bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improvement of oxygen reduction activity and stability on a perovskite oxide surface by electrochemical potential
Authors: Koohfar Sanaz , Ghasemi Masoud , Hafen Tyler , Dimitrakopoulos Georgios , Kim Dongha , Pike Jenna , Elangovan Singaravelu , Gomez Enrique D. , Yildiz Bilge 
Published: November 2023

AbstractThe instability of the surface chemistry in transition metal oxide perovskites is the main factor hindering the long-term durability of oxygen electrodes in solid oxide electrochemical cells. The instability of surface chemistry is mainly due to the segregation of A-site dopants from the lattice to the surface. Here we report that cathodic potential can remarkably improve the stability in oxygen reduction reaction and electrochemical activity, by decomposing the near-surface region of the perovskite phase in a porous electrode made of La1-xSrxCo1-xFexO3 mixed with Sm0.2Ce0.8O1.9. Our approach combines X-ray photoelectron spectroscopy and secondary ion mass spectrometry for surface and sub-surface analysis. Formation of Ruddlesden-Popper phase is accompanied by suppression of the A-site dopant segregation, and exsolution of catalytically active Co particles onto the surface. These findings reveal the chemical and structural elements that maintain an active surface for oxygen reduction, and the cathodic potential is one way to generate these desirable chemistries.
A standardized metric to enhance clinical trial design and outcome interpretation in type 1 diabetes
Authors: Ylescupidez Alyssa , Bahnson Henry T. , O’Rourke Colin , Lord Sandra , Speake Cate , Greenbaum Carla J. 
Published: November 2023

AbstractThe use of a standardized outcome metric enhances clinical trial interpretation and cross-trial comparison. If a disease course is predictable, comparing modeled predictions with outcome data affords the precision and confidence needed to accelerate precision medicine. We demonstrate this approach in type 1 diabetes (T1D) trials aiming to preserve endogenous insulin secretion measured by C-peptide. C-peptide is predictable given an individual’s age and baseline value; quantitative response (QR) adjusts for these variables and represents the difference between the observed and predicted outcome. Validated across 13 trials, the QR metric reduces each trial’s variance and increases statistical power. As smaller studies are especially subject to random sampling variability, using QR as the outcome introduces alternative interpretations of previous clinical trial results. QR can provide model-based estimates that quantify whether individuals or groups did better or worse than expected. QR also provides a purer metric to associate with biomarker measurements. Using data from more than 1300 participants, we demonstrate the value of QR in advancing disease-modifying therapy in T1D. QR applies to any disease where outcome is predictable by pre-specified baseline covariates, rendering it useful for defining responders to therapy, comparing therapeutic efficacy, and understanding causal pathways in disease.
Spatial exosome analysis using cellulose nanofiber sheets reveals the location heterogeneity of extracellular vesicles
Authors: Yokoi Akira , Yoshida Kosuke , Koga Hirotaka , Kitagawa Masami , Nagao Yukari , Iida Mikiko , Kawaguchi Shota , Zhang Min , Nakayama Jun , Yamamoto Yusuke , Baba Yoshinobu , Kajiyama Hiroaki , Yasui Takao 
Published: November 2023

AbstractExtracellular vesicles (EVs), including exosomes, are recognized as promising functional targets involved in disease mechanisms. However, the intravital heterogeneity of EVs remains unclear, and the general limitation for analyzing EVs is the need for a certain volume of biofluids. Here, we present cellulose nanofiber (CNF) sheets to resolve these issues. We show that CNF sheets capture and preserve EVs from ~10 μL of biofluid and enable the analysis of bioactive molecules inside EVs. By attaching CNF sheets to moistened organs, we collect EVs in trace amounts of ascites, which is sufficient to perform small RNA sequence analyses. In an ovarian cancer mouse model, we demonstrate that CNF sheets enable the detection of cancer-associated miRNAs from the very early phase when mice did not have apparent ascites, and that EVs from different locations have unique miRNA profiles. By performing CNF sheet analyses in patients, we identify further location-based differences in EV miRNA profiles, with profiles reflecting disease conditions. We conduct spatial exosome analyses using CNF sheets to reveal that ascites EVs from cancer patients exhibit location-dependent heterogeneity. This technique could provide insights into EV biology and suggests a clinical strategy contributing to cancer diagnosis, staging evaluation, and therapy planning.
replicAnt: a pipeline for generating annotated images of animals in complex environments using Unreal Engine
Authors: Plum Fabian , Bulla René , Beck Hendrik K. , Imirzian Natalie , Labonte David 
Published: November 2023

