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Change in population structure, policy adjustment, and China’s public pension sustainability
This study assesses the impact of population structure changes and policy adjustment on public pension sustainability. The analysis is based on actuarial models for pension income, expenditure, and accumulated balance, assessed under varying scenarios. Based on the results, China’s pension financial situation will be in deficit by around 2028, with accumulated deficit potentially as high as RMB 147,411.037 billion in 2050 without population policy adjustment. Second, matching the fertility level with replacement level will only slightly ease the pension financial situation after 2041 and cannot change the deficit trend or the time of first appearance of deficit. The short-term situation may worsen slightly. Third, delaying the legal retirement age to 65 years significantly improves the pension financial situation and ensures no accumulated pension deficit before 2050. Lastly, although increasing economic growth and reducing pension growth can significantly improve the pension financial situation, if the accumulated pension deficit does not appear before 2050, the pension growth rate should be controlled below 0 and economic growth must double in future. In conclusion, to improve the financial status of pensions and ensure elderly welfare, we should focus on reducing pension growth, increasing economic growth, and postponing the statutory retirement age.
Ultra-wide-field imaging Mueller matrix spectroscopic ellipsometry for semiconductor metrology
Abstract We propose an ultra-wide-field imaging Mueller matrix spectroscopic ellipsometry (IMMSE) system for semiconductor metrology. The IMMSE system achieves large-area measurements with a 20 mm × 20 mm field of view (FOV)—the largest FOV reported to date—and a spatial resolution of 6.5 µm. It enables the acquisition of over 10 million Mueller matrix (MM) spectra within the FOV, while a unique signal correction algorithm ensures spectrum consistency across the FOV. Leveraging this numerous MM spectra and machine learning, spatially dense metrology across the entire wafer area is achieved. This approach provides over 1987 times more metrology data and 662 times higher throughput compared to conventional point-based methods, such as scanning electron microscopy. We experimentally demonstrate the potential of the IMMSE for yield enhancement in semiconductor manufacturing by identifying spatial variations of dynamic random access memory (DRAM) structures within individual chips as well as across the wafer.
The downregulation of ubiquitin-specific peptidase 2 indicates a poor prognosis and promotes the progression of gastric cancer through focal adhesion and ECM pathway signaling
The specificity for the correlation between viscera and somato in chronic stable angina pectoris patients and healthy controls: An assessor-blinded and comparative trial
Background Although the relationship between viscera and somato remains unclear, a deeper comprehension of the relationship will maximize the diagnostic and therapeutic benefits. Therefore, this study was conducted to explore the specificity of visceral-somatic associations in the pathological state of chronic stable angina pectoris (CSAP). Methods 40 individuals with CSAP participated in the study, while 40 individuals in the healthy control group were age-matched healthy individuals. Four distinct somatic locations dispersed along the heart and lung meridians were subjected to laser doppler flowmetry, infrared thermography, and functional near-infrared spectroscopy in order to assess (1) perfusion unit (PU), (2) temperature, and (3) regional oxygen saturation (rSO2). These three outcomes represented the somatic sites’ microcirculatory, thermal, and metabolic properties. Results Regarding the microcirculatory characteristics, PU at the somatic sites of the heart meridian (Shenmen(HT7)/Shaohai(HT3)) in the CSAP group substantially increased (P < 0.05) compared to the healthy control group, while there was no statistically significant difference in PU at the somatic sites of the lung meridian between the two groups. Regarding the thermal characteristics, compared with the healthy control group, the temperature of the somatic sites of the heart meridian (Shenmen(HT7)/Shaohai(HT3)), as well as Taiyuan (LU9) of the lung meridian, increased significantly (P < 0.05). With regard to the metabolic features, rSO2 of the somatic site of the heart meridian (Shaohai (HT3)) in the CSAP group decreased significantly (P < 0.05) as compared to the healthy control group. The between-group difference in rSO2 in the lung and heart meridians (Taiyuan (LU9)/Chize (LU5)) and Shenmen (HT7), respectively, was not statistically significant. Conclusions Specific somatic sites in the heart meridian typically exhibit more significant changes in their microcirculatory, thermal, and metabolic characteristics than those in the lung meridian, thereby supporting the relative specificity for the visceral-somatic association in the disease state of CSAP. Trial registration: Clinicaltrials.gov (registration number: NCT04046640)
AI cancer driver mutation predictions are valid in real-world data
Abstract Characterizing and validating which mutations influence development of cancer is challenging. Artificial intelligence (AI) has delivered significant advances in protein structure prediction, but its utility for identifying cancer drivers is less explored. We evaluate multiple computational methods for identifying cancer driver mutations. For re-identifying known drivers, methods incorporating protein structure or functional genomic data outperform methods trained only on evolutionary data. We validate variants of unknown significance (VUSs) annotated as pathogenic by testing their association with overall survival in two cohorts of patients with non-small cell lung cancer (N = 7965 and 977). VUSs identified as pathogenic drivers by AI in KEAP1 and SMARCA4 are associated with worse survival, unlike “benign” VUSs. “Pathogenic” VUSs also exhibit mutual exclusivity with known oncogenic alterations at the pathway level, further suggesting biological validity. AI predictions thus contribute to a more comprehensive understanding of tumor genetics as validated by real-world data.
