk for depressive symptoms via self-criticism (a maladaptive consequence of their attempts to reduce cognitive dissonance). These results highlight the necessity of reducing sexual minority-related stigma and self-criticism to reduce depressive symptoms. Our findings appeal to society to further decrease prejudice and stigma, increase tolerance, and focus on the negative mental health status of gay and bisexual men.

H2Former: An Efficient Hierarchical Hybrid Transformer for Medical Image Segmentation.

Accurate medical image segmentation is of great significance for computer aided diagnosis. Although methods based on convolutional neural networks (CNNs) have achieved good results, it is weak to model the long-range dependencies, which is very important for segmentation task to build global context dependencies. The Transformers can establish long-range dependencies among pixels by self-attention, providing a supplement to the local convolution. In addition, multi-scale feature fusion and feature selection are crucial for medical image segmentation tasks, which is ignored by Transformers. However, it is challenging to directly apply self-attention to CNNs due to the quadratic computational complexity for high-resolution feature maps. Therefore, to integrate the merits of CNNs, multi-scale channel attention and Transformers, we propose an efficient hierarchical hybrid vision Transformer (H2Former) for medical image segmentation. With these merits, the model can be data-efficient for limited medical data regime. The experimental results show that our approach exceeds previous Transformer, CNNs and hybrid methods on three 2D and two 3D medical image segmentation tasks. Moreover, it keeps computational efficiency in model parameters, FLOPs and inference time. For example, H2Former outperforms TransUNet by 2.29% in IoU score on KVASIR-SEG dataset with 30.77% parameters and 59.23% FLOPs.

Progressive Multiscale Consistent Network for Multiclass Fundus Lesion Segmentation.

Effectively integrating multi-scale information is of considerable significance for the challenging multi-class segmentation of fundus lesions because different lesions vary significantly in scales and shapes. Several methods have been proposed to successfully handle the multi-scale object segmentation. However, two issues are not considered in previous studies. The first is the lack of interaction between adjacent feature levels, and this will lead to the deviation of high-level features from low-level features and the loss of detailed cues. The second is the conflict between the low-level and high-level features, this occurs because they learn different scales of features, thereby confusing the model and decreasing the accuracy of the final prediction. In this paper, we propose a progressive multi-scale consistent network (PMCNet) that integrates the proposed progressive feature fusion (PFF) block and dynamic attention block (DAB) to address the aforementioned issues. Specifically, PFF block progressively integrates multi-scale features from adjacent encoding layers, facilitating feature learning of each layer by aggregating fine-grained details and high-level semantics. As features at different scales should be consistent, DAB is designed to dynamically learn the attentive cues from the fused features at different scales, thus aiming to smooth the essential conflicts existing in multi-scale features. The two proposed PFF and DAB blocks can be integrated with the off-the-shelf backbone networks to address the two issues of multi-scale and feature inconsistency in the multi-class segmentation of fundus lesions, which will produce better feature representation in the feature space. Experimental results on three public datasets indicate that the proposed method is more effective than recent state-of-the-art methods.

CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading.

Diabetic Retinopathy (DR) grading is challenging due to the presence of intra-class variations, small lesions and imbalanced data distributions. The key for solving fine-grained DR grading is to find more discriminative features corresponding to subtle visual differences, such as microaneurysms, hemorrhages and soft exudates. However, small lesions are quite difficult to identify using traditional convolutional neural networks (CNNs), and an imbalanced DR data distribution will cause the model to pay too much attention to DR grades with more samples, greatly affecting the final grading performance. In this article, we focus on developing an attention module to address these issues. Specifically, for imbalanced DR data distributions, we propose a novel Category Attention Block (CAB), which explores more discriminative region-wise features for each DR grade and treats each category equally. In order to capture more detailed small lesion information, we also propose the Global Attention Block (GAB), which can exploit detailed and class-agnostic global attention feature maps for fundus images. By aggregating the attention blocks with a backbone network, the CABNet is constructed for DR grading. The attention blocks can be applied to a wide range of backbone networks and trained efficiently in an end-to-end manner. Comprehensive experiments are conducted on three publicly available datasets, showing that CABNet produces significant performance improvements for existing state-of-the-art deep architectures with few additional parameters and achieves the state-of-the-art results for DR grading. Code and models will be available at https://github.com/he2016012996/CABnet.