bluuebunny/crossref_metadata_2025_split huggingface dataset:
Graphene nanoplatelets reinforced Al-Cu-Mg composite fabricated using laser powder bed fusion: microstructure, mechanical properties, and wear behaviour
Authors: Pekok Mulla Ahmet , Setchi Rossitza , Ryan Michael , Brousseau Emmanuel , Han Quanquan , Gu Dongdong 
Published: July 2023

AbstractAluminium-based metal matrix composites reinforced with graphene (Gr) and its derivatives have been reported as promising composites due to their superior properties such as strength, damage tolerance, fatigue resistance, and density. However, the crack and porosity susceptibility of Aluminium 2024 Alloy (AA2024) with added Gr when fabricated using additive manufacturing techniques is not sufficiently well understood. The present work addresses this knowledge gap by focusing on the effect of graphene nanoplatelets (GNPs) and scanning speed on the AA2024 composites’ wear performance and microstructural and mechanical properties of specimens fabricated using laser powder bed fusion (LPBF). The experimental findings demonstrate that up to 0.5% presence of Gr in the composite improves its crystallite size and microhardness by up to 37.6% and 45%, respectively; however, it increases the porosity and crack formation due to the high laser power requirement. Moreover, the composites’ macroscale scratch and nanoscale wear performances showed improvements by up to 50% and 56% with higher Gr concentration (0.5%), suggesting that Gr is distributed uniformly in the structure. The improved understanding of the relationship between microstructure and mechanical characteristics of the GNPs/Al2024 composites fabricated using LPBF in terms of cracking and porosity formation is another significant contribution of this work.
Graphical abstract
Blockade of tumor-derived colony-stimulating factor 1 (CSF1) promotes an immune-permissive tumor microenvironment
Authors: Maldonado Maria del Mar , Schlom Jeffrey , Hamilton Duane H. 
Published: July 2023

AbstractThe macrophage colony-stimulating factor 1 (CSF1) is a chemokine essential for the survival, proliferation, and differentiation of mononuclear phagocytes from hemopoietic stem cells. In addition to its essential physiological role in normal tissues, the CSF1/CSF1 receptor axis is known to be overexpressed in many tumor types and associated with poor prognosis. High levels of CSF1 within the tumor microenvironment have been shown to recruit and reeducate macrophages to produce factors that promote tumor invasiveness and accelerate metastasis. In this study, we demonstrate, for the first time, that treating established syngeneic murine colon and breast carcinoma tumors with a CSF1R-blocking antibody also promotes the expansion of neoepitope-specific T cells. To assess the role of tumor-derived CSF1 in these model systems, we generated and characterized CSF1 CRISPR-Cas9 knockouts. Eliminating tumor-derived CSF1 results in decreased tumor growth and enhanced immunity against tumor-associated neoepitopes, potentially promoting an immune permissive tumor microenvironment in tumor-bearing mice. The combination of neoepitope vaccine with anti-PDL1 in the MC38 CSF1−/− tumor model significantly decreased tumor growth in vivo. Moreover, anti-CSF1R therapy combined with the adeno-TWIST1 vaccine resulted in tumor control, decreased metastasis, and a synergistic increase in CD8 T cell infiltration in 4T1 mammary tumors. Analysis of the tumor microenvironment demonstrated greater CD8 T cell infiltration and a reduction in tumor-associated macrophages following CSF1R inhibition in both tumor models. Our findings thus add to the therapeutic potential of CSF1 targeting agents by employing combinations with vaccines to modulate anti-neoepitope responses in the tumor microenvironment.
Automated airway quantification associates with mortality in idiopathic pulmonary fibrosis
Authors: Cheung Wing Keung , Pakzad Ashkan , Mogulkoc Nesrin , Needleman Sarah , Rangelov Bojidar , Gudmundsson Eyjolfur , Zhao An , Abbas Mariam , McLaverty Davina , Asimakopoulos Dimitrios , Chapman Robert , Savas Recep , Janes Sam M. , Hu Yipeng , Alexander Daniel C. , Hurst John R. , Jacob Joseph 
Published: July 2023

Abstract
Objectives
The study examined whether quantified airway metrics associate with mortality in idiopathic pulmonary fibrosis (IPF).

Methods
In an observational cohort study (n = 90) of IPF patients from Ege University Hospital, an airway analysis tool AirQuant calculated median airway intersegmental tapering and segmental tortuosity across the 2nd to 6th airway generations. Intersegmental tapering measures the difference in median diameter between adjacent airway segments. Tortuosity evaluates the ratio of measured segmental length against direct end-to-end segmental length. Univariable linear regression analyses examined relationships between AirQuant variables, clinical variables, and lung function tests. Univariable and multivariable Cox proportional hazards models estimated mortality risk with the latter adjusted for patient age, gender, smoking status, antifibrotic use, CT usual interstitial pneumonia (UIP) pattern, and either forced vital capacity (FVC) or diffusion capacity of carbon monoxide (DLco) if obtained within 3 months of the CT.

