To date, allergen immunotherapy (AIT) is the only treatment that affects the long-term development of allergic rhinoconjunctivitis and induces clinical tolerance primarily by stimulating regulatory T (Treg) cells, attenuating T helper 2 (Th2) responses and synthesis of blocking antibodies1. Conventional AIT with subcutaneous injections, sublingual tablets or drops is effective, but consumes time and resources 2.

Introduction

There is a need for a fast, efficient and safe way to induce tolerance in patients with severe allergic rhinitis. Intralymphatic immune therapy has been shown to be effective.

Methods

Patients with severe birch and timothy allergy were randomized and received three doses of 0.1 ml of birch and 5-grass allergen extracts (10,000 SQ units/ml, ALK-Abello), or birch and placebo or 5-grass and placebo by ultrasound-guided injections into inguinal lymph nodes at monthly intervals. Rhinoconjunctivitis total symptom score, medication score and rhinoconjunctivitis quality of life questionnaire were evaluated before treatment and after each birch and grass pollen season during three subsequent years. Circulating proportions of T helper subsets and allergen-induced cytokine and chemokine production were analysed by flow cytometry and Luminex.

Results

The three groups reported fewer symptoms, lower use of medication and improved quality of life during the birch and grass pollen seasons each year after treatment at an almost similar rate independently of treatment with one or two allergens. Mild local pain was the most common adverse event. IgE levels to birch decreased, whereas birch-induced IL-10 secretion increased in all three groups. IgG4 levels to birch and timothy and skin prick test reactivity remained mainly unchanged. Conjunctival challenge tests with timothy extract showed a higher threshold for allergen. In all three groups, regulatory T cell frequencies were increased 3 years after treatment.

Conclusions

Intralymphatic immunotherapy with one or two allergens in patients with grass and birch pollen allergy was safe, effective and may be associated with bystander immune modulatory responses.

IntroductionThere is a need to evaluate the safety and efficacy of intralymphatic immunotherapy (ILIT) for inducing tolerance in patients with allergic rhinitis. MethodsThirty-seven patients with seasonal allergic symptoms to birch and grass pollen and skin prick test >3 mm and/or IgE to birch and timothy >0.35 kU/L were randomized to either ILIT, with three doses of 0.1 mL of birch pollen and 5-grass pollen allergen extracts on aluminium hydroxide (10,000 SQ-U/ml; ALK-Abello) or placebo using ultrasound-guided intralymphatic injections at monthly intervals. Daily combined symptom medical score and rhinoconjunctivitis total symptom score were recorded during the peak pollen seasons the year before and after treatment. Rhinoconjunctivitis total symptom score, medication score and rhinoconjunctivitis quality of life questionnaire were recorded annually starting 2 years after treatment. Circulating proportions of T helper cell subsets and allergen-induced cytokine and chemokine production were analysed using flow cytometry and ELISA. ResultsThere were no differences between the groups related to daily combined symptom medical score the year before and after treatment. Two years after ILIT (after unblinding), the actively treated group reported significantly fewer symptoms, lower medication use and improved quality of life than did the placebo group. After the pollen seasons the year after ILIT, T regulatory cell frequencies and grass-induced IFN-gamma levels increased only in the actively treated group. ConclusionIn this randomized controlled trial, ILIT with birch and grass pollen extract was safe and accompanied by immunological changes. Further studies are required to confirm or refute the efficacy of the treatment.

Realistic appearance reproduction is of great importance in 3D printing’s applications. Halftoning as a necessary process in printing has a great impact on creating visually pleasant appearance. In this article, we study the aspects of adapting and applying Iterative Method Controlling Dot Placement (IMCDP) to halftone three-dimensional surfaces. Our main goal is to extend the 2D algorithm to a 3D halftoning approach with minor modifications. The results show high-quality reproduction for all gray tones. The 3D halftoning algorithm is not only free of undesirable artifacts, it also produces fully symmetric and wellformed halftone structures even in highlight and shadow regions.

As 3D printing is becoming increasingly popular, the demand for high quality surface reproduction is also increasing. Like in 2D printing, halftoning plays an important role in the quality of the surface reproduction. Developing advanced 3D halftoning methods for 3D printing and adapting them to the structure of the surface is therefore essential for improving surface reproduction quality. In this paper, an extension of an iterative 2D halftoning method to 3D is used to apply different halftone structures on 3D surfaces. The results show that using different halftones based on the 3D geometrical structure of the surface and/or the viewing angle in combination with the structure of the texture being mapped on the surface can potentially improve the quality of the appearance of 3D surfaces.

