Cascade analysis of awareness, willingness, uptake and adherence with regard to PrEP among young men who have sex with men (YMSM) in China: A comparison between students and non-students.

INTRODUCTION: In China, young men who have sex with men (YMSM) are one of the groups most at risk of HIV/AIDS. The uptake of pre-exposure prophylaxis (PrEP) among YMSM has not been well documented. A cascade analysis of awareness, willingness, use and adherence with regard to PrEP was conducted separately among YMSM students and non-students. METHODS: From 20 October to 30 December 2021, all adolescents aged 16-24 years were selected for the study from among MSM recruited from 31 provincial administrative regions in mainland China. Participants were included in a cross-sectional study of awareness, willingness, use and adherence with regard to PrEP among YMSM. Logistic regression modelling was used to identify factors associated with the four outcomes. RESULTS: Among 1014 student and 866 non-student YMSMs, respectively, 88.07% and 81.64% had heard of PrEP; 58.16% and 50.35% were willing to use PrEP; 7.59% and 7.62% had used PrEP; and 3.16% and 3.58% had adhered to PrEP. Among students, those living in high-risk areas and pilot cities and those who had engaged in commercial sex and group sex had a positive effect on PrEP use, and the same trends were found among non-students living in high-risk areas and pilot cities and those who had engaged in group sex. 'Daily oral' and 'flexible' PrEP use positively influenced adherence among both groups. CONCLUSIONS: A differentiation strategy of PrEP promotion should be implemented among YMSM. Material support for students, such as financial resources, should increase, while non-students should increase their level of perception of HIV risk.

Significant insights from a National survey in China: PrEP awareness, willingness, uptake, and adherence among YMSM students.

INTRODUCTION: Few studies focused on the Pre-Exposure Prophylaxis (PrEP) -related aspects, and the applicability of prior evidence to young men who have sex with men (YMSM) students was unknown. This study aimed to assess the awareness, willingness, uptake, and adherence (AWUA) to PrEP among YMSM students in China and to explore the associated factors with these stages. METHODS: A cross-sectional survey with a sizable sample of 1151 was conducted among YMSM students aged 16 and above, who self-identified as men who have sex with men(MSM) and resided in mainland China between October 20 and December 20, 2021. The chi-square test and Fisher's exact test were used for univariate analysis, followed by multivariable logistic regression analysis of influencing factors at all levels. RESULTS: According to the cascade analysis approach, 88.71% of the participants were aware of PrEP, among which 66.7% expressed willingness to use it. Among those who were willing to use PrEP, only 13.80% took it, and of those who took it, 44.68% adhered to it. The students taking PrEP were those with higher education (OR = 4.239, 95% CI: 1.334-13.467), residence in pilot cities (OR = 2.791, 95% CI: 1.498-5.198), residence in high-risk areas (OR = 5.082, 95% CI: 2.224-11.612), engagement in multi-person sexual behavior (OR = 2.186, 95% CI: 1.236-3.867), and substance use (OR = 1.908, 95% CI: 1.167-3.118). Furtherly, students with higher adherence to PrEP were likely to have receptive sexual behaviors (OR = 8.702, 95% CI: 2.070-36.592), absence of substance use (OR = 4.468, 95% CI: 1.371-14.561), and uptake of PrEP through daily oral route. (OR = 7.065, 95% CI: 1.699-29.371). CONCLUSION: YMSM students exhibit distinct patterns of "high awareness, low willingness, low uptake, and low adherence" to PrEP. Strategies for reduction in the acquisition of HIV prioritizing the current features of utilizing PrEP were urgently warranted.

Correlates of oral pre-exposure prophylaxis cessation among men who have sex with men in China: implications from a nationally quantitative and qualitative study.

