Patterns of sex behaviors and factors associated with condomless anal intercourse during the COVID-19 pandemic among men who have sex with men in Hong Kong: A cross-sectional study.

OBJECTIVES: The present study examined the patterns of sex behaviors before and during COVID-19, and identified the factors associated with condomless anal intercourse during COVID-19 from individual, interpersonal, and contextual level among men who have sex with men (MSM) in Hong Kong. METHODS: A cross-sectional study was conducted among MSM in Hong Kong. A total of 463 MSM completed a cross-sectional telephone survey between March 2021 and January 2022. RESULTS: Among all participants, the mean number of regular sex partners, non-regular sex partners, and casual sex partners during the COVID-19 period were 1.24, 2.09, and 0.08 respectively. Among those who had sex with regular, non-regular, and casual sex partner during the COVID-19 period, respectively 52.4%, 31.8% and 46.7% reported condomless anal intercourse. Compared to the pre-COVID-19 period, participants reported significantly fewer number of regular and non-regular sex partners during the COVID-19 period. However, a higher level of condomless anal intercourse with all types of sex partners during the COVID-19 period was also observed. Adjusted for significant socio-demographic variables, results from logistic regression analyses revealed that perceived severity of COVID-19 (aOR = 0.72, 95% CI = 0.58, 0.88), COVID-19 risk reduction behaviors in general (aOR = 0.68, 95% CI = 0.48, 0.96), COVID-19 risk reduction behaviors during sex encounters (aOR = 0.45, 95% CI = 0.30, 0.66), condom negotiation (aOR = 0.61, 95% CI = 0.44, 0.86), and collective efficacy (aOR = 0.79, 95% CI = 0.64, 0.98) were protective factors of condomless anal intercourse with any type of sex partners during the COVID-19 period. CONCLUSION: The COVID-19 control measures have caused a dramatic impact on the sexual behavior of MSM in Hong Kong. Interventions that promote condom use during the COVID-19 pandemic are still needed and such interventions could emphasize prevention of both COVID-19 and HIV.

Effects of Negative Attitudes towards Vaccination in General and Trust in Government on Uptake of a Booster Dose of COVID-19 Vaccine and the Moderating Role of Psychological Reactance: An Observational Prospective Cohort Study in Hong Kong.

Uptake of a booster dose of COVID-19 vaccine is effective in preventing infection and severe consequences caused by COVID-19. The present study examined the effects of negative attitudes towards vaccination in general and trust in government on uptake of a COVID-19 booster dose, as well as the moderating role of psychological reactance to pro-vaccination messages in Hong Kong. An observational prospective cohort study using online survey was conducted among 264 adults. Findings showed that, after adjustment for significant background characteristics, negative attitudes towards vaccination in general negatively predicted uptake of a booster dose, and trust in government positively predicted uptake of a booster dose. In addition, the association between negative attitudes towards vaccination in general and uptake of a booster dose was weaker among those who reported a higher level of psychological reactance. The present study highlighted the importance of improving attitudes towards vaccination in general especially among those who are not experiencing psychological reactance, and building trust in government. This study also suggested that interventions aimed at improving attitudes towards vaccination in general should seek to avoid psychological reactance, and special attention should be given to people who are experiencing psychological reactance to pro-vaccination messages.

Semantic Decomposition Network With Contrastive and Structural Constraints for Dental Plaque Segmentation.