Managing the final appearance of 3D surfaces is an interesting and essential topic in 3D printing applications. Knowledge about the parameters which influence the 3D surface reproduction quality enables engineers to achieve the final appearance as accurately as designed. Many studies have been conducted to explore numerous parameters that affect the quality of 3D surface reproduction. This work contributes to verifying the role of halftoning in increasing the 3D surface visual quality and the control over the surface appearance of a 3D printed object. The results show that applying different halftones according to the geometrical characteristics of the 3D surface could emphasize or diminish the perceived 3D geometrical structures of a shape. The experimental results are in line with the simulated outputs reported in previous work. Our findings might introduce a new approach towards having more control over 3D appearance reproduction without changing the material or printer settings.

Many image reproduction devices, such as printers, are limited to only a few numbers of printing inks. Halftoning, which is the process to convert a continuous-tone image into a binary one, is, therefore, an essential part of printing. An iterative halftoning method, called Iterative Halftoning Method Controlling the Dot Placement (IMCDP), which has already been studied by research scholars, generally results in halftones of good quality. In this paper, we propose a structure-based alternative to this algorithm that improves the halftone image quality in terms of sharpness, structural similarity, and tone preservation. By employing appropriate symmetrical and non-symmetrical Gaussian filters inside the proposed halftoning method, it is possible to adaptively change the degree of sharpening in different parts of the continuous-tone image. This is done by identifying a dominant line in the neighborhood of each pixel in the original image, utilizing the Hough Transform, and aligning the dots along the dominant line. The objective and subjective quality assessments verify that the proposed structure-based method not only results in sharper halftones, giving more three-dimensional impression, but also improves the structural similarity and tone preservation. The adaptive nature of the proposed halftoning method makes it an appropriate algorithm to be further developed to a 3D halftoning method, which could be adapted to different parts of a 3D object by exploiting both the structure of the images being mapped and the 3D geometrical structure of the underlying printed surface.

Structure-aware halftoning algorithms aim at improving their non-structure-aware version by preserving high-frequency details, structures, and tones and by employing additional information from the input image content. The recently proposed achromatic structure-aware Iterative Method Controlling the Dot Placement (IMCDP) halftoning algorithm uses the angle of the dominant line in each pixels neighborhood as supplementary information to align halftone structures with the dominant orientation in each region and results in sharper halftones, gives a more three-dimensional impression, and improves the structural similarity and tone preservation. However, this method is developed only for monochrome halftoning, the degree of sharpness enhancement is constant for the entire image, and the algorithm is prohibitively expensive for large images. In this paper, we present a faster and more flexible approach for representing the image structure using a Gabor-based orientation extraction technique which improves the computational performance of the structure-aware IMCDP by an order of magnitude while improving the visual qualities. In addition, we extended the method to color halftoning and studied the impact of orientation information in different color channels on improving sharpness enhancement, preserving structural similarity, and decreasing color reproduction error. Furthermore, we propose a dynamic sharpness enhancement approach, which adaptively varies the local sharpness of the halftone image based on different textures across the image. Our contributions in the present work enable the algorithm to adaptively work on large images with multiple regions and different textures. (C) 2022 Society for Imaging Science and Technology.

The iridescent effect produced by structural color is difficult (if not impossible) to capture and print using traditional CMYK pigments. The so called RGB reflective pigments, nonetheless, generate angle-dependent colors by light interference. A layered surface structure generated by the pigments’ particles on a substrate reflects light waves of different wavelengths at different viewing angles according to the optical principle known as the Bragg Law. In this work, we have studied the influence of different halftone structures on printed images, produced with RGB reflective inks via screen printing. The main goal was to enhance the iridescence of a printed reproduction by studying the performance of different halftone algorithms on a screen printing process. We investigated the influence of different halftone structures in creating different spatial combinations of inks on a print to reproduce the image of an iridescent feathered headdress. We applied first-order, second-order, and structure-aware FM halftones to compare how they influence the reproduction of the material appearance of the object represented in the original image. The results show that the structure-ware halftones improve the representation of the image structures and details. Therefore, it could better convey the 3D surface features that produce iridescence in real feathers.

This study proposes a digitalization framework for historic buildings. In this framework, advanced techniques, like Internet of Things (IoT), cloud computing, and artificial intelligence (AI), are utilized to create digital twins for historic buildings. A digital twin is a software representation of a physical object. This study uses digital twins to protect, predict, and optimize through analytics of real-time and historical data of selected features. Heterogeneous data of historic buildings, such as indoor environment, energy consumption metering, and outdoor climate, are collected with proper sensors or retrieved from other data sources. Then, these data are periodically uploaded and stored in the database of the cloud platform. Based on these data, AI models are trained through appropriate machine learning algorithms to monitor historic buildings, predict energy consumption, and control energy-consuming equipment autonomously to reach the balance of energy efficiency, building conservation, and human comfort. The cloud-based characteristic of our digitalization framework makes the digital twins developed in this study easy to be transplanted to many other historic buildings in Sweden and other countries.

