Sample Data Research Articles

Regional and rural-urban patterns in the prevalence of diagnosed hypertension among older U.S. adults with diabetes, 2005-2017.

Hypertension prevalence among the overall US adult population has been relatively stable during the last two decades. However, whether this stabilization has occurred across rural-urban communities and across different geographic regions is unknown, particularly among older adults with diabetes who are likely to have concomitant cardiovascular risk factors. This serial cross-sectional analysis used the 5% national sample of Medicare administrative claims data (n = 3,516,541) to examine temporal trends (2005-2017) in diagnosed hypertension among older adults with diabetes, across urban-rural communities and US census regions (Northeast, Midwest, South, and West). Joinpoint regression was used to obtain annual percent change (APC) in hypertension prevalence across rural-urban communities and geographic regions, and multivariable adjusted regression was used to assess associations between rural-urban communities and hypertension prevalence. The APC in the prevalence of hypertension was higher during 2005-2010, and there was a slowdown in the increase during 2011-2017 across all regions, with significant variations across rural-urban communities within each of the regions. In the regression analysis, in the adjusted model, older adults living in non-core (most rural) areas in the Midwest (PR = 0.988, 95% CI: 0.981-0.995) and West (PR = 0.935, 95% CI: 0.923-0.946) had lower hypertension prevalence than their regional counterparts living in large central metro areas. Although the magnitudes of these associations are small, differences in hypertension prevalence across rural-urban areas and geographic regions may have implications for targeted interventions to improve chronic disease prevention and management.

Halal–friendly attributes: a comparative study of different Muslim genders’ intentions to visit non–Islamic destinations

Purpose This study aims to examine how halal-friendly attributes at non-Islamic destinations affect the tourist attitude and visiting intentions of Muslim tourists of different genders from ASEAN countries. Design/methodology/approach The study used a structured survey with a non-probability, convenience sampling approach, targeting residents of Indonesia, Malaysia and Brunei. To assess the impact of halal-friendly attributes on destination image, tourist attitude and visiting intentions, a structural equation model was applied, analyzing the data across different genders. Findings The results indicate that the social environment exerts a positive influence on tourist attitudes for both males and females. In contrast, facilities have a negative impact on visiting intentions in both groups, with a more pronounced effect among males. While food and beverages positively influence the attitudes of female tourists, this effect is not observed in male tourists. Local factors and staff interactions show a strong positive impact on visiting intentions for males but only a marginal effect for females. The quality of service significantly boosts visiting intentions in both genders. Interestingly, tourist attitudes appear to have a negative effect on visiting intentions, with this trend being more evident in the female sample. Research limitations/implications The study is limited by its use of convenience sampling and self-reported data, which may not represent broader Indonesian perspectives and could introduce biases. Its focus on Indonesian perceptions and unexpected findings regarding tourist attitudes limit its generalizability and suggest unexplored factors affecting travel intentions. Originality/value This study offers new insights into how gender differences among Muslim tourists shape their intentions toward visiting non-Islamic destinations, emphasizing the role of halal-friendly attributes. These findings equip tourism operators and marketers with essential knowledge for developing inclusive, culturally sensitive practices, enhancing the travel experience for this significant segment of the global tourism market.

Navigating the complex dynamics of anesthesiologists’ professional identity formation in the context of their specialty training program: a phenomenographic perspective

BackgroundA specialty training program is crucial for shaping future specialist doctors, imparting clinical knowledge and skills, and fostering a robust professional identity. This study investigates how anesthesiologists develop their professional identity while navigating unique challenges specific to their specialty. The formation of professional identity in anesthesiology significantly influences doctors’ well-being, teamwork, and ultimately patient care, making it a crucial aspect of anesthesiology education. Utilizing a phenomenographic approach, the research explores the learners’ personal experiences and perspectives of professional identity formation in their specialty training programs, providing valuable insights for enhancing future anesthetic educational programs.MethodThe data for this phenomenographic study were collected through semi-structured interviews with anesthesiology trainees and specialists, guided by open-ended questions. The interviews were conducted at a Swedish university hospital, and participant selection used purposive sampling, providing rich and diverse data for analysis after 15 interviews. Iterative analysis followed the seven-step phenomenographic approach. The research team, comprising qualitative research and anesthesiology education experts, ensured result validity through regular review, discussion, and reflective practices.ResultsThe study reveals three fundamental dimensions: ‘Knowledge of Subject Matter,’ ‘Knowledge of Human Relations,’ and ‘Knowledge of Affect.’ These dimensions offer insights into how anesthesiologists comprehend anesthesiology as a profession, navigate interactions with colleagues and patients, and interpret emotional experiences in anesthesiology practice – all crucial elements in the formation of professional identity. The findings could be synthesized and further described by three conceptions: The Outcome-Driven Learner, the Emerging Collaborator, and the Self-Directed Caregiver.ConclusionThe study uncovers differing learner understandings in the development of anesthesiologists’ professional identity. Varying priorities, values, and role interpretations highlight the shortcomings of a generic, one-size-fits-all educational strategy. By acknowledging and integrating these nuanced learner perspectives, as elucidated in detail in this study, the future of anesthesia education can be improved. This will necessitate a holistic approach, intertwining both natural sciences and humanities studies, focus on tacit knowledge, and flexible teaching strategies, to guarantee thorough professional development, lifelong learning, and resilience.

