Statistical Spectral Parameter Estimation of Acoustic Signals with Applications to Byzantine Music

Digitized acoustical signals of Byzantine music performed by Iakovos Nafpliotis are used to extract the fundamental frequency of each note of the diatonic scale. These empirical results are then contrasted to the theoretical suggestions and previous empirical findings. Several parametric and non-parametric spectral parameter estimation methods are implemented. These include: (1) Phase vocoder method, (2) McAulay-Quatieri method, (3) Levinson-Durbin algorithm,(4) YIN, (5) Quinn & Fernandes Estimator, (6) Pisarenko Frequency Estimator, (7) MUltiple SIgnal Characterization (MUSIC) algorithm, (8) Periodogram method, (9) Quinn & Fernandes Filtered Periodogram, (10) Rife & Vincent Estimator, and (11) the Fourier transform. Algorithm performance was very precise. The psychophysical aspect of human pitch discrimination is explored. The results of eight (8) psychoacoustical experiments were used to determine the aural just noticeable difference (jnd) in pitch and deduce patterns utilized to customize acceptable performable pitch deviation to the application at hand. These customizations [Acceptable Performance Difference (a new measure of frequency differential acceptability), Perceptual Confidence Intervals (a new concept of confidence intervals based on psychophysical experiment rather than statistics of performance data), and one based purely on music-theoretical asymphony] are proposed, discussed, and used in interpretation of results. The results suggest that Nafpliotis\u27 intervals are closer to just intonation than Byzantine theory (with minor exceptions), something not generally found in Thrasivoulos Stanitsas\u27 data. Nafpliotis\u27 perfect fifth is identical to the just intonation, even though he overstretches his octaveby fifteen (15)cents. His perfect fourth is also more just, as opposed to Stanitsas\u27 fourth which is directionally opposite. Stanitsas\u27 tendency to exaggerate the major third interval A4-F4 is still seen in Nafpliotis, but curbed. This is the only noteworthy departure from just intonation, with Nafpliotis being exactly Chrysanthian (the most exaggerated theoretical suggestion of all) and Stanitsas overstretching it even more than Nafpliotis and Chrysanth. Nafpliotis ascends in the second tetrachord more robustly diatonically than Stanitsas. The results are reported and interpreted within the framework of Acceptable Performance Differences

Byzantine Music Intervals: An Experimental Signal Processing Approach

We used a Byzantine Music piece performed by a well recognized chanter in order to derive experimentally the mean frequencies of the first five tones (D – A) of the diatonic scale of Byzantine Music. Then we compared the experimentally derived frequencies with frequencies proposed by two theoretical scales, both representative of traditional Byzantine Music chanting. We found that if a scale is performed by a traditional chanter is very close in frequency to the frequencies proposed theoretically. We then determined an allowed frequency deviation from the mean frequencies for each tone. The concept of allowed deviation is not provided by theory. Comparing our results to the notion of pitch discrimination from psychophysics we further established that the frequency differences are minute. The Attraction Effect was tested for a secondary tone (E) and the effect is quantified for the first time. The concept of the Attraction Effect is not explained in theory in terms of frequencies of tones

Drug prescription clusters in the UK Biobank: An assessment of drug-drug interactions and patient outcomes in a large patient cohort

In recent decades, there has been an increase in polypharmacy, the concurrent administration of multiple drugs per patient. Studies have shown that polypharmacy is linked to adverse patient outcomes and there is interest in elucidating the exact causes behind this observation. In this paper, we are studying the relationship between drug prescriptions, drug-drug interactions (DDIs) and patient mortality. Our focus is not so much on the number of prescribed drugs, the typical metric in polypharmacy research, but rather on the specific combinations of drugs leading to a DDI. To learn the space of real-world drug combinations, we first assessed the drug prescription landscape of the UK Biobank, a large patient data registry. We observed distinct drug constellation patterns driven by the UK Biobank participants' disease status. We show that these drug prescription clusters matter in terms of the number and types of expected DDIs, and may possibly explain observed differences in health outcomes

Drug prescription clusters in the UK Biobank: An assessment of drug-drug interactions and patient outcomes in a large patient cohort

In recent decades, there has been an increase in polypharmacy, the concurrent administration of multiple drugs per patient. Studies have shown that polypharmacy is linked to adverse patient outcomes and there is interest in elucidating the exact causes behind this observation. In this paper, we are studying the relationship between drug prescriptions, drug-drug interactions (DDIs) and patient mortality. Our focus is not so much on the number of prescribed drugs, the typical metric in polypharmacy research, but rather on the specific combinations of drugs leading to a DDI. To learn the space of real-world drug combinations, we first assessed the drug prescription landscape of the UK Biobank, a large patient data registry. We observed distinct drug constellation patterns driven by the UK Biobank participants' disease status. We show that these drug prescription clusters matter in terms of the number and types of expected DDIs, and may possibly explain observed differences in health outcomes

AttentionDDI: Siamese attention‑based deep learning method for drug–drug interaction predictions

Background: Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pairs, it is nearly impossible to experimentally test all combinations and discover previously unobserved side effects. Therefore, machine learning based methods are being used to address this issue. Methods: We propose a Siamese self-attention multi-modal neural network for DDI prediction that integrates multiple drug similarity measures that have been derived from a comparison of drug characteristics including drug targets, pathways and gene expression profiles. Results: Our proposed DDI prediction model provides multiple advantages: (1) It is trained end-to-end, overcoming limitations of models composed of multiple separate steps, (2) it offers model explainability via an Attention mechanism for identifying salient input features and (3) it achieves similar or better prediction performance (AUPR scores ranging from 0.77 to 0.92) compared to state-of-the-art DDI models when tested on various benchmark datasets. Novel DDI predictions are further validated using independent data resources. Conclusions: We find that a Siamese multi-modal neural network is able to accurately predict DDIs and that an Attention mechanism, typically used in the Natural Language Processing domain, can be beneficially applied to aid in DDI model explainability. Keywords: Attention; Deep learning; Drug–drug interactions; Prediction; Side effect

