bluuebunny/crossref_metadata_2025 huggingface dataset:
Computer-assisted discovery of natural inhibitors for platelet-derived growth factor alpha as novel therapeutics for thyroid cancer
Authors: Khalid Hira , Sattar Farah , Ahmad Iqra , Junior Valdir Ferreira de Paula , Nishan Umar , Ullah Riaz , Dib Hanna , Omari Khaled W. , Shah Mohibullah
Published: January 2025

Platelet-derived growth factor alpha (PDGFRA) plays a significant role in various malignant tumors. PDGFRA expression boosts thyroid cancer cell proliferation and metastasis. Radiorefractory thyroid cancer is poorly differentiated, very aggressive, and resistant to radioiodine therapy. Thus, novel anticancer drugs that inhibit its metastasis are urgently required. In this context, we proposed the PDGFRA inhibitors by an optimized structure-based drug design approach. We performed a virtual screening of metabolites derived from anticancer medicinal plants (Swertia chirayita, Myristica fragrans, and Datura metel) and successfully identified seven hits, namely cis-Grossamide K, Daturafoliside O, N-cis-feruloyltyramine, Maceneolignan H, Erythro-2-(4-allyl-2, 6-dimethoxyphenoxy)-1-(3, 4, 5-trimethoxyphenyl) propan-1, 3-diol, Myrifralignan C, and stigmasteryl-3-O-β-glucoside as potential PDGFRA inhibitors. Not only the top 7 hits exhibited higher docking scores in docking simulation but also optimal drug-likeness and non-toxic profiles in pharmacokinetics analysis among 119 compounds. Our top hits are non-mutagenic, can cross the blood-brain barrier, and inhibit p-glycoprotein, while the N-cis-feruloyltyramine has the potential to become a lead compound. The protein-ligand stability of the top 3 hits, namely cis-Grossamide K, Daturafoliside O, and N-cis-feruloyltyramine, and their interactions at the potential binding site of target protein were confirmed through molecular dynamic simulations. We also analyzed pharmacophoric features for stable binding in the PDGFRA active site. These drug candidates were further characterized to predict their biological activity spectra in the human body and medicinal characteristics to know their extensive behavior in laboratory testing. This study necessitates the in-vitro and in-vivo studies to confirm the potential of our hits for the discovery of novel therapeutics against the thyroid cancer.
Integrating traditional machine learning with qPCR validation to identify solid drug targets in pancreatic cancer: a 5-gene signature study
Authors: Wang Xiaoyan , Yu Pengcheng , Jia Wei , Wan Bingbing , Ling Zhougui , Tang Yangyang
Published: January 2025

Background

Pancreatic cancer remains one of the deadliest malignancies, largely due to its late diagnosis and lack of effective therapeutic targets.

Materials and methods

Using traditional machine learning methods, including random-effects meta-analysis and forward-search optimization, we developed a robust signature validated across 14 publicly available datasets, achieving a summary AUC of 0.99 in training datasets and 0.89 in external validation datasets. To further validate its clinical relevance, we analyzed 55 peripheral blood samples from pancreatic cancer patients and healthy controls using qPCR.

Results

This study identifies and validates a novel five-gene transcriptomic signature (LAMC2, TSPAN1, MYO1E, MYOF, and SULF1) as both diagnostic biomarkers and potential drug targets for pancreatic cancer. The differential expression of these genes was confirmed, demonstrating their utility in distinguishing cancer from normal conditions with an AUC of 0.83. These findings establish the five-gene signature as a promising tool for both early, non-invasive diagnostics and the identification of actionable drug targets.

Conclusion

A five-gene signature is established robustly and has utility in diagnostics and therapeutic targeting. These findings lay a foundation for developing diagnostic tests and targeted therapies, potentially offering a pathway toward improved outcomes in pancreatic cancer management.
Weak measurements enhancing the quantum information facets of a driven Unruh–DeWitt detector
Authors: Xie Jia-Ling , Zhu Cheng-Jie , Tan Jia , Hao Xiang
Published: January 2025

We developed a Hermitian operator representation of the Unruh channel for a driven accelerated detector in the presence of external noise. This representation is then used to provide a generalized analytical approach to a non-inertial evolution subjected to quantum weak measurements. The quantum information facets were then improved by performing weak measurements before and after the quantum channel. The external noise was modeled using a phase damping channel. The prominent oscillations of the quantum information are caused by vacuum fluctuations of the quantum fields coupled to the detector. Steady values are obtained for the quantum coherence and quantum Fisher information using the Unruh effect. Thus, quantum weak measurements can effectively suppress the decoherence induced by the relativistic acceleration. By comparing with cases without weak measurements, we demonstrate that there exist some regions with optimal measurement strengths that enhance the quantum coherence and quantum Fisher information. The effects of conditional improvement on the quantum information facets are still obvious in the presence of external noise.
Effect of operational parameters on droplet deposition characteristics using an unmanned aerial vehicle for banana canopy
Authors: Yu Jiaxiang , Xu Xing , Duan Jieli , Jiang Yinlong , Yuan Haotian , Liang Huazimo , Jing Shuaijie , Yang Zhou
Published: January 2025

