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PloS One 2024The volume of human carbon (δ13C) and nitrogen (δ15N) isotope data produced in archaeological research has increased markedly in recent years. However, knowledge of...
The volume of human carbon (δ13C) and nitrogen (δ15N) isotope data produced in archaeological research has increased markedly in recent years. However, knowledge of bone remodelling, its impact on isotope variation, and the temporal resolution of isotope data remains poorly understood. Varied remodelling rates mean different elements (e.g., femur and rib) produce different temporal signals but little research has examined intra-element variability. This study investigates human bone remodelling using osteon population density and the relationship with carbon and nitrogen isotope data at a high resolution, focusing on variation through femoral cross-sections, from periosteal to endosteal surfaces. Results demonstrate considerable differences in isotope values between cross-sectional segments of a single fragment, by up to 1.3‰ for carbon and 1.8‰ for nitrogen, illustrating the need for standardised sampling strategies. Remodelling also varies between bone sections, occurring predominantly within the endosteal portion, followed by the midcortical and periosteal. Therefore, the endosteal portion likely reflects a shorter period of life closer to the time of death, consistent with expectations. By contrast, the periosteal surface provides a longer average, though there were exceptions to this. Results revealed a weak negative correlation between osteon population density and δ15N or δ13C, confirming that remodelling has an effect on isotope values but is not the principal driver. However, a consistent elevation of δ15N and δ13C (0.5‰ average) was found between the endosteal and periosteal regions, which requires further investigation. These findings suggest that, with further research, there is potential for single bone fragments to reconstruct in-life dietary change and mobility, thus reducing destructive sampling.
Topics: Humans; Femur; Bone Remodeling; Carbon Isotopes; Nitrogen Isotopes; Female; Male; Adult; Middle Aged
PubMed: 38923938
DOI: 10.1371/journal.pone.0305089 -
Translational Vision Science &... Jun 2024This study investigated the distribution of fundus tessellation density (FTD) in a Chinese pediatric population and its potential in reflecting early myopic maculopathy...
PURPOSE
This study investigated the distribution of fundus tessellation density (FTD) in a Chinese pediatric population and its potential in reflecting early myopic maculopathy (tessellated fundus).
METHODS
Participants were enrolled from kindergartens, primary schools, and middle schools, with cluster sampling in Shanghai, China. A series of ophthalmic examinations was conducted. Based on fundus photograph, FTD was quantitatively assessed using an artificial intelligence algorithm, and tessellated fundus was diagnosed by well-trained ophthalmologists.
RESULTS
A total of 14,234 participants aged four to 18 years were included, with 7421 boys (52.1%). Tessellated fundus was observed in 2200 (15.5%) participants. The median of FTD was 0.86% (range 0.0-42.1%). FTD increased with age and axial length. In the logistics regression, larger FTD was independently associated with tessellated fundus (P < 0.001). The area under curves of receiver operating characteristic curve for categorizing tessellated fundus using FTD was 0.774, and the cutoff point of FTD was 2.22%.
CONCLUSIONS
The density of fundus tessellation was consistent with the severity of myopia. FTD could help diagnose the early stage of myopic maculopathy, tessellated fundus, providing a new pattern for myopia screening and detection of early myopic fundus changes.
TRANSLATIONAL RELEVANCE
Quantification of fundus tessellation with artificial intelligence could help detect early myopic maculopathy.
Topics: Humans; Male; Adolescent; Child; Female; Fundus Oculi; Child, Preschool; China; ROC Curve; Myopia, Degenerative; Macular Degeneration; Artificial Intelligence; Photography
PubMed: 38922627
DOI: 10.1167/tvst.13.6.22 -
Metabolites May 2024While hundreds of germline genetic variants have been associated with breast cancer risk, the mechanisms underlying the impacts of most of these variants on breast...