On Stock Volatility Forecasting under Mixed-Frequency Data Based on Hybrid RR-MIDAS and CNN-LSTM Models

Most of the deep-learning algorithms on stock price volatility prediction in the existing literature use data such as same-frequency market indicators or technical indicators, and less consider mixed-frequency data, such as macro-data. Compared with the traditional model that only inputs the same-frequency data such as technical indicators and market indicators, this study proposes an improved deep-learning model based on mixed-frequency big data. This paper first introduces the reserve restricted mixed-frequency data sampling (RR-MIDAS) model to deal with the mixed-frequency data and, secondly, extracts the temporal and spatial features of volatility series by using the parallel model of CNN-LSTM and LSTM, and finally utilizes the Optuna framework for hyper-parameter optimization to achieve volatility prediction. For the deep-learning model with mixed-frequency data, its RMSE, MAE, MSLE, MAPE, SMAPE, and QLIKE are reduced by 18.25%, 14.91%, 30.00%, 12.85%, 13.74%, and 23.42%, respectively. This paper provides a more accurate and robust method for forecasting the realized volatility of stock prices under mixed-frequency data.

Small-sample Linear Profile Error Uncertainty Assessment Based On Grey System

Abstract The uncertainty assessment of the profile error of the cam profile, as defined in the national standard method, is difficult to carry out under conditions of small sample size and absence of probability distribution assumptions. This paper proposes a small-sample assessment model for the uncertainty of the profile error based on grey system. Firstly, the coordinate transformation is conducted using Vector Alignment Method（VAM）to reduce systematic errors, and the Non-Uniform Rational B-splines(NURBS)curve interpolation is utilized to fit the cam profile curve and perform error assessment. Subsequently, based on the error assessment results, Grey Information Measurement Model (GIMM) for the uncertainty of the profile error in small samples is established. This model employs Grey Relational Analysis (GRA) to eliminate outliers and evaluates the uncertainty of the profile error by solving grey correlation coefficients. Maximum-Minimum Information Measure Method (MIMM) is used to assess the optimal sample size. Finally, numerical experiments and experimental tests were conducted on the uncertainty of camshaft profile error in automobiles. A total of 15 sets of profile data were compared with Guide to the Representation of Uncertainty in Measurement(GUM) and Monte Carlo Method (MCM) under different sample sizes. The results showed that GIMM achieved evaluation with only 8 sets of data samples under small sample and poor information conditions, with an uncertainty of 0.6338, compared to 0.6346 for GUM and 0.6391 for MCM. The acceptance rate of GIMM reached 95.2%. This model outperforms other methods, providing a simplified and reliable assessment of cam profile error uncertainty.

Molecular Dynamics Simulation of Complex Reactivity with the Rapid Approach for Proton Transport and Other Reactions (RAPTOR) Software Package.

Simulating chemically reactive phenomena such as proton transport on nanosecond to microsecond and beyond time scales is a challenging task. Ab initio methods are unable to currently access these time scales routinely, and traditional molecular dynamics methods feature fixed bonding arrangements that cannot account for changes in the system's bonding topology. The Multiscale Reactive Molecular Dynamics (MS-RMD) method, as implemented in the Rapid Approach for Proton Transport and Other Reactions (RAPTOR) software package for the LAMMPS molecular dynamics code, offers a method to routinely sample longer time scale reactive simulation data with statistical precision. RAPTOR may also be interfaced with enhanced sampling methods to drive simulations toward the analysis of reactive rare events, and a number of collective variables (CVs) have been developed to facilitate this. Key advances to this methodology, including GPU acceleration efforts and novel CVs to model water wire formation are reviewed, along with recent applications of the method which demonstrate its versatility and robustness.

