This study examines the demographic makeup of composers in Western classical art song anthologies and reference materials. Generalized (noncomposer or demographic-specific) musical anthologies (n = 379) and reference books (n = 29) were collected through commercial search engines, publisher websites, academic libraries, and private collections. Each composer's gender, race, sexuality, birth nationality, approximate musical period, and number of compositions were collated and categorized from each resource. Definitions for demographic information were adapted from the United States Census Bureau and Human Rights Campaign to center the lens of a contemporary user's experience engaging with these texts. Basic statistics calculated using the composer's demographic information were multiplied by their total number of compositions to capture an aggregate understanding of what identities are likely to be encountered in such a resource. Data collection yielded 12,321 composers (unique n = 3971) and 56,847 songs with repetition. Generalized resources tend to contain the music of specific composer demographics. Included works were composed by primarily straight (n = 43,795, 77.0%), White (n = 55,661, 97.9%), or male (n = 53,864, 94.8%) composers. Z-test results showed significance when comparing musical resources with population estimates in all categories. Man, White, and Queer are significantly higher, while Woman, Black, Indigenous, People of Color (BIPOC), and Straight are lower in the aggregate data than in general population estimates. Man/White/Straight and Man/White/Queer are higher in the data, and all other groups are lower in the aggregate data than general population estimates. The widespread use of art song resources representing a select portion of the available repertoire with unknown editorial impartiality could reinforce canonic ideology by limiting exposure to diverse repertoires. A "French Song" anthology is unlikely to have any women, BIPOC, or queer representation unless designed as a "Women in French Song," "BIPOC French Composers," or "Art Song by Queer French Composers" resource instead.
Customization generation techniques have significantly advanced the synthesis of specific concepts across varied contexts. Multi-concept customization emerges as the challenging task within this domain. Existing approaches often rely on training a fusion matrix of multiple Low-Rank Adaptations (LoRAs) to merge various concepts into a single image. However, we identify this straightforward method faces two major challenges: 1) concept confusion, where the model struggles to preserve distinct individual characteristics, and 2) concept vanishing, where the model fails to generate the intended subjects. To address these issues, we introduce LoRA-Composer, a training-free framework designed for seamlessly integrating multiple LoRAs, thereby enhancing the harmony among different concepts within generated images. LoRA-Composer addresses concept vanishing through concept injection constraints, enhancing visibility via an expanded cross-attention mechanism. To combat concept confusion, concept isolation constraints are introduced, refining the self-attention computation. Furthermore, we propose two inference techniques to accelerate inference speed without performance degradation and enhance the accuracy of the generated region, respectively. Extensive experiments demonstrate that LoRA-Composer significantly outperforms standard baselines, especially in scenarios without image-based conditions such as canny edge or pose estimation.
By examining several hundred pathographies of composers, we identified numerous skin changes . We emphasize Rachmaninov's melanoma. Notable pathographies were studied in more details and shown chronologically by the composers date of birth. Skin changes in composers were usually mild and rarely fatal.
The father of Czech music, Bedřich Smetana was a brilliant, patriotic Romantic composer who spent his last decade completely deaf. He became progressively ill in his final years and passed away prematurely at 60 years old. Since then, there have been two main propositions for the etiology of his neurological symptoms, in particular his hearing loss: neurosyphilis or osteomyelitis of the temporal bone. This article compares the clinical presentation and pathology of neurosyphilis and osteomyelitis. This article infers which one is arguably the most likely cause based on Smetana's own medical history, signs and symptoms and autopsy findings. Smetana's clinical presentation and pathological results grant us a clearer picture of his neurological condition and allows us to diagnose his final neurological deterioration as complications of neurosyphilis and not osteomyelitis of the temporal bone.
