bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tests and deflection calculation method for circular concrete-filled steel tubular columns under very low-elevation lateral impact loads
Authors: Liang Jiayu , Liu Yanhui , Zhao Yichao , Xu Nan 
Published: November 2023

AbstractAn experimental investigation of circular concrete-filled steel tubular (CFST) columns subjected to very low-elevation lateral impacts was performed. Six circular CFST members were prepared for lateral impact tests according to the typical CFST columns in high-speed railway stations in China, and the impact location was at the height of the 2/9 column. The tests had three variables: the thickness of the steel tube, the impact velocity, and the axial load. The failure modes were determined in the tests, along with the time histories of the impact force and the deflection at the impact location. A finite-element analysis was performed to examine the effects of the axial load and scaling on the maximum deflection. The results show that with the increase of axial compression ratio, the impact resistance of the member first increases and then weakens. According to the travelling plastic hinge theory, a three-stage rigid plastic mechanical model was employed to describe the impact process, in which the impact location was at the non-mid-span, and a deflection calculation method for CFST applicable to any impact position was developed. A comparison with the test results indicated that deflections can be calculated with reasonable accuracy using the proposed method.
Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image
Authors: Soleimani Parisa , Farezi Navid 
Published: November 2023

AbstractThe segmentation of acute stroke lesions plays a vital role in healthcare by assisting doctors in making prompt and well-informed treatment choices. Although Magnetic Resonance Imaging (MRI) is a time-intensive procedure, it produces high-fidelity images widely regarded as the most reliable diagnostic tool available. Employing deep learning techniques for automated stroke lesion segmentation can offer valuable insights into the precise location and extent of affected tissue, enabling medical professionals to effectively evaluate treatment risks and make informed assessments. In this research, a deep learning approach is introduced for segmenting acute and sub-acute stroke lesions from MRI images. To enhance feature learning through brain hemisphere symmetry, pre-processing techniques are applied to the data. To tackle the class imbalance challenge, we employed a strategy of using small patches with balanced sampling during training, along with a dynamically weighted loss function that incorporates f1-score and IOU-score (Intersection over Union). Furthermore, the 3D U-Net architecture is used to generate predictions for complete patches, employing a high degree of overlap between patches to minimize the requirement for subsequent post-processing steps. The 3D U-Net model, utilizing ResnetV2 as the pre-trained encoder for IOU-score and Seresnext101 for f1-score, stands as the leading state-of-the-art (SOTA) model for segmentation tasks. However, recent research has introduced a novel model that surpasses these metrics and demonstrates superior performance compared to other backbone architectures. The f1-score and IOU-score were computed for various backbones, with Seresnext101 achieving the highest f1-score and ResnetV2 performing the highest IOU-score. These calculations were conducted using a threshold value of 0.5. This research proposes a valuable model based on transfer learning for the classification of brain diseases in MRI scans. The achieved f1-score using the recommended classifiers demonstrates the effectiveness of the approach employed in this study. The findings indicate that Seresnext101 attains the highest f1-score of 0.94226, while ResnetV2 achieves the best IOU-score of 0.88342, making it the preferred architecture for segmentation methods. Furthermore, the study presents experimental results of the 3D U-Net model applied to brain stroke lesion segmentation, suggesting prospects for researchers interested in segmenting brain strokes and enhancing 3D U-Net models.
Dual-color space network with global priors for photo retouching
Authors: Park Pilseo , Oh Heungmin , Kim Hyuncheol 
Published: November 2023

AbstractThere have been growing trends using deep learning-based approaches for photo retouching which aims to enhance unattractive images and make them visually appealing. However, the existing methods only considered the RGB color space, which limited the available color information for editing. To address this issue, we propose a dual-color space network that extracts color representations from multiple color spaces to provide more robust color information. Our approach is based on the observation that converting an image to a different color space generates a new image that can be further processed by a neural network. Hence, we utilize two separate networks: a transitional network and a base network, each operating in a different color space. Specifically, the input RGB image is converted to another color space (e.g., YCbCr) using color space converter (CSC). The resulting image is then passed through the transitional network to extract color representations from the corresponding color space using color prediction module (CPM). The output of the transitional network is converted back to the RGB space and fed into the base network, which operates in RGB space. By utilizing global priors from each representation in different color spaces, we guide the retouching process to produce natural and realistic results. Experimental results demonstrate that our proposed method outperforms state-of-the-art methods on the MIT-Adobe FiveK dataset, and an in-depth analysis and ablation study highlight the advantages of our approach.
Evaluation of cortical and trabecular bone structure of the mandible in patients with ankylosing spondylitis
Authors: Gulec Melike , Erturk Mediha , Tassoker Melek , Basdemirci Muserref 
Published: November 2023