Self-reported social well-being of children with hearing loss in 2023.

To study the level of social well-being for children with HL using self-completed questionnaires. The data sample relates to a total of 22 children representing a new group of children with hearing loss (HL). This new group is defined as HL detected through neonatal hearing screening and fitted with hearing technology when relevant before 6 months, received bilateral cochlear implants before one year of age followed by specific educational training using the auditory-verbal practice. The age range was from 9 to 12 years. Two self-completed questionnaires were used: The California Bullying Victimisation Scale (CBVS) and the Strengths and Difficulties Questionnaire (SDQ). The project design was a prospective case series. Self-completed assessments revealed levels of social well-being for both questionnaires comparable to populations with normal hearing. CBVS results showed that a total of 52.6% reported being 'not a victim', 36.8% peer victims and 10.5% bully victims. Results from SDQ revealed that 94.7% of the children reported being within the normal level for scores on both social strength and difficulties, 5.3% scored slightly raised/lowered and 0% had high/low scores or very high/low scores. The new group of children with HL presented with self-completed scores comparable to peers with normal hearing. It is time to raise expectations for children with HL in terms of not only outcomes on audition and spoken language but also most importantly on levels of social well-being. Furthermore, it is discussed whether this new group can also be defined as a new generation of children with HL.

MYLK*FLNB and DOCK1*LAMA2 gene–gene interactions associated with rheumatoid arthritis in the focal adhesion pathway

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease caused by a combination of genetic and environmental factors. Rare variants with low predicted effects in genes participating in the same biological function might be involved in developing complex diseases such as RA. From whole-exome sequencing (WES) data, we identified genes containing rare non-neutral variants with complete penetrance and no phenocopy in at least one of nine French multiplex families. Further enrichment analysis highlighted focal adhesion as the most significant pathway. We then tested if interactions between the genes participating in this function would increase or decrease the risk of developing RA disease. The model-based multifactor dimensionality reduction (MB-MDR) approach was used to detect epistasis in a discovery sample (19 RA cases and 11 healthy individuals from 9 families and 98 unrelated CEU controls from the International Genome Sample Resource). We identified 9 significant interactions involving 11 genes (MYLK, FLNB, DOCK1, LAMA2, RELN, PIP5K1C, TNC, PRKCA, VEGFB, ITGB5, and FLT1). One interaction (MYLK*FLNB) increasing RA risk and one interaction decreasing RA risk (DOCK1*LAMA2) were confirmed in a replication sample (200 unrelated RA cases and 91 GBR unrelated controls). Functional and genomic data in RA samples or relevant cell types argue the key role of these genes in RA.

How to animate learners to persist in learning massive open online courses? An application of the stimulus-organism-response paradigm

Purpose The purpose of this study is to propose a research model based on the stimulus–organism–response (S–O–R) model to examine whether network externality, personalization and sociability as environmental feature antecedents to learners’ learning engagement (LE) can influence their learning persistence (LP) in massive open online courses (MOOCs). Design/methodology/approach Sample data for this study were collected from learners who had experience in taking MOOCs provided by the MOOC platform launched by a well-known university in Taiwan, and 371 usable questionnaires were analyzed using structural equation modeling in this study. Findings This study proved that learners’ perceived network externality, personalization and sociability in MOOCs positively affected their cognitive LE, psychological LE and social LE elicited by MOOCs, which jointly led to their LP in MOOCs. The results support all proposed hypotheses, and the research model accounts for 76.2% of the variance in learners’ LP in MOOCs. Originality/value This study uses the S–O–R model as a theoretical base to construct learners’ LP in MOOCs as a series of the inner process, which is affected by network externality, personalization and sociability. It is worth noting that three psychological constructs including cognitive LE, psychological LE and social LE are used to represent learners’ organismic states of MOOCs usage. To date, hedonic/utilitarian concepts are more often adopted as organisms in previous studies using the S–O–R model, and psychological constructs have received lesser attention. Hence, this study’ contribution on the application of capturing psychological constructs for completely expounding three types of environmental features as antecedents to learners’ LP in MOOCs is well documented.