Rezső Seress (1899-1968), the composer of the world-famous song "Gloomy Sunday," whose life ended in suicide, is analyzed using a psychobiographical approach. The research question is how can suicide be read through Seress's self-narrations, embedded in the Hungarian cultural, social, and historical context? To answer this question, we integrate Kézdi's theory of the negative code, Wallerstein's world-systems theory, and McAdams's narrative identity theory, linking language, structure, and the self. We analyzed nine song lyrics: counted explicit and narrowly defined implicit negations, calculated negation density (per 100 words), and coded narrative tone and plot, key life events, and agency/communion cues. Throughout the analysis, interpretation is hermeneutic and context-sensitive, with numbers serving as guides. Peaks in negation density appear in the songs "Nobody Has Ever Loved Me" (1930) and especially, "Just Drink, Drink" (1940), aligning with personal and historical crises. Agency markers are minimal or defensive, while communion is consistently framed as loss. No redemptive arc is identifiable; the life story follows a contamination sequence beginning in adversity. These patterns motivate the introduction of the tragic semiperipheral self (TSS) as a culturally and structurally situated narrative type.
Dentists enjoy a wide variety of hobbies. Many are associated with music as players, singers or listeners. Wilfred Josephs started to compose as a child but his parents steered him towards a profession with a secure financial future. Although a dentist, he wrote many compositions, gaining worldwide fame when he won first prize in the La Scala International Composing Competition in Milan - the biggest musical award in the world. It was for Requiem, a setting of the Hebrew Kaddish, or mourner's prayer. Music then became his full-time occupation. Wilfred's output included many concertos and symphonies, as well as music for the large and small screen. His fame was such that he had a Society named after him. He also composed Fantasia on three notes for the British Dental Association's centenary meeting.
Crovalimab is a novel C5 inhibitor administered first intravenously and then subcutaneously in patients with paroxysmal nocturnal haemoglobinuria (PNH) naive to complement inhibition or switching from eculizumab or ravulizumab. Crovalimab showed efficacy and safety comparable to eculizumab in the pivotal COMMODORE 2 and supporting studies. We characterized crovalimab pharmacokinetics and the relationship between exposure pharmacokinetic parameters and pharmacodynamic biomarkers, efficacy and safety endpoints using pooled data (healthy volunteers [n = 9], naive [n = 210] and switched [n = 211] patients). Pharmacodynamic biomarkers included 50% complement activity and free C5; normalized lactate dehydrogenase was a marker of haemolysis. Adverse events (AEs) of special interest, related serious AEs, related Grade ≥3 AEs and infections were assessed. There was no clinically relevant difference in crovalimab concentrations between naive and switch patients. Bodyweight had a statistically significant impact on crovalimab clearances and volumes of distribution. Thus, the recommended dosing regimen used weight-based, two-tiered dosing (100 kg cutoff). Age did not have a clinically meaningful impact on crovalimab exposure. In COMMODORE 2, and the supporting COMMODORE 1 and 3 studies, complete terminal complement activity inhibition was achieved immediately at the end of the initial intravenous infusion and sustained throughout the treatment period in ≥97% of patients. Crovalimab concentrations above ≈100 μg/mL achieved complete inhibition of terminal complement activity, resulting in disease control with normalized lactate dehydrogenase ≤1.5 × upper limit of normal (ULN). There was no increased risk of AEs at higher exposure. These data confirm an effective crovalimab-dosing regimen that achieves complete terminal complement activity inhibition and disease control in patients with PNH.
Blockchain technology uses a secure and decentralised framework for transaction management and data sharing within supply chains. This is particularly crucial in the pharmaceutical industry, where product authenticity and traceability are paramount. Blockchain plays a pivotal role in preventing product loss and counterfeiting, while simultaneously enhancing transparency and efficiency throughout the supply chain. The research introduces a step-by-step approach to implementing a proof-of-concept (PoC) for Supply Chain Risk Management (SCRM) through blockchain technology. This PoC involves simulating a supply chain process to assess feasibility and measure key performance indicators. Engaging stakeholders and gathering feedback is integral to refining the blockchain-based SCRM system. The study rigorously evaluates the performance of the SCRM blockchain across various test scenarios, featuring differing numbers of organizations and clients. Multiple fabric networks are employed to assess the system's scalability and performance under diverse conditions. The results of these comprehensive tests inform practical deployment decisions and highlight areas for potential optimization and further development. So this research provides valuable insights into the application of blockchain in pharmaceutical supply chains, offering a roadmap for implementation and improving supply chain security, efficiency, and transparency.