AbstractDeep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To help overcome these limitations, we developedreplicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware.replicAntplaces 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated withreplicAntcan significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation. We also show that it increases the subject-specificity and domain-invariance of the trained networks, thereby conferring robustness. In some applications,replicAntmay even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.
Message framing to promote solar panels
Authors: Bär Dominik , Feuerriegel Stefan , Li Ting , Weinmann Markus 
Published: November 2023

AbstractGreen technologies, such as solar panels, foster the use of clean energy, yet often involve large-scale investments. Hence, adoption by retail consumers has been a key barrier. Here, we show that message framing can significantly increase customers’ serious commitment to adopting solar panels by providing empirical evidence in the field from a large-scale randomized controlled trial with a nationwide online retailer in the Netherlands (N = 26,873 participants). We design four messages aimed at promoting the purchase behavior of solar panel installations. Our messages present outcomes for oneself or for the environment and highlight cost savings versus earnings (for oneself) or reducing emissions versus generating green electricity (for the environment). Across all messages, we observe a higher rate of customers committing to solar panels compared to the baseline. However, the framing in terms of financial savings for oneself was by far the most effective, resulting in a 40% higher level of commitment than the baseline and 30% higher than the average of the other three messages, which were not significantly different in effect from each other. Our results show that message framing is cost-efficient and scalable among retail consumers to promote large-scale investments in green technologies and thus clean energy.
Microglia are not protective against cryptococcal meningitis
Authors: Mohamed Sally H. , Fu Man Shun , Hain Sofia , Alselami Alanoud , Vanhoffelen Eliane , Li Yanjian , Bojang Ebrima , Lukande Robert , Ballou Elizabeth R. , May Robin C. , Ding Chen , Velde Greetje Vande , Drummond Rebecca A. 
Published: November 2023

AbstractMicroglia provide protection against a range of brain infections including bacteria, viruses and parasites, but how these glial cells respond to fungal brain infections is poorly understood. We investigated the role of microglia in the context of cryptococcal meningitis, the most common cause of fungal meningitis in humans. Using a series of transgenic- and chemical-based microglia depletion methods we found that, contrary to their protective role during other infections, loss of microglia did not affect control of Cryptococcus neoformans brain infection which was replicated with several fungal strains. At early time points post-infection, we found that microglia depletion lowered fungal brain burdens, which was related to intracellular residence of C. neoformans within microglia. Further examination of extracellular and intracellular fungal populations revealed that C. neoformans residing in microglia were protected from copper starvation, whereas extracellular yeast upregulated copper transporter CTR4. However, the degree of copper starvation did not equate to fungal survival or abundance of metals within different intracellular niches. Taken together, these data show how tissue-resident myeloid cells may influence fungal phenotype in the brain but do not provide protection against this infection, and instead may act as an early infection reservoir.
The alarmin IL33 orchestrates type 2 immune-mediated control of thymus regeneration
Authors: Cosway Emilie J. , James Kieran D. , White Andrea J. , Parnell Sonia M. , Bacon Andrea , McKenzie Andrew N. J. , Jenkinson W. E. , Anderson Graham 
Published: November 2023

