Additive Model Research Articles

Evaluation of a Digital Scribe: Conversation Summarization for Emergency Department Consultation Calls.

We present a proof-of-concept digital scribe system as an Emergency Department (ED) consultation call-based clinical conversation summarization pipeline to support clinical documentation, and report its performance. We use four pre-trained large language models to establish the digital scribe system: T5-small, T5-base, PEGASUS-PubMed, and BART-Large-CNN via zero-shot and fine-tuning approaches. Our dataset includes 100 referral conversations among ED clinicians and medical records. We report the ROUGE-1, ROUGE-2, and ROUGE-L to compare model performance. In addition, we annotated transcriptions to assess the quality of generated summaries. The fine-tuned BART-Large-CNN model demonstrates greater performance in summarization tasks with the highest ROUGE scores (F1ROUGE-1=0.49, F1ROUGE-2=0.23, F1ROUGE-L=0.35) scores. In contrast, PEGASUS-PubMed lags notably (F1ROUGE-1=0.28, F1ROUGE-2=0.11, F1ROUGE-L=0.22). BART-Large-CNN's performance decreases by more than 50% with the zero-shot approach. Annotations show that BART-Large-CNN performs 71.4% recall in identifying key information and a 67.7% accuracy rate. The BART-Large-CNN model demonstrates a high level of understanding of clinical dialogue structure, indicated by its performance with and without fine-tuning. Despite some instances of high recall, there is variability in the model's performance, particularly in achieving consistent correctness, suggesting room for refinement. The model's recall ability varies across different information categories. The study provides evidence towards the potential of AI-assisted tools in assisting clinical documentation. Future work is suggested on expanding the research scope with additional language models and hybrid approaches, and comparative analysis to measure documentation burden and human factors.

Reconstructing an ancient fish: Three-dimensional skeletal restoration of the head of Mawsonia (Sarcopterygii, Actinistia) using CT scan, and an adjusted model for body size estimation in fossil coelacanths.

Mawsonia constitutes one of the most conspicuous fossil coelacanth taxa, due to its unique anatomy and possible maximum body size. It typifies Mesozoic coelacanth morphology, before the putative disappearance of the group in the fossil record. In this work, the three-dimensional cranial anatomy and body size estimations of this genus are re-evaluated from a recently described specimen from Upper Jurassic deposits of Uruguay. The 3D restoration was performed directly on the material based on anatomical information provided by the living coelacanth Latimeria and previous two-dimensional restorations of the head of Mawsonia. The montage was then scanned with computed tomography and virtually adjusted to generate an interactive online resource for future anatomical, taxonomic and biomechanical research. In general terms, the model constitutes a tool to improve both the anatomical knowledge of this genus and its comparison with other coelacanths. It also facilitates the evaluation of possible evolutionary trends and the discussion of particular features with potential palaeobiological implications, such as the anterior position of the eye and the development of the pseudomaxillary fold. Regarding the body size, a previous model for body size estimation based on the gular plate was submitted to OLS, RMA, segmented linear and PGLS regressions (including the evaluation of regression statistics, variance analysis, t-tests and residual analysis). The results point to a power relationship between gular and total lengths showing a better support than a simple linear relationship. The new resulting equations were applied to the studied individual and are provided for future estimates. Although an isometric evolutionary growth cannot be rejected with the available evidence, additional models developed with other bones will be necessary to evaluate possible hidden evolutionary allometric trends in this group of fishes, thus avoiding overestimates.

Normative data for insulin-like growth factor-1 (IGF-1) in healthy children and adolescents from India.

Serum IGF-1 is an important biochemical tool to diagnose and monitor GH-related disorders. However, ethnic-specific Indian data following consensus criteria for the establishment of normative data, are not available. Our objective was to generate chronological age (CA)-, bone age (BA)- and Tanner stage-specific normative data for IGF-1 in healthy Indian children and adolescents. A cross-sectional epidemiological study was conducted in schools and the community, which enrolled apparently healthy children and adolescents with robust exclusion criteria. The outcome measure was serum IGF-1 assessed using an electro-chemiluminescence immunoassay (ECLIA). The 2.5th, 5th, 10th, 25th, 50th (median), 75th, 90th, 95th, and 97.5th centiles for IGF-1 were estimated using generalized additive models. We recruited 2226 apparently healthy participants and following exclusion, 1948 (1006 boys, 942 girls) were included in the final analysis. Girls had median IGF-1 peak at CA of 13 years (321.7 ng/mL), BA of 14 years (350.2 ng/mL) and Tanner stage IV (345 ng/mL), while boys had median IGF-1 peak at CA of 15 years (318.9 ng/mL) BA of 15 years (340.6 ng/mL) and Tanner stage III (304.8 ng/mL). Girls had earlier rise, peak and higher IGF-1 values. The reference interval (2.5th-97.5th percentile) was broader during peri-pubertal ages, indicating a higher physiological variability. This study provides ethnicity-specific normative data on serum IGF-1 and will improve the diagnostic utility of IGF-1 in the evaluation and management of growth disorders in Indian children and adolescents.