We present a comparative study on the performance of two popular open-source large language models for early prediction of sepsis: Llama-3 8B and Mixtral 8x7B. The primary goal was to determine whether a smaller model could achieve comparable predictive accuracy to a significantly larger model in the context of sepsis prediction using clinical data.Our proposed LLM-based sepsis prediction system, COMPOSER-LLM, enhances the previously published COMPOSER model, which utilizes structured EHR data to generate hourly sepsis risk scores. The new system incorporates an LLM-based approach to extract sepsis-related clinical signs and symptoms from unstructured clinical notes. For scores falling within high-uncertainty prediction regions, particularly those near the decision threshold, the system uses the LLM to draw additional clinical context from patient notes; thereby enhancing the model's predictive accuracy in challenging diagnostic scenarios.A total of 2,074 patient encounters admitted to the Emergency Department at two hospitals within the University of California San Diego Health system were used for model evaluation in this study. Our findings reveal that the Llama-3 8B model based system (COMPOSER-LLMLlama) achieved a sensitivity of 70.3%, positive predictive value (PPV) of 32.5%, F-1 score of 44.4% and false alarms per patient hour (FAPH) of 0.0194, closely matching the performance of the larger Mixtral 8x7B model based system (COMPOSER-LLMmixtral) which achieved a sensitivity of 72.1%, PPV of 31.9%, F-1 score of 44.2% and FAPH of 0.020. When prospectively evaluated, COMPOSER-LLMLlama demonstrated similar performance to the COMPOSER-LLMmixtral pipeline, with a sensitivity of 68.7%, PPV of 36.6%, F-1 score of 47.7% and FAPH of 0.019 vs. sensitivity of 70.5%, PPV of 36.3%, F-1 score of 47.9% and FAPH of 0.020. This result indicates that, for extraction of clinical signs and symptoms from unstructured clinical notes to enable early prediction of sepsis, the Llama-3 generation of smaller language models can perform as effectively and more efficiently than larger models. This finding has significant implications for healthcare settings with limited resources.
The rise of AI-generated music has implications for how people derive meaning from the listening experience, including the propensity to imagine a story as music unfolds. Previous research suggests that such narrative listening requires some form of common ground between composer and listener. Therefore, people may be less likely to engage in narrative listening when they believe music is the product of an AI system rather than a human mind. We tested this possibility across two preregistered studies in which US participants (N = 399) listened to several pieces of instrumental music and reported their experience of narrative listening-whether they imagined a story and how engaging it was. When presented with unlabeled, human-composed music, participants reported imagining fewer and less engaging narratives in response to pieces they regarded as more likely computer generated than human composed (Study 1). When we experimentally manipulated the purported composer by labeling human- and AI-composed music clips as either "Human" or "AI" composed, the "AI"-labeled pieces elicited fewer and less engaging narratives than their "Human"-labeled counterparts, regardless of the actual composer (Study 2). Together, these findings suggest that ascribing music to AI is associated with-and can engender-an impoverished listening experience, devoid of the mental narratives that unfold as the composer's musical choices guide the listener's imagination. Our findings contribute to an emerging literature on perceptions of artificial creators, with practical implications for listeners, musicians, and policymakers.
Sepsis is a dysregulated host response to infection with high mortality and morbidity. Early detection and intervention have been shown to improve patient outcomes, but existing computational models relying on structured electronic health record data often miss contextual information from unstructured clinical notes. This study introduces COMPOSER-LLM, an open-source large language model (LLM) integrated with the COMPOSER model to enhance early sepsis prediction. For high-uncertainty predictions, the LLM extracts additional context to assess sepsis-mimics, improving accuracy. Evaluated on 2500 patient encounters, COMPOSER-LLM achieved a sensitivity of 72.1%, positive predictive value of 52.9%, F-1 score of 61.0%, and 0.0087 false alarms per patient hour, outperforming the standalone COMPOSER model. Prospective validation yielded similar results. Manual chart review found 62% of false positives had bacterial infections, demonstrating potential clinical utility. Our findings suggest that integrating LLMs with traditional models can enhance predictive performance by leveraging unstructured data, representing a significant advance in healthcare analytics.
