Ambient habitat noise and vibration at the Georgia Aquarium

Underwater and in-air noise evaluations were completed in performance pool systems at Georgia Aquarium under normal operating conditions and with performance sound tracks playing. Ambient sound pressure levels at in-pool locations, with corresponding vibration measures from life support system (LSS) pumps, were measured in operating configurations, from shut down to full operation. Results indicate noise levels in the low frequency ranges below 100 Hz were the highest produced by the LSS relative to species hearing thresholds. The LSS had an acoustic impact of about 10 dB at frequencies up to 700 Hz, with a 20 dB re 1 μPa impact above 1000 Hz

Electrochemical aptasensor for human osteopontin detection using a DNA aptamer selected by SELEX

A DNA aptamer with affinity and specificity for human osteopontin (OPN), a potential breast cancer biomarker, was selected using the SELEX process, considering its homology rate and the stability of its secondary structures. This aptamer exhibited a satisfactory affinity towards OPN, showing dissociation constants lower than 2.5 nM. It was further used to develop a simple, label-free electrochemical aptasensor against OPN. The aptasensor showed good sensitivity towards OPN in standard solutions, being the square wave voltammetry (SWV), compared to the cyclic voltammetry, the most sensitive technique with detection and quantification limits of 1.4 ± 0.4 nM and 4.2 ± 1.1 nM, respectively. It showed good reproducibility and acceptable selectivity, exhibiting low signal interferences from other proteins, as thrombin, with 2.610 times lower current signals-off than for OPN. The aptasensor also successfully detected OPN in spiked synthetic human plasma. Using SWV, detection and quantification limits (1.3 ± 0.1 and 3.9 ± 0.4 nM) within the OPN plasma levels reported for patients with breast cancer (0.44.5 nM) or with metastatic or recurrent breast cancer (0.98.4 nM) were found. Moreover, preliminary assays, using a sample of human plasma, showed that the aptasensor and the standard ELISA method quantified similar OPN levels (2.2 ± 0.7 and 1.7 ± 0.1 nM, respectively). Thus, our aptasensor coupled with SWV represents a promising alternative for the detection of relevant breast cancer biomarkers.The authors acknowledge the financial support from the Strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684), and from project BioTecNorte (project number NORTE-01-0145-FEDER-000004). This work was also financially supported by Project POCI-01–0145-FEDER-006984 – Associate Laboratory LSRE-LCM and by Project UID/QUI/00616/2013 – CQ-VR both funded by FEDER - Fundo Europeu de Desenvolvimento Regional through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) – and by national funds through FCT - Fundação para a Ciência e a Tecnologia, Portugal. S. Meirinho also acknowledges the research grant provided by Project UID/EQU/50020/2013.info:eu-repo/semantics/publishedVersio

Geoacoustic inversion in shallow tropical waters for a silty and sandy seabed

Seabed parameters are inverted from ambient noise measurements at two shallow tropical environments with dissimilar seabed characteristics, a silty and a sandy seabed, using an approach that matches the measured and modeled complex vertical coherence. Coherence is modeled using the Green\u27s function output from the model oases, along with theoretical formulation, for a range independent environment. Genetic algorithm is used to search the model parameter space consisting of sound speed, density, and attenuation in the sediment layers and half-space. Reasonable estimates have been obtained for the silty site, whereas the sandy site gave relatively poor parameter estimates due to reflective seabed and shipping interference

Sensitivity analysis of model parameters in geoacoustic inversion

Sensitivity of model parameters to the cost function defined by noise coherence in a shallow water environment is discussed here. Since, in inversion schemes based on matched field processing, the values of model and field parameters are compared to arrive at the best estimate, a sensitivity study helps in model parameter selection and also ensures the quality of the inversion. Model sensitivity for the acoustic properties of sediment layer and basement is checked for a layered environment in this study. In order to investigate the sensitivity of a parameter the cost function is evaluated and plotted for all possible values of the parameter values within it\u27s search bound. In this way, the distribution of cost function values should provide an indication of the parameter sensitivity. © 2013 CUST