While hundreds of germline genetic variants have been associated with breast cancer risk, the mechanisms underlying the impacts of most of these variants on breast cancer remain uncertain. Metabolomics may offer valuable insights into the mechanisms underlying genetic risks of breast cancer. Among 143 cancer-free female participants, we used linear regression analyses to explore associations between the genetic risk of breast cancer, as determined by a previously developed polygenic risk score (PRS) that included 266 single-nucleotide polymorphisms (SNPs), and 223 measures of metabolites obtained from blood samples using nuclear magnetic resonance (NMR). A false discovery rate of 10% was applied to account for multiple comparisons. PRS was statistically significantly associated with 45 metabolite measures. These were primarily measures of very low-density lipoproteins (VLDLs) and high-density lipoproteins (HDLs), including triglycerides, cholesterol, and phospholipids. For example, the strongest effect was observed with the percent ratio of medium VLDL triglycerides to total lipids (0.53 unit increase in mean-standardized ln-transformed percent ratio per unit increase in PRS; q = 0.1). While larger-scale studies are needed to confirm these results, this exploratory study presents biologically plausible findings that are consistent with previously reported associations between lipids and breast cancer risk. If confirmed, these lipids could be targeted for lifestyle and pharmaceutical interventions among women at increased genetic risk of breast cancer.
PubMed: 38921430
DOI: 10.3390/metabo14060295 -
Insects Jun 2024are predaceous mites that feed on phytophagous mites, pollens, and plant exudates and are known as one of the most potent biological pest management agents. is a...
are predaceous mites that feed on phytophagous mites, pollens, and plant exudates and are known as one of the most potent biological pest management agents. is a global mite that is difficult to manage because of its high population growth rates, necessitating alternative management measures like biological control. Regarding the functional response, the effects of temperature and prey density are some of the essential behaviors of natural enemies. This study investigates the effect of varying temperatures and prey densities on , a biological control agent for . The present results demonstrated the change in the functional response estimates when was reared at various temperatures and different prey densities. The results of the estimates regarding the searching efficiency () showed the highest value ( = 0.919) at 26 °C and the lowest value ( = 0.751) at 14 °C. The handling time per prey item () for the predatory mites changed with the temperature and prey density, showing the shortest handling time at 26 °C ( = 0.005) and the highest value at 14 °C ( = 0.015). The functional response curves matched the type II functional response model, demonstrating the inverse dependence of temperatures and prey density with a positive quadratic coefficient. The predation curves for showed a significant difference between the mean numbers of consumed at various prey densities and temperatures, illustrating a relationship between and Therefore, the results of this research may be utilized to forecast the behavior of and its usefulness in controlling populations.
PubMed: 38921159
DOI: 10.3390/insects15060444 -
Insects Jun 2024Insect development is intricately governed by hormonal signaling pathways, yet the pivotal upstream regulator that potentiates hormone activation remains largely...
Insect development is intricately governed by hormonal signaling pathways, yet the pivotal upstream regulator that potentiates hormone activation remains largely elusive. The migratory locust, , exhibits population density-dependent phenotypic plasticity, encompassing traits such as flight capability, body coloration, and behavior. In this study, we elucidated a negative correlation between population density and ontogenetic development during the nymphal stage of locusts. We found that the level of density influences the developmental trajectory by modulating transcript abundance within the ecdysone signaling pathway, with knockdown of the prothoracicotropic hormone (PTTH) resulting in developmental delay. Transcriptomic analysis of locust brains across solitary and gregarious phases revealed significant differential expression of genes involved in various pathways, including protein synthesis, energy metabolism, hormonal regulation, and immunity. Notably, knockdown experiments targeting two energy regulators, adipokinetic hormone (AKH) and (), failed to elicit changes in the developmental process in solitary locusts. However, knockdown of () significantly shortened the developmental time in higher-density populations. Collectively, our findings underscore the regulatory role of population density in determining developmental duration and suggest that an immune-related gene contributes to the observed differences in developmental patterns.
PubMed: 38921158
DOI: 10.3390/insects15060443 -
Insects Jun 2024Bark beetles are a significant link in the chain of diseases that lead to the accelerated dying of firs ( Mill.), a key species in the cultivation of stable mixed-tree...
