Search

Abstract Eukaryotic Argonaute proteins (eAgos) utilize short nucleic acid guides to target complementary sequences for RNA silencing, while prokaryotic Agos (pAgos) provide immunity against invading plasmids or bacteriophages. The Sir2-domain associated short pAgo (SPARSA) immune system defends against invaders by depleting NAD+ and triggering cell death. However, the molecular mechanism underlying SPARSA activation remains unknown. Here, we present cryo-EM structures of inactive monomeric, active tetrameric and active NAD+-bound tetrameric SPARSA complexes, elucidating mechanisms underlying SPARSA assembly, guide RNA preference, target ssDNA-triggered SPARSA tetramerization, and tetrameric-dependent NADase activation. Short pAgos form heterodimers with Sir2-APAZ, favoring short guide RNA with a 5′-AU from ColE-like plasmids. RNA-guided recognition of the target ssDNA triggers SPARSA tetramerization via pAgo- and Sir2-mediated interactions. The resulting tetrameric Sir2 rearrangement aligns catalytic residue H186 for NAD+ hydrolysis. These insights advance our understanding of Sir2-domain associated pAgos immune systems and should facilitate the development of a short pAgo-associated biotechnological toolbox.

Abstract Background Work-related musculoskeletal disorders (WMSDs) show a rapid growth trend. It has brought a huge economic burden to the society and become a serious occupational health problem that needs to be solved urgently. This study aimed to analyze the local muscle response under continuous ergonomic load, screen sensitive fatigue-related biomarkers and provide data support for the early prevention of local muscle damage and the exploration of early warning indicators. Methods Thirteen male college student volunteers were recruited to perform simulated repetitive manual lifting tasks in the laboratory. The lifting task was designed for 4 periods which lasted for 12 min in each, and then paused for 3 min for sampling. Local muscle fatigue is assesed by the Rating of perceived exertion (RPE) and the Joint analysis of sEMG spectrum and amplitude (JASA). Elbow venous blood was collected and 14 kinds of biomarkers were analyzed, which included Metabolic markers Ammonia (AMM), Lactic acid (LAC), Creatine kinase (CK), Lactate dehydrogenase (LDH), Cartilage oligomeric matrix protein (COMP), C-telopeptide of collagen I and II (CTX-I, CTX-II) and Calcium ion (Ca2+); Oxidative stress marker Glutathione (GSH); Inflammatory markers C-reaction protein (CRP), Prostaglandin E2 (PG-E2), Interleukin-6 (IL-6) and Tumor necrosis factor α (TNF-α); Pain marker Neuropeptide Y (NPY). Repeated measures analysis of variance (Repeated ANOVA), linear regression analysis, t-test and spearman correlation analysis were used to analyze the data. Results Both subjective and objective fatigue appeared at the same period. Serum AMM, LAC, CK, LDH, COMP, CTX-II, Ca2+ and NPY after fatigue were significantly higher than those before fatigue (p

Abstract Background Higher education students exhibit heightened sensitivity to environmental changes as they navigate the critical transition from adolescence to adulthood. The coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented challenges to universities worldwide, exemplifying a crisis that profoundly affects the learning outcomes and psychological status of college students. Although it is known that campus lockdown has triggered dramatic changes in lifestyles, learning outcomes, and psychological statuses, in-depth knowledge of the causal relationships among these changes remains largely unclear. Methods Here, we conducted a cross-sectional survey designed to assess the impact of campus closure during COVID-19 on lifestyle, educational performance, and anxiety levels among college students. We surveyed over 3,500 junior college, undergraduate and graduate participants from 94 colleges/universities across 30 provinces, municipalities, and autonomous regions in China.We employed structural equation modeling (SEM) to explore the relationships between changes in lifestyle, educational performance, and levels of anxiety associated with campus open or closure regulations during the COVID-19 pandemic. Results Our results discovered that sleep duration, physical activity involvement, and social connections were crucial for sustaining students' learning outcomes and mental health. The shift to online learning and campus lockdown exacerbated stressors, contributing to heightened anxiety (β = 0.066), disrupted sleep patterns, and enhanced physical activity (β = 0.070) and reduced learning effect (β = -0.059). Sleep patterns were disrupted by the campus lockdown, an effect mediated by the degradation of relationships among classmates. Nonetheless, the best-fitting SEM uncovered the intricate relationships among lifestyle changes, learning outcomes, and psychological status in response to sudden environmental changes (Fisher’s C = 80.949, P = 0.328). These results highlight the critical role of adaptable, supportive campus policies tailored to meet the diverse needs and interests of students during and beyond crises (Fisher’s C = 59.568, P = 0.809). Conclusions Our study advocates for a holistic approach that addresses the multifaceted aspects of student life to cultivate a resilient academic community. This approach contributes to a deeper understanding of the effects of sudden environmental changes on students' psychological well-being and academic performance in the post-pandemic era.

