bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep CRISPR mutagenesis characterizes the functional diversity of TP53 mutations
Authors: Funk Julianne S. , Klimovich Maria , Drangenstein Daniel , Pielhoop Ole , Hunold Pascal , Borowek Anna , Noeparast Maxim , Pavlakis Evangelos , Neumann Michelle , Balourdas Dimitrios-Ilias , Kochhan Katharina , Merle Nastasja , Bullwinkel Imke , Wanzel Michael , Elmshäuser Sabrina , Teply-Szymanski Julia , Nist Andrea , Procida Tara , Bartkuhn Marek , Humpert Katharina , Mernberger Marco , Savai Rajkumar , Soussi Thierry , Joerger Andreas C. , Stiewe Thorsten 
Published: January 2025

Abstract
The mutational landscape of TP53, a tumor suppressor mutated in about half of all cancers, includes over 2,000 known missense mutations. To fully leverage TP53 mutation status for personalized medicine, a thorough understanding of the functional diversity of these mutations is essential. We conducted a deep mutational scan using saturation genome editing with CRISPR-mediated homology-directed repair to engineer 9,225 TP53 variants in cancer cells. This high-resolution approach, covering 94.5% of all cancer-associated TP53 missense mutations, precisely mapped the impact of individual mutations on tumor cell fitness, surpassing previous deep mutational scan studies in distinguishing benign from pathogenic variants. Our results revealed even subtle loss-of-function phenotypes and identified promising mutants for pharmacological reactivation. Moreover, we uncovered the roles of splicing alterations and nonsense-mediated messenger RNA decay in mutation-driven TP53 dysfunction. These findings underscore the power of saturation genome editing in advancing clinical TP53 variant interpretation for genetic counseling and personalized cancer therapy.
A comprehensive guide to selecting suitable wavelet decomposition level and functions in discrete wavelet transform for fault detection in distribution networks
Authors: Shalby Esraa M. , Abdelaziz Almoataz Y. , Ahmed Eman S. , Abd-Elhamed Rashad Basem 
Published: January 2025

AbstractThe paper presents a comprehensive analysis of the IEEE-16 bus system under different operating conditions. It discusses the selection of suitable decomposition level and wavelet function for analyzing non-stationary signals to enhance power distribution network fault detection. MATLAB/Simulink is used to simulate the system, and transient fault current signals are processed with the MATLAB Wavelet Toolbox. The optimal decomposition level is determined by energy concentration, with the highest energy found in scales D9 (b4), D8 (b5), and D7 (b6), and D8 having the most concentration. Using MATLAB classifier learner, the article evaluates seven common mother wavelets with 53 wavelet functions, and sym3 is found to be the most efficient wavelet function in terms of training time, prediction speed, and accuracy of SVM classifiers. All fault types both symmetrical/unsymmetrical types, and various normal transient conditions such as load/capacitor/DG switching are detected/discriminated with nearly 100% accuracy at the midpoint of line 6–7 with various fault conditions, inception angles (0, 30, 45, 60, 90 and 120°) and a fault resistance of (5,10, 15, and 20 ohms). Additionally, 9 MW wind Farm is integrated at busbar 10, and various fault scenarios are simulated to assess system performance with 100% Accuracy.
Enhancing in search of Milankovitch cycles from stratigraphic record using convex optimization algorithm
Authors: Alam Syaiful , Hadian Mohamad Sapari Dwi , Hamdani Ahmad Helman , Sulaiman Noorzamzarina 
Published: January 2025

