bluuebunny/crossref_metadata_2025_split huggingface dataset:
A study on volumetric change of mandibular condyles with osteoarthritis using cone-beam computed tomography
Authors: Min Chang-Ki , Kim Kyoung-A , Lee Kyung-Eun , Suh Bong-Jik , Jung Won 
Published: May 2024

AbstractThis study aimed to quantitatively assess three-dimensional changes in the mandibular condyle with osteoarthritis using cone-beam computed tomography (CBCT). Pre- and post-treatment CBCT images of temporomandibular joints (TMJs) from 66 patients were used to assess longitudinal changes in condylar volume within individual patients using 3D slicer software. Total volume difference (dV), net increase (dV + , bone deposition), and net decrease (dV− , bone resorption) after treatment were analyzed based on clinical and radiological factors. Condyles with surface erosion at their first visit showed significantly decreased volume after treatment compared to condyles without erosion (p < 0.05). Amounts of bone resorption and deposition were higher in condyles with surface erosion (both p < 0.01). In patients with condylar erosion, the presence of joint pain was associated with a decrease in condylar volume and an increase in net resorption (both p < 0.01). When both joint pain and condylar erosion were present, patients with parafunctional habits showed reduced condylar volume after treatment (p < 0.05). Condylar volume change after treatment was negatively correlated with the duration of pain relief (R = − 0.501, p < 0.05). These results indicate that condylar erosion and TMJ pain could be significant variables affecting TMJ volume changes after treatment. Establishing appropriate treatment strategies is crucial for managing condylar erosion and TMJ pain.
Fabrication and characterization of a new eco-friendly sulfonamide-chitosan derivative with enhanced antimicrobial and selective cytotoxicity properties
Authors: Ibrahim Ahmed G. , Hamodin Ahmed G. , Fouda Amr , Eid Ahmed M. , Elgammal Walid E. 
Published: May 2024

AbstractChitosan (CH) exhibits low antimicrobial activity. This study addresses this issue by modifying the chitosan with a sulfonamide derivative, 3-(4-(N,N-dimethylsulfonyl)phenyl)acrylic acid. The structure of the sulfonamide-chitosan derivative (DMS-CH) was confirmed using Fourier transform infrared spectroscopy and Nuclear magnetic resonance. The results of scanning electron microscopy, thermal gravimetric analysis, and X-ray diffraction indicated that the morphology changed to a porous nature, the thermal stability decreased, and the crystallinity increased in the DMS-CH derivative compared to chitosan, respectively. The degree of substitution was calculated from the elemental analysis data and was found to be moderate (42%). The modified chitosan exhibited enhanced antimicrobial properties at low concentrations, with a minimum inhibitory concentration (MIC) of 50 µg/mL observed for B. subtilis and P. aeruginosa, and a value of 25 µg/mL for S. aureus, E. coli, and C. albicans. In the case of native chitosan, the MIC values doubled or more, with 50 µg/mL recorded for E. coli and C. albicans and 100 μg/mL recorded for B. subtilis, S. aureus, and P. aeruginosa. Furthermore, toxicological examinations conducted on MCF-7 (breast adenocarcinoma) cell lines demonstrated that DMS-CH exhibited greater toxicity (IC50 = 225.47 μg/mL) than pure CH, while still maintaining significant safety limits against normal lung fibroblasts (WI-38). Collectively, these results suggest the potential use of the newly modified chitosan in biomedical applications.
Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique
Authors: Bachhal Prabhnoor , Kukreja Vinay , Ahuja Sachin , Lilhore Umesh Kumar , Simaiya Sarita , Bijalwan Anchit , Alroobaea Roobaea , Algarni Sultan 
Published: May 2024

