Global Trends and Hotspots in Research on the Health Risks of Organophosphate Flame Retardants: A Bibliometric and Visual Analysis.

BACKGROUND: Organophosphate flame retardants (OPFRs) are compounds with a wide range of industrial and commercial applications and are mainly used as flame retardants and plasticizers. The global consumption of OPFRs has risen rapidly in recent decades, and they have been widely detected in environmental media. Unfortunately, OPFRs have been associated with many adverse health outcomes. The issue of the health risks of OPFRs is attracting increasing attention. Therefore, there is a need to review the current state of research and trends in this field to help researchers and policymakers quickly understand the field, identify new research directions, and allocate appropriate resources for further development of the OPFR health risk research field. METHODS: This study statistically analyzed 1162 relevant publications included in the Web of Science Core Collection from 2003-2023. The internal and external features of the literature, such as publication trends, countries, authors, journals, and keywords, were quantitatively analyzed and visually presented to identify the research hotspots, compositions, and paradigms of the field and to horizontally and vertically analyze the development trends and structural evolution of the field. RESULTS: The development of the field can be divided into three stages, and the field entered a period of rapid development in 2016. China (649 papers) is the most prolific country, followed by the United States (188 papers). The authors STAPLETON HM and WANG Y have the highest combined impact. International collaboration between countries and researchers still needs to be strengthened. Science of The Total Environment is the most frequently published journal (162 papers), and Environmental Science and Technology is the most frequently cited journal (5285 citations). Endocrine disruption, developmental toxicity, and neurotoxicity are the health effects of greatest interest. CONCLUSIONS: Future research is expected to be multidisciplinary, and research hotspots may involve a comprehensive assessment of OPFR exposure in the population, exploration of the mechanisms of endocrine-disrupting effects and in vivo metabolic processes, and examination of the health effects of OPFR metabolites.

Organophosphate esters in human serum: a relatively simple and efficient liquid chromatography-mass spectrometry method.

Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers, which are of growing concern due to their endocrine-disrupting effects, developmental toxicity, and potential carcinogenicity. However, data on human exposure to OPEs is still scarce. In this study, a relatively simple and efficient method with less serum consumption for the detection of OPEs in human serum was developed and validated. Nine OPEs in 200 µL of human serum were extracted by an acetonitrile-formic acid system and analyzed using ultra-high-performance liquid chromatography-quadrupole tandem time-of-flight high-resolution mass spectrometry. Several experiments were conducted to optimize the chromatographic and mass spectrometric conditions as well as sample preparation to obtain a more sensitive and efficient analytical protocol. The proposed method was examined in terms of its linearity, accuracy, precision, detection limit, and matrix effect. The matrix-spiked recoveries of the target OPEs ranged from 83.3% to 111.1%, with relative standard deviations between 2.7% and 16.6%. The detection limits were within (0.002 to 0.029) ng mL-1, while the quantification limits were within (0.007 to 0.098) ng mL-1. The internal standard-corrected matrix effects varied from 82.7% to 113.9%. Finally, the method was applied to detect OPEs in actual human serum samples. All nine OPEs were detected in 269 serum samples to varying degrees, with the average concentrations ranging from (0.08 to 1.77) ng mL-1. After validation, the method was found to be simple in pretreatment, high in sensitivity, good in practicality, and suitable for exposure evaluation of OPEs in populations.

In situ self-referenced intracellular two-electrode system for enhanced accuracy in single-cell analysis.

Since the emergence of single-cell electroanalysis, the two-electrode system has become the predominant electrochemical system for real-time behavioral analysis of single-cell and multicellular populations. However, due to the transmembrane placement of the two electrodes, cellular activities can be interrupted by the transmembrane potentials, and the test results are susceptible to influences from factors such as intracellular solution, membrane, and bulk solution. These limitations impede the advancement of single-cell analysis. Here, we propose a highly miniaturized and integrated in situ self-referenced intracellular two-electrode system (IS-SRITES), wherein both the working and reference electrodes are positioned inside the cell. Additionally, we demonstrated the stability (0.28 mV/h) of the solid-contact in situ Ag/AgCl reference electrode and the ability of the system to conduct standard electrochemical testing in a wide pH range (pH 6.0-8.0). Cell experiments confirmed the non-destructive performance of the electrode system towards cells and its capacity for real-time monitoring of intra- and extracellular pH values. Moreover, through equivalent circuits, finite element simulations, and drug delivery experiments, we illustrated that the IS-SRITES can yield more accurate test results and exhibit enhanced resistance to interference from the extracellular environment. Our proposed system holds the potential to enable the precise detection of intracellular substances and optimize the existing model of the electrode system for intracellular signal detection, thereby spearheading advancements in single-cell analysis.