Sleep disturbances, particularly insomnia, significantly impact individuals living with Alzheimer's disease and related dementias (ADRD), leading to accelerated cognitive decline, increased institutionalization rates, and faster disease progression. While pharmacological interventions exist, their potential side effects necessitate the exploration of safer, non-pharmacological alternatives. Music interventions have shown promise in addressing sleep disturbances among older adults, yet existing solutions are neither tailored to nor extensively tested in persons living with dementia (PLWD). This study presents the research protocol for CoMPoSER (Calming Music Personalized for Sleep Enhancement in PeRsons living with Dementia), a mobile application designed specifically for PLWD and their caregivers. In the first two phases, the study will involve the development of the application and in the third phase we will employ a pilot randomized controlled trial to assess preliminary effects of the intervention and explore its mechanism of action. The research objectives include developing and refining the CoMPoSER mobile application prototype, investigating its underlying mechanisms, and evaluating its impact on both PLWD and caregiver outcomes. By developing and systematically testing approaches that address sleep disturbances in PLWD, this study aims to expand the repertoire of evidence-based interventions available to PLWD and their families, ultimately contributing to improved quality of life and disease management.
The integration of generative artificial intelligence (GenAI) into music education markedly lowers technical barriers for predominantly novice composers, but also raises concerns about a potential erosion of human creative agency. When learners rely on text prompts to produce music with minimal subsequent involvement, they may fail to develop a sense of psychological ownership over AI-assisted creations. Drawing on the Theory of Psychological Ownership, this study examined the cognitive, affective, and behavioral processes through which perceived GenAI support relates to students' psychological ownership. Survey data were collected from 355 non-music-major undergraduates enrolled in a GenAI-assisted composition course that explicitly required iterative post-generation refinement of AI outputs. Structural equation modeling with bias-corrected bootstrapping was used to test a serial mediation model. The results showed that perceived GenAI support was positively and significantly associated with psychological ownership, and that this relationship operated through a sequential pathway involving creative self-efficacy, flow state, and learner engagement. These findings suggest that GenAI does not inherently alienate learners; when positioned as a cognitive scaffold within a human-in-the-loop design, it is associated with creative confidence, optimal immersion, and active investment of effort. The study highlights the importance of deliberately incorporating productive friction into AI-supported learning activities to elicit an "IKEA effect," thereby transforming algorithmically generated material into personally appropriated creative artifacts.
Creating interactive scenes often involves complex programming tasks. Although large language models (LLMs) like ChatGPT can generate code from natural language, their output is often error-prone, particularly when scripting interactions among multiple elements. The linear conversational structure limits the editing of individual elements, and the lack of graphical and precise control complicates visual integration. To address these issues, we integrate a context-aware modularization technique that processes textual descriptions for individual elements through separate LLM modules, with a central module managing interactions among elements. It defines a top-down structure to manage interactions, ensuring clear update logic and facilitating efficient collaboration while allowing for independent updates for each element. We design a graphical user interface, MoGraphGPT, which combines modular LLMs with enhanced graphical control to generate codes for 2D interactive scenes. It enables direct integration of graphical information and offers quick, precise control through automatically generated sliders. A comparative study with Cursor Composer shows MoGraphGPTsignificantly improves easiness, controllability, and performance in creating 2D interactive scenes with multiple visual elements in a coding-free manner. An ablation study validates the effectiveness of modularization, and an open-ended study demonstrates the usability and expressiveness of MoGraphGPT.
The term "Jael syndrome" is a symbolic analogy referring to penetrating craniofacial trauma caused, intentionally or accidentally, by blade or similar weapon in which the foreign body is kept in-situ during initial management. It is rare in clinical and medicolegal practice, but is potentially life-threatening or with neurologic and functional impact. Its name comes from an episode in the Book of Judges in the Hebrew Bible (Old Testament) in which Jael, a woman, kills the Canaanite general Sisera by hammering a tent peg through his temple while he is asleep, thus ensuring the liberation of the people of Israel. Over the centuries, the story had an enduring influence on the European artistic and cultural imagination, with numerous paintings, sculptures, musical and literary compositions memorializing the symbolic power of a unique act of violence. Artists such as Abraham Godyn and Artemisia Gentileschi, baroque sculptors, composers such as Georg Friedrich Haendel and French writers such as Victor Hugo gave the figure of Jael a lasting place in our cultural heritage. In medicine, the first description of a penetrating craniofacial injury with the foreign body in-situ was by Geoffrey Jefferson, a British neurosurgeon, in the mid-20th century, who reported a case of orbitocranial impalement. Subsequently, a few rare cases mainly involved isolated case reports or retrospective series, with consequently low levels of evidence for optimal management. Via a present-day case of penetrating facial trauma involving an arrow, this article reviews diagnostic and therapeutic issues in Jael syndrome. The case illustrates the importance of management in a well-equipped tertiary referral center with specialized medical-surgical multidisciplinary teamwork. Management is based on fundamental principles: systematized initial clinical assessment using the ABCDE (ATLS®) algorithm; not moving or removing the foreign body during the initial stage; early protection of the airway and vascular system; and adapted imaging, ideally by head-and-neck CT angiography. Current strategies favor personalized, selective management based on clinical examination, lesion topography and hemodynamic stability, rather than systematic surgical exploration. However, multicenter prospective studies are needed to refine guidelines, notably by integrating advanced imaging and minimally invasive techniques in order to improve functional prognosis in these rare but high-risk trauma.
