Service utan lokal närvaro? Förändringar av statlig direktservice i Dalarnas län under 2000-talet

During the 2000s, government authorities have reduced their local presence in rural areas. This study shows the extent of the rationalization in a case study of the region of Dalarna. Rural areas nearby larger cities have been worst hit, yet sparsely populated areas are most affected since services are withdrawn from several municipalities at the same time. Authorities use different economy measures, such as commissioning another authority to deliver local service or to give distance-based service through technical solutions. This creates multiple levels of service of varying quality. Cooperation between authorities and municipalities in order to maintain service is generally restricted to spots for information and distribution of forms but not for case-specific service. Thereby, the possibility to choose between local service and phone or web-based solutions is lost.government authorities; local direct service; co-operation of government and local authorities; regional development; Dalarna region.

A Dataset of Norwegian Hardanger Fiddle Recordings with Precise Annotation of Note and Beat Onsets

The Hardanger fiddle is a variety of the violin used in the folk music of the western and central part of southern Norway. This paper presents a dataset of several hours of recordings of Hardanger fiddle music, with note annotations of onsets, offsets and pitches, provided by the performers themselves. A subset has also been annotated with beat onset positions by the performer as well as three expert musicians. The complexity of the music genre—polyphonic, highly ornamented and with a very irregular pulsation, among other aspects—motivated the design of a new annotation software adapted to these particular needs. Beat annotation in MIR is typically recorded as positions in seconds, without explicit connection with actual musical events. In the context of music where the rhythm is carried by the melodic instrument alone, a more reliable definition of beat onsets consists in associating them with the onsets of the notes that represent the start of each beat. This latter definition of beat onsets reflects that beats are generated from within the flow of played melodic-rhythmic events, which implies that the spacing of beats may be shifting and irregular. This motivated the design of a new method for beat annotation in Hardanger fiddle music based on a selection of notes in the note annotation. Comparisons between annotators through alignment—integrated in the interface—enable them to eventually correct their annotations or observe alternative valid interpretations of any given excerpt. After dedicating a part of the note annotation dataset to the training of a machine learning model, for the task of assessing both note pitch and onset time, an F1 score of 87% can be reached. The beat annotation dataset demonstrates the necessity of developing new beat trackers adapted to Hardanger fiddle music. The dataset as well as the annotation software is made publicly available

hLMSC Secretome Affects Macrophage Activity Differentially Depending on Lung-Mimetic Environments

Mesenchymal stromal cell (MSC)-based therapies for inflammatory diseases rely mainly on the paracrine ability to modulate the activity of macrophages. Despite recent advances, there is scarce information regarding changes of the secretome content attributed to physiomimetic cultures and, especially, how secretome content influence on macrophage activity for therapy. hLMSCs from human donors were cultured on devices developed in house that enabled lung-mimetic strain. hLMSC secretome was analyzed for typical cytokines, chemokines and growth factors. RNA was analyzed for the gene expression of CTGF and CYR61. Human monocytes were differentiated to macrophages and assessed for their phagocytic capacity and for M1/M2 subtypes by the analysis of typical cell surface markers in the presence of hLMSC secretome. CTGF and CYR61 displayed a marked reduction when cultured in lung-derived hydrogels (L-Hydrogels). The secretome showed that lung-derived scaffolds had a distinct secretion while there was a large overlap between L-Hydrogel and the conventionally (2D) cultured samples. Additionally, secretome from L-Scaffold showed an HGF increase, while IL-6 and TNF-α decreased in lung-mimetic environments. Similarly, phagocytosis decreased in a lung-mimetic environment. L-Scaffold showed a decrease of M1 population while stretch upregulated M2b subpopulations. In summary, mechanical features of the lung ECM and stretch orchestrate anti-inflammatory and immunosuppressive outcomes of hLMSCs

Development of a physiomimetic model of acute respiratory distress syndrome by using ECM hydrogels and organ-on-a-chip devices