AbstractAs the primary site of T-cell development, the thymus dictates immune competency of the host. The rates of thymus function are not constant, and thymus regeneration is essential to restore new T-cell production following tissue damage from environmental factors and therapeutic interventions. Here, we show the alarmin interleukin (IL) 33 is a product of Sca1+ thymic mesenchyme both necessary and sufficient for thymus regeneration via a type 2 innate immune network. IL33 stimulates expansion of IL5-producing type 2 innate lymphoid cells (ILC2), which triggers a cellular switch in the intrathymic availability of IL4. This enables eosinophil production of IL4 to re-establish thymic mesenchyme prior to recovery of thymopoiesis-inducing epithelial compartments. Collectively, we identify a positive feedback mechanism of type 2 innate immunity that regulates the recovery of thymus function following tissue injury.
Revising the global biogeography of annual and perennial plants
Authors: Poppenwimer Tyler , Mayrose Itay , DeMalach Niv 
Published: November 2023

AbstractThere are two main life cycles in plants—annual and perennial1,2. These life cycles are associated with different traits that determine ecosystem function3,4. Although life cycles are textbook examples of plant adaptation to different environments, we lack comprehensive knowledge regarding their global distributional patterns. Here we assembled an extensive database of plant life cycle assignments of 235,000 plant species coupled with millions of georeferenced datapoints to map the worldwide biogeography of these plant species. We found that annual plants are half as common as initially thought5–8, accounting for only 6% of plant species. Our analyses indicate that annuals are favoured in hot and dry regions. However, a more accurate model shows that the prevalence of annual species is driven by temperature and precipitation in the driest quarter (rather than yearly means), explaining, for example, why some Mediterranean systems have more annuals than desert systems. Furthermore, this pattern remains consistent among different families, indicating convergent evolution. Finally, we demonstrate that increasing climate variability and anthropogenic disturbance increase annual favourability. Considering future climate change, we predict an increase in annual prevalence for 69% of the world’s ecoregions by 2060. Overall, our analyses raise concerns for ecosystem services provided by perennial plants, as ongoing changes are leading to a higher proportion of annual plants globally.
Targeting of intracellular oncoproteins with peptide-centric CARs
Authors: Yarmarkovich Mark , Marshall Quinlen F. , Warrington John M. , Premaratne Rasika , Farrel Alvin , Groff David , Li Wei , di Marco Moreno , Runbeck Erin , Truong Hau , Toor Jugmohit S. , Tripathi Sarvind , Nguyen Son , Shen Helena , Noel Tiffany , Church Nicole L. , Weiner Amber , Kendsersky Nathan , Martinez Dan , Weisberg Rebecca , Christie Molly , Eisenlohr Laurence , Bosse Kristopher R. , Dimitrov Dimiter S. , Stevanovic Stefan , Sgourakis Nikolaos G. , Kiefel Ben R. , Maris John M. 
Published: November 2023

AbstractThe majority of oncogenic drivers are intracellular proteins, constraining their immunotherapeutic targeting to mutated peptides (neoantigens) presented by individual human leukocyte antigen (HLA) allotypes1. However, most cancers have a modest mutational burden that is insufficient for generating responses using neoantigen-based therapies2,3. Neuroblastoma is a paediatric cancer that harbours few mutations and is instead driven by epigenetically deregulated transcriptional networks4. Here we show that the neuroblastoma immunopeptidome is enriched with peptides derived from proteins essential for tumorigenesis. We focused on targeting the unmutated peptide QYNPIRTTF discovered on HLA-A*24:02, which is derived from the neuroblastoma-dependency gene and master transcriptional regulator PHOX2B. To target QYNPIRTTF, we developed peptide-centric chimeric antigen receptors (PC-CARs) through a counter panning strategy using predicted potentially cross-reactive peptides. We further proposed that PC-CARs can recognize peptides on additional HLA allotypes when presenting a similar overall molecular surface. Informed by our computational modelling results, we show that PHOX2B PC-CARs also recognize QYNPIRTTF presented by HLA-A*23:01, the most common non-A2 allele in people with African ancestry. Finally, we demonstrate potent and specific killing of neuroblastoma cells expressing these HLAs in vitro and complete tumour regression in mice. These data suggest that PC-CARs have the potential to expand the pool of immunotherapeutic targets to include non-immunogenic intracellular oncoproteins and allow targeting through additional HLA allotypes in a clinical setting.
Vascular abnormalities in fellow eyes of patients with unilateral coats’ disease
Authors: Lee Seung Min , Kim Kang Hyun , Kang Hyun Goo , Choi Eun Young , Lee Junwon , Kim Min 
Published: November 2023

