CST: Complex Sparse Transformer for Low-SNR Speech Enhancement.

Speech enhancement tasks for audio with a low SNR are challenging. Existing speech enhancement methods are mainly designed for high SNR audio, and they usually use RNNs to model audio sequence features, which causes the model to be unable to learn long-distance dependencies, thus limiting its performance in low-SNR speech enhancement tasks. We design a complex transformer module with sparse attention to overcome this problem. Different from the traditional transformer model, this model is extended to effectively model complex domain sequences, using the sparse attention mask balance model's attention to long-distance and nearby relations, introducing the pre-layer positional embedding module to enhance the model's perception of position information, adding the channel attention module to enable the model to dynamically adjust the weight distribution between channels according to the input audio. The experimental results show that, in the low-SNR speech enhancement tests, our models have noticeable performance improvements in speech quality and intelligibility, respectively.

[Genetic Subtypes and Pretreatment Drug Resistance in the Newly Reported Human Immunodeficiency Virus-Infected Men Aged≥50 Years Old in Guangxi].

Objective To analyze the genetic subtypes of human immunodeficiency virus (HIV) and the prevalence of pretreatment drug resistance in the newly reported HIV-infected men in Guangxi. Methods The stratified random sampling method was employed to select the newly reported HIV-infected men aged≥50 years old in 14 cities of Guangxi from January to June in 2020.The pol gene of HIV-1 was amplified by nested reverse transcription polymerase chain reaction and then sequenced.The mutation sites associated with drug resistance and the degree of drug resistance were then analyzed. Results A total of 615 HIV-infected men were included in the study.The genetic subtypes of CRF01_AE,CRF07_BC,and CRF08_BC accounted for 57.4% (353/615),17.1% (105/615),and 22.4% (138/615),respectively.The mutations associated with the resistance to nucleoside reverse transcriptase inhibitors (NRTI),non-nucleoside reverse transcriptase inhibitors (NNRTI),and protease inhibitors occurred in 8 (1.3%),18 (2.9%),and 0 patients,respectively.M184V (0.7%) and K103N (1.8%) were the mutations with the highest occurrence rates for the resistance to NRTIs and NNRTIs,respectively.Twenty-two (3.6%) patients were resistant to at least one type of inhibitors.Specifically,4 (0.7%),14 (2.3%),4 (0.7%),and 0 patients were resistant to NRTIs,NNRTIs,both NRTIs and NNRTIs,and protease inhibitors,respectively.The pretreatment resistance to NNRTIs had much higher frequency than that to NRTIs (2.9% vs.1.3%;χ2=3.929,P=0.047).The prevalence of pretreatment resistance to lamivudine,zidovudine,tenofovir,abacavir,rilpivirine,efavirenz,nevirapine,and lopinavir/ritonavir was 0.8%, 0.3%, 0.7%, 1.0%, 1.3%, 2.8%, 2.9%, and 0, respectively. Conclusions CRF01_AE,CRF07_BC,and CRF08_BC are the three major strains of HIV-infected men≥50 years old newly reported in Guangxi,2020,and the pretreatment drug resistance demonstrates low prevalence.

Adherence to dietary guide for elderly adults and health risks of older adults in ethnic minority areas in China: a cross-sectional study.

BACKGROUND: The impact of dietary guidelines on health in ethnic minority regions needs to be further explored because of multiple sociocultural factors. Therefore, this study was conducted to analyze the association between adherence to dietary guidelines and health risks in an elderly population in an ethnic minority region. METHODS: A cross-sectional survey was conducted among 836 older adults in ethnic minority areas. They were asked to describe their daily dietary intake levels through a semi-quantitative food frequency questionnaire. The closeness coefficient for each study subject was calculated by using the technique for order preference by similarity to an ideal solution (TOPSIS), which measures the adherence to Dietary Guide for Elderly Adults (DGEA). Regression models were used to analyze the association between adherence and health risks. RESULTS: The daily food of the elderly in this area comprised cereals and vegetables. They had low intake of milk, dairy products, and water and high intake of salt. The closeness coefficient for the total population was 0.51, and the adherence of this population to dietary guidelines for the elderly was low. In both the crude model and the models adjusted for covariates, the closeness coefficient was not significantly associated with clinical indicators and health outcomes (p > 0.05). CONCLUSIONS: No association was found between adherence to large sample-based dietary guidelines and clinical indicators or health outcomes in ethnic minority populations. The applicability of dietary guidelines to ethnic minority areas and whether they yield the expected health benefits require further study.