Information and communication technologies (ICTs) help preserve historic buildings and optimize energy efficiency. This study proposes a digitalization framework for historic buildings by utilizing ICTs, such as Internet of Things (IoT), digital twins, and cloud computing. A digital twin is a digital representation of physical world assets that genuinely reflects the properties of real-world objects and processes. In this study, historic buildings are modeled with cloud-based digital twins. Indoor environmental data are collected with locally deployed sensors and ingested to a digital twin in real-time. The digital twin enables decision-makers to remotely monitor the indoor environment of a historic building and actively manipulate actuators to perform maintenance. Empowered by data analytics and artificial intelligence (AI), a digital twin can further simulate and predict state changes in a historic building to reach desired autonomous maintenance and energy saving.

Monitoring the indoor environment of historic buildings helps to identify potential risks, provide guidelines for improving regular maintenance, and preserve cultural artifacts. However, most of the existing monitoring systems proposed for historic buildings are not for general digitization purposes that provide data for smart services employing, e.g., artificial intelligence with machine learning. In addition, considering that preserving historic buildings is a long-term process that demands preventive maintenance, a monitoring system requires stable and scalable storage and computing resources. In this paper, a digitalization framework is proposed for smart preservation of historic buildings. A sensing system following the architecture of this framework is implemented by integrating various advanced digitalization techniques, such as Internet of Things, Edge computing, and Cloud computing. The sensing system realizes remote data collection, enables viewing real-time and historical data, and provides the capability for performing real-time analysis to achieve preventive maintenance of historic buildings in future research. Field testing results show that the implemented sensing system has a 2% end-to-end loss rate for collecting data samples and the loss rate can be decreased to 0.3%. The low loss rate indicates that the proposed sensing system has high stability and meets the requirements for long-term monitoring of historic buildings.

Historic buildings require good maintenance to sustain their function and preserve embodied heritage values. Previous studies have demonstrated the benefits of digitalization techniques in improving maintenance and managing threats to historic buildings. However, there still lacks a solution that can consistently organize data collected from historic buildings to reveal operating conditions of historic buildings in real-time and to facilitate various data analytics and simulations. This study aims to provide such a solution to help achieve preventive conservation. The proposed solution integrates Internet of Things and ontology to create digital twins of historic buildings. Internet of Things enables revealing the latest status of historic buildings, while ontology provides a consistent data schema for representing historic buildings. This study also gives a reference implementation by using public cloud services and open-source software libraries, which make it easier to be reused in other historic buildings. To verify the feasibility of the solution, we conducted a case study in the City Theatre, Norrköping, Sweden. The obtained results demonstrate the advantages of digital twins in providing maintenance knowledge and identifying potential risks caused by fluctuations of relative humidity.

Digital transformation in buildings accumulates massive operational data, which calls for smart solutions to utilize these data to improve energy performance. This study has proposed a solution, namely Deep Energy Twin, for integrating deep learning and digital twins to better understand building energy use and identify the potential for improving energy efficiency. Ontology was adopted to create parametric digital twins to provide consistency of data format across different systems in a building. Based on created digital twins and collected data, deep learning methods were used for performing data analytics to identify patterns and provide insights for energy optimization. As a demonstration, a case study was conducted in a public historic building in Norrköping, Sweden, to compare the performance of state-of-the-art deep learning architectures in building energy forecasting.

Kinetic modeling has been used to study the decomposition chemistry of ammonia at a wide range of temperatures, pressures, concentrations, and carrier gases mimicking the conditions in chemical vapor deposition (CVD) of metal nitrides. The modeling shows that only a small fraction of the ammonia molecules will decompose at most conditions studied. This suggests that the fact that the high NH3 to metal ratios often employed in CVD is due to the very low amount of reactive decomposition products being formed rather than due to rapid decomposition of ammonia into stable dinitrogen and dihydrogen as suggested by purely thermodynamic equilibrium models.

Chemical vapor deposition (CVD) is one of the most important techniques for depositing thin films of the group 13 nitrides (13-Ns), AIN, GaN, InN, and their alloys, for electronic device applications. The standard CVD chemistry for 13-Ns uses ammonia as the nitrogen precursor; however, this gives an inefficient CVD chemistry, forcing N/13 ratios of 100/1 or more. Here, we investigate the hypothesis that replacing the N-H bonds in ammonia with weaker N-C bonds in methylamines will permit better CVD chemistry, allowing lower CVD temperatures and an improved N/13 ratio. Quantum chemical computations show that while the methylamines have a more reactive gas-phase chemistry, ammonia has a more reactive surface chemistry. CVD experiments using methylamines failed to deposit a continuous film, while instead micrometer-sized gallium droplets were deposited. This study shows that the nitrogen surface chemistry is most likely more important to be considered than the gas-phase chemistry when searching for better nitrogen precursors for 13-N CVD.

