bluuebunny/crossref_metadata_2025_split huggingface dataset:
The RNA M5C methyltransferase NSUN2 promotes progression of hepatocellular carcinoma by enhancing PKM2-mediated glycolysis
Authors: Qi Qin , Zhong Rui , Huang Yan , Tang Yong , Zhang Xiao-wen , Liu Chang , Gao Chun-fang , Zhou Lin , Yu Jian , Wu Lu-yi 
Published: February 2025

Abstract
Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. The 5-methylcytosine (m5C) RNA methyltransferase NSUN2 is involved in cell proliferation and metastasis and is upregulated in a variety of cancers. However, the biological function and regulatory mechanism of NSUN2-mediated m5C modification have not been well studied in HCC. Our results showed that NSUN2 is upregulated and associated with poor prognosis in HCC patients after hepatectomy. NSUN2 overexpression significantly promoted HCC growth and metastasis, whereas NSUN2 knockdown had the opposite effect. m5C RNA immunoprecipitation sequencing (m5C-RIP-Seq) revealed that m5C hypermethylation correlates with mRNA overexpression and that NSUN2-mediated m5C hypermethylation promotes metabolism in HCC patients. Mechanistically, our data revealed that PKM2, a terminal enzyme in the glycolytic pathway, is a downstream target of NSUN2-mediated m5C modification. Specifically, NSUN2 could stabilize PKM2 mRNA by increasing the m5C level of the m5C site C773 in the 3′-UTR of PKM2 mRNA. In addition, rescue assays revealed that NSUN2 promotes HCC glycolysis and progression by upregulating PKM2. In conclusion, this study revealed that NSUN2-mediated m5C modification promotes glycolysis and the progression of hepatocellular carcinoma by stabilizing PKM2 mRNA, and provides a potential prognostic factor and therapeutic target for HCC patients.
Safety and efficacy of a novel ‘One-Visit, Both-Cataracts’ high-volume see-and-treat immediate sequential bilateral cataract surgery service in a public healthcare setting
Authors: Alsusa Maher , Ahmad Shakeel , Smith Zoe , Tutchings Emma , Evans Sam , Wilkinson Elizabeth , Roberts Harry 
Published: February 2025

Abstract

Purpose
To evaluate the safety and efficacy of a novel cataract surgery pathway that combines a See-and-Treat (S&T) model with Immediate Sequential Bilateral Cataract Surgery (ISBCS) at the Nightingale Hospital, Exeter, UK.

Methods
A retrospective observational study was conducted on 102 consecutive patients (204 eyes) who underwent S&T ISBCS between July 2023 and July 2024. Patients were triaged based on referral information and underwent preoperative telephone consultations. On the day of surgery, clinical assessment and bilateral cataract surgery were completed in a single visit. Data collected included patient demographics, intraoperative and postoperative outcomes, and complications.

Results
Of the 127 patients listed, 102 (84.3%) completed S&T ISBCS. No intraoperative complications were recorded. Fourteen patients (13.7%) required unplanned postoperative consultations, with most cases being non-sight-threatening and self-resolving. Cystoid macular oedema (CMO) was reported in 2.9% of eyes, with no cases of visual loss or endophthalmitis.

Conclusion
The S&T ISBCS model demonstrated safety and efficiency in delivering cataract care, with a high one-visit completion rate and low complication rates. This model offers significant time and resource savings whilst maintaining patient safety. It holds potential for broader implementation in healthcare settings facing increased demand for cataract services. Further studies are recommended to assess long-term outcomes and optimise this approach.
Discrete vulnerability to pharmacological CDK2 inhibition is governed by heterogeneity of the cancer cell cycle
Authors: Kumarasamy Vishnu , Wang Jianxin , Roti Michelle , Wan Yin , Dommer Adam P. , Rosenheck Hanna , Putta Sivasankar , Trub Alec , Bisi John , Strum Jay , Roberts Patrick , Rubin Seth M. , Frangou Costakis , McLean Karen , Witkiewicz Agnieszka K. , Knudsen Erik S. 
Published: February 2025