In recent years, as an important part of precision agricultural aviation, the plant protection unmanned aerial vehicle (UAV) has been widely studied and applied worldwide, especially in East Asia. Banana, as a typical large broad-leaved crop, has high requirements for pests and diseases control. The mechanization degree of plant protection management in banana orchard is low. Therefore, our study focuses on the effects of different flight heights (3-5 m) and droplet sizes (50-150 μm) of plant protection UAV on the droplet deposition distribution characteristics of banana canopy. And the droplet deposition distribution in banana canopy and spraying drift of plant protection UAV and ground air-assisted sprayer were compared. The results showed that droplet size was the main factor affecting droplet deposition density, coverage, uniformity and penetration on both sides of banana canopy leaves. The droplet deposition density and coverage on the adaxial side of leaves were mostly significantly larger than that on the abaxial side. The flight height of 4 m and the droplet size of 100 μm could make the adaxial side of banana canopy leaves have higher droplet deposition density (63.77 droplets per square cm) and coverage (12.75%), and can make the droplets effectively deposit on the abaxial side of banana canopy leaves, with droplet deposition density of 17.46 droplets per square cm and coverage of 1.24%. Choosing an appropriate flight height and a droplet size could improve the droplet deposition uniformity on both sides of banana canopy leaves, but the improvement was not significant. Moreover, at a same operational parameter combination, it was difficult to achieve the best droplet deposition density, coverage, uniformity and penetration at the same time. In addition, appropriately increasing the flight height and droplet size could help to improve the droplet deposition penetration on the adaxial side of banana canopy leaves, but there were few significant improvements. Compared with the plant protection UAV, the ground air-assisted sprayer had higher droplet deposition density and coverage on the abaxial side of banana canopy leaves, but had smaller droplet deposition coverage on the adaxial side. The droplet deposition density and coverage on the abaxial side of banana canopy leaves were obviously larger than the adaxial side during the spraying of ground air-assisted sprayer. The droplet drift distance of the ground air-assisted sprayer was farther than the plant protection UAV. The test results of this study can provide practical and data support for the UAV aerial application in banana orchard, and provide a valuable reference for the implementation of air-ground cooperation spraying strategy in banana orchard, which is of great significance to promote sustainable and intelligent phytoprotection of banana orchard.
Monitoring of agricultural progress in rice-wheat rotation area based on UAV RGB images
Authors: Wang Jianliang , Chen Chen , Huang Senpeng , Wang Hui , Zhao Yuanyuan , Wang Jiacheng , Yao Zhaosheng , Sun Chengming , Liu Tao
Published: January 2025

Real-time monitoring of rice-wheat rotation areas is crucial for improving agricultural productivity and ensuring the overall yield of rice and wheat. However, the current monitoring methods mainly rely on manual recording and observation, leading to low monitoring efficiency. This study addresses the challenges of monitoring agricultural progress and the time-consuming and labor-intensive nature of the monitoring process. By integrating Unmanned aerial vehicle (UAV) image analysis technology and deep learning techniques, we proposed a method for precise monitoring of agricultural progress in rice-wheat rotation areas. The proposed method was initially used to extract color, texture, and convolutional features from RGB images for model construction. Then, redundant features were removed through feature correlation analysis. Additionally, activation layer features suitable for agricultural progress classification were proposed using the deep learning framework, enhancing classification accuracy. The results showed that the classification accuracies obtained by combining Color+Texture, Color+L08CON, Color+ResNet50, and Color+Texture+L08CON with the random forest model were 0.91, 0.99, 0.98, and 0.99, respectively. In contrast, the model using only color features had an accuracy of 85.3%, which is significantly lower than that of the multi-feature combination models. Color feature extraction took the shortest processing time (0.19 s) for a single image. The proposed Color+L08CON method achieved high accuracy with a processing time of 1.25 s, much faster than directly using deep learning models. This method effectively meets the need for real-time monitoring of agricultural progress.
Effects of the narrative elaboration technique and open-ended rapport on accuracy of children’s recall according to age
Authors: Lee Seungjin , Shin Minkyeong
Published: January 2025

Introduction

This study explored the impacts of the narrative elaboration technique (NET) and open-ended rapport building on younger (n = 30, ages 5–6) and older (n = 30, ages 7–8) children’s free recall and suggestibility.

Methods

Children were randomly assigned to either a NET condition or an open-ended rapport condition after engaging in a photo-taking play session with an experimenter. Then, a novel interviewer asked them about the play session. The effects of the experimental conditions on children’s free recall and suggestibility were examined according to children’s age groups.

Results

Results revealed that open-ended rapport played a more significant positive role in young children’s free recall and suggestibility when compared to NET. No significant differences were observed in the effects of NET and open-ended rapport on older children’s memory performance.