BackgroundShoemaking industry workers are prone to work-related musculoskeletal disorders (WMSDs) due to long-term awkward postures during the work process. There is little research on the prevalence and influencing factors of WMSDs in the knee region of this industry, and it should be taken seriously. ObjectiveTo estimate the prevalence of work-related knee pain among shoemaking workers and analyze the related influencing factors. MethodsA total of 6982 shoemaking workers were selected from 26 shoemaking factories in Guangdong, Hubei, Fujian, Chongqing, Shandong, Zhejiang, and Jingxi by convenience sampling. Prevalence of work-related knee pain in past year, demographic characteristics, occupational related factors, and work posture were collected by a cross-sectional survey using the electronic version of Musculoskeletal Disorder Questionnaire. Logistic regression analysis was used to analyze the influencing factors that may lead to work-related knee pain.ResultsThis survey collected 6982 valid questionnaires with a recovery rate of 98.3%. The prevalence of work-related knee pain of shoemaking workers in the past 12 months was 13.0% (908/6982). According to the results of logistic regression analysis, compared with workers with less than 5 years of service, workers with 5-10 years of service (OR=1.21, 95%CI: 1.02, 1.45) and more than 10 years (1.53, 95%CI: 1.27, 1.83) showed a higher risk of knee WMSDs; sometimes, often and very frequent (reference : rarely or never) long-term standing (OR=1.33, 95%CI: 1.08, 1.64; OR=2.67, 95%CI: 2.10, 3.39; OR=2.75, 95%CI: 2.08, 3.63) and sometimes, often and very frequent (reference: rarely or never) long-term squatting or kneeling (OR=1.80, 95%CI: 1.47, 2.21; OR=2.43, 95%CI: 1.58, 3.75; OR=3.22, 95%CI: 1.66, 6.24) increased the risk of knee pain: long-term bending (OR=1.59, 95%CI: 1.34, 1.89) and often repeated movement of lower limbs and ankles (OR=1.48, 95%CI: 1.25, 1.75) were also risk factors for knee WMSDs among shoemaking industry workers (P

Highlights Developing a universal strategy of the p–n homojunction engineering that could significantly boost electron mobility of electron transport layer (ETL) by two orders of magnitude. Proposing a new mechanism based on p–n homojunction to explain inhibited carrier loss at buried interface. Setting a new performance benchmark as high as 25.50% for planar perovskite solar cells employing TiO2 as ETLs.

Abstract Silent information regulator 2 (Sir2) proteins typically catalyze NAD+-dependent protein deacetylation. The recently identified bacterial Sir2 domain-containing protein, defense-associated sirtuin 2 (DSR2), recognizes the phage tail tube and depletes NAD+ to abort phage propagation, which is counteracted by the phage-encoded DSR anti-defense 1 (DSAD1), but their molecular mechanisms remain unclear. Here, we determine cryo-EM structures of inactive DSR2 in its apo form, DSR2–DSAD1 and DSR2–DSAD1–NAD+, as well as active DSR2–tube and DSR2–tube–NAD+ complexes. DSR2 forms a tetramer with its C-terminal sensor domains (CTDs) in two distinct conformations: CTDclosed or CTDopen. Monomeric, rather than oligomeric, tail tube proteins preferentially bind to CTDclosed and activate Sir2 for NAD+ hydrolysis. DSAD1 binding to CTDopen allosterically inhibits tube binding and tube-mediated DSR2 activation. Our findings provide mechanistic insight into DSR2 assembly, tube-mediated DSR2 activation, and DSAD1-mediated inhibition and NAD+ substrate catalysis in bacterial DSR2 anti-phage defense systems.

We introduce a multimodal rapid identification and growth status discrimination method for morchella. Based on the unique biological characteristics and growth environmental requirements of morchella, the efficient and accurate identification of key growth stages of morchella is achieved through the integration of multimodal information acquisition technology. During the rapid identification process of the growth stage of Morchella, the Multi Stage Vision Enhanced Position Encoding Vision Transformer (MS-EP ViT) model is adopted. By introducing multi-stage input embedding, enhanced position encoding, and optimized Transformer Encoder layers, the performance of the model in identifying different growth stages of Morchella mushrooms is significantly improved. In the multimodal Morchella growth state discrimination method, text and image modalities are integrated, a Non downsampled Contourlet Transform Mask Region based Convolutional Neural Network (NSCT Mask R-CNN) model is designed, and a multimodal feature extraction strategy combining Non downsampled Contourlet Transform (NSCT) features with environmental features is explored. This strategy effectively achieves the goals of object detection and instance segmentation, and thus we have accurately evaluated the growth status of Morchella in the later stages of mulberry, young mushroom, and mature. The experimental results show that both models have achieved significant improvements in recognition accuracy and stability, and the rationality of hyperparameter settings has been verified through convergence and parameter sensitivity experiments. Overall, we provide a more accurate and efficient identification method for monitoring the growth of Morchella, which helps to better understand the growth of Morchella and provides scientific basis for optimizing its growth environment.