BACKGROUND: Several studies have demonstrated the population-level effectiveness of oral PrEP in reducing the risk of HIV infection. However, oral PrEP utilization among MSM in China remains below 1%. While existing literature has primarily focused on oral PrEP preference and willingness, there is limited exploration of the underlying factors contributing to oral PrEP cessation in China. This study aims to fill this gap by investigating the factors associated with oral PrEP cessation among MSM in China. METHODS: Assisted by MSM community organizations, we collected 6,535 electronic questionnaires from 31 regions across China, excluding Taiwan, Hong Kong, and Macau. The questionnaire focused on investigating MSM's awareness, willingness, usage, and cessation of oral PrEP. Additionally, 40 participants were randomly chosen for key informant interviews. These qualitative interviews aimed to explore the reasons influencing MSM discontinuing oral PrEP. RESULTS: We eventually enrolled 6535 participants. Among the 685 participants who had used oral PrEP, 19.70% (135/685) ceased oral PrEP. The results indicated that individuals spending > ¥1000 on a bottle of PrEP (aOR = 2.999, 95% CI: 1.886-4.771) were more likely to cease oral PrEP compared to those spending ≤ ¥1000. Conversely, individuals opting for on-demand PrEP (aOR = 0.307, 95% CI: 0.194-0.485) and those using both daily and on-demand PrEP (aOR = 0.114, 95% CI: 0.058-0.226) were less likely to cease PrEP compared to those using daily PrEP. The qualitative analysis uncovered eight themes influencing oral PrEP cessation: (i) High cost and low adherence; (ii) Sexual inactivity; (iii) Lack of knowledge about PrEP; (iv) Trust in current prevention strategies; (v) Poor quality of medical service and counseling; (vi) PrEP stigma; (vii) Partner and relationship factors; (viii) Access challenges. CONCLUSIONS: The cessation of oral PrEP among MSM in China is associated with various factors, including the cost of oral PrEP medication, regimens, individual perception of HIV risk, stigma, and the quality of medical services. It is recommended to provide appropriate regimens for eligible MSM and develop tailored combinations of strategies to enhance PrEP awareness and acceptance among individuals, medical staff, and the MSM community. The findings from this study can support the refinement of HIV interventions among MSM in China, contributing to efforts to reduce the burden of HIV in this population.

Dynamic neural reconstructions of attended object location and features using EEG.

Attention allows us to select relevant and ignore irrelevant information from our complex environments. What happens when attention shifts from one item to another? To answer this question, it is critical to have tools that accurately recover neural representations of both feature and location information with high temporal resolution. In the present study, we used human electroencephalography (EEG) and machine learning to explore how neural representations of object features and locations update across dynamic shifts of attention. We demonstrate that EEG can be used to create simultaneous time courses of neural representations of attended features (time point-by-time point inverted encoding model reconstructions) and attended location (time point-by-time point decoding) during both stable periods and across dynamic shifts of attention. Each trial presented two oriented gratings that flickered at the same frequency but had different orientations; participants were cued to attend one of them and on half of trials received a shift cue midtrial. We trained models on a stable period from Hold attention trials and then reconstructed/decoded the attended orientation/location at each time point on Shift attention trials. Our results showed that both feature reconstruction and location decoding dynamically track the shift of attention and that there may be time points during the shifting of attention when 1) feature and location representations become uncoupled and 2) both the previously attended and currently attended orientations are represented with roughly equal strength. The results offer insight into our understanding of attentional shifts, and the noninvasive techniques developed in the present study lend themselves well to a wide variety of future applications.NEW & NOTEWORTHY We used human EEG and machine learning to reconstruct neural response profiles during dynamic shifts of attention. Specifically, we demonstrated that we could simultaneously read out both location and feature information from an attended item in a multistimulus display. Moreover, we examined how that readout evolves over time during the dynamic process of attentional shifts. These results provide insight into our understanding of attention, and this technique carries substantial potential for versatile extensions and applications.

Adolescent affective psychopathic traits: the long-term outcomes of mother-infant attachment across 14 years.