Chemical vapor deposition of indium nitride (InN) is severely limited by the low thermal stability of the material, and, thus, low-temperature deposition processes such as atomic layer deposition (ALD) are needed to deposit InN films. The two chemically and structurally closely related materials—aluminum nitride and gallium nitride (GaN)—can be deposited by both plasma and thermal ALD, with ammonia (NH₃) as a nitrogen precursor in thermal processes. InN, however, can only be deposited using plasma ALD, indicating that there might be a limitation to thermal ALD with NH₃ for InN. We use quantum-chemical density functional theory calculations to compare the adsorption process of NH₃ on GaN and InN to investigate if differences in the process could account for the lack of thermal ALD of InN. Our findings show a similar reactive adsorption mechanism on both materials, in which NH₃ could adsorb onto a vacant site left by a desorbing methyl group from the surfaces. The difference in energy barrier for this adsorption indicates that the process is many magnitudes slower on InN compared to GaN. Slow kinetics would hinder NH3 from reactively adsorbing onto InN in the timeframe of the ALD growth process and, thus, limit the availability of a thermal ALD process.

Aluminum nitride (AlN), gallium nitride (GaN),and indium nitride (InN) form a family of technologicallyimportant semiconductors of high importance to light-emittingdiodes and high-frequency electronics. Although thinfilms of thesematerials are routinely manufactured by chemical vapor deposition(CVD) and atomic layer deposition (ALD), these methods are farfrom optimal and knowledge of the underlying chemical processesis lacking. In this work, we performed ab initio investigations of thesurface coverage of these materials under an ammonia-richatmosphere. Periodic density functional theory calculations wereused to test the probable surface structures, and their electronicand thermal energies were used to calculate their contribution tothe surface composition under the temperature and pressureconditions relevant for CVD and ALD processes of these materials. The results show similarities between the group of materials witha similar NHxsurface structure present for all three. Comparison of the coverage showed that at low growth temperatures, thesurface is expected to be covered by NH2, while at high temperatures, most surface sites would be vacant. The surface structureswere all found to be the most stable on AlN and least stable on InN. These results are important for further investigations of thematerial growth mechanisms.

Tris-N,N,-dimethyl-N,N -diisopropylguanidinatoindium(III) has been investigated both as a chemical vapor deposition precursor and an atomic layer deposition precursor. Although deposition was satisfactory in both cases, each report showed some anomalies in the thermal stability of this compound, warrenting further investigation, which is reported herein. The compound was found to decompose to produce diisopropylcarbodiimide both by computational modeling and solution phase nuclear magnetic resonance characterization. The decomposition was shown to have an onset at approximately 120 degrees C and had a constant rate of decomposition from 150 to 180 degrees C. The ultimate decomposition product was suspected to be bisdimethylamidoN, N,-dimethyl-N,N -diisopropylguanidinato-indium(III), which appeared to be an intractable, nonvolatile polymer. Published by the AVS.

This paper presents an approach to system-of-systems engineering for product development with the use of ontology. A proposed method for building as well as using ontology to generate and explore system-of-systems design spaces based on identified system-of-system needs is presented. The method is largely built to cover the first levels of related work, where a process for system of systems in the context of product development is introduced. Within this work, it is shown that scenarios for a system-of-systems can be used to identify needs and subsequently the system-of-systems capabilities that fulfils them. The allocation of capabilities to possible constituent systems is used to show the available design space. The proposed method of this paper therefore addresses these initial challenges and provides a framework for approaching the system-of-systems design space creation using ontology. A case study is used to test the method on a fictitious search and rescue scenario based on available resources and information from the Swedish Maritime Administration. The case study shows that a representation of a system-of-systems scenario can be created in an ontology using the method. The ontology provides a representation of the involved entities from the fictitious scenario and their existing relationships. Defined ontology classes containing conditions are used to represent the identified needs for the system-of-systems. The invocation of a description logic reasoner is subsequently used to classify and create an inferred ontology where the available system-of-systems solutions are represented as sub-classes and individuals of the defined classes representing the needs. Finally, several classes representing different possible system-of-systems needs are used to explore the available design space and to identify the most persistent solutions of the case study.

System-of-Systems Engineering (SoSE) has become a constantly growing field within product development for complex systems. Systems are becoming more and more connected with other systems and the operational environment in general. This takes the development process to new levels of complexity where high degrees of uncertainties are expected due to ever occurring changes in the operational environment, and other external factors such as politics, economy, and technology. This creates a need of being able to understand the influence of changes early in the development process and to facilitate the systems? perseverance. The focus of the development shifts from fulfilling specific requirements, to being able to meet needs and deliver capabilities over time. Additionally, modeling and simulation for complex systems and System-of-Systems (SoS) becomes a valued alternative to the economically prohibited and almost impossible prototype testing. In consideration of this problem, the presented work introduces a method for both modeling and simulation of a SoS. The method uses ontology with description logic reasoning to derive and narrow down a SoS design space which is further analyzed using Agent Based Simulation (ABS). A Search and Rescue (SAR) scenario is used as a case study to test the method. Measures of Effectiveness (MoE), based on the time it takes to find a rescue subject and the cost of doing so, are used to evaluate the SoS performances. The presented method is envisioned to be used early in the development of complex systems and SoS to increase the overall understanding of them.