AbstractAccurately identifying Milankovitch cycles has been a significant challenge in cyclostratigraphic studies, as it is essential for improving geochronology. This manuscript focuses on developing a method that distinguishes Milankovitch cycles from sedimentary noise to enhance stratigraphic precision. Despite their often-conspicuous magnitude, these periodicities frequently intertwine with noise, posing a challenge for conventional spectral analysis. Therefore, to address this issue, we have developed an algorithm that enhances the resolution of the Milankovitch signal by employing convex optimization in spectral analysis. To evaluate the effectiveness of this new algorithm, we applied it to four distinct types of local stratigraphy where the Milankovitch signal has been confirmed. These include the stratigraphic sections in the middle Miocene molluscan beds of Java and the Mahakam Delta, Pleistocene sediments of Hominin Flores, and the Towuti Lake in Sulawesi Island, Indonesia. Our findings demonstrate the preservation of all targeted signals, with a confidence level surpassing 99%. By setting the significance level to 1%, we can reject the null hypothesis, which assumes noise or the absence of a Milankovitch signal in the stratigraphic data being tested. The absence of deviations from the identified periodicities further strengthens the Milankovitch signal, underscoring the robustness of our algorithm. However, we acknowledge that achieving optimal results still hinges on the accurate selection of the initial parameters z and λ.
Strength prediction of ECC-CES columns under eccentric compression using adaptive sampling and ML techniques
Authors: Megahed Khaled 
Published: January 2025

AbstractA novel type of concrete-encased steel (CES) composite column implementing Engineered Cementitious Composites (ECC) confinement (ECC-CES) has recently been introduced, offering significantly enhanced failure behavior, ductility, and toughness when compared to conventional CES columns. This study presents an innovative method for predicting the eccentric compressive capacity of ECC-CES columns, utilizing adaptive sampling and machine learning (ML) techniques. Initially, the research introduces a finite element (FE) model for ECC-CES columns, incorporating material and geometric nonlinearities to capture the inelastic behavior of both ECC and steel through appropriate constitutive material laws. The FE model was validated against experimental data, demonstrating strong predictive accuracy. An adaptive sampling process was employed to efficiently explore the design space, resulting in a database of 2,908 FE models. Subsequently, six machine learning models were used to predict the eccentric compressive capacity based on the generated FE database. These models were thoroughly evaluated and demonstrated superior prediction accuracy compared to established design standards like EC4 and AISC360. Based on evaluation metrics, the Gaussian Process Regression (GPR), CatBoost (CATB), and LightGBM (LGBM) models emerged as the most accurate and reliable, with over 97% of the finite element (FE) samples falling within a 10% error range. While the ML models demonstrate impressive performance, their black-box nature restricts their practical use in design applications. Consequently, this study introduces a proposed design that offers competitive performance metrics. The novelty of this work lies in integrating adaptive sampling through Bayesian Optimization (BO) with the power of machine learning (ML) to generate training data that effectively covers a large input space while minimizing error. SVR, CatBoost, and GPR models demonstrated mean μ, R2, and a20-index values near 1.0, with CoV and MAPE% values consistently low, indicating highly accurate predictions across testing subsets.
A case study on using a large language model to analyze continuous glucose monitoring data
Authors: Healey Elizabeth , Tan Amelia Li Min , Flint Kristen L. , Ruiz Jessica L. , Kohane Isaac 
Published: January 2025

AbstractContinuous glucose monitors (CGM) provide valuable insights about glycemic control that aid in diabetes management. However, interpreting metrics and charts and synthesizing them into linguistic summaries is often non-trivial for patients and providers. The advent of large language models (LLMs) has enabled real-time text generation and summarization of medical data. The objective of this study was to assess the strengths and limitations of using an LLM to analyze raw CGM data and produce summaries of 14 days of data for patients with type 1 diabetes. We first evaluated the ability of GPT-4 to compute quantitative metrics specific to diabetes found in an Ambulatory Glucose Profile (AGP). Then, using two independent clinician graders, we evaluated the accuracy, completeness, safety, and suitability of qualitative descriptions produced by GPT-4 across five different CGM analysis tasks. GPT-4 performed 9 out of the 10 quantitative metrics tasks with perfect accuracy across all 10 cases. The clinician-evaluated CGM analysis tasks had good performance across measures of accuracy [lowest task mean score 8/10, highest task mean score 10/10], completeness [lowest task mean score 7.5/10, highest task mean score 10/10], and safety [lowest task mean score 9.5/10, highest task mean score 10/10]. Our work serves as a preliminary study on how generative language models can be integrated into diabetes care through data summarization and, more broadly, the potential to leverage LLMs for streamlined medical time series analysis.
Deep learning-based encryption scheme for medical images using DCGAN and virtual planet domain
Authors: Kumar Manish , Chivukula Aneesh Sreevallabh , Barua Gunjan 
Published: January 2025