Short-Term Effects of Ambient Air Pollution on Chronic Obstructive Pulmonary Disease Admissions in Jiuquan, China.

Recent findings indicate that air pollution contributes to the onset and advancement of chronic obstructive pulmonary disease (COPD). Nevertheless, there is insufficient research indicating that air pollution is linked to COPD in the region of inland northwest China. Daily hospital admission records for COPD, air pollutant levels, and meteorological factor information were collected in Jiuquan for this study between 1 January 2018 and 31 December 2019. We employed a distributed lag non-linear model (DLNM) integrated with the generalized additive model (GAM) to assess the association between air pollution and hospital admissions for COPD with single lag days from lag0 to lag7 and multiday moving average lag days from lag01 to lag07. For example, the pollutant concentration on the current day was lag0, and on the prior 7th day was lag7. The present and previous 7-day moving average pollutant concentration was lag07. Gender, age, and season-specific stratified analyses were also carried out. It is noteworthy that the delayed days exhibited a different pattern, and the magnitude of associations varied. For NO2 and CO, obvious associations with hospitalizations for COPD were found at lag1, lag01-lag07, and lag03-lag07, with the biggest associations at lag05 and lag06 [RR = 1.015 (95%CI: 1.008, 1.023) for NO2, RR = 2.049 (95%CI: 1.416, 2.966) for CO], while only SO2 at lag02 was appreciably linked to hospitalizations for COPD [1.167 (95%CI: 1.009, 1.348)]. In contrast, short-term encounters with PM2.5, PM10, and O3 were found to have no significant effects on COPD morbidity. The lag effects of NO2 and CO were stronger than those of PM2.5 and PM10. Males and those aged 65 years or older were more vulnerable to air pollution. When it came to the seasons, the impacts appeared to be more pronounced in the cold season. In conclusion, short-term encounters with NO2 and CO were significantly correlated with COPD hospitalization in males and the elderly (≥65).

Eco-climate drivers shape virome diversity in a globally invasive tick species.

Spillovers of viruses into human occur more frequently under warmer conditions, particularly arboviruses. The invasive tick species Haemaphysalis longicornis poses a significant public health threat due to its global expansion and its potential to carry a wide range of pathogens. We analyzed meta-transcriptomic data from 3595 adult H. longicornis ticks collected between 2016 and 2019 in 22 provinces across China, encompassing diverse ecological conditions. Generalized additive modelling revealed that climate factors exerted a stronger influence on the virome of H. longicornis compared to other ecological factors, such as ecotypes, distance to coastline, animal host, tick gender, and anti-viral immunity. We investigated the mechanistic understanding of how climate changes drive the tick virome using causality inference and emphasized its significance for public health. Our findings demonstrated that higher temperatures and lower relative humidity/precipitation contribute to variations in animal host diversity, leading to an increased diversity of tick virome, particularly the evenness of vertebrate associated viruses. This finding may explain the evolution of tick-borne viruses into generalists across multiple hosts, thereby increasing the probability of spillover events involving tick-borne pathogens. Deep learning projections indicate that the diversity of H. longicornis virome is expected to increase in 81.9% of regions under the SSP8.5 scenario from 2019-2030. Extension of surveillance should be implemented to avert the spread of tick-borne diseases.