Geographical distinctions of longevity indicators and their correlation with climatic factors in the area where most Chinese Yao are distributed.

Longevity research is a hot topic in the health field. Considerable research focuses on longevity phenomenon in Bama Yao Autonomous County, which has a typical karst landform and is located in Southwest China. This study aims to illustrate the spatial feature of longevity indicators in other Yao areas, to analyze the correlation between climatic factors and longevity indicators, and to provide new clues and targets for further longevity studies. We collect and integrate population, climate, and terrain data into a spatial database. The main analysis methods include spatial autocorrelation, high/low clustering, and multiscale geographically weighted regression (MGWR). Two longevity clusters are identified in Guijiang River Basin (longevity index (LI%): 2.49 ± 0.63) and Liujiang River Basin (LI%: 2.13 ± 0.60). The spatial distribution of longevity indicators is autocorrelative (Moran's I = 0.652, p < 0.001) and clustered significantly (Z score = 4.268, p < 0.001). MGWR shows that the atmospheric pressure significantly affects the spatial distribution of LI% (estimate value (EV) = - 0.566, p = 0.012), centenarity index (CI%) (EV = - 0.425, p = 0.007), UC (EV = - 0.502, p = 0.006), and CH (EV = - 0.497, p = 0.007). Rainfall significantly affects the spatial distribution of LI% (EV = 0.300, p = 0.003) and CI% (EV = - 0.191, p = 0.016). The spatial distribution of the main longevity indicators shows significant heterogeneity and autocorrelation, and they cluster in the Guijiang River and Liujiang River basins. Atmospheric pressure and rainfall may contribute to the longevity phenomenon through complex mechanisms. The longevity phenomenon in the Yao nationality in Guijiang River Basin requires further study to improve our understanding of the health effect of meteorological, environmental, and social conditions on longevity.

LPG-PCFG: An Improved Probabilistic Context- Free Grammar to Hit Low-Probability Passwords.

With the development of the Internet, information security has attracted more attention. Identity authentication based on password authentication is the first line of defense; however, the password-generation model is widely used in offline password attacks and password strength evaluation. In real attack scenarios, high-probability passwords are easy to enumerate; extremely low-probability passwords usually lack semantic structure and, so, are tough to crack by applying statistical laws in machine learning models, but these passwords with lower probability have a large search space and certain semantic information. Improving the low-probability password hit rate in this interval is of great significance for improving the efficiency of offline attacks. However, obtaining a low-probability password is difficult under the current password-generation model. To solve this problem, we propose a low-probability generator-probabilistic context-free grammar (LPG-PCFG) based on PCFG. LPG-PCFG directionally increases the probability of low-probability passwords in the models' distribution, which is designed to obtain a degeneration distribution that is friendly for generating low-probability passwords. By using the control variable method to fine-tune the degeneration of LPG-PCFG, we obtained the optimal combination of degeneration parameters. Compared with the non-degeneration PCFG model, LPG-PCFG generates a larger number of hits. When generating 107 and 108 times, the number of hits to low-probability passwords increases by 50.4% and 42.0%, respectively.

Dynamic Compensation Method for Humidity Sensors Based on Temperature and Humidity Decoupling.

Currently, integrated humidity sensors with fast-response time are widely needed. The most commonly used polyimide capacitive humidity sensor has a long response time, which is difficult to meet the need for a fast response. Most studies focusing on technology and materials have a high cost and are difficult to ensure compatability with the CMOS process. The dynamic compensation method can shorten the response time by only adding digital circuits or software processing. However, conventional compensation technology is not suitable for humidity sensors due to temperature coupling. This paper proposes a new dynamic compensation method for humidity sensors based on the decoupling of temperature factors by analyzing the coupling relationship between sensor dynamic characteristics and temperature. Simulations and experiments were used to verify the proposed method. The experimental results show that the proposed method reduces the humidity response time of the sensor by 85.6%. The proposed method can effectively shorten the response time of humidity sensors.

