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Lukas Müller,1,* Fabian Stoehr,1,* Aline Mähringer-Kunz,1 Felix Hahn,1 Arndt Weinmann,2 Roman Kloeckner1 1Department of Diagnostic and Interventional Radiology, University Medical Centre of the Johannes Gutenberg-University Mainz, Mainz, Germany; 2Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany*These authors contributed equally to this work.Correspondence: Roman KloecknerDepartment of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, Mainz, 55131, GermanyTel +49 6131 17-2019Email roman.kloeckner@unimedizin-mainz.deAbstract: Treatment of hepatocellular carcinoma (HCC) depends on the stage of disease. In the Western Hemisphere, the Barcelona Clinic Liver Cancer classification (BCLC) is the preferred staging system. Approximately one-third of patients initially present with intermediate-stage disease. For these patients, transarterial chemoembolization (TACE) is the treatment of choice. However, the intermediate-stage comprises a heterogeneous subgroup of patients with considerable differences in tumor burden and liver function. In addition, differences in individual factors that are not captured by the BCLC framework, such as the tumor growth pattern, degree of hypervascularity, and vascular supply, complicate further evaluation of these patients. Due to these differences, not all patients benefit equally from TACE. Several tools and scoring systems have been devised to provide decision-making support. All of these have shown promising initial results but failed external evaluation and have not been translated to the clinic. Nevertheless, criteria for objectifying treatment decisions in daily clinical practice are needed in all stages of disease. Therefore, this review provides a concise practical step-by-step guide on current strategies for patient selection and decision-making, with a focus on TACE, to critically evaluate the existing decision-support tools and provide a summary of the latest updates in the field.Keywords: hepatocellular carcinoma, transarterial chemoembolization, survival prediction, treatment decision support, practical step-by-step guide

Abstract Introduction Musculoskeletal ultrasound (MSUS) is integral to routine clinical diagnostics for musculoskeletal and joint disorders. This study aims to establish and validate a sonography course tailored to undergraduate medical students acquiring MSUS-specific skills at a German university. Methods A blended learning training concept, comprising 24 instruction sessions of 45 min each, was designed based on the current national guidelines of the German Society for Ultrasound in Medicine (DEGUM). This program was integrated into the clinical phase of the undergraduate students’ medical education. The self-perceived improvement in competency and the effectiveness of the course design were evaluated using a a 7-point Likert scale questionnaire. Objective learning success was evaluated via a written test and a “Direct Observation of Practical Skills” practical exam. Control groups included medical students without MSUS training (control group 1) and doctors who had completed DEGUM-certified basic MSUS courses (control group 2). Both control groups completed the written test, while control group 2 also took the practical final exam. The study involved 146 participants: 56 were allocated to the study group, 44 to control group 1, and 46 to control group 2. Results The study group rated their skills significantly higher after the course (p

This paper discusses key themes of the 15th biennial conference of the Association for Language Awareness (2020), with a focus on increasing digital engagement in language education. The COVID-19 pandemic occasioned an abrupt transition to emergency remote language teaching and learning (ERLTL) worldwide. The ALA 2020 conference was also affected by this transition; originally planned as a located conference in Geelong, Australia, it was eventually held online, a first in ALA's conference history. The current paper engages with contemporary debates of language teaching and learning in two ways. Firstly, it traces recent discussions by presenting key findings from five papers given at the conference, and secondly, via a scoping review of literature focusing on critical lessons from the pandemic regarding language teaching and learning. The review captures recent research from the Australasian region. Key debates identified in the literature include the needs of teachers and learners during the transition to online learning, and how student engagement was affected. The literatures highlight that both educators and students have been developing new practices in teaching and learning resulting from the shift to online and blended modes, which may continue to shape language education and new pedagogies in the future.