Recovery of missing samples in Orthogonal Frequency Division Multiplexing signals with optimisation using data carriers

AbstractA method is proposed for reconstructing an Orthogonal Frequency Division Multiplexing (OFDM) signal that contains data gaps, with the aim to improve demodulation. The main objective is to use the method in a passive radar application with missing data samples and to improve target detection. The OFDM signal is assumed to comply with the Digital Video Broadcasting Terrestrial standard. The proposed recovery method is based on optimisation of a novel objective function, which consists of two parts. The first part is a function of the energy in the out‐of‐band frequencies, whereas the second, and novel part, uses the location of data carriers in the constellation diagram. The method is evaluated using both simulations and real data. The authors show that the proposed method significantly improves the OFDM signal in just a few iteration steps. The proposed method improved the condition number more than a factor ten thousand millions compared to using the least square method on the out‐of‐band frequencies only. The authors also decode the symbols with the Viterbi decoding algorithm and show how the required number of iterations with the proposed algorithm depends on the amount of missing samples and on the Signal‐to‐Noise Ratio in order to achieve a Bit Error Rate of less than one in one hundred thousand millions.

Multi-Task Learning in Natural Language Processing: An Overview

Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP). However, directly training deep neural models often suffer from overfitting and data scarcity problems that are pervasive in NLP tasks. In recent years, Multi-Task Learning (MTL), which can leverage useful information of related tasks to achieve simultaneous performance improvement on these tasks, has been used to handle these problems. In this paper, we give an overview of the use of MTL in NLP tasks. We first review MTL architectures used in NLP tasks and categorize them into four classes, including parallel architecture, hierarchical architecture, modular architecture, and generative adversarial architecture. Then we present optimization techniques on loss construction, gradient regularization, data sampling, and task scheduling to properly train a multi-task model. After presenting applications of MTL in a variety of NLP tasks, we introduce some benchmark datasets. Finally, we make a conclusion and discuss several possible research directions in this field.

HGR Correlation Pooling Fusion Framework for Recognition and Classification in Multimodal Remote Sensing Data

This paper investigates remote sensing data recognition and classification with multimodal data fusion. Aiming at the problems of low recognition and classification accuracy and the difficulty in integrating multimodal features in existing methods, a multimodal remote sensing data recognition and classification model based on a heatmap and Hirschfeld–Gebelein–Rényi (HGR) correlation pooling fusion operation is proposed. A novel HGR correlation pooling fusion algorithm is developed by combining a feature fusion method and an HGR maximum correlation algorithm. This method enables the restoration of the original signal without changing the value of transmitted information by performing reverse operations on the sample data. This enhances feature learning for images and improves performance in specific tasks of interpretation by efficiently using multi-modal information with varying degrees of relevance. Ship recognition experiments conducted on the QXS-SROPT dataset demonstrate that the proposed method surpasses existing remote sensing data recognition methods. Furthermore, land cover classification experiments conducted on the Houston 2013 and MUUFL datasets confirm the generalizability of the proposed method. The experimental results fully validate the effectiveness and significant superiority of the proposed method in the recognition and classification of multimodal remote sensing data.

A novel disulfide death-related genes prognostic signature identifies the role of IPO4 in glioma progression

Background“Disulfide death,” a form of cellular demise, is triggered by the abnormal accumulation of intracellular disulfides under conditions of glucose deprivation. However, its role in the prognosis of glioma remains undetermined. Therefore, the main objective of this study is to establish prognostic signature based on disulfide death-related genes (DDRGs) and to provide new solutions in choosing the effective treatment of glioma.MethodsThe RNA transcriptome, clinical information, and mutation data of glioma samples were sourced from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), while normal samples were obtained from the Genotype-Tissue Expression (GTEx). DDRGs were compiled from previous studies and selected through differential analysis and univariate Cox regression analysis. The molecular subtypes were determined through consensus clustering analysis. Further, LASSO analysis was employed to select characteristic genes, and subsequently, a risk model comprising seven DDRGs was constructed based on multivariable Cox analysis. Kaplan-Meier survival curves were employed to assess survival differences between high and low-risk groups. Additionally, functional analyses (GO, KEGG, GSEA) were conducted to explore the potential biological functions and signaling pathways of genes associated with the model. The study also explored immune checkpoint (ICP) genes, immune cell infiltration levels, and immune stromal scores. Finally, the effect of Importin-4(IPO4) on glioma has been further confirmed through RT-qPCR, Western blot, and cell functional experiments.Results7 genes associated with disulfide death were obtained and two subgroups of patients with different prognosis and clinical characteristics were identified. Risk signature was subsequently developed and proved to serve as an prognostic predictor. Notably, the high-risk group exhibited an immunosuppressive microenvironment characterized by a high concentration of M2 macrophages and regulatory T cells (Tregs). In contrast, the low-risk group showed lower half-maximal inhibitory concentration (IC50) values. Therefore, patients in the high-risk group may benefit more from immunotherapy, while patients in the low-risk group may benefit more from chemotherapy. In addition, in vitro experiments have shown that inhibition of the expression of IPO4 leads to a significant reduction in the proliferation, migration, and invasion of glioma cells.ConclusionThis study identified two glioma subtypes and constructed a prognostic signature based on DDRGs. The signature has the potential to optimize the selection of patients for immune- and chemotherapy and provided a potential therapeutic target for glioma.