Affective psychopathic traits have been associated with increased violence and aggression. The scholarly interest in the pathways leading to affective psychopathic traits in adolescence has been growing. Infant attachment security may be one of the early life factors that contribute to the development of affective psychopathic traits. In this study, we test the infant attachment styles measured with the Strange Situation Procedure as a predictor of adolescent affective psychopathic traits measured in three dimensions: remorselessness, unemotionality, and callousness, among 1149 families in the Eunice Kennedy Shriver National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (SECCYD). Propensity scores for the four attachment styles were estimated with iterative tree-based regression models. After accounting for the potential confounding effects of demographic characteristics and child temperament with the inverse probability of treatment weighting, weighted generalized linear models revealed the association between insecure/avoidant attachment style measured one year after childbirth and higher levels of remorselessness, unemotionality, and callousness at 15 years. These findings provide insights into the long-term outcomes for attachment relationships established in early life.

Sexual Behavior Transition and Acute and Early HIV Infection Among Men Who Have Sex With Men: Evidence from an Open Cohort in China, 2011-2019.

The present study examined how sexual behaviors transit over time among men who have sex with men (MSM) and whether these transitions were associated with acute and early HIV (AEH) infection. An open cohort study was conducted using snowball sampling to recruit MSM via physical venues and Internet venues from 2011 to 2019, in China. Cox models were used to estimate the correlates of risk factors and AEH infection. We used Latent Transition Analysis (LTA) to describe behavioral profile and explore latent behavioral "trans-phenotypes," then examined the effect between different "trans-phenotypes" and AEH infection risk and explored the effect of characteristics as possible predictors of sexual behavior transition. Of 6502 MSM, a total of 1974 individuals with negative human immunodeficiency virus (HIV) tests at baseline and at least 2 visit records were included in the final analysis from 2011 to 2019. During an average 1.84 years of follow-up, 64 AEH infections were documented. In the multivariable Cox model, the association between condom use in last anal sex (HR: 0.095, 0.038-0.205), fewer sexual partners (HR: 0.375, 0.205-0.712), low frequency of condom use (HR: 3.592, 1.186-11.272), and AEH acquisition were found. The percentage of MSM with "maintain/develop safety-profile," "consistent risky-profile," and "safety-profile to risky-profile" were 52.48, 40.17, and 7.35%, respectively. Compared with "maintain\develop safety-profile," "consistent or develop risky-profile" had an increasing likelihood of AEH infection. Meanwhile, male sex workers (MSWs) or older MSM were more likely to consist or transit in "risky-profile." Having middle education is a risk factor to transit in "risky-profile" for MSM with "safety-profile" at baseline. In addition, MSM who accept health services in the past year engaged in higher proportion of developing safety-profile. Approximately half of MSM maintain or develop risky behavior with the increasing likelihood of AEH acquisition in China, which suggested that targeted and intensive interventions should be prioritized to maintain safety-profile. Clinical Trial Number: ChiCTR2000039500.

Resonant fiber-optic strain and temperature sensor achieving thermal-noise-limit resolution.

In the area of fiber-optic sensors (FOSs), the past decade witnessed great efforts to challenge the thermal-noise-level sensing resolution for passive FOS. Several attempts were reported claiming the arrival of thermal-noise-level resolution, while the realization of thermal-noise-level resolution for passive FOSs is still controversial and challenging. In this paper, an ultrahigh-resolution FOS system is presented with a sensing resolution better than existing high-resolution passive FOSs. A fiber Fabry-Perot interferometer as the sensing element is interrogated with an ultra-stable probe laser by using the Pound-Drever-Hall technique. Both strain and temperature measurements are carried out to validate the performance of the sensor. The measured noise floor agrees with the theoretical thermal noise level very well.

Anonymous Asynchronous Ratchet Tree Protocol for Group Messaging.

Signal is the first application that applies the double ratchet for its end-to-end encryption protocol. The core of the double ratchet protocol is then applied in WhatsApp, the most popular messaging application around the world. Asynchronous Ratchet Tree (ART) is extended from ratchet and Diffie-Hellman tree. It is the first group protocol that applies Forward Secrecy (FS) with Post-Compromised Security (PCS). However, it does not consider protecting the privacy of user identity. Therefore, it makes sense to provide anonymous features in the conditions of FS and PCS. In this paper, the concepts of Internal Group Anonymity (IGA) and External Group Anonymity (EGA) are formalized. On the basis of IGA and EGA, we develop the "Anonymous Asynchronous Ratchet Tree (AART)" to realize anonymity while preserving FS and PCS. Then, we prove that our AART meets the requirements of IGA and EGA as well as FS and PCS. Finally, the performance and related issues of AART are discussed.