Bark beetles are a significant link in the chain of diseases that lead to the accelerated dying of firs ( Mill.), a key species in the cultivation of stable mixed-tree stands. The aim of this work was to evaluate biotic interactions in populations of bark beetles that colonised natural traps made from firs. The tested hypothesis was that the niche breadth of the species increases with the increasing density of the population. The research was carried out in near-natural forests containing fir, growing in the Suchedniów-Oblęgorek Landscape Park in central Poland. Data were collected from 30 traps trees and 30 windfalls in the years 2010-2023. Ratz. prefers heavily weakened trees, as shown by the fact that it colonised all of the natural traps, which lack any defensive reactions. The sampling method used in the study proved effective, as confirmed by the segregation of the niches of all of the bark beetles. Using nonlinear regression (linearisable model and piecewise linear regression), models were constructed that describe the niche breadths of the bark beetles. The niche parameter is correlated with the density of colonisation. The derived models explain around 77-84% of the variation in the niche breadth of bark beetles on natural traps. The mean relative errors of estimation do not exceed 20%. The niche breadth parameter obtained from the derived regression equations may be used in models that describe-for example-the impact of observed climate change on the population dynamics of bark beetles.
PubMed: 38921137
DOI: 10.3390/insects15060422 -
Insects May 2024The dispersal strategies of a species can affect its invasion success. Investigations into the dispersal strategies of invasive species in relation to different factors...
The dispersal strategies of a species can affect its invasion success. Investigations into the dispersal strategies of invasive species in relation to different factors help improve our understanding of invasion mechanisms and provide knowledge for population management and invasion evaluation. Zacher (Acari: Tetranychidae) is an invasive species which is native to Europe but is now cosmopolitan. Here, we examined the effects of age and density on dispersal in mated females. Our results show that older females that are capable of producing more eggs within 24 h were more likely to disperse and moved longer distances than younger ones with fewer eggs. Older females spread most of their eggs out of their natal habitats and over longer distances, which reduced competition and increased offspring fitness. Females exhibited significantly increased dispersal probability and distances with an increase in population density to avoid crowding. The synchronization of dispersal and reproduction, along with the positive density-dependent dispersal strategy, may facilitate the habitat colonization and invasion speed of .
PubMed: 38921102
DOI: 10.3390/insects15060387 -
Diabetes, Metabolic Syndrome and... 2024This study assessed possible associations among physical activity (PA), sitting time (ST), metabolic syndrome (MetS), and the individual components thereof. We analyzed...
Influence of Central Obesity on Associations Between Physical Activity, Sitting Time, and Metabolic Syndrome Among Middle-Aged and Older Adults in Urban China: A Cross-Sectional Study.
OBJECTIVE
This study assessed possible associations among physical activity (PA), sitting time (ST), metabolic syndrome (MetS), and the individual components thereof. We analyzed the entire study sample and subpopulations stratified by visceral fat area (VFA). We hypothesized that individuals with elevated VFA might respond differently to modifiers of metabolic health, including PA and ST.
METHODS
This cross-sectional study, conducted between March and May 2010, enrolled 957 adults with abdominal magnetic resonance imaging (MRI) aged 40-65 years living in the urban communities in Hangzhou, China. PA and ST were recorded using the standard International Physical Activity Questionnaire (IPAQ) and categorized into three levels. The ethnicity-specific cutoff for central obesity was VFA ≥ 80 cm on MRI according to Chinese population-based research. Multiple logistic regression models were used to analyze the associations between PA, ST, MetS and its components.
RESULTS
In the total subject population, participants reporting high level of PA were at a lower risk of MetS (OR = 0.46, 95% CI: 0.25, 0.86) than those declaring low PA. In the subgroup population with VFA ≥ 80 cm (ie, with central obesity), moderate-to-high PA levels were associated with a lower risk of MetS (p for trend < 0.05) and a lower risk of decreased high-density lipoprotein cholesterol (HDL-C) concentrations (p for trend < 0.05). In addition, ST > 3 h/day was a risk factor for both MetS (p for trend < 0.05) and hypertriglyceridemia (p for trend < 0.05) in the total subject population. While in the central obesity subgroup, ST > 3 h/day was found a stronger risk factor.
CONCLUSION
Our study suggests that moderate-to-high levels of PA may have a role in prevention of MetS, and ST > 3 h/day was associated with a higher risk of MetS, particularly in individuals with central obesity.
PubMed: 38919982
DOI: 10.2147/DMSO.S457455 -
Frontiers in Artificial Intelligence 2024Osteoporosis, characterized by low bone mineral density (BMD), is an increasingly serious public health issue. So far, several traditional regression models and machine...