Plasma SOMAmer proteomics of postoperative delirium.

BACKGROUND: Postoperative delirium is prevalent in older adults and has been shown to increase the risk of long-term cognitive decline. Plasma biomarkers to identify the risk for postoperative delirium and the risk of Alzheimer's disease and related dementias are needed. METHODS: This biomarker discovery case-control study aimed to identify plasma biomarkers associated with postoperative delirium. Patients aged ≥65 years undergoing major elective noncardiac surgery were recruited. The preoperative plasma proteome was interrogated with SOMAmer-based technology targeting 1433 biomarkers. RESULTS: In 40 patients (20 with vs. 20 without postoperative delirium), a preoperative panel of 12 biomarkers discriminated patients with postoperative delirium with an accuracy of 97.5%. The final model of five biomarkers delivered a leave-one-out cross-validation accuracy of 80%. Represented biological pathways included lysosomal and immune response functions. CONCLUSION: In older patients who have undergone major surgery, plasma SOMAmer proteomics may provide a relatively non-invasive benchmark to identify biomarkers associated with postoperative delirium.

TMEM216 promotes primary ciliogenesis and Hedgehog signaling through the SUFU-GLI2/GLI3 axis.

Primary cilia are enriched in signaling receptors, and defects in their formation or function can induce conditions such as polycystic kidney disease, postaxial hexadactyly, and microphthalmia. Mammalian Hedgehog (Hh) signaling is important in the development of primary cilia, and TMEM216, a transmembrane protein that localizes to the base of cilia, is also implicated in ciliogenesis in zebrafish. Here, we found that Tmem216-deficient mice had impaired Hh signaling and displayed typical ciliopathic phenotypes. These phenomena were also observed in cells deficient in TMEM216. Furthermore, TMEM216 interacted with core Hh signaling proteins, including SUFU, a negative regulator of Hh, and GLI2/GLI3, transcription factors downstream of Hh. The competition between TMEM216 and SUFU for binding to GLI2/GLI3 inhibited the cleavage of GLI2/GLI3 into their repressor forms, which resulted in the nuclear accumulation of full-length GLI2 and the decreased nuclear localization of cleaved GLI3, ultimately leading to the activation of Hh signaling. Together, these data suggest that the TMEM216-SUFU-GLI2/GLI3 axis plays a role in TMEM216 deficiency-induced ciliopathies and Hh signaling abnormalities.

GDF11 mitigates high glucose-induced cardiomyocytes apoptosis by inhibiting the ALKBH5-FOXO3-CDR1as/Hippo signaling pathway.

Diabetic cardiomyopathy remains a formidable health challenge with a high mortality rate and no targeted treatments. Growth differentiation factor 11 (GDF11) has shown promising effects on cardiovascular diseases; however, its role and the underlying mechanism in regulating diabetic cardiomyopathy remain unclear. In this study, we developed mouse models of diabetic cardiomyopathy using leptin receptor-deficient (db/db) mice and streptozocin-induced C57BL/6 mice. The diabetic cardiomyopathy model mice exhibited apparent structural damage in cardiac tissues and a significant increase in the expression of apoptosis-related proteins. Notably, we observed a significant decreased expression of GDF11 in the myocardium of mice with diabetic cardiomyopathy. Moreover, GDF11 cardiac-specific knock-in mice (transgenic mice) exhibited improved cardiac function and reduced apoptosis. Moreover, exogenous administration of GDF11 mitigated high glucose-induced cardiomyocyte apoptosis. Mechanistically, we demonstrated that GDF11 alleviated high glucose-induced cardiomyocytes apoptosis by inhibiting the activation of the alkylation repair homolog 5 (ALKBH5)-forkhead box group O3a (FOXO3)-cerebellar degeneration-related protein 1 transcript (CDR1as)/Hippo signaling pathway. Consequently, this novel mechanism effectively counteracted myocardial cell apoptosis, providing valuable insights into potential therapeutic strategies for clinical diabetic cardiomyopathy.