AbstractCoats’ disease is an idiopathic retinal vascular disorder, known to usually occur unilaterally; however, recent studies have highlighted vascular abnormalities in the fellow unaffected eyes. This retrospective study investigated the peripheral vascular features and macular vascular structure of unaffected fellow eyes in patients with unilateral Coats’ disease using multimodal imaging tools. We analysed images of patients, including bilateral ultra-widefield imaging, fluorescein angiography (FA), ultra-widefield FA, or standard fundus photography. Available bilateral optical coherence tomography angiography (OCT-A) images were used for macular vascular structure analysis. OCT-A parameters, including foveal avascular zone (FAZ), perfusion index, and vessel density (VD) in the superficial and deep capillary plexuses (SCP, DCP), were calculated using Image J software. The mean age at diagnosis was 34.5 ± 17.9 years. The mean final best-corrected visual acuity of the affected eyes was logMAR 0.78 ± 0.79, while that of the fellow eyes was logMAR 0.04 ± 0.12. Ten fellow eyes had microaneurysms (47.6%), two had tortuous vessel abnormalities (9.5%), and 11(52.4%) had abnormal vascular findings on FA. Although there was a trend towards larger DCP FAZ (1.201 ± 0.086 vs. 1.072 ± 0.226), and lower DCP VD (8.593 ± 1.583 vs. 10.827 ± 3.392) in the affected eyes as measured by the Cirrus machine, the difference was not statistically significant between affected and fellow eyes when measured using the Zeiss Cirrus machine (P = 0.686, P = 0.343, respectively). However, when measured with the Spectralis machine, DCP FAZ was larger in affected eyes (0.828 ± 0.426 vs. 0.254 ± 0.092, P = 0.002) and DCP VD was lower in affected eyes (6.901 ± 2.634 vs. 17.451 ± 7.207, P = 0.002) compared to the fellow eyes, while other parameters showed no significant variations. These findings indicate that there may be subtle vascular abnormalities primarily located in the peripheral regions of the unaffected fellow eyes in patients with unilateral Coats’ disease, while the macular microvasculature remains unaffected.
Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images
Authors: Al-Sheikh Mona Hmoud , Al Dandan Omran , Al-Shamayleh Ahmad Sami , Jalab Hamid A. , Ibrahim Rabha W. 
Published: November 2023

AbstractMedical imaging is considered a suitable alternative testing method for the detection of lung diseases. Many researchers have been working to develop various detection methods that have aided in the prevention of lung diseases. To better understand the condition of the lung disease infection, chest X-Ray and CT scans are utilized to check the disease’s spread throughout the lungs. This study proposes an automated system for the detection multi lung diseases in X-Ray and CT scans. A customized convolutional neural network (CNN) and two pre-trained deep learning models with a new image enhancement model are proposed for image classification. The proposed lung disease detection comprises two main steps: pre-processing, and deep learning classification. The new image enhancement algorithm is developed in the pre-processing step using k-symbol Lerch transcendent functions model which enhancement images based on image pixel probability. While, in the classification step, the customized CNN architecture and two pre-trained CNN models Alex Net, and VGG16Net are developed. The proposed approach was tested on publicly available image datasets (CT, and X-Ray image dataset), and the results showed classification accuracy, sensitivity, and specificity of 98.60%, 98.40%, and 98.50% for the X-Ray image dataset, respectively, and 98.80%, 98.50%, 98.40% for the CT scans dataset, respectively. Overall, the obtained results highlight the advantages of the image enhancement model as a first step in processing.
Inter-laboratory comparison of a digital multimeter measurement in Turkey
Authors: Patan Alper M. 
Published: November 2023