Dose-Dependent Association Between Body Mass Index and Mental Health and Changes Over Time

Overweight and obesity affect 340 million adolescents worldwide and constitute a risk factor for poor mental health. Understanding the association between body mass index (BMI) and mental health in adolescents may help to address rising mental health issues; however, existing studies lack comprehensive evaluations spanning diverse countries and periods. To estimate the association between BMI and mental health and examine changes over time from 2002 to 2018. This was a repeated multicountry cross-sectional study conducted between 2002 and 2018 and utilizing data from the Health Behaviour in School-aged Children (HBSC) survey in Europe and North America. The study population consisted of more than 1 million adolescents aged 11 to 15 years, with all surveyed children included in the analysis. Data were analyzed from October 2022 to March 2023. Mental health difficulties were measured by an 8-item scale for psychological concerns, scoring from 0 to 32, where a higher score reflects greater psychosomatic issues. BMI was calculated using weight divided by height squared and adjusted for age and sex. Data were fitted by multilevel generalized additive model. Confounders included sex, living with parents, sibling presence, academic pressure, the experience of being bullied, family affluence, screen time, and physical activity. Our analysis of 1 036 869 adolescents surveyed from 2002 to 2018, with a mean (SD) age of 13.55 (1.64) years and comprising 527 585 girls (50.9%), revealed a consistent U-shaped association between BMI and mental health. After accounting for confounders, adolescents with low body mass and overweight or obesity had increased psychosomatic symptoms compared to those with healthy weight (unstandardized β, 0.14; 95% CI, 0.08 to 0.19; unstandardized β, 0.27; 95% CI, 0.24 to 0.30; and unstandardized β, 0.62; 95% CI, 0.56 to 0.67, respectively), while adolescents with underweight had fewer symptoms (unstandardized β, -0.18; 95% CI, -0.22 to -0.15). This association was observed across different years, sex, and grade, indicating a broad relevance to adolescent mental health. Compared to 2002, psychosomatic concerns increased significantly in 2006 (unstandardized β, 0.19; 95% CI, 0.11 to 0.26), 2010 (unstandardized β, 0.14; 95% CI, 0.07 to 0.22), 2014 (unstandardized β, 0.48; 95% CI, 0.40 to 0.56), and 2018 (unstandardized β, 0.82; 95% CI, 0.74 to 0.89). Girls reported significantly higher psychosomatic concerns than boys (unstandardized β, 2.27; 95% CI, 2.25 to 2.30). Compared to primary school, psychosomatic concerns rose significantly in middle school (unstandardized β, 1.15; 95% CI, 1.12 to 1.18) and in high school (unstandardized β, 2.12; 95% CI, 2.09 to 2.15). Our study revealed a U-shaped association between adolescent BMI and mental health, which was consistent across sex and grades and became stronger over time. These insights emphasize the need for targeted interventions addressing body image and mental health, and call for further research into underlying mechanisms.

Field Data Analysis of Pavement Marking Retroreflectivity and Its Relationship with Paint and Glass Bead Characteristics

Pavement marking retroreflectivity, a critical factor for safe driving, depends on the characteristics of both the paint and the embedded glass beads. However, traditional methods for predicting pavement marking service life often overlook these materials properties. This study investigates the influence of paint and glass bead characteristics on pavement marking retroreflectivity performance and addresses the characterization of glass bead size distribution by the coefficient of uniformity and curvature. Three field test sites on a Brazilian highway with various paint and glass bead combinations were evaluated. A statistical model, GAMLSS (Generalized Additive Model for Location, Scale, and Shape), was adjusted to evaluate the performance of the markings’ retroreflectivity as a function of paint and glass bead characteristics. The model revealed that well-graded glass beads increased retroreflectivity by around 10%, while paints with a higher volume of solids improved service life around 65%. Therefore, the results show that acrylic water-based paints with higher volumes of solids and well-graded glass beads with better shape characteristics should be preferred to improve pavement markings’ retroreflectivity and service life. The statistical model identified the key characteristics with the greatest impact on pavement marking retroreflectivity, offering valuable insights for real-world applications, which will assist pavement marking practitioners and road authorities in selecting appropriate materials to achieve enhanced durability.

Emergency department risk model: timely identification of patients for outpatient care coordination.