Likelihood ratio combination of multiple biomarkers via smoothing spline estimated densities.

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research.

Widefield Diamond Quantum Sensing with Neuromorphic Vision Sensors.

Despite increasing interest in developing ultrasensitive widefield diamond magnetometry for various applications, achieving high temporal resolution and sensitivity simultaneously remains a key challenge. This is largely due to the transfer and processing of massive amounts of data from the frame-based sensor to capture the widefield fluorescence intensity of spin defects in diamonds. In this study, a neuromorphic vision sensor to encode the changes of fluorescence intensity into spikes in the optically detected magnetic resonance (ODMR) measurements is adopted, closely resembling the operation of the human vision system, which leads to highly compressed data volume and reduced latency. It also results in a vast dynamic range, high temporal resolution, and exceptional signal-to-background ratio. After a thorough theoretical evaluation, the experiment with an off-the-shelf event camera demonstrated a 13× improvement in temporal resolution with comparable precision of detecting ODMR resonance frequencies compared with the state-of-the-art highly specialized frame-based approach. It is successfully deploy this technology in monitoring dynamically modulated laser heating of gold nanoparticles coated on a diamond surface, a recognizably difficult task using existing approaches. The current development provides new insights for high-precision and low-latency widefield quantum sensing, with possibilities for integration with emerging memory devices to realize more intelligent quantum sensors.

Predicting dissolved oxygen level using Young's double-slit experiment optimizer-based weighting model.

Accurate prediction of the dissolved oxygen level (DOL) is important for enhancing environmental conditions and facilitating water resource management. However, the irregularity and volatility inherent in DOL pose significant challenges to achieving precise forecasts. A single model usually suffers from low prediction accuracy, narrow application range, and difficult data acquisition. This study proposes a new weighted model that avoids these problems, which could increase the prediction accuracy of the DOL. The weighting constructs of the proposed model (PWM) included eight neural networks and one statistical method and utilized Young's double-slit experimental optimizer as an intelligent weighting tool. To evaluate the effectiveness of PWM, simulations were conducted using real-world data acquired from the Tualatin River Basin in Oregon, United States. Empirical findings unequivocally demonstrated that PWM outperforms both the statistical model and the individual machine learning models, and has the lowest mean absolute percentage error among all the weighted models. Based on two real datasets, the PWM can averagely obtain the mean absolute percentage errors of 1.0216%, 1.4630%, and 1.7087% for one-, two-, and three-step predictions, respectively. This study shows that the PWM can effectively integrate the distinctive merits of deep learning methods, neural networks, and statistical models, thereby increasing forecasting accuracy and providing indispensable technical support for the sustainable development of regional water environments.

Crotonylated BEX2 interacts with NDP52 and enhances mitophagy to modulate chemotherapeutic agent-induced apoptosis in non-small-cell lung cancer cells.

Brain expressed X-linked gene 2 (BEX2) encoded protein was originally identified to promote transcription by interacting with several transcription factors in the DNA-binding complexes. Recently, BEX2 was found to be localized in cytosol and/or mitochondria and regulate apoptosis in cancer cells and tumor growth. However, the molecular mechanism underlying its roles in cancer cells remains unclear. Here, we report that crotonylated BEX2 plays an important role in inhibiting chemotherapeutic agent-induced apoptosis via enhancing mitophagy in human lung cancer cells. BEX2 promotes mitophagy by facilitating interaction between NDP52 and LC3B. Moreover, BEX2 crotonylation at K59 is critical in the BEX2-mediated mitophagy in lung cancer cells. The K59R mutation of BEX2 inhibits mitophagy by affecting the interaction of NDP52 and LC3B. BEX2 expression is elevated after anticancer drug treatment, and its overexpression inhibits chemotherapy-induced apoptosis. In addition, inhibition of BEX2-regulated mitophagy sensitizes tumor cells to apoptosis. Furthermore, BEX2 promotes tumor growth and inhibits apoptosis by regulating mitophagy in vivo. We also confirm that BEX2 is overexpressed in lung adenocarcinoma and is associated with poor prognosis in lymph node metastasis-free cancer. Therefore, combination treatment with pharmaceutical approaches targeting BEX2-induced mitophagy and anticancer drugs may represent a potential strategy for NSCLC therapy.

Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places.