Discussion

Practical guidance is provided in terms of prioritizing open-ended rapport over NET to maximize young children’s spontaneous and accurate recall.
Cross-cultural validation and reference norms for the DCDDaily-Q questionnaire Chinese version (DCDDaily-Q-CN): evaluating children’s motor performance in activities of daily living
Authors: Huang Meihuan , Zhan Yujun , Zhou Haishan , Song Ping , Fan Yanping , Lu Yang , Chen Yijing , Lv Zhen , Liu Qing , Yun Guojun , Cao Jianguo
Published: January 2025

Background

The DCDDaily-questionnaire (DCDDaliy-Q) evaluates children’s performance and participation in motor-based activities of daily living (ADLs), meeting diagnostic criterion B for developmental coordination disorder (DCD). Currently, there are no Chinese translations or growth references available. Thus, this study aimed to culturally adapt, validate, and establish reference norms for the DCDDaily-Q in Chinese children.

Methods

The original scale was translated and culturally adapted into Chinese (DCDDaily-Q-CN) following international guidelines. Normative data of typically developing children (n = 1936, aged 5–10) were gathered from 14 randomly chosen mainstream schools in a large migrant city. Thirty children (aged 5–10 years) diagnosed with DCD were recruited through clinical referrals, and a matched control group (n = 30) was randomly selected from the reference group. Reference norms with growth curves and psychometric properties were analyzed.

Results

Sex-specific growth curves with percentiles and cut-off values of the DCDDaily-Q-CN in children aged 5–10 years were established. The instrument demonstrated excellent internal consistency across the total and the three subscales (Cronbach’s alpha = 0.83–0.91). The confirmatory factor analysis showed a good fit for the original three-factor model (CFI = 0.936, RMSEA = 0.049). Moderate to strong correlations were found between the DCDDaily-Q-CN performance total score and the DCDQ-R (rs = −0.54) and MABC-2 total scores (rs = −0.68). The total performance score effectively differentiated between children with DCD and controls (U = 9.0, p < 0.001), with a cutoff score of 45, demonstrating a sensitivity of 93% (95%CI: 77–99%) and specificity of 90% (95%CI: 74–98%).

Conclusion

The findings support that the DCDDaily-Q-CN is a reliable and valid measure to assess participation and performance in motor-based ADLs and fulfill criterion B of the diagnostic criteria for DCD.
Dynamic-budget superpixel active learning for semantic segmentation
Authors: Wang Yuemin , Stavness Ian
Published: January 2025

Introduction

Active learning can significantly decrease the labeling cost of deep learning workflows by prioritizing the limited labeling budget to high-impact data points that have the highest positive impact on model accuracy. Active learning is especially useful for semantic segmentation tasks where we can selectively label only a few high-impact regions within these high-impact images. Most established regional active learning algorithms deploy a static-budget querying strategy where a fixed percentage of regions are queried in each image. A static budget could result in over- or under-labeling images as the number of high-impact regions in each image can vary.

Methods

In this paper, we present a novel dynamic-budget superpixel querying strategy that can query the optimal numbers of high-uncertainty superpixels in an image to improve the querying efficiency of regional active learning algorithms designed for semantic segmentation.

Results

For two distinct datasets, we show that by allowing a dynamic budget for each image, the active learning algorithm is more effective compared to static-budget querying at the same low total labeling budget. We investigate both low- and high-budget scenarios and the impact of superpixel size on our dynamic active learning scheme. In a low-budget scenario, our dynamic-budget querying outperforms static-budget querying by 5.6% mIoU on a specialized agriculture field image dataset and 2.4% mIoU on Cityscapes.

Discussion

The presented dynamic-budget querying strategy is simple, effective, and can be easily adapted to other regional active learning algorithms to further improve the data efficiency of semantic segmentation tasks.
What Affects Agricultural Green Total Factor Productivity in China? A Configurational Perspective Based on Dynamic Fuzzy-Set Qualitative Comparative Analysis
Authors: Lu Danni , Zhang Xinhuan , Yang Degang , Zhang Shubao
Published: January 2025

Agricultural production faces the dual challenge of increasing output while ensuring efficient resource utilization and environmental sustainability amid escalating global climate change and relentless increases in human demand. This study used provincial panel data from China from 2001 to 2022 to address these challenges. It systematically evaluated the dynamic evolution of agricultural green total factor productivity (AGTFP) by selecting “resources” and “energy” as core input factors and adopting a dual-output approach focused on “economic” and “low-carbon” outcomes. This study thoroughly analyzed the synergistic mechanisms of factors such as natural endowment, agricultural technology, economic development, and environmental regulation, exploring their impact on AGTFP enhancement through the innovative application of the dynamic fuzzy-set qualitative comparative analysis (fsQCA) method. There was a significant upward trend in AGTFP across China, indicating notable progress in green agricultural development. Additionally, three pathways promoting AGTFP improvement were identified: resource–economy-driven, technology–policy-guided, and multifactor-synergy. Simultaneously, two modes constraining AGTFP enhancement were uncovered: economy–policy suppression and human capital–economy suppression, highlighting the pivotal role of regional economic development and the conditionality of converting natural resource advantages. Moreover, the contributions of these pathways to AGTFP exhibited notable temporal dynamics. Major economic events, such as the 2008 financial crisis and policy shifts, including the 2012 “Ecological Civilization” strategy, significantly altered the effectiveness of existing configurations. Our analysis of regional heterogeneity revealed distinct geographical patterns, with the resource–economy-driven model predominantly observed in central regions and the technology–policy-guided and multi-factor-synergy models more prevalent in central and eastern regions. These findings highlight the importance of formulating differentiated policies tailored to the specific needs and stages of development in different regions. Specifically, enhancing the synergy between resource management and economic development, optimizing technology–policy integration, and promoting coordinated multisectoral development are critical to fostering sustainable agricultural practices. This research provides crucial empirical evidence for shaping targeted policies that can drive green agricultural development across diverse regional contexts.
A Patch-Level Data Synthesis Pipeline Enhances Species-Level Crop and Weed Segmentation in Natural Agricultural Scenes
Authors: Li Tang , Burridge James , Blok Pieter M. , Guo Wei
Published: January 2025