AbstractThe difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize leaves difficult. It is critical to monitor and identify plant leaf diseases during the initial growing period to take suitable preventative measures. In this work, we propose an automated maize leaf disease recognition system constructed using the PRF-SVM model. The PRFSVM model was constructed by combining three powerful components: PSPNet, ResNet50, and Fuzzy Support Vector Machine (Fuzzy SVM). The combination of PSPNet and ResNet50 not only assures that the model can capture delicate visual features but also allows for end-to-end training for smooth integration. Fuzzy SVM is included as a final classification layer to accommodate the inherent fuzziness and uncertainty in real-world image data. Five different maize crop diseases (common rust, southern rust, grey leaf spot, maydis leaf blight, and turcicum leaf blight along with healthy leaves) are selected from the Plant Village dataset for the algorithm’s evaluation. The average accuracy achieved using the proposed method is approximately 96.67%. The PRFSVM model achieves an average accuracy rating of 96.67% and a mAP value of 0.81, demonstrating the efficacy of our approach for detecting and classifying various forms of maize leaf diseases.
Dogs understand the role of a human partner in a cooperative task
Authors: Wallner Werneck Mendes Juliana , Vindevogel Marie , van Peer Ilka , Martínez Mayte , Cimarelli Giulia , Range Friederike 
Published: May 2024

AbstractHumans are exceptionally flexible in cooperation, partly due to our ability to recognize the roles of cooperative partners. While some non-human animals understand the need for a partner in such interactions, it is unclear whether they grasp the consequences of their partner’s actions and adjust accordingly. Previous studies utilizing economic games with non-human animals yielded mixed results. We investigated dogs, known for their close cooperation with humans, in a stag hunt game. Dogs could cooperate for better rewards or defect for lower ones, while their human partners would either cooperate, never cooperate, or act randomly. We control for attraction to food, side bias, and local enhancement. Dogs were more likely to coordinate with their partners when it led to better rewards, suggesting that they understood their partner’s actions. By highlighting this cognitive skill in dogs, we advance our knowledge of the intricate mechanisms driving cooperative behavior in non-human animals.
Discovery of the first hydrothermal field along the 500-km-long Knipovich Ridge offshore Svalbard (the Jøtul field)
Authors: Bohrmann Gerhard , Streuff Katharina , Römer Miriam , Knutsen Stig-Morten , Smrzka Daniel , Kleint Jan , Röhler Aaron , Pape Thomas , Sandstå Nils Rune , Kleint Charlotte , Hansen Christian , dos Santos Ferreira Christian , Walter Maren , de Paula Santos Gustavo Macedo , Bach Wolfgang 
Published: May 2024

AbstractOceanic spreading centers north of Iceland are characterized by ultraslow spreading rates, and related hydrothermal activity has been detected in the water column and at the seafloor along nearly all ridge segments. An exception is the 500-km-long Knipovich Ridge, from where, until now, no hydrothermal vents were known. Here we report the investigation of the first hydrothermal vent field of the Knipovich Ridge, which was discovered in July 2022 during expedition MSM109. The newly discovered hydrothermal field, named Jøtul hydrothermal field, is associated with the eastern bounding fault of the rift valley rather than with an axial volcanic ridge. Guided by physico-chemical anomalies in the water column, ROV investigations on the seafloor showed a wide variety of fluid escape sites, inactive and active mounds with abundant hydrothermal precipitates, and chemosynthetic organisms. Fluids with temperatures between 8 and 316 °C as well as precipitates were sampled at four vent sites. High methane, carbon dioxide, and ammonium concentrations, as well as high 87Sr/86Sr isotope ratios of the vent fluids indicate strong interaction between magma and sediments from the Svalbard continental margin. Such interactions are important for carbon mobilization at the seafloor and the carbon cycle in the ocean.
Reducing biomass burning is key to decrease PM2.5 exposure in European cities
Authors: Zauli-Sajani Stefano , Thunis Philippe , Pisoni Enrico , Bessagnet Bertrand , Monforti-Ferrario Fabio , De Meij Alexander , Pekar Ferenc , Vignati Elisabetta 
Published: May 2024

AbstractThroughout the world, ambient fine particulate matter (PM2.5) is the environmental factor that poses the greatest risk to health and most European citizens continue to be exposed to PM2.5 levels well above World Health Organization guidelines. Here we present a comprehensive PM2.5 modelling-based source allocation assessment in 708 urban areas in Europe. The results show that urban cores, together with their commuting zones, contribute an average of 22% to urban PM2.5 concentrations levels. The residential sector is the highest source sector in 56% of cities. Its average contribution to PM2.5 formation is 27%, with a cluster of cities in Northern Italy and Eastern Europe contributing to more than 50%. Industry, agriculture and road transport show average contributions of 18%, 17% and 14%, respectively. Most emissions from residential sectors are anthropogenic primary PM2.5 which includes a condensable fraction. Furthermore, anthropogenic primary PM2.5 represents the precursor with the highest contribution in most cities (72%), contributing an average of 35% to urban PM2.5 levels. Emissions of anthropogenic primary PM2.5 by the residential sector are almost entirely (with exceptions of few countries) due to biomass burning. These results suggest that the residential sector should be a key target of any policy to improve air quality and that climate policies promoting biomass as a climate-neutral fuel could have a detrimental effect on air quality. A more integrated approach to climate and air quality policy design is desirable.
Functional connectivity associated with attention networks differs among subgroups of fibromyalgia patients: an observational case–control study
Authors: Aoe Tomohiko , Kawanaka Ryoko , Ohsone Fumio , Hara Akira , Yokokawa Tokuzo 
Published: May 2024