Laboratory experiments are one of the important means used to investigate travel choice behavior under strategic uncertainty. Many experiment-based studies have shown that the Nash equilibrium can predict aggregated route choices, while the fluctuations, whose mechanisms are still unclear, continue to exist until the end. To understand the fluctuations, this paper proposes a route-dependent attraction-based stochastic process model, which shares exactly the same behavioral foundation introduced in Part I of the study (Qi et al., 2023), i.e., route-dependent inertia and route-dependent preference. The model predictions are carefully compared with the experimental observations obtained from the congestible parallel-route laboratory experiments containing 312 subjects and eight decision-making scenarios (Qi et al., 2023). The results show that the proposed stochastic process model can precisely reproduce the random oscillations both in terms of flow switching and route flow evolution. Subsequently, an approximated model is developed to enhance the efficiency in evaluating the equilibrium distribution, providing a practical tool to evaluate the impacts of transportation policies in both long- and short-term runs. To the best of our knowledge, this paper is the first attempt to model and explain experimental phenomena by introducing stochastic process theories, as well as a successful example of applying experimental economics methodology to improve our understanding of human travel choice behavior.

This paper investigates the impact of surface effects on the propagation behavior of longitudinal waves in a nanorod. A theoretical model has been established on the basis of a newly proposed theory of elastic waves with surface effects. The surface effects comprise two components: the effect of surface energy and the effect of surface inertia. An analytical formula for the longitudinal wave velocity of a nanorod has been derived. Two inherent lengths at nanoscale have been deduced to characterize these two types of surface effects. The results indicate that the longitudinal wave in a nanorod is still nondispersive. However, an attractive phenomenon uncovered is that when the size of a rod reduces to the inherent lengths at nanoscale, the longitudinal wave velocity becomes size-dependent due to the effects of surface energy and surface inertia. The former increases the longitudinal wave velocity, whereas the latter decreases it. This can be understood as the former equivalently increasing the stiffness of the nanorod, whereas the latter enhancing its effective density. On the other hand, when the rod is at the macroscale, the longitudinal wave velocity degenerates to the classical velocity for a macroscopic rod without any surface effects. The current findings not only enhance our understanding of the size-dependent wave velocity of longitudinal waves in nanorods but also facilitate precisely designing the elastic wave nanodevices.

Abstract Thermoelectric materials are highly promising for waste heat harvesting. Although thermoelectric materials research has expanded over the years, bismuth telluride‐based alloys are still the best for near‐room‐temperature applications. In this work, a ≈38% enhancement of the average ZT (300−473 K) to 1.21 is achieved by mixing Bi0.4Sb1.6Te3 with an emerging thermoelectric material Sb2Si2Te6, which is significantly higher than that of most BiySb2−yTe3‐based composites. This enhancement is facilitated by the unique interface region between the Bi0.4Sb1.6Te3 matrix and Sb2Si2Te6‐based precipitates with an orderly atomic arrangement, which promotes the transport of charge carriers with minimal scattering, overcoming a common factor that is limiting ZT enhancement in such composites. At the same time, high‐density dislocations in the same region can effectively scatter the phonons, decoupling the electron‐phonon transport. This results in a ≈56% enhancement of the thermoelectric quality factor at 373 K, from 0.41 for the pristine sample to 0.64 for the composite sample. A single‐leg device is fabricated with a high efficiency of 5.4% at ΔT = 164 K further demonstrating the efficacy of the Sb2Si2Te6 compositing strategy and the importance of the precipitate‐matrix interface microstructure in improving the performance of materials for relatively low‐temperature applications.