Acute Respiratory Distress Syndrome is one of the more common fatal complications in COVID-19, characterized by a highly aberrant inflammatory response. Pre-clinical models to study the effect of cell therapy and anti-inflammatory treatments have not comprehensively reproduced the disease due to its high complexity. This work presents a novel physiomimetic in vitro model for Acute Respiratory Distress Syndrome using lung extracellular matrix-derived hydrogels and organ-on-a-chip devices. Monolayres of primary alveolar epithelial cells were cultured on top of decellullarized lung hydrogels containing primary lung mesenchymal stromal cells. Then, cyclic stretch was applied to mimic breathing, and an inflammatory response was induced by using a bacteriotoxin hit. Having simulated the inflamed breathing lung environment, we assessed the effect of an anti-inflammatory drug (i.e., dexamethasone) by studying the secretion of the most relevant inflammatory cytokines. To better identify key players in our model, the impact of the individual factors (cyclic stretch, decellularized lung hydrogel scaffold, and the presence of mesenchymal stromal cells) was studied separately. Results showed that developed model presented a more reduced inflammatory response than traditional models, which is in line with what is expected from the response commonly observed in patients. Further, from the individual analysis of the different stimuli, it was observed that the use of extracellular matrix hydrogels obtained from decellularized lungs had the most significant impact on the change of the inflammatory response. The developed model then opens the door for further in vitro studies with a better-adjusted response to the inflammatory hit and more robust results in the test of different drugs or cell therapy

Innovative three-dimensional models for understanding mechanisms underlying lung diseases: powerful tools for translational research

Chronic lung diseases result from alteration and/or destruction of lung tissue, inevitably causing decreased breathing capacity and quality of life for patients. While animal models have paved the way for our understanding of pathobiology and the development of therapeutic strategies for disease management, their translational capacity is limited. There is, therefore, a well-recognised need for innovative in vitro models to reflect chronic lung diseases, which will facilitate mechanism investigation and the advancement of new treatment strategies. In the last decades, lungs have been modelled in healthy and diseased conditions using precision-cut lung slices, organoids, extracellular matrix-derived hydrogels and lung-on-chip systems. These three-dimensional models together provide a wide spectrum of applicability and mimicry of the lung microenvironment. While each system has its own limitations, their advantages over traditional two-dimensional culture systems, or even over animal models, increases the value of in vitro models. Generating new and advanced models with increased translational capacity will not only benefit our understanding of the pathobiology of lung diseases but should also shorten the timelines required for discovery and generation of new therapeutics. This article summarises and provides an outline of the European Respiratory Society research seminar "Innovative 3D models for understanding mechanisms underlying lung diseases: powerful tools for translational research", held in Lisbon, Portugal, in April 2022. Current in vitro models developed for recapitulating healthy and diseased lungs are outlined and discussed with respect to the challenges associated with them, efforts to develop best practices for model generation, characterisation and utilisation of models and state-of-the-art translational potential. </p

Modeling Music : Studies of Music Transcription, Music Perception and Music Production