Species-level crop and weed semantic segmentation in agricultural field images enables plant identification and enhanced precision weed management. However, the scarcity of labeled data poses significant challenges for model development. Here, we report a patch-level synthetic data generation pipeline that improves semantic segmentation performance in natural agriculture scenes by creating realistic training samples, achieved by pasting patches of segmented plants onto soil backgrounds. This pipeline effectively preserves foreground context and ensures diverse and accurate samples, thereby enhancing model generalization. The semantic segmentation performance of the baseline model was higher when trained solely on data synthesized by our proposed method compared to training solely on real data, with an approximate increase in the mean intersection over union (mIoU) by approximately 1.1% (from 0.626 to 0.633). Building on this, we created hybrid datasets by combining synthetic and real data and investigated the impact of synthetic data volume. By increasing the number of synthetic images in these hybrid datasets from 1× to 20×, we observed a substantially performance improvement, with mIoU increasing by 15% at 15×. However, the gains diminish beyond this point, with the optimal balance between accuracy and efficiency achieved at 10×. These findings highlight synthetic data as a scalable and effective augmentation strategy for addressing the challenges of limited labeled data in agriculture.
Plio–Quaternary Tectonic Activity in the Northern Nubian Belts: The Main Driving Forces
Authors: Mantovani Enzo , Viti Marcello , Babbucci Daniele , Tamburelli Caterina
Published: January 2025

It is suggested that the occurrence of tectonic activity in the northern Nubian belts (Tell-Rif and Atlas systems) since the Late Pliocene can be interpreted as one of the processes that were produced in the central and western Mediterranean zones by the collision of the Adriatic continental promontory with the Anatolian–Aegean Tethyan system. Since then, the consumption of the residual low-buoyancy domains in the Mediterranean area was allowed by a major change in the plate mosaic and the related kinematics. The new tectonic setting started with the decoupling of a large portion of the Adriatic domain (Adria plate) from Nubia, through the formation of a long discontinuity crossing the Ionian domain (Victor Hensen–Medina fault) and the Hyblean–Pelagian domain (Sicily channel fault system). Once decoupled, the Adria plate underwent a clockwise rotation, at the expense of E–W shortening in the Hyblean–Pelagian domain and in the northern Nubian margin. The shortening in the Pelagian domain was accommodated by the northward escape of the Adventure wedge, which in turn caused the northward displacement of the eastern Maghrebian sector. The indentation of these structures into the Alpine–Apennine material lying east of the Corsica–Sardinia block induced an east to southeastward escape of wedges (southern Apennines and Calabria). This occured at the expense of the remnant Ionian Tethys oceanic domain and the thinned Adriatic margin. The extensional regime that developed in the wake of the migrating wedges led to the formation of the central and southern Tyrrhenian basins. In the northern Nubian belts, the westward push of the Adria–Hyblean–Pelagian domain has been accommodated by oroclinal bending, thrusting and uplifting across the Tell and Atlas belts. This geodynamic context might explain some features of the seismicity time pattern observed in the Tell system.
High Rate Stormwater Treatment for Water Reuse and Conservation—Review
Authors: Vigneswaran Saravanamuthu , Kandasamy Jaya , Ratnaweera Harsha
Published: January 2025

Effective stormwater management is increasingly vital due to climate change impacts, such as intensified rainfall and flooding. Urban expansion, water scarcity, and intensified agriculture demand innovative solutions like Green Stormwater Infrastructure (GSI), including vegetated biofilters, green roofs, wetlands, bioretention systems, and high-rate filtration. These systems, enhanced by natural and engineered filter materials, improve contaminant removal across diverse contexts. Modern practices prioritize retention, infiltration, and groundwater recharge over traditional rapid drainage, reframing stormwater as a resource amid rising extreme weather events. In water-scarce regions, stormwater management offers dual-use potential for drinking and non-drinking applications, addressing freshwater scarcity exacerbated by population growth and climate change. Targeting the “first flush” of pollutants after rainfall allows for more efficient, cost-effective treatment. This paper identifies three key objectives: addressing GSI limitations and exploring new technologies, evaluating treatment train combinations for cost-effective reuse, and advancing urban stormwater treatment research. Various filter media, such as those in green roofs, bioretention systems, and swales, effectively remove pollutants like nutrients, heavy metals, PAHs, and micropollutants. Granular activated carbon (GAC) filters excel at reducing heavy metals and dissolved organic carbon (DOC), with pre-screening via anthracite filters to extend GAC lifespan by trapping sediments and pollutants. Managing emerging contaminants and microplastics remains underexplored and requires further investigation.
Interdependence Between River Aquifer Groundwater Flow and Temperature–Depth Profiles: Type Curves Based on Pi Theorem and Numerical Simulations
Authors: Jiménez-Valera José Antonio , Alhama Iván , Duque Carlos , Labat David
Published: January 2025