Segmenting dental plaque from images of medical reagent staining provides valuable information for diagnosis and the determination of follow-up treatment plan. However, accurate dental plaque segmentation is a challenging task that requires identifying teeth and dental plaque subjected to semantic-blur regions (i.e., confused boundaries in border regions between teeth and dental plaque) and complex variations of instance shapes, which are not fully addressed by existing methods. Therefore, we propose a semantic decomposition network (SDNet) that introduces two single-task branches to separately address the segmentation of teeth and dental plaque and designs additional constraints to learn category-specific features for each branch, thus facilitating the semantic decomposition and improving the performance of dental plaque segmentation. Specifically, SDNet learns two separate segmentation branches for teeth and dental plaque in a divide-and-conquer manner to decouple the entangled relation between them. Each branch that specifies a category tends to yield accurate segmentation. To help these two branches better focus on category-specific features, two constraint modules are further proposed: 1) contrastive constraint module (CCM) to learn discriminative feature representations by maximizing the distance between different category representations, so as to reduce the negative impact of semantic-blur regions on feature extraction; 2) structural constraint module (SCM) to provide complete structural information for dental plaque of various shapes by the supervision of an boundary-aware geometric constraint. Besides, we construct a large-scale open-source Stained Dental Plaque Segmentation dataset (SDPSeg), which provides high-quality annotations for teeth and dental plaque. Experimental results on SDPSeg datasets show SDNet achieves state-of-the-art performance.

Operational design domain of automated vehicles at freeway entrance terminals.

The safe operation of automated vehicles (AVs) is now on the research agenda, with attention to the AV's operational design domain (ODD), which defines the conditions in which its driving automation system is designed to function. Due to the limited sight line on freeway entrance terminals, crashes involving AVs continue to occur during the merging operation. Considering that detecting range and detecting angle are critical parameters for the AV sensor's sight triangle and may differ from human driver requirements, it is urgent to provide initial ODD results to improve the AV's detection capabilities at entrance terminals. Based on sight triangle requirements, this study took a mathematical approach to quantify the relationships among AV sensor capabilities, design speed, and geometric design indicators separately for flat grades and 5 % grades. Required stopping sight distance (SSD) for the merging AV and travelled distance of the through-lane vehicle were calculated based on vehicle kinematics, common sensor capabilities, and Green Book parameter values. Using the law of cosines, the required detecting range and the detecting angle for AV sensors were calculated at various ramp and through-lane design speed combinations as the ODD constraint indicators. Two types of entrance terminals, the taper and the parallel, were considered in this study. Results show: 1) the maximum required detecting range and angle for AV sensors reached 185.0 m and 179.9 degrees, respectively; 2) the required detecting range and angle have only minor differences between the taper and parallel types; 3) the minimum acceleration lane length suggested for human drivers may not be available for AVs. These results provide two major contributions to safe operation of AVs: 1) for roadway design, the results provide general design references for acceleration lane length, merging angle, and sight-distance guarantee at freeway entrance terminals; and 2) for ODD management, results provide theoretical support for governments and traffic management departments to regulate AVs at these terminals, in some cases limiting the AV to manual driving or lower speed limits.

Robust Assembly of Cross-Linked Protein Nanofibrils into Hierarchically Structured Microfibers.

Natural, high-performance fibers generally have hierarchically organized nanosized building blocks. Inspired by this, whey protein nanofibrils (PNFs) are assembled into microfibers, using flow-focusing. By adding genipin as a nontoxic cross-linker to the PNF suspension before spinning, significantly improved mechanical properties of the final fiber are obtained. For curved PNFs, with a low content of cross-linker (2%) the fiber is almost 3 times stronger and 4 times stiffer than the fiber without a cross-linker. At higher content of genipin (10%), the elongation at break increases by a factor of 2 and the energy at break increases by a factor of 5. The cross-linking also enables the spinning of microfibers from long straight PNFs, which has not been achieved before. These microfibers have higher stiffness and strength but lower ductility and toughness than those made from curved PNFs. The fibers spun from the two classes of nanofibrils show clear morphological differences. The study demonstrates the production of protein-based microfibers with mechanical properties similar to natural protein-based fibers and provides insights about the role of the nanostructure in the assembly process.

Brain gray matter nuclei segmentation on quantitative susceptibility mapping using dual-branch convolutional neural network.