Transformer Based Binocular Disparity Prediction with Occlusion Predict and Novel Full Connection Layers.

The depth estimation algorithm based on the convolutional neural network has many limitations and defects by constructing matching cost volume to calculate the disparity: using a limited disparity range, the authentic disparity beyond the predetermined range can not be acquired; Besides, the matching process lacks constraints on occlusion and matching uniqueness; Also, as a local feature extractor, a convolutional neural network lacks the ability of global context information perception. Aiming at the problems in the matching method of constructing matching cost volume, we propose a disparity prediction algorithm based on Transformer, which specifically comprises the Swin-SPP module for feature extraction based on Swin Transformer, Transformer disparity matching network based on self-attention and cross-attention mechanism, and occlusion prediction sub-network. In addition, we propose a double skip connection fully connected layer to solve the problems of gradient vanishing and explosion during the training process for the Transformer model, thus further enhancing inference accuracy. The proposed model in this paper achieved an EPE (Absolute error) of 0.57 and 0.61, and a 3PE (Percentage error greater than 3 px) of 1.74% and 1.56% on KITTI 2012 and KITTI 2015 datasets, respectively, with an inference time of 0.46 s and parameters as low as only 2.6 M, showing great advantages compared with other algorithms in various evaluation metrics.

PassTCN-PPLL: A Password Guessing Model Based on Probability Label Learning and Temporal Convolutional Neural Network.

The frequent incidents of password leakage have increased people's attention and research on password security. Password guessing is an essential part of password cracking and password security research. The progression of deep learning technology provides a promising way to improve the efficiency of password guessing. However, the mainstream models proposed for password guessing, such as RNN (or other variants, such as LSTM, GRU), GAN and VAE still face some problems, such as the low efficiency and high repetition rate of the generated passwords. In this paper, we propose a password-guessing model based on the temporal convolutional neural network (PassTCN). To further improve the performance of the generated passwords, we propose a novel password probability label-learning method, which reconstructs labels based on the password probability distribution of the training set and deduplicates the training set when training. Experiments on the RockYou dataset showed that, when generating 108 passwords, the coverage rate of PassTCN with password probability label learning (PassTCN-PPLL) reached 12.6%, which is 87.2%, 72.6% and 42.9% higher than PassGAN (a password-guessing model based on GAN), VAEPass (a password-guessing model based on VAE) and FLA (a password-guessing model based on LSTM), respectively. The repetition rate of our model is 25.9%, which is 45.1%, 31.7% and 17.4% lower than that of PassGAN, VAEPass and FLA, respectively. The results confirm that our approach not only improves the coverage rate but also reduces the repetition rate.

Correlation between urinary contents of some metals and fasting plasma glucose levels: A cross-sectional study in China.

Many metals are involved in the pathogenesis of diabetes, but most of existing studies focused on single metals. The study of mixtures represents real-life exposure scenarios and deserves attention. This study aimed to explore the potential relationship of urinary copper (Cu), zinc (Zn), arsenic (As), selenium (Se), and strontium (Sr) contents with fasting plasma glucose (FPG) levels in 2766 participants. The levels of metals in urine were determined by inductively coupled plasma-mass spectrometry. We used linear regression models and the Bayesian kernel machine regression (BKMR) to evaluate the association between metals and FPG levels. In the multiple metals linear regression, Zn (ß = 0.434), Se (ß = 0.172), and Sr (ß = -0.143) showed significant association with FPG levels (all P < 0.05). The BKMR model analysis showed that the results of single metal association were consistent with the multiple metals linear regression. The mixture of five metals had a positive over-all effect on FPG levels, and Zn (PIP = 1.000) contributed the most to the FPG levels. Cu and As were negatively correlated with FPG levels in women. The potential interaction effect between Cu and Sr was observed in participants aged ≥ 60 years old (Pinteraction = 0.035). In summary, our results suggested that multiple metals in urine are associated with FPG levels. Further studies are needed to confirm these findings and clarify the underlying mechanisms.

Association between oil tea intake and the risk of type 2 diabetes in adults: A cross-sectional study in Gongcheng, Guangxi, China.