American Association of Heart failure nurses’ knowledge, barriers and facilitators in conducting research

BackgroundTo provide high-quality patient care, heart failure (HF) nurses must comprehend/use best evidence; however, HF nurses’ ability to do so are unknown. ObjectivesTo describe HF nurses’ research interest/involvement, confidence, facilitators/motivators, and barriers to lead/collaborate in research studies. MethodsA descriptive design with convenience sampling and online data collection (Qualtrics) were used with American Association of HF Nurses members. Recruited/included nurses (n = 145) needed to be of any educational level and currently practicing in any practice setting in the United States. A 30-item, adapted instrument assessed research interest (one-question), involvement (two-questions), confidence (two-questions), facilitators (one-question), motivators (three-questions), and barriers (21-questions). ResultsSubjects (n = 145) were Caucasian (n = 124, 86.1 %) females (n = 137, 96.5 %) with an average age of 52.5 ± 10.38 years and 26.90±12.06 years of nursing experience. Nurses were interested in conducting nursing research (7.78/10±2.37) but involvement was low. Most frequently (n = 73, 50.3 %) nurses served as principal/co-investigators. Confidence with research participation was moderate (70.28/100±26.92) and in their ability to understand/apply research findings were low (21.68/100±80.07). The most frequently reported facilitator was the ability to control their own schedule/work (n = 30, 20.7 %) and the strongest motivator (n = 107, 73.8 %) was the perception presenting nursing research/EBP impacts HF care. The greatest reported barrier was the authority to seek research funding (2.39/5 ± 1.14). Nursing experience (p=.034), interest in participating in nursing research (p=.01), and how much presenting nursing research/EBP impacted one's performance review (p<.001) added to the prediction (R2=0.499, p<.001). ConclusionsThe gained knowledge may promote development of innovative programs and educational opportunities to increase HF nurses’ research activities.

A Research on University Students’ Behavioral Intention to Use New-generation Information Technology in Intelligent Foreign Language Learning

A better understanding of how advancement in science and technology affect students’ learning behavior in an academic setting can help all educators in higher education. With the advancement of science and technology, the new-generation information technology represented by “Cloud Computing, Big Data, Internet of Things, Mobile Network, and Artificial Intelligence” has been profoundly changing the form of education, accelerating the transformation from Teaching 1.0 to Learning 2.0. To investigate the factors influencing university students using a new-generation information technology in intelligent foreign language learning, this study proposed a research model based on Technology Acceptance Model. The sample data were collected from a survey of 237 students at a university. The analysis of structural equation modeling indicated that perceived usefulness, perceived ease of use, construction of foreign language intelligence classroom, and computer self-efficacy all have a positive impact on learners’ behavioral intention. These findings suggest that more emphasis should be laid on students’ learning characteristics, advantages of a new generation of technology, as well as teachers’ intelligent literacy, to further promote the deep integration of the new-generation information technology and intelligent foreign language learning, improving the learning effectiveness ultimately and effectively.

Stability analysis of interval type-2 sampled-data polynomial fuzzy-model-based control system with a switching control scheme

AbstractThe stability of an interval type-2 (IT2) sampled-data (SD) polynomial fuzzy-model-based control system with a switching control scheme is studied in this paper. The uncertain nonlinear plant is depicted via an IT2 polynomial fuzzy model. To realize control, a switching IT2SD polynomial fuzzy controller is generated. This paper adopts a switching control scheme with a variable sampling period. The modeling domain consists of several sub-domains, and each sub-domain corresponds to a local IT2SD polynomial fuzzy controller. These local IT2SD polynomial fuzzy controllers form the switching IT2SD polynomial fuzzy controller. To aid in the stability analysis, this paper adopts a looped-functional-based technique. The imperfect premise matching concept is brought in to solve the mismatch dilemma caused by the SD control strategy and uncertainties. For decreasing the conservativeness, this paper takes into account the state information as well as the information of IT2 membership functions. The stability analysis is performed for each sub-domain, providing the potential for further relaxation. As polynomials exist in the stability conditions, this paper employs the sum-of-squares method for the stability investigation. The simulation outcomes confirm the efficacy of the proposed method.

