Computer-Aided Analysis Across the Tonal Divide: Cross-Stylistic Applications of the Discrete Fourier Transform

The discrete Fourier transform is a mathematically robust way of modeling various musical phenomena. I use the music21 Python module to interpret the pitch classes of an encoded musical score through the discrete Fourier transform (DFT). This methodology offers a broad view of the backgrounded scales and pitch-class collections of a piece. I have selected two excerpts in which the composers are very frugal with their pitch class collections—one in a tonal idiom, the other atonal. These constrained vocabularies are well suited for introducing the DFT’s methodological strengths as they pertain to score analysis

Cantus Ultimus’ MEI Neume Module and its Interoperability Across Chant Notations

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Ability-Based Methods for Personalized Keyboard Generation

This study introduces an ability-based method for personalized keyboard generation, wherein an individual's own movement and human-computer interaction data are used to automatically compute a personalized virtual keyboard layout. Our approach integrates a multidirectional point-select task to characterize cursor control over time, distance, and direction. The characterization is automatically employed to develop a computationally efficient keyboard layout that prioritizes each user's movement abilities through capturing directional constraints and preferences. We evaluated our approach in a study involving 16 participants using inertial sensing and facial electromyography as an access method, resulting in significantly increased communication rates using the personalized keyboard (52.0 bits/min) when compared to a generically optimized keyboard (47.9 bits/min). Our results demonstrate the ability to effectively characterize an individual's movement abilities to design a personalized keyboard for improved communication. This work underscores the importance of integrating a user's motor abilities when designing virtual interfaces.Comment: 20 pages, 7 figure

Challenging the MEI Neumes Module: Encoding Armenian Neumes

Chant notations are found across a large geographical area encompassing Europe and part of the Middle East (including the Levant and the historical Armenian lands). The Neumes Module represents a collective endeavour on the part of the MEI Community to capture in a machine- readable format the meaning of chant notations. In recent years intensive efforts were devoted towards improving the applicability of the MEI Neumes module, first applied almost exclusively to St Gall notation (also known as ‘East Frankish notation’), and recently extended to include the encoding of Old Hispanic, Aquitanian and square notations (MEI Neumes module, version 4.0). With this paper, we aim to contribute towards expanding the interoperability of the MEI Neumes module by testing it against the Armenian neumatic notation. From an encoding point of view, Armenian notation shows a higher degree of complexity compared to the neumatic notations so far tackled by MEI. Indeed, unlike all the neumatic notational systems hitherto dealt with by MEI, with the Armenian system we possess no information on either the melodic contour or the number of pitches associated with the neumes. Therefore, one of the fundamental elements of the current Neumes Module cannot be applied to the encoding of Armenian neumes: the ‘neume component’ , that is, a ‘sign representing a single pitched event, although the exact pitch may not be known’. Moreover, encoding Armenian neumes will serve to take even further the recent tendency to encode the visual appearance of the neumes rather than their semantics. In this paper, we outline the challenges of encoding Armenian notations with MEI and propose a solution applicable to future projects aiming at the digital analysis of the Armenian chant repertory

Classification and biological identity of complex nano shapes

Everywhere in our surroundings we increasingly come in contact with nanostructures that have distinctive complex shape features on a scale comparable to the particle itself. Such shape ensembles can be made by modern nano-synthetic methods and many industrial processes. With the ever growing universe of nanoscale shapes, names such as “nanoflowers” and “nanostars” no longer precisely describe or characterise the distinct nature of the particles. Here we capture and digitise particle shape information on the relevant size scale and create a condensed representation in which the essential shape features can be captured, recognized and correlated. We find the natural emergence of intrinsic shape groups as well-defined ensemble distributions and show how these may be analyzed and interpreted to reveal novel aspects of our nanoscale shape environment. We show how these ideas may be applied to the interaction between the nanoscale-shape and the living universe and provide a conceptual framework for the study of nanoscale shape biological recognition and identity

Preparation and Support of Patients through the Transplant Process: Understanding the Recipients' Perspectives