Aging and Disability Among Hispanics in the United States: Current Knowledge and Future Directions

Background and Objectives: Hispanics are the most rapidly aging minority population in the United States. Our objective is to provide a summary of current knowledge regarding disability among Hispanics, and to propose an agenda for future research. Research Design and Methods: A literature review was conducted to identify major areas of research. A life course perspective and the Hispanic Paradox were used as frameworks for the literature review and for identifying future areas of research. Results: Four research areas were identified: (1) Ethnic disparities in disability; (2) Heterogeneity of the U.S. older Hispanic population; (3) Risk factors for disability; and (4) Disabled life expectancy. Older Hispanics are more likely than non- Hispanic whites to be disabled or to become disabled. Disability varied by country of origin, nativity, age of migration, and duration in the United States. Important risk factors for disability included chronic health conditions, depression, and cognitive impairment. Protective factors included positive affect and physical activity. Older Hispanics have longer life expectancy than non-Hispanic whites but spend a greater proportion of old age disabled. Future research should continue to monitor trends in disability as younger generations of Hispanics reach old age. Attention needs to be given to regional variation within the United States for disability prevalence, early-life risk factors, and factors that may contribute to variation in disabled life expectancy. There is also an urgent need for interventions that can effectively prevent or delay the onset of disability in older Hispanics. Discussion and Implications: Considerable research has examined disability among older Hispanics, but continued research is needed. It is important that research findings be used to inform public policies that can address the burden of disability for older Hispanic populations

Actively Learning Reinforcement Learning: A Stochastic Optimal Control Approach

In this paper we provide a framework to cope with two problems: (i) the fragility of reinforcement learning due to modeling uncertainties because of the mismatch between controlled laboratory/simulation and real-world conditions and (ii) the prohibitive computational cost of stochastic optimal control. We approach both problems by using reinforcement learning to solve the stochastic dynamic programming equation. The resulting reinforcement learning controller is safe with respect to several types of constraints and it can actively learn about the modeling uncertainties. Unlike exploration and exploitation, probing and safety are employed automatically by the controller itself, resulting real-time learning. A simulation example demonstrates the efficacy of the proposed approach

Investigation of the effect of microplastics on the UV inactivation of antibiotic-resistant bacteria in water

This study investigated the effect of polyethylene and polyvinyl chloride microplastics on the UV fluence response curve for the inactivation of multidrug-resistant E. coli and enterococci in ultrapure water at pH 6.0 ± 0.1. In the absence of microplastics, the UV inactivation of the studied bacteria exhibited an initial resistance followed by a faster inactivation of free (dispersed) bacteria, while in the presence of microplastics, these 2 regimes were followed by an additional regime of slower or no inactivation related to microplastic-associated bacteria (i.e., bacteria aggregated with microplastics resulting in shielding bacteria from UV indicated by tailing at higher UV fluences). The magnitude of the negative effect of microplastics varied with different microplastics (type/particle size) and bacteria (Gram-negative and Gram-positive). Results showed that when the UV transmittance of the microplastic-containing water was not taken into account in calculating UV fluences, the effect of microplastics as protectors of bacteria was overestimated. A UV fluence-based double-exponential microbial inactivation model accounting for both free and microplastic-associated bacteria could describe well the disinfection data. The present study elucidated the effect of microplastics on the performance of UV disinfection, and the approach used herein to prove this concept may guide future research on the investigation of the possible effect of other particles including nanoplastics with different characteristics on the exposure response curve for the inactivation of various microorganisms by physical and chemical disinfection processes in different water and wastewater matrices.publishedVersio

Diabetic Patients are often Sub-Optimally Aware about their Disease and its Treatment

Background: Diabetes mellitus (DM) represents a continuously growing worldwide threat with major financial impact on the healthcare systems. The importance of tight glycaemic control in patients with DM type II is well established and is most effectively accomplished with the proper cooperation of both the treating physicians aswell as the treated subjects.Aims: The aim of our study was to evaluate the level of awareness of patients with DM type II about the various aspects of DM, including the nature of the disease, its precipitating factors and complications, as well as its treatment.Methodology: The patients were asked to complete anonymously a questionnaire concerning their knowledge about diabetes, its basic pathophysiology and complications, the treatment options and possible side-effects. Data were analyzed using STATA statistical software (Version 9.0).Results: Eighty patients were on oral hypoglycaemic agents (OHA), 34 on insulin while 4 were under a hybrid treatment. Among patients on OHA, 40 patients (50%) were taking a combination of them. 13.4% of the sample was aware of what DM stands for, 84.9% did not know the type of DM they were suffering from, while (85.7%) considered that obesity plays a major role in the pathogenesis of DM. Concerning the therapy of DM, only 54.83% of the patients were aware of the brand names of their antidiabetic medication, 88.2% did not know theirway of action, while 60.5% did not know the possible side effects. The majority of the sample, 60.5%, assumed that blood glucose should be measured only before meals.Conclusions: The knowledge of the subjects visiting the center for the first time was found to be inadequate. This is probably due to inadequate information, non-availability of educational material and improper guidance