Influence of radial clearance on Tresca stress in Al2O2-on-Al2O3 bearings for total hip prosthesis evaluated using finite element analysis
The association of different types of physical activity and diabetes co-morbid depression: A cross-sectional analysis
Background Diabetes co-morbid depression is a significant public health burden. Physical activity (PA) has been suggested as a potential approach to reduce the risk of diabetes co-morbid depression. Different types of PA may have different effects on diabetes co-morbid depression. Objective The aims of this study were to investigate the association between moderate to vigorous physical activity (MVPA), different types of physical activity, including work activity (WPA), transportation physical activity (TPA), recreational physical activity (RPA), sedentary behavior (SB), and co-morbid depression in participants with diabetes. Materials and methods The data for this study were derived from the 2017-2018 National Health and Nutrition Examination Survey (NHANES). A total of 642 participants aged 20 years and above were included in the study (mean age: 63.54 ± 12.08 years; 367 males and 275 females). Depression was screened by PHQ-9 in participants who were told to have diabetes by a doctor. PAs were screened by GPAQ. A binary logistic regression model was performed to analyze the association of RPA and diabetes co-morbid depression. Results The causal relationship between MVPA and diabetes co-morbid depression did not reach a significant level (P=0.949), nor did it reach in WPA (P=0.203), TPA (P=0.299) and SB (P=0.219). RPA had a significant effect on diabetes co-morbid depression (OR=0.508, 95%CI: 0.347-0.742, P<0.001), the effect remained significant after adjusted for confounding variables (OR=0.522, 95%CI: 0.356-0.789, P=0.002). Conclusions Among the various types of physical activity, only RPA was a protective factor for co-morbid depression in diabetes.
Spatially-restricted inflammation-induced senescent-like glia in multiple sclerosis and patient-derived organoids
Occupational health risks, safety essentials, and safety beliefs among construction workers in Bangladesh
Determinants of technology-based SMEs’ ability to attract talent with a Master’s degree: Case study of a city in Northeast China
Talents with a master’s degree have high-level professional skills and knowledge reserves. They play an important role in overcoming the traditional technological bottlenecks of technology-based small and medium-sized enterprises (SMEs). Taking Shenyang in Northeast China as an example of a talent outflow city, we begin by considering the attraction of technology-based SMEs’ operations to talent with a master’s degree or above. We then use correlation analysis and gray correlation analysis to analyze the influence of technology-based SMEs’ operations on attracting talent with a master’s degree as well as the correlation and correlation degree between the factors. The results show that the number of talents with a master’s degree or above in technology-based SMEs is significantly positively correlated with number of employees (X9), total assets (X3), total tax paid (X7), main business income (X5), total indebtedness (X4), and total profit (X6). These indicators are the determinants of technology-based SMEs’ ability to attract talent with a master’s degree. These results are consistent with the correlation and gray correlation analyses; thus, the results are mutually verified using two different approaches. Recommendations for talent-attraction policies in technology-based SMEs include strengthening the attraction and training of scientific and technological innovation talent, appropriately optimizing assets and liabilities, enhancing core business innovation and development, and promoting steady growth in profit tax payments. Our findings provide a basis for authorities to develop effective strategies and policies to help SMEs attract talent with a master’s degree.