Abstract
Cyclin dependent kinase 2 (CDK2) regulates cell cycle and is an emerging target for cancer therapy. There are relatively small numbers of tumor models that exhibit strong dependence on CDK2 and undergo G1 cell cycle arrest following CDK2 inhibition. The expression of P16INK4A and cyclin E1 determines this sensitivity to CDK2 inhibition. The co-expression of these genes occurs in breast cancer patients highlighting their clinical significance as predictive biomarkers for CDK2-targeted therapies. In cancer models that are genetically independent of CDK2, pharmacological inhibitors suppress cell proliferation by inducing 4N cell cycle arrest and increasing the expressions of phospho-CDK1 (Y15) and cyclin B1. CRISPR screens identify CDK2 loss as a mediator of resistance to a CDK2 inhibitor, INX-315. Furthermore, CDK2 deletion reverses the G2/M block induced by CDK2 inhibitors and restores cell proliferation. Complementary drug screens define multiple means to cooperate with CDK2 inhibition beyond G1/S. These include the depletion of mitotic regulators as well as CDK4/6 inhibitors cooperate with CDK2 inhibition in multiple phases of the cell cycle. Overall, this study underscores two fundamentally distinct features of response to CDK2 inhibitors that are conditioned by tumor context and could serve as the basis for differential therapeutic strategies in a wide range of cancers.
Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP
Authors: Birk Florian , Mahler Lucas , Steiglechner Julius , Wang Qi , Scheffler Klaus , Heule Rahel 
Published: February 2025

Abstract
To accelerate the clinical adoption of quantitative magnetic resonance imaging (qMRI), frameworks are needed that not only allow for rapid acquisition, but also flexibility, cost efficiency, and high accuracy in parameter mapping. In this study, feed-forward deep neural network (DNN)- and iterative fitting-based frameworks are compared for multi-parametric (MP) relaxometry based on phase-cycled balanced steady-state free precession (pc-bSSFP) imaging. The performance of supervised DNNs (SVNN), self-supervised physics-informed DNNs (PINN), and an iterative fitting framework termed motion-insensitive rapid configuration relaxometry (MIRACLE) was evaluated in silico and in vivo in brain tissue of healthy subjects, including Monte Carlo sampling to simulate noise. DNNs were trained on three distinct in silico parameter distributions and at different signal-to-noise-ratios. The PINN framework, which incorporates physical knowledge into the training process, ensured more consistent inference and increased robustness to training data distribution compared to the SVNN. Furthermore, DNNs utilizing the full information of the underlying complex-valued MR data demonstrated ability to accelerate the data acquisition by a factor of 3. Whole-brain relaxometry using DNNs proved to be effective and adaptive, suggesting the potential for low-cost DNN retraining. This work emphasizes the advantages of in silico DNN MP-qMRI pipelines for rapid data generation and DNN training without extensive dictionary generation, long parameter inference times, or prolonged data acquisition, highlighting the flexible and rapid nature of lightweight machine learning applications for MP-qMRI.
LFD-YOLO: a lightweight fall detection network with enhanced feature extraction and fusion
Authors: Wang Heqing , Xu Sheng , Chen Yuandian , Su Chengyue 
Published: February 2025

Abstract
Falls are one of the significant safety hazards for the elderly. Current object detection models for fall detection often suffer from high computational complexity, limiting their deployment on resource-constrained edge devices. Although lightweight models can reduce computational requirements, they typically compromise detection accuracy. To address these challenges, and considering the more lightweight architecture of YOLOv5 compared to other YOLO series models such as YOLOv8, we propose a lightweight fall detection model based on YOLOv5, named Lightweight Fall Detection YOLO (LFD-YOLO). Our method introduces a novel lightweight feature extraction module, Cross Split RepGhost (CSRG), which reduces information loss during feature map transmission. We also integrate an Efficient Multi-scale Attention (EMA) to enhance focus on the human pose. Moreover, we propose a Weighted Fusion Pyramid Network (WFPN) and utilize Group Shuffle Convolutions (GSConv) to reduce the model’s computational complexity and improve the efficiency of multi-scale feature fusion. Additionally, we design an Inner Weighted Intersection over Union (Inner-WIoU) loss to accelerate model convergence and enhance generalization. We construct a Person Fall Detection Dataset (PFDD) dataset covering diverse scenarios. Experimental results on the PFDD and the publicly available Falling Posture Image Dataset (FPID) datasets show that, compared to YOLOv5s, LFD-YOLO improves mAP0.5 by 1.5% and 1.7%, respectively, while reducing the number of parameters and calculations by 19.2% and 21.3%. Furthermore, compared to YOLOv8s, LFD-YOLO reduces the number of parameters and calculations by 48.6% and 56.1%, respectively, while improving mAP0.5 by 0.3% and 0.5%. These results demonstrate that LFD-YOLO achieves higher detection accuracy and lower computational complexity, making it well-suited for fall detection tasks.
Real-time detection of loosening torque in bolted joints using piezoresistive pressure-sensitive layer based on multi-walled carbon nanotubes reinforced epoxy nanocomposites
Authors: Sanli Abdulkadir , Demirkale Bilgehan , Kanoun Olfa 
Published: February 2025