Abnormal iron accumulation in the brain subcortical nuclei has been reported to be correlated to various neurodegenerative diseases, which can be measured through the magnetic susceptibility from the quantitative susceptibility mapping (QSM). To quantitatively measure the magnetic susceptibility, the nuclei should be accurately segmented, which is a tedious task for clinicians. In this paper, we proposed a dual-branch residual-structured U-Net (DB-ResUNet) based on 3D convolutional neural network (CNN) to automatically segment such brain gray matter nuclei. Due to memory limit, 3D-CNN-based methods typically adopted image patches, instead of the whole volumetric image, which, however, ignored the spatial contextual information of the neighboring patches, and therefore led to the accuracy loss. To better tradeoff segmentation accuracy and the memory efficiency, the proposed DB-ResUNet incorporated patches with different resolutions. By jointly using QSM and 3D T1 weighted imaging (T1WI) as inputs, the proposed method was able to achieve better segmentation accuracy over its single-branch counterpart, as well as the conventional atlas-based method and the classical 3D CNN structures. The susceptibility values and the volumes were also measured, which indicated that the measurements from the proposed DB-ResUNet was able to present high correlation with values from the manually annotated regions of interest.

Feasibility study of highway alignment design controls for autonomous vehicles.

In recent years, the development and testing of autonomous driving technology have become widespread around the world. However, due to differences in perception abilities between autonomous vehicles and human drivers, the current geometric design controls for highway alignments, designed for the human driver, may not be applicable to the autonomous vehicle (AV). Few studies, however, have systematically investigated the design controls for autonomous vehicles, though we face full driving automation in the next few decades. Because the range of modern AV sensors reaches 250 m, with expected further improvements in the near future, there is a need to determine how the sensors' perception field and perception-reaction time may affect the current road design standards developed for human drivers. This study therefore tested the feasibility of the current design controls for fully-autonomous vehicles by separately computing controls for vertical alignments and combined horizontal and vertical alignments, considering the AV's perception abilities of perception-reaction time (PRT), sensor height, and upward angle from the horizontal. The required stopping sight distance (SSD) and minimum length of sag and crest vertical curves were derived and compared with those for human drivers. Computations for combined alignments were based on Green Book coordination guidelines: as the minimum length of horizontal curve can be used for alignments adhering to guidelines, preview sight distance (PVSD) was computed for alignments that do not. Results showed that 1) AV-based design controls on vertical curves were more tolerant than those based on human drivers; and 2) the dominating criterion of sag vertical curve design control was comfort for autonomous vehicles, versus required SSD for human drivers.

Unsupervised Monocular Depth Estimation via Recursive Stereo Distillation.

Existing unsupervised monocular depth estimation methods resort to stereo image pairs instead of ground-truth depth maps as supervision to predict scene depth. Constrained by the type of monocular input in testing phase, they fail to fully exploit the stereo information through the network during training, leading to the unsatisfactory performance of depth estimation. Therefore, we propose a novel architecture which consists of a monocular network (Mono-Net) that infers depth maps from monocular inputs, and a stereo network (Stereo-Net) that further excavates the stereo information by taking stereo pairs as input. During training, the sophisticated Stereo-Net guides the learning of Mono-Net and devotes to enhance the performance of Mono-Net without changing its network structure and increasing its computational burden. Thus, monocular depth estimation with superior performance and fast runtime can be achieved in testing phase by only using the lightweight Mono-Net. For the proposed framework, our core idea lies in: 1) how to design the Stereo-Net so that it can accurately estimate depth maps by fully exploiting the stereo information; 2) how to use the sophisticated Stereo-Net to improve the performance of Mono-Net. To this end, we propose a recursive estimation and refinement strategy for Stereo-Net to boost its performance of depth estimation. Meanwhile, a multi-space knowledge distillation scheme is designed to help Mono-Net amalgamate the knowledge and master the expertise from Stereo-Net in a multi-scale fashion. Experiments demonstrate that our method achieves the superior performance of monocular depth estimation in comparison with other state-of-the-art methods.

Protein Nanofibrils and Their Hydrogel Formation with Metal Ions.