Aerospace systems are connected with the operational environment and other systems in general. The focus in aerospace product development is consequently shifting from a singular system perspective to a System-of-Systems (SoS) perspective. This increasing complexity gives rise to new levels of uncertainty that must be understood and managed to produce aerospace solutions for an ever-changing future. This paper presents an approach to using architecture frameworks, and ontologies with description logic reasoning capabilities, to break down SoS needs into required capabilities and functions. The intention of this approach is to provide a consistent way of obtaining the functions to be realized in order to meet the overarching capabilities and needs of an SoS. The breakdown with an architecture framework results in an initial design space representation of functions to be performed. The captured knowledge is then represented in an ontology with description logic reasoning capabilities, which provides a more flexible way to expand and process the initial design space representation obtained from the architecture framework. The proposed approach is ultimately tested in a search and rescue case study, partly based on the operations of the Swedish Maritime Administration. The results show that it is possible to break down SoS needs in a consistent way and that ontology with description logic reasoning can be used to process the captured knowledge to both expand and reduce an available design space representation.

This paper presents a method for generating a design space intended for aircraft design with the use of ontologies. Aircraft design, seen from a System-of-Systems (SoS) perspective, gives an idea of the complicated relationships and complexities that need to be managed and understood to develop suitable systems for an ever-changing future. Holistic SoS analyzes are used to investigate customer and stakeholder needs that then can be broken down into capabilities and subsequently functions to be performed by involved Constituent Systems (CS), sub-systems and system elements. Ontologies with description logic reasoning capabilities are then used to represent the outcome of the breakdown. An ontology development and integration process can afterwards be used to merge different ontologies together and by that map the derived functions to means that can implement them. This results in an available SoS design space that can be further processed using a description logic reasoner. The outcome of the proposed method is a reduced available design space of functions to be performed and their respective design alternatives. Search and Rescue (SAR) operations based on the Swedish Maritime Administration (SMA) are used as a case study to test the proposed method and to illustrate how it can be utilized. At the end, a design space of alternatives for a new type of search aircraft is generated with ontology and description logic reasoning.

Studying Systems of Systems (SoS) in relation to Measures of Effectiveness (MoE) is a difficult task. This is due to that SoSs include several system levels and operate in changing environments. There are also computational challenges related to modeling and simulation of SoSs and it is difficult to predict behaviors. Therefore, choosing model fidelity for system models and assessing their impact on MoE is of high relevance to the field. This paper illustrates an approach to modeling and simulation for SoSs that can be used to assess the impact of scenario parameters and to explore when level of fidelity on system level affects the MoE. This is made through a case study based on Search and Rescue (SAR) operations where a Design Of Experiments (DOE) of scenario parameters is performed and simulations of each scenario experiment is performed. The case study shows how Agent Based Simulations (ABS) can be used to obtain the MoE for different SAR missions and how the choice of model fidelity for one of the aircraft’s sensors has different effects for various scenarios. Additionally, a Visual Analytics (VA) approach is introduced and used to create a dashboard for visualizing the obtained simulation results in an interactive way. This allows users to make explorations on the resulting data and see how different scenarios influence the performance of each SoS. Furthermore, the results show that changes in scenario parameters impacts the MoE and that this is difficult to predict, at least using a quantitative method. The results point to the importance of exploration for both the scenario, using an interactive dashboard, but also to the importance of exploring the simulation model to study emerging phenomenon. The results of this approach have raised questions regarding if a more qualitative approach of studying SoSs could be beneficial to study MoE for SoSs and if inspiration could be transferred from a more scientific point of view of systems.

This paper presents a method for generating a geometrical representation of a concept aircraft from an ontology description. An ontology is used as an overarching knowledge base where entities, such as required functions, their design alternatives, and requirements can be represented. Description logic reasoning is then used to process the available design space and generate suitable concepts to fulfil desired functions, as well as indicate suitable approaches for a subsequent sizing procedure. As ontology representations are limited in terms of numerical calculation capabilities, the obtained concept information must be extracted for additional processing. Further investigations, such as statistical analyses, are consequently performed in order to expand the available information of the concept generated from the ontology. This expansion is performed to obtain estimates of required inputs for a continued geometrical sizing procedure. The outcome of the method is an estimation of the concept’s geometry and its characteristics. This information can from here be reintroduced into the ontology representation for further processing and to expand the original knowledge base. A case study is introduced to test the proposed method and to show how it can be used to estimate the characteristics of an already existing aircraft from basic requirements and configuration details. The results from the method and sizing are also compared with publicly available data for the reference aircraft to see how accurate the estimates were.

