Interlochen Center for the Arts

"Sound Experiments: The Music of the AACM." Music Reference Services Quarterly, ahead-of-print(ahead-of-print), pp. 1–2

In his 2020 book Adrian Rollini: The Life and Music of a Jazz Rambler, Ate van Delden presents a detailed account of the American musician’s rise to fame and fortune and contributions to the music ...

"Jón Leifs and the Musical Invention of Iceland." Music Reference Services Quarterly, 24(1-2), pp. 96–97

Let \mathcal{C}(\mathfrak{g},k) be the unitary modular tensor categories arising from the representation theory of quantum groups at roots of unity for arbitrary simple finite-dimensional complex Lie algebra \mathfrak{g} and positive integer levels k . Here we classify nondegenerate fusion subcategories of the modular tensor categories of local modules \mathcal{C}(\mathfrak{g},k)_R^0 where R is the regular algebra of Tannakian Rep (H)\subset\mathcal{C}(\mathfrak{g},k)_\text{pt} . We describe the decomposition of \mathcal{C}(\mathfrak{g},k)_R^0 into prime factors, and as an application we classify relations in the Witt group of nondegenerately braided fusion categories generated by the equivalency classes of \mathcal{C}(\mathfrak{so}_5,k) and \mathcal{C}(\mathfrak{g}_2,k) for k\in\mathbb{Z}_{\geq1} .

BACKGROUND Approximately 80% of those infected with COVID-19 are immune. They are asymptomatic unknown carriers who can still infect those with whom they come into contact. Understanding what makes them immune could inform public health policies as to who needs to be protected and why, and possibly lead to a novel treatment for those who cannot, or will not, be vaccinated once a vaccine is available. OBJECTIVE The primary objectives of this study were to learn if machine learning could identify patterns in the pathogen-host immune relationship that differentiate or predict COVID-19 symptom immunity and, if so, which ones and at what levels. The secondary objective was to learn if machine learning could take such differentiators to build a model that could predict COVID-19 immunity with clinical accuracy. The tertiary purpose was to learn about the relevance of other immune factors. METHODS This was a comparative effectiveness research study on 53 common immunological factors using machine learning on clinical data from 74 similarly grouped Chinese COVID-19–positive patients, 37 of whom were symptomatic and 37 asymptomatic. The setting was a single-center primary care hospital in the Wanzhou District of China. Immunological factors were measured in patients who were diagnosed as SARS-CoV-2 positive by reverse transcriptase-polymerase chain reaction (RT-PCR) in the 14 days before observations were recorded. The median age of the 37 asymptomatic patients was 41 years (range 8-75 years); 22 were female, 15 were male. For comparison, 37 RT-PCR test–positive patients were selected and matched to the asymptomatic group by age, comorbidities, and sex. Machine learning models were trained and compared to understand the pathogen-immune relationship and predict who was immune to COVID-19 and why, using the statistical programming language R. RESULTS When stem cell growth factor-beta (SCGF-β) was included in the machine learning analysis, a decision tree and extreme gradient boosting algorithms classified and predicted COVID-19 symptom immunity with 100% accuracy. When SCGF-β was excluded, a random-forest algorithm classified and predicted asymptomatic and symptomatic cases of COVID-19 with 94.8% AUROC (area under the receiver operating characteristic) curve accuracy (95% CI 90.17%-100%). In total, 34 common immune factors have statistically significant associations with COVID-19 symptoms (all c<.05), and 19 immune factors appear to have no statistically significant association. CONCLUSIONS The primary outcome was that asymptomatic patients with COVID-19 could be identified by three distinct immunological factors and levels: SCGF-β (>127,637), interleukin-16 (IL-16) (>45), and macrophage colony-stimulating factor (M-CSF) (>57). The secondary study outcome was the suggestion that stem-cell therapy with SCGF-β may be a novel treatment for COVID-19. Individuals with an SCGF-β level >127,637, or an IL-16 level >45 and an M-CSF level >57, appear to be predictively immune to COVID-19 100% and 94.8% (AUROC) of the time, respectively. Testing levels of these three immunological factors may be a valuable tool at the point of care for managing and preventing outbreaks. Further, stem-cell therapy via SCGF-β and M-CSF appear to be promising novel therapeutics for patients with COVID-19.

As an educator, performer, composer, publisher, and instrument inventor, Michael Udow has been at the forefront of percussion performance and teaching for over 50 years. In addition to playing symp...

Graphic results of a study of the application of music‐acoustical principles in the low frequencies of pipe ranks in the Aeolian‐Skinner Organ of the new Dendrinos Chapel/Recital Hall of the Interlochen Center for the Arts. The effect upon pitch and timbre as well as the intensity and frequency resulting from the sound moat under the stage of the new structure reveals a reassessment of physical features and psychological responses in musical understanding. However the direct acoustical estimations and provisions agree with earlier assumptions, especially J. F. Schouten's “subjective tones.”

DESPITE the great amount of attention focused on the secondary school mathematics curriculum in recent years, there remains a major aspect of the teaching of college preparatory mathematics which has been consistently overlooked. While there seems to be little question that the content and approach of the modern curricula are significant and relevant to the needs and purposes of the future mathematician, engineer, physicist, and statistician, the relevance of the secondary school college preparatory mathematics curriculum to the lives of the future historian, musician, teacher of English, or any articulate layman is doubtful.