Protein nanofibrils (PNFs) have been prepared by whey protein fibrillation at low pH and in the presence of different metal ions. The effect of the metal ions was systematically studied both in terms of PNF suspension gelation behavior and fibrillation kinetics. A high valence state and a small ionic radius (e.g., Sn4+) of the metal ion resulted in the formation of hydrogels already at a metal ion concentration of 30 mM, whereas an intermediate valence state and larger ionic radius (Co2+, Ni2+, Al3+) resulted in the hydrogel formation occurring at 60 mM. A concentration of 120 mM of Na+ was needed to form a PNF hydrogel, while lower concentrations showed liquid behaviors similar to the reference PNF solution where no metal ions had been introduced. The hydrogel mechanics were investigated at steady-state conditions after 24 h of incubation/gelation, revealing that more acidic (smaller and more charged) metal ions induced ca. 2 orders of magnitude higher storage modulus as compared to the less acidic metal ions (with smaller charge and larger radius) for the same concentration of metal ions. The viscoelastic nature of the hydrogels was attributed to the ability of the metal ions to coordinate water molecules in the vicinity of the PNFs. The presence of metal ions in the solutions during the growth of the PNFs typically resulted in curved fibrils, whereas an upper limit of the concentration existed when oxides/hydroxides were formed, and the hydrogels lost their gel properties due to phase separation. Thioflavin T (ThT) fluorescence was used to determine the rate of the fibrillation to form 50% of the total PNFs (t1/2), which decreased from 2.3 to ca. 0.5 h depending on the specific metal ions added.

Joint Extraction of Retinal Vessels and Centerlines Based on Deep Semantics and Multi-Scaled Cross-Task Aggregation.

Retinal vessel segmentation and centerline extraction are crucial steps in building a computer-aided diagnosis system on retinal images. Previous works treat them as two isolated tasks, while ignoring their tight association. In this paper, we propose a deep semantics and multi-scaled cross-task aggregation network that takes advantage of the association to jointly improve their performances. Our network is featured by two sub-networks. The forepart is a deep semantics aggregation sub-network that aggregates strong semantic information to produce more powerful features for both tasks, and the tail is a multi-scaled cross-task aggregation sub-network that explores complementary information to refine the results. We evaluate the proposed method on three public databases, which are DRIVE, STARE and CHASE_DB1. Experimental results show that our method can not only simultaneously extract retinal vessels and their centerlines but also achieve the state-of-the-art performances on both tasks.

Pulmonary Textures Classification via a Multi-Scale Attention Network.

Precise classification of pulmonary textures is crucial to develop a computer aided diagnosis (CAD) system of diffuse lung diseases (DLDs). Although deep learning techniques have been applied to this task, the classification performance is not satisfied for clinical requirements, since commonly-used deep networks built by stacking convolutional blocks are not able to learn discriminative feature representation to distinguish complex pulmonary textures. For addressing this problem, we design a multi-scale attention network (MSAN) architecture comprised by several stacked residual attention modules followed by a multi-scale fusion module. Our deep network can not only exploit powerful information on different scales but also automatically select optimal features for more discriminative feature representation. Besides, we develop visualization techniques to make the proposed deep model transparent for humans. The proposed method is evaluated by using a large dataset. Experimental results show that our method has achieved the average classification accuracy of 94.78% and the average f-value of 0.9475 in the classification of 7 categories of pulmonary textures. Besides, visualization results intuitively explain the working behavior of the deep network. The proposed method has achieved the state-of-the-art performance to classify pulmonary textures on high resolution CT images.

A comparison study between wide-range and high-sensitivity C-reactive protein assays (Roche Cobas c702) for low C-reactive protein concentration in patients with cardiovascular risk.