This work illustrates how a proposed method can be used to create optimization frameworks for early conceptual design studies and to increase overall knowledge at an early design stage. The method is intended to facilitate concept selection in challenging domains that typically involve multidisciplinary design problems with contradictory requirements. The main focus of the work presented here is on the conceptual design of helicopters; however, the method is intended to be applicable to early design studies in other domains as well. In short, statistics about existing helicopters are collected and compiled to provide a basis for various regression analyses. The purpose of this is to unravel relationships in the data and to obtain simple estimation models from statistical regressions that can be used in conjunction with existing formulas and equations to generate an initial helicopter design estimate. Models for each discipline, such as aerodynamics, are then created using the outcomes of the regression analyses and existing equations. Lastly, the method is used to define a multidisciplinary design optimization framework incorporating all the models obtained from the different disciplines. A case study based on search and rescue operations is used to test the proposed framework in order to obtain possible first suggestions for the baseline design of a new general-purpose search and rescue helicopter.

BACKGROUND: Current consensus is to combine a functional measure with muscle quantity to assess/confirm sarcopenia. However, the proper body size adjustment for muscle quantity is debated and sarcopenia in obesity is not well described. Further, functional measures are not muscle-specific or sensitive to etiology, and can be confounded by, for example, fitness/pain. For effective detection/treatment/follow-up, muscle-specific biomarkers linked to function are needed.

METHODS: Nine thousand six hundred and fifteen participants were included and current sarcopenia thresholds (EWGSOP2: DXA, hand grip strength) applied to investigate prevalence. Fat-tissue free muscle volume (FFMV) and muscle fat infiltration (MFI) were quantified through magnetic resonance imaging (MRI) and sex-and-body mass index (BMI)-matched virtual control groups (VCGs) were used to extract each participant's FFMV/height2 z-score (FFMVVCG). The value of combining FFMVVCG and MFI was investigated through hospital nights, hand grip strength, stair climbing, walking pace, and falls.

RESULTS: Current thresholds showed decreased sarcopenia prevalence with increased BMI (underweight 8.5%/normal weight 4.3%/overweight 1.1%/obesity 0.1%). Contrary, the prevalence of low function increased with increasing BMI. Previously proposed body size adjustments (division by height2/weight/BMI) introduced body size correlations of larger/similar magnitude than before. VCG adjustment achieved normalization and strengthened associations with hospitalization/function. Hospital nights, low hand grip strength, slow walking pace, and no stair climbing were positively associated with MFI (p < .05) and negatively associated with FFMVVCG (p < .01). Only MFI was associated with falls (p < .01). FFMVVCG and MFI combined resulted in highest diagnostic performance detecting low function.

CONCLUSIONS: VCG-adjusted FFMV enables proper sarcopenia assessment across BMI classes and strengthened the link to function. MFI and FFMV combined provides a more complete, muscle-specific description linked to function enabling objective sarcopenia detection.

Background & Aims: Sarcopenia and frailty are recognised as important factors in later stages of liver disease. However, theirrole in non-alcoholic fatty liver disease (NAFLD) is not yet fully understood. In this study we investigate the associations ofMRI-measured adverse muscle composition (AMC: low muscle volume and high muscle fat) with poor function, sarcopenia,and metabolic comorbidity within NAFLD in the large UK Biobank imaging study.

Methods: A total of 9,545 participants were included. Liver fat, fat-tissue free muscle volume, and muscle fat infiltration werequantified using a rapid MRI protocol and automated image analysis (AMRA® Researcher). For each participant, a personalisedmuscle volume z-score (sex- and body size-specific) was calculated and combined with muscle fat infiltration for AMC detection. The following outcomes were investigated: functional performance (hand grip strength, walking pace, stairclimbing, falls) and metabolic comorbidities (coronary heart disease, type 2 diabetes). Sarcopenia was detected by combiningMRI thresholds for low muscle quantity and low hand grip strength according to the European working group definition.

Results: The prevalence of sarcopenia in NAFLD (1.6%) was significantly lower (p <0.05) compared with controls without fattyliver (3.4%), whereas the prevalence of poor function and metabolic comorbidity was similar or higher. Of the 1,204 participants with NAFLD, 169 (14%) had AMC and showed 1.7–2.4× higher prevalence of poor function (all p <0.05) as well as 2.1×and 3.3× higher prevalence of type 2 diabetes and coronary heart disease (p <0.001), respectively, compared with thosewithout AMC.

Conclusions: AMC is a prevalent and highly vulnerable NAFLD phenotype displaying poor function and high prevalence ofmetabolic comorbidity. Sarcopenia guidelines can be strengthened by including cut-offs for muscle fat, enabling AMCdetection.