Results
No significant collinearity existed between AirQuant variables and clinical or functional variables. On univariable Cox analyses, male gender, smoking history, no antifibrotic use, reduced DLco, reduced intersegmental tapering, and increased segmental tortuosity associated with increased risk of death. On multivariable Cox analyses (adjusted using FVC), intersegmental tapering (hazard ratio (HR) = 0.75, 95% CI = 0.66–0.85, p < 0.001) and segmental tortuosity (HR = 1.74, 95% CI = 1.22–2.47, p = 0.002) independently associated with mortality. Results were maintained with adjustment using DLco.

Conclusions
AirQuant generated measures of intersegmental tapering and segmental tortuosity independently associate with mortality in IPF patients. Abnormalities in proximal airway generations, which are not typically considered to be abnormal in IPF, have prognostic value.

Clinical relevance statement
Quantitative measurements of intersegmental tapering and segmental tortuosity, in proximal (second to sixth) generation airway segments, independently associate with mortality in IPF. Automated airway analysis can estimate disease severity, which in IPF is not restricted to the distal airway tree.

Key Points
• AirQuant generates measures of intersegmental tapering and segmental tortuosity.
• Automated airway quantification associates with mortality in IPF independent of established measures of disease severity.
• Automated airway analysis could be used to refine patient selection for therapeutic trials in IPF.

Graphical Abstract
Trust and stakeholder perspectives on the implementation of AI tools in clinical radiology
Authors: Bergquist Magnus , Rolandsson Bertil , Gryska Emilia , Laesser Mats , Hoefling Nickoleta , Heckemann Rolf , Schneiderman Justin F. , Björkman-Burtscher Isabella M. 
Published: July 2023

Abstract
Objectives
To define requirements that condition trust in artificial intelligence (AI) as clinical decision support in radiology from the perspective of various stakeholders and to explore ways to fulfil these requirements.

Methods
Semi-structured interviews were conducted with twenty-five respondents—nineteen directly involved in the development, implementation, or use of AI applications in radiology and six working with AI in other areas of healthcare. We designed the questions to explore three themes: development and use of AI, professional decision-making, and management and organizational procedures connected to AI. The transcribed interviews were analysed in an iterative coding process from open coding to theoretically informed thematic coding.

Results
We identified four aspects of trust that relate to reliability, transparency, quality verification, and inter-organizational compatibility. These aspects fall under the categories of substantial and procedural requirements.

Conclusions
Development of appropriate levels of trust in AI in healthcare is complex and encompasses multiple dimensions of requirements. Various stakeholders will have to be involved in developing AI solutions for healthcare and radiology to fulfil these requirements.

Clinical relevance statement
For AI to achieve advances in radiology, it must be given the opportunity to support, rather than replace, human expertise. Support requires trust. Identification of aspects and conditions for trust allows developing AI implementation strategies that facilitate advancing the field.

Key Points
• Dimensions of procedural and substantial demands that need to be fulfilled to foster appropriate levels of trust in AI in healthcare are conditioned on aspects related to reliability, transparency, quality verification, and inter-organizational compatibility.
•Creating the conditions for trust to emerge requires the involvement of various stakeholders, who will have to compensate the problem’s inherent complexity by finding and promoting well-defined solutions.
Information extraction from weakly structured radiological reports with natural language queries
Authors: Dada Amin , Ufer Tim Leon , Kim Moon , Hasin Max , Spieker Nicola , Forsting Michael , Nensa Felix , Egger Jan , Kleesiek Jens 
Published: July 2023

Abstract
Objectives
Provide physicians and researchers an efficient way to extract information from weakly structured radiology reports with natural language processing (NLP) machine learning models.

Methods
We evaluate seven different German bidirectional encoder representations from transformers (BERT) models on a dataset of 857,783 unlabeled radiology reports and an annotated reading comprehension dataset in the format of SQuAD 2.0 based on 1223 additional reports.

Results
Continued pre-training of a BERT model on the radiology dataset and a medical online encyclopedia resulted in the most accurate model with an F1-score of 83.97% and an exact match score of 71.63% for answerable questions and 96.01% accuracy in detecting unanswerable questions. Fine-tuning a non-medical model without further pre-training led to the lowest-performing model. The final model proved stable against variation in the formulations of questions and in dealing with questions on topics excluded from the training set.

Conclusions
General domain BERT models further pre-trained on radiological data achieve high accuracy in answering questions on radiology reports. We propose to integrate our approach into the workflow of medical practitioners and researchers to extract information from radiology reports.