Introduction and Objectives: Autoimmune liver diseases (AILDs) are rare and require precise evaluation, which is often challenging for medical providers. Chatbots are innovative solutions to assist healthcare professionals in clinical management. In our study, ten liver specialists systematically evaluated four chatbots to determine their utility as clinical decision support tools in the field of AILDs. Materials and Methods: We constructed a 56-question questionnaire focusing on AILD evaluation, diagnosis, and management of Autoimmune Hepatitis (AIH), Primary Biliary Cholangitis (PBC), and Primary Sclerosing Cholangitis (PSC). Four chatbots -ChatGPT 3.5, Claude, Microsoft Copilot, and Google Bard- were presented with the questions in their free tiers in December 2023. Responses underwent critical evaluation by ten liver specialists using a standardized 1 to 10 Likert scale. The analysis included mean scores, the number of highest-rated replies, and the identification of common shortcomings in chatbots performance. Results: Among the assessed chatbots, specialists rated Claude highest with a mean score of 7.37 (SD = 1.91), followed by ChatGPT (7.17, SD = 1.89), Microsoft Copilot (6.63, SD = 2.10), and Google Bard (6.52, SD = 2.27). Claude also excelled with 27 best-rated replies, outperforming ChatGPT (20), while Microsoft Copilot and Google Bard lagged with only 6 and 9, respectively. Common deficiencies included listing details over specific advice, limited dosing options, inaccuracies for pregnant patients, insufficient recent data, over-reliance on CT and MRI imaging, and inadequate discussion regarding off-label use and fibrates in PBC treatment. Notably, internet access for Microsoft Copilot and Google Bard did not enhance precision compared to pre-trained models. Conclusions: Chatbots hold promise in AILD support, but our study underscores key areas for improvement. Refinement is needed in providing specific advice, accuracy, and focused up-to-date information. Addressing these shortcomings is essential for enhancing the utility of chatbots in AILD management, guiding future development, and ensuring their effectiveness as clinical decision-support tools.

The present study reports on additive manufacturing of a Ti-30Ta (at.%) high-temperature shape memory alloy (HT-SMA) using electron beam powder bed fusion (PBF-EB/M) technique. Detailed microstructure analysis was conducted to reveal the microstructural evolution along the entire process chain, i.e. from gas-atomised powder to post-processed material. PBF-EB/M processed structures with near full density and an isotropic, β-phase stabilised microstructure, i.e. equiaxed β-grains of around 20 µm in diameter with no preferred crystallographic orientation, are reported. As revealed by differential scanning calorimetry, post-process heat-treated Ti-Ta demonstrates a reversible martensitic phase transformation well above 100°C. Although partly unmolten Ta-particles after both gas atomisation and PBF-EB/M remain a challenge towards robust processing, PBF-EB/M appears to show significant potential for fabrication of Ti-Ta HT-SMAs, especially when functional metal parts and components with complex shapes are required, which are difficult to fabricate conventionally.

Animal models are an important tool in the research of chronic autoimmune diseases, like systemic lupus erythematosus (SLE). MRL-Faslpr mice are one of different lupus models that develop spontaneously an SLE-like disease with autoantibodies and immune complex deposition that leads into damage of different organs. In contrast to human SLE, both sexes of MRL-Faslpr mice develop a similar autoimmune disease. Due to the sex bias in human and the delayed disease progression in male MRL-Faslpr mice, the majority of studies have been performed in female mice. To determine the suitability of male MRL-Faslpr mice for SLE research, especially with regard to the 3 R-principle and animal welfare, analyses of phenotype, inflammation and damage with focus on kidney and spleen were performed in mice of both sexes. Female mice developed lymphadenopathy and skin lesions earlier as males. At an age of 3.5 month, more immune cells infiltrated kidney and spleen in females compared to males. At the age of 5 months, however, substantially less sex-specific differences were detected. Since other studies have shown differences between both sexes on other manifestations like autoimmune pancreatitis and Sjögren syndrome in MRL-Faslpr mice, the use of male mice as part of 3 R-principle and animal welfare must be carefully considered.

Introduction and Objectives: Autoimmune liver diseases (AILD) are rare causes hepatocellular carcinoma (HCC), and data on the efficacy and tolerability of anti-tumor therapies are scarce. This pan-European study aimed to assess outcomes in AILD-HCC patients treated with tyrosine kinase inhibitors (TKIs) or transarterial chemoembolization (TACE) compared with patients with more common HCC etiologies, including viral, alcoholic or non-alcoholic fatty liver disease. Materials and Methods: 107 patients with HCC-AILD (AIH:55; PBC:52) treated at 13 European centres between 1996 and 2020 were included. 65 received TACE and 28 received TKI therapy. 43 (66 %) were female (median age 73 years) with HCC tumor stage BCLC A (34 %), B (46 %), C (9 %) or D (11 %). For each treatment type, propensity score matching was used to match AILD to non-AILD-HCC on a 1:1 basis, yielding in a final cohort of 130 TACE and 56 TKI patients for comparative analyses of median overall survival (mOS) and treatment tolerability. Results: HCC-AILD patients showed comparable mOS to controls for both TACE (19.5 vs. 22.1 months, p = 0.9) and TKI (15.4 vs. 15.1 months, p = 0.5). Adverse events were less frequent in AILD-HCC patients than controls (33 % % vs. 62 %, p = 0.003). For TKIs, there were no significant differences in adverse events (73% vs. 86%, p = 0.2) or interruption rates (44% vs. 36 %, p = 0.7). Conclusions: In summary, this study demonstrates comparable mOS for AILD-HCC patients undergoing local and systemic treatments, with better tolerability than HCC of other causes. TKIs remain important therapeutic options for AILD-HCC patients, particularly given their exclusion from recent immunotherapy trials.