BACKGROUND AND OBJECTIVES: The associations between oil tea and type 2 diabetes (T2D) have been little studied in the population. This study aimed to evaluate whether oil tea intake is related to the reduced risk of T2D in adults. METHODS AND STUDY DESIGN: A rural-based cross-sectional study was conducted in Gongcheng Yao Autonomous County, Guangxi, southern China (2018-2019), with a total of 3178 population included in the final analysis. A multivariable logistic regression model was used to analyze the associations between the intake frequency, daily intake of oil tea and the risk of T2D. We further compared the association differences between the daily intake of oil tea and the risk of diabetes under different dietary patterns, which were generated from food frequency intake data using principal factor analysis. RESULTS: The differences in the frequency and daily intake of oil tea in both groups (diabetes group and the non-diabetes group) were statistically significant (p<0.05). After adjusting for age, sex, smoking status, physical activity, body mass index (BMI), compared with non-oil tea drinkers, intake ≥3 times /d had an inverse association with T2D (OR=0.417; 95% CI: 0.205-0.848, p<0.05); while daily intake of more than 600 mL/d but less than 900 mL/d was significantly associated with reduced T2D risk (OR=0.492; 95% CI: 0.284-0.852, p=0.011). In the Chinese traditional dietary and the plant-based dietary model, compared with the non-oil tea drinkers, the fourth intake group had a lower risk of diabetes, with an OR (95%CI) value of 0.500 (0.291-0.854) and 0.505 (0.298-0.855), respectively, but no statistical significance (All p>0.05). CONCLUSIONS: Our study suggests that oil tea was associated with a reduced risk of T2D aged 30 years or older.

Comparations of major and trace elements in soil, water and residents' hair between longevity and non-longevity areas in Bama, China.

Natural environment is an important factor affecting longevity. Soil, water and hair samples from Bama were assayed to investigate the effects of elements on the regional longevity. The concentrations of Cd, Co and Mg in soil and Co in drinking water were significantly higher in longevity area than those in non-longevity area (p < 0.05). The concentrations of K, Mo, Na, Pb and V in soil, Mg, Na, Fe, Li and Mn in drinking water and I, K, Mg, Mn, Na and Sr in hair were significantly lower in longevity area than those in non-longevity area (p < 0.05). Mg, Mn, Na and Sr in hair were affected by soil and drinking water. Our results indicate that adequate concentration of Mg in soil might benefit longevity, excessive concentrations of Na in soil, Mg, Mn and Na in drinking water and Mg, Mn, Na and Sr in hair might reduce lifespan of local residents.

[Effects of Human Immunodeficiency Virus-positive Mothers Receiving Antiretroviral Therapy to Prevent Mother-to-child Transmission on the Growth and Development of 18-month-old Children in Lingshan County of Guangxi].

Objective To evaluate the effects of antiretroviral therapy(ART)for the prevention of mother-to-child transmission(PMTCT)of acquired immune deficiency syndrome(AIDS)on the growth and development of 18-month-old children born by human immunodeficiency virus(HIV)-positive pregnant women in Lingshan County,Guangxi Zhuang Autonomous Region,and provide scientific evidence for improving the ART medication plan for PMTCT.Methods Lingshan County,ranking the first in the HIV-epidemic counties of Guangxi,was selected as the research site.According to the design of retrospective case-control study,we assigned all the subjects into the case group and the control group:(1)The case group included the HIV-positive pregnant women who had received ART for PMTCT and their HIV-negative infants in Lingshan County from 2010 to 2017.The historical cards and PMTCT data of them were collected from the national PMTCT database.(2)The control group included the healthy pregnant women and their healthy babies born in the Lingshan Maternity and Infant Hospital in 2017,and the children's growth and development data were collected.The stunted growth in children was defined as at least one of the three main indicators of body height,body weight,and head circumference below the normal range.Results The number of HIV-positive mothers and their infants in the case group was 391 and 368,respectively,and 87.21%(341/391)and 95.38%(351/368)of mothers and infants respectively received ART medication.The HIV positive rate,mortality rate,and mother-to-child transmission rate of 18-month-old children were 1.36%(5/368),4.35%(16/368),and 2.01%(5/249),respectively.The incidence of stunted growth of 18-month-old children in the case group and the control group was 42.12%(155/368)and 23.06%(101/438),respectively,with significant difference(χ2=33.520,P<0.001).Conclusion After HIV-positive mothers in Lingshan County of Guangxi received ART for PMTCT,the incidence of growth stunting in 18-month-old children increased.