Background Adverse muscle composition (MC) as measured by magnetic resonance imaging has previously been linked to poor function, comorbidity, and increased hospitalization. The aim of this study was to investigate if adverse MC predicts all-cause mortality using data from UK Biobank. Methods There were 40 178 participants scanned using a 6 min magnetic resonance imaging protocol. Images were analysed for thigh fat-tissue free muscle volume and muscle fat infiltration (MFI) using AMRA (R) Researcher (AMRA Medical, Linkoping, Sweden). For each participant, a sex, weight, and height invariant muscle volume z-score was calculated. Participants were partitioned into four MC groups: (i) normal MC, (ii) only low muscle volume [&lt;25th percentile for muscle volume z-score (population wide)], (iii) only high MFI [&gt;75th percentile (population wide, sex-specific)], and (iv) adverse MC (low muscle volume z-score and high MFI). Association of MC groups with mortality was investigated using Cox proportional-hazard modelling with normal MC as referent (unadjusted and adjusted for low hand grip strength, sex, age, body mass index, previous diagnosis of disease (cancer, type 2 diabetes and coronary heart disease), lifestyle, and socioeconomic factors (smoking, alcohol consumption, physical activity, and Townsend deprivation index). Results Muscle composition measurements were complete for 39 804 participants [52% female, mean (SD) age 64.2 (7.6) years and body mass index 26.4 (4.4) kg/m(2)]. Three hundred twenty-eight deaths were recorded during a follow-up period of 2.9 (1.4) years after imaging. At imaging, adverse MC was detected in 10.5% of participants. The risk of death from any cause in adverse MC compared with normal MC was 3.71 (95% confidence interval 2.81-4.91, P &lt; 0.001). Only low muscle volume and only high MFI were independently associated with all-cause mortality [1.58 (1.13-2.21), P = 0.007, and 2.02 (1.51-2.71), P &lt; 0.001, respectively]. Adjustment of low hand grip strength [1.77 (1.28-2.44), P &lt; 0.001] did not attenuate the associations with any of the MC groups. In the fully adjusted model, adverse MC and only high MFI remained significant (P P = 0.020) while the association with only low muscle volume was attenuated to non-significance (P = 0.560). The predictive performance of adverse MC [1.96 (1.42-2.71), P &lt; 0.001] was comparable with that of previous cancer diagnosis [1.93 (1.47-2.53), P &lt; 0.001] and smoking [1.71 (1.02-2.84), P = 0.040]. Low hand grip strength was borderline non-significant [1.34 (0.96-1.88), P = 0.090]. Conclusions Adverse MC was a strong and independent predictor of all-cause mortality. Sarcopenia guidelines can be strengthened by including cut-offs for myosteatosis enabling detection of adverse MC.

Background & Aims: Adverse muscle composition (MC) (i.e., low muscle volume and high muscle fat) has previously been linked to poor functional performance and comorbidities in non-alcoholic fatty liver disease (NAFLD). In this study we aimed to investigate associations of all-cause mortality with liver fat, NAFLD, and MC in the UK Biobank imaging study.Methods: Magnetic resonance images of 40,174 participants were analyzed for liver proton density fat fraction (PDFF), thigh fat-free muscle volume (FFMV) z-score, and muscle fat infiltration (MFI) using the AMRA (R) Researcher. Participants with NAFLD were sex-, age-, and BMI-matched to participants without NAFLD with low alcohol consumption. Adverse MC was identified using previously published cut-offs. All-cause mortality was investigated using Cox regression. Models within NAFLD were crude and subsequently adjusted for sex, age, BMI (M1), hand grip strength, physical activity, smoking, alcohol (M2), and previous cancer, coronary heart disease, type 2 diabetes (M3).Results: A total of 5,069 participants had NAFLD. During a mean (+/- SD) follow-up of 3.9 (+/- 1.4) years, 150 out of the 10,138 participants (53% men, age 64.4 [+/- 7.6] years, BMI 29.7 [+/- 4.4] kg/m2) died. In the matched dataset, neither NAFLD nor liver PDFF were associated with all-cause mortality, while all MC variables achieved significance. Within NAFLD, adverse MC, MFI and FFMV z-score were significantly associated with all-cause mortality and remained so in M1 and M2 (crude hazard ratios [HRs] 2.84, 95% CI 1.70-4.75, p &lt;0.001; 1.15, 95% CI 1.07-1.24, p &lt;0.001; 0.70, 95% CI 0.55-0.88, p &lt;0.001). In M3, the rela-tionship was attenuated for adverse MC and FFMV z-score (adjusted HRs 1.72, 95% CI 1.00-2.98, p = 0.051; 0.77, 95% CI 0.58-1.02, p = 0.069) but remained significant for MFI (adjusted HR 1.13, 95% CI 1.01-1.26, p = 0.026).Conclusions: Neither NAFLD nor liver PDFF was predictive of all-cause mortality. Adverse MC was a strong predictor of all -cause mortality in individuals with NAFLD.Impact and implications: Individuals with fatty liver disease and poor muscle health more often suffer from poor functional performance and comorbidities. This study shows that they are also at a higher risk of dying. The study results indicate that measuring muscle health (the patients muscle volume and how much fat they have in their muscles) could help in the early detection of high-risk patients and enable targeted preventative care.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