AbstractThe motivation for this article stems from the fact that medical image security is crucial for maintaining patient confidentiality and protecting against unauthorized access or manipulation. This paper presents a novel encryption technique that integrates the Deep Convolutional Generative Adversarial Networks (DCGAN) and Virtual Planet Domain (VPD) approach to enhance the protection of medical images. The method uses a Deep Learning (DL) framework to generate a decoy image, which forms the basis for generating encryption keys using a timestamp, nonce, and 1-D Exponential Chebyshev map (1-DEC). Experimental results validate the efficacy of the approach in safeguarding medical images from various security threats, including unauthorized access, tampering, and adversarial attacks. The randomness of the keys and encrypted images are demonstrated through the National Institute of Standards and Technology (NIST) SP 800-22 Statistical test suite provided in Tables 4 and 14, respectively. The robustness against key sensitivity, noise, cropping attacks, and adversarial attacks are shown in Figs. 15–18, 22–23, and 24. The data presented in Tables 5, 6, and 7 shows the proposed algorithm is robust and efficient in terms of time and key space complexity. Security analysis results are shown (such as histogram plots in Figs. 11–14 and correlation plots in Figs. 19–21). Information Entropy ($$7.9993 \pm 0.0001$$), correlation coefficient ($$\pm 0.09$$), Mean Square Error (MSE) ($$4166.3107 \pm 1645.2980$$), Peak Signal to Noise Ratio (PSNR) ($$12.2643 \pm 1.7032$$), Number of Pixel Change Rate (NPCR) ($$99.60\% \pm 0.2\%$$), and Unified Average Changing Intensity (UACI) ($$33.47\% \pm 0.1\%$$) underscore the high security and reliability of the encrypted images, are shown in Tables 8–11. Further, statistical NPCR and UACI are calculated in Tables 12 and 13, respectively. The proposed algorithm is also compared with existing algorithms, and compared values are provided in Table 15. The data presented in Tables 3–15 suggest that the proposed algorithm can opt for practical use.
Multi-level clustering and Prediction based energy efficient routing protocol to eliminate Hotspot problem in Wireless Sensor Networks.
Authors: Prince Bhaskar , Kumar Prabhat , Singh Sunil Kumar 
Published: January 2025

AbstractConserving energy of sensor nodes and ensuring balanced workloads among them are fundamental concerns in Wireless Sensor Network (WSN) design. Clustering strategies offer a promising avenue to minimize node energy consumption, thereby prolonging network lifespan. Nevertheless, numerous multi-hop routing protocols using clustering technique face the challenge of nodes nearer to the Base Station (BS) depleting their energy faster due to forwarding data from the entire network leading to premature node failure and network partitioning known as ‘hotspot problem’. The paper introduces an Energy-Efficient Mega-Cluster-Based Routing (EEMCR) protocol specially designed for expansive coverage area. The primary principle behind designing this protocol is to eliminate the hotspot problem and restrict the transmission range of nodes to the threshold distance defined by the radio energy model, thereby enhancing the overall network lifespan. The protocol adopts a centralized approach employing fixed clustering wherein the BS partitions the network into square-shaped clusters. The cluster size is determined by the threshold transmission range of the sensor radio energy model, guaranteeing that all network communication stays within this threshold distance. Four such clusters form a mega-cluster with a Mega-Cluster-Head (MCH) elected among the four Cluster Heads (CHs). The MCH role is evenly distributed among nodes of all four clusters in subsequent rounds for uniform distribution of its overhead. Implementing data aggregation at two levels (CH level as well as MCH level) leads to reduced data traffic and energy consumption throughout the network. Moreover, data collection by two data mules based on odd–even round number ensures balanced data traffic and energy distribution across the network. Analysis indicates that the proposed protocol effectively mitigates the hot-spot problem and reduces data transmission overhead of sensor nodes. In simulation, the proposed protocol on an average improves network life by 34.5%, 23.5%, 14.5% and 5.5% as compared to existing protocols FCEEC, DBSCAN, LPGCR and FBECS respectively for deployment of nodes between 600 to 1200. Also, approximately 46%, 32%, 21% and 14% of lesser sensor nodes are dead for proposed protocol in respective rounds as compared to existing protocols FCEEC, DBSCAN, LPGCR and FBECS respectively. Comparative evaluations demonstrate improved network lifetime when compared to equivalent recent routing protocols.
Numerical simulation of coal particle motion characteristics in the envelope region of spiral drum based on discrete element method
Authors: Tian Zhen , Gao Shan , Ma Lianwei , Wang He , Ge Yang 
Published: January 2025