Rapidly Spreading Human Immunodeficiency Virus Epidemic Among Older Males and Associated Factors: A Large-scale Prospective Cohort Study in Rural Southwest China.

BACKGROUND: Increasing risk of human immunodeficiency virus (HIV) heterosexual transmission can raise the potential for a more diffuse and generalized epidemic. In response to the paucity of data on HIV incidence among heterosexuals in China, we conducted a large-scale, population-based cohort study located in rural southwest China. METHODS: Baseline enrollment for the study was conducted from 2013 to 2014 and follow-up at 12 months was from 2014 to 2015 among adults 20 years or older in 3 rural counties of Southwest China. Study participants were informed of the study by brochures and leaflets distributed in outreach activities. Interviews and blood collection were conducted in private rooms. Blood samples were tested for HIV infection. RESULTS: The HIV prevalence of the sample was 0.29% (95% confidence interval [CI], 0.27-0.30) (2063 of 722,795) among the total adult population of 1,090,296 potential participants 20 years or older at baseline. Of the 720,732 individuals who tested HIV-negative at baseline, 493,990 (69%) completed the follow-up. Overall HIV incidence was 2.73 (95% CI, 2.38-3.08) per 10,000 person-years (PY) (235 of 860,627 PY). Human immunodeficiency virus incidence was associated with males, older age, less than secondary schooling and not currently being married. Human immunodeficiency virus incidence was 71.28 (95% CI, 35.21-107.35) per 10,000 PY among males aged 50 to 69 years who had less than secondary schooling and were divorced or widowed. Heterosexual sex was the dominant transmission mode for HIV seroconversions (99.0%). CONCLUSIONS: Older heterosexual males were at disproportionate risk of HIV infection. Health authorities in China need to develop and implement innovative interventions suitable for the broader population of older heterosexuals.

Research on RSS Data Optimization and DFL Localization for Non-Empty Environments.

Device-free localization (DFL) is a new technique which can estimate the target location through analyzing the shadowing effect on surrounding radio frequency (RF) links. In a relatively complex environment, the influences of random disturbance and the multipath effect are more serious. There are kinds of noises and disturbances in the received signal strength (RSS) data of RF links and the data itself can even be distorted, which will seriously affect the DFL accuracy. Most of the common filtering methods adopted in DFL field are not targeted and the filtering effects are unstable. This paper researches the characteristics of RSS data with random disturbances and proposes two-dimensional double correlation (TDDC) distributed wavelet filtering. It can filter out the random disturbances and noise while preserving the RSS fluctuations which are helpful for the DFL, thus improving the quality of RSS data and localization accuracy. Furthermore, RSS variation rules for the links are different in complex environments and hence, it is difficult for the collected training samples to cover all possible patterns. Therefore, a single machine learning model with poor generalization ability finds it difficult to achieve ideal localization results. In this paper, the Adaboost.M2 ensemble learning model based on the Gini decision tree (GDTE) is proposed to improve the generalization ability for unknown patterns. Extensive experiments performed in two different drawing rooms demonstrate that the TDDC distributed wavelet filtering and the GDTE localization model have obvious advantages compared with other methods. The localization accuracy rates of 87% and 95% can be achieved respectively in the two environments.

Disparities in HIV and syphilis prevalence and risk factors between older male clients with and without steady sex partners in southwestern rural China.