The hematopoietically-expressed homeobox gene (HHEX) played a critical role in regulating the immune system that the abnormality of which was involved in the psychopathology and cognitive deficits of psychiatric disorders. The aim of this study was to investigate the effect of HHEX rs1111875 polymorphism on the susceptibility and cognitive deficits of first-episode schizophrenic patients (FSP). We assessed cognitive function in 239 first-episode patients meeting DSM-IV for schizophrenia, and 368 healthy controls using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). The HHEX rs1111875 polymorphism was genotyped. Our results showed that the allelic and genotypic frequencies of HHEX rs1111875 polymorphism didn't differ between FSP and healthy controls (both p > 0.05) after adjusting for sex and age. Cognitive test scores in FSP were significantly lower than those in healthy controls on all scales (all p 0.05) after adjusting for covariates. There was a significant genotype (p 0.05) effect on the delayed memory score after adjusting for covariates. The HHEX rs1111875 polymorphism was significantly associated with the delayed memory score in FSP (p 0.05) after adjusting for covariates. Our findings supported that the HHEX rs1111875 polymorphism did not contribute to the susceptibility to FSP. However, this polymorphism might influence the delayed memory in FSP. Moreover, FSP had poorer cognitive function than healthy controls except for the visuospatial/constructional domain.

Introduction: We investigated trends in the use of therapeutic drugs for pregnant patients with rheumatic diseases in nine Chinese cities (Beijing, Chengdu, Guangzhou, Harbin, Hangzhou, Shanghai, Shenyang, Tianjin, and Zhengzhou) to provide a reference for drug use in clinic.Methods: Outpatient prescription data for pregnant patients diagnosed with rheumatic diseases in nine cities across China in 2016–2021 were extracted from the Hospital Prescription Cooperation Project of the Hospital Pharmacy Professional Committee of the Chinese Pharmaceutical Association. A retrospective analysis was then performed, incorporating data on patient age, defined daily doses (DDDs), defined daily cost (DDC), and other metrics.Results: In 2016–2020, more than 70% of the pregnant patients diagnosed with rheumatic diseases in these nine cities were 25 to < 35 years of age. The most common rheumatic diseases during pregnancy were antiphospholipid antibody syndrome (APS) and systemic lupus erythematosus (SLE). In terms of the routine use of daily therapeutic drugs, the DDDs of low molecular weight heparins (LMWHs), glucocorticoids, and immunosuppressive agents dominated the top three. Intravenous immunoglobulin (IVIG) and tumor necrosis factor inhibitors (TNFi) have been used since 2019 and had been in the forefront of the DDC.Conclusion: The number and total cost of prescriptions for therapeutic drugs of pregnancy complicated by rheumatic diseases, have increased significantly over the study interval. Conventional therapeutic drugs, especially glucocorticoids, LMWHs, and hydroxychloroquine were the most widely used drugs in pregnant patients with rheumatic diseases. However, IVIG and TNFi, relatively high cost, have shown gradual increases in clinical use since 2019.

BackgroundLong-term exposure to whole-body vibration (WBV) will affect the health of occupational drivers. However, research on the characteristics of WBV exposure by urban bus drivers and health risk evaluation is still insufficient. ObjectiveTo identify the characteristics of occupational WBV exposure of bus drivers serving 31 bus routes provided by three branches of Haikou Public Transport Group, and to evaluate their occupational health risks related to WBV. MethodsA total of 31 out of 142 bus routes run by three branches of Haikou Public Transport Group were selected to monitor WBV exposure of 31 bus drivers during driving. WBV parameters such as triaxial frequency weighted acceleration (awx, awy, awz) and triaxial crest factor (CFx, CFy, CFz) of the drivers were determined with a six-channel human vibration meter. Two methods, 8-hour daily value of the weighted root mean square average weighted vibration [A(8)] based on aw and 8-hour daily value of vibration dose [VDV(8)] based on vibration dose value (VDV), were used for health risk assessment and classified WBV health risk results into three levels (high, medium, and low) by the exposure action value (EAV) and exposure limit values (ELV) for A(8) and VDV(8) recommended by ISO 2631-1:1997. The two evaluation methods, A(8) and VDV(8), were compared by Fisher's exact test. ResultsRegarding the WBV parameters, the vector sum of acceleration (av) was 0.321-0.680 m·s−2, the VDV of monitoring interval was 3.824-10.174 m·s−1.75, and the VDV(8) was 6.039-13.505 m·s−1.75; their values in mean ± standard deviation were (0.480±0.100) m·s−2, (6.987±2.737) m·s−1.75, and (9.773±4.540) m·s−1.75, respectively. Positive correlations were found between awx and awz, av and awz, CFx and CFy, CFy and CFz. No bus route's WBV exposure level was graded as high health risk by either A(8) or VDV(8). The number of routes graded as low health risk by A(8) was 26, while the number by VDV(8) was 12. The consistency rates of health risk levels evaluated by the two methods were 66.7% (6/9), 54.6% (6/11), and 45.5% (5/11) for the three bus group branches, respectively. The difference in WBV health risk assessment results between the two evaluation methods was not statistically significant. ConclusionPositive correlations are found between triaxial acceleration and triaxial crest factor. There is no difference in the results of using A(8) and VDV(8) to evaluate health risks of WBV in urban bus routes.