AbstractIn order to study the movement characteristics of coal particles in the coal loading process of spiral drums, the spiral drum of a certain type of shearer was taken as the research object, and the intrinsic parameters of the materials were calibrated through the determination results of coal sample properties, the relevant parameters of coal particle adhesion were determined, and a discrete element model of spiral drum coal loading was established. The distribution of coal particle movement subsequent to the fracture of the coal wall was derived through simulation. By spatially dividing the envelope region of the spiral drum along the radial and axial directions, the number and velocity distribution of coal particles in different envelope regions were obtained. The study revealed that the number of coal particles in radial regions III and IV was significantly higher than that in regions I and II. Most of the coal particles in regions III and IV moved outward along the drum axis under the action of the spiral blades, while a small portion moved from regions III and IV towards regions I and II. The coal particles in the axial region near the outer side of the coal wall have a strong ability to flow towards the scraper conveyor, and the probability of coal particles being thrown towards the rear of the spiral drum is higher in the region near the end plate. The increase in traction speed has little effect on the velocity of coal particles in all directions within the envelope region, while the increase of drum rotation speed can significantly improve the velocity of coal particles in each region. Through statistical analysis, it was found that the coal loading rate decreases with the increasing of traction speed. As the drum speed increases, the coal loading rate first increases and then decreases. By comparing the results of industrial experiments and numerical simulations underground, the accuracy of the discrete element method used in this paper to analyze the particle motion in the envelope region of the spiral drum has been confirmed. The research results provide reference for the selection of motion parameters of coal shearer and the improving of coal loading efficiency of spiral drums.
Rapid quantitative analysis of double-stranded plasmid DNA with capillary gel electrophoresis for applications in quality control and radiation research
Authors: Hahn Marc Benjamin 
Published: January 2025

AbstractThe quantification of different structures, isoforms and types of damage in plasmid DNA is of importance for applications in radiation research, DNA based bio-dosimetry, and pharmaceutical applications such as vaccine development. The standard method for quantitative analysis of plasmid DNA damage such as single-strand breaks (SSB), double-strand breaks (DSB) or various types of base-damage is Agarose gel electrophoresis (AGE). Despite being well established, AGE has various drawbacks in terms of time consuming handling and analysis procedures. A more modern, faster, cheaper and more reliable method is capillary gel electrophoresis (CGE). However, to establish this method in biotechnology, radiation-research and related fields, certain criteria in terms of accuracy, repeatability and linearity have to be tested and protocols have to be established. This study performs the relevant tests with a common model plasmid (pUC19, double-stranded DNA with 2686 basepairs) to establish a CGE based methodology for quantitative analysis with readily available commercial CGE systems. The advantages and limitations of the methods are evaluated and discussed, and the range of applicability is presented. As a further example, the kinetics of enzyme digestion of plasmid DNA by capillary gel electrophoresis was studied. The results of the study show for a model system consisting out of pUC19, the suitability of CGE for the quantification of different types of DNA damage and the related isoforms, such as supercoiled, open-circular and linear plasmid DNA.
Association between abdominal obesity and nutritional supplement use among Iranian adults in the Shahedieh cohort study
Authors: Nouri Sina , Ghadiri-Anari Akram , Hosseini Saeed , Hazar Narjes 
Published: January 2025