INTRODUCTION
Osteoporosis, characterized by low bone mineral density (BMD), is an increasingly serious public health issue. So far, several traditional regression models and machine learning (ML) algorithms have been proposed for predicting osteoporosis risk. However, these models have shown relatively low accuracy in clinical implementation. Recently proposed deep learning (DL) approaches, such as deep neural network (DNN), which can discover knowledge from complex hidden interactions, offer a new opportunity to improve predictive performance. In this study, we aimed to assess whether DNN can achieve a better performance in osteoporosis risk prediction.
METHODS
By utilizing hip BMD and extensive demographic and routine clinical data of 8,134 subjects with age more than 40 from the Louisiana Osteoporosis Study (LOS), we developed and constructed a novel DNN framework for predicting osteoporosis risk and compared its performance in osteoporosis risk prediction with four conventional ML models, namely random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM), as well as a traditional regression model termed osteoporosis self-assessment tool (OST). Model performance was assessed by area under 'receiver operating curve' (AUC) and accuracy.
RESULTS
By using 16 discriminative variables, we observed that the DNN approach achieved the best predictive performance (AUC = 0.848) in classifying osteoporosis (hip BMD T-score ≤ -1.0) and non-osteoporosis risk (hip BMD T-score > -1.0) subjects, compared to the other approaches. Feature importance analysis showed that the top 10 most important variables identified by the DNN model were weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, smoke years, and economic level. Furthermore, we performed subsampling analysis to assess the effects of varying number of sample size and variables on the predictive performance of these tested models. Notably, we observed that the DNN model performed equally well (AUC = 0.846) even by utilizing only the top 10 most important variables for osteoporosis risk prediction. Meanwhile, the DNN model can still achieve a high predictive performance (AUC = 0.826) when sample size was reduced to 50% of the original dataset.
CONCLUSION
In conclusion, we developed a novel DNN model which was considered to be an effective algorithm for early diagnosis and intervention of osteoporosis in the aging population.
PubMed: 38919268
DOI: 10.3389/frai.2024.1355287 -
BMC Infectious Diseases Jun 2024Annual epidemics of respiratory syncytial virus (RSV) had consistent timing and intensity between seasons prior to the SARS-CoV-2 pandemic (COVID-19). However, starting...
BACKGROUND
Annual epidemics of respiratory syncytial virus (RSV) had consistent timing and intensity between seasons prior to the SARS-CoV-2 pandemic (COVID-19). However, starting in April 2020, RSV seasonal activity declined due to COVID-19 non-pharmaceutical interventions (NPIs) before re-emerging after relaxation of NPIs. We described the unusual patterns of RSV epidemics that occurred in multiple subsequent waves following COVID-19 in different countries and explored factors associated with these patterns.
METHODS
Weekly cases of RSV from twenty-eight countries were obtained from the World Health Organisation and combined with data on country-level characteristics and the stringency of the COVID-19 response. Dynamic time warping and regression were used to cluster time series patterns and describe epidemic characteristics before and after COVID-19 pandemic, and identify related factors.
RESULTS
While the first wave of RSV epidemics following pandemic suppression exhibited unusual patterns, the second and third waves more closely resembled typical RSV patterns in many countries. Post-pandemic RSV patterns differed in their intensity and/or timing, with several broad patterns across the countries. The onset and peak timings of the first and second waves of RSV epidemics following COVID-19 suppression were earlier in the Southern than Northern Hemisphere. The second wave of RSV epidemics was also earlier with higher population density, and delayed if the intensity of the first wave was higher. More stringent NPIs were associated with lower RSV growth rate and intensity and a shorter gap between the first and second waves.
CONCLUSION
Patterns of RSV activity have largely returned to normal following successive waves in the post-pandemic era. Onset and peak timings of future epidemics following disruption of normal RSV dynamics need close monitoring to inform the delivery of preventive and control measures.
Topics: Humans; COVID-19; Respiratory Syncytial Virus Infections; SARS-CoV-2; Global Health; Seasons; Respiratory Syncytial Virus, Human; Pandemics
PubMed: 38918718
DOI: 10.1186/s12879-024-09509-4