With the maturity of artificial intelligence (AI) technology, applications of AI in edge computing will greatly promote the development of industrial technology. However, the existing studies on the edge computing framework for the Industrial Internet of Things (IIoT) still face several challenges, such as deep hardware and software coupling, diverse protocols, difficult deployment of AI models, insufficient computing capabilities of edge devices, and sensitivity to delay and energy consumption. To solve the above problems, this paper proposes a software-defined AI-oriented three-layer IIoT edge computing framework and presents the design and implementation of an AI-oriented edge computing system, aiming to support device access, enable the acceptance and deployment of AI models from the cloud, and allow the whole process from data acquisition to model training to be completed at the edge. In addition, this paper proposes a time series-based method for device selection and computation offloading in the federated learning process, which selectively offloads the tasks of inefficient nodes to the edge computing center to reduce the training delay and energy consumption. Finally, experiments carried out to verify the feasibility and effectiveness of the proposed method are reported. The model training time with the proposed method is generally 30% to 50% less than that with the random device selection method, and the training energy consumption under the proposed method is generally 35% to 55% less.

Cliffs are essential refuges for endangered plants, but their inaccessibility and inadequate reliable survey methods make the assessment of the survival status of these plants difficult. We employed the drone-based Nap-of-the-Object Photography (NoOP) technique, which involves flying close to an object's surface to automatically capture high-precision images of the object surface, to investigate an endangered cliff plant, Clematis acerifolia, in North China. A drone (DJI M300 RTK) equipped with a visible-light mapping camera (DJI Zenmuse P1) was used for automated NoOP flights, enabling the collection and reconstruction of millimeter-level, high-precision three-dimensional (3D) models of the cliff plots. Across 12 cliffs, we identified and measured the precise coordinates and values of eight microtopographical and five morphological characteristic factors for 1444 C. acerifolia plants. The Ground sampling distance (GSD) of the high-precision 3D models reached 0.10 cm/pixel, and the mean value of the root mean squared error of the sampled positioning coordinates was 4.65 cm/pixel. Correlation and correspondence analyses were used for data analyses. The results indicated that the growth status of C. acerifolia was significantly influenced by a complex factor interaction, particularly plant altitude, crevice width, and crevice length. This method for cliff plant surveys using drone-based NoOP determines the parameters for such surveys and demonstrates the effectiveness and potential of this method for cliff plant investigations. The primary microtopographical factors affecting the growth and distribution status of C. acerifolia were identified, providing pointers for translocation conservation and propagation reintroduction of this endangered plant.

Fraud problems in loan application assessment cause significant losses for finance companies worldwide, and much research has focused on machine learning methods to improve the efficacy of fraud detection in some financial domains. However, diverse information falsification in individual fraud remains one of the most challenging problems in loan applications. To this end, we conducted an empirical study to explore the relationships between various fraud types and analyzed the factors influencing information fabrication. Weak relationships exist among different falsification types, and some essential factors play the same roles in different fraud types. In contrast, others have various or opposing effects on these types of frauds. Based on this finding, we propose a novel hierarchical multi-task learning approach to refine fraud-detection systems. Specifically, we first developed a hierarchical fraud category method to break down this problem into several subtasks according to the information types falsified by customers, reducing fraud identification's difficulty. Second, a heterogeneous network with a meta-path-based random walk and heterogeneous skip-gram model can solve the representation learning problem owing to the sophisticated relationships among the applicants' information. Furthermore, the final subtasks can be predicted using a multi-task learning approach with two prediction layers. The first layer provides the probabilities of general fraud categories as auxiliary information for the second layer, which is for specific subtask prediction. Finally, we conducted extensive experiments based on a real-world dataset to demonstrate the effectiveness of the proposed approach.

Government macro-control through various policies is an important way to mitigate air pollution and greenhouse gases. Therefore, environmental tax is used worldwide as an important measure. However, few studies have considered the interaction between carbon and environmental protection taxes. Additionally, different sectors differ in their energy structure, pollution emission intensity, and economic status, and previous studies rarely proposed differentiated environmental tax rates based at the sectoral level. A model framework combining the computable general equilibrium (CGE) model and Bayesian optimization (BO) algorithm is proposed to maximize GDP, meet environmental planning objectives, and explore the optimal environmental taxation scheme to realize the multi-objective optimization of the economy and environment. Meanwhile, this study compares the different impact mechanisms of environmental protection tax and carbon tax. It discusses the impacts of differentiated environmental tax rates in different sectors on the environment and economy. For example, the results show that the coordinated implementation of environmental protection and carbon tax policies and the sectoral differentiated environmental tax rates in China could better balance economic development and environmental governance. Additionally, the optimal taxation scheme could mitigate air pollution and greenhouse gases, promote economic growth, and realize sustainable economic and environmental development. Furthermore, the optimized taxation scheme positively affects the energy and industrial structures.

