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UNLABELLED: Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence. PURPOSE: Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur. METHODS: We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling. RESULTS: We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value

The article discusses the application and importance of digital technology in the early intervention and multidisciplinary treatment of malocclusion in children. It emphasizes the significance of early intervention during the critical period of children's growth and development, pointing out that the high plasticity of children's jawbones can be leveraged to achieve efficient correction through simple and gentle methods. Additionally, the article introduces digital oral habit-blocking treatments, such as the application of tongue crib appliances, and explores the potential of digital clear aligner technology in the treatment of malocclusion in children. Finally, by discussing the multidisciplinary diagnostic and therapeutic strategies for pediatric obstructive sleep apnea, the article underscores the importance of multidisciplinary team collaboration in ensuring that patients receive scientific and effective treatment.

Background Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. Methods and findings We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R.sup.2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5x10.sup.-6 and 5x10.sup.-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R.sup.2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. Conclusions In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5x10.sup.-6 or 5x10.sup.-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups., Author(s): Yong Liu 1, Xiang-He Meng 2, Chong Wu 3, Kuan-Jui Su 4, Anqi Liu 4, Qing Tian 4, Lan-Juan Zhao 4, Chuan Qiu 4, Zhe Luo 4, Martha I [...]

We tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants' feedback in a real-road scenario. We designed a ride experience-based version of the non-verbal Turing test for automated driving. Participants rode in autonomous cars as a passenger and judged whether the driver was human or AI. The AI driver failed to pass our test because passengers detected the AI driver above chance. We further investigated how human passengers ascribe humanness in our test. Based on Lewin's field theory, we advanced a computational model combining signal detection theory with pre-trained language models to predict passengers' humanness rating behaviour. We employed affective transition between pre-study baseline and corresponding post-stage emotions as the signal strength of our model. Results showed that the passengers' ascription of humanness would increase with the greater affective transition, indicating an important role of affective transition in passengers' ascription of humanness.

The influence of 82B steel with Mo （mass fraction） of 0. 02%，0. 04%，0. 07% and 0. 11% on microstructures and properties after high temperature stretching at different temperatures and the toughness mechanism of Mo element on 82B high carbon steel were studied. The results showed that：（1） During 200-300 ℃ high temperatures tensile， four groups of experimental steels had good mechanical properties ， at 300 ℃ to reach the peak， it was optimal experiment group with Mo content 0. 11% ， tensile strength increased from 1 139 MPa to 1 192 MPa， yield strength slightly reduced to 736 MPa， elongation and section shrinkage significantly increased to 22% and 72. 9%. Compared to the experimental steels with 0. 02% Mo content， the yield strength reduced by about 5%， but the tensile strength increased by 5%， the elongation and section shrinkage increased significantly， and the plasticity increased simultaneously with the Mo content， with the temperature continued to rise， the mechanical properties tended to decrease as the materials start to soften.（2） When the Mo content was 0. 02% and 0. 04%， the microstructures except carburite gradually increased with the increasing of tensile temperature， sorbite and pearlite did not appear obvious changes， and the same as room temperature， when the Mo content increased to 0. 07% and 0. 11%， sorbite and pearlite clusters are progressively finer and more evenly distributed. At the same time， four groups of experimental steels showed blue brittleness during high temperature tensile at 300 ℃，through the fractures observation found that there was Al， Si and other elements of inclusions， and failure mode was ductile fracture， the fracture can mainly be divided into the shear lip areas and fiber areas，the shear lip areas with the increase in temperature gradually reduced.

Aroma compounds are the general term for esters, aldehydes, alcohols, ketones, terpenes, organic acids and other compounds responsible for aromas, which have been widely used in food, medicine, cosmetics, agriculture and other fields. With the economic development and the improvement of the quality of life in China, the demand for aroma compounds is gradually increasing. Microbial fermentation is becoming an important way to obtain green aroma compounds. In this paper, recent progress in the production of various aroma compounds by microorganisms is summarized, and the sources and isolation of aroma-producing microorganisms, the types of microorganism-derived aroma compounds, the factors affecting the microbial production of aroma compounds and related processing technologies, and the strategies for improving the microbial production of aroma compounds are reviewed. This review is expected to provide an important reference for the research and development of aroma-producing microorganisms.

Electroencephalographic (EEG) alpha oscillations have been related to heart rate variability (HRV) and both change in Alzheimer's disease (AD). We explored if task switching reveals altered alpha power and HRV in cognitively healthy individuals with AD pathology in cerebrospinal fluid (CSF) and whether HRV improves the AD pathology classification by alpha power alone. We compared low and high alpha event-related desynchronization (ERD) and HRV parameters during task switch testing between two groups of cognitively healthy participants classified by CSF amyloid/tau ratio: normal (CH-NAT, n = 19) or pathological (CH-PAT, n = 27). For the task switching paradigm, participants were required to name the color or word for each colored word stimulus, with two sequential stimuli per trial. Trials include color (cC) or word (wW) repeats with low load repeating, and word (cW) or color switch (wC) for high load switching. HRV was assessed for RR interval, standard deviation of RR-intervals (SDNN) and root mean squared successive differences (RMSSD) in time domain, and low frequency (LF), high frequency (HF), and LF/HF ratio in frequency domain. Results showed that CH-PATs compared to CH-NATs presented: 1) increased (less negative) low alpha ERD during low load repeat trials and lower word switch cost (low alpha: p = 0.008, Cohen's d = -0.83, 95% confidence interval -1.44 to -0.22, and high alpha: p = 0.019, Cohen's d = -0.73, 95% confidence interval -1.34 to -0.13); 2) decreasing HRV from rest to task, suggesting hyper-activated sympatho-vagal responses. 3) CH-PATs classification by alpha ERD was improved by supplementing HRV signatures, supporting a potentially compromised brain-heart interoceptive regulation in CH-PATs. Further experiments are needed to validate these findings for clinical significance.