Clinical relevance statement
By reducing the need for manual searches of radiology reports, radiologists’ resources are freed up, which indirectly benefits patients.

Key Points
• BERT models pre-trained on general domain datasets and radiology reports achieve high accuracy (83.97% F1-score) on question-answering for radiology reports.
• The best performing model achieves an F1-score of 83.97% for answerable questions and 96.01% accuracy for questions without an answer.
• Additional radiology-specific pretraining of all investigated BERT models improves their performance.

Graphical Abstract
Impact of virtual reality training on mastoidectomy performance: a prospective randomised study
Authors: Tapiala Jesse , Iso-Mustajärvi Matti , Timonen Tomi , Vrzáková Hana , Dietz Aarno 
Published: July 2023

Abstract
Purpose
The opportunities for surgical training and practice in the operating room are in decline due to limited resources, increased efficiency demands, growing complexity of the cases, and concerns for patient safety. Virtual reality (VR) offers a novel opportunity to enhance surgical training and provide complementary three-dimensional experience that has been usually available in the operating room. Since VR allows viewing and manipulation of realistic 3D models, the VR environment could enhance anatomical and topographical knowledge, in particular. In this study, we explored whether incorporating VR anatomy training improves novices’ performance during mastoidectomy over traditional methods.

Methods
Thirty medical students were randomized into two groups and taught mastoidectomy in a structured manner. One group utilized a VR temporal bone model during the training while the other group used more traditional materials such as anatomy books. After the training, all participants completed a mastoidectomy on a 3D-printed temporal bone model under expert supervision. Performance during the mastoidectomy was evaluated with multiple metrics and feedback regarding the two training methods was gathered from the participants.

Results
The VR training method was rated better by the participants, and they also needed less guidance during the mastoidectomy. There were no significant differences in operational time, the occurrence of injuries, self-assessment scores, and the surgical outcome between the two groups.

Conclusion
Our results support the utilization of VR training in complete novices as it has higher trainee satisfaction and leads to at least as good results as the more traditional methods.
Inverse pattern of GABAergic system impairment in the external versus internal globus pallidus in male heroin addicts
Authors: Gos Anna , Steiner Johann , Trübner Kurt , Ungewickell Jonas , Mawrin Christian , Karnecki Karol , Kaliszan Michał , Gos Tomasz 
Published: July 2023

AbstractOpioid addiction is a global problem that has been exacerbated in the USA and Europe by the COVID-19 pandemic. The globus pallidus (GP) plays a prominent neurobiological role in the regulation of behaviour as an output station of the striato-pallidal system. GABAergic large projection neurons are the main neuronal type in the external (EGP) and internal (IGP) parts of the GP, where addiction-specific molecular and functional abnormalities occur. In these neurons, glutamate decarboxylase (GAD) with isoforms GAD 65 and 67 is a key enzyme in GABA synthesis, and experimental studies suggest GAD dysregulation in the GP of heroin addicts. Our study, which was performed on paraffin-embedded brains from the Magdeburg Brain Bank, aimed to investigate abnormalities in the GABAergic function of large GP neurons by densitometric evaluation of their GAD 65/67-immunostained thick dendrites. The study revealed a bilaterally decreased fibres density in the EGP paralleled by the increase in the IGP in 11 male heroin addicts versus 11 healthy controls (significant U-test P values). The analysis of confounding variables found no interference of age, brain volume, and duration of formalin fixation with the results. Our findings suggest a dysregulation of GABAergic activity in the GP of heroin addicts, which is consistent with experimental data from animal models and plays potentially a role in the disturbed function of basal ganglia circuit in opioid addiction.
Unlocking customer flexibilities through standardized communication interfaces
Authors: Reihs David , Bouda Fabian , Leimgruber Fabian , Machtinger Katharina , Strasser Thomas I. , Stefan Mark , Einfalt Alfred , Schober Lukas 
Published: July 2023

AbstractThe need for a more sustainable energy system is leading to more electric energy generation being connected to low voltage (LV) distribution grids. At the same time, due to accelerated growth in electric mobility and heat pumps, more and more energy consumed by end customers is supplied via LV distribution grids. These developments cause distribution networks to become less predictable and make them subject to much higher changes in supply and demand. Therefore, additional flexibility will be needed to keep such systems operating with high quality of service. The required flexibility could be provided by distributed generators as well as flexible loads. This leads to the question of how these flexibilities can be activated by distribution system operators when needed. A digital interface between distribution system operator and device operator could be implemented to communicate flexibility requirements. In this work, three possible deployment scenarios for such a digital interface are presented. A market review of available standards and commonly implemented communication protocols was conducted to identify potential candidates for the standardization of such an interface.
Using mixed methods and community participation to explore household and ambient air pollution practices in a rural community in Malawi
Authors: Phillip Eunice , Conroy Ronan M. , Walsh Aisling , Jumbe Vincent , Jewitt Sarah , Lee Shona , Stanistreet Debbi 
Published: July 2023

Abstract

Aim
Globally, household and ambient air pollution (HAAP) accounts for almost 7 million premature deaths each year. Over half of these are from incomplete biomass fuel combustion in open fires and inefficient cookstoves. Solutions to the problem remain challenging due to cost, people’s perception of pollution and unsuitability to meet user needs.