Major depressive disorder (MDD) is linked to a 61% increased risk of emergency department (ED) visits and frequent ED usage. Collaborative care management (CoCM) models target MDD treatment in primary care, but how best to prioritize patients for CoCM to prevent frequent ED utilization remains unclear. This study aimed to develop and validate a risk identification model to proactively detect patients with MDD in CoCM at high risk of frequent (≥ 3) EDvisits. This retrospective cohort study utilized electronic health records from Mayo Clinic's primary care system to develop and validate a machine learning-based risk identification model. The model predicts the likelihood of frequent ED visits among patients with MDD within a 12-month period. Data were collected from Mayo Clinic's primary care system between May 1, 2006, and December 19, 2018. Risk identification models were developed and validated using machine learning classifiers to estimate frequent ED visit risks over 12 months. The Shapley Additive Explanations model identified variables driving frequent EDvisits. The patient population had a mean (SD) age of 39.78 (16.66) years, with 30.3% being male and 6.1%experiencing frequent ED visits. The best-performing algorithm (elastic-net logistic regression) achieved an area under the curve of 0.79 (95% CI, 0.74-0.84), a sensitivity of 0.71 (95% CI, 0.57-0.82), and a specificity of 0.76 (95% CI, 0.64-0.85) in the development data set. In the validation data set, the best-performing algorithm (random forest) achieved an area under the curve of 0.79, a sensitivity of 0.83, and a specificity of 0.61. Significant variables included male gender, prior frequent ED visits, high Patient Health Questionnaire-9 score, low education level, unemployment, and use of multiple medications. The risk identification model has potential for clinical application in triaging primary care patients with MDD in CoCM, aiming to reduce future ED utilization.

Behavior, site use and demographics of shortfin devil rays, Mobula kuhlii, at a newly-discovered cleaning area in South Africa

Many shark and ray species show affinity to specific sites, making these areas critical for their survival. These include cleaning stations: locations on reefs where cleaner fish remove parasites and clean wounds, which is important for maintaining health. Cleaning stations also function as social gathering sites, or resting points, where courtship and mating can occur. In this study, we identify an aggregation site for the shortfin devil ray, Mobula kuhlii (Family Mobulidae) within the Aliwal Shoal Marine Protected Area in KwaZulu-Natal (KZN), South Africa, and document their behavior. Remote underwater video was used to collect footage of M. kuhlii being cleaned by blue streak cleaner wrasse, Labroides dimidiatus. Generalized additive models (GAMs) were used to assess environmental predictors of M. kuhlii presence on Aliwal Shoal. Mixed models were used to assess the same environmental predictors and their correlation with mean M. kuhlii cleaning duration and number of L. dimidiatus bites per second at the identified cleaning station site. M. kuhlii were present in 56% of observation days, with group numbers up to > 150 individuals. Sea surface temperature was a significant predictor for M. kuhlii presence, while a north to south current was significantly associated with longer mean cleaning durations. These results support findings of mobulid studies in KZN that show increased habitat use during summer temperatures (24–25 °C) and suggest these sites to be important for individual health and social interaction. We hope these findings can be used for development of location-specific management plans to safeguard this Endangered species.

The relationship between weight-adjusted-waist index and suicidal ideation: evidence from NHANES

BackgroundAmidst growing evidence of the intricate link between physical and mental health, this study aims to dissect the relationship between the waist-to-weight index (WWI) and suicidal ideation within a representative sample of the US population, proposing WWI as a novel metric for suicide risk assessment.MethodsThe study engaged a sample of 9500 participants in a cross-sectional design. It employed multivariate logistic and linear regression analyses to probe the association between WWI and suicidal ideation. It further examined potential nonlinear dynamics using a weighted generalized additive model alongside stratified analyses to test the relationship's consistency across diverse demographic and health variables.ResultsOur analysis revealed a significant positive correlation between increased WWI and heightened suicidal ideation, characterized by a nonlinear relationship that persisted in the adjusted model. Subgroup analysis sustained the association's uniformity across varied population segments.ConclusionsThe study elucidates WWI's effectiveness as a predictive tool for suicidal ideation, underscoring its relevance in mental health evaluations. By highlighting the predictive value of WWI, our findings advocate for the integration of body composition considerations into mental health risk assessments, thereby broadening the scope of suicide prevention strategies.

The Effects of Displaying the Time Targets of the Manchester Triage System to Emergency Department Personnel: Prospective Crossover Study.