AbstractFibromyalgia is a heterogenous chronic pain disorder diagnosed by symptom-based criteria. The aim of this study was to clarify different pathophysiological characteristics between subgroups of patients with fibromyalgia. We identified subgroups with distinct pain thresholds: those with a low pressure pain threshold (PL; 16 patients) and those with a normal pressure pain threshold (PN; 15 patients). Both groups experienced severe pain. We performed resting-state functional MRI analysis and detected 11 functional connectivity pairs among all 164 ROIs with distinct difference between the two groups (p < 0.001). The most distinctive one was that the PN group had significantly higher functional connectivity between the secondary somatosensory area and the dorsal attention network (p < 0.0001). Then, we investigated the transmission pathway of pain stimuli. Functional connectivity of the thalamus to the insular cortex was significantly higher in the PL group (p < 0.01 – 0.05). These results suggest that endogenous pain driven by top-down signals via the dorsal attention network may contribute to pain sensation in a subgroup of fibromyalgia patients with a normal pain threshold. Besides, external pain driven by bottom-up signals via the spinothalamic tract may contribute to pain sensations in another group of patients with a low pain threshold. Trial registration: UMIN000037712.
Permeability modelling in a highly heterogeneous tight carbonate reservoir using comparative evaluating learning-based and fitting-based approaches
Authors: Hajibolouri Ehsan , Roozshenas Ali Akbar , Miri Rohaldin , Soleymanzadeh Aboozar , Kord Shahin , Shafiei Ali 
Published: May 2024

AbstractPermeability modelling is considered a complex task in reservoir characterization and a key component of reservoir simulation. A common method for permeability modelling involves performing static rock typing (SRT) using routine core analysis data and developing simple fitting-based mathematical relations that link permeability to reservoir rock porosity. In the case of carbonate reservoirs, which are associated with high heterogeneities, fitting-based approaches may fail due to porosity–permeability data scattering. Accurate modelling of permeability using petrophysical well log data seems more promising since they comprise a vast array of information about the intrinsic properties of the geological formations. Furthermore, well log data exhibit continuity throughout the entire reservoir interval, whereas core data are discrete and limited in availability and coverage. In this research work, porosity, permeability and log data of two oil wells from a tight carbonate reservoir were used to predict permeability at un-cored intervals. Machine learning (ML) and fitting models were used to develop predictive models. Then, the developed ML models were compared to exponential and statistical fitting modelling approaches. The integrated ML permeability model based on Random Forest method performed significantly superior to exponential and statistical fitting-based methods. Accordingly, for horizontal and vertical permeability of test samples, the Root Mean Squared Error (RMSE) values were 3.7 and 4.5 for well 2, and 1.7 and 0.86 for well 4, respectively. Hence, using log data, permeability modelling was improved as it incorporates more comprehensive reservoir rock physics. The outcomes of this reach work can be used to improve the distribution of both horizontal and vertical permeability in the 3D model for future dynamic reservoir simulations in such a complex and heterogeneous reservoir system.
FFA-GPT: an automated pipeline for fundus fluorescein angiography interpretation and question-answer
Authors: Chen Xiaolan , Zhang Weiyi , Xu Pusheng , Zhao Ziwei , Zheng Yingfeng , Shi Danli , He Mingguang 
Published: May 2024