Subject and methods
We used mixed methods and participatory approaches to measure and understand practices and beliefs relating to HAAP in a low-resource community in Malawi. Eighty-six households were randomly sampled for the survey, fine particulate matter (PM2.5) levels were measured in 46 kitchens and four ambient locations, and 38 households were engaged during participatory transect walks. We analysed the data using descriptive and thematic analysis.

Results
Kitchen PM2.5 levels far exceeded the World Health Organization’s recommended safe levels. Open-burning practices further contributed to ambient air pollution in the community. While there was high awareness of smoke in cooking areas, participants did not associate it with adverse health outcomes. Availability and affordability of cleaner alternatives influenced household energy choices. Integrating participatory methods alongside quantitative data allowed an in-depth understanding of the community’s practices and relationship with HAAP.

Conclusion
The findings demonstrate that energy poverty is a key factor in access to clean energy sources and highlight the importance of engaging communities to design HAAP interventions that meet their physical, socioeconomic and cultural needs.
Active pairwise distance learning for efficient labeling of large datasets by human experts
Authors: Pries Joris , Bhulai Sandjai , van der Mei Rob 
Published: July 2023

AbstractIn many machine learning applications, the labeling of datasets is done by human experts, which is usually time-consuming in cases of large data sets. This raises the need for methods to make optimal use of the human expert by selecting model instances for which the expert opinion is of most added value. This paper introduces the problem of active pairwise distance learning (APDL), where the goal is to actively learn the pairwise distances between all instances. Any distance function can be used, which means that APDL techniques can e.g., be used to determine likeness between faces or similarities between users for recommender systems. Starting with an unlabeled dataset, each round an expert determines the distance between one pair of instances. Thus, there is an important choice to make each round: ‘Which combination of instances is presented to the expert?’ The objective is to accurately predict all pairwise distances, while minimizing the usage of the expert. In this research, we establish upper and lower bound approximations (including an update rule) for the pairwise distances and evaluate many domain-independent query strategies. The observations from the experiments are therefore general, and the selection strategies are ideal candidates to function as baseline in future research. We show that using the criterion max degree consistently ranks amongst the best strategies. By using this criterion, the pairwise distances of a new dataset can be labeled much more efficiently.
How do HEIs’ students accept nudging? Expert perspective analysis
Authors: Ochrana František , Korečková Jana , Kovács Radek 
Published: July 2023

AbstractThe study examines the influence of nudging on the successful studies of Higher education institutions (HEI’s) students. Data from one of the typical countries of the Central European region (Czech Republic) is analyzed. The goal of the research was to find out how students accept individual forms of nudging. For this purpose, a questionnaire survey at Czech HEIs (N = 207) and structured interviews with experts (N = 19) were conducted. This study showed that information nudges and high-touch nudges enjoyed the highest level of acceptance. A strong correlation was also found between “most irritating” and group “manipulative” nudges. These forms of nudging fluctuated around the borderline value of acceptance. A strong or moderate correlation was also found between nudges with similar content and different forms. Structured interviews showed that experts supported the use of nudges. They considered it an important tool to influence the study’s success. Experts explained the differences in the acceptance of nudges in connection with the different ages of students, the type of study, the students’ life experiences, and partly with the type of field of study. They considered parents’ education rather ambivalent.
Finding digital solutions in pandemic times: the case of appointment procedures for professors at German universities
Authors: Gerchen Anna 
Published: July 2023

AbstractIn higher education and beyond, the Covid-19 pandemic is considered to have accelerated digitalisation. While this acceleration is usually viewed in the context of the digital transformation that is characterised by its longevity, the permanence of pandemic-driven digitalisation requires investigation. Focusing on appointment procedures for professors as a central element of universities’ governance, the qualitative study presented here employed expert interviews and group discussions to investigate how German universities responded to the challenges of the Covid-19 pandemic by furthering digitalisation. Drawing on the concept of synchronous and asynchronous communication and Luhmann’s understanding of decision programmes, this article proposes differentiating between two modes of digitalisation in order to systematise the empirical findings and thus analytically distinguish pandemic-driven digitalisation from the general digital transformation. It finds that not all of the newly implemented digital solutions will be used permanently and argues that this can be understood in terms of obstructed decision programmes.