Rapid determination of seismic influence field based on mobile communication big data-A case study of the Luding Ms 6.8 earthquake in Sichuan, China.

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal-spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution.

Fraternisation and repression during the 2020–2021 attempted revolution in Belarus

ABSTRACT Through adoption of Video Data Analysis (VDA), this study analyses the 2020–2021 revolutionary attempt in Belarus which featured the largest protests in the country's post-communist history. This study asks why state repression and violence broke out in some instances and interactions, but not in others? I analyse events that took place between activists and state forces through a sample of video data featuring 144 different cases including verbal interactions between protesters and authorities; fraternisation, and whether or not activists were able to strike a positive dialogue with police or security forces. These variables are statistically analysed with relation to whether authorities repressed protesters or if they successfully managed to put crowds under control. Results reveal that fraternisation and the establishment of a positive dialogue between protesters and state forces are negatively correlated with occurrences of state repression, which yields new insight into the micro-dynamics of repression and dissent.

Development of selective isolation media for detecting the genera Actinomyces and Schaalia from oral specimens containing indigenous bacteria

To isolate specific bacteria from samples constituting the microbiota, it is essential to employ selective media that suppress the growth of resident bacteria other than specific target bacteria. Selective media for clinically important Actinomyces (including Schaalia, which was previously taxonomically classified as part of the genus Actinomyces) have been limited because they have been designed for a limited range of species within the genus and require ingredients which are difficult to prepare and handle. This study aimed to develop a selective medium [referred to as Actinomyces and Schaalia Selective Medium (ASSM)] for the isolation of a broad range of Actinomyces and Schaalia species from samples mixed with resident bacteria. The composition of ASSM includes yeast extract, agar, brain heart infusion (BHI), levofloxacin (LVFX), fosfomycin (FOM), colistin (CL) and metronidazole (MNZ). Evaluation of the medium using 24 swab samples serially collected from the roots of the teeth of a healthy individual for whom metagenome sequencing data of a saliva sample are publicly available revealed that ASSM adjusted to concentrations of LVFX 0.5 mg l−1, FOM 5 mg l−1, CL 1 mg l−1 and MNZ 2 mg l−1 and cultured anaerobically at 35 °C for 7 days enabled the isolation of Actinomyces species from 37.5 % of the samples. The inclusion of CL and MNZ in ASSM can also be useful for samples harbouring other bacterial species. The selective isolation medium is expected to contribute to studies investigating the relationship between these bacteria and their pathogenesis or disease.

Optical Properties and Possible Origins of Atmospheric Aerosols over LHAASO in the Eastern Margin of the Tibetan Plateau

The accuracy of cosmic ray observations by the Large High Altitude Air Shower Observatory Wide Field-of-View Cherenkov/Fluorescence Telescope Array (LHAASO-WFCTA) is influenced by variations in aerosols in the atmosphere. The solar photometer (CE318-T) is extensively utilized within the Aerosol Robotic Network as a highly precise and reliable instrument for aerosol measurements. With this CE318-T 23, 254 sets of valid data samples over 394 days from October 2020 to October 2022 at the LHAASO site were obtained. Data analysis revealed that the baseline Aerosol Optical Depth (AOD) and Ångström Exponent (AE) at 440–870 nm (AE440–870nm) of the aerosols were calculated to be 0.03 and 1.07, respectively, suggesting that the LHAASO site is among the most pristine regions on Earth. The seasonality of the mean AOD is in the order of spring &gt; summer &gt; autumn = winter. The monthly average maximum of AOD440nm occurred in April (0.11 ± 0.05) and the minimum was in December (0.03 ± 0.01). The monthly average of AE440–870nm exhibited slight variations. The seasonal characterization of aerosol types indicated that background aerosol predominated in autumn and winter, which is the optimal period for the absolute calibration of the WFCTA. Additionally, the diurnal daytime variations of AOD and AE across the four seasons are presented. Our analysis also indicates that the potential origins of aerosol over the LHAASO in four seasons were different and the atmospheric aerosols with higher AOD probably originate mainly from Northern Myanmar and Northeast India regions. These results are presented for the first time, providing a detailed analysis of aerosol seasonality and origins, which have not been thoroughly documented before in this region, also enriching the valuable materials on aerosol observation in the Hengduan Mountains and Tibetan Plateau.