Abstract
Bolted connections are extensively used in construction and machine design, playing critical roles in various industrial applications. Bolts and screws are, however, prone to loosening or separating due to factors such as shock, vibration, and temperature fluctuations. This self-loosening phenomenon poses a considerable challenge to the reliability of bolted connections, necessitating regular inspections to ensure their safety. In this study, we report a cost-effective technique for monitoring bolted joint loosening torque: BoltWISE (Bolt loosening detection with innovative sensors). BoltWISE employs an innovative sensor element consisting of a piezoresistive, pressure-sensitive layer made from multi-walled carbon nanotube/epoxy nanocomposites coated onto an FR4 substrate, functioning as a washer. Our approach provides high sensitivity, durability, linearity, and fast response times, with minimal hysteresis during both the tightening and loosening processes. Finite-element method simulations were conducted to determine the optimal sensor positions, ensuring fast response during bolt tightening and loosening. Our findings highlight BoltWISE as a promising, low-cost solution for efficiently detecting bolt loosening in industrial environments. Its ease of implementation and fabrication make it a valuable tool for improving safety and maintenance practices across various industries.
Vehicle game lane-changing mechanism and strategy evolution based on trajectory data
Authors: Qu Dayi , Wang Kedong , Dai Shouchen , Chen Yicheng , Cui Shanning , Yang Yuxiang 
Published: February 2025

Abstract
To improve the safety of ramp vehicles changing lane and to shorten the merging distance, this paper explores the dynamic game interaction properties of vehicles merging and the consistency of vehicles’ decision-making behaviors at the macro-microscopic levels. Using the exiD dataset and evolutionary game theory, the merging behavior of ramp vehicles is modeled to explore the effects of different driving states on the evolutionary convergence of strategies. Based on the game cost theory, the lane choice behavior of mainline vehicles is modeled. Validated by SUMO software, the results show that the model in this paper can significantly improve the safety of vehicle merging and reduce the merging distance. The mainline vehicles are more inclined to change lane and cut out in advance when facing the ramp vehicles under the influence of the change of advantage in the subsequent game.
Nutritional assessment using subjective global assessment identifies energy malnutrition and predicts mortality in patients with liver cirrhosis
Authors: Miwa Takao , Hanai Tatsunori , Nishimura Kayoko , Hirata Sachiyo , Unome Shinji , Nakahata Yuki , Imai Kenji , Suetsugu Atsushi , Takai Koji , Shimizu Masahito 
Published: February 2025

Abstract
This study aimed to evaluate whether the subjective global assessment (SGA) could effectively predict energy malnutrition, as assessed by indirect calorimetry, and mortality in hospitalized patients with cirrhosis. Energy malnutrition was defined by a nonprotein respiratory quotient (npRQ) < 0.85 using an indirect calorimetry. The usefulness of the SGA in identifying energy malnutrition and predicting mortality was assessed by the logistic regression and Cox proportional hazards models, respectively. Out of the 230 patients analyzed, 43% were found to have energy malnutrition. The distribution of SGA classifications was 54% for SGA-A, 32% for SGA-B, and 14% for SGA-C. Multivariable analysis indicated that both SGA-B (odds ratio, 3.59; 95% confidence interval [CI], 1.59–8.10) and SGA-C (odds ratio, 19.70; 95% CI, 3.46–112.00), along with free fatty acids (FFA), were independently linked to energy malnutrition. Regarding mortality, 125 patients (54%) died over a median follow-up period of 2.8 years. After adjustment, SGA-B (hazard ratio, 1.81; 95% CI, 1.08–3.03) and SGA-C (hazard ratio, 3.35; 95% CI, 1.28–8.76) were predictors of mortality in cirrhosis patients, while energy malnutrition and FFA were not. The SGA is a valuable tool for identifying energy malnutrition and predicting mortality in patients with cirrhosis.
RevCAR-mediated T-cell response against PD-L1-expressing cells turns suppression into activation
Authors: Crespo Eugenia , Loureiro Liliana R. , Stammberger Antonia , Hoffmann Lydia , Berndt Nicole , Hoffmann Anja , Dagostino Claudia , Soto Karla E. G. , Rupp Luise , Arndt Claudia , Schneider Martin , Ball Claudia R. , Bachmann Michael , Schmitz Marc , Feldmann Anja 
Published: February 2025