Vertical coherence of ambient noise in shallow water

The vertical coherence of wave breaking noise in shallow water is seen to be stable since it is influenced by seabed characteristics and hence can be used for estimating geoacoustic properties of the sea floor. Time series measurements of ambient noise taken at a shallow site of 30 m depth using a Vertical Linear Array (VLA) of hydrophones have been used to estimate coherence. Vertical coherence for the site also has been estimated using a theoretical model developed by Deane et al., 1997 [1]. Geoacoustic properties of the sediment derived based on the sediment type and measured water column sound speed are used in the model. Coherences computed using data and theory have been presented and the results are compared. It is seen that theoretical real and imaginary coherence in the 5 kHz band compares well with real and imaginary coherence from the field measurements. © 2011 IEEE

Multistep matched-field inversion for broad-band data from ASIAEX2001

This paper discusses the results of geoacoustic inversion carried out using explosive charge data from the Asian Seas International Acoustic Experiment (ASIAEX) East China Sea (ECS) Experiment. A multifrequency incoherent matched-field inversion processor and a genetic algorithm (GA) are used for the inversion. A multistep matched field inversion approach is presented, which makes use of the varying sensitivities of wave fields at various frequencies to reduce the inversion problem into a sequence of smaller inversions with fewer unknowns to estimate at each stage. Different parameters are estimated using data at different frequencies according to their sensitivities. Inversion results for different areas in the ECS region are summarized and compared with core data. © 2004 IEEE

Time-frequency representations for wideband acoustic signals in shallow water

Wideband acoustic propagation in shallow water has a unique arrival structure due to modal group speed dispersion. It is possible to unravel the modal dispersion characteristics using a proper time-frequency (TF) analysis. During the Asian Seas International Acoustics Experiment (ASIAEX) - East China Sea (ECS) experiment, signals collected by the vertical arrays have been processed using several time-frequency analysis techniques. Based upon the dispersion characteristics of acoustic propagation, theoretical time-frequency structure is used to determine the change of group delay. We present an improved time-frequency representation to provide higher resolution for TF diagrams using ECS data. The dispersion characteristics of ECS data are observed from these TF diagrams, and are verified by a theoretical model. Using this technique, higher modes can be observed with good resolution; this aids in increasing the performance of the inversion technique for long range sediment tomography

Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes

Machine learning (ML) has not yet been used to identify factors predictive for post-operative functional outcomes following arthroscopic rotator cuff repair (ARCR). We propose a novel algorithm to predict ARCR outcomes using machine learning. This is a retrospective cohort study from a prospectively collected database. Data were collected from the Surgical Outcome System Global Registry (Arthrex, Naples, FL, USA). Pre-operative and 3-month, 6-month, and 12-month post-operative American Shoulder and Elbow Surgeons (ASES) scores were collected and used to develop a ML model. Pre-operative factors including demography, comorbidities, cuff tear, tissue quality, and fixation implants were fed to the ML model. The algorithm then produced an expected post-operative ASES score for each patient. The ML-produced scores were compared to actual scores using standard test-train machine learning principles. Overall, 631 patients who underwent shoulder arthroscopy from January 2011 to March 2020 met inclusion criteria for final analysis. A substantial number of the test dataset predictions using the XGBoost algorithm were within the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) thresholds: 67% of the 12-month post-operative predictions were within MCID, while 84% were within SCB. Pre-operative ASES score, pre-operative pain score, body mass index (BMI), age, and tendon quality were the most important features in predicting patient recovery as identified using Shapley additive explanations (SHAP). In conclusion, the proposed novel machine learning algorithm can use pre-operative factors to predict post-operative ASES scores accurately. This can further supplement pre-operative counselling, planning, and resource allocation. Level of Evidence: III