The use of triage systems such as the Manchester Triage System (MTS) is a standard procedure to determine the sequence of treatment in emergency departments (EDs). When using the MTS, time targets for treatment are determined. These are commonly displayed in the ED information system (EDIS) to ED staff. Using measurements as targets has been associated with a decline in meeting those targets. This study investigated the impact of displaying time targets for treatment to physicians on processing times in the ED. We analyzed the effects of displaying time targets to ED staff on waiting times in a prospective crossover study, during the introduction of a new EDIS in a large regional hospital in Germany. The old information system version used a module that showed the time target determined by the MTS, while the new system version used a priority list instead. Evaluation was based on 35,167 routinely collected electronic health records from the preintervention period and 10,655 records from the postintervention period. Electronic health records were extracted from the EDIS, and data were analyzed using descriptive statistics and generalized additive models. We evaluated the effects of the intervention on waiting times and the odds of achieving timely treatment according to the time targets set by the MTS. The average ED length of stay and waiting times increased when the EDIS that did not display time targets was used (average time from admission to treatment: preintervention phase=median 15, IQR 6-39 min; postintervention phase=median 11, IQR 5-23 min). However, severe cases with high acuity (as indicated by the triage score) benefited from lower waiting times (0.15 times as high as in the preintervention period for MTS1, only 0.49 as high for MTS2). Furthermore, these patients were less likely to receive delayed treatment, and we observed reduced odds of late treatment when crowding occurred. Our results suggest that it is beneficial to use a priority list instead of displaying time targets to ED personnel. These time targets may lead to false incentives. Our work highlights that working better is not the same as working faster.

Association of BDNF polymorphism with gestational diabetes mellitus risk: a novel insight into genetic predisposition.

Gestational diabetes mellitus (GDM) is a prevalent metabolic disorder during pregnancy with potential long-term health implications for the mother and child. The interplay between genetics and GDM susceptibility remains an area of active research. Recently, brain-derived neurotrophic factor (BDNF) was investigated in relation to obesity and impaired glucose metabolism and pathogenesis. We aimed to investigate the association of common BDNF polymorphisms, with GDM risk in Israeli females. A cohort of 4,025 Israeli women data for BDNF common SNPs was analyzed for potential association with GDM using binary logistic regressions analysis (SPSS 29.0 and R) adjusted for confounding variables (age, T1DM, T2DM, PCOS) under different genetic models. The GDM and Non-GDM genetic frequencies for the BDNF rs925946 Tag-SNP were significantly different. The genetic frequencies were 54.16 %, and 66.91 % for the wild type (GG), 38.88 and 29.64 % for the heterozygotes (TC), and 6.94 and 3.48 % for the risk allele homozygotes (TT) for the GDM non-GDM populations, respectively. Carriers of BDNF rs925946 were significantly associated with higher risk for GDM, following the dominant genetic model (OR=1.7, 95 % CI 1.21-2.39, p=0.002), the recessive genetic model (OR=2.05, 95 % CI 1.04-4.03, p=0.03), and the additive genetic model (OR=1.62, 95 % CI 1.13-2.3, p=0.008). This association persisted after adjusting for age, T1DM, T2DM, and polycystic ovary syndrome (PCOS). Carrying BDNF rs925946 polymorphism predisposes to a higher risk of GDM pathogenesis. Its role and implications warrant further investigation, especially when considering preventive measures for GDM development.

Toxicity toward freshwater and marine water organisms of the cytostatic drugs bleomycin and vincristine and their binary mixture

Antineoplastic drugs are not effectively removed by wastewater treatment plants, ending up in surface waters. Since these drugs can interfere with the structure and functions of DNA, they pose a potential threat to aquatic biota. Unfortunately, many chemotherapeutic agents have not been studied in an environmental context. Additionally, there is a significant lack of information about the impact of anticancer drugs on marine organisms compared to freshwater species, and most studies only focus on the toxicity of single compounds rather than considering their occurrence as complex mixtures in the environment. Therefore, the aim of this study was to evaluate the ecotoxicity of two commonly used cytostatics, bleomycin and vincristine, toward six biomodels: Pseudokirchneriella subcapitata, Phaeodactylum tricornutum, Brachionus plicatilis, Brachionus calyciflorus, Thamnocephalus platyurus, and Artemia franciscana. These selected aquatic organisms are representatives of both freshwater and marine environments and belong to different trophic levels. The pharmaceuticals were investigated both individually and in combination. Binary mixture toxicity predictions were performed according to the Response Additivity and Independent Action models. Additionally, the toxicity data obtained from these experiments were utilized for risk assessment in the context of the drugs' environmental occurrence. The results indicated that freshwater species were generally more sensitive to both tested compounds than marine organisms, with T. platyurus being the most sensitive. Based on the tests performed on this biomodel, bleomycin was categorized as extremely toxic, while vincristine was considered moderately toxic. Neither of the applied models suitably predicted binary mixture toxicity, as the combination of drugs showed additive, synergistic, and antagonistic effects, suggesting that single compound toxicity data are insufficient for predicting the aquatic toxicities of cytostatics mixtures. The environmental risk of vincristine ranged from low to high, and for bleomycin varied from moderate to high, depending on the matrices examined. Therefore, further research on drug removal is recommended.