Background: People usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations. Methods: In this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models. Results: The final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables. Conclusion: In this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction.

DIRAS3 enhances RNF19B-mediated RAC1 ubiquitination and degradation in non-small-cell lung cancer cells.

Distant metastasis remains the leading cause of high mortality in patients with non-small-cell lung cancer (NSCLC). DIRAS3 is a candidate tumor suppressor protein that is decreased in various tumors. However, the regulatory mechanism of DIRAS3 on metastasis of NSCLC remains unclear. Here, we found that DIRAS3 suppressed the migration of NSCLC cells. Besides, DIRAS3 stimulated the polyubiquitination of RAC1 and suppressed its protein expression. Furthermore, RNF19B, a member of the RBR E3 ubiquitin ligase family, was observed to be the E3 ligase involved in the DIRAS3-induced polyubiquitination of RAC1. DIRAS3 could promote the binding of RAC1 and RNF19B, thus enhancing the degradation of RAC1 by the ubiquitin-proteasome pathway. Finally, the DIRAS3-RNF19B-RAC1 axis was confirmed to be associated with the malignant progression of NSCLC. These findings may be beneficial for developing potential prognostic markers of NSCLC and may provide an effective treatment strategy.

Presence of Preoperative Neurodegeneration Biofluid Markers in Patients with Postoperative Delirium.

BACKGROUND: The pathophysiology of delirium is incompletely understood, including what molecular pathways are involved in brain vulnerability to delirium. This study examined whether preoperative plasma neurodegeneration markers were elevated in patients who subsequently developed postoperative delirium through a retrospective case-control study. METHODS: Inclusion criteria were patients of 65 yr of age or older, undergoing elective noncardiac surgery with a hospital stay of 2 days or more. Concentrations of preoperative plasma P-Tau181, neurofilament light chain, amyloid ß1-42 (Aß42), and glial fibrillary acidic protein were measured with a digital immunoassay platform. The primary outcome was postoperative delirium measured by the Confusion Assessment Method. The study included propensity score matching by age and sex with nearest neighbor, such that each patient in the delirium group was matched by age and sex with a patient in the no-delirium group. RESULTS: The initial cohort consists of 189 patients with no delirium and 102 patients who developed postoperative delirium. Of 291 patients aged 72.5 ± 5.8 yr, 50.5% were women, and 102 (35%) developed postoperative delirium. The final cohort in the analysis consisted of a no-delirium group (n = 102) and a delirium group (n = 102) matched by age and sex using the propensity score method. Of the four biomarkers assayed, the median value for neurofilament light chain was 32.05 pg/ml for the delirium group versus 23.7 pg/ml in the no-delirium group. The distribution of biomarker values significantly differed between the delirium and no-delirium groups (P = 0.02 by the Kolmogorov-Smirnov test) with the largest cumulative probability difference appearing at the biomarker value of 32.05 pg/ml. CONCLUSIONS: These results suggest that patients who subsequently developed delirium are more likely to be experiencing clinically silent neurodegenerative changes before surgery, reflected by changes in plasma neurofilament light chain biomarker concentrations, which may identify individuals with a preoperative vulnerability to subsequent cognitive decline.

Public opinion changing patterns under the double-hazard scenario of natural disaster and public health event.

In the context of the COVID-19 epidemic, a "double-hazard scenario" consisting of a natural disaster and a public health event occurring simultaneously is likely to arise. Focusing on this double-hazard scenario, this study developed a new opinion dynamics model that verifies the effect of opinion dynamic in practical applications and extends the realistic meaning of the logic matrix. The new model can be used to quickly identify changing trends in public opinion about two co-occurring public safety events in China, helping the government to better anticipate and respond to these real double-hazard scenarios. The new model was tested with three real double-hazard scenarios involving natural disasters and public health events in China and the simulation results were analyzed. Using visualization and Pearson correlation coefficients to analyze more than a million items of network-wide public opinion data, the new model was found to show a good fit with reality. The study finally found that in China, public attention to both natural hazards and public health events was greater when these public safety events co-occurred (double-hazard scenario) than when they occurred separately (single-hazard scenarios). These results verify the coupling phenomenon of different disasters in a multi-hazard scenario at the information level for the first time, which is greatly meaningful for multi-hazard research.

Financial Fraud of Older Adults During the Early Months of the COVID-19 Pandemic.

