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Extensive research in Western societies has demonstrated that media reports of protests have succumbed to selection and description biases, but such tendencies have not yet been tested in the Chinese context. This article investigates the Chinese government and news media's selection and description bias in domestic protest events reporting. Using a large protest event data set from Weibo (CASM-China), we found that government accounts on Weibo covered only 0.4 per cent of protests while news media accounts covered 6.3 per cent of them. In selecting events for coverage, the news media accounts tacitly struck a balance between newsworthiness and political sensitivity; this led them to gravitate towards protests by underprivileged social groups and shy away from protests targeting the government. Government accounts on Weibo, on the other hand, eschewed reporting on violent protests and those organized by the urban middle class and veterans. In reporting selected protest events, both government and news media accounts tended to depoliticize protest events and to frame them in a more positive tone. This description bias was more pronounced for the government than the news media accounts. The government coverage of protest events also had a more thematic (as opposed to episodic) orientation than the news media.
We present the third data release from the Parkes Pulsar Timing Array (PPTA) project. The release contains observations of 32 pulsars obtained using the 64-m Parkes “Murriyang” radio telescope. The data span is up to 18 years with a typical cadence of 3 weeks. This data release is formed by combining an updated version of our second data release with ∼ 3 years of more recent data primarily obtained using an ultra-wide-bandwidth receiver system that operates between 704 and 4032 MHz. We provide calibrated pulse profiles, flux-density dynamic spectra, pulse times of arrival, and initial pulsar timing models. We describe methods for processing such wide-bandwidth observations, and compare this data release with our previous release.
HIV-1 molecular surveillance provides a new approach to explore transmission risks and targeted interventions. From January to June 2021, 663 newly reported HIV-1 cases were recruited in Zhaotong City, Yunnan Province, China. The distribution characteristics of HIV-1 subtypes and HIV-1 molecular network were analysed. Of 542 successfully subtyped samples, 12 HIV-1 strains were identified. The main strains were CRF08_BC (47.0%, 255/542), CRF01_AE (17.0%, 92/542), CRF07_BC (17.0%, 92/542), URFs (8.7%, 47/542), and CRF85_BC (6.5%, 35/542). CRF08_BC was commonly detected among Zhaotong natives, illiterates, and non-farmers and was mostly detected in Zhaoyang County. CRF01_AE was frequently detected among married and homosexual individuals and mostly detected in Weixin and Zhenxiong counties. Among the 516 pol sequences, 187 (36.2%) were clustered. Zhaotong natives, individuals aged ≥60 years, and illiterate individuals were more likely to be found in the network. Assortativity analysis showed that individuals were more likely to be genetically associated when stratified by age, education level, occupation, and reporting area. The genetic diversity of HIV-1 reflects the complexity of local HIV epidemics. Molecular network analyses revealed the subpopulations to focus on and the characteristics of the risk networks. The results will help optimise local prevention and control strategies.
Breast cancer is a high-risk disease with a high mortality rate among women. Chemotherapy plays an important role in the treatment of breast cancer. However, chemotherapy eventually results in tumours that are resistant to drugs. In recent years, many studies have revealed that the activation of Wnt/β-catenin signalling is crucial for the emergence and growth of breast tumours as well as the development of drug resistance. Additionally, drugs that target this pathway can reverse drug resistance in breast cancer therapy. Traditional Chinese medicine has the properties of multi-target and tenderness. Therefore, integrating traditional Chinese medicine and modern medicine into chemotherapy provides a new strategy for reversing the drug resistance of breast tumours. This paper mainly reviews the possible mechanism of Wnt/β-catenin in promoting the process of breast tumour drug resistance, and the progress of alkaloids extracted from traditional Chinese medicine in the targeting of this pathway in order to reverse the drug resistance of breast cancer.
Se deficiency causes impaired growth of fish skeletal muscle due to the retarded hypertrophy of muscle fibres. However, the inner mechanisms remain unclear. According to our previous researches, we infer this phenomenon is associated with Se deficiency-induced high concentration of reactive oxygen species (ROS), which could suppress the target of rapamycin complex 1 (TORC1) pathway-mediated protein synthesis by inhibiting protein kinase B (Akt), an upstream protein of TORC1. To test this hypothesis, juvenile zebrafish (45 d post-fertilisation) were fed a basal Se-adequate diet or a basal Se-deficient diet or them supplemented with an antioxidant (DL-α-tocopherol acetate, designed as VE) or a TOR activator (MHY1485) for 30 d. Zebrafish fed Se-deficient diets exhibited a clear Se-deficient status in skeletal muscle, which was not influenced by dietary VE and MHY1485. Se deficiency significantly elevated ROS concentrations, inhibited Akt activity and TORC1 pathway, suppressed protein synthesis in skeletal muscle, and impaired hypertrophy of skeletal muscle fibres. However, these negative effects of Se deficiency were partly (except that on ROS concentration) alleviated by dietary MHY1485 and completely alleviated by dietary VE. These data strongly support our speculation that Se deficiency-induced high concentration of ROS exerts a clear inhibiting effect on TORC1 pathway-mediated protein synthesis by regulating Akt activity, thereby restricting the hypertrophy of skeletal muscle fibres in fish. Our findings provide a mechanistic explanation for Se deficiency-caused retardation of fish skeletal muscle growth, contributing to a better understanding of the nutritional necessity and regulatory mechanisms of Se in fish muscle physiology.