AbstractThe relationship between the use of supplements and the presence of abdominal obesity is a topic that is currently being debated. The purpose of the present study was to evaluate the existence of such an association. The current cross-sectional investigation included a population of 9878 individuals who were the focus of the study’s initial phase in the Shahedieh cohort study, conducted in Yazd, Iran. Waist circumference was measured with the help of an inflexible tape measure. In addition, the data regarding the consumption of various supplements, including multivitamins, folic acid, ferric compounds, omega 3, calcium, vitamin D, zinc, and other supplements, were collected through interviews conducted by trained interviewers. Among the entire sample, 4785 individuals (50.6%) were found to have abdominal obesity, while 4093 individuals (43.3%) reported using at least one type of supplements. In the simple regression analysis, all of the supplements, except for multivitamins and zinc, were identified as adverse factors for abdominal obesity. However, in the multivariable regression analysis, only ferric compounds remained significant as a protective factor (OR = 0.73, 95%CI: 0.57‒0.94). The findings of this study suggest that dietary supplements, particularly ferric compounds, may have implications for managing abdominal obesity. These findings could inform public health strategies by incorporating ferric compound supplementation into dietary interventions aimed at reducing abdominal obesity prevalence. Furthermore, the potential clinical application of ferric compounds highlights the need for integrating nutritional approaches in obesity management programs. However, longitudinal studies are needed to establish causal relationships and confirm the long-term effectiveness and safety of such interventions.
Digital-twin driven alignment control method for marine shafting with air spring vibration isolation system
Authors: Liu Song , Shi Liang , Xu Wei , Hu ZeChao 
Published: January 2025

AbstractShafting alignment is crucial for marine propulsion systems and may affect the safety and stability of ship operations. Air spring vibration isolation systems (ASVISs) for marine shafting can help control the shafting alignment state by actively adjusting air spring pressures while effectively reducing the mechanical noise. However, how to accurately control the alignment state of marine shafting with air spring vibration isolation system remains a challenge. To address this issue, a digital twin (DT)-driven alignment control method is proposed in this paper. First, we design a digital twin prediction model based on the neural network to describe the data mapping relationship between the air spring pressures and shafting alignment state. Then, based on the prediction model, we transform the shafting alignment control problem into a non-linear optimization problem in which our objective is to minimize the alignment error while balancing the load on different air springs. To obtain the optimal air spring pressures, the genetic algorithm is introduced to solve the optimization problem, fully exploiting its global search capacity. Moreover, in order to achieve the optimized pressures, a soft-constrained controller based on proportional-integral-derivative (PID) algorithm is developed to accurately generate specific control policies based on the monitoring data. Finally, the feasibility and the effectiveness of the proposed alignment control method is verified with a real ASVIS.
QRFP43 modulates the activity of the hypothalamic-pituitary-thyroid axis in female sheep
Authors: Przybył Bartosz Jarosław , Szlis Michał , Misztal Anna , Wójcik-Gładysz Anna 
Published: January 2025

AbstractSince the early discovery of QRFP43, intensive research has been primarily focused on its role in the modulation of food intake. As is widely recognised, the regulation of the body’s energy status is a highly complex process involving numerous systems, hormones and neurotransmitters. Among the most important regulators of energy status, alongside the satiety and hunger centre located in the hypothalamus, is the HPT axis, which directly and indirectly affects the regulation of metabolism in all cells of the body. Therefore, it seems highly important to conduct studies aimed at elucidating how QRFP43 may impact the secretory activity of the HPT axis. The objective of this work was to investigate the role of QRFP43 in modulating HPT axis activity in sheep. The study examined mRNA and peptide expression of TRH and TSH in the hypothalamus and pituitary, as well as plasma concentrations of TSH, free T4 (FT4) and free T3 (FT3). Moreover, the relationship between QRFP34 and mRNA expression of the Dio1, Dio2, and Dio3 genes was explored in selected tissues of the HPT axis. The animals (n = 48) were randomly divided into three experimental groups: a control group receiving an ICV infusion of Ringer-Locke solution, and two experimental groups receiving ICV infusions of QRFP43 at doses of 10 and 50 µg per day. Four 50-minute ICV infusions were administered to all sheep at 30 min intervals each of three consecutive days. Hypothalamic, pituitary and thyroid glands were collected and preserved for further immunohistochemical and molecular biological analyses. Additionally, blood samples were collected during the experiment for subsequent RIA determinations. In summary, the results of the experiment have indicated that QRFP43 modulates the secretory activity of the HPT axis at all organisational levels. Moreover, QRFP43 can alter the mRNA expression profiles of DIO1, DIO2 and DIO3 in HPT tissues, leading to discrete changes in the metabolism of the cells studied and their response to signals transmitted by T4 and T3.