Preparation for heart transplant commonly includes booklets, instructional videos, personalized teaching sessions, and mentorship. This paper explores heart transplant recipients' thoughts on their preparation and support through the transplant process. Twenty-five interviews were audio-/videotaped capturing voice and body language and transcribed verbatim. Coding addressed language, bodily gesture, volume, and tone in keeping with our visual methodology. Recipients reported that only someone who had a transplant truly understands the experience. As participants face illness and life-altering experiences, maintaining a positive attitude and hope is essential to coping well. Healthcare professionals provide ongoing care and reassurance about recipients' medical status. Mentors, family members, and close friends play vital roles in supporting recipients. Participants reported that only heart transplant recipients understood the experience, the hope, and ultimately the suffering associated with living with another persons' heart. Attention needs to be focused not solely on the use of teaching modalities, but also on the development of innovative support networks. This will promote patient and caregiver engagement in self-management. Enhancing clinicians' knowledge of the existential aspects of transplantation will provide them with a nuanced understanding of the patients' experience, which will ultimately enhance their ability to better prepare and support patients and their caregivers

Prevalence and Predictors of Out-of-Target LDL Cholesterol 1 to 3 Years After Myocardial Infarction. A Subanalysis From the EYESHOT Post-MI Registry

Background: There is an incomplete understanding of the prevalence and predictors of attainment of low-density lipoprotein cholesterol (LDL-C) goal after myocardial infarction (MI). Aim: To evaluate the prevalence of achievement of LDL-C goal of 70 mg/dL, to identify the baseline features associated with suboptimal lipid control, and to assess the use of LDL-C-lowering drug therapies (LLT) beyond the first year after MI. Methods: The EYESHOT Post-MI was a prospective, cross-sectional, Italian registry, which enrolled patients presenting to cardiologist 1 to 3 years after MI. In this retrospective post-hoc analysis, patients were categorized in 2 groups according to the achievement or not of the LDL-C goal of 70 mg/dL. Univariable and multivariable logistic regression analyses were performed to identify the baseline features associate with LDL-C >= 70 mg/dL. Results: The study population included 903 patients (mean age 65.5 +/- 11.5 years). Among them, LDL-C was >= 70 mg/dL in 474 (52.5%). Male sex (p = 0.031), hypertension (p = 0.024), prior percutaneous coronary intervention (p = 0.016) and high education level (p = 0.008) were higher in the LDL-C < 70 group. At multivariable analysis, low education level was an independent predictor of LDL-C >= 70 mg/dL (OR:1.582; 95%CI, 1.156-2.165; p = 0.004). Conversely, hypertension increased the probability to achieve the LDL-C goal (OR:0.650; 95%CI, 0.443-0.954; p = 0.028). Among off-target patients, LLT was not modified in the majority of cases (67.3%), intensified in 85 (18.6%), and actually reduced in 63 patients (13.8%). Conclusions: In patients presenting to cardiologists 1 to 3 years from the last MI event, LDL-C is not under control in a large proportion of patients, particularly in those with a low education level or without hypertension. LLT is underused in this very-high-risk setting

Functional analysis of drug resistance-associated mutations in the Trypanosoma brucei Adenosine Transporter 1 (TbAT1) and the proposal of a structural model for the protein

The Trypanosoma brucei aminopurine transporter P2/TbAT1 has long been implicated in the transport of, and resistance to, the diamidine and melaminophenyl arsenical classes of drugs that form the backbone of the pharmacopoeia against African trypanosomiasis. Genetic alterations including deletions and single nucleotide polymorphisms (SNPs) have been observed in numerous strains and clinical isolates. Here, we systematically investigate each reported mutation and assess their effects on transporter function after expression in a tbat1 -/- T. brucei line. Out of a set of six reported SNPs from a reported ‘resistance allele’, none significantly impaired sensitivity to pentamidine, diminazene or melarsoprol, relative to the TbAT1-WT allele, although several combinations, and the deletion of the codon for residue F316, resulted in highly significant impairment. These combinations of SNPs, and ΔF316, also strongly impaired the uptake of [3H]-adenosine and [3H]-diminazene, identical to the tbat1-/- control. The TbAT1 protein model predicted that residues F19, D140 and F316 interact with the substrate of the transporter. Mutation of D140 to alanine resulted in an inactive transporter, whereas the mutation F19A produced a transporter with a slightly increased affinity for [3H]-diminazene, but reduced the uptake rate. The results presented here validate earlier hypotheses of drug binding motifs for TbAT1