Abstract
Applying CAR T-cell therapy to treat solid tumors is especially challenging due to the immunosuppressive tumor microenvironment (TME). While our modular RevCAR system enhances the safety and controllability of CAR T-cell therapy, effectively targeting solid tumors remains difficult. Since PD-L1 is an immune checkpoint frequently upregulated by cancer cells and their microenvironment, it is a relevant target for solid tumors. Here, we introduce a novel PD-L1 RevTM capable of redirecting RevCAR T-cells to specifically target and kill PD-L1-expressing tumor cells, becoming activated and secreting pro-inflammatory cytokines. This is shown in vitro with monolayer and 3D models, including patient-derived cultures, and in vivo. Furthermore, we demonstrate in vitro and in vivo an AND-gated targeting of cells simultaneously expressing PD-L1 and another tumor-associated antigen by the Dual RevCAR system. Our findings suggest that RevCAR-mediated targeting of PD-L1 could be a promising therapeutic approach for modulating the TME and improving solid tumor treatment.
Comparing self reported and physiological sleep quality from consumer devices to depression and neurocognitive performance
Authors: Akre Samir , Cohen Zachary D. , Welborn Amelia , Zbozinek Tomislav D. , Balliu Brunilda , Craske Michelle G. , Bui Alex A. T. 
Published: February 2025

Abstract
This study examines the relationship between self-reported and physiologically measured sleep quality and their impact on neurocognitive performance in individuals with depression. Using data from 249 participants with medium to severe depression monitored over 13 weeks, sleep quality was assessed via retrospective self-report and physiological measures from consumer smartphones and smartwatches. Correlations between self-reported and physiological sleep measures were generally weak. Machine learning models revealed that self-reported sleep quality could detect all depression symptoms measured using the Patient Health Questionnaire-14, whereas physiological sleep measures detected “sleeping too much” and low libido. Notably, only self-reported sleep disturbances correlated significantly with neurocognitive performance, specifically with processing speed. Physiological sleep was able to detect changes in self-reported sleep, medication use, and sleep latency. These findings emphasize that self-reported and physiological sleep quality are not measuring the same construct, and both are important to monitor when studying sleep quality in relation to depression.
Ensemble speed estimation in IFOC with transient detector
Authors: Arahal Manuel R. , Satué Manuel G. , Martínez‐Heredia Juana , Perales‐Esteve Manuel A. 
Published: February 2025

AbstractRotating electric machines have experienced a vast increase in use in recent times due to the electrification of various segments, including electric vehicles. Different machine types, electronic converters, and control schemes are used. In particular, the combination of indirect field oriented control (IFOC) with incremental encoders is widely utilized. Despite theoretical efforts, practical tuning of IFOC‐like structures is not easy due to non‐idealities. These arise from difficult to model phenomena appearing in the system. In particular, the latency and phase loss of the speed estimation have a negative effect on performance. This paper proposes the use of an ensemble of speed estimators to reduce these negative effects. In the proposal, ripples and latency of the speed estimations are treated as terms in a trade‐off situation. The proposal allows to obtain low ripple in a steady state combined with low latency during transients.
Dynamic charging strategy of electric vehicles in the distribution network integrated with renewable energy sources
Authors: Owais Khan Mohd , Kirmani Sheeraz , Rihan Mohd 
Published: February 2025

AbstractThe rapid expansion of electric vehicles (EVs) brings forth a range of possibilities and obstacles for the electrical grid. EVs are a hopeful way to cut down on greenhouse gas emissions and reliance on fossil fuels, but the way they charge can put a lot of stress on the grid, which can cause problems for both grid operators and EV users. The uncontrolled approach to EV charging could result in the development of new peak loads. This paper formulates a problem for the minimisation of EV charging cost while reducing the load variance using a dynamic charging strategy. This dynamic charging strategy forecasts the charging price of EV for each time stamp based on the available energy generation. The proposed formulation has also considered the seasonal variation in load profile. The model is solved using different optimisation algorithm and different scenarios have been formulated to validate the effectiveness of the optimisation problem which has been formulated to achieve the objectives of minimised charging cost and load variance. The proposed strategy has improved the charging cost by 67% from other strategies of EV charging while reduction in load variance comes out to be 69%. This proposed formulation will help in the higher adoption of EV's and will subsequently lead to sustainable growth.
Prävention der Virushepatitis B und C bei chronischer Niereninsuffizienz
Authors: Girndt Matthias 
Published: February 2025