AbstractFundus fluorescein angiography (FFA) is a crucial diagnostic tool for chorioretinal diseases, but its interpretation requires significant expertise and time. Prior studies have used Artificial Intelligence (AI)-based systems to assist FFA interpretation, but these systems lack user interaction and comprehensive evaluation by ophthalmologists. Here, we used large language models (LLMs) to develop an automated interpretation pipeline for both report generation and medical question-answering (QA) for FFA images. The pipeline comprises two parts: an image-text alignment module (Bootstrapping Language-Image Pre-training) for report generation and an LLM (Llama 2) for interactive QA. The model was developed using 654,343 FFA images with 9392 reports. It was evaluated both automatically, using language-based and classification-based metrics, and manually by three experienced ophthalmologists. The automatic evaluation of the generated reports demonstrated that the system can generate coherent and comprehensible free-text reports, achieving a BERTScore of 0.70 and F1 scores ranging from 0.64 to 0.82 for detecting top-5 retinal conditions. The manual evaluation revealed acceptable accuracy (68.3%, Kappa 0.746) and completeness (62.3%, Kappa 0.739) of the generated reports. The generated free-form answers were evaluated manually, with the majority meeting the ophthalmologists’ criteria (error-free: 70.7%, complete: 84.0%, harmless: 93.7%, satisfied: 65.3%, Kappa: 0.762–0.834). This study introduces an innovative framework that combines multi-modal transformers and LLMs, enhancing ophthalmic image interpretation, and facilitating interactive communications during medical consultation.
Persistent T cell unresponsiveness associated with chronic visceral leishmaniasis in HIV-coinfected patients
Authors: de Vrij Nicky , Pollmann Julia , Rezende Antonio M. , Ibarra-Meneses Ana V. , Pham Thao-Thy , Hailemichael Wasihun , Kassa Mekibib , Bogale Tadfe , Melkamu Roma , Yeshanew Arega , Mohammed Rezika , Diro Ermias , Maes Ilse , Domagalska Malgorzata A. , Landuyt Hanne , Vogt Florian , van Henten Saskia , Laukens Kris , Cuypers Bart , Meysman Pieter , Beyene Hailemariam , Sisay Kasaye , Kibret Aderajew , Mersha Dagnew , Ritmeijer Koert , van Griensven Johan , Adriaensen Wim 
Published: May 2024

AbstractA large proportion of HIV-coinfected visceral leishmaniasis (VL-HIV) patients exhibit chronic disease with frequent VL recurrence. However, knowledge on immunological determinants underlying the disease course is scarce. We longitudinally profiled the circulatory cellular immunity of an Ethiopian HIV cohort that included VL developers. We show that chronic VL-HIV patients exhibit high and persistent levels of TIGIT and PD-1 on CD8+/CD8- T cells, in addition to a lower frequency of IFN-γ+ TIGIT- CD8+/CD8- T cells, suggestive of impaired T cell functionality. At single T cell transcriptome and clonal resolution, the patients show CD4+ T cell anergy, characterised by a lack of T cell activation and lymphoproliferative response. These findings suggest that PD-1 and TIGIT play a pivotal role in VL-HIV chronicity, and may be further explored for patient risk stratification. Our findings provide a strong rationale for adjunctive immunotherapy for the treatment of chronic VL-HIV patients to break the recurrent disease cycle.
Influence of large-scale circulation and local feedbacks on extreme summer heat in Argentina in 2022/23
Authors: Collazo Soledad , Suli Solange , Zaninelli Pablo G. , García-Herrera Ricardo , Barriopedro David , Garrido-Perez José M. 
Published: May 2024

AbstractThe summer of 2022/23 in Argentina set a record with ten heatwaves. Here, we compare the synoptic and thermodynamic conditions of the four heatwaves with the largest spatial extent using ERA5 reanalysis data. All heatwaves were associated with mid-level anticyclonic anomalies but with different characteristics: three were quasi-stationary high-pressure systems, while one was transient. We also find that enhanced surface fluxes strongly influenced the daily temperature evolution. Furthermore, we perform an attribution exercise using the analogue technique to measure the contributions of atmospheric circulation, soil moisture, and climate change. For quasi-stationary events, the main contribution came from the circulation (up to 2 °C relative to random flow in the present climate). Conversely, the transient heatwave showed a stronger association with extreme soil moisture deficits, with an estimated increase of ~+1 °C relative to wetter conditions. Climate change has also increased the intensity of heatwaves by +0.5 to +1.2 °C relative to previous decades.
sFlt-1 impairs neurite growth and neuronal differentiation in SH-SY5Y cells and human neurons
Authors: Barron Aaron , Barrett Lauren , Tuulari Jetro J. , Karlsson Linnea , Karlsson Hasse , McCarthy Cathal M. , O'Keeffe Gerard W. 
Published: May 2024