The interaction between surface water and groundwater has been extensively studied due to its water management implications and the potential environmental impacts arising from it. Experimental studies and numerical modeling have supported analytical solutions; these solutions have been proposed for specific cases in which the aim has been to understand discharge/recharge and aquifer characterization. In this study, new graphical solutions or type curves are provided to estimate the subsurface flow and thermal–mechanical parameters in anisotropic porous media. Using the non-dimensionalization technique of the governing equations, new dimensionless groups (lumped parameters) that govern the solution of both the mechanical problem (uncoupled) and the thermal problem are obtained. From these groups, and by applying the pi theorem and examining numerical simulations of numerous cases, user-friendly type curves are obtained. The recharge flow and hydraulic conductivity are calculated when the thermal properties, geometrical parameters, and temperature variables are known. To evaluate the reliability of the type curves, two real case studies are presented: the interaction between the Guadalfeo River and the Motril-Salobreña coastal aquifer, and the artificial recharge program in the coastal aquifer of Agua Amarga in southern Spain. For verification, the groundwater flow obtained from the type curves is compared with the recharge data. In the case of the river–aquifer interaction, the recharge flow obtained is 13% less than that estimated in previous studies. Regarding the artificial recharge of the coastal aquifer, the flow obtained is 21% less than the volume irrigated over the salt marsh. The uncertainties related to hydrogeological features are considered to have the greatest influence on the error.
Assessment of Rainfall Frequencies from Global Precipitation Datasets
Authors: Yin Xueyi , Zhang Ziyang , Lin Zhi , Yin Jun
Published: January 2025

Rainfall is of vital importance to terrestrial ecosystems and its intermittent characteristics have a profound impact on plant growth, soil biogeochemical cycles, and water resource management. Rainfall frequency, one of the key statistics of rainfall intermittency, has received relatively little research attention. Leveraging scale-dependent relationships in rainfall frequencies and using various global precipitation datasets, we found most grid-scale rainfall frequencies are relatively large and do not converge to the field-scale frequencies as grid size decreases. Specifically, these differences are as high as 41.8% for the Global Precipitation Climatology Project (GPCP) and 74.8% for the fifth-generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), which are much larger than the differences in mean rainfall rates but can be partially corrected by redefining wet days with higher rainfall thresholds. These differences across most regions of the world should be interpreted as the inherent biases associated with the model structure or algorithms used for deriving precipitation data and cannot be reduced simply by increasing the data resolutions. Such biases could propagate into the hydrological process and influence the calibration of the rainfall-runoff process, one of the key nonlinear relationships in land surface modeling. We, therefore, call for urgent research into this topic to avoid misunderstandings of rainfall intermittency and ensure its proper application in various fields.
The Effects of Urban Pollution on the “Gesù Nuovo” Façade (Naples, Italy): A Diagnostic Overview
Authors: De Rosa Alessandro , Cennamo Paola , Saltarelli Chiara , Trojsi Giorgio , Rimauro Juri , Vigorito Maria Rosaria , Chianese Elena
Published: January 2025

The deterioration of stone heritage in urban environments is mainly the product of sources of air pollution like vehicular traffic and domestic heating. The results of these phenomena usually manifest as acid rain and particulate patinas, acting on the surface of stone monuments to form the so-called “black crusts”, a typical stone degradation product, mainly composed of gypsum. The aims of this study were to investigate the extent of these phenomena on the decorative apparatus of the frontal façade of Gesù Nuovo Church, in the historical centre of Naples (Italy). Preliminary diagnostics consisted of XRD and FTIR to analyse the composition of stone materials and inquire about previous restorations. The chemical characterization of black crusts was performed, using a diverse array of techniques, to highlight how different compounds are distributed along a vertical gradient and considering the proximity of specific sources of pollution (vehicle engine ignition, incense combustion, domestic heating products). Finally, molecular biology techniques were employed to identify the organisms which typically dwell in this formation and speculate about their contribution to the degradation of stone.
Seasonal Variations and Health Risk Evaluation of Trace Elements in Atmospheric PM2.5 in Liaocheng, the North China Plain
Authors: Wang Yanhui , Hou Zhanfang , Ma Jiangkai , Zhang Xiaoting , Liu Xuan , Wang Qizong , Chen Chen , Yang Kaiyue , Meng Jingjing
Published: January 2025