BACKGROUND: Low concentration C-reactive protein (CRP) has favorable prognostic significance in patients with cardiovascular risks. METHODS: We compared the wr-CRP method with the hs-CRP method both on Roche Cobas c702 analyzer for the determination of low CRP concentration (<20 mg/L) including 200 patients treated in Cardiology Department in Beijing Tsinghua Changgung Hospital (Beijing, China) from December 2018 to March 2019. RESULTS: The two methods were highly correlated (Spearman's rho = 0.995). Deming regression was used to fit the regression analysis model, giving a slope of 1.058 with an intercept of 0.008. The median method difference (wr-CRP - hr-CRP) was 0.120 mg/L (95% CI, 0.086-0.200 mg/L), and the median percent differences were 7.34% (95% CI, 4.27%-8.47%). The percent bias between both methods at the given cutoff CRP values of 1, 3, and 10 mg/L evaluated by Deming regression was 6.60%, 6.07%, and 5.88%, respectively, all of which were less than the acceptable standard (12.50%). The percentage of sample results concordant by both methods for the risk stratification was 96.0% (kappa = 0.937, P < 0.001). CONCLUSIONS: Roche wr-CRP and hs-CRP assays are highly concordant in determining low concentration CRP. Wr-CRP may be used as an alternative to hs-CRP assay on Roche Cobas c702 analyzer to assess the cardiovascular risk, considering its convenience and lower costs.

On the role of peptide hydrolysis for fibrillation kinetics and amyloid fibril morphology.

Self-assembly of proteins into amyloid-like nanofibrils is not only a key event in several diseases, but such fibrils are also associated with intriguing biological function and constitute promising components for new biobased materials. The bovine whey protein ß-lactoglobulin has emerged as an important model protein for the development of such materials. We here report that peptide hydrolysis is the rate-determining step for fibrillation of ß-lactoglobulin in whey protein isolate. We also explore the observation that ß-lactoglobulin nanofibrils of distinct morphologies are obtained by simply changing the initial protein concentration. We find that the morphological switch is related to different nucleation mechanisms and that the two classes of nanofibrils are associated with variations of the peptide building blocks. Based on the results, we propose that the balance between protein concentration and the hydrolysis rate determines the structure of the formed nanofibrils.

Reconstruction of Structurally-Incomplete Matrices With Reweighted Low-Rank and Sparsity Priors.

Most matrix reconstruction methods assume that missing entries randomly distribute in the incomplete matrix, and the low-rank prior or its variants are used to well pose the problem. However, in practical applications, missing entries are structurally rather than randomly distributed, and cannot be handled by the rank minimization prior individually. To remedy this, this paper introduces new matrix reconstruction models using double priors on the latent matrix, named Reweighted Low-rank and Sparsity Priors (ReLaSP). In the proposed ReLaSP models, the matrix is regularized by a low-rank prior to exploit the inter-column and inter-row correlations, and its columns (rows) are regularized by a sparsity prior under a dictionary to exploit intra-column (-row) correlations. Both the low-rank and sparse priors are reweighted on the fly to promote low-rankness and sparsity, respectively. Numerical algorithms to solve our ReLaSP models are derived via the alternating direction method under the augmented Lagrangian multiplier framework. Results on synthetic data, image restoration tasks, and seismic data interpolation show that the proposed ReLaSP models are quite effective in recovering matrices degraded by highly structural missing and various types of noise, complementing the classic matrix reconstruction models that handle random missing only.

Color-guided depth recovery from RGB-D data using an adaptive autoregressive model.

This paper proposes an adaptive color-guided autoregressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras. We observe and verify that the AR model tightly fits depth maps of generic scenes. The depth recovery task is formulated into a minimization of AR prediction errors subject to measurement consistency. The AR predictor for each pixel is constructed according to both the local correlation in the initial depth map and the nonlocal similarity in the accompanied high quality color image. We analyze the stability of our method from a linear system point of view, and design a parameter adaptation scheme to achieve stable and accurate depth recovery. Quantitative and qualitative evaluation compared with ten state-of-the-art schemes show the effectiveness and superiority of our method. Being able to handle various types of depth degradations, the proposed method is versatile for mainstream depth sensors, time-of-flight camera, and Kinect, as demonstrated by experiments on real systems.