ZusammenfassungDialysepatienten und das -personal sind besonders gefährdet, sich mit einer Virushepatitis zu infizieren. Die Wirksamkeit der Hepatitis-B-Impfung ist bei fortgeschrittener chronischer Niereninsuffizienz vermindert. Dies erschwert die Aufrechterhaltung einer Herdenimmunität und erfordert teilweise Impf- und Monitoringstrategien, die von denen in der Allgemeinbevölkerung abweichen. Die rationale Abwägung von Kosten und Nutzen gehören zur Impfentscheidung dazu.
Detection of rabbit and wombat warrens in broad-scale satellite imagery
Authors: McPherson Natarsha , Ostendorf Bertram 
Published: February 2025

The ability to accurately assess the spatial pattern of wild animal distributions is essential for conservation management. Warrens constructed by burrowing species offer proxies for estimating geographic occupation. We tested the feasibility of open-access satellite-based detection for two semi-fossorial mammals, the southern hairy-nosed wombat (Lasiorhinus latifrons) and European rabbit (Oryctolagus cuniculus), across the Nullarbor Plain, South Australia. Along two 100 km road sections, we collected GPS locations of warrens from ground-walked transects (~1 km) at 22 sites. Wombat and rabbit warrens were identified and digitised using Google Earth and Microsoft Bing imagery (<1.5 m resolution) for each transect. We found a significant correlation between satellite and field estimates with R2 values of 0.98 (P < 1 × 10−15) and 0.56 (P < 1 × 10−4) for wombat and rabbit warrens, respectively. User accuracy was high for both wombat (91%) and rabbit (81%) warrens. Omission in the satellite imagery was low for wombats (14%) but high for rabbit warrens (44%). However, small warrens less than 10 m in diameter accounted for 79% of rabbit warren omissions. This demonstrates that the geospatial pattern of warrens constructed by two semi-fossorial mammals can be detected and distinguished in broad-scale satellite imagery across Australia’s semi-arid landscape.
A coarse-to-fine algorithm for cross-source point cloud registration of engine blades
Authors: Chang Zhanyou , Luo Jun 
Published: February 2025

Abstract
We propose a novel coarse-to-fine algorithm, named EIDBO-TrICP, for the matching and registration of engine blade cross-source point clouds (CSPC) obtained from different types of sensors. This task is particularly challenging due to the presence of data inconsistencies such as missing data, noise, outliers, density variations, viewpoint changes, and scale differences, along with the complex structure and surface characteristics of the blade itself. The proposed method is composed of two main stages: In the coarse registration phase, we introduce an enhanced and improved dung beetle optimization algorithm (EIDBO), which improves the convergence speed and optimization accuracy of the standard dung beetle optimization algorithm by adopting a modified convergence factor. This approach effectively avoids local minima in the registration process by leveraging the global search capability of the improved dung beetle optimization algorithm. Additionally, singular value decomposition (SVD) is used to accelerate the local search convergence and enhance the final registration accuracy. In the fine registration phase, the Trimmed ICP (TrICP) algorithm is employed to ensure the precise alignment of the cross-source point clouds. The results from both synthetic data simulations and real engine blade data obtained from a compound ray coordinate measurement system demonstrate that the proposed algorithm outperforms existing methods in both efficiency and accuracy. The significance of EIDBO-TrICP lies in its ability to effectively integrate data from multiple sensors, which is crucial for the precise measurement and analysis of engine blades with complex geometries and varying structural characteristics.
The Bare Bones of the World
Authors: Costa Emanuele 
Published: February 2025

Abstract
This chapter examines the structures developed in Book I of Spinoza’s Ethics, such as causation, limitation, inherence, and conception, to show how they are deployed in the formation and demonstration of Spinoza’s most recognizable metaphysical theses: monism, necessitarianism, and the unique relation that connects Natura naturans and Natura naturata, as well as the crucial transformation of traditional accounts of causation into Spinoza’s idiosyncratic immanent causation.