BACKGROUND AND OBJECTIVES: Coronavirus disease 2019 (COVID-19) created a "perfect storm" for financial fraud targeting older adults. Guided by the Contextual Theory of Elder Abuse, we focused on individual and systemic contexts to examine how older adults became prey to financial fraud. RESEARCH DESIGN AND METHODS: In July 2020, 998 adults who were 60-98 years of age (93% White; 64% female) completed an online survey about experiences with financial fraud. Participants were recruited from gerontology research registries at Florida State University, University of Pittsburg, Virginia Tech, and Wayne State University. RESULTS: Over half (65.9%) of the respondents experienced a COVID-19-related scam attempt, with charity contributions (49%) and COVID-19 treatments (42%) being the most common. Perpetrators commonly contacted older adults electronically (47%) two or more times (64%). Although most respondents ignored the request (i.e., hung up the phone and deleted text/e-mail), 11.3% sent a requested payment, and 5.3% provided personal information. Predictors of vulnerability included contentment with financial situation, concern about finances in the aftermath of the pandemic, and wishing to talk to someone about financial decisions. Respondents targeted for a non-COVID-19 scam attempt were less likely to be targets of a COVID-19-related scam. DISCUSSION AND IMPLICATIONS: Older adults who were financially secure, worried about their financial situation, or wished they could speak with someone about their financial decisions appeared susceptible to falling victim to a fraud attempt. The high number of attempts indicates a need for a measurable and concerted effort to prevent the financial fraud of older adults.

An updated phylogeny, biogeography, and PhyloCode-based classification of Cornaceae based on three sets of genomic data.

PREMISE: A major goal of systematic biology is to uncover the evolutionary history of organisms and translate that knowledge into stable classification systems. Here, we integrate three sets of genome-wide data to resolve phylogenetic relationships in Cornaceae (containing only Cornus s.l.), reconstruct the biogeographic history of the clade, and provide a revised classification using the PhyloCode to stabilize names for this taxonomically controversial group. METHODS: We conducted phylogenetic analyses using 312 single-copy nuclear genes and 70 plastid genes from Angiosperms353 Hyb-Seq, plus numerous loci from RAD-Seq. We integrated fossils using morphological data and produced a dated phylogeny for biogeographical analysis. RESULTS: A well-resolved, strongly supported, comprehensive phylogeny was obtained. Biogeographic analyses support an origin and rapid diversification of Cornus into four morphologically distinct major clades in the Northern Hemisphere (with an eastern Asian ancestor) during the late Cretaceous. Dispersal into Africa from eastern Asia likely occurred along the Tethys Seaway during the Paleogene, whereas dispersal into South America likely occurred during the Neogene. Diversification within the northern hemisphere likely involved repeated independent colonization of new areas during the Paleogene and Neogene along the Bering Land Bridge, the North Atlantic Land Bridge, and the Tethys Seaway. Thirteen strongly supported clades were named following rules of the PhyloCode. CONCLUSIONS: Our study provides an example of integrating genomic and morphological data to produce a robust, explicit species phylogeny that includes fossil taxa, which we translate into an updated classification scheme using the PhyloCode to stabilize names.

Reinforced design method for moiré metalens with large spacing.

Although metalens has attracted many research interests for its advantages of light weight, ultrathin size, and high design freedom in realizing achromatic and aberration-free optical devices, it still lacks adjustability in zoomable optical systems. Moiré metalens, which consists of two cascaded metasurface layers, can realize large focus tuning range by the mutual rotation of the two layers, and becomes a possible solution to realize real application of reconfigurable metalenses. However, due to the spacing between the two metasurface layers, it suffers from aberration caused by diffraction, leading to a dramatically decreased efficiency with the spacing. In this paper, we propose a reinforced design method for moiré metalenses with large spacing based on diffraction optics. Simulation results demonstrate that at the wavelength of 810 nm, when the spacing of the two metasurfaces is 10λ, the focusing efficiency of the reinforced moiré metalens is 3.4 times larger than the traditional moiré metalens. Furthermore, in order to consider the situation that the spacing between the two metasurfaces cannot be controlled precisely, we also propose a reinforced design method for multiplex spacings, which can make the device maintain a high focusing efficiency (3 times larger than the traditional moiré metalens) for the spacing in a range of 6λ∼10λ. The new design method is anticipated to be applied in realizing tunable metalenses in integrated continuously zoomable optical systems.

Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach.

BACKGROUND: COVID-19 is still rampant all over the world. Until now, the COVID-19 vaccine is the most promising measure to subdue contagion and achieve herd immunity. However, public vaccination intention is suboptimal. A clear division lies between medical professionals and laypeople. While most professionals eagerly promote the vaccination campaign, some laypeople exude suspicion, hesitancy, and even opposition toward COVID-19 vaccines. OBJECTIVE: This study aims to employ a text mining approach to examine expression differences and thematic disparities between the professionals and laypeople within the COVID-19 vaccine context. METHODS: We collected 3196 answers under 65 filtered questions concerning the COVID-19 vaccine from the China-based question and answer forum Zhihu. The questions were classified into 5 categories depending on their contents and description: adverse reactions, vaccination, vaccine effectiveness, social implications of vaccine, and vaccine development. Respondents were also manually coded into two groups: professional and laypeople. Automated text analysis was performed to calculate fundamental expression characteristics of the 2 groups, including answer length, attitude distribution, and high-frequency words. Furthermore, structural topic modeling (STM), as a cutting-edge branch in the topic modeling family, was used to extract topics under each question category, and thematic disparities were evaluated between the 2 groups. RESULTS: Laypeople are more prevailing in the COVID-19 vaccine-related discussion. Regarding differences in expression characteristics, the professionals posted longer answers and showed a conservative stance toward vaccine effectiveness than did laypeople. Laypeople mentioned countries more frequently, while professionals were inclined to raise medical jargon. STM discloses prominent topics under each question category. Statistical analysis revealed that laypeople preferred the "safety of Chinese-made vaccine" topic and other vaccine-related issues in other countries. However, the professionals paid more attention to medical principles and professional standards underlying the COVID-19 vaccine. With respect to topics associated with the social implications of vaccines, the 2 groups showed no significant difference. CONCLUSIONS: Our findings indicate that laypeople and professionals share some common grounds but also hold divergent focuses toward the COVID-19 vaccine issue. These incongruities can be summarized as "qualitatively different" in perspective rather than "quantitatively different" in scientific knowledge. Among those questions closely associated with medical expertise, the "qualitatively different" characteristic is quite conspicuous. This study boosts the current understanding of how the public perceives the COVID-19 vaccine, in a more nuanced way. Web-based question and answer forums are a bonanza for examining perception discrepancies among various identities. STM further exhibits unique strengths over the traditional topic modeling method in statistically testing the topic preference of diverse groups. Public health practitioners should be keenly aware of the cognitive differences between professionals and laypeople, and pay special attention to the topics with significant inconsistency across groups to build consensus and promote vaccination effectively.

Comprehending Cornales: phylogenetic reconstruction of the order using the Angiosperms353 probe set.

PREMISE: Cornales is an order of flowering plants containing ecologically and horticulturally important families, including Cornaceae (dogwoods) and Hydrangeaceae (hydrangeas), among others. While many relationships in Cornales are strongly supported by previous studies, some uncertainty remains with regards to the placement of Hydrostachyaceae and to relationships among families in Cornales and within Cornaceae. Here we analyzed hundreds of nuclear loci to test published phylogenetic hypotheses and estimated a robust species tree for Cornales. METHODS: Using the Angiosperms353 probe set and existing data sets, we generated phylogenomic data for 158 samples, representing all families in the Cornales, with intensive sampling in the Cornaceae. RESULTS: We curated an average of 312 genes per sample, constructed maximum likelihood gene trees, and inferred a species tree using the summary approach implemented in ASTRAL-III, a method statistically consistent with the multispecies coalescent model. CONCLUSIONS: The species tree we constructed generally shows high support values and a high degree of concordance among individual nuclear gene trees. Relationships among families are largely congruent with previous molecular studies, except for the placement of the nyssoids and the Grubbiaceae-Curtisiaceae clades. Furthermore, we were able to place Hydrostachyaceae within Cornales, and within Cornaceae, the monophyly of known morphogroups was well supported. However, patterns of gene tree discordance suggest potential ancient reticulation, gene flow, and/or ILS in the Hydrostachyaceae lineage and the early diversification of Cornus. Our findings reveal new insights into the diversification process across Cornales and demonstrate the utility of the Angiosperms353 probe set.