Crowding and Binding: Not All Feature Dimensions Behave in the Same Way.

Humans often fail to identify a target because of nearby flankers. The nature and stages at which this crowding occurs are unclear, and whether crowding operates via a common mechanism across visual dimensions is unknown. Using a dual-estimation report (N = 42), we quantitatively assessed the processing of features alone and in conjunction with another feature both within and between dimensions. Under crowding, observers misreported colors and orientations (i.e., reported a flanker value instead of the target's value) but averaged the target's and flankers' spatial frequencies (SFs). Interestingly, whereas orientation and color errors were independent, orientation and SF errors were interdependent. These qualitative differences of errors across dimensions revealed a tight link between crowding and feature binding, which is contingent on the type of feature dimension. These results and a computational model suggest that crowding and misbinding are due to pooling across a joint coding of orientations and SFs but not of colors.

Related factors of quality of life of type 2 diabetes patients: a systematic review and meta-analysis.

BACKGROUND: Diabetes is a chronic disease, and it could affect both health and quality of life (QOL). A lot of studies have reported some predictors of QOL of type 2 diabetes patients. While their results were not completely consistent. So the aim of our study was finding out the related factors (including characteristics related to the disease, life styles and mental health factors) of QOL of type 2 diabetes patients. METHODS: We searched Cochrane library, EmBase, PubMed and CNKI databases for published studies that evaluated the related factors of QOL of type 2 diabetes patients by using a proper statistic method and had effect sizes (OR or ß) and 95% confidence intervals from January 1st 2000 to May 31st 2016. Any study types were acceptable, and we excluded the reviews, letters, editorials and pooled analyses. The data were analyzed using STATA software (Version 12.0; Stata Corporation). Effect sizes and 95% confidence intervals were calculated to evaluate the relationship between these factors and QOL. RESULTS: Eighteen studies were included into our systematic review and meta-analysis, totaling 57,109 type 2 diabetes patients. Do more physical exercises (The pooled ORs ranged from 0.635 to 0.825 for different scales, less than 1.00), glucose check more frequently [pooled OR (95%CI): 0.175 (0.041, 0.756)] were associated with a better QOL. Presence of complications (The pooled ORs ranged from 1.462 to 3.038 for different scales, more than 1.00), presence of hypertension [pooled OR (95%CI): 1.389 (1.173, 1644)], longer duration of diabetes [pooled OR (95%CI): 1.865 (1.088, 3.197)], diet with more red meat [pooled OR (95%CI): 2.085 (1.063, 4.089)] and depression (The pooled ORs ranged from 3.003 to 11.473 for different scales, higher than 1.00) were associated with a worse QOL. CONCLUSION: The results of this study show that physical exercise, glucose check frequently, complications, hypertension, duration of diabetes, diet with more red meat, and depression were associated with the QOL of type 2 diabetes patients.

Message Integration Authentication in the Internet-of-Things via Lattice-Based Batch Signatures.

The internet-of-things (also known as IoT) connects a large number of information-sensing devices to the Internet to collect all kinds of information needed in real time. The reliability of the source of a large number of accessed information tests the processing speed of signatures. Batch signature allows a signer to sign a group of messages at one time, and signatures' verification can be completed individually and independently. Therefore, batch signature is suitable for data integration authentication in IoT. An outstanding advantage of batch signature is that a signer is able to sign as many messages as possible at one time without worrying about the size of signed messages. To reduce complexity yielded by multiple message signing, a binary tree is usually leveraged in the construction of batch signature. However, this structure requires a batch residue, making the size of a batch signature (for a group of messages) even longer than the sum of single signatures. In this paper, we make use of the intersection method from lattice to propose a novel generic method for batch signature. We further combine our method with hash-and-sign paradigm and Fiat⁻Shamir transformation to propose new batch signature schemes. In our constructions, a batch signature does not need a batch residue, so that the size of the signature is relatively smaller. Our schemes are securely proved to be existential unforgeability against adaptive chosen message attacks under the small integer solution problem, which shows great potential resisting quantum computer attacks.