AbstractThis study aimed to examine the difference between the fractal dimension (FD) values of the mandibular trabecular bone and the panoramic mandibular index (PMI), mandibular cortical index (MCI) and mandibular cortical thickness (MCW) of patients with ankylosing spondylitis (AS) and healthy control group. A total of 184 individuals (92 cases, 92 controls), were examined in our study. PMI, MCI, and MCW values were calculated on panoramic images of all individuals. For FD values, the region of interest (ROI) was selected with the size of 100 × 100 pixels from the right-left gonial and interdental regions and 50 × 50 pixels from the condylar region. Degenerative changes in the temporomandibular joint (TMJ) region were recorded. PMI, MCI, and MCW values showed statistically significant differences between the groups (p = 0.000, p < 0.001). The radiological signs of mandibular cortical resorption were more severe in the case group than in the control group. PMI and MCW values were found to be lower in the case group than in the control group. It was determined that the number of C3 and C2 values, among the MCI values, was higher in the case group. Only the FD values of the ROI selected from the condyle region were found to be statistically significant and were lower in the case group (p = 0.026, p < 0.05). Degenerative changes in the TMJ region were significantly more frequent in the case groups (p = 0.000, p < 0.001). The fact that the mandibular cortex shows more resorptive features in individuals with AS may require further evaluation in terms of osteoporosis. Because of the low FD values of the condylar regions of these patients and the more frequent degenerative changes, the TMJ region should be followed carefully. Detailed examination of the mandibular cortex and condylar region is beneficial in patients with AS for screening and following osteoporotic changes in these individuals, which is essential for the patient’s life quality.
Ensembles of climate simulations to anticipate worst case heatwaves during the Paris 2024 Olympics
Authors: Yiou Pascal , Cadiou Camille , Faranda Davide , Jézéquel Aglaé , Malhomme Nemo , Miloshevich George , Noyelle Robin , Pons Flavio , Robin Yoann , Vrac Mathieu 
Published: November 2023

AbstractThe Summer Olympic Games in 2024 will take place during the apex of the temperature seasonal cycle in the Paris Area. The mid-latitudes of the Northern hemisphere have witnessed a few intense heatwaves since the 2003 event. Those heatwaves have had environmental and health impacts, which often came as surprises. In this paper, we search for the most extreme heatwaves in Ile-de-France that are physically plausible, under climate change scenarios, for the decades around 2024. We circumvent the sampling limitation by applying a rare event algorithm on CMIP6 data to evaluate the range of such extremes. We find that the 2003 record can be exceeded by more than 4 °C in Ile-de-France before 2050, with a combination of prevailing anticyclonic conditions and cut-off lows. This study intends to raise awareness of those unprecedented events, against which our societies are ill-prepared, in spite of adaptation measures designed from previous events. Those results could be extended to other areas of the world.
A Cre-dependent massively parallel reporter assay allows for cell-type specific assessment of the functional effects of non-coding elements in vivo
Authors: Lagunas Tomas , Plassmeyer Stephen P. , Fischer Anthony D. , Friedman Ryan Z. , Rieger Michael A. , Selmanovic Din , Sarafinovska Simona , Sol Yvette K. , Kasper Michael J. , Fass Stuart B. , Aguilar Lucero Alessandra F. , An Joon-Yong , Sanders Stephan J. , Cohen Barak A. , Dougherty Joseph D. 
Published: November 2023