OBJECTIVES/GOALS: Aim1: To develop a natural language processing (NLP) algorithm to effectively identify statin associated muscle symptoms (SAMS) in patients’electronic health records (EHRs). Aim2: To develop a machine learning model based on clinical features within EHRs that predict the likelihood of SAMS occurrences. METHODS/STUDY POPULATION: A retrospective cohort of adult patients initiated on statins within the Minnesota Fairview Healthcare System EHRs from 2010 to 2020 will be analyzed. NLP-PIER (Patient Information Extraction for Research) platform will be used to search and identify patients who developed SAMS after statin initiation. Manual annotation of clinical notes will be completed to validate the accuracy of identified SAMS cases. Then, a selection of clinical features within the EHRs will be input as predictors for machine learning algorithms development. Select machine learning classifiers will be deployed to generate models for the prediction of SAMS and the best-performing model will be selected based on model performance. RESULTS/ANTICIPATED RESULTS: The expected outcomes include generation of a fine-tuned NLP algorithm that can rigorously identify SAMS occurrences within EHRs. Further, we anticipate having a practical risk model that accurately predicts patients’risks of developing SAMS when taking statins. DISCUSSION/SIGNIFICANCE: The positive and translational impact of our research will be to equip healthcare providers with such informatics tools to improve statin adherence, ultimately promoting patient optimal health and outcomes by maximizing the tolerance and thus realizing the therapeutic benefits of statins.
According to the public data collected from the Health Commission of Gansu Province, China, regarding the COVID-19 pandemic during the summer epidemic cycle in 2022, the epidemiological analysis showed that the pandemic spread stability and the symptom rate (the number of confirmed cases divided by the sum of the number of asymptomatic cases and the number of confirmed cases) of COVID-19 were different among 3 main epidemic regions, Lanzhou, Linxia, and Gannan; both the symptom rate and the daily instantaneous symptom rate (daily number of confirmed cases divided by the sum of daily number of asymptomatic cases and daily number of confirmed cases) in Lanzhou were substantially higher than those in Linxia and Gannan. The difference in the food sources due to the high difference of the population ethnic composition in the 3 regions was probably the main driver for the difference of the symptom rates among the 3 regions. This work provides potential values for prevention and control of COVID-19 in different regions.
Dynamic interpersonal therapy (DIT) is a brief, structured psychodynamic psychotherapy with demonstrated efficacy in treating major depressive disorder (MDD). The aim of the study was to determine whether DIT is an acceptable and efficacious treatment for MDD patients in China.
Patients were randomized to 16-week treatments with either DIT plus antidepressant medication (DIT + ADM; n = 66), general supportive therapy plus antidepressant medication (GST + ADM; n = 75) or antidepressant medication alone (ADM; n = 70). The Hamilton Depression Rating Scale (HAMD) administered by blind raters was the primary efficacy measure. Assessments were completed during the acute 16-week treatment and up to 12-month posttreatment.
The group × time interaction was significant for the primary outcome HAMD (F = 2.900, df1 = 10, df2 = 774.72, p = 0.001) in the acute treatment phase. Pairwise comparisons showed a benefit of DIT + ADM over ADM at weeks 12 [least-squares (LS) mean difference = −3.161, p = 0.007] and 16 (LS mean difference = −3.237, p = 0.004). Because of the unexpected high attrition during the posttreatment follow-up phase, analyses of follow-up data were considered exploratory. Differences between DIT + ADM and ADM remained significant at the 1-, 6-, and 12-month follow-up (ps range from 0.001 to 0.027). DIT + ADM had no advantage over GST + ADM during the acute treatment phase. However, at the 12-month follow-up, patients who received DIT remained less depressed.
Acute treatment with DIT or GST in combination with ADM was similarly efficacious in reducing depressive symptoms and yielded a better outcome than ADM alone. DIT may provide MDD patients with long-term benefits in symptom improvement but results must be viewed with caution.