Atmospheric elements can cause harmful effects on air quality and human health. Despite extensive research on PM2.5, there remains a limited understanding of the seasonal variations, origins, and associated health risks of specific elements in urban areas of the North China Plain. PM2.5 samples across four seasons were collected to investigate the seasonal variations, provenance, and health risks of 18 elements in urban Liaocheng. The concentrations of PM2.5 and total detected elements (TDEs) exhibited distinct seasonal patterns, with the biggest values occurring in winter, followed by spring, autumn, and summer. Fe, Ca, Al, and K were the most plentiful elements throughout the campaign, contributing 72.2% of TDEs. The enhanced concentrations of crustal elements were due to frequent dust storms in spring. Results from positive matrix factorization suggested that the dust source was only identified in spring, accounting for the largest percentage (37.0%), while secondary oxidation made the most significant contribution (34.6%) in summer, facilitated by higher temperatures and stronger sunshine. The relative abundance (41.6%) of biomass burning was highest in autumn, ascribed to intensified agricultural waste burning during the autumn harvest, especially in October. The contribution of coal combustion in cold seasons was substantially greater than in warm seasons, highlighting the role of increased coal burning for house heating in deteriorating air quality. Potential source function analysis showed that elements in Liaocheng originated from local and neighboring regions. The carcinogenic risk from the selected elements was notably stronger for adult males than for adult females and children, while the non-carcinogenic risk was stronger for children than for adults. Overall, these findings provide ponderable insights into the contamination characteristics and sources of elements, which are useful to inform effective measures for improving air quality and aerosol modeling.
Partitioning Functional of a Class of Convex Bodies
Authors: Zhang Xinling
Published: January 2025

For each n-dimensional real Banach space X, each positive integer m, and each bounded set A⊆X with diameter greater than 0, let βX(A,m) be the infimum of δ∈(0,1] such that A⊆X can be represented as the union of m subsets of A, whose diameters are not greater than δ times the diameter of A. Estimating βX(A,m) is an important part of Chuanming Zong’s quantitative program for attacking Borsuk’s problem. However, estimating the partitioning functionals of general convex bodies in finite dimensional Banach spaces is challenging, so we will begin with the estimation of partitioning functionals for special convex bodies. In this paper, we prove a series of inequalities about partitioning functionals of convex cones. Several estimations of partitioning functionals of the convex hull of (A+u)∪(A−u) and (A+u)∪(−A−u) are also presented, where A⊆Rn−1×{0} is a convex body with the origin o in its interior, and u∈Rn∖(Rn−1×{0}). These results contribute to the study of Borsuk’s problem through Zong’s program.
Optimization of Conditions for Production of Soluble E. coli Poly(A)-Polymerase for Biotechnological Applications
Authors: Oscorbin Igor P. , Kunova Maria S. , Filipenko Maxim L.
Published: January 2025

Poly(A) polymerase (PAP 1) from Escherichia coli is the primary enzyme responsible for synthesizing poly(A) tails on RNA molecules, signaling RNA degradation in bacterial cells. In vitro, PAP 1 is used to prepare libraries for RNAseq and to produce mRNA vaccines. However, E. coli PAP 1’s toxicity and instability in low-salt buffers complicate its expression and purification. Here, we optimized the conditions for the production of recombinant PAP 1. For that, E. coli PAP 1 was expressed in seven E. coli strains with different origins and genetic backgrounds, followed by assessment of the overall protein yield, solubility, and enzymatic activity. Among the tested strains, BL21 (DE3) pLysS achieved the best balance of cell density, total PAP 1 yield, solubility, and specific activity. Rosetta 2 (DE3) and Rosetta Blue (DE3) hosting the pRARE plasmid exhibited the lowest solubility, likely due to excessive translation efficiency. Higher induction temperatures (>18 °C) exacerbated PAP 1’s insolubility. Interestingly, PAP 1 accumulation correlated with an increase in the plasmid copy number encoding the enzyme, indicating its potential utility as a surrogate marker of PAP 1 activity. These findings provide insights into optimizing E. coli PAP 1 production for biotechnological applications.
Epibrassinolide Regulates Lhcb5 Expression Though the Transcription Factor of MYBR17 in Maize
Authors: Li Hui , He Xuewu , Lv Huayang , Zhang Hongyu , Peng Fuhai , Song Jun , Liu Wenjuan , Zhang Junjie
Published: January 2025

Photosynthesis, which is the foundation of crop growth and development, is accompanied by complex transcriptional regulatory mechanisms. Research has established that brassinosteroids (BRs) play a role in regulating plant photosynthesis, with the majority of research focusing on the physiological level and regulation of rate-limiting enzymes in the dark reactions of photosynthesis. However, studies on their effects on maize photosynthesis, specifically on light-harvesting antenna proteins, have yet to be conducted. The peripheral light-harvesting antenna protein Lhcb5 is crucial for capturing and dissipating light energy. Herein, by analyzing the transcriptomic data of maize seedling leaves treated with 24-epibrassinolide (EBR) and verifying them using qPCR experiments, we found that the MYBR17 transcription factor may regulate the expression of the photosynthetic light-harvesting antenna protein gene. Further experiments using protoplast transient expression and yeast one-hybrid tests showed that the maize transcription factor MYBR17 responds to EBR signals and binds to the promoter of the light-harvesting antenna protein Lhcb5, thereby upregulating its expression. These results were validated using an Arabidopsis mybr17 mutant. Our results offer a theoretical foundation for the application of BRs to enhance the photosynthetic efficiency of maize.
The Ontological Multiplicity of Digital Heritage Models: A Case Study of Yunyan Temple, Sichuan Province, China
Authors: Tan Jie , Guo Xin , Huang Haijing
Published: January 2025