BACKGROUND: Heterosexual intercourse accounted for 93% of reported HIV cases in Guangxi, and Guangxi had 10% of China's total number of reported HIV cases. Older men are particularly vulnerable to STIs, for example, 46% of Guangxi's HIV cases were men over 50 years of age. As this is an under-studied population in China, effective prevention and control policies have yet to be developed. Thus, the aim of this study was to use a large-scale cross-sectional survey to understand the demographic and behavior factors associated with HIV and syphilis infections among older male clients of female sex workers (FSWs) in a high epidemic area of rural Guangxi, China. METHODS: A large-scale cross-sectional survey was conducted in 2012 among older male clients of FSWs in low-cost commercial sex venues. Questionnaire interviews were administered to collect sociodemographic and sexual behavior information. Blood samples were collected for HIV and syphilis infection tests. RESULTS: Of the 3485 participants, 2509 (72.0%) clients had a steady sex partner and 976 (28.0%) clients had no steady sex partner. The overall prevalence of HIV and syphilis infection were 3.0% and 3.2%, respectively. Compared to those with a steady sex partner, clients with no steady partner had higher odds of HIV infection (AOR: 1.90, 95% CI: 1.27-2.86), syphilis infection (AOR: 1.53, 95% CI: 1.02-2.30), and having factors associated with HIV or syphilis infection, including non-commercial casual sex encounters in last month (AOR: 3.29, 95% CI: 2.42-4.46), >10 years of commercial sex history (AOR: 1.31, 95% CI: 1.12-1.53), >2 incidents of commercial sex in last month (AOR: 1.53, 95% CI: 1.19-1.96), and aphrodisiac use in last month (AOR: 1.40, 95% CI: 1.16-1.70). Clients with no steady partner had lower odds of having heterosexual intercourse (AOR: 0.66, 95% CI: 0.56-0.79), awareness and knowledge of HIV/AIDS (AOR: 0.75, 95% CI: 0.64-0.88), and having had HIV tests (AOR: 0.65, 95% CI: 0.44-0.98). CONCLUSION: Older male clients of low-cost commercial sex venues in rural southwestern China are at high risk for HIV and syphilis infection, especially those with no steady sex partner. Improved interventions are urgently needed for this neglected risk population.

Point convolutional neural network algorithm for Ising model ground state research based on spring vibration.

The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations.

Retrospective clinical study analysis of skin adverse reactions related to epidermal growth factor receptor inhibitors.

Background: Epidermal growth factor receptor inhibitors (EGFRIs) represent a cornerstone in the targeted therapy of malignant tumors. While effective, dermatological adverse events (dAEs) associated with EGFRIs pose a significant challenge, often necessitating treatment discontinuation due to their severity and potential to impede the continuity of cancer therapy. Despite extensive research, the specific mechanisms and predictors of these adverse events remain poorly understood, particularly in diverse populations. This gap in knowledge underscores the need for targeted studies to better predict and manage these events, enhancing patient outcomes and adherence to life-saving therapies. Methods: This observational study was conducted at The First Affiliated Hospital of Guangxi Medical University, covering cancer patients treated with EGFRIs from 2020 to 2022. We analyzed clinical data including patient demographics, treatment specifics, and the development and timing of dAEs. The study employed SPSS 26.0 software for data analysis, focusing on the incidence of dAEs and factors influencing their occurrence. We used Kaplan-Meier and Cox regression methods to establish a predictive model for dAEs, tracking their onset and impact on treatment continuity. Results: In our study of 120 patients treated with EGFR inhibitors at The First Affiliated Hospital of Guangxi Medical University, we found a high prevalence of dAEs, with 84.2% of patients experiencing such effects. The most common manifestations were papulopustular rashes, observed as pustules in 52.5% and papules in 57.4% of cases, followed by nail lesions in 62.4% of patients, oral or other mucosal ulcers in 34.7%, and hair changes in 26.7%. The median incubation time (MIT) for dAEs was 5 weeks. We identified drug type, ethnicity, and occupation as statistically significant risk factors (P<0.05 for all) that influenced the MIT, which the Cox regression model further identified as protective factors. Nomograms were developed to assess the risk of dAEs, although it is important to note that these models have only been internally validated, lacking external validation data at this stage. Conclusions: The study highlights the high incidence of EGFRIs-associated dAEs, with specific dermatological manifestations posing significant challenges in cancer therapy. The identification of drug type, ethnicity, and occupation as influential factors on the MIT for dAEs informs clinical decisions. Our prediction model serves as a practical tool for evaluating the risk of developing dAEs over time, aiming to optimize patient management and mitigate treatment interruptions.