Distinct cell state ecosystems for nodular lymphocyte-predominant Hodgkin lymphoma
Abstract Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a rare cancer, and few studies have comprehensively investigated the immune microenvironment and rare lymphocyte-predominant (LP) cells. Here we develop a NLPHL specific lymphocyte-predominant ecotype (LPE) model to identify 34 distinct cell states across 14 cell types that co-occur within 3 LPEs for 171 cases. LPE1 and LPE2 were characterized by immunosuppressive microenvironments with high expression of B2M on LP cells, CD8 T-cell exhaustion, immune checkpoint genes expressed by follicular T-cells, and an improved freedom from progression compared to LPE3 in training (n = 109, with 65% LPE1/2) and validation cohorts (n = 62, with 61% LPE1/2). We validate the co-occurrence and co-localization of cell states using spatial transcriptomics. Protein expression of HLA-I and HLA-II on LP cells and SSTR2 on dendritic cells was predictive of LPE1 (C-statistic=0.69), LPE2 (C-statistic=0.79), and LPE3 (C-statistic=0.60). This study establishes a clinically relevant biologic categorization for NLPHL.
Unraveling the thermal decomposition and chemical ionization of methionine using ion mobility spectrometry and computational chemistry
Multi-carrier information hiding based on projection-driven vertex embedding in 3D models
To enhance the robustness of single 3D model carriers in information hiding, this paper proposes a multi-carrier steganography algorithm based on vertex projection of 3D models. The algorithm improves the embedding capacity and attack resistance by fusing multiple 3D models into a geometrically invariant space using centroid coincidence and tangent plane projection. Secret information is embedded by adjusting the position of central vertices in the projection plane of their 1-ring neighborhoods. Experimental results demonstrate that the proposed method achieves strong robustness against translation, rotation, simplification, random noise, and shear attacks. Specifically, the proposed algorithm achieves a peak SNR of 47.87 dB on the xyzrgb_dragon model, significantly outperforming other algorithms, and maintains a lower BER under various attack intensities—for instance, less than 0.1 under 30% simplification and 0.001 random noise. These results confirm the superior invisibility and robustness of the proposed multi-carrier information hiding scheme.
A conserved long-range RNA interaction in SARS-CoV-2 recruits ADAR1 to enhance virus proliferation
Mechanisms of Saposhnikovia divaricata in attenuating NaAsO2-induced neural injury via the PI3K/AKT pathway
Oral administration of crocin significantly alleviated anxiety, and depressive-like behavior following ethanol and nicotine abstinence in adolescent male rats
Artificial intelligence as a predictive tool for mental health status: Insights from a systematic review and meta-analysis
This systematic review and meta-analysis evaluates the effectiveness of AI-driven tools, particularly conversational agents (CAs), in alleviating psychological distress and improving mental health outcomes. The focus is on their impact across diverse populations, including clinical, subclinical, and older adults. A comprehensive search was conducted in PubMed, Google Scholar, Elsevier, and Scopus using specific MeSH terms and keywords such as “Artificial Intelligence,” “Machine Learning,” “Natural Language Processing,” “Depression,” and “Anxiety.” The timeframe included studies published between January 2000 and July 2024. Inclusion criteria comprised peer-reviewed original research articles, cohort studies, and case reports focusing on AI tools for mental health. Systematic reviews, secondary sources, and non-English publications were excluded. Random-effects meta-analysis was conducted using standardized mean differences, with effect sizes synthesized in forest plots. Twenty studies were included in the qualitative synthesis and six in the quantitative meta-analysis. The analysis demonstrated that AI-based CAs significantly reduce anxiety (Cohen’s d = 0.62, p < 0.01) and depression (Cohen’s d = 0.74, p < 0.001), with higher effectiveness observed in multimodal CAs compared to text-only systems. However, the long-term impact remains inconsistent due to variability in follow-up durations and methodological heterogeneity. Some studies lacked extended observation periods or reported diminished effects over time, highlighting a need for sustained intervention research. AI-based CAs, especially when integrated into mobile platforms and using multimodal interfaces, provide scalable and engaging support for mental health. While short-term benefits are evident, future studies should address long-term efficacy, methodological consistency, and ethical concerns like privacy and algorithmic bias to strengthen the utility and trust in AI interventions for mental health.