Abstract Background Alzheimer’s disease (AD) is one of the most common neurodegenerative disorders in the world, but still lack of effective drug treatment. Gynostemma Pentaphyllum (Thunb.) Makino (GpM), a Chinese medicinal herb, plays important roles in anti-inflammation, anti-oxidative stress and anti-tumor, which has been reported to ameliorate cognitive impairment of AD. However, the neuroprotective mechanism of GpM remains unclear. This study aims to investigate the targets and possible signaling pathways of GpM in the treatment of AD. Methods Active compounds of GpM and their putative target proteins were selected from Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database and Analysis Platform. AD-associated targets were identified from GeneCards, the Online Mendelian Inheritance in Man (OMIM) database and the Therapeutic Target Database (TTD). The intersecting targets of GpM and AD were identified and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were carried out to analyze the mechanism of them. Compound-target-pathway (CTP) network and protein–protein interaction (PPI) network were constructed and analyzed to elucidate the correlation between compounds, proteins and pathways. Molecular docking was performed to further demonstrate the possibility of GpM for AD. Results A total of 13 active compounds of GpM, 168 putative target proteins of compounds and 722 AD-associated targets were identified. Eighteen intersecting targets of GpM and AD were found and the epidermal growth factor receptor (EGFR), interleukin-1 beta (IL-1β), interleukin-6 (IL-6), nitric oxide synthase in endothelial (NOS3) and serum paraoxonase/arylesterase 1 (PON1) were selected as the primary targets of GpM in the treatment of AD. The neuroprotective effect of GPM was related to a variety of pathways, including amoebiasis, HIF-1 signaling pathway, cytokine-cytokine receptor interaction and so on. Conclusions Our findings elucidate the active compounds, targets and pathways of GpM involved in effects of anti-AD. The novel mechanism of GpM against AD provides more treatment options for AD.

Optimal dosing in pregnant and lactating women requires an understanding of the pharmacokinetics in the mother, fetus, and breastfed infant. Physiologically‐based pharmacokinetic (PBPK) modeling can be used to simulate untested scenarios and hence supplement clinical data to support dosing decisions. A PBPK model for the antiretroviral dolutegravir (mainly metabolized by UGT1A1) was verified using reported exposures in non‐pregnant healthy volunteers, pregnant women, and the umbilical cord, lactating mothers, and breastfed neonates. The model was then applied to predict the impact of UGT1A1 phenotypes in extensive (EM), poor (PM), and ultra‐rapid metabolizers (UM). The predicted dolutegravir maternal plasma and umbilical cord AUC in UGT1A1 PMs was 1.6‐fold higher than in EMs. The predicted dolutegravir maternal plasma and umbilical cord AUC in UGT1A1 UMs mothers was 1.3‐fold lower than in EMs. The predicted mean systemic and umbilical vein concentrations were in excess of the dolutegravir IC90 at 17, 28, and 40 gestational weeks, regardless of UGT1A1 phenotype, indicating that the standard dose of dolutegravir (50 mg q.d., fed state) is generally appropriate in late pregnancy, across UGT1A1 phenotypes. Applying the model in breastfed infants, a 1.5‐, 1.7‐, and 2.2‐fold higher exposure in 2‐day‐old neonates, 10‐day‐old neonates, and infants who were UGT1A1 PMs, respectively, compared with EMs of the same age. However, it should be noted that the exposure in breastfed infants who were UGT1A1 PMs was still an order of magnitude lower than maternal exposure with a relative infant daily dose of <2%, suggesting safe use of dolutegravir in breastfeeding women. [ABSTRACT FROM AUTHOR]