Diagnostic performance evaluation of urine HIV-1 antibody rapid test kits in a real-life routine care setting in China.

OBJECTIVES: To evaluate the diagnostic performance of urine HIV antibody rapid test kits in screening diverse populations and to analyse subjects' willingness regarding reagent types, purchase channels, acceptable prices, and self-testing. DESIGNS: Diagnostic accuracy studies PARTICIPANTS: A total of 2606 valid and eligible samples were collected in the study, including 202 samples from female sex workers (FSWs), 304 persons with injection drug use (IDU), 1000 pregnant women (PW), 100 subjects undergoing voluntary HIV counselling and testing (VCT) and 1000 students in higher education schools or colleges (STUs). Subjects should simultaneously meet the following inclusion criteria: (1) being at least 18 years old and in full civil capacity, (2) signing an informed consent form and (3) providing truthful identifying information to ensure that the subjects and their samples are unique. RESULTS: The sensitivity, specificity and area under the curve (AUC) of the urine HIV-1 antibody rapid test kits were 92.16%, 99.92% and 0.960 (95% CI: 0.952 to 0.968, p<0.001), respectively, among 2606 samples collected during on-site screenings. The kits showed good diagnostic performance in persons with IDU (AUC, 1.000; 95% CI, 1.000 to 1.000, p<0.001), PW (AUC, 0.999; 95% CI, 0.999 to 1.000, p<0.001) and FSWs (AUC, 1.000; 95% CI, 1.000 to 1.000, p<0.001). The AUC of the urine reagent kits in subjects undergoing VCT was 0.941 (95% CI: 0.876 to 0.978, p<0.001). The 'acceptable price' had the greatest influence on STUs (Pi=1.000) and PW (Pi=1.000), the 'purchase channel' had the greatest influence on subjects undergoing VCT (Pi=1.000) and persons with IDU (Pi=1.000) and the 'reagent types' had the greatest influence on FSWs (Pi=1.000). CONCLUSIONS: The rapid urine test kits showed good diagnostic validity in practical applications, despite a few cases involving misdiagnosis and underdiagnosis.

Constrain Bias Addition to Train Low-Latency Spiking Neural Networks.

In recent years, a third-generation neural network, namely, spiking neural network, has received plethora of attention in the broad areas of Machine learning and Artificial Intelligence. In this paper, a novel differential-based encoding method is proposed and new spike-based learning rules for backpropagation is derived by constraining the addition of bias voltage in spiking neurons. The proposed differential encoding method can effectively exploit the correlation between the data and improve the performance of the proposed model, and the new learning rule can take complete advantage of the modulation properties of bias on the spike firing threshold. We experiment with the proposed model on the environmental sound dataset RWCP and the image dataset MNIST and Fashion-MNIST, respectively, and assign various conditions to test the learning ability and robustness of the proposed model. The experimental results demonstrate that the proposed model achieves near-optimal results with a smaller time step by maintaining the highest accuracy and robustness with less training data. Among them, in MNIST dataset, compared with the original spiking neural network with the same network structure, we achieved a 0.39% accuracy improvement.

Repetitive transcranial magnetic stimulation promotes neurological functional recovery in rats with traumatic brain injury by upregulating synaptic plasticity-related proteins.

Studies have shown that repetitive transcranial magnetic stimulation (rTMS) can enhance synaptic plasticity and improve neurological dysfunction. However, the mechanism through which rTMS can improve moderate traumatic brain injury remains poorly understood. In this study, we established rat models of moderate traumatic brain injury using Feeney's weight-dropping method and treated them using rTMS. To help determine the mechanism of action, we measured levels of several important brain activity-related proteins and their mRNA. On the injured side of the brain, we found that rTMS increased the protein levels and mRNA expression of brain-derived neurotrophic factor, tropomyosin receptor kinase B, N-methyl-D-aspartic acid receptor 1, and phosphorylated cAMP response element binding protein, which are closely associated with the occurrence of long-term potentiation. rTMS also partially reversed the loss of synaptophysin after injury and promoted the remodeling of synaptic ultrastructure. These findings suggest that upregulation of synaptic plasticity-related protein expression is the mechanism through which rTMS promotes neurological function recovery after moderate traumatic brain injury.