Time-domain multiplexed high resolution fiber optics strain sensor system based on temporal response of fiber Fabry-Perot interferometers.

We developed a multiplexed strain sensor system with high resolution using fiber Fabry-Perot interferometers (FFPI) as sensing elements. The temporal responses of the FFPIs excited by rectangular laser pulses are used to obtain the strain applied on each FFPI. The FFPIs are connected by cascaded couplers and delay fiber rolls for the time-domain multiplexing. A compact optoelectronic system performing closed-loop cyclic interrogation is employed to improve the sensing resolution and the frequency response. In the demonstration experiment, 3-channel strain sensing with resolutions better than 0.1 nε and frequency response higher than 100 Hz is realized.

Latent class analysis suggests four classes of persons with type 2 diabetes mellitus based on complications and comorbidities in Tianjin, China: a cross-sectional analysis.

The aim of this study was to explore a new classification way in persons with type 2 diabetes mellitus based on complications and comorbidities using Latent Class Analysis, moreover, finding out the factors associated with different latent classes and making specific suggestions. In this study, 5,500 patients with type 2 diabetes mellitus from ten hospitals in Tianjin, China were selected, and the response rate was 96.2%. Latent Class Analysis was used to cluster patients. After compared the baseline characteristics, multinomial logistic regression was applied. Patients with type 2 diabetes mellitus were classified into four classes. In the univariate analysis, all variables were significant (p<0.05). According to multinomial logistic regression, we found longer duration of type 2 diabetes mellitus, family history of diabetes, older age, obesity and central obesity, female menopause, living in a suburb, having a higher 2hPG at diagnosis, smoking and drinking were associated with the prevalence of complications and comorbidities. In conclusion, LCA was shown to be an effective method for grouping patients with T2DM, which presented a nuanced approach to data reduction. Further research using LCA may be especially useful to investigate causal relationships between complications and the significant factors identified in our study.

Ultrahigh resolution optical fiber strain sensor using dual Pound-Drever-Hall feedback loops.

We present an ultrahigh resolution optical fiber strain sensor with a broad frequency range from quasi-static to several hundred hertz. The sensor consists of a π-phase shifted fiber Bragg grating for strain sensing and a fiber Fabry-Perot interferometer as reference. The laser carrier and sideband are locked to the reference and sensing elements, respectively, via two individual feedback loops, in which the Pound-Drever-Hall technique is employed to generate the error signals. The sampling rate is up to 500 samples/s in the demonstrational experiments, only limited by the updating rate of the frequency counter. The strain resolution exhibits a 1/f characteristic in the bandwidth of 0.01-250 Hz, and is better than 0.01 nϵ at 10 Hz with a dynamic range up to 149 dB. Compared with the traditional static strain sensors, the proposed sensor shows a great improvement in both resolution and sensing bandwidth, and can be a powerful tool for geophysical applications.

Rapid and long-lasting reduction of crowding through training.

Crowding is the failure to identify an object in the peripheral visual field in the presence of nearby objects. Recent studies have shown that crowding can be alleviated after several days of training, but the processes underlying this improvement are still unclear. Here we tested whether a few hundred trials within a short period of training can alleviate crowding, whether the learning is location specific, and whether the improvement reflects facilitation by target enhancement or flankers suppression. Observers were asked to identify the orientation of a letter in the periphery surrounded by two flanker letters. Observers were tested before (pretest) and after (posttest) training (600 trials). In Experiment 1 we tested whether learning is location specific or can transfer to a different location; the training and test occurred at the same or different hemifields. In a control experiment, we ruled out alternative explanations for the learning effect in Experiment 1. In Experiment 2, we assessed different components of feature selection by training with either the same flanker polarity as the pre/posttest but opposite polarity group (flanker polarity group) or the same target polarity as the pre/posttest but opposite flanker polarity (target polarity group). Following training, overall performance increased in all four conditions, but only the same-location group (Experiment 1) and the same flanker polarity (Experiment 2) showed a significant reduction in crowding as assessed by the distance at which the flankers no longer interfere with target identification, that is, the critical spacing. These results show that training can rapidly reduce crowding and that improvement primarily reflects learning to ignore the irrelevant flankers. Remarkably, in the two conditions in which training significantly reduced crowding, the benefit of short training persisted for up to a year.