This paper investigates the ontological multiplicity of digital heritage objects within the context of a digital twin project focused on Yunyan Temple, Sichuan Province, China—a site threatened by natural disasters. The project employs laser scanning and photogrammetry to generate high-resolution 3D models at varying levels of detail. The study analyzes how these digital objects support diverse analytical tasks ranging from geomorphological analysis to structural assessments and spatial sequence analysis. We present a novel four-layer data integration and service platform architecture designed to manage the complex data relationships arising from this ontological multiplicity. This includes a temporal database to support iterative refinements of conservation strategies based on ongoing monitoring. The findings highlight the dynamic role of digital objects in knowledge production and offer practical implications for database design, data management, and the development of adaptive conservation strategies for cultural heritage.
Automated Research Review Support Using Machine Learning, Large Language Models, and Natural Language Processing
Authors: Pendyala Vishnu S. , Kamdar Karnavee , Mulchandani Kapil
Published: January 2025

Research expands the boundaries of a subject, economy, and civilization. Peer review is at the heart of research and is understandably an expensive process. This work, with human-in-the-loop, aims to support the research community in multiple ways. It predicts quality, and acceptance, and recommends reviewers. It helps the authors and editors to evaluate research work using machine learning models developed based on a dataset comprising 18,000+ research papers, some of which are from highly acclaimed, top conferences in Artificial Intelligence such as NeurIPS and ICLR, their reviews, aspect scores, and accept/reject decisions. Using machine learning algorithms such as Support Vector Machines, Deep Learning Recurrent Neural Network architectures such as LSTM, a wide variety of pre-trained word vectors using Word2Vec, GloVe, FastText, transformer architecture-based BERT, DistilBERT, Google’s Large Language Model (LLM), PaLM 2, and TF-IDF vectorizer, a comprehensive system is built. For the system to be readily usable and to facilitate future enhancements, a frontend, a Flask server in the cloud, and a NOSQL database at the backend are implemented, making it a complete system. The work is novel in using a unique blend of tools and techniques to address most aspects of building a system to support the peer review process. The experiments result in a 86% test accuracy on acceptance prediction using DistilBERT. Results from other models are comparable, with PaLM-based LLM embeddings achieving 84% accuracy.
On a New Sustainable Energy Policy: Exploring a Macro-Meso-Micro Synthesis
Authors: Chatzinikolaou Dimos , Vlados Charis Michael
Published: January 2025

This study examines the differences between the emerging new energy policy and its predecessor, offering guidelines for an integrated approach to support a rapid and sustainable energy transition. Using a bibliometric and qualitative integrative analysis of 360 scientific articles on energy policy, ecosystems, and entrepreneurship—supplemented by insights from 16 experts in the European energy sector and an in-depth analysis of 89 relevant business cases—this research identifies a significant shift in priorities. Traditional concerns, such as energy security, self-sufficiency, and market liberalization, are increasingly giving way to a focus on rapid, sustainable energy transitions and development at the macrolevel. The findings reveal that contemporary energy policies are progressively prioritizing integrated strategies across the macrolevel, mesolevel, and microlevel. At the macrolevel, policies are increasingly focused on enabling efficient transitions and promoting sustainable development within an ecosystemic framework. At the mesolevel, there is a growing emphasis on strengthening regional energy ecosystems. At the microlevel, the focus increasingly shifts toward empowering energy firms through innovative organizational strategies, technological advancements, and enhanced managerial practices. The proposed integrated energy policy aims to address these broader goals while fostering diverse energy ecosystems and communities at the mesolevel. Additionally, it emphasizes the importance of empowering individual energy firms by enhancing their strategies, technological capabilities, and managerial skills. These improvements are essential for driving innovation, developing green business models, strengthening corporate social responsibility, and aligning with the principles of Resilience, Adaptability, Sustainability, and Inclusiveness (RASI).
Molecular Dynamics Simulation on the Mechanism of Shale Oil Displacement by Carbon Dioxide in Inorganic Nanopores
Authors: Li Chengshan , Xue Hongbo , Rao Liping , Yuan Fang , Xu Zhongyi , He Tongtong , Ji Chengwei , Wu Zhengbin , Yan Jiacheng
Published: January 2025

Shale oil reservoirs feature a considerable number of nanopores and complex minerals, and the impact of nano-pore confinement and pore types frequently poses challenges to the efficient development of shale oil. For shale oil reservoirs, CO2 flooding can effectively lower crude oil viscosity, enhance reservoir physical properties, and thereby increase recovery. In this paper, the CO2 displacement process in the nanoscale pores of shale oil was simulated through the molecular dynamic simulation method. The performance disparity of quartz and calcite slit nanopores was discussed, and the influences of nanoscale pore types and displacement rates on CO2 displacement behavior were further analyzed. The results demonstrate that the CO2 displacement processes of different inorganic pores vary. In contrast, the displacement efficiency of light oil components is higher and the transportation distance is longer. Intermolecular interaction has a remarkable effect on the displacement behavior of CO2 in nanopores. On the other hand, it is discovered that a lower displacement rate is conducive to the miscible process of alkane and CO2 and the overall displacement process of CO2. The displacement efficiency drops significantly with the increase in displacement velocity. Nevertheless, once the displacement speed is extremely high, a strong driving force can facilitate the forward movement of alkane, and the displacement efficiency will recover slightly.
Effects of Acorn Flour Addition on Baking Characteristics of Wheat Flour
Authors: Szabłowska Emilia , Tańska Małgorzata
Published: January 2025