Abstract
Pre-eclampsia (PE) is a hypertensive disorder of pregnancy which is associated with increased risk of neurodevelopmental disorders in exposed offspring. The pathophysiological mechanisms mediating this relationship are currently unknown, and one potential candidate is the anti-angiogenic factor soluble Fms-like tyrosine kinase 1 (sFlt-1), which is highly elevated in PE. While sFlt-1 can impair angiogenesis via inhibition of VEGFA signalling, it is unclear whether it can directly affect neuronal development independently of its effects on the vasculature. To test this hypothesis, the current study differentiated the human neural progenitor cell (NPC) line ReNcell® VM into a mixed culture of mature neurons and glia, and exposed them to sFlt-1 during development. Outcomes measured were neurite growth, cytotoxicity, mRNA expression of nestin, MBP, GFAP, and βIII-tubulin, and neurosphere differentiation. sFlt-1 induced a significant reduction in neurite growth and this effect was timing- and dose-dependent up to 100 ng/ml, with no effect on cytotoxicity. sFlt-1 (100 ng/ml) also reduced βIII-tubulin mRNA and neuronal differentiation of neurospheres. Undifferentiated NPCs and mature neurons/glia expressed VEGFA and VEGFR-2, required for endogenous autocrine and paracrine VEGFA signalling, while sFlt-1 treatment prevented the neurogenic effects of exogenous VEGFA. Overall, these data provide the first experimental evidence for a direct effect of sFlt-1 on neurite growth and neuronal differentiation in human neurons through inhibition of VEGFA signalling, clarifying our understanding of the potential role of sFlt-1 as a mechanism by which PE can affect neuronal development.
Early fault detection for rolling bearings: A meta‐learning approach
Authors: Song Wenbin , Wu Di , Shen Weiming , Boulet Benoit 
Published: May 2024

AbstractEarly fault detection (EFD) of rolling bearings aims at detecting the early symptoms of faults by monitoring small deviations of health states. Accurate EFD enables predictive maintenance and contributes to the stability of mechanical systems. In recent years, machine learning based methods have shown impressive performance on EFD. Most of the current machine learning‐based methods assume the availability for a large amount of data. However, in practice, the authors may only have a very limited amount of training data, which makes it hard to learn a reliable machine learning model. To address this concern, in this work, the authors propose to tackle EFD via meta learning. Specifically, the authors first formulate EFD as a few‐shot learning problem and then propose to tackle this problem with a metric‐based meta learning method. Furthermore, ensemble learning is further leveraged to improve the detection robustness. For the proposed method, the distribution difference from the working conditions and the bearings are considered. The experimental results on two bearing datasets show that the proposed method can achieve better EFD performance, that is, detecting incipient faults earlier while bringing in lower false alarms, compared with several frequently used EFD methods.
MSO‐DETR: Metric space optimization for few‐shot object detection
Authors: Sima Haifeng , Wang Manyang , Liu Lanlan , Zhang Yudong , Sun Junding 
Published: May 2024

AbstractIn the metric‐based meta‐learning detection model, the distribution of training samples in the metric space has great influence on the detection performance, and this influence is usually ignored by traditional meta‐detectors. In addition, the design of metric space might be interfered with by the background noise of training samples. To tackle these issues, we propose a metric space optimisation method based on hyperbolic geometry attention and class‐agnostic activation maps. First, the geometric properties of hyperbolic spaces to establish a structured metric space are used. A variety of feature samples of different classes are embedded into the hyperbolic space with extremely low distortion. This metric space is more suitable for representing tree‐like structures between categories for image scene analysis. Meanwhile, a novel similarity measure function based on Poincaré distance is proposed to evaluate the distance of various types of objects in the feature space. In addition, the class‐agnostic activation maps (CCAMs) are employed to re‐calibrate the weight of foreground feature information and suppress background information. Finally, the decoder processes the high‐level feature information as the decoding of the query object and detects objects by predicting their locations and corresponding task encodings. Experimental evaluation is conducted on Pascal VOC and MS COCO datasets. The experiment results show that the effectiveness of the authors’ method surpasses the performance baseline of the excellent few‐shot detection models.