In this paper, we focus on investigating the potential of advanced Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting for 3D scene reconstruction from aerial imagery obtained via sensor platforms with an almost nadir-looking camera. Such a setting for image acquisition is convenient for capturing large-scale urban scenes, yet it poses particular challenges arising from imagery with large overlap, very short baselines, similar viewing direction and almost the same but large distance to the scene, and it therefore differs from the usual object-centric scene capture. We apply a traditional approach for image-based 3D reconstruction (COLMAP), a modern NeRF-based approach (Nerfacto) and a representative for the recently introduced 3D Gaussian Splatting approaches (Splatfacto), where the latter two are provided in the Nerfstudio framework. We analyze results achieved on the recently released UseGeo dataset both quantitatively and qualitatively. The achieved results reveal that the traditional COLMAP approach still outperforms Nerfacto and Splatfacto approaches for various scene characteristics, such as less-textured areas, areas with high vegetation, shadowed areas and areas observed from only very few views.

Neural Radiance Fields (NeRFs) have become a rapidly growing research field with the potential to revolutionize typical photogrammetric workflows, such as those used for 3D scene reconstruction. As input, NeRFs require multi-view images with corresponding camera poses as well as the interior orientation. In the typical NeRF workflow, the camera poses and the interior orientation are estimated in advance with Structure from Motion (SfM). But the quality of the resulting novel views, which depends on different parameters such as the number and distribution of available images, the accuracy of the related camera poses and interior orientation, but also the reflection characteristics of the depicted scene, is difficult to predict. In addition, SfM is a time-consuming pre-processing step, and its robustness and quality strongly depend on the image content. Furthermore, the undefined scaling factor of SfM hinders subsequent steps in which metric information is required. In this paper, we evaluate the potential of NeRFs for industrial robot applications. To start with, we propose an alternative to SfM pre-processing: we capture the input images with a calibrated camera that is attached to the end effector of an industrial robot and determine accurate camera poses with metric scale based on the robot kinematics. We then investigate the quality of the novel views by comparing them to ground truth, and by computing an internal quality measure based on ensemble methods. For evaluation purposes, we acquire multiple datasets that pose challenges for reconstruction typical of industrial applications, like reflective objects, poor texture, and fine structures. We show that the robot-based pose determination reaches similar accuracy as SfM in non-demanding cases, while having clear advantages in more challenging scenarios. We also report results of the novel view quality for different NeRF approaches, showing that an additional online pose refinement may be disadvantageous. Finally, we present first results of applying the ensemble method to estimate the quality of the synthetic novel view in the absence of a ground truth.

This paper aims to explain potential psychological effects of algorithmic management (AM) on human-centered task design and with that also workers’ mental well-being. For this, we link research on algorithmic management (AM) with Sociomaterial System Theory and Action Regulation Theory (ART). Our main assumption is that psychological effects of sociomaterial systems, such as AM, can be explained by their impact on human action. From the synthesis of the theories, mixed effects on human-centered task design can be derived: It can be expected that AM contributes to fewer action regulation opportunities (i.e., job resources like job autonomy, transparency, predictability), and to lower intellectual demands (i.e., challenge demands like task complexity, problem solving). Moreover, it can be concluded that AM is related with more regulation problems (i.e., hindrance demands like overtaxing regulations) but also fewer regulation problems (like regulation obstacles, uncertainty). Based on these considerations and in line with the majority of current research, it can be assumed that the use of AM is indirectly associated with higher risks to workers’ mental well-being. However, we also identify potential positive effects of AM as some stressful and demotivating obstacles at work are often mitigated. Based on these considerations, the main question of future research is not whether AM is good or bad for workers, but rather how work under AM can be designed to be humane. Our proposed model can guide and support researchers and practitioners in improving the understanding of the next generation of AM systems.