My list of medically related anniversaries for 2026 (events in years ending '26 and'76) includes:• Births: Abū al-Walīd Muḥammad ibn Aḥmad ibn Rushd, the physician better known as Averroes (1126); the Flemish botanist Charles de L'Ecluse (Carolus Clusius) (1576); Amadeo Avogadro, Italian physicist (1776); Johann Spurzheim, German physician (1776); George Birkbeck, English physician (1776).• Deaths: Francis Bacon (1626); Jean Anthelme Brillat-Savarin, French lawyer and gastronome (1826); Friedrich Wilhelm Weiß, German physician and composer (1826); René Laënnec, French physician (1826); Philippe Pinel, French physician (1826); Walter Channing, American physician (1876); Victor Babeș, Romanian physician and bacteriologist (1926); William Bateson, English biologist (1926); Emile Coué, French pharmacist and psychologist (1926); Camillo Golgi, Italian anatomist (1926); Emil Kraepelin, German psychiatrist (1926); Einar Aaser, Norwegian physician (1976); Andrew Arthur Abbie, Australian anatomist and anthropologist (1976); Jacques Monod, molecular biologist (1976); George Whipple, American physician (1976); Alexander S Wiener, American physician (1976).• Biomedical texts published: Observationes medicae circa morborum acutorum historiam et curationem by Thomas Sydenham (1676); A Comment on Forty two Histories Discribed [sic] by Hippocrates in the First and Third Books of his Epidemics by John Floyer (1726); The botanical arrangement of all the vegetables naturally growing in Great Britain by William Withering (1776); De generis humani varietate nativa by Johann Friedrich Blumenbach (1776); L'uomo delinquente (Criminal Man) by Cesare Lombroso (1876); Geographical distribution of animals by Alfred Wallace (1876); The theory of the gene by Thomas Hunt Morgan (1926).• Clinical therapies introduced: George Richards Minot and William Parry Murphy (treatment of pernicious anaemia with liver, 1926).• Biochemical and bacteriological observations: Karl Wilhelm Scheele (uric acid in kidney stones, 1776); Otto Unverborden (discovery of aniline, 1826); Robert Koch (the anthrax bacillus, 1876); James Batcheller Sumner (crystallisation of jackbean urease, 1926).• Establishment of the Edinburgh Medical School (1726) and of The Body Shop (Anita Roddick, 1976).• Epidemics of typhus in Spain (1576) and Ebola virus infection in Yambuku, Zaire (1976).• Nobel prizes for physiology or medicine awarded to Baruch Samuel Blumberg for identifying Australia antigen as an indicator in the blood of hepatitis B and to Daniel Carleton Gajdusek for his work on the origin and spread of infectious diseases, particularly slow virus infections and specifically kuru (both 1926), and to Johannes Andreas Grib Fibiger for discovering the Spiroptera carcinoma (1976).
