Objective.Noninvasive blood pressure (BP) measurement technologies have been widely studied, but they still have the disadvantages of low accuracy, the requirement for frequent calibration and limited subjects. This work considers the regulation of vascular activity by the sympathetic nervous system and proposes a method for estimating BP using multiple physiological parameters.Approach.The parameters used in the model consist of heart rate variability (HRV), pulse transit time (PTT) and pulse wave morphology features extracted from electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Through four classic machine learning algorithms, a hybrid data set of 3337 subjects from two databases is evaluated to verify the ability of cross-database migration. We also recommend an individual calibration procedure to further improve the accuracy of the method.Main results.The mean absolute error (MAE) and the root mean square error (RMSE) of the proposed algorithm is 10.03 and 14.55 mmHg for systolic BP (SBP), and 5.42 and 8.19 mmHg for diastolic BP (DBP). With individual calibration, the MAE and standard deviation (SD) is -0.16 ± 7.96 (SBP) and -0.13 ± 4.50 (DBP) mmHg, which satisfied the Advancement of Medical Instrumentation (AAMI) standard. In addition, the models are used to test single databases to evaluate their performance on different data sources. The overall performance of the Adaboost algorithm is better on the Multi-parameter Intelligent Monitoring in Intensive Care Unit (MIMIC) database; the MAE between its predicted value and true value reaches 6.6mmHg (SBP) and 3.12mmHg (DBP), respectively.Significance.The proposed method considers the regulation of blood vessels and the heart by the autonomic nervous system, and verifies its effectiveness and robustness across data sources, which is promising for improving the accuracy of continuous and cuffless BP estimation.Several biofabrication methods are being investigated to produce scaffolds that can replicate the structure of the extracellular matrix. Direct-write, near-field electrospinning of polymer solutions and electrowriting of polymer melts are methods which combine fine fiber formation with computer-guided control. Research with such systems has focused primarily on synthetic polymers. To better understand the behavior of biopolymers used for direct-writing, this project investigated changes in fiber morphology, size, and variability caused by varying gelatin and acetic acid concentration, as well as process parameters such as needle gauge and height, stage speed, and interfiber spacing. Increasing gelatin concentration at a constant acetic acid concentration improved fiber morphology from large, planar structures to small, linear fibers with a median of 2.3 µm. Further varying the acetic acid concentration at a constant gelatin concentration did not alter fiber morphology and diameter throughout the range tested. Varying needle gauge and height further improved the median fiber diameter to below 2 µm and variability of the first and third quartiles to within ±1 µm of the median. Additional adjustment of stage speed did not impact the fiber morphology or diameter. C1889 Repeatable interfiber spacings down to 250 µm were shown to be capable with the system. In summary, this study illustrates the optimization of processing parameters for direct-writing of gelatin to produce fibers on the scale of collagen fibers. This system is thus capable of replicating the fibrous structure of musculoskeletal tissues with biologically relevant materials which will provide a durable platform for the analysis of single cell-fiber interactions to help better understand the impact scaffold materials and dimensions have on cell behavior.We report the stimulating effects of interfacial charge transfer process between spherical Ag nanoparticles and shuttlecock-shaped ZnO nanostructures observed by UV-visible spectroscopy and x-ray absorption spectroscopy. In specific, ZnO nanorods and shuttlecock-shaped ZnO/Ag nanostructures were developed using a simple chemical colloidal method and characterized for structural variations using XRD. The observed red shift in plasmonic peak and the increase in Urbach energy signify interfacial interactions and increased randomness in the hybrid ZnO/Ag nanostructures. Simultaneously, the enhanced intensity of deep-level emission in the ZnO/Ag hybrid suggests the increased recombination rate of electron-hole pairs. The red and blue emissions evolving with temperature subsequently suggests the presence of oxygen vacancies or zinc interstitials in the system. The decrease in intensities and emerging features in O K-edge and Zn L-edge indicates the charge transfer from Ag to ZnO at the interface of ZnO/Ag hybrids. Moreover, the differences in absorption edges with alternating light on/off conditions were analyzed for the exploitation of this ZnO-based system in various applications. To verify the reciprocal longitudinal relationships between cardiorespiratory fitness (CRF), percentage body fat (%body fat), and metabolic syndrome in Brazilian primary school students. This longitudinal study involved 420 children and adolescents followed for 3 years (2011-2014). The continuous Metabolic Syndrome (cMetSyn) score was calculated by summing adjusted z scores of glucose, systolic blood pressure, total cholesterol/high-density lipoprotein cholesterol ratio, triglycerides, and waist circumference. The CRF was assessed using running/walking tests, and %body fat was assessed through sex-specific 2-site skinfold thickness. Cross-lagged panel models were used to analyze longitudinal reciprocal relationships between CRF and %body fat with cMetSyn. Results indicated that 2011 %body fat significantly predicted both 2014 CRF scores and 2014 cMetSyn scores (P < .001); however, 2011 CRF only predicted 2014 %body fat (P < .001) but not 2014 cMetSyn (P = .103). Furthermore, 2011 cMetSyn predicted 2014 %body fat (P = .002). The model explained 36%, 48%, and 37% of the variance in 2014 CRF, %body fat, and cMetSyn, respectively. The results suggest a reciprocal inverse relationship between %body fat and metabolic syndrome risk and that %body fat may play a more important role in the risk of developing metabolic syndrome compared with CRF.The results suggest a reciprocal inverse relationship between %body fat and metabolic syndrome risk and that %body fat may play a more important role in the risk of developing metabolic syndrome compared with CRF.