IntroductionThe production of high-quality food for the growing world population on the one hand and the reduction of chemical-synthetic pesticides on the other hand represents a major challenge for agriculture worldwide. The effectiveness of a combination of microbial and non-microbial biostimulants (BSs) with various nitrogen (N) forms in pathogen defense is discussed as a promising, but still poorly understood bio-based alternative for crop protection. MethodsFor this reason, nitrate and stabilized ammonium fertilizer both combined with a consortium of Pseudomonas brassicacearum, Bacillus amyloliquefaciens, and Trichoderma harzianum as soil treatment or with a mixture of seaweed extract (Ascophyllum nodosum) together with chitosan-amended micronutrient fertilizer as foliar spray application were compared under controlled greenhouse conditions. Furthermore, a combination of microbial and different non-microbial BSs (seaweed extracts + chitosan) and micronutrients with nitrate or with stabilized ammonium fertilizer was tested under field conditions to improve nutrient availability, promote plant growth, and suppress Zymoseptoria tritici (Zt) in winter wheat. Results and discussionWhile plant-protective effects against Zt by the microbial consortium application could be observed particularly under ammonium fertilization, the application of seaweed extract–chitosan mixture expressed plant defense against Zt more strongly under nitrate fertilization. In the field trial, the combination of microbial consortium with the seaweed extract–chitosan mixture together with micronutrients zinc (Zn) and manganese (Mn) showed positive effects against Zt under ammonium fertilization, associated with increased levels of defense metabolites. Furthermore, the additional input of Zn and copper (Cu) from the chitosan application improved the micronutrient status by minimizing the risk of Zn and Cu deficiency under controlled and field conditions. The use of BSs and the inoculation of Zt did not show any effects on plant growth and yield neither under controlled greenhouse conditions nor in the field. Summarized, microbial and non-microbial BSs separately applied or even combined together as one treatment did not influence plant growth or yield but made a positive contribution to an N form-dependent promotion of pathogen defense.

Depth estimation and 3D model reconstruction from aerial imagery is an important task in photogrammetry, remote sensing, and computer vision. To compare the performance of different image-based approaches, this study presents a benchmark for UAV-based aerial imagery using the UseGeo dataset. The contributions include the release of various evaluation routines on GitHub, as well as a comprehensive comparison of baseline approaches, such as methods for offline multi-view 3D reconstruction resulting in point clouds and triangle meshes, online multi-view depth estimation, as well as single-image depth estimation using self-supervised deep learning. With the release of our evaluation routines, we aim to provide a universal protocol for the evaluation of depth estimation and 3D reconstruction methods on the UseGeo dataset. The conducted experiments and analyses show that each method excels in a different category: the depth estimation from COLMAP outperforms that of the other approaches, ACMMP achieves the lowest error and highest completeness for point clouds, while OpenMVS produces triangle meshes with the lowest error. Among the online methods for depth estimation, the approach from the Plane-Sweep Library outperforms the FaSS-MVS approach, while the latter achieves the lowest processing time. And even though the particularly challenging nature of the dataset and the small amount of training data leads to a significantly higher error in the results of the self-supervised single-image depth estimation approach, it outperforms all other approaches in terms of processing time and frame rate. In our evaluation, we have also considered modern learning-based approaches that can be used for image-based 3D reconstruction, such as NeRFs. However, due to the significantly lower quality of the resulting 3D models, we have only included a qualitative comparison between NeRF-based and conventional approaches in the scope of this work.

3D reconstruction is a long-standing research topic in the photogrammetric and computer vision communities; although a plethora of open-source and commercial solutions for 3D reconstruction have been released in the last few years, several open challenges and limitations still exist. Undoubtedly, deep learning algorithms have demonstrated great potential in several remote sensing tasks, including image-based 3D reconstruction. State-of-the-art monocular and stereo algorithms leverage deep learning techniques and achieve increased performance in depth estimation and 3D reconstruction. However, one of the limitations of such methods is that they highly rely on large training sets that are often tedious to obtain; even when available, they typically refer to indoor, close-range scenarios and low-resolution images. Especially while considering UAV (Unmanned Aerial Vehicle) scenarios, such data are not available and domain adaptation is not a trivial challenge. To fill this gap, the UAV-based multi-sensor dataset for geospatial research (UseGeo - https://usegeo.fbk.eu/home) is introduced in this paper. It contains both image and LiDAR data and aims to support relevant research in photogrammetry and computer vision with a useful training set for both stereo and monocular 3D reconstruction algorithms. In this regard, the dataset provides ground truth data for both point clouds and depth maps. In addition, UseGeo can be also a valuable dataset for other tasks such as feature extraction and matching, aerial triangulation, or image and LiDAR co-registration. The paper introduces the UseGeo dataset and validates some state-of-the-art algorithms to assess their usability for both monocular and multi-view 3D reconstruction.