Influence of hydrometeorological risk factors on child diarrhea and enteropathogens in rural Bangladesh.

A number of studies have detected relationships between weather and diarrhea. Few have investigated associations with specific enteric pathogens. Understanding pathogen-specific relationships with weather is crucial to inform public health in low-resource settings that are especially vulnerable to climate change. Our objectives were to identify weather and environmental risk factors associated with diarrhea and enteropathogen prevalence in young children in rural Bangladesh, a population with high diarrheal disease burden and vulnerability to weather shifts under climate change. We matched temperature, precipitation, surface water, and humidity data to observational longitudinal data from a cluster-randomized trial that measured diarrhea and enteropathogen prevalence in children 6 months-5.5 years from 2012-2016. We fit generalized additive mixed models with cubic regression splines and restricted maximum likelihood estimation for smoothing parameters. Comparing weeks with 30°C versus 15°C average temperature, prevalence was 3.5% higher for diarrhea, 7.3% higher for Shiga toxin-producing Escherichia coli (STEC), 17.3% higher for enterotoxigenic E. coli (ETEC), and 8.0% higher for Cryptosporidium. Above-median weekly precipitation (median: 13mm; range: 0-396mm) was associated with 29% higher diarrhea (adjusted prevalence ratio 1.29, 95% CI 1.07, 1.55); higher Cryptosporidium, ETEC, STEC, Shigella, Campylobacter, Aeromonas, and adenovirus 40/41; and lower Giardia, sapovirus, and norovirus prevalence. Other associations were weak or null. Higher temperatures and precipitation were associated with higher prevalence of diarrhea and multiple enteropathogens; higher precipitation was associated with lower prevalence of some enteric viruses. Our findings emphasize the heterogeneity of the relationships between hydrometeorological variables and specific enteropathogens, which can be masked when looking at composite measures like all-cause diarrhea. Our results suggest that preventive interventions targeted to reduce enteropathogens just before and during the rainy season may more effectively reduce child diarrhea and enteric pathogen carriage in rural Bangladesh and in settings with similar meteorological characteristics, infrastructure, and enteropathogen transmission.

RFBoost: Understanding and Boosting Deep WiFi Sensing via Physical Data Augmentation

Deep learning shows promising performance in wireless sensing. However, deep wireless sensing (DWS) heavily relies on large datasets. Unfortunately, building comprehensive datasets for DWS is difficult and costly, because wireless data depends on environmental factors and cannot be labeled offline. Despite recent advances in few-shot/cross-domain learning, DWS is still facing data scarcity issues. In this paper, we investigate a distinct perspective of radio data augmentation (RDA) for WiFi sensing and present a data-space solution. Our key insight is that wireless signals inherently exhibit data diversity, contributing more information to be extracted for DWS. We present RFBoost, a simple and effective RDA framework encompassing novel physical data augmentation techniques. We implement RFBoost as a plug-and-play module integrated with existing deep models and evaluate it on multiple datasets. Experimental results demonstrate that RFBoost achieves remarkable average accuracy improvements of 5.4% on existing models without additional data collection or model modifications, and the best-boosted performance outperforms 11 state-of-the-art baseline models without RDA. RFBoost pioneers the study of RDA, an important yet currently underexplored building block for DWS, which we expect to become a standard DWS component of WiFi sensing and beyond. RFBoost is released at https://github.com/aiot-lab/RFBoost.

Associations between blood manganese levels and sarcopenia in adults: insights from the National Health and Nutrition Examination Survey.