The G protein-coupled receptors (GPCR) sensing nutritional signals (amino acids, fatty acids, glucose, etc.) are not fully understood. In this research, we used transcriptome sequencing to analyse differentially expressed genes (DEG) in mouse mammary gland tissues at puberty, lactation and involution stages, in which eight GPCR were selected out and verified by qRT-PCR assay. It was further identified the role of GPR110-mediating nutrients including palmitic acid (PA) and methionine (Met) to improve milk synthesis using mouse mammary epithelial cell line HC11. PA but not Met affected GPR110 expression in a dose-dependent manner. GPR110 knockdown decreased milk protein and fat synthesis and cell proliferation and blocked the stimulation of PA on mechanistic target of rapamycin (mTOR) phosphorylation and sterol-regulatory element binding protein 1c (SREBP-1c) expression. In summary, these experimental results disclose DEG related to lactation and reveal that GPR110 mediates PA to activate the mTOR and SREBP-1c pathways to promote milk protein and fat synthesis.
The grey wolf optimizer (GWO) as a new intelligent optimization algorithm has been successfully applied in many fields because of its simple structure, few adjustment parameters and easy implementation. This paper mainly aims at the defects of GWO in path planning application, such as easily falling into local optimization, poor convergence and poor accuracy, and turn point grey wolf optimization (TPGWO) algorithm is proposed. First, the idea of cross-mutation and roulette is used to increase the initial population of GWO and improve the search range. At the same time, the convergence factor function is improved to become a nonlinear update. In the early stage, the search range is expanded, and in the later stage, the convergence speed is increased, while the parameters in the convergence factor function can be adjusted according to the number of obstacles and the map area to change the turning point of the function to improve the convergence speed and accuracy of the algorithm. The turning times and turning angles of the obtained path are added to the fitness function as penalty values to improve the path accuracy. The optimization test is carried out through 16 test functions, and the test results prove the convergence and robustness of TPGWO algorithm. Finally, the TPGWO algorithm is applied to the path planning of patrol robot for simulation experiments. Compared with the GWO algorithm and Particle Swarm Optimization, the simulation results show that the TPGWO algorithm has better convergence, stability and accuracy in the path planning of patrol robot.
Understanding user perceptions of interacting with the virtual world is one of the research focuses in recent years, given the rapid proliferation of virtual reality (VR) and driven to establish the metaverse. Users can generate a familiar connection between their bodies and the virtual world by being embodied in virtual hands, and hand representations can induce users’ embodiment in VR. The sense of embodiment represents the cognitive awareness of one's manifestation and includes three subcomponents: the sense of body ownership, agency and self-location. There is insufficient evidence in the literature about the effects of hand designs on the embodiment, especially based on studying its three subcomponents. This study investigates how virtual hand designs with five realism levels influence the three subcomponents of embodiment in VR. This research employs a self-report questionnaire commonly used in the literature to assess embodiment and evaluates agency and self-location by introducing implicit methods (intentional binding and proprioceptive measurement) derived from psychology. Besides, the objective data of eye tracking is used to explore the connection between embodiment and hand designs, and classifying participants’ eye tracking data to help analyze the link between embodiment and user attention. Overall, this research makes a major contribution through a systematic exploration of users’ embodied experience in VR and offers important evidence of the effects of virtual hand designs on body ownership, agency, and self-location, respectively. In addition, this study provides a valuable reference for further investigation of embodiment through implicit and objective methods, and practical design recommendations for virtual hand design in VR applications.
This study examines how maximum emotional intelligence (EI) members can contribute to the emergence of team-level organizational citizenship behaviors (OCB). Based on integrating a strategic core role and multilevel theory, we developed and tested a model in which the maximum EI score in teams results in intrateam trust and consequently impacts team OCB and examined whether the indirect effect of maximum EI on team OCB through intrateam trust would be moderated by EI diversity and trust divergence in teams. The results from 129 project teams showed that the relationship of maximum EI with team OCB is mediated by intrateam trust and this relationship is stronger for lower levels of EI diversity and trust divergence than for higher levels of diversity and divergence. The findings advance our understanding of the role of EI in team settings. Our research has important implications for organizations attempting to develop the applied value of EI.
With the dangerous and troublesome nature of hollow defects inside building structures, hollowness inspection has always been a challenge in the field of construction quality assessment. Several methods have been proposed for inspecting hollowness inside concrete structures. These methods have shown great advantages compared to manual inspection but still lack autonomy and have several limitations. In this paper, we propose a range-point migration-based non-contact hollowness inspection system with sensor fusion of ultra-wide-band radar and laser-based depth camera to extract both outer surface and inner hollowness information accurately and efficiently. The simulation result evaluates the performance of the system based on the original range-point migration algorithm, and our proposed one and the result of our system show great competitiveness. Several simulation experiments of structures that are very common in reality are carried out to draw more convincing conclusions about the system. At the same time, a set of laboratory-made concrete components were used as experimental objects for the robotic system. Although still accompanied by some problems, these experiments demonstrate the availability of an automated hollow-core detection system.