IntroductionSick building syndrome (SBS) refers to non-specific complaints, including upper-respiratory irritative symptoms, headaches, fatigue, and rash, which are usually associated with a particular building by their temporal pattern of occurrence and clustering among inhabitants or colleagues. The aim of the study was to determine the association between the clinical manifestations of sick building syndrome with outdoor pollutants and airborne pollen.MethodsIt was a descriptive and prospective observational study conducted from November 2021 to April 2022. It included subjects over 18 years old who completed an online survey on sick building syndrome (general symptoms, nasal, ocular, oropharyngeal, and skin symptoms) presented at home, housing information and personal history. The APS-330 from Pollen Sense ® was used to obtain data on pollen in the air and the local pollution monitoring system (SIMA) to obtain information regarding pollutants. For statistical analysis, SPSS version 16 was used.ResultsA total of 402 surveys were included; 91% of the subjects reported having at least 1 symptom. Females presented more general symptoms (fatigue and headache) than males. Subjects with a personal history of atopy showed a higher prevalence of practically all symptoms. Airborne pollen exposure was positively associated with mucosal symptoms in eyes and nose. Outdoor fungi spore exposure was positively associated with oculo-nasal and cutaneous symptoms in the scalp.ConclusionThis study found significant associations with female gender and a history of atopy, which suggests a higher risk for these subjects. Despite the limitations of the study, we can conclude that there is an association between the clinical manifestations of sick building syndrome with indoor and outdoor pollution.

RESUMO Objetivo verificar o risco de distúrbio alimentar pediátrico em pré-escolares que nasceram prematuros e relacionar com os aspectos neonatais, de aleitamento materno e de hábitos orais. Métodos estudo descritivo e longitudinal. A amostra foi composta por pré-escolares que nasceram prematuros e que foram acompanhados até os 24 meses de idade corrigida. A coleta de dados consistiu em análise de prontuários, entrevista e aplicação da Escala Brasileira de Alimentação Infantil. Resultados participaram 19 pré-escolares, com média de idade de 5 anos e 1 mês, nascidos prematuros (idade gestacional de 32 semanas), saudáveis e com função motora oral normal aos 2 anos de idade corrigida. A aplicação da escala permitiu identificar risco de distúrbio alimentar pediátrico em 31,57% da amostra. Não houve diferença entre os grupos com e sem risco do distúrbio quanto às variáveis neonatais, de aleitamento materno e de hábitos orais. Apenas a variável tempo de transição alimentar no período de internação neonatal apresentou diferença entre os grupos, sendo maior em crianças que não apresentaram risco para o distúrbio. Conclusão O risco de distúrbio alimentar pediátrico ocorreu em quase um terço dos prematuros em idade pré-escolar. O tempo de transição alimentar no período de internação neonatal foi a única variável que apresentou diferença entre os grupos com e sem risco para o distúrbio. Assim, pode-se refletir que o momento de introdução da alimentação oral e a forma de progressão da dieta até a via oral plena na internação neonatal podem ter relação com o comportamento alimentar em outras fases do desenvolvimento infantil.

For the last five decades, the development of "Asia-literate" students has been a key objective in Australian education policy. The contentions surrounding this educational goal have been well documented by academic literatures, policy documents and media commentary. This paper seeks to redress the absence of student voices in these debates by examining students' experiences with Asia-related curriculum in the context of Languages and History teaching and learning in secondary schools in Victoria, Australia. Engaging the lens of curriculum inquiry, we use select thematic episodes from qualitative interview data to interrogate students' understanding and evaluation of planned and experienced Asia-related curriculum. Their stories highlight the incongruences of Asia learning as framed by (and within) formal sites of learning, and students' lived experiences in other spaces. We argue that in order to effect a shift in the current expressions of Asia-related curriculum policy towards a more student-centred articulation, students' roles in shaping curricular knowledge warrant more attention and scrutiny. As our analysis suggests, a deeper understanding of students' experiences of curriculum-as-lived may be the catalyst for a long-overdue reorientation towards a curricular landscape of multiplicity, where new and authentic possibilities for Asia learning could be envisaged.