While increasing concerns arise about the health effects of environmental pollutants, the relationship between blood manganese (Mn) and sarcopenia has yet to be fully explored in the general population. This study aims to investigate the association between blood manganese (Mn) levels and sarcopenia in adults. In our study, we evaluated 8,135 individuals aged 18-59 years, utilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 2011 to 2018. We employed generalized additive model (GAM) to discern potential non-linear relationships and utilized the two-piecewise linear regression model to probe the association between blood Mn levels and sarcopenia. After adjusting for potential confounders, we identified non-linear association between blood Mn levels and sarcopenia, with an inflection point at 13.45 μg/L. The effect sizes and the confidence intervals on the left and right sides of the inflection point were 1.006 (0.996 to 1.048) and 1.082 (1.043 to 1.122), respectively. Subgroup analysis showed that the effect sizes of blood Mn on sarcopenia have significant differences in gender and different BMI groups. Our results showed that a reverse U-shaped curve between blood Mn levels and sarcopenia, with an identified the inflection point at blood Mn level of 13.45 μg/L.

Possibility and quality assessment in seafloor modeling relative to the sea surface using hybrid data

Possibility and quality assessment in seafloor modeling relative to the sea surface using hybrid data

Regression calibration of self-reported mobile phone use to optimize quantitative risk estimation in the COSMOS study.

The Cohort Study of Mobile Phone Use and Health (COSMOS) has repeatedly collected self-reported and operator-recorded data on mobile phone use. Assessing health effects using self-reported information is prone to measurement error, but operator data were available prospectively for only part of the study population and did not cover past mobile phone use. To optimize the available data and reduce bias, we evaluated different statistical approaches for constructing mobile phone exposure histories within COSMOS. We evaluated and compared the performance of four regression calibration (RC) methods (simple, direct, inverse, and generalized additive model for location, shape, and scale), complete-case (CC) analysis and multiple imputation (MI) in a simulation study with a binary health outcome. We used self-reported and operator-recorded mobile phone call data collected at baseline (2007-2012) from participants in Denmark, Finland, the Netherlands, Sweden, and the UK. Parameter estimates obtained using simple, direct, and inverse RC methods were associated with less bias and lower mean squared error than those obtained with CC analysis or MI. We showed that RC methods resulted in more accurate estimation of the relation between mobile phone use and health outcomes, by combining self-reported data with objective operator-recorded data available for a subset of participants.

Year-round vocal activity of two African barbet species

ABSTRACT Passive acoustic monitoring is a reliable method to study species behaviour and ecology, enabling the discovery of species activity patterns, especially in remote locations. Here, we conducted a year-long recording study to describe annual and circadian patterns in the vocal activity of two African barbet species, the Yellow-rumped Tinkerbird Pogoniulus bilineatus and the Western Tinkerbird Pogoniulus coryphaea. We used automated software to detect vocalisations of the two species from 4893 one-hour recordings taken in Cameroon. In total, we obtained 31,526 vocalisations of Yellow-rumped Tinkerbirds and 1318 vocalisations of Western Tinkerbirds. We used generalised additive mixed models to determine whether the month or hour of recording or meteorological conditions influenced the species’ vocal activity. Our results indicated that both tinkerbirds are likely seasonal breeders, as the highest vocal activity of both species was during the dry season. Both species exhibited smaller activity peaks in the wet season. Our study provides new information on the vocal activity patterns of two barbet species, which could be valuable in future monitoring and surveying efforts. This study provides an illustrative example of two useful technologies that facilitate studies in remote areas: passive acoustic monitoring for determining species’ activity patterns and automatic recognition software for the rapid analysis of large datasets.

The combined effects of physical and virtual models in learning cellular biology

Physical models have long been used in science education for visualization of complex cellular structure and dynamics during face-to-face lecture (F2F). Recent advancement of technology has enabled us to create virtual models and to share knowledge remotely. This study aims to find out whether physical and virtual models work synergistically to enhance student engagement in learning an undergraduate Life Sciences module. Three independent experiments were conducted to assess learning effectiveness on three biology concepts through four learning approaches: video with virtual model only, video with integration of virtual and physical model demonstration, F2F lecture using virtual model only, F2F lecture using virtual and physical models. Participants were randomly assigned to different groups each with one learning approach. Data collected through pre- and post-tests revealed that significant improvement in learning scientific concepts occurred in one of three controlled experiments when the video contains both virtual and physical models, while no obvious difference found in the other two experiments. This data suggests that well-prepared digital media alone may convey scientific information well and additional physical models do not aid in information acquisition. However, feedback survey on student learning experience showed that all participants preferred to learn from physical models. In all three experiments, students consistently voted that the physical models attracted their attention and enhanced their interests. They made better mind maps and raised more questions. These hint that a combination of digital media with physical models might improve engagement and promote higher order thinking.