According to Hamilton's rule, matrilineal-biased investment restrains men in matrilineal societies from maximising their inclusive fitness (the ‘matrilineal puzzle'). A recent hypothesis argues that when women breed communally and share household resources, a man should help his sisters' household, rather than his wife's household, as investment to the later but not the former would be diluted by other unrelated members (Wu et al., 2013). According to this hypothesis, a man is less likely to help on his wife's farm when there are more women reproducing in the wife's household, because on average he would be less related to his wife's household. We used a farm-work observational dataset, that we collected in the matrilineal Mosuo in southwest China, to test this hypothesis. As predicted, high levels of communal breeding by women in his wife's households do predict less effort spent by men on their wife's farm, and communal breeding in men's natal households do not affect whether men help on their natal farms. Thus, communal breeding by women dilutes the inclusive fitness benefits men receive from investment to their wife and children, and may drive the evolution of matrilineal-biased investment by men. These results can help solve the ‘matrilineal puzzle'.
Previous hydrocarbon explorations in the middle of the Tarim Basin indicate that strike-slip faults play an important role in the development of Ordovician carbonate reservoirs and hydrocarbon accumulation. The SB5 fault in the Tarim Basin was the target of this investigation. An evaluation of the stress in situ was carried out and provided boundary conditions to build a 3D geomechanical model. The distribution and application of present in situ stress in the strike-slip fault were studied. The results show good agreement between the absolute measured stress in situ and the modelled stresses, revealing a different stress regime along the strike-slip fault. The uplift segment belongs to a strike-slip stress state, and other areas belong to a normal fault stress state. The strike-slip fault has a significant influence on the present in situ stress distribution. The direction of the maximum horizontal stress deflects near the fault and tends to be parallel to the fault strike. This work introduces a comprehensive evaluation of the present in situ stress of the fractured carbonate reservoirs controlled by the strike-slip fault system. The present in situ stress direction can clarify the propagation direction of hydraulic fracturing and serve to evaluate the effectiveness of natural fractures.
The association between dietary Cu intake and mortality risk remains uncertain. We aimed to investigate the relationship of dietary Cu intake with all-cause mortality among Chinese adults. A total of 17 310 participants from the China Health and Nutrition Survey, a national ongoing open cohort of Chinese participants, were included in the analysis. Dietary intake was measured by three consecutive 24-h dietary recalls in combination with a weighing inventory over the same 3 d. The average intakes of the 3-d dietary macronutrients and micronutrients were calculated. The study outcome was all-cause mortality. During a median follow-up of 9·0 years, 1324 (7·6 %) participants died. After adjusting for sex, age, BMI, ever alcohol drinking, ever smoking, education levels, occupations, urban or rural residents, systolic blood pressure, diastolic blood pressure and the intakes of fat, protein and carbohydrate, the association between dietary Cu intake and all-cause mortality followed a J-shape (Pfor nonlinearity = 0·047). When dietary Cu intake was assessed as quartiles, compared with those in the first quartile (<1·60 mg/d), the adjusted hazard ratios for all-cause mortality were 0·87 (95 % CI (0·71, 1·07)), 0·98 (95 % CI (0·79, 1·21)) and 1·49 (95 % CI (1·19, 1·86)), respectively, in participants in the second (1·60–<1·83 mg/d), third (1·83–<2·09 mg/d) and fourth (≥2·09 mg/d) quartiles. A series of subgroup analyses and sensitivity analyses showed similar results. Overall, our findings emphasised the importance of maintaining optimal dietary Cu intake levels for prevention of premature death.
The treatment of water containing heavy metals has attracted increasing attention because the ingestion of such water poses risks to human health. Due to their relatively large specific surface areas and surface charges, clay minerals play a significant role in the adsorption of heavy metals in water. However, the major factors that influence the adsorption rates of clay minerals are not well understood, and thus methods to predict the sorption of heavy metals by clay minerals are lacking. A method that can identify the most appropriate clay minerals for removal of a given heavy metal, based on the predicted sorption of the clay minerals, is required. This paper presents a widely applicable deep learning neural network approach that yielded excellent predictions of the influence of the sorption ratio on the adsorption of heavy metals by clay minerals. The neural network model was based on datasets of heavy-metal parameters that are available generally. It yielded highly accurate predictions of the adsorption rate based on training data from the dataset and was able to account for a wide range of input parameters. A Pearson sensitivity analysis was used to determine the contributions of individual input parameters to the adsorption rates predicted by the neural network. This newly developed method can predict the major factors influencing heavy-metal adsorption rates. The model described here could be applied in a wide range of scenarios.