This study aimed to evaluate the impact of incorporating acorn flour (at levels ranging from 5% to 50%) on the baking properties of wheat flour (type 750). The assessment focused on key baking parameters, including fermentation properties, pasting behavior, and dough rheological characteristics such as farinographic and extensographic properties. A laboratory baking test was conducted to compare the technological properties of wheat and wheat–acorn breads, assessing dough and bread yields, oven and total losses, bread volume, and crumb hardness. Additionally, the nutritional value of selected bread variants was established. The results indicated that flour mixtures with acorn flour exhibited a significantly reduced capacity to retain gases produced during fermentation (by up to 92%) and increased resistance to gelatinization, as evidenced by lower gel viscosity (by up to 14%) and higher endpoint temperatures during pasting (by 2–4%). The inclusion of acorn flour in wheat dough notably affected its rheological properties, particularly by reducing dough extensibility (by up to 56%). However, farinographic parameters such as dough development time and stability time were extended (by 23–378% and 29–291%, respectively). High levels of acorn flour addition (>30%) resulted in bread with a dense, gummy, and less spongy crumb structure, accompanied by a reduction in loaf volume (by 40–52%). The maximal acceptable addition of acorn flour (30%) resulted in a two-fold increase in ash and fiber contents, along with decreases in carbohydrate and protein contents by 12% and 27%, respectively. These findings emphasize the need for careful formulation adjustments when incorporating acorn flour into wheat-based baked goods to balance technological efficiency and improved nutritional value.
Temporal Trends in Biologic Prescriptions for Patient with Inflammatory Bowel Disease: A Retrospective Cohort Study
Authors: Fernando Sunimal , Abu-Rgeef Reeham , Menon Shankar , So Kenji , Venugopal Kannan , Picardo Sherman
Published: January 2025

Background: Biologic therapies, such antitumour necrosis factor-alpha agents (infliximab and adalimumab), as well as newer agents (ustekinumab and vedolizumab), all have well-demonstrated safety and efficacy profiles in the management of inflammatory bowel disease (IBD). The choice of an optimal first-line biologic remains unclear due to a lack of comparative randomised trials and real-world studies; however, certain patient and disease characteristics may influence this choice. The aims of this study were to explore temporal trends in the choice of a first-line biologic therapy and the factors that influence this choice. Methodology: This study is a retrospective observational cohort study of all patients with IBD who commenced induction and completed at least one maintenance dose of a biologic therapy between 1 January 2015 and 31 December 2021. Relevant patient and disease-specific factors were collected, including history of malignancy and opportunistic infections at time of diagnosis, for each eligible patient. Factors affecting the choice of biologic therapy were compared using ANOVA and chi-square tests. Results: 280 patients were included in the study. Ustekinumab has overtaken infliximab and adalimumab as the first-line choice for Crohn’s disease since its introduction in 2018. Infliximab has remained the preferred first-line therapy for ulcerative colitis over adalimumab and vedolizumab. Ustekinumab has become he preferred biologic agent for older patients and those with a history of malignancy. Conclusions: Whilst an older agent such as infliximab is still preferred for the management of UC, novel agents such as ustekinumab are now more readily considered as a first-line agent for the management of CD.
MicroRNA 133A Regulates Squalene Epoxidase Expression in Colorectal Cancer Cells to Control Cell Proliferation and Cholesterol Production
Authors: Mo Ji-Su , Lamichhane Santosh , Sharma Grinsun , Chae Soo-Cheon
Published: January 2025

Background/Objectives: Colorectal cancer (CRC) is one of the most common cancers worldwide, with high incidence and mortality rates. MicroRNAs are endogenous and non-coding RNAs that play a pivotal role in the development and progression of various cancers by targeting specific genes. Previously, we identified MIR133A to be significantly decreased in human CRC tissues. This study aims to identify the relationship with SQLE, one of the candidate target genes of MIR133A, and study their interaction in CRC cells. Methods: Through the luciferase reporter assay, quantitative RT-PCR (qRT-PCR), and Western blot analysis. Results: We identified SQLE as a direct target gene of MIR133A. Using the MIR133A KI cell lines, which knocked-in MIR133A1 or MIR133A2 in CRC cell lines, and CRC cells transfected with siSQLE, we found that MIR133A regulated the proliferation and migration of CRC cells by modulating SQLE-mediated PIK3CA-AKT1 and CYP24A1 signaling. We also found that cholesterol production was regulated by MIR133A in CRC cells. Conclusions: Our results suggest that MIR133A is an important therapeutic target for colorectal cancer.