Suppression of a salient distractor protects the processing of target features.

We are often bombarded with salient stimuli that capture our attention and distract us from our current goals. Decades of research have shown the robust detrimental impacts of salient distractors on search performance and, of late, in leading to altered feature perception. These feature errors can be quite extreme, and thus, undesirable. In search tasks, salient distractors can be suppressed if they appear more frequently in one location, and this learned spatial suppression can lead to reductions in the cost of distraction as measured by reaction time slowing. Can learned spatial suppression also protect against visual feature errors? To investigate this question, participants were cued to report one of four briefly presented colored squares on a color wheel. On two-thirds of trials, a salient distractor appeared around one of the nontarget squares, appearing more frequently in one location over the course of the experiment. Participants' responses were fit to a model estimating performance parameters and compared across conditions. Our results showed that general performance (guessing and precision) improved when the salient distractor appeared in a likely location relative to elsewhere. Critically, feature swap errors (probability of misreporting the color at the salient distractor's location) were also significantly reduced when the distractor appeared in a likely location, suggesting that learned spatial suppression of a salient distractor helps protect the processing of target features. This study provides evidence that, in addition to helping us avoid salient distractors, suppression likely plays a larger role in helping to prevent distracting information from being encoded.

Maize Leaf Disease Recognition Based on Improved Convolutional Neural Network ShuffleNetV2.

The occurrence of maize diseases is frequent but challenging to manage. Traditional identification methods have low accuracy and complex model structures with numerous parameters, making them difficult to implement on mobile devices. To address these challenges, this paper proposes a corn leaf disease recognition model SNMPF based on convolutional neural network ShuffleNetV2. In the down-sampling module of the ShuffleNet model, the max pooling layer replaces the deep convolutional layer to perform down-sampling. This improvement helps to extract key features from images, reduce the overfitting of the model, and improve the model's generalization ability. In addition, to enhance the model's ability to express features in complex backgrounds, the Sim AM attention mechanism was introduced. This mechanism enables the model to adaptively adjust focus and pay more attention to local discriminative features. The results on a maize disease image dataset demonstrate that the SNMPF model achieves a recognition accuracy of 98.40%, representing a 4.1 percentage point improvement over the original model, while its size is only 1.56 MB. Compared with existing convolutional neural network models such as EfficientNet, MobileViT, EfficientNetV2, RegNet, and DenseNet, this model offers higher accuracy and a more compact size. As a result, it can automatically detect and classify maize leaf diseases under natural field conditions, boasting high-precision recognition capabilities. Its accurate identification results provide scientific guidance for preventing corn leaf disease and promote the development of precision agriculture.

Identification of leaf diseases in field crops based on improved ShuffleNetV2.