AbstractThe function of regulatory elements is highly dependent on the cellular context, and thus for understanding the function of elements associated with psychiatric diseases these would ideally be studied in neurons in a living brain. Massively Parallel Reporter Assays (MPRAs) are molecular genetic tools that enable functional screening of hundreds of predefined sequences in a single experiment. These assays have not yet been adapted to query specific cell types in vivo in a complex tissue like the mouse brain. Here, using a test-case 3′UTR MPRA library with genomic elements containing variants from autism patients, we developed a method to achieve reproducible measurements of element effects in vivo in a cell type-specific manner, using excitatory cortical neurons and striatal medium spiny neurons as test cases. This targeted technique should enable robust, functional annotation of genetic elements in the cellular contexts most relevant to psychiatric disease.
A fully 3D-printed versatile tumor-on-a-chip allows multi-drug screening and correlation with clinical outcomes for personalized medicine
Authors: Steinberg Eliana , Friedman Roy , Goldstein Yoel , Friedman Nethanel , Beharier Ofer , Demma Jonathan Abraham , Zamir Gideon , Hubert Ayala , Benny Ofra 
Published: November 2023

AbstractOptimal clinical outcomes in cancer treatments could be achieved through the development of reliable, precise ex vivo tumor models that function as drug screening platforms for patient-targeted therapies. Microfluidic tumor-on-chip technology is emerging as a preferred tool since it enables the complex set-ups and recapitulation of the physiologically relevant physical microenvironment of tumors. In order to overcome the common hindrances encountered while using this technology, a fully 3D-printed device was developed that sustains patient-derived multicellular spheroids long enough to conduct multiple drug screening tests. This tool is both cost effective and possesses four necessary characteristics of effective microfluidic devices: transparency, biocompatibility, versatility, and sample accessibility. Compelling correlations which demonstrate a clinical proof of concept were found after testing and comparing different chemotherapies on tumor spheroids, derived from ten patients, to their clinical outcomes. This platform offers a potential solution for personalized medicine by functioning as a predictive drug-performance tool.
Hybrid photothermal–photocatalyst sheets for solar-driven overall water splitting coupled to water purification
Authors: Pornrungroj Chanon , Mohamad Annuar Ariffin Bin , Wang Qian , Rahaman Motiar , Bhattacharjee Subhajit , Andrei Virgil , Reisner Erwin 
Published: November 2023

AbstractPhotocatalytic water splitting converts sunlight directly into storable hydrogen, but commonly involves the use of pure water and land for plant installation while generating unusable waste heat. Here we report a hybrid device consisting of a photocatalyst (PC) and a solar vapour generator (SVG) for simultaneous overall water splitting and water purification from open water sources. Specifically, an ultraviolet light-absorbing RhCrOx–Al:SrTiO3 PC is deposited on top of a floating, visible and infrared light-absorbing porous carbon SVG, which produces green fuel with a solar-to-hydrogen efficiency of 0.13 ± 0.03% and 0.95 kg m−2 h−1 of water vapour as the feed for the PC and collectable purified water. This integrated system maintains operational stability in seawater and other aqueous waste streams for over 154 h due to the isolation of the PC from contaminants in the liquid feedstock. This work provides a new concept for developing an off-grid energy production/storage solution and is a first step towards alleviating both energy and water supply challenges.
Solvation rules: aromatic interactions outcompete cation–π interactions in synthetic host–guest complexes in water
Authors: Tobajas-Curiel Gloria , Sun Qingqing , Sanders Jeremy K. M. , Ballester Pablo , Hunter Christopher A. 
Published: November 2023

Chemical double mutant cycles were used to measure the interaction of a π-box in a calix[4]pyrrole receptor with a cationic N-methyl pyridinium group and with neutral isosteric analogues in water.
Threshold Estimation and Speech Perception Under Hearing Loss Simulation: Examination of the Immersive Hearing Loss and Prosthesis Simulator
Authors: Roman Aaron M. , Pratt Sheila R. , Zhen Leslie Q. 
Published: November 2023

Purpose:
Hearing loss simulation (HLS) has been recommended for clinical teaching and counseling of patients and their families, so that they can experience hearing impairment. However, few validated procedures for simulating hearing loss are available to instructors and practicing clinicians. The aim of this study was to assess the accuracy of the Immersive Hearing Loss and Prosthesis Simulator (I-HeLPS) on reducing hearing sensitivity and word recognition to determine its adequacy for educational and clinical use.