AbstractThe standard ISO/IEC 17043:2023 specifies the general requirements for proficiency testing providers and covers the development, operation, and reporting of proficiency testing schemes. The standard aims to ensure that the measurement results obtained by different laboratories are comparable and traceable to international measurement standards. Inter-laboratory comparisons are still rare in the meteorological services network of calibration laboratories, and there are not enough bodies accredited with the ISO/IEC 17043:2023 standard in Turkey. The existing inter-laboratory comparison measurements do not fully comply with the requirements of ISO/IEC 17043:2023 and are not compatible with the measurement capabilities and scopes of the laboratories, leading to difficulties when comparing meteorological data from different laboratories. This article presents a case study of a digital multimeter interlaboratory comparison measurement in accordance with the ISO/IEC 17043:2023 standard. The results obtained were analysed using statistical methods (En score) and compared to the reference values provided by a secondary accredited calibration laboratory. The analysis showed that the measurement results obtained by the participants were consistent and generally within the acceptable range of En. The case study presented in this paper demonstrates the effectiveness of the standard in ensuring reliable measurement results in a practical setting.
Copula-based modeling and simulation of 3D systems of curved fibers by isolating intrinsic fiber properties and external effects
Authors: Weber Matthias , Grießer Andreas , Mosbach Dennis , Glatt Erik , Wiegmann Andreas , Schmidt Volker 
Published: November 2023

AbstractIn this paper we lay the foundation for data-driven 3D analysis of virtual fiber systems with respect to their microstructure and functionality. In particular, we develop a stochastic 3D model for systems of curved fibers similar to nonwovens, which is fitted to tomographic image data. By systematic variations of model parameters, efficient computer-based scenario analyses can be performed to get a deeper insight how effective properties of this type of functional materials depend on their 3D microstructure. In a first step, we consider single fibers as polygonal tracks which can be modeled by a third-order Markov chain. For constructing the transition function of the Markov chain, we formalize the intuitive notions of intrinsic fiber properties and external effects and build a copula-based transition function such that both aspects can be varied independently. Using this single-fiber model, in a second step we derive a model for the entire fiber system observed in a bounded sampling window and fit it to two different 3D datasets of nonwovens measured by CT imaging. Considering various geometric descriptors of the 3D microstructure related to effective properties of the pore space, we evaluate the goodness of model fit by comparing geometric descriptors of the 3D morphology of model realizations with those of tomographic image data.
A novel in silico scaffold-hopping method for drug repositioning in rare and intractable diseases
Authors: Tanabe Mao , Sakate Ryuichi , Nakabayashi Jun , Tsumura Kyosuke , Ohira Shino , Iwato Kaoru , Kimura Tomonori 
Published: November 2023

AbstractIn the field of rare and intractable diseases, new drug development is difficult and drug repositioning (DR) is a key method to improve this situation. In this study, we present a new method for finding DR candidates utilizing virtual screening, which integrates amino acid interaction mapping into scaffold-hopping (AI-AAM). At first, we used a spleen associated tyrosine kinase inhibitor as a reference to evaluate the technique, and succeeded in scaffold-hopping maintaining the pharmacological activity. Then we applied this method to five drugs and obtained 144 compounds with diverse structures. Among these, 31 compounds were known to target the same proteins as their reference compounds and 113 compounds were known to target different proteins. We found that AI-AAM dominantly selected functionally similar compounds; thus, these selected compounds may represent improved alternatives to their reference compounds. Moreover, the latter compounds were presumed to bind to the targets of their references as well. This new “compound-target” information provided DR candidates that could be utilized for future drug development.