This study investigated auditory-conceptual associations in children using complex audiovisual stimuli, namely musical excerpts from the Western classical repertoire and drawings. In Experiment 1, we examined whether 6- to 9-year old children were able to consistently match musical excerpts from Prokofiev's Peter and the Wolf with corresponding black-and-white images of the characters. The results confirmed robust associations, particularly for the bird, wolf and duck, while other pairings were more variable. In Experiment 2, we extended this approach by using the musical suite Saint Saëns's Carnival of the Animals, testing whether timbre influences children's audiovisual associations. Children were presented with colour images of animals alongside orchestral or piano versions of the musical excerpts that the composer associated with the animal. The results revealed that, in line with a similar study conducted recently in adults (Di Stefano et al., 2025), participants made significantly above-chance associations for the characters of the lion and the swan. However, unlike in adults, timbre had no significant effect on children's audiovisual pairings. These findings highlight the robustness of auditory-semantic associations presented through audiovisual stimuli in childhood, supporting the idea that certain audiovisual correspondences are developmentally stable, while showing that subtle nuances (i.e., differences in timbre) might emerge later on during development.
Previous studies have concluded that miRNAs may be implicated in the pathogenesis of dengue, as upregulated miRNAs were observed in blood and serum samples from infected patients. These biomarkers for dengue infection are highly promising. Among these, microRNA-21 has emerged as a major candidate, although its role in the pathogenesis of dengue infection is not clear. In this study, we predicted the target genes of miR-21 using in silico approaches and modeled guide target duplexes docked to Argonaute protein to hypothesize potential engagement with the RISC in dengue. Potential miR-21 targets and their interacting proteins were identified from public databases. Binding affinities were estimated with the help of miRWalk and miRDB, and expression across the stages of dengue was analyzed based on UniProt. Three-dimensional models of miR-21-mRNA duplexes were derived by RNA Composer and then subjected to molecular docking experiments with AGO (PDB ID: 3F73). Among them, NUDT3, MYRF, and ZNRF1 showed the highest binding affinity and were selected for molecular characterization. The mode of AGO-mediated gene silencing was further explored computationally to assess its regulatory potential. Our findings showed good agreement with previously reported interactions of miR-21 and identified new associations that may contribute to dengue pathogenesis. These genes have strong links to the progression and prognosis of disease and, hence, may serve as a potential therapeutic target. This study supports the development of RNA interference-based strategies targeting the modulation of miR-21 activity for the treatment of dengue.
A lyric essay by two writers and academics who are victim survivors of child sexual abuse (CSA) perpetrated by their biological fathers. Taking a survivor-centred approach, and referring to their own lived experience, Clare Best and Patricia Debney interrogate representations of, and allusions to, CSA in the 2025 opera Festen* in particular, and in the arts more generally, focusing on themes such as denial, use of language and the aftermath of trauma.*The opera Festen (composer Mark-Anthony Turnage, librettist Lee Hall) was first staged at The Royal Opera House, Covent Garden, London, in February 2025. Turnage and Hall based their opera on the 1998 Dogme 95 film with the same title, directed by Thomas Vinterberg. In Festen, revelations of CSA are made by Christian and Helena at their father Helge Klingefeldt's sixtieth birthday party. The drama revolves around the reactions and interactions of party guests, hotel staff and various members of the family including the perpetrator Helge and his wife Else (mother to his children).
Machine learning and statistical prediction tools proliferate across healthcare, yet the leap from development to sustained clinical impact remains elusive. This narrative review synthesizes empirical evidence on how prediction models have been embedded into routine decision‑making and what lessons can be drawn for implementation teams. Searches of PubMed, Embase, Web of Science, IEEE Xplore, and specialized informatics journals (2010-2025) identified studies describing the real-world deployment of multivariable prediction models and reporting implementation outcomes. Evidence centered on sepsis detection, deterioration, readmission, and emergency triage models. Embedding strategies ranged from interruptive pop‑ups and non‑interruptive dashboard displays to worklists and order‑set linkage. Successful deployments invested heavily in stakeholder co‑design, threshold selection, training, and performance monitoring. Comparative studies indicated that deployment of a deep‑learning sepsis model (COMPOSER (COnformal Multidimensional Prediction Of SEpsis Risk)) decreased in‑hospital mortality and improved guideline adherence relative to baseline. Major barriers included workflow misalignment, alert fatigue, lack of transparency, data quality issues, and insufficient governance structures. Few papers described ongoing monitoring. The evidence suggests that prediction models confer value only when embedded through carefully designed clinical decision support aligned with the "Five Rights" framework, supported by multidisciplinary governance and rigorous monitoring. Implementation teams should prioritize calibration and decision utility metrics over discrimination alone, establish model‑life‑cycle governance, and integrate clinician training to build trust.