Method:
Thirty-seven young adults with normal hearing completed hearing threshold and word recognition testing under normal and simulated hearing losses. The accuracy of the nominal hearing threshold settings within the I-HeLPS software was assessed with behavioral detection of frequency-modulated pure tones presented in a calibrated sound field, while listeners wore I-HeLPS headphones. The impact of the HLSs on speech perception was measured using the California Consonant Test. Hearing thresholds, word identification accuracy, and sound confusions were compared across listening conditions.

Results:
Hearing thresholds increased systematically with worse simulated hearing loss. Performance on the California Consonant Test worsened, and the number of phoneme confusions increased with simulated hearing loss severity. Most of the confusions were place confusions with near neighbors and manner confusions increased as a function of increasing severity of simulated hearing loss.

Conclusions:
The I-HeLPS accurately elevated hearing thresholds with increasing HLS severity and impacted word recognition in a manner consistent with sensorineural hearing loss. The simulations were considered reasonable for educational and clinical purposes.

Supplemental Material:

https://doi.org/10.23641/asha.24520966
AutoSourceID-Classifier
Authors: Stoppa F. , Bhattacharyya S. , Ruiz de Austri R. , Vreeswijk P. , Caron S. , Zaharijas G. , Bloemen S. , Principe G. , Malyshev D. , Vodeb V. , Groot P. J. , Cator E. , Nelemans G. 
Published: November 2023

Aims.Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification’s reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images.Methods.The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results.Results.We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C’s direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.
Optionen zur Therapie der felinen infektiösen Peritonitis – früher und heute
Authors: Krentz Daniela , Bergmann Michèle , Felten Sandra , Hartmann Katrin 
Published: November 2023

ZusammenfassungDie feline infektiöse Peritonitis (FIP) ist eine der häufigsten Infektionskrankheiten bei Katzen und verläuft unbehandelt tödlich. Bisher gibt es in Deutschland keine legal verfügbare wirksame Therapie. Therapieoptionen reichen von der symptomatischen Therapie (z. B. Glukokortikoide, Propentofyllin) über immunmodulatorische Ansätze (z. B. Interferone, Polyprenyl-Immunstimulanz) bis hin zur antiviralen Therapie mit einem Protease-Inhibitor (z. B. GC376) oder Nukleosid-Analoga (z. B. GS-441524, Remdesivir). Die symptomatische Therapie führt nicht zur Heilung der FIP, sondern nur zu einer kurzzeitigen Verbesserung der klinischen Symptome bei wenigen Katzen. Auch eine immunmodulatorische Therapie stellte sich als wenig erfolgversprechend heraus. Die antiviralen Medikamente GS-441524 und GC376 waren in mehreren Studien hochwirksam und konnten das Leben vieler an FIP erkrankten Katzen retten. Beide Wirkstoffe sind aktuell in Deutschland nicht zugelassen und können von Tierärzten nicht legal angewendet werden. Katzen dürfen aktuell nur in wenigen Ländern (z. B. Großbritannien und Australien) legal mit GS-441524 therapiert werden. GS-441524 wird daher von Katzenbesitzern in vielen anderen Ländern über den Schwarzmarkt bestellt und in Eigenregie angewendet. Dieser Artikel gibt eine Übersicht über verfügbare Therapieoptionen und einen Ausblick zur legalen Anwendung wirksamer antiviraler Medikamente.
Morbidität und Mortalität von Stute und Fohlen nach Dystokie – eine Literaturübersicht
Authors: Ellerbrock Markus , Wehrend Axel 
Published: November 2023