Vaccination plays a crucial role in preventing and controlling SARS-CoV-2 infections as well as their associated adverse outcomes. But there is a notable lack of research on the effectiveness of COVID-19 vaccination in children, particularly those young preterm-born children, who are more vulnerable to severe outcomes from SARS-CoV-2 infection. We aimed to determine the effect of vaccination with inactivated vaccines BBIBP-CorV and CoronaVac on symptomatic COVID-19 infection and related symptoms in preterm-born children aged 3–7 years after relaxation of the COVID-19 prevention and control measures in December 2022 in China. We performed a retrospective cohort study involving 242 preterm-born children aged 3–7 years and the data were collected in March 2023. Logistic regression models and modified Poisson regression models combined with entropy balancing were used to explore the associations of vaccination against SARS-CoV-2 with symptomatic COVID-19, specific symptoms, and persistent symptoms one month after recovery from COVID-19. Of the 242 recruited preterm-born children, 156 (64.5%) were vaccinated with inactivated vaccines BBIBP‐CorV and CoronaVac. After entropy balancing, the covariates were balanced between the vaccinated and the unvaccinated groups, with standardized mean difference < 0.001. Vaccination with the said SARS-CoV-2 vaccines lowered the risk of developing symptomatic COVID-19 in preterm-born children (risk ratio [RR] = 0.783; 95% confidence interval [CI]: (0.711, 0.861). Likewise, COVID-19 vaccination was associated with a decline in the risk of pneumonia (odds ratio [OR] = 0.318; 95% CI 0.110, 0.913), fever (RR = 0.710; 95% CI 0.635, 0.794), high fever (RR = 0.542; 95% CI 0.297, 0.988), sore throat (OR = 0.304; 95% CI 0.139, 0.664), and persistent symptoms (RR = 0.425; 95% CI 0.182, 0.993). Immunization with inactivated vaccines BBIBP‐CorV and CoronaVac provides protection against symptomatic COVID-19 for preterm-born children 3–7 years. [ABSTRACT FROM AUTHOR]

Steroid hormones, recognized as emerging environmental contaminants, have garnered increasing attention in recent years. The present work studied the distribution characteristics in the environment, bioaccumulation in aquatic products, and the associated environmental and health risks of typical steroid hormones from commercial freshwater aquaculture farms operating under different aquaculture modes (monoculture and polyculture). Totals of 9 and 14 steroid hormones were detected in water and sediment samples, with concentrations ranging from 0.66 ± 0.17 ng/L to 40.5 ± 5.08 ng/L and from 0.36 ± 0.08 ng/g to 123 ± 19.9 ng/g, respectively. Hazard index (HI) calculations indicated that all sampling locations were identified as medium or high risk for both water and sediment matrices. Nineteen steroids were detected in at least one type of tissue, with the concentrations in the bile, plasma, muscle, liver, and gill ranging from 5000 L/kg). Notably, despite low estimated daily intakes (EDIs) (0.43–6.43 ng/day/person to 18.7 ng/day/person) and hazard quotients (HQs) (below 4.188 × 10−7), the high bioaccumulation factors (BAFs) underscore the necessity for stringent regulatory measures by local governments. Additionally, a comparison of EDI results across different aquaculture modes and fish species revealed that steroid hormone-related health risks to humans are influenced by both the fish species and the aquaculture mode. This study indicated that the consumption of poly-cultured fish (e.g., bighead carp) may pose a greater steroid-related health risk, compared to the consumption of mono-cultured fish. [ABSTRACT FROM AUTHOR]

Highly efficient surface acoustic wave (SAW) transducers offer significant advantages for microfluidic atomization. Aiming at highly efficient atomization, we innovatively accomplish dual-surface simultaneous atomization by strategically positioning the liquid supply outside the IDT aperture edge. Initially, we optimize Lamb wave transducers and specifically investigate their performance based on the ratio of substrate thickness to acoustic wavelength. When this ratio h / λ is approximately 1.25, the electromechanical coupling coefficient of A0-mode Lamb waves can reach around 5.5% for 128° Y-X LiNbO3. We then study the mechanism of droplet atomization with the liquid supply positioned outside the IDT aperture edge. Experimental results demonstrate that optimized Lamb wave transducers exhibit clear dual-surface simultaneous atomization. These transducers provide equivalent amplitude acoustic wave vibrations on both surfaces, causing the liquid thin film to accumulate at the edges of the dual-surface and form a continuous mist. [ABSTRACT FROM AUTHOR]

Single crystal fiber (SCF) is a novel solid gain medium and technique which combines the advantages of glass fiber and single crystal, showing great potential in the field of high-power lasers. In this paper, Er, Nd: YSGG single crystals with diameters of 2 mm and lengths of 80 mm were successfully grown using the micro-pulling-down method for the first time. Then, the measurements of Laue spots and Er3+ distribution indicated that the as-grown crystals were of a high quality. The effect of co-doped Nd3+ on the Er: YSGG was systematically discussed, which demonstrated that Nd3+ can decrease the fluorescence lifetime of Er: 4I13/2 that solve the self-termination bottleneck accordingly. These results demonstrate that Er, Nd: YSGG SCFs are promising materials for the further 3 μm laser generations.