A natural choice for quantum communication is to use the relative phase between two paths of a single photon for information encoding. This method was nevertheless quickly identified as impractical over long distances, and thus a modification based on single-photon time bins has become widely adopted. It, how-ever, introduces a fundamental loss, which increases with the dimension and limits its application over long distances. Here solve this long-standing hurdle by using a few-mode-fiber space-division-multiplexing platform working with orbital-angular-momentum modes. In our scheme, we maintain the practicability provided by the time-bin scheme, while the quantum states are transmitted through a few-mode fiber in a configuration that does not introduce postselection losses. We experimentally demonstrate our proposal by successfully transmitting phase-encoded single-photon states for quantum cryptography over 500 m of few-mode fiber, showing the feasibility of our scheme.

Photonic spatial quantum states are a subject of great interest for applications in quantum communication. One important challenge has been how to dynamically generate these states using only fiber-optical components. Here we propose and experimentally demonstrate an all-fiber system that can dynamically switch between any general transverse spatial qubit state based on linearly polarized modes. Our platform is based on a fast optical switch based on a Sagnac interferometer combined with a photonic lantern and few-mode optical fibers. We show switching times between spatial modes on the order of 5 ns and demonstrate the applicability of our scheme for quantum technologies by demonstrating a measurement-device-independent (MDI) quantum random number generator based on our platform. We run the generator continuously over 15 hours, acquiring over 13.46 Gbits of random numbers, of which we ensure that at least 60.52% are private, following the MDI protocol. Our results show the use of photonic lanterns to dynamically create spatial modes using only fiber components, which due to their robustness and integration capabilities, have important consequences for photonic classical and quantum information processing.(c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

We show that telecom few-mode fiber Mach-Zehnder interferometers can be used for quantum communication protocols where the LP₀₁ and LP_11a modes are employed to encode spatial qubits.

We demonstrate an all-fiber platform for the generation and detection of spatial photonic states where combinations of LP₀₁, LP_11a and LP_11b modes are used. This scheme can be employed for quantum communication applications.

A quantum random number generator based on few-mode fiber technology is presented. The randomness originates from measurements of spatial modal quantum superpositions of the LP11a and LP11b modes. The generated sequences have passed NIST tests.

Powder bed fusion (PBF) methods offer the best material properties among metal additive manufacturing (AM) processes. Yet, alloy development for PBF is only at its infancy and has a great untapped potential. This originates from the high solidification rate within the melt pool and to exploit the full potential of materials produced by PBF methods, a diligent work lies ahead. This paper presents a high-throughput method to rapidly screen large compositional alloy intervals experimentally for their PBF feasibility, which can drastically reduce the time needed for alloy development and provide valuable data for modelling. Our method consists of two steps; co-sputtering and electron beam re-melting. First step produces an alloy gradient film on a sheet substrate. The film is then re-molted to produce a PBF mimicked microstructure. The method is successfully demonstrated on binary systems; Al-Ti,-Zr and-Nb and produced gradients in compositional ranges of 3-50 wt%Ti, 1-15 wt%Zr and 2-15 wt%Nb over a length of 200 mm. From the produced materials, the alloying efficiency could be investigated and determined regarding hardness and grain refinement. Zr shows the highest strength contribution per at% and the best grain refinement at low levels. However, at higher levels grain refinement efficiency decreases for Zr. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Grain refinement plays a central role in powder bed fusion (PBF) additive manufacturing by preventing hot cracking and thus enabling the development of high-strength alloys. However, the mechanism behind grain refinement is not fully understood for conventional casting, nor for PBF. In this work, a high throughput method have been used to produce Al-2 wt%Cu alloys with additions of Ti1-xM(Zr,Ta,V,W)(x)B-2, Al3Ti1-xM(Zr,V)(x) or AlB2 grain refiners for 0.1 &lt; x &lt; 0.9. It was found that grain size varied with x, M and the sum of Ti + M. Ti1-xMxB2 grain refiners offered no advantage over Al3Ti1-xMx. Overall, Ti and Zr provide the best grain refinement, both as Ti1-xMxB2 and Al3Ti1-xMx. However, Ti1-xZrxB2 had a grain refinement minimum around x = 0.65-0.70. The behavior was similar with Ta, but to a lesser extent. V and W had detrimental effects on grain refinement. Despite the fact that no AlB2 particles were observed, additions of B provided excellent grain refinement and was more efficient than Ti below 0.5at%. Ti1-xMxB2 lattice parameters varied with x and followed Vegards law, however, a clear relationship between grain size and epitaxial strain/lattice match could not be established. Similarly, the growth restricting factor alone was not a predictor of grain size.