ZusammenfassungEine Dystokie stellt für die Stute und das Fohlen einen lebensbedrohlichen Zustand dar. Anhand einer Literaturauswertung wurden die Morbiditäts- und Todesraten durch eine Schwergeburt bei Stute und Fohlen sowie der Einfluss auf die weitere Fruchtbarkeit der Stute analysiert. Erschwerend bei der Auswertung der Literatur ist, dass kein einheitlicher Diagnoseschlüssel genutzt wurde und dass in den meisten Veröffentlichungen nicht deutlich wird, wie umfangreich die Untersuchungen am Tier durchgeführt wurden, um zur Diagnose zu gelangen. Die Retentio secundinarum ist die häufigste Komplikation nach Schwergeburt. Weitere Komplikationen der Dystokie sind Verletzungen im weichen Geburtsweg und des Dickdarmes. Diese treten nach chirurgischer Geburtshilfe häufiger auf als nach Auszug. Bei Durchführung einer Fetotomie steigt die Gefahr von Verletzungen im Geburtsweg. Nach einer Sectio caesarea nimmt das Risiko einer Nachgeburtsverhaltung deutlich zu. Zur Darlegung eines möglichen obstetrischen Fortschritts wurden recherchierte Mortalitätsraten von Stute und Fohlen nach chirurgischer sowie konservativer Geburtshilfe in die Perioden 1970–1990 und 1991–2021 unterteilt. Die durchschnittliche maternale Todesrate nach einer Sectio caesarea liegt im Zeitraum 1970–1990 bei 18% und von 1991 bis 2021 bei 14%. Die 2 ermittelten Angaben zur Todesrate nach einer Fetotomie betrugen 29% und 10% in der Zeitspanne 1970–1990. In der Periode 1991–2021 variieren die Daten zwischen 4 und 44%, wobei sich eine durchschnittliche Todesrate von 14% ergibt. Die durchschnittliche maternale Todesrate nach Auszug unter Allgemeinanästhesie liegt bei 9%. Die Literaturangaben lagen hierbei allerdings nur für den aktuelleren Zeitraum vor und schwanken zwischen 6–29%. Die Erkrankungs- und Todesrate von Fohlen ist sehr hoch. Im Rahmen eines Kaiserschnittes werden 53% der Fohlen tot entwickelt, wobei die Todesrate in den einzelnen Fallserien aus den Jahren 1991–2021 von 13–79% variiert. Die Anzahl tot entwickelter Fohlen im Rahmen einer konservativen Geburtshilfe liegt in der gleichen Größenordnung. Postnatale Erkrankungen des Fohlens sind zum größten Teil auf die intrapartale Hypoxie während der Dystokie und Verletzungen durch die Geburtshilfe zurückzuführen. Nach Schwergeburt zeigen Stuten eine reduzierte Fertilität, was jedoch durch eine Zuchtpause im gleichen Jahr in vielen Fällen kompensiert werden kann. Insgesamt ist die Anzahl ausgewerteter Fälle aber bisher zu gering, um verbindliche Hinweise für eine Zuchtnutzung nach Dystokie formulieren zu können.
Polyendokrine Syndrome beim Hund
Authors: Strey Sina , Mischke Reinhard , Rieder Johanna 
Published: November 2023

ZusammenfassungDas autoimmune polyendokrine Syndrom (APS) ist eine beim Hund selten vorkommende
Kombination autoimmun bedingter endokriner Erkrankungen. Häufige
Kombinationen sind Hypothyreose mit Hypoadrenokortizismus oder Diabetes
mellitus. Des Weiteren können seltener Hypoparathyreoidismus und
Orchitis als Teil eines APS auftreten. Die Diagnose des APS erfolgt über
die Diagnose der jeweiligen Erkrankungen. Ebenso besteht die Therapie des APS in
der Therapie der beteiligten Erkrankungen, d.h. in der Substitution der
defizienten Hormone. Entstehen unter einer zuvor stabilen Therapie einer
endokrinen Erkrankung weitere Symptome (z.B. Polyurie, Polydipsie oder
Gewichtsverlust), sollte an die zusätzliche Entstehung anderer
Endokrinopathien wie Hypoadrenokortizismus oder Diabetes mellitus gedacht
werden. Die Diagnose der initial diagnostizierten Endokrinopathie sollte jedoch
ebenfalls kritisch hinterfragt werden. Dieser Artikel fasst Fallberichte aus dem
eigenen Patientengut sowie ausgewählte Fallberichte aus der Literatur
mit ihren Besonderheiten zusammen.