Abstract Cutaneous squamous cell carcinoma (cSCC) is the second most common nonmelanoma skin cancer characterized by high invasiveness, heterogeneity, and mainly occurs in the ultraviolet (UV)-exposed regions of the skin, but its pathogenesis is still unclear. Here, we generated single-cell transcriptome profiles for 350 cells from six primary UV-induced cSCCs, together with matched adjacent skin samples, and three healthy control skin tissues by single-cell RNA-sequencing technology based on Smart-seq2 strategy. A series of bioinformatics analyses and in vitro experiments were used to decipher and validate the critical molecular pattern of cSCC. Results showed that cSCC cells and normal keratinocytes were significantly distinct in gene expression and chromosomal copy number variation. Furthermore, cSCC cells exhibited 18 hallmark pathways of cancer by gene set enrichment analysis. Differential expression analysis demonstrated that many members belonging to S100 gene family, SPRR gene family, and FABP5 were significantly upregulated in cSCC cells. Further experiments confirmed their upregulation and showed that S100A9 or FABP5 knockdown in cSCC cells inhibited their proliferation and migration through NF-κB pathway. Taken together, our data provide a valuable resource for deciphering the molecular pattern in UV-induced cSCC at a single-cell level and suggest that S100A9 and FABP5 may provide novel targets for therapeutic intervention of cSCC in the future.

Nickel-rich layered oxides are important cathode materials for lithium batteries, but their effectiveness is compromised by air sensitivity. Using LiNiO_{2} as a prototype in ab initio molecular dynamics simulations, we find that oxygen active sites provide molecular orbitals and metal activation that work in synergy to form impurity hydroxyl and carbonate species in the presence of air. This mechanism successfully explains the high air sensitivity on the specific surfaces, the air-sensitivity transition after Li/Ni mixing, and the treatment effect of cation (Co and Mn) and anion (F and S) doping methods. Doping with sulfur ions proves to have the best protective effect against H_{2}O and CO_{2}. A series of first-principles calculations show that oxygen active sites originate from electron loss, and the activation effect of metals is limited by the type of elements, the number of coordination bonds, the coactivation effect and the Jahn-Teller distortion. While impurity formation is usually a localized phenomenon, the autoprotolysis of H_{2}O allows long-distance proton transport to support the hydrolysis reaction. The adsorption energy of H_{2}O and CO_{2} is not directly related to the air sensitivity, but is inversely proportional to the surface thermodynamic stability in the study of Li/Ni mixing. However, the concentration of Li/Ni mixing depends on the exposed surface, implying that the influence of Li/Ni mixing on air sensitivity and air adsorption capacity is uneven. Our studies contribute to realizing a cost-effective and convenient experimental strategy for treating air sensitivity of LiNiO_{2} cathode materials.

Atom localization enables a high-precision imaging of the atomic position, which has provided vast applications in fundamental and applied science. In the present work, we propose a scheme for realizing two-dimensional off-axis atom localization in a three-level Λ system. Benefiting from the use of a hybrid coupling field, which consists of one Gaussian beam and one Laguerre–Gaussian beam, our scheme shows that the atoms can be localized at arbitrary position with high spatial resolution. Considering realistic experimental parameters, our numerical simulation predicts that the atoms can be precisely localized with a spatial resolution of ∼200 nm in the range of a radial distance of a few micrometers to the beam core. Our results provide a more flexible way to localize atoms in a two-dimensional system, possibly paving one-step closer to the nanometer scale atom lithography and ultraprecise microscopy.

Root caries is a subtype of dental caries that predominantly impacts older adults. The occurrence and progression of root caries are associated with the homeostasis of dental plaque biofilm, and microbial synergistic and antagonistic interactions in the biofilm play a significant role in maintaining the oral microecological balance. The objective of the current study was to investigate the role of Veillonella parvula in the microbial interactions and the pathogenesis of root caries. The analysis of clinical samples from patients with/without root caries revealed that Veillonella and V. parvula were abundant in the saliva of patients with root caries. More importantly, a significantly increased colonization of V. parvula was observed in root carious lesions. Further in vitro biofilm and animal study showed that V. parvula colonization increased the abundance and virulence of Streptococcus mutans and Candida albicans, leading to the formation of a polymicrobial biofilm with enhanced anti‐stress capacity and cariogenicity, consequently exacerbating the severity of carious lesions. Our results indicate the critical role of V. parvula infection in the occurrence of root caries, providing a new insight for the etiological investigation and prevention of root caries. [ABSTRACT FROM AUTHOR]