Rapid and accurate identification and timely protection of crop disease is of great importance for ensuring crop yields. Aiming at the problems of large model parameters of existing crop disease recognition methods and low recognition accuracy in the complex background of the field, we propose a lightweight crop leaf disease recognition model based on improved ShuffleNetV2. First, the repetition number and the number of output channels of the basic module of the ShuffleNetV2 model are redesigned to reduce the model parameters to make the model more lightweight while ensuring the accuracy of the model. Second, the residual structure is introduced in the basic feature extraction module to solve the gradient vanishing problem and enable the model to learn more complex feature representations. Then, parallel paths were added to the mechanism of the efficient channel attention (ECA) module, and the weights of different paths were adaptively updated by learnable parameters, and then the efficient dual channel attention (EDCA) module was proposed, which was embedded into the ShuffleNetV2 to improve the cross-channel interaction capability of the model. Finally, a multi-scale shallow feature extraction module and a multi-scale deep feature extraction module were introduced to improve the model's ability to extract lesions at different scales. Based on the above improvements, a lightweight crop leaf disease recognition model REM-ShuffleNetV2 was proposed. Experiments results show that the accuracy and F1 score of the REM-ShuffleNetV2 model on the self-constructed field crop leaf disease dataset are 96.72% and 96.62%, which are 3.88% and 4.37% higher than that of the ShuffleNetV2 model; and the number of model parameters is 4.40M, which is 9.65% less than that of the original model. Compared with classic networks such as DenseNet121, EfficientNet, and MobileNetV3, the REM-ShuffleNetV2 model not only has higher recognition accuracy but also has fewer model parameters. The REM-ShuffleNetV2 model proposed in this study can achieve accurate identification of crop leaf disease in complex field backgrounds, and the model is small, which is convenient to deploy to the mobile end, and provides a reference for intelligent diagnosis of crop leaf disease.

Identifying patterns of sexual behaviors and PrEP uptake characteristics among MSM who were eligible for PrEP: A national cross-section study.

Men who have sex with men (MSM) are at a high risk of HIV infection and should be offered effective preventive measures, such as pre-exposure prophylaxis (PrEP). However, PrEP uptake among eligible MSM was not as high as desired. Diverse research findings on how risky sexual behaviors affect PrEP uptake highlight the necessity for a comprehensive investigation. Understanding the interconnectedness of different sexual behaviors is crucial for evaluating their impact on PrEP uptake among eligible MSM. Using a proportional sampling method, we recruited 5877 MSM aged 16 years and above in mainland China according to PrEP eligibility criteria. Through latent class analysis (LCA), three distinct sexual behavior patterns were identified among eligible MSM. Demographic variances and PrEP uptake among the three distinct sexual behavior patterns were examined using chi-squared tests and multinomial logistic regression. LCA revealed three patterns: low-risk (4,815 MSM), medium-risk (516 MSM), and high-risk (546 MSM). MSM aged 25 years or older with a monthly income of ≥¥8,000 were more likely to be in the medium-risk group. Those from areas with high HIV prevalence and engaging as "top" in anal sex were more likely to be in the medium- and high-risk groups. The medium- and high-risk groups had a higher willingness, uptake, and adherence rates for PrEP than the low-risk group. LCA is effective in identifying diverse sexual behavior patterns among MSM, aiding targeted interventions to enhance PrEP uptake. Addressing demographic variations and tailoring interventions for specific risk groups are crucial for promoting PrEP dissemination and reducing HIV infection risk in eligible MSM.

Lattice-based group signatures with forward security for anonymous authentication.

Group signatures allow users to sign messages on behalf of a group without revealing authority is capable of identifying the user who generated it. However, the exposure of the user's signing key will severely damage the group signature scheme. In order to reduce the loss caused by signing key leakage, Song proposed the first forward-secure group signature. If a group signing key is revealed at the current time period, the previous signing key will not be affected. This means that the attacker cannot forge group signatures regarding messages signed in the past. To resist quantum attacks, many lattice-based forward-secure group signatures have been proposed. However, their key-update algorithm is expensive since they require some costly computations such as the Hermite normal form (HNF) operations and conversion from a full-rank set of lattice vectors into a basis. In this paper, we propose the group signature with forward security from lattice. In comparison with previous works, we have several advantages: Firstly, our scheme is more effective since we only need to sample some vectors independently from a discrete Gaussian during the key-update algorithm. Secondly, the derived secret key size is linear instead of quadratic with the lattice dimensions, which is more friendly towards lightweight applications. Anonymous authentication plays an increasingly critical role in protecting privacy and security in the environment where private information could be collected for intelligent analysis. Our work contributes to the anonymous authentication in the post-quantum setting, which has wide potential applications in the IoT environment.