This dissertation presents ten studies focusing on three important subfields of music information retrieval (MIR): music transcription (Part A), music perception (Part B), and music production (Part C). In Part A, systems capable of transcribing rhythm and polyphonic pitch are described. The first two publications present methods for tempo estimation and beat tracking. A method is developed for computing the most salient periodicity (the “cepstroid”), and the computed cepstroid is used to guide the machine learning processing. The polyphonic pitch tracking system uses novel pitch-invariant and tone-shift-invariant processing techniques. Furthermore, the neural flux is introduced – a latent feature for onset and offset detection. The transcription systems use a layered learning technique with separate intermediate networks of varying depth.  Important music concepts are used as intermediate targets to create a processing chain with high generalization. State-of-the-art performance is reported for all tasks. Part B is devoted to perceptual features of music, which can be used as intermediate targets or as parameters for exploring fundamental music perception mechanisms. Systems are proposed that can predict the perceived speed and performed dynamics of an audio file with high accuracy, using the average ratings from around 20 listeners as ground truths. In Part C, aspects related to music production are explored. The first paper analyzes long-term average spectrum (LTAS) in popular music. A compact equation is derived to describe the mean LTAS of a large dataset, and the variation is visualized. Further analysis shows that the level of the percussion is an important factor for LTAS. The second paper examines songwriting and composition through the development of an algorithmic composer of popular music. Various factors relevant for writing good compositions are encoded, and a listening test employed that shows the validity of the proposed methods. The dissertation is concluded by Part D - Looking Back and Ahead, which acts as a discussion and provides a road-map for future work. The first paper discusses the deep layered learning (DLL) technique, outlining concepts and pointing out a direction for future MIR implementations. It is suggested that DLL can help generalization by enforcing the validity of intermediate representations, and by letting the inferred representations establish disentangled structures supporting high-level invariant processing. The second paper proposes an architecture for tempo-invariant processing of rhythm with convolutional neural networks. Log-frequency representations of rhythm-related activations are suggested at the main stage of processing. Methods relying on magnitude, relative phase, and raw phase information are described for a wide variety of rhythm processing tasks.Denna avhandling presenterar tio studier inom tre viktiga delområden av forskningsområdet ”Music Information Retrieval” (MIR) – ett forskningsområde fokuserat på att extrahera information från musik. Del A riktar in sig på musiktranskription, del B på musikperception och del C på musikproduktion. En avslutande del diskuterar maskininlärningsmetodiken och spanar framåt (del D). I del A presenteras system som kan transkribera musik med hänsyn till rytm och polyfon tonhöjd. De två första publikationerna beskriver metoder för att estimera tempo och positionen av taktslag i ljudande musik. En metod för att beräkna den mest framstående periodiciteten (”cepstroiden”) beskrivs, samt hur denna kan användas för att guida de applicerade maskininlärningssystemen.  Systemet för polyfon tonhöjdsestimering kan både identifiera ljudande toner samt notstarter- och slut. Detta system är både tonhöjdsinvariant samt invariant med hänseende till variationer över tid inom ljudande toner. Transkriptionssystemen tränas till att predicera flera musikaspekter i en hierarkisk struktur. Transkriptionsresultaten är de bästa som rapporterats i tester på flera olika dataset. Del B fokuserar på perceptuella särdrag i musik. Dessa kan prediceras för att modellera fundamentala perceptionsaspekter, men de kan också användas som representationer i modeller som försöker klassificera övergripande musikparametrar. Modeller presenteras som kan predicera den upplevda hastigheten samt den upplevda dynamiken i utförandet med hög precision. Medelvärdesbildade skattningar från omkring 20 lyssnare utgör målvärden under träning och evaluering. I del C utforskas aspekter relaterade till musikproduktion. Den första studien analyserar variationer i medelvärdesspektrum mellan populärmusikaliska musikstycken. Analysen visar att nivån på perkussiva instrument är en viktig faktor för spektrumdistributionen – data antyder att denna nivå är bättre att använda än genreklassificeringar för att förutsäga spektrum. Den andra studien i del C behandlar musikkomposition. Ett algoritmiskt kompositionsprogram presenteras, där relevanta musikparametrar fogas samman en hierarkisk struktur. Ett lyssnartest genomförs för att påvisa validiteten i programmet och undersöka effekten av vissa parametrar. Avhandlingen avslutas med del D, vilken placerar den utvecklade maskininlärningstekniken i ett vidare sammanhang och föreslår nya metoder för att generalisera rytmprediktion. Den första studien diskuterar djupinlärningssystem som predicerar olika musikaspekter i en hierarkisk struktur. Relevanta koncept presenteras tillsammans med förslag för framtida implementationer. Den andra studien föreslår en tempoinvariant metod för att processa log-frekvensdomänen av rytmsignaler med så kallade convolutional neural networks. Den föreslagna arkitekturen kan använda sig av magnitud, relative fas mellan rytmkanaler, samt ursprunglig fas från frekvenstransformen för att ta sig an flera viktiga problem relaterade till rytm.QC 20180427</p